CN112054861B - Secondary user selection method, medium and equipment for cooperative spectrum sensing network - Google Patents
Secondary user selection method, medium and equipment for cooperative spectrum sensing network Download PDFInfo
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Abstract
The invention discloses a selection method, a medium and equipment for secondary users in a cooperative spectrum sensing network, wherein the secondary users in the cooperative spectrum sensing network adopt an energy detector method to detect received signals; calculating a signal-to-noise ratio of the secondary user using the detected energy; modeling the correlation between the secondary users to obtain a descending correlation function; further obtaining the association degree between any two secondary users, setting the association degree between the two secondary users greater than the threshold value as 1 to represent the association, setting the association degree between the two secondary users less than the threshold value as 0 to represent the no-association, and obtaining a matrix of an association relation function, wherein the association relation function between any two secondary users represents whether the association degree exists or not; and substituting the matrix of the incidence relation function into the improved DSCN algorithm, and selecting the secondary users participating in the final fusion by using the improved DSCN algorithm to finish the user selection. The invention has the advantages of high efficiency, reduced communication overhead, low required cost and good detection effect.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a secondary user selection method, medium and equipment for a cooperative spectrum sensing network.
Background
The multi-node cooperative spectrum sensing technology (CSS) is a technology that the detection results of different secondary users are fused by utilizing the space diversity formed by sensing nodes at different geographical positions in a cognitive radio network so as to obtain the final judgment result. At present, most researches on cooperative spectrum sensing are concentrated on a detection data fusion algorithm, the global detection performance is greatly improved by using a cooperative spectrum sensing technology, the problem that the single-node detection performance is not high is solved, and the problem of hiding a terminal is also solved.
However, the number and distribution of secondary users has a great limitation on the existing cooperative spectrum sensing technology. Too many secondary users participate in the convergence, which results in too much communication overhead and energy consumption. And the detection effect is reduced due to shadow effect and multipath weakness caused by the fact that a large number of secondary users are concentrated in a small range to participate in fusion. Therefore, in practical applications, when the number and distribution of secondary users are under non-ideal conditions, it is difficult for the current cooperative spectrum sensing technology to maintain a high detection effect.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method for selecting secondary users in a cooperative spectrum sensing network, aiming at the above deficiencies in the prior art, so as to select the secondary users participating in cooperative spectrum sensing to reduce communication overhead and maintain good detection effect. The method has the characteristics of high efficiency, communication overhead reduction, low required cost and good detection effect.
The invention adopts the following technical scheme:
a secondary user selection method of a cooperative spectrum sensing network comprises the following steps:
s1, detecting the received signal by the secondary user in the cooperative spectrum sensing network by adopting an energy detector method;
s2, after the step S1 is completed, calculating the signal-to-noise ratio of the secondary user i by using the detected energy;
s3, modeling the correlation among the secondary users according to the calculation result of the step S2 to obtain a decreasing correlation function;
s4, obtaining the association degree between any two secondary users according to the correlation function of the step S3, setting a threshold value delta, setting the association degree between two secondary users i and j larger than the threshold value delta as 1 to represent the association, setting the association degree between two secondary users i and j smaller than the threshold value delta as 0 to represent the non-association, and obtaining a matrix of the association relation function, wherein the association relation function between any two secondary users represents whether the association degree exists or not;
and S5, substituting the matrix of the incidence relation function in the step S4 into the improved DSCN algorithm, and selecting the secondary users participating in the final fusion by using the improved DSCN algorithm to finish the user selection.
Specifically, in step S1, the power P received by the secondary user iiComprises the following steps:
wherein, PpuIs the power given by the primary user, diIs the distance from user i to the primary user, alpha is the path loss coefficient, beta0Is a scalar.
Specifically, in step S2, the signal-to-noise ratio γ of the secondary user iiComprises the following steps:
where σ is the noise power, PiThe received power for secondary user i.
Specifically, in step S3, the correlation function is specifically:
Crl(d)＝e-dθ
where θ is an environment-based variable and Crl (d) is the degree of association between two secondary users at a distance d.
Specifically, in step S4, the correlation matrix is:
where Crl' (i, j) represents the relationship between users i, j.
Further, the association function is specifically:
wherein d isi,jIs the distance of the secondary user i, j.
