FOURTH Generation (4G) Wireless Networks are developed

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1 2600 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 8, AUGUST 2010 Congeston-Based Prcng Resource Management n Broadband Wreless Networks Najah AbuAl, Mohammad Hayajneh, and Hossam Hassanen Abstract Supportng the dverse QoS requrements of multmeda applcatons s an essental requrement for broadband wreless access BWA) networks. Due to the projected dynamcs n traffc patterns, more capable resource management functonaltes are needed. We propose a game-theoretc, congestonbased prcng scheduler that ncorporates two sub-schemes: a bandwdth provsonng sub-scheme to address the bandwdth scarcty to provson n fourth generaton 4G) BWA technologes and an effcent packet scheduler sub-scheme. To the best of our knowledge, the proposed scheduler s the frst one to smultaneously control congeston and farness whle provdng dfferentated QoS guarantees n BWA networks. Smulaton results show that the proposed scheme realzes our objectves of controllng congeston, provdng dfferentated QoS guarantees, and caterng to proportonal farness among the dfferent network classes and among connectons wthn the same class. Index Terms Congeston control; schedulng; QoS; game theory; rado resource management RRM). I. INTRODUCTION FOURTH Generaton 4G) Wreless Networks are developed to further mprove the user s experence of access servces through hgher data access rates and mproved qualty of servce QoS) provsons. Emergng Broadband Wreless Access BWA) such as IEEE m and 3GPP s Long Term Evoluton-Advanced LTE-Advanced) are the leadng canddate technologes to be utlzed for Fourth Generaton 4G) BWA. These technologes are expected to serve a wde range of applcatons ncludng data, voce, gamng, and multmeda. 4G BWA networks are challenged by the need to guarantee the dverse QoS requrements of such applcatons over a lmted rado spectrum. Such a challenge dctates a need for capable network plannng and resource management and requres sound novel approaches to ensure QoS. To address ths need, the IEEE standard [1] defnes a servce flow framework that supports multple classes: Unsolcted Grant Servce UGS), real tme Pollng Servce rtps), non-realtme Pollng Servce nrtps), Best Effort BE) and Extended real tme Pollng Servce ErtPS). Lkewse, LTE [2] defnes Manuscrpt receved August 26, 2009; revsed February 1, 2010; accepted March 13, The assocate edtor coordnatng the revew of ths paper and approvng t for publcaton was N. Kato. N. AbuAl and M. Hayajneh are wth the College of IT, Unted Arab Emrates Unversty, Al-An, Unted Arab Emrates e-mal: {najah, mhayajneh)@uaeu.ac.ae). H. Hassanen s wth the School of Computng, Queen s Unversty, Kngston, Canada e-mal: hossam@cs.queensu.ca). Ths work was supported by grant no. 2009/053 from the Emrates Foundaton, Unted Arab Emrates, and n part by a grant from the Natural Scences and Engneerng Research Councl of Canada. The authors would lke to thank the anonymous revewers whose feedback helped mprove the qualty of ths paper. Dgtal Object Identfer /TWC /10$25.00 c 2010 IEEE two man QoS classes: the guaranteed rate class and the nonguaranteed rate class. Each traffc flow n LTE belongs to one of these two classes, and s assocated wth QoS parameters that precsely specfy the values of packet delay and loss that can be tolerated by the flow applcaton. Packet schedulng s the functon that enforces the QoS parameters n WMAX and LTE. WMAX and LTE standards do not defne a specfc packet scheduler. Several WMAX [3], [4], [5] and LTE [6], [7], [8] schedulng schemes have been recently proposed n the lterature. However, these schemes are manly desgned to maxmze network throughput or operator revenue, n addton to enforcng users flows QoS requrements. Congeston control n WMAX and LTE has receved lttle attenton n lterature. Few proposals address the ssue of desgnng QoS schedulers at the MAC layer wth explct congeston control mechansms. Song and L [9] propose a scheduler for an OFDM wreless network for a mxture of delay senstve and best effort traffc. The proposed scheme utlzes two utlty functons for each traffc type. The delay senstve traffc utlty functon s employed to meet the delay requrements of the traffc, whle the best effort utlty functon s used to control the traffc greedness. Hence, the schedulng scheme s effectvely a congeston management tool, as opposed to a congeston preventon tool. In addton, the scheduler s only trggered by the greedness of best effort traffc. The work presented n reference [10] establshes a QoS-drven adaptve congeston control framework that provdes QoS guarantees for a sngle servce flow, namely VoIP. The framework s composed of three rado resource management algorthms: admsson control, packet schedulng and load control. The guarantees for VoIP QoS requrements are realzed by the adaptve load control algorthm, whch montors the VoIP qualty and dynamcally adapts the parameters of the admsson control and the packet scheduler algorthms when congeston s projected. The adaptaton s based on a trade-off between VoIP QoS guarantees and effcent network resource usage n the mxed servces scenaro. Other proposals address the congeston control problem followng one of two approaches: ether desgnng a call admsson control CAC) scheme [11] or enhancng or redesgnng) the congeston control mechansm at the transport layer [12]. The man goal of desgnng CAC schemes s to avod congeston and mantan the delvered QoS to dfferent connectons at a target level by means of blockng new connectons. CAC schemes can avod long term network congeston but cannot adapt to short term congeston. On the other hand, addressng congeston control at the transport layer s more nvolved, especally n wreless networks. Ths s because packets may be lost due to bad channel qualty rather than congeston, and the transport layer mstakenly

