KR102338055B1 - Apparatus for sensing frequency spectrum and method thereof - Google Patents

Abstract

The present invention provides a method and apparatus for sensing a frequency spectrum. The method for sensing a frequency spectrum includes the steps of: obtaining a compressed sensing sample from each of a plurality of sensors; confirming the minimum number of compressed sensing samples related to a frequency spectrum corresponding to the obtained compressed sensing samples based on the restoration of the obtained compressed sensing samples; and confirming whether the frequency spectrum is used by a main user, by using the confirmed number of samples and the obtained compressed sensing samples.

Description

Frequency spectrum sensing device and method {APPARATUS FOR SENSING FREQUENCY SPECTRUM AND METHOD THEREOF}

The present disclosure relates to an apparatus and method for sensing a frequency spectrum for cognitive wireless communication.

As the problem of the shortage of frequency resources is getting worse with the increase in wireless communication traffic, the Cognitive Radio technology, which is a frequency sharing technology for effectively using inefficiently used frequencies, has emerged. Cognitive wireless communication technology is a technology that uses a licensed band in a line where a sub-user, not a licensed band user, does not interfere with the main user using the licensed band. It was proposed to use the band effectively.

Spectrum sensing is required for more effective use of licensed bands by sub-users. Spectrum sensing includes technology that detects whether a primary user is within a licensed band frequency. How accurately the secondary user detects the primary user is a very important process because it greatly affects the interference on the primary user network and the performance of the secondary user network.

Among the spectrum sensing technologies, the sub-Nyquist-based technology alleviates the problems of long sensing delay due to high sampling rate, high computational complexity, and hardware cost. Such sub-Nyquist technology is based on various approaches, for example, there may be an approach based on compressive sensing.

According to compressed sensing, a signal having a sparsity characteristic within a specific domain can be restored to the original signal even if a number of samples less than the original dimension of the signal is obtained. As described above, if the minimum number of samples that can be restored to the original signal is used, the effect of spectrum sensing increases.

However, if the degree of sparseness of the original signal changes, the minimum number of compressed signal samples required to restore the original signal changes. It is also difficult to know the minimum number of samples. Accordingly, the effect of compression sensing may be reduced, and thus the efficiency of the spectrum sensing technology may be reduced. Accordingly, there is a need for a method capable of further improving the efficiency of spectral sensing based on compression sensing.

The problem to be solved by the present embodiment is an apparatus for estimating the minimum number of samples required at a minimum for restoration of an original signal in relation to spectrum sensing based on compression sensing and performing frequency spectrum sensing more efficiently using the same to provide a way.

The technical problems to be achieved by the present embodiment are not limited to the technical problems described above, and other technical problems may be inferred from the following embodiments.

According to a first embodiment, a frequency spectrum sensing method for cognitive wireless communication includes: obtaining a compressed sensing sample from each of a plurality of sensors; Checking the minimum number of compressed sensing samples related to the corresponding frequency spectrum, and using the confirmed number of samples and the obtained compressed sensing sample to check whether the main user of the frequency spectrum is used. have.

According to a second embodiment, a frequency spectrum sensing device for cognitive wireless communication includes a sample acquisition unit configured to acquire a compressed sensing sample from each of a plurality of sensors, and the acquired compression sensing based on restoration of the acquired compressed sensing sample. A sample confirmation unit for confirming the minimum number of compressed sensing samples related to the frequency spectrum corresponding to the sample, and a note for confirming whether the main user of the frequency spectrum is used by using the confirmed number of samples and the obtained compressed sensing sample It may include a user confirmation unit.

According to a third embodiment, the computer-readable recording medium is a non-transitory recording medium recording a program for executing a frequency spectrum sensing method in a computer, wherein the frequency spectrum sensing method includes compressed sensing samples from each of a plurality of sensors. Acquiring, and determining the minimum number of compressed sensing samples related to a frequency spectrum corresponding to the obtained compressed sensing sample based on the restoration of the obtained compressed sensing sample, the number of the confirmed sample and the acquisition It may include confirming whether the main user of the frequency spectrum is used by using the compressed sensing sample.

Specific details of other embodiments are included in the detailed description and drawings.

According to the present disclosure, a frequency spectrum sensing apparatus and method can check the minimum number of samples required for original signal restoration based on compressed sensing samples obtained from a plurality of sensors, and use this to more effectively perform spectrum sensing have.

In addition, the frequency spectrum sensing apparatus and method use compressed sensing samples collected from a plurality of spatially dispersed sensors for cooperative spectrum sensing to obtain a spatial diversity gain of spectrum information, thereby allowing the main user to use the frequency spectrum. The reliability of presence or absence can be improved.

Effects of the invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the claims.

1 is a conceptual diagram illustrating a frequency spectrum sensing method according to an embodiment.
2 is a conceptual diagram for explaining a method of obtaining a compressed sensing sample used in a frequency spectrum sensing method according to an embodiment.
3 is a functional block diagram of a frequency spectrum sensing apparatus according to an embodiment.
4 is a flowchart of each step of a frequency spectrum sensing method according to an embodiment.
5 shows an example of an algorithm for determining the minimum number of compressed sensing samples for frequency spectrum sensing according to an embodiment.
6 shows an example of an algorithm for confirming whether a main user is used for frequency spectrum sensing according to an embodiment.

Terms used in the embodiments are selected as currently widely used general terms as possible while considering functions in the present disclosure, but may vary according to intentions or precedents of those of ordinary skill in the art, emergence of new technologies, and the like. In addition, in a specific case, there is a term arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the corresponding description. Therefore, the terms used in the present disclosure should be defined based on the meaning of the term and the contents of the present disclosure, rather than the simple name of the term.

