Stochastic Augmented Projected Gradient Methods for the Large-Scale Precoding Matrix Indicator Selection Problem

In this paper, we consider the large-scale precoding matrix indicator (PMI) selection problem at the receiver in wireless communications. The selection is based on the channel capacity of the PMI matrix in a pre-designed codebook. The quality of the PMI matrix is essential in achieving higher spectr...

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Veröffentlicht in:IEEE transactions on wireless communications Jg. 21; H. 11; S. 9553 - 9565
Hauptverfasser: Zhang, Jiaqi, Jin, Zeyu, Jiang, Bo, Wen, Zaiwen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
block coordinate descent
Channel capacity
discrete projected gradient
Gradient methods
Iterative methods
Mathematical analysis
Optimization
partial permutation
Permutations
Precoding
Precoding matrix indicator
STEM
stochastic methods
Stochastic processes
Wireless communication
Wireless communications
ISSN:1536-1276, 1558-2248
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Zusammenfassung:In this paper, we consider the large-scale precoding matrix indicator (PMI) selection problem at the receiver in wireless communications. The selection is based on the channel capacity of the PMI matrix in a pre-designed codebook. The quality of the PMI matrix is essential in achieving higher spectral efficiency. We first derive two novel formulations including a partial permutation-matrix model and an indicator-vector model for the original problem. The discrete constraints in the formulations make the problem NP-hard. Then we propose a stochastic projected gradient method augmented by block coordinate descent under various strategies. We show that the algorithms terminate in finite steps and produce sufficient descent at each iteration when the step size is chosen properly. Extensive experiments demonstrate that our proposed algorithms are able to find better PMI matrices more efficiently compared to the existing methods.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2022.3177840