Self-adaptive sensing threshold setting method and device based on main user power detection

Abstract

The invention relates to the technical field of cognitive radio, in particular to a self-adaptive perception threshold setting method and device based on main user power detection, and the method comprises the following steps: 1, calculating a high threshold and a low threshold of cognitive users SU, sending a signal-to-noise ratio of a received signal to a fusion center by each SU, and calculating a weighting factor of each user, each SU adaptively adjusts a decision threshold according to the weighting factor; 2, the SU compares the current energy with a decision threshold value, if the current energy is larger than a high threshold value, it is judged that a primary user PU exists, and if the current energy is between the high threshold value and a low threshold value, the average value of the historical energy queue is taken as the current energy, and the next round of detection is started; 3, if the average value of the historical energy is between the high threshold value and the low threshold value, calculating a single threshold value; and step 4, determining that the PU exists as long as one cognitive node detects the signal. According to the method, the processing efficiency of the algorithm is improved, and the detection performance of energy detection can be remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of cognitive radio technology, specifically to an adaptive sensing threshold setting method and apparatus based on primary user power detection. Background Technology

[0002] With the widespread adoption of mobile and internet technologies, the demand for spectrum resources has increased dramatically. Cognitive radio is a concept that promises to alleviate spectrum shortages by efficiently and dynamically utilizing the electromagnetic spectrum. To allow dynamic spectrum access, cognitive users (SUs) perform spectrum sensing. Therefore, spectrum sensing is a crucial component of cognitive radio networks, involving the detection of unused spectrum space to establish communication links for cognitive user SUs without interrupting primary user (PU) transmission. Energy-based (ED) spectrum sensing is one of the most explored approaches, but it still has significant room for improvement, particularly in terms of performance degradation at low signal-to-noise ratios. It allows the detection of unknown signals without requiring any prior information and with minimal computational and implementation complexity.

[0003] Prior art document 1 (application number CN201910293529.X) discloses a method for multi-spectrum sensing and power allocation in smart grids based on cognitive radio, including the following steps: Step S1: Calculate the power of each original channel and the power of each cognitive channel within one cycle; Step S2: Optimize energy efficiency to obtain the optimal sensing time; when the sensing time is the optimal sensing time, output the optimal power allocation scheme; Step S3: For the j-th cognitive channel, given the required minimum detection probability, calculate the test statistic and threshold based on the result of step S2; if the test statistic is greater than the threshold, determine that the primary user exists; otherwise, determine that the primary user does not exist. However, when the signal-to-noise ratio is relatively low, and the difference between noise and signal energy is not significant, the single-threshold detection method is prone to inaccurate sensing of the presence of the primary user (PU) by the primary user (SU), which will reduce the detection probability of the entire system.

[0004] Prior art document 2 (application number CN201910813502.9) discloses an energy detection method based on the dual-threshold power spectrum dual-average ratio, relating to the field of cognitive radio technology. Step one involves calculating the dual thresholds and detection statistic T under dual-threshold energy detection. DTED Determine the detection statistic T DTED Is it greater than the lower threshold γ0 and less than the upper threshold γ1? If no, proceed to step two; if yes, proceed to step three. Step two: Determine the detection statistic T. DTED If the value is greater than or equal to the upper threshold γ1, then the PU signal exists; otherwise, the detection statistic T is not checked. DTED If the value is less than the lower threshold γ0, then the PU signal does not exist; Step 3: Calculate the maximum and minimum values ​​of the power spectrum and the test statistic T using the power spectrum double average ratio algorithm.DPSRA and threshold γ DPSRA Step 4: Determine the test statistic T. DPSRA Is it greater than or equal to the threshold γ? DPSRA If yes, the PU signal exists; otherwise, it does not. However, the energy detection threshold is greatly affected by unknown noise and is difficult to determine. Secondly, the energy detection algorithm can only calculate the energy value of the signal and cannot distinguish whether it comes from a signal or noise. Furthermore, the dual-threshold energy detection algorithm increases the length of the spectrum sensing period, which further reduces the chance of the SU accessing idle frequency bands.

