Abstract
In fifth generation (5G) cellular system, massive multiple-input multiple-output (MIMO) is utilized to improve the diversity gain, reliability, link robustness, latency, and power and spectral efficiencies. However, a large number of antennas requires sophisticated signal processing to detect data. Although the detection based on maximum likelihood (ML) obtains the best performance, it is not hardware friendly because of the exponential complexity. Therefore, several iterative methods are proposed to estimate the signal without computing the inverse of equalization matrix, and hence, minimize the complexity. The Jacobi (JA) and the Gauss-Seidel (GS) methods achieve a satisfactory performance. However, large iterations’ number is in demand which produces a high computational complexity. This paper proposes a detector for massive MIMO uplink (UL) system based on the JA and GS methods. Proposed detector obtains a balance between the performance and the complexity. In this research, initialization is performed based on the JA method. After-that, the estimation is performed based on the GS method. Numerical results show that the proposed JA-GS detector outperforms the GS and the JA based detector. Moreover, proposed JA-GS based detector requires few iterations to obtain the target performance and hence, a considerable reduction in computational complexity is achieved
