Abstract
Abstract 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.
Highlights
In a digital communication system, the use of an error correcting code is mandatory
A cognitive radio receiver is an intelligent receiver able to adapt itself to a specific transmission context and to blindly estimate the transmitter parameters for selfreconfiguration purposes only with knowledge of the received data stream
Let C be an (n, k, K) convolutional code, where n is the number of outputs, k is the number of inputs, K is the constraint length, and C⊥ be a dual code of C
Summary
In a digital communication system, the use of an error correcting code is mandatory. This error correcting code allows one to obtain good immunity against channel impairments. To enhance the correction capabilities and to reduce the impact of the amount of redundancy introduced, new correcting codes are always under development This means that communication systems are in perpetual evolution. In [8], an iterative method for the blind recognition of a rate (n-1)/n convolutional encoder was proposed in a noisy environment. This method allows the identification of parameters and generator matrix of a convolutional encoder. It relies on algebraic properties of convolutional codes [9,10] and dual code [11], and is extended here to the case of rate k/n convolutional encoders.
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