Design Principles for Model Generalization and Scalable AI Integration in Radio Access Networks
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909070579990528 |
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| author | Soldati, Pablo Ghadimi, Euhanna Demirel, Burak Wang, Yu Gaigalas, Raimundas Sintorn, Mathias |
| author_facet | Soldati, Pablo Ghadimi, Euhanna Demirel, Burak Wang, Yu Gaigalas, Raimundas Sintorn, Mathias |
| contents | Artificial intelligence (AI) has emerged as a powerful tool for addressing complex and dynamic tasks in radio communication systems. Research in this area, however, focused on AI solutions for specific, limited conditions, hindering models from learning and adapting to generic situations, such as those met across radio communication systems.
This paper emphasizes the pivotal role of achieving model generalization in enhancing performance and enabling scalable AI integration within radio communications. We outline design principles for model generalization in three key domains: environment for robustness, intents for adaptability to system objectives, and control tasks for reducing AI-driven control loops. Implementing these principles can decrease the number of models deployed and increase adaptability in diverse radio communication environments. To address the challenges of model generalization in communication systems, we propose a learning architecture that leverages centralization of training and data management functionalities, combined with distributed data generation. We illustrate these concepts by designing a generalized link adaptation algorithm, demonstrating the benefits of our proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_06251 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Design Principles for Model Generalization and Scalable AI Integration in Radio Access Networks Soldati, Pablo Ghadimi, Euhanna Demirel, Burak Wang, Yu Gaigalas, Raimundas Sintorn, Mathias Machine Learning Artificial Intelligence Networking and Internet Architecture Artificial intelligence (AI) has emerged as a powerful tool for addressing complex and dynamic tasks in radio communication systems. Research in this area, however, focused on AI solutions for specific, limited conditions, hindering models from learning and adapting to generic situations, such as those met across radio communication systems. This paper emphasizes the pivotal role of achieving model generalization in enhancing performance and enabling scalable AI integration within radio communications. We outline design principles for model generalization in three key domains: environment for robustness, intents for adaptability to system objectives, and control tasks for reducing AI-driven control loops. Implementing these principles can decrease the number of models deployed and increase adaptability in diverse radio communication environments. To address the challenges of model generalization in communication systems, we propose a learning architecture that leverages centralization of training and data management functionalities, combined with distributed data generation. We illustrate these concepts by designing a generalized link adaptation algorithm, demonstrating the benefits of our proposed approach. |
| title | Design Principles for Model Generalization and Scalable AI Integration in Radio Access Networks |
| topic | Machine Learning Artificial Intelligence Networking and Internet Architecture |
| url | https://arxiv.org/abs/2306.06251 |