A multi-target relay selection method for an energy harvesting cognitive relay network

Abstract

This invention provides a multi-objective relay selection method for energy harvesting cognitive relay networks. In the context of energy harvesting cognitive relay networks, a new multi-objective function is constructed to solve discrete optimization problems by comprehensively considering network energy efficiency and signal-to-noise ratio. The multi-objective relay selection method is then rapidly obtained through a multi-objective quantum monarch butterfly optimization mechanism. This method maximizes network energy efficiency while ensuring the signal-to-noise ratio, providing a new idea and method for solving relay selection schemes in energy harvesting cognitive relay networks.

Description

Technical Field

[0001] This invention relates to a multi-target relay selection method for energy harvesting cognitive relay networks, belonging to the field of wireless communication. Background Technology

[0002] Cognitive radio technology, with its advantages of flexibility, intelligence, and adaptive configuration, can effectively utilize idle spectrum, alleviating the contradiction between scarce spectrum resources and ever-increasing data traffic, thereby improving spectrum utilization. Energy harvesting technology collects energy from the surrounding environment through specific devices, alleviating the contradiction between energy consumption and energy supply, providing a new way for wireless communication systems to utilize renewable energy, and making wireless charging possible. The combination of cognitive radio technology and energy harvesting technology has the potential to simultaneously achieve green communication and efficient spectrum utilization, becoming a hot research topic in recent years.

[0003] In energy harvesting cognitive relay networks, multiple candidate relay nodes often exist. Besides channel gain, the interference between primary users and cognitive users must be considered. This requires satisfying the primary user interference threshold while ensuring the quality of service for the cognitive users themselves. The relay selection problem is fundamental in energy harvesting cognitive relay networks and is also a typical NP-hard problem, making it difficult to find an exact solution within a finite time. A reasonable relay selection scheme is crucial for reducing power consumption and interference in cognitive relay networks and improving network performance, attracting widespread attention from scholars.

[0004] A review of existing literature revealed that Xie Xianzhong et al.'s paper, "A Relay Cooperation Scheme Based on Secondary User Willingness and Throughput in Energy Harvesting Cognitive Networks," published in the *Journal of Chongqing University of Posts and Telecommunications* (2019, 31(1):10), proposed a single-objective relay cooperation scheme with throughput as the optimization objective. This scheme utilizes the energy harvesting time allocation factor for solution, considering only network throughput without considering network energy efficiency, resulting in low practicality. G. Kalaimagal et al.'s paper, "Optimal Relay Node Selection Using Multi-Objective based Pity Beetle Optimization Algorithm for Cognitive Radio Networks," published in *Wireless Personal Communications* (2021:1-17), proposed a relay selection scheme based on a multi-objective Pity Beetle optimization algorithm. This algorithm optimizes the signal-to-interference-plus-noise ratio (SIR) for both primary and secondary users, but does not consider network energy efficiency, making it not a true multi-objective optimization problem, and the algorithm has high complexity.

[0005] A review of existing literature reveals that current relay selection methods have limited applicability, high computational complexity, and struggle to simultaneously improve signal-to-noise ratio (SNR) while maintaining energy efficiency, failing to adequately meet the multi-objective requirements of various scenarios. Therefore, this invention designs a multi-objective relay selection method based on a multi-objective quantum monarch butterfly optimization mechanism. In an energy harvesting cognitive relay network environment, considering both network energy efficiency and SNR, a new multi-objective function is constructed to solve the discrete optimization problem. The multi-objective relay selection method is then rapidly obtained through the multi-objective quantum monarch butterfly optimization mechanism, maximizing network energy efficiency while maintaining SNR. This provides a new approach and method for solving relay selection schemes in energy harvesting cognitive relay networks. Summary of the Invention

[0006] The purpose of this invention is to provide a multi-objective relay selection method for energy harvesting cognitive relay networks that simultaneously considers the signal-to-noise ratio and network energy efficiency through a multi-objective quantum monarch butterfly optimization mechanism.

[0007] The objective of this invention is achieved as follows: The steps are as follows:

[0008] Step 1: Establish a multi-objective relay selection model for the energy harvesting cognitive relay network;

[0009] Step 2: Initialize the quantum monarch butterfly population and set system parameters;

[0010] Let the maximum number of iterations be T, the iteration number be t, t∈[1,T], the size of the quantum monarch butterfly population be H, and the dimension be N; the initial quantum positions of the monarch butterflies in the population are generated randomly, and the quantum position of the m-th monarch butterfly in the t-th generation is represented as... in, For m = 1, 2, ..., H and n = 1, 2, ..., N, the position of the m-th monarch butterfly can be obtained by quantum position measurement. Measurement method is in A random number uniformly distributed between [0,1].

