CN112073138A - Double-threshold cooperative spectrum sensing method based on quantization - Google Patents
Double-threshold cooperative spectrum sensing method based on quantization Download PDFInfo
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Abstract
The invention discloses a quantization-based double-threshold cooperative spectrum sensing method, which relates to the technical field of Internet of things, and is characterized in that a spectrum sensing model is arranged, a main user and a plurality of secondary users are divided; detecting a spectrum sensing signal of the secondary user to obtain local judgment statistic; the local judgment statistic is analyzed to obtain a detection result, and whether the frequency spectrum resources are occupied or not is identified, so that the dynamic utilization of the frequency spectrum resources is realized, and the problem of shortage of the frequency spectrum resources is solved.
Description
Technical Field
The invention relates to the technical field of Internet of things, in particular to a quantization-based double-threshold cooperative spectrum sensing method.
Background
In recent years, the development of the internet of things is rapid, devices of the internet of things are continuously increased, and a large number of social security of the internet of things need to utilize wireless communication technology, so that the demand of the internet of things on radio frequency spectrum is increased sharply. Radio frequency spectrum resources are non-renewable resources, so to realize communication of more devices by using a limited frequency spectrum, some means must be adopted to improve the utilization rate of the existing frequency spectrum resources, Cognitive Radio (CR) provides a new idea of frequency spectrum sharing, the utilization rate of the frequency spectrum resources is improved by dynamic utilization of the frequency spectrum resources, and the problem of shortage of the frequency spectrum resources is relieved to a great extent.
In cognitive radio, spectrum sensing is a prerequisite for achieving spectrum sharing. The energy detection algorithm is the most widely used spectrum sensing algorithm with the advantages of no need of prior information and low computational complexity. However, the energy detection algorithm has low detection performance in a low signal-to-noise ratio environment. When the noise has uncertainty, the problem of poor detection performance exists because the detection threshold is difficult to determine, and the problem of high system overhead exists if the detection performance is not reduced.
Disclosure of Invention
The invention aims to provide a quantization-based double-threshold cooperative spectrum sensing method, which solves the problem of balance of cooperative spectrum sensing transmission overhead and detection performance.
In order to achieve the purpose, the invention adopts a quantization-based double-threshold cooperative spectrum sensing method which is characterized by comprising the following steps of setting a spectrum sensing model, and dividing a main user and a plurality of secondary users; detecting a spectrum sensing signal of the secondary user to obtain local judgment statistic; and analyzing the local judgment statistic to obtain a detection result.
In the step of detecting the spectrum sensing signal of the secondary user and acquiring the local judgment statistic, the spectrum sensing model is preprocessed, random resonance noise is added, and high threshold data and low threshold data are acquired.
After the step of obtaining the high threshold value and the low threshold value, local judgment is carried out by adopting a double-threshold energy detection algorithm based on stochastic resonance, and data of the local judgment are sent to a fusion center to obtain a judgment result of the fusion center.
The data of the local judgment is sent to a fusion center, and the step of obtaining the judgment result of the fusion center specifically comprises the following steps:
carrying out local detection on the spectrum sensing signals of each secondary user in parallel, and carrying out local judgment;
carrying out two-bit quantization on the range between the double thresholds of the high threshold and the low threshold, and dividing the range into three areas;
and in combination with the detection threshold area, the fusion center judges one bit of information uploaded by each secondary user in parallel to obtain a judgment result of the fusion center.
In the step of quantizing the range between the double thresholds of the high threshold and the low threshold by two bits and dividing the range into three regions, if the local judgment statistic is in the first region, it is judged that no master user exists in the large probability, if the local judgment statistic is in the second region, it is judged that the master user exists in the large probability, if the local judgment statistic is in the third region, the slave user does not participate in the cooperative judgment any more, the data of the local judgment is not sent to the fusion center, and the next detection of the slave user is directly entered.
The invention discloses a quantization-based double-threshold cooperative spectrum sensing method, which comprises the steps of setting a spectrum sensing model, and dividing a main user and a secondary user into a plurality of sub-users; detecting a spectrum sensing signal of the secondary user to obtain local judgment statistic; and analyzing the local judgment statistic to obtain a detection result, identifying whether the frequency spectrum resources are occupied or not, and dynamically utilizing the frequency spectrum resources to solve the problem of shortage of the frequency spectrum resources.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a signal detection block diagram of the dual-threshold cooperative spectrum sensing method of the present invention.
Fig. 2 shows three regions of two-bit quantization division of the dual-threshold cooperative spectrum sensing method of the present invention.
Fig. 3 is a local decision flow chart of the dual-threshold cooperative spectrum sensing method of the present invention.
