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1. Deep Neural Network Based Wavelength Selection and Switching in ROADM Systems

   Weiyang Mo, Craig L. (January 2018, Journal of optical communications and networking)

   Recent advances in software and hardware greatly improve the multi-layer control and management of ROADM systems facilitating wavelength switching; however, ensuring stable performance and reliable quality of transmission (QoT) remain difficult problems for dynamic operation. Optical power dynamics that arise from a variety of physical effects in the amplifiers and transmission fiber complicate the control and performance predictions in these systems. We present a deep neural network based machine learning method to predict the power dynamics of a 90-channel ROADM system from data collection and training. We further show that the trained deep neural network can recommend wavelength assignments for wavelength switching with minimal power excursions. 

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2. Open EDFA gain spectrum dataset and its applications in data-driven EDFA gain modeling

   https://doi.org/10.1364/JOCN.491901  

   Wang, Zehao; Kilper, Daniel_C; Chen, Tingjun (August 2023, Journal of Optical Communications and Networking)

   Optical networks satisfy high bandwidth and low latency requirements for telecommunication networks and data center interconnection. To improve network resource utilization, machine learning (ML) is used to accurately model optical amplifiers such as erbium-doped fiber amplifiers (EDFAs), which impact end-to-end system performance such as quality of transmission. However, a comprehensive measurement dataset is required for ML to accurately predict an EDFA’s wavelength-dependent gain. We present an open dataset consisting of 202,752 gain spectrum measurements collected from 16 commercial-grade reconfigurable optical add–drop multiplexer (ROADM) booster and pre-amplifier EDFAs under varying gain settings and diverse channel-loading configurations over 2,785 hours in total, with a total dataset size of 3.1 GB. With this EDFA dataset, we implemented component-level deep-neural-network-based EDFA models and use transfer learning (TL) to transfer the EDFA model among 16 ROADM EDFAs, which achieve less than 0.18/0.24 dB mean absolute error for booster/pre-amplifier gain prediction using only 0.5% of the full target training set. We also showed that TL reduces the EDFA data collection requirements on a new gain setting or a different type of EDFA on the same ROADM. 

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3. Accurate Representation of Signal Power Spectral Density in the Optical Network Emulation (ONE) Engine

   https://doi.org/10.1109/ICTON59386.2023.10207288  

   Latha, Aparaajitha Gomathinayakam; Rahim, Muhammad Ridwanur; Zhang, Tianliang; Hui, Rongqing; Fumagalli, Andrea (August 2023, Title (Book/Conference Proceeding) Author Component Number ISBN Year Persistent Link Series Title Article Title 2023 23rd International Conference on Transparent Optical Networks (ICTON))

   The Optical Network Emulation (ONE) engine is a software tool that offers students the opportunity to learn how to control and operate open optical (wavelength division multiplexing) transport networks, such as those based on the Open ROADM MSA standards. This paper describes multiple modelling techniques that are implemented in the ONE engine to represent the signal power spectral density at any link/fiber section of the emulated transport network. These techniques make use of polynomial fitting and deconvolution computation methods. 

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4. Tailored Dispersion of Spectro‐Temporal Dynamics in Hot‐Carrier Plasmonics

   https://doi.org/10.1002/advs.202205434  

   Kim, Andrew_S; Taghinejad, Mohammad; Goswami, Anjan; Raju, Lakshmi; Lee, Kyu‐Tae; Cai, Wenshan (January 2023, Advanced Science)

   Abstract Ultrafast optical switching in plasmonic platforms relies on the third‐order Kerr nonlinearity, which is tightly linked to the dynamics of hot carriers in nanostructured metals. Although extensively utilized, a fundamental understanding on the dependence of the switching dynamics upon optical resonances has often been overlooked. Here, all‐optical control of resonance bands in a hybrid photonic‐plasmonic crystal is employed as an empowering technique for probing the resonance‐dependent switching dynamics upon hot carrier formation. Differential optical transmission measurements reveal an enhanced switching performance near the anti‐crossing point arising from strong coupling between local and nonlocal resonance modes. Furthermore, entangled with hot‐carrier dynamics, the nonlinear correspondence between optical resonances and refractive index change results in tailorable dispersion of recovery speeds which can notably deviate from the characteristic lifetime of hot carriers. The comprehensive understanding provides new protocols for optically characterizing hot‐carrier dynamics and optimizing resonance‐based all‐optical switches for operations across the visible spectrum. 

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5. Harnessing deep neural networks to solve inverse problems in quantum dynamics: machine-learned predictions of time-dependent optimal control fields

   https://doi.org/10.1039/D0CP03694C  

   Wang, Xian; Kumar, Anshuman; Shelton, Christian R.; Wong, Bryan M. (October 2020, Physical Chemistry Chemical Physics)

   Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-dependent optimal control fields, E ( t ), that enable desired electronic transitions in reduced-dimensional quantum dynamical systems. To solve this inverse problem, we investigated two independent machine learning approaches: (1) a feedforward neural network for predicting the frequency and amplitude content of the power spectrum in the frequency domain ( i.e. , the Fourier transform of E ( t )), and (2) a cross-correlation neural network approach for directly predicting E ( t ) in the time domain. Both of these machine learning methods give complementary approaches for probing the underlying quantum dynamics and also exhibit impressive performance in accurately predicting both the frequency and strength of the optimal control field. We provide detailed architectures and hyperparameters for these deep neural networks as well as performance metrics for each of our machine-learned models. From these results, we show that machine learning, particularly deep neural networks, can be employed as cost-effective statistical approaches for designing electromagnetic fields to enable desired transitions in these quantum dynamical systems. 

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