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Serial Decoding Algorithm with Continuous Backtracking for LDPC Convolutional Codes

Ming Zhao, Bin Xu

Published Apr 22, 2019• 4 citations
Publisher
DOI
10.1007/s11277-019-06359-1

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IEEE Transactions on Information Theory•May 19, 2009•117 citations
In the Shannon Lecture at the 2019 International Symposium on Information Theory (ISIT), Professor Arikan proposed a new coding scheme called polarization-adjusted convolutional (PAC) codes. These codes exhibit excellent error-correction performance for short-to-medium block lengths. Currently, the mainstream decoding algorithms for PAC codes mainly include sequential decoding and list decoding. Fano decoding, a type of sequential decoding, offers good error-correction performance; however, its time complexity is relatively high due to the presence of extensive backtracking processes. In this paper, a fast Fano decoding algorithm based on backtracking optimization mechanism for PAC codes is proposed. The improved algorithm we put forward mitigate the time complexity of the decoding process from two aspects: path backtracking and threshold updating strategy. It enhances the error-correcting performance of the Fano decoding algorithm by introducing the first error node metric and the second error node metric. With the efforts in the above aspects, our decoding algorithm achieves good performance in both complexity and error-correction capability.
In this paper, we present a theory of quantum serial turbo codes, describe their iterative decoding algorithm, and study their performances numerically on a depolarization channel. Our construction offers several advantages over quantum low-density parity-check (LDPC) codes. First, the Tanner graph used for decoding is free of 4-cycles that deteriorate the performances of iterative decoding. Second, the iterative decoder makes explicit use of the code's degeneracy. Finally, there is complete freedom in the code design in terms of length, rate, memory size, and interleaver choice. We define a quantum analogue of a state diagram that provides an efficient way to verify the properties of a quantum convolutional code, and in particular, its recursiveness and the presence of catastrophic error propagation. We prove that all recursive quantum convolutional encoders have catastrophic error propagation. In our constructions, the convolutional codes have thus been chosen to be noncatastrophic and nonrecursive. While the resulting families of turbo codes have bounded minimum distance, from a pragmatic point of view, the effective minimum distances of the codes that we have simulated are large enough not to degrade the iterative decoding performance up to reasonable word error rates and block sizes. With well-chosen constituent convolutional codes, we observe an important reduction of the word error rate as the code length increases.