Predictive importance sampling based coverage verification for multi uav trajectory planning

Abstract

In next-generation wireless networks, unmanned aerial vehicle (UAV) networks are emerging as a promising solution for ultra-reliable low-latency communication (URLLC). A key challenge in millimeter-wave UAV networks is ensuring that mobile users are always in line-of-sight (LoS) coverage, since the current snapshot-based trajectory planning approach does not consider the mobility of the users during the decision interval, resulting in disastrous LoS gaps. For continuous coverage verification, standard uniform sampling is too computationally expensive, as it would need a large number of samples to estimate rare failure events that have latencies that are not suitable for real-time requirements. In this work, we introduce a Predictive Importance Sampling (PIS) framework that significantly decreases sample complexity by focusing verification efforts on regions where failure is predicted. Specifically, we propose a Long Short-Term Memory Mixture Density Network (LSTM-MDN) architecture to learn multimodal user trajectory distributions and introduce a defense approach based on mixture sampling to handle the robustness against the prediction error. We show that PIS yields unbiased failure probability estimates that have lower variance than uniform sampling. We then combine PIS with Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to perform coordinated multi-UAV trajectory planning based on an energy-aware multi-objective reward function that balances throughput, coverage, fairness and energy consumption. Based on the simulation results, our proposed method improves the coverage rate, throughput and verification latency in comparison with three state-of-the-art methods, thus enabling proactive coverage management for URLLC-aware UAV networks.