We introduce learned attention models into the radio machine learning domain
for the task of modulation recognition by leveraging spatial transformer
networks and introducing new radio domain appropriate transformations. This
attention model allows the network to learn a localization network capable of
synchronizing and normalizing a radio signal blindly with zero knowledge of the
signals structure based on optimization of the network for classification
accuracy, sparse representation, and regularization. Using this architecture we
are able to outperform our prior results in accuracy vs signal to noise ratio
against an identical system without attention, however we believe such an
attention model has implication far beyond the task of modulation recognition.

我们使用学习到的注意模型和适用于无线电领域的空间变换引入新的适当变换，通过优化分类精度、稀疏表示和正则化来学习一个定位网络，能够在零知识下对无线电信号进行盲同步和标准化，从而在信噪比方面胜过不带注意力机制的相同系统。