WirelessNet: An Efficient Radio Access Network Model Based on Heterogeneous Graph Neural Networks

A network digital twin can enable the ability to safely and rapidly recreate what-if scenarios of mobile networks for more cost effective and intelligent network optimization. Towards enabling a network digital twin of mobile networks, accurate and efficient radio access network models are needed. In this work, we present WirelessNet, a novel radio access network model based on Heterogeneous Message Passing Graph Neural Networks (HMPGNNs). WirelessNet represents network nodes and the underlying wireless phenomena between them as nodes and edges of different type in a heterogeneous graph. Heterogeneous graphs are fed as samples into the HMPGNN model to simulate the wireless phenomena within WirelessNet’s model architecture. Model parameters associated to the same underlying wireless phenomena are shared across network nodes. Results using system-level simulations to train and evaluate our proposal, show that WirelessNet efficiently outputs accurate downlink rates and vector representations of users, even for network deployments unseen during training, with significantly less computational runtime than a cellular network simulator and more accuracy than typical neural network architectures. With ablation experiments, we validate the downlink signal-to-interference-and-noise ratio (SINR) user equipment (UE) node feature as the most significant contributor to reconstruct downlink rates. In a more practical setting without SINR and with reference signal received power (RSRP) from serving base station (BS), WirelessNet generalizes to unseen network deployments and significantly outperforms homogeneous graph neural networks (GNNs). We further show the benefits of our proposal by implementing two network applications served by WirelessNet, namely: radio access network deployment planning and artificial intelligence/machine learning (AI/ML) model training for quality of service (QoS) prediction.