Cooperative electromagnetic spectrum sensing method and system based on quantum walrus optimization algorithm
By optimizing the weights of collaborative electromagnetic spectrum sensing using the quantum walrus optimization algorithm, the problems of low detection probability and poor robustness in existing technologies are solved, resulting in a more efficient spectrum sensing effect.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- QIANYUAN NATIONAL LABORATORY
- Filing Date
- 2026-02-27
- Publication Date
- 2026-06-05
AI Technical Summary
Existing collaborative electromagnetic spectrum sensing technologies lack effective optimization algorithms and solution methods, resulting in low detection probability, poor robustness, and difficulty in achieving efficient spectrum sensing in complex electromagnetic environments.
A cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm (QWOA) is adopted. By constructing a fitness function and combining foraging, migration and escape strategies, the weights in cooperative electromagnetic spectrum sensing are optimized to improve detection probability and robustness.
It improves the convergence speed and optimization capability of collaborative electromagnetic spectrum sensing, enhances the robustness of electromagnetic spectrum sensing, and achieves more efficient spectrum utilization.
Smart Images
Figure CN122154736A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of distributed electromagnetic spectrum sensing technology, specifically relating to a cooperative electromagnetic spectrum sensing method and system based on the quantum walrus optimization algorithm. Background Technology
[0002] Electromagnetic spectrum sensing is a key technology in cognitive radio (CR). To address the problems of low licensed spectrum utilization and scarcity of shared spectrum resources, it is necessary to adjust the current spectrum allocation strategy. In a typical CR system, licensed users are called primary users (PUs), and unlicensed users are called secondary users (SUs). During CR system operation, SUs can sense the licensed spectrum in their environment through their own equipment. When they sense that a licensed spectrum is idle at a certain moment, the SU can access this licensed frequency band for communication. When the PU re-accesses this licensed frequency band, the SU will interrupt its use of that band to ensure the PU's frequency usage experience. The introduction of CR technology provides a new direction for solving the problem of spectrum scarcity. The key to improving the utilization efficiency of idle spectrum in CR technology is how to determine spectrum availability. The main function of electromagnetic spectrum sensing technology is to reliably sense the spectrum status of the frequency band accessed by the PU. Therefore, an accurate and efficient electromagnetic spectrum sensing strategy is essential.
[0003] A key challenge in electromagnetic spectrum sensing is constructing suitable detection statistics to achieve a high detection probability. Energy detection schemes, with their low complexity and short detection time, determine the presence of a PU signal in the licensed frequency band by comparing the energy of the received signal with a preset threshold. However, in complex electromagnetic transmission environments, the signal received by a single SU node may experience severe fading, leading to the failure of the electromagnetic spectrum sensing algorithm. Cooperative electromagnetic spectrum sensing effectively addresses this issue. By integrating the detection decision information from each SU in a distributed scenario, cooperative electromagnetic spectrum sensing makes a fused decision on whether the same PU frequency band is idle, effectively improving the robustness of electromagnetic spectrum sensing.
[0004] Existing research has proposed an optimal fusion method for cooperative electromagnetic spectrum sensing. The paper "Performance Analysis and Optimization of CSS Based on Soft Decision Fusion" compares the detection performance of adaptive weighted fusion method and fixed weighted fusion method for cooperative electromagnetic spectrum sensing fusion problem, and verifies the superiority of adaptive weighted fusion cooperative electromagnetic spectrum sensing method through simulation. However, the paper does not provide a specific solution method for the optimization algorithm, nor does it conduct cooperative electromagnetic spectrum sensing performance analysis on the proposed optimization algorithm.
[0005] To address the aforementioned technical issues, it is necessary to propose a new optimization algorithm and its solution method to achieve cooperative electromagnetic spectrum sensing, while simultaneously improving the optimization capability and solution speed during cooperative electromagnetic spectrum sensing. Summary of the Invention
[0006] In view of the above, the purpose of this invention is to provide a cooperative electromagnetic spectrum sensing method and system based on the Quantum Walrus Optimization algorithm (QWOA), which effectively improves the convergence speed and optimization ability of the optimization algorithm in solving the cooperative electromagnetic spectrum sensing problem, and effectively enhances the robustness of electromagnetic spectrum sensing.
