Machine learning based CDMA power control

Author

Rohwer, Judd A. ; Abdallah, Chaouki T. ; Christodoulou, XChristos G.

Author_Institution

Telemetry Technol. Dev., Sandia Nat. Labs., Albuquerque, NM, USA

Volume

1

fYear

2003

fDate

9-12 Nov. 2003

Firstpage

207

Abstract

This paper presents binary and multiclass machine learning techniques for CDMA power control. The power control commands are based on estimates of the signal and noise subspace eigenvalues and the signal subspace dimension. Results of two different sets of machine learning algorithms are presented. Binary machine learning algorithms generate fixed-step power control (FSPC) commands based on estimated eigenvalues and SIRs. A fixed-set of power control commands are generated with multiclass machine learning algorithms. The results show the limitations of a fixed-set power control system, but also show that a fixed-set system achieves comparable performance to high complexity closed-loop power control systems.

Keywords

closed loop systems; code division multiple access; computational complexity; eigenvalues and eigenfunctions; learning (artificial intelligence); mobile radio; power control; telecommunication control; CDMA power control; binary machine learning algorithms; closed-loop power control systems; code division multiple access; complexity; eigenvalues estimation; fixed-step power control; multiclass machine learning techniques; signal estimation; Chaos; Eigenvalues and eigenfunctions; Laboratories; Machine learning; Machine learning algorithms; Multiaccess communication; Power control; Power generation; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on

Print_ISBN

0-7803-8104-1

Type

conf

DOI

10.1109/ACSSC.2003.1291898

Filename

1291898