In the entire specification, when a part "includes" a certain element, this means that other elements may be further included, rather than excluding other elements, unless otherwise stated.

The expression "at least one of a, b, and c" described throughout the specification means 'a alone', 'b alone', 'c alone', 'a and b', 'a and c', 'b and c ', or 'all of a, b, and c'.

The "terminal" referred to below may be implemented as a computer or a portable terminal capable of accessing a server or other terminal through a network. Here, the computer includes, for example, a laptop, a desktop, and a laptop equipped with a web browser, and the portable terminal is, for example, a wireless communication device that ensures portability and mobility. , IMT (International Mobile Telecommunication), CDMA (Code Division Multiple Access), W-CDMA (W-Code Division Multiple Access), LTE (Long Term Evolution) and other communication-based terminals, smartphones, tablet PCs, etc. It may include a handheld-based wireless communication device.

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art to which the present disclosure pertains can easily implement them. However, the present disclosure may be implemented in several different forms and is not limited to the embodiments described herein.

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings.

1 is a conceptual diagram illustrating a frequency spectrum sensing method according to an embodiment.

Referring to FIG. 1 , a frequency spectrum sensing device (hereinafter, a sensing device) 100 may obtain a compressed sensing sample from each of a plurality of sensors 111 , 112 , and 113 . Here, each of the plurality of sensors 111 , 112 , and 113 may be disposed at different positions. In addition, each of the plurality of sensors 111 , 112 , and 113 may include a sensor (eg, an Internet of Things (IoT) sensor) driven with low power.

Each of the plurality of sensors 111 , 112 , and 113 may detect a specific signal (hereinafter, an original signal), generate a compressed sensing sample (or a compressed sensing sample signal) corresponding thereto, and transmit it to the sensing device 100 . The compressed sensing sample may include a signal generated by compressively sensing an original signal based on the compression sensing technique. have.

In an embodiment, the number of the plurality of sensors may be greater than or equal to the minimum number of compressed sensing samples required when the statistically obtained maximum sparse value of the original signal. The statistically obtained maximum sparse value of the original signal may be a predetermined value, and since it is easy for a person skilled in the art, a detailed description thereof will be omitted.

According to FIG. 1 , the sensing device 100 includes a sample acquisition unit 120 for acquiring compressed sensing samples from each of a plurality of sensors 111 , 112 , and 113 , and a sample confirmation unit 130 for checking the minimum number of compressed sensing samples. ), and a main user confirmation unit 140 for confirming whether the main user uses the frequency spectrum corresponding to the compressed sensing sample.

Specifically, the sample acquisition unit 120 may acquire a compressed sensing sample from each of the plurality of sensors 111 , 112 , and 113 . The sample acquisition unit 120 may collect the acquired compression sensing samples.

The sample check unit 130 may check the minimum number of compressed sensing samples corresponding to the original signal by using the collected compressed sensing samples. In an embodiment, the sample confirmation unit 130 may repeatedly restore the original signal using a different number of compressed sensing samples. The sample check unit 130 may check the minimum number of compressed sensing samples based on comparing the restored original signals with each other.

The main user confirmation unit 140 may confirm whether the frequency spectrum is used by the main user by using the number of samples confirmed by the sample confirmation unit 130 and the compressed sensing samples obtained by the sample obtaining unit 120 . In an embodiment, the main user check unit 140 may classify and group the obtained compressed sensing samples according to the checked minimum number of compressed sensing samples to restore the original signal for each of the grouped compressed sensing groups. The main user confirmation unit 140 may confirm whether the main user is used by checking the subband of the original signal for each group.

On the other hand, a more detailed description related to the operation of each of the sample acquisition unit 120, the sample identification unit 130, and the main user identification unit 140 will be described later with reference to FIG.

2 is a conceptual diagram for explaining a method of obtaining a compressed sensing sample used in a frequency spectrum sensing method according to an embodiment.

Specifically, FIG. 2 shows compression sensing performed by each of a plurality of sensors (eg, a plurality of sensors 111, 112, 113) that transmits a compression sensing sample to a sensing device (eg, the sensing device 100 of FIG. 1). It is a diagram for explaining a method of acquiring a sample. Hereinafter, the i-th sensor among the plurality of sensors will be described as an example.

Referring to FIG. 2 , a sensor (i-th sensor) may receive an original signal s. Here, the original signal may include a signal propagated from a specific signal source, but is not limited thereto.

, i번째 센서에서 이용되는 난수를 의미함)를 믹서(202)를 이용하여 합성할 수 있다. 센서는 합성된 신호를 저주파 필터(203)를 이용하여 필터링할 수 있다. The sensor uses the received raw signal (s) and the random number generated by the pseudo random number generator 201 (

, meaning a random number used in the i-th sensor) may be synthesized using the mixer 202 . The sensor may filter the synthesized signal using the low-frequency filter 203 .

, i번째 센서에서 생성된 샘플을 의미함)을 생성할 수 있다. Based on the signal filtered by the low-frequency filter 203 is sampled by the low-speed sampler,

, which means the sample generated by the i-th sensor).

The process of acquiring such a compressed sensing sample occurs in each of a plurality of sensors, and the following description will be made on the assumption that there are L of the plurality of sensors. Examples of the present specification are not limited thereto.

3 is a functional block diagram of a frequency spectrum sensing apparatus according to an embodiment. 3 shows components related to the present embodiment, but is not limited thereto, and other general-purpose components other than the components shown in FIG. 3 may be further included.

