用AI生成内容构建信道地图，优化无人机飞行路径
Strategic Application of AIGC for UAV Trajectory Design: A Channel Knowledge Map Approach
- 用WGAN生成环境特征数据，解决信道数据收集慢问题
- 信道地图准确率提升，飞行路径不确定性降低30%以上
- 适合通信与无人机协同设计的研究者参考
无人飞行器（UAV）在无线通信中的应用日益广泛，但精确的信道损耗预测仍是重大挑战，制约资源优化效果。本文利用人工智能生成内容（AIGC）高效构建信道知识地图（CKM），并用于无人机轨迹设计。针对信道数据采集耗时的问题，采用基于Wasserstein的生成对抗网络（WGAN）提取环境特征并扩充数据。实验表明，所提框架显著提升了信道知识地图构建的准确性。将信道知识地图融入无人机轨迹规划后，信道增益不确定性降低，展现出提升无线通信效率的潜力。
原文摘要 · Abstract (English)
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.
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