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Reducing the time of C + L band Raman amplifiers design with an algorithm based on artificial intelligence

机译：利用基于人工智能的算法缩短C+L波段拉曼放大器设计的时间

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摘要

We present an artificial intelligence-based algorithm to efficiently reduce the time required in the design of multipump Raman amplifiers operating simultaneously in conventional (C) and long (L) bands. The performance of the amplifiers was measured in terms of on-off gain, ripple, optical signal-to-noise ratio, and noise figure, considering a single-mode fiber (SMF), a dispersion compensating fiber, as well as a photonic crystal fiber. Beyond the time reduction provided by extreme learning machine (ELM) and particle swarm optimization (PSO), the numerical simulation results show optimal gains for all fibers in the C + L band. A comparison between the proposed algorithm, the standard PSO, and the budget heuristics + multiobjective optimization based on a nondominated sorting genetic algorithm was performed. The forecast established by the ELM in the three fibers specified a root mean square error of 0.0195 in the pump wavelengths and powers test set, with a computational time of 52 s. The simulation results of the proposed PSO-based multiobjective optimization with four pumps after 100 km of SMF demonstrate an on-off gain of ～0.5 dB higher, when compared to the above-mentioned two methods.
机译：我们提出了一种基于人工智能的算法，以有效减少在传统（C）和长（L）波段同时工作的多泵浦拉曼放大器的设计时间。放大器的性能是根据开关增益、纹波、光信噪比和噪声系数来测量的，考虑了单模光纤（SMF）、色散补偿光纤以及光子晶体光纤。除了极限学习机（ELM）和粒子群优化（PSO）提供的时间缩短外，数值模拟结果还显示C+L波段内所有光纤都获得了最佳增益。对所提算法、标准PSO算法和基于非支配排序遗传算法的预算启发式+多目标优化进行了比较。ELM在三根光纤中建立的预测表明，泵浦波长和功率测试集的均方根误差为0.0195，计算时间为52 s。与上述两种方法相比，在100 km的SMF后，基于PSO的四泵多目标优化的仿真结果表明，开关增益提高了~0.5 dB。

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