A Deep Learning-Based Low Overhead Beam Selection in mmWave Communications

Abstract

Due to large amounts of available spectrum at high frequencies, millimeter-wave (mmWave) technology has gained extensive research attention in 5G communications, whereas mmWave links suffer from severe free space attenuation. Codebook-based beamforming techniques with multiple antennas can effectively alleviate this challenge with low computational complexity and low hardware cost. However, small delay and high-speed communications with beamforming techniques require beam alignment with small overhead so as to establish the wireless link quickly. In this context, this paper proposes a deep learning-based low overhead analog beam selection scheme by virtue of the super-resolution technology. To be concrete, deep neural networks are employed to conduct beam quality estimation based on partial beam measurements. Our proposed scheme can cover all the directions of arriving signals with low overhead by utilizing codebooks with different beam widths. Furthermore, for the purpose of further reducing the overhead, we formulate the beam quality prediction model based on the past beam sweepings. With these beam quality estimation and prediction model, the beam that achieves large signal-to-noise-power-ratio (SNR) can be selected based on partial beam measurements. Simulation results show that the proposed scheme can accurately estimate beam qualities and give high probability of optimal beam selections with low overhead.