Unsupervised Learning for Scalable Downlink Power Control in Cell-Free Massive MIMO

In cell-free massive multiple-input multiple-output systems, downlink power control is essential to ensure uniformly high service quality across users. Existing methods range from centralized iterative approaches requiring global channel knowledge and supervised training, to simpler distributed strategies such as fractional power control that rely on local information but perform poorly in terms of fairness. This letter proposes an unsupervised, physics-informed framework that directly optimizes max-min fairness without requiring optimal labels or user position information. The method is inherently scalable in the number of user equipment, does not require retraining when the user population changes, and can be extended to achieve full scalability with respect to both access points and users. Numerical results show that it nearly doubles the worst-user spectral efficiency compared to existing scalable schemes.

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 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.