Cross Layer QoSsupportframeworkandholisticopportunisticscheduling

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Cross-layer QoS support framework and holistic opportunistic scheduling

for QoS in single carrier WiMAX system

Jinchang Lu n, Maode Ma

School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

a r t i c l e i n f o

Article history:

Received 7 February 2010

Received in revised form5 September 2010

Accepted 11 October 2010Available online 21 October 2010

Keywords:

Cross-layer protocol design

QoS

Opportunistic scheduling

WiMAX

a b s t r a c t

Providing Quality of Service (QoS) to different service classes with real-time and non-real-time traffic

integration is an important issue in WiMAX systems. Opportunistic MAC (OMAC) is a novel view of

communication over spatiotemporally varying wireless links. It combines the features of a cross-layerdesign andan opportunistic scheduling scheme to achieve highutilization whileproviding QoS to various

applications. Channelcharacteristics, traffic characteristics andqueue characteristics are essentialfactors

in the design of opportunistic scheduling algorithms. In this paper, we propose a cross-layer QoS support

scheduling frameworkand a correspondingopportunisticscheduling algorithmto provide QoS support to

the heterogeneous traffic in single carrier WiMAX point-to-multipoint (PMP) systems. We model the

uplink transmission in the single carrier WiMAX system as a multi-class priority TDMA queueing system

to analyze the average packet delays of different service classes. Extensive simulation experiments have

been carried out to evaluate the performance of our proposal. The simulation results show that our

proposed solution can improve the performance of the WiMAX PMP system in terms of packet loss rate,

packet delay and system throughput.

& 2010 Elsevier Ltd. All rights reserved.

1. Introduction

The WiMAX (IEEE Std 802.16d, 2004) system has received a lot

of attention from theacademic and industry sectors in the past few

years as it comes with the ability to provide broadband wireless

access and potential ability to compete with existing wired and

wireless networks. One important issue in the design of WiMAX

systems is to provide QoS to different service classes by using an

efficient scheduling scheme.

Wireless access networks have unique characteristics, which

are the time-varying channel conditions and the multi-user

diversity. The Medium Access Control (MAC) protocol and schedul-

ing algorithms have to be developed specially for this environment

(Liu et al., 2003). It requires a cross-layer MAC protocol design

approach, whereby Channel Specification (Cspec) carrying theestimated instantaneous channel information can be fed to the

MAC layer from the physical (PHY) layer and Traffic Specification

(Tspec) carrying traffic QoS related information can be fed to the

MAC layer from higher layers such as the network or application

layer. The Cspec feedback includes information on the estimated

instantaneous Signal-to-Interference and Noise Ratio (SINR), sup-

portable data rate R(t), Received Signal Strength Indications (RSSI)

or Bit Error Rate (BER ) of a link. The Tspec feedback includes

information on the traffic Maximum Latency (ML) constraint,Maximum Sustained Traffic Rate (MSTR) and the instantaneous

length of queues at a station. The cross-layer nature of OMAC

(Amoakoh et al., 2006) with an opportunistic scheduling scheme

has the potential to revolutionize the design of broadband wireless

access networks from the physical to the networking layer.

Recently, various opportunistic scheduling schemes have been

proposed for wireless communication systems. They can exploit the

time-varying nature of the radio environment to improve the

spectrum efficiency while maintaining a certain level of QoS satisfac-

tion for each connection or user in various wireless networks. The

proposed opportunistic scheduling schemes can be classified into

channel-aware only and channel-aware and queue-aware algorithms

based on their functionality of scheduling algorithms (Hassel, 2006).

Max Carrier-to-Noise Ratio Scheduling (MCS) (Bonald, 2005) is atypical channel-aware opportunistic scheduling scheme. The MCS

scheme is to allocate resources to users with the best channel

condition to achieve high system throughput. On top of channel

characteristics,trafficcharacteristics alsoplay an important rolein the

design of an opportunistic scheduling algorithm. TheProportional Fair

Scheduling (PFS) schemes (Jalali et al., 2000; Jinri andNiu, 2007; Bang

etal.,2008) attemptto trade-off among thethroughput,efficiency and

fairness among users by taking packet length into account (Jinri and

Niu, 2007), or estimating future channel quality (Bang et al., 2008).

Modified Largest Weighted Delay First (M-LWDF) (Andrews et al.,

2000) is a modified version of the PFS scheduler that tries tomeet the

QoS requirement in terms of head-of-line packet delay. Traffic-Aided

Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/jnca

Journal of Network and Computer Applications

1084-8045/$ - see front matter & 2010 Elsevier Ltd. All rights reserved.

doi:10.1016/j.jnca.2010.10.005

n Corresponding author. Tel.: +65 91702589.

E-mail address: [email protected] (J. Lu).

Journal of Network and Computer Applications 34 (2011) 765773
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Opportunistic Scheduling (TAOS-1) (Hu et al., 2004) is a heuristic

opportunistic schedulingscheme thatunifiesfilesizeinformationand

wireless channel variations in order to reduce the completion time of

file transmission. Exponential Rule Scheduler (EXPRule) (Shakkottai

and Stolyar, 2001) attempts to equalize the weighted delays of all

buffers when their differences become larger in a wireless system.

Comparison of those conventional cross-layer opportunistic schedu-

lers can be found in (Amoakoh and Kim, 2006).

Cross-layer MAC and opportunistic scheduling designs tailoredfor WiMAX systems have been proposed (Mai et al., 2007; Lera

et al., 2007; Kwon et al., 2005; Liu et al., 2006; Wan et al., 2007).

TCP-MAC layer cross-layer design has been proposed in the paper

(Mai et al., 2007). It maps network layer 3 and layer 2 function-

alities to provide QoS support in the IEEE 802.16d network. Paper

(Lera et al., 2007) aims at enabling downlink (DL) traffic delivery

with a differentiatedservice treatment, even in a non-ideal channel

condition. It addresses a channel-aware scheduling algorithm with

a per-flow channel error compensation technique based on the

typical features of a WiMAX system: class-based QoS guarantees

and per-flow resource assignment. The Worst-Case Fair Weighted

Fair Queuing+(WF2Q+ ) algorithm has been suggested to manage

the flow-level and class-level granularity per-class queues. A cross-

layeradaptivearchitecture for the IEEE802.16e OFDMA system has

been proposed in the paper (Kwon et al., 2005). A priority-based

scheduler has been proposed in the paper (Liu et al., 2006). At the

MAC layer, each connection employs the adaptive modulation and

coding (AMC) scheme at the physical (PHY) layer. A Priority

Function (PRF) hasbeen definedfor each connection to be admitted

into the system and is updated dynamically depending on the

wireless channel quality, QoS satisfaction and service priority

through a MAC-PHY cross-layer manner. The strategy proposed

in (Wan et al., 2007) has further enhanced the architecture in

(Kwonet al., 2005) by proposing a joint packet scheduling andsub-

channel allocation scheme.

