Research on Signal Detection Algorithms and Circuit Structures in Large-scale MIMO Systems

Large-scale Multiple-Input Multiple-Output (MIMO) systems are crucial for improving both the spectral efficiency and reliability of communication links. Additionally, they contribute to reducing the power consumption of base stations (BS), which is a significant concern in modern wireless networks. In this context, data detection plays an essential role, particularly in the uplink of large-scale MIMO systems. However, one of the major challenges lies in the substantial increase in computational complexity required for base stations to process the data efficiently in such systems. The conventional optimal detection techniques, such as ML detection, SD detection, MMSE detection, and ZF detection, are widely employed in MIMO systems. While these methods offer optimal performance in terms of detection accuracy, they suffer from extremely high computational demands, which can be impractical for real-time implementation in large-scale networks. Under the premise of approaching the performance of the optimal calculation methods, this paper analyzes three new algorithms with lower computational complexity, including iterative algorithms, matrix approximation algorithms, and K-Best algorithms. This paper conducts a detailed study on the advantages and disadvantages of the calculation methods and circuit structures of these algorithms.