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IEICE Technical Report; IEICE Tech. Rep.

バースト誤り通信路に適する復号順序制御を行う Sum-Product 復号法の特性評価

雅人 矢部

Published Jan 1, 2004• 0 citations
OpenAlex

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復号順序制御を行う Sum-Product 復号

吉広 栗原

•Jan 1, 2003•0 citations
In this paper we present Low Density Parity Check decoding algorithm that assemble two different algorithms: Sum-Product and Bit-Flipping; we denote Bit Flipping-Sum Product Algorithm (BFSP). To reduce the bit error rate, we perform the Bit-Flipping algorithm after the Sum-Product algorithm. Simulation results over an additive white Gaussian channel show that the error performance of a LDPC codes with Bit Flipping-Sum Product decoding is within 0.2 dB of the standard Sum-Product decoding algorithms. Furthermore, the decoding complexity of the proposed BFSP algorithm is maintained at the same level as that of the standard Sum-Product algorithm.
This paper introduces a new synthesis approach called Products of EXOR-sums (POE) and demonstrates its utility when employed in an Exclusive-Or-Sum-of-Products (ESOP) based algorithm for reversible and permutative quantum circuits restricted to single output functions. Unlike previous ESOP approaches such as EXORCISM-4 which realize functions as an EXOR-ed sum of product literals, this new algorithm realizes functions as an EXOR-ed sum of POEs (EPOE). A comparison of EXORCISM-4 and EPOE circuits shows that the latter approach often produces circuits with significantly lower quantum costs.
We propose hardware-aware sum-product (SP) decoding for low-density parity-check codes. To simplify an implementation using a fixed-point number representation, we transform SP decoding in the logarithm domain to that in the decision domain. A polynomial approximation is proposed to implement an update rule of the proposed SP decoding efficiently. Numerical simulations show that the approximate SP decoding achieves almost the same performance as the exact SP decoding when an appropriate degree in the polynomial approximation is used, that it improves the convergence properties of SP and normalized min-sum decoding in the high signal-to-noise ratio regime, and that it is robust against quantization errors.