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Recurrent neural network channel estimation using measured massive MIMO data

Faghani, T; Shojaeifard, A; Wong, KK; Aghvami, AH; (2020) Recurrent neural network channel estimation using measured massive MIMO data. In: Proceedings of the 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications IEEE 2020. The Institute of Electrical and Electronics Engineers (IEEE) Green open access

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Abstract

In this work, we develop a novel channel estimation method using recurrent neural networks (RNNs) for massive multiple-input multiple-output (MIMO) systems. The proposed framework alleviates the need for channel-state-information (CSI) feedback and pilot assignment through exploiting the inherent time and frequency correlations in practical propagation environments. We carry out the analysis using empirical MIMO channel measurements between a 64T64R active antenna system and a state-of-the-art multi-antenna scanner for both mobile and stationary use-cases. We also capture and analyze similar MIMO channel data from a legacy 2T2R base station (BS) for comparison purposes. Our findings confirm the applicability of utilising the proposed RNN-based massive MIMO channel acquisition scheme particularly for channels with long time coherence and hardening effects. In our practical setup, the proposed method reduced the number of pilots used by 25%.

Type: Proceedings paper
Title: Recurrent neural network channel estimation using measured massive MIMO data
Event: 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications IEEE 2020
ISBN-13: 978-1-7281-4490-0
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/PIMRC48278.2020.9217192
Publisher version: https://doi.org/10.1109/PIMRC48278.2020.9217192
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: Channel estimation, MIMO communication, Antenna measurements, Coherence, Microprocessors, Computer architecture, Estimation
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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/10114446
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