Training FFT to Select Beams in Massive MIMO

Abstract

In this letter, we propose new trainable Fast Fourier Transform (FFT) structures to increase the accuracy of beamspace transformation in the multi-user (MU) mode of Massive Multiple Input Multiple Output (MIMO) receiver. The FFT is a widely employed beamspace transformation technique, thanks to its low transforming complexity. Unfortunately, there is a significant performance loss of MU signal detection in the FFT beamspace, as default FFT beams are not co-directed with angles of arrivals. We address this issue with trainable FFT that outperforms not only fixed FFT approaches, but also other trainable FFT techniques.

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