WDLReconNet: Compressive Sensing Reconstruction With Deep Learning Over Wireless Fading Channels
Deep learning has been exploited in compressive sensing to reduce the computational complexity of reconstruction algorithms. However, existing deep-learning-based reconstruction algorithms might result in poor performance, when applied in wireless transmission environments. First, the impact of chan...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8648366/ |
| _version_ | 1818323693092208640 |
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| author | Hancheng Lu Lei Bo |
| author_facet | Hancheng Lu Lei Bo |
| author_sort | Hancheng Lu |
| collection | DOAJ |
| description | Deep learning has been exploited in compressive sensing to reduce the computational complexity of reconstruction algorithms. However, existing deep-learning-based reconstruction algorithms might result in poor performance, when applied in wireless transmission environments. First, the impact of channel noise and fading on reconstruction has not been considered. Second, with the fully connected layers, most of these algorithms have been designed for a given sampling ratio, and thus cannot handle bandwidth variations. To tackle these problems, we propose a wireless deep learning reconstruction network (WDLReconNet). The most distinctive aspect of the WDLReconNet is that a feature enhancement layer (FEL) is designed and combined with the convolutional neural network (CNN). To combat the adverse impact from wireless transmission environments (i.e., channel noise and fading and bandwidth variations), FEL enhances features remained in compressive measurements by roughly recovering the signal based on dictionary learning. Then, this rough signal is processed by CNN to finalize reconstruction. In this way, the advantages of CNN in signal feature learning can be fully exploited for reconstruction. Furthermore, we propose fast-WDLReconNet to accelerate the training process of WDLReconNet. The experimental results demonstrate that the proposed algorithms outperform existing traditional and deep learning-based reconstruction algorithms under various scenarios. |
| first_indexed | 2024-12-13T11:16:44Z |
| format | Article |
| id | doaj.art-9089dcd3a46540a7b73b5457518f4b8a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:16:44Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9089dcd3a46540a7b73b5457518f4b8a2022-12-21T23:48:37ZengIEEEIEEE Access2169-35362019-01-017244402445110.1109/ACCESS.2019.29007158648366WDLReconNet: Compressive Sensing Reconstruction With Deep Learning Over Wireless Fading ChannelsHancheng Lu0https://orcid.org/0000-0001-8302-4996Lei Bo1Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, ChinaDepartment of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, ChinaDeep learning has been exploited in compressive sensing to reduce the computational complexity of reconstruction algorithms. However, existing deep-learning-based reconstruction algorithms might result in poor performance, when applied in wireless transmission environments. First, the impact of channel noise and fading on reconstruction has not been considered. Second, with the fully connected layers, most of these algorithms have been designed for a given sampling ratio, and thus cannot handle bandwidth variations. To tackle these problems, we propose a wireless deep learning reconstruction network (WDLReconNet). The most distinctive aspect of the WDLReconNet is that a feature enhancement layer (FEL) is designed and combined with the convolutional neural network (CNN). To combat the adverse impact from wireless transmission environments (i.e., channel noise and fading and bandwidth variations), FEL enhances features remained in compressive measurements by roughly recovering the signal based on dictionary learning. Then, this rough signal is processed by CNN to finalize reconstruction. In this way, the advantages of CNN in signal feature learning can be fully exploited for reconstruction. Furthermore, we propose fast-WDLReconNet to accelerate the training process of WDLReconNet. The experimental results demonstrate that the proposed algorithms outperform existing traditional and deep learning-based reconstruction algorithms under various scenarios.https://ieeexplore.ieee.org/document/8648366/Compressive sensing (CS)deep learningreconstruction algorithmswireless fading channelsconvolutional neural network (CNN) |
| spellingShingle | Hancheng Lu Lei Bo WDLReconNet: Compressive Sensing Reconstruction With Deep Learning Over Wireless Fading Channels IEEE Access Compressive sensing (CS) deep learning reconstruction algorithms wireless fading channels convolutional neural network (CNN) |
| title | WDLReconNet: Compressive Sensing Reconstruction With Deep Learning Over Wireless Fading Channels |
| title_full | WDLReconNet: Compressive Sensing Reconstruction With Deep Learning Over Wireless Fading Channels |
| title_fullStr | WDLReconNet: Compressive Sensing Reconstruction With Deep Learning Over Wireless Fading Channels |
| title_full_unstemmed | WDLReconNet: Compressive Sensing Reconstruction With Deep Learning Over Wireless Fading Channels |
| title_short | WDLReconNet: Compressive Sensing Reconstruction With Deep Learning Over Wireless Fading Channels |
| title_sort | wdlreconnet compressive sensing reconstruction with deep learning over wireless fading channels |
| topic | Compressive sensing (CS) deep learning reconstruction algorithms wireless fading channels convolutional neural network (CNN) |
| url | https://ieeexplore.ieee.org/document/8648366/ |
| work_keys_str_mv | AT hanchenglu wdlreconnetcompressivesensingreconstructionwithdeeplearningoverwirelessfadingchannels AT leibo wdlreconnetcompressivesensingreconstructionwithdeeplearningoverwirelessfadingchannels |