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Semantic communication has gained significant attention from researchers as apromising technique to replace conventional communication in the nextgeneration of communication systems, primarily due to its ability to reducecommunication costs. However, little literature has studied its effectivenessin multi-user scenarios, particularly when there are variations in the modelarchitectures used by users and their computing capacities. To address thisissue, we explore a semantic communication system that caters to multiple userswith different model architectures by using a multi-purpose transmitter at thebase station (BS). Specifically, the BS in the proposed framework employssemantic and channel encoders to encode the image for transmission, while thereceiver utilizes its local channel and semantic decoder to reconstruct theoriginal image. Our joint source-channel encoder at the BS can effectivelyextract and compress semantic features for specific users by considering thesignal-to-noise ratio (SNR) and computing capacity of the user. Based on thenetwork status, the joint source-channel encoder at the BS can adaptivelyadjust the length of the transmitted signal. A longer signal ensures moreinformation for high-quality image reconstruction for the user, while a shortersignal helps avoid network congestion. In addition, we propose a hybrid lossfunction for training, which enhances the perceptual details of reconstructedimages. Finally, we conduct a series of extensive evaluations and ablationstudies to validate the effectiveness of the proposed system.

Swin Transformer-Based Dynamic Semantic Communication for Multi-User with Different Computing Capacity