  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.
