Rethinking Generative Semantic Communication for Multi-User Systems with Large Language Models

The surge in connected devices in 6G with typical complex tasks requiring multi-user cooperation, such as smart agriculture and smart cities, poses significant challenges to unsustainable traditional communication. Fortunately, the booming artificial intelligence technology and the growing computational power of devices offer a promising 6G enabler: semantic communication (SemCom). However, existing deep learning-based SemCom paradigms struggle to extend to multi-user scenarios due to their increasing model size with the growing number of users and their limited compatibility with complex communication environments. Consequently, to truly empower 6G networks with this critical technology, this article rethinks generative SemCom for multi-user systems and proposes a novel framework called "M-GSC" with a large language model (LLM) as the shared knowledge base (SKB). The LLM-based SKB plays three critical roles -- complex task decomposition, semantic representation specification, and semantic translation and mapping -- for complex tasks, spawning a series of benefits such as semantic encoding standardization and semantic decoding personalization. Meanwhile, to enhance the performance of the M-GSC framework, we highlight three optimization strategies unique to this framework: extending the LLM-based SKB into a multi-agent LLM system, offloading semantic encoding and decoding, and managing communication and computational resources. Finally, a case study is conducted to demonstrate the preliminary validation of the effectiveness of the M-GSC framework in terms of efficient decoding offloading.