Methods, equipment, and media for spectrum resource allocation in multi-task UAV swarms

Abstract

This application discloses a method, device, and medium for multi-task UAV swarm spectrum resource allocation, relating to the field of UAV swarm spectrum resource allocation. The method divides the UAV swarm into multiple UAV groups, each group performing one task. The objective is to maximize the minimum transmission throughput of all UAV groups. A joint optimization algorithm for spectrum resources is applied to solve the objective function of spectrum resource allocation, yielding the optimal channel allocation and transmission power allocation solutions for the cluster leader UAV and each member UAV in each group. This invention simultaneously optimizes the channel allocation of the cluster leader UAV and member UAVs using a genetic algorithm within the joint optimization algorithm, and optimizes the transmission power of the cluster leader UAV and member UAVs using a convex optimization method within the joint optimization algorithm. This effectively improves the spectrum efficiency of the UAV swarm and ensures the fairness of UAV swarm transmission.

Description

Technical Field

[0001] This application relates to the field of spectrum resource allocation for unmanned aerial vehicle (UAV) swarms, and in particular to a method, device, and medium for spectrum resource allocation of UAV swarms for multi-tasking applications. Background Technology

[0002] In recent years, with the continuous progress and innovation of core technologies such as flight control, communication, electronics, and power, unmanned aerial vehicles (UAVs) have been continuously improved and perfected. UAVs, with their advantages of small size, low cost, strong stealth, low safety risk, and high survivability, have broad application prospects in military, public, and civilian fields, and are used to perform a wider range of tasks, such as environmental monitoring, disaster search and rescue, agricultural irrigation, and communication relay. However, due to the limitations of small UAVs in size, payload, and flight time, the model of using multiple UAV systems to enhance single UAV systems has emerged. Faced with increasing task volume and types, multi-UAV collaboration can better meet task requirements. Moreover, compared to single UAVs, UAV swarms have stronger adaptability and broader application potential, making them more suitable for multi-mission scenarios. Due to their enormous potential application value, UAV swarms have developed rapidly. Furthermore, when facing large-scale, multi-mission complex scenarios, UAV swarms can be divided into multiple groups to execute multiple tasks simultaneously, improving work efficiency while enhancing mission reliability and robustness.

[0003] While collaborative task completion by UAV swarms offers significant advantages, it also suffers from severe interference and low spectrum efficiency. To address these issues, numerous scholars have conducted in-depth research, achieving a series of results. Some existing technologies have studied the spectrum resource allocation problem in task-driven UAV communication networks, modeling the coupling relationship between tasks and spectrum allocation as a game theory model, and proposing a coalition-forming game algorithm to jointly optimize task selection and spectrum resource allocation. Other existing technologies have investigated UAV swarms performing search and rescue missions, proposing a mean-field game-based UAV energy-saving trajectory optimization algorithm that achieves high channel capacity while minimizing energy consumption. Still other existing technologies have proposed a framework for optimizing UAV trajectories and resource allocation in UAV orthogonal frequency division multiple access systems, aiming to maximize minimum average throughput. While these studies improve network transmission rates by optimizing task allocation and UAV trajectories, they do not consider the difficulty of allocation due to limited spectrum resources when UAV swarms perform multiple tasks.

