CN110649982A - Double-threshold energy detection method based on secondary user node selection - Google Patents

Double-threshold energy detection method based on secondary user node selection Download PDF

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CN110649982A
CN110649982A CN201910807837.XA CN201910807837A CN110649982A CN 110649982 A CN110649982 A CN 110649982A CN 201910807837 A CN201910807837 A CN 201910807837A CN 110649982 A CN110649982 A CN 110649982A
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齐丽娜
陈晶晶
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Nanjing Post and Telecommunication University
Nanjing University of Posts and Telecommunications
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Abstract

The invention discloses a double-threshold energy detection method based on secondary user node selection. The algorithm flow provided by the invention mainly comprises the following steps: firstly, screening out secondary users with higher signal-to-noise ratio according to the distance between the primary user and the secondary users, and then screening out a group of secondary users with low correlation according to the distance between the secondary users and the secondary users for subsequent spectrum sensing. Secondly, noise uncertainty is introduced, an upper threshold and a lower threshold in an energy detection algorithm are set, and then a mode of combining hard fusion and soft fusion judgment is adopted to make final judgment on a local result of each secondary user. The invention fully utilizes the secondary user node selection technology and the cooperative spectrum sensing technology to select a group of secondary users with high signal-to-noise ratio and low correlation, and the sensing results of the group of secondary users replace the sensing results of all secondary users, so the sensing cost is greatly reduced, and the accuracy of the sensing results is ensured by applying the improved dual-threshold energy detection algorithm to the group of secondary users.

Description

Double-threshold energy detection method based on secondary user node selection
Technical Field
The invention relates to a double-threshold energy detection method based on secondary user node selection, and belongs to the technical field of spectrum sensing.
Background
The spectrum sensing technology is used as a key part in the cognitive radio technology, and the spectrum resource idle condition in specific space and time is effectively detected before the secondary user accesses on the premise of not influencing the communication of the primary user. The method can be divided into single-node spectrum sensing and multi-node cooperative spectrum sensing according to the sensing user number. Due to the problems of multipath fading, shadow fading, hidden terminals and the like in the actual environment, the single-node spectrum sensing method cannot accurately judge the occupation or idle state of spectrum resources. The multi-node cooperative spectrum sensing method is an effective method for solving the above problems. In the cognitive radio network, each secondary user participating in cooperative sensing carries out independent spectrum sensing firstly, then sends respective local sensing results to a fusion center, and the fusion center obtains a final judgment result according to different judgment algorithms. According to the type of the uploaded data, the judgment algorithm is mainly divided into soft fusion judgment and hard fusion judgment. The hard fusion judgment means that the perception result uploaded by each user is 0 or 1, and the soft fusion judgment means that the information uploaded by each user is detection statistic. In general, the cooperative spectrum sensing performance is better as the number of secondary users participating in the cooperative spectrum sensing increases, but the sensing overhead of the system is larger and larger as the number of secondary users increases. How to find an optimal number of sub-users to guarantee the perception result is an important research direction.
Common spectrum sensing methods mainly include matched filter detection, energy detection, cyclostationary feature detection and the like. Energy detection is widely used due to the characteristics that the energy detection does not need prior information of a main user and the calculation complexity is low. However, this technique is susceptible to noise, channel uncertainty, etc., and so it is now common to incorporate cooperative spectrum sensing to reduce the effects of environmental factors. Meanwhile, the setting of the energy detection threshold is also an important factor, the threshold is too large, a large part of main user signals can be missed to be detected during the detection of secondary users, and the frequency spectrum utilization rate can be reduced if the threshold is too small.
Disclosure of Invention
The invention mainly aims at the problems of high sensing overhead and noise uncertainty in the actual system environment under the condition that all secondary users participate in sensing in cooperative spectrum sensing, and provides a dual-threshold energy detection method based on secondary user node selection.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a double-threshold energy detection method based on secondary user node selection is characterized by comprising the following steps:
1) establishing a spectrum sensing system model in a cognitive radio network, and screening out a group of secondary users with high signal-to-noise ratio according to the distance between a master user and the secondary users;
2) screening out a group of secondary users with low correlation according to the distance between the secondary users;
3) and performing cooperative spectrum sensing on the group of secondary users obtained by screening by combining a dual-threshold energy detection algorithm.