[0011] Step 3: Determine the multi-objective fitness function for multi-objective relay selection, calculate the fitness function values ​​of all individuals in the quantum monarch butterfly population, perform non-dominated sorting and crowding calculation, and establish an initial elite solution set;

[0012] Step 4: Select the globally optimal quantum position from the elite solution set, and divide the quantum monarch butterfly population into two subpopulations according to the non-dominated sorting level.

[0013] The globally optimal monarch butterfly is randomly selected from the elite solution set S. E The quantum position corresponding to the top 2% of individuals is called the globally optimal quantum position, denoted as . Based on the non-dominant ranking, the entire Monarch butterfly population is divided into two subpopulations. The H1 individuals with the highest non-dominant ranking form Monarch subpopulation 1, and the remaining individuals form Monarch subpopulation 2. The number of Monarch butterflies in subpopulation 1 is... The number of monarch butterflies in subpopulation 2 is H-H1, among which, For the migration rate of monarch butterflies, This is for rounding up;

[0014] Step 5: Update the quantum positions of monarch butterflies in subpopulation 1 and subpopulation 2 using different strategies;

[0015] Step 7: Merge the two updated subpopulations into a new quantum monarch butterfly population. According to the measurement method, measure the position of monarch butterfly individuals in the two updated subpopulations and calculate the fitness function value of all individuals in the new quantum monarch butterfly population.

[0016] Step 8: Mix the most recent two generations of quantum monarch butterfly populations, sort the non-dominated solutions and calculate the crowding degree, and update the elite solution set;

[0017] The most recent two generations of Quantum Monarch butterfly populations were mixed, and non-dominated solutions were sorted and crowding was calculated. Monarch butterfly individuals with the same non-dominated level were sorted in descending order of crowding value, and monarch butterfly individuals with a non-dominated level of 1 were selected to be added to the elite solution set S. E When the elite set S E If the number of monarch butterflies is greater than H, then the same operation as above is performed on the monarch butterflies in the elite solution set, and the top H monarch butterflies are selected as the new elite solution set S. E ;

[0018] Step 9: If the evolution has not terminated, i.e. the number of iterations is less than the preset maximum number of iterations, let t = t + 1 and return to step 4 to continue the iteration; otherwise, terminate the iteration, output the elite solution set, i.e. the Pareto front-end solution set with non-dominant level 1, and obtain the multi-objective relay selection scheme.

[0019] Furthermore, step one specifically involves: during the energy harvesting phase, the energy harvested from the environmental signal by the cognitive user source node and the nth relay node is E, respectively. s =α1Y s T and E n =α2Y n T, where α1 is the energy collection rate of the cognitive user source node, α2 is the energy collection rate of the nth relay node, T is the time slot length, and Y s and Y nLet represent the energy collected by the cognitive user source node and the nth relay node per unit time, respectively. Assuming that the cognitive user source node and the relay node transmit signals at constant power within a time slot, then the maximum transmission power of the cognitive user source node and the nth relay node are respectively... and The transmission power of the cognitive user source node and relay node must not exceed their own maximum transmission power, and the interference to the primary user receiver must not exceed the threshold value I. th The specific power control strategies for user source nodes and relay nodes are as follows: Where M is the number of relays for information transmission;

[0020] During the data transmission phase, let the signal sent by the cognitive user source node be x. s δ n Let represent the Gaussian white noise at the nth relay node, then the signal received by the nth relay node is: The relay node sends the following signals: in σ is the magnification factor. 2 Define the noise power; define parameter b. n Let b represent whether the nth relay is selected. n =1, which means that the relay is selected for information transmission. If b n =0 indicates that the relay is idle; use δ d If the Gaussian white noise at the cognitive user's destination node represents the final merged signal received by the cognitive user's destination node, then it is represented as: The system end-to-end signal-to-noise ratio is obtained as follows: System energy efficiency is defined as the ratio of the network throughput to the total power consumed by the system. Therefore, the system energy efficiency is... Where C is the throughput of the entire network, P ∑ Let be the total power consumed by the system; the multi-objective relay selection model, considering both system throughput and system energy efficiency, is the following maximum optimization equation:

[0021]