2 ABUALI et al.: CONGESTION BASED PRICING RESOURCE MANAGEMENT IN BROADBAND WIRELESS NETWORKS 2601 reacts to ths type of packet loss as f t s due to congeston. The Base Staton BS) s the man entty that s capable of detectng the network status n wreless networks and not the end nodes. Effectve MAC layer schedulng scheme at the BS n congeston wth an end-to-end scheme can better control congeston. Addtonally, most congeston control mechansms at the transport layer requre changes n the operatng systems of users devces, whch s not practcally a favorable soluton. Whle dynamc prcng s a powerful tool for controllng congeston n BWA networks, t has receved lttle attenton and s stll an open research ssue. As well, and to the best of our knowledge, there s no study that addresses dynamc prcng as a tool for controllng congeston and dfferentatng servces. The current proposals utlze prcng models to address other problems n BWA networks. For example, the authors of [13] proposed a prcng model for bandwdth sharng n heterogeneous networks of WMAX and WF technologes. Lkewse, n reference [14] a prcng model for usng WMAX as a network falure backup for a WF network s proposed. The proposal n [15] presented a market-based cogntve rado network prcng model to admt new users to an access network by servce provders that mnmzes the effect on QoS of exstng users and ncreases the servce provder revenue. The closest work to our proposal s presented n [16]. The author proposes a prcng model for WMAX UGS, rtps and BE classes. The proposed model statcally dfferentates prcng based on the class type, and s hence not dynamc. The model s threshold based, and may not be an effcent tool for reactve short term congeston control. Also, the proposed model supports only a sngle QoS metrc and left other metrcs such as the delay for future work. In ths paper, we address the vod for a congeston control scheme n BWA networks to better utlze the lmted rado spectrum. The man contrbuton of ths paper s desgnng a congeston-based prcng scheme that conssts of two noncooperatve game-theoretc prcng algorthms at two levels: class level and connecton level. The scheme s desgned to cater to farness and congeston control whle effcently dfferentatng servces n BWA networks. Usng game theory as a robust tool s motvated by the success of ts applcaton n many felds where t has been shown to provde capable solutons [17], [18], especally n stuatons where ratonal agents share a common resource whle exercsng potentally conflctng objectves. Desgnng a far schedulng scheme s acheved through exercsng proportonal farness among the dfferent classes at one level and connectons wthn same class at other level. Effectve congeston control s acheved through ntroducng a prcng parameter n a market-based scheme. The BS vares a prcng factor based on the traffc load n the network at both the class level and the connecton level. Smulaton results show that varyng the prcng factor results n controllng congeston by reducng the average throughput of the served connectons. Ths reducton s acheved wthout compromsng the farness among the classes or the QoS guarantees of the connectons, snce the average throughput acheved s larger than or equal to the mnmum data rate requested by ths connectons. We proceed by provdng an overvew of the proposed scheme n Secton II. The system model s presented n Secton III. The problem s formulated and solved at two levels: the class level and the connecton level, whch are respectvely presented n Secton IV and Secton V. In Secton VI, we evaluate the performance of our proposed approach and dscuss the results. Fnally, n Secton VII we conclude the paper. II. SCHEME OVERVIEW The scheme conssts of two congeston-based prcng subschemes, one for bandwdth provsonng at the class level and the other for schedulng at the connecton level. The two level desgn effcently guarantees the QoS requrements of connectons wthn each class. The class-level scheme solates the classes wth dverse QoS requrements. For example, for real-tme traffc, solatng VoIP traffc from vdeo streamng s requred snce VoIP traffc has a strcter delay requrements than vdeo streamng. Hence, f a VoIP packet and a vdeo streamng packet both have the same watng tme n a queue, the VoIP packet s gven hgher prorty. On the other hand, dfferentatng connectons wthn the same class enhances the QoS guarantees, especally for the traffc flows that have more than one QoS metrc. For example, wthn the same class e.g VoIP class), packets approachng ther delay bounds are gven hgher prorty than others. QoS guarantees s hence acheved by overseeng the guarantees at the two levels. Desgnng an effcent and robust bandwdth provsonng algorthm requres takng dfferent ssues nto account. The objectves of our bandwdth provsonng algorthm are as follows. 1) Enforcng the requrements of varous QoS classes; 2) Capturng and makng use of the technology s physcal and functonal specfcatons; 3) Effcently provdng for a wde range of QoS requrements; 4) Satsfyng farness among classes; and 5) Adaptng to network status, especally n congeston nstances. These objectves form the bass of our desgn and mathematcal modelng. For our purposes, we dentfy game theory as a powerful tool to desgn effcent resource management schemes wth the above characterstcs n a congested network envronment. Game theoretc approaches explore how strategc nteractons between ratonal players or agents) generate outcomes accordng to the players conflctng preferences [19], [20]. For bandwdth provsonng, we consder the players to be the dfferent classes supported by the BWA network, the players conflctng preferences as the classes QoS requrements, and the outcomes as the resources allocated wthn apredefned epoch. An ncreased demand of a specfc class for bandwdth results n decreasng the avalable bandwdth resources of the other classes n the network. Game theory can hence establsh a sutable framework to capture the nteractons of self-nterested players wth potentally conflctng nterests. At the class level, we adopt a one stage non-cooperatve congeston-based prcng game at the BS to calculate allocaton of tme slots of a gven tme frame for the dfferent classes servces n a BWA network. We call the decson epoch wthn whch ths calculaton s performed the nterclass