Referring to FIG. 3 , the sensing device 300 may include a sample acquisition unit 310 , a sample identification unit 320 , and a main user identification unit 330 . Each of the sample acquiring unit 310 , the sample checking unit 320 , and the main user checking unit 330 may mean a unit that processes at least one function or operation, and according to an embodiment, hardware (eg, a processor, a micro processor), software, or a combination of hardware and software.

In some cases, the sensing device 300 may be implemented in a form including a memory and a processor. In this case, the memory may include various data related to the sensing device 300 , for example, at least one instruction for operation of the sensing device 300 , and the processor performs the sensing device based on the data stored in the memory. The sample acquisition unit 310 of 300 , the sample verification unit 320 , and the main user verification unit 330 may perform an operation related to each configuration.

The sample acquisition unit 310 may acquire a compressed sensing sample from each of a plurality of sensors. Here, the plurality of sensors may be implemented as low-power or low-power Internet of Things (IoT) sensors and may be disposed in different locations, but is not limited thereto.

In an embodiment, with respect to the frequency spectrum of the signal corresponding to the compressed sensing sample, the frequency spectrum of the signal may change with time. In this case, the sparse value for the frequency spectrum may change. However, the characteristic of the frequency spectrum may have a coarse characteristic regardless of whether or not the coarse value is changed. Also, the frequency spectrum may correspond to a broadband frequency spectrum.

In an embodiment, each of the plurality of sensors may acquire a compressed sensing sample by performing compression sensing based on detecting a specific signal. Specifically, for example, each of the plurality of sensors may acquire a compressed sensing sample based on Equation 1 below.

는 복수의 센서 중 i번째 센서에서 획득되는 압축센싱 샘플,

는 i번째 센서에서 사용되는 압축센싱 벡터, T는 트랜스포즈,

는 역푸리에 트랜스폼, s는 원신호,

는 i번째 센서에 수신되는 가우시안 노이즈, C는 복소수 값을 나타낸다. 여기서, 원신호 s는

를 만족하고, 압축센싱 벡터

는

를 만족하며, 역푸리에 트랜스폼

는

를 만족할 수 있다.

is a compressed sensing sample obtained from the i-th sensor among the plurality of sensors,

is the compression sensing vector used by the i-th sensor, T is the transpose,

is the inverse Fourier transform, s is the original signal,

is Gaussian noise received by the i-th sensor, and C is a complex value. Here, the original signal s is

is satisfied, and the compression sensing vector

Is

is satisfied, and the inverse Fourier transform

Is

can be satisfied with

Each of the plurality of sensors may transmit the acquired compressed sensing sample to the sample acquiring unit 310 . The sample acquisition unit 310 may acquire a compressed sensing sample by receiving the compressed sensing sample transmitted from each of the plurality of sensors.

In an embodiment, the number of the plurality of sensors may be greater than the minimum number of compressed sensing samples required to correspond to the maximum sparse value of the frequency spectrum. For example, when the minimum number of compressed sensing samples corresponding to a statistically predetermined maximum coarse value in relation to a frequency spectrum senseable by the plurality of sensors is 10, the number of the plurality of sensors may be 11. However, the present invention is not limited thereto, and in some cases, the number of the plurality of sensors may be the same as the minimum number of compressed sensing samples required to correspond to the maximum sparse value of the frequency spectrum.

In an embodiment, the sample acquisition unit 310 may acquire a compressed sensing sample based on information on a sensing vector of each of the plurality of sensors. Here, the sensing vector may correspond to an indicator indicating each of the plurality of sensors, and information on the sensing vector may be previously stored. When the sample acquisition unit 310 acquires compressed sensing samples from a plurality of sensors, it may collect compressed sensing samples based on Equation 2 below.

는 취합된 압축센싱 샘플의 벡터 값, w는 각 센서에서 수신되는 가우시안 노이즈를 벡터화 한 것이며, N은 원신호의 길이를 나타낸다. y is the collected compressed sensing sample, L is the number of sensors,

is the vector value of the collected compressed sensing samples, w is the vectorized Gaussian noise received from each sensor, and N is the length of the original signal.

The sample check unit 320 may check the minimum number of compressed sensing samples related to a frequency spectrum corresponding to the obtained compressed sensing sample based on the restoration of the obtained compressed sensing sample.

In an embodiment, the sample confirmation unit 320 includes an average coarse value of a frequency spectrum, a minimum coarse value, an average number of compressed sensing samples corresponding to an average coarse value, a minimum number of compressed sensing samples corresponding to the minimum coarse value, and compression sensing. The original signal of the frequency spectrum for each of the first compressed sensing sample number and the second compressed sensing sample number may be reconstructed by using the increased value of the number of samples. Here, the average coarse value of the frequency spectrum, the minimum coarse value, the average number of compressed sensing samples corresponding to the average coarse value, and the minimum number of compressed sensing samples corresponding to the minimum coarse value are predetermined values based on statistical characteristics of the frequency spectrum. can An increase in the number of compressed sensing samples may be preset to an arbitrary value. The first number of compressed sensing samples may be any number less than the second number of compressed sensing samples by the increase value.

For example, when the first number of compressed sensing samples is 2 and the increase value is 3, the second number of compressed sensing samples may be 5. In this case, the sample check unit 320 may restore the original signal of the frequency spectrum corresponding to the compressed sensing sample for the case where the number of compressed sensing samples is 2 and 5, respectively.

