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Deep learning-assisted TeraHertz QPSK detection relying on single-bit quantization

Deep learning-assisted TeraHertz QPSK detection relying on single-bit quantization
Deep learning-assisted TeraHertz QPSK detection relying on single-bit quantization
TeraHertz (THz) wireless communication constitutes a promising technique of satisfying the ever-increasing appetite for high-rate services. However, the ultra-wide bandwidth of THz communications requires high-speed, high-resolution analog-todigital converters, which are hard to implement due to their
high complexity and power consumption. In this paper, a deep learning-assisted THz receiver is designed, which relies on singlebit quantization. Specifically, the imperfections of THz devices, including their in-phase/quadrature-phase imbalance, phase noise and nonlinearity are investigated. The deflection ratio of the maximum-likelihood detector used by our single-bit-quantization THz receiver is derived, which reveals the effect of phase offset on the demodulation performance, guiding the architecture
design of our proposed receiver. To combat the performance loss caused by the above-mentioned distortions, a twin-phase training strategy and a neural network based demodulator are proposed, where the phase offset of the received signal is compensated before sampling. Our simulation results demonstrate that the proposed deep learning-assisted receiver is capable of achieving a satisfactory bit error rate performance, despite the grave distortions encountered.
Deep learning, Demodulation, Nonlinear distortion, Phase distortion, Quantization (signal), Radio frequency, Receivers, TeraHertz communication, deep feedforward neural network, hybrid distortion, phase compensator, single-bit receiver
0090-6778
8175-8187
He, Dongxuan
35b4462c-25af-474a-9f11-435c9bb1f144
Wang, Zhaocheng
787a08f7-37ef-4c80-bbf8-8b6b5ef4316a
Quek, Tony Q. S.
e2541050-aadd-49eb-9327-aef7db3fa5f7
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
He, Dongxuan
35b4462c-25af-474a-9f11-435c9bb1f144
Wang, Zhaocheng
787a08f7-37ef-4c80-bbf8-8b6b5ef4316a
Quek, Tony Q. S.
e2541050-aadd-49eb-9327-aef7db3fa5f7
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

He, Dongxuan, Wang, Zhaocheng, Quek, Tony Q. S., Chen, Sheng and Hanzo, Lajos (2021) Deep learning-assisted TeraHertz QPSK detection relying on single-bit quantization. IEEE Transactions on Communications, 69 (12), 8175-8187.

Record type: Article

Abstract

TeraHertz (THz) wireless communication constitutes a promising technique of satisfying the ever-increasing appetite for high-rate services. However, the ultra-wide bandwidth of THz communications requires high-speed, high-resolution analog-todigital converters, which are hard to implement due to their
high complexity and power consumption. In this paper, a deep learning-assisted THz receiver is designed, which relies on singlebit quantization. Specifically, the imperfections of THz devices, including their in-phase/quadrature-phase imbalance, phase noise and nonlinearity are investigated. The deflection ratio of the maximum-likelihood detector used by our single-bit-quantization THz receiver is derived, which reveals the effect of phase offset on the demodulation performance, guiding the architecture
design of our proposed receiver. To combat the performance loss caused by the above-mentioned distortions, a twin-phase training strategy and a neural network based demodulator are proposed, where the phase offset of the received signal is compensated before sampling. Our simulation results demonstrate that the proposed deep learning-assisted receiver is capable of achieving a satisfactory bit error rate performance, despite the grave distortions encountered.

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Published date: 13 September 2021
Keywords: Deep learning, Demodulation, Nonlinear distortion, Phase distortion, Quantization (signal), Radio frequency, Receivers, TeraHertz communication, deep feedforward neural network, hybrid distortion, phase compensator, single-bit receiver

Identifiers

Local EPrints ID: 452291
URI: http://eprints.soton.ac.uk/id/eprint/452291
ISSN: 0090-6778
PURE UUID: 93ce5630-6775-444b-9fb3-43816bbc9063
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 03 Dec 2021 17:32
Last modified: 04 Aug 2022 01:33

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Contributors

Author: Dongxuan He
Author: Zhaocheng Wang
Author: Tony Q. S. Quek
Author: Sheng Chen
Author: Lajos Hanzo ORCID iD

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