Abstract
In this work, we propose a demodulator aided by a neural network (NN) for massive multiple-input multiple-output (M-MIMO) systems. In particular, we consider the uplink (UL) phase of an M-MIMO system in which users transmit utilizing a quadrature amplitude modulation (QAM) and soft-estimates are obtained via the application of the zero-forcing (ZF) combiner. Based on the ZF soft-estimates, we propose suitable features that are used in the input layer of an NN, whose task is to learn how to output hard-estimates, that is, demodulate the ZF soft-estimates. We then adopt a supervised learning perspective by performing a regression analysis and training the NN with simulated data. The performance and complexity of our NN-aided demodulator is numerically compared to those of the hard-decisor (HD) scheme used as a benchmark for a 4-QAM. Through this comparison, we show that our NN-aided demodulator is 17.3% more computationally efficient with tolerable performance losses. We argue that demodulators assisted by NNs can be a promising alternative to cheaply demodulate high-order OAMs.
Original language | English |
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Title of host publication | 2021 IEEE URUCON, URUCON 2021 |
Number of pages | 6 |
Publisher | IEEE |
Publication date | 2021 |
Pages | 166-171 |
Article number | 9647160 |
ISBN (Electronic) | 9781665424431 |
DOIs | |
Publication status | Published - 2021 |
Externally published | Yes |
Event | 2021 IEEE URUCON, URUCON 2021 - Montevideo, Uruguay Duration: 24 Nov 2021 → 26 Nov 2021 |
Conference
Conference | 2021 IEEE URUCON, URUCON 2021 |
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Country/Territory | Uruguay |
City | Montevideo |
Period | 24/11/2021 → 26/11/2021 |
Series | 2021 IEEE URUCON, URUCON 2021 |
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Bibliographical note
Funding Information:This work was supported in part by the CAPES Foundation; Finance Code 001, and in part by CNPq of Brazil under Grant 310681/2019-7.
Publisher Copyright:
© 2021 IEEE.
Keywords
- Demodulation
- Massive MIMO
- Neural Network (NN)
- Quadrature Amplitude Modulation (QAM)