Version 1
: Received: 7 July 2019 / Approved: 14 August 2019 / Online: 14 August 2019 (16:01:48 CEST)
How to cite:
Pereira de Figueiredo, F.A. Initial Results on Deep Learning-Based Pilot Contamination Mitigation in Massive MIMO Systems. Preprints2019, 2019080165. https://doi.org/10.20944/preprints201908.0165.v1
Pereira de Figueiredo, F.A. Initial Results on Deep Learning-Based Pilot Contamination Mitigation in Massive MIMO Systems. Preprints 2019, 2019080165. https://doi.org/10.20944/preprints201908.0165.v1
Pereira de Figueiredo, F.A. Initial Results on Deep Learning-Based Pilot Contamination Mitigation in Massive MIMO Systems. Preprints2019, 2019080165. https://doi.org/10.20944/preprints201908.0165.v1
APA Style
Pereira de Figueiredo, F.A. (2019). Initial Results on Deep Learning-Based Pilot Contamination Mitigation in Massive MIMO Systems. Preprints. https://doi.org/10.20944/preprints201908.0165.v1
Chicago/Turabian Style
Pereira de Figueiredo, F.A. 2019 "Initial Results on Deep Learning-Based Pilot Contamination Mitigation in Massive MIMO Systems" Preprints. https://doi.org/10.20944/preprints201908.0165.v1
Abstract
In this brief letter we report our initial results on the application of deep-learning to the massive MIMO channel estimation challenge. We show that it is possible to estimate wireless channels and that the possibility of mitigating pilot-contamination with deep-learning is possible given that the leaning model underwent an extensive training-phase and that it has been presented with a large number of different channel conditions.
Keywords
massive MIMO; pilot contamination; deep learning; machine learning
Subject
Engineering, Telecommunications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
