Adaptively Combined FIR and Functional Link Artificial Neural Network Equalizer for Nonlinear Communication Channel
This paper proposes a novel computational efficient adaptive nonlinear equalizer based on combination of finite impulse response (FIR) filter and functional link artificial neural network (CFFLANN) to compensate linear and nonlinear distortions in nonlinear communication channel. This convex nonline...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2009-04, Vol.20 (4), p.665-674 |
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| creator | Zhao, Haiquan Zhang, Jiashu |
| description | This paper proposes a novel computational efficient adaptive nonlinear equalizer based on combination of finite impulse response (FIR) filter and functional link artificial neural network (CFFLANN) to compensate linear and nonlinear distortions in nonlinear communication channel. This convex nonlinear combination results in improving the speed while retaining the lower steady-state error. In addition, since the CFFLANN needs not the hidden layers, which exist in conventional neural-network-based equalizers, it exhibits a simpler structure than the traditional neural networks (NNs) and can require less computational burden during the training mode. Moreover, appropriate adaptation algorithm for the proposed equalizer is derived by the modified least mean square (MLMS). Results obtained from the simulations clearly show that the proposed equalizer using the MLMS algorithm can availably eliminate various intensity linear and nonlinear distortions, and be provided with better anti-jamming performance. Furthermore, comparisons of the mean squared error (MSE), the bit error rate (BER), and the effect of eigenvalue ratio (EVR) of input correlation matrix are presented. |
| doi_str_mv | 10.1109/TNN.2008.2011481 |
| format | Article |
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This convex nonlinear combination results in improving the speed while retaining the lower steady-state error. In addition, since the CFFLANN needs not the hidden layers, which exist in conventional neural-network-based equalizers, it exhibits a simpler structure than the traditional neural networks (NNs) and can require less computational burden during the training mode. Moreover, appropriate adaptation algorithm for the proposed equalizer is derived by the modified least mean square (MLMS). Results obtained from the simulations clearly show that the proposed equalizer using the MLMS algorithm can availably eliminate various intensity linear and nonlinear distortions, and be provided with better anti-jamming performance. Furthermore, comparisons of the mean squared error (MSE), the bit error rate (BER), and the effect of eigenvalue ratio (EVR) of input correlation matrix are presented.</description><identifier>ISSN: 1045-9227</identifier><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 1941-0093</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNN.2008.2011481</identifier><identifier>PMID: 19244019</identifier><identifier>CODEN: ITNNEP</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Adaptive equalizer ; Adaptive filters ; Algorithms ; Applied sciences ; Artificial intelligence ; Artificial neural networks ; Bit error rate ; Channels ; Communication channels ; Computational efficiency ; Computer science; control theory; systems ; Computer simulation ; Computer systems and distributed systems. User interface ; Connectionism. Neural networks ; Detection, estimation, filtering, equalization, prediction ; Equalizers ; Exact sciences and technology ; finite impulse response (FIR) filter ; Finite impulse response filter ; functional link artificial neural network (FLANN) ; Impulse response ; Information, signal and communications theory ; Links ; Neural networks ; nonlinear channel ; Nonlinear distortion ; Nonlinear filters ; Nonlinearity ; Signal and communications theory ; Signal, noise ; Software ; Steady-state ; Studies ; Telecommunications and information theory</subject><ispartof>IEEE transaction on neural networks and learning systems, 2009-04, Vol.20 (4), p.665-674</ispartof><rights>2009 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2009</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c472t-1dce41325072661000b12221babe41b6a58a0791ed79ca52db3d96da7231013e3</citedby><cites>FETCH-LOGICAL-c472t-1dce41325072661000b12221babe41b6a58a0791ed79ca52db3d96da7231013e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4787092$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4787092$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21743181$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19244019$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhao, Haiquan</creatorcontrib><creatorcontrib>Zhang, Jiashu</creatorcontrib><title>Adaptively Combined FIR and Functional Link Artificial Neural Network Equalizer for Nonlinear Communication Channel</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNN</addtitle><addtitle>IEEE Trans Neural Netw</addtitle><description>This paper proposes a novel computational efficient adaptive nonlinear equalizer based on combination of finite impulse response (FIR) filter and functional link artificial neural network (CFFLANN) to compensate linear and nonlinear distortions in nonlinear communication channel. This convex nonlinear combination results in improving the speed while retaining the lower steady-state error. In addition, since the CFFLANN needs not the hidden layers, which exist in conventional neural-network-based equalizers, it exhibits a simpler structure than the traditional neural networks (NNs) and can require less computational burden during the training mode. Moreover, appropriate adaptation algorithm for the proposed equalizer is derived by the modified least mean square (MLMS). Results obtained from the simulations clearly show that the proposed equalizer using the MLMS algorithm can availably eliminate various intensity linear and nonlinear distortions, and be provided with better anti-jamming performance. Furthermore, comparisons of the mean squared error (MSE), the bit error rate (BER), and the effect of eigenvalue ratio (EVR) of input correlation matrix are presented.