Fig. 4 is a flow chart of centering decision of the dual-threshold cooperative spectrum sensing method of the present invention.
FIG. 5 is a graph showing the relationship between the detection performance and the signal-to-noise ratio under different noise certainty factors and different sampling points.
FIG. 6 is a comparison graph of detection probability versus signal-to-noise ratio for different noise uncertainties in accordance with the present invention.
FIG. 7 is a graph of the detection probability versus signal-to-noise ratio for different false alarm probabilities according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
In the description of the present invention, it is to be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships illustrated in the drawings, and are used merely for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention. Further, in the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Referring to fig. 1 to 5, the present invention provides a quantization-based dual-threshold cooperative spectrum sensing method, including the following steps of setting a spectrum sensing model, and dividing a primary user and a secondary user, wherein the number of the secondary users is multiple; detecting a spectrum sensing signal of the secondary user to obtain local judgment statistic; and analyzing the local judgment statistic to obtain a detection result.
In this embodiment, in the step of detecting the spectrum sensing signal of the secondary user and acquiring the local decision statistic, the spectrum sensing model is preprocessed, and random resonance noise is added to obtain high threshold data and low threshold data. After the step of obtaining the high threshold value and the low threshold value, local judgment is carried out by adopting a double-threshold energy detection algorithm based on stochastic resonance, and data of the local judgment are sent to a fusion center to obtain a judgment result of the fusion center.
In this embodiment, the step of sending the data of the local decision to the fusion center to obtain the decision result of the fusion center specifically includes:
carrying out local detection on the spectrum sensing signals of each secondary user in parallel, and carrying out local judgment;
carrying out two-bit quantization on the range between the double thresholds of the high threshold and the low threshold, and dividing the range into three areas;
and in combination with the detection threshold area, the fusion center judges one bit of information uploaded by each secondary user in parallel to obtain a judgment result of the fusion center.
In the embodiment, in the step of performing two-bit quantization on the range between the two thresholds of the high threshold and the low threshold and dividing the range into three regions, if the local judgment statistic is in the first region, it is determined that the master user does not exist in the general probability, if the local judgment statistic is in the second region, it is determined that the master user exists in the general probability, and if the local judgment statistic is in the third region, the slave user does not participate in the cooperative judgment any more, and the data of the local judgment is not sent to the fusion center, so that the next detection of the slave user is directly entered.
It can be understood that the quantization-based dual-threshold cooperative spectrum sensing method provided by the invention utilizes the characteristic that the generalized random resonance enhances the weak signal to improve the detection performance under the low signal-to-noise ratio. In the example of the present embodiment, as shown in fig. 1, the signal energy detection model at this time is:
wherein d (t) is stochastic resonance noise, wherein H0Indicating the absence of primary user signals in the detected frequency band, H1Indicating the presence of a primary user signal within the detected frequency band. y issr(t) represents the signal received by the secondary user after passing through the random resonance system. n (t) represents the background noise of the environment in which the secondary user is located, with a mean of 0 and a variance of σ2White gaussian noise. s (t) represents the primary user signal received by the secondary user with mean μ and variance
To determine d (t), we use an optimal stochastic resonance noise with a probability density distribution function expressed as:
wherein the content of the first and second substances,
indicating a dc noise of intensity. I.e., d (t) ═ d. In a low signal-to-noise environment, the values are:
using a mathematical statistical method according to ysr(t) accumulated energy values, defining decision statistics as:
wherein K is the number of sampling points. The decision statistics of the secondary users at this time obey the following normal distribution according to the law of large numbers and the central limit theorem:
suppose that the variance of the noise in the actual environment is
Wherein
And is subject to uniform distribution, rho (rho is more than or equal to 1) represents the uncertainty coefficient of noise, and let un be 10 log10ρ, then un is the fluctuation amplitude of the noise expressed in dB. Then the high threshold lambda of the dual threshold energy detection based on stochastic resonance2And a low threshold λ1Can be expressed as:
the double-threshold cooperative spectrum sensing method based on quantification firstly adopts a double-threshold energy detection algorithm based on stochastic resonance to carry out local judgment locally, and sends a judgment result to a fusion center to carry out cooperation to obtain a judgment result. The cooperative judgment is divided into two steps, the first step of cooperation is that one-bit judgment is used for fusion, local one-bit hard judgment information is sent to a fusion center, and the fusion center adopts an 'or' fusion criterion for combination.
In the present embodiment, the results of two-bit quantization are fused in cooperation. Assume that the number of secondary users participating in the collaboration detection is N. The specific algorithm steps are as follows.
