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Spectrum Sensing Method Based on Joint Reconstruction of Multiple MWC Distributed Sub-Nyquist Sampling

A technology of spectrum sensing and joint reconstruction, applied in the field of spectrum sensing, can solve problems such as difficulty in overcoming shadow effects and hidden terminals, and low success rate of spectrum sensing, to achieve overcoming hidden terminal problems, overcoming shadow effects, and high success rate of spectrum sensing Effect

Inactive Publication Date: 2014-10-22
HARBIN INST OF TECH
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  • Summary
  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The present invention aims to solve the problems of low signal-to-noise ratio, low success rate of spectrum sensing, and difficulty in overcoming shadow effects and hidden terminals in the existing sub-Nyquist sampling spectrum sensing method of a single MWC.

Method used

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  • Spectrum Sensing Method Based on Joint Reconstruction of Multiple MWC Distributed Sub-Nyquist Sampling
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  • Spectrum Sensing Method Based on Joint Reconstruction of Multiple MWC Distributed Sub-Nyquist Sampling

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specific Embodiment approach 1

[0044] Embodiment 1. The spectrum sensing method based on multiple MWC distributed sub-Nyquist sampling joint reconstruction described in the present invention is implemented by the following steps:

[0045] Step 1. Place J MWCs at J preset spatial positions, and use J different sampling matrices to obtain the radio spectrum signals of J MWCs with joint sparsity at the sub-Nyquist rate, and obtain the sampled value matrix Y j (n), j=1,2,...,J, J is a positive integer, n=1,2,...r, r is the sample value matrix Y j (n) the number of column vectors;

[0046] Step 2. Calculate the final support set The specific method is:

[0047] Calculate J sample value matrix Y j The eigenvalues ​​and eigenvectors of the correlation matrix of (n), take the eigenvectors corresponding to the 2N largest eigenvalues ​​as the transformation matrix:

[0048] T j =V r×2N

[0049] In the formula: j=1,2,...J;n=1,2,...r, N is the number of signal frequency bands, r is Y j (n) the number of colum...

specific Embodiment approach 2

[0073] Embodiment 2. The difference between this embodiment and the spectrum sensing method based on multiple MWC distributed sub-Nyquist sampling joint reconstruction described in Embodiment 1 is that in step 3, according to J MWC distributed sub-Nyquist sampling The support set of the Nyquist sampling union The frequency band position occupied by the spectrum in the spectrum sensing signal is calculated by the formula:

[0074]

[0075] achieved;

[0076] In the formula: The support set obtained for the joint reconstruction algorithm, L 0 =[(f NYQ +f s ) / 2f p ] -1 .

[0077] In this embodiment, the MWC sampling process can be regarded as a spectrum segmentation process, when the basic configuration f s =f p When ≥B, the entire spectrum is equivalent to being divided into L segments by an analog low-pass filter, and the frequency f p By setting the shift step size determines the final mixing situation, f p Further determines where each spectrum segment is plac...

specific Embodiment approach 3

[0078] Embodiment 3. The difference between this embodiment and the spectrum sensing method based on multiple MWC distributed sub-Nyquist sampling joint reconstruction described in Embodiment 1 is that in step 3, according to J MWC distributed sub-Nyquist sampling The support set of the Nyquist sampling union Spectral holes outside the occupied frequency band are calculated by the formula:

[0079]

[0080] acquired;

[0081] In the formula: Complete set of supports L 0 =[(f NYQ +f s ) / 2f p ] -1 , L=2L 0 +1.

[0082] In the present invention, in order to demonstrate the advantages of DSNS in application, the reconstruction performance of the DSNS method when comparing different numbers of MWC distribution sampling, and then compare the reconstruction power of multi-MWC distribution sampling and single MWC sampling under different signal-to-noise ratios .

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Abstract

The invention provides a spectrum sensing method based on multiple MWCs (mirror write consistency) distributed type sub-nyquist sampling joint reconstruction, and relates to a spectrum sensing method. The problems that the spectrum sensing is low in success rate, and a shadow effect and a hidden terminal are hard to overcome in the existing spectrum sensing method based on single-MWC distributed type sub-nyquist sampling joint reconstruction-based under the condition of low signal to noise ratio can be solved. The method comprises the following steps of: placing J MWCs at J preset space positions, obtaining radio-frequency spectrum signals which are different from one another and have the joint sparsity through using sub-nyquist rate by different sampling matrixes to obtain a sampling value matrix Yj(n), transporting data to a fusion center, and jointly reconstructing to obtain information; further computing the frequency spectrum holes outside of frequency band positions and s frequency band occupied by a frequency spectrum in a spectrum sensing signal; and realizing the multiple MWC (mirror write consistency) distributed type sub-nyquist sampling joint-based spectrum sensing. The method is suitable for the field of cognitive radio.

Description

technical field [0001] The invention relates to a spectrum sensing method. Background technique [0002] Cognitive radio can realize dynamic spectrum access, which provides a new solution for the efficient use of spectrum resources. Simultaneous fast spectrum sensing of multiple channels in a wide frequency band is the premise and basis for the realization of cognitive radio. However, wideband spectrum sensing has to face the pressure of high signal acquisition rate of traditional sampling methods. The traditional spectrum estimation method requires the sampling rate not to be lower than the Nyquist frequency, which is undoubtedly a big problem for RF signals with a bandwidth of GHz. At present, the development of analog-to-digital conversion hardware is still far from this requirement. distance. [0003] The newly proposed compressed sensing (CS) theory provides a new research idea for sparse signal sampling and processing. According to CS theory, sparse signals can be ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04B17/00H04B17/382
Inventor 盖建新付平乔家庆尹洪涛刘冰凤雷
Owner HARBIN INST OF TECH