Adaptive Double Threshold Spectrum Sensing Method And Software Radio Implementation Technology

In recent years,with the commercialization of 5G(5th Generation Mobile Network)technology and the rapid development of wireless communication technology,the proliferation of wireless services has brought about a shortage of spectrum resources.In order to improve the utilization of wireless resources,cognitive radio technology came into being.The technology uses spectrum sensing,intelligent learning,spectrum allocation and spectrum sharing to rationally and optimally use wireless resources.The main function of spectrum sensing is to detect the use of wireless resources,which is an important prerequisite for the normal operation of each functional module of the cognitive radio system,and plays an essential role.There are three traditional spectrum detection methods,which are matched filter algorithm,cyclic feature detection algorithm and energy detection algorithm.The energy detection algorithm does not need any prior information,which has low computational complexity and strong universality.In this paper,in response to the problems of existing energy detection algorithms,the threshold is adaptively adjusted,and the historical energy statistics are used to assist the decision,and a higher detection probability is obtained in the case of low signal-to-noise ratio.The software defined radio platform is used to test the effectiveness of the algorithm.The specific work is as follows:First,in the case of low SNR(Signal Noise Ratio),the threshold of traditional dual threshold and weighted dual threshold detection methods is fixed and inflexible.To solve this problem,an adaptive dual threshold cooperative spectrum sensing model is proposed in this thesis,which adopts the method of centralized cooperative detection.This method is to add the weighting coefficient of SNR and the basis of high and low decision thresholds on the basis of traditional double-threshold.The weighted coefficients are adjusted adaptively.The simulation results show that the algorithm has better detection performance in the case of low SNR.Second,for the double threshold detection algorithm,when the energy statistics value is between the two thresholds,it can not make a decision processing with high timeliness.An adaptive double threshold cooperative spectrum sensing algorithm based on the historical energy statistics value is proposed in this paper.The main innovation is to use the statistical value of the past historical energy information and the average value of the current energy statistics value to complete the detection decision together.It is not necessary to increase the number of detection nodes to ensure that each detection cycle can get accurate detection results,so as to improve the timeliness of spectrum detection.Combined with simulation,the effectiveness of the algorithm is verified.Thirdly,based on the USRP(Universal Software Radio Peripheral)software define radio platform,we build the spectrum sensing system of energy detection based on Python framework,which can adjust parameters and modules flexibly.The algorithm has been experimentally verified.By comparing the theoretical value with the experimental value,the effectiveness of the algorithm in practical application is determined,which provides data and method support for the hardware implementation of this type of algorithm.Compared with other algorithms in the same category,the two algorithms mentioned in this thesis have higher detection probability and lower false alarm probability.The hardware implementation determines the effectiveness of the algorithm and provides data and methods for the hardware implementation of this type of algorithm.The content of research can be effectively used in the field of cognitive radio spectrum detection,effectively improve spectrum utilization,and has good research and application prospects.