J Sign Process Syst (2016) 83:279–291DOI 10.1007/s11265-015-1061-x

Towards 5G: Context Aware Resource Allocationfor Energy Saving

Muhammad Alam1 · Du Yang1 · Kazi Huq1 · Firooz Saghezchi1 · Shahid Mumtaz1 ·Jonathan Rodriguez1

Received: 14 April 2015 / Revised: 18 September 2015 / Accepted: 7 October 2015 / Published online: 19 October 2015© Springer Science+Business Media New York 2015

Abstract With the objective of providing high quality ofservice (QoS), 5G system will need to be context-awarethat uses context information in a real-time mode dependson network, devices, applications, and the environment ofusers’. In order to continue enjoying the benefits providedby future technologies such as 5G, we need to find solutionsfor reducing energy consumption. One promising solutionis taking advantage of the context information available intoday’s networks. In this paper, we take a step towards 5Gby utilizing context information in the scheduling processas conventional packet scheduling algorithms are mainlydesigned for increasing throughput but not for the energysaving. We investigate a Context Aware Scheduling (CAS)algorithm which considers the context information of usersalong with conventional metrics for scheduling. An infor-mation model of context awareness along with a contextaware framework for resource management is also pre-

� Muhammad Alamalam@av.it.pt

Du Yangdu.yang205@gmail.com

Kazi Huqkazi.saidul@av.it.pt

Firooz Saghezchifirooz@av.it.pt

Shahid Mumtazsmumtaz@av.it.pt

Jonathan Rodriguezjonathan@av.it.pt

1 Instituto de Telecomunicacoes, University of Averio, Aveiro,Portugal

sented in this paper. CAS is simulated applying a systemlevel simulator and the results obtained show that consider-able amount of energy is saved by utilizing the context infor-mation compare to conventional scheduling algorithms.

Keywords 5G · Context information · Scheduling ·Energy efficiency

1 Introduction

In 5G Era, we need novel methods of abstraction to effi-ciently generate context-aware information, as well as newways to share context information among applications, net-works, and devices. In this sense, wireless systems play akey role as context aware enablers, as well as high capac-ity backhaul systems. This is provided by the unrelentingmotivation in the capacity increase and latency decrease,especially in the case of future 5G systems. Alternativeviews applied to “context” lead to different definitions anddifferent levels of applicability. In case of wireless networksthe context is categorized into two basic categories, UE andnetwork related context [1]. Contrary to the conventionalscheduling mechanisms, there are a number of context infor-mation available related to user equipment (UE) that can beutilized in resource management based on the motivationand scenarios e.g. transmit power, battery level, mobility,traffic type etc. In order to focus on energy saving and QoSof UE, battery level along with traffic type and channelcondition of the each UE are considered in our proposedscheduling algorithm.

Energy efficiency (EE) and low carbon strategies haveattracted a lot of concern in the recent years. Driven by therapidly increasing demand of high data-rate, the throughputof today’s wireless system has dramatically improved over

280 J Sign Process Syst (2016) 83:279–291

the last few decades. In the recent years, there is a need toimprove both the spectral efficiency as well as the EnergyEE, since the energy consumption has become an importantissue from both economic and environmental aspects; andfuture devices will be more power hungry than connectivity.The EE can be tackled through the exploitation of tech-niques and mechanisms from application to physical layer.We utilize the context information in scheduling processfor energy saving in Long Term Evolution Advanced (LTE-A). Round Robin (RR), Maximum Carrier to Interferenceratio (Max C/I) and Proportional Fair (PF) scheduling arethe three conventional and most popular scheduling meth-ods. RR allocates equal resources to all users, regardless oftheir current channel condition. On the other hand, Max C/Ischeduling aims at maximizing the total cell throughput byconsidering CQI values fed back to evolved NodeB eNBfrom the UEs. This leads to unfairness, as users that are fur-ther away from eNB (or have bad channel conditions) willnot be allocated a fair share of the radio resources. The PFalgorithm, [2], tries to provide fairness by increasing the pri-ority of a mobile user who has a relatively low value of theC/I ratio.

LTE and LTE-A aim to provide customers a new mobileexperience providing higher data rates that makes user touse more bandwidth demanding application anywhere any-time e.g. video streaming, interactive gaming etc. However,these applications are highly energy demanding and drainout the limited battery of mobile devices which is a majorchallenge for modern telecommunication systems. On onehand, this challenge makes users more reluctant to use thehigh bandwidth demanding applications but on the otherhand, it encourages the development of new architecturesand mechanisms that are more power-aware or power-efficient and contribute to the energy savings of modernmobile devices. Keeping in view the power saving, LTEuses the idea of Discontinuous Reception (DRX) and Dis-continuous Transmission (DTX) which makes the mobiledevices aware of unnecessary and continuously monitor-ing the control channels and turns the radio to an extendedsleep time and activate on defined time intervals. But thesemechanisms only extend the sleep time to contribute toenergy savings and do not considers the actual battery levelof mobile devices and other important UE related contextinformation in resource management for energy savings.There are situations when the UE’s battery level is low butgets no priority in the scheduling process. Therefore, inthis paper, we have tackle this problem by introducing newmetric in the resource management which contribute to thebattery savings of UE while providing the same QoS to UE.The major contributions of this paper are as follows:

• A detailed context architecture and framework for con-text based scheduling algorithms. The framework can

exploit any context information related to UE and eNBto achieve the desired goals based on the proposals for5G.

