Blind and Semi-Blind Maximum Likelihood Techniques for Multiuser Multichannel identification.
We investigate blind and semi-blind maximum likelihood techniques for multiuser multichannel identification. Two blind Deterministic ML methods based on cyclic prediction filters are presented [1]. The Iterative Quadratic ML (IQML) algorithm is used in [1] to solve it: this strategy does not perform...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.54.8682 2023-05-15T16:01:55+02:00 Blind and Semi-Blind Maximum Likelihood Techniques for Multiuser Multichannel identification. Elisabeth De Elisabeth De Carvalho Luc Deneire Dirk T. M. Slock The Pennsylvania State University CiteSeerX Archives application/postscript http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.8682 http://www.eurecom.fr/~deneire/pap/eusipco98.ps.gz en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.8682 http://www.eurecom.fr/~deneire/pap/eusipco98.ps.gz Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.eurecom.fr/~deneire/pap/eusipco98.ps.gz text ftciteseerx 2016-01-08T11:01:19Z We investigate blind and semi-blind maximum likelihood techniques for multiuser multichannel identification. Two blind Deterministic ML methods based on cyclic prediction filters are presented [1]. The Iterative Quadratic ML (IQML) algorithm is used in [1] to solve it: this strategy does not perform well at low SNR and gives biased estimates due to the presence of noise. We propose a modification of IQML that we call DIQML to "denoise" it and explore a second strategy called Pseudo-Quadratic ML (PQML). As proposed in [2], PQML works well only at very high SNR. The solution we present here makes it work well at rather low SNR conditions and outperform DIQML. Like DIQML, PQML is proved to be consistent, asymptotically insensitive to the initialisation and globally convergent. Furthermore, it has the same performance as DML. A semi-blind extension combining these algorithms with training sequence based approaches is also studied. Simulations will illustrate the performance of the differen. Text DML Unknown |
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description |
We investigate blind and semi-blind maximum likelihood techniques for multiuser multichannel identification. Two blind Deterministic ML methods based on cyclic prediction filters are presented [1]. The Iterative Quadratic ML (IQML) algorithm is used in [1] to solve it: this strategy does not perform well at low SNR and gives biased estimates due to the presence of noise. We propose a modification of IQML that we call DIQML to "denoise" it and explore a second strategy called Pseudo-Quadratic ML (PQML). As proposed in [2], PQML works well only at very high SNR. The solution we present here makes it work well at rather low SNR conditions and outperform DIQML. Like DIQML, PQML is proved to be consistent, asymptotically insensitive to the initialisation and globally convergent. Furthermore, it has the same performance as DML. A semi-blind extension combining these algorithms with training sequence based approaches is also studied. Simulations will illustrate the performance of the differen. |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Elisabeth De Elisabeth De Carvalho Luc Deneire Dirk T. M. Slock |
spellingShingle |
Elisabeth De Elisabeth De Carvalho Luc Deneire Dirk T. M. Slock Blind and Semi-Blind Maximum Likelihood Techniques for Multiuser Multichannel identification. |
author_facet |
Elisabeth De Elisabeth De Carvalho Luc Deneire Dirk T. M. Slock |
author_sort |
Elisabeth De |
title |
Blind and Semi-Blind Maximum Likelihood Techniques for Multiuser Multichannel identification. |
title_short |
Blind and Semi-Blind Maximum Likelihood Techniques for Multiuser Multichannel identification. |
title_full |
Blind and Semi-Blind Maximum Likelihood Techniques for Multiuser Multichannel identification. |
title_fullStr |
Blind and Semi-Blind Maximum Likelihood Techniques for Multiuser Multichannel identification. |
title_full_unstemmed |
Blind and Semi-Blind Maximum Likelihood Techniques for Multiuser Multichannel identification. |
title_sort |
blind and semi-blind maximum likelihood techniques for multiuser multichannel identification. |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.8682 http://www.eurecom.fr/~deneire/pap/eusipco98.ps.gz |
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DML |
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DML |
op_source |
http://www.eurecom.fr/~deneire/pap/eusipco98.ps.gz |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.8682 http://www.eurecom.fr/~deneire/pap/eusipco98.ps.gz |
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Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766397591445045248 |