The University of Southampton
University of Southampton Institutional Repository

Evolutionary-algorithm-assisted joint channel estimation and turbo multiuser detection/decoding for OFDM/SDMA

Evolutionary-algorithm-assisted joint channel estimation and turbo multiuser detection/decoding for OFDM/SDMA
Evolutionary-algorithm-assisted joint channel estimation and turbo multiuser detection/decoding for OFDM/SDMA
The development of evolutionary algorithms (EAs), such as genetic algorithms (GAs), repeated weighted boosting search (RWBS), particle swarm optimization (PSO), and differential evolution algorithms (DEAs), have stimulated wide interests in the communication research community. However, the quantitative performance-versus-complexity comparison of GA, RWBS, PSO, and DEA techniques applied to the joint channel estimation (CE) and turbo multiuser detection (MUD)/decoding in the context of orthogonal frequency-division multiplexing/space-division multiple-access systems is a challenging problem, which has to consider both the CE problem formulated over a continuous search space and the MUD optimization problem defined over a discrete search space. We investigate the capability of the GA, RWBS, PSO, and DEA to achieve optimal solutions at an affordable complexity in this challenging application. Our study demonstrates that the EA-assisted joint CE and turbo MUD/decoder is capable of approaching both the Cramér–Rao lower bound of the optimal CE and the bit error ratio (BER) performance of the idealized optimal maximum-likelihood (ML) turbo MUD/decoder associated with perfect channel state information, respectively, despite imposing only a fraction of the idealized turbo ML-MUD/decoder’s complexity.
0018-9545
1204-1222
Zhang, Jiankang
6add829f-d955-40ca-8214-27a039defc8a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Mu, Xiaomin
3d578909-36ba-4b16-b703-2ef63532116c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Zhang, Jiankang
6add829f-d955-40ca-8214-27a039defc8a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Mu, Xiaomin
3d578909-36ba-4b16-b703-2ef63532116c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Zhang, Jiankang, Chen, Sheng, Mu, Xiaomin and Hanzo, Lajos (2014) Evolutionary-algorithm-assisted joint channel estimation and turbo multiuser detection/decoding for OFDM/SDMA. IEEE Transactions on Vehicular Technology, 63 (3), 1204-1222.

Record type: Article

Abstract

The development of evolutionary algorithms (EAs), such as genetic algorithms (GAs), repeated weighted boosting search (RWBS), particle swarm optimization (PSO), and differential evolution algorithms (DEAs), have stimulated wide interests in the communication research community. However, the quantitative performance-versus-complexity comparison of GA, RWBS, PSO, and DEA techniques applied to the joint channel estimation (CE) and turbo multiuser detection (MUD)/decoding in the context of orthogonal frequency-division multiplexing/space-division multiple-access systems is a challenging problem, which has to consider both the CE problem formulated over a continuous search space and the MUD optimization problem defined over a discrete search space. We investigate the capability of the GA, RWBS, PSO, and DEA to achieve optimal solutions at an affordable complexity in this challenging application. Our study demonstrates that the EA-assisted joint CE and turbo MUD/decoder is capable of approaching both the Cramér–Rao lower bound of the optimal CE and the bit error ratio (BER) performance of the idealized optimal maximum-likelihood (ML) turbo MUD/decoder associated with perfect channel state information, respectively, despite imposing only a fraction of the idealized turbo ML-MUD/decoder’s complexity.

Text
tvt-hanzo-2283069-proof.pdf - Version of Record
Download (5MB)
Text
tvt2014-March.pdf - Version of Record
Download (2MB)

More information

Published date: March 2014
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 358199
URI: http://eprints.soton.ac.uk/id/eprint/358199
ISSN: 0018-9545
PURE UUID: 89602aec-a2d4-4372-8bb5-344cbc153309
ORCID for Jiankang Zhang: ORCID iD orcid.org/0000-0001-5316-1711
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 01 Oct 2013 09:19
Last modified: 18 Feb 2021 17:13

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×