Deep Learning-Enabled Semantic Communication with Structured Semantic Representation

Semantic and task-oriented communications have emerged as significant paradigm shifts for next-generation communication networks, which extracts and transmits task-relevant information rather than raw data for downstream tasks. However, most existing work focused on bit-level loss functions, such as mean square error (MSE) and cross-entropy (CE), rather than directly optimizing at the semantic level. These approaches often lack interpretability of semantic representation and result in higher system complexity and less efficient transmission for various tasks. To this end, we develop a novel task-oriented semantic communication system for multitask scenarios and further develop a semantic-level framework. This framework can extract structured semantic representation by compressing raw data with different labels into mutually orthogonal subspaces. Simulation results demonstrate that the proposed framework not only extracts structured semantic representation, but also outperforms existing benchmarks in terms of data recovery and AI inference performance.