On Multi-User Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Massive MIMO Systems

In view of reducing the complexity of signal detection in massive multiple-input multiple-output (MIMO) receivers, the use of non-coherent detection is favored over usual coherent techniques that require complex channel estimation. In this paper, a deep-learning approach to implement non-coherent mu...

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Autores principales:	Omnia Mahmoud, Ahmed El-Sayed El-Mahdy, Robert F. H. Fischer
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Deep-learning differential detection interference cancellation neural networks non-coherent detection massive MIMO Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/076884676cff42f6af42902f0fa5a644
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id	oai:doaj.org-article:076884676cff42f6af42902f0fa5a644
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spelling	oai:doaj.org-article:076884676cff42f6af42902f0fa5a6442021-11-18T00:04:22ZOn Multi-User Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Massive MIMO Systems2169-353610.1109/ACCESS.2021.3124863https://doaj.org/article/076884676cff42f6af42902f0fa5a6442021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9598851/https://doaj.org/toc/2169-3536In view of reducing the complexity of signal detection in massive multiple-input multiple-output (MIMO) receivers, the use of non-coherent detection is favored over usual coherent techniques that require complex channel estimation. In this paper, a deep-learning approach to implement non-coherent multiple-symbol detection for differential phase-shift keying (DPSK) multi-user massive MIMO systems is proposed. The proposed deep-learning implementation reduces the high computational complexity required in conventional DPSK detection techniques. Two deep-learning-based multiple-symbol differential detection receiver designs are proposed and compared with decision-feedback differential detection (DFDD), and conventional multiple-symbol differential detection (MSDD) for the same system parameters. Multiple-symbol differential sphere detection (MSDSD) is used to implement conventional MSDD. The results show that the proposed deep-learning-based multiple-symbol differential detection implementation outperforms decision-feedback differential detection and achieves an optimal performance compared to conventional multiple-symbol differential detection implemented by multiple-symbol differential sphere detection for single-user and multi-user scenarios.Omnia MahmoudAhmed El-Sayed El-MahdyRobert F. H. FischerIEEEarticleDeep-learningdifferential detectioninterference cancellationneural networksnon-coherent detectionmassive MIMOElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 148339-148352 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deep-learning differential detection interference cancellation neural networks non-coherent detection massive MIMO Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Deep-learning differential detection interference cancellation neural networks non-coherent detection massive MIMO Electrical engineering. Electronics. Nuclear engineering TK1-9971 Omnia Mahmoud Ahmed El-Sayed El-Mahdy Robert F. H. Fischer On Multi-User Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Massive MIMO Systems
description	In view of reducing the complexity of signal detection in massive multiple-input multiple-output (MIMO) receivers, the use of non-coherent detection is favored over usual coherent techniques that require complex channel estimation. In this paper, a deep-learning approach to implement non-coherent multiple-symbol detection for differential phase-shift keying (DPSK) multi-user massive MIMO systems is proposed. The proposed deep-learning implementation reduces the high computational complexity required in conventional DPSK detection techniques. Two deep-learning-based multiple-symbol differential detection receiver designs are proposed and compared with decision-feedback differential detection (DFDD), and conventional multiple-symbol differential detection (MSDD) for the same system parameters. Multiple-symbol differential sphere detection (MSDSD) is used to implement conventional MSDD. The results show that the proposed deep-learning-based multiple-symbol differential detection implementation outperforms decision-feedback differential detection and achieves an optimal performance compared to conventional multiple-symbol differential detection implemented by multiple-symbol differential sphere detection for single-user and multi-user scenarios.
format	article
author	Omnia Mahmoud Ahmed El-Sayed El-Mahdy Robert F. H. Fischer
author_facet	Omnia Mahmoud Ahmed El-Sayed El-Mahdy Robert F. H. Fischer
author_sort	Omnia Mahmoud
title	On Multi-User Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Massive MIMO Systems
title_short	On Multi-User Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Massive MIMO Systems
title_full	On Multi-User Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Massive MIMO Systems
title_fullStr	On Multi-User Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Massive MIMO Systems
title_full_unstemmed	On Multi-User Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Massive MIMO Systems
title_sort	on multi-user deep-learning-based non-coherent dpsk multiple-symbol differential detection in massive mimo systems
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/076884676cff42f6af42902f0fa5a644
work_keys_str_mv	AT omniamahmoud onmultiuserdeeplearningbasednoncoherentdpskmultiplesymboldifferentialdetectioninmassivemimosystems AT ahmedelsayedelmahdy onmultiuserdeeplearningbasednoncoherentdpskmultiplesymboldifferentialdetectioninmassivemimosystems AT robertfhfischer onmultiuserdeeplearningbasednoncoherentdpskmultiplesymboldifferentialdetectioninmassivemimosystems
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On Multi-User Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Massive MIMO Systems

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