Predictive Importance Sampling Based Coverage Verification for Multi-UAV Trajectory Planning

TL;DR

This paper introduces a predictive importance sampling framework combined with deep learning to efficiently verify coverage in multi-UAV networks, enabling real-time, reliable communication for mobile users.

Contribution

It develops a novel PIS method with LSTM-MDN and defensive sampling, improving failure probability estimation and integrating it with multi-agent trajectory planning for UAV networks.

Findings

PIS provides unbiased, low-variance failure estimates.

The integrated approach outperforms existing methods in coverage and latency.

Simulation shows improved coverage rate and throughput.

Abstract

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.