Spectral Segmented Linear Regression for Coarse Carrier Frequency Offset Estimation in Optical LEO Satellite Communications

TL;DR

This paper introduces a spectral segmented linear regression method for coarse carrier frequency offset estimation in optical LEO satellite communications, addressing challenges of large CFOs and noise.

Contribution

It proposes a robust, low-complexity NDA scheme that transforms frequency estimation into a segmented linear regression problem for improved accuracy.

Findings

Achieves accurate CFO estimation in large CFO and low SNR scenarios.

Demonstrates robustness and convergence through simulations and offline experiments.

Abstract

Carrier frequency offset estimation (CFOE) is a critical stage in modern coherent optical communication systems. Although conventional all-digital techniques perform reliably in typical fiber-optic communication links, CFOE often becomes a major bottleneck in low-symbol-rate scenarios with large carrier CFOs (approaching the signal bandwidth) and severe additive noise levels. These conditions are particularly prevalent in links between optical ground stations (OGSs) and low Earth orbit (LEO) satellites, where Doppler-induced frequency shifts of several gigahertz and atmospheric attenuation significantly degrade CFOE performance and can render traditional methods ineffective. In this paper, we propose a robust non-data-aided (NDA) scheme designed for wide-range CFOE. Such a coarse CFOE (C-CFOE) algorithm partially compensates for the CFO, enabling the operation of a subsequent fine CFOE algorithm. By applying low-complexity operations to the spectrum of the received signal, we recast the frequency estimation task as a segmented linear regression (SLR) problem. Numerical simulations in stress-test scenarios involving large CFOs, low SNR, and low symbol rates show that the proposed approach achieves good estimation accuracy and robust convergence. Experimental offline validation further confirms the practical feasibility of the method.