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- W4387475432 abstract "Massive multiple-input multiple-output (MIMO) system is one of the wireless technologies with great research significance based on channel state information (CSI). As the complexity of the CSI matrix increases, CSI feedback faces many challenges and limitations. This paper proposes a neural network composed of the encoder-decoder framework, which can effectively compress and recover CSI. The network utilizes a channel information generation module and multi-scale feature extraction module to enhance the information acquisition ability of the CSI matrix. At the same time, it reduces the computational complexity of the network by using lightweight architecture. Experimental results show that the network outperforms other advanced deep-learning methods at multiple compression rates." @default.
- W4387475432 created "2023-10-11" @default.
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- W4387475432 date "2023-10-10" @default.
- W4387475432 modified "2023-10-17" @default.
- W4387475432 title "Mal-Net: multi-scale feature extraction and attention mechanism lightweight network for CSI feedback" @default.
- W4387475432 doi "https://doi.org/10.1117/12.3006013" @default.
- W4387475432 hasPublicationYear "2023" @default.
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