Fiber-Agnostic Machine Learning-Based Raman Amplifier Models

Raman Amplifier with Integrated Dispersion-Compensating Fiber

It has been more than 20 years since fiber-optic communication became practicable, and there has been a continuing stream of technological breakthroughs. The increased diffusion of the Internet has seen the rapid introduction of dense wavelength division multiplexing (DWDM) systems. With the burgeoning increases in communications capacity in recent years 1), attention has been focused on Raman ...

Noise Suppression Phenomenon in Fiber Raman Amplifier

The problem of small bit error rate is actual in fiber Raman amplifiers. Our experimental data on the amplified spontaneous emission (ASE) with backward pumping as a base of optical noise evaluation are presented in this paper. Raman gain spectrum for useful Stokes low-powered signal is calculated and compared with obtained spectrum of non-coherent ASE noise. It is shown that output optical sig...

Model-Agnostic Interpretability of Machine Learning

Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of interpretable models has found renewed interest. I...

Analysis and Investigation of Noise Figure of Fiber Raman Amplifier

Raman amplifiers are widely used for high capacity long haul communication systems. One of the main reason for which it can be used is its improved noise figure. In this paper noise figure of Raman amplifiers is studied and analysed for different pumping schemes. The main sources of noise and their effects on the noise figure are discussed and the methods to reduce the noise figure are proposed.

Low cost incoherent pump solution for Raman fiber amplifier