3 2602 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 8, AUGUST 2010 resource allocaton phase. Wedefne four players correspondngtofourservceflow classes. Each player s assocated wth a utlty functon to address farness among the dfferent servce classes. A prcng factor s ntroduced to penalze greedy servce classes and control network congeston. At the connecton level, we desgn a sngle stage, noncooperatve congeston-based game to be executed at the BS for Down-Lnk DL) schedulng and at the Subscrber Staton SS) for UP-Lnk UL) schedulng. Players correspond to connectons wthn a same class. Smlar to the nterclass resource allocaton phase, each player s assocated wth a utlty functon to address farness among connectons of the same class. Ths phase s called the ntra class resource allocaton phase. We remark that our scheme s generc and can be appled n any class-based wreless network such as WF, HSDPA or LTE. In ths work, and partally for llustraton purposes, the performance of our algorthm s nvestgated n a WMAX settng. III. SYSTEM MODEL We propose a scheme to be mplemented at the BS n a sngle cell consstng of one BS wth several SSs. Ths s applcable n a Pont to Mult-Pont PMP) mode or a multhop relay network wth a tree-based nfrastructure. Specfcally t can be mplemented n the relay non-transparent mode, where the non-transparent node plays the role of the base staton for the moble staton. It s assumed that the BS or the non-transparent relay node) has complete channel state nformaton of all connectons through a robust feedback channel. An SS conveys ts receved sgnal to nose power rato SNR) from tself to the BS. Ths nformaton does not vary wthn a sngle frame as the wreless channel between each SS and BS on each subcarrer s assumed to undergo flat fadng that s fxed over a sngle frame perod. The Adaptve Modulaton Codng AMC) technque descrbed n IEEE standard [1] dvdes the range of the receved SNR nto seven non-overlappng regons as shown n Table I. Accordng to the receved SNR at a specfc SS, the BS decdes the sutable transmsson mode and consequently the data rate for each SS. We consder a real-tme envronment wth four classes, rtps, ertps, nrtps and BE. We do not consder the UGS class n our desgn, snce the UGS class s desgned to serve perodc constant bt rate traffc. Hence, t requres fxed number of tme slots durng the UGS connecton lfe, whch can be easly scheduled by allocatng these slots once the UGS connecton s admtted. The scheme s desgned to guarantee a mnmum transmsson rate for each connecton belongng to each class. The transmsson rate can be ncreased up to a maxmum value based on the network load. IV. BANDWIDTH PROVISIONING: CLASS LEVEL The bandwdth provsonng algorthm s desgned to calculate the number of tme slots per class. The schedulng algorthm proposed n Secton V allocates tme slots amongst connectons wthn the same class. We remark here that the bandwdth provsonng algorthm can be used as a stand alone algorthm that need not be assocated wth the schedulng algorthm proposed n Secton V. TABLE I MODULATION AND CODING SCHEMES FOR IEEE Modulaton codng) Info bts/symbol Requred SNR BPSK1/2) QPSK1/2) QPSK3/4) QAM1/2) QAM3/4) QAM2/3) QAM3/4) The bandwdth provsonng algorthm solates the servce classes by allocatng K tme-slots for each class out of K total tme-slots n a gven tme frame. In IEEE , tme frames are dvded nto constant number of tme slots wth fxed tme-slot duraton measured n mcroseconds. The slot duraton measured by number of bts transferred wthn the fxed tme-slot duraton may change on per-frame bases dependng on the channel condtons and consequently the modulaton and codng mode adapted at the current tmeframe. The total K tme-slots are completely parttoned among the classes such that the dfference between a utlty functon and a prcng functon s maxmzed. The utlty functon s defned as a logarthmc normalzed functon of the rato of each class s allocated slots to the total number of allocated slots for other classes. The utlty functon facltates optmally allocatng the frame tme-slots to the dfferent servce classes n order to guarantee proportonal farness among the servce classes. The prcng functon s defned as a weghted functon of class allocated slots. In addton to controllng congeston, the role of the prcng functon s to counter greedy demand for tme slots. We defne P1 as our frst optmzaton problem wth the objectve functon LK, K ),wherek s an array of the allocated tme-slots of all classes except the th class: P1: max K K {LK, K )=U log 1+ K ) 1) Υ + ψ logk ρ K,mn ) 2) λk }, 3) The utlty functon s defned as U log1+ K Υ ) over the set of class s tme slots denoted by K [K mn K max ]. U s ntroduced as a utlty factor to measure how much bandwdth servce class s requestng. Υ, defned as Υ v + 4 K m, s a normalzng factor of the farness measure. The parameter v s used as a tunng factor. The utlty functon n 1) ensures proportonal farness among the dfferent servce classes. The barrer functon n 2) guarantees that the new allocated tme slots are enough to support the payload of all actve connectons of class. ψ s a prortzng parameter wth U ψ. The parameter ρ s a tunng factor ntroduced to gve the network operator flexblty n decdng how much resources to allocate for classes above ther mnmum requested tme-slots, K,mn.If desred, the effect of ρ can be dsabled by choosng ρ =1. A lnear prcng functon n 3) wth prcng factor λ s ntroduced to prevent greedy use of network resources and to control congeston.

4 ABUALI et al.: CONGESTION BASED PRICING RESOURCE MANAGEMENT IN BROADBAND WIRELESS NETWORKS 2603 TABLE II OFDM FRAME DURATION AND NUMBER OF FRAMES PER SECOND. Code Frame Duraton ms) Frames per second Mappng the requested bandwdth to a maxmum and a mnmum number of tme-slots s based on the fact that IEEE standard defnes a fxed sze of tme frames and ther correspondng number of frames sent per second as shown n Table II. Gven a requested bandwdth requrement per second for each connecton n, therequested bandwdth requrement per frame s equal to the requested bandwdth per second dvded by the number of frames per second. mn Thus, gven mnmum reserved bandwdth per frame, B n ), max and maxmum sustanable bandwdth per frame B n ), the number of tme-slots for each connecton n can be represented as ˆK mn n = B mn n 4) r n ˆK n max max B n = 5) r n where r n s an average value that reflects the channel qualty over a predefned wndow sze usng a proper predcton algorthm. The mnmum and maxmum number of tme ˆK mn k slots of servce class are: K mn = C t) k=1 and K max = C t) k=1 ˆK k max, respectvely. C t) s the number of connectons of class. To represent problem P1 as a formal game theoretc problem, G1, we defne x K Υ, x