Scalable Spatial and Geometric Learning Approach for Joint Power Control and Channel Allocation

This research paper introduces an unsupervised scalable probabilistic approach for radio resource management in device-to-device (D2D) communication networks, essential for enhancing wireless data service capacity. We propose a joint optimization framework for spectrum allocation and power control, aiming to optimize the network’s mean rate while meeting minimum data rate requirements. Although deep learning (DL) models have been explored for this purpose, their scalability is constrained by the fixed sizes of their input/output features, and their effectiveness is often limited by an insufficient understanding of the network’s geometric structure. Consequently, Graph Neural Networks (GNNs) were introduced to integrate the wireless network’s topology into the learning process. However, GNNs typically lose spatial correlation data when converting the tensorized channel state information (CSI) into a graph structure. To overcome this limitation, our solution combines GNNs, convolutional neural networks (CNNs), and variational autoencoders to extract meaningful embeddings from the CSI, preserving spatial and geometric features. We also introduce an innovative graph attention mechanism that enhances the model’s focus on crucial node and edge features. Our holistic approach exploits the wireless network’s topological and spatial relationships, offering a scalable, unsupervised, and generalizable solution without the need for retraining or architectural adjustments across various wireless setups. Our findings confirm our method’s superior performance and adaptability to different wireless environments.