A Deep Learning Based Broadcast Approach for Image Semantic Communication over Fading Channels

作     者：Ma Kangning Shi Yuxuan Shao Shuo Tao Meixia Ma Kangning;Shi Yuxuan;Shao Shuo;Tao Meixia

作者机构：The Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China The School of Cyber and EngineeringShanghai Jiao Tong UniversityShanghai 200240China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2024年第21卷第7期

页      面：78-94页

核心收录：

学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 081001[工学-通信与信息系统] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported in part by the National Key R&D Project of China under Grant 2020YFA0712300 National Natural Science Foundation of China under Grant NSFC-62231022,12031011 supported in part by the NSF of China under Grant 62125108 

主　　题：broadcast approach deep learning fading channels semantic communication 

摘      要：We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel,with a finite number of channel states.A deep learning-aided broadcast approach scheme is proposed to benefit the adaptive semantic transmission in terms of different channel *** combine the classic broadcast approach with the image transformer to implement this adaptive joint source and channel coding(JSCC)***,we utilize the neural network(NN)to jointly optimize the hierarchical image compression and superposition code mapping within this *** learned transformers and codebooks allow recovering of the image with an adaptive quality and low error rate at the receiver side,in each channel *** simulation results exhibit our proposed scheme can dynamically adapt the coding to the current channel state and outperform some existing intelligent schemes with the fixed coding block.