[0007] To achieve the above-mentioned objectives, an embodiment provides a cooperative electromagnetic spectrum sensing method based on a quantum walrus optimization algorithm, comprising the following steps: To construct a cooperative electromagnetic spectrum sensing problem for distributed secondary users, the weights in solving the cooperative electromagnetic spectrum sensing detection probability are regarded as walrus quantum positions in the quantum walrus optimization algorithm. The fitness function of the walrus is constructed based on the objective of maximizing the detection probability to solve the optimal detection. The optimization objective is to minimize the fitness function. The optimal walrus position is updated and solved by combining foraging strategy, migration strategy and escape strategy as the optimal weight for optimal detection in cooperative electromagnetic spectrum sensing.
[0008] Preferably, the collaborative electromagnetic spectrum sensing problem corresponding to distributed secondary users includes: Based on the received signals of distributed secondary users, the detection statistics of a single secondary user node for the target signal, as well as the detection quantity, false alarm rate, and detection probability of the central fusion are calculated. Among them, detection probability Represented as: in, False alarm rate The noise variance matrix is... For the degrees of freedom that the noise obeys, The weight matrix for secondary user nodes, intermediate variables , To control the channel noise variance, Indicates the transmit signal power of the primary user PU. The attenuation coefficient of the channel on signal power is an intermediate variable. , Represents a diagonal matrix. and This represents the complement and inverse function of the Gaussian cumulative distribution function.
[0009] Preferably, the weights in solving the cooperative electromagnetic spectrum sensing detection probability are regarded as the quantum walrus positions in the quantum walrus optimization algorithm, and the walrus fitness function is constructed based on the objective of maximizing the detection probability to solve the optimal detection, including: Detection probability The weight matrix in Considered as the quantum position of the walrus in the quantum walrus optimization algorithm, and based on the detection probability In The fitness function for walruses is constructed as follows: in, False alarm probability, Indicates the first The actual positions of several walruses, where the actual positions of the walruses and their quantum positions satisfy a mapping relationship: in, Indicates the first In the nth iteration The quantum position sequence of a walrus express The first in k Dimensional quantum position, K Represents the dimension in the quantum position sequence. Indicates the first In the nth iteration The actual location of the walrus.
[0010] Preferably, the optimal walrus location is updated and solved by minimizing the fitness function, and by combining foraging strategies, migration strategies, and escape strategies. This includes: First, the optimal walrus quantum position is solved by minimizing the fitness function. Then, the quantum position update process is iteratively performed under foraging strategy, migration strategy, and escape strategy: the quantum rotation angle is updated, and the quantum position of the walrus is updated based on the quantum rotation angle. Then, the fitness function value corresponding to the quantum position is calculated, and the quantum position of the walrus is updated again based on the fitness function value as the input of the next strategy.
[0011] Preferably, under the foraging strategy, each walrus selects the optimal walrus quantum position as the target position. The walrus's quantum position is updated using a quantum rotation angle. The expression for updating the quantum rotation angle under the foraging strategy is: in, Indicates the first In the nth iteration The first walrus k Dimensional quantum position, for The corresponding quantum rotation angle, For the first The optimal walrus in the nth iteration k Quantum position in each dimension It is a random number, and its value range is [value range missing]. , The value is randomly selected as 1 or 2.
[0012] Preferably, under the migration strategy, each walrus moves with the location of another walrus as its target. The quantum rotation angle update expression under the migration strategy is: in, Indicates the first In the nth iteration The first walrus k Dimensional quantum position, for The corresponding quantum rotation angle, It is a random number, and its value range is [value range missing]. , The quantum position of another walrus is randomly assigned to either 1 or 2. , For another walrus index.
