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

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 rat...

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Published in:	IEEE transactions on communications Vol. 71; no. 12; p. 1
Main Authors:	Wang, Zheng, Wang, Jiaheng, Gao, Zhen, Huang, Yongming, Ng, Derrick Wing Kwan, Hanzo, Lajos
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Complexity Convergence Downlink Downlinking Iterative algorithms iterative methods Jacobian matrices low-complexity linear transmit precoding Massive MIMO Precoding Random sampling Symmetric matrices
ISSN:	0090-6778, 1558-0857
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Abstract	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 TPC algorithms compared to the existing low-complexity iterative TPC schemes.
AbstractList	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 TPC algorithms compared to the existing low-complexity iterative TPC schemes.
Author	Ng, Derrick Wing Kwan Wang, Jiaheng Gao, Zhen Huang, Yongming Hanzo, Lajos Wang, Zheng
Author_xml	– sequence: 1 givenname: Zheng orcidid: 0000-0003-3528-558X surname: Wang fullname: Wang, Zheng organization: National Mobile Communications Research Laboratory, School of Information Science and Engineering, and Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing, China – sequence: 2 givenname: Jiaheng orcidid: 0000-0002-9783-5471 surname: Wang fullname: Wang, Jiaheng organization: National Mobile Communications Research Laboratory, School of Information Science and Engineering, and Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing, China – sequence: 3 givenname: Zhen orcidid: 0000-0002-2709-0216 surname: Gao fullname: Gao, Zhen organization: School of Information and Electronics, Beijing Institute of Technology, Beijing, China – sequence: 4 givenname: Yongming orcidid: 0000-0003-3616-4616 surname: Huang fullname: Huang, Yongming organization: National Mobile Communications Research Laboratory, School of Information Science and Engineering, and Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing, China – sequence: 5 givenname: Derrick Wing Kwan orcidid: 0000-0001-6400-712X surname: Ng fullname: Ng, Derrick Wing Kwan organization: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia – sequence: 6 givenname: Lajos orcidid: 0000-0002-2636-5214 surname: Hanzo fullname: Hanzo, Lajos organization: School of Electronics and Computer Science, University of Southampton, Southampton, U.K
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SubjectTerms	Algorithms Complexity Convergence Downlink Downlinking Iterative algorithms iterative methods Jacobian matrices low-complexity linear transmit precoding Massive MIMO Precoding Random sampling Symmetric matrices
Title	Rapidly Converging Low-Complexity Iterative Transmit Precoders for Massive MIMO Downlink
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