A General Framework for Scalable UE-AP Association in User-Centric Cell-Free Massive MIMO based on Recurrent Neural Networks
This addresses scalability and robustness issues in user-centric cell-free massive MIMO networks for telecommunications, though it appears incremental as it builds on existing deep learning methods.
This study tackled the challenge of access point and user equipment association in cell-free massive MIMO networks by introducing a deep learning algorithm based on Bidirectional Long Short-Term Memory cells and a hybrid probabilistic methodology, which enhanced scalability without retraining and demonstrated superiority over heuristic alternatives in numerical results.
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 leveraging Bidirectional Long Short-Term Memory cells and a hybrid probabilistic methodology for weight updating. This approach enhances scalability by adapting to variations in the number of UEs without requiring retraining. Additionally, the study presents a training methodology that improves scalability not only with respect to the number of UEs but also to the number 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 their superiority over widely used heuristic alternatives.