Multi-User Beamforming with Deep Reinforcement Learning in Sensing-Aided Communication
This work addresses beam drifting in mobile mmWave communications, offering a robust solution for improving throughput, though it appears incremental as it builds on existing sensing-aided communication concepts.
The paper tackles beam failure in mmWave communications by optimizing sensing-aided beam allocation for mobile users, using a DRL-assisted method that achieves a considerable throughput gain over conventional and heuristic methods without requiring user feedback.
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 Cramér-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.