2019 IEEE Wireless Communications and Networking Conference (WCNC) 2019
DOI: 10.1109/wcnc.2019.8885419
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Algorithm and Architecture for Path Metric Aided Bit-Flipping Decoding of Polar Codes

Abstract: Polar codes attract more and more attention of researchers in recent years, since its capacity achieving property. However, their error-correction performance under successive cancellation (SC) decoding is inferior to other modern channel codes at short or moderate blocklengths. SC-Flip (SCF) decoding algorithm shows higher performance than SC decoding by identifying possibly erroneous decisions made in initial SC decoding and flipping them in the sequential decoding attempts. However, it performs not well whe… Show more

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Cited by 6 publications
(3 citation statements)
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References 16 publications
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“…Similar problems that occur in linear modulation have been addressed using LLR clipping [43]- [52], LLR scaling [53]- [56], bit flipping [57], [58], and LLR generation through machine learning [59]. Currently, the best method of solving this problem is to use machine learning to generate LLRs.…”
Section: Introductionmentioning
confidence: 99%
“…Similar problems that occur in linear modulation have been addressed using LLR clipping [43]- [52], LLR scaling [53]- [56], bit flipping [57], [58], and LLR generation through machine learning [59]. Currently, the best method of solving this problem is to use machine learning to generate LLRs.…”
Section: Introductionmentioning
confidence: 99%
“…This method achieves slightly improved performance for the SCF approach [46], and also illustrates an increase in the reliability metric compared to the basic SCL approach. Next, we will consider flexible and generalized approaches to the SCLF concept to achieve the optimal decoding performance.…”
Section: ) Basic Flipping Methodmentioning
confidence: 88%
“…In this work, we focus on improving the area efficiency of hybrid polar decoding by adopting a pipelined architecture, which is similar to our previous work [30]. Here, the main contributions of this work are summarized as follows:…”
Section: Introductionmentioning
confidence: 95%