A General Framework for Scalable UE-AP Association in User-Centric Cell-Free Massive MIMO Based on Recurrent Neural Networks

This study addresses the challenge of access point (AP) and user equipment (UE) association in cell-free massive MIMO networks. It introduces a deep learning algorithm based on Bidirectional Long Short-Term Memory cells (a specific type of Recurrent Neural Network) and a hybrid probabilistic methodology for weight updating. This approach enhances scalability by adapting to variations in the number of UEs without requiring retraining. In addition, the study proposes a novel master-centric methodology for managing the AP-UE association process, which helps improve scalability not only with respect to the number of UEs, but also to that of APs. Furthermore, a variant of the proposed AP-UE algorithm ensures robustness against pilot contamination effects, a critical issue arising from pilot reuse in channel estimation. Extensive numerical results validate the effectiveness and adaptability of the proposed methods, demonstrating clear advantages over widely used heuristic alternatives.