Modeling EDFA Gain Ripple and Filter Penalties With Machine Learning for Accurate QoT Estimationdoi:10.1109/JLT.2020.2975081Ankush MahajanKonstantinos ChristodoulopoulosRicardo MartinezSalvatore SpadaroRaul MunozIEEE
Since each span length is different, and our erbium-doped fiber amplifiers (EDFAs) have minimal gain of 20 dB (to compensate the loss of approximately 100 km of fiber), we fixed all EDFA gains to be 20 dB and use tunable attenuator integrated in each EDFA to ensure the proper ...
The machine learning enabled RWA algorithms, RSA algorithms, and RCSA algorithms are elaborated, analyzed and compared in detail. In addition, the applications of machine learning in the QoT estimation, traffic estimation, and crosstalk prediction, etc., are also elaborated. Based on the existing ...
3. Biosensors Assisted by Machine Learning As was mentioned before, ML is a subfield of artificial intelligence (AI) that provides another way to gain insight into complex data [137]. ML uses computational systems to simulate human learning and gives the algorithm the ability to recognize and ac...
Machine Learning-Based EDFA Gain Model. In Proceedings of the 2018 European Conference on Optical Communication (ECOC), Rome, Italy, 23–27 September 2018; pp. 1–3. [Google Scholar] Morais, R.M.; Pedro, J. Machine Learning Models for Estimating Quality of Transmission in DWDM Networks. J...
This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain and phase spectra of a vector Brillouin optical time-domain analysis (VBOTDA). The PML framework is used to predict the Brillouin frequency shift (BFS...
[65] also proposed a multi-functional ML model based on CNN, where MFI and OSNR estimation are realized simultaneously. Different from the use of asynchronous amplitude histogram [95], the eye diagram of the received signal is used as the feature input. Since CNN has the ability of self-...
Finally, it gets to the start using the gain function in 3D space for each fitting factor since our model is based on 3D scoring of each feature in the space where point is moved in x, y, and z values in space (logical tracking during classifier learning). Algorithm 2 aims to use ...
In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 h, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in ...
estimation Climatology average method Empirical orthogonal function Multiple linear regressions Quantitative precipitation forecasting Multiple nonlinear regressions Machine learning Multiple linear regression Neural networks Wavelet-based neural network Auto regressive integrated moving average United States Geological ...