Res-DNN based signal detection algorithm for end-to-end MIMO systems

Deep learning can improve the effect of signal detection by extracting the inherent characteristics of wireless communication data.To solve the tradeoff between the performance and complexity of MIMO system signal detection, an end-to-end MIMO system signal detection scheme based on deep learning wa...

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Main Authors: Guoquan LI, Yonghai XU, Jinzhao LIN, Zhengwen HUANG
Format: Article
Language:zho
Published: China InfoCom Media Group 2022-03-01
Series:物联网学报
Subjects:
Online Access:http://www.wlwxb.com.cn/thesisDetails#10.11959/j.issn.2096-3750.2022.00256
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author Guoquan LI
Yonghai XU
Jinzhao LIN
Zhengwen HUANG
author_facet Guoquan LI
Yonghai XU
Jinzhao LIN
Zhengwen HUANG
author_sort Guoquan LI
collection DOAJ
description Deep learning can improve the effect of signal detection by extracting the inherent characteristics of wireless communication data.To solve the tradeoff between the performance and complexity of MIMO system signal detection, an end-to-end MIMO system signal detection scheme based on deep learning was proposed.The encoder and the decoder based on residual deep neural network replace the transmitter and the receiver of the wireless communication system respectively, and they were trained in an end-to-end manner as a whole.Firstly, the features of the input data were extracted by encoder, then the communication model was established and was sent to the zero forcing detector for preliminary detection.Finally, the detection signal was reconstructed through the decoder.Simulation results show that the proposed detection scheme is superior to the same type of algorithm, and the detection performance is significantly better than that of the MMSE detection algorithm at the expense of a certain time complexity.
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institution OA Journals
issn 2096-3750
language zho
publishDate 2022-03-01
publisher China InfoCom Media Group
record_format Article
series 物联网学报
spelling doaj-art-69b5a8b709ec438ab3c535e6c81cf4952025-08-20T02:09:21ZzhoChina InfoCom Media Group物联网学报2096-37502022-03-016657259648902Res-DNN based signal detection algorithm for end-to-end MIMO systemsGuoquan LIYonghai XUJinzhao LINZhengwen HUANGDeep learning can improve the effect of signal detection by extracting the inherent characteristics of wireless communication data.To solve the tradeoff between the performance and complexity of MIMO system signal detection, an end-to-end MIMO system signal detection scheme based on deep learning was proposed.The encoder and the decoder based on residual deep neural network replace the transmitter and the receiver of the wireless communication system respectively, and they were trained in an end-to-end manner as a whole.Firstly, the features of the input data were extracted by encoder, then the communication model was established and was sent to the zero forcing detector for preliminary detection.Finally, the detection signal was reconstructed through the decoder.Simulation results show that the proposed detection scheme is superior to the same type of algorithm, and the detection performance is significantly better than that of the MMSE detection algorithm at the expense of a certain time complexity.http://www.wlwxb.com.cn/thesisDetails#10.11959/j.issn.2096-3750.2022.00256deep learning;MIMO system;signal detection;Res-DNN;end-to-end
spellingShingle Guoquan LI
Yonghai XU
Jinzhao LIN
Zhengwen HUANG
Res-DNN based signal detection algorithm for end-to-end MIMO systems
物联网学报
deep learning;MIMO system;signal detection;Res-DNN;end-to-end
title Res-DNN based signal detection algorithm for end-to-end MIMO systems
title_full Res-DNN based signal detection algorithm for end-to-end MIMO systems
title_fullStr Res-DNN based signal detection algorithm for end-to-end MIMO systems
title_full_unstemmed Res-DNN based signal detection algorithm for end-to-end MIMO systems
title_short Res-DNN based signal detection algorithm for end-to-end MIMO systems
title_sort res dnn based signal detection algorithm for end to end mimo systems
topic deep learning;MIMO system;signal detection;Res-DNN;end-to-end
url http://www.wlwxb.com.cn/thesisDetails#10.11959/j.issn.2096-3750.2022.00256
work_keys_str_mv AT guoquanli resdnnbasedsignaldetectionalgorithmforendtoendmimosystems
AT yonghaixu resdnnbasedsignaldetectionalgorithmforendtoendmimosystems
AT jinzhaolin resdnnbasedsignaldetectionalgorithmforendtoendmimosystems
AT zhengwenhuang resdnnbasedsignaldetectionalgorithmforendtoendmimosystems