Efficient spectrum sensing method based on support vector machine

Abstract

The invention discloses an efficient spectrum sensing method based on a support vector machine. The efficient spectrum sensing method comprises the following steps: S1, inputting a to-be-sensed receiving signal; S2, preprocessing the to-be-perceived received signal through PCA (principal component analysis), and decomposing a covariance matrix of the to-be-perceived received signal by adopting Dullet decomposition to obtain feature statistics; S3, obtaining a label of a to-be-perceived received signal through an energy detection algorithm, and forming a sample training set by the obtained label and the obtained feature statistics; S4, inputting the formed sample training set into a support vector machine SVM classifier for training to obtain a spectrum classifier; and S5, inputting the collected data into a spectrum classifier for processing to obtain a classification result. According to the method, the high spectrum recognition rate can still be achieved under the condition of the low signal-to-noise ratio, meanwhile, due to introduction of the non-progressive threshold, the progressive threshold changes along with the environment, and spectrum sensing is more accurate.

Description

technical field [0001] The invention relates to the technical field of digital communication, in particular to an efficient spectrum sensing method based on a support vector machine. Background technique [0002] The traditional spectrum allocation method is static, resulting in the spectrum not being fully utilized, thus making spectrum resources increasingly scarce and limiting the development of wireless communications. [0003] The traditional energy detection algorithm mainly uses the energy of the signal received by the SU as the criterion. The process is as follows: first pass the received signal through a band-pass filter, remove the out-of-band signal, and then input it to the A / D converter to obtain a discrete time domain The signal is then sampled according to Shannon's theorem, and the statistic is obtained after calculating the signal energy. Compared with the preset threshold, if it is greater than the threshold, the spectrum is occupied, otherwise, the spectru...

AI Technical Summary

Problems solved by technology

Therefore, uncertain factors such as environment and noise have a greater impact on the perception results.

Images