Modulation recognition algorithm based on mixed attention prototype network
Q3 Engineering
引用次数: 0
Abstract
针对极少量带标签样本条件下的通信信号调制识别难题, 提出一种基于混合注意力原型网络的调制识别算法。综合元学习和度量学习的思想, 在原型网络框架下通过特征提取模块将信号映射至一个新的特征度量空间, 并通过比较该空间内各类原型与查询信号之间的距离确定查询信号调制样式。根据通信信号IQ分量的时序特点设计了由卷积神经网络和长短时记忆网络级联的特征提取模块, 并引入卷积注意力机制提升关键特征的权重; 采用基于Episode的训练策略, 使算法可泛化到新的信号识别任务中。仿真结果表明, 所提算法在每类信号只有5个带标签样本(5-way 5-shot)时平均识别率可达85.68%。基于混合注意力原型网络的调制识别算法
A modulation recognition algorithm based on a hybrid attention prototype network is proposed to address the modulation recognition problem of communication signals with very few labeled samples. Integrating the ideas of meta learning and metric learning, the signal is mapped to a new feature metric space through a feature extraction module within the prototype network framework, and the modulation style of the query signal is determined by comparing the distances between various prototypes and the query signal in this space. A feature extraction module was designed based on the temporal characteristics of the communication signal IQ component, which is cascaded by a convolutional neural network and a long and short term memory network. The convolutional attention mechanism was introduced to enhance the weight of key features; Adopting an Episode based training strategy, the algorithm can be generalized to new signal recognition tasks. The simulation results show that the proposed algorithm has an average recognition rate of 85.68% when there are only 5 labeled samples (5-way 5-shot) for each type of signal.
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