Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/80783
DC Field | Value | Language |
---|---|---|
dc.contributor | Photonics Research Centre | en_US |
dc.creator | Lu, XY | en_US |
dc.creator | Lu, C | en_US |
dc.creator | Yu, WX | en_US |
dc.creator | Qiao, L | en_US |
dc.creator | Liang, SY | en_US |
dc.creator | Lau, APT | en_US |
dc.creator | Chi, N | en_US |
dc.date.accessioned | 2019-05-28T01:09:21Z | - |
dc.date.available | 2019-05-28T01:09:21Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/80783 | - |
dc.language.iso | en | en_US |
dc.publisher | Optical Society of America | en_US |
dc.rights | © 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement (https://www.osapublishing.org/library/license_v1.cfm#VOR-OA) | en_US |
dc.rights | © 2019 Optical Society of America. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved. | en_US |
dc.rights | Journal © 2019 | en_US |
dc.rights | The following publication Xingyu Lu, Chao Lu, Weixiang Yu, Liang Qiao, Shangyu Liang, Alan Pak Tao Lau, and Nan Chi, "Memory-controlled deep LSTM neural network post-equalizer used in high-speed PAM VLC system," Opt. Express 27, 7822-7833 (2019) is available at https://dx.doi.org/10.1364/OE.27.007822 | en_US |
dc.title | Memory-controlled deep LSTM neural network post-equalizer used in high-speed PAM VLC system | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 7822 | en_US |
dc.identifier.epage | 7833 | en_US |
dc.identifier.volume | 27 | en_US |
dc.identifier.issue | 5 | en_US |
dc.identifier.doi | 10.1364/OE.27.007822 | en_US |
dcterms.abstract | Linear and nonlinear impairments severely limit the transmission performance of high-speed visible light communication systems. Neural network-based equalizers have been applied to optical communication systems, which enables significantly improved system performance, such as transmission data rate and distance. In this paper, a memory-controlled deep long short-term memory (LSTM) neural network post-equalizer is proposed to mitigate both linear and nonlinear impairments in pulse amplitude modulation (PAM) based visible light communication (VLC) systems. Both 1.15-Gbps PAM4 and 0.9Gbps PAM8 VLC systems are successfully demonstrated, based on a single red-LED with bit error ratio (BER) below the hard decision forward error correction (HD-FEC) limit of 3.8 x 10(-3). Compared with the traditional finite impulse response (FIR) based equalizer, the Q factor performance is improved by 1.2dB and the transmission distance is increased by one-third in the same experimental hardware setups. Compared with traditional nonlinear hybrid Volterra equalizers, the significant complexity and system performance advantages of using a LSTM-based equalizer is demonstrated. To the best of our knowledge, this is the first demonstration of using deep LSTM in VLC systems. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Optics express, 4 Mar. 2019, v. 27, no. 5, p. 7822-7833 | en_US |
dcterms.isPartOf | Optics express | en_US |
dcterms.issued | 2019 | - |
dc.identifier.isi | WOS:000460170000153 | - |
dc.identifier.pmid | 30876338 | - |
dc.identifier.eissn | 1094-4087 | en_US |
dc.description.validate | 201905 bcrc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Lu_Memory-controlled_LSTM_VLC.pdf | 3.12 MB | Adobe PDF | View/Open |
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