Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908406468575232 |
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| author | Kontes, Georgios Michalopoulos, Diomidis S. Ghimire, Birendra Mutschler, Christopher |
| author_facet | Kontes, Georgios Michalopoulos, Diomidis S. Ghimire, Birendra Mutschler, Christopher |
| contents | AI/ML research has predominantly been driven by domains such as computer vision, natural language processing, and video analysis. In contrast, the application of AI/ML to wireless networks, particularly at the air interface, remains in its early stages. Although there are emerging efforts to explore this intersection, fully realizing the potential of AI/ML in wireless communications requires a deep interdisciplinary understanding of both fields. We provide an overview of AI/ML-related discussions in 3GPP standardization, highlighting key use cases, architectural considerations, and technical requirements. We outline open research challenges and opportunities where academic and industrial communities can contribute to shaping the future of AI-enabled wireless systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11466 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks Kontes, Georgios Michalopoulos, Diomidis S. Ghimire, Birendra Mutschler, Christopher Machine Learning AI/ML research has predominantly been driven by domains such as computer vision, natural language processing, and video analysis. In contrast, the application of AI/ML to wireless networks, particularly at the air interface, remains in its early stages. Although there are emerging efforts to explore this intersection, fully realizing the potential of AI/ML in wireless communications requires a deep interdisciplinary understanding of both fields. We provide an overview of AI/ML-related discussions in 3GPP standardization, highlighting key use cases, architectural considerations, and technical requirements. We outline open research challenges and opportunities where academic and industrial communities can contribute to shaping the future of AI-enabled wireless systems. |
| title | Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.11466 |