[0013] Preferably, under the escape strategy, the walrus does not set a target position but instead wanders randomly within the solution space. The quantum rotation angle update expression under the escape strategy is: in, Indicates the first In the nth iteration The first walrus k Dimensional quantum position, for The corresponding quantum rotation angle.
[0014] Preferably, updating the quantum position of a walrus based on a quantum rotation angle includes: in, and Indicates the first Second and third In the nth iteration The first walrus k Dimensional quantum position, for The corresponding quantum rotation angle.
[0015] Preferably, after calculating the fitness function value corresponding to the quantum position, the walrus's quantum position is updated based on the fitness function value, including: First, the quantum position is mapped to the actual position, and then the corresponding fitness function value is calculated. Then, the quantum position is updated according to the fitness function value as follows: in, and They represent the first In the nth iteration The actual location sequence of the walruses and They represent the first Second and third In the nth iteration The quantum position sequence of a walrus This represents the fitness function value.
[0016] To achieve the above-mentioned objectives, this invention also provides a cooperative electromagnetic spectrum sensing system based on the quantum walrus optimization algorithm, including a memory and one or more processors. The memory stores executable code, and when the one or more processors execute the executable code, they implement the above-mentioned cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm.
[0017] Compared with the prior art, the beneficial effects of the present invention include at least the following: This invention establishes an optimization problem for cooperative electromagnetic spectrum sensing based on weighted fusion. By mapping candidate solutions of the optimization algorithm to quantum states, and designing an iterative optimization mechanism for the quantum positions of candidate solutions based on foraging, migration, and escape strategies, this invention effectively improves the convergence speed and optimization ability of the optimization algorithm in solving the cooperative electromagnetic spectrum sensing problem, enhances the ability to solve the theoretical boundary of cooperative electromagnetic spectrum sensing, and thus effectively improves the robustness of electromagnetic spectrum sensing. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 This is a schematic diagram of the collaborative electromagnetic spectrum sensing architecture provided in the embodiment; Figure 2This is a flowchart of the cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm provided in the embodiment; Figure 3 This is a flowchart of the solution process based on the quantum walrus optimization algorithm provided in the embodiment; Figure 4 This is a comparison chart of the convergence speeds of the Quantum Walrus Optimization Algorithm (QWOA), Walrus Optimization Algorithm (WOA), and Particle Swarm Optimization Algorithm (PSO) provided in the embodiments; Figure 5 This is a comparison chart of the changes in the fitness functions QWOA, WOA, and PSO as a function of the number of iterations provided in the example. Figure 6 This is a comparison chart of detection probabilities under different false alarm rates for the QWOA and WOA algorithms provided in the embodiment; Figure 7 This is a comparison chart of detection probabilities under different false alarm rates for the QWOA and PSO algorithms provided in the embodiment; Figure 8 This is a comparison chart of the fitness function values obtained by the QWOA, WOA, and PSO algorithms provided in the embodiment under different false alarm rates. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the scope of protection of this invention.
[0021] The cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm provided in the embodiment is applied to... Figure 1 The cooperative electromagnetic spectrum sensing architecture shown includes a licensed primary user (PU) and distributed unlicensed secondary users (SUs) sharing the same electromagnetic spectrum resources with the primary user (PU). The statistical energy of the SUs is detected by an energy detector. At the same time, the statistical energy is superimposed with noise V when it is transmitted in the control channel. The statistical energy with superimposed noise is weighted by W and then fused in the Fusion Center (FC).
[0022] like Figure 2 As shown in the embodiment, the cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm includes the following steps: S1. Construct an optimization problem for weighted fusion cooperative electromagnetic spectrum sensing corresponding to distributed secondary users. Treat the weights in solving the detection probability of cooperative electromagnetic spectrum sensing as the walrus quantum positions in the quantum walrus optimization algorithm. Construct the walrus fitness function based on the objective of maximizing the detection probability of solving the optimal detection.