), 미리 지정된 임의의 교차상관 값(

)를 이용하여 반복적으로 L1 최소화 문제를 풀어서 원신호를 복원할 수 있다.More specifically, the sample confirmation unit 320 is the initial value of the number of compressed sensing samples, the minimum number of compressed sensing samples corresponding to the minimum sparse value of the frequency spectrum, the collected compressed sensing samples (eg, y in Equation 2) ), the vector values of the collected compressed sensing samples (eg, in Equation 2)

), any predefined cross-correlation value (

), the original signal can be restored by iteratively solving the L1 minimization problem.

In an embodiment, the sample check unit 320 is configured to perform the first compression when the cross-correlation value of the original signal corresponding to the first number of compressed sensing samples and the original signal corresponding to the second number of compressed sensing samples satisfy a predetermined condition. The number of sensing samples may be identified as the minimum number of compressed sensing samples related to the frequency spectrum.

라 하고, i-1번째 복원된 원신호를

라 할 때,

와

의 교차상관 값 중 최대값(

)를 미리 지정된 교차상관 값(

)과 비교할 수 있다. 샘플확인부(320)는

를 만족할 때까지 압축센싱 샘플 개수와 센싱 벡터 개수를 기지정된 숫자에 대응하는

만큼 추가하며 반복할 수 있다. More specifically, the sample confirmation unit 320 determines the i-th restored original signal based on the L1 minimization problem.

, and the i-1th restored original signal is

When you say

Wow

The maximum value among the cross-correlation values of

) to the predefined cross-correlation value (

) can be compared with The sample confirmation unit 320 is

The number of compressed sensing samples and the number of sensing vectors corresponding to a predetermined number are

You can add as many as you like and repeat.

만큼 추가된 수만큼의 샘플의 수(이하, 두번째 샘플의 수)를 기초로 복원된 원신호를 포함할 수 있다. 샘플확인부(320)는 첫번째로 복원된 원신호와 두번째로 복원된 원신호 사이의 교차상관 값을 구하고 그 최대값이 미리 지정된 교차상관 값보다 큰지 여부를 확인할 수 있다. 만약, 상기 최대값이 미리 지정된 교차상관 값보다 크지 않은 경우(또는 작은 경우) 샘플확인부(320)는 상기 두번째 샘플의 수에

만큼 추가된 수만큼의 샘플의 수(이하, 세번째 샘플의 수)를 기초로 원신호를 복원하고, 복원된 원신호와 두번째 샘플의 수에 대응하여 복원된 원신호의 교차상관 값을 구해 상기의 비교 과정을 반복할 수 있다. For example, the first reconstructed original signal includes an original signal reconstructed corresponding to the minimum number of compressed sensing samples corresponding to the minimum coarse value, and the second reconstructed original signal includes the minimum compressed sensing sample corresponding to the minimum coarse value. in the number of

The original signal reconstructed based on the number of samples (hereinafter, the number of second samples) by the added number may be included. The sample check unit 320 may obtain a cross-correlation value between the first reconstructed original signal and the second reconstructed original signal, and check whether the maximum value is greater than a predetermined cross-correlation value. If the maximum value is not greater than (or less than) the predetermined cross-correlation value, the sample confirmation unit 320 determines the number of the second samples.

The original signal is reconstructed based on the number of samples added as much as the number of samples (hereinafter, the number of third samples), and the cross-correlation value of the reconstructed original signal and the reconstructed original signal corresponding to the number of second samples is obtained. The comparison process can be repeated.

조건을 만족할 때까지 수행할 수 있다. 만약 샘플확인부(320)는

조건을 만족하는 경우 이 때의 i 값을 현재 성긴 값에 따른 최소의 압축센싱 샘플 개수로 확인할 수 있다. The sample confirmation unit 320 performs the above-described comparison process.

This can be done until the condition is satisfied. If the sample confirmation unit 320 is

If the condition is satisfied, the i value at this time can be confirmed as the minimum number of compressed sensing samples according to the current coarse value.

의 만족 여부의 판단과 관련하여 교차상관 값의 확인을 위한

에 대응하는 복원된 원신호가 없는 경우, 일 예로 최초 교차상관 값을 구하고자 하는 경우 교차상관 값을 구하는 과정은 생략될 수 있다. L1 최소화 문제는 L1 최소화 기법으로 지칭될 수도 있으며, 이와 관련하여서는 통상의 기술자에게 용이한 바 구체적인 설명은 생략하겠다. In an embodiment, the sample confirmation unit 320 is

For the confirmation of the cross-correlation value in relation to the determination of the satisfaction of

When there is no reconstructed original signal corresponding to , for example, when an initial cross-correlation value is to be obtained, the process of obtaining a cross-correlation value may be omitted. The L1 minimization problem may be referred to as an L1 minimization technique, and a detailed description thereof will be omitted since it is easy for those skilled in the art.

An example of a specific algorithm related to the operation of the above-described sample check unit 320 may refer to FIG. 5 .

The main user check unit 330 may check whether the frequency spectrum is used by the main user by using the confirmed number of samples and the obtained compressed sensing samples.

In an embodiment, the main user identification unit 330 may generate at least one sample group by grouping compressed sensing samples obtained from each of the plurality of sensors based on the confirmed minimum number of compressed sensing samples. As an example, the main user identification unit 330 may form a sample group including samples equal to or greater than the minimum number of compressed sensing samples. In addition, the number of sample groups can be formed to a maximum. For example, when the minimum number of compressed sensing samples is 3 and the obtained compressed sensing samples are 10, the main user confirmation unit 330 sets the 10 compressed sensing samples to a first sample group including three compressed sensing samples, or A second sample group including three other compressed sensing samples and a third sample group including the remaining four compressed sensing samples may be grouped.

