Multiple Input Multiple Output
Multiple-input multiple-output (MIMO) systems utilize multiple antennas at both transmitting and receiving ends to enhance communication efficiency and reliability. Current research focuses on optimizing MIMO performance across diverse applications, including wireless communication, video processing, and radar sensing, employing techniques like deep learning, reinforcement learning, and novel signal processing algorithms to address challenges such as channel estimation, beamforming, and resource allocation. These advancements are driving improvements in data throughput, spectral efficiency, and robustness in various fields, from cellular networks and satellite communications to image processing and robotics.
Papers
Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A model-based deep learning approach
Xiangyu Zhang, Zening Wang, Haiyang Zhang, Luxi Yang
Lightweight and Flexible Deep Equilibrium Learning for CSI Feedback in FDD Massive MIMO
Yifan Ma, Wentao Yu, Xianghao Yu, Jun Zhang, Shenghui Song, Khaled B. Letaief