• A detailed design and working of context informationbased signaling in LTE-A.

• A context base battery priority scheduling algorithm forthe low battery devices in congested scenarios whereUE has limited access to recharging and utilizing highdata rate demanding applications.

• Implementation of context information based module insystem level simulator for LTE-A.

Most of the traditional schedulers consider only the through-put but not the energy related information in the scheduling;therefore, we go beyond the state-of-the-art and develop anew scheduler which exploits the context information ofUE for energy saving and QoS. The rest of the paper isorganized as follows: in Section 2, we present the relatedwork, Section 3 covers the scenario, the detail descrip-tion of problem formulation is provided in Section 4 whilethe proposed scheduling algorithm along with the pseudo-code and flowchart is presented in Section 5. Section 6gives details about the context based framework for schedul-ing, Section 7 details the representation of the contextinformation followed by the the acquisition of contextinformation in Section 8. The details of the system levelsimulator along with simulations results are presented inSection 9 and finally, we concluded the paper in the lastsection.

2 Related Work

When the mobile devices are powered on unnecessarily foran extended period of time they consume useful battery,which is considered one of the main reasons for energyconsumption in both infrastructure and ad hoc networks.This problem is tackled by the introduction of proper sleepor idle modes of the mobile devices which is reported in[3, 4]. Therefore, to take advantage of the sleep or idlemodes for energy saving 3GPP [5] has standardized discon-tinuous Transmission (DTX) and Discontinuous Reception(DRX). Similarly, in [6] the study evaluates several dif-ferent parameter settings for LTE’s DRX, and attempts todiscover a reasonable trade-off between VoIP performanceand user terminal battery life. But these mechanisms con-tribute to energy saving only by extending the UE sleep timewhile ignoring the consideration of the context informationin the scheduling process. For instance, to guarantee theQoS for real-time flows and also to minimize energy con-sumption of mobile devices a work is presented in [7]. Thescheduling problem is formulated as an integer linear pro-gram to minimize the total number of active frames to save

J Sign Process Syst (2016) 83:279–291 281

energy consumption. The aftermentioned work adopts thescheduling process to contribute only to the mobile termi-nal’s sleep time thus ignoring the context information e.g.amount of battery power remaining for application in useetc. A detailed survey is presented on opportunistic schedul-ing in [8]. Opportunistic scheduling is considered to takeadvantage of the available information such as the channelquality and other QoS parameters (i.e., throughput, delay,and jitter) that consents the scheduler to the proper trans-mission resources for user [8]. An opportunistic schedulingmechanism for OFDM systems to minimize the overalltransmission power is presented in [9]. Energy efficient andlow complexity scheduling mechanisms for uplink cognitivecellular networks is presented in [10] along with a compari-son of RR and opportunistic scheduling for EE. It is proventhat RR is more energy efficient than opportunistic schedul-ing while providing the same QoS. Furthermore, existingschedulers mostly rely on either single parameter e.g. chan-nel quality, throughput etc.[11–13], or a combination ofmore than one parameters, e.g. traffic type, channel qualityor QoS metrics (jitter, delay) etc. But still these works arelimited and do not go beyond the existing state-of-the-artwork for considering context information n the schedulingprocess.

On the other hand, some recent works consider the uti-lization of the context information in the scheduling process.To improve the QoS of, a context-aware resource allo-cation (CARA) scheduling scheme, for cellular wirelessnetworks, is presented in [14]. This scheduling mechanism

is transaction-based and considers the running application’sforeground/background state as context information. Eachtransaction flow is provided a finish time, QoS require-ments, and the context information attached. However,CARA considers the context information which is limitedto the application in use, and contributes only the QoSimprovement.

3 Scenario

The proposed scenario for the context based scheduling isdepicted in Fig. 1. The scenario shows an LTE-A cell hav-ing an eNB and several UEs randomly deployed inside thecell. Each UE gathers its required context information intoa Context list (CX-list) and send to the eNB on the LTE-Auplink feedback channel. A context based database is main-tained at each eNB which is updated each time when the UEtriggers or when there is a change in the UE’s dynamic con-text information e.g. battery level, channel quality etc. EachUE once associated with a eNB, this particular eNB willcreate a temporary profile for this UE which contains UE’stemporary identity as well as key context information asso-ciated with the corresponding bearers including prioritizedbit-rate and etc. The context information will be utilizedfor eNB downlink and uplink scheduling to guarantee QoS.The created profile for each UE will be removed once theUE moves into another cell, switches into idle mode, orswitches off.

eNB

Context based

UE profiles

CX-list

%

%

%

%

%

%

%

%

%

Core Network

Context InformationMME

UE

UE

UE

UE

UE

UE

UE

UE

UE

Figure 1 Scenario for the investigated context based scheduling.

