• Title/Summary/Keyword: Multiple-bit-flipping

Search Result 3, Processing Time 0.014 seconds

Generalized SCAN Bit-Flipping Decoding Algorithm for Polar Code

  • Lou Chen;Guo Rui
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.17 no.4
    • /
    • pp.1296-1309
    • /
    • 2023
  • In this paper, based on the soft cancellation (SCAN) bit-flipping (SCAN-BF) algorithm, a generalized SCAN bit-flipping (GSCAN-BF-Ω) decoding algorithm is carried out, where Ω represents the number of bits flipped or corrected at the same time. GSCAN-BF-Ω algorithm corrects the prior information of the code bits and flips the prior information of the unreliable information bits simultaneously to improve the block error rate (BLER) performance. Then, a joint threshold scheme for the GSCAN-BF-2 decoding algorithm is proposed to reduce the average decoding complexity by considering both the bit channel quality and the reliability of the coded bits. Simulation results show that the GSCAN-BF-Ω decoding algorithm reduces the average decoding latency while getting performance gains compared to the common multiple SCAN bit-flipping decoding algorithm. And the GSCAN-BF-2 decoding algorithm with the joint threshold reduces the average decoding latency further by approximately 50% with only a slight performance loss compared to the GSCAN-BF-2 decoding algorithm.

Multiple Node Flip Fast-SSC Decoding Algorithm for Polar Codes Based on Node Reliability

  • Rui, Guo;Pei, Yang;Na, Ying;Lixin, Wang
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.16 no.2
    • /
    • pp.658-675
    • /
    • 2022
  • This paper presents a fast-simplified successive cancellation (SC) flipping (Fast-SSC-Flip) decoding algorithm for polar code. Firstly, by researching the probability distribution of the number of error bits in a node caused by channel noise in simplified-SC (SSC) decoder, a measurement criterion of node reliability is proposed. Under the guidance of the criterion, the most unreliable nodes are firstly located, then the unreliable bits are selected for flipping, so as to realize Fast-SSC-Flip decoding algorithm based on node reliability (NR-Fast-SSC-Flip). Secondly, we extended the proposed NR-Fast-SSC-Flip to multiple node (NR-Fast-SSC-Flip-ω) by considering dynamic update to measure node reliability, where ω is the order of flip-nodes set. The extended algorithm can correct the error bits in multiple nodes, and get good performance at medium and high signal-to-noise (SNR) region. Simulation results show that the proposed NR-Fast-SSC-Flip decoder can obtain 0.27dB and 0.17dB gains, respectively, compared with the traditional Fast-SSC-Flip [14] and the newly proposed two-bit-flipping Fast-SSC (Fast-SSC-2Flip-E2) [18] under the same conditions. Compared with the newly proposed partitioned Fast-SSC-Flip (PA-Fast-SSC-Flip) (s=4) [18], the proposed NR-Fast-SSC-Flip-ω (ω=2) decoder can obtain about 0.21dB gain, and the FER performance exceeds the cyclic-redundancy-check (CRC) aided SC-list (CRC-SCL) decoder (L=4).

Multi-resolution Lossless Image Compression for Progressive Transmission and Multiple Decoding Using an Enhanced Edge Adaptive Hierarchical Interpolation

  • Biadgie, Yenewondim;Kim, Min-sung;Sohn, Kyung-Ah
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.11 no.12
    • /
    • pp.6017-6037
    • /
    • 2017
  • In a multi-resolution image encoding system, the image is encoded into a single file as a layer of bit streams, and then it is transmitted layer by layer progressively to reduce the transmission time across a low bandwidth connection. This encoding scheme is also suitable for multiple decoders, each with different capabilities ranging from a handheld device to a PC. In our previous work, we proposed an edge adaptive hierarchical interpolation algorithm for multi-resolution image coding system. In this paper, we enhanced its compression efficiency by adding three major components. First, its prediction accuracy is improved using context adaptive error modeling as a feedback. Second, the conditional probability of prediction errors is sharpened by removing the sign redundancy among local prediction errors by applying sign flipping. Third, the conditional probability is sharpened further by reducing the number of distinct error symbols using error remapping function. Experimental results on benchmark data sets reveal that the enhanced algorithm achieves a better compression bit rate than our previous algorithm and other algorithms. It is shown that compression bit rate is much better for images that are rich in directional edges and textures. The enhanced algorithm also shows better rate-distortion performance and visual quality at the intermediate stages of progressive image transmission.