In this paper, we propose a Gradient Descent Bit-Flipping (GDBF) decoding with momentum, which considers past updates to provide inertia to the decoding process. We show that GDBF or randomized GDBF decoders with momentum may closely approach the floating-point Belief-Propagation decoding performance, and even outperform it in the error-floor region, especially for graphs with high connectivity degree.

翻译：在本文中,我们建议采用渐渐潜伏点位解码(GDBF ), 以动向解码(GDBF ), 认为过去更新为解码过程提供了惰性。 我们表明GDBF或随机化GDBF解码器的动向可能会接近浮点信仰-促进解码性能,甚至会超过误差层区域,特别是连接度高的图表。