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Space-time correlation GLRT (generalized likehood ratio test) method based on oversampling

A detection method and oversampling technology, which is applied in the field of spectrum sensing algorithm, can solve the problem that the spatial correlation between channel time correlation receiving antennas cannot accumulate a large number of signal samples, etc., and achieve high real-time requirements, strong robustness, and high-efficiency sensing Effect

Inactive Publication Date: 2012-04-11
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the existing GLRT detection algorithm fails to make full use of channel time correlation, spatial correlation between receiving antennas and cannot accumulate a large number of signal samples in a short time, and proposes a space-time detection algorithm based on oversampling Related GLRT Detection Methods

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  • Space-time correlation GLRT (generalized likehood ratio test) method based on oversampling
  • Space-time correlation GLRT (generalized likehood ratio test) method based on oversampling
  • Space-time correlation GLRT (generalized likehood ratio test) method based on oversampling

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Embodiment Construction

[0021] The technical solutions in the embodiments of the present invention are clearly and completely described below. It should be understood that the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0022] The schematic flow chart of the method of the present invention is as figure 1 As shown, it includes: obtaining the sample matrix of the oversampled signal; calculating the average energy of the received signal; calculating the relevant statistics; calculating the detection statistics;

[0023] The specific implementation steps are as follows:

[0024] Step 1: Cognitive radio users respectively perform oversampling on the received signals of M receiving antennas with an oversampling rate of L, an...

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Abstract

The invention discloses a space-time correlation GLRT (generalized likehood ratio test) method based on oversampling. The method comprises the following specific steps: acquiring an oversampling signal sample matrix; calculating the average energy of receiving signals; calculating correlative statistic; calculating test statistic; and comparing the obtained test statistic with a preset judgment threshold to perform test judgment. According to the method disclosed by the invention, the average energy of the receiving signals is obtained through oversampling the receiving signals; and in the process of calculating the test statistic, the U calculation correlative statistic is obtained through utilizing the space-time correlation of an MIMO (multiple-input multiple-output) channel, namely utilizing the space-time correlation matrix eigen value decomposition of a normalization channel, so that compared with the existing frequency spectrum detection algorithm, the method disclosed by the invention has better detection performance, cannot be affected by noise variance estimation error and has strong robustness to the noise variance estimation error.

Description

technical field [0001] The invention belongs to the technical field of communication, and relates to a spectrum sensing algorithm in a cognitive radio (Cognitive Radio). Background technique [0002] Cognitive radio technology is proposed to solve the current scarcity of spectrum resources, and spectrum sensing algorithm is one of the key technologies of cognitive radio. In order to avoid the cognitive radio system from causing harmful interference to licensed users, it is required that the spectrum sensing method can reliably detect licensed user signals under low signal-to-noise ratio. [0003] Existing MIMO (Multiple-Input Multiple-Output) spectrum detection algorithms include Matched Filter Detection (Matched Filter Detection, MF), Energy Detection (Energy Detection, ED), Cyclostationary Feature Detection, etc. However, these algorithms have their own significant advantages and disadvantages. The cyclic spectrum detection algorithm needs to know the frequency periodic ...

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Application Information

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IPC IPC(8): H04B17/00H04B17/382H04L1/06
Inventor 罗军王军李强李少谦
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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