Spectrum Sensing In Cognitive Radio Based On Eigenvalue-based Detector And Consecutive Detection Mechanism

Driven by explosive growth of mobile data traffic and massive device connectivity,next generation wireless communication systems need to make full utilization of the limited spectrum resources with high spectral efficiency,energy efficiency and cost efficiency.Cognitive radio is viewed as a promising solution to the spectrum scarcity problem,and spectrum sensing is one of the key technologies to ensure its practical application.In this thesis,eigenvalue-based cooperative spectrum sensing(CSS)algorithms and consecutive spectrum detection mechanism are studied respectively,with the following two aspects addressed in detail:1.In the centralized CSS model,various eigenvalue-based CSS algorithms are investigated.In order to achieve better sensing performance,a simulation platform is built to compare various CSS algorithms and analyze their theoretical basis,advantages and disadvantages.The eigenvalue-based CSS algorithms are not only superior to the energy detector in terms of reliability,but also easy to implement as the blind detectors.However,most of the existing CSS algorithms only use the maximum eigenvalue of the covariance matrix of the received signal to form the global test statistic,which discards the remaining eigenvalues and does not fully extract all the information hidden in the received signal.At the same time,the existing algorithms are mostly based on the first-order eigenvalues and do not fully exploit the information buried in the received signal.In order to solve the problems,higher moments of the eigenvalues are introduced to enhance the existing CSS algorithms.Higher moments of the eigenvalues can be used to expand the gap between the global test statistics under different hypotheses and excavate the hidden information in the received signal.Therefore,a CSS algorithm based on high-order moments of eigenvalues is proposed.Simulation results show that the proposed algorithm can effectively improve the spectrum sensing performance.2.For the consecutive spectrum detection mechanism developed in this thesis,it is expected to:(1)propose a consecutive spectrum detection mechanism;(2)improve the sensing agility of the primary user's(PU's)behavior;and(3)reduce computational implementation complexity.First,a sliding window is designed,where the secondary users(SU)observe the licensed spectrum with an assemblage of multiple time slots of the received signals,and then consecutive spectrum detection is carried out by estimating the cardinality of the signal assemblage.The sensing agility of the mechanism is closely related to the width and the number of the time slots in the sliding window.Based on the existing criteria,signal sample power-based approaches are proposed,demanding no computational complexity caused by the eigenvalue decomposition of the covariance matrix.At the same time,approaches based on signal contiguity and combinations of different algorithms are proposed to relieve the high ratio of false alarm or missed detection when the algorithms are utilized on themselves only.These approaches can also reduce the error of estimation and achieve the quick response to PU signal.Finally,a consecutive spectrum detection algorithm based on signal assemblage mean value iteration is proposed,which does not require eigenvalue decomposition of the covariance matrix,and the computational complexity is therefore remarkably low.Simulation results show that the proposed mechanism achieves agile response under a certain SINR.