KR20170018274A - System for coopeative spectrum sensing using sub-nyquist sampling and method thereof - Google Patents

System for coopeative spectrum sensing using sub-nyquist sampling and method thereof Download PDF

Info

Publication number
KR20170018274A
KR20170018274A KR1020150142064A KR20150142064A KR20170018274A KR 20170018274 A KR20170018274 A KR 20170018274A KR 1020150142064 A KR1020150142064 A KR 1020150142064A KR 20150142064 A KR20150142064 A KR 20150142064A KR 20170018274 A KR20170018274 A KR 20170018274A
Authority
KR
South Korea
Prior art keywords
user terminal
frequency band
probability
primary user
detecting
Prior art date
Application number
KR1020150142064A
Other languages
Korean (ko)
Other versions
KR101776666B1 (en
Inventor
신요안
정홍규
김광열
Original Assignee
숭실대학교산학협력단
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 숭실대학교산학협력단 filed Critical 숭실대학교산학협력단
Priority to US15/224,679 priority Critical patent/US9967117B2/en
Publication of KR20170018274A publication Critical patent/KR20170018274A/en
Application granted granted Critical
Publication of KR101776666B1 publication Critical patent/KR101776666B1/en

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
    • G01R23/16—Spectrum analysis; Fourier analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
    • G01R23/02—Arrangements for measuring frequency, e.g. pulse repetition rate; Arrangements for measuring period of current or voltage

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The present invention relates to a cooperative spectrum sensing system using sub-Nyquist sampling and a method therefor.
A cooperative spectrum sensing system comprising a plurality of secondary user terminals and a convergence center for detecting a frequency band occupied by a primary user terminal according to the present invention, the plurality of secondary user terminals comprising: A receiving unit for receiving a signal transmitted from a terminal; A sampling unit for sub-Nyquist sampling the signal at a predetermined downsampling rate; An energy detector for detecting energy of the sampled signal and detecting a frequency band occupied by the primary user terminal; And an operation unit for calculating a probability of detecting a frequency band occupied by the primary user terminal and a probability of erroneous detection using the spectrum of the frequency band and transmitting the calculation result to the fusion center.
According to the present invention, a secondary user who borrows a frequency band in a cognitive radio network can use a low-speed analog-to-digital converter to transmit a signal to a primary user within a range that does not interfere with a primary user, It is possible to expect a cost reduction effect for constructing a spectrum sensing system which should be the most prior in a cognitive wireless network.

Description

TECHNICAL FIELD [0001] The present invention relates to a cooperative spectrum sensing system using sub-Nyquist sampling and a cooperative spectrum sensing system using sub-

The present invention relates to a cooperative spectrum sensing system using subnetworkquest sampling and a method thereof. More particularly, the present invention relates to a cooperative spectrum sensing system using subnetworkquest sampling, 0001] The present invention relates to a cooperative spectrum sensing system using sub-Nyquist sampling for sensing a band and a method thereof.

The US Federal Communications Commission (FCC) has announced that many of the frequency bands are not being used at the same time and that they are inefficient in time and space. Recently, researches on cognitive radio networks (CRNs) have been actively carried out in order to solve the frequency shortage problem and to utilize the frequency band efficiently.

A cognitive wireless network is an intelligent network in which a secondary user borrowing a frequency band opportunistically accesses an empty frequency band without interfering with the primary user, the licensee for that frequency band. To construct a cognitive wireless network, the secondary user must perform accurate and fast broadband spectral sensing for an empty frequency band.

However, broadband spectral sensing requires very fast sampling in order to satisfy the Nyquist Rate, which is costly to implement an analog-to-digital converter (ADC) for high-speed sampling. Therefore, there is a problem that the hardware implementation is practically impossible.

More specifically, for broadband spectrum sensing, implementation of an analog-to-digital converter by Nyquist sampling, implementation of a sensing system using a compressive sensing technique, implementation of a sensing system using sub-Nyquist sampling, There are three major research areas.

