  Beam codebooks are a new feature of massive multiple-input multiple-output
(M-MIMO) in 5G new radio (NR). Codebooks comprised of beamforming vectors are
used to transmit reference signals and obtain limited channel state information
(CSI) from receivers via the codeword index. This enables large arrays that
cannot otherwise obtain sufficient CSI. The performance, however, is limited by
the codebook design. In this paper, we show that machine learning can be used
to train site-specific codebooks for initial access. We design a neural network
based on an autoencoder architecture that uses a beamspace observation in
combination with RF environment characteristics to improve the synchronization
signal (SS) burst codebook. We test our algorithm using a flexible dataset of
channels generated from QuaDRiGa. The results show that our model outperforms
the industry standard (DFT beams) and approaches the optimal performance
(perfect CSI and singular value decomposition (SVD)-based beamforming), using
only a few bits of feedback.
