In this paper, we study the tradeoffs between complexity and reliability for decoding large linear block codes. We show that using artificial neural networks to predict the required order of an ordered statistics based decoder helps in reducing the average complexity and hence the latency of the decoder. We numerically validate the approach through Monte Carlo simulations.

翻译：在本文中,我们研究了解码大型线性区块代码的复杂程度和可靠性之间的权衡。我们表明,使用人工神经网络来预测一个基于解码器的定序统计数据所需的顺序有助于降低平均复杂性,从而降低解码器的潜伏度。我们通过蒙特卡洛模拟对方法进行数字验证。