136 maheswara rao 22 a.maheswara rao1 cd

Proceedings of International Conference on Innovation in Electronics and Communication Engineering (ICIECE-2012)

20-21, July 2012, GNI Hyderabad Page

A Channel State Analysis of ARS and CRAS in

802.16 WiMax Networks

A.Maheswara Rao

1, S.Varadarajan

2 and M.N.Giriprasad

3

1Research Scholor, JNTU; 2College of Engineering,SVU,Tirupati; 3JNTU Anantapur. [email protected],[email protected] , [email protected]

Abstract-Channel error is one of the factors which disrupt

the fair allocation of any traffic. In this paper, we propose

a channel condition based rate allocation predictive rate

control technique, using queue length and bandwidth

requirement information. Thus the value of predictive rate

control helps the base station to predict the future traffic

load and adjust the rate based on it. By simulation results,

we show that our proposed scheme attains better

throughput and fairness with reduced delay.

Key Words: Adaptive Route Scheduling, Channel Rate

Allocation Scheme, Quality of Service, SINR, Wimax,

Highest Urgency First, Best effort service rtps, nrtps,

UGS.

I. INTRODUCTION

1.1. WiMAX Networks

WiMAX is considers as one of the emerging wireless access

technologies for next generation all-IP networks. The

advantages of high bit rate and large area coverage with a

single BS are the basic advantages that WiMAX exhibits. It

provides operators with the possibilities to offer connectivity

to end users in a beneficial way [1]. It can be an excellent

substitute for providing last-mile access in wireless

metropolitan area network (WMAN) as it provides advantages

of high speed, cost effective, swift and easy installation. It

supports a large number of applications in regions where it is

not possible to deploy even wired infrastructure economically

or technically [2]. These features of WiMax make it adjustable

to various related fields like in all mobile service,

entertainment, m-commerce, m-learning and many mobile

healthcare systems [3,11].

The increase of wireless technology, along with the massive

boom over the growth of internet has tremendously increased

the need for wireless data services. The next-generation

wireless communication systems has to render multi-

functional services with higher data rate as well as time-

varying data rates, with numerous quality of service (QoS)

constraints. The next generation wireless services such as 4G

networks like WiMAX need to compensate traffic flow of

heterogeneous mix of real and non-real time traffic with

applications taking into account the widely varying and

diverse QoS guarantee [4].

1.2 Rate Allocation in WiMAX Networks

In WiMAX Networks, each end-host or devices have to

maintain numerous applications and in the mean time, the

system has to maintain various requirements such as bit rate

and latency. Consider a corporate user who is participating in

a video conference call, at the same time may be uploading

some relevant files to a remote server and browsing web pages

for reference simultaneously. The situation becomes complex

in the presence of many such users under one roof in which

each access the wireless network. This situation can easily

make the traffic to be congested with multiple varying

applications flows from multiple devices. In this situation,

problem of resource allocation arises naturally. As multimedia

application needs uninterrupted data flow, the rate allocation

of data along the network channel becomes critical.

The rate allocation is needed in WiMAX network to provide

QoS guarantees as well as fairness among the users.

Challenges in the design of a rate allocation policy in WiMAX

network are multi-fold. Like;

IEEE 802.16 provides formidable MAC along with multiple

services having various quality requirements. This

requirements includes the real-time service with fixed bit rate

(UGS), real-time service with variable bit rates and a bounded

delay (rtPS), the non-real-time service with variable bit rates

but insensitive to delay (nrtPS), and the best effort service

(BE). [5]

In real-time applications characteristics such as latency

requirements and distortion-rate (DR) varies tremendously.

For example, a high-definition (HD) video sequence

containing action oriented scenes needs much higher data rate

to attain the same quality as a static head-and-shoulder news

clip for a mobile device.

mailto:[email protected],[email protected]

mailto:[email protected]



The need of packet delivery of each packet is at most

important in video streaming applications to ensure

continuous media play out when compared to lesser

applications like file transfer or web browsing.[6]

1.3. Problem Identification & Proposed Solution

In our work, we propose to develop a channel condition based

rate allocation method which takes into account the channel

error which is rarely addressed due to its exceptional

occurrence in the traffic service. Our proposed approach on

rate allocation is based on two phases, admission control

phase and rate control phase.

Yi-Neng Lin et.al. [7] have proposed a bandwidth allocation

algorithm, Highest Urgency First (HUF), to overcome those

challenges with the physical-layer being OFDMA-TDD,

which is the most ubiquitous physical-layer technology for the

WiMAX systems. Here the scheme translates the needed size

to number of slots according to the current MCS when a frame

starts, and then designates the bandwidth according to the

necessity of the data/request. The Downlink and uplink sub-

frames are decided upon by obtaining the bandwidth for the

most urgent requests and divide equally the remaining

bandwidth for others. Independently in the downlink and

uplink, the HUF distribute bandwidth to every mobile station

in accordance with a pre-calculated U-factor which takes into

account the three important factors; urgency, priority and

fairness.

