CN105141385B - Multiband cooperative cognitive frequency spectrum sensing method - Google Patents

Abstract

The invention relates to a multi-band cooperative cognitive spectrum sensing method. Each user sends its own signal-to-noise ratio, confidence level, service requirements, and spectrum detection results of multiple primary users to a spectrum sensing fusion center, and the spectrum sensing fusion center calculates 1. Screen the primary cooperative sub-users, and then assign the number of frequency bands to the primary cooperative sub-users; calculate the quotient value between the root mean square value of the signal-to-noise ratio of all primary sub-users and the signal-to-noise ratio of each primary sub-user, according to each quotient value and the preset threshold of signal-to-noise ratio, select the secondary users participating in the collaboration, and adjust the detection probability of secondary users; combine the signal-to-noise ratio of each secondary user and the adjusted average detection probability, Go back to the step of selecting secondary users to get the final selected secondary users who finally participate in the cooperation, and use the weighted OR criterion to cooperate with the global detection probability as the final detection result of the spectrum sensing fusion center, avoiding low signal-to-noise ratio or poor performance Secondary users have a bad influence on the overall detection performance.

Description

Multi-band Cognitive Cognitive Spectrum Sensing Method

technical field

The invention relates to the field of spectrum detection, in particular to a multi-band cooperative cognitive spectrum sensing method.

Background technique

With the continuous development of wireless communication technology, emerging technologies marked by LTE, Wi-Fi, satellite communication and collaborative communication have emerged one after another, emerging in endlessly. These communication technologies put forward higher demands on wireless spectrum resources, thus making the spectrum resources more tense, and cognitive radio technology (Cognitive Radio, CR) emerges under this background.

The basic approach of cognitive radio is that first, the secondary user (or cognitive user, cognitive user) uses spectrum sensing to continuously detect the authorized spectrum resources in the surrounding environment; Under the condition that the frequency spectrum is occupied and the transmission performance is almost unaffected, the secondary user adaptively adjusts the transceiver equipment, and adjusts the transceiver equipment to the idle frequency spectrum for communication. When the secondary user perceives (or detects) that there is a signal of the primary user, the secondary user must quickly vacate the channel for the primary user to use, thereby avoiding interference to the normal communication of the primary user, thereby improving spectrum resource utilization.

In order to reduce the adverse effects of many factors such as multipath fading, shadowing effect and noise uncertainty on the detection performance in the actual environment, cooperative spectrum sensing methods based on multiple secondary users have been proposed continuously. The detection result of each secondary user is sent to the spectrum sensing fusion center for fusion to achieve the purpose of spectrum sensing.

However, most of the existing cooperative spectrum sensing methods are only for single-band sensing. In order to improve spectrum utilization, multi-band cooperative sensing methods have become a new research hotspot. However, in multi-band cooperative sensing, since the detection performance and signal-to-noise ratio of each user are not completely excellent, when all secondary users participate in multi-band cooperative sensing, the low signal-to-noise ratio or detection performance Poor secondary users can have a bad impact on the overall detection performance.

Contents of the invention

The technical problem to be solved by the present invention is to provide a multi-band cooperative cognitive spectrum sensing method that can avoid the adverse impact of secondary users with low signal-to-noise ratio or poor detection performance on the overall detection performance.

The technical solution adopted by the present invention to solve the above-mentioned technical problems is: a multi-band cooperative cognitive spectrum sensing method, which is characterized in that it includes the following steps in sequence:

(1) In the cognitive network, the number of primary users is N ₁ , the number of secondary users is N ₂ , the number of spectrum sensing fusion centers is 1, and the primary users independently occupy their respective frequency bands in the spectrum; N ₂ Each secondary user independently obtains its own SNR _j and the spectrum detection results of the frequency bands occupied by N ₁ primary users, and respectively obtains the SNR _j , spectrum detection results, detection confidence P _j and service requirements R _j is sent to the spectrum sensing fusion center, where,

The primary user is denoted as PU _i , the secondary user is denoted as CR _j , the spectrum sensing fusion center is denoted as FC, business requirements R _j ∈ [0,N ₁ ], P _j ∈ [0,1], detection confidence P _d,ji is the detection probability of secondary user CR _j to primary user PU _i ; 1≤i≤N ₁ , 1≤j≤N ₂ , N ₁ ≥2, N ₂ ≥2;

(2) The spectrum sensing fusion center FC receives the signal-to-noise ratio SNR _j , spectrum detection results, detection confidence P _j and service requirements R _j sent by each user CR _j , and judges that the signal-to-noise ratio SNR _j of the secondary user is greater than the expected When the selected signal-to-noise ratio screening value SNR _chosen , select the secondary user corresponding to the signal-to-noise ratio as the primary secondary user participating in the collaborative detection, and record the primary secondary user as CR' _t , and perform step (3), otherwise, Select the spectrum detection result corresponding to the secondary user with the highest SNR as the final detection result of the spectrum sensing fusion center FC; where,

