Machine Learning-Based AP Selection in User-Centric Cell-free Multiple-Antenna Networks
User-centric cell-free (UCCF) massive multiple-input multiple-output (MIMO) systems are considered a viable solution to realize the advantages offered by cell-free (CF) networks, including reduced interference and consistent quality of service while maintaining manageable complexity. In this paper, we propose novel learning-based access point (AP) selection schemes tailored for UCCF massive MIMO systems. The learning model exploits the dataset generated from two distinct AP selection schemes, based on large-scale fading (LSF) coefficients and the sum-rate coefficients, respectively. The proposed learning-based AP selection schemes could be implemented centralized or distributed, with the aim of performing AP selection efficiently. We evaluate our model's performance against CF and two heuristic clustering schemes for UCCF networks. The results demonstrate that the learning-based approach achieves a comparable sum-rate performance to that of competing techniques for UCCF networks, while significantly reducing computational complexity.