III. Proposed Rate Allocation Scheme

As we have stated in section 1.3, our method is based on a

channel conditioning which is based on rate allocation. We

take into account the channel error, which is a rare but a

potential threat which occurs in traffic services. our scheme

has basically two phases; an admission control phase which

initially estimates the channel condition and then admits the

flow in a urgency based technique and the second phase takes

on rate control . Our scheme is based on a BS centric model,

where BS analyzes the channel error both for uplink and

downlink request.

3.1 Admission Control Phase

3.1.1. Channel Error Estimation

The Admission control phase is carried on by initially

determining channel error. The channel error for any traffic is

determined by the channel state which is based on many

external and internal factors. We determine the channel error

by using the signal to interference plus noise ratio (SINR). [8]

We determine the channel condition with respect to Shannon–

Hartley theorem. We initialize weight to each user with

respect to the channel condition (C) value. The Shannon–

Hartley theorem is given as,

)1(2 SINRLogC kk bps/Hz -- (1)

The weight value of C determines the channel error and the

use of that particular channel. If a channel has a channel

condition Ck above a threshold level Cth., then the channel is

considered to be a bad channel. Similarly the channels with Ck

below Cth are considered as good. The bad channels are not

considered for maximizing the smooth rate control.

3.1.2 Admission Control

The admission control is the resource allocation phase, where

the data is transmitted based on different criteria like data size,

bandwidth availability, traffic types, urgency and priority. As

we discussed above, in WiMax, there exists five scheduling

classes and each possessing different data scheduling

methods. Thus all these criteria’s should be taken into account

when considering the admission control. The simulation

topology depicted in figue1.A number of MSs and a BSs are

connected via gateway to a video conference end point and an

FTP server(7,10).

Our estimation on admission control is based on “priority of

services” scheme. Here we take into account two factors; the

data request from BS or SS and the traffic class. The equation

for the “priority for services” is given by;

PoS = RD+ST (2)

Where RD is the requested bandwidth and is calculated by

TDreq

Dreq - 1 RD (3)

Dreq is the node’s requested bandwidth .

TDreqis the maximum bandwidth



And ST is the value assigned to each traffic service types as 1,

0.80, 0.60,0.40 and 0.20 in order as per the priority of the

services: UGS, ertPS, rtPS, nrtPS, BE.

From the channel error estimation (eq 1 & 2), we have

analyzed the channel as either good channel or bad channel.

The good channel admits the services to pass through in a

priority basis by calculating the PoS. If the channel condition

is bad, the pending traffic services are buffered in the same

prioritized manner. The buffered traffic is transmitted when

the channel resumes to good condition. In case of a buffer

overflow, the traffic service is transmitted according to their

priority by utilizing other channels having low priority traffic.

In case of same priority value, there will be traffic congestion.

So the proposed method makes use of earliest deadline

technique to schedule the traffic using an Urgency factor

(UF). It is given by using the request’s residual time Tr and

request’s execution time Te. Thus the equation for UF is given

by;

eUF

TeTr1

1 (4)

Thus our equation of earliest deadline technique takes into

account both PoS and UF (eq 3 and 4) to allocate traffic for

similar priority data.

PoS = PoS + UF (5)

Algorithm for Earliest Deadline Technique

In incoming traffic flow

At T=1

1. If Ck < Cth,, then

1.1 channel is marked as good channel,

1.2 assign priority using (2)

1.3 schedule the traffic

2. Else if Ck > Cth, then

2.1 mark the channel as bad.

2.2 put each traffic in separate queues

q1,q2,q3…….qk

2.3. If Qlen(i) > Qmax , where i=1,2…k, then

2.3.1 schedule the traffic using (5).

2.4. End if.

3. End if

4. T = T + 1 ,

5. Repeat from 1.0

Fig 2.Admission Control Phase

3.2 Rate Control Phase

We perform predictive rate control for the traffic flows in this

phase to provide a controlled traffic flow. As we have

discussed already, our scheme is a BS centric model. Here the

BS decides the rate allocation and traffic control of both

uplink(UL) as well downlink(DL). Thus the BS needs to be

periodically updated about the traffic load and demand

requests of SS for both UL and DL. In the uplink scheduling,

each SS periodically sends its queue length and bandwidth

requirement information to update the rate to BS.

