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arXiv · 2503.04278

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

Giovanni Di Gennaro·Amedeo Buonanno·Gianmarco Romano·Stefano Buzzi·Francesco A. N. Palmieri
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Abstract

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.

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Giovanni Di Gennaro, Amedeo Buonanno, Gianmarco Romano, Stefano Buzzi, Francesco A. N. Palmieri. 2025-03-06. A General Framework for Scalable UE-AP Association in User-Centric Cell-Free Massive MIMO based on Recurrent Neural Networks. https://arxiv.org/abs/2503.04278

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