Each secondary user (N ═ 1,2, …, N) participating in the collaboration performs local detection in parallel, and the detection flow is as shown above.Suppose the local decision statistic for the nth secondary user is recorded as Ysr,nThe local double threshold is marked as lambdan1、λn2. Will Ysr,nAnd λn1、λn2Comparing to obtain a local judgment result Ln，LnThe values of (a) are as follows:
if the local decision statistic Ysr,nEntering a second step between double thresholds; the range between the two thresholds is quantized by two bits and divided into three regions, An＝(2λn1+λn2)/3，Bn＝(λn1+2λn2)/3. If in the first region λ, as shown in FIG. 4n1≤Ysr,n≤AnIf the primary user is not present in the second area B, the primary user is considered to be present in a high probabilityn≤Ysr,n≤λn2If the primary user exists in the third area A, the primary user is considered to exist in a high probabilityn＜Ysr,n＜BnAnd if the signals are in the severe uncertain area, judging whether the main user exists difficultly, and the secondary user does not participate in cooperative judgment any more, and does not send the detection result to the fusion center to directly enter the next detection. The two-bit quantitative detection result can be recorded as
Where ND denotes no decision is made. Finally, a local decision result LS can be obtainedn，LSnThe values of (a) are as follows:
finally, LS is addednSending to the fusion center, the fusion center firstly making hard decision for one bit information uploaded by each secondary user in parallel, and the detection threshold takes the noise uncertainty into accountThe result is more accurate, so the 'or' criterion is adopted by the hard decision criterion of the fusion center. That is, the result of the first step cooperation decision can be expressed as:
otherwise, if all the bit information received by the fusion center does not have '1', namely D1When the value is equal to 0, fusion judgment is carried out on all received two-bit information, and the two-bit information S is obtainednAfter the conversion into the corresponding decimal system, the statistics of the two-bit soft information obtained by the fusion center is as follows:
wherein ΛiIf the number of users in the ith area is, the soft decision result of the two-bit information obtained by the fusion center is:
if M secondary users send 1-bit data and U secondary users send two-bit data, N-M-U secondary users do not participate in cooperative detection. The final decision result of the fusion center is R:
it can be understood that the decision result is the final detection result, and whether the spectrum resource is used or not can be identified. The invention discloses a quantization-based double-threshold cooperative spectrum sensing method, which comprises the steps of setting a spectrum sensing model, and dividing a main user and a secondary user into a plurality of sub-users; detecting a spectrum sensing signal of the secondary user to obtain local judgment statistic; and analyzing the local judgment statistic to obtain a detection result, identifying whether the frequency spectrum resources are occupied or not, and dynamically utilizing the frequency spectrum resources to solve the problem of shortage of the frequency spectrum resources.
In addition, in order to further explain the effect of the quantization-based dual-threshold cooperative spectrum sensing method, simulation comparison is performed.
The fusion center carries out two-step fusion judgment, wherein the first step is hard fusion judgment of one-bit information, and the second step is soft fusion judgment of two-bit information. Let the detection probability and false alarm probability of the first step cooperation be P respectivelyd_HAnd Pf_H. According to the algorithm, the 'or' judgment criterion is adopted in the first step of cooperative judgment, and the following steps are adopted:
wherein P isd_nAnd Pf_nThe false alarm probability and the detection probability for the nth secondary user in the first step are respectively:
let the detection probability and false alarm probability of the second step cooperation be P respectivelyD_SAnd PF_SThe prior analysis shows that the soft data decision of U two-bit information has lambda1The detection statistic of the secondary users is located in a first region having Λ2The detection statistics of the secondary users are located in a second area, using Δf,n1And Δd,n1Respectively representing the false alarm probability and the detection probability of the nth secondary user in the first area by deltaf,n2And Δd,n2Respectively represent the n-thThe false alarm probability and the detection probability of the secondary user in the second area are as follows:
Δf,n1＝P(λn1≤Ysr,n＜An|H0)
Δf,n2＝P(Bn≤Ysr,n＜λn2|H0)
Δd,n1＝P(λn1≤Ysr,n＜An|H1)
Δd,n2＝P(Bn≤Ysr,n＜λn2|H1)
then, the decision conditions when a master user exists are as follows:
will omega1，ω2After simplification of the value substitution of
This has the following:
wherein
Indicating a rounding down. Then there are:
then the global detection probability P of the present algorithmDAnd false alarm probability PFRespectively as follows:
the detection performance of the quantization-based dual-threshold cooperative spectrum sensing method is related to the detection probability and the false alarm probability of the detection statistics falling in the first region, the second region and the third region. If the probability of falling in the third area is increased, it means that the effective number of secondary users participating in the cooperative detection is reduced, and the detection performance is reduced. If all the detection statistics of the secondary users falling between the double thresholds are located in the third area, which is equivalent to that only M secondary users falling outside the double thresholds participate in cooperation, the detection probability and the false alarm probability are respectively:
the simulation parameters of the data are as follows: mean noise σ2The signal mean μ is 0.05, the false alarm probability is 0.05, and the secondary users participating in the collaboration are assumed to be 5. Monte Carlo simulation is adopted, and the simulation times are 5000 times.
