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一种采用步进策略的极化码增强信念传播翻转译码器。

An Enhanced Belief Propagation Flipping Decoder for Polar Codes with Stepping Strategy.

作者信息

Zhang Xiaojun, Liu Yimeng, Chen Chengguan, Guo Hua, Zeng Qingtian

机构信息

College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

State Key Laboratory of High-End Server and Storage Technology, Jinan 250101, China.

出版信息

Entropy (Basel). 2022 Aug 3;24(8):1073. doi: 10.3390/e24081073.

Abstract

The Belief Propagation (BP) algorithm has the advantages of high-speed decoding and low latency. To improve the block error rate (BLER) performance of the BP-based algorithm, the BP flipping algorithm was proposed. However, the BP flipping algorithm attempts numerous useless flippings for improving the BLER performance. To reduce the number of decoding attempts needed without any loss of BLER performance, in this paper a metric is presented to evaluate the likelihood that the bits would correct the BP flipping decoding. Based on this, a BP-Step-Flipping (BPSF) algorithm is proposed which only traces the unreliable bits in the flip set (FS) to flip and skips over the reliable ones. In addition, a threshold β is applied when the magnitude of the log-likelihood ratio (LLR) is small, and an enhanced BPSF (EBPSF) algorithm is presented to lower the BLER. With the same FS, the proposed algorithm can reduce the average number of iterations efficiently. Numerical results show the average number of iterations for EBPSF-1 decreases by 77.5% when = 256, compared with the BP bit-flip-1 (BPF-1) algorithm at Eb/N0 = 1.5 dB.

摘要

置信传播(BP)算法具有高速解码和低延迟的优点。为了提高基于BP算法的误块率(BLER)性能,提出了BP翻转算法。然而,BP翻转算法为了提高BLER性能进行了大量无用的翻转尝试。为了在不损失BLER性能的情况下减少所需的解码尝试次数,本文提出了一种度量标准来评估比特纠正BP翻转解码的可能性。基于此,提出了一种BP步长翻转(BPSF)算法,该算法只跟踪翻转集中不可靠的比特进行翻转,跳过可靠的比特。此外,当对数似然比(LLR)的幅度较小时应用阈值β,并提出了一种增强型BPSF(EBPSF)算法以降低BLER。在相同的翻转集下,所提算法能够有效减少平均迭代次数。数值结果表明,当码长 = 256时,在Eb/N0 = 1.5 dB条件下,与BP比特翻转1(BPF - 1)算法相比,EBPSF - 1的平均迭代次数减少了77.5%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d78/9407095/558a3520a154/entropy-24-01073-g001.jpg

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