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.

翻译：面向集成感知与通信（ISAC）系统的资源分配需优化，以在动态多用户环境中综合考虑跨层数据流量与队列状态，平衡通信与感知模块的需求。本文研究面向低延迟通信与最小化感知参数估计误差的跨层ISAC波束分配问题。为处理实际数据缓冲区动态与无线信道时变间的复杂耦合，提出一种基于深度强化学习（DRL）的辅助方法。该方法无需显式信道状态信息，通过利用感知观测结果降低反馈开销。仿真验证表明，DRL框架能有效考虑缓冲区状态，在分配资源时自适应无线环境。所提多波束方案在仅适度增加延迟的情况下提升了整体吞吐量。最终，DRL辅助的波束管理实现了与完美角度偏移（AoD）先知基准相当的通信与感知性能。这些成果推动了ISAC系统智能资源管理的前沿发展。