CN110740006A - optimized Internet of things multi-band cooperative spectrum sensing method - Google Patents

optimized Internet of things multi-band cooperative spectrum sensing method Download PDF

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CN110740006A
CN110740006A CN201911023382.9A CN201911023382A CN110740006A CN 110740006 A CN110740006 A CN 110740006A CN 201911023382 A CN201911023382 A CN 201911023382A CN 110740006 A CN110740006 A CN 110740006A
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马涛
郭偶凡
何迎利
王俊椋
李超
瞿静文
葛红舞
张宇新
严喻冬
王海冬
梁伟
陈民
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Nari Information and Communication Technology Co
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Abstract

The invention discloses an optimized Internet of things multi-band cooperative spectrum sensing method, which comprises the steps of dividing the whole available frequency band into non-overlapping frequency bands, respectively carrying out double-threshold energy detection based on noise uncertainty on each frequency sub-band, introducing multi-band cooperative sensing, carrying out joint judgment on a hard judgment result by a cognitive user only, adopting a 'majority' judgment rule for the joint judgment, fusing statistics of the sub-frequency bands under the condition that no main user signal exists in the joint judgment result, and obtaining a final judgment result by adopting an optimal limit of soft judgment based on a minimum total error probability rule.

Description

optimized Internet of things multi-band cooperative spectrum sensing method
Technical Field
The invention relates to optimized Internet of things multi-band cooperative spectrum sensing methods, and belongs to the technical field of radio information transmission.
Background
However, due to static and fixed allocation strategies, spectrum resources are not fully utilized, Cognitive Radio (CR) is effective technologies for solving spectrum emergency, and has application in the Internet of things , spectrum sensing is used as which is a key technology of cognitive radio, and spectrum holes in communication can be detected, so that the utilization rate of a spectrum in a wireless network, particularly a power Internet of things, is effectively improved.
The spectrum sensing means that an authorized frequency band is continuously detected, whether the frequency band is occupied by a master user is judged, and the use condition of the detected frequency band is obtained. The spectrum sensing is to find out spectrum holes and ensure that a cognitive user uses an idle frequency band under the condition that normal communication of a master user is not influenced. With the continuous evolution of cognitive radio technology, the perception method is also continuously improved. The conventional sensing technology can be divided into primary user transmitting end detection and primary user receiving end detection.
The detection method of the transmitting terminal is provided earlier, the technology is developed more mature, the design complexity is low, the implementation is easy, but the technology is not providedWhen multipath effect and shadow fading are more serious in a line environment, the detection performance is influenced by the reduction of the strength of a received signal, and a perception model can be expressed as binary hypothesis problems0An assumption, H, indicating the absence of primary user signals in the detection band1Indicating the assumption of the presence of a primary user signal on the detected frequency band. Suppose that the detected statistic is YcAnd the decision is limited to λ, then the decision criterion is H0:Yc< lambda or H1:YcLambda is greater than. The detection performance of spectrum detection techniques is often expressed in four concepts: probability of detection PdFalse alarm probability PfProbability of missed detection PmAnd an idle probability Pn. In practical applications, the detection probability and the false alarm probability are often used as important indicators of detection performance, and Receiver Operating Characteristics (ROC) are also represented by the two probabilities. The higher the detection probability, the better, but it also represents the interference degree of the cognitive user to the authorized user, and the higher the detection probability, the greater the interference. The false alarm probability represents the utilization rate of the frequency spectrum, and the lower the false alarm probability, the higher the utilization rate of the frequency spectrum.
The energy detection is a spectrum sensing method applying the most general, and is incoherent detection methods, wherein the energy detection is to perform time domain sampling on a received signal, calculate the square of a modulus value after FFT operation, and compare the square of the modulus value with a decision limit after accumulation and averaging.
