Sim2Field: End-to-End Development of AI RANs for 6G
Abstract
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.
Cite
@article{arxiv.2509.23528,
title = {Sim2Field: End-to-End Development of AI RANs for 6G},
author = {Russell Ford and Hao Chen and Pranav Madadi and Mandar Kulkarni and Xiaochuan Ma and Daoud Burghal and Guanbo Chen and Yeqing Hu and Chance Tarver and Panagiotis Skrimponis and Vitali Loseu and Yu Zhang and Yan Xin and Yang Li and Jianzhong Zhang and Shubham Khunteta and Yeswanth Guddeti Reddy and Ashok Kumar Reddy Chavva and Mahantesh Kothiwale and Davide Villa},
journal= {arXiv preprint arXiv:2509.23528},
year = {2025}
}