Intellicise model transmission for semantic communication in intelligence-native 6G networks

Yining Wang, Shujun Han, Xiaodong Xu, Meng Rui, Haotai Liang, Dong Chen, Zhang Ping
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引用次数: 0

Abstract

To facilitate emerging applications and demands of edge intelligence (EI)-empowered 6G networks, model-driven semantic communications have been proposed to reduce transmission volume by deploying artificial intelligence (AI) models that provide abilities of semantic extraction and recovery. Nevertheless, it is not feasible to preload all AI models on resource-constrained terminals. Thus, in-time model transmission becomes a crucial problem. This paper proposes an intellicise model transmission architecture to guarantee the reliable transmission of models for semantic communication. The mathematical relationship between model size and performance is formulated by employing a recognition error function supported with experimental data. We consider the characteristics of wireless channels and derive the closed-form expression of model transmission outage probability (MTOP) over the Rayleigh channel. Besides, we define the effective model accuracy (EMA) to evaluate the model transmission performance of both communication and intelligence. Then we propose a joint model selection and resource allocation (JMSRA) algorithm to maximize the average EMA of all users. Simulation results demonstrate that the average EMA of the JMSRA algorithm outperforms baseline algorithms by about 22%.
在智能原生 6G 网络中进行智能模型传输以实现语义通信
为了促进边缘智能(EI)赋能的 6G 网络的新兴应用和需求,有人提出了模型驱动语义通信,通过部署具有语义提取和恢复能力的人工智能(AI)模型来减少传输量。然而,在资源有限的终端上预载所有人工智能模型是不可行的。因此,及时传输模型成为一个关键问题。本文提出了一种智能模型传输架构,以保证语义通信模型的可靠传输。通过使用实验数据支持的识别误差函数,提出了模型大小与性能之间的数学关系。我们考虑了无线信道的特性,并推导出了瑞利信道上模型传输中断概率(MTOP)的闭式表达式。此外,我们还定义了有效模型精度(EMA)来评估通信和智能的模型传输性能。然后,我们提出了一种联合模型选择和资源分配(JMSRA)算法,以最大化所有用户的平均 EMA。仿真结果表明,JMSRA 算法的平均 EMA 优于基准算法约 22%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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