Deep Learning Applications in MIMO Systems

Deep learning has emerged as a promising approach to tackle the challenges in multiple-input and multiple-output (MIMO) systems and has demonstrated its potential to improve system performance significantly. This paper studies two main applications that can improve the MIMO system performance and spectrum utilization. The first proposed application is an algorithmic approximation used to reduce computational complexity and time taken by known algorithms. The second application is used for the inversion of unknown functions in a system and channel estimation. This paper reviews several use cases of DL in MIMO systems, including channel estimation, precoding, and beamforming. We investigate the application of deep learning through neural networks to address different challenges in MIMO systems. We highlight the benefits and enhancements of deep learning compared to conventional methods and demonstrate how it can improve the performance of MIMO systems.