However, basically, a system based on Nyquist sampling requires a high-speed signal converter. This has a disadvantage that expensive equipment must be purchased. Therefore, recently, there has been a research on system construction using compression sensing and sub-Nyquist sampling Is actively proceeding.

In this case, the compression sensing technique has a disadvantage in that it has a very high complexity in the process of recovering a signal compressed under the Nyquist sampling rate to the original signal, but the subnetworkquest sampling technique has a very low complexity in the implementation of the algorithm have.

However, there is a problem that it is impossible to obtain a closed form of the sensing performance due to the irregular spectrum occupation of the primary user in the cognitive radio network and the aliasing effect by the Nyquist sampling.

The technique which is the background of the present invention is disclosed in Korean Patent Registration No. 1189904 (October 10, 2012 announcement).

SUMMARY OF THE INVENTION The present invention has been made in order to solve the above problems, and it is an object of the present invention to provide a method and apparatus for a secondary user who borrows a frequency band in a cognitive radio network, A cooperative spectrum sensing system using sub-Nyquist sampling for sensing a frequency band that is not being used by a primary user and a method therefor.

In addition, the present invention is a method of analyzing the detection performance using sub-Nyquist sampling in a state in which the degree of sparsity of a frequency band to be used is known, The present invention also provides a cooperative spectrum sensing system using sub-Nyquist sampling and a method thereof.

According to an aspect of the present invention, there is provided a cooperative spectrum sensing system including a plurality of secondary user terminals and a convergence center for detecting a frequency band occupied by a primary user terminal, The secondary user terminal comprises: a receiving unit for receiving a signal transmitted from the primary user terminal; A sampling unit for sub-Nyquist sampling the signal at a predetermined downsampling rate; An energy detector for detecting energy of the sampled signal and detecting a frequency band occupied by the primary user terminal; And an operation unit for calculating a probability of detecting a frequency band occupied by the primary user terminal and a probability of erroneous detection using the spectrum of the frequency band and transmitting the calculation result to the fusion center.

The energy detecting unit determines that the corresponding frequency band is occupied by the primary user terminal if the detected energy is equal to or higher than the threshold value and determines that the corresponding frequency band is not occupied by the primary user terminal if the detected energy is lower than the threshold can do.

The operation unit can calculate the probability of detecting the frequency band occupied by the primary user terminal by the following equation.

Figure pat00001

Where i is the index of the secondary user terminal, m is the channel,? Is the threshold,

Figure pat00002
The size of the index set included in the frequency axis in the corresponding channel,
Figure pat00003
Is a signal-to-noise ratio (SNR) in a spectrum bin, and L is a down-sampling rate.

The operation unit can calculate the probability of erroneously detecting the frequency band occupied by the primary user terminal by the following equation.

Figure pat00004

The convergence center receives results calculated from the plurality of secondary user terminals sampled at the same downsampling rate, and if the downsampling rate is smaller than the reference value, It is possible to calculate the probability of detecting the frequency band occupied by the differential user terminal and the probability of false detection.

Figure pat00005
,

Figure pat00006

here,

Figure pat00007
Is the probability of detection,
Figure pat00008
Is the probability of false detection.

If the downsampling rate is equal to or greater than the reference value, the convergence center can calculate the probability of detecting the frequency band occupied by the primary user terminal and the probability of false detection by the OR-rule as shown in the following equation .

Figure pat00009
,

Figure pat00010

here,

Figure pat00011
Is the probability of detection,
Figure pat00012
Is the probability of false detection.

In addition, a sensing method using a cooperative spectrum sensing system including a plurality of secondary user terminals and a convergence center for detecting a frequency band occupied by a primary user terminal according to an exemplary embodiment of the present invention, Receiving a signal transmitted from the primary user terminal; Sub-Nyquist sampling the signal at a predetermined down-sampling rate of the secondary user terminal; Detecting energy of the sampled signal by the secondary user terminal and detecting a frequency band occupied by the primary user terminal; Calculating a probability of detecting a frequency band occupied by the primary user terminal and a probability of detecting an error by using the spectrum of the frequency band; And transmitting the calculation result to the fusion center by the secondary user terminal.

