用扩散模型生成无线资源分配方案，速度快且泛化强。
Graph Signal Diffusion Models for Wireless Resource Allocation
- 基于图神经网络的扩散模型，条件生成最优资源分配。
- 时间共享生成的功率分配，近似最优吞吐率与最小速率。
- 适用于多种网络状态，迁移能力出色，适合实际部署。
针对具有图结构干扰的无线网络中的约束遍历资源优化问题，我们训练了一个扩散模型策略，以匹配专家条件分布下的资源分配。通过使用原始对偶（专家）算法，为每个训练网络实例生成原始迭代解，作为对应专家条件分布的采样结果。我们将资源分配视为定义在已知信道状态图上的随机图信号。采用基于图神经网络（GNN）块的U-Net层次架构实现扩散模型，条件依赖于信道状态和额外节点状态。推理阶段，学习到的生成模型通过直接从近似最优条件分布中采样分配向量，实现了对迭代专家策略的近似。在功率控制案例研究中，我们证明：通过时间共享生成的功率分配，可实现近最优的遍历和速率效用及近可行的遍历最小速率，在不同网络状态下表现出强大的泛化与可迁移性。
原文摘要 · Abstract (English)
We consider constrained ergodic resource optimization in wireless networks with graph-structured interference. We train a diffusion model policy to match expert conditional distributions over resource allocations. By leveraging a primal-dual (expert) algorithm, we generate primal iterates that serve as draws from the corresponding expert conditionals for each training network instance. We view the allocations as stochastic graph signals supported on known channel state graphs. We implement the diffusion model architecture as a U-Net hierarchy of graph neural network (GNN) blocks, conditioned on the channel states and additional node states. At inference, the learned generative model amortizes the iterative expert policy by directly sampling allocation vectors from the near-optimal conditional distributions. In a power-control case study, we show that time-sharing the generated power allocations achieves near-optimal ergodic sum-rate utility and near-feasible ergodic minimum-rates, with strong generalization and transferability across network states.
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