F
Faisal Nadeem Khan
Researcher at Hong Kong Polytechnic University
Publications - 52
Citations - 1702
Faisal Nadeem Khan is an academic researcher from Hong Kong Polytechnic University. The author has contributed to research in topics: Optical performance monitoring & Computer science. The author has an hindex of 18, co-authored 45 publications receiving 1207 citations. Previous affiliations of Faisal Nadeem Khan include Universiti Sains Malaysia & Tsinghua University.
Papers
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Journal ArticleDOI
Optical Performance Monitoring: A Review of Current and Future Technologies
TL;DR: The development of various OPM techniques for direct-detection systems and digital coherent systems are reviewed and future OPM challenges in flexible and elastic optical networks are discussed.
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An Optical Communication's Perspective on Machine Learning and Its Applications
TL;DR: The mathematical foundations of basic ML techniques from communication theory and signal processing perspectives are described, which in turn will shed light on the types of problems in optical communications and networking that naturally warrant ML use.
Journal ArticleDOI
Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks
Faisal Nadeem Khan,Kangping Zhong,Xian Zhou,Waled Hussein Al-Arashi,Changyuan Yu,Chao Lu,Alan Pak Tao Lau +6 more
TL;DR: The use of DNNs in combination with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in digital coherent receivers is experimentally demonstrated.
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Modulation Format Identification in Coherent Receivers Using Deep Machine Learning
Faisal Nadeem Khan,Kangping Zhong,Waled Hussein Al-Arashi,Changyuan Yu,Chao Lu,Alan Pak Tao Lau +5 more
TL;DR: A novel technique for modulation format identification (MFI) in digital coherent receivers is proposed by applying deep neural network (DNN) based pattern recognition on signals' amplitude histograms obtained after constant modulus algorithm (CMA) equalization.
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Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks.
TL;DR: Results of numerical simulations demonstrate that the proposed technique can effectively classify all these widely-used modulation formats with an overall estimation accuracy of 99.6% and also in the presence of various link impairments.