Massive MIMO Demodulation Aided by NN

Gabriel Polvani, Victor Croisfelt, Taufik Abrao

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review


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 languageEnglish
Title of host publication2021 IEEE URUCON, URUCON 2021
Number of pages6
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication date2021
ISBN (Electronic)9781665424431
Publication statusPublished - 2021
Event2021 IEEE URUCON, URUCON 2021 - Montevideo, Uruguay
Duration: 24 Nov 202126 Nov 2021


Conference2021 IEEE URUCON, URUCON 2021
Series2021 IEEE URUCON, URUCON 2021

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.


  • Demodulation
  • Massive MIMO
  • Neural Network (NN)
  • Quadrature Amplitude Modulation (QAM)


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