Adaptive channel allocation spectrum etiquette for cognitive radio networks

In this work, we propose a game theoretic framework to analyze the behavior of cognitive radios for distributed adaptive channel allocation. We define two different objective functions for the spectrum sharing games, which capture the utility of selfish users and cooperative users, respectively. Based on the utility definition for cooperative users, we show that the channel allocation problem can be formulated as a potential game, and thus converges to a deterministic channel allocation Nash equilibrium point. Alternatively, a no-regret learning implementation is proposed for both scenarios and it is shown to have similar performance with the potential game when cooperation is enforced, but with a higher variability across users. The no-regret learning formulation is particularly useful to accommodate selfish users. Non-cooperative learning games have the advantage of a very low overhead for information exchange in the network. We show that cooperation based spectrum sharing etiquette improves the overall network performance at the expense of an increased overhead required for information exchange

[1]  Joseph Mitola Cognitive Radio for Flexible Mobile Multimedia Communications , 2001, Mob. Networks Appl..

[2]  H. Yamaguchi,et al.  Active interference cancellation technique for MB-OFDM cognitive radio , 2004, 34th European Microwave Conference, 2004..

[3]  Keith B. Hall,et al.  Fair and Efficient Solutions to the Santa Fe Bar Problem , 1910 .

[4]  Jeffrey H. Reed,et al.  Convergence of cognitive radio networks , 2004, 2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733).

[5]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[6]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[7]  Michael A. Temple,et al.  Cognitive radio - an adaptive waveform with spectral sharing capability , 2005, IEEE Wireless Communications and Networking Conference, 2005.

[8]  David J. Goodman,et al.  Network Assisted Power Control for Wireless Data , 2001, Mob. Networks Appl..

[9]  Gunes Ercal,et al.  On No-Regret Learning, Fictitious Play, and Nash Equilibrium , 2001, ICML.

[10]  R. Michael Buehrer,et al.  WSN15-4: A Game-Theoretic Framework for Interference Avoidance in Ad hoc Networks , 2006, IEEE Globecom 2006.

[11]  Jeffrey H. Reed,et al.  GAME THEORY AND INTERFERENCE AVOIDANCE IN DECENTRALIZED NETWORKS , 2004 .

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

[13]  J. Lansford,et al.  UWB coexistence and cognitive radio , 2004, 2004 International Workshop on Ultra Wideband Systems Joint with Conference on Ultra Wideband Systems and Technologies. Joint UWBST & IWUWBS 2004 (IEEE Cat. No.04EX812).

[14]  D. Fudenberg,et al.  The Theory of Learning in Games , 1998 .

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

[16]  Robert P. Gilles,et al.  On the Role of Game Theory in the Analysis of Software Radio Networks , 2002 .