Generative AI-Enabled Semantic Communication: State-of-the-Art, Applications, and the Way Ahead

The rapid advancement of generative artificial intelligence (GenAI) has introduced novel opportunities for semantic communication (SemCom) systems. This survey offers a comprehensive overview of GenAI-enabled SemCom, connecting theoretical foundations with practical applications. Initially, we introduce the fundamental concepts of SemCom and explore how generative models augment traditional communication paradigms. The paper systematically reviews state-of-the-art methodologies, including variational autoencoders, generative adversarial networks, diffusion models, and other GenAI frameworks within SemCom contexts. We classify GenAI in SemCom based on its GenAI architecture, communication modality, and application tasks. Additionally, we present detailed case studies that demonstrate real-world applications in smart healthcare, intelligent transportation systems, and smart agriculture. These case studies exemplify how generative SemCom can fulfill semantic tasks while preserving the communication fidelity. Finally, we identify emerging research directions and discuss open challenges that merit further investigation. This survey constitutes a valuable resource for researchers and practitioners aiming to comprehend and implement GenAI techniques in next-generation communication systems.