  We consider a radio resource management (RRM) problem in a multi-user
wireless network, where the goal is to optimize a network-wide utility function
subject to constraints on the ergodic average performance of users. We propose
a state-augmented parameterization for the RRM policy, where alongside the
instantaneous network states, the RRM policy takes as input the set of dual
variables corresponding to the constraints. We provide theoretical
justification for the feasibility and near-optimality of the RRM decisions
generated by the proposed state-augmented algorithm. Focusing on the power
allocation problem with RRM policies parameterized by a graph neural network
(GNN) and dual variables sampled from the dual descent dynamics, we numerically
demonstrate that the proposed approach achieves a superior trade-off between
mean, minimum, and 5th percentile rates than baseline methods.
