Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems
This letter proposes a deep convolutional neural network (DCNN) approach for adaptive modulation and coding in practical multiple-input, multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Our target is to maximize the throughput and fulfill a packet error rate constraint...
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| Veröffentlicht in: | IEEE wireless communications letters 2019-06, Vol.8 (3), p.665-668 |
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| container_title | IEEE wireless communications letters |
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| creator | Elwekeil, Mohamed Jiang, Shibao Wang, Taotao Zhang, Shengli |
| description | This letter proposes a deep convolutional neural network (DCNN) approach for adaptive modulation and coding in practical multiple-input, multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Our target is to maximize the throughput and fulfill a packet error rate constraint. We consider practical impairments of MIMO-OFDM receiver, such as imperfect timing synchronization, carrier frequency offset correction, and channel estimation. We treat the estimated channel state information and the noise standard deviation as input features to the DCNN. The main advantages of the proposed approach are: 1) it learns the characteristics of the MIMO-OFDM channel properly and predicts the suitable modulation and coding scheme and 2) it does not need complex features selection. |
| doi_str_mv | 10.1109/LWC.2018.2881978 |
| format | Article |
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Our target is to maximize the throughput and fulfill a packet error rate constraint. We consider practical impairments of MIMO-OFDM receiver, such as imperfect timing synchronization, carrier frequency offset correction, and channel estimation. We treat the estimated channel state information and the noise standard deviation as input features to the DCNN. The main advantages of the proposed approach are: 1) it learns the characteristics of the MIMO-OFDM channel properly and predicts the suitable modulation and coding scheme and 2) it does not need complex features selection.</description><identifier>ISSN: 2162-2337</identifier><identifier>EISSN: 2162-2345</identifier><identifier>DOI: 10.1109/LWC.2018.2881978</identifier><identifier>CODEN: IWCLAF</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>adaptive modulation and coding (AMC) ; Artificial neural networks ; Carrier frequencies ; Channel estimation ; Coding ; deep convolutional neural networks ; Encoding ; MIMO communication ; Modulation ; Multiple-input ; multiple-output (MIMO) ; Neural networks ; OFDM ; Orthogonal Frequency Division Multiplexing ; orthogonal frequency division multiplexing (OFDM) ; Quadrature amplitude modulation ; Receivers ; Synchronism ; Training ; Wireless networks</subject><ispartof>IEEE wireless communications letters, 2019-06, Vol.8 (3), p.665-668</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-8c28297fd7e71a90cfa8bd1486d919c61917ed41458909d67baa168a2cac12e63</citedby><cites>FETCH-LOGICAL-c291t-8c28297fd7e71a90cfa8bd1486d919c61917ed41458909d67baa168a2cac12e63</cites><orcidid>0000-0003-2924-4706 ; 0000-0001-9454-4997 ; 0000-0002-7937-5870</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8540019$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8540019$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Elwekeil, Mohamed</creatorcontrib><creatorcontrib>Jiang, Shibao</creatorcontrib><creatorcontrib>Wang, Taotao</creatorcontrib><creatorcontrib>Zhang, Shengli</creatorcontrib><title>Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems</title><title>IEEE wireless communications letters</title><addtitle>LWC</addtitle><description>This letter proposes a deep convolutional neural network (DCNN) approach for adaptive modulation and coding in practical multiple-input, multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Our target is to maximize the throughput and fulfill a packet error rate constraint. We consider practical impairments of MIMO-OFDM receiver, such as imperfect timing synchronization, carrier frequency offset correction, and channel estimation. We treat the estimated channel state information and the noise standard deviation as input features to the DCNN. The main advantages of the proposed approach are: 1) it learns the characteristics of the MIMO-OFDM channel properly and predicts the suitable modulation and coding scheme and 2) it does not need complex features selection.