282 J Sign Process Syst (2016) 83:279–291

4 Problem Formulation

In this section, we formulate our problem. We define thefollowing notations:

k User indexK Total number of active usersj Resource block indexJ Total number of resource blocksE Energy (Joules)Ptx Transmit power per resource block at the eNB

(Watts)N0 AWGN noise varianceF File size (Bits)

hkj Instantaneous channel impulse response for the k-th

user at j-th RB, including path-loss and shadowing(Complex value)

δkj If j -th RB assigned to k-th user, then δk

j = 1;

otherwise δkj = 0 (Binary value)

PcRx Power consumption at the UE for receiving (Watts)

We target a scenario, where: 1) there are K active users ina single cell, all connected to one eNB; 2) file downloadapplication is considered for all users, the required file sizeis Fk (bits) for the k-th user; 3) the remaining battery levelof the k-th user is Ek (Joule).

Figure 2 Flow chart of theproposed algorithm.

J Sign Process Syst (2016) 83:279–291 283

We made the following assumptions:

1) The up-to-date remaining battery level of each user Ek

(Joule) is known at the eNB through an error-free delay-free feedback channel.

2) The required download file size Fk is known at eNB.3) The active number of users K is smaller than the

number of RBs in every transmission time interval(TTI).

4) Scheduling is performed every TTI (1 ms) in a RRfashion.

5) Equal power allocation over frequency/time isemployed at the eNB. For every resource block, thetransmit power is denoted as Ptx .

The problem is formulated as follows

maxK∑

k

⎛

⎜⎜⎜⎜⎜⎝Ek − FkPcRx

∑Jj log 2

(1 +

∣∣∣hkj

∣∣∣2δkj Ptx

N0

)

⎞

⎟⎟⎟⎟⎟⎠(1)

Subject to the following constraints

J∑

j

δkj = LCM(J, K)/K

Ek > 0

Fk > 0

5 Proposed Algorithm

We investigate a context aware scheduling (CAS) algorithmwhich reduces the energy consumption by considering thecontext information based on the work presented in [15].Most of the conventional schedulers, make decisions basedon the throughput/QoS and instantaneous channel conditionas part of a cross-layer scheduling approach. However, newfactors that should be considered to enhance the system per-formance are the cost of energy per bit and the requiredenergy (in battery level). In this context, the scheduling met-ric of a packet scheduler considers the ratio of the transmitenergy to the number of transmitted bits [15] and multi-ply with the remaining battery level energy. For this reason,in a system with limited transmit energy, it is more effi-cient to allocate physical resource blocks (PRBs) to theusers that require the least ratio of the transmit energy tothe number of transmission bits and have low remainingenergy. Thus, in the proposed packet scheduling scheme,the scheduling metric selects the UEs to be allocated inan order from lower to higher of the ratio of the trans-mit energy, Em

u , to the number of transmission bits Bmu ,

of the PRB m and BLu, battery level of the UE u asfollows:

ϒ(u, m) = argminu,m

Emu

Bmu

= argminu,m

P mu T

Bmu

∗ BLu (2)

where ϒ(u, m) is the scheduling metric which denotes theindex of selected UE u and PRB m respectively; energy isthe multiple of power and time (P m

u .T ).

UE Context Architecture

UE eNB

Context Provider

Context Reasoner

Context Filter

Decision Engine

Context ManagerPolicy SetConfigura�on

Profile

Context Info

Control Info

Schedulling Process

BS Context UEs context

Context Based Priority calcula�on

Context based scheduler

Context Provider

Context Manager

Decision And implementa�on EngineCore Network

MME

S-GW

P-GW

Internet

Context Informa�on

Context

Figure 3 Context architecture and framework for proposed scheduling algorithm.

284 J Sign Process Syst (2016) 83:279–291

Table 1 LTE QoS classes [17].

QCI Upper bound packet error rate Upper bound delay budget (ms)

1 10−2 100

2 10−3 150

3 10−3 50

4 10−6 300

5 10−6 100

6 10−6 300

7 10−3 100

8 10−6 300

9 10−6 300

As presented in [16], P mu = �(Bm

u )

hmu

, we can redefine themetric as follows:

argminu,m

P mu T

Bmu

∗ BLu = argminu,m

�(Bmu )T

hmu · Bm

u

∗ BLu (3)

Let ˆP mu denote the maximum transmit power at the trans-

mitter that can be assigned for the UE u and the PRB m.Equation 2 can be rewritten as –

ϒ(u, m) = argminu,m

⎛

⎜⎜⎝T(

�mu

�(Bmu )

+ 1ˆP mu

)Bm

u

⎞

⎟⎟⎠ ∗ BLu (4)

Because, arg min(x) = arg max( 1x). Finally, the schedul-

ing metric can be expressed as

ϒ(u, m) = argmaxu,m

(�m

u(E(Bm

u )/Bmu

) ∗ 1

BLu

)(5)

The proposed energy efficient scheduler allocates thePRB m to the UE with larger excess channel gain whichis distant to the required received energy per bit and lower

Table 2 Representation of Battery level.

Index Battery level Index Battery level

0 ≥ 30 % 0 [75 %–100 %]

1 < 30 % 1 [50 %–75 %]

2 [25 %–50 %]

3 [0 %–25 %]

battery level as in Eq. 5. The flowchart of the algorithm isshown in Fig. 2.

6 Context Based Scheduling Frameworkand Architecture

An information model of context awareness is presented inFig. 3. This model basically illustrates how Context Infor-mation (Cx Info) is extracted and processed by variousfunctional blocks in the context aware architecture. The out-come of this information model is the implementation ofenergy saving strategies based on the given context settings.Following is the brief description of different modules ofour context architecture.

