Abstract
We demonstrate the use of meta-heuristics algorithms for flatness optimization of optical frequency combs (OFCs). Without any additional component for flatness compensation, the laser alone is explored when driven by optimized bias current and radio frequency (RF) driving signals composed by multiple harmonics. The bias current amplitude and RF harmonic amplitudes and relative phases are optimized using particle swarm optimization (PSO) and differential evolution (DE) algorithms. The numerical results lead to a 9 lines-GS-laser-based OFC spectrum with 2.9 dB flatness. An online experimental optimization using the DE algorithm results in a 7-line-GS-laser-based OFC with 2 dB flatness.
Highlights
The current increase of data traffic in optical networks has encouraged the use of optical frequency combs (OFC) in Nyquist-dense-wavelength division multiplexing (N-DWDM) [1,2], orthogonal frequency division multiplexing (OFDM) [3], and comb-based super-channel systems [4]
We demonstrate the use of meta-heuristics algorithms for flatness optimization of optical frequency combs (OFCs)
In achieving OFCs with narrower flatness using the model, the major limitations come from the number of harmonics in the laser driving signal, the laser modulation bandwidth, its linewidth enhancement factor, and the optimization constraints over the OFC frequency window around the maximum power and minimum bias current
Summary
The current increase of data traffic in optical networks has encouraged the use of optical frequency combs (OFC) in Nyquist-dense-wavelength division multiplexing (N-DWDM) [1,2], orthogonal frequency division multiplexing (OFDM) [3], and comb-based super-channel systems [4]. The laser-based OFC performance can be improved by driving the device with an RF signal composed of multiple harmonics and an optimized tailored driving signal In this setup, the degrees of freedom are the amplitudes and relative phases of the. Harmonics composing the RF signal and the bias current This is a complex optimization where the goal is to find the driving signal characteristics that lead to a target OFC performance [18] in terms of CNR, number of lines, and flatness. A proof-of-concept experimental demo is presented in Section 5., firstly describing the experimental setup and applying the DE algorithm in an online experimental optimization of the flatness of a DML-based OFC.
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