Unmanned aerial vehicle (UAV) networks are emerging as a promising solution for ultra-reliable low-latency communication (URLLC) in next-generation wireless systems. A key challenge in millimeter wave UAV networks is maintaining continuous line of sight (LoS) coverage for mobile users, as existing snapshot-based trajectory planning methods fail to account for user mobility within decision intervals, leading to catastrophic coverage gaps. Standard uniform sampling for continuous coverage verification is computationally prohibitive, requiring huge number of samples to estimate rare failure events with latencies incompatible with real-time requirements. In this work, we propose a predictive importance sampling (PIS) framework that drastically reduces sample complexity by concentrating verification efforts on predicted failure regions. Specifically, we develop a long short-term memory mixture density network (LSTM-MDN) architecture to capture multimodal user trajectory distributions and combine it with defensive mixture sampling for robustness against prediction errors. We prove that PIS provides unbiased failure probability estimates with lower variance than uniform sampling. We then integrate PIS with multi-agent deep deterministic policy gradient (MADDPG) for coordinated multi-UAV trajectory planning using an adaptive multi-objective reward function balancing throughput, coverage, fairness, and energy consumption. Lastly, the simulation results show how our suggested method outperforms three other state-of-the-art methods in terms of coverage rate, throughput, and verification latency, making proactive coverage management for URLLC-aware UAV networks feasible.

翻译：无人机网络正成为下一代无线系统中超可靠低延迟通信的一种有前景的解决方案。毫米波无人机网络的一个关键挑战是为移动用户维持连续的视距覆盖，因为现有的基于快照的轨迹规划方法未能考虑决策间隔内的用户移动性，从而导致灾难性的覆盖间隙。用于连续覆盖验证的标准均匀采样在计算上是不可行的，它需要大量样本来估计罕见故障事件，其延迟与实时要求不相容。在本工作中，我们提出了一个预测重要性采样框架，通过将验证工作集中在预测的故障区域上，极大地降低了样本复杂度。具体而言，我们开发了一种长短期记忆混合密度网络架构，以捕获多模态用户轨迹分布，并将其与防御性混合采样相结合，以增强对预测误差的鲁棒性。我们证明了预测重要性采样提供了比均匀采样方差更小的无偏故障概率估计。然后，我们将预测重要性采样与多智能体深度确定性策略梯度相结合，使用一个平衡吞吐量、覆盖率、公平性和能耗的自适应多目标奖励函数，进行协调的多无人机轨迹规划。最后，仿真结果表明，我们提出的方法在覆盖率、吞吐量和验证延迟方面优于其他三种最先进的方法，使得面向超可靠低延迟通信的无人机网络的主动覆盖管理成为可能。