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Title: Classification of error correcting codes and estimation of interleaver parameters in a noisy transmission environment
Authors: Swaminathan, Ramabadran
Madhukumar, A. S.
Keywords: Adaptive modulation and coding
Blind/semi-blind estimation
Issue Date: 2017
Source: Swaminathan, R., & Madhukumar, A. S. (2017). Classification of Error Correcting Codes and Estimation of Interleaver Parameters in a Noisy Transmission Environment. IEEE Transactions on Broadcasting, 63(3), 463-478.
Series/Report no.: IEEE Transactions on Broadcasting
Abstract: Channel encoder, which includes a forward error correcting (FEC) code followed by an interleaver, plays a vital role in improving the error performance of digital storage and communication systems. In most of the applications, the FEC code and interleaver parameters are known at the receiver to decode and de-interleave the information bits, respectively. But the blind/semi-blind estimation of code and interleaver parameters at the receiver will provide additional advantages in applications such as adaptive modulation and coding, cognitive radio, non-cooperative systems, etc. The algorithms for the blind estimation of code parameters at the receiver had previously been proposed and investigated for known FEC codes. In this paper, we propose algorithms for the joint recognition of the type of FEC codes and interleaver parameters without knowing any information about the channel encoder. The proposed algorithm classify the incoming data symbols among block coded, convolutional coded, and uncoded symbols. Further, we suggest analytical and histogram approaches for setting the threshold value to perform code classification and parameter estimation. It is observed from the simulation results that the code classification and interleaver parameter estimation are performed successfully over erroneous channel conditions. The proposed histogram approach is more robust against the analytical approach for noisy transmission environment and system latency is one of the important challenges for the histogram approach to achieve better performance.
ISSN: 0018-9316
DOI: 10.1109/TBC.2017.2704436
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: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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