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

A technology of sampling covariance and spectrum sensing, which is applied in transmission monitoring, electrical components, transmission systems, etc., can solve the problem of low frequency detection probability, achieve the effect of improving detection probability, improving spectrum utilization rate, and high application value

Active Publication Date: 2019-03-29
HANGZHOU DIANZI UNIV
View PDF4 Cites 20 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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 decompo...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • SVM efficient spectrum sensing method based on Cholesky decomposition sampling covariance matrix
  • SVM efficient spectrum sensing method based on Cholesky decomposition sampling covariance matrix
  • SVM efficient spectrum sensing method based on Cholesky decomposition sampling covariance matrix

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0027] The embodiments of the present invention will be described in detail below in conjunction with specific embodiments and drawings.

[0028] The support vector machine (SVM) efficient spectrum sensing method based on feature extraction and Chomsky decomposition provided by the present invention can be used in the field of information and communication engineering technology, and is not limited to the communication described in detail in the following embodiments field. The following typical fields are selected to illustrate the specific implementation manners of the present invention.

[0029] In this embodiment, the SVM high-efficiency spectrum sensing method of Cholesky decomposing the sampling covariance matrix is ​​realized through the following steps in turn: Step 1, the sensing signal constructs the covariance matrix, Cholesky decomposes the covariance matrix to obtain the lower triangular matrix, and constructs the The corresponding statistic X is used as a training...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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 ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): H04B17/382
CPCH04B17/382
Inventor 包建荣聂建园王天枢刘超姜斌
Owner HANGZHOU DIANZI UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap