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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.20277 |
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| _version_ | 1866908550930890752 |
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| author | Uslu, Yigit Berkay Hadou, Samar Bidokhti, Shirin Saeedi Ribeiro, Alejandro |
| author_facet | Uslu, Yigit Berkay Hadou, Samar Bidokhti, Shirin Saeedi Ribeiro, Alejandro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20277 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative Diffusion Models for Resource Allocation in Wireless Networks Uslu, Yigit Berkay Hadou, Samar Bidokhti, Shirin Saeedi Ribeiro, Alejandro Machine Learning Signal Processing 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. |
| title | Generative Diffusion Models for Resource Allocation in Wireless Networks |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2504.20277 |