[0022] Furthermore, in step three, the multi-objective function for optimizing the maximum value of the position of the m-th monarch butterfly is: Wherein, the fitness function of objective 1 is the signal-to-noise ratio, and the fitness function of objective 2 is the energy efficiency, i.e. The non-dominated sorting process is as follows: For the m-th position Calculate the dominant position The number of monarch butterflies q m and location The set of positions dominated If the dominant position The number q m =0, then the non-dominated rank of the monarch butterfly at that position is 1; for each monarch butterfly with a non-dominated rank of 1, traverse the set of positions it dominates. Each position in Calculate the dominant position The number of monarch butterflies q g If q g -1 = 0, then the position The positions of monarch butterflies are stored in set Q, where the non-dominance ranking is 2. The above process is repeated for each monarch butterfly in set Q, thus obtaining the set of positions with a non-dominance ranking of 3. This process is repeated until the non-dominance rankings of all monarch butterfly positions are obtained. The dominance relationship is determined as follows: for any two monarch butterfly positions... and like and If at least one strict inequality holds, then the position is called a position. Dominate It is a non-dominant position; conversely, if and If at least one strict inequality holds, then the position is called a position. Dominate It is a non-dominant position; if neither of the above two conditions is met, then the position is... and There is no dominance relationship between them; the crowding degree calculation is performed on monarch butterflies with the same non-dominance level. Assume there are o positions with a non-dominance level of Δ, and the fitness function value f... z Sort in ascending order, z = 1 or 2. Define the crowding degree of the monarch butterflies whose fitness function has the minimum and maximum values ​​as ∞. The crowding degree values ​​of other monarch butterflies are calculated as follows: in, and Let be the fitness function values ​​of the target z at the next and previous positions, respectively. and Let z be the maximum and minimum values ​​of the fitness function for the objective z. For each monarch butterfly, the crowding degree corresponding to each objective function is calculated as described above, and the sum of all crowding degree components is the final crowding degree value for that monarch butterfly. Monarch butterflies with a non-dominant level of 1 and a large crowding degree value are added to the elite solution set S. E middle.

[0023] Furthermore, in step five, the update strategies for the quantum position and quantum rotation angle of the m1th monarch butterfly in the nth dimension in subpopulation 1 are as follows:

[0024]

[0025]

[0026] in, and Let represent the quantum rotation angle and the quantum position in the nth dimension of the updated m1-th monarch butterfly, respectively. The quantum position of the m1th monarch butterfly in generation t, in the nth dimension, is represented by r1 = 1, 2, ..., H1, n = 1, 2, ..., N; the individual monarch butterfly label r1 is randomly selected from monarch butterfly subpopulation 1, and the individual monarch butterfly label r2 is randomly selected from monarch butterfly subpopulation 2. For the migration rate of monarch butterflies, For the Monarch butterfly migration cycle, a1, a2, and All are random numbers that follow a uniform distribution in the interval [0,1], and β1 is the quantum position control parameter of monarch butterfly population 1;

[0027] In subpopulation 2, the update strategies for the quantum position and quantum rotation angle of the m2th monarch butterfly in the nth dimension are as follows:

[0028]

[0029]

[0030] in, and Let represent the quantum rotation angle and the quantum position in the nth dimension of the m2th monarch butterfly after the update, respectively. Let r represent the nth dimension quantum position of the m2th monarch butterfly in generation t, where m2 = H1+1, H1+2, ..., H, and n = 1, 2, ..., N. The individual monarch butterfly number r3 is randomly selected from monarch butterfly subpopulation 2. The nth dimension represents the globally optimal quantum position of the quantum monarch butterfly population. For Monarch butterfly adjustment rates, a3, a4, a5, and All are random numbers that follow a uniform distribution in the interval [0,1], and β2 is the quantum position control parameter of monarch butterfly population 2.

[0031] Compared with the prior art, the beneficial effects of the present invention are: (1) In order to improve the spectrum utilization rate, save energy effectively, alleviate the contradiction between energy consumption and energy supply, and realize the concept of green communication, the present invention combines energy harvesting technology with cognitive relay network to construct an energy harvesting cognitive relay network system model.

[0032] (2) This invention solves the problem of multi-objective relay selection in discrete optimization cognitive relay networks. In view of the problem that existing relay selection methods cannot simultaneously guarantee signal-to-noise ratio and energy efficiency, a novel multi-objective relay selection method based on the multi-objective quantum monarch butterfly optimization mechanism is designed. This method can solve multi-objective relay selection problems in real life. The designed method has stable performance and can find the optimal multi-objective relay selection scheme in a short time.

[0033] (3) Compared to traditional monarch butterfly optimization mechanisms and quantum monarch butterfly optimization mechanisms, which can only solve single-objective problems of continuous optimization, the multi-objective quantum monarch butterfly optimization mechanism designed in this invention can be used to solve multi-objective problems of discrete optimization. During population update, the quantum state evolution rule is adopted, which improves convergence, reduces the time complexity of the algorithm, and has strong optimization capabilities. This provides a new approach to solving the relay selection problem in cognitive relay networks and has good generalizability. Attached Figure Description

[0034] Figure 1 A schematic diagram of the multi-objective relay selection method for the multi-objective quantum monarch butterfly optimization mechanism.

[0035] Figure 2 This study presents the nondominated solutions searched by the nondominated sorting genetic method and the multi-objective quantum monarch butterfly optimization mechanism, which simultaneously consider signal-to-noise ratio and energy efficiency.

[0036] Figure 3 This includes the non-dominated solution searched by the multi-objective quantum monarch butterfly optimization mechanism, which considers both signal-to-noise ratio and energy efficiency, when the number of relays is 20, as well as the single-objective solutions of the monarch butterfly optimization method and the political optimization method.

