Article (Scientific journals)
Data-driven Precoded MIMO Detection Robust to Channel Estimation Errors
Mayouche, Abderrahmane; Alves Martins, Wallace; Chatzinotas, Symeon et al.
2021In IEEE Open Journal of the Communications Society
Peer reviewed
 

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This article has been accepted for publication in a future issue of the IEEE Open Journal of the Communications Society, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/OJCOMS.2021.3079643


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Keywords :
MIMO detection; Precoding; Machine learning; Channel coding; Imperfect CSIT
Abstract :
[en] We study the problem of symbol detection in downlink coded multiple-input multiple-output (MIMO) systems with precoding and without the explicit knowledge of the channel-state information (CSI) at the receiver. In this context, we investigate the impact of imperfect CSI at the transmitter (CSIT) on the detection performance. We first model the CSIT degradation based on channel estimation errors to investigate its impact on the detection performance at the receiver. To mitigate the effect of CSIT deterioration at the latter, we propose learning based techniques for hard and soft detection that use downlink precoded pilot symbols as training data. We note that these pilots are originally intended for signal-to-interference-plus-noise ratio (SINR) estimation. We validate the approach by proposing a lightweight implementation that is suitable for online training using several state-of-the-art classifiers. We compare the bit error rate (BER) and the runtime complexity of the proposed approaches where we achieve superior detection performance in harsh channel conditions while maintaining low computational requirements. Specifically, numerical results show that severe CSIT degradation impedes the correct detection when a conventional detector is used. However, the proposed learning-based detectors can achieve good detection performance even under severe CSIT deterioration, and can yield 4-8 dB power gain for BER values lower than 10-4 when compared to the classic linear minimum mean square error (MMSE) detector.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Mayouche, Abderrahmane ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Alves Martins, Wallace ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Chatzinotas, Symeon  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Ottersten, Björn ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
External co-authors :
no
Language :
English
Title :
Data-driven Precoded MIMO Detection Robust to Channel Estimation Errors
Publication date :
2021
Journal title :
IEEE Open Journal of the Communications Society
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
European Projects :
H2020 - 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems
FnR Project :
FNR11607830 - Exploiting Interference For Physical Layer Security In 5g Networks, 2017 (01/02/2018-31/07/2021) - Bjorn Ottersten
Funders :
CE - Commission Européenne [BE]
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