Massive MIMO pre-coding algorithm based on truncated Kapteyn series expansion

Man, Yongqiang; Li, Zhengquan; Yan, Feng; Xing, Song; Shen, Lianfeng

doi:10.1109/iccs.2016.7833644

Cited by 8 publications

(2 citation statements)

References 10 publications

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“…Thus, by truncating Equation ( 12) and taking the first N terms, we obtain the RZF precoding matrix with the truncated Kapteyn polynomial expansion as [24]…”

Section: Truncated Kapteyn Series Expansion Algorithmmentioning

confidence: 99%

Improved Massive MIMO RZF Precoding Algorithm Based on Truncated Kapteyn Series Expansion

Xue

Man

et al. 2019

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In order to reduce the computational complexity of the inverse matrix in the regularized zero-forcing (RZF) precoding algorithm, this paper expands and approximates the inverse matrix based on the truncated Kapteyn series expansion and the corresponding low-complexity RZF precoding algorithm is obtained. In addition, the expansion coefficients of the truncated Kapteyn series in our proposed algorithm are optimized, leading to further improvement of the convergence speed of the precoding algorithm under the premise of the same computational complexity as the traditional RZF precoding. Moreover, the computational complexity and the downlink channel performance in terms of the average achievable rate of the proposed RZF precoding algorithm and other RZF precoding algorithms with typical truncated series expansion approaches are analyzed, and further evaluated by numerical simulations in a large-scale single-cell multiple-input-multiple-output (MIMO) system. Simulation results show that the proposed improved RZF precoding algorithm based on the truncated Kapteyn series expansion performs better than other compared algorithms while keeping low computational complexity.

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“…Thus, by truncating Equation ( 12) and taking the first N terms, we obtain the RZF precoding matrix with the truncated Kapteyn polynomial expansion as [24]…”

Section: Truncated Kapteyn Series Expansion Algorithmmentioning

confidence: 99%

Improved Massive MIMO RZF Precoding Algorithm Based on Truncated Kapteyn Series Expansion

Xue

Man

et al. 2019

Information

Self Cite

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“…Hence, a number of low-complexity TPC schemes have been proposed for implementing the matrix inversion by polynomial expansion or iterative methods [19]- [25]. Specifically, the Neumann series (NS) and Kapteyn series (KS) based on polynomial expansion are applied for linear TPC in [26], [27]. Compared to polynomial expansion, approximating the matrix inversion by iterative methods is more promising as a benefit of its faster convergence.…”

Section: Introductionmentioning

confidence: 99%

Rapidly Converging Low-Complexity Iterative Transmit Precoders for Massive MIMO Downlink

Wang

Gao

et al. 2023

IEEE Trans. Commun.

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In this paper, rapidly converging low-complexity iterative transmit precoding (TPC) techniques are proposed for the massive multiple-input multiple-output (MIMO) downlink. First of all, the proposed random block-based iterative TPC (RBI-TPC) algorithm performs its iterations by updating multiple rather than a single component at each instant, where the updating order of each block containing multiple components relies on the samples randomly sampled from a discrete distribution. Based on the analytically derived convergence rate, we demonstrate that improved convergence is achieved by the block-based update mechanism conceived since the correlation between multiple components can be beneficially exploited. Then, the random sampling that determines the updating order is studied. By applying conditional random sampling, the updating order is optimized based on the latest updates for attaining more rapid convergence. We also demonstrate that the associated updating order may become deterministic under specific conditions so that a fixed but optimized updating order can be used for facilitating the practical implementations, which paves the way for conceiving the ordered block-based iterative TPC (OBI-TPC) algorithm. Finally, the concept of successive over-relaxation (SOR) is adopted for further convergence improvement and simulations are presented to illustrate the performance improvements of the proposed RBI and OBI

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A Low-Complexity Massive MIMO Precoding Algorithm Based on Chebyshev Iteration

Zhang

Shen

et al. 2017

IEEE Access

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Massive MIMO pre-coding algorithm based on truncated Kapteyn series expansion

Cited by 8 publications

References 10 publications

Improved Massive MIMO RZF Precoding Algorithm Based on Truncated Kapteyn Series Expansion

Improved Massive MIMO RZF Precoding Algorithm Based on Truncated Kapteyn Series Expansion

Rapidly Converging Low-Complexity Iterative Transmit Precoders for Massive MIMO Downlink

A Low-Complexity Massive MIMO Precoding Algorithm Based on Chebyshev Iteration

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