Game Theory for Resource Allocation in Wireless Networks

Wireless technologies and devices are becoming increasingly ubiquitous in modern society. Wireless resources are natural and fixed, whereas wireless technologies and devices are increasing day-by-day, resulting in spectrum scarcity. As a consequence, efficient use of limited wireless resources has become an issue of vital importance in wireless systems. As demand increases, management of limited wireless resources for optimal allocation becomes crucial. Optimal allocation of limited wireless resources results in quick and reliable dissemination of information to larger service areas. Recently, game theory has emerged as an efficient tool to help optimally allocate wireless resources. Game theory is an optimization technique based on strategic situations and decision-making, and has found its application in numerous fields. The first part of this chapter presents a review of game theory and its application in resource allocation at different layers of the protocol stack of the network model. As shown by a recent study, static assignment of frequency spectrum by governmental bodies, such as FCC (Federal Communications Commission) in the United States, is inefficient since the licensed systems do not always fully utilize their frequency bands. In such a scenario, unlicensed secondary (cognitive radio) users can identify the idle spectrum bands and use them opportunistically. In order to access the licensed spectrum dynamically and opportunistically, the dynamic spectrum access functionality needs to be incorporated in the next generation (XG) wireless networks. Different game theory approaches for dynamic spectrum access are discussed in the second part of the chapter.

[1]  Ben Y. Zhao,et al.  Utilization and fairness in spectrum assignment for opportunistic spectrum access , 2006, Mob. Networks Appl..

[2]  Allen B. MacKenzie,et al.  Game Theory for Wireless Engineers , 2006, Game Theory for Wireless Engineers.

[3]  Ananthram Swami,et al.  A Survey of Dynamic Spectrum Access: Signal Processing and Networking Perspectives , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[4]  Diego Moreira Alves Speech Synthesis and Recognition Based on Mobile Computing Application , 2009 .

[5]  Mihaela van der Schaar,et al.  Spectrum Access Games and Strategic Learning in Cognitive Radio Networks for Delay-Critical Applications , 2009, Proceedings of the IEEE.

[6]  Srinivasan Seshan,et al.  Selfish behavior and stability of the internet: a game-theoretic analysis of TCP , 2002, SIGCOMM.

[7]  Allen B. MacKenzie,et al.  Using game theory to analyze wireless ad hoc networks , 2005, IEEE Communications Surveys & Tutorials.

[8]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[9]  Hamid Aghvami,et al.  Cognitive Radio game for secondary spectrum access problem , 2009, IEEE Transactions on Wireless Communications.

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

[11]  R. Michael Buehrer,et al.  Interference avoidance in networks with distributed receivers , 2009, IEEE Transactions on Communications.

[12]  Michael L. Honig,et al.  Distributed interference compensation for wireless networks , 2006, IEEE Journal on Selected Areas in Communications.

[13]  S. Sethi,et al.  A survey of Stackelberg differential game models in supply and marketing channels , 2007 .

[14]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[15]  S. Kassam,et al.  Robust signal processing for communication systems , 1983, IEEE Communications Magazine.

[16]  Gongjun Yan,et al.  Signal processing techniques for spectrum sensing in cognitive radio systems: Challenges and perspectives , 2009, 2009 First Asian Himalayas International Conference on Internet.

[17]  Michael L. Honig,et al.  Auction-Based Spectrum Sharing , 2006, Mob. Networks Appl..

[18]  Tim Hawkins,et al.  Towards A Game Theoretic Understanding of Ad-Hoc Routing , 2005, GDV@CAV.

[19]  Wei Yu,et al.  Distributed multiuser power control for digital subscriber lines , 2002, IEEE J. Sel. Areas Commun..

[20]  Qing Zhao,et al.  Decentralized dynamic spectrum access for cognitive radios: cooperative design of a non-cooperative game , 2009, IEEE Transactions on Communications.

[21]  Catherine Rosenberg,et al.  A game theoretic framework for bandwidth allocation and pricing in broadband networks , 2000, TNET.

[22]  Cem U. Saraydar,et al.  Efficient power control via pricing in wireless data networks , 2002, IEEE Trans. Commun..

[23]  L. Shapley,et al.  REGULAR ARTICLEPotential Games , 1996 .

[24]  Zhu Han,et al.  Cooperative Game Theory for Distributed Spectrum Sharing , 2007, 2007 IEEE International Conference on Communications.

[25]  J. Nash Equilibrium Points in N-Person Games. , 1950, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Zhu Han,et al.  Non-cooperative resource competition game by virtual referee in multi-cell OFDMA networks , 2007, IEEE Journal on Selected Areas in Communications.

[27]  R. Michael Buehrer,et al.  A game-theoretic framework for interference avoidance , 2009, IEEE Transactions on Communications.

[28]  Chao Zou,et al.  A Game Theoretic DSA-Driven MAC Framework for Cognitive Radio Networks , 2008, 2008 IEEE International Conference on Communications.

[29]  Rui J. P. de Figueiredo Cognitive signal processing: An emerging technology for the prediction of behavior of complex human/machine systems , 2009, ICCCAS 2009.

[30]  Abhay Parekh,et al.  Spectrum sharing for unlicensed bands , 2005, IEEE Journal on Selected Areas in Communications.