BS calculates the rate by predicting the future load value with

respect to the Queue length variations and the bandwidth

requirements. The allocated fair bandwidth ABWi of SSi is

calculated according to;

ABWi = Li

BWLi (6)

Where BW is the total available bandwidth and Li is the local

traffic load.

The queue length (QLi) is the available buffered value in the

queue. The QLi value is calculated by calculating the buffered

values between two consecutive intervals. The QL value is

calculated according to ;

QLi(t+1) = QL(t)+ iij

m

J

ij ABWFnaTR )(

1

(7)

Where,

TRij is the traffic in SSi in session – ij

Fij is the forward delay from the session source to SSi



a is a binary value, in which 0 indicates the traffic is inactive

and 1 denotes the traffic to be active.

Let QLi(t) , QLi(t+1), QLi(t+2) , ……. be the sequence of queue

lengths at time instants t,t+1,t=2,…

Then the queue length variation ΔV can be calculated as

ΔV = QLi(t+2) - QLi(t+1) (8)

where ΔV is the prediction of queue variation at time t+2.

If ΔV > 0, then the queue length has an increasing tendency;

otherwise,

if ΔV < 0, the queue length is likely to decrease.

Taking into account the equation (6) and (8), we can

determine the rate control value. Our equation for predictive

rate control depends on the value of ΔV. Thus our predictive

rate control equation is;

ARi= ABWi ΔV (9)

Thus the value of predictive rate control helps the base station

to predict the future traffic load and adjust the rate based on it.

So if there is uneven traffic in the channel, the predicted

values help to control it with traffic rate adjustment. This

helps in providing a controlled traffic flow services along the

channel.

IV. Simulation Results

4.1. Simulation Model and Parameters

To simulate the proposed scheme, network simulator (NS2) is

used. The proposed scheme has been implemented over IEEE

802.16 MAC protocol. In the simulation, clients (SS) and the

base station (BS) are deployed in a 1000 meter x 1000 meter

region for 100 seconds simulation time. All nodes have the

same transmission range of 250 meters. In the simulation, the

video traffic (Variable Bit Rate) is used.

The simulation settings and parameters are summarized in

table 1.

Table 1: Simulation Settings

Area Size 1000 X 1000

Mac 802.16

Clients 20

Radio Range 250m

Simulation Time 100 sec

Routing Protocol DSDV

Traffic Source CBR, VBR

Video Trace JurassikH263-256k

Physical Layer OFDM

Packet Size 1500 bytes

Frame Duration 0.005

Transmission Rate 250Kb,500Kb,750Kb

1000Kb

Error Rate 0.01,0.02,….0.05

4.2 Results

A. Effect of Varying Channel Error Rates

In the initial experiment, we vary the channel error rate as

0.01, 0.02, 0.03, 0.04 and 0.05. The results are compared

between Adaptive Resource scheduling and Channel Rate

Allocation Scheme for various error rates.

Fig 3. Comparsion of bandwidth between ARS and CRAS

Fig 4. Comparsion of end to end delay between ARS and

CRAS



Fig 5. Comparsion of fairness between ARS and CRAS

Fig: 3 give the aggregated bandwidth for VBR traffic. From

the figure, it can be seen that CRAS has received more

bandwidth when compared with ARS.

Fig: 4 give the average end-to-end delay for VBR traffic.

From figure, we can see that the average end-to-end delay of

the proposed CRAS protocol is less when compared to the

ARS.

Fig: 5 give the fairness index for VBR traffic. From the figure,

it can be seen that CRAS achieves more fairness when

compared with ARS.

Analysis

Normally, when the channel error rate is increased, the

received bandwidth of all the flows will tend to decrease. As it

can be seen from the figure 2, the bandwidth of all the flows

slightly decreases, when the error rate is increased. As per the

proposed algorithm, the received bandwidth for VBR is

more, when compared with ARS.

V. Conclusion

In this paper, we have proposed a rate allocation mechanism

for 802.16 WiMax networks. It introduces a base station (BS)

centric rate allocation model based on channel error. Our

model has two phases; Admission Control Phase and Rate

Control Phase. In the first phase, initially we determine the

channel error and estimate the channel as good or bad. After

determining the channel state, a priority of service scheme,

sends the traffic in prioritized manner in good condition as

well as when bad channel resumes to its healthy state. During

buffer overflow in bad condition state, we transfer the traffic

through low priority channel using earliest deadline technique.

The second phase predicts the future load using a rate control

technique. Our technique of rate control depends on the

periodic value of each SS’s queue length and bandwidth

requirement information. By simulation results, we have

shown that our proposed scheme attains better throughput and

fairness with reduced delay.

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