In this paper, we investigated semantic communication for multi-task processing using an information-theoretic approach. We introduced the concept of a "semantic source", allowing multiple semantic interpretations from a single observation. We formulated an end-to-end optimization problem taking into account the communication channel, maximizing mutual information (infomax) to design the semantic encoding and decoding process exploiting the statistical relations between semantic variables. To solve the problem we perform data-driven deep learning employing variational approximation techniques. Our semantic encoder is divided into a common unit and multiple specific units to facilitate cooperative multi-task processing. Simulation results demonstrate the effectiveness of our proposed semantic source and system design when statistical relationships exist, comparing cooperative task processing with independent task processing. However, our findings highlight that cooperative multi-tasking is not always beneficial, emphasizing the importance of statistical relationships between tasks and indicating the need for further investigation into the semantically processing of multiple tasks.

翻译：本文采用信息论方法研究了面向多任务处理的语义通信。我们引入了"语义信源"的概念，使得单一观测能够产生多种语义解释。通过考虑通信信道特性，我们构建了一个端到端优化问题，利用语义变量间的统计关系，以最大化互信息（infomax）为目标设计语义编码与解码过程。为解决该问题，我们采用变分近似技术进行数据驱动的深度学习。所提出的语义编码器被划分为公共单元和多个专用单元，以支持协作式多任务处理。仿真结果表明：当存在统计关联时，我们提出的语义信源与系统设计在协作任务处理相较于独立任务处理展现出显著优势。然而，研究同时揭示协作多任务处理并非始终有效，这凸显了任务间统计关系的重要性，并指出需要进一步探究多任务的语义处理机制。