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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2506.16070 |
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| _version_ | 1866912440047894528 |
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| author | Rathakrishnan, Mathushaharan Gayan, Samiru Singh, Rohit Kaur, Amandeep Inaltekin, Hazer Edirisinghe, Sampath Poor, H. Vincent |
| author_facet | Rathakrishnan, Mathushaharan Gayan, Samiru Singh, Rohit Kaur, Amandeep Inaltekin, Hazer Edirisinghe, Sampath Poor, H. Vincent |
| contents | It is envisioned that 6G networks will be supported by key architectural principles, including intelligence, decentralization, interoperability, and digitalization. With the advances in artificial intelligence (AI) and machine learning (ML), embedding intelligence into the foundation of wireless communication systems is recognized as essential for 6G and beyond. Existing radio access network (RAN) architectures struggle to meet the ever growing demands for flexibility, automation, and adaptability required to build self-evolving and autonomous wireless networks. In this context, this paper explores the transition towards AI-driven RAN (AI-RAN) by developing a novel AI-RAN framework whose performance is evaluated through a practical scenario focused on intelligent orchestration and resource optimization. Besides, the paper reviews the evolution of RAN architectures and sheds light on key enablers of AI-RAN including digital twins (DTs), intelligent reflecting surfaces (IRSs), large generative AI (GenAI) models, and blockchain (BC). Furthermore, it discusses the deployment challenges of AI-RAN, including technical and regulatory perspectives, and outlines future research directions incorporating technologies such as integrated sensing and communication (ISAC) and agentic AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16070 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards AI-Driven RANs for 6G and Beyond: Architectural Advancements and Future Horizons Rathakrishnan, Mathushaharan Gayan, Samiru Singh, Rohit Kaur, Amandeep Inaltekin, Hazer Edirisinghe, Sampath Poor, H. Vincent Signal Processing It is envisioned that 6G networks will be supported by key architectural principles, including intelligence, decentralization, interoperability, and digitalization. With the advances in artificial intelligence (AI) and machine learning (ML), embedding intelligence into the foundation of wireless communication systems is recognized as essential for 6G and beyond. Existing radio access network (RAN) architectures struggle to meet the ever growing demands for flexibility, automation, and adaptability required to build self-evolving and autonomous wireless networks. In this context, this paper explores the transition towards AI-driven RAN (AI-RAN) by developing a novel AI-RAN framework whose performance is evaluated through a practical scenario focused on intelligent orchestration and resource optimization. Besides, the paper reviews the evolution of RAN architectures and sheds light on key enablers of AI-RAN including digital twins (DTs), intelligent reflecting surfaces (IRSs), large generative AI (GenAI) models, and blockchain (BC). Furthermore, it discusses the deployment challenges of AI-RAN, including technical and regulatory perspectives, and outlines future research directions incorporating technologies such as integrated sensing and communication (ISAC) and agentic AI. |
| title | Towards AI-Driven RANs for 6G and Beyond: Architectural Advancements and Future Horizons |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2506.16070 |