WTSCNet: A CSI Feedback Network Based on Wavelet Transform Convolution and Attention Mechanism Article Swipe
Yongli An
,
Shuoyang Lu
,
Yixiao Liu
,
Yingchao Liu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6411543/v1
YOU?
·
· 2025
· Open Access
·
· 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.
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Metadata
- 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
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410146195Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-6411543/v1Digital Object Identifier
- Title
-
WTSCNet: A CSI Feedback Network Based on Wavelet Transform Convolution and Attention MechanismWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-07Full publication date if available
- Authors
-
Yongli An, Shuoyang Lu, Yixiao Liu, Yingchao LiuList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-6411543/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-6411543/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-6411543/latest.pdfDirect OA link when available
- Concepts
-
Computer science, Convolution (computer science), Encoder, Artificial intelligence, Feature (linguistics), Wavelet, Channel (broadcasting), Wavelet transform, Feature extraction, Algorithm, Pattern recognition (psychology), Artificial neural network, Telecommunications, Philosophy, Operating system, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.multi-scale | 92 |
| abstract_inverted_index.outperforms | 102 |
| abstract_inverted_index.performance | 4 |
| abstract_inverted_index.processing, | 36 |
| abstract_inverted_index.traditional | 25 |
| abstract_inverted_index.Experimental | 97 |
| abstract_inverted_index.incorporates | 86 |
| abstract_inverted_index.information. | 42 |
| abstract_inverted_index.integration. | 96 |
| abstract_inverted_index.computational | 82 |
| abstract_inverted_index.reconstruction | 113 |
| abstract_inverted_index.attention-based | 127 |
| abstract_inverted_index.spatial-channel | 94 |
| abstract_inverted_index.multi-resolution | 73 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.21648176 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |