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arXiv · 2608.15990

Gaussianization-Based Parameter Estimation for Gamma-Gamma and Lognormal-Rician Turbulence Channels

Maoke Miao·Bo Liu·Xinyu Zhang·Xiao-Yu Chen
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Abstract

Accurate parameter estimation for atmospheric turbulence channels is challenging because the probability density functions of the Gamma-Gamma (GG) and Lognormal-Rician (LR) models involve special functions and numerical integrations. This paper proposes two Gaussianization parameter estimators for GG and LR turbulence channels, i.e., the quantile-transformation (QT) estimator and the Box-Cox estimator. The QT estimator employs bidirectional cross-transformation together with higher-order statistical matching, whereas the Box-Cox estimator constructs an approximate likelihood by incorporating the transformation Jacobian. Asymptotic analysis identifies skewness as the leading-order deviation from Gaussianity under weak-to-moderate turbulence and yields asymptotic expressions for the Box-Cox power parameters. In addition, a physics-informed regularizer based on the extended Rytov-theory mapping from the Rytov variance to the GG shape parameters is introduced to improve parameter identifiability. Simulation results demonstrate that both estimators provide robust performance under noiseless and noisy conditions, while the physics-informed regularization term can improve the parameter estimation performance by at least three orders of magnitude compared with the iterative moment-based estimator and the method-of-moments/convex-optimization estimator in specific turbulence scenarios.

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Maoke Miao, Bo Liu, Xinyu Zhang, Xiao-Yu Chen. 2026-08-17. Gaussianization-Based Parameter Estimation for Gamma-Gamma and Lognormal-Rician Turbulence Channels. https://arxiv.org/abs/2608.15990

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