Utility-optimal random-access control

This paper designs medium access control (MAC) protocols for wireless networks through the network utility maximization (NUM) framework. A network-wide utility maximization problem is formulated, using a collision/persistence-probabilistic model and aligning selfish utility with total social welfare. By adjusting the parameters in the utility objective functions of the NUM problem, we can also control the tradeoff between efficiency and fairness of radio resource allocation. We develop two distributed algorithms to solve the utility-optimal random-access control problem, which lead to random access protocols that have slightly more message passing overhead than the exponential-backoff protocols, but significant potential for efficiency and fairness improvement. We provide readily-verifiable sufficient conditions under which convergence of the proposed algorithms to a global optimality of network utility can be guaranteed, and numerical experiments that illustrate the value of the NUM approach to the complexity-performance tradeoff in MAC design.

[1]  David K. Smith,et al.  Mathematical Programming: Theory and Algorithms , 1986 .

[2]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[3]  Frank Kelly,et al.  Rate control for communication networks: shadow prices, proportional fairness and stability , 1998, J. Oper. Res. Soc..

[4]  Raj Jain,et al.  A Quantitative Measure Of Fairness And Discrimination For Resource Allocation In Shared Computer Systems , 1998, ArXiv.

[5]  Steven H. Low,et al.  Optimization flow control—I: basic algorithm and convergence , 1999, TNET.

[6]  Voon Chin Phua,et al.  Wireless lan medium access control (mac) and physical layer (phy) specifications , 1999 .

[7]  Jean C. Walrand,et al.  Fair end-to-end window-based congestion control , 2000, TNET.

[8]  Vaduvur Bharghavan,et al.  Achieving MAC layer fairness in wireless packet networks , 2000, MobiCom '00.

[9]  Brahim Bensaou,et al.  On max-min fairness and scheduling in wireless ad-hoc networks: analytical framework and implementation , 2001, MobiHoc '01.

[10]  Rakesh V. Vohra,et al.  Mathematics of the Internet , 2001 .

[11]  Richard J. La,et al.  Utility-based rate control in the Internet for elastic traffic , 2002, TNET.

[12]  Leandros Tassiulas,et al.  Maxmin fair scheduling in wireless networks , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[13]  Steven H. Low,et al.  A duality model of TCP and queue management algorithms , 2003, TNET.

[14]  Brahim Bensaou,et al.  Fair bandwidth sharing algorithms based on game theory frameworks for wireless ad-hoc networks , 2004, IEEE INFOCOM 2004.

[15]  Leandros Tassiulas,et al.  Achieving proportional fairness using local information in Aloha networks , 2004, IEEE Transactions on Automatic Control.

[16]  Feng Guang-zeng IEEE Standard 802.16 , 2005 .

[17]  Derong Liu The Mathematics of Internet Congestion Control , 2005, IEEE Transactions on Automatic Control.

[18]  Lijun Chen,et al.  Joint congestion control and media access control design for ad hoc wireless networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[19]  Koushik Kar,et al.  Cross-layer rate control for end-to-end proportional fairness in wireless networks with random access , 2005, MobiHoc '05.

[20]  A. Robert Calderbank,et al.  Jointly optimal congestion and contention control based on network utility maximization , 2006, IEEE Communications Letters.

[21]  A. Robert Calderbank,et al.  Utility-Optimal Medium Access Control: Reverse and Forward Engineering , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

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

[23]  A. Robert Calderbank,et al.  Reverse-Engineering MAC: A Non-Cooperative Game Model , 2007, IEEE Journal on Selected Areas in Communications.