Blind recovery of k/n rate convolutional encoders in a noisy environment

http://jwcn.eurasipjournals.com/content/2011/1/168International audienceIn order to enhance the reliability of digital transmissions, error correcting codes are used in every digital communication system. To meet the new constraints of data rate or reliability, new coding schemes are currently being developed. Therefore, digital communication systems are in perpetual evolution and it is becoming very difficult to remain compatible with all standards used. A cognitive radio system seems to provide an interesting solution to this problem: the conception of an intelligent receiver able to adapt itself to a specific transmission context. This article presents a new algorithm dedicated to the blind recognition of convolutional encoders in the general k/n rate case. After a brief recall of convolutional code and dual code properties, a new iterative method dedicated to the blind estimation of convolutional encoders in a noisy context is developed. Finally, case studies are presented to illustrate the performances of our blind identification method

Blind Identification of Block Interleaved Convolution Code Parameters

Most of the digital communication system uses forward error correction (FEC) in addition with interleaver to achieve reliable communication over a noisy channel. To get useful information from intercepted data, in non-cooperative context, it is necessary to have algoritihms for blind identification of FEC code and interleaver parameters. In this paper, a matrix rank-based algebraic algorithm for the joint and blind identification of block interleaved convolution code parameters for cases, where interleaving length is not necessarily an integer multiple of codeword length is presented. From simulations, it is observed that the code rate and block interleaver length are identified correctly with probability of detection equal to 1 for bit error rate values of less than or equal to 10-4

A Fast Method for Blind Identification of Punctured Convolutional Codes

The existing method for blind identification of a punctured convolutional code involves searching for dual words and the puncturing pattern exhaustively. As the length of the dual words and the code rate increase, the computational complexity of this method expands exponentially. To address this problem, a fast scheme for blind identification of punctured convolutional codes is proposed. First, a recursive algorithm for solving the parity check equation set is proposed. The dual word and generator polynomial bases of the punctured convolutional code are estimated by using the recursive algorithm. After this, by using the structural properties of the generator matrix of the blocked code, possible generator matrices of the punctured convolutional code are obtained. Finally, since a generator polynomial of the parent convolutional code can be recovered from any column of its polycyclic pseudocirculant matrix, the corresponding generator matrix of the parent code and the puncturing pattern are reconstructed simultaneously from an estimation of the generator matrix of the punctured code. The reconstructed generator matrix of the parent code with a minimal constraint length is determined to be the identification result. Simulation experiments show the effectiveness of the proposed method. As there is no need to search for the dual word and puncturing pattern exhaustively, the method can achieve fast identification of punctured convolutional codes. Additionally, the method is robust to bit errors in the received sequence

Classification Based on Euclidean Distance Distribution for Blind Identification of Error Correcting Codes in Noncooperative Contexts

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Influence du mapping sur la reconnaissance d'un système de communication

Le contexte de cette thèse est la reconnaissance de systèmes de communication dans un contexte non coopératif. Nous nous intéressons au problème de la reconstruction de codes convolutifs et à la reconstruction du mapping (la bijection utilisée pour associer une séquence binaire à un signal modulé). Nous avons élaboré une nouvelle méthode statistique qui à partir d'une séquence binaire bruitée observée permet de détecter si une séquence binaire est codée par un codeur convolutif. Cette méthode consiste à former des blocs de séquence suffisamment grands pour contenir le support d'une équation de parité et à compter le nombre de blocs identiques. Elle a l'avantage de fournir la longueur du code utilisé lorsque le mapping est inconnu. Cette méthode peut également être utilisée pour reconstruire le dual d'un code convolutif lorsque le mapping est connu. Nous proposons par ailleurs un algorithme de reconnaissance de mapping basé sur le parcours de classes d'équivalences. Deux types de classes sont définies. Nous disposons d'un signal bruité partiellement démodulé (démodulé avec un mapping par défaut) et supposons que les données sont codées par un codeur convolutif. Nous utilisons la reconnaissance d'un tel code comme testeur et parcourons enfin les classes d'équivalences faisant apparaître une structure de codes convolutifs. Cette classification améliore la complexité de la recherche pour les petites constellations (4 et 8-PSK). Dans le cas des constellations 16 à 256-QAM l'algorithme est appliqué aux mappings Gray ou quasi-Gray. L'algorithme ne fournit pas un résultat unique mais il permet de trouver un ensemble de mappings possibles à partir de données bruitées.The context of this thesis is the recognition of communication systems in a non-cooperative context. We are interested in the convolutional code reconstruction problem and in the constellation labeling reconstruction (the mapping used to associate a binary sequence to a modulated signal). We have defined a new statistical method for detecting if a given binary sequence is a noisy convolutional code-word obtained from an unknown convolutional code. It consists in forming blocks of sequence which are big enough to contain the support of a parity check equation and counting the number of blocks which are equal. It gives the length of the convolutional code without knowledge of the constellation labeling. This method can also be used to reconstruct the dual of a convolutional code when the constellation labeling is known. Moreover we propose a constellation labeling recognition algorithm using some equivalence classes. Two types of classes are defined: linear and affine. We observe a noisy signal which is partially demodulated (with a default labeling) and assume that the data are coded by a convolutional encoder. Thus we use the reconstruction of a code as a test and run through the classes which reveal a code structure. This classification improves the complexity of the search for small constellations (4-PSK and 8-PSK). In case of 16-QAM to 256-QAM constellations we apply the algorithm to Gray or quasi-Gray labelings. The algorithm does not give a unique result but it allows to find a small set of possible constellation labelings from noisy data.PARIS-JUSSIEU-Bib.électronique (751059901) / SudocSudocFranceF