[0004] Some existing technologies address the issue of low efficiency caused by drones only being able to join one alliance in traditional alliance formation by proposing an overlapping alliance formation game theory algorithm to optimize task resource allocation through partial cooperation among overlapping alliance members. Other existing technologies maximize the throughput of drone-assisted relay networks by jointly optimizing transmission power, bandwidth, and drone deployment. Some existing technologies study joint channel and time slot selection in alliance-based multi-drone networks considering different drone communication needs through latent game theory. These studies consider forming drone swarms in an alliance manner, optimizing spectrum resource utilization by optimizing drone alliance formation and drone deployment, thereby improving the transmission efficiency of drone swarms, but they do not consider the heterogeneous characteristics of drone swarms. Some existing technologies consider the impact of spectrum resources on reconnaissance performance, studying cooperative reconnaissance and spectrum access schemes for task-driven heterogeneous drone alliance networks. Based on traditional Pareto order and selfish order, they propose an alliance expected altruistic order that maximizes alliance utility, as well as a joint bandwidth allocation and alliance formation algorithm to achieve stable alliance partitioning. Some existing technologies consider joint deployment, computational offloading, and resource allocation optimization in alliance-based UAV MEC networks, but neglect interference within the alliance. Other existing technologies have studied the grouping and allocation problem of heterogeneous UAV swarms under complex multi-tasking scenarios, proposing a clustering-then-matching method based on improved K-means and delayed reception algorithms to improve the optimality and timeliness of heterogeneous UAV swarms when performing complex multi-tasking, but they do not address the spectrum resource allocation problem. Based on the above research, traditional spectrum resource allocation methods are often ill-suited to the complexities of UAV swarm multi-tasking scenarios. Furthermore, UAV swarms are typically composed of UAVs with different functions, whose communication technologies and spectrum requirements vary, further increasing the complexity of spectrum resource allocation. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, and medium for allocating spectrum resources in a multi-task UAV swarm, and to provide a joint optimization algorithm for spectrum resources. In this algorithm, a genetic algorithm is used to simultaneously optimize the channel allocation of the cluster leader UAV and member UAVs, and a convex optimization method is used to optimize the transmission power of the cluster leader UAV and member UAVs. This can effectively improve the spectrum efficiency of the UAV swarm and ensure the fairness of UAV swarm transmission.

[0006] To achieve the above objectives, this application provides the following solution:

[0007] Firstly, this application provides a method for allocating spectrum resources for multi-tasking UAV swarms, including:

[0008] The system acquires the number of drone groups within the target task area, the number of member drones in each drone group, the number of channels, and the location information of each drone. Each drone group includes a leader drone and several member drones. Each drone group performs one task within the target task area.

[0009] For each UAV group, the first transmission throughput between the cluster leader UAV and the ground control station is calculated based on the channel allocation and transmission power of the cluster leader UAV, and the second transmission throughput between the member UAV and the cluster leader UAV within the UAV group is calculated based on the channel allocation and transmission power of the member UAVs.

[0010] For each drone group, the smaller of the first transmission throughput and the second transmission throughput in the group is taken as the transmission throughput of the drone group, and a spectrum resource allocation objective function is constructed with the goal of maximizing the minimum transmission throughput of all drone groups.

[0011] The spectrum resource joint optimization algorithm is applied to solve the spectrum resource allocation objective function, yielding the optimal channel allocation and transmission power allocation solutions for the cluster leader UAV and for each member UAV in each UAV group. The spectrum resource joint optimization algorithm includes a genetic algorithm and a convex optimization method. The genetic algorithm is used to solve for the optimal channel allocation solution for the cluster leader UAV and member UAVs, and the convex optimization method is used to solve for the optimal transmission power allocation solution for the cluster leader UAV and member UAVs.

[0012] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aforementioned multi-task-oriented UAV swarm spectrum resource allocation method.

[0013] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for allocating spectrum resources for multi-task UAV swarms.

[0014] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0015] This invention provides a method, device, and medium for spectrum resource allocation in multi-tasking UAV swarms. In studying the spectrum resource allocation problem of UAV swarms performing multiple tasks, the UAV swarm is divided into multiple groups. Considering the heterogeneity of the UAV groups and the situations of spectrum reuse and co-channel interference, channel allocation and transmission power optimization are performed on the cluster leader UAV and member UAVs to maximize the minimum transmission throughput of all groups. A joint spectrum resource optimization algorithm is proposed for solving this problem. Specifically, a genetic algorithm is used to simultaneously optimize the channel allocation of the cluster leader UAV and member UAVs, and a convex optimization method is used to optimize the transmission power of the cluster leader UAV and member UAVs step by step. Applying the allocation algorithm of this invention effectively improves the throughput of the minimum UAV group and ensures the fairness of transmission among the UAV groups. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is an application environment diagram of a multi-task-oriented UAV swarm spectrum resource allocation method according to an embodiment of this application;

[0018] Figure 2 A flowchart illustrating a method for allocating spectrum resources for multi-tasking unmanned aerial vehicle (UAV) swarms according to an embodiment of this application;

[0019] Figure 3 This is a schematic diagram of a multi-tasking drone swarm system model provided in an embodiment of this application;

[0020] Figure 4 This is a schematic diagram illustrating the convergence of a joint optimization algorithm for spectrum resources provided in an embodiment of this application.

