Strategic Application of AIGC for UAV Trajectory Design: A Channel Knowledge Map Approach
This work addresses resource optimization for UAV-based wireless communication, but it is incremental as it applies existing AI methods to a specific domain problem.
The paper tackled the challenge of accurate channel loss prediction for UAVs in wireless communication by using AIGC to construct Channel Knowledge Maps and design trajectories, resulting in improved CKM accuracy and reduced channel gain uncertainty to enhance efficiency.
Unmanned Aerial Vehicles (UAVs) are increasingly utilized in wireless communication, yet accurate channel loss prediction remains a significant challenge, limiting resource optimization performance. To address this issue, this paper leverages Artificial Intelligence Generated Content (AIGC) for the efficient construction of Channel Knowledge Maps (CKM) and UAV trajectory design. Given the time-consuming nature of channel data collection, AI techniques are employed in a Wasserstein Generative Adversarial Network (WGAN) to extract environmental features and augment the data. Experiment results demonstrate the effectiveness of the proposed framework in improving CKM construction accuracy. Moreover, integrating CKM into UAV trajectory planning reduces channel gain uncertainty, demonstrating its potential to enhance wireless communication efficiency.