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Summary: Statistical Learning Theory to Evaluate The
Performance of Game Theoretic Power Control
Algorithms for Wireless Data in Arbitrary Channels
M. Hayajneh & C. T. Abdallah
Dept. of Electrical & Computer Engr., Univ. of New Mexico,
EECE Bldg., Albuquerque, NM 87131-1356, USA.
{hayajneh, chaouki}@ eece.unm.edu
Abstract--In this paper we use statistical learning theory to eval-
uate 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 specifi-
cally, we simulated the case of Rayleigh flat fading channel. With
the help of a relatively small number of training samples, the re-
sults suggest the learnability of the utility function classes defined
by changing the users power (adjusted parameter) for each user's
utility function.
I. INTRODUCTION
In game theoretic power control algorithms used in wireless
code division multiple access (CDMA) cellular systems, the ob-
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