Signal recognition is one of the significant and challenging tasks in the signal processing and communications field. It is often a common situation that there's no training data accessible for some signal classes to perform a recognition task. Hence, as widely-used in image processing field, zero-shot learning (ZSL) is also very important for signal recognition. Unfortunately, ZSL regarding this field has hardly been studied due to inexplicable signal semantics. This paper proposes a ZSL framework, signal recognition and reconstruction convolutional neural networks (SR2CNN), to address relevant problems in this situation. The key idea behind SR2CNN is to learn the representation of signal semantic feature space by introducing a proper combination of cross entropy loss, center loss and reconstruction loss, as well as adopting a suitable distance metric space such that semantic features have greater minimal inter-class distance than maximal intra-class distance. The proposed SR2CNN can discriminate signals even if no training data is available for some signal class. Moreover, SR2CNN can gradually improve itself in the aid of signal detection, because of constantly refined class center vectors in semantic feature space. These merits are all verified by extensive experiments with ablation studies.

中文翻译：

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SR2CNN：用于信号识别的零散学习

信号识别是信号处理和通信领域中一项重大且具有挑战性的任务之一。通常情况是，某些信号类别没有可访问的训练数据来执行识别任务。因此，如在图像处理领域中广泛使用的，零散学习（ZSL）对于信号识别也非常重要。不幸的是，由于无法解释的信号语义，关于这一领域的ZSL几乎没有被研究过。本文提出了一种ZSL框架，信号识别和重建卷积神经网络（SR2CNN），以解决这种情况下的相关问题。SR2CNN背后的关键思想是通过引入交叉熵损失，中心损失和重构损失的适当组合来学习信号语义特征空间的表示，以及采用适当的距离度量空间，以使语义特征具有比最大类内距离更大的最小类间距离。即使没有训练数据可用于某些信号类别，所提出的SR2CNN也可以区分信号。此外，由于语义特征空间中的类中心向量不断细化，因此SR2CNN可以借助信号检测逐渐改善自身。这些优点均通过大量的消融实验得到了验证。因为语义特征空间中的类中心向量不断完善。这些优点均通过大量的消融实验得到了验证。因为语义特征空间中的类中心向量不断完善。这些优点均通过大量的消融实验得到了验证。