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Task-Oriented Scene Graph-Based Semantic Communications With Adaptive Channel Coding
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DOI：10.1109/TWC.2024.3450697.png)
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Abstract
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Semantic communications have shown great potential in reducing the transmitted data amount through the powerful capability to extract and transmit essential semantic information. Although existing works have achieved certain transmission efficiency, challenges such as efficiently interpretable semantic extraction, and dynamically adaptive channel coding have not been fully explored. In this paper, we tackle these challenges by proposing a task-oriented scene graph-based semantic communication system with adaptive channel coding, named GRACE, to perform image retrieval task. To enhance the interpretability of semantic communications and reduce semantic redundancy, we introduce a scene graph semantic encoder. This encoder fully exploits informative scene graph semantics, effectively extracting scene graphs and performing further semantic coding. Additionally, to handle variable channel conditions in real-world scenarios, we develop a semantic-aware adaptive channel coding to adapt to channel conditions and reduce the communication resources. At the receiver, the image retrieval task is accomplished based on the recovered scene graph semantics. The experimental results demonstrate the superiority of the proposed system compared to other communication systems in terms of task-execution performance, robustness against channel variations, transmission efficiency, and computational complexity.
Keywords:
Semantics
Feature extraction
Image retrieval
Data mining
Receivers
Symbols
Noise
Semantic communications
scene graph
image semantic coding
deep learning
Journal
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10.7
Papers:
1.3W
Citations:
5.3W
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