[0037] Figure 4 This presents the non-dominated solution searched by the multi-objective quantum monarch butterfly optimization mechanism, which considers both signal-to-noise ratio and energy efficiency, when the number of relays is 30, as well as the single-objective solutions of the monarch butterfly optimization method and the political optimization method. Detailed Implementation

[0038] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0039] The steps of this invention are as follows:

[0040] Step 1: Establish a multi-objective relay selection model for the energy harvesting cognitive relay network.

[0041] The energy harvesting cognitive relay network consists of primary users and cognitive users. Each primary user comprises a pair of primary user transmitters and receivers, while each cognitive user consists of a pair of cognitive user source nodes, a cognitive user destination node, and N relay nodes. Primary users and cognitive users share a licensed frequency band with bandwidth W. It is assumed that there is no direct communication link between the cognitive user source nodes and the cognitive user destination nodes, and that the entire system operates in half-duplex time-division multiple access mode, with all channels subject to independent and identically distributed Rayleigh fading. Within a time slot, a complete cognitive user communication consists of two phases: energy harvesting and data transmission. During energy harvesting, the cognitive user source nodes and relay nodes harvest energy from surrounding environmental signals and convert it into electrical energy for storage and information transmission. During data transmission, the cognitive user source nodes send signals to each relay node, which interferes with the primary user receiver. The relay nodes amplify the received signals and forward them to the cognitive user destination node, which also interferes with the primary user receiver. The channel fading between any node i and node j is given by... Among them, κ i,j Let γ be the distance between node i and node j, γ be the fading coefficient, and β be the path loss factor. During the data transmission phase, the channel fading from the cognitive user source node to the nth relay node is h. s,n The channel fading from the cognitive user source node to the primary user receiver is h. s,a The channel fading from the nth relay node to the cognitive user's destination node is h. n,d The channel fading from the nth relay node to the primary user receiver is h. n,a The transmit power of the cognitive user source node is represented by P. s Let P represent the transmit power of the nth relay node. n Let's assume that the transmission power of the primary user's transmitter has a negligible impact on the cognitive user due to the long distance.

[0042] During the energy harvesting phase, the energy harvested from the environmental signal by the cognitive user source node and the nth relay node is E, respectively. s =α1Y s T and E n =α2Y n T, where α1 is the energy collection rate of the cognitive user source node, α2 is the energy collection rate of the nth relay node, T is the time slot length, and Y s and Y n These represent the energy collected by the cognitive user source node and the nth relay node per unit time, respectively. Assuming the cognitive user source node and the relay node transmit signals at constant power within a time slot, the maximum transmission power of the cognitive user source node and the nth relay node are respectively... and Cognitive users need to meet power constraints, meaning that the transmission power of the cognitive user's source node and relay node cannot exceed their own maximum transmission power, and the interference to the primary user's receiver cannot exceed a threshold value I. th The specific power control strategies for user source nodes and relay nodes are as follows: Where M represents the number of relays used for information transmission.

[0043] During the data transmission phase, let the signal sent by the cognitive user source node be x. s δ n Let represent the Gaussian white noise at the nth relay node, then the signal received by the nth relay node is: The relay node receives signal y n The signal is then amplified and forwarded to the destination node; therefore, the relay node's transmitted signal is... in σ is the magnification factor. 2 Let b be the noise power. n Let b represent whether the nth relay is selected. n =1, which means that the relay is selected for information transmission. If b n =0 indicates that the relay is idle. Use δ d If the Gaussian white noise at the cognitive user's destination node represents the final merged signal received by the cognitive user's destination node, then it can be expressed as:

[0044] Assuming the noise around the relay node and the cognitive user's destination node is the same white Gaussian noise, the end-to-end signal-to-noise ratio of the system can be calculated as follows: System energy efficiency is defined as the ratio of the network throughput to the total power consumed by the system. Therefore, the system energy efficiency is... Where C is the throughput of the entire network, P ∑ This represents the total power consumed by the system.

[0045] The multi-objective relay selection problem, which simultaneously considers system throughput and system energy efficiency, is optimized by the following maximum value equation:

[0046]

[0047] Step 2: Initialize the quantum monarch butterfly population and set system parameters.

[0048] Let the maximum number of iterations be T, the iteration number be t, where t∈[1,T], and the size of the quantum monarch butterfly population be H, with dimension N. The initial quantum positions of the monarch butterflies in the population are generated randomly. The quantum position of the m-th monarch butterfly in the t-th generation can be represented as... in, For m = 1, 2, ..., H and n = 1, 2, ..., N, the position of the m-th monarch butterfly can be obtained by quantum position measurement. Measurement method is in It is a random number uniformly distributed between [0,1].

[0049] Step 3: Determine the multi-objective fitness function for multi-objective relay selection, calculate the fitness function value of all individuals in the quantum monarch butterfly population, perform non-dominated sorting and crowding calculation, and establish an initial elite solution set.

