In the 6G era, innovative MIMO technologies are crucial for enhancing system robustness, reliability, and transmission rates while addressing spectrum shortages, complexity, and network delays. This talk will present our recent advancements in machine learning and optimization techniques for next-generation MIMO systems. First, we introduce a deep-unfolding framework that uses an iterative algorithm-induced deep-unfolding neural network in matrix form to tackle communication system challenges. This approach maximizes the sum-rate in massive MIMO systems via beamforming design by transforming iterative algorithms into a layer-wise structure with trainable parameters. We extend this deep-unfolding algorithm to jointly design channel acquisition and hybrid analog-digital (AD) beamforming through end-to-end learning in mmWave MIMO systems. Second, we propose a double-reconfigurable intelligent surface (RIS)-assisted radar-communication coexistence system. Deploying two RISs enhances communication signals and reduces interference. Our goal is to optimize RIS and radar beamforming to maximize communication performance while preserving radar detection. We simplify this problem using auxiliary variables and develop a penalty dual decomposition (PDD)-based optimization algorithm. Lastly, we explore a semantic-centric MIMO system with our Feature Allocation for Semantic Transmission (FAST) framework. FAST addresses the mismatch between feature importance and wireless channels by integrating an importance evaluator, employing channel prediction for future CSI estimation, and allocating transmission time slots to features. This framework enhances semantic communication performance in image transmission, applicable to both precoding-free and precoding-based MIMO systems in the space-time domain.

Yunlong Cai received his Ph.D. in Electronic Engineering from the University of York, UK, in 2010. From 2010 to 2011, he worked as a postdoctoral researcher at the Electronics and Communication Laboratory of CNAM University, France. Since February 2011, he has been with the College of Information Science and Electronic Engineering at Zhejiang University, where he is currently a professor. He has been a visiting scholar at Georgia Institute of Technology, McGill University, and the University of California, Irvine. His research interests include signal processing and optimization in communications, transceiver design for multi-antenna systems, UAV communications, and machine learning in communications. In these fields, he has published over 130 papers in prestigious IEEE journals such as JSAC, SPL, TWC, TCOM, JSTSP, and TVT, with several papers recognized as Highly Cited Papers (top 1% in Essential Science Indicators, ESI). Additionally, he has published more than 100 papers in international conferences. Prof. Cai currently serves as an Associate Editor for IEEE Transactions on Communications (TCOM) and a Senior Area Editor for IEEE Signal Processing Letters (SPL). He was the Lead Guest Editor for the IEEE Journal on Selected Areas in Communications (JSAC) Special Issue on "Next Generation Advanced Transceiver Technologies." He also served as the General Chair of the 18th IEEE International Symposium on Wireless Communication Systems (ISWCS 2022), held in Hangzhou in October 2022.