The number of primary secondary users is N' ₂ , the signal-to-noise ratio corresponding to the primary secondary user CR' _t is SNR' _t , the detection confidence is P' _t , and the service requirement is R' _t , 1≤t≤N' ₂ ≤ N ₂ ; the number of spectrum detection results received by the spectrum sensing fusion center FC is N ₁ × N ₂ ;

(3) The spectrum sensing fusion center FC assigns the number of frequency bands C _t to be detected to the primary secondary user CR' _t according to the confidence P' _t of the primary secondary user CR' _t and the service demand R' _t :

(3-1) According to the confidence degree P' _t of each primary selected secondary user CR' _t , respectively normalize the confidence P' _t of each primary selected secondary user CR' _t to obtain each primary selected secondary user Normalized confidence value of _CR't

(3-2) The normalized confidence value corresponding to each primary user CR' _t obtained according to step (3-1) Calculate the number of frequency bands C _t that need to be detected when the spectrum sensing fusion center FC assigns the primary user CR' _t to be detected:

(4) The spectrum sensing fusion center FC calculates the root mean square value of the signal-to-noise ratio of all primary secondary users according to the signal-to-noise ratio SNR' _t of primary secondary users CR' _t participating in cooperative detection And let the signal-to-noise ratio SNR' _t = γ _t , where the root mean square value of the signal-to-noise ratio is calculated as follows:

(5) The spectrum sensing fusion center FC calculates the root mean square value of the signal-to-noise ratio of all primary secondary users in turn The quotient η _t between the signal-to-noise ratio SNR' _t of each primary secondary user CR' _t , where

(6) The spectrum sensing fusion center FC calculates and obtains the SNR preset threshold λ and the SNR optimal threshold λ _optimal , and respectively calculates and obtains the SNR quotient value η _t and the SNR preset threshold λ according to The size relationship, select the secondary user CR” _k to participate in the collaboration, and the signal-to-noise ratio of the secondary user CR” _k is SNR” _k , where,

(6-1) The spectrum sensing fusion center FC acquires the signal-to-noise ratio set {SNR' _t } of the primary-selected slave users according to the received SNR sets {SNR' _t } corresponding to the N' ₂ primary-selected slave users. The maximum value of the noise ratio, record the maximum value of the signal-to-noise ratio as SNR'max;

(6-2) Take the obtained SNR maximum value SNR'max as a reference, and make the SNR' _t of the N' ₂ primary users CR' _t with the SNR maximum value SNR'max respectively quotient processing, and calculate the initial threshold λ _t corresponding to each primary user SNR' _t , where,

λ _t = |SNR' _t /SNR' _max |, t=1,2,...,N' ₂ , N' ₂ ≤ N ₂ ;

(6-3) According to the normalized confidence value of each primary selection from the user CR' _t and the signal-to-noise ratio quotient η _t , calculate the joint screening parameter value ξ _t of each primary secondary user CR' _t , and select the secondary user CR" _k participating in the collaboration according to the joint screening parameter value ξ _t , where the complex The number of selected users CR” _k is M, t=1,2,...,N' ₂ , k=1,2,...,M, M≤N' ₂ :

If the joint screening parameter value ξ _t is within the preset value range [ξ _a , ξ _b ], that is, when ξ _a ≤ ξ _t ≤ ξ _b , then select the primary user corresponding to the joint screening parameter value ξ _t as complex Select a secondary user and participate in collaborative detection; otherwise, the primary secondary user will not be selected;

(6-4) According to the signal-to-noise ratio preset threshold λ in step (6-3), obtain the cooperative detection performance curves of M re-selected slave users CR" _k under the OR criterion and the AND criterion respectively, wherein,

OR criterion:

AND criteria: k=1,2,...,M, M≤N' ₂ ≤N ₂ ;

Among them, P _{d, k} is the average detection probability of the k-th re-selected slave user CR” _k , P _{fa, k} is the average false alarm probability of the k-th re-selected slave user CR” _k ; P _{d, ks} is the re-selected The detection probability of the s-th frequency band allocated from user CR” _k , P _fa,ks is the false alarm probability of the s-th frequency band allocated from user CR” _k to it; Q _d is the global Detection probability, Q _fa is the global false alarm probability after collaborative detection; ω _k represents the weight coefficient of signal-to-noise ratio CR" _k , SNR" _k is the signal-to-noise ratio of the kth re-selected user CR" _k , SNR" _max Indicates the maximum value of the SNR of the M secondary users, and SNR" _min represents the minimum value of the SNR of the M secondary users;

