Generative Network Layer for Communication Systems with Artificial Intelligence
This addresses data efficiency for communication systems, but it is incremental as it builds on existing GenAI and network layer concepts.
The paper tackles the problem of high data rate requirements in communication networks by introducing a generative network layer that uses Generative AI at intermediate nodes to generate content from compressed prompts, achieving over 100% improvement in required data rates in a case study with image generation.
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.