[0005] The technical problem this invention aims to solve is the low detection accuracy of traditional energy detection algorithms. To address this, an adaptive sensing threshold setting method based on primary user power detection is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide an adaptive sensing threshold setting method and apparatus based on primary user power detection, with a weighting factor δ. i It depends on the number of cognitive users N and the signal-to-noise ratio η. i It changes with the changes in the number of cognitive users N and the signal-to-noise ratio η, by adaptively adjusting the number of cognitive users N and the signal-to-noise ratio η. i This adjusts the high threshold. and low threshold Make the detection probability P d Achieving optimality. Single threshold. It is with the main user's transmit power E i It changes with the changes, and adaptively adjusts the primary user transmit power E. i This adjusts the single threshold. Make the detection probability P d To achieve optimal results.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] The adaptive sensing threshold setting method based on primary user power detection includes the following steps:

[0009] S10: Receive the transmitted signal from the primary user PU, and calculate the high threshold λ of the cognitive user SU based on the false alarm probability, missed detection probability, noise variance, and power of the received signal. H and low threshold λ L ;

[0010] S20, the signal-to-noise ratio η of the transmitted signal i Go to the fusion center and set the weighting factor of each SU to δ. i ;

[0011] S30, according to the δ iAdaptive adjustment of high decision threshold and low decision threshold The δ i According to the number N of SU, the η i The detection probability P adapts to changes in the form of [variable name] and changes accordingly. d To achieve the optimal;

[0012] S40, Obtain energy statistics E i And update the historical energy queue, comparing it with the current energy E. i and decision threshold:

[0013] like Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center; if Then it is an empty channel; if Historical energy queue average Take the current energy and start the next round of detection, while setting the flag to 1; if Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center;

[0014] S50, in the second round of testing, if Calculate a single threshold like Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center;

[0015] S60, when the fusion center receives the decision signal, it calculates the detection probability and the false alarm probability.

[0016] Preferably, the high threshold λ in S10 H and the low threshold λ L The calculation process is as follows:

[0017] The high threshold λ of the SU is calculated according to formulas (1) and (2). H and low threshold λ L ;

[0018]

[0019]

[0020] Where Q is the standard Gaussian complementary cumulative distribution function; P f It is the probability of a false alarm; P m It is the probability of missed detection; It is the noise variance; is the average power of the received signal; M is the number of detection sampling points.

[0021] Preferably, the weighting factor δ in S20 i The calculation process is shown in formula (3):

[0022]

[0023] Where, δ i η is the weighting factor for the i-th cognitive user, N is the number of cognitive users, and η is the weighting factor for the ith cognitive user. i It is the signal-to-noise ratio of the signal received by each cognitive user.

[0024] Preferably, each SU in S30 is based on the weighting factor δ i The decision threshold is adaptively adjusted using formulas (3), (4), and (5):

[0025]

[0026]

[0027] in, It is the adaptive high threshold for the i-th cognitive user. It is the adaptive low threshold for the i-th cognitive user.

[0028] Preferably, the single threshold calculation process in S50 is as shown in formula (6):

[0029]

[0030] Where M is the number of samples detected; It is the noise variance; E i This is the primary user's transmit power.

[0031] Preferably, the calculation process for the number of samples M is as shown in formula (7):

[0032]

[0033] Where ε is the noise power; Q is the standard Gaussian complementary cumulative distribution function; δ i It is a weighting factor; η i It is the signal-to-noise ratio of the signal received by each cognitive user; P f It is the probability of a false alarm; P d It is the detection probability.

[0034] Preferably, in S50, according to the single threshold Adaptive adjustment of primary user transmit power E i The single threshold Based on the primary user's transmit power E i The detection probability P adapts to changes in the form of [variable name] and changes accordingly. d To achieve optimal results.

[0035] Preferably, the detection probability P in S60 dand the false alarm probability P f The calculation process is shown in formulas (8) and (9):

[0036]

[0037]

[0038] Where N is the number of cognitive users, It is the detection probability of the i-th cognitive user. It is the false alarm probability of the i-th cognitive user.

