State-Augmented Learnable Algorithms for Resource Management in Wireless Networks

TL;DR

This paper introduces a state-augmented algorithm for resource management in wireless networks that incorporates dual variables, ensuring near-optimal solutions and demonstrating superiority over baseline methods in power control tasks.

Contribution

The paper proposes a novel state-augmented approach that integrates dual variables into RRM policies, improving feasibility and optimality in wireless resource management.

Findings

The algorithm achieves feasible and near-optimal resource management decisions.

It outperforms baseline methods in wireless power control experiments.

Graph neural network parameterizations enhance the algorithm's effectiveness.

Abstract

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.