Compressed sensing spectrum detecting method under blind sparse condition

A technology of compressed sensing and spectrum detection, which is applied to electrical components, transmission monitoring, transmission systems, etc., can solve problems such as slow convergence speed of detection algorithms, missing spectrum detection, and increased algorithm complexity, and achieve convergence and complexity. Low degree, good real-time effect

Inactive Publication Date: 2014-06-18
HARBIN INST OF TECH
View PDF4 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, in general, unlicensed users (SUs) often do not know the signal information of authorized users (PUs), and traditional algorithms require prior information on signal sparsity
Under the premise that the sparsity is unknown, if the traditional algorithm estimates the signal sparsity too high, the convergence s

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Compressed sensing spectrum detecting method under blind sparse condition
  • Compressed sensing spectrum detecting method under blind sparse condition
  • Compressed sensing spectrum detecting method under blind sparse condition

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0029] Specific implementation manner 1: The spectrum detection method of compressed sensing under blind sparse conditions in this implementation manner is specifically prepared according to the following steps:

[0030] Step 1. A mathematical model established based on the compressed sensing theory for the antenna received signal x of length N Perform optimization iterative solution; where the measurement matrix is ​​Θ, the signal after compression sampling is y, f is the sparse basis coefficient to be sought, Ψ is the transformation basis matrix, and Φ is the Gaussian random matrix;

[0031] Step 2: Under the premise that the measurement matrix Θ satisfies the RIP property of the matrix, simplify the mathematical model obtained in Step 1 to obtain the convex optimization problem of the signal;

[0032] Step 3. Use the greedy tracking algorithm to perform correlation detection on the convex optimization problem of the signal, and obtain the most relevant element, and merge it with t...

Example Embodiment

[0042] Specific embodiment two: this embodiment is different from specific embodiment one in that: in step one, the mathematical model established on the antenna received signal x of length N according to the compressed sensing theory The specific problem of the optimization iterative solution is described as follows:

[0043] (1) The length of the signal received by the antenna is N, and the compressed sampling rate is K sub-bands are randomly selected from N frequency bands as frequency bands occupied by authorized users, and M is the number of compressed sampling points;

[0044] (2) N×1 dimensional noise-free signal x_o obtained by inverse Fourier transformation of the frequency band occupied by authorized users; x_o∈R N , The inverse Fourier transform method is x_o=Ψf, f is the sparse base coefficient to be sought;

[0045] (3) If there are only K elements in f that are non-zero, then the signal x_o is said to be sparse under the transformation basis of Ψ, and the original sign...

Example Embodiment

[0053] Specific embodiment three: this embodiment is different from specific embodiment one or two in that in step two, under the premise that the measurement matrix is ​​Θ and satisfies the RIP property of the matrix, the mathematical model obtained in step one is simplified to obtain the convex optimization problem of the signal The specific process is:

[0054] Although the 0-norm algorithm of compressed sensing is optimal, it is an NP-hard problem. In order to find the sparsest solution, an exhaustive list is required This possibility is that the algorithm complexity is extremely high; relevant data show that under the premise that the measurement matrix Θ satisfies the RIP property of the matrix, MATLAB software is used as the simulation software, input: sampling vector y, compressed sensing measurement matrix Θ=ΦΨ, iteration Termination threshold s, the purpose of the algorithm in the invention is to output the spectrum sensing result under the premise that the above variab...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a compressed sensing spectrum detecting method under the blind sparse condition. The aim of spectrum detecting under the condition that the sparseness is unknown is achieved. The compressed sensing spectrum detecting method under the blind sparse condition includes the steps of (1) conducting optimization iteration solving on a built mathematic model (please see the specifications for the formula), (2) conducting simplification to obtain convex optimization problem of reconstitution signals, (3) obtaining an element with the maximum correlation, merging the element with a support set of last-time iteration, and obtaining a new support set, (4) obtaining a residual error, (5) obtaining a contribution value and outputting spectrum detecting results (please see the specifications for the formula) and the like. The mode is applied to the compressed sensing spectrum detecting method.

Description

technical field [0001] The invention relates to a frequency spectrum detection method of compressed sensing under the condition of blind sparseness. Background technique [0002] Spectrum is a precious resource for wireless communications. With the increasing demand of users for high-speed data services, the demand for spectrum resources in communication systems is also increasing, and spectrum resources are increasingly scarce. In order to improve the utilization of spectrum resources and meet the needs of users for high-speed data transmission, cognitive radio technology emerges as the times require. Cognitive radio can utilize spectrum holes for data transmission of unlicensed users (SUs) without affecting data transmissions of authorized users (PUs). [0003] According to the Nyquist sampling law, in the conversion process from analog to digital, in order not to lose information, it must be sampled at a sampling rate at least twice the highest frequency of the signal to...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): H04B17/00H04B17/382
Inventor 高玉龙张蔚马永奎朱尤祥张中兆陈艳平
Owner HARBIN INST OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap