KR101822555B1 - Apparatus and method for spectrum sensing via compressed sensing algorithm - Google Patents

Abstract

The present invention relates to a method and device to detect a spectrum by using compressed sensing algorithm. According to a first embodiment of the present invention, the spectrum sensing device includes: a signal receiving part receiving a sparse signal from an external device; a support selecting part selecting at least one subspace based on the distance from a subspace, in which the sparse signal is located, as a support indicating a row of a measurement matrix for the restoration of the sparse signal; a support management part including the selected support in a support set; and a signal restoring part estimating and restoring an original sparse signal based on the support included in the support set.

Description

TECHNICAL FIELD [0001] The present invention relates to a method and apparatus for detecting a spectrum using a compression sensing algorithm,

A method and apparatus for detecting a spectrum signal using a subspace matching pursuit algorithm, which is a compression sensing algorithm for restoring a sparse signal, .

Sampling at more than twice the analog signal frequency bandwidth in obtaining the analog signal from most digital devices today can restore the signal completely and completely.

Such a Nyquist sampling method can completely restore a signal, but the amount of sampling data is rapidly increased for a large-capacity signal, and the efficiency is degraded.

A compressive sensing method of restoring a signal close to the original signal while achieving a high compression ratio by using a sub-Nyquist sampling method of sampling a signal with a smaller number of samples than a normal Nyquist signal Was studied.

The compression sensing method for recovering the original signal from the sparse signal is applied to various fields, and is particularly used for sensing a signal in a spectrum in the wireless communication field.

At this time, a simultaneous matching pursuit algorithm, which is an algorithm for restoring a sparse signal to a original signal, is the most widely used for simple and excellent performance.

However, when the sparse signal is reconstructed using the simultaneous orthogonal matching permutation, there is a problem that the reconstruction performance decreases as the number of spectra increases.

Korean Patent Registration No. 10-0788706 discloses a method of encoding / decoding a wideband speech signal using a matching pursuit formatted wave model including a damping factor as a method of encoding / decoding a wideband speech signal. However, as described above, the problem of deteriorating the reconstruction performance as the number of spectra increases is not solved.

Therefore, a technique for solving the above-described problems is required.

On the other hand, the background art described above is technical information acquired by the inventor for the derivation of the present invention or obtained in the derivation process of the present invention, and can not necessarily be a known technology disclosed to the general public before the application of the present invention .

One embodiment of the present invention is directed to selecting a plurality of supports in each iteration process using a partial space permutation algorithm.

It is also an object of the present invention to select an optimized support by performing a double optometry on a plurality of supports selected through a subspace matching pursuit algorithm.

According to a first aspect of the present invention, there is provided an apparatus for sensing a spectrum, comprising: a signal receiver for receiving a sparse signal from an external device; A support selection unit for selecting at least one subspace based on a distance from a subspace where the sparse signal is located, as a support indicating an index of a column of the measurement matrix, a support for including the selected at least one support in a support set And a signal restoring unit for estimating and restoring the original signal based on at least one support included in the management unit and the support set.

According to a second aspect of the present invention there is provided a method for a spectrum sensing apparatus to sense a spectrum comprising the steps of receiving a sparse signal from an external device and providing a representation of the index of a column of the measurement matrix used for reconstruction of the sparse signal, Selecting at least one subspace based on a distance from a subspace in which the signal is located, including the selected at least one support in a support set, and based on at least one support included in the support set And recovering the original signal.

According to one of the above-mentioned objects of the present invention, an embodiment of the present invention can select a plurality of supports and increase the probability of becoming a final support through a double optometry method.

Further, according to any one of the means for solving the problems of the present invention, it is possible to improve the recovery performance of the original signal by restoring the sparse signal using a high probability support.

In addition, according to any one of the means for solving the problems of the present invention, it is possible to efficiently detect a sparse signal in a spectrum when a signal in which energy exists in a wide frequency band has scarcity.

The effects obtained by the present invention are not limited to the above-mentioned effects, and other effects not mentioned can be clearly understood by those skilled in the art from the following description will be.