The cyclostationary filtering detection mainly utilizes the cyclostationary characteristic of a main user signal to separate the signal from noise in a time-invariant stationary noise environment. The characteristics of the signal mean value, the autocorrelation function and the like which change with the period are easily obtained by studying the autocorrelation characteristics of the signal, and since the noise does not have the characteristics, the signal and the noise can be separated by utilizing the statistical characteristics, which are also called cyclostationary characteristics. The advantages of the cyclostationary filtering detection are that noise and signals can be distinguished, and the detection performance is high. Meanwhile, the method has the obvious defects that only specific types of signals can be detected, namely only signals with the cyclostationary property can be detected, and the method is long in detection time and high in calculation complexity.
The matched filtering detection is that the structural characteristics of a main user signal, such as pulse waveform, a modulation mode, a data packet format and the like, need to be known in advance, and a filter which is consistent with the characteristics of the main user signal is added at a receiving end, so that the signal-to-noise ratio of the signal at the receiving end is maximum.
Whether the cognitive user is a single user or multiple users can also divide the perception technology into single-user perception and multi-user cooperative perception. Both the transmitting end detection and the receiving end detection are single-user sensing technologies, however, in an actual communication scene, many factors such as shadow fading, multipath fading and the like all affect the performance of spectrum sensing. The detection performance of a single user is limited by the adverse factors, so that the detection performance of the whole system is reduced. The cooperative spectrum sensing can effectively reduce the influence of the adverse factors. The cooperative spectrum sensing is to generate cooperative gain by using diversity, so as to improve the detection performance of the whole system. The current research on cooperative spectrum sensing technology mostly focuses on utilizing space diversity, that is, cognitive users distributed at different positions perform cooperative sensing. The cooperative spectrum sensing also generates higher complexity and extra overhead while improving the system detection performance.
And for the cooperative spectrum sensing system with the fusion center, the fusion center performs fusion judgment on the obtained sensing data. The common hard decision fusion criterion includes ' OR ' and ' most criterion, maximum posterior probability fusion criterion, Bayes fusion detection criterion, etc. The predecessors have done much work on cooperative spectrum sensing in practical applications. The cooperative spectrum sensing technology can also utilize cooperation among a plurality of frequency bands, namely, the detection frequency band is divided into a plurality of sub-frequency bands, each sub-frequency band is subjected to spectrum sensing respectively, and the result of cooperative sensing among the sub-frequency bands is used as the detection result of the cognitive user on the frequency band.
Disclosure of Invention
The invention aims to provide optimized Internet of things multi-band cooperative spectrum sensing methods, which are used for performing dual-threshold cooperative spectrum sensing by using the uncertainty of noise in the actual environment and can effectively improve the performance of spectrum sensing.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the embodiment of the invention provides optimized Internet of things multi-band cooperative spectrum sensing methods, which comprise the following steps:
dividing the detected frequency band into K sub-frequency bands;
counting energy perception statistics for each sub-frequency band;
judging the energy perception statistic of each sub-frequency band by adopting double thresholds;
according to the dual-threshold judgment result of each sub-frequency band, sending a spectrum sensing result to a fusion center, wherein the spectrum sensing result comprises the following steps:
if the judgment result of the sub-frequency bands with the number exceeding the set number is the main user signal existence limit, the cognitive user judges that the main user signal exists in the detection frequency band, and sends the perception result of the main user signal existence to the fusion center;
if the judgment results of all the sub-frequency bands indicate that the main user signals do not exist, judging that the main user signals of the detection frequency bands do not exist, and sending a sensing result that the main user signals do not exist to the fusion center;
under other conditions, transmitting the energy perception statistics of all the sub-frequency bands to a fusion center for fusion;
and the fusion center gives a judgment result according to the fusion result.
Further , the step of counting energy perception statistics for each sub-band comprises:
Figure BDA0002247918670000031
wherein, Xi,jDetecting energy perception statistics of the jth sub-frequency band for the ith cognitive user, wherein M is the number of sampling points and xi,j(k) Indicating that the ith cognitive user detects the signal received by the jth sub-band at the kth moment.
