A method, system and medium for selecting cluster heads of unmanned aerial vehicle swarms for spectrum sharing

Abstract

The present application discloses a method, system and medium for selecting cluster heads of drone swarms for spectrum sharing, and relates to the technical field of cluster head election. The method comprises: collecting actual information of communication scenarios and drone swarms, and preprocessing the actual information to obtain communication scenario attribute parameter information and drone node attribute parameter information; performing initial formation of the drone swarm based on the communication scenario attribute parameter information and drone node attribute parameter information; iteratively optimizing the initial formation of the drone swarm using the genetic-grey wolf optimization algorithm to obtain cluster head election results and spectrum allocation results of the drone swarm. The present invention can ensure that spectrum resources are more reasonably and efficiently utilized in drone swarms through the obtained spectrum allocation results. This helps to reduce the waste of spectrum resources and improve the utilization rate of spectrum resources, thereby meeting the spectrum requirements of drone swarms during communication.

Description

Technical Field

[0001] The present application relates to the technical field of cluster head election, and more specifically, to a method, system and medium for selecting cluster heads of drone groups for spectrum sharing. Background Art

[0002] With the rapid development of modern drone swarm communication technology and the increasing shortage of electromagnetic spectrum resources, cognitive radio spectrum sharing technology has become one of the important methods to solve the shortage of spectrum resources. Compared with single drone communication, drone swarm communication has the advantages of anti-damage and high coordination. In the cluster control method and centralized control method, one or more cluster head nodes need to be selected in drone swarm communication. At present, the main methods for selecting cluster heads of drone swarms include the minimum ID algorithm, the highest node degree algorithm, the weighted clustering algorithm, the LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm and the swarm intelligence optimization algorithm.

[0003] In the existing technology, the clustering optimization method based on the improved gray wolf algorithm considers the relative mobility of drone nodes and the relative distance between nodes for clustering, and comprehensively considers four key factors: node residual energy, highest node degree, communication status and task type, and uses the improved gray wolf optimization algorithm to elect the best cluster head. The weighted cluster head election algorithm based on stability improvement (SI-WCSA) determines the size of the cluster according to the assigned task, and then comprehensively considers four factors: node mobility, energy, node degree and distance, and uses a combined weighting method based on hierarchical analysis method and entropy method to calculate the index weights, so as to effectively select the optimal cluster head. The cluster head selection method based on machine learning, namely the Cluster Head Machine Learning (CH-ML) model, aims to optimize the cluster head election process in wireless sensor networks under the Internet of Things environment, and determine the best cluster head by predicting the residual energy of sensor nodes.

[0004] However, the clustering optimization method based on the improved gray wolf algorithm has certain challenges in terms of computational complexity and parameter adjustment, and may face problems such as environmental interference and hardware limitations in actual deployment. The adaptability of the weighted cluster head election algorithm based on stability improvement in extreme environments, the computational complexity of the algorithm, and the scalability in large-scale drone networks still face huge challenges. The prediction and training of the machine learning model in the machine learning-based cluster head selection method may take a long time, which will affect the real-time response capability of the network. Most of the current drone swarm cluster head election algorithms only consider factors such as node energy, node degree, network life, and reliability, and do not take spectrum resources into consideration, resulting in the selected drone swarm cluster head failing to improve the overall frequency efficiency of the drone swarm. Summary of the invention

[0005] In response to at least one defect or improvement need in the prior art, the present invention provides a method, system and medium for selecting a drone swarm cluster head for spectrum sharing, which are used to solve the problem that most drone swarm cluster head election algorithms in the prior art only consider factors such as node energy, node degree, network life, reliability, etc., but do not take spectrum resources into consideration, resulting in the selected drone swarm cluster head failing to improve the overall frequency utilization efficiency of the drone swarm.

[0006] To achieve the above object, according to a first aspect of the present invention, a method for selecting a cluster head of a drone group for spectrum sharing is provided, comprising:

[0007] Collect the actual information of the communication scene and the drone group, and pre-process the actual information to obtain the communication scene attribute parameter information and the drone node attribute parameter information;

[0008] Perform initial formation of the drone swarm based on communication scenario attribute parameter information and drone node attribute parameter information;

[0009] The genetic-grey wolf optimization algorithm is used to iteratively optimize the initial formation of the UAV swarm, and the cluster head election results and spectrum allocation results of the UAV swarm are obtained.

[0010] In a possible implementation, the initial formation of the drone swarm is iteratively optimized using the genetic-grey wolf optimization algorithm to obtain the cluster head election result and spectrum allocation result of the drone swarm, and also includes:

[0011] Initialize the wolf pack parameters and randomly generate several parent wolf packs according to the initial formation of the drone group;

[0012] The spectrum utility function of the entire drone swarm is calculated based on the initial formation of the drone swarm, and the three optimal solutions of the spectrum utility function are determined as Wolf, Wolf and Wolf;

[0013] Traversal Wolf, Wolf and All possible positions of the wolf in each dimension, and mutate them one by one to get the first Wolf;

[0014] Will Wolf, Wolf, Wolf and first Wolf Cross Random Generation 2 Wolves select individuals from all individuals and put them into the next generation population to obtain the offspring population;

[0015] When the preset number of iterations is reached, the latest The cluster head election results and spectrum allocation results of wolves as drone swarms.

