A Statistical Linear Precoding Scheme Based on Random Iterative Method for Massive MIMO Systems

In this paper, the random iterative method is introduced to massive multiple-input multiple-output (MIMO) systems for the efficient downlink linear precoding. By adopting the random sampling into the traditional iterative methods, the matrix inversion within the linear precoding schemes can be appro...

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Vydané v:	IEEE transactions on wireless communications Ročník 21; číslo 12; s. 10115 - 10129
Hlavní autori:	Wang, Zheng, Gower, Robert M., Zhang, Cheng, Lyu, Shanxiang, Xia, Yili, Huang, Yongming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Approximation Complexity Complexity theory Convergence convergence analysis and enhancement Downlink Downlinking Iterative methods Jacobian matrices large-scale MIMO linear precoding low complexity Massive MIMO Mathematical analysis matrix inversion Optimization Precoding Random sampling Symmetric matrices
ISSN:	1536-1276, 1558-2248
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Abstract	In this paper, the random iterative method is introduced to massive multiple-input multiple-output (MIMO) systems for the efficient downlink linear precoding. By adopting the random sampling into the traditional iterative methods, the matrix inversion within the linear precoding schemes can be approximated statistically, which not only achieves a faster exponential convergence with low complexity but also experiences a global convergence without suffering from the various convergence requirements. Specifically, based on the random iterative method, the randomized iterative precoding algorithm (RIPA) is firstly proposed and we show its approximation error decays exponentially and globally along with the number of iterations. Then, with respect to the derived convergence rate, the concept of conditional sampling is introduced, so that further optimization and enhancement are carried out to improve both the convergence and the efficiency of the randomized iterations. After that, based on the equivalent iteration transformation, the modified randomized iterative precoding algorithm (MRIPA) is presented, which achieves a better precoding performance with low-complexity for various scenarios of massive MIMO. Finally, simulation results based on downlink precoding in massive MIMO systems are given to show the system gains of RIPA and MRIPA in terms of performance and complexity.
AbstractList	In this paper, the random iterative method is introduced to massive multiple-input multiple-output (MIMO) systems for the efficient downlink linear precoding. By adopting the random sampling into the traditional iterative methods, the matrix inversion within the linear precoding schemes can be approximated statistically, which not only achieves a faster exponential convergence with low complexity but also experiences a global convergence without suffering from the various convergence requirements. Specifically, based on the random iterative method, the randomized iterative precoding algorithm (RIPA) is firstly proposed and we show its approximation error decays exponentially and globally along with the number of iterations. Then, with respect to the derived convergence rate, the concept of conditional sampling is introduced, so that further optimization and enhancement are carried out to improve both the convergence and the efficiency of the randomized iterations. After that, based on the equivalent iteration transformation, the modified randomized iterative precoding algorithm (MRIPA) is presented, which achieves a better precoding performance with low-complexity for various scenarios of massive MIMO. Finally, simulation results based on downlink precoding in massive MIMO systems are given to show the system gains of RIPA and MRIPA in terms of performance and complexity.
Author	Lyu, Shanxiang Xia, Yili Huang, Yongming Wang, Zheng Zhang, Cheng Gower, Robert M.
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SubjectTerms	Algorithms Approximation Complexity Complexity theory Convergence convergence analysis and enhancement Downlink Downlinking Iterative methods Jacobian matrices large-scale MIMO linear precoding low complexity Massive MIMO Mathematical analysis matrix inversion Optimization Precoding Random sampling Symmetric matrices
Title	A Statistical Linear Precoding Scheme Based on Random Iterative Method for Massive MIMO Systems
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