Generative AI-driven Semantic Communication Networks: Architecture, Technologies and Applications

TL;DR

This paper reviews how generative AI enhances semantic communication networks, focusing on architecture, technologies, applications, and network management to support AI-generated content efficiently.

Contribution

It introduces a GAI-driven SemCom framework, detailing models for content creation, transmission, and network management, highlighting innovations in resource efficiency and application scenarios.

Findings

GAI significantly improves semantic extraction and transmission.

GAI-driven SemCom enables efficient resource utilization.

Potential applications include autonomous driving, smart cities, and the Metaverse.

Abstract

Generative artificial intelligence (GAI) has emerged as a rapidly burgeoning field demonstrating significant potential in creating diverse contents intelligently and automatically. To support such artificial intelligence-generated content (AIGC) services, future communication systems should fulfill much more stringent requirements (including data rate, throughput, latency, etc.) with limited yet precious spectrum resources. To tackle this challenge, semantic communication (SemCom), dramatically reducing resource consumption via extracting and transmitting semantics, has been deemed as a revolutionary communication scheme. The advanced GAI algorithms facilitate SemCom on sophisticated intelligence for model training, knowledge base construction and channel adaption. Furthermore, GAI algorithms also play an important role in the management of SemCom networks. In this survey, we first overview the basics of GAI and SemCom as well as the synergies of the two technologies. Especially, the GAI-driven SemCom framework is presented, where many GAI models for information creation, SemCom-enabled information transmission and information effectiveness for AIGC are discussed separately. We then delve into the GAI-driven SemCom network management involving with novel management layers, knowledge management, and resource allocation. Finally, we envision several promising use cases, i.e., autonomous driving, smart city, and the Metaverse for a more comprehensive exploration.