Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. However, little literature has studied its effectiveness in multi-user scenarios, particularly when there are variations in the model architectures used by users and their computing capacities. To address this issue, we explore a semantic communication system that caters to multiple users with different model architectures by using a multi-purpose transmitter at the base station (BS). Specifically, the BS in the proposed framework employs semantic and channel encoders to encode the image for transmission, while the receiver utilizes its local channel and semantic decoder to reconstruct the original image. Our joint source-channel encoder at the BS can effectively extract and compress semantic features for specific users by considering the signal-to-noise ratio (SNR) and computing capacity of the user. Based on the network status, the joint source-channel encoder at the BS can adaptively adjust the length of the transmitted signal. A longer signal ensures more information for high-quality image reconstruction for the user, while a shorter signal helps avoid network congestion. In addition, we propose a hybrid loss function for training, which enhances the perceptual details of reconstructed images. Finally, we conduct a series of extensive evaluations and ablation studies to validate the effectiveness of the proposed system.

翻译：语义通信作为有望替代传统通信的下一代通信系统关键技术，因其能有效降低通信开销而获得研究者广泛关注。然而，现有文献鲜有研究其在多用户场景中的有效性，特别是当用户采用不同模型架构且计算能力存在差异时。针对这一问题，本文探索了一种通过基站部署多用途发射器来适配具有不同模型架构的多用户语义通信系统。具体而言，所提框架中的基站采用语义编码器和信道编码器对图像进行编码传输，而接收端则利用本地信道解码器和语义解码器重构原始图像。基站端的联合源信道编码器能够根据信噪比和用户计算能力，有效提取并压缩面向特定用户的语义特征。基于网络状态，该联合编码器可自适应调整传输信号长度：较长信号可为用户提供更丰富的高质量图像重建信息，较短信号则有助于避免网络拥塞。此外，我们提出了一种混合损失函数用于训练，以增强重建图像的感知细节。最后，通过一系列全面评估和消融实验验证了所提系统的有效性。