Latent Thompson Sampling-Based mmWave Receive Beam Measurement and Selection to Tackle User Orientation Changes and Mobility

Beamforming enables millimeter-wave communications to achieve high data rates in 5G and beyond systems. However, accurate beam alignment entails a large training overhead. User device orientation changes and mobility can rapidly lead to beam misalignment and lower the data rate. They also make the beam gains a non-stationary random processes. We propose a comprehensive and novel approach called latent Thompson sampling-based beam selection (LTBS), which combines latent Thompson sampling to track the angle of arrival (AoA) as a latent state, receive beam subset selection based on the sampled AoA in a manner compliant with the 5G new radio standard, rate adaptation, and data beam selection based on predicted throughput. We propose two variants of LTBS that trade-off between complexity and accuracy in modeling millimeter-wave channels. The prior update and channel gain prediction in one of the variants are based on the realistic spatial channel model (SCM). We propose variations that employ windowing to also tackle lateral user mobility, which alters the AoA and the channel statistics. Our numerical results show that the proposed methods track the AoA in a manner robust to user orientation changes and provide higher average data rates compared to conventional and state-of-the-art learning-based beam selection methods.