X, wth X [x mn x max ] beng the acton profle or the strategy space of the th player the th servce class). Consequently, the objectve functon n problem P1 s modfed as follows G1x, X,λ ): max { x X L x, X,λ ) }, I, 6) wth L x, X,λ ) gven by L x, X,λ ) = U log 1 + x ) 7) + ψ logx ρ x mn ) λ x G1x, X,λ ) s a Noncooperatve Game wth Prcng NGP) n whch player decdes ts next decson x based on local nformaton X,λ ) that maxmzes ts objectve functon L x, X,λ ). The vector of players actons decsons) s denoted by X =x 1,x 2,x 3,x 4 ), whle X s the vector X wthout the th element. The prcng factor, to prevent greedy usage of the tme slots resource, λ s equal to λ Υ. Fnally, I = {1, 2, 3, 4} s the ndexng set of the players. The lmts of the acton profle X, namely x mn and x max are set such that Υ x mn = K mn and x max Υ = K max, respectvely. To smplfy representaton, L wll be used as a smplfed form of L x, X,λ ). The optmal value x that maxmzes L n 6) s the feasble soluton of the followng equaton or Thus, L x = U 1+x + ψ x ρ x mn λ =0, I 8) x 2 Ξ x + φ =0 9) ) x = Ξ φ 2 Ξ 2, I 10) + U+ψ λ and φ ρ x mn. Then the optmal number of tme slots of where Ξ 1 +ρ x mn ψ U ρ x mn λ servce class, K can be expressed n terms of x as follows K = x Υ, I 11) In order to guarantee that x > 0, the utlty factor U should be lower bounded by U >λ 1 ρ x mn ) ψ 12) A. Exstence of a Nash equlbrum operatng pont In ths subsecton we demonstrate the exstence of Nash Equlbrum NE) operatng pont n the game G1. The exstence of a NE pont s guaranteed f the objectve functon s quasconcave and optmzed on a convex strategy space [20]. We denote the NE operatng pont by K allocated tme-slots vector of the four dfferent servce classes) where K =K1, K2,K 3,K 4 ), and at equlbrum K s gven by 4 K = x Υ = x Km + v, I 13) To prove the exstence of NE operatng pont K of G1, t s enough to prove that L n 6) s quasconcave functon n x gven X on the convex set X =[x mn x max ]. Hence, we calculate the second dervatve of G1 and proof that t s always negatve on the compact convex set X. The second order dervatves of L n 6) wth respect to x s 2 L x 2 = U 1 + x ) 2 ψ x ρ x mn 14) ) 2 < 0, x X, I Therefore, L s a strctly concave functon on the compact convex set X, whch mples that L s a quasconcave functon on X. Therefore, an NE pont K exsts. Also, as a result of strct concaveness, the unconstraned maxmzer x s unque. B. Unqueness of the NE operatng pont of G1 Before provng the unqueness of the NE pont K of the game G1, we present the followng proposton: Proposton 1: Gven the actons of the other players QoS classes) X, the best response that the player can decde s b X )=mn {x,xmax }. 15) Proof: It s obvous that x s the unconstraned maxmzer of the objectve functon L gven the other players decsons X. However, f ths maxmzer s not feasble,.e. x >xmax, then the best decson that maxmzes L s x max

5 2604 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 8, AUGUST 2010 as L / x > 0 n the nterval {x : x mn <x x },whch means that the objectve functon s ncreasng n ths nterval. Thus, gven X, b X )=x max maxmzes L f x s not feasble and ths concludes the proof. Recall that K = x Υ whch s a one-to-one mappng relatonshp, consequently the best response functon n 15) can be wrtten n terms of the number of tme slots K as follows b K )=mn{k,k max }, 16) where K = K 1,K 2,K 3,K 4 ) and K s the vector K wthout the th element. Defne the best response vector functon bk) as follows: bk) b 1 K 1 ),b 2 K 2 ),b 3 K 3 ),b 4 K 4 )) 17) In lght of the followng theorem based on the observatons n [21], we can prove the unqueness of the NE operatng pont K of game G1. Theorem 1: A non-cooperatve game wth a standard best response functon adopts a unque equlbrum pont f t exsts. At ths stage and for completeness, we provde the defnton of a generc standard functon ΘK) as follows [21]: Defnton 1: A vector functon ΘK) s called a standard vector functon f t satsfes the followng: 1) Postvty: ΘK) > 0,.e. each element s postve, 2) Monotoncty: f K > ˆK then ΘK) Θ ˆK) entry wse), and 3) Scalablty: δ >1, δθk) > Θδ K) entry wse). Thus, to prove the unqueness of K we only need to prove that bk) s a standard functon. Postvty of the best response bk) s clear from the fact that the strategy space of each player X s a convex and compact subset of the postve real numbers R +. To prove that bk) s a monotone functon n K t s enough to prove that each of ts components s a monotone functon n K. To do so, let K > ˆK. Then 4 b K) =b K )=x Υ = x v + K m ), I whle b ˆK) =b ˆK )=x ˆΥ = x v + 4 ˆK m ), I 18) 19) By examnng the equatons 18) and 19), one can see that b K) >b ˆK),.e. t s a monotone functon n K, then the monotoncty of the best response functon bk) s mpled. Consder δ>1. Then 4 δb K) =δx v + K m ), I. 20) On the other hand, b δ K) =x v + δ 4 K m ), I. 21) Equatons 20) and 21) show that δbk) >bδ K),.e. bk) s a scalable standard functon. Then the unqueness of the NE pont s guaranteed based on Theorem 1. C. Pareto optmalty of the NE pont for G1 In ths Subsecton, we nvestgate the effcency of the NE operatng pont K. We prove that t s a Pareto optmal operatng pont,.e. t s the best operatng pont that the players servce classes) can reach n a non-cooperatve manner at equlbrum. Smlar to [19], a Pareto optmal operatng pont s defned as follows. Defnton 2: The vector K s Pareto optmal f there exsts no other vector K > K such that L K) L K ) for all Iand L K) >L K ) for some I. The followng Lemma demonstrate the Pareto optmalty of K. Lemma 1: The NE operatng pont K of the game G1 s a Pareto optmal operatng pont. Proof: Wthout loss of generalty, let K = π K, I, whereπ > 1, then ) L K, K π K ) = U log 1+ v + π m Km 22) + ψ logπ K ρ K,mn ) λπ K The behavor of L n 22) wth respect to π can be found by dfferentatng L wth respect to π as follows L K, K ) U = π v + 4 π m Km + π K ) ψ + π K ρ K mn λ K K fd π K ), I, 23) where π π D π π π 4 Recall that K s the unconstraned maxmzer of L n 1), therefore at equlbrum L K, K ) U K = K=K v + 4 K m + K ) ψ + K ρ λ =0 K,mn fk ), I. 24) Snce K > 0 and by observng the two equatons 23) and 24), one can conclude that fd π K ) <fk )=0, π > 1, whch mples that L K, K ) < 0. π That s, when L n 22) decreases, π ncreases. Thus, accordng to Defnton 2, K s a Pareto optmal operatng pont.

