Adaptive online power control scheme based on the evolutionary game theory

In view of the remarkable growth in the number of users and the limited network resource, an efficient network management is very important and has been an active area of research over the years. Especially, during wireless network operations, adaptive power control is an effective way to enhance the network performance. In this study, a new online power control scheme is proposed based on the evolutionary game theory. To converge a desirable network equilibrium, the proposed scheme adaptively adjusts a transmit power level in a distributed online manner. For the efficient network management, the online approach is dynamic and flexible that can adaptively respond to current network conditions. With a simulation study, the author demonstrates that the proposed scheme improves the network performance under widely diverse network environments.

[1]  E. de Souza An evolutionary game-theoretic approach to congestion control , 2005 .

[2]  V. Georgiev Using Game Theory to Analyze Wireless Ad Hoc Networks . ” , 2008 .

[3]  Shan Huang,et al.  A Game Theory Based WiMAX Uplink Power Control Algorithm , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[4]  Narayan B. Mandayam,et al.  Pricing and power control for joint network-centric and user-centric radio resource management , 2004, IEEE Transactions on Communications.

[5]  Yezekael Hayel,et al.  An evolutionary game approach for the design of congestion control protocols in wireless networks , 2008, WiOpt 2008.

[6]  Yan Guo,et al.  A Novel Distributed Power Control Algorithm Based on Game Theory , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[7]  Kyung Sup Kwak,et al.  Sub-optimum superimposed training design for estimating of mobile OFDM channels , 2009, 2009 Fourth International Conference on Communications and Networking in China.

[8]  Andrea J. Goldsmith,et al.  Distributed power and admission control for time varying wireless networks , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[9]  Sungwook Kim Online energy efficient routing approach for QoS-sensitive wireless sensor networks , 2009, 2009 International Conference on Information Networking.

[10]  Bo Li,et al.  Non-cooperative power control for wireless ad hoc networks with repeated games , 2007, IEEE Journal on Selected Areas in Communications.

[11]  Yongkang Xiao,et al.  Game theory models for IEEE 802.11 DCF in wireless ad hoc networks , 2005, IEEE Communications Magazine.

[12]  R. M. Buehrer,et al.  Game theoretic analysis of joint link adaptation and distributed power control in GPRS , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[13]  Liu Zhanjun,et al.  An interference avoidance power control algorithm based on game theory , 2010, 2010 Second Pacific-Asia Conference on Circuits, Communications and System.

[14]  Nicholas R. Jennings,et al.  Self-organized routing for wireless microsensor networks , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[15]  Tijani Chahed,et al.  Framework for Resource Allocation in Heterogeneous Wireless Networks Using Game Theory , 2006, EuroNGI Workshop.

[16]  Yanli Hou,et al.  Research on power control algorithm based on game theory in cognitive radio system , 2010, 2010 2nd International Conference on Signal Processing Systems.

[17]  Juha Leino,et al.  Applications of Game Theory in Ad Hoc Networks , 2003 .

[18]  Moh Lim Sim,et al.  Fair power control for wireless ad hoc networks using game theory with pricing scheme , 2010, IET Commun..

[19]  Eduard A. Jorswieck,et al.  Energy-Efficient Power Control and Receiver Design in Relay-Assisted DS/CDMA Wireless Networks via Game Theory , 2011, IEEE Communications Letters.

[20]  T. Yi,et al.  Effect of time delay and evolutionarily stable strategy. , 1997, Journal of theoretical biology.

[21]  H. Vincent Poor,et al.  Energy-efficient power and rate control with QoS constraints: a game-theoretic approach , 2006, IWCMC '06.

[22]  Andrew Byde,et al.  Applying evolutionary game theory to auction mechanism design , 2003, EEE International Conference on E-Commerce, 2003. CEC 2003..

[23]  Li Jian-dong,et al.  Joint rate and power control based on game theory in cognitive radio networks , 2009, 2009 Fourth International Conference on Communications and Networking in China.

[24]  Liu Yu-tao Power Control Algorithm Based on Game Theory in Cognitive Radio Networks , 2009 .

[25]  J. Hofbauer,et al.  Evolutionary game dynamics , 2011 .

[26]  Pramod K. Varshney,et al.  An integrated adaptive bandwidth-management framework for QoS-sensitive multimedia cellular networks , 2004, IEEE Transactions on Vehicular Technology.