[0023] In the embodiment, when constructing the optimization problem of weighted fusion cooperative electromagnetic spectrum sensing, the received signal of each distributed secondary user is represented as: (1) in, , This represents the number of SU nodes. This indicates that the main user PU is transmitting a signal. Indicates the first The channel attenuation experienced by each node is generally considered to be constant within a single sensing range. This represents the noise component in the received signal. and The binary hypothesis indicating whether a signal exists or not. This indicates the received signal of the SU node.
[0024] The method for energy detection by sampling a single SU node, the energy detection statistics are expressed as follows: (2) in, This represents the number of sampling points.
[0025] After obtaining the detection statistics of a single SU for the target signal Then, the detection statistics need to be transmitted to the Fusion Center (FC) via the control channel for detection fusion. Considering the non-ideal characteristics of the control channel, noise will be superimposed on the detection statistics during transmission in the control channel. Let the... The noise of each control channel is Then we have the following formula: (3) The central fusion detection volume The expression is: (4) in, The weights represent the weights of the weighted fusion of the k-th node. This represents the detection statistics of the superimposed noise at the k-th node.
[0026] exist Assuming that each receiving node contains only noise, the false alarm probability of the fusion center can be derived. The expression, and its detailed derivation process, are as follows: The output of the energy detector Obeying the degree of freedom The chi-square distribution, when the degrees of freedom When it is large enough, according to the central limit theorem, it can be approximated as following a Gaussian distribution with a mean of 1 / 2. The variance is Control channel noise It follows a mean of 0 and a variance of . Gaussian distribution, due to and Independent of each other, the fusion center receives the first The decision results of each sensor follow a Gaussian distribution with a mean of 1. The variance is ; Can be seen as The sum of several independent Gaussian random variables; given the mean and variance, the probability density function and cumulative distribution function can be obtained, along with the mean of the fused central detection. and variance The expression is: (5) (6) in For noise variance To control the channel noise variance, Weight coefficient matrix ， , This represents a diagonal matrix.
[0027] Based on formulas (5) and (6), the expression for the false alarm probability is derived as follows: (7) in, This represents the complement of the Gaussian cumulative distribution function. The detection threshold is expressed as: (8) Similarly, in Under the following assumptions, since the received signal contains both signal and noise, the detection probability of the fusion center can be derived. The expression, and its detailed derivation process, are as follows: The mean is The variance is ,in ,in, This represents the sequence of signals transmitted by the primary user (PU). This indicates the power of the signal transmitted by the primary user (PU).
[0028] exist Assuming mean and variance The expression is: (9) (10) in, The detection probability is obtained according to formulas (9) and (10). expression: (11) Detection threshold Substituting the expression into formula (11) yields the detection probability. The expression is: (12) function It has the property of monotonically decreasing, and optimal detection aims to maximize the detection probability; therefore, it can be regarded as a... function The process of minimizing the expression, where the weight vector Considered as the quantum position of the walrus in the quantum walrus optimization algorithm, it is the variable to be solved; Therefore, the fitness function expression can be derived as follows: (13) in, This represents the probability of a false alarm. Indicates the first The actual position of a walrus and its quantum position satisfy a mapping relationship: (14) in, Indicates the first In the nth iteration The quantum position sequence of a walrus express The first in k Dimensional quantum position, K Represents the dimension in the quantum position sequence. Indicates the first In the nth iteration The actual location of each walrus, and the quantum walrus population size are... .
[0029] S2 takes minimizing the fitness function as the optimization objective, and combines foraging strategy, migration strategy, and escape strategy to update and solve for the optimal walrus position as the optimal weight for optimal detection in cooperative electromagnetic spectrum sensing.
