WiCI Webinar: "Toward Intelligent 6G O-RAN: Automated Control and Optimization"

[Zhang, Hongwei [E CPE]]

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Date: Thursday, August 6, 2026
Time: 11:00 AM CT

Abstract : This talk presents our research toward transforming 6G O-RAN into an intelligent, application-aware, and perception-enabled wireless infrastructure. First, under the theme of AI for RAN, I will introduce deep reinforcement learning (DRL) approaches for automated O-RAN control and optimization, with an emphasis on graph-based network representations that capture the dynamic relationships among users, cells, and radio resources. Second, under RAN for AI, I will demonstrate how intelligent handover control and mobility management can improve the reliability and latency of edge AI offloading applications. Finally, I will present our work on integrated sensing and communication (ISAC) in O-RAN, which extends network architectures and radio signals to support environmental sensing and perception. Together, these efforts illustrate a unified vision in which 6G O-RAN not only uses AI to optimize network operations but also provides reliable connectivity for distributed AI and serves as a programmable platform for perceiving the physical environment.

Bio: 

Huacheng Zeng is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University. Before joining Michigan State University, he was an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Louisville and a Senior System Engineer at Marvell Semiconductor. He received his Ph.D. degree in computer engineering from Virginia Tech in 2015. His research focuses on AI-enabled wireless communication, networking, and sensing systems, with particular interests in 6G, O-RAN, edge AI, reinforcement learning, and integrated sensing and communication. He received the NSF CAREER Award in 2019.

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