Research On Spectrum Sensing For Cyclic Spectrum Of Communication Signals Based On Compressive Sensing

In order to adapt to the rapid development of wireless communication technology and make full use of the valuable wireless spectrum resources to improve the spectrum utilization,cognitive radio is proposed.Cognitive radio can obtain the information of the surrounding spectrum environment through the sensing units,and then dynamically adjust the wireless parameters to access the free available frequency of the wireless communication,which improves the spectrum utilization.However,the spectrum detection technology is limited by the Nyquist sampling theorem.It is difficult to apply to the broadband spectrum sensing.In order to solve this problem,our paper introduces the compressive sensing theory.It can reconstruct the cyclic spectrum of signals from compressive samples obtained from low speed ADCs,and then ues the cyclic spectrum to detect spectrum based on cyclic feature detection technology.The main contents of this paper are divided into two parts shown as follows:1.Cyclic spectral compressive sensing and wide-band spectrum detection based on a single task: Based on the cyclostationary characteristics presented in the process of modulation,a single task cyclic spectral compressive sensing model based on a single cognitive user is established.A smoothed L0 norm algorithm is applied to reconstruct the cyclic spectrum of the signal from the samples obtained in the model,which is then used to detect the spectrum.The simulation results show that the spectrum detection based the reconstructed cyclic spectrum performs better than the energy detection,and has good robustness to the noise.2.Cyclic spectral compressive sensing and wide-band spectrum detection based on multi-task: a multi-task cyclic spectral compressive sensing algorithm based multi-cooperative users is proposed for the single task algorithm is sensitive to channels fading.Due to the prior distribution of each secondary users' cyclic spectrum sharing the same group of super parameters,we use multitask Bayes compressive sensing model to reconstruct the cyclic spectrum of signals.Then,these reconstructed cyclic spectrum are used for spectrum detection collaboratively,which will get a certain spatial diversity gain to counteract the fading of channels.At the same time,to solve the problem that a little change of the prior noise parameter will greatly affect algorithm performance,we modify the likelihood function model,and an improved multitask Bayesian compression perception algorithm is derived by using Bayesian inference.The simulation results show that the cyclic spectral compressive sensing algorithm based on multi-task are better than the one based on a single task.When there exists frequency selective fading in channels,the detection performance of the cyclic spectral compressive sensing algorithm based on multi-task is better than the one based on a single task.