Blind Recognition of (n−1)/n Rate Punctured Convolutional Encoders in a Noisy Environment

Abstract

Blind recognition of error-correcting code is an essential problem to decode intercepted data. In this paper, a method dedicated to the blind recognition of punctured convolutional encoders is presented. The blind recognition of such encoders is of great significance, because convolutional encoders are embedded in most digital transmission systems where the puncturing principle is used to increase the code rate. After a brief review of the principle of puncturing codes, a method mainly based on the Walsh-Hadamard transform is presented for blind recognition of both the mother code and the puncturing pattern when the received bits are erroneous. Compared to existing techniques, our algorithm has advantages of robustness and efficiency. Experiments are conducted to illustrate the performances of this new blind recognition method. Index Terms—Blind Recognition, convolutional code, punctured pattern, walsh-hadamard transform. I. INTRODUCTION Most digital transmission systems are encoded to enhance the communication quality. Redundancy bits are appended in the informative binary data stream to better withstand channel noise. In a non-cooperative context, in order to perform information analysis, it is necessary to decode intercepted data with no knowledge of the parameters of the code. In this case, the blind recognition problem needs to be addressed. Convolutional codes are a class of important codes due to their flexibility in code length, soft decodability, short decoding delay and their role as component codes in parallelly serially concatenated codes. Puncturing allows convolutional codes to flexibly change rates and is widely used in applications where high code rates are required and where rate adaptivity is desired. In this paper, we only focus on communications encoded with punctured convolutional codes. This article is not the first to deal with blind recognition of convolutional codes in a noisy environment. A systematic algebraic approach for the reconstruction of linear and convolutional error