Optimization of 125-$\mu$m Heterogeneous Multi-Core Fibre Design Using Artificial Intelligence
| Autor: | Xun Mu, Alessandro Ottino, Georgios Zervas, Filipe Ferreira |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Physics
business.industry Particle swarm optimization Multiplicity (mathematics) Atomic and Molecular Physics and Optics Cutoff frequency Permutation Signal-to-noise ratio (imaging) Transmission (telecommunications) Bandwidth (computing) Orders of magnitude (speed) Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | IEEE Journal of Selected Topics in Quantum Electronics. 28:1-13 |
| ISSN: | 1558-4542 1077-260X |
| DOI: | 10.1109/jstqe.2021.3104821 |
| Popis: | We propose an automated heterogeneous trench-assisted multi-core fibre (MCF) design method. This method uses neural networks to speed up coating loss estimation by $\sim$ $10^6$ times and using particle swarm optimization (PSO) algorithm to explore the optimal MCF design under various objectives and properties constraints. The latter reduces the permutation evaluations by ten orders of magnitude compared with the brute force method. The artificial intelligence (AI)-based method is used to design MCFs on two objectives: minimizing crosstalk (XT) and maximizing effective mode area ( $A_\text {eff}$ ). By optimizing XT with different $A_\text {eff}$ and cutoff wavelength constraints combinations for 6-core fibres, we achieved −92.1 dB/km ultra-low XT for C+L band fibre and −64 dB/km for E+S+C+L-band fibre. Meanwhile, we explored the upper limit of $A_\text {eff}$ given different bandwidth constraints resulting in a 6.82 relative core multiplicity factor. We performed capacity analysis of fibres for two transmission lengths. It is shown that bandwidth is the dominant factor while the increase brought by $A_\text {eff}$ and the penalty caused by XT are relevantly small. Our fibres exceed the cutoff-limited capacity of the 7-core fibre in literature by 35.1% and 84.8% for 1200 km and 6000 km transmission respectively. |
| Databáze: | OpenAIRE |
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