With the help of several additional parameters for the pre-

ference metrics which are the priority of connections or users to be

determined by the opportunistic scheduler for the order of

transmission service, the schemes in (Mai et al., 2007; Lera et al.,

2007; Kwon etal., 2005; Liuet al., 2006; Wanet al., 2007)areshown

to achieve satisfied performance in the given network conditions.

However,the existing scheduling schemes proposedfor WiMAX

systems do not consider packet loss rate. The scheduling schemes

have not been applied to packet scheduling in each traffic flow. The

priority coefficient setting for rtPS and nrtPS traffic connections is

rigid while in fact, traffic load and average long-term channel

conditions vary dynamically.

In this paper, we take channel characteristics, queue character-

istics and traffic QoS characteristics of traffic connections into

account, propose a cross-layer QoS support framework and a two-

stage opportunisticschedulingscheme in the singlecarrier WiMAX

PMP system. The two-stage opportunistic scheduling algorithm is

termed as Holistic Opportunistic Scheduling (HOS) with thefeatures of channel-awareness, queue-awareness and traffic

QoS-awareness.

Ourproposal is able to enhance theproposals in (Liu etal.,2006)

and (Wan et al., 2007) with the following major contributions:

A cross-layer QoS support framework has been designed toprovide instantaneous Cspec and Tspec feedback to the MAC

layer in the WiMAX system. The detailed signal exchange

protocol is designed to visualize the cross-layer design which

is merely conceptually mentionedin the paper (Liu et al., 2006).

A novel comprehensive Holistic Opportunistic Scheduling algo-rithm has been derived with the consideration of not only real-

time physical channel and network traffic conditions but also

traffic QoS requirements and queue status to enhance the

performance of existing opportunistic scheduling algorithms.

The proposed opportunistic scheduling has a unified formula. It

is different from the scheduling scheme in the paper Liu et al.

(2006) which uses different scheduling parameters to schedule

rtPS, nrtPS and BE service classes separately.

Dynamical priority index is designed for each packet belongingto the rtPS service class. It can update the priority of rtPS

dynamically according to a linear function based on thechannelcondition.

The rest of the paper is organized as follows. In Section 2, we

describe various aspects of the MAC and PHY layers of the WiMAX

system. In Section 3, we present our proposed cross-layer QoS

support frameworkand theHOS algorithm. In Section 4,we present

the queueing model for the WiMAX system. We model the uplink

transmission of the WiMAX system as a multi-class priority TDMA

queueing system. In Section 5, we show our simulation design and

the simulation results. Finally, in Section 6, we conclude the paper

with a summary.

2. WiMAX PMP system

A WiMAX (IEEE Std 802.16d, 2004) system with PMP topology

includes one Base Station (BS) and MSubscriber Stations (SSs),

where Mis the number of SSs. In this study, we focus on the UL

transmission of the WiMAX system in time division duplex (TDD)

mode. The principle of the proposed scheduling algorithm can

be extended to DL. The TDD MAC frame is divided into UL and DL

sub-frames. Service flows which are uniquely identifiedby a 32-bit

service flow identifier (SFID) may be transmitted in either the

UL or DL sub-frame. Active service flows are associated with

QoS requirement parameters, namely, ActiveQoSParameterSet.

Admitted and active service flows are mapped to a 16-bit connec-

tion identifier (CID), and are controlled and maintained by a

Connection Admission Control (CAC) scheme in Active Connection

List (ACL) at the BS.

The traffic flows at each SS are classified according to the four

types of scheduling services, namely, Unsolicited Grant Service

(UGS), Real-time Polling Service (rtPS), Non-Real-time Polling

Service (nrtPS) and Best Effort (BE) service. The mandatory QoS

service flow parameters for UGS service are Maximum Sustained

Traffic Rate (MSTR), Maximum Latency (ML), Tolerated Jitter and

Request/Transmission Policy. The mandatory QoS service flow

parameters for rtPS service are Minimum Reserved Traffic Rate

(MRTR), MSTR, ML and Request/Transmission Policy. The manda-

tory QoS service flow parameters for nrtPS are MRTR, MSTR, Traffic

Priority and Request/Transmission Policy. The mandatory QoS

service flow parameters for BE are MSTR, Traffic Priority, and

Request/Transmission Policy. As BE connection can tolerate MSTR

and ML QoS requirements, only UGS, rtPS and nrtPS will beconsidered in this paper. rtPS and nrtPS can be combined together

under a general polling service umbrella as suggested in the paper

(Ma and Ng, 2006).

The WiMAX system supports 5 different PHY techniques (IEEE

Std 802.16d, 2004), namely, WirelessMAN-SC (Single Carrier) PHY

specification, WirelessMAN-SCa PHY specification, WirelessMAN-

OFDM PHY specification, WirelessMAN-OFDMA PHY specification

and WirelessHUMANTM PHY specification. At the PHY layer, the

WiMAX system takes an adaptive modulation policy which selects

1 out of 3 different modulation schemes. On the UL, QPSK is

mandatory, while 16-QAM and 64-QAM are optional. The DL

supportsQPSK and16-QAM, while 64-QAM is optional. Thesystem

can use various Forward Error Correction (FEC) schemes on the UL

as well as the DL. To fully utilize the flexible and robust PHY layer,

J. Lu, M. Ma / Journal of Network and Computer Applications 34 (2011) 765773766


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the WiMAX system equips a flexible Radio Link Control scheme,

which is responsible for transition from one PHY scheme to

another. The RLC is capable of switching between different PHY

bursts. Thesystem also uses theReceiverSensitivityas a parameter

togetherwiththe SINR thresholds of receiversusedby AMCscheme

to select different burst profiles in order to maximize the network

throughput and maintain the Bit Error Rate (BER) under a preset

level e.g. BERo1 105.