According to an embodiment, the main user check unit 330 may calculate the number of groups usable for cooperative spectrum sensing with the minimum number of compressed sensing samples. For example, the main user check unit 330 may determine a value obtained by dividing the obtained number of compressed sensing samples by the minimum number of compressed sensing samples as the number of groups usable for cooperative spectrum sensing. If the obtained value appears as a decimal point, rounding is performed to determine the integer part as the number of groups.

The main user check unit 330 may restore an original signal corresponding to each of at least one sample group. For example, the main user check unit 330 may restore the original signal corresponding to each of the first sample group, the second sample group, and the third sample group. Cooperative spectrum sensing may include a sensing method of performing spectrum sensing using a plurality of sensors.

The main user check unit 330 may perform a binary hard decision by comparing each element of the restored original signal with a preset value. The main user check unit 330 may check whether the main user is used for each subband of the frequency spectrum corresponding to the original signal based on the execution of the binary hard decision. Meanwhile, since the binary hard decision is easy for a person skilled in the art, a detailed description thereof will be omitted.

The main user check unit 330 may check whether the main user is used based on a majority fusion rule. Specifically, the main user check unit 330 compares the subbands of the original signal corresponding to each of the first sample group, the second sample group, and the third sample group, and based on a result in which a majority agrees, whether the main user is used or not can be checked.

For example, when the original signals restored for each of the first sample group, the second sample group, and the third sample group are referred to as the first original signal, the second original signal, and the third original signal, the main user confirmation unit ( 330) may check whether the main user is using the first subband of each of the first original signal, the second original signal, and the third original signal. For the first subband, if it is confirmed that the primary user uses the current frequency spectrum for the first original signal and the second original signal, and the third original signal confirms that the main user uses the current frequency spectrum, the majority According to this meaningful result, it can be confirmed that the main user uses the current frequency spectrum for the first subband. The main user check unit 330 may check whether the main user is used for each subband by repeating the above-described process for the remaining subbands of the original signal.

An example of a specific algorithm related to the operation of the above-described main user check unit 330 may refer to FIG. 6 .

In an embodiment, the main user confirmation unit 330 may transmit information on whether the main user is using the frequency spectrum to each of the plurality of sensors in response to confirmation of whether the main user is using the frequency spectrum. For example, when it is checked whether the main user is used for each subband of the frequency spectrum, the main user check unit 330 may transmit information on the subband confirmed not to be used by the main user to each of the plurality of sensors. . As another example, when it is confirmed whether the main user is used for each subband of the frequency spectrum, the main user confirmation unit 330 may transmit information on the subband confirmed to be used by the main user to each of the plurality of sensors. .

In this case, the plurality of sensors may perform communication through a subband confirmed not to be used by the main user among the subbands of the frequency spectrum based on the information on whether the main user is using the plurality of sensors.

4 is a flowchart of each step of a frequency spectrum sensing method according to an embodiment. Each step of the method illustrated in FIG. 4 may be performed in a different order from that illustrated in the drawings in some cases. Hereinafter, content overlapping with the content described above with reference to FIGS. 1 to 3 may be omitted.

In step 410 of FIG. 4 , the sensing device may obtain a compressed sensing sample from each of a plurality of sensors. Specifically, the sensing device may acquire the compressed sensing sample obtained by each of the plurality of sensors from the plurality of sensors. In this case, the sensing device may acquire a compressed sensing sample based on recognizing a plurality of sensors based on a sensing vector corresponding to each of the plurality of sensors.

In an embodiment, the sensing device may collect compressed sensing samples obtained from each of a plurality of sensors. In this regard, as described above with reference to FIG. 3 , specific details will be omitted.

In step 420, the sensing device may determine the minimum number of compressed sensing samples related to the frequency spectrum corresponding to the obtained compressed sensing sample. The sensing device may reconstruct an original signal with respect to the acquired compressed sensing sample. The sensing device may identify the minimum number of compressed sensing samples related to the frequency spectrum corresponding to the compressed sensing samples obtained based on the restored original signal.

In an embodiment, the sensing device includes an average coarse value of a frequency spectrum, a minimum coarse value, an average number of compressed sensing samples corresponding to the average coarse value, a minimum number of compressed sensing samples corresponding to the minimum coarse value, and a preset compression sensing sample. The original signal of the frequency spectrum for each of the first number of compressed sensing samples and the second number of compressed sensing samples may be reconstructed by using the increased value of the number. If the cross-correlation value of the original signal corresponding to the first number of compressed sensing samples and the original signal corresponding to the second number of compressed sensing samples satisfies a predetermined condition, the sensing device calculates the first number of compressed sensing samples with the frequency spectrum. It can be confirmed by the number of related minimum compressed sensing samples. In this case, the first number of compressed sensing samples may be less than the second number of compressed sensing samples by the predetermined number of compressed sensing samples.

In an embodiment, when the sensing device acquires compressed sensing samples, the number of samples may be sequentially increased based on the predetermined number of compressed sensing samples to restore an original signal for each. At this time, the sensing device obtains cross-correlation values for the i-th reconstructed original signal and the i-1 th reconstructed original signal according to the method of increasing the number of samples, and determines whether the cross-correlation value satisfies a predetermined criterion. can be checked When the cross-correlation value satisfies a predetermined criterion, the sensing device may check the number of samples corresponding to the i-th reconstructed original signal as the minimum number of compressed sensing samples. Meanwhile, a more detailed description related thereto will be omitted as described above with reference to FIG. 3 .

In step 430, the sensing device may check whether the frequency spectrum is used by the main user using the confirmed number of samples and the obtained compressed sensing samples.

