• Title/Summary/Keyword: soft-decision list decoder

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Efficient VLSI Architecture for Factorization in Soft-Decision Reed-Solomon List Decoding (연판정 Reed-Solomon 리스트 디코딩의 Factorization을 위한 효율적인 VLSI 구조)

  • Lee, Sung-Man;Park, Tae-Guen
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.47 no.11
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    • pp.54-64
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    • 2010
  • Reed-Solomon (RS) codes are the most widely used error correcting codes in digital communications and data storage. Recently, Sudan found algorithm of list decoder for RS codes. List decoder has larger decoding radius than conventional hard-decision decoding algorithms and return more than one candidate polynomial. But, the algorithm includes interpolation and factorization step that demand massive computations. In this paper, an efficient architecture and processing schedule are proposed. The architecture consists of R-MAC, memories, and control unit. The R-MAC computes both of RC and PU steps that are main part of the factorization algorithm. The proposed architecture can achieve higher hardware utilization efficiency (HUE) and throughput by using efficient processing schedule and memory architecture. Also, the architecture can be designed flexibly with scalability for various applications. We design and synthesize our architecture using Dongbu-Anam $0.18{\mu}m$ standard cell library and the maximum clock frequency is 330MHz.

Area-efficient Interpolation Architecture for Soft-Decision List Decoding of Reed-Solomon Codes (연판정 Reed-Solomon 리스트 디코딩을 위한 저복잡도 Interpolation 구조)

  • Lee, Sungman;Park, Taegeun
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.3
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    • pp.59-67
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    • 2013
  • Reed-Solomon (RS) codes are powerful error-correcting codes used in diverse applications. Recently, algebraic soft-decision decoding algorithm for RS codes that can correct the errors beyond the error correcting bound has been proposed. The algorithm requires very intensive computations for interpolation, therefore an efficient VLSI architecture, which is realizable in hardware with a moderate hardware complexity, is mandatory for various applications. In this paper, we propose an efficient architecture with low hardware complexity for interpolation in soft-decision list decoding of Reed-Solomon codes. The proposed architecture processes the candidate polynomial in such a way that the terms of X degrees are processed in serial and the terms of Y degrees are processed in parallel. The processing order of candidate polynomials adaptively changes to increase the efficiency of memory access for coefficients; this minimizes the internal registers and the number of memory accesses and simplifies the memory structure by combining and storing data in memory. Also, the proposed architecture shows high hardware efficiency, since each module is balanced in terms of latency and the modules are maximally overlapped in schedule. The proposed interpolation architecture for the (255, 239) RS list decoder is designed and synthesized using the DongbuHitek $0.18{\mu}m$ standard cell library, the number of gate counts is 25.1K and the maximum operating frequency is 200 MHz.

Syndrome Check aided Fast-SSCANL Decoding Algorithm for Polar Codes

  • Choangyang Liu;Wenjie Dai;Rui Guo
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.18 no.5
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    • pp.1412-1430
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    • 2024
  • The soft cancellation list (SCANL) decoding algorithm for polar codes runs L soft cancellation (SCAN) decoders with different decoding factor graphs. Although it can achieve better decoding performance than SCAN algorithm, it has high latency. In this paper, a fast simplified SCANL (Fast-SSCANL) algorithm that runs L independent Fast-SSCAN decoders is proposed. In Fast-SSCANL decoder, special nodes in each factor graph is identified, and corresponding low-latency decoding approaches for each special node is propose first. Then, syndrome check aided Fast-SSCANL (SC-Fast-SSCANL) algorithm is further put forward. The ordinary nodes satisfied the syndrome check will execute hard decision directly without traversing the factor graph, thereby reducing the decoding latency further. Simulation results show that Fast-SSCANL and SC-Fast-SSCANL algorithms can achieve the same BER performance as the SCANL algorithm with lower latency. Fast-SSCANL algorithm can reduce latency by more than 83% compared with SCANL, and SC-Fast-SSCANL algorithm can reduce more than 85% latency compared with SCANL regardless of code length and code rate.