Adaptive Wireless Image Transmission Transformer Architecture for Image Transmission and Reconstruction
The advancement of 6G (6th Generation Mobile Networks) communication technology has posed challenges for traditional communication network architectures in meeting the demands for communication efficiency and quality. Semantic communication technology, characterized by its “understand before transmi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/21/6772 |
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| author | Hongcheng Li Geming Xia Chaodong Yu Yuze Zhang Hongfeng Li |
| author_facet | Hongcheng Li Geming Xia Chaodong Yu Yuze Zhang Hongfeng Li |
| author_sort | Hongcheng Li |
| collection | DOAJ |
| description | The advancement of 6G (6th Generation Mobile Networks) communication technology has posed challenges for traditional communication network architectures in meeting the demands for communication efficiency and quality. Semantic communication technology, characterized by its “understand before transmit” approach, has emerged as a pivotal technology driving the progress of 6G due to its ability to enhance communication efficiency and quality. The Wireless Image Transmission Transformer (WITT) model, which operates as a semantic communication system leveraging vision transformer technology for the transmission of semantic images, has shown efficacy in transmitting input images through processes of feature extraction and channel adaptation. This study introduces an advanced channel adaptive module that is informed by deep learning methodologies and the adaptive modulation principles of the Variational Information Bottleneck (VIB). This innovation enhances the original WITT model, resulting in the development of the Adaptive Wireless Image Transmission Transformer (ADWITT) architecture. Comprehensive experimental results have unequivocally shown that the transmission performance of the ADWITT architecture substantially surpasses that of the conventional WITT (Wavelet Image Transmission Technique) model, particularly in scenarios characterized by harsh and detrimental channel conditions. These findings underscore the robustness and adaptability of the ADWITT approach, which is poised to improve the field of image transmission by offering superior performance and resilience in environments where traditional methods falter. |
| format | Article |
| id | doaj-art-120f3bbfbb004b87b49bc80281d4e13f |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-120f3bbfbb004b87b49bc80281d4e13f2024-11-08T14:40:53ZengMDPI AGSensors1424-82202024-10-012421677210.3390/s24216772Adaptive Wireless Image Transmission Transformer Architecture for Image Transmission and ReconstructionHongcheng Li0Geming Xia1Chaodong Yu2Yuze Zhang3Hongfeng Li4School of Computer Science, National University of Defense Technology, Changsha 410000, ChinaSchool of Computer Science, National University of Defense Technology, Changsha 410000, ChinaSchool of Computer Science, National University of Defense Technology, Changsha 410000, ChinaSchool of Computer Science, National University of Defense Technology, Changsha 410000, ChinaSchool of Computer Science, National University of Defense Technology, Changsha 410000, ChinaThe advancement of 6G (6th Generation Mobile Networks) communication technology has posed challenges for traditional communication network architectures in meeting the demands for communication efficiency and quality. Semantic communication technology, characterized by its “understand before transmit” approach, has emerged as a pivotal technology driving the progress of 6G due to its ability to enhance communication efficiency and quality. The Wireless Image Transmission Transformer (WITT) model, which operates as a semantic communication system leveraging vision transformer technology for the transmission of semantic images, has shown efficacy in transmitting input images through processes of feature extraction and channel adaptation. This study introduces an advanced channel adaptive module that is informed by deep learning methodologies and the adaptive modulation principles of the Variational Information Bottleneck (VIB). This innovation enhances the original WITT model, resulting in the development of the Adaptive Wireless Image Transmission Transformer (ADWITT) architecture. Comprehensive experimental results have unequivocally shown that the transmission performance of the ADWITT architecture substantially surpasses that of the conventional WITT (Wavelet Image Transmission Technique) model, particularly in scenarios characterized by harsh and detrimental channel conditions. These findings underscore the robustness and adaptability of the ADWITT approach, which is poised to improve the field of image transmission by offering superior performance and resilience in environments where traditional methods falter.https://www.mdpi.com/1424-8220/24/21/67726Gsemantic communicationdeep learningchannel adaptationWITT model |
| spellingShingle | Hongcheng Li Geming Xia Chaodong Yu Yuze Zhang Hongfeng Li Adaptive Wireless Image Transmission Transformer Architecture for Image Transmission and Reconstruction Sensors 6G semantic communication deep learning channel adaptation WITT model |
| title | Adaptive Wireless Image Transmission Transformer Architecture for Image Transmission and Reconstruction |
| title_full | Adaptive Wireless Image Transmission Transformer Architecture for Image Transmission and Reconstruction |
| title_fullStr | Adaptive Wireless Image Transmission Transformer Architecture for Image Transmission and Reconstruction |
| title_full_unstemmed | Adaptive Wireless Image Transmission Transformer Architecture for Image Transmission and Reconstruction |
| title_short | Adaptive Wireless Image Transmission Transformer Architecture for Image Transmission and Reconstruction |
| title_sort | adaptive wireless image transmission transformer architecture for image transmission and reconstruction |
| topic | 6G semantic communication deep learning channel adaptation WITT model |
| url | https://www.mdpi.com/1424-8220/24/21/6772 |
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