Fundamental Limitation of Semantic Communications:Neural Estimation for Rate-Distortion  

作　　者：Dongxu Li Jianhao Huang Chuan Huang Xiaoqi Qin Han Zhang Ping Zhang 

机构地区：[1]School of Science and Engineering and the Fu-ture Network of Inelligence Institute,the Chinese University of Hong Kong,Shenzhen 518172,China [2]Department of Electrical and Electronic Engineering,the University of Hong Kong,Hong Kong 999077,China [3]School of Information and Communication Engineering and the State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China

出　　处：《Journal of Communications and Information Networks》2023年第4期303-318,共16页通信与信息网络学报（英文）

基　　金：supported in part by the Natural Science Foundation of China under Grants 62022070,62341112,62293480,and 62293481,in part by Shenzhen high-tech zone project under Grant KC2022KCCX0041,in part by the key project of Shenzhen under Grant JCYJ20220818103006013,in part by the Shenzhen Outstanding Talents Training Fund 202002,in part by the Guangdong Provincial Key Laboratory of Future Networks of Intelligence under Grant 2022B1212010001,and in part by the Shenzhen Key Laboratory of Big Data and Artificial Intelligence under Grant ZDSYS201707251409055.

摘　　要：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 

分 类 号：TN91[电子电信—通信与信息系统]