Semantic Communication Meets Edge Intelligence

TL;DR

This paper explores integrating semantic communication with edge intelligence to improve data efficiency and reduce overheads in AI-driven applications like IoT, emphasizing edge-based training and resource optimization.

Contribution

It introduces an edge-driven approach for semantic extraction and demonstrates how edge intelligence can enhance semantic communication efficiency and generalization capabilities.

Findings

Edge-driven training reduces computational overheads.

Semantic-aware resource optimization improves IoT performance.

Enhanced generalization of AI agents with SemCom.

Abstract

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).