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Deep-learning-aided joint channel estimation and data detection for spatial modulation

Deep-learning-aided joint channel estimation and data detection for spatial modulation
Deep-learning-aided joint channel estimation and data detection for spatial modulation
Deep neural network (DNN)-aided spatial modulation (SM) is conceived. In particular, a pair of DNN structures are designed for replacing the conventional model-based channel estimators and detectors. As our first prototype, the conventional DNN estimates the channel relying on the pilot symbols and then carries out data detection in a data-driven manner. By contrast, our new DeepSM scheme is proposed for operation in more realistic time-varying channels, which updates the channel state information (CSI) at each time-slot (TS) before detecting the data. Hence, our novel DeepSM scheme is capable of performing well even in highly dynamic communication environments. Finally, our simulations show that the proposed DeepSM outperforms the conventional model-based channel estimation and data detection for transmission over time-varying channels.
2169-3536
Xiang, Luping
56d951c0-455e-4a67-b167-f6c8233343b1
Liu, Yusha
711a72e8-e8be-4be4-a79d-ea1413e7012a
Luong, Thien
a15fc6c2-8387-4e11-aae6-64553eb9770c
Maunder, Robert
76099323-7d58-4732-a98f-22a662ccba6c
Yang, Lie-Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Xiang, Luping
56d951c0-455e-4a67-b167-f6c8233343b1
Liu, Yusha
711a72e8-e8be-4be4-a79d-ea1413e7012a
Luong, Thien
a15fc6c2-8387-4e11-aae6-64553eb9770c
Maunder, Robert
76099323-7d58-4732-a98f-22a662ccba6c
Yang, Lie-Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Xiang, Luping, Liu, Yusha, Luong, Thien, Maunder, Robert, Yang, Lie-Liang and Hanzo, Lajos (2020) Deep-learning-aided joint channel estimation and data detection for spatial modulation. IEEE Access, 8. (doi:10.1109/ACCESS.2020.3032627).

Record type: Article

Abstract

Deep neural network (DNN)-aided spatial modulation (SM) is conceived. In particular, a pair of DNN structures are designed for replacing the conventional model-based channel estimators and detectors. As our first prototype, the conventional DNN estimates the channel relying on the pilot symbols and then carries out data detection in a data-driven manner. By contrast, our new DeepSM scheme is proposed for operation in more realistic time-varying channels, which updates the channel state information (CSI) at each time-slot (TS) before detecting the data. Hence, our novel DeepSM scheme is capable of performing well even in highly dynamic communication environments. Finally, our simulations show that the proposed DeepSM outperforms the conventional model-based channel estimation and data detection for transmission over time-varying channels.

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Deeplearning-SM - Accepted Manuscript
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Accepted/In Press date: 17 October 2020
Published date: 20 October 2020

Identifiers

Local EPrints ID: 444726
URI: http://eprints.soton.ac.uk/id/eprint/444726
ISSN: 2169-3536
PURE UUID: 1e742cbb-b6a2-4038-a7e9-e7886fda23d0
ORCID for Luping Xiang: ORCID iD orcid.org/0000-0003-1465-6708
ORCID for Robert Maunder: ORCID iD orcid.org/0000-0002-7944-2615
ORCID for Lie-Liang Yang: ORCID iD orcid.org/0000-0002-2032-9327
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 02 Nov 2020 17:31
Last modified: 02 Nov 2020 17:31

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Contributors

Author: Luping Xiang ORCID iD
Author: Yusha Liu
Author: Thien Luong
Author: Robert Maunder ORCID iD
Author: Lie-Liang Yang ORCID iD
Author: Lajos Hanzo ORCID iD

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