[2510.18299] Physics-Informed Parametric Bandits for Beam Alignment in mmWave Communications

Computer Science > Machine Learning arXiv:2510.18299 (cs) [Submitted on 21 Oct 2025 (v1), last revised 1 Mar 2026 (this version, v2)] Title:Physics-Informed Parametric Bandits for Beam Alignment in mmWave Communications Authors:Hao Qin, Thang Duong, Ming F. Li, Chicheng Zhang View a PDF of the paper titled Physics-Informed Parametric Bandits for Beam Alignment in mmWave Communications, by Hao Qin and 3 other authors View PDF HTML (experimental) Abstract:In millimeter wave (mmWave) communications, beam alignment and tracking are crucial to combat the significant path loss. As scanning the entire directional space is inefficient, designing an efficient and robust method to identify the optimal beam directions is essential. Since traditional bandit algorithms require a long time horizon to converge under large beam spaces, many existing works propose efficient bandit algorithms for beam alignment by relying on unimodality or multimodality assumptions on the reward function's structure. However, such assumptions often do not hold (or cannot be strictly satisfied) in practice, which causes such algorithms to converge to choosing suboptimal beams. In this work, we propose two physics-informed bandit algorithms \textit{pretc} and \textit{prgreedy} that exploit the sparse multipath property of mmWave channels - a generic but realistic assumption - which is connected to the Phase Retrieval Bandit problem. Our algorithms treat the parameters of each path as black boxes and maintain ...