[0039] Preferably, the adaptive sensing threshold setting device based on primary user power detection includes:

[0040] The adaptive adjustment module receives the transmitted signal from the primary user (PU) and calculates the high threshold λ of the cognitive user (SU) based on the false alarm probability, missed detection probability, noise variance, and power of the received signal. H and low threshold λ L The signal-to-noise ratio η of the transmitted signal i Go to the fusion center and set the weighting factor of each SU to δ. i According to the δ i Adaptive adjustment of high decision threshold and low decision threshold The δ i According to the number N of SU, the η i The detection probability P adapts to changes in the form of [variable name] and changes accordingly. d To achieve the optimal;

[0041] A dual-threshold comparison module is used to obtain the energy statistics value E. i And update the historical energy queue, comparing it with the current energy E. i and decision threshold: if Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center; if Then it is an empty channel; if Historical energy queue average Take the current energy and start the next round of detection, while setting the flag to 1; if Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center;

[0042] A single threshold comparison module is used to compare the average energy. To make further decisions; in the second round of testing, if Calculate a single threshold like Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center;

[0043] The decision module is used to calculate the detection probability and false alarm probability when the fusion center receives the decision signal.

[0044] Preferably, the single threshold in the single threshold comparison module Adaptive adjustment of primary user transmit power E i The single threshold Based on the primary user's transmit power E i The detection probability P adapts to changes in the form of [variable name] and changes accordingly. d To achieve optimal results.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] 1. This invention incorporates an additional algorithm between the two thresholds in the detection algorithm, improving processing efficiency and significantly enhancing energy detection performance. The adaptive dual-threshold energy detection algorithm exhibits significantly better detection performance than the traditional single-threshold energy detection method even under low signal-to-noise ratio conditions. By adding two more thresholds to the traditional single-threshold energy detection threshold, creating a triple-threshold algorithm, the impact of noise uncertainty and low signal-to-noise ratio on sensing performance in the sensing environment can be reduced.

[0047] 2. This invention provides an adaptive sensing threshold setting method and apparatus based on primary user power detection. The proposed improved algorithm combines the advantages of adaptive single-threshold and dual-threshold methods. Cognitive users perform local decision-making using a three-threshold energy detection method, and then the local decision results uploaded by the cognitive users are fused and decided at the fusion center, thereby making the detection more accurate and effectively reducing the system processing time. Simultaneously, the algorithm proposed in this invention has low computational complexity, and the adaptive adjustment of the three thresholds optimizes the detection probability.

[0048] 3. This invention utilizes a weighting factor that adaptively changes with the number of cognitive users and the signal-to-noise ratio. Simultaneously, the weighting factor serves as a coefficient for the adaptive change of the dual threshold, while the single threshold adaptively changes with the primary user's transmit power. This ensures that cognitive users have superior spectrum sensing performance with the same number of sampling points, guaranteeing effective use of spectrum resources without interfering with the primary user. This invention can find the optimal sensing time and optimal power allocation strategy, maximizing data rate or energy efficiency while satisfying all constraints. Attached Figure Description

[0049] Figure 1 This is a flowchart of the algorithm of the present invention;

[0050] Figure 2 This is a detection probability diagram under the influence of noise in this invention;

[0051] Figure 3This is an error probability diagram under different signal-to-noise ratio conditions of the present invention;

[0052] Figure 4 This is a graph showing the relationship between the threshold and error probability under different signal-to-noise ratio conditions according to the present invention.

