用生成式扩散模型学习无线网络资源分配策略，实现近优性能。
Generative Diffusion Models for Resource Allocation in Wireless Networks
- 通过扩散模型模仿专家策略生成最优资源分配样本
- 在功率控制案例中达到近似最优的系统效用
- 基于图神经网络实现对多种网络配置的泛化能力
本文提出一种监督训练算法，利用生成式扩散模型（GDM）学习随机资源分配策略。将资源分配问题建模为在满足稳态服务质量（QoS）约束下最大化稳态效用函数。给定来自近似最优解的随机专家策略样本，训练GDM策略以模仿该专家，并从最优分布中生成新样本。通过顺序执行生成样本，实现近优性能。为使模型能泛化到一类网络配置，采用图神经网络（GNN）参数化逆向扩散过程。在功率控制的案例研究中展示了数值结果。
原文摘要 · Abstract (English)
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.
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