Turbo multi-user detection for OFDM/SDMA systems relying on differential evolution aided iterative channel estimation
Turbo multi-user detection for OFDM/SDMA systems relying on differential evolution aided iterative channel estimation
A differential evolution (DE) algorithm aided iterative channel estimation and turbo multi-user detection (MUD) scheme is proposed for multi-user multi-input multiple-output aided orthogonal frequency-division multiplexing/spacedivision multiple-access (OFDM/SDMA) systems. The proposed scheme iteratively exchanges the estimated channel information and the detected data between the channel estimator and MUD employing a turbo technique, which gradually improves the accuracy of the channel estimation and the MUD, especially for the first iteration. Quadrature amplitude modulation (QAM) is employed in most wireless standards by virtue of providing a high throughput. However, the optimal maximum likelihood (ML)-MUD becomes extremely complex for employment in QAM-aided multi-user systems. Hence, two different DE aided MUD schemes, the DE aided minimum symbol error rate (MSER)-MUD as well as the discrete DE aided ML-MUD, were developed, and their achievable performance versus complexity was characterized. The simulation results demonstrate that the proposed DE aided channel estimator is capable of approaching the Cramer-Rao lower bound with just two or three iterations. The ultimate bit error rate lower-bound of the single-user additive white Gaussian noise scenario has been approached in the range of E_{b}/N_{0}\ge 10 dB and E_{b}/N_{0}\ge 6 dB for the DE aided MSER-MUD and the discrete DE aided ML-MUD, respectively.
1621-1633
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
June 2012
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
(2012)
Turbo multi-user detection for OFDM/SDMA systems relying on differential evolution aided iterative channel estimation.
IEEE Transactions on Communications, 60 (6), .
Abstract
A differential evolution (DE) algorithm aided iterative channel estimation and turbo multi-user detection (MUD) scheme is proposed for multi-user multi-input multiple-output aided orthogonal frequency-division multiplexing/spacedivision multiple-access (OFDM/SDMA) systems. The proposed scheme iteratively exchanges the estimated channel information and the detected data between the channel estimator and MUD employing a turbo technique, which gradually improves the accuracy of the channel estimation and the MUD, especially for the first iteration. Quadrature amplitude modulation (QAM) is employed in most wireless standards by virtue of providing a high throughput. However, the optimal maximum likelihood (ML)-MUD becomes extremely complex for employment in QAM-aided multi-user systems. Hence, two different DE aided MUD schemes, the DE aided minimum symbol error rate (MSER)-MUD as well as the discrete DE aided ML-MUD, were developed, and their achievable performance versus complexity was characterized. The simulation results demonstrate that the proposed DE aided channel estimator is capable of approaching the Cramer-Rao lower bound with just two or three iterations. The ultimate bit error rate lower-bound of the single-user additive white Gaussian noise scenario has been approached in the range of E_{b}/N_{0}\ge 10 dB and E_{b}/N_{0}\ge 6 dB for the DE aided MSER-MUD and the discrete DE aided ML-MUD, respectively.
Text
TCOM-TPS-11-0400R2.pdf
- Accepted Manuscript
Text
tcom2012-6.pdf
- Version of Record
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Published date: June 2012
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 273148
URI: http://eprints.soton.ac.uk/id/eprint/273148
PURE UUID: 4fc513f3-9cd9-466b-9864-4d976ce018c3
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Date deposited: 30 Jan 2012 15:42
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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