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IEEE COMSOC MMTC E-LetterEditor’s Selected Paper RecommendationH. Kushwaha, Y. Xing, R. Chandramouli, and H. Heffes, “Reliable multimedia transmissionover cognitive radio networks using fountain codes,” Proceedings of the IEEE, vol. 96, no. 1,pp. 155-165, Jan. 2008Deploying wireless real-time multimediaapplications encounters many challenges, such asconsiderable bandwidth requirement, stringentdelay constraints, and reliable link transmission.The end-to-end final received multimedia qualityis mainly dominated by the link capacity and linkquality. However, radio spectrum is a limitedresource and a major portion of the spectrum hasbeen already allocated. Recently, FederalCommunications Commission (FCC)proceedings propose the notion of secondaryspectrum access to improve spectrum efficiencyby allowing dynamic access to the vacant partsof the spectrum owned by the primary user (PU)to become accessible temporarily by a secondaryuser (SU). This dynamic access of spectrum bysecondary users can be realized by adoptingcognitive radios. Cognitive radio is a wirelesscommunication paradigm in which either thenetwork or the wireless node itself changesparticular transmission or reception parametersto execute its tasks efficiently without interferingwith the licensed users or other cognitive radios.In cognitive radio networks, a multimediatransmission application can select a set ofsubchannels (SC) from different PUs to establisha communication link. Note that a link canconsist of multiple different SCs at differentfrequencies. The advantages are (1) to havehigher overall throughput contributed frommultiple SCs; (2) to add path diversity fromdifferent SCs in the network to facilitate errorprotection during streaming the multimediacontent. On the other hand, the inherent problemof distributing media over multiple SCs is thecoordination required between the SCs. In orderto efficiently utilize the limited spectral resource,the multimedia applications need to carefullycoordinate the packet scheduling. Besides, theapplications also need to overcome theinterference from PUs. Thus, error protectionmechanism needs to be brought into scenes toensure the successful content reconstruction.The aforementioned two problems occurred incognitive radio networks can be alleviated byadopting fountain codes [1][2]. An idealfountain code has the property that an infiniteamount of parity packets from k informationpackets can be generated and the entire sourcemessage can be reliably reconstructed from any n(n>k) received encoding packets. In this paper,the authors propose to apply fountain codes tomultimedia transmission over cognitive networksscenario to achieve two goals simultaneously.First, the transmitters can spread the packets overdifferent SCs with no need of coordinationbetween them since the receiver cares about theamount (instead of order or type) of correctlyreceived packets. Secondly, it works as a channelcode to resist the packet loss effect from PUinterference and other channel conditions.More specifically, the authors study the problemof optimizing the spectral resources in thesecondary usage scenario for multimediaapplications with respect to the number ofavailable SCs and primary user occupancy of theSCs. Analysis and simulations detail the optimalnumber of SCs and optimal fountain codes toachieve optimal spectral efficiency.Researches on multimedia over cognitive radionetworks are gaining more and more interests.Authors in [3] study the channel selectionproblem for multimedia over cognitive networks.The scenario of multimedia multicast overcognitive networks is studied in [4].[1] J. Wagner, J. Chakareski, and P. Frossard,"Streaming of scalable video from multiple serversusing rateless codes”, in IEEE ICME, Jul. 2006.[2] H. Jenkac; T. Stockhammer, and W. Xu,"Asynchronous and reliable on-demand mediabroadcast", IEEE Network Magazine, vol. 20, no. 2,pp.14-20, March/April 2006..[3] H. P. Shiang and M. van der Schaar, "Queuingbaseddynamic channel selection for heterogeneousmultimedia applications over cognitive radionetworks," IEEE Trans. Multimedia, Vol. 10, no.5, pp.896~909, Aug. 2008[4] D. Hu; S. Mao; J.H. Reed, "On video multicast incognitive radio networks", INFOCOM 2009. pp.2222–2230.* The Column Editor recommending this paper is Dr.Guan-Ming Suhttp://www.comsoc.org/~mmc/ 34/41 Vol.4, No.7, August 2009
