论文标题
具有多个接收天线的深度调制识别：一种端到端的功能学习方法
Deep Modulation Recognition with Multiple Receive Antennas: An End-to-end Feature Learning Approach
论文作者
论文摘要
使用深神经网络的调节识别表现出与常规算法相比的希望。但是，大多数现有的研究都集中在单个接收天线上。在本文中，引入了两个端到端特征学习深度体系结构，以进行多个接收天线的调制识别。第一个基于多视图卷积神经网络，通过将来自不同天线的信号视为3D对象的不同视图，并设计适用于多特-Antenna信号的特征融合的视图池的位置和操作。考虑到无线通信中接收天线的瞬时SNR可能会有所不同，我们进一步提出了权重学习卷积神经网络，该卷积神经网络使用权重学习模块自动学习用于梳理不同接收天线的特征的权重以执行多端纳纳信号的端到端特征学习。结果表明，端到端特征学习深度体系结构的表现优于现有算法，而拟议的权重学习卷积神经网络则取得了最佳性能。
Modulation recognition using deep neural networks has shown promising advantages over conventional algorithms. However, most existing research focuses on single receive antenna. In this paper, two end-to-end feature learning deep architectures are introduced for modulation recognition with multiple receive antennas. The first is based on multi-view convolutional neural network by treating signals from different receive antennas as different views of a 3D object and designing the location and operation of view-pooling layer that are suitable for feature fusion of multi-antenna signals. Considering that the instantaneous SNRs could be different among receive antennas in wireless communications, we further propose weight-learning convolutional neural network which uses a weight-learning module to automatically learn the weights for feature combing of different receive antennas to perform end-to-end feature learning of multi-antenna signals. Results show that both end-to-end feature learning deep architectures outperform the existing algorithm, and the proposed weight-learning convolutional neural network achieves the best performance.
