Deep Refinement-Based Joint Source Channel Coding over Time- Varying Channels
In recent developments, deep learning (DL)-based joint source-channel coding (JSCC) for wireless image transmission has made significant strides in performance enhancement. Nonetheless, the majority of existing DL-based JSCC methods are tailored for scenarios featuring stable channel conditions, not...
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| Vydáno v: | IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC s. 1 - 6 |
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IEEE
21.04.2024
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| ISSN: | 1558-2612 |
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| Abstract | In recent developments, deep learning (DL)-based joint source-channel coding (JSCC) for wireless image transmission has made significant strides in performance enhancement. Nonetheless, the majority of existing DL-based JSCC methods are tailored for scenarios featuring stable channel conditions, notably a fixed signal-to-noise ratio (SNR). This specialization poses a limitation, as their performance tends to wane in practical scenarios marked by highly dynamic channels, given that a fixed SNR inadequately represents the dynamic nature of such channels. In response to this challenge, we introduce a novel solution, namely deep refinement-based JSCC (DRJSCC). This innovative method is designed to seamlessly adapt to channels ex-hibiting temporal variations. By leveraging instantaneous channel state information (CSI), we dynamically optimize the encoding strategy through re-encoding the channel symbols. This dynamic adjustment ensures that the encoding strategy consistently aligns with the varying channel conditions during the transmission process. Specifically, our approach begins with the division of encoded symbols into multiple blocks, which are transmitted progressively to the receiver. In the event of changing channel conditions, we propose a mechanism to re-encode the remaining blocks, allowing them to adapt to the current channel conditions. Experimental results show that the DRJSCC scheme achieves comparable performance to the other mainstream DL-based JSCC models in stable channel conditions, and also exhibits great robustness against time-varying channels. |
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| AbstractList | In recent developments, deep learning (DL)-based joint source-channel coding (JSCC) for wireless image transmission has made significant strides in performance enhancement. Nonetheless, the majority of existing DL-based JSCC methods are tailored for scenarios featuring stable channel conditions, notably a fixed signal-to-noise ratio (SNR). This specialization poses a limitation, as their performance tends to wane in practical scenarios marked by highly dynamic channels, given that a fixed SNR inadequately represents the dynamic nature of such channels. In response to this challenge, we introduce a novel solution, namely deep refinement-based JSCC (DRJSCC). This innovative method is designed to seamlessly adapt to channels ex-hibiting temporal variations. By leveraging instantaneous channel state information (CSI), we dynamically optimize the encoding strategy through re-encoding the channel symbols. This dynamic adjustment ensures that the encoding strategy consistently aligns with the varying channel conditions during the transmission process. Specifically, our approach begins with the division of encoded symbols into multiple blocks, which are transmitted progressively to the receiver. In the event of changing channel conditions, we propose a mechanism to re-encode the remaining blocks, allowing them to adapt to the current channel conditions. Experimental results show that the DRJSCC scheme achieves comparable performance to the other mainstream DL-based JSCC models in stable channel conditions, and also exhibits great robustness against time-varying channels. |
| Author | Yu, Guanding Zhang, Guangyi Li, Hanlei Pan, Junyu Cai, Yunlong |
| Author_xml | – sequence: 1 givenname: Junyu surname: Pan fullname: Pan, Junyu email: junyupan@zju.edu.cn organization: College of Information Science and Electronic Engineering, Zhejiang University,Hangzhou,China – sequence: 2 givenname: Hanlei surname: Li fullname: Li, Hanlei email: hanleili@zju.edu.cn organization: College of Information Science and Electronic Engineering, Zhejiang University,Hangzhou,China – sequence: 3 givenname: Guangyi surname: Zhang fullname: Zhang, Guangyi email: zhangguangyi@zju.edu.cn organization: College of Information Science and Electronic Engineering, Zhejiang University,Hangzhou,China – sequence: 4 givenname: Yunlong surname: Cai fullname: Cai, Yunlong email: ylcai@zju.edu.cn organization: College of Information Science and Electronic Engineering, Zhejiang University,Hangzhou,China – sequence: 5 givenname: Guanding surname: Yu fullname: Yu, Guanding email: yuguanding@zju.edu.cn organization: College of Information Science and Electronic Engineering, Zhejiang University,Hangzhou,China |
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| SubjectTerms | Adaptation models Deep learning Image coding Image communication Receivers Symbols Wireless communication |
| Title | Deep Refinement-Based Joint Source Channel Coding over Time- Varying Channels |
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