Multiple-Input Multiple-Output (MIMO) systems have many applications in Wireless and Mobile Networks. This technique can be used to improve the spectral efficiency of the system and increase the capacity of the channel, avoid the fading of the channel and to avoid interference. In addition, can be used for security as well as tracking and detection. Hence the performance analysis of these systems is of foremost importance. The chapter covers mainly the techniques for performance analysis of MIMO systems such as the Hybrid Filtering Technique for MIMO-OFDM Systems, artificial intelligence solutions beyond 5G radio access networks, 5G-V2X, Spectral Efficiency, and Pilot Contaminated Massive MIMO Systems, SBPA in Crowded Massive MIMO Systems, Algorithms for Automatic Selection of Antennas in a MIMO, Deep Neural Network for Compressive Sensing and Application to Massive MIMO Channel Estimation, MIMO-OFDM System with Diverse Transformation for 5G Applications, Timing Offset Estimation and Correction in OFDM Assisted Massive MIMO Systems, single-cell massive MIMO systems. Various channel estimation methods for MIMO-OFDM and massive MIMO-GFDM systems are discussed, including Taylor-Based Least Square Estimation, Elman Recurrent Neural Network, and Polynomial Expansion-Based MMSE Channel Estimation. Hybrid optimizer-based recurrent neural network long short-term memory, convex combination-based algorithms, and deep learning are also addressed for channel estimation in 5G systems. Optimized pilot-based channel estimation and large-scale MIMO SC-FDMA uplink systems have also been explored. Additionally, robust beamforming and spatial precoding techniques are being studied.