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

M. Hayajneh, C. T. Abdallah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2003 IEEE Wireless Communications and Networking Conference, WCNC 2003
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages723-728
Number of pages6
ISBN (Electronic)0780377001
DOIs
Publication statusPublished - 2003
Externally publishedYes
Event2003 IEEE Wireless Communications and Networking Conference: The Dawn of Pervasive Communication, WCNC 2003 - New Orleans, United States
Duration: Mar 16 2003Mar 20 2003

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume2
ISSN (Print)1525-3511

Other

Other2003 IEEE Wireless Communications and Networking Conference: The Dawn of Pervasive Communication, WCNC 2003
Country/TerritoryUnited States
CityNew Orleans
Period3/16/033/20/03

ASJC Scopus subject areas

  • Engineering(all)

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