efficient utilization of bandwidth for ofdm wsn
TRANSCRIPT
Efficient Utilization of Bandwidth for OFDM WSN
Submitted by:
Ansar Saeed Hashmi
2008-Ph.D-E-01
Supervised by:
Dr. Noor M. Sheikh
Department of Electrical Engineering
University of Engineering and Technology, Lahore,
Pakistan.
2
Efficient Utilization of Bandwidth for OFDM WSN
Submitted to the faculty of the Electrical Engineering Department of
the University of Engineering and Technology Lahore
in partial fulfillment of the requirements for the Degree of
Doctor of Philosophy
in
Electrical Engineering
Approval on _________________
Internal Examiner External Examiner
Chairman Dean
Department of Electrical Engineering Faculty of Electrical Engineering
Department of Electrical Engineering
University of Engineering and Technology Lahore
3
Declaration
I hereby declare that the work contained in this thesis is my own, except where explicitly stated
otherwise. Moreover, this work has not been submitted to obtain another degree or professional
qualification.
Signed: ________________
Date: _________________
4
Acknowledgments
I would like to express my gratitude to my supervisor Professor Dr. Noor M. S. for his
valuable guidance, support, direction, motivation, encouragement and patience
throughout this research work.
Also I would like to thank the members of the post graduate committee for their
cooperation, support, facilitation and encouragement. I wish to extend my appreciation
to all those Professors who have helped and supported my research work. I owe my
thanks to all those who provided generous help.
I very especially would like to thank to the members of ASRB Committee of UET for
giving me tremendous opportunity to complete the research work and its processing.
I would like to thank to the directorate of research of UET for continuing support. Also
special thanks to Administrative staff like Mr. Islam of the post graduate Lab. for
cooperation and generous support along with fast and efficient response to all the
administrative matters.
I must thank my family members for their endurance, sacrifice, patience, support,
consistent encouragement and inspiration in continuing the project even in desperate
and gloomy times when things got stuck.
5
This work is dedicated to my parents and family members.
6
Contents
Acknowledgments
List of Acronyms and Abbreviations
List of Figures
List of Tables
Abstract
1. Introduction and Background
1.1. Wireless Sensor Networks (WSNs)
1.1.1. WSN Challenges
1.1.2. Seismic monitoring WSN
1.1.3. Oil and Gas Exploration WSN
1.2. IEEE 802.11b (WiFi)
1.3. IEEE 802.16 (WiMAX)
1.4. Area of Research
1.5. Motivation
1.6. Objectives of the Thesis
1.7. Main Contributions of the Thesis
1.8. Structure of the Thesis
2. Literature Review and Related work
2.1. Factors influencing Capacity of WiMAX, including System overheads
2.1.1. Cyclic Prefix G
2.1.2. Modulation
2.1.3. Forward Error Correction (FEC)
2.1.4. Adaptive Communication
2.1.5. Radio Resource Management
2.1.6. QoS Scheduling
7
2.1.7. Sub-carriers allocation
2.1.8. Communication Overheads
2.1.8.1. Minimum Allocation Unit (MAU)
2.1.8.2. Contention region
2.1.8.3. Initial ranging
2.1.8.4. Preamble
2.1.8.5. Management overheads
2.1.8.5.1. Sub headers
2.1.8.5.2. Packing and Fragmentation
2.2. Techniques for Improvement of throughput (Capacity)
2.2.1. Modulation Techniques
2.2.2. Power Techniques
2.2.3. Various Coding Techniques
2.2.4. Adaptive Techniques
2.2.5. Sub-carrier Grouping Techniques
2.2.6. Frame and symbol size
2.2.7. MAU size
2.2.8. Packing and Fragmentation
2.2.9. Resource allocation through Scheduling
2.2.10. Diversity through MIMO system
2.3. Related work in Adaptive Technique and Problems
2.3.1. Related work in Adaptive Technique
2.4. Proposed Work
3. IEEE 802.16 (WiMAX) and System Overheads
3.1. IEEE 802.16 (WiMAX)
3.2. IEEE 802.16 (WiMAX) Frame Resources
3.3. IEEE 802.16 (WiMAX) Resources (Slots) Capacity
3.4. Resources for Frame Overheads
3.4.1. Frame Overheads
3.4.1.1. Downlink Subframe Overheads
3.4.1.1.1. Connections overhead
3.4.1.1.2. DL-MAP and IE’s
3.4.1.1.3. UL-MAP and IE’s
3.4.1.1.4. Uplink Subframe Overheads
3.5. Frame Resource Group Set
4. System Model (DL)
8
4.1. System Description
4.2. DL Minimum Traffic Model for WSN nodes
4.3. Channel Model
4.4. MMPP Traffic Model
5. Performance Resources Estimation of DL Minimum Traffic
5.1. Packet Scheduling by BS
5.1.1. Known MCS Distribution
5.1.2. Unknown MCS Distribution
5.2. Queuing Analysis
5.2.1. State Transition Probability
5.2.2. Queuing Parameters Estimation
5.2.2.1. Average Queue Length
5.2.2.2. Arrival Process
5.2.2.3. Service Process
5.2.2.4. Throughput
5.2.2.5. Probability of Packet Drop
5.2.2.6. Packet Delay
5.2.3. Performance Resources Estimation for DL Minimum Traffic Model
5.3. For One WiMAX Cell DL Minimum Traffic Resource Estimation (Block Diagram)
6. UL Subframe Resource Utilization, Adaptive Frame Shift and Proposed Algorithm
6.1. UL Subframe Resource Utilization
6.1.1. Maximum Resources Utilization
6.1.2. Minimum Resources Utilization
6.2. Adaptive UL-Subframe Resource Distribution
6.2.1. Increasing UL-Subframe resources
6.2.2. Decreasing UL-Subframe resources
6.3. Main Blocks of Proposed Algorithm
6.3.1. Main Algorithm
6.3.2. Frame Resource Group Set
6.3.3. DL Minimum Traffic Resources Algorithm
6.3.4. UL Subframe Utilization
6.3.5. Adaptive Resource Distribution
6.3.5.1. Increasing Resources
6.3.5.2. Reducing and Restoring to Normal Resources
9
7. Numerical Analysis and Simulation Results
7.1. Preliminary Uplink Performance Analysis
7.2. Numerical Analysis (Algorithm) and Simulation Environment
7.2.1. Algorithm’s Mathematics Results (MATLAB)
7.2.2. NS2 Simulation Results
7.3. Numerical and Simulation Results and Discussion
7.3.1. DL Minimum Traffic Parameters Estimation By Algorithm Results
7.3.2. DL Minimum Traffic Simulation Results
7.3.3. UL Maximum and Normal Traffic Simulation Results
8. Conclusions
8.1. Future Enhancements
References
10
List of Acronyms and Abbreviations
ACK Acknowledgment
ADC Analog to Digital Converters
AMC Adaptive Modulation and Coding
AP Access Point
ARQ Auto Repeat Request
ATM Asynchronous Transfer Mode
BE Best Effort (services for scheduling)
BER Bit Error Rate
BPSK Binary Phase Shift Keying (Binary Digital Modulation)
BS Base Station
BSS Basic Service Set
BW Bandwidth
CID Connection Identifier
CONS Connections
CP Cyclic Prefix (G ratio)
CQI Channel Quality Information
CQICH Channel Quality Information Channel
CRC Cyclic Redundancy Check
CSI Channel State Information
CSMA/CA Carrier Sense Multiple Access/Collision Avoid
db Data Base
11
DCD Downlink Channel Descriptor
DL Downlink
DL-MAP Downlink Medium Access Protocol
DTMC Discrete Time Markov Chain
FCH Frame Control Header
FDD Frequency Division Duplex
FEC Forward Error Correction
FFT Fast Fourier Transform
FIFO First In First Out
FTP File Transfer Protocol
FUSC Full Usage of Subchannels
GPC Grant per Connection
GPSS Grant per Subscriber Station
GSM Global System for Mobile Communication
IDC Index of Dispersion for Counts
Km Kilometer
LA Learning Automata
MAC Media Access Control
MAP Markovian Arrival Process
MAU Minimum Allocation Unit
Mbps Mega bits per sec
MCS Modulation and Coding Scheme
MHz Mega Hertz
MIMO Multiple-In Multiple-Out
MISO Multiple-In Singe-Out
12
MMPP Markov Modulated Poisson Process
NIST National Institute of Standards and Technology, USA
NLOS Non Line Of Sight
nrtPS Non-real-time Polling Services (for scheduling)
NS2 Network Simulator 2
OFDM Orthogonal Frequency Division Multiplexing
OFDMA Orthogonal Frequency Division Multiple Access
OS Operating System
OSI Open System Interface
pdf Probability Density Function
PDU Protocol Data Unit
PHY Physical
PMP Point to Multipoint
PUSC Partial Usage of Subchannels
QAM Quadrature Amplitude Modulation
QoS Quality of Service
QPSK Quadrature Phase Shift Keying (Digital Modulation)
RLC Radio Link Control
RNG-REQ Ranging Request
RNG-RSP Ranging Response
RTG Receive/transmit Transition Gap
rtPS Real-time Polling Services (for scheduling)
Rx Receive
SDU Service Data Unit
SNR Signal to Noise Ratio
13
sq Square
SS Subscriber Station
STC Space Time Coding
TCP/IP Transmission Control Protocol / Internet Protocol
TDD Time Division Duplex
TDM Time Division Multiplexed
TDMA Time Division Multiple Access
TTG Transmit/receive Transmission Gap
Tx Transmit
UCD Uplink Channel Descriptor
UDP User Datagram Protocol
UGS Unsolicited Grant Service (for scheduling)
UL Uplink
UL-MAP Uplink Medium Access Protocol
VOIP Voice on IP
WEIRD WiMAX extension to Isolated Research Data Networks
WiFi Wireless Fidelity, (IEEE 802.11 certification consortium)
WiMAX Worldwide Interoperability for Microwave Access Forum, (Technology mainly
based on IEEE 802.16)
WSN Wireless Sensor Network
14
List of Figures
Figure 1: General Architectures of WSN, with Two Tier elaboration. 20
Figure 2: Three-component signal of the seismic station. 22
Figure 3: 802.16-WiFi WSN with BS, WiFi router sink Nodes and sensor nodes 23
Figure 4: WiMAX lower layers including MAC and PHY 24
Figure 5: TDD frame structure for WiMAX 25
Figure 6: OFDM DL and UL ratio 26
Figure 7: Frequency-domain representation of OFDM symbol. 27
Figure 8: Proposed Research Work Overview. 29
Figure 9: Main Focus of DL Minimum Traffic Resources Estimation. 29
Figure 10: Symbols, Tiles and Slots formation for UL PUSC. 48
Figure 11: Symbols, Tiles and Slots formation for DL PUSC. 49
Figure 12: (a) WiMAX DL ON-OFF Traffic Model for WSN. 54
Figure 13: (b) 50% Nodes ON and 50% Nodes OFF. 54
Figure 14: MCS regions divided by SNR around BS. 56
Figure 15: System Model based on MMPP Traffic Model, DL queue, Packet Arrival and Service
rates (packets/frame). 58
Figure 16: System Queue Model based on DTMC. 63
Figure 17: For One WiMAX Cell DL Min. Traffic Resource Estimation. 68
Figure 18: Main Algorithm. 74
Figure 19: DL Minimum Traffic Resource Estimation Algorithm. 75
Figure 20: In simulations N (15) WiMAX Nodes are placed in a circle around a BS. 78
15
Figure 21: Aggregate UL Throughput at 1:1 ratio for Low, Medium and High data rates. 78
Figure 22: Aggregate UL Packet delay at 1:1 ratio for Low, Medium and High data rates. 79
Figure 23: Aggregate UL Packets at 1:1 ratio for Low, Medium and High data rates. 79
Figure 24: Aggregate UL Packet drop at 1:1 ratio for Low, Medium and High data rates. 80
Figure 25: Aggregate UL Throughput at 2:1, 1:1, 1:2, 1:3 ratios for High data rate. 80
Figure 26: Aggregate UL Packet delay at 2:1, 1:1, 1:2, 1:3 ratios for High data rate. 81
Figure 27: Aggregate UL Packets at 2:1, 1:1, 1:2, 1:3 ratios for High data rate. 81
Figure 28: Aggregate UL Packet drop at 2:1, 1:1, 1:2, 1:3 ratios for High data rate. 82
Figure 29: For UL Total Throughput of Packets at different ratios for High and Medium data
rates, in the simulation time. 82
Figure 30: Individual Packet Throughput of UL at 1:1 ratio for Low data rate, in simulation time.
83
Figure 31: Individual Packet Throughput of UL at 1:1 ratio for Medium data rate, in simulation
time. 83
Figure 32: Individual Packet Throughput of UL at 1:1 ratio for High data rate, in simulation time.
84
Figure 33: Throughput of the DL Min. Traffic model by Algorithm. 87
Figure 34: Number of Packets of the DL Min. Traffic model by Algorithm. 88
Figure 35: Packet Delay of the DL Min. Traffic model by Algorithm. 88
Figure 36: Probability of Packet Drop of the DL Min. Traffic model by Algorithm. 89
Figure 37: Throughput of the DL Min. Traffic model by Simulation. 90
Figure 38: Packets Received of the DL Min. Traffic model by Simulation. 90
Figure 39: Packet Delay of the DL Min. Traffic model by Simulation. 91
Figure 40: Probability of Packet Drop of the DL Min. Traffic model by Simulation. 91
Figure 41: Packet Drop of the DL Min. Traffic model by Simulation. 92
Figure 42: Throughput of the UL Traffic by Simulation. 93
Figure 43: Packets Received of the UL Traffic by Simulation. 94
16
Figure 44: Packet Delays of the UL Traffic by Simulation. 94
Figure 45: Probability of Packet Drop of the UL Traffic by Simulation. 95
Figure 46: Packet Drop of the UL Traffic by Simulation. 95
Figure 47: Maximum and Minimum Delays of Nodes of the UL Traffic by Simulation. 96
17
List of Tables
Table 1: Different Frame Durations for WiMAX. 44
Table 2: Data rates for different Modulation and coding schemes in Mb/sec. 46
Table 3: PUSC Parameters of Subcarriers, Frequency and Timing for OFDMA. 47
Table 4: Normal ratio 1: 1 Resources of Symbols, Subcarriers, Tiles, Clusters and Slots. 48
Table 5: Slot Capacity of various MCS Levels. 49
Table 6: MCS regions divided by SNR. 57
Table 7: Simulation Parameters for UL Performance Analysis. 76
Table 8: Valid Preferred DL:UL ratio’s. 85
Table 9: Simulation Parameters. 86
18
Abstract
Wireless Sensor Networks (WSNs) have gained popularity in a lot of emerging areas since their
evolution, like the monitoring of natural phenomena concerning geosciences in various
disciplines. Some relevant areas include the monitoring of oil and gas exploration field, earth
quake and volcanic eruptions etc. In these processes real time phenomena are monitored in
remote fields and after data collection through WSN, it is ported to the far flung research centers
for further investigation and decision making. Here 2-tier WSN is considered with lower tier of
sensor nodes in WiFi and upper tier of WiMAX as a backhaul for data transportation to distant
research facility. However WiMAX has already popularity for applications with main
configuration of downlink data delivery direction, like serving internet hot spots and similar
amenities. This innovative research work is concerned with previously mentioned phenomena
monitoring which needs high data throughput efficiency in the uplink direction. For dense sensor
node concentration, in order to transport consolidated output form all sensor nodes in real time
phenomena of impulsive nature, sufficient uplink throughput is needed with low latency which
forms a bottleneck in these cases for WiMAX backhaul. Real time applications constrain end-to-
end delay and hence throughput which severely affect the performance and the accuracy of the
monitoring. To the system only fixed bandwidth is available, for which resources of slots have to
be shared between downlink and uplink. Here WiMAX OFDM in TDD mode is considered.
Initially analysis has been presented to point out main important constituent parameters of
WiMAX which contribute to throughput.
This research work proposes a solution to enhance uplink bandwidth allocation efficiency for
these phenomena through adaptive shift of WiMAX frame ratio. In this regard for WiMAX
adaptive ratio shift researchers have used a number of scenarios but all of them are not very
efficient and have drawbacks. Some of them are using either fixed margins or some other are
using fixed step sizes for upward or downward (increment) without any calculation. If a link
(uplink or downlink) is running near full capacity then any kind of sudden appearance of internal
system overheads in the form of system message or broadcast may bring the link to
fragmentation. And fragmentation successively may contribute to increase the link overheads
further, causing either increased delay or some other problem related to packet drop, re-
transmission or transmission failure. If problems are induced, this may take relatively longer at a
reduced data transfer rate. In order to avoid similar problems, relevant calculations have to be
performed to guarantee good QoS values of maximum link throughput while keeping low delay
and packet drop, which needs adequately more resources.
In fact keeping in view the complex trade-off between QoS parameters and system resources, the
optimization problem is formulized to maximize the uplink throughput while keeping the latency
and packet drop of downlink to minimum limit, to facilitate the efficient operation of uplink’s
19
momentarily bulky traffic. A novel solution to this problem is obtained through the incorporation
of stochastic processes with random variable in finite state space. The analytical and
mathematical expressions are contributed for the different analytical models. A MMPP traffic
model is formed for OFDMA transmission. This is supported with a DTMC system model for
queuing theoretic performance modeling. Analytical and numerical values of performance
parameters like throughput, packet delay and probability of packet drop are estimated for
resource allocation through mathematical models of stochastic process. First of all to restrict the
downlink traffic to minimum level a traffic pattern is defined from downlink to WSN sensor
nodes. While considering packet scheduling, two scenarios are taken into account, one is
concerned with known MCS distribution and the other is concerned with unknown MCS
distribution. In this process mainly affecting factors of frame overheads are also taken into
account. Then by using all these analytic and mathematical models, an algorithm is formulated to
find out minimum optimal resource requirement on down link after considering these QoS
parameters. Through this manipulation rest of the resources can be transferred to facilitate uplink
operation.
Another DTMC model is designed to check and quantify the uplink frame utilization. When
uplink frame utilization is beyond normal, the frame resources have to be incremented to
previously calculated maximum limit by adaptively remapping the frame ratio. Also when
utilization on uplink reduces below normal, it is remapped to normal frame ratio. The main
algorithm is formed by appropriately including all these previous analytical models. This sets the
adaptive ratio to the best suitable maximum value, to facilitate the uplink bulky traffic, and saves
the link from congestion and slowing down. This also ensures minimization of previously
mentioned errors of delay, drop or timeout related problems. This process achieves highest
degree of convergence in just one step by providing maximum throughput on uplink without
degrading QoS parameters on the downlink operation.
In order to prove the results firstly analytical results are obtained from the computations of the
algorithm in MATLAB. Secondly intensive simulations are conducted around Rayleigh flat
fading channel through models in three steps. One simulation model gives results for downlink
minimum traffic, next model is for uplink maximum traffic and another model is for uplink
normal ratio traffic. All these results together prove the accuracy and superiority of the algorithm
by showing an outstanding uplink bandwidth efficiency enhancement without degrading the
downlink operation.
Future research directions are to further enhance the analytical models for more states. Also
more variations to overhead analysis can be added by more realistic models and with more
overhead reduction techniques.
20
Chapter 1.
Introduction and Background
1.1. Wireless Sensor Networks (WSNs)
This research work is related to high throughput OFDM backed wireless sensor networks
(WSNs). Initially wired sensor networks started into deployments, but after increased popularity
and ease of installation and maintenance of wireless connectivity, WSN started dominating, [1].