(6-5) According to the collaborative detection performance curves under the OR criterion and the AND criterion, the maximum detection probabilities Q _{(OR, d)-max} and Q _{(AND, d)-max} under the OR criterion and the AND criterion are respectively obtained, and The maximum value of Q _{(OR, d)-max} and Q _{(AND, d)-max} Q _d-max , and the signal-to-noise ratio preset threshold corresponding to the best detection performance value Q _d-max is the signal-to-noise ratio The optimal threshold, the optimal threshold of SNR is λ _optimal ; where, Q _d-max = max(Q _{(OR, d)-max} , Q _{(AND, d)-max} );

(7) According to the obtained SNR optimal threshold λ _optimal , obtain the multiple-selection slave user CR” corresponding to the SNR optimal threshold λ _optimal , obtain the adjustment factor α of the multiple-selection slave user CR” and other M- The adjustment factor α _k of one reselected slave user CR” _k , and adjust the average false alarm probability of reselected slave users CR” and CR” _k according to the adjustment factors α and α _k respectively. After the reselected slave user CR” is adjusted The average false alarm probability is denoted as P _fa , and the average false alarm probability adjusted from user CR” _k is denoted as P _fa,k ; where,

P _fa,k =α _k P _fa ,k=1,2,...,M-1;

Among them, α _k is the adjustment factor of the secondary user CR" _k , which is used to adjust the average false alarm probability according to the signal-to-noise ratio SNR" _k of the _secondary user CR" _k itself; SNR of k multiple choices from user CR"_k;

(8) According to the adjustment factor α _k of the M secondary users obtained in step (7) and the corresponding adjusted average false alarm probability P _fa,k , calculate the adjusted decision threshold of the secondary user CR” _k λ” _k and average detection probability P _d,k , where,

in, k=1,2,...,M, M≤N'₂; n is the number of sampling points;

(9) According to the signal-to-noise ratio SNR” _k of the M reselected slave users in step (8) and the adjusted average detection probability P _d,k obtained, return to step (6), and re-select the M reselected slave users Select among T final users CR"' _t participating in the collaboration, and use the weighted OR criterion to obtain the global detection probability after collaboration as the final detection result of the spectrum sensing fusion center FC, where 1≤t≤T≤M≤ _N'2 .

Further, the weighted OR criterion in the step (9) is as follows:

Among them, P' _{d, ts} is the detection probability of the sth frequency band allocated by the final selected slave user CR"' _t , and P' _{fa, ts} is the sth frequency band allocated by the final selected slave user CR"' _t False alarm probability; P' _d,t is the average detection probability of the tth reselected final user CR"' _t , P' _fa,t is the tth reselected final user CR"' _t The average false alarm probability of ; Q' _d is the global detection probability after cooperative detection, Q' _fa is the global false alarm probability after cooperative detection; M' is the number of reselected final slave users; ω' _t is reselected The weighting coefficient of the final selection from the user CR”' _t .

Compared with the prior art, the present invention has the advantage that each user sends its own signal-to-noise ratio, confidence level, service requirements and spectrum detection results of multiple primary users to the spectrum sensing fusion center, and the spectrum sensing fusion center Calculate and screen primary cooperative secondary users, delete secondary users with poor detection performance and low signal-to-noise ratio, and then allocate the number of frequency bands to primary cooperative secondary users; calculate the root mean square value of the signal-to-noise ratio of all primary primary secondary users and The quotient value between the signal-to-noise ratios of the secondary users, according to the relationship between each quotient value and the preset threshold value of the signal-to-noise ratio, select the reselected secondary users participating in the collaboration, and adjust the detection probability of the reselected secondary users; The signal-to-noise ratio of the secondary user and the adjusted average detection probability return to the step of selecting multiple secondary users to obtain the final selected secondary users participating in the collaboration, and use the global detection probability of the weighted OR criterion cooperation as the spectrum sensing fusion center The final detection results can avoid the bad impact of low signal-to-noise ratio or poor performance secondary users on the overall detection performance.

Description of drawings

FIG. 1 is a schematic diagram of a cognitive network structure in an embodiment of the present invention;

FIG. 2 is a schematic flow chart of a multi-band cooperative cognitive spectrum sensing method according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of a simulation result of a multi-band cooperative cognitive spectrum sensing method and a traditional AND criterion cooperative spectrum sensing according to an embodiment of the present invention.

detailed description

The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

As shown in Figure 1, in the cognitive network, the number of primary users is N ₁ , the number of secondary users is N ₂ , and the number of spectrum sensing fusion centers is 1, where the primary user is denoted as PU _i , and the secondary user It is denoted as CR _j , and the spectrum sensing fusion center is denoted as FC; 1≤i≤N ₁ , 1≤j≤N ₂ , N ₁ ≥2, N ₂ ≥2.

