AI 中文总结
针对带宽受限无人机群的弹性覆盖问题，提出PL-MARL框架，引入运动感知推理引擎应对稀疏信号等问题，实现计算与通信权衡。仿真显示其在极端情况下能保持良好性能，验证了主动推理是鲁棒空中协调的有效方案。
AI 中文摘要
本文提出预测轻量级多智能体强化学习（PL-MARL）框架，以确保带宽受限无人机群的弹性覆盖。为应对稀疏信号和信息老化导致的协调崩溃，引入运动感知推理引擎，通过物理先验主动重建邻居轨迹。该方法实现了高效的计算与通信权衡，将结构弹性与信号频率解耦。仿真表明，PL-MARL在极端信号稀缺和节点故障下保持卓越覆盖和任务连续性。结果验证了主动推理是一种可扩展、低延迟的鲁棒空中协调解决方案，有效最小化控制开销，为有效载荷服务保留频谱并确保抗干扰弹性。
英文摘要
This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Aware Inference Engine that proactively reconstructs neighbor trajectories via physical priors. This approach enables an efficient computation-for-communication trade-off, decoupling structural resilience from signaling frequency. Simulations confirm that PL-MARL maintains superior coverage and mission continuity under extreme signaling scarcity and node failure. Our results validate proactive inference as a scalable, low-latency solution for robust aerial coordination, effectively minimizing control overhead to preserve spectrum for payload services while ensuring resilience against interference.
CommentsAccepted for publication in IEEE Wireless Communications Letters. ©2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses
Journal refIEEE Wireless Communications Letters, vol. 15, pp. 4390-4394, 2026
