Cognitive radio network power control method based on harris eagle optimization algorithm

Abstract

The application discloses a cognitive wireless network power control method based on a Harris eagle optimization algorithm, and comprises the following steps: firstly, a cognitive wireless network model is established according to the control requirements of the cognitive wireless network; secondly, a target function based on SINR is established by using the Nash equilibrium principle for the communication quality of each secondary user in the cognitive wireless network; thirdly, a constrained optimization problem model is proposed for coping with the minimum communication quality constraint of the secondary user, the interference temperature constraint of each secondary user to the primary user and the total sum constraint of the transmission power of all secondary users; finally, the Harris eagle optimization algorithm is improved to have the ability to solve the constrained optimization problem model and solve the constrained optimization problem. The application guarantees the optimal power configuration under the current network environment on the premise of ensuring the communication quality of each primary and secondary user; in addition, the Harris eagle optimization algorithm is improved to have excellent performance when solving the above constrained optimization problem.

Description

Technical Field

[0001] This invention relates to wireless network power control technology, and in particular to a cognitive wireless network power control method based on the Harris Eagle optimization algorithm. Background Technology

[0002] Cognitive Radio Networks (CRNs) are a widely applicable network type based on intelligent technologies. They achieve dynamic availability and efficient utilization of spectrum by sensing idle frequency bands and spectrum in the environment, thus fully utilizing radio spectrum resources and improving network coverage and performance. In recent years, CRN technology has developed rapidly, attracting numerous researchers. CRNs have wide applications; for example, they can utilize idle spectrum in agriculture to enable agricultural IoT applications, such as farmland IoT monitoring, accurate weather forecasting, species identification, and automated spraying. They can also be applied to large industrial equipment sectors, such as steel mills, mines, and shipyards, using intelligent sensing technology to make maintenance and operation data transmission more reliable. The emergence of CRNs aims to address the problems of insufficient spectrum utilization and network congestion caused by the fixed and limited spectrum resources in existing wireless communication systems. Power control technology, as a crucial component of CRNs, enables dynamic availability and efficient utilization of spectrum, thereby improving network coverage, reliability, and performance stability. Furthermore, power control can reduce unnecessary interference and collisions in the network, improve spectrum utilization efficiency and network capacity, while also reducing chip latency, node power consumption, and extending network lifetime. In summary, research on cognitive wireless network power control technology is of great significance, playing a crucial role in improving the utilization efficiency of wireless spectrum resources and the overall performance stability of the network. However, the currently used distributed control method has certain limitations: since each node can only use its own information to make power control decisions, the lack of global information exchange between nodes may lead to local optima rather than global optima. This means that distributed control may not be able to achieve power optimization for the entire network. Secondly, because distributed control relies only on local information, node decisions may be affected by local interference or noise. Such local interference may lead to instability and inconsistency in power control. Summary of the Invention

[0003] Purpose of the invention: In view of the current research status and practical application background of cognitive wireless network power control, the present invention provides a cognitive radio network power control method based on the Harris Eagle optimization algorithm.

[0004] Technical solution: The cognitive wireless network power control method based on the Harris Eagle optimization algorithm of the present invention includes the following steps:

[0005] A cognitive wireless network model is established based on the control requirements of cognitive wireless networks;

[0006] A SINR-based objective function is established to assess the communication quality of each user in a cognitive wireless network, based on the Nash equalization principle.

[0007] To address the constraints of minimum communication quality for secondary users, interference temperature constraints of each secondary user on the primary user, and the total transmit power constraint of all secondary users, a constrained optimization problem model is proposed. This model enables power allocation for secondary users while satisfying the primary and secondary user constraints. Specifically, the minimum communication quality constraint for secondary users is used to ensure the communication quality of the secondary users themselves, the interference temperature constraints of each secondary user on the primary user are used to ensure that the communication quality of the primary user is not interfered with, and the total transmit power constraint of all secondary users is used to ensure that the total spectrum resources are sufficient.

[0008] The Harris Eagle optimization algorithm is improved to enable it to solve the constrained optimization problem model, and the improved Harris Eagle optimization algorithm is used to solve the constrained optimization problem; wherein, the improved Harris Eagle optimization algorithm is to rewrite the fitness function in the original Harris Eagle algorithm into an objective function that includes a penalty function and a flag to determine whether the current operator violates the constraint.

