2025/05/09 by Xiyu Wang, Wang, Xiyu, Gilberto Berardinelli +7
Engineering · #Advanced MIMO Systems Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #Indoor and Outdoor Localization Technologies #Machine Learning (cs.LG) #Millimeter-Wave Propagation and Modeling #Networking and Internet Architecture (cs.NI) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2505.05956
openalex publication_date 2025/05/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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.