In the step 2), the low-relevance secondary user screening mode is as follows:
Figure BDA0002184161100000021
wherein, Crela(i, j) represents the correlation between the ith and jth secondary users, y is the distance between the ith and jth secondary users, C (y) represents the correlation function between two secondary users with the distance of y, 0 and 1 respectively represent the irrelevance and the high correlation, and tau is the correlation threshold value.
In step 1), the power P of a master user signal received by the ith slave user from the master useriExpressed as:
Figure BDA0002184161100000022
wherein d isiIs the distance, P, between the ith secondary user and the primary userrIs the signal power received by the ith secondary user from the primary user, beta represents the invariant fading factor during multipath fading, and alpha is the path loss factor;
thus, the received signal-to-noise ratio at the ith secondary user can be expressed as:
Figure BDA0002184161100000023
wherein, PiIs the power, σ, of the primary user signal received at the ith secondary user obtained from equation (1)2Is the noise variance at the ith secondary user; and (3) selecting the secondary users with high signal-to-noise ratio by the formula (2), and recording the number of the screened secondary users as K1.
More specifically, in step 2), one sub-user SU of K1 sub-users obtained by screening is randomly selectedtAs target secondary user, other secondary users and SUtPerforming correlation comparison, and screening out the relevant SUtA group of low relevance secondary users; the correlation function c (y) between two secondary users is expressed as:
C(y)＝e-xy (3)
where y is the distance between two secondary users and x is an environmental factor; the correlation function C (y) between two sub-users is taken as [0,1 ] by the formula (3)]0,1 correspond to uncorrelated, fully correlated respectively; correlation C between ith and jth secondary usersrela(i, j) is quantified as:
Figure BDA0002184161100000024
in equation (4), y is the distance between the ith and jth secondary users, i, j ∈ {1,2, …, K1}, τ is the correlation threshold, and 0 and 1 respectively represent no correlation and high correlation.
In step 3), the detection statistic T of each secondary useriIs defined as:
Figure BDA0002184161100000025
Xi(t) is a signal of a master user received at the ith secondary user, and t is the moment; n is the number of samples;
introducing noise uncertainty rho, and determining the range of real noise as follows:
Figure BDA0002184161100000026
wherein the content of the first and second substances,
Figure BDA0002184161100000027
representing the local (i.e. at a certain secondary user, the same applies below) true noise power, i.e. the local gaussian white noise power (i.e. the noise variance) And the power of other uncertain noise; rho represents the noise uncertainty and is the ratio of the local real noise power and the local white Gaussian noise power;
Figure BDA0002184161100000029
wherein n denotes the abbreviation of "noise",specifically to the noise variance at each secondary user.
The upper and lower thresholds of the dual threshold energy detection algorithm for the secondary user are set as follows:
Figure BDA0002184161100000031
Figure BDA0002184161100000032
wherein, Pf,reqIs the maximum false alarm probability value allowed; q-1(. is the inverse of a Gaussian Q function; n is the number of samples; p represents the noise uncertainty, which is the ratio of the local (i.e. at a certain secondary user) true noise power and the local white gaussian noise power;
Figure BDA00021841611000000316
representing the variance of the noise; lambda [ alpha ]0Is a lower threshold; lambda [ alpha ]1Is an upper threshold;
the perception result of each secondary user will be determined according to the following decision rule:
Figure BDA0002184161100000033
wherein D isiRepresenting the result of the local judgment, and ND representing that the result falls in the fuzzy area without judgment; 0 represents that a master user does not exist, and 1 represents that the master user exists; and sending the local judgment result to the fusion center.
