Du Kaixuan, Wan Pin, Wang Yonghua, Ai Xiongzhi, Chen Huang
School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
Hubei Key Laboratory of Intelligent Wireless Communications, South-Central University for Nationalities, Wuhan 430074, China.
Entropy (Basel). 2020 Jan 12;22(1):94. doi: 10.3390/e22010094.
Due to the scarcity of radio spectrum resources and the growing demand, the use of spectrum sensing technology to improve the utilization of spectrum resources has become a hot research topic. In order to improve the utilization of spectrum resources, this paper proposes a spectrum sensing method that combines information geometry and deep learning. Firstly, the covariance matrix of the sensing signal is projected onto the statistical manifold. Each sensing signal can be regarded as a point on the manifold. Then, the geodesic distance between the signals is perceived as its statistical characteristics. Finally, deep neural network is used to classify the dataset composed of the geodesic distance. Simulation experiments show that the proposed spectrum sensing method based on deep neural network and information geometry has better performance in terms of sensing precision.
由于无线电频谱资源的稀缺性以及需求的不断增长，利用频谱感知技术来提高频谱资源的利用率已成为一个热门的研究课题。为了提高频谱资源的利用率，本文提出了一种将信息几何与深度学习相结合的频谱感知方法。首先，将感知信号的协方差矩阵投影到统计流形上。每个感知信号都可以被视为流形上的一个点。然后，将信号之间的测地距离视为其统计特征。最后，使用深度神经网络对由测地距离组成的数据集进行分类。仿真实验表明，所提出的基于深度神经网络和信息几何的频谱感知方法在感知精度方面具有更好的性能。
