Fast Beam Alignment via Pure Exploration in Multi-armed Bandits

TL;DR

This paper introduces a bandit-based algorithm for fast beam alignment in millimeter-wave communications, significantly reducing latency by exploiting correlations and heteroscedastic properties among beams.

Contribution

The paper proposes the Two-Phase Heteroscedastic Track-and-Stop algorithm, a novel pure exploration method that groups beams to efficiently identify the optimal beam with fewer samples.

Findings

The algorithm outperforms baseline methods in synthetic and semi-practical data.

It effectively reduces beam alignment latency in mmWave systems.

Theoretical analysis confirms its efficiency and accuracy.

Abstract

The beam alignment (BA) problem consists in accurately aligning the transmitter and receiver beams to establish a reliable communication link in wireless communication systems. Existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair. This incurs a significant latency when the number of antennas is large. In this work, we develop a bandit-based fast BA algorithm to reduce BA latency for millimeter-wave (mmWave) communications. Our algorithm is named Two-Phase Heteroscedastic Track-and-Stop (2PHT\&S). We first formulate the BA problem as a pure exploration problem in multi-armed bandits in which the objective is to minimize the required number of time steps given a certain fixed confidence level. By taking advantage of the correlation structure among beams that the information from nearby beams is similar and the heteroscedastic property that the variance of the reward of an arm (beam) is related to its mean, the proposed algorithm groups all beams into several beam sets such that the optimal beam set is first selected and the optimal beam is identified in this set after that. Theoretical analysis and simulation results on synthetic and semi-practical channel data demonstrate the clear superiority of the proposed algorithm vis-\`a-vis other baseline competitors.