In the step (3), according to a normal cumulative distribution function, the detection probability P of the main user under the non-cooperative conditiond,2thAnd false alarm probability Pf,2thAre respectively defined as follows:
Figure BDA0002184161100000035
detection probability Q of primary user under cooperative conditiond,2thSum false alarm probability Qf,2thAre respectively defined as follows:
Figure BDA0002184161100000036
Figure BDA0002184161100000037
wherein M is the number of secondary users participating in the cooperation; p (T)i>λ ρ | H1) represents that a master user exists in the actual environment, and the probability of the existence of the master user is correctly judged; p (T)i>λ ρ | H0) represents the probability that no master user exists in the actual environment but the master user is judged to exist by mistake; q is a Gaussian function; h1 indicates the presence of a primary user; h0 denotes a case where a master user does not exist;
Figure BDA0002184161100000038
μ0and
Figure BDA00021841611000000310
respectively representing the mean and variance under the H0 assumption,μ1and
Figure BDA00021841611000000312
mean and variance under H1 assumption, respectively;which represents the variance of the noise, is,
Figure BDA00021841611000000314
representing the primary user signal variance. The invention provides a double-threshold energy detection method based on secondary user node selection, which mainly comprises the following two aspects:
1) firstly, screening out secondary users with high signal-to-noise ratio according to the distance between the primary user and the secondary users and under the condition of considering the environmental factors such as multipath fading and the like. The signal-to-noise ratio at each secondary user is:
Figure BDA00021841611000000315
Piis the signal power, σ, of the primary user received at each secondary user2Is the noise variance. Gamma rayiA comparison is made with a predetermined noise threshold value of 1dB above which a high signal-to-noise ratio is indicated. The number of the filtered secondary users is recorded as K1.
And then screening out a group of secondary users with low relevance according to the distance between the secondary users. Based on the first stage selection, a SU of K1 secondary users is randomly selectedtAs target secondary users, the other secondary users sequentially carry out correlation detection with the target secondary users, and SU is screened outtAnd recording the number of the filtered secondary users as K2 in the inner group of secondary users with low relevance.The correlation between the two secondary users is quantified as:
Figure BDA0002184161100000041
where y is the distance between the ith and jth secondary users, i, j ∈ {1,2, …, K1}, c (y) is the correlation at distance y, τ is a predetermined correlation threshold, and 0 and 1 represent uncorrelated and highly correlated secondary users, respectively.
2) The method comprises the steps that a double-threshold energy detection algorithm is applied to K2 screened secondary users for cooperative spectrum sensing, and in an actual environment, various interferences (such as noise interference, factors such as environment fading and the like, and small changes of noise variance) are contained in a communication system, so that uncertainty of a detection result of a primary user can be caused. We therefore introduce a noise uncertainty p, determining the true noise power in the following range:
Figure BDA0002184161100000042
Figure BDA0002184161100000044
and the power of local real noise, namely the sum of the power of local white gaussian noise and other uncertain noise is represented, and rho is the ratio of the real noise to the white gaussian noise. Then, the lower and upper thresholds of the available dual-threshold energy detection algorithm are respectively set as:
Figure BDA0002184161100000045
and λ1λ ρ, where λ is an energy detection threshold preset according to Neyman-Pearson. And finally, according to a normal cumulative distribution function, the detection probability and the false alarm probability of the main user under the cooperation condition are respectively defined as follows:
Figure BDA0002184161100000046
Figure BDA0002184161100000047
wherein, Pd,2thAnd Pf,2thRespectively, the detection and false alarm probabilities of the primary user under the non-cooperative condition, and M is the number of secondary users participating in the cooperation.
Compared with the prior art, the invention has the beneficial effects that: according to the method, a secondary user node selection technology and a cooperative spectrum sensing technology are fully utilized, a group of secondary users with high signal-to-noise ratio and low correlation is screened out from all secondary users participating in cooperative spectrum sensing, the group of secondary users can well replace all secondary users to obtain a cooperative spectrum sensing result, the sensing overhead is greatly reduced due to the fact that the number of the secondary users participating in spectrum sensing is reduced, and meanwhile, the accuracy of the spectrum sensing result in a noise environment can be guaranteed by combining a dual-threshold energy detection algorithm introducing noise uncertainty.
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FIG. 1 is a schematic diagram of a system model of the present invention;
FIG. 2 is a diagram of dual threshold energy detection.
Detailed Description
The present invention will be described in further detail with reference to examples.
