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.
191910-191919
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
20 October 2020
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).
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.
Text
Deeplearning-SM
- Accepted Manuscript
Text
Deep-Learning-Aided Joint Channel Estimation
- Version of Record
More information
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
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Date deposited: 02 Nov 2020 17:31
Last modified: 24 Apr 2024 01:42
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