Deep Learning-Based Cooperative CSI Feedback via Multiple Receiving Antennas in Massive MIMO

Liang, Xin; Shen, Jinghan; Chang, Haoran; Xu, Gu; Zhang, Lin

doi:10.1109/icc45855.2022.9839111

Cited by 7 publications

(1 citation statement)

References 19 publications

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“…The first DLbased framework, CsiNet [5], utilizes convolutional neural networks (CNNs) and significantly outperforms CS methods. Since then, further advancements have extended the original scene, including channel implicit reciprocity [6], time correlation using LSTM [7], spatial correlation among multi-users [8], etc., while more researchers focus on improving the autoencoder architecture to enhance CSI feedback accuracy. Among them, [9] increases the receptive field and extracts deeper channel features to achieve performance improvement.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Deep Network for Efficient CSI Feedback in Massive MIMO Systems

Sun

Liang

et al. 2021

IEEE Wireless Commun. Lett.

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The efficacy of massive multiple-input multiple-output (MIMO) techniques heavily relies on the accuracy of channel state information (CSI) in frequency division duplexing (FDD) systems.Many works focus on CSI compression and quantization methods to enhance CSI reconstruction accuracy with lower feedback overhead. In this letter, we propose CsiConformer, a novel CSI feedback network that combines convolutional operations and self-attention mechanisms to improve CSI feedback accuracy. Additionally, a new quantization module is developed to improve encoding efficiency. Experiment results show that CsiConformer outperforms previous state-of-the-art networks, achieving an average accuracy improvement of 17.67% with lower computational overhead.

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