Graph Neural Networks-Based User Pairing in Wireless Communication Systems

TL;DR

This paper introduces an unsupervised graph neural network approach for user pairing in wireless systems, significantly improving scheduling efficiency and scalability over traditional methods like k-means and SUS.

Contribution

The paper presents a novel GNN-based method for user pairing that outperforms existing algorithms and adapts to network size changes without retraining.

Findings

49% better sum rate than k-means at 20 dB SNR

95% better sum rate than SUS at 20 dB SNR

Model handles dynamic network sizes without performance loss

Abstract

Recently, deep neural networks have emerged as a solution to solve NP-hard wireless resource allocation problems in real-time. However, multi-layer perceptron (MLP) and convolutional neural network (CNN) structures, which are inherited from image processing tasks, are not optimized for wireless network problems. As network size increases, these methods get harder to train and generalize. User pairing is one such essential NP-hard optimization problem in wireless communication systems that entails selecting users to be scheduled together while minimizing interference and maximizing throughput. In this paper, we propose an unsupervised graph neural network (GNN) approach to efficiently solve the user pairing problem. Our proposed method utilizes the Erdos goes neural pipeline to significantly outperform other scheduling methods such as k-means and semi-orthogonal user scheduling (SUS). At 20 dB SNR, our proposed approach achieves a 49% better sum rate than k-means and a staggering 95% better sum rate than SUS while consuming minimal time and resources. The scalability of the proposed method is also explored as our model can handle dynamic changes in network size without experiencing a substantial decrease in performance. Moreover, our model can accomplish this without being explicitly trained for larger or smaller networks facilitating a dynamic functionality that cannot be achieved using CNNs or MLPs.