A cooperative spectrum sensing system using sub-Nyquist sampling according to the present invention is characterized in that a secondary user borrowing a frequency band in a cognitive radio network uses a low-speed analog-to- It is possible to sense a frequency band that is not used by the primary user within a range that does not cause interference to the primary user, and thus it is possible to expect a cost reduction effect for constructing a spectrum sensing system that should be the most advanced in the cognitive wireless network.

Further, according to the present invention, sub-Nyquist sampling is used in a state in which the degree of sparsity of the frequency band to be used, that is, the degree of uniformity in the spectrum, is recognized, so that superior spectral sensing performance can be obtained even through a small number of samples , There is an excellent effect in terms of complexity.

1 is a configuration diagram of a cooperative spectrum sensing system using sub-Nyquist sampling according to an embodiment of the present invention.
2 is a configuration diagram of a secondary user terminal according to an embodiment of the present invention.
3 is a flowchart illustrating an operation flow of a cooperative spectrum sensing method using sub-Nyquist sampling according to an embodiment of the present invention.
FIG. 4 is a graph comparing results obtained by performing Nyquist sampling and sub-Nyquist sampling.
5 is a graph for comparing ROC performance when sampling is performed at different sampling rates at the same SNR.
6 is a graph for comparing the ROC performance of the AND-rule and the OR-rule at the same SNR.
7 is a graph showing the ROC performance of the OR-rule at various SNRs.
8 is a graph illustrating ROC performance of an AND-rule at various SNRs.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. The terms used are terms selected in consideration of functions in the embodiments, and the meaning of the terms may vary depending on the subject, the intention or the precedent of the operator, and the like. Therefore, the meaning of the terms used in the following embodiments is defined according to the definition when specifically defined in this specification, and unless otherwise defined, it should be interpreted in a sense generally recognized by those skilled in the art.

First, a cooperative spectrum sensing system using sub-Nyquist sampling according to an embodiment of the present invention will be described with reference to FIGS. 1 and 2. FIG.

FIG. 1 is a configuration diagram of a cooperative spectrum sensing system using sub-Nyquist sampling according to an embodiment of the present invention, and FIG. 2 is a configuration diagram of a secondary user terminal according to an embodiment of the present invention.

As shown in FIG. 1, the cooperative spectrum sensing system using sub-Nyquist sampling according to an embodiment of the present invention includes a primary user terminal 100, a secondary user terminal 200, and a convergence center 300.

The primary user terminal 100 refers to a user who primarily uses a specific frequency band, for example, a license holder for a corresponding frequency band such as KBS, SKT, and the like.

The secondary user terminal 200 is a user who borrows a frequency band. More specifically, the secondary user terminal 200 detects a frequency band occupied by the primary user terminal 100 in the corresponding frequency band and gives interference to the primary user terminal 100 Access to the fre- quency band is used.

1, the secondary user terminal 200 for detecting a frequency band occupied by the primary user terminal 100 may be constituted by a plurality of units. As shown in FIG. 2, the secondary user terminal 200 includes a receiving unit 210, An energy detection unit 230, and an operation unit 240. The operation unit 240 includes an operation unit 220, an energy detection unit 230,

The receiving unit 210 receives a signal transmitted from the primary user terminal 100.

The sampling unit 220 performs sub-Nyquist sampling on the signal received through the receiver 210 at a predetermined downsampling rate.

At this time, the sub-Nyquist sampling is performed at a predetermined down sampling rate compared to the Nyquist sampling in which the entire signal for the frequency band is received and is sampled at a high speed through the analog-to-digital converter performing the high-speed sampling And performs low-speed sampling.

Therefore, the sampling unit 220 samples only the received signal at a predetermined down-sampling rate in the entire frequency band.

