论文标题
通过图形神经网络的资源分配在自由空间光学领aul网络中
Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks
论文作者
论文摘要
本文研究了自由空间光学（FSO）Fronthaul网络中的最佳资源分配。最佳分配最大化了平均加权总容量，但要受功率限制和数据拥塞限制。根据链接的瞬时通道状态信息（CSI），考虑自适应功率分配和节点选择。通过参数化资源分配策略，我们将问题提出为无监督的统计学习问题。我们考虑使用小规模训练参数来利用FSO网络结构的策略参数化来利用图形神经网络（GNN）。 GNN被证明可以保留与网络中资源分配策略的置换率匹配的置换量比。开发了原始的双重学习算法是为了以无模型的方式训练GNN，在此不需要系统模型的知识。数值模拟呈现出GNN相对于基线策略的强劲性能，具有相等的功率分配和随机节点选择。
This paper investigates the optimal resource allocation in free space optical (FSO) fronthaul networks. The optimal allocation maximizes an average weighted sum-capacity subject to power limitation and data congestion constraints. Both adaptive power assignment and node selection are considered based on the instantaneous channel state information (CSI) of the links. By parameterizing the resource allocation policy, we formulate the problem as an unsupervised statistical learning problem. We consider the graph neural network (GNN) for the policy parameterization to exploit the FSO network structure with small-scale training parameters. The GNN is shown to retain the permutation equivariance that matches with the permutation equivariance of resource allocation policy in networks. The primal-dual learning algorithm is developed to train the GNN in a model-free manner, where the knowledge of system models is not required. Numerical simulations present the strong performance of the GNN relative to a baseline policy with equal power assignment and random node selection.
