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Machine learning based CDMA power control

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/20272

Machine learning based CDMA power control

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Title: Machine learning based CDMA power control
Author: Abdallah, Chaouki T.; Rohwer, Judd A.; Christodoulou, Christos G.
Subject(s): Chaos
Eigenvalues and eigenfunctions
Machine learning
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.
Date: 2003-11-09
Publisher: IEEE
Citation: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 2003, 1: 207-211
Description: Digital Object Identifier: 10.1109/ACSSC.2003.1291898
URI: http://hdl.handle.net/1928/20272
ISBN: 0-7803-8104-1

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