A deep learning based broadcast approach for image semantic communication over fading channels

We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel, with a finite number of channel states. A deep learning-aided broadcast approach scheme is proposed to benefit the adaptive semantic transmission in terms of different channel states. We combine the classic broadcast approach with the image transformer to implement this adaptive joint source and channel coding (JSCC) scheme. Specifically, we utilize the neural network (NN) to jointly optimize the hierarchical image compression and superposition code mapping within this scheme. The learned transformers and codebooks allow recovering of the image with an adaptive quality and low error rate at the receiver side, in each channel state. The simulation results exhibit our proposed scheme can dynamically adapt the coding to the current channel state and outperform some existing intelligent schemes with the fixed coding block.