The following describes the multi-band cooperative cognitive spectrum sensing method in this embodiment with reference to FIG. 1 and FIG. 2 . Wherein, the multi-band cooperative cognitive spectrum sensing method includes the following steps in sequence:

(1) In the cognitive network, the number of primary users is N ₁ , the number of secondary users is N ₂ , the number of spectrum sensing fusion centers is 1, and the primary users independently occupy their respective frequency bands in the spectrum; N ₂ Each secondary user independently obtains its own SNR _j and the spectrum detection results of the frequency bands occupied by N ₁ primary users, and respectively obtains the SNR _j , spectrum detection results, detection confidence P _j and service requirements R _j is sent to the spectrum sensing fusion center, where,

The primary user is denoted as PU _i , the secondary user is denoted as CR _j , the spectrum sensing fusion center is denoted as FC, business requirements R _j ∈ [0,N ₁ ], P _j ∈ [0,1], detection confidence P _d,ji is the detection probability of secondary user CR _j to primary user PU _i ; 1≤i≤N ₁ , 1≤j≤N ₂ , N ₁ ≥2, N ₂ ≥2;

(2) The spectrum sensing fusion center FC receives the signal-to-noise ratio SNR _j , spectrum detection results, detection confidence P _j and service requirements R _j sent by each user CR _j , and judges that the signal-to-noise ratio SNR _j of the secondary user is greater than the expected When the selected signal-to-noise ratio screening value SNR _chosen , select the secondary user corresponding to the signal-to-noise ratio as the primary secondary user participating in the collaborative detection, and record the primary secondary user as CR' _t , and perform step (3), otherwise, Select the spectrum detection result corresponding to the secondary user with the highest SNR as the final detection result of the spectrum sensing fusion center FC; where,

The number of primary secondary users is N' ₂ , the signal-to-noise ratio corresponding to the primary secondary user CR' _t is SNR' _t , the detection confidence is P' _t , and the service requirement is R' _t , 1≤t≤N' ₂ ≤ N ₂ ; the number of spectrum detection results received by the spectrum sensing fusion center FC is N ₁ × N ₂ ;

(3) The spectrum sensing fusion center FC assigns the number of frequency bands C _t to be detected to the primary secondary user CR' _t according to the confidence P' _t of the primary secondary user CR' _t and the service demand R' _t :

(3-1) According to the confidence degree P' _t of each primary selected secondary user CR' _t , respectively normalize the confidence P' _t of each primary selected secondary user CR' _t to obtain each primary selected secondary user Normalized confidence value of _CR't

(3-2) The normalized confidence value corresponding to each primary user CR' _t obtained according to step (3-1) Calculate the number of frequency bands C _t that need to be detected when the spectrum sensing fusion center FC assigns the primary user CR' _t to be detected:

(4) The spectrum sensing fusion center FC calculates the root mean square value of the signal-to-noise ratio of all primary secondary users according to the signal-to-noise ratio SNR' _t of primary secondary users CR' _t participating in cooperative detection And let the signal-to-noise ratio SNR' _t = γ _t , where the root mean square value of the signal-to-noise ratio is calculated as follows:

(5) The spectrum sensing fusion center FC calculates the root mean square value of the signal-to-noise ratio of all primary secondary users in turn The quotient η _t between the signal-to-noise ratio SNR' _t of each primary secondary user CR' _t , where

(6) The spectrum sensing fusion center FC calculates and obtains the SNR preset threshold λ and the SNR optimal threshold λ _optimal , and respectively calculates and obtains the SNR quotient value η _t and the SNR preset threshold λ according to The size relationship, select the secondary user CR” _k to participate in the collaboration, and the signal-to-noise ratio of the secondary user CR” _k is SNR” _k , where,

(6-1) The spectrum sensing fusion center FC acquires the signal-to-noise ratio set {SNR' _t } of the primary-selected slave users according to the received SNR sets {SNR' _t } corresponding to the N' ₂ primary-selected slave users. The maximum value of the noise ratio, record the maximum value of the signal-to-noise ratio as SNR'max;

(6-2) Take the obtained SNR maximum value SNR'max as a reference, and make the SNR' _t of the N' ₂ primary users CR' _t with the SNR maximum value SNR'max respectively quotient processing, and calculate the initial threshold λ _t corresponding to each primary user SNR' _t , where,

λ _t = |SNR' _t /SNR' _max |, t=1,2,...,N' ₂ , N' ₂ ≤ N ₂ ;

(6-3) According to the normalized confidence value of each primary selection from the user CR' _t and the signal-to-noise ratio quotient η _t , calculate the joint screening parameter value ξ _t of each primary secondary user CR' _t , and select the secondary user CR" _k participating in the collaboration according to the joint screening parameter value ξ _t , where the complex The number of selected users CR” _k is M, t=1,2,...,N' ₂ , k=1,2,...,M, M≤N' ₂ :

If the joint screening parameter value ξ _t is within the preset value range [ξ _a , ξ _b ], that is, when ξ _a ≤ ξ _t ≤ ξ _b , then select the primary user corresponding to the joint screening parameter value ξ _t as complex Select a secondary user and participate in collaborative detection; otherwise, the primary secondary user will not be selected;

(6-4) According to the signal-to-noise ratio preset threshold λ in step (6-3), obtain the cooperative detection performance curves of M re-selected slave users CR" _k under the OR criterion and the AND criterion respectively, wherein,

