Belief propagation list bit-flip decoder for polar codes

Yang, Yumin; Yaoyue, Hu; Pan, Zhiwen; Liu, Nan; Xia, Shenjie

doi:10.1007/s11432-019-2801-6

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Recently, the work in [26] merged the partitioned SC-flip idea with high-speed SC-flip decoders and proposed an improved partitioned fast-SSC-flip (PFSSC-flip) decoder. Inspired by the SCFlip decoding algorithms, the authors in [27][28][29] further extended the bit-flip algorithm for belief propagation (BP) decoding.…”

Section: Introductionmentioning

confidence: 99%

Parity-Check-CRC Concatenated Polar Codes SSCFlip Decoder

et al. 2022

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Successive cancellation flip decoding requires a large number of extra successive cancellation decoding attempts at low signal-to-noise ratios (SNRs), resulting in high decoding complexity. In addition, it has a long decoding latency. Although modifications have been proposed in successive cancellation flip decoding, these still have high computational complexity at low SNRs due to a huge number of additional successive cancellation decoding attempts. It is desirable to detect the unsuccessful successive cancellation decoding process at an early stage in the additional successive cancellation flip attempts and stop it that can reduce the decoding complexity. This paper combines the parity-check-CRC concatenated polar codes with the low-latency simplified successive cancellation decoding and proposes a parity-check-CRC concatenated polar codes simplified successive cancellation flip (PC-CRC-SSCFlip) decoder. It further employs the parity-check vector to identify the unsuccessful simplified successive cancellation flip decoding at an early stage and terminates so that it can minimize the decoding complexity on average. Additionally, this work proposes an error-prone flipping list by incorporating the empirically observed indices based on channel-induced error distribution along with the first bit of each Rate-1 node. The proposed technique can identify more than one error-prone bit through a flipping list and correct them. In addition, the parity-check vector further narrows down the search space for the identification of erroneous decisions. Simulation results show that 60% of unsuccessful additional successive cancellation decoding attempts terminate early rather than decode the whole codeword. The proposed PC-CRC-SSCFlip decoder has approximately 0.7 dB and 0.3 dB gains over successive cancellation and successive cancellation flip decoders, respectively, at a fixed block error rate (BLER) = 10−3. Additionally, it reduces the average computational complexity and decoding latency of the successive cancellation flip decoder at low-to-medium SNRs while approaching successive cancellation decoding complexity at medium-to-high SNRs.

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Section: Introductionmentioning

confidence: 99%

Parity-Check-CRC Concatenated Polar Codes SSCFlip Decoder

et al. 2022

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“…The flipping set which is constructed by a convolutional neural network-based imitation learning scheme was presented in [19], such that useless flipping attempts are avoided. [20] proposed a noised-aided BPL bit-flipping decoder which is superior to the CA-SCL decoder at the cost of numbers of decoding iterations. In [21], a low-complexity two-level post-processing algorithm was proposed to modify the false converged error efficiently in the BPL decoder.…”

Section: Introductionmentioning

confidence: 99%

Higher-Order Belief Propagation Correction Decoder for Polar Codes

Zhang

Xing

et al. 2022

Entropy

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Belief propagation (BP) decoding for polar codes has been extensively studied because of its inherent parallelism. However, its performance remains inferior to that of successive cancellation list decoding (SCL) due to the structure of the decoding graph. To improve the block error rate (BLER) performance, the BP correction (BPC) decoding, a post-processing scheme that corrects prior knowledge of the identified code bit, improves convergence by executing additional iterations on the failed BP decoder. Moreover, the BPC decoder demonstrates a better decoding performance than the BP-based bit-flipping decoder. Nevertheless, the additional decoding attempts lead to increased latency. In this article, a modified BPC decoder is proposed to reduce the number of decoding attempts by redefining the correction rules. A new metric is designed to effectively identify the corrected location. Numerical results show that the proposed modified BPC decoder achieves a slight improvement in BLER compared with the original BPC, with a dramatic reduction in average complexity. Furthermore, a higher-order version, named MBPC-Ω, is extended to further improve the performance, where the Ω is the maximum correction order. Numerical results show that the higher-order modified BPC achieves a similar BLER performance to existing multiple bit-flipping BP decoders but has around half the latency overhead. In addition, the proposed MBPC-2 decoder performs better than the cyclic redundancy check-aided SCL (CA-SCL) decoder with list size 4 and is slightly worse than the CA-SCL with list size 8 in high signal-to-noise ratio (SNR) regions but with significant decoding latency reduction.

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Belief propagation list bit-flip decoder for polar codes

Cited by 2 publications

References 18 publications

Parity-Check-CRC Concatenated Polar Codes SSCFlip Decoder

Parity-Check-CRC Concatenated Polar Codes SSCFlip Decoder

Higher-Order Belief Propagation Correction Decoder for Polar Codes

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