Problem
Research questions and friction points this paper is trying to address.
trajectory optimization
curse of dimensionality
interference-limited
multi-UAV networks
real-time coordination
Rate-Aware Quantum-Inspired Trajectory Learning for Interference-Limited Multi-UAV Networks
🤖 AI Summary
This work addresses the challenge of trajectory optimization in interference-limited multi-UAV networks, where the curse of dimensionality leads to prohibitive computational overhead for coordination. To mitigate this issue, the authors propose a rate-aware quantum annealing graph compression (RA-QAGC) approach that uniquely integrates quantum-inspired graph compression with a rate-aware mechanism, coupled with decentralized reinforcement learning to guide UAVs toward high-throughput regions adaptively. The proposed framework effectively alleviates the curse of dimensionality while preserving system scalability and quality-of-service guarantees. Experimental results demonstrate that the method achieves a total throughput of 59.4 Mbps and a prioritized user throughput of 23.9 Mbps, representing improvements of approximately 15% and 34%, respectively, over baseline schemes.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Quantum-Inspired Optimization
Trajectory Learning
Interference-Limited UAV Networks
Rate-Aware Graph Abstraction
Decentralized Reinforcement Learning
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