OR criterion:

AND criteria: k=1,2,...,M, M≤N' ₂ ≤N ₂ ;

Among them, P _{d, k} is the average detection probability of the k-th re-selected slave user CR” _k , P _{fa, k} is the average false alarm probability of the k-th re-selected slave user CR” _k ; P _{d, ks} is the re-selected The detection probability of the s-th frequency band allocated from user CR” _k , P _fa,ks is the false alarm probability of the s-th frequency band allocated from user CR” _k to it; Q _d is the global Detection probability, Q _fa is the global false alarm probability after collaborative detection; ω _k represents the weight coefficient of signal-to-noise ratio CR" _k , SNR" _k is the signal-to-noise ratio of the kth re-selected user CR" _k , SNR" _max Indicates the maximum value of the SNR of the M secondary users, and SNR" _min represents the minimum value of the SNR of the M secondary users;

(6-5) According to the collaborative detection performance curves under the OR criterion and the AND criterion, the maximum detection probabilities Q _{(OR, d)-max} and Q _{(AND, d)-max} under the OR criterion and the AND criterion are respectively obtained, and The maximum value of Q _{(OR, d)-max} and Q _{(AND, d)-max} Q _d-max , and the signal-to-noise ratio preset threshold corresponding to the best detection performance value Q _d-max is the signal-to-noise ratio The optimal threshold, the optimal threshold of SNR is λ _optimal ; where, Q _d-max = max(Q _{(OR, d)-max} , Q _{(AND, d)-max} );

(7) According to the obtained SNR optimal threshold λ _optimal , obtain the multiple-selection slave user CR” corresponding to the SNR optimal threshold λ _optimal , obtain the adjustment factor α of the multiple-selection slave user CR” and other M- The adjustment factor α _k of one reselected slave user CR” _k , and adjust the average false alarm probability of reselected slave users CR” and CR” _k according to the adjustment factors α and α _k respectively. After the reselected slave user CR” is adjusted The average false alarm probability is denoted as P _fa , and the average false alarm probability adjusted from user CR” _k is denoted as P _fa,k ; where,

P _fa,k =α _k P _fa ,k=1,2,...,M-1;

Among them, α _k is the adjustment factor of the secondary user CR" _k , which is used to adjust the average false alarm probability according to the signal-to-noise ratio SNR" _k of the _secondary user CR" _k itself; SNR of k multiple choices from user CR"_k;

(8) According to the adjustment factor α _k of the M secondary users obtained in step (7) and the corresponding adjusted average false alarm probability P _fa,k , calculate the adjusted decision threshold of the secondary user CR” _k λ” _k and average detection probability P _d,k , where,

in, k=1,2,...,M, M≤N'₂; n is the number of sampling points;

(9) According to the signal-to-noise ratio SNR” _k of the M reselected slave users in step (8) and the adjusted average detection probability P _d,k obtained, return to step (6), and re-select the M reselected slave users Select among the T final users CR”' _t participating in the collaboration, and use the weighted OR criterion to obtain the global detection probability after collaboration as the final detection result of the spectrum sensing fusion center FC, where the weighted OR criterion is as follows:

Among them, P' _{d, ts} is the detection probability of the sth frequency band allocated by the final selected slave user CR"' _t , and P' _{fa, ts} is the sth frequency band allocated by the final selected slave user CR"' _t False alarm probability; P' _d,t is the average detection probability of the tth reselected final user CR"' _t , P' _fa,t is the tth reselected final user CR"' _t The average false alarm probability of ; Q' _d is the global detection probability after cooperative detection, Q' _fa is the global false alarm probability after cooperative detection; M' is the number of reselected final slave users; ω' _t is reselected The weighting coefficient of the final selection from the user CR"' _t , 1≤t≤T≤M≤N' ₂ .

Fig. 3 shows a schematic diagram of the simulation results of the multi-band cooperative cognitive spectrum sensing method in the present invention, and simulates the traditional AND cooperative detection method at the same time. Wherein, in the traditional AND cooperative detection method, all secondary users participate in cooperative spectrum sensing detection. The simulation conditions are as follows:

It is assumed that in the cognitive radio network, the number N ₁ of primary users PU is 2, 3, 5 and 7 respectively, and the number N ₂ of slave users CR gradually increases from 2 to 11; the signal-to-noise ratios of slave users are respectively -5dB, -8dB, -10dB, -13dB, -16dB, -17dB, -19dB, -23dB, -25dB and -27dB, all adopt energy detection from the user.