Context Provider is the source of Cx Info. This Cx Infois obtained directly from the radio environment (e.g. fromterminal measurement or network) without any processing.For instance, the information can be battery levels of MTsor signal strength to determine distance between UEs andeNB. If the mapping to the scheduling framework is con-sidered, the context provider resides in both terminal andnetwork side. The policy set is the set of strategies that canbe used by radio, in other words it imposes constraints onthe radio functionalities. The context manager is respon-sible for Cx Info processing to provide refined Cx Infofor the decision engine. It consists of two blocks: ContextReasoner and Context Filter. The Context Reasoner col-lects raw Cx Info and generates rules for context filteringbased on the constraints from the policy set. The reasonermay need to process the Cx Info to generate rules; how-ever it will not alter the information content. The ContextFilter filters the Cx Info based on the rules generated by

UE eNB S-GW P-GW(PCEF) PDN

PCRF

SPR

EPS BearerRadio bearer

S1 Bearer

S5/S8Bearer

Dataflow1(streaming)

Packet flow (video)

Packet flow (audio)

Data flow2 (video conference)

Example bearer

Temp UE profile

Figure 4 QoS information flow.

J Sign Process Syst (2016) 83:279–291 285

Figure 5 Mapping of physicalchannels and radio resources.

Sub-H forControl 1

Sub-H forControl 1

Sub-H forSDU 1 ... MAC

control 1MAC

control 2MACSDU 1 ...

MAC Header

PUSCHPRACH

UE1UE2

PUCCH format 2 (UE1/UE2/...)

PUCCH format 2 (UE1/UE2/...)

RB1

RB N

Slot 1 Slot 2 ...

RB1

RB N

Slot 1 Slot 2 ...

PDCCH

PDSCH

SIB2

1) Uplink mapping 2) Downlink mapping

3) MAC frame combined context informa�on and service data

the context reasoner and output the high level operationalCx Info for the decision engine. The decision engine is thecore of the context awareness framework. It makes deci-sion based on operational Cx Info from the context managerand the constraints from the policy set. The decision will beimplemented or used as a knowledge build-up. Configura-tion profiles represent a knowledge database built based onprevious decisions. They can be seen as results of learningprocess. For example in a learning process, the implemen-tation of a decision will be evaluated. A good (energyefficient) decision will be given a higher score. Good deci-sions with high scores are likely to be repeated in the futureif the context setting permits. The framework is shown inthe Fig. 3. The context related to scheduling is gathered atthe UE and signaled to the eNB. The eNB gathers the infor-mation from all the connected UEs and its own informationin UE context provider module. The collected information

is filtered in the context manager and passed to the schedul-ing process where the context based priority calculationalgorithm calculates the priority of each UE, based on thecontext parameters; which are battery level, channel qualityand traffic type. The scheduling is performed in presence ofnetwork policies provided by policy set module. The sched-uled decisions are passed to decision and implementationmodule.

7 The Representation of Context Information

Context information, such as the received SINR, usually is avalue/vector in continuous domain, which contains infiniteentropy and cannot be processed by today’s digital systems.The common method is to predefine a table, which dividesthe original infinite-number of continuous values/vectors

Table 3 LTE-A defined list of control formats.

PUCCH format Release Application No. of UCI bits No of PUCCH bits

1 R8 SR 1 1

1a R8 1 bit HARQ-ACK and optional SR 1 or 2 1 or 2

1b R8 2 bit HARQ-ACK and optional SR 2 or 3 2 or 3

2 R8 CQI, PMI, RI ≤ 11 20

2a R8 CQI, PMI, RI and 1 bit HARQ-ACK ≤ 12 21

2b R8 CQI, PMI, RI and 2 bit HARQ-ACK ≤ 13 22

3 R10 20 bits HARQ-ACK and Optional SR ≤ 21 48

286 J Sign Process Syst (2016) 83:279–291

RRMAdmission Control,

Link Adaptation,Channel resources

Maganemnet,Scheduling, Power

control and Interference calculations

Context Aware schedulling (CAS)

(RR, MCI, PF),HAndover, HARQ.

Mobility models&User deployment

Computation of system level

metrics(Energy Efficiency, spectrul efficiency

etc.)

Look Up table from the PHY layer of the

system

System Level SimulatorSimulator Mode

Dynamic/Combined

snapshot

Cell Deployment

Context Aware Module

Battery level, Channel quality indicator, Traffic information, application in use etc.

Context Information:BS context

information, UE context information

Figure 6 Components of system level simulator.

into finite-number of discrete regions. All the values withinone region are represented by the index of this region. Forexample, LTE-A standardize a QoS table having 9 elementsshown in Table 1. The index of each element in the table iscalled Quality Classification Indicator, which could be rep-resented using 4 bits. Similarly, the SINR values are mappedinto 16 combinations of modulation and coding schemes.

Following the same strategy, we could pre-define a tableto represent the battery level. Two examples are given belowand represented in Table 2. The first one set 30 % of remain-ing battery as the alarm threshold, which requires 1 bitto represent these two elements. While the other one pro-vides have four elements, and requires at least 2 bits forrepresentation. Having a longer table can represent more

Table 4 Simulations parameters.

