Tracking the Best Beam for a Mobile User via Bayesian Optimization

TL;DR

This paper introduces a Bayesian optimization method for efficient beam selection in 5G, balancing performance and overhead without extensive historical data, and demonstrates its effectiveness in simulation scenarios.

Contribution

It proposes a novel Bayesian optimization approach for dynamic beamset selection in 5G, reducing measurement overhead while maintaining high signal quality.

Findings

Effective beamset adaptation over time

Reduced measurement overhead in simulations

Maintains high RSRP with fewer measurements

Abstract

The standard beam management procedure in 5G requires the user equipment (UE) to periodically measure the received signal reference power (RSRP) on each of a set of beams proposed by the basestation (BS). It is prohibitively expensive to measure the RSRP on all beams and so the BS should propose a beamset that is large enough to allow a high-RSRP beam to be identified, but small enough to prevent excessive reporting overhead. Moreover, the beamset should evolve over time according to UE mobility. We address this fundamental performance/overhead trade-off via a Bayesian optimization technique that requires no or little training on historical data and is rooted on a low complexity algorithm for the beamset choice with theoretical guarantees. We show the benefits of our approach on 3GPP compliant simulation scenarios.