As can be seen from Figure 3, under the condition that the number of master users and the number of slave users are certain, the global detection probability in the present invention is greater than the detection probability of traditional AND cooperative detection, which shows that the cooperative spectrum sensing method in the present invention has better At the same time, it can also be found that under the conditions of the cooperative detection method and the number of master users, with the increase of the number of slave users, the global detection probability after cooperative detection gradually increases; in the cooperative detection method and the number of slave users Under certain conditions, with the increase of the number of primary users, the global detection probability after cooperative detection gradually decreases. It can be seen that, compared with the traditional AND cooperative detection method, the multi-band cognitive cooperative spectrum sensing method in the embodiment of the present invention avoids the bad influence of secondary users with low signal-to-noise ratio or poor detection performance, and the overall cooperative detection performance is greatly improved. The improvement has better detection performance.

Claims (1)

1. The multi-band cooperative cognitive spectrum sensing method is characterized in that, comprising the following steps in sequence: (1) In the cognitive network, the number of primary users is N ₁ , the number of secondary users is N ₂ , the number of spectrum sensing fusion centers is 1, and the primary users independently occupy their respective frequency bands in the spectrum; N ₂ Each secondary user independently obtains its own SNR _j and the spectrum detection results of the frequency bands occupied by N ₁ primary users, and respectively obtains the SNR _j , spectrum detection results, detection confidence P _j and service requirements R _j is sent to the spectrum sensing fusion center, where, The primary user is denoted as PU _i , the secondary user is denoted as CR _j , the spectrum sensing fusion center is denoted as FC, business requirements R _j ∈ [0,N ₁ ], P _j ∈ [0,1], detection confidence P _d,ji is the detection probability of secondary user CR _j to primary user PU _i ; 1≤i≤N ₁ , 1≤j≤N ₂ , N ₁ ≥2, N ₂ ≥2; (2) The spectrum sensing fusion center FC receives the signal-to-noise ratio SNR _j , spectrum detection results, detection confidence P _j and service requirements R _j sent by each user CR _j , and judges that the signal-to-noise ratio SNR _j of the secondary user is greater than the expected When the selected signal-to-noise ratio screening value SNR _chosen , select the secondary user corresponding to the signal-to-noise ratio as the primary secondary user participating in the collaborative detection, and record the primary secondary user as CR' _t , and perform step (3), otherwise, Select the spectrum detection result corresponding to the secondary user with the highest SNR as the final detection result of the spectrum sensing fusion center FC; where, The number of primary secondary users is N' ₂ , the signal-to-noise ratio corresponding to the primary secondary user CR' _t is SNR' _t , the detection confidence is P' _t , and the service requirement is R' _t , 1≤t≤N' ₂ ≤ N ₂ ; the number of spectrum detection results received by the spectrum sensing fusion center FC is N ₁ × N ₂ ; (3) The spectrum sensing fusion center FC assigns the number of frequency bands C _t to be detected to the primary secondary user CR' _t according to the confidence P' _t of the primary secondary user CR' _t and the service demand R' _t : (3-1) According to the confidence degree P' _t of each primary selected secondary user CR' _t , respectively normalize the confidence P' _t of each primary selected secondary user CR' _t to obtain each primary selected secondary user Normalized confidence value of _CR't $\begin{array}{cc}\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{{\mathrm{<span\; class="notranslate">\; P</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}<span\; class="notranslate">\; =</span>\frac{{{\mathrm{<span\; class="notranslate">\; P</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}{\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{{\mathrm{<span\; class="notranslate">\; N</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}{{\mathrm{<span\; class="notranslate">\; P</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}<span\; class="notranslate">\; ,</span>& \mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&le;</span>{{\mathrm{<span\; class="notranslate">\; N</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ;</span>\end{array}$ (3-2) The normalized confidence value corresponding to each primary user CR' _t obtained according to step (3-1) Calculate the number of frequency bands C _t that need to be detected when the spectrum sensing fusion center FC assigns the primary user CR' _t to be detected: $\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{{\mathrm{<span\; class="notranslate">\; P</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}<span\; class="notranslate">\; \&Center\; Dot;</span>{{\mathrm{<span\; class="notranslate">\; N</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>& \mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&le;</span>{{\mathrm{<span\; class="notranslate">\; N</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ;</span>\end{array}$ (4) The spectrum sensing fusion center FC calculates the root mean square value of the signal-to-noise ratio of all primary secondary users according to the signal-to-noise ratio SNR' _t of primary secondary users CR' _t participating in cooperative detection And let the signal-to-noise ratio SNR' _t = γ _t , where the root mean square value of the signal-to-noise ratio is calculated as follows: $\begin{array}{cc}\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; \&gamma;</span>}}<span\; class="notranslate">\; =</span>\sqrt{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{{\mathrm{<span\; class="notranslate">\; N</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{{\mathrm{<span\; class="notranslate">\; N</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{{\mathrm{<span\; class="notranslate">\; SNR</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; ,</span>& {{\mathrm{<span\; class="notranslate">\; N</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ;</span>\end{array}$ (5) The spectrum sensing fusion center FC calculates the root mean square value of the signal-to-noise ratio of all primary secondary users in turn The quotient η _t between the signal-to-noise ratio SNR' _t of each primary secondary user CR' _t , where $\begin{array}{ccc}{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; |</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; \&gamma;</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; \&gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; ,</span>& \mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; ...