Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144740
Title: Parameter identification of Reed-Solomon codes over noisy environment
Authors: Swaminathan, Ramabadran
Madhukumar, A. S.
Wang, Guohua
Ting, Shang Kee
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Swaminathan, R., Madhukumar, A. S., Wang, G., & Ting, S. K. (2017). Parameter identification of Reed-Solomon codes over noisy environment. Proceedings of the 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), 1-5. doi:10.1109/VTCFall.2017.8287916
Conference: 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)
Abstract: Blind reconstruction of code parameters plays a vital role in non-cooperative applications. It also provides additional advantages in applications such as adaptive modulation and coding, reconfigurable receivers, etc. In this paper, blind estimation algorithm is proposed to identify Reed-Solomon (RS) code parameters at the receiver over noisy channels. Simulation results validating the proposed algorithm are given for various test cases. Further, the accuracy of estimation of RS code parameters using the proposed algorithm is also evaluated for various M-ary quadrature amplitude modulation schemes. From the simulation results, it is observed that the accuracy of estimation improves with a decrease in code dimension and modulation order and the proposed algorithm outperforms other algorithms in the prior works.
URI: https://hdl.handle.net/10356/144740
ISBN: 978-1-5090-5935-5
DOI: 10.1109/VTCFall.2017.8287916
Schools: School of Computer Science and Engineering 
Rights: © 2017 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/VTCFall.2017.8287916
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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