[0009] Furthermore, in a cognitive wireless network, a primary user network consisting of A primary users and a secondary user network consisting of B secondary users coexist. Each user has its own transmitting and receiving equipment. When users in a cognitive wireless network send information simultaneously, it will cause interference to other users. However, due to a good communication protocol between primary users, interference between primary users is not considered to exist. In this scenario, the expression for the signal strength received by primary and secondary users, i.e., the cognitive wireless network model, is as follows:

[0010]

[0011]

[0012] Where, q s,k q p,u The signal strengths received by the k-th secondary user and the u-th primary user are t and t, respectively. s,k t s,l t p,n The transmit power of secondary user k, other secondary users excluding secondary user k, and all primary users are t, respectively. p,u The transmit power of the primary user u, ξ s,k ξ p,u ξ represents the channel gain between the k-th secondary user and the u-th primary user and their respective receivers. l,k ζ n,k , These represent the interference channel gains between the secondary user and the k-th secondary user, between the primary user and the k-th secondary user, and between the secondary user and the u-th primary user, respectively. s,k v p,u These represent the background noise of the k-th secondary user and the u-th primary user when receiving information, respectively.

[0013] Furthermore, the objective function based on SINR is:

[0014]

[0015] Where, ψ k Let ψ be the signal-to-noise ratio and interference ratio of secondary user k at its current transmit power. m#n It is the minimum signal-to-noise ratio and interference ratio required to ensure the basic communication quality of each secondary user, i.e., the minimum SINR.

[0016] Furthermore, the SINR calculation method is as follows:

[0017]

[0018] Where, ψ k Let SINR be the signal-to-noise ratio (SNR) and interference ratio (INR) of secondary user k at its current transmit power. Let ξ be the variance of the background noise at the k-th sub-user. k,k Let be the channel gain from transmitter k to receiver for secondary user k. Furthermore, the minimum communication quality constraint for secondary users is:

[0019] ψ k ≥ψ m#n k = 1, ..., B

[0020] The interference temperature constraints of each secondary user on the primary user are as follows:

[0021]

[0022] Among them, t s,n For the secondary user n's transmit power, ζ n,l C represents the channel gain from secondary user n to primary user 0. l The maximum allowable interference signal strength for each primary user, depending on the specific task requirements;

[0023] The total transmit power constraint for all secondary users is:

[0024]

[0025] Among them, t max This represents the maximum total transmit power for all secondary users.

[0026] The constrained optimization problem model is then represented as:

[0027] Target:

[0028] Constraint: ψ k ≥ψ m#n k = 1, ..., B

[0029]

[0030]

[0031] Furthermore, the improved Harris Eagle optimization algorithm is as follows:

[0032] The fitness function in the original Harris Eagle optimization algorithm, which is the performance index for evaluating the merits of candidate solutions, can be rewritten in the following form:

[0033] fitneAs = [obj conA]

[0034] Where obj is the objective function to which the penalty function is added, and cons is a flag to determine whether the current operator violates the constraints. If the constraints are violated, it is assigned a value of 1, and if the constraints are obeyed, it is assigned a value of 0. The operator is the candidate solution generated by the Harris Eagle algorithm.

[0035] Based on the operator's violation of constraints, a penalty function is added to correct the iteration direction; then, the objective function with the added penalty function is expressed as follows:

[0036] obj=obj1+10×error1+error2+10 10 ×errCr3

[0037] Where, obj1 is the value of the objective function independent variable of the constrained optimization problem after a left shift of length 1; error1 is the value that violates the minimum communication quality constraint for secondary users in the constrained optimization problem down to the constraint boundary; error2 is the value that violates the interference temperature constraint of each secondary user to the primary user in the constrained optimization problem down to the constraint boundary; error3 is the value that violates the constraint of the sum of the transmit power of all secondary users down to the constraint boundary; C u The maximum interference signal strength allowed by the primary user u.