[0053] Figure 5 This is a graph showing the relationship between the threshold and error probability under different sampling numbers according to the present invention;

[0054] Figure 6 This is a graph showing the relationship between signal-to-noise ratio and sample complexity under different algorithms of this invention;

[0055] Figure 7 This is a graph showing the relationship between the number of cognitive users and the detection probability under a signal-to-noise ratio of -20dB, as presented in this invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Please see Figures 1 to 7 This invention provides an adaptive sensing threshold setting method and apparatus based on primary user power detection, the technical solution of which is as follows:

[0058] The adaptive sensing threshold setting method based on primary user power detection includes the following steps:

[0059] S10: Receive the transmitted signal from the primary user PU, and calculate the high threshold λ of the cognitive user SU based on the false alarm probability, missed detection probability, noise variance, and power of the received signal. H and low threshold λ L ;

[0060] The high threshold λ mentioned in S10 H and the low threshold λ L The calculation process is as follows:

[0061] The high threshold λ of the SU is calculated according to formulas (1) and (2). H and low threshold λ L ;

[0062]

[0063]

[0064] Where Q is the standard Gaussian complementary cumulative distribution function; P fIt is the probability of a false alarm; P m It is the probability of missed detection; It is the noise variance; is the average power of the received signal; M is the number of detection sampling points.

[0065] S20, the signal-to-noise ratio η of the transmitted signal i Go to the fusion center and set the weighting factor of each SU to δ. i ;

[0066] The weighting factor δ mentioned in S20 i The calculation process is shown in formula (3):

[0067]

[0068] Where, δ i η is the weighting factor for the i-th cognitive user, N is the number of cognitive users, and η is the weighting factor for the ith cognitive user. i It is the signal-to-noise ratio of the signal received by each cognitive user.

[0069] S30, according to the δ i Adaptive adjustment of high decision threshold λ Hi and low decision threshold λ Li The δ i According to the number N of SU, the η i The detection probability P adapts to changes in the form of [variable name] and changes accordingly. d To achieve the optimal;

[0070] Each SU in S30 is based on the weighting factor δ i The decision threshold is adaptively adjusted using formulas (3), (4), and (5):

[0071]

[0072]

[0073] in, It is the adaptive high threshold for the i-th cognitive user. It is the adaptive low threshold for the i-th cognitive user.

[0074] S40, Obtain energy statistics E i And update the historical energy queue, comparing it with the current energy E. i and decision threshold:

[0075] like Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center; if Then it is an empty channel; if Historical energy queue average Take the current energy and start the next round of detection, while setting the flag to 1; if Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center;

[0076] S50, in the second round of testing, if Calculate a single threshold like Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center;

[0077] The single threshold calculation process described in S50 is shown in formula (6):

[0078]

[0079] Where M is the number of samples detected; It is the noise variance; E i This is the primary user's transmit power.

[0080] The calculation process for the number of samples M is shown in formula (7):

[0081]

[0082] Where ε is the noise power; Q is the standard Gaussian complementary cumulative distribution function; δ i It is a weighting factor; η i It is the signal-to-noise ratio of the signal received by each cognitive user; P f It is the probability of a false alarm; P d It is the detection probability.

[0083] S50 is based on the single threshold Adaptive adjustment of primary user transmit power E i The single threshold Based on the primary user's transmit power E i The detection probability P adapts to changes in the form of [variable name] and changes accordingly. d To achieve optimal results.

[0084] S60, when the fusion center receives the decision signal, it calculates the detection probability and the false alarm probability.

[0085] The detection probability P described in S60 d and the false alarm probability P f The calculation process is shown in formulas (8) and (9):

[0086]

[0087]

[0088] Where N is the number of cognitive users, It is the detection probability of the i-th cognitive user. It is the false alarm probability of the i-th cognitive user.

[0089] An adaptive sensing threshold setting device based on primary user power detection includes:

[0090] The adaptive adjustment module receives the transmitted signal from the primary user (PU) and calculates the high threshold λ of the cognitive user (SU) based on the false alarm probability, missed detection probability, noise variance, and power of the received signal. H and low threshold λ L The signal-to-noise ratio η of the transmitted signal i Go to the fusion center and set the weighting factor of each SU to δ. i According to the δ i Adaptive adjustment of high decision threshold and low decision threshold The δ i According to the number N of SU, the η i The detection probability P adapts to changes in the form of [variable name] and changes accordingly. d To achieve the optimal;

[0091] A dual-threshold comparison module is used to obtain the energy statistics value E. i And update the historical energy queue, comparing it with the current energy E. i and decision threshold: if Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center; if Then it is an empty channel; if Historical energy queue average Take the current energy and start the next round of detection, while setting the flag to 1; if Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center;

[0092] A single threshold comparison module is used to compare the average energy. To make further decisions; in the second round of testing, if Calculate a single threshold like Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center;

[0093] The decision module is used to calculate the detection probability and false alarm probability when the fusion center receives the decision signal.

