Education
- M.S. in Electrical Engineering - University at Buffalo, SUNY (2012)
- B.S. in Electrical Engineering - China Agricultural University, Beijing (2010)
Research Interests
Si Chen received his M.S. in Electrical Engineering from the University at Buffalo, The State University of New York (SUNY), successfully defending his Master thesis titled “Groundwave Modelling and Online Simulation System for Advanced HF Radio Networking” on May 11, 2012. He worked in the Wireless Networks and Embedded Systems Laboratory under the supervision of Professor Tommaso Melodia. Prior to his graduate studies, Si received his B.S. in Electrical Engineering from the College of Engineering, China Agricultural University, Beijing, China in 2010. His research interests focused on cognitive radio and dynamic spectrum access networks, optimization of wireless communication networks, and greenhouse monitoring with wireless sensor networks.
Publications
2025
Conference Papers
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.
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