Multi-Task Semantic Communication: A Mutual Information-Aided Semi-Supervised Approach

In this paper, we design an end-to-end digital semantic communication system to transmit semantic symbols that simultaneously facilitate image classification tasks and reconstruction tasks. By training a mutual information-assisted joint source-channel coding (MIJSCC) framework, the learned semantic representation can incorporate both pixel-level generative information for reconstruction and structural discriminative information for classification, which are obtained label-free via global and local mutual information estimation and maximization, as well as mean square error (MSE) minimization. Then, the high-resolution semantic representation is quantized into finite constellation symbols to satisfy the hardware constraint on discrete control in practical radio frequency systems. Considering dynamic channel conditions in practical communication systems, we further design an adaptive MIJSCC framework with attention-based semantic enhancement (A-MIJSCC), which allows for the sequential activation of varying dimensions of the semantic representation according to channel signal-to-noise ratio. Compared to existing semantic communication frameworks that are dominated by end target and labels, the MIJSCC addresses the semi-supervised learning of intermediate semantics. Simulation results show that the proposed MIJSCC supports both image classification and reconstruction via task-agnostic semantic extraction, whose performance surpasses the benchmark frameworks. It is also demonstrated that the A-MIJSCC method facilitates the adaptive semantic transmission under varying channel conditions, which effectively reduces the transmission overhead while preserving task performance.