[0038] Furthermore, when using the improved Harris Eagle optimization algorithm to solve constrained optimization problems, the selection of candidate solutions should first consider whether the operator conforms to the constraints, and then consider the magnitude of the objective function value. The specific logic is as follows:

[0039] Assuming operators X and Y exist, their superiority is compared as follows:

[0040] If both X and Y are subject to constraints, the one with the larger objective function value is considered better.

[0041] If X obeys the constraints and Y does not, then X is the better one.

[0042] If X does not comply with the constraints, but Y does, then Y is the better one.

[0043] If neither X nor Y complies with the constraints, then the one with the larger objective function value is considered better.

[0044] The algorithm still uses the above sorting method to evaluate the merits of operators during internal iterations.

[0045] Based on the same inventive concept, the cognitive wireless network power control system based on the Harris Eagle optimization algorithm of the present invention includes:

[0046] The model building module is used to build a cognitive wireless network model according to the control requirements of the cognitive wireless network.

[0047] The objective function construction module is used to establish a SINR-based objective function for the communication quality of each user in a cognitive wireless network, based on the Nash equalization principle.

[0048] The constrained optimization problem model building module is used to propose a constrained optimization problem model to address constraints such as the minimum communication quality of secondary users, the interference temperature of each secondary user to the primary user, and the total transmit power of all secondary users, thereby achieving secondary user power allocation while satisfying the primary and secondary user constraints.

[0049] The algorithm improvement and model solving module is used to improve the Harris Eagle optimization algorithm so that it has the ability to solve the constrained optimization problem model, and to use the improved Harris Eagle optimization algorithm to solve the constrained optimization problem.

[0050] Based on the same inventive concept, the present invention provides a cognitive wireless network power control device based on the Harris Eagle optimization algorithm, the device comprising:

[0051] Memory containing executable program code;

[0052] A processor coupled to the memory;

[0053] The processor calls the executable program code stored in the memory to execute the cognitive wireless network power control method based on the Harris Eagle optimization algorithm as described above.

[0054] Based on the same inventive concept, the present invention provides a computer-readable storage medium storing computer instructions, which, when invoked, are used to execute the cognitive wireless network power control method based on the Harris Eagle optimization algorithm described above.

[0055] Beneficial effects: Compared with the prior art, the significant technical effects of the present invention are as follows:

[0056] (1) This invention establishes an optimization model for the cognitive wireless network power control problem with constraints, which has certain application significance for solving the cognitive wireless network power control problem using optimization, game theory and other methods.

[0057] (2) Based on the constrained optimization problem model, the present invention improves the Harris Eagle optimization algorithm so that it can better solve the cognitive wireless network power control problem, so that the final power allocation can achieve better communication quality while satisfying the constraints.

[0058] (3) The present invention uses the improved Harris Eagle optimization algorithm to solve the above-mentioned constrained optimization problem and achieves better power control results, realizing the dynamic availability and efficient utilization of the spectrum, which has great practical value. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the implementation of the method of this invention;

[0060] Figure 2 This is a diagram of a cognitive wireless network;

[0061] Figure 3 This is a graph showing the iterative effect of the method of the present invention on the optimal objective function value for power control of cognitive wireless networks;

[0062] Figure 4 This is a diagram showing the effect of the method of this invention iteratively calculating the optimal power allocation when the constraints are violated. Detailed Implementation

[0063] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0064] To overcome the limitations of existing technologies, optimization algorithms can be applied to power control in cognitive wireless networks. These algorithms can globally optimize network power configuration without being restricted by inter-node communication. The advantage of these algorithms lies in their ability to reduce power waste and maximize the performance of individual nodes by considering global information across the entire network.

[0065] Intelligent optimization algorithms are optimization algorithms based on artificial intelligence and machine learning, capable of automatically searching for and optimizing optimal solutions to problems. In the power control problem of cognitive wireless networks, intelligent optimization algorithms can help determine the optimal power control strategy, thereby improving key indicators such as network performance, resource efficiency, and coverage. Compared with traditional optimization algorithms, intelligent optimization algorithms have the following advantages:

[0066] 1. High adaptability: Intelligent optimization algorithms can adaptively search for and optimize the optimal solution, adapting to the solution of problems with different complexities, nonlinearities and multiple parameters.

