In this paper we use statistical learning theory to evaluate the performance of game theoretic power control algorithms for wireless data in arbitrary channels, i.e., no presumed channel model is required. To show the validity of statistical learning theory in this context, we studied a flat fading channel, and more specifically, we simulated the case of Rayleigh flat fading channel. With the help of a relatively small number of training samples, the results suggest the learnability of the utility function classes defined by changing the user power (adjusted parameter) for each user's utility function.
2003 IEEE Wireless Communications and Networking
AWGN, Context modeling, Multiaccess communication, Game theory
Abdallah, Chaouki T. and M. Hayajneh. "Statistical learning theory to evaluate the performance of game theoretic power control algorithms for wireless data in arbitrary channels." 2003 IEEE Wireless Communications and Networking (2003): 723-728. doi:10.1109/WCNC.2003.1200459.