The energy detector 230 detects the energy of the sampled signal from the sampling unit 220 and detects the frequency band occupied by the primary user terminal 100.

The energy detector 230 determines that the corresponding frequency band is occupied by the primary user terminal 100 if the detected energy is greater than or equal to a threshold value, It is judged that it has not been occupied.

The calculating unit 240 calculates the probability of detecting the frequency band occupied by the primary user terminal 100 and the probability of erroneous detection using the uniformity of the spectrum of the frequency band, 300).

The fusion center 300 receives the results calculated from the plurality of secondary user equipments 200 sampled at the same down sampling rate and outputs the calculated result to the primary user terminal 200 via the AND- And calculates the probability of detecting the frequency band occupied by the frequency band 100 and the probability of false detection.

Hereinafter, a sensing method using a cooperative spectrum sensing system according to an embodiment of the present invention will be described in detail.

FIG. 3 is a flowchart illustrating an operation flow of a cooperative spectrum sensing method using sub-Nyquist sampling according to an embodiment of the present invention, and a specific operation of the present invention will be described with reference to FIG.

According to the cooperative spectrum sensing method using sub-Nyquist sampling according to the embodiment of the present invention, the primary user terminal 100 transmits signals to a plurality of secondary user terminals 200 (S310).

Then, each of the secondary user terminals 200 subnetworks a signal received in step S310 at a predetermined downsampling rate (S320).

At this time, the sub-Nyquist sampling receives a small amount of signal at a set down sampling rate compared with the Nyquist sampling in which the entire signal for the frequency band is received and is sampled at a high speed through the analog-to-digital converter performing high- Thereby performing low-speed sampling.

Accordingly, in step S320, only the received signal is sampled at a predetermined down-sampling rate in the entire frequency band.

More specifically, the received signal received by the secondary user terminal 200 i in the perceived wireless network is expressed as Equation 1 below, and the sub-Nyquist sampling is performed at the down sampling rate L through Equations 2 and 3 do.

In this case, Equation (3) is the Fourier transform of Equation (2).

Figure pat00013

here,

Figure pat00014
S (t) is the transmission signal of the primary user terminal 100,
Figure pat00015
Represents the noise received by the index i of the secondary user terminal 200.

Figure pat00016

here,

Figure pat00017
M denotes a channel, L denotes a sampling interval, and T denotes a sampling time.

Figure pat00018

here,

Figure pat00019
Is a function defined for two variables with integer values, where n represents a signal at a predetermined downsampling rate.

In order to analyze the frequency axis, a discrete impulse train (Discrete Impulse Train)

Figure pat00020
And a Discrete Fourier Transform (DFT)
Figure pat00021
Are calculated as shown in the following equations (4) and (5).

Figure pat00022

Here, N represents the number of signals generated through sampling.

Figure pat00023

In this case, Equation (2) is represented on the frequency axis as expressed by Equation (6) below, and the relationship between sub-Nyquist sampling and Nyquist sampling can be found by the following Equation (7).

Figure pat00024

Here, Y [k] and X [k] are discrete Fourier transforms of y [n] and x [n]

Figure pat00025
Is a circular convolution and 1 / N is a scaling factor for simply expressing the detection probability to be analyzed later.

Figure pat00026

here,

Figure pat00027
Means the modulo operator.

Next, the secondary user terminal 200 detects the energy of the sampled signal in step S320 and detects the frequency band occupied by the primary user terminal 100 in step S340.

At this time, if the energy detected in step S340 is greater than the threshold value, the secondary user terminal 200 determines that the corresponding frequency band is occupied by the primary user terminal 100. If the energy is smaller than the threshold value, It is determined that the terminal 100 is not occupied.

As a result, the aliasing effect of the sub-Nyquist sampling can be observed through Equation (7) and it can be seen that the Nyquist sampling signal can not be restored because L signals are added.

