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A SVM Efficient Spectrum Sensing Method Based on Cholesky Decomposition Sampling Covariance Matrix

A sampling covariance, spectrum sensing technology, applied in transmission monitoring, electrical components, transmission systems, etc., can solve the problem of low spectrum detection probability, and achieve the effect of improving detection probability, reducing sample dimension, and high application value

Active Publication Date: 2021-04-30
HANGZHOU DIANZI UNIV
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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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  • A SVM Efficient Spectrum Sensing Method Based on Cholesky Decomposition Sampling Covariance Matrix
  • A SVM Efficient Spectrum Sensing Method Based on Cholesky Decomposition Sampling Covariance Matrix
  • A SVM Efficient Spectrum Sensing Method Based on Cholesky Decomposition Sampling Covariance Matrix

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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...

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Abstract

The invention discloses an SVM efficient spectrum sensing method based on Cholesky decomposition sampling covariance matrix, which is completed by the following steps: S1, using Cholesky decomposition of the covariance matrix of the sensing signal to construct statistics; S2, marking the statistics label; S3, using the statistic and the corresponding label as a training sample set, using the SVM algorithm to train the sample to obtain a convex quadratic programming problem; S4, using a sequence optimization algorithm to solve the convex quadratic programming problem, and obtaining SVM classifier; S5, using the SVM classifier to classify the main user state. The method of the present invention can reduce the correlation between the PU signal and the noise, reduce the number of sample sets, increase the distance between the PU signal and the noise, and improve the detection probability through Cholesky decomposition of the preprocessing of the sensing signal sampling covariance matrix, and Reduced spectrum sensing complexity.

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 ...

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Application Information

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Patent Type & Authority Patents(China)
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
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