Parameters Values

Carrier frequency fc 2 GHz

Bandwidth 10 MHz

Duplex mode FDD

Noise density −174 dBm/Hz

Fast fading model Rayleigh fading using Pedestrian B model (6 taps, SISO) Urban

Log-normal shadowing variance σ (dB) LOS σ = 4 (dB), NLOS σ = 8 (dB)

Number of cells Multiple cell

Number of users 50

BS transmit power 43 dBm

Received SINR threshold −3 dBm

Average snapshot 100

Time transmission interval 1 ms (sub-frame)

Number of resource block 50 RB in each slot, 7 symbol, number of subcarriers per RB=12,total subcarrier=600

Link adaptation EESM( Exp Effective SINR Mapping)

Traffic model Data (File)

Radio resource management CAS, RR

Turbo decoder Max Log Map (8 iterations)

HARQ Chase combining, Number of process=6,Retransmission interval=6 ms,Max Nb of retransmission=3

AMC PERtarget 10 %

CQI delay Each TTI, with 2 ms delay

J Sign Process Syst (2016) 83:279–291 287

information, while it also increases the complexity and sig-naling overhead. Hence, there is a trade-off between theinformation accuracy and complexity plus overhead.

8 The Acquisition of Context Information

Two types of context information, eNB context and UEs’context, are shown in Fig. 4. The eNB’s context is mea-sured, update, and stored at each eNB, which is easy toobtain. UEs’ context information can be categorized intotwo types according to its lifetime: quasi-/stable pre-definedcontext information with less or no changes; and unsta-ble measured context information with frequently changes.For example, UE’s price plan and its expected QoS for dif-ferent applications are pre-defined with low change rate;while the channel quality and battery level need to be mea-sured with high variation. In this section, we will discuss theacquisition of these two types of UE context information.

8.1 Quasi-/stable Pre-defined UEs’ Context InformationObtained from Core Networks

The quasi-/stable context information is stored in the corenetwork, and transmitted to eNB via backhaul links. Takingthe QoS information as an example, the process is illus-trated in Fig. 4. As an IP-connected network, all services(voice, data, etc.) in LTE-A are connected to the externalPacket Data Network (PDN) through the Packet Data Net-work Gateway (P-GW). It is natural to configure the qualityof service at the P-GW. Since the expected service qualityis related to charging, an element called Policy and Charg-ing Enforcement Function (PCEF) is embedded in P-GW.This PCEF is responsible to communicate with other twonetwork elements in the core network. One is called Pol-icy and Charging Function (PCRF), which provides policyand charging control rules. The other one is called Sub-scription Profile Repository (SPR), which contains users’

Figure 7 Total remaining battery (%) vs simulation time (TTIs).

subscription information such as his/her subscribed priceplan.

Having the information of how much a UE is willingto pay, the P-GW configures QoS for difference servicesthrough a mechanism named Evolved Packet Service (EPS)bearer. A EPS bearer could be considered as a bi-directionaldata pipe as a logical connection between the UE and theP-GW. It consisted by three other logical bearers, S5/S8bearer, S1 bearer and radio bearer as shown in Fig. 4. Oncea UE is switched on and connected to the network, a defaultEPS bearer will be set-up, and be remained until this UEswitched off. More than one EPS bearer can be set up fordifference services. Each EPS bearer is associated with aspecific QoS, which defines how the data will be trans-ferred using parameters such as error-rate, delay as shownin Table 1 (but not limited to these). One example of ESPbearer is also demonstrated in Fig. 4. This bearer defineda QoS suitable for real-time video transmission, for exam-ple QoS having low delay. In this bearer, two service dataflows—one for video streaming from the UE to the network,and one for video conference from the network to the UE aresupported. Within each data flow, one or more packet flows(e.g. audio and video) are contained for supporting this ser-vice. LTE gives the same QoS to all the packet flows withina particular EPS bearer.

The eNBs are not suitable candidate for storage thesequasi-/static pre-defined UE context information because:1) The operators want to reduce the cost of eNBs; 2) eNBcompletely lose connection with certain mobile nodes onceit moves out from this eNB’s coverage area. However, oncea UE is associated with an eNB, this eNB will create atemporary profile for this UE, which contains this UE’s tem-porary identity as well as key QoS parameters associatedwith the corresponding bearers including priority prioritizedbit-rate and etc. These QoS information will be utilized foreNB downlink and uplink scheduling. This profile will bedeleted when this UE moves into another cell, switches intoidle mode, or switches off.

Figure 8 Average battery consumption vs number of users.

288 J Sign Process Syst (2016) 83:279–291

8.2 Unstable Measured UEs’ Context InformationObtained from Uplink Feedback Channel

The unstable measured UEs’ context information, such asthe channel quality and battery level, is obtained fromthe UE and signaled via uplink feedback channel. Thereare two feedback modes: periodic and aperiodic. Peri-odic mode is carried on a regular interval according tothe average variable rate of the context information. Forexample, the channel quality indicator is influence by themulti-path fading, and has a faster variable rate than theRank Indicator, which is more influenced by shadowing.Hence CQI feedback is carried out 32 times more fre-quent than the RI feedback. By contrast, aperiodic nodeusually carried out on-demand or for abnormal situa-tion. Aperiodic mode usually has a higher priority thanperiodic mode.

