Adaptive Semantic Communication for Multi-User Systems with Non-Uniform Deep Architectures

Semantic communication is going to be the backbone of the next wireless generation communication, thanks to its advantages in reducing the communication volume, bandwidth efficiency, and robustness against channel noise. These impressive capabilities are enabled by adopting the advancements of deep learning (DL) in the design framework of the joint sourcechannel coding (JSCC) for the semantic communication system. However, in multi-user semantic communication scenarios, it is often infeasible to equip all users with identical DL architectures due to the heterogeneous computing capabilities across devices. Therefore, we consider a multi-user semantic communication scenario, where the user device is deployed with a different DL model to reflect the heterogeneity in computing capacity. Then, we proposed a training framework to enhance the communication performance and prevent the catastrophic forgetting phenomenon of Deep-JSCC design with the assistance of a temporary decoder. Finally, we provide a comprehensive simulation to illustrate the efficiency of the training framework against various benchmark settings under different types of channels and noise levels.