1 is a block diagram illustrating a spectral sensing apparatus according to an embodiment of the present invention.
2 is a flowchart illustrating a method of spectral sensing according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, which will be readily apparent to those skilled in the art. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

Throughout the specification, when a part is referred to as being "connected" to another part, it includes not only "directly connected" but also "electrically connected" with another part in between . Also, when an element is referred to as "comprising ", it means that it can include other elements as well, without departing from the other elements unless specifically stated otherwise.

Hereinafter, the definition of terms used in the present invention will be given priority.

In the following, 'spectrum' is an electromagnetic wave divided by the wavelength or frequency, and the frequency is arranged in order of wavelength. Detecting a signal in such a spectrum is called 'spectral sensing'.

And 'measurement matrix' is a matrix for restoring the original signal vector from a K-sparse signal (with K nonzero elements of the signal vector).

'Support' is the index of the column of the measurement matrix multiplied by the non-zero element of the K-scarce signal vector.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram illustrating a spectral sensing apparatus 100 according to an embodiment of the present invention.

The spectral sensing device 100 may communicate via the network N. [ The network N may be a local area network (LAN), a wide area network (WAN), a value added network (VAN), a personal area network (PAN) mobile radio communication network, Wibro (Wireless Broadband Internet), Mobile WiMAX, HSDPA (High Speed Downlink Packet Access) or satellite communication network.

The spectrum sensing apparatus 100 may be implemented as a computer, a portable terminal, a television, a wearable device, or the like, which can be connected to a remote server through a network N or connected to other terminals and servers. Here, the computer includes, for example, a notebook computer, a desktop computer, a laptop computer, and the like, each of which is equipped with a web browser (WEB Browser), and the portable terminal may be a wireless communication device , Personal Communication System (PCS), Personal Digital Cellular (PDC), Personal Handyphone System (PHS), Personal Digital Assistant (PDA), Global System for Mobile communications (GSM), International Mobile Telecommunication (IMT) (W-CDMA), Wibro (Wireless Broadband Internet), Smart Phone, Mobile WiMAX (Mobile Worldwide Interoperability for Microwave Access) (Handheld) based wireless communication device. In addition, the television may include an Internet Protocol Television (IPTV), an Internet television (TV), a terrestrial TV, a cable TV, and the like. Further, the wearable device is an information processing device of a type that can be directly worn on a human body, for example, a watch, a glasses, an accessory, a garment, shoes, and the like. The wearable device can be connected to a remote server via a network, Lt; / RTI >

The spectrum sensing apparatus 100 can recover a sparse original signal corresponding to a sparse signal received from a sparse signal received from the outside and determine a spectrum having a final energy based on the restored sparse signal.

Each component of the spectrum sensing apparatus 100 will be described in more detail with reference to FIG.

First, the spectrum sensing apparatus 100 may include a signal receiving unit 110. The signal receiving unit 110 can receive a sparse signal from an external device (not shown).

That is, the signal receiving unit 110 can receive a sparse signal, which is a K-sparse sparse signal vector having K non-zero elements from an external device.

Meanwhile, the spectrum sensing apparatus 100 may include a support selection unit 120. The support selection unit 120 is a support that indicates an index of a column of a measurement matrix used for restoration of a sparse signal and selects at least one subspace in a close order in accordance with the distance from the subspace where the sparse signal is located For which a subspace matching pursuit algorithm can be used.

That is, the support selection unit 120 receives a residual signal obtained by subtracting a projected signal from a sparse signal received from the signal reception unit 110, and a sparse signal estimated based on the measurement matrix The user can repeat the process of selecting and supporting at least one partial space adjacent thereto.

For example, in the initial iteration, the support selection unit 120 can select a support based on the sparse signal received from the signal reception unit 110 and the measurement matrix.

Thereafter, the support selection unit 120 may transmit the support set including the selected support to the signal reconstruction unit 140, and may receive the re-estimated second sparse signal from the signal reconstruction unit 140 to select the support Can be repeated.

That is, the support selection unit 120 can select at least one partial space in the closest order in the partial space in which the estimated original signal is present.

Meanwhile, the spectrum sensing apparatus 100 may include a support manager 130. The support management unit 130 may include at least one selected support in the support set.

