mlsanityData to 14 Oct 2026

Generative Diffusion Models for Resource Allocation in Wireless Networks

arXiv 2504.20277 ↗First posted 28 Apr 2025cs.LG eess.SPcited by 6

On the map: Diffusion models → Diffusion for compression and wireless

Abstract (arXiv)

This paper proposes a supervised training algorithm for learning stochastic resource allocation policies with generative diffusion models (GDMs). We formulate the allocation problem as the maximization of an ergodic utility function subject to ergodic Quality of Service (QoS) constraints. Given samples from a stochastic expert policy that yields a near-optimal solution to the constrained optimization problem, we train a GDM policy to imitate the expert and generate new samples from the optimal distribution. We achieve near-optimal performance through the sequential execution of the generated samples. To enable generalization to a family of network configurations, we parameterize the backward diffusion process with a graph neural network (GNN) architecture. We present numerical results in a case study of power control.

Key facts. Cited by 6 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 18 of its 36 references that are in the map. First posted on arXiv 28 Apr 2025.

Builds on

18 of 36 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Diffusion Models as Network Optimizers: Explorations and Analysis1 Nov
  2. 2024Diffusion Model Based Resource Allocation Strategy in Ultra-Reliable Wireless Networked Control Systems22 Jul
  3. 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization10 Aug

Other references · 15

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  5. 2018Convolutional Neural Network Architectures for Signals Supported on Graphscited by 95
  6. 2020Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysiscited by 81
  7. 2020Graph Neural Networks: Architectures, Stability and Transferabilitycited by 80
  8. 2018Towards Optimal Power Control via Ensembling Deep Neural Networkscited by 38
  9. 2021Learning Decentralized Wireless Resource Allocations with Graph Neural Networkscited by 25
  10. 2019Experienced Deep Reinforcement Learning with Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communicationcited by 21
  11. 2022State-Augmented Learnable Algorithms for Resource Management in Wireless Networkscited by 14
  12. 2024DiffSG: A Generative Solver for Network Optimization with Diffusion Modelcited by 14
3 more
  1. 2023Stability of Graph Convolutional Neural Networks through the lens of small perturbation analysiscited by 5
  2. 2025DRESS: Diffusion Reasoning-based Reward Shaping Scheme For Intelligent Networkscited by 2
  3. 2024Generative Model for Joint Resource Management in Multi-Cell Multi-Carrier NOMA Networkscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2025Graph Signal Generative Diffusion Modelscited by 5
  2. 2025Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regressioncited by 3
  3. 2026Constrained Diffusion Models with Primal-Dual Inferencecited by 2
  4. 2026Generative Diffusion Models of Stochastic Graph Signalscited by 2
  5. 2026Unrolled Neural Networks for Constrained Optimizationcited by 0
  6. 2026Learning Coverage- and Power-Optimal Transmitter Placement from City Maps: A Comparative Study of Direct and Indirect Neural Approachescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Graph Signal Diffusion Models for Wireless Resource Allocation
  2. 2025Generative Resource Allocation for 6G O-RAN with Diffusion Policies
  3. 2025Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art
  4. 2025Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models
  5. 2025Diffusion Models for Future Networks and Communications: A Comprehensive Survey
  6. 2024DRL Optimization Trajectory Generation via Wireless Network Intent-Guided Diffusion Models for Optimizing Resource Allocation
  7. 2025Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach
  8. 2024Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges