Beam Scheduling for Cross-Layer ISAC: A Deep Reinforcement Learning Approach
This work addresses the challenge of resource allocation in ISAC systems for dynamic multi-user environments, offering an intelligent approach that reduces feedback overhead.
The paper proposes a deep reinforcement learning (DRL)-assisted beam allocation method for cross-layer integrated sensing and communication (ISAC) systems, achieving low-latency communication and minimizing sensing estimation error. The DRL framework adapts to dynamic environments and buffer status, improving throughput with modest delay increases, and performs close to a genie-aided benchmark with perfect AoD knowledge.
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.