The uplink feedback channel could be the PhysicalUplink Share Channel (PUSCH) or the Physical UplinkControl Channel (PUCCH) depends on the states of UE.More explicitly

1) If the mobile is in connection mode and has datawaiting for transmission via the PUSCH, the contextinformation will be multiplex with the data at MAClayer, and transmitted back to the eNB.

2) If the mobile is in connection mode but has not data fortransmission, context information will be transmittedvia the PUCCH.

3) If the mobile is in idle mode, this mobile needs toinvoke a random access request via Physical RandomAccess Channel (PRACH), re-establish the connectionwith the eNodeB, and then feedback its context infor-mation via PUSCH or PUCCH as described previously.

In addition, for energy saving purpose, it is more appro-priate for an idle-mode mobile to report in aperiodicmode trigger by the change of context information sta-tus, for example, the battery level dropped below a certainthreshold.

A simplified mapping of several physical channels(related to this paper) and radio resources are shown inFig. 5. The outermost parts of the uplink band are reservedfor PUCCH. The rest of the uplink bandwidth is mainlyused by the PUSCH. Some resource blocks are reservedfor PRACH. For downlink radio resources, a few sym-bols (varies from frame to frame) at the beginning of eachsubframe are reserved for control information such as Phys-ical Downlink Control Channel (PDCCH). The rest of thesubframe is reserved to downlink data transmission as thePhysical Downlink Shared Channel (PDSCH).

The PUSCH is allocated to individual mobile in units ofresource blocks within each sub-frame. An uplink sched-uler at the eNB will decide allocate which resource blocksto which UE, and sending the UE a scheduling grant on thePDCCH. This grants permission for the mobile to transmitand states all the transmission parameters it should follow,such as transport block size, the resource block allocationand the modulation scheme. If one UE has data to trans-mit, it will initial a scheduling request through PUCCH, andreceive such a scheduling grant. If this UE also has contextinformation to feedback, it will concatenate its Service DataUnit (SDU) with context information (denoted as control) asshown in Fig. 5. The type of the context information, theirlength, and their place in the combined packet are includedin the MAC header.

The PUCCH is also shared by all UEs. An individualmobile transmits the PUCCH using two resource blocks,which occupies 1 ms and at the opposite sides of the fre-quency band. To efficiently utilize the limited PUCCHbandwidth, these two resource blocks are further sharedby several UEs by using different cyclic shift or orthogo-nal sequence index, which are assigned to this mobile byeNB. Moreover, LTE-A standard has pre-defined a list ofcontrol formats, which are shown in Table 3. The resourceblocks in the PUCCH are reserved for different control for-mat. The way of reserving which resource blocks for whattype of control format is again decided by the eNB, andadvertised in the System Information Block No.2 (SIB 2)via PUSCH. As illustrated in the Fig. 5, the two highlightedresource blocks are reserved for transmitting a combinationof CQI/PMI/RI. UE1 and UE2 both want to transmit thesethree types of information. As a result, they will spread thesecontrol information with their own orthogonal sequence,and occupy these two resource blocks.

9 Simulation Setup and Results

This section presents the simulation results and analyses ofour devised algorithm. Figure 6 demonstrates the compo-nent of the simulator we use for our simulation purpose.The simulation parameters set for proposed scenario to sim-ulate is shown in Table 4. From Fig. 7, it can be observedthat the battery consumption of UEs is reduced by around10 % compared to the benchmark algorithm RR. It is alsoobserved that our algorithm effectively saves more energyas simulation time increases, and reaches optimal resultswithin 100 TTIs due to the context aware information in thescheduling. For instance, if we consider battery level as acontext entity inside the context aware module, it demon-

J Sign Process Syst (2016) 83:279–291 289

Figure 9 CDF vs energyconsumption.

strates that the lower the battery level the higher the priorityof our scheduling, while also considering other parameters.Therefore, in our investigated algorithm, low battery leveland minimum energy per bit is assigned higher schedul-ing priority which eventually leads to reduced batteryconsumption.

For the scheduling process, CAS considers the remain-ing battery of each UE along with channel quality, trafficdemand and adaptive coding which are mostly ignored inthe previous algorithms. As result, CAS saves energy andthe average remaining battery level of the active users aremuch higher compared to conventional RR. Figure 8 rep-resents the average remaining battery level compared withnumber of users. This figure depicts that CAS, on average,saves more energy compared to conventional schedulingalgorithms. Figure 9 shows the Cumulative Density Func-tion (CDF) of the UEs’ energy consumption. With theproposed method, almost 50 % of the users consume energywhich is a value 0.5 mJ. Using the conventional RR, only20 % of UEs’ consume the same amount of energy; the gain

Figure 10 Overall comparison of CAS with conventional RR.

is 25–30 %. This gain is achieved due to the context awareinformation available at context module for each mobileuser. The context aware module provides the context to theRRMmodule to consider the battery level of each user in thescheduling process and to adapted its power according to thetraffic load in each cell. Thus, the proposed algorithm savesenergy and increase the number of UEs to be scheduled.

