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A Spectrum Sensing Method

A spectrum sensing and covariance technology, applied in the spectrum sensing field of spectrum sensing and deep convolutional neural network architecture, which can solve the problems of algorithm performance dependence, algorithm performance degradation, failure and so on.

Active Publication Date: 2020-01-17
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

One of the main disadvantages of these model-driven algorithms is that the performance of the algorithms is highly dependent on the accuracy of the detection statistics model. Once the statistical model has uncertainty or cannot be obtained, the performance of the algorithm will drop sharply or fail.

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Embodiment Construction

[0033] The present invention will be described in detail below in conjunction with the accompanying drawings and simulation examples, so that those skilled in the art can better understand the present invention.

[0034] Consider a multi-antenna cognitive radio scenario. Such as figure 1 As shown, a cognitive radio terminal collects N observation vectors through an M-element antenna system for spectrum sensing. Let x(n)=[x 1 (n), x 2 (n),...,x M (n)] T Represents the nth observation vector (n=0,1,…,N-1), where x m (n) yes The nth discrete-time sample at the mth antenna. Therefore, the spectrum sensing problem under multiple antennas can be expressed as a binary hypothesis testing problem:

[0035]

[0036] Among them, H 1 and H 0 Respectively represent two hypothesis tests of the existence of the main user and the absence of the main user, s(n) represents the signal vector, u(n) represents the mean value is 0, and the covariance is Independent and identically d...

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Abstract

The invention belongs to the technical field of wireless communications, relates to a spectrum sensing method, and more specifically relates to a spectrum sensing method of depth convolution neural network architecture based on covariance sensing. The method mainly comprises the following steps: sampling: collecting observation vectors of N moments through an M-ary multi-antenna system, and marking the observation vectors as X; acquiring the detection statistics: constructing a detection statistics model based on a deep neural network, and using data obtained in step S1 to train, obtaining thetrained deep neural network model, and obtaining the detection statistic T(X) according to the obtained observation vectors; and judging: comparing the detection statistics with a threshold gamma: ifT(X) is greater than gamma, then judging that a main user signal exists, and if not, then judging that the main user signal does not exist.

Description

technical field [0001] The invention belongs to the technical field of wireless communication and relates to a spectrum sensing method, more specifically to a spectrum sensing method based on a covariance sensing deep convolutional neural network architecture. Background technique [0002] With the rapid development of wireless communication services, people have an increasing demand for spectrum resources, and the huge demand for spectrum makes available spectrum resources increasingly scarce. The traditional fixed allocation of frequencies leads to underutilization of most existing frequency bands, which is also common worldwide. Facing the spectrum crisis, cognitive radio technology emerges as the times require, and its basic idea is spectrum sharing or spectrum reuse. Therefore, spectrum sensing technology for detecting spectrum holes has become an important prerequisite and core link for cognitive radio to realize dynamic spectrum access. [0003] The design of detect...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04B17/382
CPCH04B17/382
Inventor 刘畅梁应敞
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA