A State-Augmented Approach for Learning Optimal Resource Management Decisions in Wireless Networks. (arXiv:2210.16412v2 [cs.LG] UPDATED)

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 …

arxiv decisions management networks state wireless