Improved Wideband Spectrum Sensing Research Based On Bayesian Theory

Cognitive Radio can alleviate the shortage of spectrum resources effectively,as one of the key technologies of cognitive radio,spectrum sensing can let cognitive users utilize the authorized spectrum with opportunities through rapid and accurate sensing of authorized users.However,due to the limitation of Nyquist sampling theorem,the existing hardware can't meet the requirements of realizing the narrowband spectrum sensing in wideband.Therefore,Compressive Sensing is introduced to wideband spectrum sensing by domestic and foreign scholars,sampling signal at sub-Nyquist rate,reducing the hardware requirement of wideband spectrum sensing.The compressive sensing based on Bayesian theory has its unique advantages in a relatively sparse scene and blind detection environment.Based on the Bayesian Compressive Sensing,diving into the signal recovery technology of wideband spectrum sensing,a high efficient and reliable wideband spectrum sensing algorithm is established by utilizing the prior knowledge and statistic theory.The main innovations of this paper are as follows:(1)Begins with a analysis of the problems of basis function selection in the iterative process of the Bayesian Compressive sensing,a Prior Knowledge based Bayesian Compressive sensing algorithm is proposed,and the selection of basis functions is improved by the introduction of prior knowledge.According to the difference of prior knowledge,the corresponding improved algorithm is proposed.At first,the PU probability from spectrum prediction is used as prior knowledge,PU Probability Prediction based Bayesian Compressive Sensing is proposed,improving the initialization process of Bayesian Compressive Sensing,accelerating the convergence,reducing the selection of redundant basis functions and signal reconstruction time,optimizing the algorithm performance.The simulation results show that the improved algorithm performs faster than original algorithm with same reconstruction accuracy,have better anti?noise performance.(2)Considering the signal property as prior knowledge,the block-sparse signal's structural properties that the primary users appear in block is introduced.Then a Block-sparse based Bayesian Compressive Sensing is proposed,improving the iterative process of Bayesian Compressive Sensing,reducing the iterations and the selection of redundant basis functions,optimizing the algorithm performance.The simulation results show that the improved algorithm has higher detection probability and lower signal reconstruction time.(3)In the blind detection environment of wideband spectrum sensing,around the problem of sparsity unknown,this paper analyze the errorbar's significance for adaptive sensing matrix,and utilize the property of errorbar,using support vector machine to study the relationship between errorbar and sampling rate,proposing the Adaptive Step based Bayesian Compressive Sensing which change the process of gradually increasing measurements into the process of dynamically choosing variable step.The proposed algorithm can reduce the times of recovery signal and optimize the algorithm performance.The simulation results show that the improved algorithm can dynamically increase the sampling rate and reduce the times of reconstruct signal,meanwhile,ensure spectrum detection performance.The research of this paper shows that the improved wideband spectrum sensing algorithm base on Bayesian theory can sensing the authorized users more quickly and accurately,and also work well in the blind detection environment.