期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 71, 期 11, 页码 11972-11987出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2022.3194870
关键词
Cell-free massive MIMO; computational com- plexity; convergence; iterative methods; linear precoding; matrix inversion; parallel computation
资金
- Rohit Sharma Professorship
- TELUS
- Natural Sciences and Engineering Research Council(NSERC) of Canada
Cell-free massive multiple-input multiple-output (mMIMO) systems, which distribute a large number of access points over the coverage area to serve users jointly, require efficient methods to calculate the precoding matrix. This study examines several iterative methods and proposes the hyper-power iterative inversion method, which demonstrates fast convergence, high accuracy, and strong numerical stability. The hyper-power method is a promising candidate for matrix inversion in cell-free mMIMO system precoders.
Cell-free massive multiple-input multiple-output (mMIMO) systems are an alternative topology for mMIMO deployment, wherein a large number of access points are distributed over the coverage area to jointly serve users. Linear precoding methods such as zero-forcing are sufficient to achieve near-optimal performance in mMIMO systems. However, a key challenge in implementing these precoders can be a channel matrix inversion operation, which results in significant computational complexity in systems with large-scale antenna arrays. Hence, instead of direct matrix inversion, we examine several iterative methods to calculate the precoding matrix in a cell-free mMIMO system. We investigate their computational complexity and convergence rate in the presence of small- and large-scale fading and spatial correlation between antennas. Notably, we demonstrate that some iterative methods previously proposed for conventional (co-located) mMIMO do not always converge for cell-free mMIMO. Our main focus is the hyper-power iterative inversion method, which can be applied to both matrix inverses and pseudoinverses with guaranteed convergence; its factorized version also reduces its computational complexity. Although the hyper-power method does not reduce the complexity compared to direct matrix inversion, it converges quickly with high accuracy and strong numerical stability, and is conducive to parallel computation. These qualities make it a good candidate for matrix inversion in cell-free mMIMO system precoders.
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