Dynamic bandwidth allocation in wireless networks using a Shahshahani gradient based extremum seeking control

In this paper we propose an extremum seeking approach based on evolutionary game theory, to solve the dynamic bandwidth allocation problem in wireless networks. The algorithm proposed aims to maximize a general utility function associated to the network, defined as the sum of the individual utilities of the agents (users) that belong to the network. Due to the complex time-varying nature of the available bandwidth, the quality of the links across the population of agents, and the parameters and structure of the utilities of the agents, a non-model based control is required. In this context, the extremum seeking algorithm proposed allows the optimal on-line bandwidth allocation, based only on the individual measurements of the utility function of each agent. We show how the extremum seeking control converges to a neighborhood of the optimal allocation, maximizing the general utility function of the network. This function can be externally manipulated by a network manager aiming to achieve a tradeoff between a “throughput” and a “fairness” behavior in the resource allocation solution.

[1]  Miroslav Krstic,et al.  Multivariable Newton-based extremum seeking , 2011, IEEE Conference on Decision and Control and European Control Conference.

[2]  Ying Tan,et al.  Non-local stability of a multi-variable extremum-seeking scheme , 2011, 2011 Australian Control Conference.

[3]  Ying Tan,et al.  ON NON-LOCAL STABILITY PROPERTIES OF EXTREMUM SEEKING CONTROL , 2005 .

[4]  John N. Tsitsiklis,et al.  Bayesian proportional resource allocation games , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[5]  Peter Marbach,et al.  Downlink resource allocation and pricing for wireless networks , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[6]  Frank Kelly,et al.  Charging and rate control for elastic traffic , 1997, Eur. Trans. Telecommun..

[7]  Suwatchai Kamonsantiroj,et al.  An auction-based dynamic bandwidth allocation with sensitivity in a wireless networked control system , 2009, Comput. Ind. Eng..

[8]  Azad Ghaffari,et al.  Multivariable Newton-based extremum seeking , 2012, Autom..

[9]  Victor O. K. Li,et al.  Utility-oriented adaptive QoS and bandwidth allocation in wireless networks , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[10]  Bob Briscoe,et al.  Economic models for resource control in wireless networks , 2002, The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications.

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

[12]  S. Shenker.  Fundamental Design Issues for the Future Internet , 1995 .

[13]  William H. Sandholm,et al.  Population Games And Evolutionary Dynamics , 2010, Economic learning and social evolution.

[14]  Nicanor Quijano,et al.  The Ideal Free Distribution: Theory and Engineering Application , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  Andrew T. Campbell,et al.  A Utility-Based Approach for Quantitative Adaptation in Wireless Packet Networks , 2001, Wirel. Networks.

[16]  Andrew T. Campbell,et al.  A programmable MAC framework for utility-based adaptive quality of service support , 2000, IEEE Journal on Selected Areas in Communications.

[17]  Andres Pantoja,et al.  A Population Dynamics Approach for the Dispatch of Distributed Generators , 2011, IEEE Transactions on Industrial Electronics.

[18]  M. Zorzi,et al.  On the throughput and fairness performance of heterogeneous downlink packet traffic in a locally centralized CDMA/TDD system , 2002, Proceedings IEEE 56th Vehicular Technology Conference.

[19]  Michael L. Honig,et al.  Slow-rate utility-based resource allocation in wireless networks , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

[20]  Zhimei Jiang,et al.  Channel quality dependent scheduling for flexible wireless resource management , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[21]  R. Ordóñez,et al.  Numerical Optimization-based Extremum Seeking Control of LTI Systems , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[22]  B. Olsen,et al.  Approximate fair bandwidth allocation: A method for simple and flexible traffic management , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[23]  Mo-Yuen Chow,et al.  Behavioral control based adaptive bandwidth allocation in a system of Unmanned Ground Vehicles , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[24]  Wanjiun Liao,et al.  Utility-based optimal resource allocation in wireless networks , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[25]  Vaduvur Bharghavan,et al.  Achieving application level fairness through utility-based wireless fair scheduling , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[26]  Vasaka Visoottiviseth,et al.  Adaptive bandwidth allocation for per-station fairness on wireless access router , 2010, 2010 10th International Symposium on Communications and Information Technologies.

[27]  M. Krstić,et al.  Real-Time Optimization by Extremum-Seeking Control , 2003 .

[28]  S. Shahshahani,et al.  A New Mathematical Framework for the Study of Linkage and Selection , 1979 .

[29]  Josef Hofbauer,et al.  Evolutionary Games and Population Dynamics , 1998 .

[30]  Ming-Syan Chen,et al.  A General Framework of Time-Variant Bandwidth Allocation in the Data Broadcasting Environment , 2010, IEEE Transactions on Knowledge and Data Engineering.

[31]  Ying Tan,et al.  A unifying approach to extremum seeking: Adaptive schemes based on estimation of derivatives , 2010, 49th IEEE Conference on Decision and Control (CDC).

[32]  Nicanor Quijano,et al.  A Shahshahani Gradient based extremum seeking scheme , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).