Learning-Based Predictive Transmitter-Receiver Beam Alignment in Millimeter Wave Fixed Wireless Access Links

Abstract

Millimeter wave (mmwave) fixed wireless access is a key enabler of 5G and beyond small cell network deployment, exploiting the abundant mmwave spectrum to provide Gbps backhaul and access links. Large antenna arrays and extremely directional beamforming are necessary to combat the mmwave path loss. However, narrow beams increase sensitivity to physical perturbations caused by environmental factors. To address this issue, in this paper we propose a predictive transmit-receive beam alignment process. We construct an explicit mapping between transmit (or receive) beams and physical coordinates via a Gaussian process, which can incorporate environmental uncertainties. To make full use of underlying correlations between transmitter and receiver and accumulated experiences, we further construct a hierarchical Bayesian learning model and design an efficient beam predictive algorithm. To reduce dependency on physical position measurements, a reverse mapping that predicts physical coordinates from beam experiences is further constructed. The designed algorithms enjoy two folds of advantages. Firstly, thanks to Bayesian learning, a good performance can be achieved even for a small sample setting as low as 10 samples in our scenarios, which drastically reduces training time and is therefore very appealing for wireless communications. Secondly, in contrast to most existing algorithms that only predict one beam in each time-slot, the designed algorithms generate the most promising beam subset, which improves robustness to environment uncertainties. Simulation results demonstrate the effectiveness and superiority of the designed algorithms against the state of the art.