论文标题
通道编码的神经相互信息估计：最新的估计器，分析和性能比较
Neural Mutual Information Estimation for Channel Coding: State-of-the-Art Estimators, Analysis, and Performance Comparison
论文作者
论文摘要
最近，基于深度学习的物理层设计，即使用密集的神经网络作为编码器和解码器，最近引起了极大的兴趣。但是，尽管这种方法是自然训练数据驱动的，但使用标准渠道模型模仿无线通道的动作，这仅部分反映了物理基础真相。最近，已经提出了基于神经网络的相互信息（MI）估计器，该估计量直接从输入输出测量中提取信道操作，并将这些输出馈入通道编码器。这是一个有希望的方向，因为新的设计范式是完全自适应和基于数据的。本文实施了此类MI估计器的最新改进，理论上分析了它们对渠道编码问题的适用性，并比较了其性能。为此，提出了使用\ emph {```````reverse Jensen）''''}''''的新的MI估计器了。
Deep learning based physical layer design, i.e., using dense neural networks as encoders and decoders, has received considerable interest recently. However, while such an approach is naturally training data-driven, actions of the wireless channel are mimicked using standard channel models, which only partially reflect the physical ground truth. Very recently, neural network based mutual information (MI) estimators have been proposed that directly extract channel actions from the input-output measurements and feed these outputs into the channel encoder. This is a promising direction as such a new design paradigm is fully adaptive and training data-based. This paper implements further recent improvements of such MI estimators, analyzes theoretically their suitability for the channel coding problem, and compares their performance. To this end, a new MI estimator using a \emph{``reverse Jensen''} approach is proposed.