There are some limitations and bottlenecks for high throughput WSNs [2], [3].
A wireless sensor network (WSN) is a wireless network consisting of spatially distributed
autonomous devices using sensors to cooperatively monitor physical or environmental
phenomena [1], [2]. This is originally motivated by military applications such as battlefield
surveillance, but now it is used in many civilian application areas as well.
Figure 1: General Architectures of WSN, with Two Tier elaboration.
These nodes consist of Sensing unit (accelerometer), Processing unit, ADC Converters,
Transceiver unit and Power unit, which are all small, compact and highly integrated, [61]. These
nodes have OS, db, and algos. in shortcut versions. Also these have limited power supply,
processor speed, memory space and bandwidth, [61]. Processing data consumes less power and
transmitting data takes more power, so these nodes use energy saving techniques. WSN can be of
single tier but for larger geographical coverage these can be of multiple tiers [2], [3], [61]. The
architecture of common WSN types is shown in Figure 1. Here two tiers WSN is elaborated
WSN
Architecture
Flat Hierarchical
Mesh, Ad-Hoc,
Peer-to-Peer
Two Tier &
Multi-Tier
Cluster-
Based
Cluster-
Tree
Hexagonal
21
which is the main focus of this research work. In two-tier WSN under consideration here, upper
tier is with high throughput TDD based OFDMA of IEEE 802.16 (WiMAX) and lower tier is of
WiFi sensor nodes, collecting data from seismic sensors, [14], [15], [16], [17], [18].
1.1.1. WSN Challenges
Although there are many prevailing challenges faced by WSN, as in [61], some are listed below.
However in this thesis mainly Network Capacity and its bottlenecks are under prime
consideration, [2], [3].
– Network capacity
– Deployment strategies
– Node localization
– Fault tolerance
– Security
– Energy saving
– Auto-reconfiguration
1.1.2. Seismic monitoring WSN
From WSN seismic data is collected for earthquake predictions and volcanic activities
monitoring, as mentioned in [5]-[11]. The details of seismo-acoustic data collection from sensors
like accelerometer, seismometer, seismograph, microphone/geophone arrays along with
deployment strategies are mentioned in [5]-[11]. These sensor units may be imotes or micaz with
7 MHz to around 416 MHz processor units. Different deployment strategies have been discussed
on numerous locations for variety of related areas of volcanic research. Since the post processing
of data through specific algorithms, takes place at a remote research facility or laboratory, so
huge data has to be transferred through wireless links with high speed. The remote data transfer
link’s bottleneck has been mentioned as a severe telecommunication bottleneck. It has been
identified that due to large amount of data, the slow link may get time out and another problem
may start, leading to either re-transmission or transmission failure [5]-[11].
European FP6 project, WEIRD (WiMAX extension to Isolated Research Data Networks) is
under research for heterogeneous networks backed by 802.16 (WiMAX) for the deployment
across Europe, [12]. This project is one of the practical implementations for volcanic activity
monitoring through sensors, [12], [13]. One of the main WEIRD tasks relies upon implementing
four test beds located in different European countries. A scenario envisages the wireless real-
time data links between seismological sensors deployed over a volcano and the central site of the
“Osservatorio Vesuviano” in Italy or of the Icelandic Meteorological Office. These real time data
can be used to analyze volcano status and evolution, and to share information with the scientist
community all over the Europe. WEIRD project is also facing throughput bottlenecks for uplink
backbone of WiMAX communication, [12]. Figure 2 shows about 30 seconds of the seismic
22
signal gathered during the test bed, from seismograph, [13]. For this much duration data
collected, if transferred through serial link of 9600 bps GSM connection takes about fifteen
minutes, which is an unacceptable delay in emergency cases.
Apart from other challenges the main challenge for high throughput WSN is network capacity
depending upon radio link, [12], [13] and its key ingredient is optimal bandwidth utilization. In
future as other resource requirement is increasing linearly, the bandwidth requirement is
increasing exponentially.
For Emergency Management, OFDM based 802.16 (WiMAX) has been proposed as a broadband
wireless communication system, [19], which inter-connect several heterogeneous networks
including WiFi and WSN.
Figure 2: Three-component signal of the seismic station, [13].
Apart from this for nodes of real time streaming video applications, high volume data transfer for
uplink also faces bottlenecks, [20].
1.1.3. Oil and Gas Exploration WSN
WSN with larger geographical coverage for high data throughput are utilized in Oil and Gas
exploration fields for seismic data collection [4]. These fields possess typically 2000 nodes per
sqkm over large areas, with dense number of nodes ranging from 20,000 to 30,000
simultaneously active. The field can be extremely large like more than 30 sq km. The data load is
of 150kbps to 160kbps for one set of seismic sensor nodes formed from a group of three sensors,
[5], [6]. This collaboratively forms huge data, and requires scalability and very high data
throughput to forward data to data-centre for further research and analysis work, [4], [5], [6].
In this research work 2-tier WSN is considered as in Figure 1, with WiMAX and WiFi, as
specified in [5], [6], [14], [15], [61]. This is showing one WiMAX cell with one base station
(BS) in the upper tier. In the lower tier WiFi router sink Nodes are shown, which are attached to
numerous sensor nodes for seismic data collection. For simplicity, hereafter WiMAX router sink
Node is referred as Node and WiFi sensor node is referred as node in the rest of this article. The
lower tier of WiFi, 802.11b has adequate bandwidth for the group of its nodes, like a typical
throughput of 4Mbits/sec and maximum of 11Mbits/sec for an elementary popular version, [26],
23
[27]. So lower tier has sufficient throughput, but the upper tier of WiMAX is facing throughput
bottlenecks for consolidated Uplink operation, as in [16]-[18]. Here WiMAX is backhaul to the
far flung research facility, where collected data have to be immediately investigated and
analyzed for predictions and decisions.
The capacity for Nodes accommodation of a wireless cell is defined by range and throughput. In
the case of Oil and Gas field, range is not an issue, since the number of located Nodes is quite
congested. However the number of Nodes, with an accumulative throughput in this scenario is
limited by the WiMAX uplink capacity, [19].
Base Station (BS)
802.16
802.1680
2.16
router
Node
sensor
nodes
WiFi
Network
OFDM Wireless
802.16 (WiMAX)
WAN
802.1
1
802.11
802.1
1
802.11
802.11
sensor
nodes
sensor
nodes
802.11
master
sensor
node
Figure 3: 802.16-WiFi WSN with BS, WiFi router sink Nodes and sensor nodes
1.2. IEEE 802.11b (WiFi)
The lower tier for WSN under consideration is of WiFi, 802.11b. It uses Contention based MAC,
CSMA/CA with connectionless protocols of data transmission. This is cheaper technology and
commonly adapted with adequate range. It has a smaller cell with basic service set (BSS) and has
a central base station called access point (AP) with a number of wireless stations (sensor nodes in
this case). In the scenario of this research work AP is actually replaced by router sink Node to
connect to WiMAX network as in Figure 3.
WiFi has a typical throughput of 4-5Mbits/sec and maximum of 11Mbits/sec for this elementary
version, [26], [27]. The lower tier has sufficient throughput, but the upper tier of WiMAX is
facing bottlenecks for throughput for consolidated Uplink operation. Therefore no data
overheads are considered for WiFi and it is assumed that all data can be transferred to WiMAX
24
without any bottleneck.
1.3. IEEE 802.16 (WiMAX)
The IEEE 802.16 (WiMAX) is a promising telecommunication technology due to its high speed
data rates, low cost of deployment and large coverage area. This standard describes the air-
interface between Base Station (BS) and Subscriber Station (SS), [21], [22]. Its online
documentation is given by [23]-[25]. In point to multipoint (PMP) mode BS centrally
coordinates the transmission between BS and SSs and there is direct communication between BS
and SSs. Orthogonal Frequency Division Multiplexing (OFDM) is the basic technique and its
enhancement is Orthogonal Frequency Division Multiple Access (OFDMA). The lower two
layers of OSI model of MAC and physical (PHY) are shown in Figure 4 below. It uses Grant
based MAC (TDM/TDMA) along with connection oriented protocol. In fact WiMAX is the
closest practical crystallization of IEEE 802.16.
Figure 4: WiMAX lower layers including MAC and PHY
Its duplexing techniques are frequency division duplex (FDD) and time division duplex (TDD).
TDD technology is more flexible for uplink/downlink bandwidth allocation and its applications
have more benefits. Also TDD is more popular due to its cheap products and for the ability of
asymmetric traffic usage. This research work is concerned with high throughput WiMAX of
TDD based OFDMA which is the backhaul (upper-tier) of wireless sensor networks, as given in
25
earlier Figure 3. The main focus is on TDD parameters for dynamic bandwidth allocation. This is
to be analyzed on frame by frame basis.
In 802.16 (WiMAX) for point to multipoint (PMP) operation BS transmits on downlink a time
division multiplexed (TDM) signal, in which individual subscriber stations are allocated time
slots serially. Access in the uplink direction is by time-division multiple access (TDMA), [21].
For duplexing, it uses time-division duplexing TDD, in which the uplink and downlink share a
channel but do not transmit simultaneously.
Figure 5: TDD frame structure for WiMAX
The detailed TDD frame structure for WiMAX has been presented in Figure 5. This is mainly
divided in Downlink and Uplink subframes. OFDM Symbols are in horizontal direction in time
space and in vertical direction subcarriers are grouped in subchannels in frequency domain.
Downlink sub-frame starts with preamble and frame control header (FCH). Next it has broadcast
control, which comprises downlink medium access protocol (DL-MAP) and uplink medium
access protocol (UL-MAP). These indicate physical transition on the downlink as well as
bandwidth allocations and burst profiles on uplink, [21]. The size of DL-MAP also depends on
the number of active SSs. The size of UL-MAP is also variable and depends upon number of
active SSs. Next are the TDM slots of each SS, as prescribed by DL-MAP. At the end there is
26
Tx/Rx transition gap separating the downlink sub-frame with uplink sub-frame. The uplink sub-
frame starts with initial maintenance (ranging) opportunities. The next are bandwidth request
contention opportunities. Afterwards SS scheduled data is placed one by one as previously
arranged in UL-MAP. Each SS sends data in its own time slot.
Figure 6: OFDM DL and UL ratio
The frame resources are distributed between downlink and uplink subframes, as shown in Figure
6. This figure also shows that the resources can be adaptively distributed between subframes. BS
centrally controls all the traffic on the downlink, and in advance on the uplink. So apart from
data in downlink, BS sends broadcast for all the SSs. A subscriber station can know whether a
burst contains traffic destined for it or not. This capability allows a SS to skip bursts in the
downlink sub-frame that contains no relevant traffic, [21].
In initial ranging SS performs initial access process for initial power leveling and ranging by
using ranging request (RNG-REQ). SS’s transmit time adjustment and power adjustment, are
done in advance through the response (RNG-RSP). In initial ranging request SS adjusts
transmission parameters in a loop for best suitability. BS monitors the uplink quality and
commands SS to use a particular burst profile, [21]. During normal operation Radio Link Control
(RLC) monitors the link conditions and forces the SS to change the burst profile accordingly.
There are two methods for SS to request a change in downlink burst profile depending on the
SS’s mode of operation either grant per connection (GPC) or grant per SS (GPSS), [21].
The size of FFT used is equal to the number of subcarriers in the frame. All the subcarriers are
not used for data. But some contribute to left or right guard subcarriers, some other may be DC
(or Null) subcarriers and some serve as Pilot subcarriers for signal calibration or similar uses.
The overall organization of subcarriers is shown in Figure 7, whereas only Data subcarriers are
27
for user data.
Figure 7: Frequency-domain representation of OFDM symbol.
1.4. Area of Research
In WSN deployment important arising issues are scalability and data analysis. Real time data is
always analyzed in a far flung office (laboratory) and has to be efficiently transported there. In
order to facilitate data transfer, highly efficient wireless links are needed. Also the increased
uplink efficiency can significantly contribute to larger cell sizes, which can cover more sensor
nodes. This can provide ease of deployment economically, through exorbitant cost savings.
In this research work, 2-tier WSN as presented in Figure 3, has high data load on the wireless
uplink of upper tier protocol of 802.16 (WiMAX). This is because, a lot of sensor nodes
deployed in the field, can be transmitting simultaneously real time impulsive data in the WiFi
network of lower tier. The wireless gateway router sinks Nodes (access points) will route all the
traffic to the 802.16 Base Station through uplink. Since main data sent on 802.16 (WiMAX)
network is in the wireless uplink direction, therefore from Base Station highest uplink efficiency
is required to forward the data as fast as possible to support real time usage.
Normally 802.16 (WiMAX) has bandwidth availability mainly for downlink, but uplink has
comparatively low performance, [32]. Because presently this protocol is getting popular for the
applications to serve hot spots like for providing the Internet and similar type of areas [17], [19],
which have main traffic flow in the downlink direction. In these areas the downlink data is given
more consideration, since this is the main data flow.
On the other hand the cell size for 802.16 is limited by two factors, firstly by all users aggregate
uplink/downlink throughput and secondly by range within the cell. But here in the under
consideration WSN the main limiting factor is the throughput, which may squeeze the size of the
cell to support fewer sensor nodes. In order to monitor the real time phenomena of impulsive
28
nature more uplink throughputs are highly preferred, since real time constrains end-to-end delay
and hence throughput. Which severely affect the performance and the accuracy to monitor the
real time impulsive phenomenon of this kind.
1.5. Motivation
The common approach for sharing the bandwidth between downlink, DL and uplink, UL
subframes is through adaptive distribution of the resources. However it can be seen that in most
of the cases of earlier research as detailed subsequently, some fixed margins (or fixed step sizes)
are being used either for upper or lower limit definition. These fixed margins (or fixed step sizes)
without any calculation are usually either over estimated or under estimated. If this is upper
margin then may be under utilized or if this is lower margin then may be over utilized. Also
fixed margins do not specify the dynamics of the traffic. If the link traffic is on the verge of
bottleneck then the sudden appearance of any kind of overhead, may be in the form of
management message or broadcast, can bring the link to find delay, drop, fragmentation or repeat
request. Even in worst cases it may find session timeout, restart of transmission or transmission
failure. Overheads further increase with slow fragmented link causing even tighter and lesser
throughput along with other problems.
However by dimensioning the traffic, its behavior can be kept well within limits. The DL traffic
pattern has been defined here for WSN, and its resources have been calculated. The DL traffic is
more systematic in this scenario as compared to UL traffic. The UL traffic is rather abrupt and
bursty in nature. If adequate resources are available it will provide relief from fragmentation and
further deterioration. By the use of sufficient resources, delay comes within the good limits and
packet drop reduces to minimum, ultimately fragmentation reaches to minimum by saving the
extra overheads. Here MMPP traffic model has been defined for OFDMA based transmission
along with DTMC queuing system to gauge the traffic related parameters of delay, drop and
throughput. This will provide adequate resources according to the minimum requirement of DL
and will enable to transfer rest of the resources to UL to facilitate the bulky traffic. Ultimately
overheads are reduced, when resources are adequate on downlink and uplink has maximum
resources and this way congestion is avoided to provide a relief from slowing down link.
1.6. Objectives of the Thesis
In this thesis uplink bottlenecks of WiMAX in the upper tier of 2-tietr WSN have been
addressed. The strategies have been designed through different models to overcome these
shortcomings by estimating the resource requirement and by redistributing the unused resources
optimally. This research work enhances the uplink performance without degrading the downlink
operation. The uplink bottlenecks of a cell for a congested field are mainly restricted by
throughput. In the WiMAX cell considered here, congested WSN are deployed for collecting the
seismic data from areas like Oil and Gas exploration field or earth quake monitoring site. The
models of stochastic process are used to solve the optimization problem with random variables in
29
finite state space. Figure 8 is showing the block overview of proposed research work. Main focus
is to find out minimum resource requirement for DL. For this, DL minimum traffic model is
designed to reduce extra traffic and to limit the system resources. Also the objective of this
model is to transform the DL traffic pattern to ON/OFF format. This way it can be easily adapted
for MMPP model for further analysis which is a stochastic process. After this, queuing behavior
is analyzed with a DTMC queue model for DL, for inter frame dependency. The depth of
analysis is extended to packet and frame level. The inter-relationship of DL minimum traffic
model, MMPP traffic model and DTMC queue model has been further shown in Figure 9. This
shows the formation of the groups of 10 master sensor nodes on WiFi network, transmitting to
WiMAX router Nodes in the cell. Also the parameters for DTMC queue model for DL are
identified.
One WiMAX
Cell DL
Minimum
Traffic Model
Channel
Model
MMPP
Traffic
Model
DTMC
Queue
Model For
DL
DL Min Traffic
Performance
Parameters
Estimation
Resource
Group Set
Formation of
Algorithm for
DL Min, UL
Max Resources
Simulation
models for
verification of
results
UL Max & DL
Min Resources
Estimation For
Adaptive Shift
UL Frame
Utilization
Model
WiMAX
Overheads
Estimation
For DL
Figure 8: Proposed Research Work Overview.
MMPP
Traffic Model
MMPP
Traffic
Service rate
n packets/frame
DTMC Queue
Model For DL
Arrival rate
k packets/frame
To One
WiMAX Cell
Assumed DL
Minimum
Traffic Model
DL Min
Traffic
Up to Max. 10
master sensor
nodes in Wi-Fi
master
sensor
nodeGroups of 10
master sensor
nodes
Figure 9: Main Focus of DL Minimum Traffic Resources Estimation.
30
For resource allocation partial usage of subcarrier (PUSC) method of distribution is used. The
relevant derivations of the final Algorithm are implemented in MATLAB. And with variety of
resources the properties of parameters like throughput, delay and packet drop are analyzed to
determine minimum suitable DL resources and maximum UL resources. Later on simulation
models are setup in NIST WiMAX module on top of NS2, to gauge the efficiency of the
designed algorithm. The main blocks carry following objectives concisely.
One objective of the module of DL minimum traffic is to define a strategy to reduce the
DL traffic to minimum possible level, for reducing the system resources to the lowest
limit. And secondly this module transforms the DL traffic by streamlining and organizing
to ON/OFF format to make it acceptable to the next module of MMPP traffic for further
processing.
DL traffic is further encapsulated in the format of MMPP traffic model which supports
the analysis of the traffic at packet level. This model also includes the Bernoulli and
Binomial distributions for traffic. This in coordination of previous module supports the
derivation for traffic related expressions by making it more systematic.
Next is the DL queue which is modeled in consistency with DTMC queue structure to
examine the behavior and derive the expressions for packet arrival rate/frame and service
rate/frame along with throughput, delay and probability of packet drop.
The module for resource group set provides the method in PUSC for the valid list of
resource sets for DL and UL from low to high usage.
The objective of overhead part is to include all those variable and fixed overheads which
have significant effect on DL traffic and resource distribution. This covers all the entities
which are up to slot level and have countable contribution to overheads.
This is important to know the UL frame utilization for the distribution of resources.
When UL frame utilization is high and more resources are needed then some vacant
resources from DL can be transferred adaptively to UL subframe to facilitate the bulky
UL traffic. The UL frame resources are assumed like DTMC filling process and
expressions have been derived to calculate maximum and minimum frame usage, which
define a boundary for adaptive shift.
By utilizing all the derivations of the previous modules an algorithm has been designed
which determines minimum estimated resources for DL minimum traffic by working
iteratively and by taking into account the traffic quality parameters like throughput,
number of packets, delay and probability of packet drop. The vacant resources of DL can
be transferred to UL, to make UL resources maximum.
