Unsupervised Learning - Based Downlink Power Allocation for CF-mMIMO Networks

Abstract

Cell-free massive MIMO (CF-mMIMO) is a transformative wireless network technology that surmounts conventional cellular network limitations concerning coverage, capacity, and interference management. Despite offering numerous benefits, CF-mMIMO also presents significant challenges, particularly in signal processing and power allocation. This paper introduces an unsupervised learning framework for downlink (DL) power allocation in CF-mMIMO networks, utilizing only large scaling fading coefficients instead of the hard-to-obtain exact user equipment (UE) locations or channel state information. We consider the sum spectral efficiency (sum-SE) optimization objective and investigate two distinct precoding schemes-maximum ratio (MR) and regularized zero-forcing (RZF)-for multi-antenna access points (APs). A custom loss function is formulated to maximize the sum-SE at each UE while accounting for pilot contamination and ensuring that power budget constraints are satisfied at each AP. 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. The simulation results demonstrate that the proposed unsupervised learning approach outperforms existing methods in terms of SE, showcasing an improvement up to 20%. The proposed unsupervised neural network also approximates the optimal solutions generated by convex solvers while significantly reducing computational complexity.