论文标题
动量梯度下降碎片解码
Gradient Descent Bit-Flipping Decoding with Momentum
论文作者
论文摘要
在本文中，我们提出了一种动量解码的梯度下降位点（GDBF），它考虑了过去的更新以提供解码过程的惯性。我们表明，具有动量的GDBF或随机GDBF解码器可能会紧密接近浮点信念传播的解码性能，甚至在误差层区域中的表现都超越了它，尤其是对于具有较高连接度的图形。
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.
