Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107263
DC Field | Value | Language |
---|---|---|
dc.contributor | Photonics Research Centre | en_US |
dc.creator | Khan, FN | en_US |
dc.creator | Zhong, K | en_US |
dc.creator | Al-Arashi, WH | en_US |
dc.creator | Yu, C | en_US |
dc.creator | Lu, C | en_US |
dc.creator | Lau, APT | en_US |
dc.date.accessioned | 2024-06-13T01:04:58Z | - |
dc.date.available | 2024-06-13T01:04:58Z | - |
dc.identifier.issn | 1041-1135 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107263 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication F. N. Khan, K. Zhong, W. H. Al-Arashi, C. Yu, C. Lu and A. P. T. Lau, "Modulation Format Identification in Coherent Receivers Using Deep Machine Learning," in IEEE Photonics Technology Letters, vol. 28, no. 17, pp. 1886-1889, 1 Sept. 2016 is available at https://doi.org/10.1109/LPT.2016.2574800. | en_US |
dc.subject | Coherent detection | en_US |
dc.subject | Deep machine learning | en_US |
dc.subject | Modulation format identification | en_US |
dc.title | Modulation format identification in coherent receivers using deep machine learning | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1886 | en_US |
dc.identifier.epage | 1889 | en_US |
dc.identifier.volume | 28 | en_US |
dc.identifier.issue | 17 | en_US |
dc.identifier.doi | 10.1109/LPT.2016.2574800 | en_US |
dcterms.abstract | We propose a novel technique for modulation format identification (MFI) in digital coherent receivers by applying deep neural network (DNN) based pattern recognition on signals' amplitude histograms obtained after constant modulus algorithm (CMA) equalization. Experimental results for three commonly-used modulation formats demonstrate MFI with an accuracy of 100% over a wide optical signal-to-noise ratio (OSNR) range. The effects of fiber nonlinearity on the performance of MFI technique are also investigated. The proposed technique is non-data-aided (NDA) and avoids any additional hardware on top of standard digital coherent receiver. Therefore, it is ideal for simple and cost-effective MFI in future heterogeneous optical networks. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE photonics technology letters, 1 Sept. 2016, v. 28, no. 17, p. 1886-1889 | en_US |
dcterms.isPartOf | IEEE photonics technology letters | en_US |
dcterms.issued | 2016-09-01 | - |
dc.identifier.scopus | 2-s2.0-84978958278 | - |
dc.identifier.eissn | 1941-0174 | en_US |
dc.description.validate | 202403 bckw | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0817 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation China | en_US |
dc.description.fundingText | Hong Kong Government General Research Fund | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6661468 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Khan_Modulation_Format_Identification.pdf | Pre-Published version | 1.68 MB | Adobe PDF | View/Open |
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