Physics-Informed Parametric Bandits for Beam Alignment in mmWave Communications

TL;DR

This paper introduces physics-informed bandit algorithms for efficient beam alignment in mmWave communications, leveraging channel sparsity to outperform existing methods in diverse environments.

Contribution

The paper proposes two novel physics-informed bandit algorithms, pretc and prgreedy, that exploit mmWave channel sparsity for improved beam alignment and tracking.

Findings

Outperform existing algorithms in synthetic and real-world datasets.

Demonstrate robustness across diverse channel environments.

Show improved convergence and accuracy in beam selection.

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 optimal estimates of them based on sampled historical rewards. \textit{pretc} starts with a random exploration phase and then commits to the optimal beam under the estimated reward function. \textit{prgreedy} performs such estimation in an online manner and chooses the best beam under current estimates. Our algorithms can also be easily adapted to beam tracking in the mobile setting. Through experiments using both the synthetic DeepMIMO dataset and the real-world DeepSense6G dataset, we demonstrate that both algorithms outperform existing approaches in a wide range of scenarios across diverse channel environments, showing their generalizability and robustness.