6 ABUALI et al.: CONGESTION BASED PRICING RESOURCE MANAGEMENT IN BROADBAND WIRELESS NETWORKS 2605 D. Class tme-slots allocaton algorthm In reference [21], t was proven that both synchronous and asynchronous algorthms wth standard best response functons converge to the same pont. Therefore, we consder asynchronous tme slots allocaton control algorthms that converge to the unque Nash equlbrum pont K of game G1. In ths algorthm, the players servce classes) update ther numbers of tme slots as follows Assume class m updates ts requred number of tme slots at tme tcs 1, where these tme tcs refer to the teraton ndces n the set T m = {t m1,t m2,...}, wth t mn <t mn+1 and t m0 =0 for all m I.LetT = {t 1,t 2, }where T = T 1 T4 wth t n <t n+1. Defne K to be the vector of numbers of tme slots pcked randomly from the total strategy space K = K 1 K4. Algorthm 1: Consder the game G1 gven n 6) and generate a sequence of tme slots vectors as follows: a) Set the tme slots vector at tme t 0 =0: K0) = K, let k =1 b) For all m I, such that t k T m : Gven Kt k 1 ), calculate U m t k ) >λ m t k 1 )1 ρ x mn m ) ψ m t k 1 ), and ψ m t k ) = 0.01U m t k ), then x mt k ) = argmax x m X m L m x m, X m t k 1 ),λ m Kt k 1 ))), thenset Kmt k ) = x mt k )v m + 4 j =m K jt k 1 )) and let the number of tme slots K m t k ) = b m t k ) = mnkmt k ),Km max ) c) If Kt k ) = Kt k 1 ) stop and declare the Nash equlbrum number of tme slots vector as Kt k ),elselet k := k +1andgotob). The output of Algorthm 1 s the optmal allocated tme slots for the dfferent servce classes, whch can be computed n polynomal tme [22]. The work presented n [22] proved that a mult-player game can be computed n polynomal tme f t s guaranteed that the game s a pure Nash equlbrum problem. Our proposed algorthm s mathematcally formulated as a pure Nash equlbrum problem as opposed to a randomzed Nash equlbrum), hence based on the study conducted n [22], the algorthm computaton can run n a polynomal tme. In the followng secton, we calculate the optmal allocated tme slots per connecton wthn the same class. V. DOWNLINK/UPLINK GAME-THEORETIC SCHEDULING ALGORITHMS: CONNECTION LEVEL After calculatng the optmal number of tme-slots for each QoS servce class nterclass bandwdth allocaton phase), we ntroduce our game theoretc schedulng algorthm based on a formulaton for the optmzaton problem P2 below. The objectve functon of P2 s to optmally allocate tme slots among connectons of same class ntraclass bandwdth allocaton phase) usng the results of the nterclass phase. P2: max B B {LB, B )= ˆU log 1+ B Θ ) 25) + ˆψ logb ˆρ B,mn ) 26) The utlty functon n 25), ˆU log 1+ B Θ ), s ntroduced to provde proportonal farness among the dfferent connectons 1 We dstngush the tme tcs from the tme slots of the tme frame as the algorthm teraton ndces. of a gven servce class through the rato B Θ,whereΘ c + M B m and the set of connecton s allocated bandwdth s denoted by B =[B mn,b max ]. ˆU s a weghtng factor to the utlty functon, consder t as the utlty factor. Ths factor s ntroduced to provde the network operator the flexblty to enforce users servce level agreements based on how much bandwdth they are requestng or/and what delay bounds ther applcatons can tolerate. The network operators can decde on the granularty of ths factor based on ther polces. For example, ˆU 1 can be a functon of Δt to cater for the flows 1 m requred bound, where Δt 1 m s the resdual tme to reach the latency bound of connecton m. Hence, n ths example, the weghtng factor, ˆU, can be used to prortze users who spend longer tme n the system watng for the servce. The functon ˆψ logb ˆρ B,mn ) n 26) s the connecton s mnmum requrement constrant wth ˆU ˆψ.Gven that WMAX and LTE defne a maxmum data rate per traffc flow, we use ˆρ as a tunng factor to provde the network operator wth the flexblty to decde how much resources to allocate for connectons above ther mnmum requested bandwdth, B,mn. If the network operator s polcy s to allocate the mnmum requred bandwdth of the connecton, then ˆρ s set to 1. The problem n P2 s genercally formulated to be equally appled for all defned servce classes n the network. The parameters ˆU, ˆρ, c,and ˆψ n P2 can be utlzed when needed as prortzaton varables among connectons wthn a same class based on the connectons QoS bounds. For example, ˆU can be a functon of the delay bound of a connecton for example; the ertps or rtps class connectons n WMAX or GBR class connectons n LTE) to prortze connectons approachng there delay bound wthn the same class. Another example s utlzng ˆρ to serve network operators polces towards the content of traffc connectons of the same class. Thus, small values of ˆρ ndcate that the network operator s not concerned whether a traffc class, say HTTP s starved. Problem P2 s not a formal game theoretc problem. To reformulate t, we defne y B Θ, y Y, wth Y = [y mn,y max ] beng the strategy space of the th connecton. Consequently, the objectve functon n problem P2 s modfed as follows G2y, Y ): max { y Y J y, Y ) }, M, 27) wth J y, Y ) gven by J y, Y ) = ˆU log 1 + y ) 28) + ˆψ logy ˆρ y mn ) G2y, Y ) s a non-cooperatve bandwdth control game NBG), where connecton decdes ts next decson, y based on the values gven by the vector decsons, Y ) such that the objectve functon J y, Y ) s maxmzed. The vector Y s the vector Y = y 1,y 2,,y M ) wthout the th element, where M s the total number of connectons of a gven class, and the boundary of connecton s decson Y ) s lower bounded by y mn and upper bounded by y max. y mn s related to B mn by Θ y mn = B mn and y max by y max Θ = B max, respectvely. To fnd the optmal value y that maxmzes the objectve functon J y, Y ), we calculate the frst dervatve of J as

