SVM efficient spectrum sensing method based on Cholesky decomposition sampling covariance matrix

Abstract

The invention discloses an SVM efficient spectrum sensing method based on a Cholesky decomposition sampling covariance matrix. The method is implemented by adopting the following steps: S1, a covariance matrix of a Cholesky decomposition sensing signal is used to construct a statistical quantity; S2, the statistical quantity is marked with a label; S3, the statistical quantity and the corresponding label are used as a training sample set, and samples are trained by adopting an SVM algorithm to obtain a convex quadratic programming problem; S4, the convex quadratic programming problem is solvedby adopting a sequence optimization algorithm to obtain an SVM classifier; and S5, states of main users are classified by adopting the SVM classifier. According to the method, through the preprocessing of the Cholesky decomposition sensing signal sampling covariance matrix, the correlation between a PU signal and a noise can be reduced, and the number of sample sets is reduced, so that the distance between the PU signals and the noise is increased, the detection probability is improved, and the spectrum sensing complexity is reduced.

Description

technical field [0001] The invention belongs to the field of digital communication, and in particular relates to a support vector machine efficient spectrum sensing method based on Chomsky decomposition sampling covariance matrix to realize spectrum sensing of cognitive radio. Background technique [0002] Traditional wireless spectrum resources are statically allocated according to authorization, which limits the flexibility of wireless communication. Cognitive radio (CR) dynamically allocates spectrum resources, which improves spectrum utilization. Among them, spectrum sensing is the premise of CR, and its main goal is that cognitive devices quickly and intelligently identify unoccupied available spectrum in the frequency band, so that more users can opportunistically use the resource. The actual wireless environment is quite complex, and the pure traditional cognitive radio spectrum sensing technology cannot meet the higher requirements in the future: supporting a large ...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the above-mentioned technical problems, the present invention proposes a SVM efficient spectrum sensing method based on Cholesky decomposition sampling covariance matrix to improve the problem of low spectrum detection probability under low signal-to-noise ratio, that is, to improve spectrum utilization

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