Si Chen

Education

M.S. in Electrical Engineering - University at Buffalo, SUNY (2012)
B.S. in Electrical Engineering - China Agricultural University, Beijing (2010)

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

Journals & Magazines

T. Chen, C. I, and T. Melodia. “O-RAN's Role in Shaping 6G: Industry Perspectives on Open and Smart RAN.” IEEE Wireless Communications Magazine (2025)Journal

DOI Link

Conference Papers

R. Ford, H. Chen, P. Madadi, M. Kulkarni, X. Ma, D. Burghal, G. Chen, Y. Hu, C. Tarver, P. Skrimponis, Y. Zhang, Y. Xin, J. Zhang, S. Khunteta, Y. Reddy, A. Chavva, M. Kothiwale, and D. Villa. “Sim2Field: End-to-End Development of AI RANs for 6G.” Proceedings of the 2nd ACM Workshop on Open and AI RAN (2025)Conference

DOI Link

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.

2024

Journals & Magazines

T. Ngo, M. Ngo, B. Chen, G. Gemmi, E. Baena, M. Polese, T. Melodia, W. Chien, and T. Quek. “Consistent and Repeatable Testing of O-RAN Distributed Unit (O-DU) across Continents.” Proceedings of the First Workshop on Research and Innovation in Testing and Integration for Open Radio Access Networks (RitiRAN) (2024)Preprint

DOI arXiv

2018

Conference Papers

F. Pop, B. Herrera, C. Cassella, G. Chen, E. Demirors, R. Guida, T. Melodia, and M. Rinaldi. “Novel pMUT-Based Acoustic Duplexer for Underwater and Intra-Body Communication.” 2018 IEEE International Ultrasonics Symposium (IUS) (2018)Conference

DOI

B. Herrera, E. Demirors, G. Chen, R. Guida, F. Pop, N. Dave, C. Cassella, T. Melodia, and M. Rinaldi. “pMUT-based High Data Rate Ultrasonic Wireless Communication Link for Intra-Body Networks.” Proc. of ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) (2018)Conference

DOI

2014

Journals & Magazines

S. D'Oro, L. Galluccio, G. Morabito, S. Palazzo, L. Chen, and F. Martignon. “Defeating jamming with the power of silence: a game-theoretic analysis.” IEEE Transactions on Wireless Communications (2014)Journal

DOI

2012

Conference Papers

E. Koski, S. Chen, S. Pudlewski, and T. Melodia. “Network Simulation for Advanced HF Communications Engineering.” IET Intl. Conf. on Ionospheric Radio Systems and Techniques (IRST) (2012)Conference

DOI