Faghani, T;
Shojaeifard, A;
Wong, KK;
Aghvami, AH;
(2019)
Deep Learning-Based Decision Region for MIMO Detection.
In:
Proceedings of the 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC).
IEEE: Istanbul, Turkey, Turkey.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Deep Learning-Based Decision Region for MIMO Detection |
| Event: | 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) |
| ISBN-13: | 9781538681107 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/PIMRC.2019.8904346 |
| Publisher version: | https://doi.org/10.1109/PIMRC.2019.8904346 |
| 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: | AWGN channels, error statistics, fading channels, learning (artificial intelligence), MIMO communication, neural nets, pattern classification, telecommunication computing |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10088505 |
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