[0094] The single threshold in the single threshold comparison module Adaptive adjustment of primary user transmit power E i The single threshold Based on the primary user's transmit power E iadapts and changes accordingly to make the detection probability P d reach the optimum.

[0095] As an implementation manner of the present invention, refer to Figure 1 , the algorithm flowchart of the present invention.

[0096] As an implementation manner of the present invention, refer to Figure 2 , the detection probability graph under the influence of noise. The number of samples 10 < M < 5000. Experimental data shows that when the number of samples decreases, the detection probability decreases accordingly. However, the detection probability shows higher robustness at higher signal-to-noise ratios.

[0097] As an implementation manner of the present invention, refer to Figure 3 , the error probability graph under different signal-to-noise ratio conditions. Experimental data shows that only under low signal-to-noise ratio conditions does the error probability decrease accordingly. When the noise uncertainty is 1.14, the error probability of the adaptive threshold is lower than that of the traditional energy detection and single threshold. When the signal-to-noise ratio is higher, the error probability decreases accordingly. Compared with the single threshold, the error rate of the adaptive threshold algorithm decreases by 30% under the signal-to-noise ratio of -15dB. The adaptive threshold algorithm reduces both the false alarm probability and the missed detection probability while reducing the overall error probability.

[0098] As an implementation manner of the present invention, refer to Figure 4 , the relationship graph between the threshold and the error probability under different signal-to-noise ratio conditions. Experimental data shows that when using the adaptive double-threshold algorithm, the error probability decreases. When the signal-to-noise ratio is -20dB and -15dB, the noise uncertainty is 1.12, 1.14, 1.16, and the number of samples M = 2000. When the adaptive threshold reaches the optimized value, the overall error rate drops to the minimum. When the noise uncertainty is 1.12 and the signal-to-noise ratio is -15dB, the adaptive optimized threshold is 0.78. When the noise uncertainty is 1.16 and the signal-to-noise ratio is -20dB, the adaptive optimized threshold is 0.84. As the noise uncertainty increases, the adaptive optimized threshold and the error probability also increase.

[0099] As an implementation manner of the present invention, refer to Figure 5 , the relationship graph between the threshold and the error probability under different numbers of samples. Experimental data shows that when the noise uncertainty is 1.14 and the signal-to-noise ratio is -20dB, as the number of samples N increases, the error probability further decreases. This decrease proves that the proposed formula (6) is reasonable and verifies that as the number of samples increases, the minimum is achieved under the optimal threshold. When the adaptive threshold is 0.77 and the number of samples is 200, 1000, 2000 respectively, the minimum error rates are 0.85, 0.5, 0.3. Ideal sensing performance can be achieved by adjusting the number of samples N with higher noise uncertainty and lower signal-to-noise ratio.

[0100] As one embodiment of the present invention, refer to Figure 6 The graph shows the relationship between signal-to-noise ratio (SNR) and sample complexity under different algorithms. Experimental data shows that when using energy detection and single-threshold algorithms, the noise uncertainty is 1.12, and the SNR is -14dB < η. i A signal-to-noise ratio (SNR) <-12dB, with a sharp increase in the number of samples N, indicates a sensing failure. i When the value is -12dB, the number of samples N decreases significantly. Using an adaptive thresholding method can avoid this sensing failure.

[0101] As one embodiment of the present invention, refer to Figure 7 The graph shows the relationship between the number of cognitive users and the detection probability under a signal-to-noise ratio (SNR) of -20dB. Experimental data shows that the adaptive three-threshold energy detection algorithm, with 11 cognitive users, has a detection probability close to 1 under a SNR of -20dB. Among the five algorithms, the higher the number of cognitive users, the greater the detection probability.

