Rate-Aware Quantum-Inspired Trajectory Learning for Interference-Limited Multi-UAV Networks
For UAV network operators, this work provides a scalable, interference-aware coordination method that improves throughput and QoS in both disaster and normal scenarios.
The paper tackles the curse of dimensionality in trajectory optimization for multi-UAV networks in interference-limited environments. The proposed RA-QAGC scheme achieves 59.4 Mbps total throughput and 23.9 Mbps priority-user throughput, outperforming baselines by 15% and 34% respectively.
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.