Device-Edge Digital Semantic Communication with Trained Non-Linear Quantization

Powered by deep learning, semantic communication is an intelligent communication paradigm, aiming to transmit useful information in the semantic domain. In most existing work, robust semantic features can be learned against wireless channel degradation, and directly transmitted in an analog fashion. However, analog semantic communication raises various challenges to the existing system from hardware/protocol to encryption issues. In this paper, we propose a novel non-linear quantization module to efficiently quantify semantic features. A sparse scaling vector is further incorporated to reduce the dimension of transmitted semantic features. Experimental results demonstrate that the proposed nonlinear quantization achieves better performance than the linear quantization method, and the performance of the digital system achieves better performance.