[0030] like Figure 3 As shown, the iterative solution process using the quantum walrus optimization algorithm is as follows: First, the initial optimal walrus quantum position is determined with the goal of minimizing the fitness function. Specifically, a multidimensional nonlinear minimization search is performed on the fitness function. The swarm optimal solution is the quantum position corresponding to the point that minimizes the fitness function. The expression for the quantum position of the swarm optimal solution is as follows: (15) Then, the quantum position update process is iteratively performed under foraging, migration, and escape strategies: the quantum rotation angle is updated, and the walrus's quantum position is updated based on the quantum rotation angle. Then, the fitness function value corresponding to the quantum position is calculated, and the walrus's quantum position is updated again based on the fitness function value as the input for the next strategy. The detailed process is as follows: Under the foraging strategy, each walrus selects the optimal walrus as its target position. The walrus's quantum position is updated using the quantum rotation angle. The expression for updating the quantum rotation angle under the foraging strategy is: (16) in, for The corresponding quantum rotation angle, For the first The optimal walrus in the nth iteration k Quantum position in each dimension It is a random number, and its value range is [value range missing]. , The value is randomly selected as 1 or 2.
[0031] The quantum position of the walrus is then updated based on the quantum rotation angle as follows: (17) in, Indicates the first In the nth iteration The first walrus k Dimensional quantum position.
[0032] quantum position Mapped to actual location Then, the corresponding fitness function value is calculated, and the walrus's quantum position is updated based on the fitness function value. (18) in, and They represent the first In the nth iteration The actual location sequence of the walruses and They represent the first Second and third In the nth iteration The quantum position sequence of a walrus This represents the fitness function value.
[0033] Under the migration strategy, each walrus moves with the location of another walrus as its target. The quantum rotation angle update expression under the migration strategy is: (19) Among them, the quantum position of another walrus , For another walrus index.
[0034] The quantum rotation angle obtained using formula (19) Similarly, the quantum position of the walrus under the migration strategy is updated according to formulas (17) and (18).
[0035] Under the escape strategy, the walrus does not set a target position but instead wanders randomly within the solution space. This stage reflects the algorithm's ability to find the optimal solution randomly. The expression for updating the quantum rotation angle under the escape strategy is: (20) in, Indicates the first In the nth iteration The first walrus k Dimensional quantum position, for The corresponding quantum rotation angle.
[0036] The quantum rotation angle obtained using formula (20) Similarly, the walrus quantum position under the escape strategy is updated according to formulas (17) and (18).
[0037] The walrus quantum position is iteratively executed under the above foraging strategy, migration strategy, and escape strategy. Finally, the iteration stops after the preset number of iterations is met, and the optimal walrus quantum position is output as the optimal weight vector.
[0038] To verify the superiority of the proposed method in terms of convergence and optimization capability, four sets of simulations were set up for comparative verification. The proposed algorithm is denoted as QWOA, the Walrus Optimization Algorithm as WOA, and the Particle Swarm Optimization Algorithm as PSO. The number of cognitive nodes (SU) was set to 60, and the maximum number of iterations was set to [missing information]. The walrus population number is 200. Given a value of 100, assuming the received channel noise follows a zero-mean Gaussian distribution, the noise variance column vector is... The parameters are set as follows: [2,2.5,0.9,2.7,1.3,3.3,2,2.5,0.9,2.7,2,2.5,0.9,2.7,1.3,2.4,0.8,2.6,1.2,3.2,1.9,2.4,0.8,2.6,1.9,2.4,0.8,2.6,1.2,2.1,2.6,1,2.8,1.4,2,2.5,0.9,2.7,1.3,3.3,2,2.5,0.9,2.7,2,2.5,0.9,2.7,1.3]; Assuming the control channel noise follows a zero-mean Gaussian distribution, the control channel noise variance column vector... Set to: [1.3,0.8,2,3.8,2.3,0.4,1.3,0.8,2,3.1,1.3,0.8,2,3.1,1.3,1.4,0.9,2.1,3.9,2.4,0.5,1.4,0.9,2,4.1.1,1.4,0.9,2,4.1.1,1.4,1.2,0.7,1.9,3.7,2.2,0.3,1.2,0.7,2,2.0.9,1.2,0.7,1.9,3,1.2,1.3,0.8,2,3.8,2.3,0.4,1.3,0.8,2,3.1,1.3,0.8,2,3.1,1.3].
