The development of emerging applications, such as autonomous transportation systems, is expected to result in an explosive growth in mobile data traffic. As the available spectrum resource becomes more and more scarce, there is a growing need for a paradigm shift from Shannon's Classical Information Theory (CIT) to semantic communication (SemCom). Specifically, the former adopts a “transmit-before-understanding” approach while the latter leverages artificial intelligence (AI) techniques to “understand-before-transmit,” thereby alleviating bandwidth pressure by reducing the amount of data to be exchanged without negating the semantic effectiveness of the transmitted symbols. However, the semantic extraction (SE) procedure incurs costly computation and storage overheads. In this article, we introduce an edge-driven training, maintenance, and execution of SE. We further investigate how edge intelligence can be enhanced with SemCom through improving the generalization capabilities of intelligent agents at lower computation overheads and reducing the communication overhead of information exchange. Finally, we present a case study involving semantic-aware resource optimization for the wireless powered Internet of Things (IoT).

中文翻译：

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语义通信遇上边缘智能

新兴应用的发展，如自主交通系统，预计将导致移动数据流量的爆炸式增长。随着可用频谱资源变得越来越稀缺，越来越需要从香农的经典信息理论 (CIT) 到语义通信 (SemCom) 的范式转变。具体来说，前者采用“先传输后理解”的方式，而后者则利用人工智能（AI）技术“先理解后传输”，从而在不否定语义的情况下通过减少要交换的数据量来缓解带宽压力。传输符号的有效性。然而，语义提取（SE）过程会产生昂贵的计算和存储开销。在这篇文章中，我们介绍了一种边缘驱动的训练、维护、和SE的执行。我们进一步研究了如何通过 SemCom 以较低的计算开销提高智能代理的泛化能力并减少信息交换的通信开销来增强边缘智能。最后，我们提出了一个案例研究，涉及无线供电物联网 (IoT) 的语义感知资源优化。