Statistical learning theory to evaluate the performance of game theoretic power control algorithms for wireless data in arbitrary channels

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 fiat 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 users power (adjusted parameter) for each user's utility function.