D2-JSCC: Digital Deep Joint Source-channel Coding for Semantic Communications

Semantic communications (SemCom) have emerged as a new paradigm for supporting sixth-generation applications with high communication efficiencies. Most existing SemCom techniques utilize deep neural networks (DNNs) to implement analog source-channel mappings, which are incompatible with existing dig...

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Veröffentlicht in:IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops (Print) S. 1 - 7
Hauptverfasser: Huang, Jianhao, Yuan, Kai, Huang, Chuan, Huang, Kaibin
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 02.09.2024
Schlagworte:
Channel coding
Digital communication
Distortion
Feature extraction
Image coding
Image communication
Land mobile radio
Semantic communication
Signal to noise ratio
Source coding
ISSN:2166-9589
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Zusammenfassung:Semantic communications (SemCom) have emerged as a new paradigm for supporting sixth-generation applications with high communication efficiencies. Most existing SemCom techniques utilize deep neural networks (DNNs) to implement analog source-channel mappings, which are incompatible with existing digital communication architectures. To address this issue, this paper proposes a novel framework of digital deep joint source-channel coding (\mathrm{D}^{2}-JSCC) targeting image transmission in SemCom. The framework features digital source and channel codings that are jointly optimized to reduce the end-to-end (E2E) distortion. First, deep source coding with an adaptive density model is designed to efficiently extract and encode semantic features according to their different distributions. Second, channel coding is employed to protect encoded features against channel distortion. To facilitate their joint design, the E2E distortion is characterized as a function of the source and channel rates. Then to minimize the E2E distortion, we propose an efficient two-step algorithm to find the optimal trade-off between the source and channel rates for a given channel signal-to-noise ratio (SNR). Via experiments on simulating the \mathbf{D}^{2}-JSCC with different channel codes and real datasets, the proposed framework is observed to outperform the classic deep JSCC and separation-based approaches.
ISSN:2166-9589
DOI:10.1109/PIMRC59610.2024.10817394