The double-threshold energy detection method based on secondary user node selection specifically comprises the following steps:
1) firstly, selecting a secondary user node: a system model is shown in FIG. 1. In the first step, a secondary user with high signal-to-noise ratio is selected according to the distance between a primary user and a secondary user and under the condition of considering multipath fading. Because secondary users that derive a high signal-to-noise ratio from the primary user can receive a stronger primary user signal, the presence and absence of the primary user can be determined in a more accurate manner than other secondary users. It is assumed that the distance between the primary user and the respective secondary user is known. The primary user signal power P received by each secondary user (i-th secondary user) from the primary useriExpressed as:
Figure BDA0002184161100000051
wherein d isiIs the distance, P, between the ith secondary user and the primary userrThe signal power of the main user received by the autonomous user of the ith user, beta represents the invariable fading factor during multipath fading, and alpha is the path loss factor. It follows that the received signal-to-noise ratio at each sub-user (i-th sub-user) can be expressed as:
Figure BDA0002184161100000052
wherein, PiIs the power, σ, of the primary user signal received at the ith secondary user obtained from equation (1)2Is the noise variance. Selecting the secondary user with high signal-to-noise ratio by the formula (2), wherein the threshold value of the signal-to-noise ratio is set to be 1dB and gammaiGreater than 1dB appears to have a high signal-to-noise ratio. The number of the filtered secondary users is recorded as K1.
And then, in the second step, screening out a group of secondary users with low relevance according to the distance between the secondary users and the secondary users. Due to the spatial correlation between the secondary users, all the secondary users at a short distance will experience almost the same fading effect, and such secondary users will exhibit similar perceptual results and the cooperative perceptual performance will be degraded. So this phase randomly selects one SU of K1 sub-userstAs target secondary user, other secondary users and SUtPerforming correlation comparison, and screening out the relevant SUtA group of low relevance secondary users. The correlation function between two secondary users can be expressed as:
C(y)＝e-xy (3)
where y is the distance between two secondary users and x is an environmental factor. The correlation function C (y) between two sub-users is taken as [0,1 ] according to the formula (3)]Corresponding to uncorrelated (0) and fully correlated (1), respectively. The correlation C of the ith and jth secondary usersrela(i, j) can be quantified as:
Figure BDA0002184161100000053
where y is the distance between the ith and jth sub-users, i, j ∈ {1,2, …, K1}, and τ is a fixed correlation threshold, which is set to 0.4 in the present invention. In the formula (4), 0 and 1 represent uncorrelated and highly correlated, respectively. Without loss of generality, the 1 st secondary user is assumed as a target secondary user, and the subsequent secondary users are sequentially subjected to correlation comparison with the 1 st secondary user, and finally a group of secondary users including the 1 st secondary user is screened (namely, the number of the screened secondary users is K2).
2) The system model considered in the invention comprises a main user, a fusion center and M secondary users. The binary hypothesis model for spectrum sensing by each secondary user is as follows:
Figure BDA0002184161100000054
wherein, Xi(t) is the signal of the primary user received at the ith secondary user, s (t) is the signal sent by the primary user, ni(t) is a mean of 0 and a variance ofAdditive white Gaussian noise of hiIs the gain of the perceptual channel between the ith secondary user and the primary user. H0 and H1 correspond to the absence and presence of primary users, respectively. t represents the time.
Detection statistic T for each secondary useriIs defined as:
Figure BDA0002184161100000061
where N is the number of samples (i.e., randomly selected portions of the M secondary users). The detection statistic follows a normal Gaussian distribution, as follows:
Figure BDA0002184161100000062
wherein the content of the first and second substances,
Figure BDA0002184161100000063
μ0and
Figure BDA0002184161100000064
respectively representing the mean and variance under the H0 assumption,
Figure BDA0002184161100000065
Figure BDA0002184161100000066
μ1and
Figure BDA0002184161100000067
mean and variance under the H1 assumption are indicated, respectively.
Figure BDA0002184161100000068
Which represents the variance of the noise, is,
Figure BDA0002184161100000069
representing the primary user signal variance.
After local detection, each secondary user obtains a binary decision result, and the result is sent to the fusion center for further decision. The decision mode at the fusion center adopts an OR decision mode, and the mode can obviously improve the detection probability of the main user.
According to the Neyman-Pearson theorem, the threshold lambda of the single-threshold energy detection algorithm is set as follows:
Figure BDA00021841611000000610
wherein, Pf,reqIs the maximum allowable false alarm probability value, typically set to 0.1. Q-1(. is) the inverse of a gaussian Q function whose complementary cumulative distribution function is defined as follows:
Figure BDA00021841611000000611
wherein z is the lower limit of anomalous integration and u is the integrand
Figure BDA00021841611000000616
The integral variable of (2).