[0016] In a possible implementation, the wolf pack parameters are initialized, and a number of parent wolf packs are randomly generated according to the initial formation of the drone group, and also include:

[0017] Wolf pack size , maximum number of iterations , mutation points Initialize;

[0018] Each cluster head node in the initial formation of the drone swarm is encoded, and several parent wolf packs are randomly generated.

[0019] In a possible implementation, the initial formation of the drone swarm includes the working bandwidth of the drone nodes; the spectrum utility function of the entire drone swarm is calculated according to the initial formation of the drone swarm, and the three optimal solutions of the spectrum utility function are determined as Wolf, Wolf and Wolf, also includes:

[0020] The total spectrum efficiency of the drone swarm downlink and the variance of the total throughput of the drone swarm downlink are calculated according to the initial formation of the drone swarm;

[0021] The spectrum utility function of the entire drone swarm is calculated based on the total spectrum efficiency of the drone swarm downlink, the variance of the total throughput of the drone swarm downlink, and the working bandwidth of the drone nodes.

[0022] In one possible implementation, traversing Wolf, Wolf and All possible positions of the wolf in each dimension, and mutate them one by one to get the first Wolf, also includes:

[0023] According to the mutation vector Wolf, Wolf and The wolf mutates, and the mutation vector is:

[0024] ;

[0025] in, , represents the dimension sequence number of the population vector, express Between A random number, express A random sequence in Indicates The position changes, is the mutation operator.

[0026] In one possible implementation, Wolf, Wolf, Wolf and first Wolf Cross Random Generation 2 Wolves select individuals from all individuals and put them into the next generation population to obtain the offspring population, which also includes:

[0027] Will Wolf, Wolf, Wolf and first The wolf undergoes random crossover mutation to obtain the second Wolf;

[0028] According to the greedy principle, Wolf, Wolf, wolf and second The wolves select the individuals that will be put into the next generation of the population;

[0029] Among them, the second The wolves and the individuals released into the next generation population form the offspring population.

[0030] In one possible implementation, Wolf, Wolf, Wolf and first The wolf undergoes random crossover mutation to obtain the second Wolf, also includes:

[0031] Wolf, Wolf, Wolf and first Wolves constitute the individuals to be mutated, and the mutation process is as follows:

[0032] ;

[0033] in, is the crossover operator, represents a population with an evolutionary number of G+1.

[0034] In one possible implementation, according to the greedy criterion, Wolf, Wolf, wolf and second The wolves select individuals to be put into the next generation population, including:

[0035] calculate Wolf, Wolf, wolf and second The fitness function of each wolf is to put the individual with a larger fitness function among the parent and offspring individuals into the next generation population, as follows:

[0036] ;

[0037] in, Indicates about The fitness function of .

[0038] According to a second aspect of the present invention, a spectrum sharing-oriented drone cluster head election system is also provided, comprising:

[0039] An information collection module is configured to collect actual information of the communication scene and the drone group, and pre-process the actual information to obtain communication scene attribute parameter information and drone node attribute parameter information;

[0040] An initial formation module, which is configured to perform an initial formation of the drone group based on the communication scenario attribute parameter information and the drone node attribute parameter information;

[0041] The iterative optimization module is configured to use the genetic-grey wolf optimization algorithm to iteratively optimize the initial formation of the drone swarm to obtain the cluster head election result and spectrum allocation result of the drone swarm.

[0042] According to the third aspect of the present invention, a storage medium is also provided, which stores a computer program executable by an access authentication device. When the computer program runs on the access authentication device, the access authentication device executes any step of the above-mentioned method for selecting a cluster head of a drone group for spectrum sharing.

[0043] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0044] The present invention provides a method for selecting a cluster head of a drone group for spectrum sharing. The method uses a genetic-grey wolf optimization algorithm to iteratively optimize the initial formation of the drone group. The method can fully utilize the characteristics of the genetic algorithm, which has strong global search capabilities and is not easy to fall into local optimal solutions, and the gray wolf optimization algorithm, which has the advantages of fast convergence speed and high search efficiency. The genetic-grey wolf optimization algorithm makes the optimization process more efficient and intelligent, and can quickly find the optimal or suboptimal cluster head election results and spectrum allocation schemes. It can ensure that spectrum resources are used more reasonably and efficiently in the drone group, which helps to reduce the waste of spectrum resources and improve the utilization rate of spectrum resources, thereby meeting the spectrum requirements of the drone group during the communication process. It can be adjusted and optimized according to different communication scenarios and the actual information of the drone group, thereby enhancing the adaptability and flexibility of the drone group, so that the drone group can maintain efficient and stable operation under different communication environments and task requirements, and improve the overall performance and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A schematic diagram of a flow chart of an embodiment of a method for selecting a cluster head of a drone group for spectrum sharing provided by the present invention;

