Fast reinforcement learning algorithm for mobile power control in cellular communication systems

Author

Gao, X.Z. ; Gao, X.M. ; Ovaska, S.J.

Author_Institution

Inst. of Intelligent Power Electron., Helsinki Univ. of Technol., Espoo, Finland

Volume

4

fYear

1997

fDate

12-15 Oct 1997

Firstpage

3883

Abstract

A fast reinforcement learning algorithm based on Muller´s method is first proposed. This new algorithm converges much faster than the conventional approach, and therefore is more suitable to be used in on-line applications. The authors apply the fast reinforcement learning algorithm into the power control of cellular phones. The channel tracking error can be minimized in the mobile power control scheme. Simulation experiments demonstrate that the harmful deep fading is greatly compensated and the response overshoot is small

Keywords

cellular radio; convergence of numerical methods; learning (artificial intelligence); learning systems; simulation; telecommunication control; telecommunication power supplies; cellular communication systems; cellular phones; channel tracking error; convergence; deep fading compensation; fast reinforcement learning algorithm; mobile power control; on-line applications; response overshoot; simulation experiments; Cellular phones; Communication systems; Convergence; Delay effects; Error correction; Learning; Power control; Power electronics; Stochastic processes; Uniform resource locators;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on

Conference_Location

Orlando, FL

ISSN

1062-922X

Print_ISBN

0-7803-4053-1

Type

conf

DOI

10.1109/ICSMC.1997.633277

Filename

633277