AP Selection in Uplink Cell-Free Massive MIMO: An Unsupervised Heterogeneous GNN Approach

We examine an uplink cell-free (CF) massive multiple-input multiple-output (MIMO) system in which multiple-antenna access points (APs) are connected to a central processing unit (CPU) via unlimited capacity front-haul links, and each user equipment (UE) is equipped with a single antenna. In such a system, optimizing AP selection to maximize the sum spectral efficiency (SE) while meeting real-time communication requirements is challenging. To address this, we develop a novel approach using a heterogeneous graph neural network (HetGNN), which effectively captures the complex relationships between AP and UE nodes. Given the difficulty in obtaining ground truth for optimal AP selection, we employ an unsupervised HetGNN model that directly optimizes AP selection through its loss function. Simulation results demonstrate that our approach achieves an average improvement of at least 8% compared to existing methods and surpasses an offline approach using the Simulated Annealing method. Our approach also results in high fairness scores on Jain's Fairness Index; averaging greater than 0.845 per UE and exceeding 0.9 as the system scales. Our computation time analysis shows that the average inference time remains low and within acceptable limits for communication systems.