CKM-Driven Communication-Aware UAV Intelligent Trajectory Optimization for Urban Inspection

2026-06-23

AI Summary

This work addresses the challenge of low communication reliability in urban drone inspection caused by spatially heterogeneous wireless channels. To this end, the authors propose an intelligent path-planning framework that integrates channel modeling with trajectory decision-making. The approach introduces, for the first time, a diffusion model to construct a temporal cumulative Channel Knowledge Map (CKM), which is then combined with a graph attention network and the Soft Actor-Critic (SAC) reinforcement learning algorithm to enable end-to-end communication-aware optimization—from global waypoint sequencing to local continuous trajectory control. Notably, the method guides multiple drones to proactively avoid communication-degraded regions without requiring real-time channel feedback. Simulation results demonstrate that the proposed framework significantly enhances both flight efficiency and communication reliability by enabling drones to effectively traverse high-quality channel regions.

Abstract

Unmanned aerial vehicles (UAVs) are increasingly employed in urban inspection tasks, where reliable communication is critical but challenging due to the severe spatial channel heterogeneity. To address the issue, in this paper, we focus on the communication-aware path planning for multi-UAV tasks, and propose a channel knowledge map (CKM)-driven trajectory planning framework which integrates the channel modeling and trajectory decision-making. Specifically, we apply the diffusion model to construct a time-accumulated CKM and achieve the accurate perception with low flight overhead, which leverages the sparse observation data to reconstruct the high-fidelity global channel quality distribution. Based on the CKM, we propose a global-to-local graph attention network soft actor-critic algorithm. The graph attention network optimizes the complex combinatorial node ordering problem, generating an optimal and communication-aware sequence for the inspection targets. Subsequently, the soft actor-critic algorithm performs continuous action control to ensure the smoothness of the flight path and dynamically avoid communication attenuation areas. Simulation results demonstrate that the proposed method effectively guides UAVs through high-quality channel regions without dependence on real-time channel feedback, significantly improving both the trajectory efficiency and communication reliability.

Problem

• UAV trajectory optimization
• communication-aware planning
• urban inspection
• channel heterogeneity
• reliable communication

Innovation

• Channel Knowledge Map
• Diffusion Model
• Graph Attention Network
• Soft Actor-Critic
• Communication-Aware Trajectory Optimization