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

Abstract

Massive Multiple-Input Multiple-Output (MIMO) technology is a fundamental component of next-generation wireless communication systems, offering improved spectral efficiency, higher data rates, and enhanced network reliability. However, the performance of massive MIMO systems heavily depends on accurate channel estimation, which remains a significant challenge due to pilot contamination, hardware impairments, and computational complexity. Traditional channel estimation techniques, such as Least Squares (LS) and Minimum Mean Square Error (MMSE), often struggle to provide optimal performance in large-scale antenna systems. To overcome these limitations, machine learning (ML)-based approaches have emerged as promising solutions, enabling efficient channel estimation and system optimization through data-driven models. This review paper provides an in-depth analysis of various machine learning techniques applied to channel estimation in massive MIMO systems, including supervised learning, deep learning, and reinforcement learning-based methods. We discuss the advantages, limitations, and computational trade-offs of these techniques while comparing them with conventional estimation methods. Additionally, we explore hybrid approaches that integrate traditional and ML-based methods to enhance estimation accuracy and reduce processing complexity. Through a comprehensive survey of recent advancements, this paper highlights key research challenges, open issues, and future directions in the field. The findings indicate that machine learning-driven channel estimation can significantly improve the efficiency and adaptability of massive MIMO systems, paving the way for intelligent and self-optimizing wireless networks.