[0039] The received signal passes through the channel attenuation coefficient vector for: [0.3,0.4,0.6,0.2,0.3,0.2,0.7,0.4,0.1,0.2,0.3,0.4,0.6,0.2,0.30.5,0.6,0.7,0.4,0.5,0.4,0.7,0.6,0.3,0.4,0.5,0.6,0.8,0.4,0.5,0.4,0.5,0.7,0.3,0.4,0.3,0.6,0.5,0.2,0.3,0.4,0.5,0.7,0.3,0.4,0.5,0.7,0.3,0.4,0.3,0.6,0.5,0.2,0.3,0.4,0.5,0.7,0.3,0.4].
[0040] The false alarm probability of the fusion center (FC) is set to 0.08.
[0041] Figure 4 This is a comparison chart of the convergence speeds of QWOA, WOA, and PSO. Figure 5 The graph compares the changes in fitness function of QWOA, WOA, and PSO with the number of iterations. Compared with the other two algorithms, the proposed algorithm QWOA has better convergence speed and solution effect in solving the cooperative electromagnetic spectrum sensing objective function problem.
[0042] Figure 6 This is a comparison chart of detection probabilities for QWOA and WOA under different false alarm rates. Figure 7 This is a comparison chart of detection probabilities under different false alarm rates for QWOA and PSO. Figure 8 A comparison chart of fitness function values for QWOA, WOA, and PSO under different false alarm rates is presented. With other parameters remaining constant, the cooperative electromagnetic spectrum sensing method based on the proposed algorithm achieves better results under different false alarm rates when the false alarm rate is changed, demonstrating that the proposed algorithm effectively improves the robustness of electromagnetic spectrum sensing.
[0043] Based on the same inventive concept, the embodiment also provides a cooperative electromagnetic spectrum sensing system based on the quantum walrus optimization algorithm, including a memory and one or more processors. The memory stores executable code, and when the one or more processors execute the executable code, it is used to implement the above-mentioned cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm, specifically including the following steps: S1, construct the optimization problem of weighted fusion cooperative electromagnetic spectrum sensing corresponding to distributed secondary users, regard the weights in solving the detection probability of cooperative electromagnetic spectrum sensing as the walrus quantum position in the quantum walrus optimization algorithm, and construct the walrus fitness function based on the objective of maximizing the detection probability of solving the optimal detection. S2 takes minimizing the fitness function as the optimization objective, and combines foraging strategy, migration strategy, and escape strategy to update and solve for the optimal walrus position as the optimal weight for optimal detection in cooperative electromagnetic spectrum sensing.
[0044] The computing device provided in this embodiment, at the hardware level, includes not only a processor and memory, but also internal buses, network interfaces, memory, and other hardware required for business operations. The memory is non-volatile memory. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to implement the cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm described in S1-S2 above. Of course, besides software implementation, this invention does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution entity of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.
[0045] The specific embodiments described above illustrate the technical solution and beneficial effects of the present invention in detail. It should be understood that the above description is only the most preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, additions, and equivalent substitutions made within the scope of the principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A cooperative electromagnetic spectrum sensing method based on a quantum walrus optimization algorithm, characterized in that, Includes the following steps: We construct an optimization problem for weighted fusion cooperative electromagnetic spectrum sensing corresponding to distributed secondary users. We regard the weights in solving the detection probability of cooperative electromagnetic spectrum sensing as the walrus quantum positions in the quantum walrus optimization algorithm. We construct the walrus fitness function based on the objective of maximizing the detection probability of solving the optimal detection. The optimization objective is to minimize the fitness function. The optimal walrus position is updated and solved by combining foraging strategy, migration strategy and escape strategy as the optimal weight for optimal detection in cooperative electromagnetic spectrum sensing.
2. The cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm according to claim 1, characterized in that, The optimization problem of constructing weighted fusion cooperative electromagnetic spectrum sensing for distributed secondary users includes: Based on the received signals of distributed secondary users, the detection statistics of a single secondary user node for the target signal, as well as the detection quantity, false alarm rate, and detection probability of the central fusion are calculated. Among them, detection probability Represented as: in, This represents the probability of a false alarm. The noise variance matrix is... For the degrees of freedom that the noise obeys, The weight matrix for secondary user nodes, intermediate variables , To control the channel noise variance, Indicates the main user's transmitted signal power. The attenuation coefficient of the channel on the signal power is an intermediate variable. , Represents a diagonal matrix. and This represents the complement and inverse function of the Gaussian cumulative distribution function.
