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Intellicise communication system: model-driven semantic communications

The Journal of China Universities of Posts and Telecommunications 2022年第1期29卷 2-12页

作者： Zhang Ping Xu Xiaodong Dong Chen Han Shujun Wang Bizhu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

As one of the critical technologies for the 6 th generation mobile communication system(6 G) mobile communication systems, artificial intelligence(AI) technology will provide complete automation for connecting the virtual and physical worlds. In order to construct the future ubiquitous intelligent network, people are beginning to rethink how mobile communication systems transmit and exploit intelligent information. This paper proposes a new communication paradigm, called the Intellicise communication system: model-driven semantic communication. Intellicise communication system is built on top of the traditional communication system and innovatively adds a new feature dimension on top of the traditional source coding, which enables the communication system to evolve from the traditional transmission of bit to the transmission of "model". Like the semantic base(Seb) for semantic communication, the model is considered as the new feature obtained from the joint source-channel coding. The sink node can re-construct the original signal based on the received model and the encoded sequence. In addition, the performance evaluation metrics and the implementation details of the Intellicise communication system are discussed in this paper. Finally, preliminary results of model-driven image transmission in the Intellicise communication system are presented.

关键词： Intellicise communication system semantic communications model driven the 6th generation mobile communication system artificial intelligence

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Fundamental Limitation of semantic communications:Neural Estimation for Rate-Distortion

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Journal of communications and Information Networks 2023年第4期8卷 303-318页

作者： Dongxu Li Jianhao Huang Chuan Huang Xiaoqi Qin Han Zhang Ping Zhang School of Science and Engineering and the Fu-ture Network of Inelligence Institute the Chinese University of Hong KongShenzhen 518172China Department of Electrical and Electronic Engineering the University of Hong KongHong Kong 999077China School of Information and Communication Engineering and the State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

This paper studies the fundamental limit of semantic communications over the discrete memoryless channel.We consider the scenario to send a semantic source consisting of an observation state and its corresponding semantic state,both of which are recovered at the receiver.To derive the performance limitation,we adopt the semantic rate-distortion function(SRDF)to study the relationship among the minimum compression rate,observation distortion,semantic distortion,and channel capacity.For the case with unknown semantic source distribution,while only a set of the source samples is available,we propose a neural-network-based method by leveraging the generative networks to learn the semantic source distribution.Furthermore,for a special case where the semantic state is a deterministic function of the observation,we design a cascade neural network to estimate the SRDF.For the case with perfectly known semantic source distribution,we propose a general Blahut-Arimoto(BA)algorithm to effectively compute the SRDE.Finally,experimental results validate our proposed algorithms for the scenarios with ideal Gaussian semantic source and some practical datasets.

关键词： semantic communications semantic ratedistortion generative network Blahut-Arimoto algorithm

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