Beam Training in mmWave Vehicular Systems: Machine Learning for Decoupling Beam Selection

TL;DR

This paper proposes machine learning-based location-aided methods for decoupling beam selection in mmWave vehicular systems, reducing overhead while maintaining high performance in dynamic environments.

Contribution

It introduces ML-based approaches for decoupling beam selection between UE and BS, quantifies performance gaps, and demonstrates comparable results to joint selection using simulations.

Findings

Decoupled beam selection with location info at BS performs comparably to joint selection.

Decoupled beam selection without location approaches joint performance with sufficient beam sweeps.

Performance gaps are quantified for different decoupling scenarios.

Abstract

Codebook-based beam selection is one approach for configuring millimeter wave communication links. The overhead required to reconfigure the transmit and receive beam pair, though, increases in highly dynamic vehicular communication systems. Location information coupled with machine learning (ML) beam recommendation is one way to reduce the overhead of beam pair selection. In this paper, we develop ML-based location-aided approaches to decouple the beam selection between the user equipment (UE) and the base station (BS). We quantify the performance gaps due to decoupling beam selection and also disaggregating the UE's location information from the BS. Our simulation results show that decoupling beam selection with available location information at the BS performs comparable to joint beam pair selection at the BS. Moreover, decoupled beam selection without location closely approaches the performance of beam pair selection at the BS when sufficient beam pairs are swept.