Blind Recognition of Block Code Parameters in the Presence of High SNR Using Statistical Techniques

Document Type : Original Article

Authors

Abstract

Blind recognition of error correction codes parameters from intercepted bit-stream at the receiver side, is highly considered in military and commercial applications. In fact, identification of the encoding scheme used in the transmitter without any prior information, is a challenging task to the adversary. Several methods have been presented for blind code recognition. In this paper, a statistical method for recognition of  the length of the code word and the length of the block of information is presented. This scheme not only is resistant to error, but also its performance sustains in long codes. In this work, the method has been tested using some clustering algorithms such as K-Means and Jenks Natural Breaks. Then, a novel method to extract features of systematic binary linear block codes has been presented. Simulation results in MATLAB show that the proposed method, in addition to having low computational complexity and high performance rate, have an acceptable result in identifying systematic block codes with long lengths and even at high error levels.

Keywords


[1]   Filiol, E. “Reconstruction of Convolutional Encoders over GF (q)”; IMA Int. Conf. Cryptography and Coding 1997, 101-109.##
[2]   Rice, B. “Determining the Parameters of a Rate 1/n Convolutional Encoder over GF (q)”; Third Int. Con. Finite Fields and Applications, 1995.##
[3]   Valembois, A. “Detection and Recognition of a Binary Linear Code”; Discrete Applied Mathematics 2001, 111, 199-218.##
[4]   Cluzeau, M.; Finiasz, M. “Recovering a Code's Length and Synchronization from a Noisy Intercepted Bitstream”; IEEE Int. Sympos. Information Theory 2009, 2737-2741.##
[5]   Barbier J.; Letessier, J. “Forward Error Correcting Codes Characterization Based On Rank Properties”; Int. Conf. Wireless Communications & Signal Processing 2009, 1-5.##
[6]   Cluzeau, M. “Block Code Reconstruction Using Iterative Decoding Techniques”; IEEE Int. Sympos. Information Theory 2006, 2269-2273.##
[7]   Wang, J.; Yue, Y.; Yao, J. “A Method of Blind Recognition of Cyclic Code Generator Polynomial”; 6th Int. Conf. Wireless Communications Networking and Mobile Computing (WiCOM) 2010, 1-4.##
[8]   Teimouri, M.; Motlagh, H.; Haddadi, M. “Blind Recognition of BCH Product Codes”; J. Electrical Eng. 2017, 1, 49-54.##
[9]   Naseri A.; Maymanat, M. “Proposed Algorithm for Channel Coding Detection in Communication Surveillance Systems”; J. Passive Defence Sci & Technol. 2011, 2, 101-110.##
[10] Xia T.; Wu, H. C. “Novel Blind Identification of LDPC Codes Using Average LLR of Syndrome a Posteriori Probability”; IEEE Trans. Signal Processing 2014, 62, 632-640.##
[11] Moosavi R.; Larsson, E. G. “Fast Blind Recognition of Channel Codes”; IEEE Trans. Communications 2014, 62, 1393-1405.##
[12] Swaminathan R.; Madhukumar, A. “Classification of Error Correcting Codes and Estimation of Interleaver Parameters in a Noisy Transmission Environment”; IEEE Trans. Broadcasting 2017, 63, 463-478.##
[13] Mao, D. “Performance Bound Analysis on Hamming-Weight-Analysis Algorithm for Blind Recognition of Linear Block Codes”; Int. Conf. Communicatins and Networking in China 2017, 323-331.##
[14] Sharma, A.; Pillai, N. R. “Blind Recognition of Parameters of Linear Block Codes from Intercepted Bit Stream”; Int. Conf. Computing, Communication and Automation 2016, 1262-1266.##
[15] Lin S.;  Costello, D. J. “Error Control Coding” Pearson-Prentice Hall, 2004.##
[16] Likas, A.;  Vlassis, N.; Verbeek, J. J. “The Global k-Means Clustering Algorithm”; Pattern Recognition 2003, 36, 451-461.##
 [17] Jenks, G. F. “Optimal Data Classification for Choropleth Maps,” Department of Geographiy, University of Kansas Occasional Paper, 1977.##
[18]Nasseri A.;  Moghaddam, G. S. “The Proposed Intelligent Algorithm for Process Section  in the Radar Interception Systems”; J. Passive Defence Sci. & Technol. 2011, 1, 87-98.##