Hierarchical Deep Learning for Joint Turbulence and PE Estimation in Multi-Aperture FSO Systems

TL;DR

This paper introduces a hierarchical deep learning approach combined with a novel multi-aperture hardware design to jointly estimate turbulence, pointing errors, and AoA in FSO systems, significantly improving accuracy and robustness.

Contribution

It presents the first practical joint estimation method for turbulence, pointing errors, and AoA using a new hardware architecture and hierarchical deep learning, reducing complexity and enhancing performance.

Findings

Achieves near-MAP estimation accuracy

Reduces computational cost by orders of magnitude

Outperforms end-to-end learning baselines

Abstract

Accurate characterization of free-space optical (FSO) channels requires joint estimation of transmitter pointing errors, receiver angle-of-arrival (AoA) fluctuations, and turbulence-induced fading. However, existing literature addresses these impairments in isolation, since their multiplicative coupling in the received signal severely limits conventional estimators and prevents simultaneous recovery. In this paper, we introduce a novel multi-aperture FSO receiver architecture that leverages spatial diversity across a lens array to decouple these intertwined effects. Building on this hardware design, we propose a hierarchical deep learning framework that sequentially estimates AoA, transmitter pointing error, and turbulence coefficients. This decomposition significantly reduces learning complexity and enables robust inference even under strong atmospheric fading. Simulation results demonstrate that the proposed method achieves near-MAP accuracy with orders-of-magnitude lower computational cost, and substantially outperforms end-to-end learning baselines in terms of estimation accuracy and generalization. To the best of our knowledge, this is the first work to demonstrate practical joint estimation of these three key parameters, paving the way for reliable, turbulence-resilient multi-aperture FSO systems.