|Keywords:||polar coding; polar codes; channel polarization; mismatched decoding; robust code design; generalized likelihood ratio test (GRLT); coding for non-stationary channels; extremal channels; information combining; Gallager's E0|
|Full text PDF:||http://infoscience.epfl.ch/record/203886|
The general subject considered in this thesis is a recently discovered coding technique, polar coding, which is used to construct a class of error correction codes with unique properties. In his ground-breaking work, Arikan proved that this class of codes, called polar codes, achieve the symmetric capacity – the mutual information evaluated at the uniform input distribution – of any stationary binary discrete memoryless channel with low complexity encoders and decoders requiring in the order of $O(N\log N)$ operations in the block-length $N$. This discovery settled the long standing open problem left by Shannon of finding low complexity codes achieving the channel capacity. Polar codes are not only appealing for being the first to 'close the deal'. In contrast to most of the existing coding schemes, polar codes admit an explicit low complexity construction. In addition, for symmetric channels, the polar code construction is deterministic; the theoretically beautiful but practically limited "average performance of an ensemble of codes is good, so there must exist one particular code in the ensemble at least as good as the average'' formalism of information theory is bypassed. Simulations are thus not necessary in principle for evaluating the error probability which is shown in a study by Telatar and Arikan to scale exponentially in the square root of the block-length. As such, at the time of this writing, polar codes are appealing for being the only class of codes proved, and proved with mathematical elegance, to possess all of these properties. Polar coding settled an open problem in information theory, yet opened plenty of challenging problems that need to be addressed. This novel coding scheme is a promising method from which, in addition to data transmission, problems such as data compression or compressed sensing, which includes all types of measurement processes like the MRI or ultrasound, could benefit in terms of efficiency. To make this technique fulfill its promise, the original theory has been, and should still be, extended in multiple directions. A significant part of this thesis is dedicated to advancing the knowledge about this technique in two directions. The first one provides a better understanding of polar coding by generalizing some of the existing results and discussing their implications, and the second one studies the robustness of the theory over communication models introducing various forms of uncertainty or variations into the probabilistic model of the channel. See the fulltext of the thesis for the complete abstract.