Enhancing the Reliability of Multiuser Image Semantic Communication in Wireless Networks

The rapid growth of the mobile Internet has led to an increasing demand for reliable transmission of various data types, particularly images shared among multiple users over wireless networks. Traditional communication systems face challenges in managing large-scale image transmissions. Semantic communication, focusing on conveying meaning rather than raw bits, offers a promising solution. For semantic communication, reliability hinges on two key factors: successful transmission and successful understanding. Taking image semantic communication (ISC) systems as an example, successful transmission ensures the physical delivery of the image, while successful understanding refers to the correct interpretation of its semantics. To address these requirements, we design a semantic extraction and reconstruction module, called STC-based on Swin Transformer and convolutional neural network that enables parallel semantic processing and incorporates enhanced channel-aware attention mechanism, which is then integrated with residual blocks to form a joint source-channel coding (JSCC) model for semantic extraction, compression, and reconstruction. To optimize ISC reliability, we design a system utility function integrating the impacts of successful transmission and comprehension. We formulate a utility maximization problem for joint semantic compression rate (SCR) selection and resource allocation, solved by a carefully-designed joint SCR selection and resource allocation (JSSRA) algorithm based on the hierarchical soft actor-critic method. Simulation results demonstrate that our JSCC model significantly improves image quality compared to other learning-based methods while maintaining computational efficiency. Meanwhile, the JSSRA algorithm enhances system utility by 15%-50% compared to existing resource allocation methods. These results validate the effectiveness and superiority of our proposed methods in multi-user ISC systems.