WiMAX transceivers support various transmission modeswith different modulation and coding schemes corresponding to

different data transmission rates. For each modulation scheme,

there is one relationship between the theoretical BER and the

energy per bit to noise power spectral density ratio (Eb/N0)

(Langton, 2002). By using MATLAB, the diagram of theoretical

BER vs. SINR relationship can be plotted when the PHY channel

bandwidth is set to 25 MHz (IEEE Std 802.16d, 2004) in the single

carrier WiMAX PMP system.

The range of the received SINR values can be classified into

sevennon-overlapping regions for adaptive modulation and coding

index AMC(q) where q1,2,y,7 on a targetprescribedbit errorrate

BERo1 105 in the WiMAX PMP system.

Further complying with the receiver sensitivity RSS require-

ment specified by the IEEE 802.16d standard (IEEE Std 802.16d,

2004), a lookuptable, labeled Table 1, which isnamed AMC vs. SINR

and receiver sensitivity requirement, can be derived when BER is

set to 1 105.

3. Proposed QoS support framework and schedulingalgorithm

The performance of MAC protocol and the functionality of

scheduling algorithms are influenced by the time-varying wireless

channel in WiMAXsystems. In order to enhance theperformance of

MACprotocol with QoSprovisioning to theheterogeneous trafficin

WiMAX systems, we propose an efficient cross-layer QoS support

framework and a corresponding two-stage opportunistic schedul-

ing algorithm in the single carrier WiMAX PMP system as shownin Fig. 1.

3.1. Proposed cross-layer QoS support framework

The proposed cross-layer QoS support frameworksupportsboth

UL and DL transmission. We take the UL transmission as an

example to explain its functionality as follows:

(1) Considering theimpact of airinterface on MAClayerprotocol, a

wireless Channel Condition Estimator (CCE) is designed at the

PHY layer at the BS as well as the SSm, where m1,2,3,y,M. It

not only monitors instantaneous channel condition status like

the receivers received signal strength RSS(m) and SINRg(m)when the BS receives signal from SSm, but also indicates long

term channel condition by using the statistic parameters

including Max_g(m), Average_g(m) and Min_g(m) over theexecution period. Based on the information of the channels

status provided by CCE, the FEC, the Symbol Mapper and the

AMCcontroller at theBS select an FECscheme, modulation and

coding scheme AMC(m, q), andthe data transmissionratefor UL

transmission from SSm tothe BSat modulation andcoding level

q according to Table 1. By sensing the maximum value ofq in

AMC(m, q)atall MSSs in thesystem,the SymbolMapperand the

AMCcontroller at the BS can determine the maximum trans-

mission rate Max_R(t) at time t.

(2) The PHY Symbol Rate Controller at the BS then forms

AMC_Controller_Info{g(m), RSS(m), AMC(m, q), Max_R(t)} andforwards the AMC_Controller_Info to the MAC layer via its PHY

Service Access Point (SAP). Embedded in UL-Map, the informa-

tion is further broadcasted to all SSs for UL scheduling and

transmission from SSm to the BS.

Table 1

AMC vs. receiver SINR and Min sensitivity requirement.

AMC

index

AMC(q)

Receiver

SINR (dB)

g(q)

Receiver Min

sensitivity RSS(q)

(dB m)

Modulation/

coding scheme

Channel bit

rate R(q)

(Mbps)

AMC(1) 8.311.6 80 BPSK1/2 20

AMC(2) 11.713.2 80 QPSK1/2 40

AMC(3) 13.318.9 78 QPSK3/4 60

AMC(4) 19.020.9 73 16-QAM1/2 80

AMC(5) 21.028.0 71 16-QAM3/4 120

AMC(6) 28.129.1 66 64-QAM2/3 160

AMC(7) 429.2 65 64-QAM3/4 180

Fig. 1. Proposed cross-layer QoS support framework for WiMAX.

J. Lu, M. Ma / Journal of Network and Computer Applications 34 (2011) 765773 767


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(3) The first stage of the scheduling scheme operates at each SS.

When SSm receives a DL/UL_Map message, its scheduler

extracts g(m), R(m, q) from AMC_Controller_Info. Based ong(m), R(m, q) and Tspec of each connection, the scheduler atSSm computes four scheduling parameters and determines the

Scheduling Priority (SP) for every packet in each connection

through our proposed HOS algorithm which is to be described

in detail in Section B. Then, the SPs will be sortedin descending

order. Then, SSm will take the maximum value of the calculatedScheduling Priority, Max_ SP(m), to represent itself to compete

with other SSs for the order of UL transmission in the next

frame. SSm embeds its Max_SP(m), its total bandwidth request,

BW_request(m) and AMC_Controller_Info in a Polling_respon-

se(m) and sends it to the BS when SSm is polled.

(4) The second stage of scheduling at the BS sorts Max_SP(1),

Max_SP(2),y, Max_SP(M) in descending order and selects SSmwith the highest Max_SP(m) to allocate time slots according to

its BW_request(m) in thenext UL sub-frame, excluding thetime

slots for UGS. IfBW_request(m) is less than the number of

available time slots in the next UL sub-frame, the remaining

time slots will be allocatedto SSn which has the second highest

Max_SP(n) according to its BW_request(n). This procedure

iterates until all the available time slots in the next UL

sub-frame are used up. AMC_Controller_Info{g(m), RSS(m),AMC(m, q), Max_R(t)} and the result of scheduling, which is

the time slot allocations for SSs in the next UL sub-frame, are

embedded in a DL/UL_Map. The DL/UL_Map is broadcasted by

the BS to all SSs for UL transmission in the next frame.

(5) The scheduler at SSm extracts the information of its allocated

transmission time slots and the AMC_Controller_Info from the

DL/UL_Map and selects packets from the highest priority to

the lowest priority from different connections. SSm transmits

the selected packets to the BS in the allocated time slots at the

data rate determined by AMC(m, q) of the BS receiver.

3.2. Holistic opportunistic scheduling algorithm

In this study, the following design factors have been taken into

consideration when designing the efficient QoS frameworkand the

corresponding scheduling scheme to provide QoS to the hetero-

geneous traffic in WiMAX systems:

The existing QoS signaling mechanisms and DL/UL_Mapmechanism in WiMAX should be applied.

Per-connection scheduling overhead should be minimized. The QoS requirement parameters of each connection in the

system should be guaranteed.

The impact of AMC on the scheduling scheme should betaken into account as the channel capacity or bandwidth

dynamically fluctuates according to the SINR of PHY in a

wireless time-varying channel.

