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.

传统的网络层的作用是通过中间网络节点从源到目的地传输数据包副本。我们提出了一个使用中间或边缘网络节点的生成式网络层，并分析了它对网络中所需数据速率的影响。我们进行了一个案例研究，其中生成式网络层通过生成式 AI 生成由大幅压缩的潜在表示组成的提示的图像。在图像质量约束下的网络流分析结果显示，生成式网络层可以在所需数据速率方面实现超过 100% 的改进。