Generative Artificial Intelligence for Beamforming in Low-Altitude Economy
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916946475220992 |
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| author | Sun, Geng Qi, Jia Zhang, Chuang Liu, Xuejie Wang, Jiacheng Niyato, Dusit Liu, Yuanwei Kim, Dong In |
| author_facet | Sun, Geng Qi, Jia Zhang, Chuang Liu, Xuejie Wang, Jiacheng Niyato, Dusit Liu, Yuanwei Kim, Dong In |
| contents | The growth of low-altitude economy (LAE) has driven a rising demand for efficient and secure communication. However, conventional beamforming optimization techniques struggle in the complex LAE environments. In this context, generative artificial intelligence (GenAI) methods provide a promising solution. In this article, we first introduce the core concepts of LAE and the roles of beamforming in advanced communication technologies for LAE. We then examine their interrelation, followed by an analysis of the limitations of conventional beamforming methods. Next, we provide an overview of how GenAI methods enhance the process of beamforming, with a focus on its applications in LAE. Furthermore, we present a case study using a generative diffusion model (GDM)-based algorithm to enhance the performance of aerial collaborative beamforming-enabled remote secure communications in LAE and simulation results verified the effectiveness of the proposed algorithms. Finally, promising research opportunities are identified. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15079 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative Artificial Intelligence for Beamforming in Low-Altitude Economy Sun, Geng Qi, Jia Zhang, Chuang Liu, Xuejie Wang, Jiacheng Niyato, Dusit Liu, Yuanwei Kim, Dong In Networking and Internet Architecture The growth of low-altitude economy (LAE) has driven a rising demand for efficient and secure communication. However, conventional beamforming optimization techniques struggle in the complex LAE environments. In this context, generative artificial intelligence (GenAI) methods provide a promising solution. In this article, we first introduce the core concepts of LAE and the roles of beamforming in advanced communication technologies for LAE. We then examine their interrelation, followed by an analysis of the limitations of conventional beamforming methods. Next, we provide an overview of how GenAI methods enhance the process of beamforming, with a focus on its applications in LAE. Furthermore, we present a case study using a generative diffusion model (GDM)-based algorithm to enhance the performance of aerial collaborative beamforming-enabled remote secure communications in LAE and simulation results verified the effectiveness of the proposed algorithms. Finally, promising research opportunities are identified. |
| title | Generative Artificial Intelligence for Beamforming in Low-Altitude Economy |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2504.15079 |