论文标题
无线网络中资源管理的国家提出的可学习算法
State-Augmented Learnable Algorithms for Resource Management in Wireless Networks
论文作者
论文摘要
我们考虑多用户无线网络中的资源管理问题，可以将其视为优化网络范围的公用事业功能，这受到用户在整个网络的长期平均绩效的限制。我们提出了一种国家提升的算法，用于解决上述无线电资源管理（RRM）问题，在此问题中，与瞬时网络状态一起，RRM策略将其作为输入对应于约束的一组双重变量的输入，这些变量取决于在执行过程中侵蚀了多少约束。从理论上讲，我们表明，拟议的国家提出的算法会导致可行且近乎最佳的RRM决策。此外，侧重于使用图神经网络（GNN）参数化的无线功率控制问题，我们证明了所提出的RRM算法优于基线方法的优越性。
We consider resource management problems in multi-user wireless networks, which can be cast as optimizing a network-wide utility function, subject to constraints on the long-term average performance of users across the network. We propose a state-augmented algorithm for solving the aforementioned radio resource management (RRM) problems, where, alongside the instantaneous network state, the RRM policy takes as input the set of dual variables corresponding to the constraints, which evolve depending on how much the constraints are violated during execution. We theoretically show that the proposed state-augmented algorithm leads to feasible and near-optimal RRM decisions. Moreover, focusing on the problem of wireless power control using graph neural network (GNN) parameterizations, we demonstrate the superiority of the proposed RRM algorithm over baseline methods across a suite of numerical experiments.
