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Low complexity iterative MLSE equalization of M-QAM signals in extremely long rayleigh fading channels

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dc.contributor.author Myburgh, HC
dc.contributor.author Olivier, JC
dc.date.accessioned 2010-01-08T16:19:08Z
dc.date.available 2010-01-08T16:19:08Z
dc.date.issued 2009-05
dc.identifier.citation Myburgh, HC and Olivier, JC. 2009. Low complexity iterative MLSE equalization of M-QAM signals in extremely long rayleigh fading channels. IEEE EUROCON 2009 Saint-Petersburg, Russia 18-23 May 2009, pp 1632-1637 en
dc.identifier.isbn 978-1-4244-3861-7
dc.identifier.uri http://hdl.handle.net/10204/3862
dc.description Copyright: 2009 Institute of Electrical and Electronics Engineering (IEEE). Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. en
dc.description.abstract This work proposes a neural network based iterative Maximum Likelihood Sequence Estimation (MLSE) equalizer, able to equalize signals in M-arry Quadrature Amplitude Modulation (M-QAM) modulated systems in a mobile fading environment with extremely long channels. Its computational complexity is linear in the data block length and approximately independent of the channel memory length, whereas conventional equalization algorithms have computational complexity linear in the data block length but exponential in the channel memory length. Its performance is compared to the Viterbi MLSE equalizer for short channels and it is shown that the proposed equalizer has the ability to equalize M-QAM signals in systems with hundreds of memory elements, achieving matched filter bound performance with perfect channel state information (CSI) knowledge in uncoded systems. The proposed equalizer is evaluated in a frequency selective Rayleigh fading environment. en
dc.language.iso en en
dc.publisher Institute of Electrical and Electronics Engineering (IEEE) en
dc.subject Computational complexity en
dc.subject Neural network en
dc.subject M-arry quadrature amplitude modulation en
dc.subject M-QAM en
dc.subject Maximum likelihood sequence estimation en
dc.subject MLSE en
dc.subject Rayleigh fading channels en
dc.subject IEEE EUROCON 2009 en
dc.title Low complexity iterative MLSE equalization of M-QAM signals in extremely long rayleigh fading channels en
dc.type Conference Presentation en
dc.identifier.apacitation Myburgh, H., & Olivier, J. (2009). Low complexity iterative MLSE equalization of M-QAM signals in extremely long rayleigh fading channels. Institute of Electrical and Electronics Engineering (IEEE). http://hdl.handle.net/10204/3862 en_ZA
dc.identifier.chicagocitation Myburgh, HC, and JC Olivier. "Low complexity iterative MLSE equalization of M-QAM signals in extremely long rayleigh fading channels." (2009): http://hdl.handle.net/10204/3862 en_ZA
dc.identifier.vancouvercitation Myburgh H, Olivier J, Low complexity iterative MLSE equalization of M-QAM signals in extremely long rayleigh fading channels; Institute of Electrical and Electronics Engineering (IEEE); 2009. http://hdl.handle.net/10204/3862 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Myburgh, HC AU - Olivier, JC AB - This work proposes a neural network based iterative Maximum Likelihood Sequence Estimation (MLSE) equalizer, able to equalize signals in M-arry Quadrature Amplitude Modulation (M-QAM) modulated systems in a mobile fading environment with extremely long channels. Its computational complexity is linear in the data block length and approximately independent of the channel memory length, whereas conventional equalization algorithms have computational complexity linear in the data block length but exponential in the channel memory length. Its performance is compared to the Viterbi MLSE equalizer for short channels and it is shown that the proposed equalizer has the ability to equalize M-QAM signals in systems with hundreds of memory elements, achieving matched filter bound performance with perfect channel state information (CSI) knowledge in uncoded systems. The proposed equalizer is evaluated in a frequency selective Rayleigh fading environment. DA - 2009-05 DB - ResearchSpace DP - CSIR KW - Computational complexity KW - Neural network KW - M-arry quadrature amplitude modulation KW - M-QAM KW - Maximum likelihood sequence estimation KW - MLSE KW - Rayleigh fading channels KW - IEEE EUROCON 2009 LK - https://researchspace.csir.co.za PY - 2009 SM - 978-1-4244-3861-7 T1 - Low complexity iterative MLSE equalization of M-QAM signals in extremely long rayleigh fading channels TI - Low complexity iterative MLSE equalization of M-QAM signals in extremely long rayleigh fading channels UR - http://hdl.handle.net/10204/3862 ER - en_ZA


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