Beam Scheduling for Cross-Layer ISAC: A Deep Reinforcement Learning Approach

作者：Xiyu Wang, Gilberto Berardinelli, Hei Victor Cheng, Petar Popovski, Ramoni Adeogun · 发表于：arXiv (Cornell University) · 年份：2026 · 研究领域：Advanced Wireless Communication Technologies、Radar Systems and Signal Processing、Distributed Sensor Networks and Detection Algorithms

Resource allocation in integrated sensing and communication (ISAC) systems needs to be optimized to balance the requirements of the communication and sensing modules considering complicated cross-layer data traffic and queue status in dynamic multi-user environments. This paper studies the beam allocation for cross-layer ISAC that achieves low-latency communication and minimizes sensing parameters estimation error. To handle the complex coupling between practical data buffer dynamics and varying wireless channels, we propose a deep reinforcement learning (DRL)-assisted approach. Rather than relying on explicit channel state information, the DRL-assisted beam allocation reduces feedback overhead by leveraging sensing observations. Simulation results verify that the DRL framework effectively takes buffer status into account and adapts to the wireless environment while allocating resources. The proposed multi-beam scheme improves overall throughput with only modest delay increases. Finally, the DRL-assisted beam management achieves both communication and sensing performance close to that of the genie-aided benchmark with perfect angle-of-departure (AoD) knowledge. These contributions advance the state-of-the-art intelligent resource management for ISAC systems.