[0050] The multi-objective function for optimizing the maximum position of the m-th monarch butterfly is: Wherein, the fitness function of objective 1 is the signal-to-noise ratio, and the fitness function of objective 2 is the energy efficiency, i.e. For the entire Quantum Monarch butterfly population, the positions of all individuals are substituted into the fitness function for calculation, yielding the fitness function values ​​for all Quantum Monarch butterfly individuals. Non-dominated sorting and crowding calculations are then performed. The non-dominated sorting process is as follows: For the m-th position... Calculate the dominant position The number of monarch butterflies q m and location The set of positions dominated If the dominant position The number q m =0, then the non-dominated rank of the monarch butterfly at that position is 1; for each monarch butterfly with a non-dominated rank of 1, traverse the set of positions it dominates. Each position in Calculate the dominant position The number of monarch butterflies q g If q g -1 = 0, then the position The non-dominant ranks of monarch butterflies are stored in set Q, where the non-dominant rank is ranked 2. The above process is repeated for each monarch butterfly in set Q, thus obtaining the set of positions ranked 3rd in non-dominant rank. This process is repeated until the non-dominant ranks of all monarch butterfly positions are obtained. The dominance relationship is determined as follows: for any two monarch butterfly positions... and like and If at least one strict inequality holds, then the position is called a position. Dominate This is a non-dominant position. Conversely, if and If at least one strict inequality holds, then the position is called a position. Dominate It is a non-dominant position. If neither of the above two conditions is met, then the position... and There is no dominance relationship between them. Crowding calculation is performed on Monarch butterflies with the same non-dominant level. Assume there are o positions with a non-dominant level of Δ, and the fitness function value f is... z Sort in ascending order, z = 1 or 2. Define the crowding degree of the monarch butterflies whose fitness function has the minimum and maximum values ​​as ∞. The crowding degree values ​​of other monarch butterflies are calculated as follows: in, and Let be the fitness function values ​​of the target z at the next and previous positions, respectively. and Let z be the maximum and minimum values ​​of the fitness function for objective z. The crowding degree corresponding to each objective function of each monarch butterfly is calculated as described above, and the sum of the crowding degree components is the final crowding degree value for that monarch butterfly. Monarch butterflies with a non-dominant level of 1 and a large crowding degree value are added to the elite solution set S. E middle.

[0051] Step four: Select the globally optimal quantum position from the elite solution set, and divide the quantum monarch butterfly population into two subpopulations according to the non-dominated sorting level.

[0052] The globally optimal monarch butterfly is randomly selected from the elite solution set S. E The quantum position corresponding to the top 2% of individuals is called the globally optimal quantum position, denoted as . Based on the non-dominant ranking, the entire Monarch butterfly population is divided into two subpopulations. The H1 individuals with the highest non-dominant ranking form Monarch subpopulation 1, and the remaining individuals form Monarch subpopulation 2. The number of Monarch butterflies in subpopulation 1 is... The number of monarch butterflies in subpopulation 2 is H-H1, among which, For the migration rate of monarch butterflies, This is for rounding up.

[0053] Step 5: Update the quantum positions of monarch butterflies in subpopulation 1 and subpopulation 2 using different strategies.

[0054] In subpopulation 1, the update strategies for the quantum position and quantum rotation angle of the m1th monarch butterfly in the nth dimension are as follows:

[0055]

[0056]

[0057] in, and Let represent the quantum rotation angle and the quantum position in the nth dimension of the updated m1-th monarch butterfly, respectively. Let r represent the n-th dimension quantum position of the m1-th monarch butterfly in generation t, where m1 = 1, 2, ..., H1, and n = 1, 2, ..., N. Monarch butterfly individual label r1 is randomly selected from monarch butterfly subpopulation 1, and monarch butterfly individual label r2 is randomly selected from monarch butterfly subpopulation 2. For the migration rate of monarch butterflies, For the Monarch butterfly migration cycle, a1, a2, and All are random numbers that follow a uniform distribution in the interval [0,1], and β1 is the quantum position control parameter of monarch butterfly population 1.

[0058] In subpopulation 2, the update strategies for the quantum position and quantum rotation angle of the m2th monarch butterfly in the nth dimension are as follows:

[0059]

[0060]

[0061] in, and Let represent the quantum rotation angle and the quantum position in the nth dimension of the m2th monarch butterfly after the update, respectively. Let r represent the nth dimension quantum position of the m2th monarch butterfly in generation t, where m2 = H1+1, H1+2, ..., H, and n = 1, 2, ..., N. The individual monarch butterfly number r3 is randomly selected from monarch butterfly subpopulation 2. The nth dimension represents the globally optimal quantum position of the quantum monarch butterfly population. For Monarch butterfly adjustment rates, a3, a4, a5, and All are random numbers that follow a uniform distribution in the interval [0,1], and β2 is the quantum position control parameter of monarch butterfly population 2.

