Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (8): 1159-1170.

### A New Algorithm for Encoder Recognition of Turbo Code Components

Zirong Honga,*, Bo Danb, and Zhaojun Wub

1. aSchool of Electric and Electronic Engineering, Lanzhou Petrochemical Polytechnic, Lanzhou, 730060, China;
bNaval Aviation University, Yantai, 264001, China
• Submitted on  ;  Revised on  ; Accepted on
• Contact: *E-mail address: hzrsll@163.com
• About author:Zirong Hong is a Lecturer of the Lanzhou Petrochemical Polytechnic. His research interests include robotics and signal processing.Bo Dan is an lecturer of the Naval aviation university. His research interests include machine learning and artificial intelligence. He has published more than 20 papers and undertaken many projects such as national natural fund and national equipment pre-research for the 13th five-year plan.Zhaojun Wu is a Ph.D. candidate at Naval aviation university. His research interests include channel code identification.

Abstract: In order to address the drawbacks of recognition algorithm used for recursive system convolutional codes (RSC), such as low fault tolerance and high computational complexity, a novel recognition algorithm was proposed in this study. Firstly, it clearly demonstrated that the rational fraction in binary domain is capable of expanding into recurring series, and the problem of cyclic period could be solved by means of impulse response and analytical matrix. Secondly, in order to reduce the workload of computation placed on the algorithm, a polynomial database was constructed by traversing the constructed RSC code. Then, the specific matrix operation was conducted. In case of a correct ergodic polynomial, the result vector code weight tends to be significantly larger than the ergodic polynomial is incorrect, so as to realize the recognition of the polynomial. Finally, as revealed by the theoretical analysis, the fault-tolerance of the proposed algorithm was solely relevant to the code weight of the recurring series rather than the coding constraint length. The simulation results validated not only the effectiveness of the algorithm but also the correctness of the fault-tolerant performance analysis. When the error code reached as high as 0.1, the recognition rate of some polynomials was higher, and the computational complexity was lower compared to the existing algorithms.