Further to step , the signal received during the cognitive user detection is calculated as follows:
wherein s isi,j(k) Representing the primary user signal n after attenuation, delay and loss of the radio channeli,j(k) Representing the received noise signal, H0Indicating absence of primary user signal, H1Indicating that a main user signal exists, and L indicating the number of cognitive users.
Further , the dual thresholds are determined as:
wherein λ is1,iIs the minimum limit, lambda, of the ith cognitive user2,iMaximum limit, Q, for the ith cognitive user-1(. cndot.) is the inverse of a Gaussian complementary integral function Q (. cndot.), M is the number of sampling points, Pf,iIn order to be the probability of a false alarm,
Figure BDA0002247918670000035
indicating the standard noise power, β is the noise uncertainty.
Further , the determining the energy perception statistic of each sub-band by using a dual threshold includes:
if the detected energy perception statistic is greater than the maximum limit lambda2,iJudging that a master user signal exists;
if the detected energy perception statistic is less than the minimum limit lambda1,iJudging that the master user signal does not exist;
if the detected energy perception statistic lies between the maximum limit and the minimum limit, then no decision is made directly.
And , if the judgment result that the sub-frequency band exceeds K/2 is that the main user signal exists, the cognitive user judges that the main user signal exists in the detection frequency band.
Further , the sending the energy perception statistics of all sub-bands to a fusion center for fusion includes:
and performing equal gain fusion on all sub-band detection energy perception statistics, or performing equal gain fusion on all sub-band detection energy perception statistics larger than the minimum limit.
Further , the fusion center uses the optimal soft decision limit based on the minimum total error probability criterion as the decision limit.
Further , the optimal limit for the soft decisions based on the minimum total error probability criterion is:
Figure BDA0002247918670000036
wherein λ isoptFor soft decision optimum bound, μ0And σ0Mean and variance, mu, respectively, which are absent in the primary user1And σ1Mean and variance, P, respectively, of the primary user0Probability of absence of primary user, P1The probability of the existence of the primary user.
The invention achieves the following beneficial effects:
in the invention, the fusion center combines the perception data of all cognitive users by adopting the maximum ratio, the judgment limit of the fusion center adopts the optimal limit of soft-judgment cooperative spectrum perception based on the minimum error probability criterion, the cooperative spectrum perception based on double-threshold and optimal judgment limit energy detection is realized, and both software simulation and actual spectrum perception environmental tests show that the detection performance is improved.
When the cognitive user performs local detection, the detection frequency band is subdivided into a plurality of sub-frequency bands, each sub-frequency band is detected by adopting a double threshold value, the detection results of the sub-frequency bands are fused and then serve as the local sensing result of the cognitive user to be sent to a fusion center, final fusion judgment is performed, the sensing performance in the Internet of things can be effectively improved by multi-frequency band cooperative spectrum sensing, and the result shows that the method has higher detection probability.
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FIG. 1 is a schematic diagram of a local dual-threshold decision criterion in the present invention;
FIG. 2 is a flow chart of local dual threshold decision in the present invention;
FIG. 3 is a flow chart of a soft decision method of the fusion center of the present invention;
FIG. 4 is a schematic diagram of a detection frequency band divided into K sub-bands according to the present invention;
FIG. 5 is a schematic diagram of local multiband cooperative spectrum sensing according to the present invention;
FIG. 6 is a flow chart of local multiband cooperative spectrum sensing in the present invention;
FIG. 7 is a ROC graph of two methods for local total fusion according to an embodiment of the present invention;
FIG. 8 is a ROC plot of the MCSDO method at different K values according to an embodiment of the present invention;
fig. 9 is a ROC graph under different cooperative sensing methods in the embodiment of the present invention.
Detailed Description
The present invention is further described in , the following examples are only for the purpose of more clearly illustrating the technical solutions of the present invention, and the protection scope of the present invention should not be limited thereby.
