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

arXiv: https://arxiv.org/abs/2312.02984 · HTML: https://mlsanity.com/paper/2312.02984/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 20 of its 25 references that are in the map.
- First posted on arXiv 13 Nov 2023.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 20 of 20)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Semantic Image Synthesis via Diffusion Models](https://mlsanity.com/paper/2207.00050/) (2022)
- [You Only Need Adversarial Supervision for Semantic Image Synthesis](https://mlsanity.com/paper/2012.04781/) (2020)
- [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020)
- [Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis](https://mlsanity.com/paper/1910.06809/) (2019)
- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022)
- [Semantically Multi-modal Image Synthesis](https://mlsanity.com/paper/2003.12697/) (2020)
- [Robust Semantic Communications with Masked VQ-VAE Enabled Codebook](https://mlsanity.com/paper/2206.04011/) (2022)
- [A Unified Multi-Task Semantic Communication System for Multimodal Data](https://mlsanity.com/paper/2209.07689/) (2022)
- [Generative Semantic Communication: Diffusion Models Beyond Bit Recovery](https://mlsanity.com/paper/2306.04321/) (2023)
- [Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach](https://mlsanity.com/paper/2208.08239/) (2022)
- [Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme](https://mlsanity.com/paper/2302.13580/) (2023)
- [Learned Perceptual Image Enhancement](https://mlsanity.com/paper/1712.02864/) (2017)
- [SAMScore: A Content Structural Similarity Metric for Image Translation Evaluation](https://mlsanity.com/paper/2305.15367/) (2023)

## Built on by (most cited first, 5 of 5)

- [Semantics-Guided Diffusion for Deep Joint Source-Channel Coding in Wireless Image Transmission](https://mlsanity.com/paper/2501.01138/) (2025)
- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [Rate-Adaptive Generative Semantic Communication Using Conditional Diffusion Models](https://mlsanity.com/paper/2409.02597/) (2024)
- [Diff-GOn: Enhancing Diffusion Models for Goal-Oriented Communications](https://mlsanity.com/paper/2412.06980/) (2024)
- [LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency](https://mlsanity.com/paper/2412.17839/) (2024)

## Abstract (arXiv)

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.