Wang, Zheng;
Xu, Xiong;
Qiang, Xiaodan;
Li, Kezhi;
(2021)
Learning Vector Quantization-Aided Detection for MIMO Systems.
IEEE Communications Letters
, 25
(3)
pp. 874-878.
10.1109/LCOMM.2020.3039528.
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Abstract
In this letter, the learning vector quantization (LVQ) from machine learning (ML) is adopted into the large-scale multiple-input multiple-output (MIMO) detection to improve the detection performance. Inspired by the decision region from lattice decoding, the random Gaussian noises are applied in the proposed learning vector quantization-aided detection (LVQD) algorithm for data generation. Then, based on the classification, supervised learning is activated to update the targeted prototype vector iteratively, so as to a better detection performance. Meanwhile, the decoding radius in lattices is also used to serve as a preprocessing for LVQD, which leads to an efficient detection without performance loss. Finally, simulation results confirm that considerable performance gain can be achieved by the proposed LVQD algorithm, which suits well for suboptimal detection schemes.
| Type: | Article |
|---|---|
| Title: | Learning Vector Quantization-Aided Detection for MIMO Systems |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/LCOMM.2020.3039528 |
| Publisher version: | https://doi.org/10.1109/LCOMM.2020.3039528 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Learning vector quantization, large-scale MIMO detection, lattice decoding, machine learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10182778 |
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