标题： 基于学习的集成感知与通信系统资源管理 显示英文标题

标题： Learning-Based Resource Management in Integrated Sensing and Communication Systems

摘要： 在本文中，我们解决在配备雷达和通信单元的集成感知与通信系统中的自适应时间分配任务。 双功能雷达-通信系统的任务包括为跟踪多个目标分配停留时间，并利用剩余时间向估计的目标位置进行数据传输。 我们引入了一种新的约束深度强化学习（CDRL）方法，旨在在时间预算约束下优化跟踪和通信之间的资源分配，从而提高目标通信质量。 我们的数值结果证明了我们提出的CDRL框架的效率，证实了其在严格遵守时间约束的同时，在高度动态环境中最大化通信质量的能力。 显示英文摘要

摘要： 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.