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Efficient Statistical Linear Precoding for Downlink Massive MIMO Systems
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DOI：10.1109/TWC.2024.3419137.png)
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Abstract
En 中文
In this paper, we study low-complexity linear precoding for downlink massive multiple-input multiple-output (MIMO) systems, exploiting a statistical method. In sharp contrast to traditional linear precoding algorithms, our proposed efficient randomized iterative precoding algorithm (ERIPA) not only avoids costly matrix inversion but also considers the complexity reduction of matrix multiplication involved, thus enabling more efficient linear precoding. Additionally, ERIPA is demonstrated to have both exponentially fast and global convergence, making it adaptable to various practical scenarios of massive MIMO. We also investigate the convergence phenomenon of ERIPA in relation to the selection of the sampling distribution during random iterations. After that, the concept of conditional sampling is introduced to ERIPA such that significant system potential can be beneficially exploited in terms of both precoding performance and computational complexity. Finally, simulation results regarding the downlink massive MIMO are presented to confirm the superiorities of the proposed ERIPA.
Keywords:
Precoding
Convergence
Complexity theory
Downlink
Massive MIMO
Iterative algorithms
Jacobian matrices
linear precoding
low complexity
global convergence
iterative methods
convergence analysis and enhancement
Journal
IF:
10.7
Papers:
1.3W
Citations:
5.3W