Advanced Learning Architectures and Sufficient Statistics for Beam Selection in mmWave Bands with Multi-Path Propagation

In this paper, we investigate the applicability of machine and deep learning (ML/DL) techniques to uplink beam selection problems with hybrid beamforming architectures. The goal is to select the element in the codebook of analog beamformers (ABF) yielding the highest sum-rate. The multi-antenna system operates in 5GNR’s Frequency Range 2 (mmWave band). In this context, the presence of multi-path propagation along with the use of multi-carrier signals precludes the use of (single) angle-of-arrival information as an input to the learning system. Therefore, we investigate alternative statistics such as the singular vector/values of the (multi-carrier) channel matrix, the average covariance matrix, or the covariance matrix at a given subcarrier. Besides, we propose a novel ML/DL architecture enabling a continuous operation of the system which avoids the spectral efficiency losses associated to periodically switching to a dedicated ABF for the estimation of such statistics.Grant numbers : ARISTIDES - Aprendizaje Estadístico e Inferencia para Sistemas de Comunicación de Alta Dimensionalidad (RTI2018-099722-B-I00).© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works