CSI feedback algorithm for massive MIMO systems based on SFNet

被引：0

作者：

Zhang, Yun ^{[1
]}

Huang, Jingwei ^{[1
]}

Xu, Sunwu ^{[1
]}

Gao, Gui ^{[1
]}

Yu, Shujuan ^{[1
]}

Zhao, Shengmei ^{[2
]}

机构：

[1] College of Electronic and Optical Engineering, College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing

[2] School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing

来源：

Tongxin Xuebao/Journal on Communications | 2025年 / 46卷 / 06期

基金：

中国国家自然科学基金;

关键词：

channel state information; CSI feedback; deep learning; massive MIMO;

D O I：

10.11959/j.issn.1000-436x.2025097

中图分类号：

学科分类号：

摘要：

To address the issues of high computational complexity, low feedback accuracy, and neglect of quantization loss in existing deep learning-based channel state information (CSI) feedback methods for frequency-division duplex massive multiple-input multiple-output (MIMO) systems, the deep learning algorithm SFNet for CSI feedback was proposed. SFNet integrated a traditional convolutional neural network (CNN) and Transformer architecture, incorporating a spatial-frequency block designed to leverage global information and a multi-scale adaptive spatial attention gate for fusing local and global features. Fast Fourier convolution and a dynamic feature fusion mechanism were utilized to activate more input information, adjust the receptive field, selectively highlight spatially correlated features, suppress interference, and allow the network to achieve advanced performance with extremely low computational complexity. The experimental results show that the proposed algorithm achieves advanced estimation performance with significantly low computational complexity. Furthermore, the trained model exhibits strong robustness across various environments. © 2025 Editorial Board of Journal on Communications. All rights reserved.

引用

页码：196 / 208

页数：12

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