According to equation (8), the threshold λ is a function of the sampling rate and the noise variance, and if the noise variance is constant, λ is also constant. However, in an actual situation, the communication system includes various factors such as environmental fading and noise interference, and a small change in noise variance may have a large influence on λ, thereby affecting the accuracy of the primary user detection result. In order to make the method closer to the actual environment, noise uncertainty ρ is introduced, and the range of true noise is determined as follows:
Figure BDA00021841611000000612
wherein the content of the first and second substances,
Figure BDA00021841611000000613
representing the power of local real noise, namely the sum of the power of local white Gaussian noise and the power of other uncertain noise; ρ represents the noise uncertainty, which is the ratio of the local true noise power and the local white gaussian noise power. According to the formulas (6) and (10), the upper and lower thresholds (lambda) for the local detection of the secondary user can be obtained0Is a lower threshold; lambda [ alpha ]1For the upper threshold) are set as follows:
Figure BDA00021841611000000614
Figure BDA00021841611000000615
an improved model of a dual threshold energy detection system is shown in fig. 2. The perception result of each secondary user is determined according to the following judgment rules:
Figure BDA0002184161100000071
wherein D isiThe result of the local decision is shown, and ND shows that the result falls in the fuzzy area, and no decision is made. In M sub-users, if K local judgment results are ND, the K sub-users send detection statistics to a fusion center, and the rest M-K sub-users send specific 0 or 1 judgment results (0 indicates that a main user does not exist, and 1 indicates that the main user exists). For the secondary users which have been screened, the detection statistics which need to be sent are considered to be reduced in comparison with the previous detection statistics, and the perception overhead is greatly reduced.
According to normal cumulative distribution function, the detection probability P of the primary user under the non-cooperative conditiond,2thAnd false alarm probability Pf,2thAre respectively defined as follows:
Figure BDA0002184161100000072
Figure BDA0002184161100000073
the detection probability Q of the primary user in the case of cooperationd,2thSum false alarm probability Qf,2thAre respectively defined as follows:
Figure BDA0002184161100000074
Figure BDA0002184161100000075
wherein, P (T)i>λ ρ | H1) represents that a master user exists in the actual environment, and the probability of the existence of the master user is correctly judged; p (T)i>λ ρ | H0) indicates the probability that there is no primary user in the actual environment, but it is erroneously determined that a primary user exists. Q is a gaussian function.
Figure BDA0002184161100000076
μ0Mean under H0 assumption;
Figure BDA0002184161100000077
μ1mean under H1 assumption;
Figure BDA0002184161100000078
represents the variance under the H0 assumption;
Figure BDA0002184161100000079
represents the variance under the H1 assumption;which represents the variance of the noise, is,
Figure BDA00021841611000000711
representing the primary user signal variance.
The basic structure, essential features and advantages of the invention have been shown and described above. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are intended to illustrate the structure of the present invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention, which is intended to be covered by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (6)

1. A double-threshold energy detection method based on secondary user node selection is characterized by comprising the following steps:
1) establishing a spectrum sensing system model in a cognitive radio network, and screening out a group of secondary users with high signal-to-noise ratio according to the distance between a master user and the secondary users;
2) screening out a group of secondary users with low correlation according to the distance between the secondary users;
3) and performing cooperative spectrum sensing on the group of secondary users obtained by screening by combining a dual-threshold energy detection algorithm.
2. The dual-threshold energy detection method based on secondary user node selection according to claim 1, wherein in step 2), the low-correlation secondary user screening manner is as follows:
Figure FDA0002184161090000011
wherein, Crela(i, j) represents the correlation between the ith and jth secondary users, y is the distance between the ith and jth secondary users, C (y) represents the correlation function between two secondary users with the distance of y, 0 and 1 respectively represent the irrelevance and the high correlation, and tau is the correlation threshold value.
3. The method as claimed in claim 1, wherein in step 1), the power P of the primary user signal received by the ith secondary user from the primary user is determinediExpressed as:
Figure FDA0002184161090000012
wherein d isiIs the distance, P, between the ith secondary user and the primary userrIs the signal power received by the ith secondary user from the primary user, beta represents the invariant fading factor during multipath fading, and alpha is the path loss factor;
thus, the received signal-to-noise ratio at the ith secondary user can be expressed as:
Figure FDA0002184161090000013
wherein, PiIs the power, σ, of the primary user signal received at the ith secondary user obtained from equation (1)2Is the noise variance at the ith secondary user; and (3) selecting the secondary users with high signal-to-noise ratio by the formula (2), and recording the number of the screened secondary users as K1.
