M-JSCC: An Asymmetric Semantic Communication Architecture for 6G Intelligent Networks

Semantic communication (SC) is considered a critical technology for breaking through the Shannon limit and achieving low-latency, high-capacity 6 G transmission. However, previous SC systems have typically employed a symmetrical architecture to enhance data recovery capabilities, resulting in a strong coupling between the encoder and decoder. In this paper, we introduce a novel asymmetric SC system, termed masked joint source-channel coding (M-JSCC), which significantly enhances the encoder's versatility by allowing it to adapt to different decoder models tailored to specific task requirements. Moreover, we abandon traditional convolutional neural networks and adopt the innovative transformer to increase model capacity further. Additionally, we empower the model with data generation capabilities to combat interference and distortion during wireless transmission, achieving robust semantic transmission. As a result, extensive experiments verify that our M-JSCC achieves better semantic understanding and performance across various tasks and different channel conditions.