计算机科学
调制(音乐)
卷积神经网络
人工智能
星座
机器学习
任务(项目管理)
深度学习
实施
星座图
信号(编程语言)
网络拓扑
代表(政治)
模式识别(心理学)
频道(广播)
电信
哲学
误码率
物理
操作系统
管理
天文
政治
政治学
法学
经济
程序设计语言
美学
作者
Shengliang Peng,Hanyu Jiang,Huaxia Wang,Hathal Alwageed,Yu Zhou,Marjan Mazrouei Sebdani,Yu-Dong Yao
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2019-03-01
卷期号:30 (3): 718-727
被引量:340
标识
DOI:10.1109/tnnls.2018.2850703
摘要
Deep learning (DL) is a new machine learning (ML) methodology that has found successful implementations in many application domains. However, its usage in communications systems has not been well explored. This paper investigates the use of the DL in modulation classification, which is a major task in many communications systems. The DL relies on a massive amount of data and, for research and applications, this can be easily available in communications systems. Furthermore, unlike the ML, the DL has the advantage of not requiring manual feature selections, which significantly reduces the task complexity in modulation classification. In this paper, we use two convolutional neural network (CNN)-based DL models, AlexNet and GoogLeNet. Specifically, we develop several methods to represent modulated signals in data formats with gridlike topologies for the CNN. The impacts of representation on classification performance are also analyzed. In addition, comparisons with traditional cumulant and ML-based algorithms are presented. Experimental results demonstrate the significant performance advantage and application feasibility of the DL-based approach for modulation classification.
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