Open AccessMassive MIMO Detection Algorithms Based on MMSE-SIC, ZF-MIC, Neumann Series Expansion, Gauss-Seidel, and Jacobi Method
Author(s) -
Zixuan Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1438/1/012006
Subject(s) - neumann series , jacobi method , mimo , algorithm , minimum mean square error , computational complexity theory , mathematics , inversion (geology) , iterative method , computation , gauss–seidel method , von neumann architecture , computer science , telecommunications , mathematical analysis , channel (broadcasting) , statistics , paleontology , structural basin , estimator , biology , pure mathematics
The massive multiple-input multiple-output (MIMO) systems is an important technology in the fifth generation of mobile communication. To get the result of a MIMO system require some algorithm to approximate the precise result as the computation complexity is too large. There are several methods that have been advanced in the fitting, like zero forcing (ZF) method or minimum mean-square error (MMSE) method. However, in massive MIMO system, these methods require further simplify because of the increasing complexity of matrix inversion. In the papers, many methods were presented to get the approximation matrix: like MMSE-SIC, ZF-MIC, Gauss-Seidel, Jacobi and Neumann series expansion. The Jacobi’s iterative method and Newton’s iterative method both use iteration to approach the MMSE estimation. Their BER performance can outperform current methods and require less computational complexity. Almost every method can get a nearly ideal fit when the number of users or antennas is large enough. But when the number is small, the approximation will not be very accurate.