[0047] Figure 2 The present invention provides Figure 1 A schematic flow chart of an embodiment of step S103;

[0048] Figure 3 A schematic diagram of the structure of an embodiment of a spectrum sharing-oriented drone cluster head election system provided by the present invention;

[0049] Figure 4 A schematic diagram of the structure of a drone cluster head election device for spectrum sharing provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0051] The terms "first", "second", "third", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0052] The present invention provides a method, system and medium for selecting a cluster head of a drone group for spectrum sharing, which are described below respectively.

[0053] See also Figure 1 , Figure 1 A flow chart of an embodiment of a method for selecting a cluster head of a drone group for spectrum sharing provided by the present invention is provided. In a specific embodiment of the present invention, a method for selecting a cluster head of a drone group for spectrum sharing is disclosed, including:

[0054] S101, collecting actual information of the communication scene and the drone group, and preprocessing the actual information to obtain communication scene attribute parameter information and drone node attribute parameter information;

[0055] S102, performing initial formation of the drone group based on the communication scenario attribute parameter information and the drone node attribute parameter information;

[0056] S103. Use the genetic-grey wolf optimization algorithm to iteratively optimize the initial formation of the drone swarm to obtain the cluster head election result and spectrum allocation result of the drone swarm.

[0057] In the above embodiment, it is necessary to collect relevant data of the current communication scene, including but not limited to geographic location information, obstacle distribution, electromagnetic interference conditions, and potential communication demand hotspots, to form a comprehensive description of the communication scene. At the same time, it is also necessary to record the actual information of the drone group in detail, including key attributes such as the location, speed, remaining power, communication capability, and mission type of each drone. This information is then pre-processed to extract communication scene attribute parameter information and drone node attribute parameter information that are critical to subsequent decision-making, ensuring the accuracy and practicality of the data.

[0058] Based on the above preprocessed parameter information, the method performs an initial formation of the UAV swarm. This process takes into account the relative position relationship between UAVs, the stability of the communication link, and the expected mission requirements, aiming to initially build a UAV formation architecture that is conducive to information transmission and can effectively cover the target area. The initial formation not only provides a basic framework for subsequent optimization, but also ensures the basic operational capability of the UAV swarm in complex environments.

[0059] The initial formation of the drone swarm is iteratively optimized using the genetic-grey wolf optimization algorithm, which combines the global search capability of the genetic algorithm and the fast convergence characteristics of the gray wolf optimization algorithm. It aims to guide the drone swarm to find the optimal cluster head election scheme and spectrum allocation strategy while maintaining the stability of the formation by simulating the hunting behavior of gray wolf groups in nature. During the iteration process, the algorithm continuously evaluates various possible formation configurations and spectrum allocation schemes, and screens and optimizes them according to the preset fitness function (which may involve multiple dimensions such as communication efficiency, energy consumption, and spectrum utilization) until the predetermined number of iterations or convergence conditions are reached, thereby outputting the final drone swarm cluster head election results and spectrum allocation results.

[0060] In a specific embodiment, the attribute parameter information of the communication scenario mainly includes the base station location, the base station transmission power, and the statistical parameter information of the Rayleigh fading model (i.e., the path loss index, the standard deviation of the shadow effect, the additive Gaussian white noise power, and the variance of the Rayleigh fading envelope, etc.), and the attribute parameter information of the nodes in the drone cluster mainly includes the number of nodes, node location, transmission power, receiving power, antenna gain, available frequency channels, cluster head node number, frequency channel number occupied by the communication between the drone member nodes and the cluster head node, frequency channel number occupied by the communication between the drone cluster head nodes and the cluster head nodes, and frequency channel number occupied by the communication between the drone cluster head nodes and the ground base station, etc.

[0061] Statistical parameter information of the Rayleigh fading model. In the model, the probability density of the signal envelope conforms to the Rice distribution, and its probability density function is expressed as follows:

[0062] ;

[0063] in, represents the signal envelope, represents the signal envelope variance, represents the sight distance component, represents the first kind of zero-order modified Bessel function. When When , that is, when there is no line-of-sight link, Rayleigh distribution can be obtained. At this time, the channel fading model is a Rayleigh fading model, and its probability density function is expressed as:

[0064] ;

[0065] Among them, when the signal envelope obeys Rayleigh distribution, the square envelope obeys exponential distribution, and its probability density function is expressed as:

[0066] ;

[0067] The large-scale channel fading model of UAV swarm communication is established as:

[0068] ;

[0069] in, is the UAV transmission power, is the received power, Indicates the reference distance The path loss is is the path loss exponent, is the distance from the transmitter to the receiver, is a log-normally distributed random variable with zero mean and standard deviation Represents the power change caused by the shadow effect.

