Abstract
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.
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References
(March 11, 2005) Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies. FCC Report and Order, FCC-05-57A1
Chakravarthy VD, Shaw AK, Temple MA, Stephens JP (2005) Cognitive radio—An adaptive waveform with spectral sharing capability. In: Wireless Communications and networking conference, vol 2, 2005 IEEE, pp. 724–729, March 13–17 2005
Freund Y, Schapire R (1995) A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the Second European Conference on Computational Learning Theory. Springer, Berlin Heidelberg New York, pp 23–37
Goodman DJ, Mandayam NB (2001) Network assisted power control for wireless data. Mob Netw Appl 6(5):409–415
Greenwald A, Jafari A (2003) A class of no-regret algorithms and game-theoretic equilibria. In: Proceedings of the 2003 Computational Learning Theory Conference, pp. 1–11, August
Jafari A, Greenwald A, Gondek D, Ercal G (2001) On no-regret learning, fictitious play, and nash equilibrium. In: Proceedings of the Eighteenth International Conference on Machine Learning, Williamstown, pp. 226–223, June
Lansford J (2004) UWB coexistence and cognitive radio. In: Ultra Wideband Systems, 2004. Joint with Conference on Ultrawideband Systems and Technologies, 2004 International Workshop Joint UWBST and IWUWBS, Kyoto, Japan, pp. 35–39, May 18–21 2004
Mahmood H (2002) Investigation of low rate channel codes for asynchronous DS-CDMA. M.Sc Thesis, University of Ulm, Ulm, Germany
Menon R, MacKenzie A, Buehrer R, Reed J (2004) Game theory and interference avoidance in decentralized networks. In: SDR Forum technical conference, Phoenix, Arizona, November 15–18 2004
Mitola J III (1999) Cognitive radio for flexible mobile multimedia communications. In: IEEE 1999 Mobile multimedia conference (MoMuC), San Diego, California
Mitola J III (2000) Cognitive radio: an integrated agent architecture for software defined radio. Doctor of Technology Dissertation, Royal Institute of Technology (KTH), Sweden, pp. 45–49, May 2000
Monderer D, Shapley L (1996) Potential games. Games Econom Behav 14:124–143
Neel J, Reed JH, Gilles RP (2002) The role of game theory in the analysis of software radio networks. In: SDR forum technical conference, San Diego, California, November
Neel J, Reed JH, Gilles RP (2004) Convergence of cognitive radio networks. In: Wireless Communications and Networking Conference 2004 IEEE, vol 4, pp. 2250–2255, March 21–25 2004
Yamaguchi H (2004) Active interference cancellation technique for MB-OFDM cognitive radio. In: 34th European Microwave Conference 2004, vol 2, Amsterdam, The Netherlands, pp. 1105–1108, October 13 2004
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Nie, N., Comaniciu, C. Adaptive Channel Allocation Spectrum Etiquette for Cognitive Radio Networks. Mobile Netw Appl 11, 779–797 (2006). https://doi.org/10.1007/s11036-006-0049-y
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DOI: https://doi.org/10.1007/s11036-006-0049-y