Sim2Field: End-to-End Development of AI RANs for 6G
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arXiv
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2025
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| author | Ford, Russell Chen, Hao Madadi, Pranav Kulkarni, Mandar Ma, Xiaochuan Burghal, Daoud Chen, Guanbo Hu, Yeqing Tarver, Chance Skrimponis, Panagiotis Loseu, Vitali Zhang, Yu Xin, Yan Li, Yang Zhang, Jianzhong Khunteta, Shubham Reddy, Yeswanth Guddeti Chavva, Ashok Kumar Reddy Kothiwale, Mahantesh Villa, Davide |
| author_facet | Ford, Russell Chen, Hao Madadi, Pranav Kulkarni, Mandar Ma, Xiaochuan Burghal, Daoud Chen, Guanbo Hu, Yeqing Tarver, Chance Skrimponis, Panagiotis Loseu, Vitali Zhang, Yu Xin, Yan Li, Yang Zhang, Jianzhong Khunteta, Shubham Reddy, Yeswanth Guddeti Chavva, Ashok Kumar Reddy Kothiwale, Mahantesh Villa, Davide |
| contents | Following state-of-the-art research results, which showed the potential for significant performance gains by applying AI/ML techniques in the cellular Radio Access Network (RAN), the wireless industry is now broadly pushing for the adoption of AI in 5G and future 6G technology. Despite this enthusiasm, AI-based wireless systems still remain largely untested in the field. Common simulation methods for generating datasets for AI model training suffer from "reality gap" and, as a result, the performance of these simulation-trained models may not carry over to practical cellular systems. Additionally, the cost and complexity of developing high-performance proof-of-concept implementations present major hurdles for evaluating AI wireless systems in the field. In this work, we introduce a methodology which aims to address the challenges of bringing AI to real networks. We discuss how detailed Digital Twin simulations may be employed for training site-specific AI Physical (PHY) layer functions. We further present a powerful testbed for AI-RAN research and demonstrate how it enables rapid prototyping, field testing and data collection. Finally, we evaluate an AI channel estimation algorithm over-the-air with a commercial UE, demonstrating that real-world throughput gains of up to 40% are achievable by incorporating AI in the physical layer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23528 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sim2Field: End-to-End Development of AI RANs for 6G Ford, Russell Chen, Hao Madadi, Pranav Kulkarni, Mandar Ma, Xiaochuan Burghal, Daoud Chen, Guanbo Hu, Yeqing Tarver, Chance Skrimponis, Panagiotis Loseu, Vitali Zhang, Yu Xin, Yan Li, Yang Zhang, Jianzhong Khunteta, Shubham Reddy, Yeswanth Guddeti Chavva, Ashok Kumar Reddy Kothiwale, Mahantesh Villa, Davide Networking and Internet Architecture Following state-of-the-art research results, which showed the potential for significant performance gains by applying AI/ML techniques in the cellular Radio Access Network (RAN), the wireless industry is now broadly pushing for the adoption of AI in 5G and future 6G technology. Despite this enthusiasm, AI-based wireless systems still remain largely untested in the field. Common simulation methods for generating datasets for AI model training suffer from "reality gap" and, as a result, the performance of these simulation-trained models may not carry over to practical cellular systems. Additionally, the cost and complexity of developing high-performance proof-of-concept implementations present major hurdles for evaluating AI wireless systems in the field. In this work, we introduce a methodology which aims to address the challenges of bringing AI to real networks. We discuss how detailed Digital Twin simulations may be employed for training site-specific AI Physical (PHY) layer functions. We further present a powerful testbed for AI-RAN research and demonstrate how it enables rapid prototyping, field testing and data collection. Finally, we evaluate an AI channel estimation algorithm over-the-air with a commercial UE, demonstrating that real-world throughput gains of up to 40% are achievable by incorporating AI in the physical layer. |
| title | Sim2Field: End-to-End Development of AI RANs for 6G |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2509.23528 |