[0067] 2. Fast convergence speed: Intelligent optimization algorithms can find the optimal solution in a complex search space and converge quickly, finding the optimal solution in a relatively short time.

[0068] 3. Strong robustness: Intelligent optimization algorithms can handle data noise and uncertainty, effectively avoid distortion and errors in data processing, and improve the robustness and accuracy of data analysis and optimization.

[0069] 4. Wide applicability: Intelligent optimization algorithms are applicable to various optimization problems, including continuous and discrete problems, multi-objective and multi-constraint problems, as well as parameter optimization and feature selection problems.

[0070] On the other hand, the Harris Hawks Optimization algorithm is a novel evolutionary algorithm proposed by Chinese-American mathematician Lyongyen Wei in 2018. Inspired by the hunting behavior of the Harris Hawk, this algorithm employs a population-based evolutionary strategy to solve optimization problems. Given its strong applicability and relatively fast convergence, the Harris Hawks Optimization algorithm demonstrates superiority in solving the constrained optimization problem of cognitive wireless network power control.

[0071] This invention discloses a cognitive wireless network power control method based on the Harris Eagle optimization algorithm. First, a multi-primary-user and secondary-user communication network model is established according to the cognitive wireless network control requirements. Then, an objective function based on SINR is established for the communication quality of each secondary user in the cognitive wireless network, using the Nash equalization principle. To address constraints such as minimum communication quality of secondary users, interference temperature constraints of each secondary user to the primary user, and the total transmit power constraint of all secondary users, a constrained optimization problem model is proposed, thereby achieving secondary user power allocation while satisfying primary and secondary user constraints. Finally, the Harris Eagle optimization algorithm is improved based on the established optimization problem model to achieve optimal configuration in solving the secondary user power allocation problem in the cognitive wireless network. This invention establishes a cognitive wireless network model and then establishes an optimization problem model based on the Nash equalization principle to solve the secondary user power control problem, achieving optimal power configuration in the current network environment while ensuring the communication quality of each primary and secondary user. Furthermore, this invention improves the Harris Eagle optimization algorithm to exhibit superior performance in solving the aforementioned constrained optimization problem.

[0072] Example 1

[0073] like Figure 1As shown, the present invention provides a cognitive wireless network power control method based on the Harris Eagle optimization algorithm, which specifically includes the following steps:

[0074] S1. Based on the cognitive wireless network control requirements, establish a multi-master user and secondary user communication network model, i.e., a cognitive wireless network model.

[0075] In a cognitive wireless network with shared spectrum, a primary user network consisting of A primary users and a secondary user network consisting of B secondary users coexist. Each user has its own transmitting and receiving equipment. When users in the cognitive wireless network send information simultaneously, it will cause interference to other users. For example... Figure 2 As shown, squares represent transmitters, triangles represent receivers, and the primary user's transmitter and receiver are called a user pair, as are the secondary user's transmitter and receiver. In this context, the expressions for the signal strength received by the primary and secondary users, i.e., the cognitive wireless network model, are as follows:

[0076]

[0077]

[0078] Where, q s,k q p,u The signal strengths received by the k-th secondary user and the u-th primary user are t and t, respectively. s,k t s,l t p,n The transmit power of secondary user k, other secondary users excluding secondary user k, and all primary users are t, respectively. p,u The transmit power of the primary user u, ξ s,k ξ p,u ξ represents the channel gain between the k-th secondary user and the u-th primary user and their respective receivers. l,k ζ n,k , These represent the interference channel gains between the secondary user and the k-th secondary user, between the primary user and the k-th secondary user, and between the secondary user and the u-th primary user, respectively. s,k v p,u Let $\mathbf$ and $\mathbf$ be the background noise when the k-th secondary user and the u-th primary user receive information, respectively. Based on the above model, the main sources of interference for secondary users are interference between secondary users, interference from primary users to secondary users, and background noise. The interference experienced by primary users mainly comes from secondary users and background noise.

[0079] S2. To assess the communication quality of each user in a cognitive wireless network, an objective function based on SINR (signal-to-interference-plus-noise ratio) is established using the Nash equalization principle.

