基于元学习的小样本调制识别算法

针对基于深度学习的调制识别算法对带标签样本需求量过大的问题，采用基于元学习思想的多任务训练策略，通过大量不同的任务训练网络来获取一种跨任务的信号识别能力，使得网络在面对新信号类别时仅需少量样本就能快速适应。为更全面地提取信号样本的特征，设计了一种由卷积神经网络和长短时记忆网络并联组成的混合特征并行网络，通过度量样本特征向量间距离的方式完成识别任务；并引入可同时考虑信号类内与类间距离的联合损失函数，以使信号样本特征在度量空间内的分布能更加紧凑，从而实现更高效的相似性比对。实验结果表明，该算法在仅有5个带标签信号样本条件下最高可达到88.43%的识别准确率。

A Few Shot Modulation Recognition Algorithm Based on Meta Learning

Aimed at the problem that the demands of the modulation recognition algorithm based on deep learning for the labeled samples are too heavy, a multi task training strategy based on meta learning is adopted. This strategy obtains a ability of cross task signal recognition through a large number of different task training networks to make the network quickly adapt to new signal categories with a small number of samples. In order to extract the features from signal samples more comprehensively, a hybrid parallel feature extraction network is designed, completing the recognition task by measuring the distance between sample feature vectors. And a joint loss function is introduced to take both inter class and intra class distance into account, making the samples realize comparison more efficiently after feature extraction. The experimental results show that the algorithm can achieve a highest recognition accuracy of 88.43% when there are only five labeled signal samples.