Wideband Spectrum Sensing Based on Riemannian Distance for Cognitive Radio Networks.
Detecting the signals of the primary users in the wideband spectrum is a key issue for cognitive radio networks. In this paper, we consider the multi-antenna based signal detection in a wideband spectrum scenario where the noise statistical characteristics are assumed to be unknown. We reason that the covariance matrices of the spectrum subbands have structural constraints and that they describe a manifold in the signal space. Thus, we propose a novel signal detection algorithm based on Riemannian distance and Riemannian mean which is different from the traditional eigenvalue-based detector (EBD) derived with the generalized likelihood ratio criterion. Using the moment matching method, we obtain the closed expression of the decision threshold. From the considered simulation settings, it is shown that the proposed Riemannian distance detector (RDD) has a better performance than the traditional EBD in wideband spectrum sensing.
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China. luqiuyuan@bit.edu.cn.
Qiuyuan
Q
Lu
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China. adrien_yang@bit.edu.cn.
Shengzhi
S
Yang
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China. liufan92@bit.edu.cn.
Fan
F
Liu
Riemannian distance; Riemannian mean; cognitive radio; information geometry; moment matching; wideband spectrum sensing