| ABSTRACT |
Massive Multiple-Input Multiple-Output (MIMO) systems are a key enabler for next-generation
wireless communication, offering high data rates, improved spectral efficiency, and robust connectivity. A critical
challenge in massive MIMO is the accurate and efficient estimation of Channel State Information (CSI), which
becomes increasingly complex with the rise in the number of antennas and users. Traditional channel estimation
techniques such as Least Squares (LS) and Minimum Mean Square Error (MMSE) are often limited by pilot
contamination, high dimensionality, and computational complexity. To address these limitations, machine learning
(ML) techniques have emerged as powerful tools for data-driven channel estimation. This review provides a
comprehensive analysis of recent ML-based methods for channel estimation in massive MIMO systems, including
supervised, unsupervised, and reinforcement learning approaches. The paper compares these techniques with
conventional methods in terms of estimation accuracy, complexity, and adaptability to dynamic environments. Key
challenges such as data requirements, model generalization, and real-time implementation are also discussed. Finally,
the review highlights future research directions, including the use of federated learning, graph neural networks, and
explainable AI for intelligent channel estimation. The integration of machine learning into massive MIMO has the
potential to significantly enhance wireless communication performance in 5G and beyond. |
| AUTHOR |
Khushboo Raj, Jyoti Verma, Santosh Yadav
M.Tech. in Digital Communication, Millenium Institute of Technology and Science, Bhopal, Madhya Pradesh, India
Department of Electrical and Electronics Engineering, MITS, Bhopal, Madhya Pradesh, India |