7 2606 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 8, AUGUST 2010 follows J y = ˆU 1+y + ˆψ y ˆρ y mn =0, M 29) Hence, y = ˆU ˆρ y,mn ˆψ ˆψ + ˆU, M 30) and the optmal bandwdth allocated to a connecton s gven by M B = y c + B m. 31) We omt the proofs of exstence, unqueness and Pareto optmalty of NE operatng pont of game G2 as they are almost smlar to the proofs presented above for game G1. A. Connectons bandwdth allocaton control algorthm In ths subsecton, we present the algorthm for allocatng the control bandwdth that converges to the unque Nash equlbrum pont B of game G2. At each teraton of the algorthm, the connectons update ther allocated bandwdth as follows untl convergence: Assume class m updates ts requred number of tme slots at tme tcs n the set ˆTm = {ˆt m1, ˆt m2,...}, wth ˆt mn < ˆt mn+1 and ˆt m0 =0for all m M.Let ˆT = {ˆt 1, ˆt 2, } where ˆT = ˆT 1 ˆTM wth ˆt n < ˆt n+1 and defne B to be the values of the bandwdths vector randomly chosen from the total strategy space B = B 1 BM. Algorthm 2: Consder the game G2 gven n 27) and generate a sequence of bandwdth vectors as follows: a) Set the bandwdth vector at tme ˆt 0 =0: B0) = B, let k =1 b) For all m M, such that ˆt k ˆTm : Gven Bˆt k 1 ), calculate ψ m t k )=0.01U m t k ),thenym ˆt k ) = argmax y m Y m J m y m, Y m ˆt k 1 )), then set Bm ˆt k ) = ym ˆt k )c m + M j =m B jˆt k 1 ) and let the bandwdth B m ˆt k )=b m ˆt k )=mnbmˆt k ),B m,max ) c) If Bˆt k ) = Bˆt k 1 ) stop and declare the Nash equlbrum number of tme slots vector as Bˆt k ),elselet k := k +1andgotob). The output of Algorthm 2 s the optmal number of tme slots per connecton, whch can be computed n a polynomal tme [22], snce the game s a pure Nash equlbrum problem. In the followng secton, we evaluate the performance of the nterclass and ntra-class schemes through smulaton. VI. PERFORMANCE EVALUATION A. Smulaton setup We evaluate the performance of our proposed congestonbased prcng scheme n an IEEE settng. We mplemented a smulator comprsng a TDD cell wth one BS and several SSs. The wreless channel s modeled based on the Nakagam-m channel model whch s adopted to accurately descrbe the statstcal varaton of the channel gans between the BS and the SSs based on OFDM channel multplexng. The AMC module defned by the IEEE standard dvdes the range of the receved SNR nto seven non-overlappng regons where the dvdng thresholds are evaluated based on a target prescrbed bt error rate BER). Accordng to the receved SNR at a specfc SS, the module n the PHY layer of BS decdes the sutable transmsson mode for each SS as shown n Table I. Each transmsson mode conssts of a modulaton and codng par amed at effcently usng the bandwdth whle satsfyng a prescrbed BER. Nodes are randomly placed over a smulaton grd of 5000m 5000m. Number of subscrber statons smulated are 1 to 30. Each subscrber staton can smultaneously have dfferent types of connectons ncludng voce, vdeo, FTP or background traffc. In loadng the network, the rato between the dfferent traffc types s mantaned for each experment. The rato s read Voce:Vdeo:FTP:Background. For example, the rato 1:2:1:3 means that Vdeo and Background are respectvely 2 and 3 tmes ether voce or FTP traffc. We shall refer to each loadng nstance by loadng rato, e.g. loadng rato 1:2:1:3. The loadng rato s used for studyng the farness of the algorthm n supportng dfferent types of applcatons wth dfferent data rate requrements when the network s congested. For example, the experment scenaro wth loadng rato of 1:1:1:1 and 3:1:1:1 studes the effect of voce traffc whch has the hghest prorty n the system) on the farness of the algorthm when the number of voce flows s equal to the other flow types number, and when the number of voce flows s the largest number of flows n the system. The effect of the loadng rato s dscussed n Subsecton VI-D. The frame sze s fxed at a value of 10 ms equally dvded between UL and DL traffc, the OFDM symbol duraton s 12.5 μs, and the rate of frames s 100 frames/second. Tme slots are allocated for actve flows at the begnnng of each frame. The DL bandwdth s smulated as 20 MHz. The channel qualty of each SS remans constant per frame, but s allowed to vary from frame to frame. B. Traffc model For connectons traffc model, we adopt the traffc model presented n [23], a model specfcally desgned and tested for WMAX smulaton). Ths model mplements VoIP traffc for the ErtPS class, vdeo streamng traffc forthertps class, FTP traffc forthenrtps class and background traffc forthebe class. Table III shows the traffc model used n smulaton. Each smulaton scenaro s repeated 20 tmes. MRTR n table s the Mnmum Reserved Traffc Rate, MSTR s the Maxmum Sustaned Traffc Rate and ML s the Maxmum Latency. Snce we are nterested n studyng the performance of the scheme n congeston status, connectons are admtted to the network based on a generc CAC algorthm based on ther MRTR value the desgn of a CAC algorthm s out of the scope of ths work). However, connectons can send traffc at a rate equal to or hgher than MRTR and up to ther MSTR, whch then results n congeston. C. Performance metrcs We utlze the followng metrcs n our performance evaluaton: Average Throughput: The rate of packets successfully transmtted durng the smulaton tme. Ths metrc s used to understand the effect of load, congeston and relatve prorty of the classes and connectons.

8 ABUALI et al.: CONGESTION BASED PRICING RESOURCE MANAGEMENT IN BROADBAND WIRELESS NETWORKS 2607 TABLE III TRAFFIC MODEL Servce Traffc Type MRTR kbps) MSTR kbps) ML ms) Packet bytes) ErtPS Voce rtps Stream vdeo nrtps FTP BE Background Average queung delay: The tme between the arrval of a packet to the departure of the packet from the queue. The value s reported n mllseconds ms) and s averaged over the number of packets. Packet loss: The percentage of packets dropped from the queue out of all the packets that arrved nto the queue. The metrc ndcates the percentage of packets that mssed ther delay bounds. Both average delay and packet loss wll allow us to determne how effectvely the scheme satsfes the QoS requrements of real-tme connectons. Farness ndex: Farness s measured among all connectons nterclass farness). We use Jan s farness ndex to calculate nterclass farness, whch s defned as n ) 2 x Jan s Farness Index = =1 n x 2 =1 n, 32) where n s the total number of connectons n the network. To calculate nterclass farness usng Jan s ndex, we use normalzed average throughput for x. The average throughput of a connecton s normalzed wth respect to the MRTR of the connecton. D. Results We evaluate the performance of our scheme based on the desred characterstcs dscussed n Subsecton II. The performance of the algorthm s evaluated under lght and heavy loadng condtons. Both farness and satsfacton of QoS requrements are studed for the dfferent classes and for connectons wthn the same class. For each smulaton scenaro, experments are repeated 20 tmes and results are obtaned wth 95% confdence nterval lower than Fgure 1 shows the performance of the scheme under lght