[0080] The objective function based on SINR is as follows. First, the SINR calculation method is as follows:

[0081]

[0082] Where, ψ k Let S be the signal-to-noise ratio (SNR) and interference ratio (INR) of secondary user k at its current transmit power, which is the SINR value of secondary user k. Let ξ be the variance of the background noise at the k-th sub-user. k,k Let be the channel gain from transmitter k to receiver for secondary user k.

[0083] In establishing a constrained optimization problem model, we assume that all users are equally important, meaning that each user has the same network communication quality requirements, in order to achieve good overall communication quality. Based on the Nash equilibrium principle, we derive the following objective function:

[0084]

[0085] Where, ψ m#n It is the minimum signal-to-noise ratio and interference ratio required to ensure the basic communication quality of each secondary user, i.e., the minimum SINR.

[0086] S3. To address the constraints of minimum communication quality for secondary users, interference temperature of each secondary user to the primary user, and total transmit power of all secondary users, a constrained optimization problem model is proposed to achieve power allocation for secondary users while satisfying the constraints of primary and secondary users.

[0087] When secondary users engage in network communication, they must first ensure their own communication quality, namely, the minimum signal-to-noise ratio and interference ratio. Therefore, the minimum communication quality constraint for secondary users is:

[0088] ψ k ≥ψ m#n , k=1,……,B (5)

[0089] In practical applications, the transmit power of this user must not only meet its own network communication quality requirements, but also ensure that the communication quality of the primary user is not interfered with. The interference temperature constraint of each secondary user on the primary user in the optimization problem is achieved by the following formula:

[0090]

[0091] Among them, t s,n For the secondary user n's transmit power, ζ n,l C represents the channel gain from secondary user n to primary user 0. l The maximum allowable interference signal strength for each master user is determined based on the different needs of the task.

[0092] Given the limited total spectrum resources, the total transmit power of each secondary user should be limited to a threshold, i.e., the sum of the transmit power of all secondary users is constrained as follows:

[0093]

[0094] Among them, t ma4 This is the upper limit of the total transmit power for all secondary users.

[0095] Combining step S2, the overall understanding of the wireless network power control optimization problem, i.e., the constrained optimization problem model, can be expressed as follows:

[0096] Target:

[0097] Constraint: ψ k ≥ψ m#n ,k=1,……,B(9)

[0098]

[0099]

[0100] S4. Improve the Harris Eagle optimization algorithm based on the constrained optimization problem model established in step S3, so that it can achieve the optimal configuration in solving the power allocation of secondary users in cognitive wireless networks.

[0101] The original Harris Eagle optimization algorithm is an intelligent optimization algorithm for solving unconstrained optimization problems. To solve the aforementioned constrained optimization problems, the original Harris Eagle optimization algorithm needs to be improved as follows.

[0102] Fitness Function: This is a constrained optimization problem. Therefore, when calculating the operator (the operator is a candidate solution generated by the Harris Eagle algorithm, where the elements of the solution are the transmit power of each sub-user, and the number of elements is the number of sub-users), it is also necessary to check whether it violates the constraints. Therefore, the fitness function (i.e., the performance index for evaluating the quality of candidate solutions) in the original Harris Eagle algorithm is rewritten as follows:

[0103] fitness=[Cbj cCns] (12)

[0104] Wherein, obj is the objective function to which the penalty function is added, and the specific calculation method is shown in equation (13). const is a flag to determine whether the current operator violates the constraint. If the constraint is violated, it is assigned a value of 1, and if the constraint is obeyed, it is assigned a value of 0.

[0105] As an intelligent optimization algorithm, the objective function value of an operator significantly influences the overall iteration direction of the algorithm. However, for some operators, even when violating constraints, the objective function value can still significantly impact the algorithm's iteration direction. Therefore, a penalty function should be added to correct the iteration direction based on the constraint violation. The objective function with the penalty function is expressed as follows:

[0106] Cbj=obj1+10×error1+error2+10 10 ×error3(13)

[0107]

[0108]

[0109]

[0110]