The embodiment of the invention provides optimized Internet of things multi-band cooperative spectrum sensing methods, which are specifically implemented as follows:
Spectrum sensing modeling
The spectrum sensing of single node energy detection in the Internet of things is modeled as binary hypothesis problems:
Figure BDA0002247918670000041
wherein x isi(k) The signal received by the ith cognitive user at the kth moment is represented; si(k) Representing a master user signal after the attenuation, the delay and the loss of a wireless channel; n isi(k) Representing the received noise signal. H0Indicating absence of primary user signal, H1Indicating the presence of a primary user signal, ni(k) And si(k) Independent of each other, L represents the number of cognitive users.
Two, single node spectrum sensing
Based on the analysis of single-node energy perception statistics, the detection probability and the false alarm probability of single-node frequency spectrum perception are calculated, and the optimized energy detection frequency spectrum perception judgment is -limit, energy perception does not need to know any information of a main user in advance, and the method is simple to implement, low in complexity and widely applied to .
And the cognitive user compares the signal energy perceived in the perception time with a preset judgment limit to obtain a local perception result.
The energy perception statistic is:
Figure BDA0002247918670000051
wherein M is the number of sampling points, XiThe signal energy received by the ith cognitive user in the perception time. When M is sufficiently large, X is known from the central limit theoremiIt can be approximated as a gaussian distribution:
Figure BDA0002247918670000052
wherein the content of the first and second substances,
Figure BDA0002247918670000053
representing the noise power, αiRepresenting the signal-to-noise ratio of the ith cognitive user. Will detect statistic XiCompared with the preset decision limit lambda, the detection probability P of energy perception can be obtainedd,iAnd probability of false alarm Pf,iComprises the following steps:
Figure BDA0002247918670000054
Figure BDA0002247918670000055
wherein Q (-) is a Gaussian complementary integration function.
Given the false alarm probability, the decision limit can be found as:
Figure BDA0002247918670000056
wherein Q is-1(. cndot.) is the inverse function of Q (-).
Cooperative spectrum sensing based on double threshold and optimal decision energy-limited detection
The embodiment of the invention provides a cooperative spectrum sensing method based on double thresholds and energy-limited detection of optimal decision , namely a CSDO (complementary binary data optimized) method, and obtains higher detection probability and lower false alarm probability.
In an actual wireless environment, noise of a receiving end not only comprises white gaussian noise but also comprises other interference noise, although noise power after the noise is mixed randomly changes along with position and time, the noise power of the receiving end generally changes within a certain range of , the change degree of the noise power is called as the uncertainty of the noise, as can be seen from formula (6), a local decision limit is directly related to the noise power, and when the noise power dynamically changes, the local detection performance is directly influenced.
Defining a noise uncertainty interval, namely:
wherein the content of the first and second substances,
Figure BDA0002247918670000062
which is representative of the actual noise power,
Figure BDA0002247918670000063
representing the standard noise power, β is the noise uncertainty, assuming the upper bound of β is C (in dB), the maximum noise uncertainty that exists in the system, and 10lg β is at [ -C, C]Are uniformly distributed.
Based on the noise uncertainty model and the energy detection fixed false alarm probability, the detection double limit can be expressed as:
Figure BDA0002247918670000064
Figure BDA0002247918670000065
the hard decision means that the cognitive user judges whether the master user signal exists or not by comparing the detection statistic with the preset list .
The soft decision means that when the detection statistic of the cognitive user is between the preset double limits, the decision is not directly made, but the detection statistic is fused, and finally the decision is made.
In the embodiment of the invention, the local double limit decision criterion is as shown in fig. 1, when the detection statistic is outside the uncertain region, the hard decision is directly adopted, and when the detection statistic is larger than lambda2Local judgement as H1(ii) a When the detection statistic is less than lambda1Then, it is locally judged as H0. When the detection statistic is located in the uncertain region, the local cannot directly make a decision because it cannot be distinguished whether the statistic is influenced by the signal of the main user or the noise fluctuation, and therefore, the processing of the part of the statistic directly influences the overall detection performance. In an embodiment of the invention, the detection statistics are selected to be sent to the fusion center.