The formulation used in this algorithm is implemented in MATLAB forming a test bed
for examining various characteristics of DL traffic for a particular set of resources with
respect to throughput, delay and probability of packet drop.
For proving the authentication of the algorithm’s results, comprehensive simulation
models are designed in NIST WiMAX module on top of NS2. So a simulation test bed is
formed for OFDMA based WiMAX under Rayleigh flat fading channel to verify the
31
analytical results of the algorithm. The results are proved through intensive simulations
by three models, one for DL and two for UL, keeping in view the quality performance
parameters of throughput, delay and packet drop. Also the analysis and the evaluation of
the effects of different system pertaining parameters like guard time, modulation, code
rate, symbols, slots, and DL/UL ratio were conducted on previous quality of service
parameters to achieve best performance metrics.
1.7. Main Contributions of the Thesis
The research work in this thesis contributes following achievements.
The design of DL minimum traffic model is proposed which reduces the DL resources to
minimum level and links to next model of MMPP.
Next MMPP traffic model is presented which helps in the derivation of traffic related
expressions.
A DTMC queue structure is defined for the analysis of DL queue behavior for quality of
service related parameters.
The analytical expressions are derived for the estimation of quality of service parameters
of throughput, delay and probability of packet drop along with packet arrival and service
rates per frame for specific set of resources.
Detailed analysis is carried out to include the resources occupied by DL overheads and
some UL overheads.
Novel strategies have been incorporated to estimate UL frame utilization, using DTMC
framework for maximum and minimum usage.
The expressions are derived to estimate minimum DL and maximum UL resources
respectively, to shift adaptively between normal and maximum UL ratio, when UL needs
more resources.
An algorithm has been designed by using the previously defined modules, which
enhances the efficiency of UL bandwidth utilization through optimization, without
degrading the DL performance.
A test bed has been developed in MATLAB by implementing the algorithm for the
examination and estimation of DL resource related quality parameters.
Also a simulation test bed has been developed for OFDMA based WiMAX under
Rayleigh flat fading channel to verify the results of the algorithm. This test bed is also
used to analyze the effects of system related parameters on quality of service parameters.
The designed algorithm provides highest level of instant convergence through adaptive
shift to maximum level of UL resources in just one step, which increases UL throughput
to maximum level while keeping good QoS parameters of delay and packet drop, and
without degrading the DL operation.
Due to maximum resources allocation for UL the chance of error for faulty service
32
through delay, packet drop or session restart reduces to minimum. This will have better
ability to handle congestion and forward impulsive kind of real time traffic.
Also the increased uplink efficiency will significantly contribute to larger cell sizes,
which will cover more sensor nodes and coverage area, providing ease of deployment and
economically exorbitant cost saving.
This research work has produced following comprehensive and relevant research publications as
principal author.
Ansar H., Noor M., “Bandwidth Utilization Efficiency Enhancement for OFDM based WSN”,
Accepted, International Journal of Communication Systems (IJCS, John Wiley Online Library,
Aug.), vol. 31, no. 15, 2018. (IF=1.6)
Ansar H., Noor M., “Analysis for the importance of OFDM uplink frame resource adequacy, to
keep good QoS parameters”, in submission.
1.8. Structure of the Thesis
In Chapter 1 basic introduction is provided about WSN, WiFi, WiMAX and WSN under
consideration for research. The research area is explained and problem statement is formulated.
The objectives of the research work and main contributions are explained.
The remainder of the thesis is organized as follows. Chapter 2 describes literature review and
related work. Initially all the factors influencing the WiMAX throughput are discussed in detail
including overheads. All the previous similar techniques for throughput enhancement by
adaptive shift are discussed.
In Chapter 3 the detail introduction of WiMAX is presented. In the beginning all the parameters
influencing WiMAX throughput are elaborated through example. The permutations for resource
allocation are discussed. The smallest allocate able resource unit of slot is explained in detail.
Later on, frame overheads estimation methods are presented. Next, the procedure for “frame
resource group set” is included.
In Chapter 4 system model is explained. The DL minimum traffic model is presented. Later on
channel model, MMPP model and DTMC queuing model are explained.
In Chapter 5 performance estimation parameters of DL for QoS are presented in detail. Here two
schemes for packet scheduling are discussed for known and unknown MCS distributions. A
pictorial overview of DL minimum traffic model is also presented.
In Chapter 6 UL subframe utilization is gauged. The procedures are defined to identify the need
for adaptive resource distribution for maximum UL resources and normal UL resources. The
main algorithm is explained which estimates the adequate resources for DL and maximum
resources for UL, and estimates the UL frame utilization for adaptive shift of resources. Later on,
the DL minimum traffic resource estimation algorithm for QoS parameters, is explained.
In Chapter 7 numerical analysis and simulation results are discussed. Firstly simulation analysis
33
is provided for the effect of UL resource utilization on QoS parameters of packet delay and drop
along with throughput. Next are the results of the algorithm for DL minimum traffic. Afterwards
three simulation models provide results for (a) DL minimum traffic, (b) UL maximum traffic and
(c) UL normal traffic for comparison. The results are discussed appropriately, to achieve the
main goal of UL maximum resource allocation.
Finally chapter 8 provides conclusions and future directions.
34
Chapter 2.
Literature Review and Related work
2.1. Factors influencing Capacity of WiMAX, including System overheads
In literature review all those main factors and parameters have been identified which influence
the capacity of WiMAX channel. These entities provide a deeper insight into WiMAX
technology in relation to throughput, and these are kept at optimal level wherever possible in this
research work, to contribute to enhanced performance in the devised procedures onward (on
proving stages). Here some kinds of overheads are also mentioned, which influence capacity.
2.1.1. Cyclic Prefix G
Cyclic prefix of a symbol is denoted by G. This is a fraction of symbol’s time and carries no new
data. But, this is a repeat of the last portion of the useful symbol time appended in the beginning.
This is a margin for variable delay spread by Inter symbol Interference (ISI) and time
synchronization errors. This is a kind of overhead, if this is shorter then can be less expensive
depending upon the situation, [58-60]. For lower value of G the throughput gets better for all
modulation and coding rates.
G = 1/2m where m = (2,3,4,5)
2.1.2. Modulation
Assuming 64-QAM modulation having 6 bits per symbol, the raw Capacity of 7MHz channel for
best G of 1/32 is 34.9Mbps without coding rate, but this reduces to only 3.9Mbps when using
BPSK of 1bit per symbol. Different Modulation schemes are used according to the channel
conditions, which affect channel capacity. Also the available radio spectrum constrains the size
of the channels in frequency bandwidth and fixes the size of raw capacity, [29]. Because the
channel is not necessarily of fixed size but can vary with time as environmental conditions
change for NLOS channels. In practical systems channel can be changing, affecting different
parameters and this can introduce errors, [29]. Errors can be due to imperfect transmission, air-
link, and imperfect detection.
2.1.3. Forward Error Correction (FEC)
To cater errors redundant bits are added in the information before transmitting, so on receiving
side error detection and correction can be done, called forward error correction, (FEC), [29].
35
Adding redundant bits adds overhead which further reduces 7 MHz channel capacity. If using
64-QAM for highest coding rate of ¾ the capacity now reduces to 26.18 Mbps and for BPSK for
½ rate reduces to only 2.92 Mbps. When channel conditions deteriorate or such that it cannot
support 64-QAM, but may marginally support BPSK which can work in weaker channel
conditions at lower rate, if conditions improve system can shift back to 64-QAM for a higher
speed, this is called adaptation.
2.1.4. Adaptive Communication
In wireless communications the channel conditions are not constant but may keep on changing
due to surrounding and environmental conditions. Due to this reason channel is monitored and
Channel State Information (CSI) is received at receiver and is fed back to the transmitter. After
feedback this CSI is used at transmitter to estimate the channel and adapt parameters like
modulation, coding scheme (coding variable rate, variable length code), power and other system
factors to get best results. The channel state information for signal to noise ratio (SNR) is
available at the transmitter, using pilot signal, [42]. The performance of adaptive communication
system is determined by feedback and also processing delay, constraints of coding, modulation
schemes and the rate of channel variation, [28]. So, adaptive communication performs much
better than older fixed margin scenarios by utilizing each opportunity of getting best throughput,
without wasting resources. Adaptive systems respond immediately to the channel variations
rather than working on previously assumed calculations. In 802.16 after initial maintenance the
Subscriber Station is required to continuously monitor the transmission quality and control the
burst profile. The Base Station may also monitor the received transmission quality and instruct
the SS to switch to a different uplink burst profile. Such adaptation capability permits the system
to switch to a more robust PHY technology during harsh channel conditions and back to more
efficient schemes when the channel is reasonably good. The radio link control’s (RLC)
adaptation process is designed to be a continuous process, attempting to strike an optimal
balance between efficiency and robustness. There are quite a few resource parameters, which can
be used appropriately to adapt accordingly to dynamically control the transmission. To achieve
QoS management it needs adaptive traffic management mechanisms. In 802.16 networks the
QoS is fully controlled by the Base Station, i.e. the B.S. has to handle all the data traffic as well
as to deal with its prioritization, [29].
2.1.5. Radio Resource Management
RRM can switch between different Phy burst profiles on per-frame and per-SS basis, by using
SNR profile. Combinations of Phy modulation and FEC schemes used between BS and SS are
called uplink and down link burst profiles. Maximum possible bit rate is a function of different
modulation formats adopted according to the changing channel state, and choice of modulation
format is a function of channel state.
36
2.1.6. QoS Scheduling
Qos is a mechanism for the assurance of network performance associated with data stream
concerning delay, throughput, etc. A packet coming in the network can be transmitted, or if
network is congested can be dropped, but if queue is available, it can be place in the queue for
later on delivery. Multiple priority queues are available for different classes of services.
Scheduling policy determines how and when to process packets in these queues. Scheduling
services is data handling mechanism. BS controls UL and DL scheduling. Scheduler calculates
throughput requirements for UL and DL traffic. The DL is mainly broadcast and scheduler fills
in each burst with respect to the QoS parameters of the frames in the queue. Whereas, UL
scheduling uses a more complex grant/poll scheme, as it requires the BS and individual SS to
coordinate. In PMP operation BS is the Central Scheduler for UL and DL. Four types of
scheduling services are available in 802.16 (WiMAX) UGS scheduling, rtPS scheduling, nrtPS
scheduling, BE scheduling. Although different types of QoS classes had been defined by the
IEEE 802.16 standard, the scheduling architecture is left to be vendor specific. Designing an
efficient scheduling algorithm provides high throughput and minimum delay, is challenging for
system developers. Some scheduling improvements are proposed in [33], [59]. As different SSs
are located at different locations so they find channel in different channel states for each of them.
Scheduler should provide highly efficient use of radio resources.
2.1.7. Sub-carriers allocation
For downlink BS has more power to transmit so it transmits on all the sub-carriers. But for up-
link SSs has limited power to transmit. So the OFDM PHY allows the uplink channel to be
subdivided into multiple sub-channels so that the SS can concentrate their transmission power
into fewer data sub-carriers in each symbol. This allows multiple SSs to share the channel
simultaneously, to increase the efficiency for using the uplink channel. Sub-channels affect the
channel capacity, by changing the minimum allocation unit on the uplink, [21].
2.1.8. Communication Overheads
When some resources for data are not fully and efficiently used then some part left unused
becomes an overhead. Also, some other kinds of overheads are useful but carry no useful data
information. In 802.16, there are various points where improper utilization can create extra
overheads, some are listed below apart form others. Any unused space of some resource is also
contributing to overhead. These include poorly filled frames, in which some space is left blank,
without data, [21]. For this Minimum Allocation Unit (MAU) size must match frame size and
should be in consistency with data type, [29].
2.1.8.1. Minimum Allocation Unit (MAU)
If the size of MAU is very closely fitted to the size of packet over the channel, then the capacity
37
of the channel increases.
2.1.8.2. Contention region
Similarly there is a contention area which is like time slots in open poll available. The allocation
of this area and utilization should be properly organized to avoid any wastage of useful
bandwidth.
2.1.8.3. Initial ranging
During initial access SS needs to set parameters with BS for proper start of communication and
joining the network. The allocation of this area is very important, although there is no useful data
to transfer through this area.
2.1.8.4. Preamble
At various points before beginning communication some recognition space for timing and
similar purpose is added, without carrying data information.
2.1.8.5. Management overheads
2.1.8.5.1. Sub headers
There are various headers and sub headers added with the data PDU, which do not carry user
data but they format the data. Also CRC is used which is important part but caries no user data.
Grant Management headers used for bandwidth allocation are also overhead, [39].
2.1.8.5.2. Packing and Fragmentation
These are the processes which make the MAC PDU by combining or splitting to fit in the
available space. The headers of these contribute to overhead.
2.2. Techniques for Improvement of throughput (Capacity)
The basic prevailing techniques for improvement of throughput have been reviewed here. The
optimum values pertaining to these techniques have been used on all possible occasions, for the
proving stages of enhanced performance procedures.
2.2.1. Modulation Techniques
For higher rates more favorable channel conditions are required which increase throughput but
38
need more power. Various modulation techniques provide different number of bits to be carried
per unit. Also for more efficient modulation more power is required for the same range. The
lowest one BPSK is the most robust and least efficient for throughput, because it has the ability
to work in the worst channel conditions among all the other modulation techniques, [28].
2.2.2. Power Techniques
At times when channel is deteriorating more power is needed to transmit. Deterioration can be
caused due to various reasons like rain storm, snow storm, wind storm, multi-path interference or
some other problem. These cause different kinds of fading at various frequencies. In order to
cater these, transmitter power has to be increased accordingly for successful transmission. In this
way packet error rate and bit error rate is dependent on received signal strength, [29].
2.2.3. Various Coding Techniques
Initially with different codes for code rate and code length, like technique were used. Various
coding techniques have been researched for efficient output rate. When the block of input data is
coded, output from different coding techniques gives different block sizes. For FEC in 802.16
(WiMAX) Reed-Solomon and convolutional codes are inherently used. But other codes like
turbo codes are also researched for better results. The output block size for different code length
varies. With more advanced coding schemes the efficiency can be enhanced further, [32].
2.2.4. Adaptive Techniques
Modulation constellations along with coding length and rate combinations for better efficiency of
bandwidth according to the channel conditions are used. For higher efficient modulation schemes
power is also adjusted, because they need more power for the same range. Since channel
conditions can be different for different users, these parameters are varied accordingly per user,
per frame basis adaptively for highest possible channel capacity. IEEE 802.16 systems controlled
by the base stations support adaptive modulation and coding on both downlink and uplink, and
adaptive power control on the uplink, [28].
2.2.5. Sub-carrier Grouping Techniques
As various users for Uplink usage are located at different places in the field, so channel
conditions are different for them. The SSs in 802.16 are allocated sub-channels (group of sub-
carriers), because user station has less power and it cannot transmit efficiently in total available
sub-carriers. In uplink channel is divided into multiple sub-channels. This lets multiple SSs share
the channel simultaneously. Sub-channels affect the channel capacity indirectly by changing the
MAU on the uplink. There can be sub-channels like 1, 2, 4, 8, 16, [21]. The smallest allocation
unit is one sub-channel which consists of like 192/16 = 12 sub-carriers in frequency by one
OFDM symbol in time. In that case the coded and uncoded block size is 1/16 of the value. This
39
can improve the air-link utilization by matching the allocation to the amount of data being sent.
For example, without sub-channels a SS using 64-QAM 2/3 code rate has an uncoded minimum
allocation unit of 96 Bytes. If the SS needs to send 6 Bytes in a frame then 90 Bytes of the
allocation could have wasted. However if a single sub-channel is allocated, the uncoded
minimum allocation unit is 96 × 1/6 = 6 Bytes.
The group of sub-carriers they use suffer from different channel conditions. So, adaptively per
user basis depending upon SNR and BER values, the system parameters are adjusted. The
allocation of sub-channels (sub-carriers) can also be done adaptively to evenly distribute the
other system parameters for overall more efficiency of the uplink, [21], [22]. Key issues of
system level modeling for WiMAX are presented in [30]. In WiMAX sub-channel (sub-carriers)
allocation techniques called permutations, are used, like PUSC, full usage of subcarriers (FUSC)
and adaptive modulation and coding (AMC) schemes. Out of these, PUSC is more common and
used in this research work.
2.2.6. Frame and symbol size
If the data frame sizes are the best matching with the system channel filling block, then the
wasted space will be at minimum and system can be highly efficient. In case the frame size is
such that the system resources can be filled only partially, then there will be some wasted space.
The frame is made of whole number of symbols (per frame). Since the symbol time varies with
channel width there is no way to have a whole number of symbols fit into a fixed frame length in
every case. So there can be small gap at the end of each frame that is unused. This has greatest
impact for shorter frame lengths, [28].
2.2.7. MAU size
One of the strengths of OFDM technology is its ability to send very small amount of information
using as few as a single sub-carrier for one symbol time. For example using the highest order
modulation (64-QAM), a single data sub-carrier could be used to send as few as 6 bits data at a
time. Therefore channel usage is highly granular. In terms of channel capacity this helps to
reduce the amount of wasted bandwidth in sending packets over the channel because the
allocation can be closely fitted to the size of the packet. In that case, the aggregate capacity of the
channel increases due to more efficient usage. There are some restrictions on how 802.16 OFDM
PHY organizes the data sub-carriers into Minimum Allocation Unit (MAU). In the OFDM the
MAU’s useful capacity (Bytes) is variable and depends on the chosen modulation and coding,
[29].
However there can be cases when the amount of data to be sent in a burst spills over a MAU
boundary and in those cases a nearly empty MAU is sent representing additional channel
overhead. The additional overhead represented by fractionally occupied MAUs is variable. For
uplink the number of bursts depends on number of active SS in a frame. The number of uplink
bursts per frame will be generally higher than downlink for this reason, [29].
40
2.2.8. Packing and Fragmentation
Packing and Fragmentation features at the MAC layer can be used to adjust the size of the
packets to be sent (or received) to the MAU. Fragmentation refers to splitting a MAC SDU
across multiple MAC PDUs, for better packing of MAC SDUs into the available OFDM
frequency-time resources by using all data sub-carriers in each OFDM symbol. This increases
link efficiency. Packing is converse of fragmentation, and combines two or more MAC SDUs
into a single MAC PDU. Without these the overhead of fractionally used MAUs will be higher,
because the scheduler will have fewer options to size the packets to the bandwidth allocations,
[29].
2.2.9. Resource allocation through Scheduling
The base station scheduler has considerable freedom in arranging the packets to be sent into a
burst. Packet scheduling algorithms are implemented at both the BS and SSs. A scheduling
algorithm at SS is required to distribute the bandwidth from the BS among its connections. A
scheduling algorithm at SS is not needed if the BS grants bandwidth to each connection for SS
separately i.e. the Grant per Connection (GPC) procedure is followed. If the Grant per Subscriber
Station (GPSS) is followed, the scheduling algorithm at the SS needs to decide on the allocation
of bandwidth among its connections, [21]. Uplink scheduling task is more challenging as the
necessary information of SSs such as queue size for the uplink scheduling is not available. An
uplink algorithm at the BS has to coordinate its decision with all the SSs whereas, a downlink
algorithm is only concerned in communicating the decision locally to the BS. An adaptive power
control scheme has been proposed to keep the transmission rate at a higher level. So an adaptive
opportunistic scheduling algorithm is formed, [33]. Also, the SNR thresholds trigger the AMC
and FEC combinations, [59].
