论文标题
思维：最大基于信息的神经解码器
MIND: Maximum Mutual Information Based Neural Decoder
论文作者
论文摘要
我们正在协助对学习架构的发展，并将其应用于数字通信系统。本文中，我们考虑检测/解码问题。我们旨在为这项任务开发最佳的神经体系结构。最佳标准的定义是一个基本步骤。我们建议使用通道输入输入信号对的共同信息（MI），从而使传输密码字的A-posteriori信息的最小化鉴于通信通道输出观察。 A-tosteriori信息的计算是一项艰巨的任务，对于大多数渠道而言，它尚不清楚。因此，必须学会。对于这样一个目标，我们提出了一个基于判别公式的新型神经估计量。这导致了共同信息神经解码器（思维）的推导。开发的神经体系结构不仅能够解决未知通道中的解码问题，还可以返回对编码方案获得的平均MI的估计以及解码误差概率。报告了几个数值结果，并将其与最大的A型和最大似然解码策略进行了比较。
We are assisting at a growing interest in the development of learning architectures with application to digital communication systems. Herein, we consider the detection/decoding problem. We aim at developing an optimal neural architecture for such a task. The definition of the optimal criterion is a fundamental step. We propose to use the mutual information (MI) of the channel input-output signal pair, which yields to the minimization of the a-posteriori information of the transmitted codeword given the communication channel output observation. The computation of the a-posteriori information is a formidable task, and for the majority of channels it is unknown. Therefore, it has to be learned. For such an objective, we propose a novel neural estimator based on a discriminative formulation. This leads to the derivation of the mutual information neural decoder (MIND). The developed neural architecture is capable not only to solve the decoding problem in unknown channels, but also to return an estimate of the average MI achieved with the coding scheme, as well as the decoding error probability. Several numerical results are reported and compared with maximum a-posteriori and maximum likelihood decoding strategies.
