With unprecedented increases in traffic load in today’s wireless networks, design challenges shift from the wireless network itself to the computational support behind the network. In this vein, there is new interest in non-conventional computing paradigms, such as quantum and physics-inspired computing, due to their potential to dramatically accelerate signal processing and optimization tasks. In this talk, I will discuss how these emerging computing approaches can help overcome fundamental computational limitations and reshape wireless system architectures for next-generation (NextG) networks with substantially enhanced performance, scalability, and efficiency. The talk will span topics ranging from specialized solver designs to the redesign of Radio Access Network (RAN) baseband processing. I will also briefly highlight broader implications of these non-traditional computing approaches beyond wireless communications, including their potential impact on AI systems and large-scale optimization problems.

Minsung Kim is an Assistant Professor in the Department of Computer Science and WINLAB at Rutgers University. Prior to joining Rutgers, he was a postdoctoral associate at Yale University. He received his Ph.D. in Computer Science from Princeton University and his B.E. in Electrical Engineering (Great Honors) from Korea University. His research focuses on quantum and emerging computing systems for next-generation wireless networks. His work has been published in the premier venues of mobile computing and wireless networking, such as ACM SIGCOMM and MobiCom. He is a recipient of the Qualcomm Innovation Fellowship (North America) and the Princeton SEAS Award for Excellence. He was named a Siebel Scholar and an ACM MobiSys Rising Star.