[0111] Where, obj1 is the value of the objective function of the constrained optimization problem after a left shift of length 1 (ensuring that no complex numbers appear during the algorithm operation when the operator yields a small SINR value, and that the monotonicity of the logarithmic function is unique and does not change the final optimization direction), error1 is the value that violates the minimum communication quality constraint of the secondary user in the constrained optimization problem to the constraint boundary, error2 is the value that violates the interference temperature constraint of each secondary user to the primary user in the constrained optimization problem to the constraint boundary, and error3 is the value that violates the constraint of the sum of the transmit power of all secondary users to the constraint boundary. C u The maximum interference signal strength allowed by the primary user u. Among the three constraints, when the operator does not comply with the constraint, the magnitude of its deviation value differs greatly. Therefore, the coefficient of the corresponding deviation in the final objective function (i.e., the objective function with the penalty function added) obj is adjusted to ensure that the overall iteration direction is not limited by the influence of a single constraint.

[0112] Sorting method: When selecting the merits of candidate solutions, we should first consider whether the operator conforms to the constraints, and then consider the magnitude of the objective function value. The specific logic is as follows:

[0113] Assuming operators X and Y exist, their superiority is compared as follows:

[0114] If both X and Y are subject to constraints, the one with the larger objective function value is considered better.

[0115] If X obeys the constraints and Y does not, then X is the better one.

[0116] If X does not comply with the constraints, but Y does, then Y is the better one.

[0117] If neither X nor Y complies with the constraints, then the one with the larger objective function value is considered better.

[0118] The algorithm still uses the above sorting method to evaluate the merits of operators during internal iterations.

[0119] In summary, the proposed method establishes a mathematical model of the relationship between the transmit power of each user and the final network communication quality. Based on the actual application scenario, it proposes several constraints. Finally, based on this constrained optimization problem model, the Harris Eagle optimization algorithm is improved to better solve the cognitive wireless network power control problem, so that the final power allocation can achieve better communication quality while satisfying the constraints.

[0120] Example 2

[0121] The cognitive wireless network power control system based on the Harris Eagle optimization algorithm disclosed in this invention includes:

[0122] The model building module is used to build a cognitive wireless network model according to the control requirements of the cognitive wireless network.

[0123] The objective function construction module is used to establish a SINR-based objective function for the communication quality of each user in a cognitive wireless network, based on the Nash equalization principle.

[0124] The constrained optimization problem model building module is used to propose a constrained optimization problem model to address constraints such as the minimum communication quality of secondary users, the interference temperature of each secondary user to the primary user, and the total transmit power of all secondary users, thereby achieving secondary user power allocation while satisfying the primary and secondary user constraints.

[0125] The algorithm improvement and model solving module is used to improve the Harris Eagle optimization algorithm so that it has the ability to solve the constrained optimization problem model, and to use the improved Harris Eagle optimization algorithm to solve the constrained optimization problem.

[0126] Example 3

[0127] The present invention discloses a cognitive wireless network power control device based on the Harris Eagle optimization algorithm, the device comprising:

[0128] Memory containing executable program code;

[0129] A processor coupled to the memory;

[0130] The processor calls the executable program code stored in the memory to execute the cognitive wireless network power control method based on the Harris Eagle optimization algorithm as described above.

[0131] The device may include: a memory storing executable program code; a processor coupled to the memory; the processor calling the executable program code stored in the memory to perform the steps in the cognitive wireless network power control method based on the Harris Eagle optimization algorithm described in Embodiment 1.

[0132] The memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the memory may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as a "hard disk drive"). A program / utility having a set (at least one) of program modules may be stored in, for example, memory. Such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules typically perform the functions and / or methods described in the embodiments of the present invention.

[0133] The processor executes various functional applications and data processing by running programs stored in memory, such as the method provided in Embodiment 1 of the present invention.

[0134] Example 4

[0135] Embodiment 4 of the present invention also provides a computer-readable storage medium storing computer instructions, which, when invoked, are used to execute the cognitive wireless network power control method based on the Harris Eagle optimization algorithm described above.

[0136] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0137] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0138] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0139] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as MATLAB, Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0140] Of course, the computer-executable instructions provided in the embodiments of the present invention are not limited to the above-described method operations, but can also perform related operations in the methods provided in any embodiment of the present invention.

