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

Authors

Chaouki T. Abdallah
M. Hayajneh

Document Type

Article

Publication Date

3-20-2003

Abstract

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.

Publisher

IEEE

Publication Title

2003 IEEE Wireless Communications and Networking

ISSN

1525-3511

Issue

2

First Page

723

Last Page

728

DOI

10.1109/WCNC.2003.1200459

Language (ISO)

English

Sponsorship

IEEE

Keywords

AWGN, Context modeling, Multiaccess communication, Game theory

Recommended Citation

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.