  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.
