Graph Neural Network Performance Analysis in the Cell-Free Massive MIMO Wireless Systems

To address challenging issues in wireless communications, researchers have developed deep learning (DL)-based techniques, yielding encouraging outcomes. However, commonly adopted neural network (NN) architectures, such as convolutional (CNNs) and multi-layer perceptrons, are derived from DL for image processing applications and are not specifically tailored to tackle wireless network challenges. Consequently, they often exhibit poor generalization and scalability issues in large-scale networks and unknown network environments. Graph neural networks (GNNs) have gained recent popularity as a solution to these challenges due to their ability to effectively utilize domain knowledge and graph topology in wireless communications. GNN-based techniques demonstrate nearly optimal performance in massive networks and show good generalization across various system configurations. To leverage GNNs in wireless systems, we have designed a cell-free massive MIMO (CF-mMIMO) system, presented as a GNN structure. The proposed CF-mMIMO system is employed to analyze the effectiveness of different GNN models and various optimization algorithms. The system exhibits effective performance against optimal benchmarks and provides valuable insights for the next-generation research community.