A 3-D plot is demonstrated in Fig. 10. In this figure,we show an overall comparison between the proposedalgorithm and the conventional RR in terms of energy con-sumption, number of users and simulation frames. Here, weassume all the context entities (battery level, CQI and traf-fic) that are defined in the proposed algorithm section. Itcan be observed that the proposed approaches saves almost0.2 mJ of energy in contrast to the benchmark RR algorithm.

10 Conclusion

In this paper, we present a Context Aware Scheduling(CAS) algorithm for 5G based on LTE-A exploiting thecontext information. CAS goes beyond the state-of-the-art and exploits the context information of UE for energysaving and guarantee the requested QoS. Furthermore, wepresent an information model for context awareness whichillustrates how context information is extracted and pro-cessed by various functional blocks in the context awarearchitecture of UE. The presented architecture is not onlyused for radio resource management, but can further beutilized in various context based mechanisms. The paperalso discuss the design of context information based sig-naling in LTE-A that can be used in the future technolo-gies. A context aware module is implemented in a system

290 J Sign Process Syst (2016) 83:279–291

level simulator to test the efficiency of the proposed CASand to provide a comparison with conventional schedul-ing. The simulation results show that CAS has the potentialto save energy compared to conventional RR scheduling.In fact, the battery consumption of the UEs are reducedby 10–15 % by using CAS in contrast to conventional RRscheduling.

References

1. Alam, M., Albano, M., Radwan, A.R., & Rodriguez, J. (2010).Context parameter prediction to prolong mobile terminal bat-tery life. In Proceedings of the international mobile mul-timedia communications conf.—mobimedia, Lisboa, Portugal(pp. 1–10).

2. Jalali, A., Padovani, R., & Pankaj, R. (2000). Data throughput ofcdma-hdr a high efficiency-high data rate personal communicationwireless system. In Vehicular technology conference proceedings,2000. VTC 2000-Spring Tokyo. 2000 IEEE 51st (Vol. 3, pp. 1854–1858).

3. Marsan, M., Chiaraviglio, L., Ciullo, D., & Meo, M. (2011).Switch-off transients in cellular access networks with sleepmodes. In 2011 IEEE international conference on communica-tions workshops (ICC) (pp. 1–6).

4. Marsan, M.A., Chiaraviglio, L., Ciullo, D., & Meo, M. (2010).A simple analytical model for the energy-efficient activation ofaccess points in dense wlans. In Proceedings of the 1st interna-tional conference on energy-efficient computing and networking,ser. e-Energy ’10 (pp. 159–168). New York: ACM. [Online].Available: doi:10.1145/1791314.1791340.

5. (2010). 3GPP TS 25.214, Physical Layer Procedures (FDD),release 8, v8.9.0.

6. (2009). Waqas ahmad khan. Impact of DRX on VoIP Performanceand Battery Life in LTE, master thesis.

7. Wu, C.Y., Ho, H.J., & Lee, S.L. (2012). Minimizing energy con-sumption with qos constraints over ieee 802.16e networks. Com-puter Communications, 35(14), 1672–1683. [Online]. Available:doi:10.1016/j.comcom.2012.06.012.

8. Asadi, A., & Mancuso, V. (2013). A survey on opportunisticscheduling in wireless communications (pp. 1–18).

9. Wong, C.Y., Cheng, R., Lataief, K., &Murch, R. (1999). Multiuserofdm with adaptive subcarrier, bit, and power allocation. IEEEJournal on Selected Areas in Communications, 17(10), 1747–1758.

10. Ding, H., Ge, J., Benevides da Costa, D., & Jiang, Z. (2010).Energy-efficient and low-complexity schemes for uplink cognitivecellular networks. IEEE Communications Letters, 14(12), 1101–1103.

11. Kelly, F. (1997). Charging and rate control for elastic traffic.European Transactions on Telecommunications, 8, 1.

12. Viswanath, P., Tse, D., & Laroia, R. (2002). Opportunistic beam-forming using dumb antennas. IEEE Transactions on InformationTheory, 48(6), 1277–1294.

13. Kushner, H., & Whiting, P. (2004). Convergence of proportional-fair sharing algorithms under general conditions. IEEE Transac-tions on Wireless Communications, 3(4), 1250–1259.

14. Proebster, M., Kaschub, M., & Valentin, S. (2011). Context-aware resource allocation to improve the quality of service ofheterogeneous traffic. In 2011 IEEE International Conference onCommunications (ICC) (pp. 1–6).

15. Song, J., Gil, G.-T., & Kim, D.-H. (2010). Packet-scheduling algo-rithm by the ratio of transmit power to the transmission bits in3gpp lte downlink. EURASIP Journal on Wireless Communica-tions and Networking, 2010, pp. 6:1–6:13. [Online]. Available:doi:10.1155/2010/251281.

16. Wong, C.Y., Cheng, R., Lataief, K., &Murch, R. (1999). Multiuserofdm with adaptive subcarrier, bit, and power allocation. IEEEJournal on Selected Areas in Communications, 17(10), 1747–1758.

17. 3GPP TS 23.203 Policy and charging control architecture.Retrieved 13 Feb 2015.