</span><span\; class="notranslate">\; ,</span>{{\mathrm{<span\; class="notranslate">\; N</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>& {{\mathrm{<span\; class="notranslate">\; N</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ;</span>\end{array}$ (6) The spectrum sensing fusion center FC calculates and obtains the SNR preset threshold λ and the SNR optimal threshold λ _optimal , and respectively calculates and obtains the SNR quotient value η _t and the SNR preset threshold λ according to The size relationship, select the secondary user CR” _k to participate in the collaboration, and the signal-to-noise ratio of the secondary user CR” _k is SNR” _k , where, (6-1) The spectrum sensing fusion center FC acquires the signal-to-noise ratio set {SNR' _t } of the primary-selected slave users according to the received SNR sets {SNR' _t } corresponding to the N' ₂ primary-selected slave users. The maximum value of the noise ratio, record the maximum value of the signal-to-noise ratio as SNR'max; (6-2) Take the obtained SNR maximum value SNR'max as a reference, and make the SNR' _t of the N' ₂ primary users CR' _t with the SNR maximum value SNR'max respectively quotient processing, and calculate the initial threshold λ _t corresponding to each primary user SNR' _t , where, λ _t = |SNR' _t /SNR' _max |, t=1,2,...,N' ₂ , N' ₂ ≤ N ₂ ; (6-3) According to the normalized confidence value of each primary selection from the user CR' _t and the signal-to-noise ratio quotient η _t , calculate the joint screening parameter value ξ _t of each primary secondary user CR' _t , and select the secondary user CR" _k participating in the collaboration according to the joint screening parameter value ξ _t , where the complex The number of selected users CR” _k is M, t=1, 2,..., N' ₂ , k=1, 2,..., M, M≤N' ₂ : If the joint screening parameter value ξ _t is within the preset value range [ξ _a , ξ _b ], that is, when ξ _a ≤ ξ _t ≤ ξ _b , then select the primary user corresponding to the joint screening parameter value ξ _t as complex Select a secondary user and participate in collaborative detection; otherwise, the primary secondary user will not be selected; (6-4) According to the signal-to-noise ratio preset threshold λ in step (6-3), obtain the cooperative detection performance curves of M re-selected slave users CR" _k under the OR criterion and the AND criterion respectively, wherein, OR criterion: AND criteria: $\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; k</span>}}{\overset{{{\mathrm{<span\; class="notranslate">\; N</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}\underset{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}}}{{{\mathrm{<span\; class="notranslate">\; N</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; k</span>}}{\overset{{{\mathrm{<span\; class="notranslate">\; N</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}\underset{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}}}{{{\mathrm{<span\; class="notranslate">\; N</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}<span\; class="notranslate">\; ;</span>\end{array}$ Among them, P _{d, k} is the average detection probability of the k-th re-selected slave user CR” _k , P _{fa, k} is the average false alarm probability of the k-th re-selected slave user CR” _k ; P _{d, ks} is the re-selected The detection probability of the s-th frequency band allocated from user CR” _k , P _fa,ks is the false alarm probability of the s-th frequency band allocated from user CR” _k to it; Q _d is the global Detection probability, Q _fa is the global false alarm probability after collaborative detection; ω _k represents the weight coefficient of signal-to-noise ratio CR" _k , SNR" _k is the signal-to-noise ratio of the kth re-selected user CR" _k , SNR" _max Indicates the maximum value of the SNR of the M secondary users, and SNR" _min represents the minimum value of the SNR of the M secondary users; (6-5) According to the collaborative detection performance curves under the OR criterion and the AND criterion, the maximum detection probabilities Q _{(OR, d)-max} and Q _{(AND, d)-max} under the OR criterion and the AND criterion are respectively obtained, and The maximum value of Q _{(OR, d)-max} and Q _{(AND, d)-max} Q _d-max , and the signal-to-noise ratio preset threshold corresponding to the best detection performance value Q _d-max is the signal-to-noise ratio The optimal threshold, the optimal threshold of SNR is λ _optimal ; where, Q _d-max = max(Q _{(OR, d)-max} , Q _{(AND, d)-max} ); (7) According to the obtained SNR optimal threshold λ _optimal , obtain the multiple-selection slave user CR” corresponding to the SNR optimal threshold λ _optimal , obtain the adjustment factor α of the multiple-selection slave user CR” and other M- The adjustment factor α _k of one reselected slave user CR” _k , and adjust the average false alarm probability of reselected slave users CR” and CR” _k according to the adjustment factors α and α _k respectively. After the reselected slave user CR” is adjusted The average false alarm probability is denoted as P _fa , and the average false alarm probability adjusted from user CR” _k is denoted as P _fa,k ; where, P _fa,k =α _k P _fa ,k=1,2,...,M-1; $\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\frac{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; SNR</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}<span\; class="notranslate">\; -</span>{{\mathrm{<span\; class="notranslate">\; SNR</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; SNR</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; \&prime;</span>}}}<span\; class="notranslate">\; ,</span>& \mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; ...