3. The cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm according to claim 2, characterized in that, The weights in solving the cooperative electromagnetic spectrum sensing detection probability are regarded as the quantum walrus positions in the quantum walrus optimization algorithm. The walrus's fitness function is constructed based on the objective of maximizing the detection probability to solve for optimal detection, including: Detection probability The weight matrix in Considered as the quantum position of the walrus in the quantum walrus optimization algorithm, and based on the detection probability In The fitness function for walruses is constructed as follows: in, This represents the probability of a false alarm. Indicates the first The actual positions of the walruses and their quantum positions satisfy a mapping relationship: in, Indicates the first In the nth iteration The quantum position sequence of a walrus express The first in k Dimensional quantum position, K Represents the dimension in the quantum position sequence. Indicates the first In the nth iteration The actual location of the walrus.
4. The cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm according to claim 1, characterized in that, The optimization objective is to minimize the fitness function, and the optimal walrus location is updated and solved by combining foraging, migration, and escape strategies, including: First, the optimal walrus quantum position is solved by minimizing the fitness function. Then, the quantum position update process is iteratively performed under foraging strategy, migration strategy, and escape strategy: the quantum rotation angle is updated, and the quantum position of the walrus is updated based on the quantum rotation angle. Then, the fitness function value corresponding to the quantum position is calculated, and the quantum position of the walrus is updated again based on the fitness function value as the input of the next strategy.
5. The cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm according to claim 4, characterized in that, Under the foraging strategy, each walrus selects the optimal walrus quantum position as its target position. The walrus's quantum position is updated using a quantum rotation angle. The expression for updating the quantum rotation angle under the foraging strategy is: in, Indicates the first In the nth iteration The first walrus k Dimensional quantum position, for The corresponding quantum rotation angle, For the first The optimal walrus in the nth iteration k Quantum position in each dimension It is a random number, and its value range is [value range missing]. , The value is randomly selected as 1 or 2.
6. The cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm according to claim 4, characterized in that, Under the migration strategy, each walrus moves with the location of another walrus as its target. The quantum rotation angle update expression under the migration strategy is: in, Indicates the first In the nth iteration The first walrus k Dimensional quantum position, for The corresponding quantum rotation angle, It is a random number, and its value range is [value range missing]. , The quantum position of another walrus is randomly assigned to either 1 or 2. , For another walrus index.
7. The cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm according to claim 4, characterized in that, Under the escape strategy, the walrus does not set a target position but instead wanders randomly within the solution space. The quantum rotation angle update expression under the escape strategy is: in, Indicates the first In the nth iteration The first walrus k Dimensional quantum position, for The corresponding quantum rotation angle.
8. The cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm according to claim 4, characterized in that, Updating the quantum position of walruses based on quantum rotation angles includes: in, and Indicates the first Second and third In the nth iteration The first walrus k Dimensional quantum position, for The corresponding quantum rotation angle.
9. The cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm according to claim 4, characterized in that, After calculating the fitness function value corresponding to the quantum position, the walrus's quantum position is updated based on the fitness function value, including: First, the quantum position is mapped to the actual position, and then the corresponding fitness function value is calculated. Then, the quantum position is updated according to the fitness function value as follows: in, and They represent the first Second and third Iteration of the first The actual location sequence of the walruses and They represent the first Second and third In the nth iteration The quantum position sequence of a walrus This represents the fitness function value.
10. A cooperative electromagnetic spectrum sensing system based on a quantum walrus optimization algorithm, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that... When the one or more processors execute the executable code, they are used to implement the cooperative electromagnetic spectrum sensing method based on the quantum walrus optimization algorithm as described in any one of claims 1-9.