用深度强化学习优化雷达通信系统的时隙分配，提升动态环境下的通信质量。
Learning-Based Resource Management in Integrated Sensing and Communication Systems
- 基于约束深度强化学习，动态分配雷达跟踪与通信时长。
- 在时长受限下显著提升目标通信质量，适应快速变化环境。
- 适合研究智能资源管理的无线系统研究人员参考。
本文针对配备雷达和通信单元的集成感知与通信系统，解决自适应时间分配问题。双功能雷达-通信系统需为多目标跟踪分配驻留时间，并利用剩余时间向估计的目标位置传输数据。我们提出一种新型约束深度强化学习（CDRL）方法，在时间预算约束下优化跟踪与通信之间的资源分配，从而提升目标通信质量。数值结果表明，所提出的CDRL框架在高度动态环境中能有效最大化通信质量，同时严格遵守时间约束。
原文摘要 · Abstract (English)
In this paper, we tackle the task of adaptive time allocation in integrated sensing and communication systems equipped with radar and communication units. The dual-functional radar-communication system's task involves allocating dwell times for tracking multiple targets and utilizing the remaining time for data transmission towards estimated target locations. We introduce a novel constrained deep reinforcement learning (CDRL) approach, designed to optimize resource allocation between tracking and communication under time budget constraints, thereby enhancing target communication quality. Our numerical results demonstrate the efficiency of our proposed CDRL framework, confirming its ability to maximize communication quality in highly dynamic environments while adhering to time constraints.
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