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)

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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%.

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