人工智能通信系统的生成网络层

Mathias Thorsager;Israel Leyva-Mayorga;Beatriz Soret;Petar Popovski
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摘要

网络层的传统角色是通过中间网络节点将数据包副本从源传输到目的地。我们提出了一种在中间或边缘网络节点使用生成式人工智能(GenAI)的生成式网络层,并分析了它对网络所需数据速率的影响。我们进行了一项案例研究,GenAI 辅助节点根据提示生成图像,这些提示由大量压缩的潜在表征组成。在图像质量限制条件下进行的网络流量分析结果表明,生成网络层可将所需数据传输率提高 100%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generative Network Layer for Communication Systems With Artificial Intelligence
The traditional role of the network layer is the transfer of packet replicas from source to destination through intermediate network nodes. We present a generative network layer that uses Generative AI (GenAI) at intermediate or edge network nodes and analyze its impact on the required data rates in the network. We conduct a case study where the GenAI-aided nodes generate images from prompts that consist of substantially compressed latent representations. The results from network flow analyses under image quality constraints show that the generative network layer can achieve an improvement of more than 100% in terms of the required data rate.
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