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arXiv · 2303.17301

Tracking the Best Beam for a Mobile User via Bayesian Optimization

Lorenzo Maggi·Ryo Koblitz·Qiping Zhu·Matthew Andrews
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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.

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Lorenzo Maggi, Ryo Koblitz, Qiping Zhu, Matthew Andrews. 2023-03-30. Tracking the Best Beam for a Mobile User via Bayesian Optimization. https://arxiv.org/abs/2303.17301

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