Amalia Eka Rakhmania, Hudiono, Umi Anis Ro’isatin, and Nurul Hidayati
Channel Estimation Methods in Massive MIMO: A Comparative Review of Machine Learning and Traditional Techniques
Massive Multiple Input Multiple Output (MIMO) has emerged as a crucial technology in 5G and future 6G networks, offering unprecedented improvements in capacity, energy efficiency, and spectral efficiency. A key challenge for Massive MIMO systems is accurate and efficient channel estimation, which significantly impacts system performance. Traditional channel estimation methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE) have been widely employed, but their limitations, particularly in complex and dynamic environments, have led to the exploration of more sophisticated approaches, including machine learning (ML)-based techniques. This review aims to compare traditional channel estimation methods with modern machine learning-based techniques in Massive MIMO systems, providing insights into their performance, computational complexity, and scalability. Furthermore, this paper outlines potential future research directions, emphasizing the integration of machine learning, optimization techniques, and hardware-friendly design for enhanced performance.
Reference:
DOI: 10.36244/ICJ.2025.1.3
Please cite this paper the following way:
Amalia Eka Rakhmania, Hudiono, Umi Anis Ro’isatin, and Nurul Hidayati, "Channel Estimation Methods in Massive MIMO: A Comparative Review of Machine Learning and Traditional Techniques ", Infocommunications Journal, Vol. XVII, No 1, March 2025, pp. 19-31., https://doi.org/10.36244/ICJ.2025.1.3