Automatic recognition of type of FEC codes in a robust environment

Forward error correcting (FEC) channel codes play a vital role in improving the reliability of digital transmissions. In practice, accurate information about the type of FEC codes and code parameters used for encoding must be known at the receiver end. However, in an espionage and counter-espionage...

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Bibliographic Details
Main Author: Khng, Qian Yu
Other Authors: A S Madhukumar
Format: Final Year Project
Language:English
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/10356/66810
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Institution: Nanyang Technological University
Language: English
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Summary:Forward error correcting (FEC) channel codes play a vital role in improving the reliability of digital transmissions. In practice, accurate information about the type of FEC codes and code parameters used for encoding must be known at the receiver end. However, in an espionage and counter-espionage context where there is a need for communication intelligence, the code types and code parameters are unknown, and blind recognition of the same is mandatory for efficiency in cracking the encryption. This paper describes a methodology for classifying FEC codes without a priori knowledge of the coding scheme or code parameters, in both erroneous and non-erroneous environments. Different classes of FEC codes exhibit unique characteristics due to their structural differences, thus novel methodologies were specifically developed for use in a classification framework to pick up on these features variances. Analyses of these features will allow an uninformed observer to reverse-engineer the encoder structure and subsequently decode the data. After a brief recall of the two main branches of FEC codes – Block codes and Convolutional codes, and a recapitulation of reduced row echelon form matrix properties, a new iterative method dedicated to the blind or automatic recognition of these two FEC codes is developed. Case studies are presented to illustrate the performance of the blind recognition method in error free scenarios. The same method is then modified to provide rigor in classification in erroneous environments.