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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Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 21; no. 12; pp. 10115 - 10129
Main Authors: Wang, Zheng, Gower, Robert M., Zhang, Cheng, Lyu, Shanxiang, Xia, Yili, Huang, Yongming
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2022.3182407