D2D Power Allocation via Quantum Graph Neural Network

Increasing wireless network complexity demands scalable resource management. Classical GNNs excel at graph learning but incur high computational costs in large-scale settings. We present a fully quantum Graph Neural Network (QGNN) that implements message passing via Parameterized Quantum Circuits (PQCs). Our Quantum Graph Convolutional Layers (QGCLs) encode features into quantum states, process graphs with NISQ-compatible unitaries, and retrieve embeddings through measurement. Applied to D2D power control for SINR maximization, our QGNN matches classical performance with fewer parameters and inherent parallelism. This end-to-end PQC-based GNN marks a step toward quantum-accelerated wireless optimization.

RSSI prediction using Machine Learning models

In this study, we present a method to predict the Received signal strength indication (RSSI) in an area of the base station. Traditional attenuated wave propagation models are often time consuming as well as computationally complex, depending on the unique factors of the medium. This study focuses on providing a solution to predict signal quality using coordinate values of many points in the considering area. We apply machine learning models such as linear regression, Support Vector Machine (SVM) or Decision tree model, to directly predict the RSSI of many points in the range of a base station without computing the complex parameters of the attenuated propagation model. The effectiveness of RSSI prediction was evaluated by mean square error (MSE) and mean absolute error (MAE). The stage of training and testing machine learning models in the research uses data that are the actual measurement results during the research process.