Initialization and Rate-Quality Functions for Generative Network Layer Protocols
This work addresses the problem of integrating GenAI into communication networks for compression, providing a practical foundation for network engineers, but it is incremental as it builds on existing protocols and methods.
The paper tackles the challenge of evaluating the quality of approximations generated by GenAI in communication networks as a function of the rate between source and GenAI nodes, presenting a method- and data-agnostic initialization protocol for learning rate-quality functions. Results show successful estimation with as few as 2 images and positive gains over JPEG after 1-18 post-learning transmissions.
Generative AI (GenAI) creates full content based on compact prompts. While GenAI has been used for applications where the generated content is returned to the prompt sender, it can play a vital role in extending the capacity of communication networks by transmitting compact prompts through links with limited capacity and, then, generating and forwarding approximations from the GenAI to the destination. This poses the challenge of evaluating the quality of those approximations as a function of the rate between the source and the GenAI node, while accounting for the communication overhead of learning. We present a method- and data-agnostic initialization protocol for learning rate-quality functions in GenAI-aided networks, defining three variants: (1) source-oriented, (2) node-oriented, and (3) destination-oriented. Each of them has different messaging flows based on where quality measurements are performed. The protocol augments node discovery protocols (e.g., MCP, A2A) when sources lack confidence in advertised model performance. We illustrate operation via statistical determination of required learning data, and validate using two prompting approaches. Results show successful rate-quality estimation with as few as 2 images, and positive gains over JPEG after just 1-18 post-learning transmissions, providing a practical, compression-agnostic foundation for GenAI-based network compression.