Towards a Foundation Model for Communication Systems
Fuente:
arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918026987700224 |
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| author | Buffelli, Davide Das, Sowmen Lin, Yu-Wei Vakili, Sattar Wang, Chien-Yi Attarifar, Masoud Nath, Pritthijit Shiu, Da-shan |
| author_facet | Buffelli, Davide Das, Sowmen Lin, Yu-Wei Vakili, Sattar Wang, Chien-Yi Attarifar, Masoud Nath, Pritthijit Shiu, Da-shan |
| contents | Artificial Intelligence (AI) has demonstrated unprecedented performance across various domains, and its application to communication systems is an active area of research. While current methods focus on task-specific solutions, the broader trend in AI is shifting toward large general models capable of supporting multiple applications. In this work, we take a step toward a foundation model for communication data--a transformer-based, multi-modal model designed to operate directly on communication data. We propose methodologies to address key challenges, including tokenization, positional embedding, multimodality, variable feature sizes, and normalization. Furthermore, we empirically demonstrate that such a model can successfully estimate multiple features, including transmission rank, selected precoder, Doppler spread, and delay profile. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14603 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards a Foundation Model for Communication Systems Buffelli, Davide Das, Sowmen Lin, Yu-Wei Vakili, Sattar Wang, Chien-Yi Attarifar, Masoud Nath, Pritthijit Shiu, Da-shan Artificial Intelligence Machine Learning Signal Processing Artificial Intelligence (AI) has demonstrated unprecedented performance across various domains, and its application to communication systems is an active area of research. While current methods focus on task-specific solutions, the broader trend in AI is shifting toward large general models capable of supporting multiple applications. In this work, we take a step toward a foundation model for communication data--a transformer-based, multi-modal model designed to operate directly on communication data. We propose methodologies to address key challenges, including tokenization, positional embedding, multimodality, variable feature sizes, and normalization. Furthermore, we empirically demonstrate that such a model can successfully estimate multiple features, including transmission rank, selected precoder, Doppler spread, and delay profile. |
| title | Towards a Foundation Model for Communication Systems |
| topic | Artificial Intelligence Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2505.14603 |