A Compressed Sampling and Dictionary Learning Framework for WDM-Based Distributed Fiber Sensing

TL;DR

This paper introduces a novel compressed sampling and dictionary learning approach for fiber-optic distributed sensing, improving signal reconstruction and impairment detection using wavelength-tunable lasers.

Contribution

It develops an alternating minimization algorithm that estimates sparse signals and dictionary parameters, accounting for imperfect prior knowledge in fiber sensing.

Findings

Effective sparse signal recovery demonstrated through simulations.

Experimental validation shows accurate impairment localization.

Algorithm handles dictionary coherence and uncertain parameters.

Abstract

We propose a compressed sampling and dictionary learning framework for fiber-optic sensing using wavelength-tunable lasers. A redundant dictionary is generated from a model for the reflected sensor signal. Imperfect prior knowledge is considered in terms of uncertain local and global parameters. To estimate a sparse representation and the dictionary parameters, we present an alternating minimization algorithm that is equipped with a pre-processing routine to handle dictionary coherence. The support of the obtained sparse signal indicates the reflection delays, which can be used to measure impairments along the sensing fiber. The performance is evaluated by simulations and experimental data for a fiber sensor system with common core architecture.