Therefore, in order to detect the signal of the primary user terminal 100 based on Equation (7), the energy of the signal sampled by the following Equation (8) is detected to determine the presence or absence of the signal.

Figure pat00028

Here,

Figure pat00029
Is a reference value for determining the presence or absence of a signal,
Figure pat00030
Is an index set included in the frequency axis in the corresponding channel,
Figure pat00031
The
Figure pat00032
.

That is, if the detected energy is equal to or greater than the threshold value,

Figure pat00033
), And if it is less than the threshold value, the signal is not received (
Figure pat00034
).

At this time

Figure pat00035
Is modeled as a multi-variate Gaussian distribution with an average of 0,
Figure pat00036
The variance of S [k]
Figure pat00037
Is equal and the variance of noise
Figure pat00038
Assuming that we normalize
Figure pat00039
Can be expressed by the following equation (9). &Quot; (9) "

Figure pat00040

here,

Figure pat00041
Is an index set including Spectral Support among L signals added in a channel,
Figure pat00042
The degree of freedom
Figure pat00043
Means a Chi-square distribution.

Next, the secondary user terminal 200 calculates the probability of detecting the frequency band occupied by the primary user terminal 100 and the probability of false detection using the uniformity of the spectrum for the frequency band ( S350), and transmits the calculated result to the fusion center 300 (S360).

That is, according to the analysis as described above

Figure pat00044
and
Figure pat00045
in
Figure pat00046
(Probability Density Function) < / RTI >

Figure pat00047

Figure pat00048

here,

Figure pat00049
The
Figure pat00050
Signal-to-noise ratio (SNR) in the " Spectrum Bin "
Figure pat00051
The
Figure pat00052
Satisfy
Figure pat00053
. Therefore, the probability of detecting the frequency band occupied by the primary user terminal 100 (correct detection probability) and the probability of false alarm (False Alarm Probability) are calculated by the following equations (12) and (13), respectively.

Figure pat00054

Figure pat00055

However, as shown in Equations (12) and (13), in the case of sub-Nyquist sampling, spectrum support

Figure pat00056
), It is assumed that the accurate detection probability can be obtained by using the following lemma in the present invention.

In a frequency band that knows a certain degree of spectrum (s), a specific frequency index is used for spectral support

Figure pat00057
) Is P = s / NL,
Figure pat00058
The probability that two or more signals in the spectrum support are added by the sub-Nyquist sampling converges to zero.

In other words,

Figure pat00059
The equation (12) and (13) are summarized as the following equations (14) and (15).

Figure pat00060

Figure pat00061

Where i is the index of the secondary user terminal 200, m is the channel,? Is the threshold,

Figure pat00062
The size of the index set included in the frequency axis in the corresponding channel,
Figure pat00063
Is the signal-to-noise ratio (SNR) in the spectral bin, and L is the sampling down-sampling rate.

Next, the fusion center 300 receives the calculated results from the plurality of secondary user terminals 200 sampled at the same down sampling rate (S370).

Next, the fusion center 300 calculates the probability of detecting the frequency band occupied by the primary user terminal 100 and the probability of false detection through AND-rule or OR-rule according to the down sampling rate ( S380).

More specifically, the fusion center 300 considers Hard Decision for cooperative sensing. In the hard decision combination, the probability of detection by the AND-rule is calculated by Equation (16), and the probability of false detection is (17).

Figure pat00064

Figure pat00065

In addition, the probability of detection by the OR-rule is calculated by Expression (18), and the probability of false detection is calculated by Expression (19).

Figure pat00066

Figure pat00067

That is, in the present invention, it is also possible to set the number of signals (N) * down-sampling rate (L) generated through sampling so as to satisfy the lemma in all processes.

4 to 8 show simulation results for demonstrating the performance of the present invention.

Before explaining the simulation results,

Figure pat00068
The total bandwidth of MHz
Figure pat00069
Hz divided by the same bandwidth
Figure pat00070
Channels, and two primary user terminals 100 are assumed to randomly occupy non-overlapping channels.