[0141] Experimental simulation

[0142] The cognitive wireless network model described above will be modeled using MATLAB, and the effectiveness and superiority of the power control of the cognitive wireless network will be verified using the method of this invention.

[0143] First, a cognitive wireless network consisting of 5 secondary users and 2 primary users is established. The distances between each network communication pair (i.e., each user pair) are shown in the table below:

[0144] Table 1. Distance between user pairs

[0145]

[0146] Where SU represents the secondary user communication pair, PU represents the primary user communication pair, and T and R represent the transmitter and receiver, respectively. The channel gain between user communication pairs is calculated as follows:

[0147]

[0148] Among them, Z k,l Let μ be the distance between user pair o and user pair l, where μ is a constant with a value of 0.097.

[0149] Minimum communication quality requirements for each user ψ min The maximum allowable interference signal strength C for each master user is 0.01. l From 0 to 10 -9 Random numbers between, and the total transmit power threshold t for each user. ma4 The value is 6.5, and the background noise is in the range [0, 10]. -10 The random number is generated by the Harris Eagle optimization algorithm, which is set to a population size of 50 and an iteration count of 500. Figure 3 This is a graph showing the change in the objective function value of the optimal operator (i.e., the candidate solution ranked highest by the algorithm) during the iteration process. Figure 4 This represents the case where the corresponding operator violates the constraints, where 0 indicates compliance with the constraints and 1 indicates non-compliance. The final transmit powers allocated to each user are 0.1674, 3.000, 0.3200, 1.035, and 2.518, respectively. Under these transmit powers, the SINR values ​​for each user are 0.0274, 0.1752, 0.0459, 0.0622, and 0.3010, respectively. The above results comply with all constraints and ensure good communication quality.

Claims

1. A cognitive wireless network power control method based on the Harris Eagle optimization algorithm, characterized in that, Includes the following steps: A cognitive wireless network model is established based on the control requirements of cognitive wireless networks; A SINR-based objective function is established to assess the communication quality of each user in a cognitive wireless network, based on the Nash equalization principle. To address the constraints of minimum communication quality for secondary users, interference intensity from each secondary user to the primary user, and total transmit power of all secondary users, a constrained optimization problem model is proposed. This model enables power allocation for secondary users while satisfying the primary and secondary user constraints. Specifically, the minimum communication quality constraint for secondary users ensures their own communication quality, the interference intensity constraint from each secondary user to the primary user ensures that the primary user's communication quality is not interfered with, and the total transmit power constraint of all secondary users ensures that the total spectrum resources are sufficient. An improved Harris Eagle optimization algorithm is proposed to enable it to solve the constrained optimization problem model. The improved algorithm is then used to solve the constrained optimization problem. Specifically, the fitness function in the original Harris Eagle algorithm is rewritten to include a penalty function as the objective function and a flag indicating whether the current operator violates the constraints. The improved Harris Eagle optimization algorithm is as follows: The fitness function in the original Harris Eagle optimization algorithm, which is the performance index for evaluating the merits of candidate solutions, can be rewritten in the following form: ； in, The objective function to which the penalty function is added, A flag is used to determine whether the current operator violates the constraints. If the constraints are violated, the flag is set to 1; if the constraints are obeyed, the flag is set to 0. The operator is the candidate solution generated by the Harris Eagle algorithm. Based on the operator's violation of constraints, a penalty function is added to correct the iteration direction; then, the objective function with the added penalty function is expressed as follows: ； in, The value of the objective function's independent variable in a constrained optimization problem after a left shift of length 1. The value at the constraint boundary is the value that violates the minimum communication quality constraint for secondary users in a constrained optimization problem. The value of the disturbance intensity of each secondary user to the primary user on the constraint boundary in a constrained optimization problem is given. The value at the constraint boundary is the sum of the transmit power of all secondary users that violates the constraint.

