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http://hdl.handle.net/2080/21
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DC Field | Value | Language |
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dc.contributor.author | Patra, S K | - |
dc.contributor.author | Mulgrew, B | - |
dc.date.accessioned | 2005-04-22T13:12:01Z | - |
dc.date.available | 2005-04-22T13:12:01Z | - |
dc.date.issued | 1998-07 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—II: ANALOG AND DIGITAL SIGNAL PROCESSING, VOL. 45, NO. 7, P 812 - 820 | en |
dc.identifier.uri | http://hdl.handle.net/2080/21 | - |
dc.description | 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 | A normalized Bayesian solution is derived for digital communication channel equalization which uses estimates of scalar channel states. This equalizer is termed as a normalized Bayesian equalizer with scalar channel states (NBEST). The relationship between the NBEST and fuzzy equalizers is derived and computational aspects of fuzzy equalizers are investigated using different types of fuzzy basis functions. It is shown that the fuzzy equalizer in general demands much lower computational complexity than the optimum equalizer. Ways to further reduce the computation complexity of fuzzy equalizers is proposed and their performance evaluated. A novel scheme to select a subset of channel states close to the received vector, resulting in considerable reduction in the computational complexity, is also proposed. A fuzzy equalizer with this modified membership function is shown to perform close to the Bayesian equalizer. | en |
dc.format.extent | 285270 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | IEEE | en |
dc.subject | Bayesian equalizers | en |
dc.subject | digital communication systems | en |
dc.subject | fuzzy systems | en |
dc.title | Efficient Architecture for Bayesian Equalization Using Fuzzy Filters | en |
dc.type | Article | en |
Appears in Collections: | Journal Articles |
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