FIG. 4 shows a signal-to-noise ratio (SNR)

Figure pat00071
(b) and the down sampling rate L = 8 (c), respectively, for the signals generated by the Nyquist sampling (a) and the down-sampling rate Show. As can be seen from FIG. 4, it can be seen that the performance improves as the sub-Nyquist sampling rate approaches the Nyquist sampling rate due to the noise.

 FIG. 5 is a graph for comparing the ROC performance when samples are sampled at different sampling rates at the same SNR, and FIG. 6 is a graph for comparing the ROC performance of AND-rule and OR-rule at the same SNR.

More specifically, FIG. 5 shows the SNR

Figure pat00072
dB shows the performance of ROC (Receiver Operating Characteristic) after two secondary users 200 perform spectral sensing. FIG. 6 shows ROC performance after the hard decision technique is performed in the fusion center 300. FIG. As can be seen from FIG. 6, it can be seen that the OR-rule significantly outperforms the AND-rule.
Figure pat00073
and
Figure pat00074
We can confirm that lemma 1 is satisfied with high probability.

FIG. 7 is a graph showing ROC performance of an OR-rule at various SNRs, and FIG. 8 is a graph illustrating ROC performance of an AND-rule at various SNRs.

7 and 8 show the cooperative sensing performance of the OR-rule and the AND-rule according to various SNRs, and both methods show a perfect detection performance when the SNR is 5dB or more.

As described above, the cooperative spectrum sensing system using the sub-Nyquist sampling according to the embodiment of the present invention and the method thereof enable a secondary user who borrows a frequency band in a perceived wireless network to use a low-speed analog- It is possible to sense a frequency band that is not used by the primary user within a range that does not interfere with the primary user who is the licensee of the corresponding frequency band, thereby reducing the cost of establishing a spectrum sensing system Effect can be expected.

In addition, by using sub-Nyquist sampling in the state of recognizing the degree of sparsity of the frequency band to be used, that is, in the spectrum, superior spectral sensing performance can be obtained even with a small number of samples, Has a very good effect.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, Therefore, the present invention should be construed as a description of the claims which are intended to cover obvious variations that can be derived from the described embodiments.

100: primary user terminal 200: secondary user terminal
210: Receiving unit 220: Sampling unit
230: Energy detection unit 240:
300: Convergence Center

Claims (12)

A cooperative spectrum sensing system comprising a plurality of secondary user terminals and a convergence center for detecting a frequency band occupied by a primary user terminal,
Wherein the plurality of secondary user terminals comprise:
A receiver for receiving a signal transmitted from the primary user terminal;
A sampling unit for sub-Nyquist sampling the signal at a predetermined downsampling rate;
An energy detector for detecting energy of the sampled signal and detecting a frequency band occupied by the primary user terminal; And
And a calculation unit for calculating a probability of detecting a frequency band occupied by the primary user terminal and a probability of erroneous detection using the spectrum of the frequency band and transmitting the calculation result to the fusion center, Cooperative Spectrum Sensing System Using Quist Sample. The method according to claim 1,
Wherein the energy detector comprises:
Determining that the corresponding frequency band is occupied by the primary user terminal if the detected energy is greater than or equal to a threshold value and determining that the corresponding frequency band is not occupied by the primary user terminal if the detected energy is below the threshold, Cooperative Spectrum Sensing System Using Sampling. The method according to claim 1,
The operation unit,
A cooperative spectrum sensing system using sub-Nyquist sampling for calculating a probability of detecting a frequency band occupied by the primary user terminal according to the following equation:
Figure pat00075