The generic scheduling algorithms may not be adequate for wireless networks. In wire-line links,
the bandwidth is constant, while in wireless links it is varying with time due to interference,
fading and shadowing. Also due to different physical locations, different SSs may perceive
different channel quality at the same instance, called multiuser diversity. So wire-line algorithms
do not cater bad channel conditions. Two algorithm classes considered are 1) non-opportunistic
scheduling and 2) opportunistic scheduling. Non-opportunistic scheduling algorithms assume the
channel is either in a good or a bad state and user per flow queuing, while opportunistic
scheduling considers more comprehensive information on channel quality. Channel state-
dependent packet scheduling considers location dependent and time dependent channel
conditions. When an SS receives a unicast request polling, it sends a bandwidth request. The
bandwidth request contains the length of its uplink data connection queue, [33]. The maximum
Signal-to-Interference ratio scheduler is based on the allocation of radio resources to subscriber
stations which have the highest Signal-to-Interference ratio, [33]. Some scheduling
improvements are proposed in [33], [59]. Scheduling and call admission control methods have
41
also been used for increasing throughput. Each SS is allocated a certain number of time slots in
some/all of the subcarriers. So some overhead is needed because some slots are needed to be
allocated for control messages, [40], [42]. By considering multitude of services using realistic
physical and mac layer models, delay and blocking can be investigated. The mechanism of
packet scheduling process in WiMAX is one of the main entities which also affect the
throughput and delay related issues.
2.2.10. Diversity through MIMO system
The general method called Multiple-in Multiple-Out (MIMO) channel estimation, combines
signals from M transmitters (M-IN) and N receivers (N-Out) with the goal to enhance the fade
resistance and to increase the combined channel spectral efficiency, or some combination of
both. A special case of MIMO is MISO Multiple-in Single-Out for Space Time Coding (STC).
This gives an extra link margin in fading NLOS environments. This gives additional link
protection and higher availability in NLOS conditions. The system performance has been
evaluated for variety of features like different types of MIMO schemes, receiver structures and
frequency reuse schemes in [31].
2.3. Related work in Adaptive Technique and Problems
Although there are many areas in which research has improved the WiMAX performance, in this
thesis mainly the distribution of the resources of slots between DL and UL sub-frames has been
considered (through packet scheduling). This will enhance the throughput and hence the
efficiency of frame utilization. The slots are not only used for data but these are also used for
control messages. As DL side is also used for broadcasting control messages including DL-Map,
UL-Map, so there are more overheads on this side. This performance enhancement can be
achieved through adaptive split between DL & UL sub-frames. There are quite a few efforts for
adaptive split between DL & UL from different angles of research. Most of the efforts are in the
area of physical frame resources measurements. In the physical layer there are resources of
subcarriers and symbols, which together form the slots. In fact the frame utilization for these
resources has been tried to gauge, mostly from different perspectives.
1) In [34], Iwan Adhicandra has shown a way for adaptively allocating resource between
DL and UL subframes. It has been started with a larger DL portion and afterwards UL borrows
some resources from DL. But DL resources have been just gauged by fixed margins without any
calculations. This leaves huge flaws like DL over utilization. The triggering point for DL is set at
high value of less than 60% frame utilization and upper limit is defined at 70%, before resetting
to initial value, along with the least working boundary of 10 symbols for DL. So the
redistributable margin in DL is very constrained, and overall efficiency improvement is
relatively low. The traffic related deeper insight cannot be estimated because no traffic related
parameters like delay measures are involved. The traffic considered is just simple IP, best effort
and for only four SSs.
42
2) Rastin Pries in [35] has used another approach for adaptively allocating the resources to
DL and UL. He has chosen a minimum of 65% limit for collective usage of DL and UL
subframes, which is quite high. Also he has used a step size of 5%, to increase the resources of
the higher usage demand side, but it does not include upper limit definition on either side. A
fixed step size like this may not always match the capacity of the resource units exactly, so this
can produce unjustified distribution. He has considered the traffic which is to some extent
symmetrical on both sides like VOIP, but much improvement has not been shown. Also on
asymmetrical traffic like web and ftp, although he has shown comparatively lower delays, but
these are still quite high of more than 250ms. However this does not specify anything about
accommodating bursty uplink traffic of shorter duration. Also the number of supported SSs is
quite less and with only low traffic rates.
3) In [36], author has described another technique for channel split ratio, through learning
automata process. LA is a finite state machine, which via a repetitive learning process of a
stochastic environment, offers an optimal action from a pool. The environment reacts to the
action to produce a feedback and further repetitions produce final action. He has considered very
small span of allocation for bursty traffic. There is no analysis for delay and service rate
concerned parameters, but this only shows slot usage. In some cases results are very close to
static approach.
4) In [37], author has proposed cubic spline extrapolation ratio determination algorithm for
the estimation of adaptive ratio of the next frame. This algorithm maintains a fixed size history
that helps the extrapolation of the next value based on the previous values already added to the
history. In this case no traffic dynamics is used to train the history vector. So convergence is
rather slow, and unaware of immediate traffic pattern changes. Also author has considered very
small range of DL : UL ratio.
5) In [38], split ratio for TDD subframes has been derived with respect to TCP/IP flow
parameters, by undertaking the returned back acknowledgment. However WiMAX system level
parameters are not taken into account, not even overheads. Also this scenario does not specify
delay analysis.
6) In [27], scheduling through priority queue size for reserved traffic is used, but shows only
5% improvement.
2.4. Proposed Work
In this research work an algorithm is proposed to enhance the bandwidth utilization efficiency
for OFDM backed WSN, without degrading the performance of downlink. A minimum DL
traffic model is designed which defines downlink traffic pattern for sensor nodes. The bandwidth
utilization of DL has been estimated by using a Markov modulated Poisson process (MMPP)
based model, along with Discrete Time Markov Chain (DTMC) queuing structure. In this regard
different performance parameters like throughput, probability of packet drop and delay has been
estimated for minimum set of resources, which have been shown graphically for different
number of Nodes. Overheads have also been considered to find out the final DL resources. The
43
uplink data traffic is measured to find out full frame utilization condition. At that time the uplink
resources are adaptively increased to the previously calculated value to increase the sub-frame
capacity. The simulation results prove the specified improvement in UL data traffic. Here
algorithm is derived for OFDM based backhaul of WiMAX but with relevant changes this
algorithm can also be used for OFDM backhaul of Long Term Evolution (LTE) services.
44
Chapter 3.
IEEE 802.16 (WiMAX) and System Overheads
3.1. IEEE 802.16 (WiMAX)
WiMAX has a scalable use of any spectrum width which may vary from 1.25 MHz to 28 MHz.
The size of accompanying FFT also varies accordingly. For a 10 MHz channel, FFT size is 1024,
which shows that channel is divided into 1024 equally spaced discrete narrow band subcarriers.
The capacity of each subcarrier depends upon the modulation order used. Different modulation
schemes like BPSK, QPSK, 16-QAM, 64-QAM are available, which are used in different
channel conditions and provide different throughputs [21]. The higher order modulations need
more power and the lower order is more robust for working in the worst channel conditions. All
the subcarriers are not used for data, some are used for safety zone which are called guard
subcarriers, as shown in Figure 7. These physical slots are for guard subcarriers of right and left
transition gaps (RTG and TTG) between subframes. Some others are used for reference
frequency, called DC subcarriers. A number of subcarriers are used to monitor the quality of the
channel, called pilot subcarriers. Only remaining subcarriers are available for data transmission.
Hence these subcarriers which are not used for data form a kind of operational overhead which
affect capacity, [42]. WiMAX frame duration may vary from 2.5ms to 20ms according to the
configuration as shown in Table 1, with larger frames carrying more data.
Table 1: Different Frame Durations for WiMAX.
Code
Frame
duration
(ms)
Frames
per second
Symbols per Frame
(symbol duration = 12.5μs)
Total Symbols
per second
0 2.5 400 200 80000
1 4 250 320 80000
2 5 200 400 80000
3 8 125 640 80000
4 10 100 800 80000
5 12.5 80 1000 80000
6 20 50 1600 80000
7-255 reserved - - -
IEEE 802.16 defines a number of uplink scheduling mechanisms like polling, bandwidth grants
45
and contention regions to provide four types of scheduling services Unsolicited Grant Services
(UGS), Real-time Polling Service (rtPS), Non-real-time Polling Service (nrtPS), and Best Effort
(BE). The resources in WiMAX frame are fixed which are distributed between uplink and
downlink subframes. OFDMA is acting between physical (PHY) layer and media access control
(MAC) layers. Here it is allowing multiple SSs to make use of different bandwidth resources in
both time and frequency domains. Different Nodes are allocated with these time and frequency
resources in units of slots. The slots are the smallest distributable unit of time and frequency
which carry data for different entities and slots are allocated to Nodes. The standard has no
specific algorithms for utilizing the available bandwidth by OFDMA, so this is a new
challenging research area of efficient utilization of the available bandwidth [37]. There are a
number of ways to group the subcarriers in subchannels like partial usage of subchannel (PUSC),
full usage of subchannel (FUSC) and adaptive modulation and coding (AMC). Among these
PUSC is the most common technique, [58]-[60] and in this research work only PUSC is
considered for slot allocation.
The raw capacity of a WiMAX channel depends upon a number of factors as given by following
equation. This is further explained with an example.
Craw = Data rate = number of un-coded bits per OFDM symbol / OFDM symbol duration
Craw = coding-rate · B· n · Ld · log2(M) / L (1 + G) (1)
Whereas, parameters are explained as below,
fs = sampling frequency = FLOOR(n · B)
f = one subcarrier spacing = fs / NFFT = n · B / NFFT
Tb = useful symbol time = 1 /(one subcarrier spacing) = 1/f
Tg = Guard Time (CP time) = G · (useful symbol time) = G · 1/f
Tsym = OFDM Symbol duration = useful symbol time + Guard Time
= 1/f + G ·1/f = Tb + G·Tb = Tb + Tg
= 1/f ( 1 + G )
Tsf = sampling Time = 1/fs = Tb / NFFT
The rest of the parameters are explained with example values.
B = channel BW (bandwidth) = 7 MHz
L = NFFT = Total subcarriers or FFT size = 256
Lower frequency Guard subcarriers = 28
Upper frequency Guard subcarriers = 27
Nused = Total used subcarriers = L – 28 – 27 – DC subcarriers = 200
Ld = Total data subcarriers = Nused – Pilot subcarriers = 192
n = sampling factor =8/7
46
G = Cyclic Prefix (Guard Time) =1/16
Constellation = 16-QAM
Coding rate = 3/4
M = 16
log2(M) = 4
Craw = ¾ · 7 · 106 · 8/7 · 192 · 4 / 256 ( 1 + 1/16) = 16.94 Mb/s
For 7 MHz channel Table 2 gives the detail of data rates for different Cyclic Prefix (G ratio) and
Modulation and coding schemes in Mb/sec. It can be seen that for higher order modulating
schemes data rates are greater. Also as G ratio reduces, the data rate increases.
Table 2: Data rates for different Modulation and coding schemes in Mb/sec.
G ratio BPSK
1/2
QPSK
1/2
QPSK
3/4
16-QAM
½
16-QAM
¾
64-QAM
2/3
64-QAM
3/4
1/32 2.92 5.82 8.73 11.64 17.45 23.27 26.18
1/16 2.82 5.65 8.47 11.29 16.94 22.59 25.41
1/8 2.67 5.33 8.00 10.67 16.00 21.33 24.00
1/4 2.40 4.80 7.20 9.60 14.40 19.20 21.60
3.2. IEEE 802.16 (WiMAX) Frame Resources
A WiMAX frame has resources in the form of subcarriers in frequency domain and symbols in
time domain, which mutually form slots. A slot is the smallest allocation unit carrying data, for
different entities. The total symbols of the frame are distributed between DL and UL subframes
according to the DL & UL ratio, [21], [22]. However these resources are not always fully utilized
depending upon the traffic conditions of DL & UL. WiMAX TDD has no fixed DL & UL ratio
but it is adaptive and can be changed as shown in Figure 6. The DL is also a broadcast channel
for BS to send the control information to SSs like frame control header (FCH), DL-MAP, UL-
MAP, UCD, DCD, initial ranging and BW contention. Using this information SS knows the
allocations of slots in the DL and UL sub-frames, to share the uplink to BS.
These WiMAX resources of symbols, subcarriers and slots, are discussed here in detail. For
PUSC permutation the parameters like subcarriers, frequency, sampling ratio and timings for
different bandwidths of 1.25 to 20 MHz are listed in Table 3 for OFDMA and also for 3.5 MHz
of OFDM, [21], [22].
For 10MHz channel at normal ratio of 1:1 the detailed formation of number of resources for
symbols, subcarriers, tiles, clusters and slots is given in Table 4. Also the distribution of
47
resources in the form of symbols, subcarriers, subchannels, tiles, clusters and slots is shown in
Figures 10 and 11. For 10MHz channel it has 1024 subcarriers (FFT) out of which only 720 are
used for data and 120 for pilot signaling. In PUSC for UL there are 35 subchannels having each
24 subcarriers and this group of subcarriers is divided into 6 tiles with 4 subcarriers each, over 3
symbols span. For DL there are 30 subchannels with each having 28 subcarriers and this group
of subcarriers is divided into 2 clusters with 14 subcarriers each, over 2 symbols. A slot consists
of 28 subcarriers over 2 symbols in DL. However in UL a slot consists of 24 subcarriers over 3
symbols. So in general, one slot carries 48 data subcarriers × symbols overall for DL and UL.
Table 3: PUSC Parameters of Subcarriers, Frequency and Timing for OFDMA.
Parameters Values
OFDM-
PHY
OFDMA-PHY
(a) DL
System bandwidth (MHz) 3.5 1.25 5 10 20
FFT size / Subcarriers 256 128 512 1024 2084
Subcarriers per cluster - 14 14 14 14
Number of subchannels - 3 15 30 60
Number of Left-guard subcarriers 28 22 46 92 184
Number of Right-guard subcarriers 27 21 45 91 183
Number of guard subcarriers 55 43 91 183 367
Number of null and guard subcarriers 56 44 92 184 368
Number of used subcarriers 200 85 421 841 1681
Number of pilot subcarriers 8 12 60 120 240
Number of data subcarriers 192 72 360 720 1440
(b) UL
System bandwidth (MHz) - 1.25 5 10 20
FFT size - 128 512 1024 2084
Number of guard subcarriers - 31 103 183 367
Number of used subcarriers - 97 409 841 1681
(c) General
Sampling factor 8/7 28/25
Sampling frequency fs MHz 4 1.4 5.6 11.2 22.4
Sampling time (1/fs , n sec) 250 128 512 1024 2048
Subcarrier spacing (ƒ ,kHz) 7.81 10.93
48
Useful symbol time (Tb = 1/, s) 128 91.4
Guard time (Tg=Tb/8, s) 16 11.4
OFDMA symbol time (Ts = Tb + Tg, s) 144 102.8
Table 4: Normal ratio 1: 1 Resources of Symbols, Subcarriers, Tiles, Clusters and Slots.
Configurations Downlink Uplink
DL/UL Symbols excluding preamble 22 24
Ranging, CQI and ACK (column symbols) N/A 3
Number of Subchannels 30 35
Number of symbols columns per Cluster (DL)/Tile
(UL)
2 3
Number of subcarriers per Cluster (DL)/ Tile (UL) 14 4
SymbolsSubcarriers per Cluster (DL)/Tile (UL) 28 12
SymbolsData Subcarriers per Cluster (DL)/Tile
(UL)
24 8
Number of pilots per Cluster (DL)/Tile (UL) 4 4
Number of Clusters (DL) / Tiles (UL) per Slot 2 6
Subcarriers Symbols per Slot 28* 2 = 56 12*6 = 72
Data Subcarriers Symbols per Slot 24*2 = 48 8*6 = 48
Subcarriers Symbols per DL/UL Subframe 330*56=18480 245*72=17640
Number of Slots 22*30/2=330 21*35/3=245
Figure 10: Symbols, Tiles and Slots formation for UL PUSC, [21].
49
Figure 11: Symbols, Tiles and Slots formation for DL PUSC, [21].
3.3. IEEE 802.16 (WiMAX) Resources (Slots) Capacity
Table 5: Slot Capacity of various MCS Levels.
Modulation Bits per
symbol
Coding
Rate
DL Bytes per slot=
Bps*CR*48
UL bytes
per slot
BPSK ½ 1 0.5 3 3
QPSK 1/8 2 0.125 1.5 1.5
QPSK 1/4 2 0.25 3 3
QPSK 1/2 2 0.5 6 6
QPSK 3/4 2 0.75 9 9
QAM-16 1/2 4 0.5 12 12
QAM-16 2/3 4 0.67 16 16
QAM-16 3/4 4 0.75 18 18
QAM-64 1/2 6 0.5 18 -
QAM-64 2/3 6 0.67 24 -
QAM-64 3/4 6 0.75 27 N/A
QAM-64 5/6 6 0.83 30 N/A
50
For different MCS levels and coding rates, bits per symbol are given in Table 5 and relevant slot
capacity is determined. The QAM-64 5/6 is the most efficient one with slot capacity of 30 bytes
and BPSK 1/2 is least economical with a slot capacity of only 3 bytes. But the system related
broadcast data is always transmitted using a slower MCS level of either BPSK or QPSK 1/2 rate
for maximum reliability, and to ensure to reach the last limit of the cell, because of its robust
performance in worst channel conditions. Previously calculated parameters for resources of slots,
like number of slots and slot capacity will be used in subsequent analytical and simulations
models.
3.4. Resources for Frame Overheads
3.4.1. Frame Overheads
Frame overheads are of many types, but here only directly concerned, are considered. A
WiMAX OFDMA frame is a constitution of subcarriers in frequency domain and symbols in
time space respectively. But only subcarriers for data are contributing to throughput. First of all
space for RTG and TTG is set apart in physical slots, which consists of (right and left) transition
gaps of UL & DL. The affect of cyclic prefix of symbol is relatively less and is not taken into
account here. Only PUSC permutation is mainly considered here, in which slots for DL & UL
can be calculated by following rule, [21], [22].
SUL = HUL-SCH · YUL-SYMBOL · 1/3 , SDL = HDL-SCH · YDL-SYMBOL · 1/2. (2)
HUL-SCH and HDL-SCH are the respective number of subchannels for UL & DL, and YUL-SYMBOL and
YDL-SYMBOL are number of symbols for UL & DL respectively. Other areas of overhead include
Auto Repeat Request (ARQ), Channel Quality Information Channel (CQICH), QoS flow
reserved slots and different types of connections reserved slots. Here ARQ and CQICH are not
considered, since these contribute quite less. The frequency of downlink and uplink channel
descriptor (DCD and UCD) is only once in a period of up to maximum 10 sec, so it is also not
considered. There are mainly two kinds of overheads fixed and variable for DL and UL. Primary
concern is to gauge the overheads faced by DL minimum traffic model. So for UL mainly fixed
overheads and those, which appear in DL as UL-MAP, are considered. The total frame overhead
slots for DL minimum traffic include DL & UL overheads and can be given as,
Total DL Min Traffic Frame Overhead Slots = STDL-MIN-OH = SDL-OH + SUL-OH . (3)
The MAP messages are broadcast by using the MCS level of QPSK ½, which takes 6 bytes per
slot. All overhead slots are computed using this MCS level.
51
3.4.1.1. Downlink Subframe Overheads
For DL fixed overhead of preamble takes two symbols for synchronization and one symbol for
Frame Control Header (FCH), [21], [22], [59]. The main broadcast messages are of MAC
management, DL-MAP and UL-MAP which carry information about symbol allocation for
Nodes. Also these have fixed and variable parts. The DL overheads can be summed up as below,
and each item is computed subsequently.
