Spectrum sensing method based on Hilbert-Huang transform

Abstract

The invention discloses a spectrum sensing method and device based on Hilbert-Huang transform, wherein the method comprises the steps of: obtaining a signal to be detected, and performing EMD decomposition of the signal to be detected, so that n corresponding IMF components are obtained, wherein the signal to be detected is a wireless spectrum signal; obtaining the Hilbert spectrum of each IMF component, and superposing the Hilbert spectrums respectively corresponding to the first IMF component to the ith IMF component, so that the total Hilbert spectrum is obtained, wherein i is less than n; accumulating the total Hilbert spectrum in terms of time to obtain a marginal spectrum; and, judging whether the marginal spectrum is greater than or equal to the pre-set judgement threshold value or not, if so, determining that a main user signal exits in the signal to be detected, and if not, determining that the main user signal does not exist in the signal to be detected. By means of the technical scheme provided by the invention, the local feature of the wireless spectrum signal can be reflected accurately; and thus, the spectrum sensing accuracy rate can be greatly improved.

Description

technical field [0001] The present invention relates to the field of cognitive radio technology, and more specifically, to a spectrum sensing method and device based on Hilbert-Huang transform. Background technique [0002] Cognitive radio technology is considered to be an important way to achieve dynamic spectrum sharing and alleviate the increasing shortage of wireless spectrum resources. Spectrum sensing is the core technology and implementation basis of cognitive radio. The goal of spectrum sensing is to protect primary users from interference. Reliable, accurate and fast detection of the primary user on a specific frequency band. [0003] Traditional sensing methods are all based on energy sensing. The basic idea is to compare the energy of a signal within a specific time with a preset threshold and make a decision. Specifically, its implementation principle may include: after the received signal is A / D transformed, then undergoes FFT (Fourier Transform) and then calcu...

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Problems solved by technology

[0005] The time-frequency processing method used in the above spectrum sensing method is Fourier Transform (FourierTransform), but Fourier Transform is a typical linear and steady-state transformation, suitable for global analysis of linear stationary signals, and can only obtain the full spatial spectrum of the signal , but it is difficult to obtain the local characteristics of the signal, and there are limitations in the analysis of non-stationary, nonlinear frequency-time-varying signals, which in turn causes the accuracy of spectrum sensing to be greatly reduced

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