Spectrum Sensing Method Based on Support Vector Machine

Abstract

The invention relates to a spectrum sensing method. In this method, the spectrum sensing problem is modeled as a signal classification problem. After data processing and feature extraction, the support vector machine is used as a classifier to realize spectrum sensing. The steps are as follows: firstly, collect signals in the presence and absence of primary user signals respectively; secondly, extract features and mark labels; thirdly, optimize parameters and use training data for learning to obtain the optimal classification hyperplane; Fourth, extract the characteristics of the signal to be tested, and use the obtained classification hyperplane for discrimination to achieve spectrum sensing capabilities. For the convenience of description, the ratio of the maximum and minimum eigenvalues ​​of the signal covariance matrix is ​​selected as the classification feature. In specific applications, other features, such as signal energy, signal spectrum, and cyclic spectrum, can also be selected as classification features. The invention not only solves the problem that the decision threshold is difficult to set in the common spectrum sensing method, but also has superior sensing performance.

Description

technical field [0001] The invention relates to the technical field of wireless communication spectrum sensing, in particular to a support vector machine-based spectrum sensing method. Background technique [0002] Spectrum sensing is the key technology of current cognitive radio, and also the key technology of future intelligent wireless systems, military communications and interference countermeasures. [0003] As a radio technology that improves the utilization of spectrum resources, cognitive radio is based on the fact that on the one hand, spectrum resources for wireless communication are scarce, and there are fewer and fewer spectrums available for allocation (especially the frequency band below 5 GHz). On the one hand, the utilization rate of part of the authorized spectrum is very low, which undoubtedly causes a great waste of spectrum resources. The basic starting point of cognitive radio is that, without affecting the normal communication of authorized users using...

AI Technical Summary

Problems solved by technology

[0010] Support vector machines can effectively solve linearly separable binary classification problems. For two different types of training samples, the training data set can be expressed as , , represents the training data vector, Indicates the dimensionality of the training data vector, express Classification, , is the number of training data; the support vector machine selects the hyperplane that maximizes the classification interval

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