Unmanned aerial vehicle (UAV) can provide on-demand, high-capacity connectivity in disaster and normal situation. However, it faces a challenge of curse of dimensionality in trajectory optimization, where interference-limited environments and vast search spaces make real-time coordination computationally expensive. To overcome this challenge, we propose the Rate-Aware Quantum-Annealed Graph Condensation (RA-QAGC) scheme, which combines rate-aware graph abstraction with decentralized reinforcement learning to enable scalable, interference-aware UAV coordination. By identifying high throughput locations and guiding UAV trajectory adaptation toward throughput-optimal regions, RA-QAGC effectively balances network capacity by maintaining quality-of-service (QoS) requirements. Simulation results demonstrate the proposal outperformed over existing schemes by achieving 59.4 Mbps total throughput and 23.9 Mbps priority-user throughput, representing gains of approximately 15% and 34%, respectively, over the baseline schemes.
感知速率的量子启发轨迹学习：针对干扰受限的多无人机网络 / Rate-Aware Quantum-Inspired Trajectory Learning for Interference-Limited Multi-UAV Networks
本文提出了一种融合速率感知图抽象与量子退火技术的轨迹学习算法（RA-QAGC），让多架无人机在信号干扰严重且决策空间巨大的复杂环境中，能够快速、智能地调整飞行路径，从而在保障用户服务质量的同时，显著提升网络总吞吐量和优先用户的通信速率。
源自 arXiv: 2606.25480