[0062] Step 7: Merge the two updated subpopulations into a new quantum monarch butterfly population. Based on the measurement method, measure the positions of monarch butterfly individuals in the two updated subpopulations and calculate the fitness function values ​​of all individuals in the new quantum monarch butterfly population.

[0063] Step 8: Mix the most recent two generations of Quantum Monarch butterfly populations, sort the non-dominated solutions and calculate the crowding degree, and update the elite solution set.

[0064] The most recent two generations of Quantum Monarch butterfly populations were mixed, and non-dominated solutions were sorted and crowding was calculated. Monarch butterfly individuals with the same non-dominated level were sorted in descending order of crowding value, and monarch butterfly individuals with a non-dominated level of 1 were selected to be added to the elite solution set S. E When the elite solution set SE If the number of monarch butterflies is greater than H, then the same operation as above is performed on the monarch butterflies in the elite solution set, and the top H monarch butterflies are selected as the new elite solution set S. E .

[0065] Step 9: If the evolution has not terminated, i.e. the number of iterations is less than the preset maximum number of iterations, let t = t + 1 and return to step 4 to continue the iteration; otherwise, terminate the iteration, output the elite solution set, i.e. the Pareto front-end solution set with non-dominant level 1, and obtain the multi-objective relay selection scheme.

[0066] exist Figure 2 In this context, the non-dominated solution of the non-dominated sequencing genetic method is denoted as NSGA-II. Figure 3 and Figure 4 In the equation, the solution for the signal-to-noise ratio of the monarch butterfly optimization method is denoted as MBO-SNR, the solution for the energy efficiency of the monarch butterfly optimization method is denoted as MBO-EE, the solution for the signal-to-noise ratio of the political optimization method is denoted as PO-SNR, and the solution for the energy efficiency of the political optimization method is denoted as PO-EE.

[0067] In the simulation, it is assumed that the location of the primary user transmitter in the energy harvesting cognitive relay network is (5,20), the location of the primary user receiver is (25,20), the location of the cognitive user source node is (0,0), and the location of the cognitive user destination node is (30,0). The relay nodes are uniformly distributed within a circle with a radius of 5 and a center of (15,0). The primary user and the cognitive user share a licensed frequency band with a bandwidth of W = 1MHz. γ represents the channel fading, which follows a complex Gaussian distribution with a mean of 0 and a variance of 1. The path loss factor β = 4, the energy harvesting rate is α1 = α2 = 0.5, and the interference threshold is I. th = -20dBW, noise power spectral density is σ 2 = -110dBW / Hz. The parameter settings for the multi-objective quantum monarch butterfly optimization mechanism are as follows: quantum population size is 100, T = 500, β1=β2=0.01. To facilitate comparison of the performance of the proposed multi-objective quantum monarch butterfly optimization mechanism's multi-objective relay selection method with existing non-dominated sorting genetic methods, the non-dominated sorting genetic method is applied to the multi-objective relay selection problem. Figure 2The two methods were compared. The population size and number of termination iterations of the non-dominated sorting genetic method are the same as those of the multi-objective quantum monarch butterfly optimization method. Other parameter settings for the non-dominated sorting genetic method are referenced in "Application of NSGA-II algorithm to the spectrum assignment problem inspectrum sharing networks" (Applied Soft Computing. 2016, 39(C): 88-198). To facilitate comparison of the dominance relationship between the proposed multi-objective quantum monarch butterfly optimization mechanism's multi-objective relay selection method and existing single-objective monarch butterfly methods and political optimization methods, the monarch butterfly optimization method and political optimization method were applied to the single-objective relay selection problem. Figure 3 and 4 The three methods were compared. The population size and number of termination iterations of the Monarch butterfly optimization method and the political optimization method are the same as those of the multi-objective quantum Monarch butterfly optimization method. The other parameter settings of the Monarch butterfly optimization method are referenced in "Monarch butterfly optimization" (Neural Computing and Applications, 2019), and the other parameter settings of the political optimization method are referenced in "Political Optimizer: A novel socio-inspired meta-heuristic for global optimization" (Knowledge-Based Systems, 2020, 195:105709).

[0068] Figure 2 To simultaneously consider the non-dominated sorting genetic method and the quantum monarch butterfly optimization mechanism to find the non-dominated solution, the number of relay nodes is 20, and the energy collected by the cognitive user source node per unit time is Y. s =20, the energy collected by the relay node per unit time is Y. n =12. The proposed multi-objective relay selection method based on the quantum monarch butterfly optimization mechanism has better optimization results, and the obtained solution can dominate most of the solutions obtained by the non-dominated sorting genetic method.