IEEE COMSOC MMTC E-LetterFocused Technology Advances SeriesDistributed Algorithm Design for Network Optimization Problems with CoupledObjectivesJianwei Huang, the Chinese University of Hong Kong, Chinajwhuang@ie.cuhk.edu.hkIn order to optimize the performance of variouscommunication and networking systems, weoften model it as a Network UtilityMaximization (NUM) problem [1]. In the NUMformulation, we associate each network entity(user) with a utility function representing itsperformance/satisfaction/happiness, and thusmaximizing the total network utility is equivalentto maximizing the network performance. Due tothe distributed and heterogeneous nature oftoday’s network, one of the key challenges ishow to design distributed algorithms that canachieve the global optimal NUM solution.The difficulty in distributed algorithm designoften lies in the coupling nature of the NUMproblem. There exist two kinds of coupling:coupled constraints (e.g., limited total networkresource) and coupled objective functions (e.g.,due to interferences or collisions among wirelessnodes). The coupled constraints can bedecomposed using the dual or primaldecompositions which have been widely usedand studied (see [1] [2] and numerous referencestherein). The coupled objective functions,however, are more difficult to deal with and lessstudied. One recent innovative approach ofdealing with coupled objective functions is touse “consistency pricing” [3], which is mainlysuitable for strictly convex NUM formulations,involves significant amount of message passingamong users, and requires updating the variablesusing small step-sizes which often leads to slowconvergence.Here we establish a new framework of designingdistributed algorithm for NUM with coupledobjective functions. The key idea is to “reverseengineer”the algorithm based on the KKTcondition of the NUM problem, which involves“localizing the global objective function” anddesigning suitable physical message passingmechanism. A key feature of the correspondingalgorithm is that it does involve use any smallstep-sizes, thus typically has much faster andmore robust convergence compared with theconsistence pricing approach. Moreover, thealgorithm also works for certain cases where thestrictly convexity of the NUM problem can notbe directly proved using the standardoptimization approach.To be more concrete, we will consider a networkof a set of K users. Each user k has a coupledutility function U k (x k ,x -k ) that depends on bothhis own local decision variable x k and otherusers’ decision variables x -k =(x 1 ,…,x k-1 ,x k+1 ,x K ).An example of the utility function would be thedata rate achieved by a wireless user, which onlydepends on its own transmission power but alsoon the interference (and other other users’transmission power). User k can choose x k from afeasible set X k =[X k min ,X k max ]. Denote x as thevector containing all users’ decision variablesand X as the set containing all users’ feasible sets.We want to solve the following NUM problem:maxx∈XK∑k = 1( )Ukxk , x−k.(1)We assume that the utility function U k (x k ,x -k ) isincreasing and strictly concave in x k but notnecessarily concave in x. This means thatProblem (1) is not necessarily a strictly concavemaximization problem in variables x, and thusmight have more than one local/global optimalsolution. Solving such a problem is difficult ingeneral even through centralized computation.Our target is to solve the problem in a distributedfashion under certain technical conditions.The starting point of our algorithm design is theKarush–Kuhn–Tucker (KKT) conditions ofProblem (1), which are the necessary conditionsfor a global optimal solution. A KKT point x*needs to satisfy the following for each k,⎛ ∗ ∗ ⎞ ⎛ ∗ ∗ ⎞∂ Uk ⎜ xk , x−k ⎟ ∂ Uj ⎜ xk , xk ⎟⎝ ⎠ ⎝ − ⎠ ∗ ∗+ λkµ∗ ∑= −∗k,∂xkj≠k∂xk(2)maxminλ k( x k− Xk) = 0 , µk( Xk− xk) = 0,(3)∗ ∗λk, µk≥ 0.(4)Our task is to design a distributed algorithm thatis guaranteed to converge to the KKT set. If theKKT set is a singleton set, then thehttp://www.comsoc.org/~mmc/ 35/41 Vol.4, No.7, August 2009
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