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Soft-decision spectrum sensing method based on compression non-reconstruction

A spectrum sensing, local spectrum sensing technology, applied in transmission monitoring, electrical components, transmission systems, etc., can solve the problem of reducing pressure, not giving full play to the theoretical advantages of compressed sampling, and not giving a closed solution expression for the performance of the sensing algorithm after compressed sampling and other problems, to achieve the effect of reducing the sampling rate, reducing the transmission pressure, and increasing the detection accuracy.

Active Publication Date: 2015-01-07
THE 36TH INST OF CENT MILITARY COMMISSION EQUIP DEV DEPT
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Problems solved by technology

In recent years, there are also literatures on the application of compressed sampling theory in spectrum detection, such as wideband spectrum detection algorithm based on compressed sensing, compressed sensing signal detection method based on sampled value digital features, and sub-Nyquist sampling and cyclic feature detection. Some of the above algorithms need to reconstruct the original signal from the observation value, but only reduce the pressure on transmission, without giving full play to the advantages of the compressed sampling theory, and some do not give the performance of the post-compressed sampling sensing algorithm Closed solution expression, just verified by simulation

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  • Soft-decision spectrum sensing method based on compression non-reconstruction
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Embodiment Construction

[0012] The present invention will be further explained below in conjunction with the drawings.

[0013] Attached figure 1 A flowchart of a soft decision spectrum sensing method based on compressed non-reconstruction is given, which includes the following steps:

[0014] In the first step, each secondary user establishes a binary hypothesis test model, H 0 Indicates that the main user does not exist, H 1 Indicates that the primary user exists, assuming that the signal model received by the i-th secondary user without compression sampling can be expressed as

[0015] H 0 : R i (k)=n i (k)

[0016] H 1 : R i (k)=x i (k)+n i (k)

[0017] Where r i (k) is the signal received by the i-th secondary user at the k-th sampling moment; x i (k) represents the primary user signal that reaches the i-th secondary user through the fading channel, with a mean value of 0 and a variance of n i (k) represents the channel noise of the i-th secondary user, assuming it is additive white Gaussian noise, the m...

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Abstract

The invention discloses a soft-decision spectrum sensing method based on compression non-reconstruction. The method mainly comprises the steps that firstly, a sensing model is established, secondary users establish a local spectrum sensing model, a binary presumptive model is adopted, and the energy detecting method is adopted under the non-cooperative mode; secondly, compressed sampling is carried out, and the secondary users utilize a two-dimensional Gaussian measurement matrix to carry out compressed sampling on a receiving signal; thirdly, under the situation that the compressed sampling data are not reconstructed, the secondary users directly utilize the compressed sampling data to establish the test statistics amount based on energy, whether a primary user exists or not is not judged, and the test statistics amount is transmitted to a fusion center; fourthly, the soft decision of the fusion center is carried out, and after the fusion center collects the test statistics amount sent by all the secondary users, the unified test statistics amount is established and compared with the detecting threshold value, and a final decision result is given.

Description

Technical field [0001] The invention relates to a spectrum sensing method for a distributed spectrum monitoring system, in particular to a soft decision spectrum sensing method based on compressed non-reconstruction. Background technique [0002] Spectrum sensing is a key technology in cognitive radio networks, and an important means to determine whether the primary user exists and to find idle channels. How to quickly and accurately sense the spectrum occupancy within the monitoring range is an important issue faced by spectrum sensing. Currently, distributed spectrum monitoring systems based on wireless sensor networks use cognitive radio technology to realize the perception and utilization of spectrum. In order to overcome the influence of multipath fading and channel fading, cooperative detection methods such as soft decision and hard decision are usually adopted. Under the same circumstances, the performance of soft decision is better than hard decision, but soft decision n...

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Application Information

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IPC IPC(8): H04B17/00
Inventor 吴昊陈勇柳永祥赵杭生许金勇邵震洪
Owner THE 36TH INST OF CENT MILITARY COMMISSION EQUIP DEV DEPT
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