Mélanie Marazin

Blind Recognition of Linear Space–Time Block Codes: A Likelihood-Based Approach

Blind recognition of communication parameters is a research topic of high importance for both military and civilian communication systems. Numerous studies about carrier frequency estimation, modulation recognition as well as channel identification are available in literature. This paper deals with the blind recognition of the space-time block coding (STBC) scheme used in multiple-input-multiple-output (MIMO) communication systems. Assuming there is perfect synchronization at the receiver side, this paper proposes three maximum-likelihood (ML)-based approaches for STBC classification: the optimal classifier, the second-order statistic (SOS) classifier, and the code parameter (CP) classifier. While the optimal and the SOS approaches require ideal conditions, the CP classifier is well suited for the blind context where the communication parameters are unknown at the receiver side. Our simulations show that this blind classifier is more easily implemented and yields better performance than those available in literature.

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

In 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.

Dual Code Method for Blind Identification of Convolutional Encoder for Cognitive Radio Receiver Design

Digital communication systems are in perpetual evolution in order to respond to the new user expectations and to new applications transmissions constraints, in term of data rate or reliability. With this fast development of new communication standards, it becomes very difficult for users and also for communications devices producers, to stay compatible with all standards used and with the oncoming ones. For that reason, cognitive radio systems seem to provide an interesting solution to this problem. The conception of intelligent receiver able to adapt itself to a specific transmission context and to blindly estimate the transmitter parameters is a promising solution for the future. In such context, new coding schemes like turbocodes, Low-Density Parity-Check (LDPC) or concatenated codes are developed to increase transmission robustness without significant degradation of the data rate. It is why we have developed a method, described in this paper, dedicated to the blind identification of convolutional encoders usually used in many standards. Moreover, an analysis of the method performances is detailed.

Algebraic method for blind recovery of punctured convolutional encoders from an erroneous bitstream

To enhance the quality of transmissions, all digital communication systems use error-correcting codes. By introducing some redundancy in the informative binary data stream, they allow one to better withstand channel impairments. The design of new coding schemes leads to a perpetual evolution of the digital communication systems and, thus, cognitive radio receivers have to be designed. Such receivers will be able to blind estimate the transmitter parameters. In this study, an algebraic method dedicated to the blind identification of punctured convolutional encoders is presented. The blind identification of such encoders is of great interest, because convolutional encoders are embedded in most digital transmission systems where the puncturing principle is used to increase the code rate to reduce the loss of the information data rate because of the redundancy introduced by the encoder. After a brief recall of the principle of puncturing codes and the construction of the equivalent punctured code, a new method dedicated to the blind identification of both the mother code and the puncturing pattern is developed when the received bits are erroneous. Finally, case studies are presented to illustrate the performances of our blind identification method.

Blind Identification of Convolutional Encoder Parameters over GF(2m) in the Noiseless Case

In most digital communication systems, error correcting codes are essential to achieve good immunity to channel impairment. Due to the complexity of the encoding process, but especially of the decoding process, most research on error correcting codes is restricted to binary data which are elements of the Galois field GF(2). Reed Solomon codes have been the most commonly used so far as non-binary (GF(q)) error correcting codes. Recently, low complexity decoding algorithms for non-binary LDPC and non-binary turbo-codes have been developed. In this paper, the design of convolutional codes over the non-binary Galois field, with cardinal 2^m (GF(2^m)), is presented in order to blind identify their parameters. An extension of our blind identification method dedicated to convolutional codes over GF(2) is developed for convolutional encoders over GF(2^m). Finally, the impact of Galois field parameters on the blind estimation of convolutional encoder parameters over GF(2^m) is investigated.

Some interesting dual-code properties of convolutional encoder for standards self-recognition

For enhancement of the quality of digital transmissions, standards are in continual evolution, which generates compatibility problems. Cognitive radio systems permit one to solve this problem through the design of intelligent receivers. However, such receivers must be able to adapt themselves to a specific transmission context. This requires the development of new methods in order to blindly estimate error-correcting codes. Coding schemes such as turbocode, composed of convolutional codes, belong to a family of error-correcting codes in use in many standards. In most of the methods designed to identify convolutional encoders the algebraic properties are used implicitly. However usually, these dedicated properties are neither explicated, nor detailed, nor demonstrated. The study reported here investigates the algebraic properties of convolutional encoders, useful for blind recognition methods in the cognitive radio context and more specially the algebraic relationships between different forms of a convolutional code and its corresponding dual code. Moreover, some simulation results are presented to show the relevance of these properties for the blind identification of the convolutional encoder.