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Dynamic-SCFlip Decoding of Polar Codes

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TLDR
In this article, a generalized successive cancellation flip (SCFlip) decoding of polar codes is proposed, where one or several positions are flipped from the standard SC decoding to correct the trajectory of the SC decoding.
Abstract
This paper proposes a generalization of the recently introduced successive cancellation flip (SCFlip) decoding of polar codes, characterized by a number of extra decoding attempts, where one or several positions are flipped from the standard SC decoding. To make such an approach effective, we first introduce the concept of higher order bit flips and propose a new metric to determine the bit flips that are more likely to correct the trajectory of the SC decoding. We then propose a generalized SCFlip decoding algorithm, referred to as dynamic-SCFlip (D-SCFlip), which dynamically builds a list of candidate bit flips, while guaranteeing that the next attempt has the highest probability of success among the remaining ones. Simulation results show that D-SCFlip is an effective alternative to SC-list decoding of polar codes, by providing very good error correcting performance, with an average computation complexity close to the one of the SC decoder.

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Citations
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Proceedings ArticleDOI

Partitioned successive-cancellation list decoding of polar codes

TL;DR: It is shown that with careful selection of list sizes and number of partitions, the proposed algorithm can outperform conventional SCL while requiring less memory.
Proceedings ArticleDOI

Improved successive cancellation flip decoding of polar codes based on error distribution

TL;DR: Two techniques to improve the SC-Flip decoding algorithm for low-rate codes, based on the observation of channel-induced error distributions are proposed, including a fixed index selection (FIS) scheme to avoid the substantial implementation cost of LLR selection and sorting with no cost on error-correction performance.
Proceedings ArticleDOI

Improved Successive Cancellation Flip Decoding of Polar Codes Based on Error Distribution

TL;DR: In this paper, the authors proposed two techniques to improve the error-correction performance of SC-Flip decoding for low-rate codes, based on the observation of channel-induced error distributions.
Journal ArticleDOI

Improved Bit-Flipping Algorithm for Successive Cancellation Decoding of Polar Codes

TL;DR: The average log-likelihood ratio (LLR) values and their distribution related to the erroneous bit-channels are investigated, and the Thresholded SC-Flip decoding algorithm is developed, which shows negligible loss compared with the error-correction performance obtained when all single-errors are corrected.
Proceedings ArticleDOI

Partitioned Successive-Cancellation Flip Decoding of Polar Codes

TL;DR: In this article, the authors proposed a partitioned SC-Flip (PSCF) decoding algorithm, which outperforms SC-flip in terms of error-correction performance and average computational complexity.
References
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Journal ArticleDOI

Channel Polarization: A Method for Constructing Capacity-Achieving Codes for Symmetric Binary-Input Memoryless Channels

TL;DR: The paper proves that, given any B-DMC W with I(W) > 0 and any target rate R< I( W) there exists a sequence of polar codes {Cfrn;nges1} such that Cfrn has block-length N=2n, rate ges R, and probability of block error under successive cancellation decoding bounded as Pe(N,R) les O(N-1/4) independently of the code rate.
Journal ArticleDOI

Regular and irregular progressive edge-growth tanner graphs

TL;DR: Simulation results show that the PEG algorithm is a powerful algorithm to generate good short-block-length LDPC codes.
Proceedings ArticleDOI

List decoding of polar codes

TL;DR: It appears that the proposed list decoder bridges the gap between successive-cancellation and maximum-likelihood decoding of polar codes, and devise an efficient, numerically stable, implementation taking only O(L · n log n) time and O( L · n) space.
Journal ArticleDOI

List Decoding of Polar Codes

TL;DR: Simulations show that the resulting performance is very close to that of maximum-likelihood decoding, even for moderate values of L, and it is shown that such a genie can be easily implemented using simple CRC precoding.
Journal ArticleDOI

Efficient Design and Decoding of Polar Codes

TL;DR: It is shown that Gaussian approximation for density evolution enables one to accurately predict the performance of polar codes and concatenated codes based on them.
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