The development of emerging applications, such as autonomous transportation systems, are 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).
翻译:随着现有频谱资源越来越少,越来越需要从香农的经典信息理论(CIT)向语义通信(SemCom)进行范式转变。具体地说,前者采用了“传输之前理解”的方法,而后者则利用人工智能技术“在传输之前了解”,从而减轻带宽压力,办法是在不否定传输符号的语义效力的情况下减少数据交换量,从而降低数据交换量。然而,语义提取(SE)程序需要付出昂贵的计算和存储间接费用。在本篇文章中,我们引入了边缘驱动培训、维护和实施SE。我们进一步调查如何通过提高SemCom智能剂在较低计算间接费用上的通用能力,降低信息交流的通信管理费。最后,我们介绍了一项案例研究,涉及对物质无线动力互联网进行语义认知资源优化。