Deep Learning-Based Decision Region for MIMO Detection

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4 Citations (Scopus)

Abstract

In this work, a deep learning-based symbol detection method is developed for multi-user multiple-input multiple-output (MIMO) systems. We demonstrate that the linear threshold-based detection methods, which were designed for AWGN channels, are suboptimal in the context of MIMO fading channels. Furthermore, we propose a MIMO detection framework which replaces the linear thresholds with decision boundaries trained with neural network (NN) classifiers. The symbol error rate (SER) performance of the proposed detection model is compared against conventional methods under state-of-the-art system parameters. Here, we report to up to a 2 dB gain in SER performance using the proposed NN classifiers, allowing for exploiting higher-order modulation schemes, or transmitting with reduced power. The underlying gain in performance may be further enhanced from improvements to the NN architecture and hyper-parameter optimization.
Original languageEnglish
Title of host publication2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2019-September
ISBN (Electronic)9781538681107
DOIs
Publication statusPublished - 21 Nov 2019
Event30th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2019 - Istanbul, Turkey
Duration: 8 Sept 201911 Sept 2019

Conference

Conference30th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2019
Country/TerritoryTurkey
CityIstanbul
Period8/09/201911/09/2019

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