Learning Decentralized Wireless Resource Allocations with Graph Neural Networks

TL;DR

This paper introduces a novel decentralized wireless resource allocation method using Aggregation Graph Neural Networks and primal-dual learning, effectively handling delays and asynchrony in dynamic networks.

Contribution

It develops Agg-GNNs for local information processing and combines them with model-free primal-dual learning to optimize resource allocation in decentralized wireless networks.

Findings

Superior performance over baseline strategies in simulations

Permutation equivariance property facilitates adaptation to network changes

Effective handling of delays and asynchrony in decentralized settings

Abstract

We consider the broad class of decentralized optimal resource allocation problems in wireless networks, which can be formulated as a constrained statistical learning problems with a localized information structure. We develop the use of Aggregation Graph Neural Networks (Agg-GNNs), which process a sequence of delayed and potentially asynchronous graph aggregated state information obtained locally at each transmitter from multi-hop neighbors. We further utilize model-free primal-dual learning methods to optimize performance subject to constraints in the presence of delay and asynchrony inherent to decentralized networks. We demonstrate a permutation equivariance property of the resulting resource allocation policy that can be shown to facilitate transference to dynamic network configurations. The proposed framework is validated with numerical simulations that exhibit superior performance to baseline strategies.