An Interpretable Mapping From a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization
In this paper, we propose a scheme that utilizes the optimization ability of artificial intelligence (AI) for optimal transceiver-joint equalization in compensating for the optical filtering impairments caused by wavelength selective switches (WSS). In contrast to adding or replacing a certain modul...
Gespeichert in:
| Veröffentlicht in: | Journal of lightwave technology 2021-09, Vol.39 (17), p.5449-5458 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 5458 |
|---|---|
| container_issue | 17 |
| container_start_page | 5449 |
| container_title | Journal of lightwave technology |
| container_volume | 39 |
| creator | Zhai, Zhiqun Jiang, Hexun Fu, Mengfan Liu, Lei Yi, Lilin Hu, Weisheng Zhuge, Qunbi |
| description | In this paper, we propose a scheme that utilizes the optimization ability of artificial intelligence (AI) for optimal transceiver-joint equalization in compensating for the optical filtering impairments caused by wavelength selective switches (WSS). In contrast to adding or replacing a certain module of existing digital signal processing (DSP), we exploit the similarity between a communication system and a neural network (NN). By mapping a communication system to an NN, in which the equalization modules correspond to the convolutional layers and other modules can been regarded as static layers, the optimal transceiver-joint equalization coefficients can be obtained. In particular, the DSP structure of the communication system is not changed. Extensive numerical simulations are performed to validate the performance of the proposed method. For a 65 GBaud 16QAM signal, it can achieve a 0.76 dB gain when the number of WSSs is 16 with a -6 dB bandwidth of 73 GHz. |
| doi_str_mv | 10.1109/JLT.2021.3086301 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_9447958</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9447958</ieee_id><sourcerecordid>2572665980</sourcerecordid><originalsourceid>FETCH-LOGICAL-c333t-58cffa9394b82a2e1e088f4b8177a7bd22f679cad09cfb85b4a22ccc5c586aeb3</originalsourceid><addsrcrecordid>eNo9kM1PAjEQxRujiYjeTbw08bzYj-22PRKCCkE5iOemW7pmkd0ubVeDf71FiKfJzLz3JvMD4BajEcZIPswXqxFBBI8oEgVF-AwMMGMiIwTTczBAnNJMcJJfgqsQNgjhPBd8AMK4hbM2Wt95G3W5tfBFd13dfsBH7xqo4cQ1Td_WRsfatfBtH6JtYHRp82p7r7epxG_nP2HlPFx2sW7SbOV1G4ytv6zP5q5uI5zuer2tf_5SrsFFpbfB3pzqELw_TleT52yxfJpNxovMUEpjxoSpKi2pzEtBNLHYIiGq1GDONS_XhFQFl0avkTRVKViZa0KMMcwwUWhb0iG4P-Z23u16G6LauN636aQijJOiYFKgpEJHlfEuBG8r1fn0hN8rjNQBrUpo1QGtOqFNlrujpbbW_stlnnPJBP0FpQR21g</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2572665980</pqid></control><display><type>article</type><title>An Interpretable Mapping From a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization</title><source>IEEE Electronic Library (IEL)</source><creator>Zhai, Zhiqun ; Jiang, Hexun ; Fu, Mengfan ; Liu, Lei ; Yi, Lilin ; Hu, Weisheng ; Zhuge, Qunbi</creator><creatorcontrib>Zhai, Zhiqun ; Jiang, Hexun ; Fu, Mengfan ; Liu, Lei ; Yi, Lilin ; Hu, Weisheng ; Zhuge, Qunbi</creatorcontrib><description>In this paper, we propose a scheme that utilizes the optimization ability of artificial intelligence (AI) for optimal transceiver-joint equalization in compensating for the optical filtering impairments caused by wavelength selective switches (WSS). In contrast to adding or replacing a certain module of existing digital signal processing (DSP), we exploit the similarity between a communication system and a neural network (NN). By mapping a communication system to an NN, in which the equalization modules correspond to the convolutional layers and other modules can been regarded as static layers, the optimal transceiver-joint equalization coefficients can be obtained. In particular, the DSP structure of the communication system is not changed. Extensive numerical simulations are performed to validate the performance of the proposed method. For a 65 GBaud 16QAM signal, it can achieve a 0.76 dB gain when the number of WSSs is 16 with a -6 dB bandwidth of 73 GHz.