Can I read Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High Spectrum Efficiency on EtoBox?

Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High Spectrum Efficiency by Wijesinghe, Achintha; Zhang, Songyang; Wanninayaka, Suchinthaka; Wang, Weiwei; Ding, Zhi is a scholarly article available to read on EtoBox.

What is Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High Spectrum Efficiency about?

The latest advances in artificial intelligence (AI) present many unprecedented opportunities to achieve much improved bandwidth saving in communications. Unlike conventional communication systems focusing on packet transport, rich datasets and AI makes it possible to efficiently transfer only the information most critical to the goals of message recipients. One of the most exciting advances in generative AI known as diffusion model presents a unique opportunity for designing ultra-fast communication systems well beyond language-based messages. This work presents an ultra-efficient communication design by utilizing generative AI-based on diffusion models as a specific example of the general goal-oriented communication framework. To better control the regenerated message at the receiver output, our diffusion system design includes a local regeneration module with finite dimensional noise latent. The critical significance of noise latent control and sharing residing on our Diff-GO is the ability to introduce the concept of "local generative feedback" (Local-GF), which enables the transmitter to monitor the quality and gauge the quality or accuracy of the message recovery at the semant

Author

Wijesinghe, Achintha; Zhang, Songyang; Wanninayaka, Suchinthaka; Wang, Weiwei; Ding, Zhi

Published

2023

Language

EN