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Autoencoder with fitting network for Terahertz wireless communications: A deep learning approach

Autoencoder with fitting network for Terahertz wireless communications: A deep learning approach
Autoencoder with fitting network for Terahertz wireless communications: A deep learning approach
Terahertz wireless communication has been regarded as an emerging technology to satisfy the ever-increasing demand of ultra-high-speed wireless communications. However, affected by the imperfections of cheap and energy-efficient Terahertz devices, Terahertz signals suffer from serve hybrid distortions, including in-phase/quadrature imbalance, phase noise and nonlinearity, which degrade the demodulation performance significantly. To improve the robustness against these hybrid distortions, an improved autoencoder is proposed, which includes coding the transmitted symbols at the transmitter and decoding the corresponding signals at the receiver. Moreover, due to the
lack of information of Terahertz channel during the training of the autoencoder, a fitting network is proposed to approximate the characteristics of Terahertz channel, which provides an approximation of the gradients of loss. Simulation results show that our proposed autoencoder with fitting network can recover the
transmitted symbols under serious hybrid distortions, and improves the demodulation performance significantly.
Terahertz wireless communication, autoencoder, hybrid distortion, signal demodulation
1673-5447
172-180
Huang, Zhaohui
19be76c4-801f-420f-98c9-98942cc36e5e
He, Dongxuan
04c37be2-641c-4bf2-bb04-199d7242f474
Chen, Jiaxuan
5b02b5a8-1840-48cf-8cf1-dc74fb705d63
Wang, Zhaocheng
34b3f898-ca5e-4e68-a495-cc920f21cc0e
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Huang, Zhaohui
19be76c4-801f-420f-98c9-98942cc36e5e
He, Dongxuan
04c37be2-641c-4bf2-bb04-199d7242f474
Chen, Jiaxuan
5b02b5a8-1840-48cf-8cf1-dc74fb705d63
Wang, Zhaocheng
34b3f898-ca5e-4e68-a495-cc920f21cc0e
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Huang, Zhaohui, He, Dongxuan, Chen, Jiaxuan, Wang, Zhaocheng and Chen, Sheng (2022) Autoencoder with fitting network for Terahertz wireless communications: A deep learning approach. China Communications, 19 (3), 172-180. (doi:10.23919/JCC.2022.03.012).

Record type: Article

Abstract

Terahertz wireless communication has been regarded as an emerging technology to satisfy the ever-increasing demand of ultra-high-speed wireless communications. However, affected by the imperfections of cheap and energy-efficient Terahertz devices, Terahertz signals suffer from serve hybrid distortions, including in-phase/quadrature imbalance, phase noise and nonlinearity, which degrade the demodulation performance significantly. To improve the robustness against these hybrid distortions, an improved autoencoder is proposed, which includes coding the transmitted symbols at the transmitter and decoding the corresponding signals at the receiver. Moreover, due to the
lack of information of Terahertz channel during the training of the autoencoder, a fitting network is proposed to approximate the characteristics of Terahertz channel, which provides an approximation of the gradients of loss. Simulation results show that our proposed autoencoder with fitting network can recover the
transmitted symbols under serious hybrid distortions, and improves the demodulation performance significantly.

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ChinaCom2022-March - Author's Original
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ChinaCom2022-Mar - Accepted Manuscript
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More information

Accepted/In Press date: 10 March 2022
Published date: March 2022
Additional Information: © China Communications Magazine Co., Ltd.
Keywords: Terahertz wireless communication, autoencoder, hybrid distortion, signal demodulation

Identifiers

Local EPrints ID: 456045
URI: http://eprints.soton.ac.uk/id/eprint/456045
ISSN: 1673-5447
PURE UUID: e25fad40-9f60-4ca9-8793-dd664b69096e

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Date deposited: 12 Apr 2022 17:12
Last modified: 10 Aug 2022 17:30

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Contributors

Author: Zhaohui Huang
Author: Dongxuan He
Author: Jiaxuan Chen
Author: Zhaocheng Wang
Author: Sheng Chen

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