Spectrum sensing method based on alpha-divergence of information geometry

Abstract

The present invention belongs to the wireless communication method technical field and relates to a spectrum sensing method based on the alpha-divergence of information geometry. The method includes the following steps that: a cognitive radio model is established, a noise environment is simulated, so that N noise covariance matrices Rk are generated, and one of the N noise covariance matrices Rk is adopted as a unit to be detected R; the Riemann mean overlineR of the N noise covariance matrices Rk is calculated through using a natural gradient descent algorithm, a geometric distance L betweenthe detection unit R and the Riemann mean overlineR is calculated; n geometric distances L are sequenced in a descending order, and a threshold value T is calculated; simulation is performed, so thata plurality of unknown signal covariance matrices are generated, and the alpha-divergence of the information geometry is adopted to solve distances D between the signal covariance matrices and a knownsignal matrix in manifold; and the threshold value T is compared with the distances D, if D is larger than T, signals to be sensed exist, otherwise, only noises exist. The method of the invention hasthe advantages of simple and practical calculation process, high spectrum sensing efficiency and high spectrum sensing stability.

Description

technical field [0001] The present invention relates to the technical field of wireless communication methods, and more specifically, to a spectrum sensing method based on α-divergence of information geometry. Background technique [0002] The rapid development of wireless communication technology has brought a lot of convenience to our life, and at the same time, the demand for spectrum resources is also increasing. As a limited natural resource, wireless spectrum is becoming increasingly tense, which restricts the development of wireless communication technology and affects high-quality communication services to a certain extent. At present, the spectrum management systems of various countries are roughly the same, and the available spectrum resources are divided into two parts, including authorized fixed frequency bands and unlicensed frequency bands for open use, and the utilization rate of spectrum resources in different frequency bands is very different; in some The u...

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Problems solved by technology

However, the received sensing signal in the actual environment includes noise, which will affect the detection performance of traditional spectrum sensing methods

In addition, the disadvantage of the energy detection algorithm is that it is easily affected by noise fluctuations, and the detection performance is very sensitive to the uncertainty of the noise; the disadvantage of the cyclostationary feature detection algorithm is that it has high complexity and reduces the sensitivity of the system; while the matched filter detection The disadvantage of the algorithm is that it needs prior information of the authorized user signal, and its versatility is poor; since the positive definite matrix CFAR detection is based on the Riemann mean (geometric mean) calculation of the clutter power, when the sample data has abnormal values ​​(outliers) When , the Riemann mean (geometric mean) may change greatly, resulting in unstable detection performance

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