arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2402.13073eess.SP
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迈向智能通信:大模型赋能的语义通信

Towards Intelligent Communications: Large Model Empowered Semantic Communications

Huiqiang Xie, Zhijin Qin, Xiaoming Tao, Zhu Han

AI总结:

本文系统研究大模型赋能的语义通信,提出集成记忆模块的新架构,展示应用优势,讨论关键设计并指出未来研究方向。

摘要:

深度学习赋能的语义通信通过有效交换数据背后的语义,展现出显著提升传输效率并缓解频谱稀缺的巨大潜力。近年来,拥有数十亿参数的大模型的出现,展现了非凡的类人智能,通过增强语义理解和上下文理解,为推进语义通信提供了有前景的途径。本文系统地研究了从潜在应用到系统设计的大模型赋能语义通信系统。首先,我们提出了一种新的语义通信架构,通过引入记忆模块,将大模型无缝集成到语义通信中。然后,展示了典型应用以说明新架构的优势。此外,我们讨论了从模块设计到系统训练的实现新语义通信系统的关键设计。最后,确定了潜在的研究方向,以推动大模型赋能的语义通信发展。

英文摘要:

Deep learning enabled semantic communications have shown great potential to significantly improve transmission efficiency and alleviate spectrum scarcity, by effectively exchanging the semantics behind the data. Recently, the emergence of large models, boasting billions of parameters, has unveiled remarkable human-like intelligence, offering a promising avenue for advancing semantic communication by enhancing semantic understanding and contextual understanding. This article systematically investigates the large model-empowered semantic communication systems from potential applications to system design. First, we propose a new semantic communication architecture that seamlessly integrates large models into semantic communication through the introduction of a memory module. Then, the typical applications are illustrated to show the benefits of the new architecture. Besides, we discuss the key designs in implementing the new semantic communication systems from module design to system training. Finally, the potential research directions are identified to boost the large model-empowered semantic communications.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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