Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High
  Spectrum Efficiency
Achintha Wijesinghe Songyang Zhang Suchinthaka Wanninayaka Weiwei Wang Zhi Ding
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 semantic system receiver. To
this end, we propose a new low-dimensional noise space for the training of
diffusion models, which significantly reduces the communication overhead and
achieves satisfactory message recovery performance. Our experimental results
demonstrate that the proposed noise space and the diffusion-based generative
model achieve ultra-high spectrum efficiency and accurate recovery of
transmitted image signals. By trading off computation for bandwidth efficiency
(C4BE), this new framework provides an important avenue to achieve exceptional
computation-bandwidth tradeoff.