Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144731
Title: Blind reconstruction of Reed-Solomon encoder and interleavers over noisy environment
Authors: Swaminathan, Ramabadran
Madhukumar, A. S.
Wang, Guohua
Ting, Shang Kee
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Swaminathan, R., Madhukumar, A. S., Wang, G., & Ting, S. K. (2018). Blind reconstruction of Reed-Solomon encoder and interleavers over noisy environment. IEEE Transactions on Broadcasting, 64(4), 830-845. doi:10.1109/TBC.2018.2795461
Journal: IEEE Transactions on Broadcasting 
Abstract: Blind estimation of code and interleaver parameters is useful in smart storage systems and ubiquitous communication applications such as adaptive modulation and coding, reconfigurable radio systems, non-cooperative radio systems, etc. In this paper, we analyze Reed-Solomon (RS) encoded data stream and propose blind estimation algorithms to identify RS code parameters. We also provide algorithms to estimate block interleaver parameters from RS coded and block interleaved data stream. In addition, synchronization compensation through appropriate bit/symbol positioning is integrated with the proposed code and interleaver parameter estimation algorithms. Simulation results validating the proposed algorithms are given for various test cases involving both erroneous and non-erroneous scenarios. Moreover, the accuracy of estimation of RS code and block interleaver parameters are also given with detailed inferences for different modulation schemes, codeword length, and code dimension values. It has been inferred that the accuracy of parameter estimation improves with decrease in code dimension and codeword length values of RS codes. Further, the accuracy of estimation of lower modulation order schemes is better when compared to higher modulation order schemes as expected. It has also been noted that the proposed code and interleaver parameter estimation algorithms for noisy environment consistently outperform the algorithms proposed in the prior works.
URI: https://hdl.handle.net/10356/144731
ISSN: 0018-9316
DOI: 10.1109/TBC.2018.2795461
Schools: School of Computer Science and Engineering 
Rights: © 2018 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/TBC.2018.2795461
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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