[0070] Among them, the number and location of drone nodes conform to the Poisson cluster process, which Including the parent process and the child process, first through the Poisson point process The parent process that generates the Poisson cluster process is when a circle with these points as the center and a radius of Multiple points are randomly generated uniformly within the circle, and their probability density function is expressed as follows:

[0071] ;

[0072] in, is the coordinate of the sub-process point relative to the parent process point, is the Euclidean distance from the child process point to the parent process point. In the random variable All values , each value conforms to the Poisson distribution in a certain area, that is:

[0073] ;

[0074] in, is the number of points of the subprocess, Poisson point process density.

[0075] Compared with the prior art, the present embodiment provides a method for selecting a cluster head of a drone group for spectrum sharing, which uses the genetic-grey wolf optimization algorithm to iteratively optimize the initial formation of the drone group. The genetic algorithm can make full use of the characteristics of the genetic algorithm with strong global search capability and not easy to fall into the local optimal solution, and the gray wolf optimization algorithm has the advantages of fast convergence speed and high search efficiency. The genetic-grey wolf optimization algorithm makes the optimization process more efficient and intelligent, and can quickly find the optimal or suboptimal cluster head election results and spectrum allocation schemes. It can ensure that spectrum resources are used more reasonably and efficiently in the drone group, which helps to reduce the waste of spectrum resources and improve the utilization rate of spectrum resources, thereby meeting the spectrum requirements of the drone group in the communication process. It can be adjusted and optimized according to different communication scenarios and the actual information of the drone group, thereby enhancing the adaptability and flexibility of the drone group, so that the drone group can maintain efficient and stable operation under different communication environments and task requirements, and improve the overall performance and reliability.

[0076] See also Figure 2 , Figure 2 The present invention provides Figure 1 In some embodiments of the present invention, the initial formation of the drone group is iteratively optimized using the genetic-grey wolf optimization algorithm to obtain the cluster head election result and spectrum allocation result of the drone group, and further includes:

[0077] S201, initializing wolf group parameters, and randomly generating a number of parent wolf groups according to the initial formation of the drone group;

[0078] S202, calculating the spectrum utility function of the entire drone group according to the initial formation of the drone group, and determining the three optimal solutions of the spectrum utility function as Wolf, Wolf and Wolf;

[0079] S203, traversal Wolf, Wolf and All possible positions of the wolf in each dimension, and mutate them one by one to get the first Wolf;

[0080] S204, will Wolf, Wolf, Wolf and first Wolf Cross Random Generation 2 Wolves select individuals from all individuals and put them into the next generation population to obtain the offspring population;

[0081] S205. When the preset number of iterations is reached, the latest The cluster head election results and spectrum allocation results of wolves as drone swarms.

[0082] In the above embodiment, the key parameters in the gray wolf optimization algorithm are initialized, including but not limited to the wolf group size, number of iterations, search space range, etc., and a number of parent wolf group individuals are randomly generated according to the initial formation of the drone group (such as the number, position, speed, etc. of drones). Each individual represents a possible cluster head election scheme and spectrum allocation strategy.

[0083] According to the initial formation of the drone swarm, the spectrum utility function of the entire drone swarm is calculated. The spectrum utility function comprehensively considers multiple factors such as the effective use of spectrum resources, communication interference between drones, and data transmission rate. By comparing the spectrum utility function values ​​of different individuals, the three optimal solutions of the spectrum utility function are determined as α wolf (optimal solution), β wolf (second-best solution) and δ wolf (third-best solution). These three wolves will play a leading role in the subsequent iteration process.

[0084] Traverse all possible positions of α wolf, β wolf and δ wolf in each dimension, try to perform small mutation operations on these positions to explore new solution space. Evaluate the spectral utility function for each mutated position, and select the best mutation result as the first ω wolf (i.e., the new candidate solution).

[0085] The α wolf, β wolf, δ wolf and the first ω wolf are crossed, and the second ω wolf (i.e., another set of new candidate solutions) is generated by randomly combining some of the characteristics of these leader wolves. From all individuals including the leader wolf and ω wolf, screening is performed according to the value of the spectral utility function, and the best individuals are selected to be put into the next generation population to form a offspring population, ensuring the diversity and evolutionary direction of the population.

[0086] Repeat steps S202 to S204 until the preset iteration stop condition is reached (in a preferred embodiment of the present invention, the preset iteration stop condition is reaching the maximum number of iterations). When the iteration ends, the latest α wolf is output as the cluster head election result and spectrum allocation result of the drone swarm. At this time, α wolf represents the optimal solution found in the current search space, which can maximize the spectrum utility of the drone swarm while ensuring the rationality of cluster head election and communication efficiency.

[0087] In some embodiments of the present invention, the wolf pack parameters are initialized, and a plurality of parent wolf packs are randomly generated according to the initial formation of the drone group, further comprising:

[0088] Wolf pack size , maximum number of iterations , mutation points Initialize;

[0089] Each cluster head node in the initial formation of the drone swarm is encoded, and several parent wolf packs are randomly generated.