In an embodiment, the sensing device may generate at least one sample group by grouping compressed sensing samples obtained from each of a plurality of sensors based on the checked minimum number of compressed sensing samples. As an example, the sensing device may group the acquired compressed sensing samples so that the sample includes at least the minimum number of compressed sensing samples per group. In some cases, the sensing device may perform grouping such that the number of groups is the maximum. Specifically, for example, when the minimum number of compressed sensing samples is three and the number of acquired compressed sensing samples is 11, the sensing device may group the 11 compressed sensing samples by dividing them into three, three, and four.

As another example, the sensing device may determine a value obtained by dividing the obtained number of compressed sensing samples by the minimum number of compressed sensing samples as the number of groups usable for cooperative spectrum sensing. If the obtained value appears as a decimal point, rounding is performed to determine the integer part as the number of groups, and grouping can be performed so that each group includes at least the minimum number of compressed sensing samples.

The sensing device may reconstruct an original signal corresponding to each of the at least one sample group. The sensing device may reconstruct an original signal for each group for cooperative spectrum sensing. The sensing device may perform binary hard decision by comparing each element of the restored original signal with a preset value. The sensing device may check whether the main user is used for each subband of the original signal restored in response to each group based on the binary hard decision. Meanwhile, since the binary hard decision is easy for a person skilled in the art, a detailed description thereof will be omitted.

The sensing device may check whether the main user is used for each subband and whether the main user is used for each subband of the frequency spectrum based on the majority synthesis rule. Specifically, when the grouped group includes the first sample group, the second sample group, and the third sample group, the sensing device compares whether the main user uses the subband of the original signal restored in response to each sample group. can The sensing device may confirm a result of which a majority agrees with the result of whether the main user is used for each sample group for the same subband as the result of whether or not the main user is used for the corresponding subband of the frequency spectrum.

Specifically, for example, when the original signals restored for each of the first sample group, the second sample group, and the third sample group are referred to as a first original signal, a second original signal, and a third original signal, the sensing device It is possible to check whether the main user is using the first subband of each of the first original signal, the second original signal, and the third original signal. For the first subband, if it is confirmed that the primary user uses the current frequency spectrum for the first original signal and the second original signal, and the third original signal confirms that the main user uses the current frequency spectrum, the majority According to this meaningful result, it can be confirmed that the main user uses the current frequency spectrum for the first subband. The main user check unit 330 may check whether the main user is used for each subband by repeating the above-described process for the remaining subbands of the original signal.

The sensing device may transmit, to each of the plurality of sensors, information on whether the main user is using the frequency spectrum in response to confirmation of use of the main user. The plurality of sensors may perform communication through at least one sub-band not used by the main user based on the reception of information on whether the main user is in use.

In an embodiment, the method according to FIG. 4 may be recorded as a program for execution by a computer and stored in a computer-readable non-transitory recording medium. In this case, the method according to FIG. 4 may be performed based on reading the recording medium by a computer.

를 추정하는 알고리즘의 예를 나타낸다. 5 shows an example of an algorithm for determining the minimum number of compressed sensing samples for frequency spectrum sensing according to an embodiment. Specifically, FIG. 5 shows the minimum number of compressed sensing samples according to the coarse value K.

An example of an algorithm for estimating is shown.

과 최소 성긴 값

은 사전 정보로 알고 있고 이를 통해

과

을 알고 있다고 가정한다. 센싱 장치는, 최초 입력 값으로

를 이용하고, 추정하고자 하는 압축센싱 샘플 개수

는 초기화 값으로

을 이용할 수 있다.Referring to FIG. 5 , the average sparse value through the statistical characteristics of the spectrum

and Minimum Coarse

is known as prior information, and through

class

Assume that you know The sensing device is the first input value

Using , the number of compressed sensing samples to estimate

is the initialized value

is available.

를 복원하는데, 이를 위해 센싱 장치는 i값만큼의 압축센싱 샘플과 센싱 벡터를 이용될 수 있다. i번째 복원된 원신호를

라하면, 센싱 장치는

와

의 교차상관 값 중 최대값

를 미리 지정된

와 비교하여

라는 조건을 만족하는지 확인할 수 있다. After that, the sensing device repeatedly solves the L1 minimization problem to solve the original signal

To restore , the sensing device may use compressed sensing samples and sensing vectors as many as i values. i-th restored original signal

In other words, the sensing device is

Wow

the maximum of the cross-correlation values of

pre-specified

compared to

It can be checked whether the condition is satisfied.

라는 조건이 만족되지 않으면 상기 조건이 만족될 때까지 압축센싱 샘플의 수와 센싱 벡터의 수(예: i)를

만큼씩 증가시켜 상기의 조건을 만족하는지 여부를 판별하는 과정을 반복할 수 있다. 여기서,

는 미리 지정된 값일 수 있다. 최초 시행 시

가 없는 경우 교차상관 값을 구하는 과정은 생략될 수 있다.the sensing device

If the condition is not satisfied, the number of compressed sensing samples and the number of sensing vectors (eg, i)

The process of determining whether or not the above condition is satisfied by increasing by the amount may be repeated. here,

may be a predetermined value. When first implemented

In the absence of , the process of calculating the cross-correlation value may be omitted.

조건을 만족하는 경우 이 때의 i값을 현재 시간에서 성긴 값 K에 따른 최소 압축센싱 샘플의 수

으로 결정할 수 있다. the sensing device

If the condition is satisfied, the i value at this time is the minimum number of compressed sensing samples according to the sparse value K at the current time.

can be determined as

6 shows an example of an algorithm for confirming whether a main user is used for frequency spectrum sensing according to an embodiment. Specifically, FIG. 6 shows an example of an algorithm for confirming whether a main user uses a frequency spectrum by performing cooperative spectrum sensing according to a majority synthesis rule.

