摘要
在认知无线电网络中,高效且准确的频谱感知是必不可少的一个环节。针对传统机器学习算法在频谱感知训练慢的难题,提出一种基于卷积神经网络的正交频分复用(orthogonal frequency division multiplexing,OFDM)频谱感知方法,将深度学习在图像处理上的优势应用到OFDM信号频谱感知中。该方法首先分析OFDM信号的循环自相关和频谱感知模型,对循环自相关进行归一化灰度处理,形成循环自相关灰度图;然后以LeNet-5网络为基础设计卷积神经网络分层地对训练数据进行学习,提取出更加抽象的特征;最后将测试数据输入到训练好的卷积神经网络模型,完成频谱感知。仿真实验表明,该方法能够完成OFDM信号的频谱感知,在低信噪比条件下具有较高的检测概率。
Efficient and accurate spectrum sensing is a necessary part in cognitive radio networks.To solve the slow training problem of the traditional machine learning algorithm in spectrum sensing,an orthogonal frequency division multiplexing(OFDM)spectrum sensing method based on convolutional neural networks is proposed.The advantage of deep learning in image processing is applied to the spectrum sensing of OFDM signals.Firstly,the spectrum sensing model of OFDM signals and the cyclic autocorrelation are analyzed.The cyclic autocorrelation is normalized and transformed by the gray level.Then,the convolutional neural network is designed based on the LeNet-5network to learn the training data for more abstract features hierarchically.Finally,the testing data is input into the trained convolutional neural network model,and the spectrum sensing is completed.Simulation results show that this method can complete the spectrum sensing task of OFDM signals,and has a high detection probability under low signal-to-nose ratio.
作者
张孟伯
王伦文
冯彦卿
ZHANG Mengbo;WANG Lunwen;FENG Yanqing(Electronic Countermeasure Institute,National University of Defense Technology, Hefei 230037, China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2019年第1期178-186,共9页
Systems Engineering and Electronics
基金
国家自然科学基金(61273302
61671454)
国防科技创新特区项目(17-H863-01-ZT-003-204-03)资助课题
关键词
正交频分复用
频谱感知
循环自相关
卷积神经网络
orthogonal frequency division multiplexing (OFDM)
spectrum sensing
cyclic autocorrelation
convolutional neural network