Specifically, step S5 specifically includes:
s501, setting a threshold value of a signal-to-noise ratio
If the signal-to-noise ratio gamma of the secondary useriIs greater than
Put the secondary user into the collection
Otherwise, abandoning;
s502, setting a threshold eta1Selecting a secondary user, if the number of associated users of the corresponding secondary user is greater than eta1If not, the corresponding secondary user is not set as a key user;
s503, after a father key user is selected, a cluster is established, and all related points of the corresponding father key user are placed in the corresponding cluster;
s504, setting a threshold eta2Sequentially judging all other users in the corresponding cluster, if the number of the associated users of a certain secondary user is more than eta2If not, the corresponding secondary user is not set as a key user;
s505, after each sub-key user is selected, all the related users of the corresponding user are put into the cluster, and the judgment is continued according to the method of the step S504 until all the secondary users in the corresponding cluster are judged;
s506, selecting a user with the largest number of associated users in the corresponding cluster as a user participating in fusion;
and S507, continuously selecting the users which are not in the cluster and have not accessed, selecting other secondary users participating in the fusion according to the steps S503-S506, and when all the secondary users in the corresponding cooperative spectrum sensing network are judged, selecting the secondary users participating in the fusion to send sensing results to the fusion center for spectrum sensing fusion.
Another aspect of the invention is a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods described.
Another aspect of the present invention is a computing device, including:
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention relates to a selection method of secondary users of a cooperative spectrum sensing network, which uses a descending correlation function by modeling the distance between the secondary users and a receiver and loss index, and aims to determine the correlation between the secondary users; setting a threshold value of the correlation to determine whether the correlation between the two secondary users is good; by setting the incidence matrix, the aim is to facilitate the fusion center to store the correlation relationship among all the secondary users; by using the improved DSCN algorithm, representative secondary users participating in cooperative spectrum sensing fusion are selected, so that the influence of shadow effect is reduced, and meanwhile, the communication overhead is greatly reduced.
Furthermore, prior information of the main user signal does not need to be known in advance, and the method is an optimal spectrum sensing scheme for detecting any zero mean value and counting independent signals. The energy detector determines whether the channel is idle by measuring the received signal strength.
Further, the signal-to-noise ratio of the secondary user i is calculated for the calculation of the subsequent steps.
Further, the degree of association between any two secondary users is calculated for the calculation of the association matrix in step S4.
Further, a total matrix of the correlation degrees between any two secondary users is obtained through calculation and is used for improved DSCN algorithm calculation.
Further, the association degree between two secondary users is intuitively judged, wherein 1 represents the existence of the correlation, and 0 represents the nonexistence of the correlation.
Further, setting a threshold value, and excluding secondary users with too low signal-to-noise ratio, so as to ensure high detection probability and low false alarm probability; and selecting a key secondary user meeting the requirement according to the number of users associated with the secondary user and setting a cluster. And setting another threshold, judging all other secondary users in the key user range according to the number of the associated users, and putting all the conforming users into the cluster by analogy. And finally, selecting a secondary user with the largest number of associated users in the cluster as an object participating in fusion. The purpose of setting a cluster is to represent the user group with stronger relevance as a representative user, reduce the number of users participating in fusion and reduce shadow effect. The purpose of selecting the secondary user with the largest number of stroke-associated users as the object to participate in the fusion is to select the most representative secondary user to participate in the fusion.
In summary, the invention selects representative secondary users in the cooperative spectrum sensing network to participate in spectrum fusion, so as to save communication overhead of each secondary user and reduce the influence of shadow effect on the premise of ensuring good detection effect.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a diagram of a modified DSCN algorithm;
FIG. 2 is a diagram of initially generated randomly distributed secondary users;
FIG. 3 is a distribution diagram of the filtered secondary users with too low SNR;
FIG. 4 is a diagram of a secondary user profile selected to participate in a convergence using a modified DSCN algorithm;
FIG. 5 is a comparison graph of detection probability for an overall communication network using/not using this method;
FIG. 6 is a graph of false alarm probability versus total communication network usage/non-usage of this method;
fig. 7 is a graph comparing communication overhead for an overall communication network using/not using this method.
