Massive MIMO System using Channel Estimation and Machine Learning Technique: A Review

Abstract

Next-generation smart air interface solution for wireless local area network is mixture of MIMO-OFDM (Multiple-input multiple-output) with (Orthogonal frequency division multiplexing). To discuss and predict the use of machine learning and deep learning based on MIMO communications, channel estimation, signal detection and selection in OFDM systems. Massive Multiple-Input Multiple-Output (MIMO) technology is a fundamental enabler of next-generation wireless networks, offering significant improvements in spectral efficiency, energy efficiency, and network capacity. However, accurate channel estimation remains a critical challenge due to the large number of antennas and dynamic wireless environments. Traditional channel estimation techniques, such as Least Squares (LS) and Minimum Mean Square Error (MMSE), often suffer from high computational complexity and performance degradation in low-SNR conditions. To address these limitations, machine learning (ML)-based approaches have emerged as a promising solution, leveraging data-driven models to enhance channel estimation accuracy and adaptability.