Neural network based BER prediction for 802.16e channel
The prediction of bit error rate (BER) in IEEE 802.16e mobile wireless MAN network is investigated here. The state of the channel is estimated on symbol by symbol basis on a realistic fading environment. The state of a channel is modeled as nonlinear and temporal system. Neural network method is the...
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| creator | Gowrishankar Babu H.S., Ramesh Satyanarayana, P.S. |
| description | The prediction of bit error rate (BER) in IEEE 802.16e mobile wireless MAN network is investigated here. The state of the channel is estimated on symbol by symbol basis on a realistic fading environment. The state of a channel is modeled as nonlinear and temporal system. Neural network method is the best system to predict and analyze the behaviors of such nonlinear and temporal system. In this context, BER prediction by k symbol ahead is investigated by two different recurrent neural network architectures such as recurrent radial basis function (RRBF) network and echo state network (ESN). The predicted BER will match very well with the simulation results. |
| doi_str_mv | 10.1109/SOFTCOM.2007.4446119 |
| format | Conference Proceeding |
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The state of the channel is estimated on symbol by symbol basis on a realistic fading environment. The state of a channel is modeled as nonlinear and temporal system. Neural network method is the best system to predict and analyze the behaviors of such nonlinear and temporal system. In this context, BER prediction by k symbol ahead is investigated by two different recurrent neural network architectures such as recurrent radial basis function (RRBF) network and echo state network (ESN). 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The state of the channel is estimated on symbol by symbol basis on a realistic fading environment. The state of a channel is modeled as nonlinear and temporal system. Neural network method is the best system to predict and analyze the behaviors of such nonlinear and temporal system. In this context, BER prediction by k symbol ahead is investigated by two different recurrent neural network architectures such as recurrent radial basis function (RRBF) network and echo state network (ESN). The predicted BER will match very well with the simulation results.</description><subject>Bit error rate</subject><subject>Fading</subject><subject>Frequency estimation</subject><subject>Frequency synchronization</subject><subject>Neural networks</subject><subject>Nonlinear distortion</subject><subject>OFDM</subject><subject>Predictive models</subject><subject>Quality of service</subject><subject>Wireless networks</subject><isbn>9789536114931</isbn><isbn>9536114933</isbn><isbn>953611495X</isbn><isbn>9789536114955</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1j8FKw0AURUdEUGu-QBfzA4nvZebNZJYa2ipUA1rBXZlMXzAakzKJiH9vwbo692wuHCGuEDJEcNfP1WJdVg9ZDmAzrbVBdEfi3JHaL-3o9Vgkzhb_rvBUJOP4DgDojNWFORP2kb-i72TP0_cQP2TtR97K2_mT3EXetmFqh142Q5QF5BkaluHN9z13F-Kk8d3IyYEz8bKYr8u7dFUt78ubVdqipSltnFaefO4JGPZiiQECUWCtG2OR0AYPiEWuGmM05VQ78A7ZGm9srdRMXP79tsy82cX208efzaFV_QKu00WE</recordid><startdate>200709</startdate><enddate>200709</enddate><creator>Gowrishankar</creator><creator>Babu H.S., Ramesh</creator><creator>Satyanarayana, P.S.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200709</creationdate><title>Neural network based BER prediction for 802.16e channel</title><author>Gowrishankar ; Babu H.S., Ramesh ; Satyanarayana, P.S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-f943a5a2a50e0f9475e00c55ce44f671517ca011823f664525b90a91e76a67b33</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Bit error rate</topic><topic>Fading</topic><topic>Frequency estimation</topic><topic>Frequency synchronization</topic><topic>Neural networks</topic><topic>Nonlinear distortion</topic><topic>OFDM</topic><topic>Predictive models</topic><topic>Quality of service</topic><topic>Wireless networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Gowrishankar</creatorcontrib><creatorcontrib>Babu H.S., Ramesh</creatorcontrib><creatorcontrib>Satyanarayana, P.S.</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>Gowrishankar</au><au>Babu H.S., Ramesh</au><au>Satyanarayana, P.S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Neural network based BER prediction for 802.16e channel</atitle><btitle>2007 15th International Conference on Software, Telecommunications and Computer Networks</btitle><stitle>SOFTCOM</stitle><date>2007-09</date><risdate>2007</risdate><spage>1</spage><epage>5</epage><pages>1-5</pages><isbn>9789536114931</isbn><isbn>9536114933</isbn><eisbn>953611495X</eisbn><eisbn>9789536114955</eisbn><abstract>The prediction of bit error rate (BER) in IEEE 802.16e mobile wireless MAN network is investigated here. The state of the channel is estimated on symbol by symbol basis on a realistic fading environment. The state of a channel is modeled as nonlinear and temporal system. Neural network method is the best system to predict and analyze the behaviors of such nonlinear and temporal system. In this context, BER prediction by k symbol ahead is investigated by two different recurrent neural network architectures such as recurrent radial basis function (RRBF) network and echo state network (ESN). The predicted BER will match very well with the simulation results.</abstract><pub>IEEE</pub><doi>10.1109/SOFTCOM.2007.4446119</doi><tpages>5</tpages></addata></record> |
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| subjects | Bit error rate Fading Frequency estimation Frequency synchronization Neural networks Nonlinear distortion OFDM Predictive models Quality of service Wireless networks |
| title | Neural network based BER prediction for 802.16e channel |
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