Strategic Application of AIGC for UAV Trajectory Design: A Channel Knowledge Map Approach
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909608250966016 |
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| author | Zhang, Chiya Wang, Ting Han, Rubing Gong, Yuanxiang |
| author_facet | Zhang, Chiya Wang, Ting Han, Rubing Gong, Yuanxiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00386 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Strategic Application of AIGC for UAV Trajectory Design: A Channel Knowledge Map Approach Zhang, Chiya Wang, Ting Han, Rubing Gong, Yuanxiang Artificial Intelligence 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. |
| title | Strategic Application of AIGC for UAV Trajectory Design: A Channel Knowledge Map Approach |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2412.00386 |