On the Effectiveness of Task-oriented Digital Semantic Communication System

Task-oriented digital semantic communication systems utilize neural networks to extract and transmit taskrelevant features, which significantly improves the communication efficiency compared to traditional communication systems. These systems also ensure compatibility with modern digital communication frameworks, and thus attract much attention in emerging resource-constrained communication scenarios. However, existing research has limitations in optimizing the effectiveness of transmitted features, which hinders further improvements in system performance. In this paper, two regularization terms are proposed to address this limitation. First, a transmission effectiveness regularization term is introduced to enhance the downstream task performance by reducing the redundant information in the transmitted features. Second, a semi-orthogonal regularization term is proposed to optimize the vector quantization module, which improves quantization efficiency and model generalization by minimizing the similarity between codewords. These proposed regularization terms are then incorporated into the objective function of an existing robust digital semantic communication system. Furthermore, a new implementation scheme and a corresponding optimization objective is presented. Experimental results demonstrate that, the presented scheme achieves better inference performance with low latency and exhibits stronger robustness in the case of mismatched training and testing channels compared to the baseline schemes.