[0090] In the above embodiment, the wolf pack size is the total number of individuals in the wolf pack, that is, the number of potential solutions processed simultaneously in the algorithm. A larger wolf pack size can increase the coverage of the search space, but it may also lead to an increase in computational cost. Therefore, it needs to be reasonably set according to the complexity of the actual problem and the availability of computing resources.

[0091] Maximum number of iterations That is, the maximum number of iterations that will be executed before the algorithm stops. The maximum number of iterations determines the length of time the algorithm searches the solution space and is an important parameter for controlling the algorithm's convergence speed and search accuracy.

[0092] Number of mutation points It is the number of genes (or decision variables) that will be changed for each individual in the mutation operation. Mutation is an important means of introducing new solutions in genetic algorithms, exploring new solution space areas by changing certain genes of individuals. The setting of the number of mutation points needs to balance exploration and stability to avoid the algorithm from converging prematurely or falling into a local optimum.

[0093] Each cluster head node in the initial formation of the drone swarm is encoded. The encoding method can be selected according to the specific characteristics of the problem. For example, binary encoding can be used to represent the selection state of the cluster head node, or real number encoding can be used to represent the location of the cluster head node or spectrum allocation.

[0094] According to the encoded initial formation information of the drone group, several parent wolf pack individuals are randomly generated. Each individual represents a possible cluster head election and spectrum allocation scheme, and the value of its gene (or decision variable) is randomly selected in the search space. In this way, the algorithm can have a diverse solution set at the initial stage, providing a rich search starting point for the subsequent optimization process.

[0095] In some embodiments of the present invention, the initial formation of the drone swarm includes the working bandwidth of the drone nodes; the spectrum utility function of the entire drone swarm is calculated according to the initial formation of the drone swarm, and the three optimal solutions of the spectrum utility function are determined as Wolf, Wolf and Wolf, also includes:

[0096] The total spectrum efficiency of the drone swarm downlink and the variance of the total throughput of the drone swarm downlink are calculated according to the initial formation of the drone swarm;

[0097] The spectrum utility function of the entire drone swarm is calculated based on the total spectrum efficiency of the drone swarm downlink, the variance of the total throughput of the drone swarm downlink, and the working bandwidth of the drone nodes.

[0098] In the above embodiment, the spectrum utility function of the drone swarm is , which is expressed as follows:

[0099] ;

[0100] in, is the working bandwidth of all drone nodes, is the total spectrum efficiency of the drone swarm downlink, is the variance of the total downlink throughput of the drone swarm.

[0101] Among them, the total spectrum efficiency of the drone swarm downlink in step , which is expressed as follows:

[0102]

[0103] in, , , It represents the access relationship among the cluster head node, cluster member nodes and ground base station, namely:

[0104] ;

[0105] ;

[0106] ;

[0107] , , It represents the frequency channel allocation scheme among cluster head nodes, cluster member nodes and ground base stations, namely:

[0108] ;

[0109] , , Indicates that the drone node uses the frequency channel The signal-to-interference-plus-noise ratio (SINR) when receiving the signal is:

[0110] ;

[0111] ;

[0112] ;

[0113] in, , , Indicates that the drone node uses the frequency channel The power of the co-channel interference signal received when receiving the signal, assuming that the UAV communication signal will not leak adjacent channels during the propagation process, that is, do not consider the cross-frequency interference, only consider the interference on the same channel, that is, co-channel interference, can be expressed as:

[0114]

[0115]

[0116]

[0117] in Representation Node Frequency Channel Receiving Node The received signal power when transmitting the signal, Representation Node Frequency Channel Receiving Node The Gaussian white noise power when transmitting the signal.

[0118] Among them, the variance of the total downlink throughput of the drone swarm in step , which is expressed as follows:

[0119]

[0120] in, Indicates that the node adopts Frequency Channel Receiving Node The channel bandwidth occupied when transmitting signals, throughput is the sum of the throughput of all drone nodes and base stations, expressed as follows:

[0121]

[0122] is the mean throughput, expressed as follows:

[0123] .

[0124] In some embodiments of the present invention, traversing Wolf, Wolf and All possible positions of the wolf in each dimension, and mutate them one by one to get the first Wolf, also includes:

[0125] According to the mutation vector Wolf, Wolf and The wolf mutates, and the mutation vector is:

[0126] ;

[0127] in, , represents the dimension sequence number of the population vector, express Between A random number, express A random sequence in Indicates The position changes, is the mutation operator.

[0128] In some embodiments of the present invention, Wolf, Wolf, Wolf and first Wolf Cross Random Generation 2 Wolves select individuals from all individuals and put them into the next generation population to obtain the offspring population, which also includes:

[0129] Will Wolf, Wolf, Wolf and first The wolf undergoes random crossover mutation to obtain the second Wolf;

[0130] According to the greedy principle, Wolf, Wolf, wolf and second The wolves select the individuals that will be put into the next generation of the population;

[0131] Among them, the second The wolves and the individuals released into the next generation population form the offspring population.

