Spatial Spectrum Sensing-Based Device-to-Device Cellular Networks

Abstract

Ultra-densification is one of the main features of 5G networks. In an ultra-dense network, how to conduct interference management and spectrum allocation is a challenging issue. Spectrum sensing in cognitive radio networks is a distributed and efficient way to resolve this issue in ultra-dense networks. However, most of the studies on spectrum sensing only focus on sensing temporal spectrum opportunities where one or multiple primary users are active, which does not make full use of spectrum opportunities in the spatial location domain. To overcome the shortcomings of conventional temporal spectrum sensing, we study the problem of spatial spectrum sensing, which senses spatial spectrum opportunities in wireless networks. In this paper, the performance of spatial spectrum sensing and its application in sensing-based device-to-device (D2D) cellular networks are analyzed using stochastic geometry. Specifically, by modeling the locations of active transmitters as a Poisson point process, the spatial spectrum sensing problem is formulated using the framework of a detection theory. Closed-form expressions are obtained for the sensing threshold, probabilities of spatial detection, and false alarm. Furthermore, analytical throughput for D2D users and cellular users under both channel inversion and constant power allocation cases are derived. The optimal sensing radius that maximizes the defined network metric is obtained numerically. Finally, the simulation and numerical results are presented to verify our theoretical analysis.