[0021] Figure 5 A schematic diagram illustrating the maximum-minimum group throughput as a function of the number of channels, provided in an embodiment of this application;

[0022] Figure 6 A schematic diagram illustrating the maximum-minimum group throughput as a function of the number of groups, provided in an embodiment of this application;

[0023] Figure 7 This is a schematic diagram comparing the performance of different solutions provided in an embodiment of this application;

[0024] Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] The multi-task-oriented UAV swarm spectrum resource allocation method provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send the number of UAV groups in the target task area, the number of member UAVs in each UAV group, the number of channels, and the location information of each UAV to server 104. After receiving the number of UAV groups in the target task area, the number of member UAVs in each UAV group, the number of channels, and the location information of each UAV, server 104 calculates the first transmission throughput between the cluster head UAV and the ground control station for each UAV group based on the channel allocation and transmission power of the cluster head UAV, and calculates the second transmission throughput between the member UAVs and the cluster head UAVs in their respective UAV groups based on the channel allocation and transmission power of the member UAVs. For each UAV group, the smaller of the first transmission throughput and the second transmission throughput in the group is taken as the transmission throughput of the UAV group, and a spectrum resource allocation objective function is constructed with the goal of maximizing the minimum transmission throughput of all UAV groups. The spectrum resource allocation objective function is solved by applying a spectrum resource joint optimization algorithm to obtain the optimal channel allocation solution and the optimal transmission power allocation solution for the cluster head UAV in each UAV group, as well as the optimal channel allocation solution and the optimal transmission power allocation solution for each member UAV. Server 104 can feed back the optimal channel allocation and transmission power allocation solutions for the cluster leader drone and each member drone in each drone group to terminal 102. Furthermore, in some embodiments, the multi-task drone cluster spectrum resource allocation method can also be implemented separately by server 104 or terminal 102. For example, terminal 102 can directly construct the spectrum resource allocation objective function based on the number of drone groups in the target task area, the number of member drones in each drone group, the number of channels, and the location information of each drone, and solve it using a joint spectrum resource optimization algorithm. Alternatively, server 104 can obtain the number of drone groups in the target task area, the number of member drones in each drone group, the number of channels, and the location information of each drone from the data storage system, and construct the spectrum resource allocation objective function and solve it using a joint spectrum resource optimization algorithm.

[0028] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0029] In one exemplary embodiment, such as Figure 2 As shown, a method for allocating spectrum resources for multi-tasking UAV swarms is provided. This method is executed by computer devices, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 204. Wherein:

[0030] Step 201: Obtain the number of drone groups in the target task area, the number of member drones in each drone group, the number of channels, and the location information of each drone; each drone group includes a cluster leader drone and several member drones; each drone group performs one task within the target task area.

[0031] This invention considers a scenario where a ground control station O and a drone swarm consisting of multiple drones perform multiple tasks over a large area, such as... Figure 3 As shown. Considering the large size of the mission area (target mission area) and the large number of tasks to be performed, the mission area can be divided into G non-overlapping regions, each of which is handled by a drone swarm for reconnaissance. The entire drone swarm is divided into G groups according to the target area to perform tasks. Each group consists of a cluster leader drone and N member drones, resulting in a total of G groups and a cluster leader drone. The set of cluster leader drones can be represented as... The collection of member drones can be represented as The drone swarm has M (M≥G) available channels, each with a bandwidth of B0. The set of available channels is represented as follows: Each cluster leader UAV uses a different channel to transmit reconnaissance information to the ground control station. Member UAVs within the cluster share channels from other cluster leader UAVs via underlay to transmit reconnaissance information. This achieves shared access to M available channels for intra-cluster transmission and transmission between the cluster leader UAV and the ground control station.

[0032] Step 202: For each UAV group, calculate the first transmission throughput between the cluster leader UAV and the ground control station based on the channel allocation and transmission power of the cluster leader UAV, and calculate the second transmission throughput between the member UAVs and the cluster leader UAVs within their respective UAV groups based on the channel allocation and transmission power of the member UAVs.

