Introduction

Massive multiple-input multiple-output (MIMO) technology has emerged as a promising approach for the next generation of wireless communication systems, as it offers increased spectral efficiency, higher energy efficiency, and improved user experience. However, as the number of antennas at the base station (BS) grows significantly, the computational complexity and the amount of data to be processed also increase, leading to significant challenges in signal processing. Sparse signal processing techniques have been proposed to address these challenges by exploiting the sparsity of the wireless channels, which often have a small number of significant paths. By exploiting this sparsity, sparse signal processing can reduce the computational complexity and improve the performance of massive MIMO systems. Some of the most popular sparse signal processing techniques used in massive MIMO systems include compressed sensing, matching pursuit, and message passing algorithms. These techniques can be used to design efficient algorithms for channel estimation (CE), detection, and precoding, and can significantly reduce the computational complexity of massive MIMO systems while maintaining high performance. Overall, the combination of massive MIMO and sparse signal processing offers a powerful framework for addressing the challenges of next-generation wireless communication systems.