Where i is the index of the secondary user terminal, m is the channel,? Is the threshold,
Figure pat00076
The size of the index set included in the frequency axis in the corresponding channel,
Figure pat00077
Is a signal-to-noise ratio (SNR) in a spectrum bin, and L is a down-sampling rate. The method of claim 3,
The operation unit,
A cooperative spectrum sensing system using sub-Nyquist sampling for calculating a probability of erroneously detecting a frequency band occupied by the primary user terminal according to the following equation.
Figure pat00078
 The method according to claim 3 or 4,
The fusion center comprises:
And if the downsampling rate is smaller than the reference value, it is determined by the primary user terminal according to an AND-rule as expressed by the following equation: < EMI ID = Cooperative Spectrum Sensing System using Sub Nyquist Sampling to Compute the Probability of Occupied Frequency Band and the Probability of Failure Detection:
Figure pat00079
,
Figure pat00080

here,
Figure pat00081
Is the probability of detection,
Figure pat00082
Is the probability of false detection. 6. The method of claim 5,
The fusion center comprises:
If the down-sampling rate is equal to or greater than the reference value, sub-Nyquist sampling is performed to calculate the probability of detecting the frequency band occupied by the primary user terminal and the probability of false detection by the OR-rule, Cooperative Spectrum Sensing System:
Figure pat00083
,
Figure pat00084

here,
Figure pat00085
Is the probability of detection,
Figure pat00086
Is the probability of false detection. A sensing method using a cooperative spectrum sensing system including a plurality of secondary user terminals and a convergence center for detecting a frequency band occupied by a primary user terminal,
Receiving a signal transmitted by the secondary user terminal from the primary user terminal;
Sub-Nyquist sampling the signal at a predetermined down-sampling rate of the secondary user terminal;
Detecting energy of the sampled signal by the secondary user terminal and detecting a frequency band occupied by the primary user terminal;
Calculating a probability of detecting a frequency band occupied by the primary user terminal and a probability of detecting an error by using the spectrum of the frequency band; And
And transmitting the result of the calculation to the fusion center by the secondary user terminal. 8. The method of claim 7,
Wherein the step of detecting the frequency band comprises:
Determining that the corresponding frequency band is occupied by the primary user terminal if the detected energy is greater than or equal to a threshold value and determining that the corresponding frequency band is not occupied by the primary user terminal if the detected energy is below the threshold, Cooperative Spectrum Sensing Method Using Sampling. 8. The method of claim 7,
Wherein the calculating comprises:
A cooperative spectrum sensing method using sub-Nyquist sampling for calculating a probability of detecting a frequency band occupied by the primary user terminal according to the following equation:
Figure pat00087

Where i is the index of the secondary user terminal, m is the channel,? Is the threshold,
Figure pat00088
The size of the index set included in the frequency axis in the corresponding channel,
Figure pat00089
Is a signal-to-noise ratio (SNR) in a spectrum bin, and L is a down-sampling rate. 10. The method of claim 9,
Wherein the calculating comprises:
A cooperative spectrum sensing method using sub-Nyquist sampling for calculating a probability of erroneously detecting a frequency band occupied by the primary user terminal according to the following equation.
Figure pat00090
 11. The method according to claim 9 or 10,
The fusion center receiving results calculated from the plurality of secondary user terminals sampled at the same downsampling rate, respectively; And
If the down-sampling rate is smaller than the reference value, the convergence center calculates the probability of detecting the frequency band occupied by the primary user terminal and the probability of false detection by the AND-rule according to the following equation A method for cooperative spectrum sensing using sub-Nyquist sampling comprising:
Figure pat00091
,
Figure pat00092

here,
Figure pat00093
Is the probability of detection,
Figure pat00094
Is the probability of false detection. 12. The method of claim 11,
If the down-sampling rate is equal to or greater than the reference value, the convergence center calculates the probability of detecting the frequency band occupied by the primary user terminal and the probability of false detection by OR-rule, Cooperative Spectrum Sensing Method Using Sub-Nyquist Sampling Containing More:
Figure pat00095
,
Figure pat00096

here,
Figure pat00097
Is the probability of detection,
Figure pat00098
Is the probability of false detection.
KR1020150142064A 2015-08-07 2015-10-12 System for coopeative spectrum sensing using sub-nyquist sampling and method thereof KR101776666B1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US15/224,679 US9967117B2 (en) 2015-08-07 2016-08-01 Cooperative spectrum sensing system using sub-nyquist sampling and method thereof