</description><subject>adaptive modulation and coding (AMC)</subject><subject>Artificial neural networks</subject><subject>Carrier frequencies</subject><subject>Channel estimation</subject><subject>Coding</subject><subject>deep convolutional neural networks</subject><subject>Encoding</subject><subject>MIMO communication</subject><subject>Modulation</subject><subject>Multiple-input</subject><subject>multiple-output (MIMO)</subject><subject>Neural networks</subject><subject>OFDM</subject><subject>Orthogonal Frequency Division Multiplexing</subject><subject>orthogonal frequency division multiplexing (OFDM)</subject><subject>Quadrature amplitude modulation</subject><subject>Receivers</subject><subject>Synchronism</subject><subject>Training</subject><subject>Wireless networks</subject><issn>2162-2337</issn><issn>2162-2345</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEFPwkAQRjdGEwlyN_Gyiefizra0u0dSREmKPSjhuFnaaVIo3brbavj3tkKYyzeH900yj5BHYFMAJl-SbTzlDMSUCwEyEjdkxCHkHveD2e1196N7MnFuz_oJGXAQI7JZIDY0NvWPqbq2NLWu6Ad29j_aX2MPjhbG0qSsD3Se66bVA-VoWdP1ap166XKxptvSYoXO0c-Ta_HoHshdoSuHk0uOyWb5-hW_e0n6torniZdxCa0nMi64jIo8wgi0ZFmhxS6HQIS5BJmFICHCPIBgJiSTeRjttIZQaJ7pDDiG_pg8n-821nx36Fq1N53tf3CK8yDov_flQLEzlVnjnMVCNbY8antSwNTgT_X-1OBPXfz1ladzpUTEKy5mAWMg_T-70Gpe</recordid><startdate>20190601</startdate><enddate>20190601</enddate><creator>Elwekeil, Mohamed</creator><creator>Jiang, Shibao</creator><creator>Wang, Taotao</creator><creator>Zhang, Shengli</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>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0003-2924-4706</orcidid><orcidid>https://orcid.org/0000-0001-9454-4997</orcidid><orcidid>https://orcid.org/0000-0002-7937-5870</orcidid></search><sort><creationdate>20190601</creationdate><title>Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems</title><author>Elwekeil, Mohamed ; Jiang, Shibao ; Wang, Taotao ; Zhang, Shengli</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-8c28297fd7e71a90cfa8bd1486d919c61917ed41458909d67baa168a2cac12e63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>adaptive modulation and coding (AMC)</topic><topic>Artificial neural networks</topic><topic>Carrier frequencies</topic><topic>Channel estimation</topic><topic>Coding</topic><topic>deep convolutional neural networks</topic><topic>Encoding</topic><topic>MIMO communication</topic><topic>Modulation</topic><topic>Multiple-input</topic><topic>multiple-output (MIMO)</topic><topic>Neural networks</topic><topic>OFDM</topic><topic>Orthogonal Frequency Division Multiplexing</topic><topic>orthogonal frequency division multiplexing (OFDM)</topic><topic>Quadrature amplitude modulation</topic><topic>Receivers</topic><topic>Synchronism</topic><topic>Training</topic><topic>Wireless networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Elwekeil, Mohamed</creatorcontrib><creatorcontrib>Jiang, Shibao</creatorcontrib><creatorcontrib>Wang, Taotao</creatorcontrib><creatorcontrib>Zhang, Shengli</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>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE wireless communications letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Elwekeil, Mohamed</au><au>Jiang, Shibao</au><au>Wang, Taotao</au><au>Zhang, Shengli</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems</atitle><jtitle>IEEE wireless communications letters</jtitle><stitle>LWC</stitle><date>2019-06-01</date><risdate>2019</risdate><volume>8</volume><issue>3</issue><spage>665</spage><epage>668</epage><pages>665-668</pages><issn>2162-2337</issn><eissn>2162-2345</eissn><coden>IWCLAF</coden><abstract>This letter proposes a deep convolutional neural network (DCNN) approach for adaptive modulation and coding in practical multiple-input, multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Our target is to maximize the throughput and fulfill a packet error rate constraint. We consider practical impairments of MIMO-OFDM receiver, such as imperfect timing synchronization, carrier frequency offset correction, and channel estimation. We treat the estimated channel state information and the noise standard deviation as input features to the DCNN. The main advantages of the proposed approach are: 1) it learns the characteristics of the MIMO-OFDM channel properly and predicts the suitable modulation and coding scheme and 2) it does not need complex features selection.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/LWC.2018.2881978</doi><tpages>4</tpages><orcidid>https://orcid.org/0000-0003-2924-4706</orcidid><orcidid>https://orcid.org/0000-0001-9454-4997</orcidid><orcidid>https://orcid.org/0000-0002-7937-5870</orcidid></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | adaptive modulation and coding (AMC) Artificial neural networks Carrier frequencies Channel estimation Coding deep convolutional neural networks Encoding MIMO communication Modulation Multiple-input multiple-output (MIMO) Neural networks OFDM Orthogonal Frequency Division Multiplexing orthogonal frequency division multiplexing (OFDM) Quadrature amplitude modulation Receivers Synchronism Training Wireless networks |
| title | Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems |
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