Channel-Aware Deep Joint Source-Channel Coding for Multi-Task Oriented Semantic Communication
Existing deep learning (DL)-based semantic communication generally employs deep neural networks (DNNs) for semantic extraction at a fixed dimension and incorporates channel state information (CSI) to facilitate semantic transmission. However, the optimal dimension of the semantic information to tran...
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| Published in: | IEEE wireless communications letters Vol. 14; no. 5; pp. 1521 - 1525 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Piscataway
IEEE
01.05.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2162-2337, 2162-2345 |
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| Abstract | Existing deep learning (DL)-based semantic communication generally employs deep neural networks (DNNs) for semantic extraction at a fixed dimension and incorporates channel state information (CSI) to facilitate semantic transmission. However, the optimal dimension of the semantic information to transmit varies with wireless channel conditions, trading off between channel uses and performance depending on the channel condition. To strike the balance, this letter proposes a channel-aware deep joint source-channel coding (CA-DJSCC) for multi-task oriented semantic communication. Specifically, we formulate an optimization problem to characterize the intricate and hard-to-quantify relationship between the channel condition and the optimal dimension. To achieve the channel-adaptive dimension, we develop a joint source and channel encoder, pruning elements based on semantic importance. Correspondingly, we also design a joint source and channel decoder, introducing a reference signal to enhance the multi-task performance. Simulation results demonstrate that the CA-DJSCC achieves superior performance in terms of both image reconstruction and classification tasks, with over 44.1% reduction in average channel uses compared to typical baselines. |
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| AbstractList | Existing deep learning (DL)-based semantic communication generally employs deep neural networks (DNNs) for semantic extraction at a fixed dimension and incorporates channel state information (CSI) to facilitate semantic transmission. However, the optimal dimension of the semantic information to transmit varies with wireless channel conditions, trading off between channel uses and performance depending on the channel condition. To strike the balance, this letter proposes a channel-aware deep joint source-channel coding (CA-DJSCC) for multi-task oriented semantic communication. Specifically, we formulate an optimization problem to characterize the intricate and hard-to-quantify relationship between the channel condition and the optimal dimension. To achieve the channel-adaptive dimension, we develop a joint source and channel encoder, pruning elements based on semantic importance. Correspondingly, we also design a joint source and channel decoder, introducing a reference signal to enhance the multi-task performance. Simulation results demonstrate that the CA-DJSCC achieves superior performance in terms of both image reconstruction and classification tasks, with over 44.1% reduction in average channel uses compared to typical baselines. |
| Author | Wang, Biqi Jiang, Feng Wang, Shen Gu, Run Li, Mengyu Xu, Wei |
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| SubjectTerms | Artificial neural networks Coding Communication Data mining Decoding deep learning Feature extraction Image reconstruction joint source and channel coding Machine learning multi-task execution Multitasking Optimization Reference signals Semantic communication Semantics Signal to noise ratio Training Vectors Visualization Wireless communications |
| Title | Channel-Aware Deep Joint Source-Channel Coding for Multi-Task Oriented Semantic Communication |
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