A Summary and Analysis of Low Complexity Methods for Massive MIMO Systems

2018 International Conference on Advanced Electronic Materials, Computers and Materials Engineering

材料科学;无线电电子学;计算机科学

Chen, Siyu^1

Department of Electronic Information Engineering, Sichuan University, No. 4, First South Section, First Ring Road, Wuhou District, Chengdu City, Sichuan Province, China^1

关键词: Gauss-Seidel method;    Improved convergence;    Linear minimum mean square errors;    Massive MIMO systems;    Massive multiple-input- multiple-output system (MIMO);    Neumann series expansion;    Spectral efficiencies;    Symmetric successive over relaxations;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/439/5/052031/pdf
DOI  :  10.1088/1757-899X/439/5/052031

来源: IOP

【 摘 要 】

The massive multiple-input multiple-output (MIMO) systems help increase the wireless channel capacity and spectral efficiency manifold, and the coverage as well as power consumption. In MIMO systems, the linear minimum mean-square error (MMSE) can achieve the near-performance. However, along with the huge number of the BS-to-user antennas at the BS station, the computational complexity becomes a serious problem due to computing matrix inversion. Many methods were proposed to improve complexity, for instance, Neumann series expansion method, Jacobi methods and conjugate gradient (CG) method, but it is still costly. And recently many other methods on account of the above methods were proposed, respectively. Such as Gauss-Seidel method, Symmetric Successive Over Relaxation (SSOR) method, further improved Jacobi method based on soft-output massive MIMO detection scheme, pre-conditioner conjugate gradient (PCG) based on SSOR, split pre-conditioned conjugate gradient (SPCG) method, etc, which all show the reduced complexity, improved convergence and BER performance.