# Diffusion Model Based Resource Allocation Strategy in Ultra-Reliable Wireless Networked Control Systems

arXiv: https://arxiv.org/abs/2407.15784 · HTML: https://mlsanity.com/paper/2407.15784/

## Key facts

- Cited by 9 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 7 of its 13 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Placed in

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

## Builds on (most cited first, 7 of 7)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Experienced Deep Reinforcement Learning with Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication](https://mlsanity.com/paper/1911.03264/) (2019)
- [Wireless Network Design for Control Systems: A Survey](https://mlsanity.com/paper/1708.07353/) (2017)
- [Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach](https://mlsanity.com/paper/2210.00673/) (2022)
- [Optimization Theory Based Deep Reinforcement Learning for Resource Allocation in Ultra-Reliable Wireless Networked Control Systems](https://mlsanity.com/paper/2311.16895/) (2023)
- [Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models](https://mlsanity.com/paper/2311.09349/) (2023)

## Built on by (most cited first, 9 of 9)

- [Generative Diffusion Models for Resource Allocation in Wireless Networks](https://mlsanity.com/paper/2504.20277/) (2025)
- [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025)
- [Graph Signal Diffusion Models for Wireless Resource Allocation](https://mlsanity.com/paper/2604.05175/) (2026)
- [Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression](https://mlsanity.com/paper/2506.18748/) (2025)
- [Constrained Diffusion Models with Primal-Dual Inference](https://mlsanity.com/paper/2606.17192/) (2026)
- [Generative Diffusion Models of Stochastic Graph Signals](https://mlsanity.com/paper/2607.06833/) (2026)
- [Multi-Agent Conditional Diffusion Model with Mean Field Communication as Wireless Resource Allocation Planner](https://mlsanity.com/paper/2510.22969/) (2025)
- [CoDiPhy: A General Framework for Applying Denoising Diffusion Models to the Physical Layer of Wireless Communication Systems](https://mlsanity.com/paper/2503.10297/) (2025)
- [From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks](https://mlsanity.com/paper/2605.25531/) (2026)

## Abstract (arXiv)

Diffusion models are vastly used in generative AI, leveraging their capability to capture complex data distributions. However, their potential remains largely unexplored in the field of resource allocation in wireless networks. This paper introduces a novel diffusion model-based resource allocation strategy for Wireless Networked Control Systems (WNCSs) with the objective of minimizing total power consumption through the optimization of the sampling period in the control system, and blocklength and packet error probability in the finite blocklength regime of the communication system. The problem is first reduced to the optimization of blocklength only based on the derivation of the optimality conditions. Then, the optimization theory solution collects a dataset of channel gains and corresponding optimal blocklengths. Finally, the Denoising Diffusion Probabilistic Model (DDPM) uses this collected dataset to train the resource allocation algorithm that generates optimal blocklength values conditioned on the channel state information (CSI). Via extensive simulations, the proposed approach is shown to outperform previously proposed Deep Reinforcement Learning (DRL) based approaches with close to optimal performance regarding total power consumption. Moreover, an improvement of up to eighteen-fold in the reduction of critical constraint violations is observed, further underscoring the accuracy of the solution.