Massive Multiple Input Multiple Output

Massive Multiple Input Multiple Output (MIMO) systems aim to significantly boost wireless communication speeds by employing numerous antennas at both transmitting and receiving ends. Current research heavily focuses on mitigating the challenges of efficient channel estimation and feedback in these systems, employing deep learning architectures like variational autoencoders, graph neural networks, and transformers, alongside optimized algorithms such as approximate message passing and projected gradient ascent, to reduce computational complexity and power consumption. These advancements are crucial for realizing the potential of massive MIMO in next-generation wireless networks, improving spectral efficiency, and enabling applications like high-precision indoor localization and energy-efficient communication.