The local decision criterion is:
Figure BDA0002247918670000066
wherein, YiAnd obtaining a local judgment result for the ith cognitive user.
Wherein, YiAnd obtaining a local judgment result for the ith cognitive user. A local decision criteria flow diagram is shown in fig. 2.
The fusion center carries out soft decision on the detection statistics of the N cognitive users, and can be regarded as carrying out times of single limited soft decision cooperative spectrum sensing on the N usersiThe combination was carried out as shown in the following formula.
Figure BDA0002247918670000067
Wherein, wiIs a weighting factor.
Y is obtained from the formula (3)cMean and variance in absence of primary user and presence of primary user: mu.s0、σ0、μ1、σ1。
The soft decision optimal limit based on the minimum total error probability criterion can obtain the total error probability PeMinimum:
Pe＝P0PF+P1PM(12)
PM＝1-PD(13)
wherein, P0Probability of absence of primary user, P1Probability of existence of primary user, PMFor the probability of missed detection, the soft decision optimum limit lambda based on the minimum total error probability criterionoptComprises the following steps:
Figure BDA0002247918670000071
the Optimal limit of soft decision based on the minimum total error probability criterion is used as the limit in soft decision of the fusion center, and a CSDO algorithm (Cooperative Spectrum sensing algorithm based on Double limit and Optimal decision limit energy detection) is proposed to fully utilize the information of each cognitive user.
Defining cognitive user local detection probability Pd,iLocal false alarm probability Pf,iLocal miss probability Pm,iLocal idle probability Pn,iComprises the following steps:
Pd,i＝P(Xi＞λ2,i|H1) (15)
Pf,i＝P(Xi＞λ2,i|H0) (16)
Pm,i＝P(Xi＜λ1,i|H1) (17)
Pn,i＝P(Xi＜λ1,i|H0) (18)
obtaining the detection probability P of the systemd,csdoAnd false alarm probability Pf,csdoRespectively as follows:
Figure BDA0002247918670000072
Figure BDA0002247918670000073
fourth, based on double threshold and optimum decision energy-limited detection multi-band cooperative spectrum sensing
On the basis of the CSDO method, a multiband cooperative spectrum sensing method based on double threshold and optimal decision energy-limited detection, namely an MCSDO method, is provided, and higher detection probability and lower false alarm probability are obtained.
When each cognitive user performs local detection, the detected frequency band is divided into K sub-frequency bands as shown in fig. 4, each sub-frequency band is respectively subjected to double-threshold energy detection based on noise uncertainty to obtain the detection result of each sub-frequency band, and X is used for detecting the energy of each sub-frequency bandi,jRepresenting the energy perception statistic of the ith cognitive user for detecting the jth sub-frequency band, the calculation mode and the second part XiThe calculation methods of the method are the same, the sub-bands are not overlapped, the cognitive user respectively carries out double-threshold energy detection on the sub-bands to obtain a hard decision result or detection energy perception statistic, and carries out multi-band combined decision on the hard decision result obtained by the sub-bands, as shown in figure 5.
The flow chart of the local multi-band cooperative spectrum sensing is shown in figure 6, the cognitive user carries out double-threshold energy detection on each sub-band respectively to obtain the detection energy sensing statistic and the hard decision result of each sub-band, and the hard decision result obtained on each sub-band has the result that the signal detection statistic exceeds the maximum lambda limit as the result that the signal detection statistic exceeds the maximum lambda limit, wherein the K is the criterion that the cognitive user only carries out the joint decision on the hard decision result and the 'majority' criterion is adopted for the joint decision2Then, the cognitive user judges the existence of a main user signal of a detection frequency band and sends H to the fusion center1When all sub-bands result in signal detection statistics less than a minimum lambda limit1When the master user signal is judged to be absent, H is sent to the fusion center0(ii) a In addition to the above two cases, in other cases, the cognitive user needs to fuse the statistics of all sub-bands, and then send the statistics to the fusion center for final judgment.