[0069] Figure 3 To find the non-dominated solution searched by the quantum monarch butterfly optimization mechanism that simultaneously considers signal-to-noise ratio and energy efficiency, and the single-objective solutions of the monarch butterfly optimization method and the political optimization method, the number of relay nodes is 20, and the energy collected by the cognitive user source node per unit time is Y. s =20, the energy collected by the relay node per unit time is Y.n =12.

[0070] Figure 4 To find the non-dominated solution searched by the quantum monarch butterfly optimization mechanism, which simultaneously considers signal-to-noise ratio and energy efficiency, and the single-objective solutions of the monarch butterfly optimization method and the political optimization method, the number of relay nodes is 30, and the energy collected by the cognitive user source node per unit time is Y. s =12, the energy Y collected by the relay node per unit time. n =12.

[0071] from Figure 3 and Figure 4 As can be seen, the proposed relay selection method dominates both the single-objective Monarch Butterfly optimization method and the political optimization method, and the obtained non-dominated solutions are uniformly distributed. The quantum Monarch Butterfly optimization mechanism is effective for different numbers of relay nodes, demonstrating the wide applicability of the proposed method. In practice, different solutions can be selected according to different needs. For example, when a high signal-to-noise ratio is required, some energy efficiency can be sacrificed, and then a suitable solution can be selected from the solution set as the solution to the practical problem.

Claims

1. A multi-target relay selection method for an energy harvesting cognitive relay network, characterized in that, The steps are as follows: Step 1: Establish a multi-objective relay selection model for the energy harvesting cognitive relay network; In the energy harvesting phase, the energy harvested by the cognitive user source node and the nth relay node from the ambient signal is E s = α1Y s T and E n = α2Y n T, α1 is the energy harvesting rate of the cognitive user source node, α2 is the energy harvesting rate of the nth relay node, T is the time slot length, Y s and Y n represent the energy harvested by the cognitive user source node and the nth relay node in unit time, respectively; If the cognitive user source node and the relay node transmit signals at constant power within a time slot, then the maximum transmission power of the cognitive user source node and the nth relay node are respectively... and The transmission power of the cognitive user source node and relay node cannot exceed its own maximum transmission power, and the interference to the primary user receiver cannot exceed the threshold I th The specific power control strategy of the cognitive user source node and relay node is as follows: Where M is the number of relays for information transmission; During the data transmission phase, the cognitive user source node sends signal x. s δ n Let represent the Gaussian white noise at the nth relay node, then the signal received by the nth relay node is: The relay node sends the following signals: σ is the magnification factor. 2 Noise power; Definition of parameter b n to indicate whether the nth relay is selected, if b n = 1, it means that the relay is selected for information transmission, if b n = 0, it means that the relay is idle; δ d represents the Gaussian white noise at the destination node of the cognitive user, then the final combined signal received at the destination node of the cognitive user is represented as: The end-to-end signal-to-noise ratio of the system is obtained as follows: The system energy efficiency is defined as the ratio of the network throughput to the total power consumed by the system. Therefore, the system energy efficiency is: Where C is the throughput of the entire network, P ∑ Let be the total power consumed by the system; the multi-objective relay selection model, considering both system throughput and system energy efficiency, is the following maximum optimization equation: Step 2: Initialize the quantum monarch butterfly population and set system parameters; Let the maximum number of iterations be T, the iteration number be t, t∈[1,T], the size of the quantum monarch butterfly population be H, and the dimension be N; the initial quantum positions of the monarch butterflies in the population are generated randomly, and the quantum position of the m-th monarch butterfly in the t-th generation is represented as... in, The position of the m-th monarch butterfly can be obtained by quantum position measurement. Measurement method is in A random number uniformly distributed between [0,1]. Step 3: Determine the multi-objective fitness function for multi-objective relay selection, calculate the fitness function values ​​of all individuals in the quantum monarch butterfly population, perform non-dominated sorting and crowding calculation, and establish an initial elite solution set; Step 4: Select the globally optimal quantum position from the elite solution set, and divide the quantum monarch butterfly population into two subpopulations according to the non-dominated sorting level. The globally optimal monarch butterfly is randomly selected from the elite solution set S. E The quantum position corresponding to the top 2% of individuals is called the globally optimal quantum position, denoted as . Based on the non-dominant ranking, the entire Monarch butterfly population is divided into two subpopulations. The H1 individuals with the highest non-dominant ranking form Monarch subpopulation 1, and the remaining individuals form Monarch subpopulation 2. The number of Monarch butterflies in subpopulation 1 is... The number of monarch butterflies in subpopulation 2 is H-H1, among which, For the migration rate of monarch butterflies, This is for rounding up; Step 5: Update the quantum positions of monarch butterflies in subpopulation 1 and subpopulation 2 using different strategies; Step 7: Merge the two updated subpopulations into a new quantum monarch butterfly population. According to the measurement method, measure the position of monarch butterfly individuals in the two updated subpopulations and calculate the fitness function value of all individuals in the new quantum monarch butterfly population. Step 8: Mix the most recent two generations of quantum monarch butterfly populations, sort the non-dominated solutions and calculate the crowding degree, and update the elite solution set; The most recent two generations of Quantum Monarch butterfly populations were mixed, and non-dominated solutions were sorted and crowding was calculated. Monarch butterfly individuals with the same non-dominated level were sorted in descending order of crowding value, and monarch butterfly individuals with a non-dominated level of 1 were selected to be added to the elite solution set S. E When the elite set S E If the number of monarch butterflies is greater than H, then the same operation as above is performed on the monarch butterflies in the elite solution set, and the top H monarch butterflies are selected as the new elite solution set S. E ; Step 9: If the evolution has not terminated, i.e. the number of iterations is less than the preset maximum number of iterations, let t = t + 1 and return to step 4 to continue the iteration; otherwise, terminate the iteration, output the elite solution set, i.e. the Pareto front solution set with non-dominant level 1, and obtain the multi-objective relay selection scheme.

