基于大语言模型的语义通信:现状,挑战与展望

Large Language Model-Based Semantic Communications:Status,Challenges,and Prospects

语义通信有望成为下一代无线网络关键技术之一。但现有语义通信方案仍面临几个问题,如语义难以数学建模与优化、系统语义理解能力有限。大语言模型的出现为解决这些问题提供了可能性。首先回顾了基于深度学习的语义通信,接着分析了将大语言模型应用于语义通信的优势,包括在语义理解和生成等方面的突出表现。随后详细介绍了基于大语言模型的语义通信的最新进展,展示了该技术在提升系统的语义解析能力、提高信息传输效率方面的显著成果。然而,这种方法仍面临一些开放性问题,包括计算和资源需求、适应性和泛化性、大模型幻觉、以及数据隐私和安全性等挑战。最后,讨论了基于大语言模型的语义通信可能的应用场景,如数据爆发式传输、人机通信以及在恶劣环境下的通信,展示了其在多个领域中的潜在价值和广泛应用前景。

Semantic communications are expected to be a key technology for next-generation wireless networks.However,current semantic communication approaches still face several issues,such as difficulties in modeling and optimization,and limited semantic understanding,The emergence of large language models offers the potential to address these problems,This paper frst reviews deep learning-based semantic communications and then analyzes the advantages of applying large language models to semantic communications,including the outstanding performance in semantic understanding and generation.Subsequently,the latest developments in semantic communications based on large language models are introduced in detail,demonstrating the significant achievements in enhancing system semantic parsing capabilities and improving information transmission efficiency.However,this approach still encounters some open challenges,including computational and resource requirements,adaptability and generalization,large model illusion,as well as data privacy and security.Finally,potential application scenarios of semantic communication based on large language models are discussed,such as data burst transmission,human-machine communication,and communication in harsh environments,demonstrating its potential value and broad application prospects across multiple domains.