Combined Deep Learning and SOR Detection Technique for High Reliability in Massive MIMO Systems

In this paper, a novel iterative detection technique that combines deep learning (DL) and the approximated algorithm of successive over relaxation (SOR) is proposed to achieve high reliability and reduce the computational complexity. Recently, as the demanded data rates increase, the massive multipl...

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Autores principales:	Jun-Yong Jang, Chan-Yeob Park, Beom-Sik Shin, Hyoung-Kyu Song
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Massive MIMO MIMO detection deep learning DNN SOR iterative detector Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/d0d86f43fcf743998cbb33968d66b70b
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spelling	oai:doaj.org-article:d0d86f43fcf743998cbb33968d66b70b2021-11-18T00:07:55ZCombined Deep Learning and SOR Detection Technique for High Reliability in Massive MIMO Systems2169-353610.1109/ACCESS.2021.3125002https://doaj.org/article/d0d86f43fcf743998cbb33968d66b70b2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9599698/https://doaj.org/toc/2169-3536In this paper, a novel iterative detection technique that combines deep learning (DL) and the approximated algorithm of successive over relaxation (SOR) is proposed to achieve high reliability and reduce the computational complexity. Recently, as the demanded data rates increase, the massive multiple-input and multiple-output (MIMO) system has drawn attention in wireless communication. In massive MIMO, the implementation of traditional detectors for high reliability has become impractical, and the reduction for the complexity of detectors has emerged as a practical implementation challenge. The existing DL-based detection technique of orthogonal approximate message passing network (OAMPNet) can provide high detection performance. However, the computational complexity is too high for the implementation in massive MIMO systems. The proposed detection technique uses SOR algorithm to reduce the computational complexity, and the relaxation parameter of SOR is adaptively determined by a learning algorithm. A non-linear estimator using the DL algorithm is combined with the SOR algorithm to achieve high reliability, and regardless of the size of the MIMO system, only the size of the DL architecture determines the complexity of the non-linear estimator. Simulation results show that the proposed detector outperforms the conventional linear detector based on minimum mean square error (MMSE) and achieves high reliability with lower complexity than OAMPNet in various channel environments with spatial correlation.Jun-Yong JangChan-Yeob ParkBeom-Sik ShinHyoung-Kyu SongIEEEarticleMassive MIMOMIMO detectiondeep learningDNNSORiterative detectorElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 148976-148987 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Massive MIMO MIMO detection deep learning DNN SOR iterative detector Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Massive MIMO MIMO detection deep learning DNN SOR iterative detector Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jun-Yong Jang Chan-Yeob Park Beom-Sik Shin Hyoung-Kyu Song Combined Deep Learning and SOR Detection Technique for High Reliability in Massive MIMO Systems
description	In this paper, a novel iterative detection technique that combines deep learning (DL) and the approximated algorithm of successive over relaxation (SOR) is proposed to achieve high reliability and reduce the computational complexity. Recently, as the demanded data rates increase, the massive multiple-input and multiple-output (MIMO) system has drawn attention in wireless communication. In massive MIMO, the implementation of traditional detectors for high reliability has become impractical, and the reduction for the complexity of detectors has emerged as a practical implementation challenge. The existing DL-based detection technique of orthogonal approximate message passing network (OAMPNet) can provide high detection performance. However, the computational complexity is too high for the implementation in massive MIMO systems. The proposed detection technique uses SOR algorithm to reduce the computational complexity, and the relaxation parameter of SOR is adaptively determined by a learning algorithm. A non-linear estimator using the DL algorithm is combined with the SOR algorithm to achieve high reliability, and regardless of the size of the MIMO system, only the size of the DL architecture determines the complexity of the non-linear estimator. Simulation results show that the proposed detector outperforms the conventional linear detector based on minimum mean square error (MMSE) and achieves high reliability with lower complexity than OAMPNet in various channel environments with spatial correlation.
format	article
author	Jun-Yong Jang Chan-Yeob Park Beom-Sik Shin Hyoung-Kyu Song
author_facet	Jun-Yong Jang Chan-Yeob Park Beom-Sik Shin Hyoung-Kyu Song
author_sort	Jun-Yong Jang
title	Combined Deep Learning and SOR Detection Technique for High Reliability in Massive MIMO Systems
title_short	Combined Deep Learning and SOR Detection Technique for High Reliability in Massive MIMO Systems
title_full	Combined Deep Learning and SOR Detection Technique for High Reliability in Massive MIMO Systems
title_fullStr	Combined Deep Learning and SOR Detection Technique for High Reliability in Massive MIMO Systems
title_full_unstemmed	Combined Deep Learning and SOR Detection Technique for High Reliability in Massive MIMO Systems
title_sort	combined deep learning and sor detection technique for high reliability in massive mimo systems
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/d0d86f43fcf743998cbb33968d66b70b
work_keys_str_mv	AT junyongjang combineddeeplearningandsordetectiontechniqueforhighreliabilityinmassivemimosystems AT chanyeobpark combineddeeplearningandsordetectiontechniqueforhighreliabilityinmassivemimosystems AT beomsikshin combineddeeplearningandsordetectiontechniqueforhighreliabilityinmassivemimosystems AT hyoungkyusong combineddeeplearningandsordetectiontechniqueforhighreliabilityinmassivemimosystems
_version_	1718425252325752832

Combined Deep Learning and SOR Detection Technique for High Reliability in Massive MIMO Systems

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