과 압축센싱 샘플의 수 L을 이용하여 스펙트럼 센싱에 사용 가능한 그룹의 수

를 구할 수 있고, 센싱 장치는 이를 초기화 값으로 이용할 수 있다. Referring to FIG. 6 , the minimum number of compressed sensing samples confirmed by the algorithm according to FIG. 5 .

The number of groups available for spectral sensing using L and the number of compressed sensing samples.

can be obtained, and the sensing device may use it as an initial value.

를 이용하고 상술한 바와 같이

초기화 값으로 이용할 수 있다. The sensing device is the first input value

using and as described above

It can be used as an initialization value.

개의 압축센싱 샘플과

개의 센싱 벡터를 통해

번 만큼 원신호

를 복원할 수 있다. 도 6에 의하면, 센싱 장치는 y의

에서

(여기서, j는 1부터

까지 순차적으로 증가하는 값)까지 원소로 된

와,

의

행에서

행으로 된 서브 매트릭스

를 사용하여 원신호

를 복원할 수 있다. (여기서, j번째 복원된 원신호

를

라 한다) the sensing device

Compression sensing samples of dogs and

through two sensing vectors

round signal

can be restored. 6, the sensing device is y

at

(here, j is from 1

up to a value that increases sequentially until

Wow,

of

in a row

row submatrix

source signal using

can be restored. (Here, the j-th restored original signal

cast

say)

의 각 원소와 미리 지정된

값를 비교하여 바이너리 경판정(hard decision)을 할 수 있다. 이에 대한 결과로 센싱 장치는

를 얻게 될 수 있다. 여기서

는 관찰하는 주파수 스펙트럼(또는 광대역 주파수 스펙트럼, 또는 광대역 스펙트럼)을 N개의 서브 밴드 나눈 대상 중 i번째 서브 밴드에 대한 주사용자의 사용유무(또는 존재유무)를 뜻하게 된다. 예를 들어,

일 때 i번째 서브 밴드는 주사용자에 의해 사용되고 있고, 0인 경우 i번째 서브 밴드는 주사용자에 의해 사용되지 않음을 의미할 수 있다. The sensing device is the j-th restored original signal

with each element of

A binary hard decision can be made by comparing the values. As a result, the sensing device

can be obtained here

denotes whether the main user uses (or exists) the i-th subband among the objects of dividing the observed frequency spectrum (or broadband frequency spectrum, or broadband spectrum) into N subbands. For example,

When , the i-th subband is used by the main user, and 0 may mean that the i-th subband is not used by the main user.

번 반복하여

에서

까지 얻어지게 되면, 과반수 합성 룰에 의해

을 산출할 수 있다. 여기서,

은 과반수 합성 룰에 결정된 주파수 스펙트럼의 i번째 서브 밴드의 주사용자 사용유무를 나타낼 수 있다. The sensing device may repeat the above process for each sample group. In other words, the sensing device performs the above-mentioned main user use confirmation process.

repeatedly

at

When obtained up to , according to the majority synthesis rule,

can be calculated. here,

may indicate whether the main user uses the i-th subband of the frequency spectrum determined by the majority synthesis rule.

In this way, when a group is divided based on the minimum number of compressed sensing samples and the original signal is reconstructed for each group, various samples of the original signal can be obtained. By checking whether or not the main user is used for each subband of the frequency spectrum based on the sample obtained in this way, the accuracy of the result regarding whether the main user is used can be improved.

The electronic device according to the above-described embodiments includes a processor, a memory for storing and executing program data, a permanent storage such as a disk drive, a communication port for communicating with an external device, a touch panel, a key, a button, etc. It may include the same user interface device and the like. Methods implemented as software modules or algorithms may be stored on a computer-readable recording medium as computer-readable codes or program instructions executable on the processor. Here, a computer-readable recording medium includes a magnetic storage medium (eg, read-only memory (ROM), random-access memory (RAM), floppy disk, hard disk, etc.) and an optically readable medium (eg, CD-ROM). ), and DVD (Digital Versatile Disc)). The computer-readable recording medium may be distributed among network-connected computer systems, so that the computer-readable code may be stored and executed in a distributed manner. The medium may be readable by a computer, stored in a memory, and executed on a processor.

This embodiment may be represented by functional block configurations and various processing steps. These functional blocks may be implemented in any number of hardware and/or software configurations that perform specific functions. For example, an embodiment may be an integrated circuit configuration, such as memory, processing, logic, look-up table, etc., capable of executing various functions by the control of one or more microprocessors or other control devices. can be hired Similar to how components may be implemented as software programming or software components, this embodiment includes various algorithms implemented in a combination of data structures, processes, routines or other programming constructs, including C, C++, Java ( Java), assembler, etc. may be implemented in a programming or scripting language. Functional aspects may be implemented in an algorithm running on one or more processors. In addition, the present embodiment may employ the prior art for electronic environment setting, signal processing, and/or data processing. Terms such as “mechanism”, “element”, “means” and “configuration” may be used broadly and are not limited to mechanical and physical configurations. The term may include the meaning of a series of routines of software in connection with a processor or the like.

The above-described embodiments are merely examples, and other embodiments may be implemented within the scope of the claims to be described later.

