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

arXiv: https://arxiv.org/abs/2606.24979 · HTML: https://mlsanity.com/paper/2606.24979/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 23 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G](https://mlsanity.com/paper/2309.07460/) (2023)
- [Radio Map Based 3D Path Planning for Cellular-Connected UAV](https://mlsanity.com/paper/1912.00021/) (2019)
- [5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning](https://mlsanity.com/paper/2106.05370/) (2021)
- [How Much Data is Needed for Channel Knowledge Map Construction?](https://mlsanity.com/paper/2312.06966/) (2023)
- [Dynamic Trajectory Optimization and Power Control for Hierarchical UAV Swarms in 6G Aerial Access Network](https://mlsanity.com/paper/2508.18702/) (2025)
- [Hierarchical Low-Altitude Wireless Network Empowered Air Traffic Management](https://mlsanity.com/paper/2509.03386/) (2025)
- [Trajectory Optimization for Cellular-Enabled UAV with Connectivity and Battery Constraints](https://mlsanity.com/paper/2307.16207/) (2023)
- [Strategic Application of AIGC for UAV Trajectory Design: A Channel Knowledge Map Approach](https://mlsanity.com/paper/2412.00386/) (2024)

## Abstract (arXiv)

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.