Empowering Wireless Network Applications with Deep Learning-based Radio Propagation Models
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929469534502912 |
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| author | Bakirtzis, Stefanos Yapar, Cagkan Fiore, Marco Zhang, Jie Wassell, Ian |
| author_facet | Bakirtzis, Stefanos Yapar, Cagkan Fiore, Marco Zhang, Jie Wassell, Ian |
| contents | The efficient deployment and operation of any wireless communication ecosystem rely on knowledge of the received signal quality over the target coverage area. This knowledge is typically acquired through radio propagation solvers, which however suffer from intrinsic and well-known performance limitations. This article provides a primer on how integrating deep learning and conventional propagation modeling techniques can enhance multiple vital facets of wireless network operation, and yield benefits in terms of efficiency and reliability. By highlighting the pivotal role that the deep learning-based radio propagation models will assume in next-generation wireless networks, we aspire to propel further research in this direction and foster their adoption in additional applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12193 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Empowering Wireless Network Applications with Deep Learning-based Radio Propagation Models Bakirtzis, Stefanos Yapar, Cagkan Fiore, Marco Zhang, Jie Wassell, Ian Signal Processing Machine Learning Networking and Internet Architecture The efficient deployment and operation of any wireless communication ecosystem rely on knowledge of the received signal quality over the target coverage area. This knowledge is typically acquired through radio propagation solvers, which however suffer from intrinsic and well-known performance limitations. This article provides a primer on how integrating deep learning and conventional propagation modeling techniques can enhance multiple vital facets of wireless network operation, and yield benefits in terms of efficiency and reliability. By highlighting the pivotal role that the deep learning-based radio propagation models will assume in next-generation wireless networks, we aspire to propel further research in this direction and foster their adoption in additional applications. |
| title | Empowering Wireless Network Applications with Deep Learning-based Radio Propagation Models |
| topic | Signal Processing Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2408.12193 |