4. The method of claim 3, wherein the method comprises selecting a dual threshold energy based on a secondary user nodeIn step 2), randomly selecting one sub-user SU of K1 sub-users obtained by screeningtAs target secondary user, other secondary users and SUtPerforming correlation comparison, and screening out the relevant SUtA group of low relevance secondary users; the correlation function c (y) between two secondary users is expressed as:
C(y)＝e-xy (3)
where y is the distance between two secondary users and x is an environmental factor; the correlation function C (y) between two sub-users is taken as [0,1 ] by the formula (3)]0,1 correspond to uncorrelated, fully correlated respectively; correlation C between ith and jth secondary usersrela(i, j) is quantified as:
Figure FDA0002184161090000014
in equation (4), y is the distance between the ith and jth secondary users, i, j ∈ {1,2, …, K1}, τ is the correlation threshold, and 0 and 1 respectively represent no correlation and high correlation.
5. The dual-threshold energy detection method based on node selection of secondary users as claimed in claim 1, wherein in step 3), the detection statistic T of each secondary user isiIs defined as:
Figure FDA0002184161090000021
Xi(t) is a signal of a master user received at the ith secondary user, and t is the moment; n is the number of samples;
introducing noise uncertainty rho, and determining the range of real noise as follows:
wherein the content of the first and second substances,
Figure FDA0002184161090000023
representing the power of local real noise, namely the sum of the power of local white Gaussian noise and the power of other uncertain noise; rho represents the noise uncertainty and is the ratio of the local real noise power and the local white Gaussian noise power;
Figure FDA0002184161090000024
specifically refers to the noise variance at each secondary user;
the upper and lower thresholds of the dual threshold energy detection algorithm for the secondary user are set as follows:
Figure FDA0002184161090000025
Figure FDA0002184161090000026
wherein, Pf,reqIs the maximum false alarm probability value allowed; q-1(. is the inverse of a Gaussian Q function; n is the number of samples; rho represents the noise uncertainty and is the ratio of the local real noise power and the local white Gaussian noise power;
Figure FDA0002184161090000027
representing the variance of the noise; lambda [ alpha ]0Is a lower threshold; lambda [ alpha ]1Is an upper threshold;
the perception result of each secondary user will be determined according to the following decision rule:
Figure FDA0002184161090000028
wherein D isiRepresenting the result of the local judgment, and ND representing that the result falls in the fuzzy area without judgment; 0 represents that a master user does not exist, and 1 represents that the master user exists; and sending the local judgment result to the fusion center.
6. Root of herbaceous plantThe method as claimed in claim 5, wherein in step (3), according to the normal cumulative distribution function, the detection probability P of the primary user under the non-cooperative condition is determinedd,2thAnd false alarm probability Pf,2thAre respectively defined as follows:
Figure FDA0002184161090000029
Figure FDA00021841610900000210
detection probability Q of primary user under cooperative conditiond,2thSum false alarm probability Qf,2thAre respectively defined as follows:
Figure FDA0002184161090000031
wherein M is the number of secondary users participating in the cooperation; p (T)i>λ ρ | H1) represents that a master user exists in the actual environment, and the probability of the existence of the master user is correctly judged; p (T)i>λ ρ | H0) represents the probability that no master user exists in the actual environment but the master user is judged to exist by mistake; q is a Gaussian function; h1 indicates the presence of a primary user; h0 denotes a case where a master user does not exist;
Figure FDA0002184161090000033
μ0and
Figure FDA0002184161090000035
respectively representing the mean and variance under the H0 assumption,
Figure FDA0002184161090000036
μ1andmean and variance under H1 assumption, respectively;
Figure FDA0002184161090000038
which represents the variance of the noise, is,representing the primary user signal variance.
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CN112054861A (en) * 2020-09-01 2020-12-08 西北工业大学 Secondary user selection method, medium and equipment for cooperative spectrum sensing network
CN112099057A (en) * 2020-09-17 2020-12-18 重庆大学 Double-threshold cooperation GNSS interference detection algorithm based on fuzzy logic
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