A Deep Learning Approach for User-Centric Clustering in Cell-Free Massive MIMO Systems
This work addresses a critical bottleneck in optimizing resource allocation for cell-free massive MIMO systems, offering a scalable approach to improve network performance, though it is incremental as it applies existing deep learning methods to a known problem.
The paper tackles the complex combinatorial problem of user-AP association in cell-free massive MIMO systems by proposing a deep learning solution to maximize sum spectral efficiency and control active connections, with numerical results demonstrating effectiveness under imperfect channel conditions.
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.