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대한전자공학회 학술대회

Node Monitoring Algorithm with Piecewise Linear Function Approximation for Efficient LDPC Decoding

오빈빈, 진호, 서희종

Published Nov 1, 2010• 0 citations
OpenAlex

Abstract

We propose an efficient algorithm for reducing the complexity of LDPC code decoding by using node monitoring (NM) and Piecewise Linear Function Approximation (NP). This algorithm was simulated in order to verify its efficiency. Simulation results show that the complexity of our NM algorithm is improved to about 20%, compared with well-known methods.

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Node Monitoring Algorithm with Piecewise Linear Function Approximation for Efficient LDPC Decoding

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IEEE Transactions on Communications•Jul 1, 2005•27 citations
We propose an efficient algorithm for reducing the complexity of LDPC code decoding by using node monitoring (NM) and Piecewise Linear Function Approximation (NP). This algorithm was simulated in order to verify its efficiency. Simulation results show that the complexity of our NM algorithm is improved to about 20%, compared with well-known methods.
In this paper, we propose an efficient algorithm for reducing the complexity of LDPC code decoding by using node monitoring (NM) and Piecewise Linear Function Approximation (NP). This NM algorithm is based on a new node-threshold method, and the message passing algorithm. Piecewise linear function approximation is used to reduce the complexity for more. This algorithm was simulated in order to verify its efficiency. Simulation results show that the complexity of our NM algorithm is reduced to about 20%, compared with thoes of well-known method.
본 논문에서는 노드 모니터링(NM)과 Piecewise Linear Function Approximation(: NP)을 사용하여 LDPC 디코딩 알고리듬의 계산복잡도를 감소시키는 알고리듬을 제안한다. 이 알고리듬은 기존의 알고리듬보다도 더 효율적이다. 제안된 알고리즘이 기존의 방법보다도 개선되었다는 것을 확인하기 위해서 모의실험을 하였다. 실험결과, 제안된 알고리즘의 계산은 기존의 방법에 비해 약 20 % 향상되었음을 확인하였다. In this paper, we proposed an efficient algorithm using Node monitoring (NM) and Piecewise Linear Function Approximation(: NP) for reducing the complexity of LDPC code decoding. Proposed NM algorithm is based on a new node-threshold method together with message passing algorithm. Piecewise linear function approximation is used to reduce the complexity of the algorithm. This new algorithm was simulated in order to verify its efficiency. Complexity of our new NM algorithm is improved to about 20% compared with well-known methods according to simulation results.
This paper proposes the simplified sum-product (SSP) decoding algorithm to improve BER performance for low-density parity-check codes (LDPC). The sum-product algorithm achieves the highest BER performance among the LDPC decoding algorithms. However, based on the hyperbolic tangent and inverse hyperbolic tangent functions for the check node updates, the sum-product (SP) algorithm requires high computational complexity. To reduce computational complexity, the proposed SSP algorithm adopts a piecewise linear approximation, the logarithmic and exponential functions, which can replace multiplications and divisions with additions and subtractions. In addition, the proposed SSP algorithm can simplify both the ln[tanh(x)] and tanh-1[exp(x)] by using two quantization tables which can reduce tremendous computational complexity. Simulation results show that the proposed SSP algorithm can improve about 0.8 dB of bit error ratio (BER) performance compared with the modified sum-product algorithm.
In this paper, an algorithm for low-density parity-check (LDPC) codes with reduced complexity is presented. The complexity reduction is achieved by calculating a linear function for updating the check nodes in each iteration, instead of an exponential and logarithmic function. The parameters of the linear function are optimized by density evolution. Simulation results show that there is nearly no loss in the performance by using this approximation compared to the exact calculation of the belief propagation decoding algorithm, even for very large block lengths.
Reduced-Complexity Decoding of LDPC Codes Various log-likelihood-ratio-based belief-propagation (LLR- BP) decoding algorithms and their reduced-complexity derivatives for low-density parity-check (LDPC) codes are presented. Numerically accurate representations of the check-node update computation used in LLR-BP decoding are described. Furthermore, approximate representation of the decoding computations are shown to achieve a reduction in complexity, by simplifying the check-node update or symbol-node update, or both. In particular, two main approaches for simplified check-node updates are presented that are based on the so-called min-sum approximation coupled with either a normalization term or an additive offset term. Density evolution is used to analyze the performance of these decoding algorithms, to determine the optimum values of the key parameters, and to evaluate finite quantization effects. Simulation results show that these reduced-complexity decoding algorithms for LDPC codes achieve a performance very close to that of the BP algorithm. The unified treatment of decoding techniques for LDPC codes presented here provides flexibility in selecting the appropriate scheme from a performance, latency, computational complexity, and memory-requirement perspective.