SDL-OH = SDL-OH-PAM + SDL-OH-FCH + SDL-OH-DM-UM + SDL-OH-CONS . (4)
SDL-OH-PAM = 2 · YDL-SYMBOL , SDL-OH-FCH =1 · YDL-SYMBOL ,
SDL-OH-DM-UM = SDL-OH-DM + SDL-OH-UM .
3.4.1.1.1. Connections overhead
When Node N joins BS for communication, connections are assigned in pair, for both DL and
UL, with connection id (CID), a 16 bit value. Each CID is unique and for one type of service
only like broadcast, multicast, unicast, transport, management, initial ranging, etc. Each Node is
assigned normally CIDs for basic, primary, secondary management and secondary transport data
connections. In DL, each CID for Node is allocated at least one slot. And in general each active
Node is allocated four slots at least. So service flow related minimum number of slots for N
Nodes can be equal to total number of connections CONSN, for all Nodes as given below by
Equation (5), [21], [22], [62], [63], [64]. Whereas, ncon-max is the maximum number of
connections for one Node and n(ncon) stands for one connection for each combination.
N
n
n
n
conNCONSOHDL
con
con
nnCONSS
1 1
max
)( . (5)
3.4.1.1.2. DL-MAP and IE’s
In Equation (6) DMFIX is the fixed part of DL-MAP with the size of 96 bits plus 32 bits of CRC,
making 16 bytes, but with compression its size is 80 bits plus 32 bits of CRC, making 14 bytes,
[21], [22], [59] and with some different optional fields its size is 13 bytes, (taking 6 slots), [63],
[64]. Addition of an IE of length 44 bits plus (16)*(XCID the number of CIDs), (take a minimum
of 2 slots), [21], [22], [64]. The variable part grows with the addition of each information
element IECON of 7.5 bytes (takes 2 slots), for each defined connection of Equation (5). And this
makes total slots of SCONS, [64]. After four allocations if there is overflow, then 2 more slots are
allocated, [64]. Both parts are as given below in Equation (6),
IE = 44 + (16) ∙ XCID 2 slots= SIE , SCONS (IECON) = 2 · SDL-OH-CONS .
52
SDL-OH-DM = DMFIX + SIE + SCONS (IECON) = 6 + 2 + 2 · SDL-OH-CONS . (6)
Although this variable part method of [64], takes more slots than normal to encapsulate the
actual data volume. However necessary slots may be further trimmed down which encapsulate
the actual final data volume.
3.4.1.1.3. UL-MAP and IE’s
In Equation (7) UMFIX is the fixed part of UL-MAP with 4 fields of 7 bytes, but with
compression it takes 5 bytes, [21], [22] and with some more optional fields it takes 8 bytes,
(takes 4 slots), [64]. The variable part depends upon the number of IE’s from Nodes requesting
bandwidth for UL. Each IE takes 4 bytes and almost one slot SIE-UL for burst definition, but if
filled then takes two slots, [64]. The aggregate slots are given as below,
SDL-OH-UM = UMFIX + N · SIE-UL = 4 + N · 1. (7)
3.4.1.1.4. Uplink Subframe Overheads
The fixed overhead for UL includes contention regions of initial ranging with two symbols,
bandwidth request with one symbol and optional preamble from one to two symbols, [21], [22],
[59]. The total overhead slots can be given as below,
SUL-OH = SUL-OH-PAM + SUL-OH-IR + SUL-OH-BW-RQ . (8)
SUL-OH-PAM = 0/1/2 , SUL-OH-IR = 2 ·YUL-SYMBOL , SUL-OH-BW-RQ = 1 · YUL-SYMBOL .
3.5. Frame Resource Group Set
The frame resources in terms of symbols, subcarriers and slots are distributed among DL and UL
subframes. The total frame symbols are allocated between DL & UL such that if YT-SYMBOL are
total symbols and YDL-SYMBOL are used for DL then YUL-SYMBOL = YT-SYMBOL - YDL-SYMBOL are left
for UL. As PUSC permutation is considered here, so DL slots occupy 2 symbols and UL slots
occupy 3 symbols. Therefore best recommendation is to make YUL-SYMBOL a multiple of 3 and YDL-
SYMBOL should be of the form 2k+1, where k is any natural number and 1 is for FCH of DL. For
PUSC the distribution can be further converted to slots by using Equation (2) for DL and UL, for
different steps of up to down ratios, making a frame resource group set. These steps (levels) of
resources are used in an algorithm of section 6.3. By using this procedure, a table has been
formed in section 7.2.
53
Chapter 4.
System Model (DL)
4.1. System Description
In this research work only one cell has been considered comprising of two tier WSN, as shown in
Figure 12. There is one BS in the centre with TDD-based WiMAX of IEEE802.16 standard,
having OFDMA transmission in the upper tier. This is the formation of upper tier of WSN. The
lower tier comprises of WiFi IEEE802.11b router Nodes which collect data from sensor nodes.
Lower tier has sufficient bandwidth for the group of its nodes, like 4 Mb/s a typical with a
maximum of 11Mb/s. However the aggregate bandwidth of the uplink from all router Nodes
forms the bottleneck. WiMAX TDD has frames constitution and the resources are shared
between DL and UL subframes. In order to anticipate the bursty traffic of UL, here DL traffic
has been modeled and dimensioned. The resources for needed DL minimum traffic have been
estimated and rest of the resources can be transferred to the UL traffic when it becomes burstily
high although for a shorter duration. In WiMAX the smallest distributable resource is
represented by unit of slot, which is a two dimensional entity of symbol in time domain and
subcarriers in frequency domain. In the physical layer packets are assumed to be using PUSC
scheme. Here one slot carries 48 data subcarriers × symbols. Each frame carries transmission
overhead for both UL and DL subframes. In DL subframe, there is preamble, frame control
header (FCH), DL-MAP and UL-MAP messages along with data bursts. The MAP messages are
broadcasted to indicate the start and end, size, and encoding of data bursts. So, to accomplish this
some modeling has been done onwards. The WiMAX resources are represented by units of slots.
4.2. DL Minimum Traffic Model for WSN nodes
In normal mode WiMAX DL traffic can be, more than minimum traffic as defined subsequently.
But in minimum traffic mode for DL traffic some rules are defined. During operation WiMAX
DL sends data to router Nodes for further transmission to sensor nodes on WiFi network, of
lower tier. This data is for monitoring the health of the link and sensor nodes. Also there are
instructions for controlling the operation of the nodes (WiFi sensor nodes), like transmitting data
to the uplink [5], [6]. The volume of this DL data is much smaller than the sensor data which is
sent to uplink. These sensor nodes are capable of processing data for transmitting and receiving
at a rate of 50kbps to 60kbps, [5], [6]. Let’s call a combination of three sensor nodes a master
sensor node, which work in coordination for seismic data collection and transmission at an
aggregate rate of 150kbps to 160kbps, [5], [6]. Each master sensor node takes 0.04 sec to transfer
800 bytes at 160kbps. For ten master sensor nodes it will take 0.4 sec serially (or one by one) in
54
other words to transfer each 800 bytes, which makes ON time -1 equal to 0.4 sec. It is assumed
that 800 bytes for one master sensor node can include three packets of 256 bytes for each of three
destined sensor nodes of the combination. This is further assumed that 256 bytes are sufficient
for the operation of one sensor node in the minimum downlink traffic mode, since only
instruction sets needed are for monitoring and controlling. Hence the router Node (WiFi sink
router Node/WiMAX Node) can have the structure of data traffic in ON/OFF mode for ten
master sensor nodes, as shown in Figure 12 (a). In Figure 13 (b) it is shown that 50% Nodes are
ON and 50% Nodes are OFF. This will further reduce the DL traffic. In order to reduce the
traffic further another selection can be to consider only 20% ON Nodes, if total number of Nodes
is much higher with lower throughput. The percentage of ON Nodes should be a meaningful
combination.
ON OFF
1/
1/b
1 2 3 4 5 6 7 8 9 10
10 master sensor nodes
one router Node ON/OFF traffic modes
(a)
Figure 12: (a) WiMAX DL ON-OFF Traffic Model for WSN.
50% 50%
ON
1/
OFF
1/b
ON
OFF
(b)
Figure 13: (b) 50% Nodes ON and 50% Nodes OFF.
Following are the assumptions for minimum traffic model from WiMAX DL to WSN.
Each WiFi (master) sensor node is a combination of three simple sensor nodes working
55
in coordination.
Each WiMAX router Node is attached to maximum ten (master) sensor nodes of WiFi.
All the sensor nodes attached to one router Node are of the same operational properties,
like make, model, processor, memory, OS, etc.
One router Node has all those sensor nodes preferably which need same instruction set
for monitoring and controlling.
All simple sensor nodes can transmit and receive data, at a rate of 50kbps to 60kbps. So
a master sensor node can transmit and receive data at an aggregate rate of 150kpbs to
160kbps.
A data packet size of 800 bytes on DL is used for master sensor node, a combination of
three sensor nodes. These 800 bytes can carry three 256 bytes packets for each
individual node.
This is the minimum data these sensor nodes need for operation from the remote
application server, for monitoring and control.
Sensor nodes are allocated through uniform distribution.
With reference to the above suppositions a relationship can be created between the ON and OFF
durations of DL traffic. The mean on and off periods as mentioned previously for router Node
DL traffic are denoted by -1 and b-1 respectively. The total number of router Nodes is
represented by N and the percentage of ON Nodes is given by . The total ON Nodes are
denoted by and total OFF Nodes are denoted by . Hence following relationships are derived,
100
N (9)
100
)100( N (10)
b
)100(11 (11)
If -1 has a value of 0.4 sec as previously stated, N is taken 10 Nodes, is 50%, then is 5, is
5 and b-1 is also 0.4sec. But if is 20%, then is 2, is 8 and b-1 is 1.6sec. This makes the
constituent parts of the WiMAX DL traffic in minimum mode.
56
4.3. Channel Model
For WiMAX OFDMA/TDMA based downlink transmission, there are multiple traffic channels
called subcarriers, available for all the SSs (router Nodes). The channel state information
represented by signal to noise (SNR) ratio is a feedback signal at transmitter through pilot signal,
[42]. Through adaptive modulation and coding (AMC) the maximum number of bits per symbol
cb, a subcarrier can transmit per unit time at a particular time slot can be given as a function of
SNR, and Pber , maximum bit error rate (BER) as below.
)5ln(
5.11log
2ber
b Pc . (12)
BPSK
QPSK1/2
QPSK3/4
16-QAM1/2
16-QAM3/4
64-QAM2/3
64-QAM3/4
BS
Figure 14: MCS regions divided by SNR around BS.
Actually the regions of modulation and coding scheme (MCS) are defined according to the value
of SNR, as shown in Table 6. These MCS regions are also shown around BS in circular form in
Figure 14. These regions start with the highest SNR value which is located closest to BS, like 64-
57
QAM 3/4. And these regions end with the lowest SNR value which is located furthest away from
BS, like BPSK.
Table 6: MCS regions divided by SNR.
Modulation Coding
Rate
Bits/Symbol (BPS) Coding Rate *BPS Receiver SNR
(dB)
BPSK ½ 1 0.5 3.0
QPSK ½ ½ 2 1 6.0
QPSK ¾ ¾ 2 1.5 8.5
16-QAM ½ ½ 4 2 11.5
16-QAM ¾ ¾ 4 3 15.0
64-QAM 2/3 2/3 6 4 19.0
64-QAM ¾ ¾ 6 4.5 21.0
The partition between regions of MCS c , can be derived from Equation (12) as given by [44],
[45], is stated as below.
5.1
)5ln()12(
berc
cP
. (13)
So, for c= 0, 1, . . .,C number of MCS levels for downlink operation, there will be C+1 steps and
c regions of (instantaneous SNR) denoted by c . If c < c+1 then modulation level c is
applied. The instantaneous SNR, has probability density function (pdf) of P() in the field
area of a cell. For zero level of c no packet will be transmitted. The average SNR is denoted by
avg and for Rayleigh flat fading channel pdf is given as under by [45].
avgavg
p
exp
1)( . (14)
Hence, after solving above Equation (14) in following Equation (15), this produces the
probability P(c) of falling in cth MCS level for a Node, as given by Equation (16) below.
58
1
)()(c
c
dpcP (15)
avg
c
avg
ccP
1
expexp)( . (16)
If channel has B bandwidth and sc subcarriers, then each subcarrier has a bandwidth of = B/sc.
If the length of a time slot is Ts and c is the MCS level used, having cb bit rate from Equation
(12), with a packet of length Lp bits, then packet transmission rate Tr per time slot for this Node
and subcarrier is given by,
P
bsr
L
cTT
)( . (17)
This is for extending the depth of analysis to the subcarrier level and slot level. However, here
only fixed Nodes are considered, which do not face severe changing conditions, as faced by
mobile Nodes.
4.4. MMPP Traffic Model
Max
Traffic
lmax
Min
Traffic
lmin
rmin rmaxMMPP
Traffic
BS Downlink
Scheduling
FIFO Queue
MMPP Traffic
Model
Arrival rate
k packets/frame Service rate
n packets/frame
Figure 15: System Model based on MMPP Traffic Model, DL queue, Packet Arrival and
Service rates (packets/frame).
59
All the traffic generated in a cell for DL minimum traffic model, as defined earlier, will be
utilized here as an ON-OFF traffic model, making two-state MMPP model, like in ATM circuits,
[46]-[48]. Thus the packet arrival at a Node follows a two-state MMPP process which is identical
for all Nodes of the same queue, [51]. The main entities of this MMPP traffic model, along with
downlink FIFO scheduling queue of BS in one cell have been shown in Figure 15 and explained
onwards. The MMPP is a stochastic process having a Poisson process whose intensity is
interpreted through the states of the Markov chain. Hence Poisson process is modulated by the
Markov chain making MMPP a special case of the Markovian arrival process called MAP.
MMPP can capture the inter-frame dependency between consecutive frames, making it favorite
for multi-Node traffic design. In this process superposition of all the two state MMPP sources
takes place, [50].
In this model, there are two states of Traffic Max (maximum) and Min (minimum) Traffic, with
lmax and lmin the packet arrival rates and rmax and rmin the transition rates respectively, between
the two states of the MMPP model. So, for all the packets being transmitted on the downlink of
the BS, there is a simple FIFO scheduling queue, which serves packets on first in first out basis
at its arrival and service rates of packets/frame. The on and off durations of -1 and b-1 are
respectively mean periods distributed exponentially. The probability of on and off intervals can
be obtained as below,
11
1
b
onp ,
11
1
b
boffp . (18)
The design parameters include transition rate matrix R and the Poisson arrival rate matrix for
two-state MMPP process as given below.
maxmax
minmin
rr
rrR ,
max
min
0
0
l
lΛ . (19)
The average on Nodes are given by Navg. Here A is the emission rate for the time duration of the
node (master node) Tnode . The average arrival rate is given by lavg . However the actual arrival
rate depends upon the state of the Markov chain and the total number of states of the Markov
chain model. The binomial and Bernoulli distributions of N independent on-off Nodes form the
stationary distribution of n . The four parameters rmax , rmin , lmax , lmin are determined by using
the Index of Dispersion for counts (IDC) matching technique, [46]-[48]. The expression for
IDC() has been derived using the basic method as described by H. Heffes , [46]. There are also
other methods like moment based matching technique and matching technique, [49]. Whereas,
60
Navg= N ∙ pon , A = 1 / Tnode , lavg = N ∙ A ∙ pon .
)1)(()(
))((2
minmax
2minmax
min
IDCr
avg
avgavg
lll
llll. (20)
)1)(()(
)()(2
minmax
min2
maxmax
IDCr
avg
avgavg
lll
llll. (21)
nNon
nonn pp
n
N
)1( . (22)
avg
avg
N
m
m
N
n
nn
A
0
0min
l ,
N
Nm
m
N
Nn
n
avg
avg
n
A
1
1
max
l . (23)
The IDC() is given by following expression, with T being the packet arrivals at fixed intervals
during on period.
2
2
)(
)1(1)(
b
IDC . (24)
61
Chapter 5.
Performance Resources Estimation of DL Minimum
Traffic
5.1. Packet Scheduling by BS
The number of packets scheduled by BS for a frame depends on a few factors like the available
capacity of the channel, which depends upon the level of MCS used, due to the channel
conditions. For higher MCS level more capacity is available. There are two ways to evaluate the
probability of number of packets scheduled, using different levels of MCS as described below.
5.1.1. Known MCS Distribution
If the distribution of the MCS level used for packet transmission is known, then the probability,
that BS schedules n packets from the downlink queue is given by,
max
0
)(
)(
h
h
sch
hF
nFnP ,
l
NMINTDL
S
Sh
maxmax
. (25)
Here probability depends on variety of MCS distributions. Where, F(n) represent all those
instances, which contribute n packets for scheduling during one frame of downlink, and the other
number of packet instances distribution is given by F(h). Here, Smaxl is the number of slots
required to transmit one packet, using the highest MCS level available for the downlink, and
STDL-MIN-N is the total number of available slots for the downlink.
5.1.2. Unknown MCS Distribution
If the MCS distribution is unknown in advance, then the number of scheduled packets depends
on the channel condition of each individual packet. If k packets are scheduled for a particular
frame with k = x1 + x2 + … + xC , where xc is denoting the packets modulated by cth MCS
level. At this frame, k needs less than STDL-MIN-N (St), total slots but if for k + 1 more slots are
needed than STDL-MIN-N, then only k packets will be scheduled. Let Xk and X’k+1 represent these
two cases as follows,
62
C
c
ccck xkxxxxX
1
21 0,|),,,( . (26)
C
c
ccccck xxxkxxxxX
1
211 1',1'|)',,','(' . (27)
The cases ( ) for k packets scheduling are as below,
tktkkk SXSXX )'(&)(| 1 .
(28)
The possible set of scheduling for successful transmission is given by ψk and thus an index
probability function Ic(Xk) is defined for different MCS levels as below,
.,0
)(,1)(
1
otherwise
XifXI
kkkc
. (29)
Therefore, the probability Psch of k packets being scheduled has two parts as given below,
)&Pr()( 11 kkkksch XXkP .
C
c
kc
C
c c
c
X
sch XIcPx
xcPkkP
kk 11
)()(1!
)(!)(
. (30)
Here P(c) is taken from Equation (16). This expression for probability Psch is the sum of two
products, one is the probability satisfying k packets in a particular MCS distribution and other is
not satisfying with k+1 packets.
There are many factors, which affect the channel conditions making the possibility of different
MCS levels. However, these conditions are more stable for fixed subscriber stations as compared
to mobile stations and hence the possibility of MCS level change is much less in fixed station
than mobile one. In this work only fixed stations are considered.
5.2. Queuing Analysis
The estimation of the performance of downlink can be done through the related parameters, as
63
specified here. The trends of queuing packets in the downlink queue are analyzed with the help
of DTMC queuing model, [56], [57]. Figure 16 is showing this model and each state is
representing the number of packets queued in the downlink queue. A discrete-time Markov chain
Qn |n=0,1, . . . is a random sequence, such that given Q0 , . . . , Qn , the next random variable
Qn+1 depends only on Qn through the following transition probability,
P[Qn+1 = j| Qn =i, Qn-1 =in-1, . . . , Q0 =i0] = P[Qn+1 = j| Qn =i] = Pij . (31)
The transitions to and from each state are represented by arrows. These transitions can be
represented by a state transition matrix for downlink queue.