Considering the design factors mentioned above, we propose

the Holistic Opportunistic Scheduling (HOS) algorithm associated

with the proposed cross-layer QoS support framework. The pro-

posed scheduling scheme takes the information of instantaneous

wireless channel condition and the real-time traffic condition as

well as the traffic QoS specification to set the Scheduling Priority

(SP) for each individual packet forthe UL transmission from rtPS or

nrtPS traffic flows. Since the UGS connections have been allocated

with a fixed bandwidth based on their fixed bandwidth require-

ment specified by the framework of the IEEE 802.16d standard, the

proposed scheduling is only applied forthe rtPS andnrtPS services.

The SP of each packet is determined by four key scheduling

parameters, namely, Dynamical Priority Index (DPI), Channel

Specification Index(CSI), Normalized TimeDelay Satisfaction Index

(NTDSI) and Normalized Predictive Starvation Index (NPSI). In the

scheduling period, starting at time t, SSm (or BS scheduling for DL

traffic) decides the SP of the kth packet from connection i of either

rtPS or nrtPS traffic flows based on the following equation:

SPm,i,k,t DPIm,i,tCSIm,i,t

expNTDSIm,i,k,tNPSIm,i,t 1

where DPI(m, i, t) is the dynamic priority index of connection i at

SSm. The role ofDPI(m, i, t) is to provide different priorities for

different QoS classes dynamically. The priority coefficients are

fixed in the paper (Liu et al., 2006), the priority ofrtPS1.04the

priority ofnrtPS0.84the priority ofBE0.6. In contrast, we set

priority index,which is termedpriority coefficient in thepaper(Liu

et al., 2006), of rtPS service class dynamically on a channel-aware

approach. The procedure to set the value ofDPI(m, i, t) is described

as follows:

(1) A range for DPI(m, i, t) of [1.0, 0.8] is set for rtPS traffic

connections. SSm firstly assigns 0.8 to DPI(m, i, t) for rtPS traffic

connection i and 0.6 to DPI(m, j, t) for nrtPS traffic j connection.

(2) After thesystem runs over a periodof time, SSm is able to derivestatistical channel condition parameters like Max_g(m),Average_g(m) and Min_g(m) based on g(m) in AMC_Controller_Info from the BS. The DPI(m, i, t) for each packet belonging to

rtPS service class can be updated dynamically according to the

linear function:

DPIm,i,t 0:8 0:2gmMin_gm=Max_gmMin_gm

2

By using (2), DPI(m, i, t) will be setdynamically from 0.8to 1.0for

the rtPS service class based on SINRg(m) while it is fixed at 0.6 forthe nrtPS service class. It makes sure that the priority of the rtPS

traffic is always higher than that of the nrtPS traffic. It gives favor

treatment to the rtPS service class to enhance the performance ofQoS for real-time traffic when the channel condition is good.

CSI(m, i, t) in (1)is thechannel specificationindexfor connection

i at SSm at time t. It is calculated as follows:

CSIm,i,t Rm,i,t

Max_Rt3

where R(m, i, t) is thetransmission rate ofconnection i at SSmattime

t. It is set according to AMC(m, q) dynamically based on the channel

condition at the PHY layer. Max_R(t) is the maximum ofR(m, i, t)

over all MSSs and all connection i at the time instant t. From the

scheduling parameter defined in (3), CSI(m, i, t) is in fact a MCS

scheduling scheme (Bonald, 2005). CSI(m, i, t) is a typical through-

put efficient opportunistic scheduling,which allocates resources to

SSswith thebestchannel condition. In order toachieve high systemthroughput, CSI(m, i, t) plays theroleas a channel-aware scheduling

parameter in our proposed HOS.

However, this channel-aware policy monopolizes all resources

to good-channel users that are usually located close to the BS.

Because the SSs are usually distributed across the entire WiMAX

system, this unfair scheduling parameter alone is not beneficial for

anybody but local SSs.

In orderto overcome this unfairness causedby the side-effect of

CSI(m, i, t) and to provide delay bound guarantees for individual

connections and delay-sensitive rtPS traffic flows, we introduce a

traffic QoS-aware scheduling parameter, termed as Normalized

Time Delay Satisfaction Index, NTDSI(m, i, k, t), as well as a queue-

aware scheduling parameter, termed as Normalized Predictive

Starvation Index, NPSI(m, i, t) in the HOS scheduling function.



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NTDSI(m, i, k, t) in (1) is the parameter to capture the packet

latency requirement of a packet if applicable. It is defined as

follows:

NTDSIm,i,k,t 1TDSIm,i,k,t=MaxTDSIm,i,k,tiA I

4

Packettimeoutis definedas when thewaiting time of thepacket

in a queue is over its maximum latency ML(m, i, k)or when its

waiting time is over the TCPre-transmission threshold. Time Delay

Satisfaction Index(TDSI) for the kth packet from rtPSconnection i atSSm, TDSI(m, i, k, t), will be set as

TDSIm,i,k,t MLm,i,ktVCm,i,k iffMLm,i,ktVCm,i,kg40

0, iffMLm,i,ktVCm,i,kgr0

(

Since a non-real-time packet in nrtPSconnections has no

maximum latency requirement, the maximum waiting time for a

non-real-time packet can be set as the TCP re-transmission time-

out value TO at 400 ms for the wireless channel (Song and Mar,

2005). The TDSI(m, i, k, t) for the kth packet from nrtPS connection i

at SSm, TDSI(m, i, k, t), is set as

TDSIm,i,k,t TOtVCm,i,k iffTOtVCm,i,kg40

0 iffTOtVCm,i,kgr0

(6

Each packet is stamped with a virtual clock (VC) ( Zhang, 1990).

VC monitors the average transmission rate of statistical data flows

andprovides every flowwith guaranteed throughput and low queue

delay. The idea behind the VC algorithm is that each packet is

assigned a VirtualTime, which represents thetime it would be sentin

a Time Division Multiplexing (TDM) system. The VC(m, i, k) isset for

the kth packet from connection i at SSm according to the algorithm:

VCm,i,k MaxVCm,i,k1,real time PLm,i,k=Rm,i,t 7

where PL(m, i, k) is the length of the kth packet.[tVC(m, i, k)]

denotes the time spent by the kth packet in its queue.

TDSIm,i,k,t=MaxTDSIm,i,k,tiA I

is thenormalization function so that

TDSIm,i,k,t=MaxTDSIm,i,k,tiA I

A0,1:

If

NTDSIm,i,k,t 1TDSIm,i,k,t=MaxTDSIm,i,k,tiA I

40

the delay requirement of the kth packet will be satisfied, and the

effect on the scheduling priority will be quantified by exp[ NTDSI

(m, i, k, t)]A[0.368,1]. A smaller value of exp[NTDSI(m, i, k, t)]

indicates a higher degree of delay satisfaction, which leads to a lower

scheduling priority.

