A neural network model applied to the detection of digital signals
This work presents an artificial neural network (ANN) approach for the signal decision problems associated with digital communication system receivers which use modulation schemes whose signal elements belong to finite bidimensional constellations. The decision system proposed, named a neural decode...
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| creator | Fernandes, M.A.C. Neto, A.D.D. Bezerra, J.B. |
| description | This work presents an artificial neural network (ANN) approach for the signal decision problems associated with digital communication system receivers which use modulation schemes whose signal elements belong to finite bidimensional constellations. The decision system proposed, named a neural decoder (ND), is a multilayer perceptron neural network trained with a backpropagation algorithm and models a maximum-likelihood receiver. The ND training process and simulation results of their performance, regarding a conventional receiver, are presented for some of the modulation systems studied. |
| doi_str_mv | 10.1109/ITS.1998.713132 |
| format | Conference Proceeding |
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The ND training process and simulation results of their performance, regarding a conventional receiver, are presented for some of the modulation systems studied.</description><subject>Artificial neural networks</subject><subject>Constellation diagram</subject><subject>Digital communication</subject><subject>Digital modulation</subject><subject>Maximum likelihood decoding</subject><subject>Multi-layer neural network</subject><subject>Multilayer perceptrons</subject><subject>Neodymium</subject><subject>Neural networks</subject><subject>Signal detection</subject><isbn>0780350308</isbn><isbn>9780780350304</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1998</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj0tLAzEUhQMiqLVrwVX-wIy5k9dkWYuPQsGFdV0yyU2NTifDTET89wbas_kW5wGHkDtgNQAzD5vdew3GtLUGDry5IDdMt4xLxll7RZbz_MWKuFFS6WvyuKID_ky2L8i_afqmx-Sxp3Yc-4ie5kTzJ1KPGV2OaaApUB8PMZfGHA-D7edbchkKcHnmgnw8P-3Wr9X27WWzXm2rCLrJFVhgnRadZEo4j66zXgslZDAKWxSyhFoMxnQeeHCeK1UcptAoaJwGwxfk_rQbEXE_TvFop7_96SX_B7F7R1o</recordid><startdate>1998</startdate><enddate>1998</enddate><creator>Fernandes, M.A.C.</creator><creator>Neto, A.D.D.</creator><creator>Bezerra, J.B.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1998</creationdate><title>A neural network model applied to the detection of digital signals</title><author>Fernandes, M.A.C. ; Neto, A.D.D. ; Bezerra, J.B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i172t-1a10b74b5064cdecbad74645f96e8e451728ef99bd13fcd366f9606e9612c7193</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Artificial neural networks</topic><topic>Constellation diagram</topic><topic>Digital communication</topic><topic>Digital modulation</topic><topic>Maximum likelihood decoding</topic><topic>Multi-layer neural network</topic><topic>Multilayer perceptrons</topic><topic>Neodymium</topic><topic>Neural networks</topic><topic>Signal detection</topic><toplevel>online_resources</toplevel><creatorcontrib>Fernandes, M.A.C.</creatorcontrib><creatorcontrib>Neto, A.D.D.</creatorcontrib><creatorcontrib>Bezerra, J.B.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Fernandes, M.A.C.</au><au>Neto, A.D.D.</au><au>Bezerra, J.B.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A neural network model applied to the detection of digital signals</atitle><btitle>ITS'98 Proceedings. SBT/IEEE International Telecommunications Symposium (Cat. No.98EX202)</btitle><stitle>ITS</stitle><date>1998</date><risdate>1998</risdate><spage>279</spage><epage>283 vol.1</epage><pages>279-283 vol.1</pages><isbn>0780350308</isbn><isbn>9780780350304</isbn><abstract>This work presents an artificial neural network (ANN) approach for the signal decision problems associated with digital communication system receivers which use modulation schemes whose signal elements belong to finite bidimensional constellations. The decision system proposed, named a neural decoder (ND), is a multilayer perceptron neural network trained with a backpropagation algorithm and models a maximum-likelihood receiver. The ND training process and simulation results of their performance, regarding a conventional receiver, are presented for some of the modulation systems studied.</abstract><pub>IEEE</pub><doi>10.1109/ITS.1998.713132</doi></addata></record> |
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| ispartof | ITS'98 Proceedings. SBT/IEEE International Telecommunications Symposium (Cat. No.98EX202), 1998, p.279-283 vol.1 |
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| language | eng |
| recordid | cdi_ieee_primary_713132 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Constellation diagram Digital communication Digital modulation Maximum likelihood decoding Multi-layer neural network Multilayer perceptrons Neodymium Neural networks Signal detection |
| title | A neural network model applied to the detection of digital signals |
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