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A Two-Step Neural Network Based Beamforming in MIMO without Reference Signal

With the deployment of large scale antenna array in millimeter wave (mmWave) band, the resolution of beamforming has been dramatically improved. To reduce the long beam-training process using reference signal (RS) in codebook-based high resolution beamforming, hierarchical codebook is often used to reduce the number of beam-training symbols. However, the large beam-training overhead is still the bottleneck for overall system performance improvement in term of the true achievable data rate. In this paper, with the angle reciprocity in frequency duplex division (FDD) system, a neural network based line of sight path angle of arrival (LAoA) estimation algorithm is proposed for beam selection, in order to achieve the non-RS-aided codebook-based beamforming. To further achieve high accuracy LAoA estimation, two-step neural network models are designed to capture the relationship between the receiving signal and the corresponding LAoA. The numerical results show that the proposed algorithm outperforms the benchmark algorithm in terms of sum weighted data rate (SWR) and sum data rate (SR). In the low signal to noise ratio (SNR) environments with a couple of uplink signal snapshots, our algorithm also performs better than MUSIC based beam selection algorithm.