Degraded broadcast channels (DBC) are a typical multi-user communications scenario. There exist classic transmission methods, such as superposition coding with successive interference cancellation, to achieve the DBC capacity region. However, semantic communications method over DBC remains lack of in-depth research. To address this, we design a fusion-based multi-user semantic communications system for wireless image transmission over DBC in this paper. The proposed architecture supports a transmitter extracting semantic features for two users separately, and learns to dynamically fuse these semantic features into a joint latent representation for broadcasting. The key here is to design a flexible image semantic fusion (FISF) module to fuse the semantic features of two users, and to use a multi-layer perceptron (MLP) based neural network to adjust the weights of different user semantic features for flexible adaptability to different users channels. Experiments present the semantic performance region based on the peak signal-to-noise ratio (PSNR) of both users, and show that the proposed system dominates the traditional methods.

翻译：退化广播信道（DBC）是一种典型的多用户通信场景。现有经典传输方法（如采用连续干扰消除的叠加编码）可实现DBC的容量域。然而，针对DBC的语义通信方法尚缺乏深入研究。为此，本文设计了一种基于融合的多用户语义通信系统，用于退化广播信道下的无线图像传输。所提出的架构支持发射端分别为两用户提取语义特征，并学习将这些语义特征动态融合为联合潜在表示进行广播。关键技术在于设计灵活的图像语义融合（FISF）模块以融合两用户的语义特征，并采用基于多层感知机（MLP）的神经网络调整不同用户语义特征的权重，从而灵活适应不同用户的信道条件。实验基于两用户的峰值信噪比（PSNR）展示了语义性能域，并表明所提系统性能优于传统方法。