[0033] The leader drone and member drones are drones with different functions, so they need to be considered separately when allocating channels and transmission power. Therefore, this invention takes into account the heterogeneous characteristics of drone groups.

[0034] Step 203: For each drone group, the transmission throughput of the drone group is the smaller of the first transmission throughput and the second transmission throughput in the group, and a spectrum resource allocation objective function is constructed with the goal of maximizing the minimum transmission throughput of all drone groups.

[0035] Step 204: Apply the spectrum resource joint optimization algorithm to solve the spectrum resource allocation objective function, and obtain the optimal channel allocation and transmission power allocation solutions for the cluster leader UAV and for each member UAV in each UAV group; the spectrum resource joint optimization algorithm includes a genetic algorithm and a convex optimization method; the genetic algorithm is used to solve the optimal channel allocation solution for the cluster leader UAV and member UAVs; the convex optimization method is used to solve the optimal transmission power allocation solution for the cluster leader UAV and member UAVs.

[0036] To address the mutual interference problem in multi-task UAV swarms under limited spectrum resources, steps 201 to 204 above are implemented to study a multi-task spectrum resource sharing allocation method from two dimensions: channel allocation and power allocation. To ensure fairness in the transmission throughput of all groups, considering spectrum reuse and co-channel interference, a joint spectrum resource optimization algorithm is proposed with the goal of maximizing the minimum group transmission throughput. This algorithm simultaneously optimizes the channel allocation of the cluster leader UAV and member UAVs using a genetic algorithm, and optimizes the transmission power of the cluster leader UAV and member UAVs step by step using a convex optimization method. The method proposed in this invention can effectively improve the spectrum efficiency of the group and ensure the fairness of group transmission.

[0037] In another exemplary embodiment of this application, the process of obtaining the first transmission throughput and the second transmission throughput in step 202 is as follows:

[0038] (a) Transmission throughput between the cluster-head UAV and the ground control station, i.e., the first transmission throughput

[0039] All cluster-head UAVs use different channels, therefore there is no common-channel interference between them. However, considering that the channels used by the cluster-head UAVs are shared by other group member UAVs, the transmission link between the cluster-head UAVs and the ground control station may be subject to common-channel interference from other group member UAVs. In this invention, the channel allocation matrix of the cluster-head UAVs is defined as follows: when This indicates that channel m is used by the cluster head UAV g, and the channel allocation matrix of the member UAVs is represented as follows: when This indicates that channel m is used by UAV n, a member of group g. Therefore, the throughput of the cluster leader UAV g using channel m to transmit data to the ground control station can be expressed as:

[0040]

[0041] Where B0 is the channel bandwidth, γ g,m The signal-to-interference-plus-noise ratio (SIR) is expressed by the following formula:

[0042]

[0043] in and Let d represent the transmission power of the cluster leader drone g and the member drones within the j-th drone group, respectively. g,O and d nj,O Let G and N represent the distances from the cluster leader UAV g to the ground control station and the distances from the nth member UAV in the j-th UAV group to the ground control station, respectively. and The value is 1 or 0, which indicates whether the cluster leader UAV of the g-th group and the member UAV n of the j-th group use channel m, respectively. α is the path loss exponent, and δ0 is the unit noise power.

[0044] (b) Transmission throughput between member drones and cluster head drones, i.e., the second transmission throughput

[0045] Within each drone swarm, member drones perform reconnaissance missions and transmit the reconnaissance information to the cluster leader drone. Because member drones share the same transmission channel with other cluster leader drones, their transmissions are susceptible to interference from other cluster leader drones. Furthermore, member drones may also use the same channel, causing further interference. Therefore, the signal-to-interference-plus-noise ratio (SIR) of member drone n in the g-th drone swarm using channel m can be expressed as:

[0046]

[0047] Here, group j is different from group g, and group i is different from group j. Let represent the transmission power of member drone n in the g-th group, the transmission power of the cluster head drone in the j-th group, and the transmission power of other member drones different from member drone n, respectively. d represents the distance from member drone n in group g to the cluster head drone of that group. j,g and d ig,g Let $j$ represent the distance from cluster leader drone $j$ to cluster leader drone $g$, and $i$ represent the distance from member drone $i$ to cluster leader drone $g$, respectively. and The value is 1 or 0, which indicates whether the cluster leader drone j and the member drone i in the g-th group use channel m, respectively.

