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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pei-Chuan Tsai Chih-Ming Chen Ying-ping Chen |
| Copyright Year | 2012 |
| Description | Author affiliation: Department of Computer Science, National Chiao Tung University, HsinChu City, TAIWAN (Pei-Chuan Tsai; Chih-Ming Chen; Ying-ping Chen) |
| Abstract | Luby Transform (LT) codes are a new member in the family of forward error correction codes without a fixed code rate. The property called rateless is attractive to researchers in last decade, and lots of studies have been proposed and attempted to improve the performance of LT codes. One variation is the use of a sparse degree distribution instead of a full one referred to in the encoding process of LT codes to reduce the search space. Observing a fact that the ability of a sparse degree distribution is limited by the nonempty degrees, we introduce a tag selection scheme to choose reasonable sparse degrees for LT codes in this paper. We firstly investigate the influence of different degrees on the error rate of LT codes and then propose a general selection algorithm based on our observations. After that, the covariance matrix adaptation evolution strategy (CMA-ES) is applied to find the optimal sparse degree distributions of which the degrees are defined by our selection algorithm. Finally, the experimental results are presented as evidence to show the proposed scheme is effective and practical. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 778409 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467315104 |
| e-ISBN | 9781467315098 |
| e-ISBN | 9781467315081 |
| DOI | 10.1109/CEC.2012.6252861 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Error probability Encoding Optimization Solitons Decoding Evolutionary computation Robustness |
| Content Type | Text |
| Resource Type | Article |
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