SINR Distribution-Based LSFC and Nakagami-m Estimation in Power-Weighted RZF CF-mMIMO

Abstract

Cell-free massive multiple-input multiple-output is a promising distributed architecture for future wireless networks, where accurate signal-to-interference-plus-noise ratio modeling is essential for channel-parameter inference. This letter proposes a distribution-driven estimator for downlink power-weighted regularized zero-forcing systems using paired instantaneous signal-to-interference-plus-noise ratio and access point–user equipment loading observations. A tractable Gamma-ratio/Beta-prime model is developed for likelihood-based recovery of serving-link large-scale fading coefficients and a global Nakagami- $m$ factor. Simulations with fractional power control show accurate cumulative distribution function fitting and low-error joint recovery.