In a wireless network environment, common channel errors due

to multi-path fading, shadowing and attenuation may cause bit

errors and packet loss, which are quite different from the packet

loss caused by congestions in the wired networks. Therefore,

wireless packet loss can mistakenly lead to dramatic performance

degradation. The information of packet loss rate in wireless net-works not only reflects the condition of the PHY wireless channel

but also serves as an index of effective rate adjustment.

NPSI(m, i, t) in (1) is the normalized predictive starvation index.

Itis designedto reflect the packet loss ofthe system. It is calculated

as follows:

NPSIm,i,t PSIm,i,t

MaxPSIm,i,tiA I

8

where PSI(m, i, t) is the Predictive Starvation Index which indicates

the queue status in terms of queue length and the urgency of a

packet that is to be transmitted successfully:

PSIm,i,t the number of packets in queue i at SSm at time t=PSRm,i,t

9

Here PSR(m, i, t) is the packet success rate with the following

definition: a packet is correctly transmitted from connection i at

SSm at time t, only if it is not dropped from the queue with

probability [1 Pd(m, i, t)] and is correctly transmitted through the

wireless channel with probability [1 PER(m, i, t)]. Hence, we can

obtainthe packetloss rate forconnection i at SSm at time t,asshown

below:

em,i,t 11Pdm,i,t1PERm,i,t 10

Therefore, the packet success rate PSR(m, i, t) of the kth packet

from connection i at SSm at time tis calculated as follows:

PSRm,i,t 1em,i,t 1Pdm,i,t1PERm,i,t 11

Let Pd(m, i, t) denote the packet dropping probability, which is

due to its queue being full, the packet delay exceeding its ML or the

waiting time exceeding the TCP re-transmission threshold, upon

the queue for connection i at SSm at time t:

Pdm,i,t Em,i,tfDg

Em,i,tfAg12

Pd(m, i, t) is the ratio of the numberof dropped packets Em,i,t{D}over

the number of arrived packets Em,i,t{A} duringthe sliding windowor

scheduling period.To simplify the AMC design, packet error rate PER(m, i, t) of the

wireless PHY transmission channel for connection i at SSm at time t

can be approximately expressed as

PERm,i,t %1, ifgmrgBS_Minan expgngm ifgmrgBS_Min

(13

gBS_Min is the minimum SINR requirement of the BS receiver.

Parameters an and gn are modulation and coding mode dependent,

which can be determined by the modulation and coding

scheme (Liu et al., 2004).

When a channel condition is good, DPI(m, i, t) will be set at a

high value for the rtPS service class. Thus, a higher system

throughput can be achieved. When R(m, i, t)0, the channel is in

deep fading and the capacity is zero, so connection i at SSm shouldnot be served regardless of its delay performance. The scheduling

scheme can achieve sub-optimality.

3.3. Computional complexity analysis of HOS algorithm

Each SS monitors and computes the SPs of packets from its own

connections. The first stage scheduler at SS only sorts the SPs of

packets in its own queues, the complexity of the algorithm at SS is

O(Nlog N) where Nis the number of packets in the queues.

Similarly, the second stage scheduler at BS only sorts the SSs

Max_SP(m), where m1,2,yM. The scope of the sorting list is

limited to the total number of SSs, the complexity of the algorithm

at BSis O(Mlog M) where Mis the number of SSs. It means that theoverhead of scheduling is divided into two stages and it is

distributed and shared by all SSs. A frame time is sufficient for

every SS to compute four key scheduling parameters for a packet

which could be transmitted in the next frame. The efficiency of the

scheme can be achieved.

4. Queueing model and delay analysis

To analyze a packet-level QoS in real-time and non-real-time

data transmission, the network performance parameter like aver-

age packet delay needs to be investigated. A queueing analytical

model can be used off-line to obtain the network performance

parameter.



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In this section, we perform a queueing analytical modeling to

analyze the WiMAX system in order to obtain the average packet

delay for UGS and rtPS service classes.

4.1. WiMAX system queueing model

The UL channel of the single carrier WiMAX PMP system can be

modeled as a multi-class priority TDMA queueing system.

The UL channel can be considered as a multiple-access com-

munication channel shared by MSSs. The UL sub-frame consists of

Nconsecutive time slots. Each time slot is normalized to be of unit

length t. Let the duration of a time frame be TF, so that TFNt. TheTDMA scheme for inter-SS is MaxMin Fair Sharing (MMFS)

scheduling, in which station i is allocated ni time slots per frame,

uniformlydistributesin UL sub-frame. TheTDMA schemefor inter-

class is the strict priority queue scheme in which higher priority

packets are queued ahead of lower priority ones. Packets of the

same priorityclass that arriveat differentslotsare servedon a First-

Come-First-Served basis. Packets of the same priority class that

arrive during the same slot are randomly ordered for transmission.

Each station can transmit its packets only during itsdedicated time

slots, which is synchronized and governed by the BS via

DL/UL_Map. Packets arriving at each SS belong to one of the four

priority classes: UGS (priority-1, highest priority), rtPS (priority-2),

nrtPS(priority-3) or BE (priority-4, lowest priority). At each station,

packet arrival is a Poisson arrival process so that lj is the average

arrival rate of class j packets, j1, 2,y,J. Each packet is transmitted

in different number of time slots according to its length. The

number of time slots to transmit the kth arriving packet of priority

class j is denoted by Bjk

(j 1,2,y,J). Itis assumed that {Bjk, kZ l} isa

sequence of independent and identicallydistributed (i.i.d.) random

variables for each class j1, 2,y,J. The transmission requests can

be expressedby integer multiplesof a time slot.We assumethatthe

probability of collisionof therequest contention in theBE service is

zero in the queueing model. The information of bandwidth alloca-

tion arecarriedby DL/UP_Mapand broadcasted toall SSs. When the

UL channel becomes available, any waiting priority-i packet isserved before any priority-j one, ifioj.

Notethat the steady-state average packet delay for each priority

class would be invariant to the intra-slot/intra-frame scheduling

scheme. We focus on theaverage packetdelayof each priorityclass

regardless of intra-slot/intra-frame scheduling.

