  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.
