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

2024-08-16

AI Summary

To address scalability bottlenecks in 6G multi-user semantic communication—including model bloat, cross-user semantic inconsistency, and poor adaptability to dynamic communication environments—this paper proposes M-GSC, a generative semantic communication framework leveraging large language models (LLMs). Methodologically, it pioneers the use of an LLM as a shared knowledge base to jointly support task decomposition, standardized semantic representation, and cross-user semantic mapping. It introduces a协同 mechanism integrating semantic encoding standardization with personalized decoding, and proposes three key optimizations: multi-agent LLM scaling, semantic codec offloading, and joint communication-computation resource management. Experimental results demonstrate that M-GSC significantly improves decoding offloading efficiency, achieving high scalability, low overhead, and robust environmental adaptability in complex application scenarios such as smart agriculture and smart cities.

Technology Category

• Multiagent Systems: Agent Communication
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

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 its increasing model size with the growing number of users and its limited compatibility with complex communication environments. Consequently, to truly empower 6G networks with this critical technology, this article rethinks generative SemCom for multi-user system and proposes a novel framework called ``M-GSC"with the large language model (LLM) as the shared knowledge base (SKB). The LLM-based SKB plays three critical roles, that is, 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 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 on the effectiveness of the M-GSC framework in terms of efficient decoding offloading.

Problem

• Enhances multi-user semantic communication
• Leverages large language models for 6G
• Optimizes resource management in communication

Innovation

• LLM-based shared knowledge base
• Multi-agent LLM system
• Semantic encoding standardization