Because each sub-frequency band belongs to the same cognitive users, the signal-to-noise ratios of all sub-frequency bands of the same cognitive users are considered to be the same, so that the equal gain fusion is adopted in the sub-frequency band information fusion, and the two local fusion schemes are provided (1) the equal gain fusion is carried out on all sub-frequency band detection statistics, namely the local full fusion of the cognitive users, and (2) all sub-frequency band detection statistics larger than lambda are called as local full fusion of the cognitive users1The equal gain fusion is carried out on the sub-frequency band detection statistics, and the fusion is called the local part fusion of the cognitive user.
Examples
In this embodiment, the measured signal-to-noise ratios of the cognitive users are respectively: -1.39dB, -0.95dB, -1.29dB, average signal-to-noise ratio-1.2 dB. The whole multi-band cooperative sensing system is repeatedly detected 10000 times.
When the number K of the sub-frequency segments is 4 and the cognitive user locally performs full fusion on the detection statistics, a Receiver operating characteristic ROC curve (Receiver operating characteristic) of the MCSDO method compared with the CSDO method provided by the invention is shown in fig. 7. It can be seen that the MCSDO method performs better than the CSDO method in practical applications. Such as the false alarm probability Pf＝16.At 47%, the detection probability P of the MCSDO methodd75.68%, detection probability P of CSDO methodd51.12%, the detection probability of the MCSDO method is higher than that of the CSDO method by 24.56%, and the superiority of the MCSDO method when the cognitive user locally performs full fusion on the detection statistics is verified on the USRP. This is because the introduction of the multi-band cooperative spectrum sensing improves the local detection performance of the cognitive user, i.e. lower false alarm probability and higher detection probability.
When the number K of the sub-frequency segments changes and the local full integration is adopted, the ROC curve of the MCSDO method is shown in fig. 8. When the number of sub-bands K is reduced to 1, the MCSDO method is degenerated to the CSDO method. As can be seen from fig. 8, as the number K of the sub-frequency segments increases, the detection performance of the system also increases. However, the increase of the number of the sub-bands simultaneously means that the local calculation load of the cognitive user is increased, the sensing time is increased, and the throughput of the system is reduced, so that the reasonable number of the sub-bands should be selected according to requirements in practical application.
When the number K of the sub-bands is 4 and the cognitive user locally fuses the detection statistics, an ROC curve of the MCSDO method versus the CSDO method is shown in fig. 9. It can be seen that, when the partial fusion is adopted locally, the detection performance of the MCSDO method is still better than that of the CSDO method. Such as the false alarm probability PfWhen the MCSDO method is 16.47 percent, the detection probability P in partial fusion is adoptedd96.3%, the detection probability P of MCSDO method when adopting full fusiond75.68%, detection probability P of CSDO methoddComparing the three, the MCSDO method and the CSDO method is obviously superior in performance no matter the local full fusion or partial fusion is adopted for the detection statistics, wherein the detection performance when the partial fusion is adopted is superior to that when the full fusion is adopted, because the results after the double limit detection of each sub-band are fully utilized in the partial fusion, the hard decision result H is improved1The influence of soft decision information in the final decision of the fusion center is reduced, and the hard decision result H is reduced0When full integration is adopted, the final detection performance can be influenced only when the sub-frequency band exceeding half judges that the main user signal existsThe improvement is obtained.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (9)

1, optimized Internet of things multi-band cooperative spectrum sensing method, which is characterized by comprising the following steps:
dividing the detected frequency band into K sub-frequency bands;
counting energy perception statistics for each sub-frequency band;
judging the energy perception statistic of each sub-frequency band by adopting double thresholds;
according to the dual-threshold judgment result of each sub-frequency band, sending a spectrum sensing result to a fusion center, wherein the spectrum sensing result comprises the following steps:
if the judgment result of the sub-frequency bands with the number exceeding the set number is the main user signal existence limit, the cognitive user judges that the main user signal exists in the detection frequency band, and sends the perception result of the main user signal existence to the fusion center;
if the judgment results of all the sub-frequency bands indicate that the main user signals do not exist, judging that the main user signals of the detection frequency bands do not exist, and sending a sensing result that the main user signals do not exist to the fusion center;
under other conditions, transmitting the energy perception statistics of all the sub-frequency bands to a fusion center for fusion;
and the fusion center gives a judgment result according to the fusion result.
