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High-energy-efficiency spectrum sensing method based on support vector machine (SVM)

A technology of support vector machine and spectrum sensing, applied in the direction of reducing energy consumption, advanced technology, climate sustainability, etc., can solve problems such as low signal-to-noise ratio, linear inseparability of binary classification problems, etc., achieve high detection rate, and enhance representation effect , the effect of improving throughput

Active Publication Date: 2019-03-26
CHINA ACADEMY OF SPACE TECHNOLOGY
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Problems solved by technology

However, when the signal-to-noise ratio is very low and the number of sampling points is small, the binary classification problem gradually becomes linearly inseparable

Method used

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  • High-energy-efficiency spectrum sensing method based on support vector machine (SVM)
  • High-energy-efficiency spectrum sensing method based on support vector machine (SVM)
  • High-energy-efficiency spectrum sensing method based on support vector machine (SVM)

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Embodiment Construction

[0055] Aiming at the deficiencies of the prior art, the present invention uses a classification model to perform spectrum detection to improve the performance of spectrum sensing. The basic idea is to represent the signal by constructing a feature vector, and then learn an SVM model to judge the occupancy of the spectrum.

[0056] The present invention solves the problem of relatively poor detection performance of the spectrum sensing algorithm based on energy detection due to unused signal prior information. The traditional energy detection algorithm essentially establishes a linear classifier, then thresholds the statistics, and finally Get the verdict. However, when the signal-to-noise ratio is very low and the number of sampling points is small, the binary classification problem gradually becomes linearly inseparable.

[0057] Therefore, it is considered to solve the binary classification problem based on the SVM model. SVM uses the Gaussian kernel function to expand the d...

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Abstract

The invention provides a high-energy-efficiency spectrum sensing method based on a support vector machine (SVM). Firstly, energy statistics amounts of a main user signal and a noise are calculated, energy vectors are obtained, and then residual vectors of the main user signal and the noise are obtained; secondly, an SVM classification model is trained offline by utilizing an eigenvector, and Gaussian kernel parameters are optimized on the basis of simulated annealing; and finally, characteristic expressions of received signals are calculated, classification is carried out by using the SVM classification model, and whether the signals include a main user or not is judged so as to judge a frequency spectrum state.

Description

technical field [0001] The invention relates to spectrum sensing technology in the field of cognitive radio, in particular to an energy-efficient spectrum sensing method based on a support vector machine. Background technique [0002] Usually, a spectrum is only allocated to primary users (PUs) and perceived users cannot access this frequency band. However, the primary user does not occupy this frequency band all the time, so the idle frequency band leads to lower spectrum utilization. Spectrum sensing can detect unoccupied frequency bands, so that sensing users can use this frequency band when the primary user is not occupied, improving spectrum utilization. [0003] The spectrum sensing algorithm based on energy detection is the most commonly used sensing model. This scheme does not require obtaining prior knowledge about the primary user signal. Researchers have successively proposed single-threshold and double-threshold spectrum sensing algorithms. In order to furthe...

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Application Information

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IPC IPC(8): H04W16/14H04W24/08H04B17/382
CPCH04B17/382H04W16/14H04W24/08Y02D30/70
Inventor 李久超刘枫李亚秋张千陈明章
Owner CHINA ACADEMY OF SPACE TECHNOLOGY
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