2. The cognitive wireless network power control method based on the Harris Eagle optimization algorithm according to claim 1, characterized in that, In cognitive wireless networks, A primary user network consisting of primary users and... A secondary user network consisting of multiple secondary users coexists, with each user having its own transmitting and receiving equipment. When users in a cognitive wireless network simultaneously transmit information, it can cause interference to other users. However, primary users, due to a robust communication protocol, are not considered to experience interference. In this scenario, the expression for the signal strength received by primary and secondary users, i.e., the cognitive wireless network model, is as follows: ； ； in, , The first The first user, the first The signal strength received by each primary user , , Secondary users Excluding secondary users Transmit power of all secondary users and all primary users Main user The transmission power, , The first The first user, the first Channel gain between each primary user and their respective receiver , , From the second user to the first From the first secondary user to the primary user, the number of users... The first user, the second user to the first Interference channel gain between primary users , The first The first user, the first Background noise when a primary user receives information.

3. The cognitive wireless network power control method based on the Harris Eagle optimization algorithm according to claim 1, characterized in that, The objective function based on SINR is: ； in, For secondary users Signal-to-noise ratio and interference ratio at the current transmit power It is the minimum signal-to-noise ratio and interference ratio required to guarantee the basic communication quality of each secondary user, i.e., the minimum SINR. This refers to the number of secondary users.

4. The cognitive wireless network power control method based on the Harris Eagle optimization algorithm according to claim 3, characterized in that, The SINR calculation method is as follows: ； in, , , Secondary users Excluding secondary users Transmit power of all secondary users and all primary users , From the second user to the first From the first secondary user to the primary user, the number of users... Interference channel gain between individual users For secondary users Signal-to-noise ratio and interference ratio at the current transmit power, i.e., secondary user SINR value, For the first Variance of background noise at each user location For secondary users Channel gain from transmitter to receiver.

5. The cognitive wireless network power control method based on the Harris Eagle optimization algorithm according to claim 1, characterized in that, The minimum communication quality constraint for secondary users is: ； in, For secondary users Signal-to-noise ratio and interference ratio at the current transmit power These are the minimum signal-to-noise ratio and interference ratio required to ensure basic communication quality for each secondary user. This refers to the number of secondary users; The interference intensity constraints for each secondary user on the primary user are as follows: ； in, For secondary users Transmission power, For secondary users To the main user Channel gain, To determine the maximum allowable interference signal strength for each primary user, depending on the specific task requirements. Number of primary users; The total transmit power constraint for all secondary users is: ； in, For secondary users The transmission power, This represents the maximum total transmit power for all secondary users. The constrained optimization problem model is then represented as: Target: ; constraint: ; ； 。 6. The cognitive wireless network power control method based on the Harris Eagle optimization algorithm according to claim 5, characterized in that, When using the improved Harris Eagle optimization algorithm to solve constrained optimization problems, the selection of candidate solutions should first consider whether the operators comply with the constraints, and then consider the magnitude of the objective function value. The specific logic is as follows: Assuming operators X and Y exist, their superiority is compared as follows: If both X and Y are subject to constraints, the one with the larger objective function value is considered better. If X obeys the constraints and Y does not, then X is the better one. If X does not comply with the constraints, but Y does, then Y is the better one. If neither X nor Y complies with the constraints, then the one with the larger objective function value is considered better. The algorithm still uses the above sorting method to evaluate the merits of operators during internal iterations.

7. A system for the cognitive wireless network power control method based on the Harris Eagle optimization algorithm as described in any one of claims 1-6, characterized in that, include: The model building module is used to build a cognitive wireless network model according to the control requirements of the cognitive wireless network. The objective function construction module is used to establish a SINR-based objective function for the communication quality of each user in a cognitive wireless network, based on the Nash equalization principle. The constrained optimization problem model building module is used to propose a constrained optimization problem model to address constraints such as the minimum communication quality of secondary users, the interference intensity of each secondary user to the primary user, and the total transmit power of all secondary users, thereby achieving secondary user power allocation while satisfying the primary and secondary user constraints. The algorithm improvement and model solving module is used to improve the Harris Eagle optimization algorithm so that it has the ability to solve the constrained optimization problem model, and to use the improved Harris Eagle optimization algorithm to solve the constrained optimization problem.

8. A cognitive wireless network power control device based on the Harris Eagle optimization algorithm, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the cognitive wireless network power control method based on the Harris Eagle optimization algorithm as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when invoked, are used to execute the cognitive wireless network power control method based on the Harris Eagle optimization algorithm as described in any one of claims 1-6.

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