Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/144728
Title: | Blind estimation of code parameters for product codes over noisy channel conditions | Authors: | Swaminathan, Ramabadran Madhukumar, A. S. Wang, Guohua |
Keywords: | Engineering::Computer science and engineering | Issue Date: | 2020 | Source: | Swaminathan, R., Madhukumar, A. S. & Wang, G. (2020). Blind estimation of code parameters for product codes over noisy channel conditions. IEEE Transactions on Aerospace and Electronic Systems, 56(2), 1460-1473. doi:10.1109/TAES.2019.2934308 | Journal: | IEEE Transactions on Aerospace and Electronic Systems | Abstract: | Product codes are multidimensional codes constructed using multiple component codes. In this paper, novel algorithms are proposed for the blind estimation of two-dimensional product code parameters over the noisy channel conditions considering Reed-Solomon and Bose-Chaudhuri-Hocquenghem as component codes. The performance of the algorithms in terms of probability of the correct estimation is investigated for different code parameters. It is observed that the accuracy improves with the decrease in modulation order and code dimension values. | URI: | https://hdl.handle.net/10356/144728 | ISSN: | 0018-9251 | DOI: | 10.1109/TAES.2019.2934308 | Schools: | School of Computer Science and Engineering | Rights: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TAES.2019.2934308 | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Journal Articles |
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Blind Estimation of Code Parameters for Product Codes Over Noisy Channel Conditions.pdf | 652.71 kB | Adobe PDF | ![]() View/Open |
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