</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ;</span>\end{array}$ $\begin{array}{cc}\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; SNR</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; \&prime;</span>}}<span\; class="notranslate">\; =</span>\sqrt{\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\frac{{<span\; class="notranslate">\; (</span>{{\mathrm{<span\; class="notranslate">\; SNR</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; m</span>}}}<span\; class="notranslate">\; ,</span>& \mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; \&le;</span>{{\mathrm{<span\; class="notranslate">\; N</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ;</span>\end{array}$ Among them, α _k is the adjustment factor of the secondary user CR" _k , which is used to adjust the average false alarm probability according to the signal-to-noise ratio SNR" _k of the _secondary user CR" _k itself; SNR of k multiple choices from user CR"_k; (8) According to the adjustment factor α _k of the M secondary users obtained in step (7) and the corresponding adjusted average false alarm probability P _fa,k , calculate the adjusted decision threshold of the secondary user CR” _k λ” _k and average detection probability P _d,k , where, $\begin{array}{c}\mathrm{<span\; class="notranslate">\; \&lambda;</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&lsqb;</span>\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; Q</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; \&rsqb;</span>\\ <span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&lsqb;</span>\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; Q</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&delta;</span>}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; \&rsqb;</span>\\ <span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&lsqb;</span>\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; Q</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\frac{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; SNR</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; \&prime;</span>}}<span\; class="notranslate">\; -</span>{{\mathrm{<span\; class="notranslate">\; SNR</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; SNR</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; \&prime;</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; \&rsqb;</span>\end{array}<span\; class="notranslate">\; ;</span>$ ${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; Q</span>}<span\; class="notranslate">\; \&lsqb;</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\sqrt{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; \&CenterDot;</span>{{\mathrm{<span\; class="notranslate">\; SNR</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ;</span>$ $\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}{<span\; class="notranslate">\; \&lsqb;</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; )</span>\sqrt{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}{{\mathrm{<span\; class="notranslate">\; SNR</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}<span\; class="notranslate">\; \&rsqb;</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&CenterDot;</span>{<span\; class="notranslate">\; (</span>{{\mathrm{<span\; class="notranslate">\; SNR</span>}}^{<span\; class="notranslate">\; \&prime;</span><span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ;</span>$ in, n is the number of sampling points; (9) According to the signal-to-noise ratio SNR” _k of the M reselected slave users in step (8) and the adjusted average detection probability P _d,k obtained, return to step (6), and re-select the M reselected slave users Select among T final users CR"' _t participating in the collaboration, and use the weighted OR criterion to obtain the global detection probability after collaboration as the final detection result of the spectrum sensing fusion center FC, where 1≤t≤T≤M≤ N'₂; where: The weighted OR criterion is as follows: $\begin{array}{cc}{{\mathrm{<span\; class="notranslate">\; Q</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; m</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}{<span\; class="notranslate">\; \&Pi;</span>}}{{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{{\mathrm{<span\; class="notranslate">\; P</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& {{\mathrm{<span\; class="notranslate">\; Q</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; m</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}{<span\; class="notranslate">\; \&Pi;</span>}}{{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{{\mathrm{<span\; class="notranslate">\; P</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>\end{array}$ $\begin{array}{ccccc}{{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>\frac{{{\mathrm{<span\; class="notranslate">\; P</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}}}{\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; m</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{{\mathrm{<span\; class="notranslate">\; P</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}}}<span\; class="notranslate">\; ,</span>& {{\mathrm{<span\; class="notranslate">\; P</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; t</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; m</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\underset{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}{{\mathrm{<span\; class="notranslate">\; P</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}}}{{\mathrm{<span\; class="notranslate">\; m</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}<span\; class="notranslate">\; ,</span>& {{\mathrm{<span\; class="notranslate">\; P</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; t</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; m</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\underset{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}{{\mathrm{<span\; class="notranslate">\; P</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}}}{{\mathrm{<span\; class="notranslate">\; m</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}<span\; class="notranslate">\; ,</span>& \mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; ...</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; m</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; m</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ;</span>\end{array}$ Among them, P' _{d, ts} is the detection probability of the sth frequency band allocated by the final selected slave user CR"' _t , and P' _{fa, ts} is the sth frequency band allocated by the final selected slave user CR"' _t False alarm probability; P' _d,t is the average detection probability of the tth reselected final user CR"' _t , P' _fa,t is the tth reselected final user CR"' _t The average false alarm probability of ; Q' _d is the global detection probability after cooperative detection, Q' _fa is the global false alarm probability after cooperative detection; M' is the number of reselected final slave users; ω' _t is reselected The weighting coefficient of the final selection from the user CR”' _t .