In this letter, 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.

中文翻译：

---

用于无线网络上协作多任务处理的语义通信


在这封信中，我们使用信息论方法研究了多任务处理的语义通信。我们引入了 “语义源” 的概念，允许从单个观察中进行多种语义解释。我们制定了一个端到端的优化问题，同时考虑到通信渠道，最大化互信息 （infomax），以利用语义变量之间的统计关系来设计语义编码和解码过程。为了解决这个问题，我们采用变分近似技术进行数据驱动的深度学习。我们的语义编码器分为一个通用单元和多个特定单元，以促进协作式多任务处理。仿真结果表明，当存在统计关系时，我们提出的语义源和系统设计的有效性，将协作任务处理与独立任务处理进行比较。然而，我们的研究结果强调，协作式多任务处理并不总是有益的，强调了任务之间统计关系的重要性，并表明需要进一步研究多个任务的语义处理。