A soft-in soft-out detection approach using partial Gaussian approximation
This paper concerns the implementation of the soft-in soft-out detector in an iterative detection system. A detection approach is proposed based on the properties of Gaussian functions. In this approach, for the computation of the APP (a posteriori probability) of a concerned symbol, the other symbo...
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| Format: | Conference Paper |
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2012
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| Online Access: | http://hdl.handle.net/20.500.11937/29829 |
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curtin-20.500.11937-29829 |
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curtin-20.500.11937-298292018-03-29T09:08:37Z A soft-in soft-out detection approach using partial Gaussian approximation Guo, Q. Fang, L. Huang, D. Nordholm, Sven This paper concerns the implementation of the soft-in soft-out detector in an iterative detection system. A detection approach is proposed based on the properties of Gaussian functions. In this approach, for the computation of the APP (a posteriori probability) of a concerned symbol, the other symbols are distinguished based on their contributions to the APP of the concerned symbol, and the symbols with less contributions are treated as Gaussian variables to reduce the computational complexity. The exact APP detector and the well-known LMMSE (linear minimum mean square error) detector are two special cases of the proposed detector. Simulation results show that the proposed detector can significantly outperform the LMMSE detector, and achieve a good trade-off between complexity and performance. © 2012 IEEE. 2012 Conference Paper http://hdl.handle.net/20.500.11937/29829 10.1109/WCSP.2012.6542820 restricted |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Curtin University Malaysia |
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Curtin Institutional Repository |
| collection |
Online Access |
| description |
This paper concerns the implementation of the soft-in soft-out detector in an iterative detection system. A detection approach is proposed based on the properties of Gaussian functions. In this approach, for the computation of the APP (a posteriori probability) of a concerned symbol, the other symbols are distinguished based on their contributions to the APP of the concerned symbol, and the symbols with less contributions are treated as Gaussian variables to reduce the computational complexity. The exact APP detector and the well-known LMMSE (linear minimum mean square error) detector are two special cases of the proposed detector. Simulation results show that the proposed detector can significantly outperform the LMMSE detector, and achieve a good trade-off between complexity and performance. © 2012 IEEE. |
| format |
Conference Paper |
| author |
Guo, Q. Fang, L. Huang, D. Nordholm, Sven |
| spellingShingle |
Guo, Q. Fang, L. Huang, D. Nordholm, Sven A soft-in soft-out detection approach using partial Gaussian approximation |
| author_facet |
Guo, Q. Fang, L. Huang, D. Nordholm, Sven |
| author_sort |
Guo, Q. |
| title |
A soft-in soft-out detection approach using partial Gaussian approximation |
| title_short |
A soft-in soft-out detection approach using partial Gaussian approximation |
| title_full |
A soft-in soft-out detection approach using partial Gaussian approximation |
| title_fullStr |
A soft-in soft-out detection approach using partial Gaussian approximation |
| title_full_unstemmed |
A soft-in soft-out detection approach using partial Gaussian approximation |
| title_sort |
soft-in soft-out detection approach using partial gaussian approximation |
| publishDate |
2012 |
| url |
http://hdl.handle.net/20.500.11937/29829 |
| first_indexed |
2018-09-06T21:31:22Z |
| last_indexed |
2018-09-06T21:31:22Z |
| _version_ |
1610895308817956864 |