Radar Enhanced Multi-Armed Bandit for Rapid Beam Selection in Millimeter Wave Communications

TL;DR

This paper introduces a radar-enhanced multi-armed bandit approach for millimeter wave beam selection, significantly reducing exploration time by focusing on radar-detected mobile targets, thus improving communication throughput.

Contribution

The paper presents a novel integration of radar with MAB algorithms to efficiently identify optimal beams in millimeter wave systems, reducing latency and complexity.

Findings

Reduced exploration time by focusing on radar-detected beams

Improved throughput through targeted beam search

Effective distinction between static and mobile targets

Abstract

Multi-arm bandit (MAB) algorithms have been used to learn optimal beams for millimeter wave communication systems. Here, the complexity of learning the optimal beam linearly scales with the number of beams, leading to high latency when there are a large number of beams. In this work, we propose to integrate radar with communication to enhance the MAB learning performance by searching only those beams where the radar detects a scatterer. Further, we use radar to distinguish the beams that show mobile targets from those which indicate the presence of static clutter, thereby reducing the number of beams to scan. Simulations show that our proposed radar-enhanced MAB reduces the exploration time by searching only the beams with distinct radar mobile targets resulting in improved throughput.