load. Gven that the network s not congested, t s expected to have all traffc classes achevng a throughput hgher than ther mnmum requred bandwdth. The x axs n ths and the followng fgures ndcates the loadng rato assocated wth each pont of evaluaton. We note n Fg. 1 that for loadng ratos 1:3:1:1, 1:1:3:1, 1:1:1:3, the average throughput of the more loaded classes respectvely vdeo, FTP and background) s the lowest. Ths s due to the contenton among same class connectons for the fxed amount of bandwdth allocated for these classes. However, the voce class does not undergo the same trend, achevng almost a constant average throughput of 40kbps. Ths s because voce s assgned the hghest prorty n the network, and also due to the voce class s low traffc requrements Averag ge Throughput 1:1 1:1:1 2:1 1:1:1 3:1 1:1:1 1:2 2:1:1 1:3 3:1:1 1:1 1:2:1 1:1 1:3:1 BE FTP Vdeo Voce Fg. 1. Average throughput n Kbps) of BE, FTP, vdeo and voce under lght loaded network. Average Delay :1:1:1 2:1:1:1 3:1:1:1 Voce 1:2:1:1 1:3:1:1 Vdeo Fg. 2. Average delay n ms) of vdeo and voce traffc under lght loaded network. MRTR=12.5kbps and MSTR=64kbps). The latter justfcaton s also emphaszed by observng the vdeo traffc results, whch show the hghest throughput under the loadng rato 3:1:1:1. Snce vdeo s prortzed second after voce, and because the requrements of the voce connecton are small, the bandwdth avalable for vdeo traffc s hgher n ths case than that under other loadng ratos. Vdeo traffc, consequently, explots ths avalablty to acheve the hgher throughput. Snce the network s lghtly loaded, the average delay n Fg. 2 s much lower than classes delay bounds. Smlarly, the packet loss of the audo and vdeo traffc n ths experment s zero. Fgure 3 shows the results when the network s heavly loaded. Ths s an mportant experment as t evaluates our scheme n a congeston settng. The fgure shows that the scheme was able to adapt to the network status as the number 1:1:2:1 1:1:3:1 1:1 1:1:2 1:1:1:2 1:1 1:1:3 1:1:1:3

9 2608 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 8, AUGUST 2010 Avera age Throughput BE FTP Vdeo Voce :1:1:1 2:1:1:1 3:1:1:1 1:2:1:1 Fg. 3. Average throughput n Kbps) of BE, FTP, vdeo and voce under heavy loaded network. 1:3:1:1 1:1:2:1 1:1:3:1 1:1:1:2 1:1:1:3 Packet Loss 7% 6% 5% 4% 3% 2% 1% 0% 1:1:1:1 2:1:1:1 3:1:1:1 1:2:1:1 1:3:1:1 1:1:2:1 1:1:3:1 1:1:1:2 1:1:1:3 Fg. 5. Average packet loss of vdeo traffc under heavy loaded network Average Delay Voce Vdeo 0 1:1:1:1 2:1:1:1 3:1:1:1 1:2:1:1 1:3:1:1 1:1:2:1 1:1:3:1 1:1:1:2 1:1:1:3 Fg. 6. classes. Jans ndex farness among voce, vdeo, FTP and BE connecton Fg. 4. Average delay n ms) of vdeo and voce traffc under heavy loaded network. of connectons changed for the dfferent classes. We observe that the lowest voce throughput occurs at loadng ratos 2:1:1:1 and 3:1:1:1. Understandng ths outcome s ntutve, because n these loadng ratos, more voce traffc connectons are competng for the allocated bandwdth. The same behavor can be observed for vdeo, FTP and background traffc. However, the average throughput of each class 1kbps for BE, 45kpbs for FTP, 64kbps for vdeo and 14kbps for voce) shows that the mnmum bandwdth requred by each class s acheved, despte the fact that the connectons are recevng lower allocatons than the lghtly loaded experment. Fgure 4 also shows the effect of ncreasng the number of FTP connectons on the average vdeo throughput. The value of the average throughput of vdeo traffc s 68kbps and 67kbps when the loadng rato s respectvely 1:1:2:1 and 1:1:3:1. These throughput values are almost the requred mnmum bandwdth of the vdeo traffc. Snce the allocated bandwdth for the FTP class s larger, the allocated bandwdth for the vdeo traffc s smaller. Recall that both classes have hgh bandwdth requrements 45kbps for FTP and 64kbps for vdeo). More mportantly, the scheme s response ndcates ts robustness n handlng congeston nstances. In Fg. 4, the delay of voce and vdeo traffc s hgher than that of the lght load setup. In the fgure, the measured delay does not exceed the delay bounds for VoIP and vdeo traffc. Note that packets queued beyond ther delay bounds are dropped and ther delay s not ncluded n calculatng the average delay. The fgure shows that the delay of the voce s less when the network s more loaded wth FTP and background traffc 1:1:2:1, 1:1:3:1, 1:1:1:2 and 1:1:1:3) than when more loaded wth vdeo traffc loadng ratos 1:2:1:1 and 1:3:1:1). Ths s due to the fact that FTP and BE traffc are delay nsenstve, whle vdeo traffc s a delay senstve traffc. Accordngly, voce and vdeo packets approachng ther delay bound wll compete for the current tme slot of transmsson at the scheduler. The results n the fgure also reveal that the delay ncreases as the number of connectons ncreases. Ths s due to the contenton among connectons of the same class. We remark here that the major nfluence on connectons delay performance s due to the scheduler. However, the bandwdth provsonng algorthm controls the delay through controllng the amount of tme-slots allocated to each real tme class. It s also worth observng that the dfference between the average delay and the delay bound for voce traffc s smaller than that of the vdeo traffc, and that the average delay for vdeo traffc s almost at ts requred delay bound at all loadng ratos. These results llustrate the effectveness of the proposed algorthm as t consstently prortzes voce over vdeo. For packet loss, the results n Fg. 5 should be coupled wth those n Fg. 4. Fgure 5 shows that the packet loss has the largest value at a loadng rato of 1:3:1:1, whle Fg. 4 shows mnmum delay at ths loadng rato. Gven that the delay of dropped packets s not ncluded n the delay calculatons, and that the packets dropped from the head of the queue allow other queued packets to be sent wth lower delay, the delay of the vdeo class s lower at ths loadng rato. Fgure 6 presents the results of farness among the dfferent classes wth the network heavly loaded. The results show that maxmum farness.e. farness ndex = 1) s acheved when

