WTSCNet: A CSI Feedback Network Based on Wavelet Transform Convolution and Attention Mechanism Article Swipe
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· 2025
· Open Access
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· DOI: https://doi.org/10.21203/rs.3.rs-6411543/v1
To enhance the performance of massive MIMO systems, efficient downlink CSI compression and feedback are crucial in FDD mode. Deep learning (DL)-based methods surpass traditional compressed sensing but often rely on CNNs designed for image processing, neglecting essential channel and spatial information. This paper proposes WTSCNet, a novel CSI feedback network integrating wavelet transform convolution and attention mechanisms to balance network complexity and feature extraction. The encoder employs wavelet transform convolution for multi-resolution feature extraction, improving spatial information capture while reducing computational cost. The decoder incorporates the CARBlock module to enhance multi-scale and spatial-channel feature integration. Experimental results show that WTSCNet outperforms CNN-based methods like CRNet, achieving a 6.41 dB improvement in reconstruction accuracy at low compression ratios, while reducing complexity by 9.9M parameters compared to attention-based TransNet+ with a 0.3 dB accuracy gain. The proposed model offers a robust and efficient solution for CSI compression feedback.
Related Topics To Compare & Contrast
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-6411543/v1
- https://www.researchsquare.com/article/rs-6411543/latest.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410146195