FIG. 5 is a graph showing the relationship between the detection performance and the signal-to-noise ratio of the proposed detection algorithm at different noise certainty factors and different sampling points, with the simulation parameter PfThe noise uncertainty un is 0.5dB, 1dB, 3dB, 5dB, and the number of sampling points K is 1000, 3000, 5000, 7000, and 9000, respectively. As can be seen from FIG. 4, increasing the number of sampling points can effectively increase the sampling performance under low noise uncertainty, and increasing K and detecting performance under high noise uncertainty when K is greater than or equal to 3000The improvement is small due to the signal-to-noise ratio wall. Therefore, when there is uncertainty in noise, the larger the number of sampling points is, the better the detection performance is not necessarily. The uncertainty of noise uncertainty in the actual environment is considered, and the number K of sampling points is 3000 for the purpose of balancing the detection performance and the rapid detection.
Fig. 6 is a comparison graph of the algorithm deployed and the conventional energy detection algorithm ED, the energy detection algorithm SRED based on the generalized stochastic resonance, and the algorithm Referenc under different noise uncertainties. The simulation parameters are as follows: probability of false alarm Pf0.05 and 5. The comparison shows that the sensitivity of the algorithm to noise uncertainty is lower than that of the ED and SRED algorithms under the condition of not improving the false alarm probability, and the detection performance of the algorithm is greatly improved compared with that of the SRED and ED algorithms under the condition of low signal to noise ratio. The detection performance of the algorithm is similar to that of the literature algorithm, but the algorithm is not involved in cooperation because the data uploaded to the fusion center between the double thresholds is a 2-bit value and is discarded when the detection statistics are in a severe confusion area, and the algorithm uploaded between the double thresholds is an energy value, so that the system overhead of the algorithm is greatly reduced compared with that of the literature algorithm, and the calculation amount of the algorithm is also reduced.
FIG. 7 is a graph of the detection probability versus the signal-to-noise ratio for different false alarm probabilities. The simulation parameters are as follows: n is 5, un is 3dB, and K is 3000. As can be seen, increasing the false alarm probability can improve detection performance, especially in low signal-to-noise ratio environments. Therefore, in some areas where the primary user is not particularly sensitive, the false alarm probability can be increased appropriately to improve the detection performance of the secondary user, so that the secondary user has more possibility to access the network.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A double-threshold cooperative spectrum sensing method based on quantization is characterized by comprising the following steps of setting a spectrum sensing model, and dividing a main user and a plurality of secondary users;
detecting a spectrum sensing signal of the secondary user to obtain local judgment statistic;
and analyzing the local judgment statistic to obtain a detection result.
2. The quantization-based dual-threshold cooperative spectrum sensing method of claim 1, wherein in the step of detecting the spectrum sensing signal of one of said secondary users to obtain the local decision statistic,
preprocessing the spectrum sensing model, and adding stochastic resonance noise to obtain high threshold data and low threshold data.
3. The quantization-based dual-threshold cooperative spectrum sensing method of claim 2, wherein after the step of obtaining the high threshold value and the low threshold value,
and carrying out local judgment by adopting a double-threshold energy detection algorithm based on stochastic resonance, and sending data of the local judgment to a fusion center to obtain a judgment result of the fusion center.
4. The double-threshold cooperative spectrum sensing method based on quantization as claimed in claim 3, wherein the step of sending the data of the local decision to the fusion center to obtain the decision result of the fusion center specifically comprises:
carrying out local detection on the spectrum sensing signals of each secondary user in parallel, and carrying out local judgment;
carrying out two-bit quantization on the range between the double thresholds of the high threshold and the low threshold, and dividing the range into three areas;
and in combination with the detection threshold area, the fusion center judges one bit of information uploaded by each secondary user in parallel to obtain a judgment result of the fusion center.
5. The method for sensing double-threshold cooperative spectrum based on quantization as claimed in claim 4, wherein in the step of quantizing the range between the double thresholds of the high threshold and the low threshold by two bits and dividing the range into three regions, if the local decision statistic is in the first region, it is determined that no primary user exists in the large probability, if the local decision statistic is in the second region, it is determined that a primary user exists in the large probability, if the local decision statistic is in the third region, the secondary user does not participate in the cooperative decision any more, the data of the local decision is not sent to the fusion center, and the next secondary user is directly detected.
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