5.2.1. State Transition Probability
The state transition matrix P is representing all the state transitions of the DTMC queue, with
Qmax the maximum size of the queue. So, each element pi,j of matrix P is a 2 × 2 matrix
representing the transition probability of the number of queued packets.
Q0 QmaxQ3Q2Q1
P0,1
P0,2
P1,0
P3,0
P0,3
P0,max
P2,0
Pmax,0
Figure 16: System Queue Model based on DTMC.
maxmax,2max,1max,0max,
max,22,21,20,2
max,12,11,10,1
max,02,01,00,0
QQQQQ
Q
Q
Q
pppp
pppp
pppp
pppp
P
. (32)
It specifies if there are i queuing packets in the current frame, then there will be j queuing
packets in the next frame. If the number of queuing packets is i for the current frame, and n
64
packets are scheduled, then (j - max(i - n, 0)) should arrive to make the number of packets to be j
in the queue of the next frame. Therefore each element of the matrix P can be given as below,
max
0
, )())0,max((
n
n
schji nPnijp DU , l
NMINTDL
S
Sn
maxmax
. (33)
Whereas, the value of Psch(n) can be taken either from Equation (25) or (30), as given previously.
ΛRΛU1)( . (34)
Here, U is the transition probability matrix and D(k) is the diagonal probability matrix, as given
below. The diagonal elements of D(k) denote the probability of k packets being transmitted on
downlink in the frame duration Tmf, in each state of the two-state MMPP model, [55], [57].
!
)(0
0!
)(
)()(
max
)(min
max
min
k
eT
k
eT
kmf
mf
Tkmf
Tkmf
l
l
l
l
D . (35)
Then by using the transition matrix P in Equation (32) the steady state probability matrix M of
the two state MMPP model can be calculated as follows, [52]-[56], and 1 is a column matrix of
ones,
M · P = M and M · 1 = 1. (36)
The state of matrix M defines the number of packets in the queue, as below. It is a 1 × 2(Qmax
+1) matrix.
M = (M(0) M(1) M(2) . . . M(2Qmax +1)) . (37)
The probability of k packets being in the queue can be obtained as follows,
(k) = M (2k) + M (2k+1). (38)
Now the steady state probability matrix ss of the DTMC model of Figure 16 is given as below,
by using Equations (37) and (38),
65
ss = (ss(0) ss(1) ss(2) . . . ss(Qmax)). (39)
Next part describes the calculations of queuing parameters from the above queuing analysis.
5.2.2. Queuing Parameters Estimation
5.2.2.1. Average Queue Length
By using Equation (39), the average queue length Qavg can be given by the following, [55], [57],
max
0
)(
Q
k
avg kkQ . (40)
5.2.2.2. Arrival Process
The average packet arrival rate avg at the queue for the two state MMPP model in frame
duration is given as,
1Ds
max
0
)(
AN
k
avg kk . (41)
In above equation Amax is the maximum emission of packets during frame Tmf. The matrix s can
be solved through s · U = s, and 1 is a column matrix of ones.
5.2.2.3. Service Process
The average packet service rate avg of the queue for the two state MMPP model in frame
duration is given as,
)()(),min(maxmax
00
nPkkn sch
Q
k
n
n
avg
, l
NMINTDL
S
Sn
maxmax
. (42)
5.2.2.4. Throughput
If Lpak is the size of the downlink packet then average downlink throughput avg can be given as,
mf
pakavgavg
T
L
. (43)
66
5.2.2.5. Probability of Packet Drop
The packet dropping probability Pdrop of the downlink can be given as below,
avg
avgdropP
1 . (44)
5.2.2.6. Packet Delay
For packet delay firstly lMP is calculated, then queue throughput QMP is computed and this
contributes to packet delay D by using Little’s Law, [57], as following,
maxmin
maxminminmax
rr
rrMP
lll . (45)
)1( dropMPQMP P l , QMP
avgQD
. (46)
5.2.3. Performance Resources Estimation of DL Minimum Traffic Model
The above consolidation provides an estimation of the parameters of the DL minimum traffic.
Here resources are represented by slots available for downlink. So downlink minimum traffic
model needs minimum slots (resources), for minimum acceptable delay and lower probability of
packet drop for highly efficient throughput. These parameters can be calculated for the specific
resources of the traffic. There is some lower limit for the minimum slots, as the number of slots
is reduced beyond the required slots limit, this will increase the delay and hence the probability
of packet drop will increase. Further reducing the slots will further increase delay along with the
probability of packet drop and finally packet will start dropping out. The minimum acceptable
ranges of delay and probability of packet drop are already known and can be set as the last limit.
Therefore DL final number of minimum resources (slots) STDL-MIN-N should satisfy these limits of
packet delay and drop. So an iterative process is devised, starting from minimum slots (levels) in
the frame resource group set (defined previously). And keep on increasing slots onwards until
the delay and drop limit requirement is satisfied. This procedure is used in an algorithm in
section 6.3. Some resources are required for the need of overheads also, and these must be
allocated in the system as identified previously.
5.3. For One WiMAX Cell DL Minimum Traffic Resource Estimation (Block Diagram)
67
Previously presented analytical modules are interlinked with each other. Figure 17 presents the
overview of this inter-relationship of analytical modules. This inter-relationship of the analytical
modules has been consolidated in the following lines.
First of all a WiMAX cell is shown with BS in the centre. Then around the BS, there are
router Nodes which are attached to master sensor nodes of WiFi network.
Each WiMAX router Node is attached to maximum ten master sensor nodes of WiFi
network. The dotted lines show OFF Nodes and solid lines show ON Nodes.
The DL minimum traffic model is shown at the BS. Here fifty percent Nodes are OFF
and fifty percent Nodes are ON.
The traffic generated for DL minimum traffic model in BS and destined for router Nodes,
is transformed to two state MMPP model for further analysis. The two states of MMPP
traffic model are Max (maximum) traffic and Min (minimum) traffic.
Now this traffic enters in the DL system queue, which is modeled as DTMC to check the
behavior of traffic.
For a specific known set of WiMAX DL system resources, the traffic related parameters
are analytically computed, like arrival rate (packets per frame), service rate (packets per
frame), throughput, packet delay and packet drop. By using this methodology, the
minimum resources for DL minimum traffic for adequate values of quality of service
parameters can be determined.
68
BS
WiMAX Router
Node (ON)
WiMAX Router
Node (OFF)
WiF
i 10
mas
ter
sens
or n
odesOn
e W
iMA
X C
ell
Ass
um
ed
DL
Min
Tra
ffic
Mo
de
l
50
%5
0%
ON
1/
OF
F
1/bO
N
OF
F
Ma
x T
raffic
lm
ax
Min
T
raffic
lm
in
rmin
rmax
MM
PP
Tra
ffic
MM
PP
Tra
ffic
Mo
de
l
DL
Min
Tra
ffic
Q0
Qm
ax
Q3
Q2
Q1
P0
,1
P0
,2
P1
,0
P3
,0
P0
,3
P0
,ma
x
P2
,0
Pm
ax,
0
Se
rvic
e r
ate
n p
ack
ets
/fra
me
Arr
iva
l ra
te
k p
ack
ets
/fra
me
DL
Sys
tem
Qu
eu
e
DT
MC
Mo
de
l
Fig
ure
17
: F
or
On
e W
iMA
X C
ell
DL
Min
Tra
ffic
Re
sou
rce
Est
ima
tion
.
Th
rou
gh
pu
t
Pa
cke
t D
ela
y
Pa
cke
t D
rop
Figure 17: For One WiMAX Cell DL Min. Traffic Resource Estimation.
69
Chapter 6.
UL Subframe Resource Utilization, Adaptive Frame
Shift and Proposed Algorithm
6.1. UL Subframe Resource Utilization
The uplink subframe resource utilization is required to be measured for decision making of either
increasing resources or decreasing already enhanced resources. This will identify, that when UL
subframe resources are either in bottleneck due to overuse, or unfilled due to under use. Hence
this will determine the triggering point after collecting and analyzing system statistics. For a UL
subframe of a given number of V slots, frame resource utilization can be computed. Here packets
are coming from a queue and filling the frame slots. This can be modeled as a discrete time
Markov chain, since each time, this process is independent and not related to the past, [56], [57].
So DTMC Vn |n=0,1, . . . can be defined as a random sequence, such that given V0 , . . . , Vn ,
the next random variable Vn+1 will only depend on Vn according to the following transition
probability,
Pu[Vn+1 = j| Vn =i, Vn-1 =in-1, . . . , V0 =i0] = Pu[Vn+1 = j| Vn =i] = Puij (47)
It is assumed, as the packets arrive and fill the frame slots as a DTMC sequence, this counting
can be placed in Cu matrix like form as below,
maxmax,2max,1max,0max,
max,22,21,20,2
max,12,11,10,1
max,02,01,00,0
VuVuVuVuV
Vuuuu
Vuuuu
Vuuuu
cccc
cccc
cccc
cccc
uC .
maxmax,2max,1max,0max,
max,22,21,20,2
max,12,11,10,1
max,02,01,00,0
VuVuVuVuV
Vuuuu
Vuuuu
Vuuuu
pppp
pppp
pppp
pppp
Pu. (48)
This is covering all the possible transitions, from the number of packets in the current frame to
70
the new number of packets in the next frame. The actual size of the matrix Cu is the maximum
possible total no of given slots V, in the UL subframe. The actual size of the matrix Cu is
determined by the possible maximum total number of given slots Vmax, in the UL subframe.
Afterwards, probabilities can be calculated, by taking the sum of each row of Cu and by dividing
each element of that row by the row’s total sum, to form the probability matrix Pu, as given
above in Equation (48).
Then, by using matrix Pu stationary distribution U of the DTMC can be computed as below and
here U is 1 × V matrix, [52], [56]. Also 1 is a column matrix of ones.
U · Pu = U and U · 1 = 1. (49)
U = (U(0) U(1) U(2) . . . U(Vmax) ) . (50)
All the slots utilization is depicted by the steady state distribution matrix, U. The maximum
value of the vth element of the U distribution shows that this vth slot is in highest utilization and
its minimum value shows that this particular slot is in lowest utilization. These statistics of slot
utilization can be collected for batches of 5 frames each to ensure steady state. These methods
are used in the algorithm, later on.
6.1.1. Maximum Resources Utilization
Hence, for a given set of V slots, the subframe resource utilization is maximum, if the highest
order or near the highest order slots in U distribution are showing maximum utilization. If a
batch is showing maximum resource utilization then for given V slots resource utilization is
maximum.
6.1.2. Minimum Resources Utilization
So, for a given set of V slots, the subframe resource utilization is minimum, if only the lowest
order or near the lowest order slots in U distribution are showing maximum utilization. If a
batch is showing minimum resource utilization then for given V slots resource utilization is
minimum.
6.2. Adaptive UL-Subframe Resource Distribution
This method of adaptive resource allocation provides instant convergence of very high degree.
The grand total resources of the frame, SGT for normal operation are divided between DL as STDL-
NOR and UL as STUL-NOR for normal ratio of 1:1 as below,
71
SGT = STDL-NOR + STUL-NOR . (51)
So, the DL minimum resources, STDL-MIN for maximum efficiency can be calculated here as in
Equation (52) below. These consist of two parts, one set of resources for regular Node data STDL-
MIN-N, which have been calculated already (in section 5.3 Queuing Analysis) and other one STDL-
MIN-OH for overheads as taken from Equation (3) (section 3.4, Resources for Frame Overheads).
These processes are contributing to an algorithm, onward.
STDL-MIN = STDL-MIN-N + STDL-MIN-OH . (52)
6.2.1. Increasing UL-Subframe resources
When the UL resources start showing bottleneck, as identified before by the method of UL
resource utilization section previously, then more resources are needed to upgrade the
performance. The UL resources for maximum efficiency STUL-MAX can be calculated from
Equation (51) and Equation (52) as below,
STUL-MAX = SGT - STDL-MIN . (53)
At this point the previously calculated maximum UL resources of Equation (53) are allocated
adaptively by re-mapping the DL & UL ratio, for increasing the UL bandwidth to available
maximum limit. Hence, all the vacant resources of the DL are transferred adaptively to the UL to
achieve maximum efficiency.
6.2.2. Decreasing UL-Subframe resources
At some point, the UL resources are needed to be decreased for normal operation, when the
already enhanced resources are no more in use. This point is identified by the UL minimum
resource utilization section previously. Then, the UL resources can be restored to normal, as
given by Equation (51), by re-mapping adaptively the DL & UL frame ratio to normal.
6.3. Main Blocks of Proposed Algorithm
6.3.1. Main Algorithm
Here all the previous steps have been concisely placed in an algorithm of Figure 18 for
systematic graphical view like flow chart. The main Algorithm of Figure 18 is consolidating all
the processing steps. So, in the first step of DL Minimum Traffic Resource Estimation, system
72
will select the minimum level from “Resource Group Set” to estimate packet delay and drop. If
with this level, values of packet delay and drop are not satisfied, then it will increase to higher
level of “Resource Group Set” until these values are well satisfied and with in desired range. At
this point system will estimate the required resources. In next step system will measure UL
Subframe utilization. If utilization is full system will adaptively increase the resources to the
previously proposed level. Otherwise if utilization is found to be at minimum, then it will
adaptively reduce the already increased resources to defined normal ratio. If this is the last frame,
process will end otherwise it will loop back and repeat to measure the UL Subframe Utilization
to check the full status until end.
The Figure 19 is showing the detailed steps of DL Minimum Traffic Resource Estimation
Algorithm. After initialization Channel Model is placed. Then is System Model which consists
of DL minimum Traffic Model, MMPP Traffic Model and DTMC Queue Model. Next step is
DTMC Queue Analysis, which provides Steady State Distribution Analysis. Afterwards
Performance Estimation Process uses the Number of Packets Scheduled per Frame and Packet
Arrival and Service Rate per Frame, to estimate the performance parameters like throughput,
delay and packet drop. In the last, Frame Overheads are computed for DL & UL to produce the
final Resource Metrics. The relevant blocks of the main algorithm are concisely briefed here to
refer to the previously discussed detail, as below.
6.3.2. Frame Resource Group Set
The frame resources are defined as slots contributed by symbols and subcarriers. The frame
resource group set has been explained in a previous section 3.5 and by using this method Table 8
has been formed in section 7.2 of Analysis and Simulation Environment.
6.3.3. DL Minimum Traffic Resources Algorithm
This is part of main Algorithm and its further detail is given in Figure 19 and covered in chapters
4 and 5 previously. Moreover Frame Overheads are discussed in section 3.4.
6.3.4. UL Subframe Utilization
This module of UL subframe utilization has been explained in section 6.1. This part of
Algorithm here calculates UL subframe maximum and minimum resource utilization,
accordingly.
6.3.5. Adaptive Resource Distribution
Adaptive resource distribution is calculated in section 6.2.
6.3.5.1. Increasing Resources
73
In this part of the Algorithm resources are increased adaptively to the previously calculated value
as given in Equation (53).
6.3.5.2. Reducing and Restoring to Normal Resources
This part of the Algorithm reduces the resources to normal ratio, as given in Equation (51).
74
START
DL Min. Traffic
Resources Estimation
Measure UL Subframe
Utilization
IF FULL
Adaptively Increase UL
Subframe Resource
Allocation
Measure UL Subframe
Utilization
IF Almost Min.
Adaptively Restore to Normal
UL Subframe Allocation
IF Last FRAME
STOP
NO
YES
NO
YES
NO
YES
From “Resource
Group Set” Start with
Minimum Level
IF Min.
Delay & Drop
OK
Increase to
Next Level
Resources
YES
NO
Figure 18: Main Algorithm.
75
Initialization
Channel Model
System Model
MMPP Traffic
Model
DTMC Queue
Model
DL Min. Traffic
Model
DTMC Queue
Analysis
Steady State
Distribution
Analysis
Performance Estimation Processes Number of Packets Scheduled per Frame
Packet Arrival Rate per Frame
Packet Service Rate per Frame
Performance Parameters Throughput
Delay
Probability of Packet Drop
Frame Overheads
DL Overheads
UL Overheads
Final Resource
Metrics
End
Figure 19: DL Minimum Traffic Resource Estimation Algorithm.
76
Chapter 7.
Numerical Analysis and Simulation Results
7.1. Preliminary Uplink Performance Analysis
Here a simulation test bed has been created to examine the behavior of WiMAX UL traffic. For
this performance evaluation process of WiMAX, a 10 MHz bandwidth Rayleigh flat fading
channel has been used in TDD mode. In order to explain the importance of adequate resources,
simulations have been conducted for different set of resources with different traffic data rates
like low, medium and high. In these simulations different dl:ul ratios are used like 1:1, 2:1, 1:2,
1:3. All the WiMAX simulations are conducted for 50 seconds duration and from 25th second to
35th second simulation is for packet emission. This fundamental process is shown explicitly in
the basic Figures 30, 31, and 32, which are for single source only. All the other Figures are
showing the simulation for packet emission time of 25th second to 35th second. When the
simulation process starts there are some initial procedures, which take some time to complete
and get the environment stabilized. This analysis provides a deeper insight of the quality of
service parameters like throughput, packet delay and packet drop. The effect of different other
system parameters, have also been analyzed. More detail of used parameters is given in Table 7
as below. In these simulations fifteen WiMAX Nodes are used, which are placed in a circle of 50
meters radius, as shown in Figure 20. More on simulation processing is explained in next
sections 7.2 and 7.2.2.
Table 7: Simulation Parameters for UL Performance Analysis.
PHY Profile Type Wireless OFDMA 10 MHz
UL Modulation QAM64-3/4
Packets type IP, UDP
Packet size 1500 bytes
Frame UL / DL Ratio 1:1, 2:1, 1:2, 1:3
Nodes in a Circle of Radius 50 meters
BS attached to a server 1
Number of Nodes 15
Packet Intervals (sec) 0.05, 0.025, 0.0125
(Low, Medium, High Traffic)
Here data rates of low, medium and high are used with respective packet intervals of 0.05sec,
77
0.025sec, 0.0125sec. The packet size is 1500 bytes and WiMAX best effort service is used, along
with UDP on IP. Figure 21 shows aggregate UL throughput for these three data rates. For high
data rate throughput is not as high as expected, like medium data rate. The reason for this is
explained by Figure 22, which shows large packet delay for high data rate as compared to other
rates. The medium and low data rates have quite low packet delay. Also, Figure 23 shows less
aggregate number of packets for high data rate as compared to other rates. Similarly Figure 24
shows large aggregate packet drop for high data rate whereas for other rates aggregate packet
drop is quite low, almost zero. This can be clearly seen that up to medium data rate packet delay
and drop are quite low, along with good throughput. However, for high data rate resources are
not adequate to provide low values of packet delay and drop. And, therefore throughput is also
not good for high data rate.