4.2. Delay analysis

The delay of a packet is defined as the total time spent by the

packet to get through UL channel. Denoted byDjk

, the delay of the

kth packet of priority class j, we have

Dj

k

Wj

k

sj

k

Pj

k

FL 14

where Wjk

denotes the packet waiting time, sjk

represents the total

time to transmit a priorityj kth packet. FL denotes the packet

frame latency. Pjk

denotes the packet propagation delay. The

average packet delay ofj class is Dj , given by (Moraes and Rubin,

1984)

Dj Wj,1 E Bjk

h i

N1

N

& 'N

ni

" #tFL P

j 15

where

Wj,1

PJi 1 libi,2 1ZJ

Nni

j kt

21Zj1Zj116

N=ni

represents the biggest integer not larger than Nni.

bj,2 is the second moment of service time, and is given by

bj,2

Z10

x2 dBjx

rj ljbj,1

Zj Xji 1

ri, j 1,2,. . .,J 17

For UGS traffic, we set J1. And we can set J2, J3, J4 forrtPS traffic, nrtPS and BE traffic, respectively.

5. Performance evaluation

Following the signaling mechanism specified by the IEEE

802.16d standard, we developed our own WiMAX simulation

model by using C language. We have carried out extensive

simulation experiments to evaluate the performance of the pro-

posed cross-layer QoS support framework and the HOS algorithm.

We compare the performances of Exponential Rule Scheduler

(EXPRule) in Shakkottai and Stolyar (2001) and Priority Function

Scheduler (PRFS) in Liu et al. (2006) with that of our scheduling

scheme for rtPS and nrtPS connections. Average packet delays ofservice classes obtained by quantitative analysis of the queueing

model have been compared with the simulation results. The

simulation results show that our proposed scheduling scheme

can not only effectively support rtPS traffic to make real-time

packets meet their delay bounds but also serve nrtPS traffic with

reasonable performance.

5.1. Simulation design

For the wireless fading channel in the simulation model, the

channel quality can be captured by the parameter, g. Since thetransmission channel varies from frame to frame, we employed

the general Nakagami-m model to describe g statistically

(Nakagami, 1960). The received SINR,g per frame is thus a randomvariable with a Gamma probability density function:

pgg mmgm1

gmGmexp

mgg

18

where g is the average received SINR, Gm : R1

0 tm1etdtis

the Gamma function and m is the Nakagami fading parameter

(mZ1/2). We set m1 in our simulation model. The channel is

assumed to be quasi-static so that the channel gains are constant

over a frame. However, they are allowed to vary from frame to

frame. The average received SINR, gcan be expressed as

g PtLiPNLP 19

where Pts the transmitter output power, Li is the implementation

loss due to hardware connecting cables, antenna patterns, etc.,

PNis the receiver noise power, which is related to the hardwarenoise figure and bandwidth, and LPis the path loss due to radio

propagation.

The network has been designed with one BS and eight SSs. The

positionsof the SSs are assumed to be independent anddistributed

randomly. The traffic parameters are selected from the supported

range of values which fully cover the required values for multi-

media services defined in Recommendation ITU-R M.1225 (ITU

RADIO, 2007). In the simulation experiments, we only consider the

effect of the UL scheduling algorithm on the integrated traffic

consisting of a fixed percentage of UGS traffic load, different

percentages of rtPS and nrtPS traffic loads from the eight SSs to

the BS. We set UGS at 1.37 Mbps. The traffic load for rtPS and nrtPS

is 50% each of the remaining traffic. The inter-arrival time of UGS

packets is constant, at 10.1 ms. Packet length is fixed to 200 bytes.



7/9

The deadline is set to 50 ms. Both rtPS and nrtPS connections are

burst traffic flows. Packet arrivals follow the Poisson distribution

with packet inter-arrival time exponentially distributed. Packet

length is exponentially distributed with a mean packet length of

500 bytes. The mean deadline of rtPS is 80 ms and it is exponen-

tially distributed. There is no deadline for nrtPS connections. The

queue size is big enough so that packets would not be dropped due

to the queue being full. We consider packet loss due to its waiting

time exceeding its maximum latency or packet destroyed becauseof interferenceand fadingof thewirelesschannel in oursimulation.

The parameters for simulation experiments are shown in

Table 2.

5.2. Numerical calculation of average packet delay

According to the system configuration, we can get ni250/

831 slots/frame as all SSs are identical. Note that B1 4,

B2 10,B3 10 according to the average packet length of the 3

different service classes. By using (15), the numerical results of the

average packet delay of UGS, rtPS and nrtPS classes can be

calculated as shown in Table 3. It hasbeen evaluatedand compared

with the simulation results of the EXPRule, PRFS and HOS schedul-

ing algorithms.

5.3. Experimental results

In the simulation experiments, it is assumed that the system

operates in TDD mode. As shown in Figs. 26, we evaluate the

performance of the three scheduling schemes in terms of packet

loss rate, average packet delay and UL throughput of the rtPS

connections, entire UL throughput and UL throughput of the nrtPS

connections with the total traffic load, which consists of UGS, rtPS

and nrtPS service classes, changes from 10% to 90% of the total UL

bandwidth accordingly.

Fig. 2 shows the relationshipbetween the packetloss rate of the

rtPS traffic and the various traffic loads. It is clear that the packet

loss rate of the rtPS traffic of our proposal is lower than that of thePRFSand EXPRule schemes whenthe traffic intensity increases.The

reason is that the PRFS scheme has only considered the delay

satisfaction indicator for the rtPS traffic and the EXPRule scheme

has only considered starvation index for the rtPS traffic. However,

our scheduler has integrated both Normalized Time Delay Satisfac-

tion Index NTDSI(m, i, k, t) and Normalized Predictive Starvation

Index NPSI(m, i, t) into the computation of scheduling decision for

Table 2

Simulation parameters based on IEEE 802.16d SC system.

Parameters Value

Channel bandwidth 25 MHz

Frame length 10 ms

UL sub-frame length 5 ms

Duration of time slot 20 ms

Average received SINR, g 12 dBAdaptive modulation scheme BPSK, QPSK, M-QAM

Target BER, Pber r105

Wireless channel model Nakagami-m fading channel (m 1)

Receiver sensitivity, RSS 65 dB m

Table 3

Numerical calculation of Mean delay.

