Unsupervised Learning for Distributed Downlink Power Allocation in Cell-Free mMIMO Networks

Abstract

Cell-free massive multiple-input multiple-output (CF-mMIMO) surmounts conventional cellular network limitations in terms of coverage, capacity, and interference management. This paper aims to introduce a novel unsupervised learning framework for the downlink (DL) power allocation problem in CF-mMIMO networks, utilizing only large-scale fading (LSF) coefficients as input, rather than the hard-to-obtain exact user location or channel state information (CSI). Both centralized and distributed CF-mMIMO power control learning frameworks are explored, with deep neural networks (DNNs) trained to estimate power coefficients while addressing the constraints of pilot contamination and power budgets. For both learning frameworks, the proposed approach is utilized to maximize three well-known power control objectives under maximum-ratio and regularized zero-forcing precoding schemes: 1) sum of spectral efficiency, 2) minimum signal-to-interference-plus-noise ratio (SINR) for max-min fairness, and 3) product of SINRs for proportional fairness, for each of which customized loss functions are formulated. The proposed unsupervised learning approach circumvents the arduous task of training data computations, typically required in supervised learning methods, bypassing the use of conventional complex optimization methods and heuristic methodologies. Furthermore, an LSF-based radio unit (RU) selection algorithm is employed to activate only the contributing RUs, allowing efficient utilization of network resources. Simulation results demonstrate that our proposed unsupervised learning framework outperforms existing supervised learning and heuristic solutions, showcasing an improvement of up to 20% in spectral efficiency and more than 40% in terms of energy efficiency compared to state-of-the-art supervised learning counterparts.