</description><identifier>ISSN: 0733-8724</identifier><identifier>EISSN: 1558-2213</identifier><identifier>DOI: 10.1109/JLT.2021.3086301</identifier><identifier>CODEN: JLTEDG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptive filters ; Artificial intelligence ; Artificial neural networks ; Coherent transceiver ; Communication systems ; Communications systems ; Convolution ; Digital signal processing ; Equalization ; Finite impulse response filters ; Mapping ; Modules ; neural network ; Neural networks ; optical filtering impairments ; Optical transmitters ; Optimization ; Switches ; transceiver-joint equalization ; Transceivers</subject><ispartof>Journal of lightwave technology, 2021-09, Vol.39 (17), p.5449-5458</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c333t-58cffa9394b82a2e1e088f4b8177a7bd22f679cad09cfb85b4a22ccc5c586aeb3</citedby><cites>FETCH-LOGICAL-c333t-58cffa9394b82a2e1e088f4b8177a7bd22f679cad09cfb85b4a22ccc5c586aeb3</cites><orcidid>0000-0002-2437-0220 ; 0000-0002-6168-2688 ; 0000-0002-6039-9063</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9447958$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9447958$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhai, Zhiqun</creatorcontrib><creatorcontrib>Jiang, Hexun</creatorcontrib><creatorcontrib>Fu, Mengfan</creatorcontrib><creatorcontrib>Liu, Lei</creatorcontrib><creatorcontrib>Yi, Lilin</creatorcontrib><creatorcontrib>Hu, Weisheng</creatorcontrib><creatorcontrib>Zhuge, Qunbi</creatorcontrib><title>An Interpretable Mapping From a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization</title><title>Journal of lightwave technology</title><addtitle>JLT</addtitle><description>In this paper, we propose a scheme that utilizes the optimization ability of artificial intelligence (AI) for optimal transceiver-joint equalization in compensating for the optical filtering impairments caused by wavelength selective switches (WSS). In contrast to adding or replacing a certain module of existing digital signal processing (DSP), we exploit the similarity between a communication system and a neural network (NN). By mapping a communication system to an NN, in which the equalization modules correspond to the convolutional layers and other modules can been regarded as static layers, the optimal transceiver-joint equalization coefficients can be obtained. In particular, the DSP structure of the communication system is not changed. Extensive numerical simulations are performed to validate the performance of the proposed method. For a 65 GBaud 16QAM signal, it can achieve a 0.76 dB gain when the number of WSSs is 16 with a -6 dB bandwidth of 73 GHz.</description><subject>Adaptive filters</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Coherent transceiver</subject><subject>Communication systems</subject><subject>Communications systems</subject><subject>Convolution</subject><subject>Digital signal processing</subject><subject>Equalization</subject><subject>Finite impulse response filters</subject><subject>Mapping</subject><subject>Modules</subject><subject>neural network</subject><subject>Neural networks</subject><subject>optical filtering impairments</subject><subject>Optical transmitters</subject><subject>Optimization</subject><subject>Switches</subject><subject>transceiver-joint equalization</subject><subject>Transceivers</subject><issn>0733-8724</issn><issn>1558-2213</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM1PAjEQxRujiYjeTbw08bzYj-22PRKCCkE5iOemW7pmkd0ubVeDf71FiKfJzLz3JvMD4BajEcZIPswXqxFBBI8oEgVF-AwMMGMiIwTTczBAnNJMcJJfgqsQNgjhPBd8AMK4hbM2Wt95G3W5tfBFd13dfsBH7xqo4cQ1Td_WRsfatfBtH6JtYHRp82p7r7epxG_nP2HlPFx2sW7SbOV1G4ytv6zP5q5uI5zuer2tf_5SrsFFpbfB3pzqELw_TleT52yxfJpNxovMUEpjxoSpKi2pzEtBNLHYIiGq1GDONS_XhFQFl0avkTRVKViZa0KMMcwwUWhb0iG4P-Z23u16G6LauN636aQijJOiYFKgpEJHlfEuBG8r1fn0hN8rjNQBrUpo1QGtOqFNlrujpbbW_stlnnPJBP0FpQR21g</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>Zhai, Zhiqun</creator><creator>Jiang, Hexun</creator><creator>Fu, Mengfan</creator><creator>Liu, Lei</creator><creator>Yi, Lilin</creator><creator>Hu, Weisheng</creator><creator>Zhuge, Qunbi</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-2437-0220</orcidid><orcidid>https://orcid.org/0000-0002-6168-2688</orcidid><orcidid>https://orcid.org/0000-0002-6039-9063</orcidid></search><sort><creationdate>20210901</creationdate><title>An Interpretable Mapping From a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization</title><author>Zhai, Zhiqun ; Jiang, Hexun ; Fu, Mengfan ; Liu, Lei ; Yi, Lilin ; Hu, Weisheng ; Zhuge, Qunbi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c333t-58cffa9394b82a2e1e088f4b8177a7bd22f679cad09cfb85b4a22ccc5c586aeb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adaptive filters</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Coherent transceiver</topic><topic>Communication systems</topic><topic>Communications systems</topic><topic>Convolution</topic><topic>Digital signal processing</topic><topic>Equalization</topic><topic>Finite impulse response filters</topic><topic>Mapping</topic><topic>Modules</topic><topic>neural network</topic><topic>Neural networks</topic><topic>optical filtering impairments</topic><topic>Optical transmitters</topic><topic>Optimization</topic><topic>Switches</topic><topic>transceiver-joint equalization</topic><topic>Transceivers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhai, Zhiqun</creatorcontrib><creatorcontrib>Jiang, Hexun</creatorcontrib><creatorcontrib>Fu, Mengfan</creatorcontrib><creatorcontrib>Liu, Lei</creatorcontrib><creatorcontrib>Yi, Lilin</creatorcontrib><creatorcontrib>Hu, Weisheng</creatorcontrib><creatorcontrib>Zhuge, Qunbi</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Journal of lightwave technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhai, Zhiqun</au><au>Jiang, Hexun</au><au>Fu, Mengfan</au><au>Liu, Lei</au><au>Yi, Lilin</au><au>Hu, Weisheng</au><au>Zhuge, Qunbi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Interpretable Mapping From a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization</atitle><jtitle>Journal of lightwave technology</jtitle><stitle>JLT</stitle><date>2021-09-01</date><risdate>2021</risdate><volume>39</volume><issue>17</issue><spage>5449</spage><epage>5458</epage><pages>5449-5458</pages><issn>0733-8724</issn><eissn>1558-2213</eissn><coden>JLTEDG</coden><abstract>In this paper, we propose a scheme that utilizes the optimization ability of artificial intelligence (AI) for optimal transceiver-joint equalization in compensating for the optical filtering impairments caused by wavelength selective switches (WSS). In contrast to adding or replacing a certain module of existing digital signal processing (DSP), we exploit the similarity between a communication system and a neural network (NN). By mapping a communication system to an NN, in which the equalization modules correspond to the convolutional layers and other modules can been regarded as static layers, the optimal transceiver-joint equalization coefficients can be obtained. In particular, the DSP structure of the communication system is not changed. Extensive numerical simulations are performed to validate the performance of the proposed method. For a 65 GBaud 16QAM signal, it can achieve a 0.76 dB gain when the number of WSSs is 16 with a -6 dB bandwidth of 73 GHz.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JLT.2021.3086301</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-2437-0220</orcidid><orcidid>https://orcid.org/0000-0002-6168-2688</orcidid><orcidid>https://orcid.org/0000-0002-6039-9063</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0733-8724 |
| ispartof | Journal of lightwave technology, 2021-09, Vol.39 (17), p.5449-5458 |
| issn | 0733-8724 1558-2213 |
| language | eng |
| recordid | cdi_ieee_primary_9447958 |
| source | IEEE Electronic Library (IEL) |
| subjects | Adaptive filters Artificial intelligence Artificial neural networks Coherent transceiver Communication systems Communications systems Convolution Digital signal processing Equalization Finite impulse response filters Mapping Modules neural network Neural networks optical filtering impairments Optical transmitters Optimization Switches transceiver-joint equalization Transceivers |
| title | An Interpretable Mapping From a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T03%3A03%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20Interpretable%20Mapping%20From%20a%20Communication%20System%20to%20a%20Neural%20Network%20for%20Optimal%20Transceiver-Joint%20Equalization&rft.jtitle=Journal%20of%20lightwave%20technology&rft.au=Zhai,%20Zhiqun&rft.date=2021-09-01&rft.volume=39&rft.issue=17&rft.spage=5449&rft.epage=5458&rft.pages=5449-5458&rft.issn=0733-8724&rft.eissn=1558-2213&rft.coden=JLTEDG&rft_id=info:doi/10.1109/JLT.2021.3086301&rft_dat=%3Cproquest_RIE%3E2572665980%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2572665980&rft_id=info:pmid/&rft_ieee_id=9447958&rfr_iscdi=true |