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[Author] Yujin ZHENG(2hit)

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  • Hardware Implementation of Euclidean Projection Module Based on Simplified LSA for ADMM Decoding

    Yujin ZHENG  Junwei ZHANG  Yan LIN  Qinglin ZHANG  Qiaoqiao XIA  

     
    LETTER-Coding Theory

      Pubricized:
    2022/05/20
      Vol:
    E105-A No:11
      Page(s):
    1508-1512

    The Euclidean projection operation is the most complex and time-consuming of the alternating direction method of multipliers (ADMM) decoding algorithms, resulting in a large number of resources when deployed on hardware platforms. We propose a simplified line segment projection algorithm (SLSA) and present the hardware design and the quantization scheme of the SLSA. In simulation results, the proposed SLSA module has a better performance than the original algorithm with the same fixed bitwidths due to the centrosymmetric structure of SLSA. Furthermore, the proposed SLSA module with a simpler structure without hypercube projection can reduce time consuming by up to 72.2% and reduce hardware resource usage by more than 87% compared to other Euclidean projection modules in the experiments.

  • An Enhanced HDPC-EVA Decoder Based on ADMM

    Yujin ZHENG  Yan LIN  Zhuo ZHANG  Qinglin ZHANG  Qiaoqiao XIA  

     
    LETTER-Coding Theory

      Pubricized:
    2021/04/02
      Vol:
    E104-A No:10
      Page(s):
    1425-1429

    Linear programming (LP) decoding based on the alternating direction method of multipliers (ADMM) has proved to be effective for low-density parity-check (LDPC) codes. However, for high-density parity-check (HDPC) codes, the ADMM-LP decoder encounters two problems, namely a high-density check matrix in HDPC codes and a great number of pseudocodewords in HDPC codes' fundamental polytope. The former problem makes the check polytope projection extremely complex, and the latter one leads to poor frame error rates (FER) performance. To address these issues, we introduce the even vertex algorithm (EVA) into the ADMM-LP decoding algorithm for HDPC codes, named as HDPC-EVA. HDPC-EVA can reduce the complexity of the projection process and improve the FER performance. We further enhance the proposed decoder by the automorphism groups of codes, creating diversity in the parity-check matrix. The simulation results show that the proposed decoder is capable of cutting down the average decoding time for each iteration by 30%-60%, as well as achieving near maximum likelihood (ML) performance on some BCH codes.