Adaptive Deep Joint Source-Channel Coding for One-to-Many Wireless Image Transmission

Deep learning based joint source-channel coding (DJSCC) has recently made significant progress and emerged as a potential solution for future wireless communication. However, there are still several crucial issues that necessitate further in-depth exploration to enhance the efficiency of DJSCC, such...

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Vydáno v:IEEE transactions on broadcasting Ročník 71; číslo 3; s. 914 - 929
Hlavní autoři: Luo, Lei, He, Ziyang, Wu, Junjie, Guo, Hongwei, Zhu, Ce
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.09.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation models
Bandwidth
bandwidth adaptability
Bandwidths
Channel coding
Coding
Complexity
Deep joint source-channel coding
gradient normalization
Image coding
Image communication
Image reconstruction
Image transmission
Modules
neighboring attention
Source coding
Time-varying channels
Training
Wireless communication
Wireless communications
ISSN:0018-9316, 1557-9611
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Shrnutí:Deep learning based joint source-channel coding (DJSCC) has recently made significant progress and emerged as a potential solution for future wireless communication. However, there are still several crucial issues that necessitate further in-depth exploration to enhance the efficiency of DJSCC, such as channel quality adaptability, bandwidth adaptability, and the delicate balance between efficiency and complexity. This work proposes an a daptive d eep joint source-channel coding scheme tailored for one-to- m any wireless i mage t ransmission scenarios (ADMIT). First, to effectively improve transmission performance, neighboring attention is introduced as the backbone for the proposed ADMIT method. Second, a channel quality adaptive module (CQAM) is designed based on multi-scale feature fusion, which seamlessly adapts to fluctuating channel conditions across a wide range of channel signal-to-noise ratios (CSNRs). Third, to be precisely tailored to different bandwidth resources, the channel gained adaptive module (CGAM) dynamically adjusts the significance of individual channels within the latent space, which ensures seamless varying bandwidth accommodation with a single model through bandwidth adaptation and symbol completion. Additionally, to mitigate the imbalance of loss across multiple bandwidth ratios during the training process, the gradient normalization (GradNorm) based training strategy is leveraged to ensure adaptive balancing of loss decreasing. The extensive experimental results demonstrate that the proposed method significantly enhances transmission performance while maintaining relatively low computational complexity. The source codes are available at: https://github.com/llsurreal919/ADMIT .
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ISSN:0018-9316
1557-9611
DOI:10.1109/TBC.2025.3559003