Blind adaptive multiuser detection using a recurrent neural network

Shubao Liu, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Multiuser detection has gained much attention in recent years for its potential to greatly improve the capacities of CDMA communication systems. In this paper, a recurrent neural network is presented for solving the nonlinear optimization problem involved in the multiuser detection in CDMA. Compared with other neural networks, the presented neural network can globally converge to the exact optimal solution of the nonlinear optimization problem with nonlinear constraints and has relatively low structural complexity. Computer simulation results are presented to show the optimization capability. The performance in CDMA communcation systems is also studied by means of simulation.

Original languageEnglish
Title of host publication2004 International Conference on Communications, Circuits and Systems
Pages1071-1075
Number of pages5
Publication statusPublished - 2004
Externally publishedYes
Event2004 International Conference on Communications, Circuits and Systems - Chengdu, China
Duration: 27 Jun 200429 Jun 2004

Publication series

Name2004 International Conference on Communications, Circuits and Systems
Volume2

Conference

Conference2004 International Conference on Communications, Circuits and Systems
Country/TerritoryChina
CityChengdu
Period27/06/0429/06/04

Keywords

  • Blind adaptive detection
  • CDMA
  • Multiuser Detection
  • Nonlinear optimization
  • Recurrent neural networks

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