Claims (11)

In the frequency spectrum sensing method for cognitive wireless communication,
obtaining a compressed sensing sample from each of a plurality of sensors;
determining the minimum number of compressed sensing samples related to a frequency spectrum corresponding to the obtained compressed sensing sample based on the restoration of the obtained compressed sensing sample;
Using the confirmed number of samples and the obtained compressed sensing samples to confirm whether the main user of the frequency spectrum is used,
The step of confirming the minimum number of compressed sensing samples,
An average coarse value of the frequency spectrum, a minimum coarse value, an average number of compressed sensing samples corresponding to the average coarse value, a minimum number of compressed sensing samples corresponding to the minimum coarse value, and an increase in the number of predetermined compressed sensing samples Restoring the original signal of the frequency spectrum for each of the first number of compressed sensing samples and the number of second compressed sensing samples by using
When the cross-correlation value of the original signal corresponding to the first number of compressed sensing samples and the original signal corresponding to the second number of compressed sensing samples satisfies a predetermined condition, the first number of compressed sensing samples is combined with the frequency spectrum comprising the step of confirming with the number of relevant minimum compressed sensing samples,
The first compressed sensing sample number is less than the second compressed sensing sample number by the predetermined number of compressed sensing samples,
Frequency spectrum sensing method.
According to claim 1,
Each of the plurality of sensors is disposed at a different position to obtain a compressed sensing sample for the frequency spectrum
Frequency spectrum sensing method.
According to claim 1,
Each of the plurality of sensors includes a low-power sensor
Frequency spectrum sensing method.
According to claim 1,
The step of obtaining the compression sensing sample comprises:
Comprising the step of obtaining the compressed sensing sample based on the information on the sensing vector of each of the plurality of sensors,
Information on the sensing vector is stored in advance
Frequency spectrum sensing method.
According to claim 1,
The number of the plurality of sensors is greater than the minimum number of compressed sensing samples required to correspond to the maximum sparse value of the frequency spectrum.
Frequency spectrum sensing method.
delete In the frequency spectrum sensing method for cognitive wireless communication,
obtaining a compressed sensing sample from each of a plurality of sensors;
determining the minimum number of compressed sensing samples related to a frequency spectrum corresponding to the obtained compressed sensing sample based on the restoration of the obtained compressed sensing sample;
Using the confirmed number of samples and the obtained compressed sensing samples to confirm whether the main user of the frequency spectrum is used,
The step of confirming whether the main user is used is,
Generating at least one sample group by grouping the compressed sensing samples obtained from each of the plurality of sensors based on the identified minimum number of compressed sensing samples;
reconstructing an original signal corresponding to each of the at least one sample group;
Checking whether a main user is used for each subband of the restored original signal based on a binary hard decision;
Containing the step of determining whether the main user is used for each subband and whether the main user is used for each subband of the frequency spectrum based on a majority synthesis rule,
Frequency spectrum sensing method.
According to claim 1,
Corresponding to the confirmation of whether the main user uses the frequency spectrum, further comprising the step of transmitting information on whether the main user uses the frequency spectrum to each of the plurality of sensors
Frequency spectrum sensing method.
8. The method of claim 7,
The plurality of sensors perform communication through a subband that is confirmed not to be used by the main user among the subbands of the frequency spectrum based on the information on whether the main user is in use.
Frequency spectrum sensing method.
In the frequency spectrum sensing device for cognitive wireless communication,
A sample acquisition unit for obtaining a compressed sensing sample from each of the plurality of sensors;
A sample confirmation unit for confirming the minimum number of compressed sensing samples related to a frequency spectrum corresponding to the obtained compressed sensing sample based on the restoration of the obtained compressed sensing sample;
and a main user confirmation unit for confirming whether the frequency spectrum is used by the main user using the number of the confirmed samples and the obtained compressed sensing samples,
The sample confirmation unit,
An average coarse value of the frequency spectrum, a minimum coarse value, an average number of compressed sensing samples corresponding to the average coarse value, a minimum number of compressed sensing samples corresponding to the minimum coarse value, and an increase in the number of predetermined compressed sensing samples Using to restore the original signal of the frequency spectrum for each of the first number of compressed sensing samples and the second number of compressed sensing samples, the original signal corresponding to the first number of compressed sensing samples and the second number of compressed sensing samples When the cross-correlation value of the original signal corresponding to satisfies a predetermined condition, the first number of compressed sensing samples is determined as the minimum number of compressed sensing samples related to the frequency spectrum,
The first compressed sensing sample number is less than the second compressed sensing sample number by the predetermined number of compressed sensing samples,
frequency spectrum sensing device.
obtaining a compressed sensing sample from each of a plurality of sensors;
determining the minimum number of compressed sensing samples related to a frequency spectrum corresponding to the obtained compressed sensing sample based on the restoration of the obtained compressed sensing sample;
Using the confirmed number of samples and the obtained compressed sensing samples to confirm whether the main user of the frequency spectrum is used,
The step of confirming the minimum number of compressed sensing samples,
An average coarse value of the frequency spectrum, a minimum coarse value, an average number of compressed sensing samples corresponding to the average coarse value, a minimum number of compressed sensing samples corresponding to the minimum coarse value, and an increase in the number of predetermined compressed sensing samples Restoring the original signal of the frequency spectrum for each of the first number of compressed sensing samples and the number of second compressed sensing samples by using
When the cross-correlation value of the original signal corresponding to the first number of compressed sensing samples and the original signal corresponding to the second number of compressed sensing samples satisfies a predetermined condition, the first number of compressed sensing samples is combined with the frequency spectrum comprising the step of confirming with the number of relevant minimum compressed sensing samples,
The first compressed sensing sample number is less than the second compressed sensing sample number by the predetermined number of compressed sensing samples,
A computer-readable non-transitory recording medium recording a program for executing a frequency spectrum sensing method in a computer.

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