论文标题

用深CNN学习星座图,以进行准确的调制识别

Learning Constellation Map with Deep CNN for Accurate Modulation Recognition

论文作者

Doan, Van-Sang, Huynh-The, Thien, Hua, Cam-Hao, Pham, Quoc-Viet, Kim, Dong-Seong

论文摘要

调制分类被认为是信号检测和解调之间的中间步骤,已广泛部署在几个现代的无线通信系统中。尽管过去几十年来已经研究了许多方法来确定传入信号的调制格式,但它们经常揭示大多数传统机器学习算法学习无线电特征的障碍。为了克服这一缺点,我们通过利用与星座图兼容的深度学习来提出一种准确的调制分类方法。特别是,开发了一个卷积神经网络,以熟练地学习灰度星座图像的最相关的无线电特征。深网是由多个处理块指定的,其中每个块中的几个分组和不对称的卷积层通过流动流中的结构来组织特征富集。这些块通过跳过连接连接,以防止消失的梯度问题,同时有效地保留整个网络中的信息。关于八个数字调制的星座图像数据集进行了几次密集的模拟,提议的深网络在多路径雷利褪色通道下,在0 dB信噪比(SNR)时达到了大约87%的分类准确性,并进一步超过了一些基于基于群集的调制分类的深层模型。

Modulation classification, recognized as the intermediate step between signal detection and demodulation, is widely deployed in several modern wireless communication systems. Although many approaches have been studied in the last decades for identifying the modulation format of an incoming signal, they often reveal the obstacle of learning radio characteristics for most traditional machine learning algorithms. To overcome this drawback, we propose an accurate modulation classification method by exploiting deep learning for being compatible with constellation diagram. Particularly, a convolutional neural network is developed for proficiently learning the most relevant radio characteristics of gray-scale constellation image. The deep network is specified by multiple processing blocks, where several grouped and asymmetric convolutional layers in each block are organized by a flow-in-flow structure for feature enrichment. These blocks are connected via skip-connection to prevent the vanishing gradient problem while effectively preserving the information identify throughout the network. Regarding several intensive simulations on the constellation image dataset of eight digital modulations, the proposed deep network achieves the remarkable classification accuracy of approximately 87% at 0 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel and further outperforms some state-of-the-art deep models of constellation-based modulation classification.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源