From Figure 25 to Figure 28 these parameters are calculated for high data rate at dl:ul ratios of
2:1, 1:1, 1:2, 1:3 respectively. It can be seen from Figure 25 and Figure 27 that the aggregate
throughput and aggregate packets are maximum for 1:3 ratio, although these are minimum for
2:1 ratio, where resources are few for uplink. In Figure 26, it can be seen that aggregate packet
delay is maximum for 2:1 ratio, however it is minimum for 1:3 ratio, where resources are more
adequate. Similarly, Figure 28 is showing maximum aggregate packet drop at 2:1 ratio and it is
minimum at 1:3 ratio due to sufficient resources. In Figure 28 for high data rate, at 1:3 ratio
packet drop after Node 10 increases due to saturation of resources and gets close to the high data
rate at 1:2 ratio. So in Figure 27 high data rate at 1:3 ratio shows less packets after Node 10 and
gets close to the high data rate at ratio 1:2. Figure 29 is showing the total throughput of packets
per second during 10 seconds of simulation time, for medium data rate at 1:1 ratio and high data
rate at 1:1, 2:1, 1:2, 1:3 ratios respectively. Here time axis are shown for this reason for the
simulation time of packet emission from 25th second to 35th second and extended beyond to
show the packet behavior once the emission stops at 35th second. It can be seen that total
throughput of packets per second is minimum for 2:1 ratio, but maximum for 1:3 ratio where
resources are more sufficient. Also medium data rate at 1:1 ratio (more resources) has more total
throughput per second than high data rate at 2:1 ratio, with few resources. For high data rate at
1:3 ratio in the beginning Nodes start contributing to the throughput with ample resources, until
throughput goes to higher saturation level due to unavailability of resources. Throughput stays at
this high level and at the end it finishes quickly with sharp drop as the packet emission beyond
35 seconds stops and remaining packets are processed relatively fast.
Figure 30 shows throughput of individual packets from single source, for low data rate at 1:1
ratio for 10 seconds of simulation time from 25th to 35th seconds. Here numerous packets have
throughput above average and few packets have throughput below average. Figure 31 shows
throughput of individual packets from single source, for medium data rate at 1:1 ratio for 10
seconds of simulation time. Here all the packets are more close to average throughput. Figure 32
shows throughput of individual packets from singe source, for high data rate at 1:1 ratio for 10
seconds of simulation time. It starts with a high throughput, but drops immediately to a very low
throughput, due to inadequate resources.
78
The comparisons in these simulations provide a true picture for the adequacy of resources for a
given data rate to provide high throughput and low packet delay and drop. This analysis specifies
the importance and need of adequate resources for a given data rate. These adequate resources
can be derived through calculations and analytical models.
Nodes in 50m radius circle
Wireless Nodes
Figure 20: In simulations N (15) WiMAX Nodes are placed in a circle around a BS.
Aggregate Throughput for Uplink (dl:ul) 1:1
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Number of Nodes
Agg
rega
te T
hrou
ghpu
t (kb
ps)
Low Data rate
Medium Data rate
High Data rate
Figure 21: Aggregate UL Throughput at 1:1 ratio for Low, Medium and High data rates.
79
Aggregate Packet Delay Uplink (dl:ul)1:1
0
2
4
6
8
10
12
14
16
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Number of Nodes
Tim
e de
lay
(sec
)
Low Data rate
Medium Data rate
High Data rate
Figure 22: Aggregate UL Packet delay at 1:1 ratio for Low, Medium and High data rates.
Aggregate Packets for Uplink (dl:ul) 1:1
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0 2 4 6 8 10 12 14 16
Number of Nodes
Num
ber o
f Pac
kets
Low Data rate
Medium Data rate
High Data rate
Figure 23: Aggregate UL Packets at 1:1 ratio for Low, Medium and High data rates.
80
Aggregate Packet drop Uplink (dl:ul)1:1
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Number of Nodes
Num
ber
of P
acke
ts d
rop
Low Data rate
Medium Data rate
High Data rate
Figure 24: Aggregate UL Packet drop at 1:1 ratio for Low, Medium and High data rates.
Aggregate Throughput Uplink
0
2000
4000
6000
8000
10000
12000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Number of Nodes
Agg
rega
te th
roug
hput
(kb
ps)
High Data rate (dl:ul)2:1
High Data rate (dl:ul)1:1
High Data rate (dl:ul)1:2
High Data rate (dl:ul)1:3
Figure 25: Aggregate UL Throughput at 2:1, 1:1, 1:2, 1:3 ratios for High data rate.
81
Aggregate Packet Delay Uplink
0
5
10
15
20
25
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Number of Nodes
Del
ay T
ime
(sec
)
High Data rate(dl:ul)2:1
High Data rate(dl:ul)1:1
High Data rate(dl:ul)1:2
High Data rate(dl:ul)1:3
Figure 26: Aggregate UL Packet delay at 2:1, 1:1, 1:2, 1:3 ratios for High data rate.
Figure 27: Aggregate UL Packets at 2:1, 1:1, 1:2, 1:3 ratios for High data rate.
Aggregate Packets Uplink
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Number of Nodes
Num
ber o
f Pac
kets
High Data rate (dl:ul)2:1
High Data rate(dl:ul)1:1
High Data rate(dl:ul)1:2
high Data rate(dl:ul)1:3
82
Aggregate Packet Drop Uplink
0
1000
2000
3000
4000
5000
6000
7000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Number of Nodes
Num
ber
of P
acke
ts
High Data rate (dl:ul)2:1
High Data rate (dl:ul)1:1
High Data rate (dl:ul)1:2
High Data rate(dl:ul)1:3
Figure 28: Aggregate UL Packet drop at 2:1, 1:1, 1:2, 1:3 ratios for High data rate.
Total Throughput Packet per sec Uplink
0
2
4
6
8
10
12
14
16
25 27 29 31 33 35 37
Simulation Time 25-35 (sec)
Tot
al th
roug
hput
pac
kets
per
sec
(M
bps)
Medium Data rate (dl:ul) 1:1
High Data rate (dl:ul) 1:1
High Data rate (dl:ul) 2:1
High Data rate (dl:ul) 1:2
High Data rate (dl:ul) 1:3
Figure 29: For UL Total Throughput of Packets at different ratios for High and Medium
data rates, in the simulation time.
83
Individual Packet Throughput Uplink (dl:ul) 1:1
0
50
100
150
200
250
300
350
400
450
500
0 5 10 15 20 25 30 35 40
Simulation Time 25-35 (sec)
Eac
h P
acke
t thr
ough
put (
kbps
)
Low Data rate
Figure 30: Individual Packet Throughput of UL at 1:1 ratio for Low data rate, in
simulation time.
Individual Packet Throughput Uplink (dl:ul) 1:1
0
50
100
150
200
250
300
350
400
450
0 5 10 15 20 25 30 35 40
Simulation Time 25-35 (sec)
Eac
hl P
acke
t thr
ough
put (
kbps
)
Medium Data rate
Figure 31: Individual Packet Throughput of UL at 1:1 ratio for Medium data rate, in
simulation time.
84
Figure 32: Individual Packet Throughput of UL at 1:1 ratio for High data rate, in
simulation time.
7.2. Numerical Analysis (Algorithm) and Simulation Environment
Here same test bed of simulation of previous section is used for the performance evaluation of
WiMAX. And also a 10 MHz bandwidth Rayleigh flat fading channel has been used in TDD
mode. The MAC frame duration is 5 ms which consists of 48 symbols and (FFT of) 1024 OFDM
subcarriers, out of which 720 are for data use and 120 are for pilot use. The PUSC permutation is
used in which one DL slot consists of one subchannel and two OFDMA symbols and one UL
slot consists of one subchannel and three OFDMA symbols. One slot carries 48 subcarriers ×
symbols. Through frame resource group section 3.5 a group set is formed for this formation, with
all possible DL:UL ratio combinations except the last one which is just minimum, as given by
vertical lines in Table 8, below. For this combination normal ratio is 24:24 however fixed
overhead of 3 symbols for DL & also for UL (section for frame overheads) are taken apart,
making it 21:21 (20:21, more suitable in PUSC) ratio for data.
For numerical and simulation results N (15) Nodes are placed in one cell of WiMAX around one
BS in a circle uniformly, as in Figure 20. All Nodes are using the highest MCS level of QAM-
64 3/4 for DL carrying 27 bytes per slot and QAM-16 3/4 for UL carrying 16 bytes per slot.
Individual Packet Throughput Uplink (dl:ul)1:1
0
50
100
150
200
250
300
0 5 10 15 20 25 30 35 40
Simulation Time 25-35 (sec)
Eac
h P
acke
t thr
ough
put (
kbps
)
High Data rate
85
These Nodes are attached in WSN formation. All the traffic considered here is of BE service
flow by using UDP on IP. The packet size for UL is 1500 bytes and for DL minimum traffic
model is 800 bytes and 2 × 400 bytes (2 packet sizes for comparison, for algorithm). All
computations in MATLAB are on per second basis and of NS2 are for 10 seconds duration.
Table 8: Valid Preferred DL:UL ratio’s.
DL
Symbols 20 18 14 12 8 6 2 1
DL Slots 300 270 210 180 120 90 30 15
UL
Symbols 21 24 27 30 33 36 39 39
UL Slots 245 280 315 350 385 420 455 455
Firstly the Algorithm is implemented in MATLAB and its results for DL are compared with DL
simulation results. Secondly UL simulations at the calculated ratio are compared with the
existing generic built in WiMAX algorithm of normal ratio.
7.2.1. Algorithm’s Mathematics Results (MATLAB)
The previously designed DL minimum traffic resource estimation Algorithm’s mathematics is
implemented in MATLAB by using the above frame resource group set of Table 8. The
Equations (9-11), (18-25) and (32-46) are mainly used in this Algorithm to calculate resources
for adequate values of throughput, number of packets, delay and probability of packet drop, for
the increasing number of Nodes. This Algorithm starts with the lowest number of resources of
slots and computes the delay and probability of drop. If computed delay and drop values are
higher than lowest acceptable limit then according to need, it increases to next higher level of
resources to facilitate better delay and drop values. The packet delay in WiMAX should be less
than 200 ms and final limit of 150 ms is better as per recommendations. Although network
allows a maximum delay up to 600 ms (may be deteriorating the network performance). This
part of MATLAB estimates resources for variable data traffic. In this process a matrix of 102 ×
102 size is developed to solve as much linear equations. The Index of dispersion for counts is
calculated and the average queue length is computed. The average arrival rate and the average
number of serviced packets during time period, T are calculated. Similarly packet drop
probability per Node, packet delay per Node and throughputs of Nodes are calculated.
86
7.2.2. NS2 Simulation Results
Here NS2 (Network Simulator 2) version 2.34 has been used for simulation models, with a
WiMAX module from NIST. The detailed parameters used in simulation are mentioned in Table
9 below. By adding the estimated resources of 30 slots as calculated from Algorithm (MATLAB)
above for DL Minimum traffic to frame overheads, final DL:UL ratio comes out to be 13:35
symbols. However after fixed over heads only useful symbols left are 10:32. Since 2 redundant
symbols on the UL side are not very justified in PUSC so these are transferred to DL for full
utilization, making final highly efficient adaptive DL:UL ratio of 15:33 symbols. Out of it 12:30
symbols are used for data and various frame overheads, whereas in slots it comes out to be a
ratio of 180:350. This is final and highly efficient adaptive ratio for UL maximum traffic & DL
minimum traffic. The comparison of UL designed maximum traffic ratio is done with the normal
1:1 frame ratio traffic, in the subsequent simulations. Since NS2 performs one simulation at a
time for either DL or UL, so first DL simulations are performed and later on UL simulations are
conducted. For this different simulation Tcl scripts have been written to perform the simulations.
And the post processing of the out put data is done in awk scripts. In awk scripts derived
formulas were used to determine the following network parameters for UL & DL.
• Aggregate Throughput per Node
• Aggregate Packets per Node
• Aggregate Packet delay (ETE (end-to-end)) per Node
• Max & Min Packets delay per Node
• Aggregate Packets drop per Node
• Individual Packet Throughput
• Per Node Total Throughput per sec (above section 7.1)
• Per Node Total Instantaneous Throughput per sec (above section 7.1)
Table 9: Simulation Parameters.
PHY Profile Type Wireless OFDMA Channel Bandwidth 10MHz
Cyclic Prefix 1/8 Channel Fading Rayleigh Fading
Permutation PUSC Queue Length 50
Routing Protocol DSDV Queue Type Drop Tail Priority
Queue
Total Symbols 48 DL:UL Normal
ratio
24:24 Symbols
Modulation UL 16-QAM ¾ Modulation DL 64-QAM ¾
Protocol / Service Flow IP , UDP / BE Traffic UL / DL
CBR / EXP ON-
OFF
Simulation Duration 50 seconds Data Packets
Emission
10 seconds , (25-35)
seconds
Total Frames Expected 10,000 Packet size in bytes DL 800 / UL 1500
87
7.3. Numerical and Simulation Results and Discussion
7.3.1. DL Minimum Traffic Parameters Estimation By Algorithm Results
This Algorithm has estimated the required resources for DL minimum traffic model for packet
size of 800 bytes, to be 30 slots from the frame resource group set. The comparison of these
resources has been shown for up to 50 Nodes.
For these resources number of packets gets double with half size packet of 400 bytes, as can be
seen from Figure 34. In Figure 33, although for 10 Nodes there are total 133 packets of 800 bytes
and 266 packets of 400 bytes per second, but the throughput is same around 850kbps. For 8
Nodes there are (800 bytes) total 108 packets per second at a throughput of 690kbps. It can be
seen in Figure 33 that throughput increases up to 15 Nodes and then saturation starts with smooth
curve, showing completely utilized resources beyond that point. From Figure 35 this is clear that
packet delay up to Node 10 is very small but later on it increase sharply from Node 15 to 25.
Also from Figure 36 the probability of packet drop is zero up to 10 Nodes but later on it keeps on
increasing for subsequent Nodes, which is showing the trend of dropping packets. Here it should
be noted that in Figure 35 for half size data packets delay is also half. For 10 Nodes packet delay
is around 10 ms and less for fewer than 10 Nodes.
Figure 33: Throughput of the DL Min. Traffic model by Algorithm.
88
Figure 34: Number of Packets of the DL Min. Traffic model by Algorithm.
Figure 35: Packet Delay of the DL Min. Traffic model by Algorithm.
89
Figure 36: Probability of Packet Drop of the DL Min. Traffic model by Algorithm.
7.3.2. DL Minimum Traffic Simulation Results
This simulation is run for 8 Nodes with the implementation of DL Minimum traffic ON-OFF
model for WSN, through NS2 procedure for exponential on-off traffic. The total simulation time
is 50 seconds, however it has processed completely from 9998 to 9999 frames starting at 0.00919
second. From Figure 38 it can be seen that for 10 seconds of data packets emission, total packets
generated per second is around average of 109 for almost 2000 frames making a total of 1092
packets. Figure 37 shows, these packets contribute an aggregate throughput of 746kbps.
Figure 39 depicts that individual Node has packet delay from 5ms to 7ms. Figure 40 provides the
probability of packet drop which is zero for all Nodes throughout the 10 seconds of data
emission. Therefore, Figure 41 proves that no packet is dropped for all 8 Nodes throughout the
10 seconds of data emission. These simulation results and analysis prove the perfection of the
DL minimum traffic parameters estimation Algorithm as the simulation results are matching with
Algorithm results.
90
Figure 37: Throughput of the DL Min. Traffic model by Simulation.
Figure 38: Packets Received of the DL Min. Traffic model by Simulation.
91
Figure 39: Packet Delay of the DL Min. Traffic model by Simulation.
Figure 40: Probability of Packet Drop of the DL Min. Traffic model by Simulation.
92
Figure 41: Packet Drop of the DL Min. Traffic model by Simulation.
7.3.3. UL Maximum and Normal Traffic Simulation Results
This simulation provides comparison of the calculated UL maximum ratio traffic with the traffic
of the existing built in WiMAX algorithm of normal ratio (1:1). Different number of Nodes like
6, 7 and 8 with solid signs for UL maximum traffic and 4, 5, 6, 7 and 8 with non-solid signs for
normal ratio are used for performance comparison. Solid signs show maximum UL traffic Nodes
and non-solid signs show normal ratio Nodes. In Figure 42 the 6 solid Nodes of UL maximum
traffic show maximum aggregate throughput near 9Mbps, but only 4 non-solid Nodes can follow
this rate for a lower aggregate throughput at normal ratio. Similarly it can be seen that other 7
and 8 solid Nodes are accommodating 2 more Nodes each at higher aggregate throughput than
normal ratio non-solid Nodes of 5 and 6. The throughput for non-solid 7 and 8 Nodes of normal
ratio further deteriorates.
In Figure 43 this is clear that solid Nodes are showing more aggregate packets than non-solid
Nodes. The 6 solid Nodes have more aggregate packets but only 4 non-solid Nodes follow this
rate for a lower aggregate number of packets at normal ratio. Also 7 and 8 solid Nodes show
more aggregate packets than non-solid 5 and 6 Nodes of normal ratio at the same rate. At normal
ratio the performance of non-solid 7 and 8 Nodes is further deteriorated. In Figure 44 solid
Nodes show minimum delays as compared to non-solid Nodes. The packet delay for 6 solid
93
Nodes is below 20ms whereas only 4 non-solid Nodes at normal ratio could fulfill this margin.
For 7 solid Nodes packet delay shoots to 350ms on the other hand only 5 non-solid Nodes could
come near this range at 370ms. Similarly 8 solid Nodes show delay of 450ms whereas only 6
non-solid Nodes could reach near this point at 590ms. The cases of 7 and 8 non-solid Nodes get
further worst with delays of 590ms and 690ms respectively. In Figure 45 probability of packet
drop is only 0.001 for 6 solid Nodes but only 4 non-solid Nodes are in this range and for 7 solid
Nodes this is 0.052 however only 5 non-solid Nodes are near it at 0.071. Also for 8 solid Nodes
this value further goes down to 0.163 to 0.182 and only 6 non-solid Nodes could achieve only
0.22. For non-solid 7 and 8 Nodes this is the poorest at 0.334 and 0.42 respectively.
In Figure 46 packet dropping situation is the worst for normal ratio Nodes. For 6 solid Nodes
only one packet drops per Node with an aggregate of 6 packets drop whereas only 4 non-solid
Nodes are in this range with an aggregate of 4 packets drop. For 7 and 8 solid Nodes per Node
packets drop are 65 and 204 respectively, with an aggregate packets drop of 458 and 1678
respectively. However at normal ratio 4, 5, 6, 7 and 8 non-solid Nodes have per Node packets
drop of 1, 89, 275, 417 and 512 with aggregate packets drop of 4, 445, 1674, 2921 and 4168
respectively. In Figure 47 the darker color maximum and minimum delays for Nodes on
maximum UL ratio, are lesser as compared to lighter color delays of normal 1:1 ratio Nodes.
Figure 42: Throughput of the UL Traffic by Simulation.
94
Figure 43: Packets Received of the UL Traffic by Simulation.
Figure 44: Packet Delays of the UL Traffic by Simulation.
95
Figure 45: Probability of Packet Drop of the UL Traffic by Simulation.
Figure 46: Packet Drop of the UL Traffic by Simulation.
96
Figure 47: Maximum and Minimum Delays of Nodes of the UL Traffic by Simulation.
These simulation results prove that the performance of solid Nodes of maximum UL ratio is
outstanding in all the aspects of throughput, number of packets, delay and packet drop, as
compared to non-solid Nodes of normal ratio. The 6 solid Nodes accommodate 2 Nodes more
than 4 non-solid Nodes with the same level of performance. The solid Nodes accommodate 2
more Nodes, ahead of non-solid Nodes of normal ratio, with equal performance level in all these
aspects.
Thus, the analysis shows that the mathematically computed Algorithm results are perfectly
matching with the DL simulation results in all the areas of performance measurement aspects
like throughput, delay and packet drop. Hence this proves the outstanding performance of the
proposed mathematical Algorithm for the estimation of resources with quality of service
performance parameters. Also, above results of intensive simulations for UL, demonstrate the
superiority of the proposed scheme, by providing adaptively (around) 50% of enormous
performance improvement over normal ratio algorithm. And it can be further enhanced just by
reducing the overhead resources. Also for WSN the proposed DL minimum traffic model
efficiently reduces the resource utilization.