[0132] In the above embodiment, random crossover operations are performed on α wolf, β wolf, δ wolf (these three solutions represent the best, second best and third best solutions in the current population) and the first ω wolf (which may represent a relatively good solution but not in the top three). Crossover operation is a commonly used method in genetic algorithms. New offspring individuals are generated by exchanging some genes of two parent individuals. In this example, the crossover operation involves the exchange of elements in the solution vector or some form of weighted average. Next, the individuals obtained after the crossover are subjected to mutation operation. Mutation operation is an important means to introduce new genes and increase population diversity. In this example, mutation can be manifested as a slight adjustment or random change to certain elements in the solution vector. Through the above crossover and mutation process, the second ω wolf is obtained, which is a new solution that may have a higher fitness value.

[0133] According to the greedy criterion, individuals to be placed in the next generation population are selected from α wolf, β wolf, δ wolf and the second ω wolf:

[0134] The greedy criterion is a simple and effective selection strategy that can select the currently available optimal solution. In this case, the advantages and disadvantages of α wolf, β wolf, δ wolf and the second ω wolf are evaluated according to the spectrum utility function value (or other fitness function value).

[0135] First, the spectral utility function values ​​of the four solutions are compared, and the three best solutions are selected as part of the next generation population. If the fitness value of the second ω wolf is higher than that of one of the top three solutions (such as δ wolf), it will replace that solution and enter the next generation population.

[0136] In addition, in order to maintain the diversity of the population, it may be necessary to randomly select a certain number of individuals from other individuals in the current population (not the top three and not the first wolf) to join the next generation population. These individuals may be selected through other mechanisms (such as roulette selection, tournament selection, etc.).

[0137] After completing the above selection process, a sub-population consisting of α wolf (or updated better solution), β wolf (or updated sub-optimal solution), δ wolf (or updated third-optimal solution), second ω wolf and several individuals selected by other mechanisms is obtained. This sub-population will serve as the basis for the next round of iterations to continue the optimization search.

[0138] In some embodiments of the present invention, Wolf, Wolf, Wolf and first The wolf undergoes random crossover mutation to obtain the second Wolf, also includes:

[0139] Wolf, Wolf, Wolf and first Wolves constitute the individuals to be mutated, and the mutation process is as follows:

[0140] ;

[0141] in, is the crossover operator, represents a population with an evolutionary number of G+1.

[0142] In some embodiments of the present invention, according to the greedy criterion, Wolf, Wolf, wolf and second The wolves select individuals to be put into the next generation population, including:

[0143] calculate Wolf, Wolf, wolf and second The fitness function of each wolf is to put the individual with a larger fitness function among the parent and offspring individuals into the next generation population, as follows:

[0144] ;

[0145] in, Indicates about The fitness function of .

[0146] In order to better implement the drone cluster head election method for spectrum sharing in the embodiment of the present invention, based on the drone cluster head election method for spectrum sharing, please refer to Figure 3 , Figure 3 The present invention is a schematic diagram of the structure of an embodiment of a drone cluster head election system for spectrum sharing provided by the present invention. The embodiment of the present invention provides a drone cluster head election system 300 for spectrum sharing, including:

[0147] An information collection module 310 is configured to collect actual information of the communication scene and the drone group, and pre-process the actual information to obtain communication scene attribute parameter information and drone node attribute parameter information;

[0148] An initial formation module 320, which is configured to perform an initial formation of the drone group based on the communication scenario attribute parameter information and the drone node attribute parameter information;

[0149] The iterative optimization module 330 is configured to use the genetic-grey wolf optimization algorithm to iteratively optimize the initial formation of the drone group to obtain the cluster head election result and spectrum allocation result of the drone group.

[0150] It should be noted here that: the system 300 provided in the above embodiment can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above method embodiments, which will not be repeated here.

[0151] See also Figure 4 , Figure 4 A schematic diagram of the structure of a drone swarm cluster head election device for spectrum sharing provided by an embodiment of the present invention. Based on the above-mentioned drone swarm cluster head election method for spectrum sharing, the present invention also provides a drone swarm cluster head election device for spectrum sharing. The drone swarm cluster head election device for spectrum sharing can be a computing device such as a mobile terminal, a desktop computer, a notebook, a PDA, and a server. The drone swarm cluster head election device 400 for spectrum sharing includes a processor 410, a memory 420, and a display 430. Figure 4 Only some components of the drone cluster head election device for spectrum sharing are shown, but it should be understood that it is not required to implement all of the shown components, and more or fewer components may be implemented instead.