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KR20150111585 2015-08-07
KR1020150111585 2015-08-07

Publications (2)

Publication Number Publication Date
KR20170018274A true KR20170018274A (en) 2017-02-16
KR101776666B1 KR101776666B1 (en) 2017-09-11

Family

ID=58265024

Family Applications (1)

Application Number Title Priority Date Filing Date
KR1020150142064A KR101776666B1 (en) 2015-08-07 2015-10-12 System for coopeative spectrum sensing using sub-nyquist sampling and method thereof

Country Status (1)

Country Link
KR (1) KR101776666B1 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114244458A (en) * 2021-11-16 2022-03-25 中国科学院上海微系统与信息技术研究所 Total-blind spectrum sensing method of sub-Nyquist sampling front end

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114244458A (en) * 2021-11-16 2022-03-25 中国科学院上海微系统与信息技术研究所 Total-blind spectrum sensing method of sub-Nyquist sampling front end
CN114244458B (en) * 2021-11-16 2022-11-11 中国科学院上海微系统与信息技术研究所 Total-blind spectrum sensing method of sub-Nyquist sampling front end

Also Published As

Publication number Publication date
KR101776666B1 (en) 2017-09-11

Similar Documents

Publication Publication Date Title
US9379924B2 (en) Cognitive radio spectrum sensing with improved edge detection of frequency bands
Song et al. Spectrum sensing in cognitive radios based on enhanced energy detector
US10348939B2 (en) Detection of the presence of television signals embedded in noise using cyclostationary toolbox
US9967117B2 (en) Cooperative spectrum sensing system using sub-nyquist sampling and method thereof
TW200830749A (en) Systems and methods for detecting the presence of a transmission signal in a wireless channel
De Vito A review of wideband spectrum sensing methods for cognitive radios
Rashid et al. Spectrum sensing measurement using GNU Radio and USRP software radio platform
CN106656372B (en) Frequency band interference detection method of frequency hopping system
Chaitanya et al. Real time hardware implementable spectrum sensor for cognitive radio applications
KR101776666B1 (en) System for coopeative spectrum sensing using sub-nyquist sampling and method thereof
Hamid et al. Spectrum sensing through spectrum discriminator and maximum minimum eigenvalue detector: A comparative study
Rwodzi Energy-detection based spectrum sensing for cognitive radio on a real-time SDR platform
Sun et al. A novel wideband spectrum sensing system for distributed cognitive radio networks
JP5252430B2 (en) Signal detection method, program, information storage medium, and sensor
Guibene et al. Distribution discontinuities detection using algebraic technique for spectrum sensing in cognitive radio
KR101755240B1 (en) Signal detection apparatus using compressive sensing and method thereof
KR100916180B1 (en) An Enhanced Energy Detector for identification of the VSB and WMP signal in the IEEE 802.22 System and Detecting Method
Vartiainen et al. False alarm rate analysis of the FCME algorithm in cognitive radio applications
KR101494966B1 (en) Method and apparatus for wideband spectrum sensing in cognitive radio
Verma et al. Throughput maximization by alternative use of single and double thresholds based energy detection method
TWI493539B (en) Methods for determining whether a signal includes a wanted signal and apparatuses configured to determine whether a signal includes a wanted signal
Zayen et al. Performance comparison for low complexity blind sensing techniques in cognitive radio systems
CN105812104B (en) Carrier polymerizing method and device and terminal device
Thennattil et al. A novel approach in cooperative spectrum sensing for cognitive radio
Rauniyar et al. Cooperative adaptive threshold based energy and matched filter detector in cognitive radio networks

Legal Events

Date Code Title Description
A201 Request for examination
E902 Notification of reason for refusal
GRNT Written decision to grant