Abstract
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.
Highlights
In today’s increasingly crowded wireless spectrum environment, cognitive radio (CR) networks are considered a promising technology to mitigate the contradiction between fixed spectrum allocation and efficient utilization, which has received sustained attention in recent years [1,2]
We can see that for Riemannian distance detector (RDD), Riemannian mean (RM) estimation outperforms the arithmetic mean (AM)
We propose a novel wideband spectrum sensing (WSS) detector based on Riemannian distance for multi-antenna
Summary
In today’s increasingly crowded wireless spectrum environment, cognitive radio (CR) networks are considered a promising technology to mitigate the contradiction between fixed spectrum allocation and efficient utilization, which has received sustained attention in recent years [1,2]. In [13], the wideband spectrum sensing (WSS) problem is partitioned into four basic elements to study, namely, system modeling, performance metrics, sampling schemes and detection algorithms. As a promising cutting-edge discipline, information geometry studies the problems in the field of statistics and information science by applying modern differential geometry method on Riemannian manifolds It has been widely used in machine learning, medical imaging, radar signal processing, signal classification and other research areas [15,16,17,18,19,20]. Inspired by these studies, we consider using the information geometry theory to design the spectrum sensing method, rather than the normed linear space theory.
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