用于增强无人机群双基地ISAC系统中感知估计的强化学习
Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms
AI总结：
针对无人机群双基地ISAC系统的感知增强需求，提出采用集中训练分布式执行的多智能体强化学习框架，结合传输功率自适应技术，在多场景下实现了稳健可扩展的感知性能提升。
摘要：
本文提出了一种新颖的多智能体强化学习（Multi-Agent Reinforcement Learning，MARL）框架，用于增强以无人机（unmanned aerial vehicle，UAV）群作为感知雷达的集成感知与通信（integrated sensing and communication，ISAC）网络。通过将无人机的定位与轨迹优化建模为部分可观测马尔可夫决策过程，我们开发了一种采用集中式训练、分布式执行的MARL方法，以最大化整体感知性能。具体而言，我们实现了一种分布式协作MARL策略，使无人机能够制定有效的通信协议，从而提升其环境感知能力与运行效率。此外，我们为MARL方案加入了传输功率自适应技术，以减轻通信无人机之间的干扰并优化通信协议的效率。尽管复杂度有所提升，我们的方案在多种场景下均表现出稳健的性能与适应性，为未来的ISAC网络提供了一种可扩展且高性价比的增强方案。
英文摘要：
This paper introduces a novel Multi-Agent Reinforcement Learning (MARL) framework to enhance integrated sensing and communication (ISAC) networks using unmanned aerial vehicle (UAV) swarms as sensing radars. By framing the positioning and trajectory optimization of UAVs as a Partially Observable Markov Decision Process, we develop a MARL approach that leverages centralized training with decentralized execution to maximize the overall sensing performance. Specifically, we implement a decentralized cooperative MARL strategy to enable UAVs to develop effective communication protocols, therefore enhancing their environmental awareness and operational efficiency. Additionally, we augment the MARL solution with a transmission power adaptation technique to mitigate interference between the communicating drones and optimize the communication protocol efficiency. Moreover, a transmission power adaptation technique is incorporated to mitigate interference and optimize the learned communication protocol efficiency. Despite the increased complexity, our solution demonstrates robust performance and adaptability across various scenarios, providing a scalable and cost-effective enhancement for future ISAC networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
