Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electronic and Information Engineering | en_US |
| dc.contributor.advisor | Yu, Changyuan (EIE) | en_US |
| dc.creator | Luo, Huaijian | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/12124 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Intelligent algorithms enabled optical network monitoring and optimization | en_US |
| dcterms.abstract | In this thesis, we proposed three intelligent algorithms for power optimization and optical performance monitoring (OPM), respectively, to intelligently achieve maximum transmission capacity and optical multi-parameter monitoring for coherent fiber optic communication networks. | en_US |
| dcterms.abstract | For launch power optimization, we propose using the optimization algorithm, simulated annealing (SA) algorithm, to find the optimal set of power parameters (power slopes and offsets) of the three communication bands (C+L+S band). The goal is to maximize the transmission capacity by considering dynamic Raman gain and channel-dependent parameters in ultra-wideband wavelength-division multiplexing (UWB WDM) systems. We show that, compared to brute-force searching (BFS), the SA algorithm can achieve a comparable and even higher transmission capacity. Meanwhile, the SA algorithm's searching speed is much faster. Different optimizing strategies can also be used to balance the capacity-spectral flatness trade-off. Moreover, this method can be used to optimize the launch power distribution for arbitrary optical fiber UWB WDM systems. | en_US |
| dcterms.abstract | For optical performance monitoring, a cost-effective deep learning-assisted scheme is proposed to simultaneously achieve modulation format identification (MFI), baud rate identification (BRI), chromatic dispersion identification (CDI), and optical signal-to-noise ratio (OSNR) estimation of optical signals. This method is based on multi-task learning (MTL) neural network model with adaptive asynchronous delay tap plot (AADTP) and asynchronous amplitude histogram (AAH) by direct detection in the intermediate nodes of optical networks. This scheme is transparent to the baud rate. In comparison to a single input, the combined inputs of AADTP and AAH improve the accuracies of the MTL model. Signals in two formats, two baud rates, and three CD situations are used to verify this scheme. MFI, BRI, and CDI have the best accuracies of 100%, 99.81%, and 99.83%, respectively. Meanwhile, the average mean absolute error (MAE) of OSNR estimation is 0.2867 dB. This method is cost-effective and practical to be applied at intermediate nodes to construct smart optical networks. | en_US |
| dcterms.abstract | The other novel OPM method based on Fourier transform spectrum analysis (FTSA) is designed for OSNR estimation, modulation format, and baud rate identification (MF-BRI) in the presence of fiber nonlinearities. The FTSA, which consists of two-stage Mach-Zehnder interferometer (MZI) arrays, is used to obtain interference intensities, which reflect the spectral features of signals. Then, without knowing the configuration of the communication network, the mapping between the OPM parameters and modulated interference intensity (MII) is characterized using artificial neural networks (ANNs). The results demonstrate that optical performance parameters can be monitored simultaneously. Meanwhile, under complex transmission conditions with frequency offsets and delay jitters, the accuracy of MF-BRI is 94.8%, and most (over 86%) OSNR monitoring errors are less than 1 dB. The FTSA can also be fabricated on a silicon on insulator (SOI) platform with a large fabrication tolerance and has a wide working bandwidth to support the entire optical communication band. As a result, the proposed OPM method is capable of integration and miniaturization, and it is promising to be widely deployed at intermediate nodes of optical fiber networks. | en_US |
| dcterms.extent | xxiv, 147 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2022 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Optical fiber communication | en_US |
| dcterms.LCSH | Fiber optics | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
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