2. The multi-target relay selection method for an energy harvesting cognitive relay network according to claim 1, characterized in that, In step three, the multi-objective function for optimizing the maximum position of the m-th monarch butterfly is: Wherein, the fitness function of objective 1 is the signal-to-noise ratio, and the fitness function of objective 2 is the energy efficiency, i.e. The non-dominated sorting process is as follows: For the m-th position Calculate the dominant position The number of monarch butterflies q m and location The set of positions dominated If the dominant position The number q m =0, then the non-dominated rank of the monarch butterfly at that position is 1; for each monarch butterfly with a non-dominated rank of 1, traverse the set of positions it dominates. Each position in Calculate the dominant position The number of monarch butterflies q g If q g -1 = 0, then the position The positions of monarch butterflies are stored in set Q, where the non-dominance ranking is 2. The above process is repeated for each monarch butterfly in set Q, thus obtaining the set of positions with a non-dominance ranking of 3. This process is repeated until the non-dominance rankings of all monarch butterfly positions are obtained. The dominance relationship is determined as follows: for any two monarch butterfly positions... and like and If at least one strict inequality holds, then the position is called a position. Dominate It is a non-dominant position; conversely, if and If at least one strict inequality holds, then the position is called a position. Dominate It is a non-dominant position; if neither of the above two conditions is met, then the position is... and There is no dominance relationship between them; the crowding degree calculation is performed on monarch butterflies with the same non-dominance level. Assume there are o positions with a non-dominance level of Δ, and the fitness function value f... z Sort in ascending order, z = 1 or 2. Define the crowding degree of the monarch butterflies whose fitness function has the minimum and maximum values ​​as ∞. The crowding degree values ​​of other monarch butterflies are calculated as follows: in, and Let be the fitness function values ​​of the target z at the next and previous positions, respectively. and Let z be the maximum and minimum values ​​of the fitness function for the objective z. For each monarch butterfly, the crowding degree corresponding to each objective function is calculated as described above, and the sum of all crowding degree components is the final crowding degree value for that monarch butterfly. Monarch butterflies with a non-dominant level of 1 and a large crowding degree value are added to the elite solution set S. E middle.

3. The multi-target relay selection method for an energy harvesting cognitive relay network according to claim 1, characterized in that, In step five, the update strategies for the quantum position and quantum rotation angle of the m1th monarch butterfly in the nth dimension in subpopulation 1 are as follows: in, and Let represent the quantum rotation angle and the quantum position in the nth dimension of the updated m1-th monarch butterfly, respectively. The quantum position of the m1th monarch butterfly in the tth generation, in the nth dimension, is represented by r1 = 1, 2, ..., H1, n = 1, 2, ..., N; the individual monarch butterfly label r1 is randomly selected from monarch butterfly subpopulation 1, and the individual monarch butterfly label r2 is randomly selected from monarch butterfly subpopulation 2. For the migration rate of monarch butterflies, For the monarch butterfly migration cycle, a1, a2, and All are random numbers that follow a uniform distribution in the interval [0,1], and β1 is the quantum position control parameter of monarch butterfly population 1; In subpopulation 2, the update strategies for the quantum position and quantum rotation angle of the m2th monarch butterfly in the nth dimension are as follows: in, and Let represent the quantum rotation angle and the quantum position in the nth dimension of the m2th monarch butterfly after the update, respectively. Let r represent the nth dimension quantum position of the m2th monarch butterfly in the tth generation, where m2 = H1+1, H1+2, ..., H, and n = 1, 2, ..., N. The individual monarch butterfly number r3 is randomly selected from monarch butterfly subpopulation 2. The nth dimension represents the globally optimal quantum position of the quantum monarch butterfly population. For Monarch butterfly adjustment rates, a3, a4, a5, and All are random numbers that follow a uniform distribution in the interval [0,1], and β2 is the quantum position control parameter of monarch butterfly population 2.

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