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.
|Title of host publication
|2003 IEEE Wireless Communications and Networking Conference, WCNC 2003
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2003
|2003 IEEE Wireless Communications and Networking Conference: The Dawn of Pervasive Communication, WCNC 2003 - New Orleans, United States
Duration: Mar 16 2003 → Mar 20 2003
|IEEE Wireless Communications and Networking Conference, WCNC
|2003 IEEE Wireless Communications and Networking Conference: The Dawn of Pervasive Communication, WCNC 2003
|3/16/03 → 3/20/03
ASJC Scopus subject areas
- General Engineering