That is, the support manager 130 may include the support selected in the process of repeating the process of selecting the support by the support selector 120 described above in the support set, or may remove the support in the support set.

At this time, the support management unit 130 may perform a double optometry for verifying whether or not at least one of the supports included in the support set is the same as the selected support. In this way, the support management unit 130 can determine the optimal support to be included in the support set.

For example, the support management unit 130 may perform the double optometry by checking whether there is the same support as the support selected through the second iteration in the support set including the selected support in the first iteration. The support manager 130 may then include the same support as the selected support in the support set and remove it from the support set for the different supports.

By repeating this process, the optimal support for restoring the sparse signal is selected, and the number of supports is constantly limited, so that the restoration power of the signal can be improved without deteriorating the performance even if the number of spectra is increased.

Meanwhile, the spectrum sensing apparatus 100 may include a signal restoration unit 140. The signal restoring unit 140 may restore the original signal based on at least one support included in the support set.

To this end, the signal restoration unit 140 may estimate the sparse signal using the residual signal and the measurement matrix, and may provide the sparse signal to the support selection unit 120. [

For example, the signal restoration unit 140 may receive a support set from the support selection unit 120, and generate a residual signal by subtracting the projected signal from the received sparse signal, Can be calculated. The signal restoring unit 140 may calculate the second residual signal by subtracting the signal projected on the space formed by the support included in the updated support set in the existing first residual signal and use the second residual signal and the measurement matrix Thereby estimating the sparse signal.

The signal reconstruction unit 140 may provide the estimated sparse signal to the support selection unit 120 to update the support set.

The signal restoring unit 140 can repeat the above-described process until the residual signal becomes zero, and stops the iterative process of estimating the sparse signal and selecting the support when the residual signal becomes zero.

Thereafter, the signal restoring unit 140 can restore the original sparse signal using the sparse signal received by the signal receiving unit 110, based on the finally selected support set.

The spectral sensing method according to the embodiment shown in FIG. 2 includes the steps of time-series processing in the spectral sensing apparatus 100 shown in FIG. Therefore, even if omitted from the following description, the above description of the spectrum sensing apparatus 100 shown in FIG. 1 can be applied to the spectrum sensing method according to the embodiment shown in FIG.

First, the spectrum sensing apparatus 100 can receive a sparse signal from an external device (S2001).

Then, the spectrum sensing apparatus 100 can select at least one support using the scarce signal received in step S2001 in order to select a support (S2002).

That is, the spectral sensing device 100 can select from a position on the vector of the sparse signal in close order, by supporting at least one subspace, for which the spectral sensing device 100 can use a subspace matching perch algorithm.

The spectral sensing device 100 may then include at least one selected support in the support set (S2003).

Thereafter, the spectrum sensing apparatus 100 can determine whether the residual signal obtained by subtracting the projected signal from the space included in the support set in the sparse signal is 0 (S2004).

If the residual signal is not 0 in step S2004, the spectrum sensing apparatus 100 can estimate the original signal based on the support set (S2005). For example, the spectral sensing apparatus 100 may estimate a source signal through a residual signal and a measurement matrix.

Then, the spectrum sensing apparatus 100 may select at least one adjacent subspace from the original sparse signals estimated in step S2005, in the order of close proximity (S2006).

Thereafter, the spectrum sensing apparatus 100 can compare whether the support selected in step S2006 is identical to the support included in the support set (S2007), and can update the support set (S2008).

That is, the spectrum sensing apparatus 100 can perform a double optometry to determine whether there is the same support between the support included in the support set and at least one support selected in step S2006.

Thereafter, the spectrum sensing apparatus 100 may repeat steps S2005 to S2008 until the residual signal becomes zero.

However, if the residual signal is 0 in step S2004, the spectrum sensing apparatus 100 can recover the original signal using the measurement matrix and the rare signal received in step S2001 (S2009).

By using the partial space matching permutation algorithm as described above, it is possible to repeatedly select the subspace by supporting the partial space, but it is possible to select the optimal support by checking whether or not the same support exists in the support set.

