Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107006
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | en_US |
dc.creator | Khan, FN | en_US |
dc.creator | Lu, C | en_US |
dc.creator | Lau, APT | en_US |
dc.date.accessioned | 2024-06-07T00:59:34Z | - |
dc.date.available | 2024-06-07T00:59:34Z | - |
dc.identifier.issn | 0013-5194 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107006 | - |
dc.language.iso | en | en_US |
dc.publisher | The Institution of Engineering and Technology | en_US |
dc.rights | © The Institution of Engineering and Technology 2016 | en_US |
dc.rights | This is the peer reviewed version of the following article: Khan, F.N., Lu, C. and Lau, A.P.T. (2016), Joint modulation format/bit-rate classification and signal-to-noise ratio estimation in multipath fading channels using deep machine learning. Electron. Lett., 52: 1272-1274, which has been published in final form at https://doi.org/10.1049/el.2016.0876. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. | en_US |
dc.title | Joint modulation format/bit-rate classification and signal-to-noise ratio estimation in multipath fading channels using deep machine learning | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1272 | en_US |
dc.identifier.epage | 1274 | en_US |
dc.identifier.volume | 52 | en_US |
dc.identifier.issue | 14 | en_US |
dc.identifier.doi | 10.1049/el.2016.0876 | en_US |
dcterms.abstract | A novel algorithm for simultaneous modulation format/bit-rate classification and non-data-aided (NDA) signal-to-noise ratio (SNR) estimation in multipath fading channels by applying deep machine learning-based pattern recognition on signals’ asynchronous delay-tap plots (ADTPs) is proposed. The results for three widely-used modulation formats at two different bit-rates demonstrate classification accuracy of 99.8%. In addition, NDA SNR estimation over a wide range of 0−30 dB is shown with mean error of 1 dB. The proposed method requires low-speed, asynchronous sampling of signal and is thus ideal for low-cost multiparameter estimation under real-world channel conditions. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Electronics letters, July 2016, v. 52, no. 14, p. 1272-1274 | en_US |
dcterms.isPartOf | Electronics letters | en_US |
dcterms.issued | 2016-07 | - |
dc.identifier.scopus | 2-s2.0-84976639938 | - |
dc.identifier.eissn | 1350-911X | en_US |
dc.description.validate | 202405 bcch | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0846 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingText | Hong Kong Government General Research Fund under project number PolyU152079/14E | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6655515 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Lu_Joint_Modulation_Format.pdf | Pre-Published version | 1.01 MB | Adobe PDF | View/Open |
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