Blind adaptive multiuser detection using a recurrent neural network

Shubao Liu; Jun Wang

Blind adaptive multiuser detection using a recurrent neural network

Shubao Liu, Jun Wang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

9 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 language	English
Title of host publication	2004 International Conference on Communications, Circuits and Systems
Pages	1071-1075
Volume	2
Publication status	Published - 2004
Externally published	Yes
Event	2004 International Conference on Communications, Circuits and Systems - Chengdu, China Duration: 27 Jun 2004 → 29 Jun 2004

Publication series

Name
Volume	2

Conference

Conference	2004 International Conference on Communications, Circuits and Systems
Place	China
City	Chengdu
Period	27/06/04 → 29/06/04

Research Keywords

Blind adaptive detection
CDMA
Multiuser Detection
Nonlinear optimization
Recurrent neural networks

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@inproceedings{11576e9a18f14e8eb2ef32e6e6d7a852,

title = "Blind adaptive multiuser detection using a recurrent neural network",

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.",

keywords = "Blind adaptive detection, CDMA, Multiuser Detection, Nonlinear optimization, Recurrent neural networks",

author = "Shubao Liu and Jun Wang",

year = "2004",

language = "English",

isbn = "0780386477",

volume = "2",

pages = "1071--1075",

booktitle = "2004 International Conference on Communications, Circuits and Systems",

note = "2004 International Conference on Communications, Circuits and Systems ; Conference date: 27-06-2004 Through 29-06-2004",

}

TY - GEN

T1 - Blind adaptive multiuser detection using a recurrent neural network

AU - Liu, Shubao

AU - Wang, Jun

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

KW - Blind adaptive detection

KW - CDMA

KW - Multiuser Detection

KW - Nonlinear optimization

KW - Recurrent neural networks

UR - http://www.scopus.com/inward/record.url?scp=11244260395&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-11244260395&origin=recordpage

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 0780386477

SN - 9780780386471

VL - 2

SP - 1071

EP - 1075

BT - 2004 International Conference on Communications, Circuits and Systems

T2 - 2004 International Conference on Communications, Circuits and Systems

Y2 - 27 June 2004 through 29 June 2004

ER -

Blind adaptive multiuser detection using a recurrent neural network

Abstract

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Research Keywords

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