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Wenwu XIE, Ming XIONG, Ziqing REN, et al., “Research on Semantic Communication Based on Joint Control Mechanism of Shallow and Deep Neural Network,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–14, xxxx doi: 10.23919/cje.2023.00.278
Citation: Wenwu XIE, Ming XIONG, Ziqing REN, et al., “Research on Semantic Communication Based on Joint Control Mechanism of Shallow and Deep Neural Network,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–14, xxxx doi: 10.23919/cje.2023.00.278

Research on Semantic Communication Based on Joint Control Mechanism of Shallow and Deep Neural Network

doi: 10.23919/cje.2023.00.278
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  • Author Bio:

    Wenwu XIE received the M.S. and Ph.D. degrees in Communication Engineering from the Huazhong Normal University in 2004, 2007 and 2017. He is currently a Professor at Hunan Institute of Science and Technology. His research interests cover communication algorithm, internet of thing, and evolutionary computing. (Email: gavinxie@hnist.edu.cn)

    Ming XIONG postgraduate of School of Information Science and Engineering, Hunan Institute of Science and Technology, His research interests include artificial intelligence communication, semantic communication. (Email: 812211120161@vip.hnist.edu.cn)

    Ziqing REN female, is a undergraduate student of Software Engineering in the School of Computer Science, Hefei Normal University, Her research interest is semantic communication. of Computer Science, Hefei Normal University, major research direction: semantic communication. (Email: 708868550@qq.com)

    Ji WANG is currently an Associate Professor with the Department of Electronics and Information Engineering, Central China Normal University, China. His research interests include space information networks and B5G wireless communications. (Email: jiwang@ccnu.edu.cn)

    Zhihe YANG received the M.S.degrees in Computer Technology from Huazhong University of Science and Technology in 2006. He is currently a Professor at Hunan Institute of Science and Technology. His research interests cover internet of thing, Intelligent computing and intelligent information system. (Email: 602641715@qq.com)

  • Corresponding author: Email: jiwang@ccnu.edu.cn
  • Received Date: 2023-08-11
  • Accepted Date: 2023-08-11
  • Available Online: 2023-08-11
  • With the rapid development of deep learning, various semantic communication models are emerging, but the current semantic communication models still have much room for improvement in the coding layer. For this reason, a joint-residual neural networks (Joint-ResNets) framework based on the joint control of shallow neural networks (SNNs) and deep neural networks (DNNs) is proposed to cope with the problems in semantic communication coding. The framework synergizes SNNs and DNNs based on their shared utility, and uses variable weight $\alpha$ term to control the ratio of SNNs and DNNs to fully utilize the simplicity of SNNs and the richness of DNNs. The article details the construction of the Joint-ResNets framework and its canonical use in classical semantic communication models, and illustrates the control mechanism of the variable weight $\alpha$ term in the Joint-ResNets framework and its importance in balancing the model complexity between SNNs and DNNs. The article takes the task-oriented communication model in the device edge collaborative reasoning system as an example for experimentation and analysis. The experimental validation shows that DNNs and SNNs can be combined in a more effective way to standardize semantic coding, which improves the overall predictive performance, interpretability, and robustness of semantic communication models, and this framework is expected to bring new breakthroughs in the field of semantic communication.
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