Multi-User Beamforming with Deep Reinforcement Learning in Sensing-Aided Communication

TL;DR

This paper proposes a deep reinforcement learning approach to optimize multi-user beamforming in mmWave communication, leveraging sensing echoes to improve throughput and robustness without user feedback.

Contribution

It introduces a DRL-based beam allocation policy that dynamically manages multiple beams using sensing data, outperforming heuristic methods.

Findings

DRL method significantly improves throughput over traditional methods.

The approach is robust to varying user speeds.

No user feedback or prior state information needed.

Abstract

Mobile users are prone to experience beam failure due to beam drifting in millimeter wave (mmWave) communications. Sensing can help alleviate beam drifting with timely beam changes and low overhead since it does not need user feedback. This work studies the problem of optimizing sensing-aided communication by dynamically managing beams allocated to mobile users. A multi-beam scheme is introduced, which allocates multiple beams to the users that need an update on the angle of departure (AoD) estimates and a single beam to the users that have satisfied AoD estimation precision. A deep reinforcement learning (DRL) assisted method is developed to optimize the beam allocation policy, relying only upon the sensing echoes. For comparison, a heuristic AoD-based method using approximated Cram\'er-Rao lower bound (CRLB) for allocation is also presented. Both methods require neither user feedback nor prior state evolution information. Results show that the DRL-assisted method achieves a considerable gain in throughput than the conventional beam sweeping method and the AoD-based method, and it is robust to different user speeds.