Precoder Design for Massive MIMO Downlink With Matrix Manifold Optimization

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Author(s)Sun, Rui
Author(s)Wang, Chen
Author(s)Lu, An-An
Author(s)Gao, Xiqi
Author(s)Xia, Xiang-Gen
Date Accessioned2024-05-21T17:36:09Z
Date Available2024-05-21T17:36:09Z
Publication Date2024-02-12
DescriptionThis article was originally published in IEEE Transactions on Signal Processing. The version of record is available at: https://doi.org/10.1109/TSP.2024.3364914. © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. See https://www.ieee.org/publications/rights/index.html for more information. This article will be embargoed until 02/12/2026.
AbstractWe investigate the weighted sum-rate (WSR) maximization linear precoder design for massive multiple-input multiple-output (MIMO) downlink. We consider a single-cell system with multiple users and propose a unified matrix manifold optimization framework applicable to total power constraint (TPC), per-user power constraint (PUPC) and per-antenna power constraint (PAPC). We prove that the precoders under TPC, PUPC and PAPC are on distinct Riemannian submanifolds, and transform the constrained problems in Euclidean space to unconstrained ones on manifolds. In accordance with this, we derive Riemannian ingredients, including orthogonal projection, Riemannian gradient, Riemannian Hessian, retraction and vector transport, which are needed for precoder design in the matrix manifold framework. Then, Riemannian design methods using Riemannian steepest descent, Riemannian conjugate gradient and Riemannian trust region are provided to design the WSR-maximization precoders under TPC, PUPC or PAPC. Riemannian methods do not involve the inverses of the large dimensional matrices during the iterations, reducing the computational complexities of the algorithms. Complexity analyses and performance simulations demonstrate the advantages of the proposed precoder design.
SponsorThis work was supported by the National Key R&D Program of China under Grant 2018YFB1801103, the Jiangsu Province Basic Research Project under Grant BK20192002, the Fundamental Research Funds for the Central Universities under Grant 2242022k60007, the Key R&D Plan of Jiangsu Province under Grant BE2022067, the Huawei Cooperation Project, and the National Natural Science Foundation of China under Grants 62394294 and 62371125.
CitationR. Sun, C. Wang, A. -A. Lu, X. Gao and X. -G. Xia, "Precoder Design for Massive MIMO Downlink With Matrix Manifold Optimization," in IEEE Transactions on Signal Processing, vol. 72, pp. 1065-1080, 2024, doi: 10.1109/TSP.2024.3364914.
ISSN1941-0476
URLhttps://udspace.udel.edu/handle/19716/34419
Languageen_US
PublisherIEEE Transactions on Signal Processing
Keywordslinear precoding
Keywordsmanifold optimization
Keywordsper-antenna power constraint
Keywordsper-user power constraint
Keywordstotal power constraint
Keywordsweighted sum-rate
TitlePrecoder Design for Massive MIMO Downlink With Matrix Manifold Optimization
TypeArticle
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