Iterative decoding algorithms for low-density parity-check (LDPC) codes are known to be subject to decoding failures that result in high error floors in the bit error rate performance curves. Recently, researchers became aware that the error floor was a result of a number of properties of the code's graph representation, including cycles, stopping sets, trapping sets, absorbing sets, and pseudo-codewords.
This research aims at analyzing the error floor phenomenon for these structures and developing improved code representations as well as decoder architectures to mitigate the error floor. The convolutional counterparts of LDPC codes, namely LDPC convolutional codes will also be examined in this scope. It is proposed to extend the results to the decoding of conventional codes with denser graph representations, where the error floor issue is even more severe.
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