Deep CM-CNN for Spectrum Sensing in Cognitive Radio
Deep CM-CNN for Spectrum Sensing in Cognitive Radio
复制标题
用于认知无线电频谱感知的深度 CM-CNN
DOI：
10.1109/jsac.2019.2933892
复制
发表时间：
2019-10-01
影响因子：
16.4
通讯作者：
Liang, Ying-Chang
中科院分区：
文献类型：
--
作者：
Liu, Chang;Wang, Jie;Liang, Ying-Chang
频谱感知的关键问题之一是检验统计量的设计。现有的方法一般利用基于模型的特征作为检验统计量，如能量和特征值。然而，这些特征不能准确地表征真实的环境。受此启发，在本文中，我们使用深度神经网络（DNN）来智能地探索数据驱动的测试统计量。首先，我们介绍了一个基于DNN的检测框架，其中基于DNN的似然比测试（DNN-LRT）的推导，以保证所设计的测试统计量的最优性。作为所开发的基于DNN的框架的实现，我们使用样本协方差矩阵作为卷积神经网络（CNN）的输入，并提出了一种基于协方差矩阵感知CNN（CM-CNN）的频谱感知算法，进一步提高了性能。此外，我们还提供了所提出的方法的理论分析。据我们所知，这是第一次分析基于CNN的方法的理论性能。仿真结果表明，该方法的性能接近于最优检测器。在信噪比为-18dB的情况下，该方法的检测概率为96.7%，虚警概率为1.9%，明显优于传统方法。
One of the key problems in spectrum sensing is to design the test statistic. Existing methods generally exploit the model-based features as the test statistic, such as energies and eigenvalues. However, these features could not accurately characterize the real environment. Motivated by this, in this paper, we use a deep neural network (DNN) to intelligently explore the data-driven test statistic. Firstly, we introduce a DNN-based detection framework, where a DNN-based likelihood ratio test (DNN-LRT) is derived to guarantee the optimality of the designed test statistic. As a realization of the developed DNN-based framework, we use the sample covariance matrix as the input of a convolutional neural network (CNN), and propose a covariance matrix-aware CNN (CM-CNN)-based spectrum sensing algorithm, which further improves the performance. In addition, we also provide the theoretical analysis of the proposed method. To the best of our knowledge, it's the first time to analyze the theoretical performance of CNN-based methods. Finally, simulation results demonstrate that the performance of the proposed method is close to that of the optimal detector. Particularly, the proposed method could achieve a detection probability of 96.7% with a false alarm probability of 1.9% at SNR = -18dB, which significantly outperforms the conventional methods.