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.
翻译:我们考虑多用户无线网络的资源管理问题,这种网络可以被描绘为优化整个网络的公用事业功能,但受整个网络用户长期平均绩效的限制。 我们建议采用国家强化算法来解决上述无线电资源管理问题,在这种算法中,与即时网络状态一起,RRM政策将一系列与制约相对应的双重变量作为投入,这些变量的演变取决于执行过程中违反制约的程度。 我们理论上表明,拟议中的国家强化算法导致近乎最佳的RRM决定。 此外,我们侧重于利用图形神经网络参数化的无线电源控制问题,我们展示了拟议的RRM算法在一系列数字实验中优于基线方法的优势。