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.
20 - 23 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.
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e-pub ahead of print date: 2013
Venue - Dates:
IEEE Congress on Evolutionary Computation, Cancun, Mexico, 2013-06-20 - 2013-06-23
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
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Date deposited: 12 Jun 2013 10:20
Last modified: 18 Mar 2024 03:14
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Contributors
Author:
Jiankang Zhang
Author:
Sheng Chen
Author:
Xiaomin Mu
Author:
Lajos Hanzo
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