ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks

TL;DR

This paper introduces ENGNN, a novel GNN architecture with edge-update capabilities for efficient radio resource management in wireless networks, achieving high performance and low latency.

Contribution

The paper proposes an edge-update mechanism for GNNs, enabling handling of both node and edge variables, which improves performance and generalization in wireless resource management.

Findings

Achieves higher sum rate than existing methods.

Reduces computation time significantly.

Generalizes well across various network configurations.

Abstract

In order to achieve high data rate and ubiquitous connectivity in future wireless networks, a key task is to efficiently manage the radio resource by judicious beamforming and power allocation. Unfortunately, the iterative nature of the commonly applied optimization-based algorithms cannot meet the low latency requirements due to the high computational complexity. For real-time implementations, deep learning-based approaches, especially the graph neural networks (GNNs), have been demonstrated with good scalability and generalization performance due to the permutation equivariance (PE) property. However, the current architectures are only equipped with the node-update mechanism, which prohibits the applications to a more general setup, where the unknown variables are also defined on the graph edges. To fill this gap, we propose an edge-update mechanism, which enables GNNs to handle both node and edge variables and prove its PE property with respect to both transmitters and receivers. Simulation results on typical radio resource management problems demonstrate that the proposed method achieves higher sum rate but with much shorter computation time than state-of-the-art methods and generalizes well on different numbers of base stations and users, different noise variances, interference levels, and transmit power budgets.