Mu Mimo

Multi-user multiple-input multiple-output (MU-MIMO) systems aim to enhance wireless communication efficiency by simultaneously serving multiple users with multiple antennas. Current research heavily utilizes deep learning, employing neural networks like graph neural networks and autoencoders, to optimize various aspects of MU-MIMO, including precoding, channel estimation, and detection, often addressing challenges posed by limited feedback and hardware constraints like low-resolution phase shifters. These advancements are crucial for improving data rates and spectral efficiency in 5G and beyond networks, particularly in millimeter-wave communication where efficient beamforming is critical.