AERIS: Offline Policy Improvement for Multi-UAV Integrated Sensing and Communication

2026-08-26

AI Summary

本文提出AERIS框架，通过离线学习固定飞行日志来改进多无人机集成感知与通信的策略，设计了STAR-CRDT算法以提高通信质量、感知可靠性和飞行安全性。

Abstract

Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) is a promising 6G paradigm, but dynamic multi-UAV ISAC control must jointly balance communication quality, sensing reliability, and flight safety under stochastic mobility. Existing optimization methods often require repeated global non-convex solving, while online reinforcement learning (RL) depends on risky trial-and-error flights that may cause sensing loss or collision-risk events. This paper proposes AERIS, an offline policy improvement framework for multi-UAV ISAC. AERIS learns from fixed flight logs under centralized training and decentralized execution, so each UAV acts from local histories while training uses logged global information to assess team-level effects. We further design STAR-CRDT, an offline multi-agent RL algorithm that performs support-aware local action rectification and distills only trusted improvements into the decentralized actor. We prove an offline-support policy improvement guarantee. Experiments show that STAR-CRDT improves the main ISAC objective return by 29.3% over the strongest baseline. It further improves communication sum rate, sensing pass rate, and sensing margin by 3.4%, 4.8%, and 69.1%, while reducing collision-risk events by 54.2%. On unseen real-road maps built from OpenStreetMap data, STAR-CRDT still obtains the best return.

Problem

• UAV
• ISAC
• dynamic control
• communication quality
• sensing reliability

Innovation

• offline policy improvement
• multi-UAV ISAC
• centralized training decentralized execution
• support-aware action rectification
• trusted improvements