Classification Based on Euclidean Distance Distribution for Blind Identification of Error Correcting Codes in Noncooperative Contexts - 2018


The use of channel code is mandatory in current digital communication systems. It allows us to access the knowledge on the receiver side despite the presence of noise. In this Project, we are interested in the blind identification of the parameters of a slip-up correcting code from a received noisy knowledge stream. The literature provides a massive quantity of contributions for this drawback in the onerous-call case however few within the soft-decision case. It is well known that soft-call strategies enable vital gain in decoding techniques. Thence, we have a tendency to propose an algorithm which is able to spot the length of a code through a classification method from the bits likelihood values. It highlights a difference of behavior between an freelance identically distributed sequence and an encoded one. This method will not rely on any a priori data regarding the encoder concerned. Indeed, the distribution of n-length code words in an n-dimensional area depends on the encoder characteristics. Some areas of this n-dimensional house are left vacant as a result of of the redundancy added by the encoder. Despite the presence of noise, it's still attainable to detect this phenomenon. Furthermore, an adaptation of a collisions technique based on the birthday paradox offers us access to an estimation of the code dimension. Finally, we investigate the performance of this estimation ways to indicate their efficiency.

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