The University of Southampton
University of Southampton Institutional Repository

Benchmarking capabilities of evolutionary algorithms in joint channel estimation and turbo multi-User detection/decoding

Benchmarking capabilities of evolutionary algorithms in joint channel estimation and turbo multi-User detection/decoding
Benchmarking capabilities of evolutionary algorithms in joint channel estimation and turbo multi-User detection/decoding
Joint channel estimation (CE) and turbo multi-user detection (MUD)/decoding for space-division multiple-access based orthogonal frequency-division multiplexing communication has to consider both the decision-directed CE optimisation on a continuous search space and the MUD optimisation on a discrete search space, and it iteratively exchanges the estimated channel information and the detected data between the channel estimator and the turbo MUD/decoder to gradually improve the accuracy of both the CE and the MUD. We evaluate the capabilities of a group of evolutionary algorithms (EAs) to achieve optimal or near optimal solutions with affordable complexity in this challenging application. Our study confirms that the EA assisted joint CE and turbo MUD/decoder is capable of approaching both the Cram\'er-Rao lower bound of the optimal channel estimation and the bit error ratio performance of the idealised optimal turbo maximum likelihood (ML) MUD/decoder associated with the perfect channel state information, respectively, despite only imposing a fraction of the complexity of the idealised turbo ML-MUD/decoder.
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 (2013) Benchmarking capabilities of evolutionary algorithms in joint channel estimation and turbo multi-User detection/decoding. IEEE Congress on Evolutionary Computation, Cancun, Mexico. 19 - 22 Jun 2013. 9 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Joint channel estimation (CE) and turbo multi-user detection (MUD)/decoding for space-division multiple-access based orthogonal frequency-division multiplexing communication has to consider both the decision-directed CE optimisation on a continuous search space and the MUD optimisation on a discrete search space, and it iteratively exchanges the estimated channel information and the detected data between the channel estimator and the turbo MUD/decoder to gradually improve the accuracy of both the CE and the MUD. We evaluate the capabilities of a group of evolutionary algorithms (EAs) to achieve optimal or near optimal solutions with affordable complexity in this challenging application. Our study confirms that the EA assisted joint CE and turbo MUD/decoder is capable of approaching both the Cram\'er-Rao lower bound of the optimal channel estimation and the bit error ratio performance of the idealised optimal turbo maximum likelihood (ML) MUD/decoder associated with the perfect channel state information, respectively, despite only imposing a fraction of the complexity of the idealised turbo ML-MUD/decoder.

Text
M-cec2013.pdf - Version of Record
Download (292kB)
Text
P-cec2013.pdf - Other
Download (613kB)

More information

e-pub ahead of print date: 2013
Venue - Dates: IEEE Congress on Evolutionary Computation, Cancun, Mexico, 2013-06-19 - 2013-06-22
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 353308
URI: http://eprints.soton.ac.uk/id/eprint/353308
PURE UUID: e4de3366-6656-4286-9c20-d57b6a904d22
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: 12 Jun 2013 10:20
Last modified: 18 Feb 2021 17:13

Export record

Contributors

Author: Jiankang Zhang ORCID iD
Author: Sheng Chen
Author: Xiaomin Mu
Author: Lajos Hanzo ORCID iD

University divisions

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.

×