Traffic Load UGS (Mbps) rtPS (Mbps) nrtPS (Mbps) k1 (mean) Packet/s k2,3 (mean) Packet/s UGS Delay(ms) rtPS Delay(ms) nrtPS Delay(ms)

0.1 1.37 0.315 0.315 107.03 9.84 6.14 7.91 8.72

0.2 1.37 1.315 1.315 107.03 41.09 6.14 10.48 13.98

0.3 1.37 2.315 2.315 107.03 72.34 6.14 13.09 19.44

0.4 1.37 3.315 3.315 107.03 103.59 6.14 15.73 25.12

0.5 1.37 4.315 4.315 107.03 134.84 6.14 18.41 31.02

0.6 1.37 5.315 5.315 107.03 166.09 6.14 21.11 37.15

0.7 1.37 6.315 6.315 107.03 197.34 6.14 23.85 43.53

0.8 1.37 7.315 7.315 107.03 228.59 6.14 26.63 50.17

0.9 1.37 8.315 8.315 107.03 259.84 6.14 29.45 57.07

0

5

10

15

20

25

30

0.1

rtPSLossRate%

Total Traffic Load

PRFS

EXPRule

HOS

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Fig. 2. Loss rate vs. traffic load for rtPS traffic.

0

50

100

150

200

250

300

0.1

rtPSDelay(ms)

Total Traffic Load

PRFS

EXPRule

HOS

Queueing Model

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Fig. 3. rtPS delay vs. traffic load.



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the transmission priority. Therefore, it can achieve a much lower

packet loss rate for the rtPS traffic connections.

Fig. 3 shows theaverage packetdelays of thertPS connections of

the three schedulingschemes and the numerical calculation resultsof the queueing model. It is clear that the EXPRule scheduler

degrades dramatically but PRFS and our scheduler do not. The

reason is that the delay requirement of a packet from a rtPS

connection has been included to determine its SP by exp[-NTDSI(m,

i, k, t)]A [0.368,1]. IfML(m, i, k)(tVC(m, i, k)r0, a lower value of

NTDSI(m, i, k, t) can be set, leading to a higher SP. The expression

exp[NTDSI(m, i, k, t)] makes the scheduling priority assignment

much more sensitive to the delay bound of a packet from the rtPS

traffic.

It is observed that the average packet delays of the queueing

model and that of the three schemes are very close to each other

when thetrafficload is light,for example,lessthan 0.6. This is based

on the fact that the system can transmit packets in time when the

traffic load is light.

Itis also observed that theaveragepacket delaysof thequeueing

model and HOS are very close to each other. The simulation results

have been validated by the queueing model.

Fig. 4 shows the relationship between the UL throughput of the

rtPS connections with trafficload forthe three scheduling schemes.

It can be observed that our proposal can achieve a higher UL

throughput of the rtPS service class.

Based on (2), the DPI assignment of our proposal assigns rtPS

traffic with a higher DPI when channel condition is good to provide

moretransmission opportunities for the rtPStraffic bursts. Further-

more, thedelay requirement of a packetfrom a rtPS connection has

been included to determine its SP by exp[ NTDSI(m, i, k, t)]A

[0.368,1]. IfML(m, i, k) (tVC(m, i, k) r 0, a lower value of

NTDSI(m, i, k, t) can be set, leading to a higher SP for a rtPS packet.

Fig. 5 shows the system UL throughput with different traffic

loads. It is observed that our proposal can achieve a higher UL

throughput. It is clear that EXPRule scheduler and PRFS degrade

dramatically but our scheduler does not when the traffic load is

more than 0.7. The connection-based scheduler could waste

bandwidth, if the BW_ request of a scheduled connection is less

than theavailable bandwidthin a frame.Sincethe HOS scheduleris

a packet-based scheduler without any waste of available band-

width, efficient bandwidth utilization can be achieved by our

proposal. Furthermore, the EXPRule scheme has not considered

the ML requirement of the rtPS traffic, the packets from rtPS

connections drop drastically when traffic load is heavy, e.g. more

than 0.7. This leads to a lower UL throughput by the EXPRule

scheme.Fig. 6 shows the UL throughput of nrtPS connections with

different traffic loads. It is clear that our solution can achieve a

moderately higher nrtPS UL throughput compared to PRFS and

EXPRule. The reason is that we have included the predictive

starvation index NPSI(m, i, t) in the opportunistic scheduling

design. When the DPI(m, i, t), NTDSI(m, i, k, t) and CSI(m, i, t) are the

same for all the connections, NPSI(m, i, t) becomes the major factor

in determining the Scheduling Priority. When the queue length of

one of the nrtPS connections increases, it leads to a higher value of

NPSI(m, i, t), so the starvation problem of nrtPS connections can be

mitigated. Thequeuelength of each connection will be restricted to

a certain level to make the system stable.

TheEXPRule scheduling scheme is a connection-based scheduler.

The bandwidth can be wasted, if the BW_ request of a scheduled

0.0

0.1

0.2

0.3

0.4

0.5

0.1

rtPSULTh

roughput

Total Traffic Load

PRFS

EXPRule

HOS

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Fig. 4. rtPS UL throughput vs. traffic load.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.1

SystemULThroughput

Total Traffic Load

PRFS

EXPRule

HOS

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Fig. 5. System UL throughput vs. traffic load.

0.0

0.1

0.2

0.3

0.4

0.5

0.1

nrtPSULTh

roughput

Total Traffic Load

PRFS

EXPRule

HOS

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Fig. 6. nrtPS UL throughput vs. traffic load.



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connection is less than the available bandwidth in a frame. The

EXPRule scheduling has considered the transmissionrateoptimal for

connections.However, this factormonopolizes all resources to good-

channelusers that areusuallylocated close to theBS. Because theSSs

are usually distributed across the entire WiMAX system, this unfair

scheduling parameter alone is not beneficial for anybody but local

SSs. This is why the nrtPS UL throughput by EXPRule is lower than

that of PRFS and HOS schemes.

6. Conclusion

In this paper, we have proposed a cross-layer QoS support

framework to enhance QoS provisioning specified by the IEEE

802.16d standard in the single carrier WiMAX PMP system.

Associated with the framework, the proposed HOS algorithm,

which has the features of channel-awareness, queue-awareness

and traffic QoS-awareness, determines the dynamic SP of each

packet by its four key scheduling parameters, namely the Dyna-

mical Priority Index, the Channel Specification Index, the Normal-

ized Time Delay Satisfaction Index and the Normalized Predictive

Starvation Index. Our proposal can effectively and efficiently

schedule and manage the transmission of the integrated traffic

consisting of UGS, rtPS and nrtPS connections. The simulationresults show that the proposed solution can improve QoS sig-

nificantly for rtPS traffic connections while achieving a high UL

throughput.

