提出量子启发轨迹学习方法，提升干扰受限多无人机网络吞吐量。
Rate-Aware Quantum-Inspired Trajectory Learning for Interference-Limited Multi-UAV Networks
- 结合速率感知图压缩与分布式强化学习优化轨迹
- 总吞吐率达59.4 Mbps，优先用户达23.9 Mbps
- 适合高密度无人机协同场景的实时调度
无人机可在灾情与日常场景中提供按需的高容量连接，但轨迹优化面临维度诅咒问题，干扰环境与巨大搜索空间导致实时协调计算成本高昂。为此，本文提出速率感知量子退火图凝聚（RA-QAGC）方案，融合速率感知图抽象与分布式强化学习，实现可扩展、干扰感知的无人机协同。通过识别高吞吐区域并引导无人机向最优吞吐区调整轨迹，有效平衡网络容量并满足服务质量（QoS）要求。仿真结果表明，该方案总吞吐量达59.4 Mbps，优先用户吞吐量达23.9 Mbps，相比基线方案分别提升约15%和34%。
原文摘要 · Abstract (English)
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.
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