Pu, Chuan Hsian (2011) SOVA decoding in symmetric alpha-stable noise. In: Symposium on Information & Computer Sciences (1st).
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Abstract
Soft-Output Viterbi Algorithm (SOVA) is one type of recovery memory-less Markov Chain and is used widely to decode convolutional codes. Fundamentally, conventional SOVA is designed on the basis of Maximum A-Posteriori Probability (APP) with the assumption of normal distribution. Therefore, conventional SOVA fails miserably in the presence of symmetric alpha stable noise S\ensuremathα S which is one form of stable random processes widely accepted for impulsive noise modeling. The author studies and has improved the performance of conventional SOVA by introducing Cauchy function into path-metric calculation. Substantial performance improvement was gained from Mento Carlo Simulation for SOVA based turbo codes.
| Item Type: | Conference or Workshop Item (Paper) |
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| Additional Information: | Author is affiliated to Taylor's University, American Degree Transfer Programme. |
| Uncontrolled Keywords: | turbo codes; Soft Output Viterbi Algorithm (SOVA); Maximum A-Posteriori (MAP); estimation; cauchy metric; Symmetric Alpha Stable(S\ensuremathα S) Distribution; non-gaussian CHANNEL. |
| Subjects: | Q Science > QA Mathematics |
| Divisions: | Others > Non Sunway Academics |
| Depositing User: | Administrator Admin |
| Date Deposited: | 16 Oct 2012 09:20 |
| Last Modified: | 16 Oct 2012 09:20 |
| URI: | http://eprints.sunway.edu.my/id/eprint/111 |
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