Soft-decision-driven sparse channel estimation and turbo equalization for MIMO underwater acoustic communications
Soft-decision-driven sparse channel estimation and turbo equalization for MIMO underwater acoustic communications
Multi-input multi-output (MIMO) detection based on turbo principle has been shown to provide a great enhancement in the throughput and reliability of underwater acoustic (UWA) communication systems. Benefits of the iterative detection in MIMO systems, however, can be obtained only when a high quality channel estimation is ensured. In this paper, we develop a new soft-decision-driven sparse channel estimation and turbo equalization scheme in the triply selective MIMO UWA. First, the Homotopy recursive least square dichotomous coordinate descent (Homotopy RLS-DCD) adaptive algorithm, recently proposed for sparse single-input single-output (SISO) system identification, is extended to adaptively estimate rapid time-varying MIMO sparse channels. Next, the more reliable a posteriori soft-decision symbols, instead of the hard decision symbols or the a priori softdecision symbols, at the equalizer output, are not only feedback to the Homotopy RLS-DCD based channel estimator but also to the minimum mean-square-error (MMSE) equalizer. As the turbo iterations progress, the accuracy of channel estimation and the quality of the MMSE equalizer are improved gradually, leading to the enhancement in the turbo equalization performance. This also allows the reduction in pilot overhead. The proposed receiver has been tested by using the data collected from the SHLake2013 experiment. The performance of the receiver is evaluated for various modulation schemes, channel estimators and MIMO sizes. Experimental results demonstrate that the proposed a posteriori soft-decision-driven sparse channel estimation based on the Homotopy RLS-DCD algorithm and turbo equalization offer considerable improvement in system performance over other turbo equalization schemes.
A posteriori soft-decision, a priori soft-decision, channel estimation, DCD iterations, Homotopy iterations, multiple-input multiple-output (MIMO), recursive least-squares (RLS), sparse channel, turbo equalization, underwater acoustic communication
1-18
Zhang, Youwen
7aa2372a-67e3-4f9e-8acb-22e604c34fd5
Zakharov, Yuriy
2abf7642-edba-4f15-8b98-4caca66510f6
Li, Jianghui
9c589194-00fa-4d42-abaf-53a32789cc5e
Zhang, Youwen
7aa2372a-67e3-4f9e-8acb-22e604c34fd5
Zakharov, Yuriy
2abf7642-edba-4f15-8b98-4caca66510f6
Li, Jianghui
9c589194-00fa-4d42-abaf-53a32789cc5e
Zhang, Youwen, Zakharov, Yuriy and Li, Jianghui
(2018)
Soft-decision-driven sparse channel estimation and turbo equalization for MIMO underwater acoustic communications.
IEEE Access, .
(doi:10.1109/ACCESS.2018.2794455).
Abstract
Multi-input multi-output (MIMO) detection based on turbo principle has been shown to provide a great enhancement in the throughput and reliability of underwater acoustic (UWA) communication systems. Benefits of the iterative detection in MIMO systems, however, can be obtained only when a high quality channel estimation is ensured. In this paper, we develop a new soft-decision-driven sparse channel estimation and turbo equalization scheme in the triply selective MIMO UWA. First, the Homotopy recursive least square dichotomous coordinate descent (Homotopy RLS-DCD) adaptive algorithm, recently proposed for sparse single-input single-output (SISO) system identification, is extended to adaptively estimate rapid time-varying MIMO sparse channels. Next, the more reliable a posteriori soft-decision symbols, instead of the hard decision symbols or the a priori softdecision symbols, at the equalizer output, are not only feedback to the Homotopy RLS-DCD based channel estimator but also to the minimum mean-square-error (MMSE) equalizer. As the turbo iterations progress, the accuracy of channel estimation and the quality of the MMSE equalizer are improved gradually, leading to the enhancement in the turbo equalization performance. This also allows the reduction in pilot overhead. The proposed receiver has been tested by using the data collected from the SHLake2013 experiment. The performance of the receiver is evaluated for various modulation schemes, channel estimators and MIMO sizes. Experimental results demonstrate that the proposed a posteriori soft-decision-driven sparse channel estimation based on the Homotopy RLS-DCD algorithm and turbo equalization offer considerable improvement in system performance over other turbo equalization schemes.
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08262651
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Accepted/In Press date: 12 January 2018
e-pub ahead of print date: 17 January 2018
Keywords:
A posteriori soft-decision, a priori soft-decision, channel estimation, DCD iterations, Homotopy iterations, multiple-input multiple-output (MIMO), recursive least-squares (RLS), sparse channel, turbo equalization, underwater acoustic communication
Identifiers
Local EPrints ID: 418030
URI: http://eprints.soton.ac.uk/id/eprint/418030
ISSN: 2169-3536
PURE UUID: 0246729d-13d5-4ef7-a161-c83d048aa6ed
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Date deposited: 21 Feb 2018 17:30
Last modified: 15 Mar 2024 18:37
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Author:
Youwen Zhang
Author:
Yuriy Zakharov
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