Distributed Rate Allocation for Wireless Networks

This paper develops a distributed algorithm for rate allocation in wireless networks that achieves the same throughput region as optimal centralized algorithms. This cross-layer algorithm jointly performs medium access control and physical-layer rate adaptation. The paper establishes that this algorithm is throughput-optimal for general rate regions. In contrast to on-off scheduling, rate allocation enables optimal utilization of physical-layer schemes by scheduling multiple rate levels. The algorithm is based on local queue-length information, and thus the algorithm is of significant practical value. An important application of this algorithm is in multiple-band multiple-radio throughput-optimal distributed scheduling for white-space networks. The algorithm requires that each link can determine the global feasibility of increasing its current data-rate. In many classes of networks, any one link's data-rate primarily impacts its neighbors and this impact decays with distance. Hence, local exchanges can provide the information needed to determine feasibility. Along these lines, the paper discusses the potential use of existing physical-layer control messages to determine feasibility. This can be considered as a technique analogous to carrier sensing in carrier sense multiple access (CSMA) networks.

[1]  Upendra Dave,et al.  Applied Probability and Queues , 1987 .

[2]  Thomas M. Cover,et al.  Elements of information theory (2. ed.) , 2006 .

[3]  Jean C. Walrand,et al.  A Distributed CSMA Algorithm for Throughput and Utility Maximization in Wireless Networks , 2010, IEEE/ACM Transactions on Networking.

[4]  Jean C. Walrand,et al.  Distributed Random Access Algorithm: Scheduling and Congestion Control , 2009, IEEE Transactions on Information Theory.

[5]  Minghua Chen,et al.  Markov Approximation for Combinatorial Network Optimization , 2013, IEEE Transactions on Information Theory.

[6]  Alexander L. Stolyar.  Dynamic Distributed Scheduling in Random Access Networks , 2005 .

[7]  Shilpa Achaliya,et al.  Cognitive radio , 2010 .

[8]  Jinsung Lee,et al.  Implementing utility-optimal CSMA , 2009, 2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[9]  Devavrat Shah,et al.  Network adiabatic theorem: an efficient randomized protocol for contention resolution , 2009, SIGMETRICS '09.

[10]  A. Robert Calderbank,et al.  Layering as Optimization Decomposition: A Mathematical Theory of Network Architectures , 2007, Proceedings of the IEEE.

[11]  Leandros Tassiulas,et al.  Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks , 1992 .

[12]  R. Srikant,et al.  Stable scheduling policies for fading wireless channels , 2005, IEEE/ACM Transactions on Networking.

[13]  Jian Ni,et al.  Q-CSMA: Queue-Length-Based CSMA/CA Algorithms for Achieving Maximum Throughput and Low Delay in Wireless Networks , 2009, IEEE/ACM Transactions on Networking.

[14]  Sriram Vishwanath,et al.  On the stability region of amplify-and-forward cooperative relay networks , 2009, 2009 IEEE Information Theory Workshop.

[15]  Richard L. Tweedie,et al.  Markov Chains and Stochastic Stability , 1993, Communications and Control Engineering Series.

[16]  Vikram Srinivasan,et al.  Dynamic spectrum access in DTV whitespaces: design rules, architecture and algorithms , 2009, MobiCom '09.

[17]  Atilla Eryilmaz,et al.  Stable scheduling policies for fading wireless channels , 2003, IEEE International Symposium on Information Theory, 2003. Proceedings..

[18]  P. Gupta,et al.  Optimal Throughput Allocation in General Random-Access Networks , 2006, 2006 40th Annual Conference on Information Sciences and Systems.

[19]  R. Srikant,et al.  A tutorial on cross-layer optimization in wireless networks , 2006, IEEE Journal on Selected Areas in Communications.

[20]  Axthonv G. Oettinger,et al.  IEEE Transactions on Information Theory , 1998 .

[21]  Asuman E. Ozdaglar,et al.  Achievable rate region of CSMA schedulers in wireless networks with primary interference constraints , 2007, 2007 46th IEEE Conference on Decision and Control.

[22]  John Odentrantz,et al.  Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues , 2000, Technometrics.

[23]  Sriram Vishwanath,et al.  Q-CMRA: Queue-Based Channel-Measurement and Rate-Allocation , 2012, IEEE Transactions on Wireless Communications.

[24]  Alexandre Proutière,et al.  Performance of random medium access control, an asymptotic approach , 2008, SIGMETRICS '08.

[25]  Jean Walrand,et al.  Approaching throughput-optimality in a distributed CSMA algorithm: collisions and stability , 2009, MobiHoc S3 '09.

[26]  H. Vincent Poor,et al.  Convergence and tradeoff of utility-optimal CSMA , 2009, 2009 Sixth International Conference on Broadband Communications, Networks, and Systems.

[27]  J. Walrand,et al.  Approaching Throughput-optimality in a Distributed CSMA Algorithm with Contention Resolution , 2009 .

[28]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[29]  P. Lawson,et al.  Federal Communications Commission , 2004, Bell Labs Technical Journal.

[30]  Leandros Tassiulas,et al.  Resource Allocation and Cross-Layer Control in Wireless Networks , 2006, Found. Trends Netw..

[31]  J. Walrand,et al.  Convergence Analysis of a Distributed CSMA Algorithm for Maximal Throughput in a General Class of Networks , 2008 .

[32]  J. Dai.  On Positive Harris Recurrence of Multiclass Queueing Networks: A Unified Approach Via Fluid Limit Models , 1995 .

[33]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..