</description><subject>Adaptive equalizer</subject><subject>Adaptive filters</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Bit error rate</subject><subject>Channels</subject><subject>Communication channels</subject><subject>Computational efficiency</subject><subject>Computer science; control theory; systems</subject><subject>Computer simulation</subject><subject>Computer systems and distributed systems. User interface</subject><subject>Connectionism. Neural networks</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Equalizers</subject><subject>Exact sciences and technology</subject><subject>finite impulse response (FIR) filter</subject><subject>Finite impulse response filter</subject><subject>functional link artificial neural network (FLANN)</subject><subject>Impulse response</subject><subject>Information, signal and communications theory</subject><subject>Links</subject><subject>Neural networks</subject><subject>nonlinear channel</subject><subject>Nonlinear distortion</subject><subject>Nonlinear filters</subject><subject>Nonlinearity</subject><subject>Signal and communications theory</subject><subject>Signal, noise</subject><subject>Software</subject><subject>Steady-state</subject><subject>Studies</subject><subject>Telecommunications and information theory</subject><issn>1045-9227</issn><issn>2162-237X</issn><issn>1941-0093</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kcFrFDEUh4MotlbvgiCDoJ6mvpdkJpPjsrRaWLYg9RwymQymnUm2yYyl_evNdocKHnrJy0u-9x3ej5D3CKeIIL9dbbenFKDJByJv8AU5RsmxBJDsZb4Dr0pJqTgib1K6BkBeQf2aHKGknAPKY5JWnd5N7o8d7ot1GFvnbVecX_wstM919mZyweuh2Dh_U6zi5HpnXO63do6PZboL8aY4u5314B5sLPoQi23wQxbpuFeOs3dG7zXF-rf23g5vyateD8m-W-oJ-XV-drX-UW4uv1-sV5vScEGnEjtjOTJagaB1jQDQIqUUW93m97bWVaNBSLSdkEZXtGtZJ-tOC8oQkFl2Qr4evLsYbmebJjW6ZOwwaG_DnFQjqrwgykUmvzxL1gKxgabJ4Kf_wOswx7ygpCRSYFJIniE4QCaGlKLt1S66Ucd7haD2uamcm9rnppbc8sjHxTu3o-3-DSxBZeDzAuhk9NBH7Y1LTxxFwRk-ij4cOGetffrmohEgKfsLJHWnrg</recordid><startdate>20090401</startdate><enddate>20090401</enddate><creator>Zhao, Haiquan</creator><creator>Zhang, Jiashu</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QP</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TK</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20090401</creationdate><title>Adaptively Combined FIR and Functional Link Artificial Neural Network Equalizer for Nonlinear Communication Channel</title><author>Zhao, Haiquan ; Zhang, Jiashu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c472t-1dce41325072661000b12221babe41b6a58a0791ed79ca52db3d96da7231013e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Adaptive equalizer</topic><topic>Adaptive filters</topic><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Bit error rate</topic><topic>Channels</topic><topic>Communication channels</topic><topic>Computational efficiency</topic><topic>Computer science; control theory; systems</topic><topic>Computer simulation</topic><topic>Computer systems and distributed systems. User interface</topic><topic>Connectionism. Neural networks</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>Equalizers</topic><topic>Exact sciences and technology</topic><topic>finite impulse response (FIR) filter</topic><topic>Finite impulse response filter</topic><topic>functional link artificial neural network (FLANN)</topic><topic>Impulse response</topic><topic>Information, signal and communications theory</topic><topic>Links</topic><topic>Neural networks</topic><topic>nonlinear channel</topic><topic>Nonlinear distortion</topic><topic>Nonlinear filters</topic><topic>Nonlinearity</topic><topic>Signal and communications theory</topic><topic>Signal, noise</topic><topic>Software</topic><topic>Steady-state</topic><topic>Studies</topic><topic>Telecommunications and information theory</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Haiquan</creatorcontrib><creatorcontrib>Zhang, Jiashu</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>Pascal-Francis</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhao, Haiquan</au><au>Zhang, Jiashu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptively Combined FIR and Functional Link Artificial Neural Network Equalizer for Nonlinear Communication Channel</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNN</stitle><addtitle>IEEE Trans Neural Netw</addtitle><date>2009-04-01</date><risdate>2009</risdate><volume>20</volume><issue>4</issue><spage>665</spage><epage>674</epage><pages>665-674</pages><issn>1045-9227</issn><issn>2162-237X</issn><eissn>1941-0093</eissn><eissn>2162-2388</eissn><coden>ITNNEP</coden><abstract>This paper proposes a novel computational efficient adaptive nonlinear equalizer based on combination of finite impulse response (FIR) filter and functional link artificial neural network (CFFLANN) to compensate linear and nonlinear distortions in nonlinear communication channel. This convex nonlinear combination results in improving the speed while retaining the lower steady-state error. In addition, since the CFFLANN needs not the hidden layers, which exist in conventional neural-network-based equalizers, it exhibits a simpler structure than the traditional neural networks (NNs) and can require less computational burden during the training mode. Moreover, appropriate adaptation algorithm for the proposed equalizer is derived by the modified least mean square (MLMS). Results obtained from the simulations clearly show that the proposed equalizer using the MLMS algorithm can availably eliminate various intensity linear and nonlinear distortions, and be provided with better anti-jamming performance. Furthermore, comparisons of the mean squared error (MSE), the bit error rate (BER), and the effect of eigenvalue ratio (EVR) of input correlation matrix are presented.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>19244019</pmid><doi>10.1109/TNN.2008.2011481</doi><tpages>10</tpages></addata></record> |
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| subjects | Adaptive equalizer Adaptive filters Algorithms Applied sciences Artificial intelligence Artificial neural networks Bit error rate Channels Communication channels Computational efficiency Computer science control theory systems Computer simulation Computer systems and distributed systems. User interface Connectionism. Neural networks Detection, estimation, filtering, equalization, prediction Equalizers Exact sciences and technology finite impulse response (FIR) filter Finite impulse response filter functional link artificial neural network (FLANN) Impulse response Information, signal and communications theory Links Neural networks nonlinear channel Nonlinear distortion Nonlinear filters Nonlinearity Signal and communications theory Signal, noise Software Steady-state Studies Telecommunications and information theory |
| title | Adaptively Combined FIR and Functional Link Artificial Neural Network Equalizer for Nonlinear Communication Channel |
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