Muhammad Alam holdsa PhD. degree in Computerscience from university ofAveiro. In 2009, he becamea Researcher at Instituto deTelecomunicacoes – Aveiro(Portugal) and concludedhis Ph.D. in MAP-i Doc-toral program Portugal in2014. He has been involvedin several research projectssuch as C2POWER, PEACE,SmartVision and ICSI. Cur-rently, he is a Post-doctoralresearcher at the Instituto deTelecomunicacoes—Polo de

Aveiro, Portugal, working in the EU funded ICSI project. He is theauthor of several Journal and conference research papers. His researchinterests include Wireless Communication, Vehicular Communication,ITS and Context aware systems.

Du Yang received her BEng.degree from the BeijingUniversity of Posts andTelecommunications (China),in 2005; and her MSc. andPh.D. degrees from Univer-sity of Southampton (UK), in2006 and 2010 respectively.She was a recipient of theMobile VCE Scholarship.She worked as a Post-doctoralresearcher at the Instituto deTelecomunicacoes—Polo deAveiro, Portugal, workingin the EU funded WHERE2project. Currently, she is

working as Core Network Engineer at Huawei Technologies, U.K.Her research interests include MIMO techniques, multi-hop relayingcommunication, position information assisted communication, jointPHY and MAC layer optimization in LTE standard.

J Sign Process Syst (2016) 83:279–291 291

Kazi Huq received the B.Sc.degree in computer scienceand engineering from Ahsan-ullah University of Scienceand Technology, Dhaka,Bangladesh, in 2003; theM.Sc. degree in electricalengineering from BlekingeInstitute of Technology,Blekinge, Sweden, in 2006;and the Ph.D. degree in elec-trical engineering from theUniversity of Aveiro, Aveiro,Portugal, in 2014. Since April2014, he has been a SeniorResearch Engineer with the

Instituto de Telecomunicacoes, Polo de Aveiro, Portugal. He is theauthor of several publications, including conferences, journals, anda book chapter. His research activities include fifth-generation (5G),energy-efficient wireless communication, radio resource managementfor green cellular networks, and coordinated scheduling.

Firooz Saghezchi receivedthe MSc degree in ElectricalEngineering-CommunicationSystems from Shiraz Uni-versity, Shiraz, Iran in2003 and the BSc degreein Electrical Engineering-Telecommunications fromUniversity of Tabriz, Tabriz,Iran in 2000. He secured alecturer position at ElectricalEngineering Department ofIslamic Azad University ofGarmsar, Garmsar, Iran for sixyears. Then, he joined 4TELLWireless Communication

Research Group at Instituto de Telecomunicacoes, Aveiro, Portugalin 2010, where he has been involved in several European researchprojects such as HURRICANE, C2POWER and E2SG. He is currentlypursuing his PhD under the umbrella of MAP-tele Doctoral Pro-gramme in Telecommunications, a joint degree offered by Universityof Minho, University of Aveiro and University of Porto in Portugal.He has authored several scientific works including book chapters,journal and conference publications and served as an active reviewerand TPC member for several high-profile journals and conferences.His research interests include 5G, energy efficiency, cooperativecommunications, game theory, demand response and smart grid.

Shahid Mumtaz receivedhis MSc. degree from theBlekinge Institute of Tech-nology, Sweden and hisPh.D. degree from Univer-sity of Aveiro, Portugal. Heis now a senior researchengineer at the Instituto deTelecomunicacoes—Polo deAveiro, Portugal, workingin EU funded projects. Hehas been involved in severalEC R&D Projects (5GPP-Speed-5G, CoDIV, FUTON,C2POWER, GREENET,GREEN-T, ORCALE,

ROMEO, FP6, and FP7) in the field of green communication and nextgeneration wireless systems. In EC projects, he holds the position oftechnical manager, where he oversees the project from a scientific andtechnical side, managing all details of each work packages which givesthe maximum impact of the project’s results for further developmentof commercial solutions. He has been also involved in two Portuguesefunded projects (SmartVision & Mobilia) in the area of networkingcoding and development of system level simulator for 5G wirelesssystem. His research interests include MIMO techniques, multi-hoprelaying communication, cooperative techniques, cognitive radios,game theory, energy efficient framework for 4G, position informationassisted communication, joint PHY and MAC layer optimization inLTE standard. He is author of several books, conference, journals andbook chapter publications.

Jonathan Rodriguez receivedhis Masters degree in Elec-tronic and Electrical Engineer-ing and Ph.D from the Uni-versity of Surrey (UK), in1998 and 2004 respectively. In2005, he became a researcherat the Instituto de Telecomu-nicacoes (IT)-Portugal wherehe was a member of the Wire-less Communications Scien-tific Area. In 2008, he becamea Senior Researcher wherehe established the 4TELLResearch Group (http://www.av.it.pt/4TELL/) targeting next

generation mobile networks with key interests on green communi-cations, cooperation, security, and electronic circuit design. Since itsinception, the group has steadily grown and now Dr. Rodriguez isresponsible for supervising 36 research staff, including a project port-folio of over 25 research grants. He has served as project coordinatorfor major international research projects, that includes Eureka LOOPand FP7 C2POWER, whilst serving as technical manager for FP7COGEU and FP7 SALUS. Since 2009, he became an Invited Profes-sor at the University of Aveiro (PT) and Honorary Visiting Researcherat the University of Bradford (UK). He is author of more than 300scientific works, that includes 6 books. His professional affiliationsinclude: Senior Member of the IEEE and Chartered Engineer (CEng)since 2013, and Fellow of the IET (2015).