GNN Approach for Cell-Free Massive MIMO: Graph Neural Network Solution for Power Control
Core Concepts
Graph Neural Networks offer a superior solution to power control in Cell-Free Massive MIMO systems, outperforming traditional methods.
Abstract
Introduction to Cell-Free Massive MIMO and its challenges.
Importance of downlink power control in CFmMIMO.
Comparison of Machine Learning approaches for power control.
Development of a Graph Neural Network solution for max-min power control.
Detailed explanation of the GNN structure and data preprocessing.
Training process and loss function used in GNN.
Numerical results showcasing the performance and complexity of GNN compared to SOCP.
Conclusion highlighting the scalability and generalizability of the proposed GNN solution.
A GNN Approach for Cell-Free Massive MIMO
Stats
"Our GNN model contains 9 hidden layers, i.e., T = 10."
"The loss function used for training is the mean square error of the per-user SINR."
Quotes
"The fully connected layers in [14] can incur large training complexity."
"GNN inherently satisfies this property. Therefore, such a data augmentation is not needed."