Parameter estimation algorithm of convolutional codes with solving cost function based on conjugate gradient

被引：0
|
作者：
Chen Z. [1 ,2 ]
Lu L. [1 ]
Sun Z. [1 ]
Sun R. [1 ]
机构：
[1] School of Information and Communication Engineering, Harbin Engineering University, Harbin
[2] Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2022年 / 44卷 / 10期
关键词：
conjugate gradient method; optimization method; parameter estimation of convolutional codes; recursive systematic convolutional codes; soft-decision demodulati on;
D O I：
10.12305/j.issn.1001-506X.2022.10.29
中图分类号：
学科分类号：
摘要：
Convolutional codes are often used as sub-codes of high performance codes such as concatenated codes and turbo codes. Correct parameter estimation of convolutional codes is the basis of recognition of concatenated codes and turbo codes, which requires that the estimation algorithms of convolutional codes should have strong robustness against channel noise. The key to such purpose is to make use of the soft-decesion demodulation received sequence. In this paper, a cost function model based on exponential function is proposed according to the linear constraint relation between symbols of recursive systematic convolutional codes. The parameter estimation of convolutional codes is transformed into the minimal value of the cost function. And the optimization is accomplished via a simple iterative process by conjugate gradient. Simulation results show that, compared with the existing algorithms, the new algorithm significantly improves the performance while it is also applicable to the estimation of general convolutional codes and has a fast convergence speed. © 2022 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码：3235 / 3242
页数：7
相关论文
共 30 条
  • [1] VILA-VALLS J, NAVARRO M, CLOSASP, Et al., Synchronization challenges in deep space communications, IEEE Aerospace and Electronic Systems Magazine, 34, 1, pp. 16-27, (2019)
  • [2] YANG C, ZHAN M, DENG Y, Et al., Error-correcting performance comparison for polar codes, LDPC codes and convolutional codes in high-performance wireless, Proc. of the 6th International Conference on Information, Cybernetics, and Computational Social Systems, pp. 258-262, (2019)
  • [3] QIU M, WU X W, AMAT A G I, Et al., Analysis and design of partially information-and partially parity-coupled Turbo codes, IEEE Trans, on Communications, 69, 4, pp. 2107-2122, (2021)
  • [4] SHAO S, HAILES P, WANG T Y, Et al., Survey of turbo, LDPC, and polar decoder ASIC implementations, IEEE Communications Surveys & Tutorials, 21, 3, pp. 2309-2333, (2019)
  • [5] LIU Y, XIANG L, MAUNDER R G, Et al., Hybrid iterative detection and decoding of near-instantaneously adaptive Turbo-coded sparse code multiple access, IEEE Trans, on Vehicular Technology, 70, 5, pp. 4682-4692, (2021)
  • [6] MENEZLA F, BENDELHOUM M S, MAHDJOUB Z, Et al., The effect of error correcting codes in the chain of transmission and comparison between the performances of these codes, Proc. of the 6th International Conference on Image and Signal Processing and Their Applications, (2020)
  • [7] ZOU Y, LU P Z., A new generalization of key equation, Chinese Journal of Computers, 29, 5, pp. 711-718, (2006)
  • [8] WANG F H, HUANG Z T, ZHOU Y Y., A method for blind recognition of convolution code based on euclidean algorithm, Proc. of the International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1414-1417, (2007)
  • [9] LIU J, WANG X J, ZHOU X Y., Blind recognition of convolutional coding based on Walsh-Hadamard transform, Journal of Electronics & Information Technology, 32, 4, pp. 884-888, (2010)
  • [10] ZHANG L M, LIU J, ZHONG Z G., Blind recognition of (n, l, m) convolutional codes based on modified Walsh-Hadamard transform[J], Journal of Electronics & Information Technology, 40, 4, pp. 839-845, (2018)
123