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A Lightweight Design to Convolution-Based Deep Learning CSI Feedback
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DOI：10.1109/LCOMM.2024.3424434.png)
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Abstract
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In frequency division duplex mode, the user equipment sends downlink channel state information (CSI) to the base station for feedback. However, high-dimensional CSI can cause a large feedback overhead. Although convolution-based deep learning methods help compress and recover CSI, the redundant features among the CSI feature maps extracted by the convolution operator cause efficiency decay. This letter applies the Ghost module, which generates feature maps from a handful of primary features, to reduce redundancy and improve feedback efficiency. Additionally, a lightweight neural network, called GCRNet, is proposed based on the Ghost module. Compared with CLNet, GCRNet reduces complexity by an average of 22.15% while maintaining comparable performance.
Keywords:
Convolution
Feature extraction
Task analysis
Redundancy
Decoding
Artificial neural networks
Vectors
CSI feedback
deep learning
lightweight design
feature efficiency
convolution neural network
Journal
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4.4
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1.3W
Citations:
2.2W
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