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, where semantic features of data are transmitted using artificial intelligence algorithms to attain high communication efficiencies. Most existing SemCom techniques utilize deep neural networ...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications Vol. 43; no. 4; pp. 1246 - 1261
Main Authors: Huang, Jianhao, Yuan, Kai, Huang, Chuan, Huang, Kaibin
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Algorithms
Artificial intelligence
Artificial neural networks
Channel coding
Codes
Coding
Decoding
Deep learning
deep source coding
digital deep joint source-channel coding
Digital imaging
Distortion
Image coding
Image transmission
joint source-channel rate control
Optimization
Semantic communication
Semantic communications
Semantics
Signal to noise ratio
Source coding
Symbols
ISSN:0733-8716, 1558-0008
Online Access:Get full text
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Summary:Semantic communications (SemCom) have emerged as a new paradigm for supporting sixth-generation applications, where semantic features of data are transmitted using artificial intelligence algorithms to attain 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 (D2-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 prior model is designed to encode semantic features according to their 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 via the analysis of the Bayesian model and Lipschitz assumption on the DNNs. Then to minimize the E2E distortion, a two-step algorithm is proposed to control the source-channel rates for a given channel signal-to-noise ratio. Simulation results reveal that the proposed framework outperforms classic deep JSCC and mitigates the cliff and leveling-off effects, which commonly exist for separation-based approaches.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2025.3531546