State-Augmented Learnable Algorithms for Resource Management in Wireless Networks
This addresses resource allocation challenges in wireless networks, offering an incremental improvement for network operators.
The paper tackles resource management in multi-user wireless networks by proposing a state-augmented algorithm that incorporates dual variables to handle constraints, achieving feasible and near-optimal decisions with demonstrated superiority over baselines in wireless power control experiments.
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.