10 ABUALI et al.: CONGESTION BASED PRICING RESOURCE MANAGEMENT IN BROADBAND WIRELESS NETWORKS 2609 The proposed scheme s mplemented n a muncpalty WF solar-powered test-bed. The proposed scheme s coded n C- language and ntegrated nto an open-source Lnux based operatng system of the access ponts. As for future work, the authors wll study the performance of the scheme n mtgatng congeston and meetng the QoS requrements of dfferent types of real-tme and non real-tme applcatons carred by the muncpalty WF test-bed. Fg. 7. Average throughput n Kbps) of BE, FTP, vdeo and voce for loadng rato 1:1:1:1 vs. the prcng factor λ. all traffc types are equally loaded.e. loadng rato 1:1:1:1). The farness ndex decreases when any traffc type s loaded 3 tmes the mnmum rato.e. at loadng ratos 3:1:1:1, 1:3:1:1, 1:1:3:1 and 1:1:1:3). Ths decrease s especally pronounced wth voce or vdeo.e. 3:1:1:1 and 1:3:1:1). Because these two classes have hghest prortes, schedulng and bandwdth allocaton of these classes reduce resources left for the FTP and BE class, drectly decreasng the farness ndex. Despte ths reducton, however, the ndex s mantaned at a relatvely large value 0.94). The fgure hence reveals that our objectve of desgnng a far congeston control resource management scheme s fulflled. The farness results under lght load are not shown as the experments always yeld a farness ndex of 1 for all loadng ratos. Fnally, Fg. 7 shows the average throughput aganst dfferent values of the prcng factor λ. Asthefgure shows, an ncrease of the prcng factor results n decreasng the average throughput. Hence, the prcng factor λ s effectve n preventng greedy use of network resources and n controllng congeston as desred. Nevertheless, the requred MRTR of connectons s mantaned even for large values of λ. VII. CONCLUSIONS AND FUTURE WORK In ths paper, we addressed the vod for a congeston control scheme n BWA networks by presentng a gametheoretc model that controls congeston through dynamc prcng whle provdng for farness and QoS guarantees. The scheme conssts of two congeston-based prcng sub-schemes, each over a dfferent operatonal phase: one for bandwdth provsonng at the class level operatng n an nter-class phase and the other for schedulng at the connecton level operatng n an ntra-class phase. We defned characterstcs and requrementstodesgnaneffcent and robust nterclass bandwdth provsonng algorthm. These characterstcs are used as the gudelnes for the desgn and the performance evaluaton of the nterclass algorthm. The nterclass algorthm s desgned as a general algorthm rrespectve of the underlyng technology as long as t s tme frame based. Smulaton results show that the proposed scheme at the class level s able to meet these characterstcs. 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11 2610 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 8, AUGUST 2010 [20] D. Fudenberg and J. Trole, Game Theory. The MIT Press, [21] R. D. Yates, A framework for uplnk power control n cellular rado systems, IEEE J. Sel. Areas Commun., vol. 13, no. 7, pp , Sep [22] C. Papadmtrou, A. Fabrkant, and K. Talwar, The complexty of pure Nash equlbra, n Proc. Thrty-Sxth Annual ACM Symposum on theory of Computng, [23] B. Km and Y Hur, Applcaton Traffc Model for WMAX Smulaton. POSDATA, Ltd, Najah AbuAl receved her B.Sc. and M.Sc. degrees n Electrcal Engneerng n 1989 and 1995 respectvely from Unversty of Jordan, Amman, Jordan and her PhD degree n 2006 n Computer Networks n Electrcal Engneerng department at Queen s Unversty, Kngston, Canada. She joned the College of Informaton Technology, Unted Arab Emrates Unversty Al An, UAE), as an Assstant Professor wth the Computer Networks Engneerng track. She had a postdoctoral fellowshp at the School of Computng, Queen s Unversty from January 2006 to August She worked as an nstructor and the head of the Engneerng Department at Queen Noor College n Jordan from 1995 to Her research nterests comprse wred and wreless communcaton networks. Specfcally, analytcal and measurement based network performance management and Qualty of Servce and resource management of sngle and mult-hop wreless networks. Mohammad Hayajneh SM 01, M 04) receved hs B.Sc and M.Sc n Electrcal Engneerng majorng n Electroncs and Communcatons from Jordan Unversty of Scence and Technology n 1995, 1998, respectvely. He receved hs PhD n Electrcal Engneerng from Unversty of New Mexco Albuquerque, USA) n In September 2004 he joned the College of Informaton Technology, Unted Arab Emrates Unversty Al An, UAE), as an Assstant Professor wth the Computer System Engneerng track. Before jonng the PhD program at Unversty of New Mexco, Dr. Hayajneh held several postons n the academa: he was lecturer and head of the Telecom. Department n the Insttute of Scence for Telecom and Technology n Ryadh, KSA from And from he was an nstructor at Prncess Sumaya Unversty of Technology, PSUT) Amman, Jordan. Dr. Hayajneh research nterests nclude: power control for wreless data networks, WMAX cross-layer schedulng, resource allocaton and prcng n wreless data networks, MIMO systems, performance analyss and modelng of OFDMA based wreless networks, Cooperatve networks. Hossam Hassanen s wth the School of Computng at Queen s Unversty workng n the areas of broadband, wreless and varable topology networks archtecture, protocols, control and performance evaluaton. Dr. Hassanen obtaned hs Ph.D. n Computng Scence from the Unversty of Alberta n He s the founder and drector of the Telecommuncaton Research TR) Lab trl n the School of Computng at Queen s. Dr. Hassanen has more than 350 publcatons n reputable journals, conferences and workshops n the areas of computer networks and performance evaluaton. He has delvered several plenary talks and tutorals at key nternatonal venues, ncludng Unconventonal Computng 2007, IEEE ICC 2008, IEEE CCNC 2009, IEEE GCC 2009, IEEE GIIS 2009, ASM MSWIM 2009 and IEEE Globecom Dr. Hassanen has organzed and served on the program commttee of numerous nternatonal conferences and workshops. He also serves on the edtoral board of a number of Internatonal Journals. He s a senor member of the IEEE, and s currently char of the IEEE Communcaton Socety Techncal Commttee on Ad hoc and Sensor Networks TC AHSN). Dr. Hassanen s the recpent of Communcatons and Informaton Technology Ontaro CITO) Champons of Innovaton Research award n He receved several best paper awards, ncludng at IEEE Wreless Communcatons and Network 2007), IEEE Global Communcaton Conference 2007), IEEE Internatonal Symposum on Computers and Communcatons 2009), IEEE Local Computer Networks Conference 2009) and ACM Wreless Communcaton and Moble Computng 2010). Dr. Hassanen s an IEEE Communcatons Socety Dstngushed Lecturer.