[0102] In summary, the high threshold λ mentioned in S10 H and the low threshold λ L The calculation process is as follows:

[0103] The high threshold λ of the SU is calculated according to formulas (1) and (2). H and low threshold λ L ;

[0104]

[0105]

[0106] Where Q is the standard Gaussian complementary cumulative distribution function; P f It is the probability of a false alarm; P m It is the probability of missed detection; It is the noise variance; is the average power of the received signal; M is the number of detection sampling points.

[0107] The weighting factor δ mentioned in S20 i The calculation process is shown in formula (3):

[0108]

[0109] Where, δ i η is the weighting factor for the i-th cognitive user, N is the number of cognitive users, and η is the weighting factor for the ith cognitive user. i It is the signal-to-noise ratio of the signal received by each cognitive user.

[0110] Each SU in S30 is based on the weighting factor δ iThe decision threshold is adaptively adjusted using formulas (3), (4), and (5):

[0111]

[0112]

[0113] in, It is the adaptive high threshold for the i-th cognitive user. It is the adaptive low threshold for the i-th cognitive user.

[0114] This invention employs an adaptive dual-threshold energy detection algorithm, which sets more accurate thresholds.

[0115] The single threshold calculation process described in S50 is shown in formula (6):

[0116]

[0117] Where M is the number of samples detected; It is the noise variance; E i This is the primary user's transmit power.

[0118] The calculation process for the number of samples M is shown in formula (7):

[0119]

[0120] Where ε is the noise power; Q is the standard Gaussian complementary cumulative distribution function; δ i It is a weighting factor; η i It is the signal-to-noise ratio of the signal received by each cognitive user; P f It is the probability of a false alarm; P d It is the detection probability.

[0121] S50 is based on the single threshold Adaptive adjustment of primary user transmit power E i The single threshold Based on the primary user's transmit power E i The detection probability P adapts to changes in the form of [variable name] and changes accordingly. d To achieve optimal results.

[0122] This invention employs an adaptive single-threshold energy detection algorithm. The single threshold combined with the dual-threshold threshold forms a triple threshold, which facilitates spectrum resource management and enables timely detection of the presence of primary users. This allows for the effective utilization of idle spectrum by recognizing users without interfering with primary users, thereby improving spectrum utilization.

[0123] The detection probability P described in S60 d and the false alarm probability P fThe calculation process is shown in formulas (8) and (9):

[0124]

[0125]

[0126] Where N is the number of cognitive users, It is the detection probability of the i-th cognitive user. It is the false alarm probability of the i-th cognitive user.

[0127] This invention sends the decision results of the cognitive user to the fusion center, which makes the final decision using "OR". By using the above three cases to determine the presence of the master user, it has higher detection performance and faster perception speed. Under the conditions of low signal-to-noise ratio and low sampling points, the detection results of this method are higher than those of traditional methods. Under the condition of low sampling points, it can perceive the presence of the master user at a faster speed, effectively avoiding unnecessary interference to the master user.

[0128] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An adaptive sensing threshold setting method based on primary user power detection, characterized in that, Includes the following steps: S10: Receive the transmitted signal from the primary user PU, and calculate the high threshold λ of the cognitive user SU based on the false alarm probability, missed detection probability, noise variance, and power of the transmitted signal. H and low threshold λ L ; S20, the signal-to-noise ratio η of the transmitted signal is... i Go to the fusion center and set the weighting factor of each SU to δ. i ; S30, according to the δ i Adaptive adjustment of high decision threshold and low decision threshold The δ i According to the number N of SU, the η i The detection probability P adapts to changes in the form of [variable name] and changes accordingly. d To achieve the optimal; S40, Obtain energy statistics E i And update the historical energy queue, comparing it with the current energy E. i and decision threshold: like Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center; if Then it is an empty channel; if Historical energy queue average Take the current energy and start the next round of detection, while setting the flag to 1; if Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center; S50, in the second round of testing, if Calculate a single threshold like Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center; S60, when the fusion center receives the decision signal, it calculates the detection probability and the false alarm probability.