[0152] In some embodiments, the memory 420 may be an internal storage unit of the drone swarm cluster head election device 400 for spectrum sharing, such as a hard disk or memory of the drone swarm cluster head election device 400 for spectrum sharing. In other embodiments, the memory 420 may also be an external storage device of the drone swarm cluster head election device 400 for spectrum sharing, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the drone swarm cluster head election device 400 for spectrum sharing. Further, the memory 420 may also include both an internal storage unit and an external storage device of the drone swarm cluster head election device 400 for spectrum sharing. The memory 420 is used to store application software and various data installed in the drone swarm cluster head election device 400 for spectrum sharing, such as the program code for installing the drone swarm cluster head election device 400 for spectrum sharing. The memory 420 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a drone group cluster head election program 440 for spectrum sharing is stored in the memory 420, and the drone group cluster head election program 440 for spectrum sharing can be executed by the processor 410, thereby realizing the drone group cluster head election method for spectrum sharing of each embodiment of the present application.

[0153] In some embodiments, the processor 410 can be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code stored in the memory 420 or process data, such as executing a drone cluster head election method for spectrum sharing.

[0154] In some embodiments, the display 430 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 430 is used to display information on the spectrum sharing-oriented drone cluster head election device 400 and to display a visual user interface. The components 410-430 of the spectrum sharing-oriented drone cluster head election device 400 communicate with each other via a system bus.

[0155] In one embodiment, when the processor 410 executes the drone cluster head election program 440 for spectrum sharing in the memory 420, the steps in the above drone cluster head election method for spectrum sharing are implemented.

[0156] This embodiment further provides a computer-readable storage medium, on which a drone cluster head election program for spectrum sharing is stored. When the drone cluster head election program for spectrum sharing is executed by a processor, the following steps are implemented:

[0157] Collect the actual information of the communication scene and the drone group, and pre-process the actual information to obtain the communication scene attribute parameter information and the drone node attribute parameter information;

[0158] Perform initial formation of the drone swarm based on communication scenario attribute parameter information and drone node attribute parameter information;

[0159] The genetic-grey wolf optimization algorithm is used to iteratively optimize the initial formation of the UAV swarm, and the cluster head election results and spectrum allocation results of the UAV swarm are obtained.

[0160] In summary, the present invention provides a method for selecting a cluster head of a drone group for spectrum sharing, which uses a genetic-grey wolf optimization algorithm to iteratively optimize the initial formation of the drone group. It can make full use of the characteristics of the genetic algorithm, which has strong global search capabilities and is not easy to fall into the local optimal solution, and the gray wolf optimization algorithm, which has the advantages of fast convergence speed and high search efficiency. The genetic-grey wolf optimization algorithm makes the optimization process more efficient and intelligent, and can quickly find the optimal or suboptimal cluster head election results and spectrum allocation schemes. It can ensure that spectrum resources are used more reasonably and efficiently in the drone group, which helps to reduce the waste of spectrum resources and improve the utilization rate of spectrum resources, thereby meeting the spectrum requirements of the drone group during the communication process. It can be adjusted and optimized according to different communication scenarios and the actual information of the drone group, thereby enhancing the adaptability and flexibility of the drone group, so that the drone group can maintain efficient and stable operation under different communication environments and task requirements, and improve the overall performance and reliability.

[0161] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0162] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0163] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0164] In the several embodiments provided in the present application, it should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of the system or unit can be electrical or other forms.

[0165] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0167] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0168] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0169] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0170] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0171] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for selecting cluster heads of drone groups for spectrum sharing, characterized in that: include: Collecting actual information of the communication scene and the drone group, and preprocessing the actual information to obtain communication scene attribute parameter information and drone node attribute parameter information; Performing an initial formation of the drone group based on the communication scenario attribute parameter information and the drone node attribute parameter information; The genetic-grey wolf optimization algorithm is used to iteratively optimize the initial formation of the drone swarm, and the cluster head election result and spectrum allocation result of the drone swarm are obtained; The method of iteratively optimizing the initial formation of the drone group by using the genetic-grey wolf optimization algorithm to obtain the cluster head election result and spectrum allocation result of the drone group also includes: Initialize the wolf pack parameters and randomly generate several parent wolf packs according to the initial formation of the drone group; The spectrum utility function of the entire drone swarm is calculated based on the initial formation of the drone swarm, and the three optimal solutions of the spectrum utility function are determined as Wolf, Wolf and Wolf; Traversal Wolf, Wolf and All possible positions of the wolf in each dimension, and mutate them one by one to get the first Wolf; Will Wolf, Wolf, Wolf and first Wolf Cross Random Generation 2 Wolves select individuals from all individuals and put them into the next generation population to obtain the offspring population; When the preset number of iterations is reached, the latest The results of cluster head election and spectrum allocation of wolves as drone swarms; Among them, the Wolf, Wolf, Wolf and first Wolf Cross Random Generation 2 Wolves select individuals from all individuals and put them into the next generation population to obtain the offspring population, which also includes: Will Wolf, Wolf, Wolf and first The wolf undergoes random crossover mutation to obtain the second Wolf; According to the greedy principle, Wolf, Wolf, wolf and second The wolves select the individuals that will be put into the next generation of the population; Among them, the second The wolves and the individuals put into the next generation population form the offspring population; Among them, the Wolf, Wolf, Wolf and first The wolf undergoes random crossover mutation to obtain the second Wolf, also includes: Wolf, Wolf, Wolf and first Wolves constitute the individuals to be mutated, and the mutation process is as follows: ； in, is the crossover operator, represents a population with an evolutionary number of G+1; Among them, the greedy criterion is Wolf, Wolf, wolf and second The wolves select individuals to be put into the next generation population, including: calculate Wolf, Wolf, wolf and second The fitness function of each wolf is to put the individual with a larger fitness function among the parent and offspring individuals into the next generation population, as follows: ； in, Indicates about The fitness function of .

