Yang Kai, Huang Zhitao, Wang Xiang, Li Xueqiong
State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China.
Sensors (Basel). 2019 May 16;19(10):2270. doi: 10.3390/s19102270.
Spectrum sensing is one of the technologies that is used to solve the current problem of low utilization of spectrum resources. However, when the signal-to-noise ratio is low, current spectrum sensing methods cannot well-handle a situation in which the prior information of the licensed user signal is lacking. In this paper, a blind spectrum sensing method based on deep learning is proposed that uses three kinds of neural networks together, namely convolutional neural networks, long short-term memory, and fully connected neural networks. Experiments show that the proposed method has better performance than an energy detector, especially when the signal-to-noise ratio is low. At the same time, this paper also analyzes the effect of different long short-term memory layers on detection performance, and explores why the deep-learning-based detector can achieve better performance.
频谱感知是用于解决当前频谱资源利用率低问题的技术之一。然而，当信噪比很低时，当前的频谱感知方法无法很好地处理授权用户信号先验信息缺失的情况。本文提出了一种基于深度学习的盲频谱感知方法，该方法同时使用了三种神经网络，即卷积神经网络、长短期记忆网络和全连接神经网络。实验表明，所提出的方法比能量检测器具有更好的性能，尤其是在信噪比很低时。同时，本文还分析了不同长短期记忆层对检测性能的影响，并探究了基于深度学习的检测器能够实现更好性能的原因。
