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The latest advances in artificial intelligence (AI) present manyunprecedented opportunities to achieve much improved bandwidth saving incommunications. Unlike conventional communication systems focusing on packettransport, rich datasets and AI makes it possible to efficiently transfer onlythe information most critical to the goals of message recipients. One of themost exciting advances in generative AI known as diffusion model presents aunique opportunity for designing ultra-fast communication systems well beyondlanguage-based messages. This work presents an ultra-efficient communicationdesign by utilizing generative AI-based on diffusion models as a specificexample of the general goal-oriented communication framework. To better controlthe regenerated message at the receiver output, our diffusion system designincludes a local regeneration module with finite dimensional noise latent. Thecritical significance of noise latent control and sharing residing on ourDiff-GO is the ability to introduce the concept of "local generative feedback"(Local-GF), which enables the transmitter to monitor the quality and gauge thequality or accuracy of the message recovery at the semantic system receiver. Tothis end, we propose a new low-dimensional noise space for the training ofdiffusion models, which significantly reduces the communication overhead andachieves satisfactory message recovery performance. Our experimental resultsdemonstrate that the proposed noise space and the diffusion-based generativemodel achieve ultra-high spectrum efficiency and accurate recovery oftransmitted image signals. By trading off computation for bandwidth efficiency(C4BE), this new framework provides an important avenue to achieve exceptionalcomputation-bandwidth tradeoff.

Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High Spectrum Efficiency