2. The method for selecting a cluster head of a drone group for spectrum sharing according to claim 1, characterized in that: The wolf pack parameters are initialized, and a number of parent wolf packs are randomly generated according to the initial formation of the drone group, and further includes: Wolf pack size , maximum number of iterations , mutation points Initialize; Each cluster head node in the initial formation of the drone swarm is encoded, and several parent wolf packs are randomly generated.

3. The method for selecting a cluster head of a drone group for spectrum sharing according to claim 1, characterized in that: The initial formation of the drone group includes the working bandwidth of the drone node; the spectrum utility function of the entire drone group is calculated according to the initial formation of the drone group, and the three optimal solutions of the spectrum utility function are determined as Wolf, Wolf and Wolf, also includes: Calculate the total spectrum efficiency of the drone swarm downlink and the variance of the total throughput of the drone swarm downlink according to the initial formation of the drone swarm; The spectrum utility function of the entire drone swarm is calculated based on the total spectrum efficiency of the drone swarm downlink, the variance of the total throughput of the drone swarm downlink, and the working bandwidth of the drone node.

4. The method for selecting a cluster head of a drone group for spectrum sharing according to claim 1, characterized in that: The traversal Wolf, Wolf and All possible positions of the wolf in each dimension, and mutate them one by one to get the first Wolf, also includes: According to the mutation vector Wolf, Wolf and The wolf mutates, and the mutation vector is: ； in, , represents the dimension sequence number of the population vector, express Between A random number, express A random sequence in Indicates The position changes, is the mutation operator.

5. A UAV cluster head election system for spectrum sharing, characterized in that: include: An information collection module is configured to collect actual information of the communication scene and the drone group, and pre-process the actual information to obtain communication scene attribute parameter information and drone node attribute parameter information; An initial formation module, configured to perform an initial formation of the drone group based on the communication scenario attribute parameter information and the drone node attribute parameter information; An iterative optimization module is configured to iteratively optimize the initial formation of the drone swarm using a genetic-grey wolf optimization algorithm to obtain a cluster head election result and a spectrum allocation result of the drone swarm; The method of iteratively optimizing the initial formation of the drone group by using the genetic-grey wolf optimization algorithm to obtain the cluster head election result and spectrum allocation result of the drone group also includes: Initialize the wolf pack parameters and randomly generate several parent wolf packs according to the initial formation of the drone group; The spectrum utility function of the entire drone swarm is calculated based on the initial formation of the drone swarm, and the three optimal solutions of the spectrum utility function are determined as Wolf, Wolf and Wolf; Traversal Wolf, Wolf and All possible positions of the wolf in each dimension, and mutate them one by one to get the first Wolf; Will Wolf, Wolf, Wolf and first Wolf Cross Random Generation 2 Wolves select individuals from all individuals and put them into the next generation population to obtain the offspring population; When the preset number of iterations is reached, the latest The results of cluster head election and spectrum allocation of wolves as drone swarms; Among them, the Wolf, Wolf, Wolf and first Wolf Cross Random Generation 2 Wolves select individuals from all individuals and put them into the next generation population to obtain the offspring population, which also includes: Will Wolf, Wolf, Wolf and first The wolf undergoes random crossover mutation to obtain the second Wolf; According to the greedy principle, Wolf, Wolf, wolf and second The wolves select the individuals that will be put into the next generation of the population; Among them, the second The wolves and the individuals put into the next generation population form the offspring population; Among them, the Wolf, Wolf, Wolf and first The wolf undergoes random crossover mutation to obtain the second Wolf, also includes: Wolf, Wolf, Wolf and first Wolves constitute the individuals to be mutated, and the mutation process is as follows: ； in, is the crossover operator, represents a population with an evolutionary number of G+1; Among them, the greedy criterion is Wolf, Wolf, wolf and second The wolves select individuals to be put into the next generation population, including: calculate Wolf, Wolf, wolf and second The fitness function of each wolf is to put the individual with a larger fitness function among the parent and offspring individuals into the next generation population, as follows: ； in, Indicates about The fitness function of .

6. A storage medium, characterized in that: It stores a computer program executable by an access authentication device. When the computer program runs on the access authentication device, the access authentication device executes the steps of the method according to any one of claims 1 to 4.

Images