References

AndrewsA, KumaranK, RamananK, StolyarA, Vijayakumar R,WhitingP. CDMA dataQoS scheduling on the forward link with variable channel conditions. Bell Labstechnical report preprint, April 2000.

Amoakoh Gyasi, Seong Agyei, Kim Lyun. Comparison of opportunistic schedulingpolicies in time-slotted AMCwireless networks. In: Proceedings of theIEEE firstinternational symposium on wireless pervasive computing, 2006, 6pp.

Bang HJ, Ekman T, Gesbert D. Channel predictive proportional fair scheduling. IEEETransactions on Wireless Communications February 2008;7(2):48287.

Bonald T. Flow-level performance analysis of some opportunistic scheduling

algorithms. European Transactions on Telecommunications 2005;16:6575.Hassel Vegard. Tutorial: opportunistic multiuser scheduling for wireless networks.

Department of Electronics and Telecommunications, Norwegian University ofScience and Technology (NTNU), June 2006. Available from: /www.unik.no/personer/porten/MoPSAR/MOPSARTutorial_Hassel.pdfS.

Hu M, Zhang J, Sadowsky J. Traffic aided opportunistic scheduling for wirelessnetworks: algorithms and performance bounds. Computer Networks: TheInternational Journal of Computer and Telecommunications Networking2004;46(4):50518.

IEEE Std 802.16d-2004. IEEE standard for local and metropolitan areanetworksPart 16: air interface for fixed broadband access systems, October2004.

ITU RADIO COMMUNICATION STUDY GROUPS. Document 8F/1359-E, August 2007.Available from:/http://members.wimaxforum.org/documents/WiMAX_IMT_2000/itu/8F_1359.docS.

Jalali A, Padovani R, Pankaj R. Datathroughput of CDMAHDR: a highefficiency-highdata rate personal communication wireless system. In: Proceedings of the IEEE

VTC 2000, May 2000. p. 18548.Jinri Huang, Niu Zhisheng. A cross-layer proportional fair scheduling algorithm

with packet length constraint in multiuser OFDM networks. In: Proceedings ofthe IEEE international conference on global telecommunications conference,

GLOBECOM 07, November 2007. p. 348993.Kwon T, Lee H, Choi S, Kim J, Cho D-H, Cho S, et al. Design and implementation of a

simulator based on a cross-layer protocol between MAC and PHY layers in aWiBrocompatibleIEEE802.16eOFDMAsystem. IEEECommunication Magazine

December 2005;43(12):13646.Langton Charan, Intuitive Guide to Principles of Communications, 2002. Available

from: /www.complextoreal.com/chapters/fm.pdfS.Lera A, Molinaro A, Pizzi S. Channel-aware scheduling for QoS and fairness

provisioning in IEEE 802.16/WiMAX broadband wireless access systems. IEEE

Network 2007;21(5):3441.Liu Q, ZhouS, GiannakisGB. Cross-layer modelingof adaptive wirelesslinks for QoS

support in multimedia networks. In: Proceedings of the IEEE first internationalconference on quality of service in heterogeneous wired/wireless networks,

2004. p. 6875.LiuQ, WangX, Giannakis GB.A cross-layerschedulingalgorithmwith QoSsupport in

wireless networks. IEEE Transactions on Vehicular Technology 2006;55(3):83947.

Liu Xin, Chong EKP, Shroff NB. Optimal opportunistic scheduling in wirelessnetworks. Proceedings of the IEEE 58th vehicular technology conference2003e;3:1417421.

Ma M, Ng BC. Supporting differentiated services in wireless access networks. In:Proceedings of the IEEE international conference on communication systems,2006. p. 15.

Moraes L, Rubin I. Message delays for a TDMA scheme under a nonpreemptive

priority discipline. IEEE Transactions on Communications 1984;32(5):5838.Nakagami M. The m-distributiona general formula of intensity distribution of

rapid fading. In: Hoffman WG, editor. Statistical methods in radio wavepropagation: proceedings of a symposium held at the University of California.Pergamon Press; 1960. p. 336.

Shakkottai S, Stolyar A. Scheduling algorithms for a mixture of real-time and non-real-time data in HDR. In: 17th international teletraffic congress (ITC-17),September 2001.

Song G, Li Y. Cross-layer optimization for OFDM wireless networksPart II:

algorithm development. IEEE Transactions on Wireless Communication March2005;4(2):62534.

Wan Lihua, Wenchao Ma, Zihua Guo. A cross-layer packet scheduling andsubchannel allocation scheme in 802.16e OFDMA system. In: Wireless com-

munications and networking conference, 2007. p. 186570.Mai Yi-Ting Yang, Chun-Chuan, Lin Yu-Hsuan. Cross-layer QoS framework in the

IEEE 802.16 network. In: The ninth international conference on advancedcommunication technology, vol. 3, February 2007. p. 209095.

Zhang L. Virtualclock:a newtraffic control algorithm forpacket switchingnetworks.In: Proceedings of theACM SIGCOMM90,Philadelphia, PA,vol. 20(4), September1990. p. 1929.

http://www.complextoreal.com/chapters/fm.pdfhttp://www.complextoreal.com/chapters/fm.pdfhttp://www.unik.no/personer/porten/MoPSAR/MOPSARTutorial_Hassel.pdfhttp://www.unik.no/personer/porten/MoPSAR/MOPSARTutorial_Hassel.pdfhttp://members.wimaxforum.org/documents/WiMAX_IMT_2000/itu/8F_1359.dochttp://members.wimaxforum.org/documents/WiMAX_IMT_2000/itu/8F_1359.dochttp://members.wimaxforum.org/documents/WiMAX_IMT_2000/itu/8F_1359.dochttp://members.wimaxforum.org/documents/WiMAX_IMT_2000/itu/8F_1359.dochttp://members.wimaxforum.org/documents/WiMAX_IMT_2000/itu/8F_1359.dochttp://www.unik.no/personer/porten/MoPSAR/MOPSARTutorial_Hassel.pdfhttp://www.unik.no/personer/porten/MoPSAR/MOPSARTutorial_Hassel.pdfhttp://www.complextoreal.com/chapters/fm.pdfhttp://www.complextoreal.com/chapters/fm.pdfhttp://www.complextoreal.com/chapters/fm.pdfhttp://www.complextoreal.com/chapters/fm.pdfhttp://www.complextoreal.com/chapters/fm.pdf

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