2. The adaptive sensing threshold setting method based on primary user power detection according to claim 1, characterized in that: The high threshold λ mentioned in S10 H and the low threshold λ L The calculation process is as follows: The high threshold λ of the SU is calculated according to formulas (1) and (2). H and low threshold λ L ; Where Q is the standard Gaussian complementary cumulative distribution function; P f It is the probability of a false alarm; P m It is the probability of missed detection; It is the noise variance; is the average power of the received signal; M is the number of detection sampling points.

3. The adaptive sensing threshold setting method based on primary user power detection according to claim 1, characterized in that: The weighting factor δ mentioned in S20 i The calculation process is shown in formula (3): Where, δ i η is the weighting factor for the i-th cognitive user, N is the number of cognitive users, and η is the weighting factor for the ith cognitive user. i It is the signal-to-noise ratio of the signal received by each cognitive user.

4. The adaptive sensing threshold setting method based on primary user power detection according to claim 1, characterized in that: Each SU in S30 is based on the weighting factor δ i The decision threshold is adaptively adjusted using formulas (3), (4), and (5): in, It is the adaptive high threshold for the i-th cognitive user. It is the adaptive low threshold for the i-th cognitive user.

5. The adaptive sensing threshold setting method based on primary user power detection according to claim 1, characterized in that: The single threshold calculation process described in S50 is shown in formula (6): Where M is the number of samples detected; It is the noise variance; E i This is the primary user's transmit power.

6. The adaptive sensing threshold setting method based on primary user power detection according to claim 6, characterized in that: The calculation process for the number of samples M is shown in formula (7): Where ε is the noise power; Q is the standard Gaussian complementary cumulative distribution function; δ i It is a weighting factor; η i It is the signal-to-noise ratio of the signal received by each cognitive user; P f It is the probability of a false alarm; P d It is the detection probability.

7. The adaptive sensing threshold setting method based on primary user power detection according to claim 6, characterized in that: S50 is based on the single threshold Adaptive adjustment of primary user transmit power E i The single threshold Based on the primary user's transmit power E i The detection probability P adapts to changes in the form of [variable name] and changes accordingly. d To achieve optimal results.

8. The adaptive sensing threshold setting method based on primary user power detection according to claim 1, characterized in that: The detection probability P described in S60 d and the false alarm probability P f The calculation process is shown in formulas (8) and (9): Where N is the number of cognitive users, It is the detection probability of the i-th cognitive user. It is the false alarm probability of the i-th cognitive user.

9. An adaptive sensing threshold setting device based on primary user power detection, characterized in that, include: The adaptive adjustment module receives the transmitted signal from the primary user (PU) and calculates the high threshold λ of the cognitive user (SU) based on the false alarm probability, missed detection probability, noise variance, and power of the received signal. H and low threshold λ L The signal-to-noise ratio η of the transmitted signal i Go to the fusion center and set the weighting factor of each SU to δ. i According to the δ i Adaptive adjustment of high decision threshold and low decision threshold The δ i According to the number N of SU, the η i The detection probability P adapts to changes in the form of [variable name] and changes accordingly. d To achieve the optimal; The dual-threshold comparison module is used to obtain the energy statistics value E. i And update the historical energy queue, comparing it with the current energy E. i and decision threshold: if Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center; if Then it is an empty channel; if Historical energy queue average Take the current energy and start the next round of detection, while setting the flag to 1; if Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center; A single threshold comparison module is used to compare the average energy. To make further decisions; in the second round of testing, if Calculate a single threshold like Then it is determined that the primary user (PU) exists, and a decision signal is sent to the fusion center; The decision module is used to calculate the detection probability and false alarm probability when the fusion center receives the decision signal.

10. The adaptive sensing threshold setting device based on primary user power detection according to claim 9, characterized in that: The single threshold in the single threshold comparison module Adaptive adjustment of primary user transmit power E i The single threshold Based on the primary user's transmit power E i The detection probability P adapts to changes in the form of [variable name] and changes accordingly. d To achieve optimal results.

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