In this paper, we introduce a neural-augmented decoder for Turbo codes called TINYTURBO . TINYTURBO has complexity comparable to the classical max-log-MAP algorithm but has much better reliability than the max-log-MAP baseline and performs close to the MAP algorithm. We show that TINYTURBO exhibits strong robustness on a variety of practical channels of interest, such as EPA and EVA channels, which are included in the LTE standards. We also show that TINYTURBO strongly generalizes across different rate, blocklengths, and trellises. We verify the reliability and efficiency of TINYTURBO via over-the-air experiments.

翻译：在本文中,我们为涡轮代码引入了一个神经强化解码器,称为 TINYTURBO 。 TINYTURBO 的复杂程度与古典最大MAP算法相近,但比最大log-MAP 基线可靠得多,而且运行过程接近MAP 算法。 我们显示, TINYTURBO 在各种实际感兴趣的渠道,如EPA 和 EVA 频道上表现出很强的稳健性, 这些渠道都包含在LTE 标准中。 我们还显示, TINYTURBO 在不同的速度、 区长和 三角形之间非常笼统。 我们通过超空实验来验证 TINYTURB 的可靠性和效率 。