Semantic Communication Meets Edge Intelligence

TLDR

Emerging applications such as autonomous transportation are expected to generate explosive mobile data traffic, and as spectrum becomes scarce, a shift from Shannon’s Classical Information Theory to semantic communication—leveraging AI to understand before transmitting—has been proposed to reduce bandwidth usage, though semantic extraction incurs significant computational and storage costs. This article proposes an edge‑driven approach to train, maintain, and execute semantic extraction, aiming to enhance edge intelligence by improving agent generalization at lower computational overhead and reducing communication overhead. A case study demonstrates semantic‑aware resource optimization for wireless‑powered Internet of Things devices.

Abstract

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).