GenAI-SC: Generative AI-Empowered Semantic Communications for Fading Channels in 6G

Semantic communication (SC) has recently emerged as a pivotal technology for enabling sixth-generation (6G) networks, attracting growing research interest and notable advances. However, most existing SC systems have been designed based on either additive white Gaussian noise (AWGN) channels with fading effects being neglected or fading channels with the assumption of perfect channel state information (CSI) availability. In practice, channel fading is non-negligible and perfect CSI is challenging to obtain. Particularly, in some scenarios, even imperfect CSI cannot be acquired. To enable more effective SC in fading channels, this article presents a generative AI (GenAI)-empowered SC (GenAI-SC) framework, which incorporates a modular GenAI-based anti-fading module (GAFM) adaptable to three levels of CSI awareness, i.e., CSI-free, CSI-inferable and CSI-estimable. To demonstrate the advantages of GenAI-SC, we conduct case studies for both text and image applications in fading channels, focusing on the CSI-free case where the GAFM is implemented. The results show that GenAI-SC outperforms conventional SC in terms of both semantic information recovery and system robustness in various fading environments. They also demonstrate that GenAI-SC can adapt to new fading conditions by fine-tuning only the GAFM model, without the need to retrain the semantic encoder and decoder. We further outline several promising future research directions, including the enhancement of security and robustness, and the development of efficient and scalable architectures, which are expected to inspire more studies and some practical applications of SC.