Strategic Application of AIGC for UAV Trajectory Design: A Channel Knowledge Map Approach

TL;DR

This paper introduces a novel AI-driven framework using AIGC and WGANs to efficiently construct channel knowledge maps, significantly improving UAV trajectory planning and wireless communication performance.

Contribution

It presents a new method combining AIGC and WGANs for rapid, accurate channel knowledge map construction to optimize UAV trajectories in wireless networks.

Findings

Enhanced CKM accuracy demonstrated in experiments

Reduced channel gain uncertainty in UAV trajectories

Improved wireless communication efficiency

Abstract

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.