2. The optimized IOT multiband cooperative spectrum sensing method according to claim 1, wherein the statistics of energy sensing statistics for each sub-band includes:
wherein, Xi,jDetecting energy perception statistics of the jth sub-frequency band for the ith cognitive user, wherein M is the number of sampling points and xi,j(k) Indicating that the ith cognitive user detects the signal received by the jth sub-band at the kth moment.
3. The optimized IOT multiband cooperative spectrum sensing method according to claim 2, wherein the signals received during cognitive user detection are calculated as follows:
wherein s isi,j(k) Representing the primary user signal n after attenuation, delay and loss of the radio channeli,j(k) Representing the received noise signal, H0Indicating absence of primary user signal, H1Indicating that a main user signal exists, and L indicating the number of cognitive users.
4. The optimized IOT multiband cooperative spectrum sensing method according to claim 1, wherein the dual threshold is determined as:
Figure FDA0002247918660000013
Figure FDA0002247918660000014
wherein λ is1,iIs the minimum limit, lambda, of the ith cognitive user2,iMaximum limit, Q, for the ith cognitive user-1(. cndot.) is the inverse of a Gaussian complementary integral function Q (. cndot.), M is the number of sampling points, Pf,iIn order to be the probability of a false alarm,
Figure FDA0002247918660000015
indicating the standard noise power, β is the noise uncertainty.
5. The optimized IOT multiband cooperative spectrum sensing method according to claim 4, wherein the determining the energy sensing statistics of each sub-band by using double thresholds includes:
if the detected energy perception statistic is greater than the maximum limit lambda2,iJudging that a master user signal exists;
if the detected energy perception statistic is less than the minimum limit lambda1,iJudging that the master user signal does not exist;
if the detected energy perception statistic lies between the maximum limit and the minimum limit, then no decision is made directly.
6. The optimized IOT multi-band cooperative spectrum sensing method according to claim 1, wherein if a judgment result that the sub-bands exceed K/2 is that a main user signal exists, the cognitive user judges that the main user signal exists in the detection band.
7. The optimized IOT multiband cooperative spectrum sensing method according to claim 1, wherein the sending the energy sensing statistics of all sub-bands to a fusion center for fusion includes:
and performing equal gain fusion on all sub-band detection energy perception statistics, or performing equal gain fusion on all sub-band detection energy perception statistics larger than the minimum limit.
8. The optimized IOT multiband cooperative spectrum sensing method according to claim 1, wherein the fusion center employs a soft-decision optimal limit based on a minimum total error probability criterion as a decision limit.
9. The optimized IOT multiband cooperative spectrum sensing method according to claim 8, wherein the optimal limit of soft decisions based on the minimum total error probability criterion is:
Figure FDA0002247918660000021
wherein λ isoptFor soft decision optimum bound, μ0And σ0Mean and variance, mu, respectively, which are absent in the primary user1And σ1Mean and variance, P, respectively, of the primary user0Probability of absence of primary user, P1The probability of the existence of the primary user.
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CN115664563A (en) * 2022-10-20 2023-01-31 电子科技大学 Passive cooperative spectrum sensing method based on energy characteristic geometric symmetry
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