The spectral sensing method according to the embodiment described with reference to FIG. 2 may also be implemented in the form of a recording medium including instructions executable by a computer such as a program module executed by a computer. Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. In addition, the computer-readable medium may include both computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Communication media typically includes any information delivery media, including computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, or other transport mechanism.

The spectral sensing method according to an embodiment of the present invention may also be implemented as a computer program (or a computer program product) including instructions executable by a computer. A computer program includes programmable machine instructions that are processed by a processor and can be implemented in a high-level programming language, an object-oriented programming language, an assembly language, or a machine language . The computer program may also be recorded on a computer readable recording medium of a type (e.g., memory, hard disk, magnetic / optical medium or solid-state drive).

Thus, a spectral sensing method according to an embodiment of the present invention can be implemented by a computer program as described above being executed by a computing device. The computing device may include a processor, a memory, a storage device, a high-speed interface connected to the memory and a high-speed expansion port, and a low-speed interface connected to the low-speed bus and the storage device. Each of these components is connected to each other using a variety of buses and can be mounted on a common motherboard or mounted in any other suitable manner.

Where the processor may process instructions within the computing device, such as to display graphical information to provide a graphical user interface (GUI) on an external input, output device, such as a display connected to a high speed interface And commands stored in memory or storage devices. As another example, multiple processors and / or multiple busses may be used with multiple memory and memory types as appropriate. The processor may also be implemented as a chipset comprised of chips comprising multiple independent analog and / or digital processors.

The memory also stores information within the computing device. In one example, the memory may comprise volatile memory units or a collection thereof. In another example, the memory may be comprised of non-volatile memory units or a collection thereof. The memory may also be another type of computer readable medium such as, for example, a magnetic or optical disk.

And the storage device can provide a large amount of storage space to the computing device. The storage device may be a computer readable medium or a configuration including such a medium and may include, for example, devices in a SAN (Storage Area Network) or other configurations, and may be a floppy disk device, a hard disk device, Or a tape device, flash memory, or other similar semiconductor memory device or device array.

It will be understood by those skilled in the art that the foregoing description of the present invention is for illustrative purposes only and that those of ordinary skill in the art can readily understand that various changes and modifications may be made without departing from the spirit or essential characteristics of the present invention. will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed may also be implemented in a combined form.

The scope of the present invention is defined by the appended claims rather than the detailed description and all changes or modifications derived from the meaning and scope of the claims and their equivalents are to be construed as being included within the scope of the present invention do.

100: Spectrum sensing device
110:
120: Support selection part
130:
140:

Claims (9)

An apparatus for sensing a spectrum,
A signal receiving unit for receiving a sparse signal from an external device;
A support selector for selecting at least one subspace based on a distance from a subspace where the sparse signal is located, as a support indicating an index of a column of a measurement matrix used for reconstruction of the sparse signal;
A support manager for including at least one selected support in a support set; And
And a signal reconstruction unit for estimating and restoring a source signal based on at least one support included in the support set. The method according to claim 1,
The support management unit,
And updates the support set based on at least one support selected based on the estimated source-sparse signal. 3. The method of claim 2,
The support management unit,
And verifies the identity of at least one of the supports included in the support set with the selected at least one support. The method of claim 3,
The support management unit,
And removes from the support set a support that is not identical to the selected at least one support. A method for a spectrum sensing device to sense a spectrum,
Receiving a sparse signal from an external device;
Selecting at least one subspace based on a distance from a subspace where the sparse signal is located, as a support indicating an index of a column of a measurement matrix used for reconstruction of the sparse signal;
Including at least one selected support in a support set; And
Recovering a source-sparse signal based on at least one support included in the support set. 6. The method of claim 5,
The spectral sensing method includes:
And updating the support set based on at least one support selected based on the estimated source-sparse signal. The method according to claim 6,
Wherein updating the support set comprises:
Verifying whether the selected set of supports is the same as the selected at least one of the supports included in the support set. 8. The method of claim 7,
Wherein updating the support set comprises:
And removing from the support set a support that is not the same as the selected at least one support. 9. A computer program stored on a recording medium for performing the method according to any one of claims 5 to 8.

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