Detailed Description
The invention discloses a secondary user selection method of a cooperative spectrum sensing network, which comprises the following steps:
s1, the secondary user detects the received signals by adopting an energy detector method, and the energy received by each user is as follows:
wherein, PiIs the power received by the secondary user i, PpuIs the power given by the primary user, diIs the distance from user i to the primary user, alpha is the path loss coefficient, beta0Is a scalar;
s2, after the secondary user detects the signal, calculating the signal-to-noise ratio of the user i by using the detected energy;
the method specifically comprises the following steps:
where σ is the noise power, γiIs the signal-to-noise ratio of secondary user i;
s3, modeling the correlation among the users to obtain a decreasing correlation function;
the method specifically comprises the following steps:
Crl(d)＝e-dθ
where θ is an environment-based variable, and Crl (d) is the degree of association between two secondary users at a distance d;
s4, setting a threshold value delta, setting the association degree between two secondary users i and j larger than the threshold value delta as 1 to represent the association, and setting the association degree between the two secondary users i and j smaller than the threshold value delta as 0 to represent the non-association, so as to obtain an association relation function;
the method specifically comprises the following steps:
where Crl' (i, j) represents the relationship between users i, j, di,jIs the distance of the secondary user i, j.
The correlation matrix obtained by the above function is:
and S5, selecting the secondary users participating in the final fusion by using the improved DSCN algorithm.
Referring to fig. 1, the specific steps are as follows:
s501, setting a threshold value of a signal-to-noise ratio
If the signal-to-noise ratio gamma of the secondary useriIs greater than
Put this user into the collection
Otherwise, abandoning;
s502, setting a threshold eta1Selecting a secondary user if the number of associated users of the user is greater than eta1If the key user is not set as the parent key user, the key user is set as the parent key user;
s503, after a father key user is selected, a cluster is established, and all related points of the father key user are placed in the cluster;
s504, setting a threshold eta2Sequentially judging all other users in the cluster, if the number of the associated users is more than eta2If the current user is not the key user, setting the current user as a sub key user, otherwise, not setting the current user as the key user;
s505, after each sub-key user is selected, putting all users related to the user into a cluster, and continuing judging according to the method of S504 until all users in the cluster are judged;
s506, selecting a user with the largest number of associated users in the cluster as a user participating in fusion;
and S507, continuously selecting the users which are not in the cluster and have not been visited, and selecting other secondary users participating in the fusion according to the steps of S503-S506.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
400 secondary users randomly distributed in a 100km x 100km area, wherein each secondary user detects signals of a main user by using an energy detection method;
calculating the signal energy power detected by each secondary user using step S1;
calculating a signal-to-noise ratio for each secondary user relative to the primary user using step S2;
calculating the correlation between every two secondary users by using the steps S3 and S4 and obtaining a correlation matrix;
the secondary users participating in the fusion are selected using step S5.
Referring to fig. 2, 3 and 4, 400 secondary users randomly generated in a range of 100km × 100km, fig. 3 is a distribution of the remaining secondary users after removing the secondary users with too low signal-to-noise ratio, and fig. 4 is a distribution of the secondary users participating in the cooperative spectrum sensing fusion selected by using the DSCN algorithm. Generally, according to the difference of the number and distribution of initial secondary users, 20-40 secondary users are screened to participate in final fusion.
Referring to fig. 5, the fusion policy used in the simulation is an "and" policy, because it can reduce the probability of interference of secondary users when the primary user is present. Simulation results show that under the condition that the target false alarm probability is 0.001-0.002, the detection probability range of the traditional cooperative spectrum sensing is 0.1-1, and the detection probability range of the cooperative spectrum sensing using the method is 0.9-1. When the number of the secondary users is large, the detection probability of the method is far higher than that of the traditional cooperative spectrum sensing. At the same time, the detection probability of both methods decreases as the number of secondary users participating in the fusion increases, because the "and" rule is that the fusion result is the primary user only if all detect the presence of the same primary user.
Referring to fig. 6, the fusion policy used by the emulation is an or policy, because when the primary user does not exist, it can occupy the channel as long as one secondary user perceives that the channel is free. Simulation results show that under the condition that the target detection probability is 0.999 and 0.998, the false alarm probability range of the traditional cooperative spectrum sensing is 0.08-0.1, and the detection probability range of the cooperative spectrum sensing using the method is 10-13～10-5The false alarm probability using this method is much lower than that using conventional cooperative spectrum sensing. Meanwhile, the false alarm probabilities of the two methods are increased along with the increase of the number of the secondary users participating in the fusion, because the OR rule judges that the primary user exists as long as one secondary user detects the existence of the primary user.
Referring to fig. 7, when the number of initial secondary users varies from 1 to 100, the number of secondary users participating in the fusion using the method is always less than 20, which greatly saves communication overhead compared with the conventional method.
In summary, the method for selecting the secondary user of the cooperative spectrum sensing network according to the present invention obtains a decreasing correlation function by modeling the distance between the secondary user and the receiver and the loss exponent. And establishing an incidence relation matrix to store the correlation relation among the secondary users by setting a correlation threshold. And selecting representative secondary users to participate in cooperative spectrum sensing fusion by using a modified DSCN algorithm. On the premise of ensuring good detection effect, the communication overhead of each secondary user is greatly saved, and the influence of shadow effect is reduced.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (8)
1. A secondary user selection method of a cooperative spectrum sensing network is characterized by comprising the following steps:
s1, detecting the received signal by the secondary user in the cooperative spectrum sensing network by adopting an energy detector method;
s2, after the step S1 is completed, calculating the signal-to-noise ratio of the secondary user i by using the detected energy;
s3, modeling the correlation among the secondary users according to the calculation result of the step S2 to obtain a decreasing correlation function;
s4, obtaining the association degree between any two secondary users according to the correlation function of the step S3, setting a threshold value delta, setting the association degree between two secondary users i and j larger than the threshold value delta as 1 to represent the association, setting the association degree between two secondary users i and j smaller than the threshold value delta as 0 to represent the non-association, and obtaining a matrix of the association relation function, wherein the association relation function between any two secondary users represents whether the association degree exists or not;
s5, substituting the matrix of the incidence relation function in the step S4 into the improved DSCN algorithm, selecting the secondary users participating in the final fusion by using the improved DSCN algorithm, and completing the user selection, wherein the specific steps are as follows:
s501, setting a threshold value of a signal-to-noise ratio
If the signal-to-noise ratio gamma of the secondary useriIs greater than
Put the secondary user into the collection
Otherwise, abandoning;
s502, setting a threshold eta1Selecting a secondary user, if the number of associated users of the corresponding secondary user is greater than eta1If not, the corresponding secondary user is not set as a key user;
s503, after a father key user is selected, a cluster is established, and all related points of the corresponding father key user are placed in the corresponding cluster;
s504, setting a threshold eta2Sequentially judging all other users in the corresponding cluster, if the number of the associated users of a certain secondary user is more than eta2If not, the corresponding secondary user is not set as a key user;
s505, after each sub-key user is selected, all the related users of the corresponding user are put into the cluster, and the judgment is continued according to the method of the step S504 until all the secondary users in the corresponding cluster are judged;
s506, selecting a user with the largest number of associated users in the corresponding cluster as a user participating in fusion;
and S507, continuously selecting the users which are not in the cluster and have not accessed, selecting other secondary users participating in the fusion according to the steps S503-S506, and when all the secondary users in the corresponding cooperative spectrum sensing network are judged, selecting the secondary users participating in the fusion to send sensing results to the fusion center for spectrum sensing fusion.
2. The cooperative spectrum sensing network secondary user selection method as claimed in claim 1, wherein in step S1, the secondary user i receives power PiComprises the following steps:
wherein, PpuIs the power given by the primary user, diIs the distance from user i to the primary user, alpha is the path loss coefficient, beta0Is a scalar.
3. The cooperative spectrum sensing network secondary user selection method as claimed in claim 1, wherein in step S2, the signal-to-noise ratio γ of the secondary user iiComprises the following steps:
where σ is the noise power, PiThe received power for secondary user i.
4. The cooperative spectrum sensing network secondary user selection method according to claim 1, wherein in step S3, the correlation function specifically is:
Crl(d)＝e-dθ
where θ is an environment-based variable and Crl (d) is the degree of association between two secondary users at a distance d.
5. The cooperative spectrum sensing network secondary user selection method according to claim 1, wherein in step S4, the correlation matrix is:
where Crl' (i, j) represents the relationship between users i, j.
6. The cooperative spectrum sensing network secondary user selection method according to claim 5, wherein the association relation function is specifically:
wherein d isi,jIs the distance of the secondary user i, j.
7. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-6.
8. A computing device, comprising:
one or more processors, memory, and one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-6.
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