A Deep Learning Approach for User-Centric Clustering in Cell-Free Massive MIMO Systems

TL;DR

This paper introduces a deep learning method using LSTM networks to efficiently solve user clustering in cell-free massive MIMO systems, enhancing system capacity and fairness despite imperfect channel information.

Contribution

It presents a scalable deep learning approach for user association in cell-free massive MIMO, avoiding retraining and handling imperfect channel data.

Findings

Effective in maximizing spectral efficiency

Operates without retraining for different user scenarios

Robust against pilot contamination effects

Abstract

Contrary to conventional massive MIMO cellular configurations plagued by inter-cell interference, cell-free massive MIMO systems distribute network resources across the coverage area, enabling users to connect with multiple access points (APs) and boosting both system capacity and fairness across user. In such systems, one critical functionality is the association between APs and users: determining the optimal association is indeed a combinatorial problem of prohibitive complexity. In this paper, a solution based on deep learning is thus proposed to solve the user clustering problem aimed at maximizing the sum spectral efficiency while controlling the number of active connections. The proposed solution can scale effectively with the number of users, leveraging long short-term memory cells to operate without the need for retraining. Numerical results show the effectiveness of the proposed solution, even in the presence of imperfect channel state information due to pilot contamination.