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GLRT (General Likelihood Ratio Test) detection method based on oversampling

A detection method and oversampling technology, applied in the field of communication, can solve the problems of accumulating a large number of signal samples and the failure of the GLRT detection algorithm to take advantage of channel correlation, etc., to achieve strong robustness, short sensing time, and good detection performance

Inactive Publication Date: 2012-04-04
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 utilize channel correlation and cannot accumulate a large number of signal samples in a short time, and proposes a GLRT detection method based on oversampling

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  • GLRT (General Likelihood Ratio Test) detection method based on oversampling
  • GLRT (General Likelihood Ratio Test) detection method based on oversampling
  • GLRT (General Likelihood Ratio Test) detection method based on oversampling

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Embodiment Construction

[0020] 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.

[0021] 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;

[0022] The specific implementation steps are as follows:

[0023] Step 1: Cognitive radio users oversample the received signals of M receiving antennas with an oversampling rate of L, and then compose the sample ...

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Abstract

The invention discloses a GLRT (General Likelihood Ratio Test) detection method based on oversampling, which is proposed for the problem that the signal correlation cannot be utilized and large quantities of signal samples cannot be accumulated in a short time in the current GLRT detection algorithm. The method particularly comprises the following steps of: obtaining an oversampled signal sample matrix; calculating average energy of the received signals; calculating associate statistics; calculating detection statistical quantity; and comparing the obtained detection statistical quantity withthe predefined judgment threshold for detection and judgment. By the way of oversampling the received signals to obtain the average energy of the received signals and utilizing the time correlation of the MIMO (Multiple Input Multiple Output) channel in calculation of the statistical quantity, the GLRT detection method based on oversampling disclosed by the invention has better detection performance, is not influenced by the noise variance estimation error and has strong robustness on the noise variance error, compared with the traditional frequency spectrum algorithm.

Description

technical field [0001] The invention belongs to the technical field of communication, and relates to a frequency spectrum detection 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 detection 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 spectrum detection methods 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 (CFD) wait. However, these algorithms have their own significant advantages and disadvantages. The cyclic spectrum detection algorithm needs to know the...

Claims

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Application Information

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IPC IPC(8): H04B17/00H04B17/309
Inventor 罗军王军李强李少谦
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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