The methods used for frame overheads analysis and estimation (in advance) follow WiMAX
standard and NS2 procedures, but some NS2 procedures are not highly efficient for resource
allocation. Further improvement can be achieved by saving allocation resources by making the
97
slots completely filled and by avoiding any resource wastage from slightly or partially used slots.
For reducing overheads if persistent scheduling is used rather than dynamic scheduling, then the
availability of vacant resources will increase tremendously.
98
Chapter 8.
Conclusions
This thesis is about WSN for the monitoring of natural phenomena concerning geosciences, like
monitoring of oil and gas exploration fields, earth quake and volcanic eruptions. In this research
work 2-tier WSN is investigated, with lower tier of sensor nodes in WiFi and upper tier of
WiMAX as a backhaul for data transportation to distant research facility. But normally WiMAX
has popularity for applications with main configuration of downlink data delivery direction, like
serving internet hot spots. In this scenario WiMAX can provide good throughput only in
downward direction but for main uplink operation necessary modifications are needed. When
monitored data is collected from dense sensor nodes concentration in real time phenomena of
impulsive nature, it needs sufficient uplink throughput to transport the consolidated output of all
sensor nodes with low latency. However for the backhaul of WiMAX, this forms a bottleneck for
uplink. Here WiMAX OFDM in TDD mode is considered and initially all the important
constituent parameters are identified which contribute to throughput. The research has been
extended up to the smallest distributable unit of resource called slot.
This research work has presented manifestations to enhance uplink bandwidth allocation
efficiency for these phenomena by using adaptive shift of WiMAX frame ratio. On the other
hand un-sufficient resources may cause packet delay, drop, session timeout, transmission restart
or transmission failure. However QoS parameters are defined for throughput, packet delay and
packet drop in order to avoid transmission problems. Hence the optimization problem is
formulized to maximize the uplink throughput while keeping the latency and packet drop of
downlink to minimum level. A Rayleigh flat fading channel model is used for simulations. The
necessary analytical expressions are derived for the proposed models. Firstly DL minimum
traffic model is proposed to reduce the resources of DL to only very necessary requirement and
to transform it in the ON/OFF format. The necessary expressions are derived for this model.
Secondly for OFDMA based transmission a MMPP traffic model of two states is formed. All the
traffic originating for DL minimum traffic model is transformed to ON/OFF traffic for MMPP
99
model, for analysis. Thirdly DL queue has been transformed to a DTMC queuing theoretic
performance model for the estimation of DL minimum traffic resources for the best value of
throughput, and minimum packet delay and drop. Fourthly an analytical model for frame
overheads computations has been provided, which includes both DL & UL. Although two
scenarios have been considered for packet scheduling, one is concerned with known MCS
distribution and the other is for unknown MCS distribution, but for analysis only known MCS
distribution is used. All these analytical and mathematical models contribute for the formation of
a mathematical algorithm to estimate optimal resource requirement on downlink after focusing
on the best values of these QoS parameters.
Fifthly another DTMC model has been proposed to estimate and quantify the uplink frame
utilization as maximum or minimum for the adaptive shift of resources. Finally main algorithm is
formulated by using all these analytical models. This algorithm estimates DL minimum traffic
resources and then calculates UL maximum traffic resources for adaptively controlling the frame
dl:ul ratio to the best optimized point. When uplink frame utilization is beyond normal, uplink
frame resources have to be incremented to previously calculated maximum limit by adaptively
remapping the frame ratio. And when utilization on uplink reduces below normal, it is restored
back to normal ratio by adaptively remapping. This process enhances facilitation to the uplink
bulky traffic and saves the link from congestion and slowing down. By this method those
problems are also minimized which are caused by delay, packet drop and session timeout. The
reaction of this method is quite fast in just one step convergence it provides maximum
throughput on uplink without degrading QoS parameters of downlink operation.
For the demonstration of validation of the results first of all analytical and mathematical
expressions of the algorithm are implemented in MATLAB to produces numerical values of the
result. These results are shown graphically for DL minimum traffic. Then intensive simulations
are conducted through designed models in three stages. The first simulation model at the frame
ratio calculated by algorithm, produces results for DL minimum traffic, which match very
closely with the results of algorithm to prove its accuracy. The results of second simulation
model represent UL enhanced performance at the frame ratio calculated by algorithm. The third
simulation model gives results for UL normal ratio operation. The second simulation model’s
results of UL enhanced performance show clear improvement around 50% over the existing
100
generic built-in WiMAX algorithm of UL normal ratio (third simulation model) operation, while
keeping good QoS parameters and without degrading the DL operation. This proves the
superiority of the proposed algorithm by comprehensively proving the results. The proposed
algorithm has shown significant efficiency enhancement while keeping good QoS parameters
which is not found in the existing adaptive ratio techniques.
8.1. Future Enhancements
The proposed framework of these analytical models can be extended by increasing states. The
DL minimum traffic model and MMPP model can be designed for more states like 3 and 4. This
will increase the resolution and give added performance enhancement, but this will take more
steps with increased complication. However this will support more when Nodes will be operating
at lower throughput but with increased population. Also more research work can be done on
overheads analysis for further reduction. One way is by using more realistic model with reduced
overheads. Next is by using more overhead reduction techniques like persistent scheduling
schemes rather than dynamic scheduling.
Here algorithm is derived for OFDM backhaul of WiMAX, but by making relevant changes this
algorithm can also be used for OFDM backhaul of Long Term Evolution (LTE) services.
101
References
[1] D. Estrin, L. Girod, et al. “Instrumenting the world with wireless sensor networks”, IEEE
Proc. of the Inter. Conf. on Acoustics, Speech and Signal Proc., May 2001.
[2] Mario Alves, “The WSN Standards”, Cooperating Objects Network Excellence 7FP,
CONET consortium, European Conference on WSN, Ireland, Feb 2009.
[3] Mario Alves, “Enabling ubiquitous computing and cyber physical systems with wireless
sensor/actuator networks”, Research Centre, FCT, Italy, Sep 2008.
[4] Bob Heath, “Land seismic: the move towards the mega-channel”, First Break, Feb 2008.
[5] Stefano Savazzi, Umberto Spagnolin, “Synchronous ultra wide band Wireless Sensors
Networks for Oil and Gas exploration”, in IEEE Symposium on Computers & Comms.,
Italy, pp. 907 – 912, July 2009.
[6] S. Savazzi, L. Goratti, U. Spagnolini, M. Latva-aho, “Short-range Wireless Sensor
Networks for high density seismic monitoring”, in Proc. of WWRF 22nd meeting, Paris,
France, 5-7 May 2009.
[7] Roman Lara Cueva, Rodolfo Gordillo Orquera, Ivan Londono, “Towards a New Volcano
Monitoring System Using Wireless Sensor Networks”, in The International Conference
Series on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP),
Australia, April 2013.
[8] Rui Tan, Guoliang Xing, Jinzhu Chen, Wen-Zhan Song, Renjie Huang, “Quality-driven
Volcanic Earthquake Detection using Wireless Sensor Networks”, Michigan State
University, Georgia State University and Washington State University, in Real-Time
Systems Symposium (RTSS), IEEE 31st, pp. 271 – 280, 2010.
[9] Werner Allen, J. Johnson, M. Ruiz, J. Lees, M. Welsh, “Monitoring volcanic eruptions
with a wireless sensor network”, Harvard Univ., Boston, MA, USA, Wireless Sensor
Networks, Second European Workshop on Publication, pp. 108–120, 31 Jan.-2 Feb. 2005.
[10] Werner Allen, K. Lorincz, M. Ruiz, O. Marcillo, J. Johnson, J. Lees, M. Welsh,
“Deploying a wireless sensor network on an active volcano”, Internet Computing, IEEE,
Div. of Eng. Appl. Sci., Harvard Univ., Cambridge, MA, USA, vol. 10, no. 2, pp. 18 – 25,
March-April 2006.
[11] R. Scarpa and R. I. Tilling (Eds.), “Monitoring and mitigation of volcanic hazards”,
Springer Verlag Berlin Heidelberg, pp. 845, 1996.
[12] Web site of the WEIRD Project with URL: http//www.ist-weird.eu/ dated Jan 2013.
[13] Silvano Mignanti, Gabriele Tamea, Ilaria Marchetti, Mario Castellano, Antonio Cimmino
et al., “WEIRD Testbeds with Fixed and Mobile WiMAX Technology for User
Applications, Telemedicine and Monitoring of Impervious Areas”, 4th International
Conference on Testbeds and Research Infrastructures for the Development of Networks
& Communities, TridentCom, ICST, Innsbruck, Austria, March 18–20, 2008.
102
[14] Zeashan Hameed Khan, Denis Genon Catalot, “Hierarchical Wireless Network
Architecture for Distributed Applications”, in Int. Conf. on Wireless & Mobile Comms.,
Aug 2009.
[15] Anis Koubaa, Mario Alves, “A Two-tiered Architecture for Real-Time Communication
in Large-Scale Wireless Sensor Networks: Research Challenges”, in ECRTS05, Spain,
July 2005.
[16] Dr. Rashid A. Saeed, Amran Naemat, “WiMAX-WiFi Synergy for Next Generation
Heterogynous Network”, WiMAX New Developments, InTech Pub., Dec 2009.
[17] Hui-Tang Lin, Ying-You Lin, Wang-Rong Chang, “An Integrated WiMAX/WiFi
Architecture with QoS Consistency over Broadband Wireless Networks”, in IEEE
Consumer Comms. And Networking Conf., Feb 2009.
[18] Man Cheuk Ng, M. V. “From WiFi to WiMAX: Techniques for High-Level IP reuse
across different OFDM protocols”, in IEEE 5th ICFMMC, Computer Society, USA, Jun
2007.
[19] Romand Fantacci, Francesco Chiti, “A Broadband Wireless Communications Systems for
Emergency Management”, IEEE Wireless Communications, Jun 2008.
[20] Will Hrudey, Ljiljana Trajkovic, “Streaming Video Content Over IEEE 802.16/WiMAX
Broadband Access”, Opnetwork 2008, USA, Dec 2008.
[21] IEEE Std. 802.16-2004, IEEE Standard for Local and metropolitan area networks - Part
16: Air Interface for Fixed Broadband Wireless Access Systems (2004).
[22] IEEE Std 802.16e-2005, Amendment to IEEE Standard for Local and Metropolitan Area
Networks - Part 16: Air Interface for Fixed Broadband Wireless Access Systems -
Physical and Medium Access Control Layers for Combined Fixed and Mobile Operation
in Licensed Bands.
[23] IEEE Standard for Air Interface for Broadband Wireless Access Systems - 2012
(Revision of IEEE Std 802.16-2009) , dated Jun 2015.
[24] IEEE 802.16: BROADBAND WIRELESS METROPOLITAN AREA NETWORKS
(MANs)
[25] WiMAX Forum White Paper, Mobile WiMAX—part I: a technical overview and
performance evaluation. [Online]. Available: http://www.wimaxforum.org dated: Jun
2016.
[26] IEEE Standard for Information technology--Telecommunications and information
exchange between systems Local and metropolitan area networks--Specific requirements
Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY)
Specifications. 802.11-2012, (IEEE 802.11™: Wireless LANs), dated: Jun 2016.
[27] M. Nguyen et al., “QoS-aware dynamic resource allocation for wireless broadband access
networks”, EURASIP Journal on Wireless Communications and Networking, vol. 104,
2014.
[28] Carl Eklund, Roger B. Marks, Kenneth L. Stanwood, Manasi Navare, “IEEE Standard
802.16 a technical overview of the wireless man air interface for broadband wireless
103
access”, IEEE Communications Magazine, Jun 2002.
[29] Sr Telecom, “WiMAX Capacity White Paper”, Sr Telecom Broadband Inc, Canada, Aug
2006.
[30] F. Wang, A. Ghosh, C. Sankaran, P. Fleming, F. Hsieh, and S. J. Benes, “Mobile
WiMAX systems: performance and evolution", IEEE Commun. Mag., vol. 46, no. 10, pp.
41-49, Oct. 2008.
[31] R. Jain, C. So-In, and A.-K. A. Tamimi, “System-level modeling of IEEE 802.16e mobile
WiMAX networks: key issues", IEEE Wireless Commun., vol. 15, no. 5, pp. 73-79, Oct.
2008.
[32] Mario Marchese, Senior Member IEEE, “Optimal Bandwidth Provision at WiMAX MAC
Service Access Point on Uplink Direction”, Int. Conf. on Comms., UK, Jun 2007.
[33] Loutfi Nuaymi, Aymen Belghith, “Comparison of WiMAX scheduling algorithms and
proposals for the rtPS QoS class”, in European Wireless Conf., Czech Republic, Jun 2008.
[34] Iwan Adhicandra, “Adaptive Subframe Allocation in WiMAX Networks”, in 33rd
International Conference on Telecommunications and Signal Processing- TSP 2010.
[35] R. Pries, D. Staehle, D. Marsico, “IEEE 802.16 Capacity Enhancement Using an
Adaptive TDD Split”, in Vehicular Technology Conference, VTC Spring 2008. IEEE, pp.
1539-1543.
[36] Sarigiannidis A., Nicopolitidis P., Papadimitriou G., Sarigiannidis P., ”Using learning
automata for adaptively adjusting the downlink-to-uplink ratio in IEEE 802.16e wireless
networks”, in IEEE Symposium on Computers and Communications (ISCC), Kerkyra, pp.
353 – 358, July 2011.
[37] Panagiotis Sarigiannidis, Angelos Michalas, “On Effectively Determining the Downlink-
to-uplink Sub-frame Width Ratio for Mobile WiMAX Networks Using Spline
Extrapolation”, in Panhellenic Conference on Informatics, IEEE Computer Society,
Kastonia, pp. 139 – 143, Oct 2009.
[38] Chih-He Chiang, Wanjiun Liao, Tehuang Liu, Iam Kin Chan, and Hsi-Lu Chao,
“Adaptive Downlink and Uplink Channel Split Ratio Determination for TCP-Based Best
Effort Traffic in TDD-Based WiMAX Networks”, IEEE Journal on Selected Areas in
Communications, vol. 27, no. 2, pp. 182-190, Feb. 2009.
[39] Ardian Ulvan, Vit Andrlik, Robert Bestak, “The Overhead and Efficiency Analysis on
WiMAX’s MAC Management Message”, Internetworking Indonesia Journal, Spring,
USA, Vol. 1, No. 1, pp 3-9, 2009.
[40] J. Cai, X. Shen, and J. W. Mark, “Downlink resource management for packet
transmission in OFDM wireless communication systems”, in Proc. IEEE
GLOBECOM’03, vol. 6, pp. 2999-3003, Dec. 2003.
[41] P. E. Omiyi and H. Haas, “Improving time-slot allocation in 4th Generation
OFDM/TDMA TDD radio access networks with innovative channel-sensing”, in Proc.
ICC’04, vol. 6, pp. 3133-3137, June 2004.
[42] S. Coleri, M. Ergen, A. Puri, and A. Bahai, “Channel estimation techniques based on
104
pilot arrangement in OFDM systems”, IEEE Trans. Broadcast., vol. 48, no. 3, pp. 223-
229, Sept. 2002.
[43] Dusit Niyato and Ekram Hossain, “Queue Aware Uplink Bandwidth Allocation and Rate
Control for Polling Service in IEEE 802.16 Broadband Wireless Networks”, IEEE Trans.,
Mobile Computing, vol. 5, no. 6, pp. 668-679, 2006.
[44] D. Niyato and E. Hossain, “Queue-aware uplink bandwidth allocation for polling services
in 802.16 broadband wireless networks", in Proc. IEEE Globecom, vol. 6, pp. 3702-3706,
Nov. 2005.
[45] Dusit Niyato and Ekram Hossain, “Queuing Analysis of OFDM/TDMA Systems”, in
Proc. IEEE Communications Society, IEEE GLOBECOM 2005, pp. 3712–3716.
[46] H. Heffes and D. M. Lucantoni, “A Markov modulated characterization of packetized
voice and data traffic and related statistical multiplexer performance”, IEEE J. Select.
Areas Commun., vol. 4, no. 6, pp. 856-868, Sept. 1986.
[47] A. Baiocchi, N. B. Melazzi, M. Listanti, A. Roveri, and R. Winkler, “Loss performance
analysis of an ATM multiplexer loaded with high speed ON-OFF sources”, IEEE J.
Select. Areas Commun., vol. 9, no. 3, pp. 388-393, Apr. 1991.
[48] H. Shah and Le-Ngoc T., “MMPP modeling of aggregated ATM traffic”, in Proc. IEEE
Canadian Conf. Electrical Computer Engineering, vol. 1, pp. 129-132, May 1998.
[49] S.-B. Kim, M.-Y. Lee, and M.-J. Kim, “Σ-matching technique for MMPP modeling of
heterogeneous ON-OFF sources”, in Proc, IEEE Globecom, vol. 2, pp. 1090-1094, Nov.
1994.
[50] A. T. Andersen and B. F. Nielsen, “An application of superpositions of two-state
Markovian sources to the modeling of self-similar behaviour”, in Proc. IEEE INFOCOM,
Kobe, Japan, pp. 196–204, Apr. 1997.
[51] L. B. Le, E. Hossain, and A. S. Alfa, “Queuing analysis for radio link level scheduling in
a multi-rate TDMA wireless network”, in Proc. IEEE GLOBECOM’04, vol. 6, pp. 4061–
4065, Nov.–Dec. 2004.
[52] M. F. Neuts, Matrix Geometric Solutions in Stochastic Models - An Algorithmic
Approach, John Hopkins Univ. Press, Baltimore, MD, 1981.
[53] Carl D. Meyer, Matrix Analysis and Applied Linear Algebra, SIAM, Philadelphia, USA,
2000.
[54] S.M. Ross, Introduction to probability models, Academic Press, 9th edition 2007.
[55] Gregory F. Lawler, Introduction to Stochastic Processes, Chapman & Hall/CRC, Florida,
USA, 2006.
[56] Roy D. Yates, David J. Goodman, Probability and Stochastic Processes, A Friendly
Introduction for Electrical and Computer Engineers, John Wiley & Sons Ltd. , USA,
2005.
[57] Moshe Zukerman, Introduction to Queuing Theory and Stochastic Teletraffic Models, EE
Department, City University of Hong Kong, 2015.
[58] J. Andrews, A. Ghosh, and R. Muhamed, Fundamentals of WiMAX: understanding
105
broadband wireless networking, Prentice Hall, February 2007.
[59] Loutfi Nuaymi, WiMAX Technology for Broadband Wireless Access, John Wiley & Sons
Ltd. , France, 2007.
[60] Yang Xiao, WiMAX/MobileFi, Auerbach Publications, New York, USA, 2008.
[61] Kazem Sohraby, Daniel Minoli, Taieb Znati, Wireless Sensor Networks, Technology,
Protocols, and Applications, John Wiley & Sons, Inc., New Jersey, USA, 2007.
[62] The Network Simulator NS2: Documentation, www.isi.edu/nsnam/ns/ns-
documentation.html dated: Jun 2016.
[63] The NS2 Manual : www.isi.edu/nsnam/ns/doc/ns_doc.pdf dated: Jun 2016.
[64] The Network Simulator NS2, NIST add-on IEEE 802.16 model (MAC+PHY), January
2009:
[65] David McMahon, MATLAB Demystified, The McGraw-Hill Companies, Inc., USA, 2007.
[66] Dr. Brian Vick, MATLAB Commands and Functions, Virginia Tech, USA.