[0048] Therefore, the throughput of data transmission by member drone n in the g-th group using channel m can be expressed as:

[0049]

[0050] In another exemplary embodiment of this application, the process of constructing the spectrum resource allocation objective function in step 203 is as follows:

[0051] This invention addresses the issue of fairness in group transmission, and its design objective is to optimize the channel allocation matrix for cluster-head UAVs. and transmission power matrix and the channel allocation matrix of the drones in the group. and transmission power matrix This maximizes the minimum throughput of all drone swarms. Each drone swarm has two data transmission links: one from member drones to the swarm leader and one from the swarm leader to the ground control station. Therefore, the throughput of each drone swarm is determined by the smaller of the two links, which can be expressed as: To facilitate the solution, this invention introduces... The minimum throughput of all drone swarms is then equivalent to maximizing η(θ). G ,P G ,θ N ,P N ).

[0052] Therefore, the spectrum resource allocation problem in the multi-task scenario of this invention can be expressed as:

[0053]

[0054] In the above formula (5), C1 indicates that the channel allocation matrix takes the value of 1 or 0, which respectively indicates whether the cluster head drone and the member drones in the group occupy channel m; C2 indicates that the cluster head drone and the member drones in the same group will not occupy the same channel m; C3 indicates that the cluster head drones in different groups do not occupy the same channel m; C4 and C5 indicate that each cluster head drone and member drone uses only one channel; C6 and C7 indicate the transmission power constraints when the cluster head drone and the member drones in any group use the channel; C8 and C9 indicate that the throughput of any drone group is less than or equal to the throughput constraints of the link from the cluster head drone to the control center and the link from the member drone to the cluster head drone; C10 indicates that the throughput of the group is greater than the objective function η.

[0055] In another exemplary embodiment of this application, since the objective function sought by the present invention involves two binary variables θ G θ N and two continuous variables P G P N Therefore, the problem in the above formula (5) is a mixed-integer nonlinear programming problem (MINLP), which is difficult to solve directly. In order to reduce the complexity of the problem, the problem is decomposed into two sub-problems: channel allocation and transmission power optimization, and the BCD algorithm is used for alternating optimization. That is, based on the objective function obtained in step 203 (i.e., formula (5)), this invention proposes a spectrum resource joint optimization algorithm to solve the objective function, decomposes the objective function into multiple sub-problems for solving, including the channel allocation optimization problem of the cluster head UAV and member UAVs, and the transmission power optimization problem of the cluster head UAV and member UAVs. For solving the channel allocation optimization sub-problem, this invention proposes a genetic algorithm that can simultaneously optimize the channel allocation of the cluster head UAV and the channel allocation of the member UAVs. When solving the transmission power optimization problem, since the cluster head UAV and member UAVs influence and restrict each other, it is difficult to optimize two continuous variables at the same time. Therefore, this invention considers optimizing the transmission power of the cluster head UAV and the transmission power of the member UAVs separately. To address the subproblem of optimizing the transmission power of the cluster-head UAV, this invention employs a convex optimization method, transforming a non-convex problem into a convex one, which can then be solved using the CVX solver. Similarly, to address the subproblem of optimizing the transmission power of member UAVs, this invention also employs a convex optimization method, transforming a non-convex problem into a convex one, which can then be solved using the CVX solver. This invention uses Block Coordinate Descent (BCD) to iteratively optimize the above three subproblems, ultimately obtaining the optimal solution to the objective function sought by this invention. Therefore, as shown in Table 1, the iterative optimization process of the joint spectrum resource optimization algorithm is as follows.

[0056] Table 1 Joint Optimization Algorithm for Spectrum Resources

[0057]

[0058]

[0059] Based on Table 1, step 204 specifically includes:

[0060] (1) Set initial values ​​for the transmission power variables of the cluster head UAV and the member UAVs, and substitute these initial values ​​into the spectrum resource allocation objective function to obtain the channel allocation optimization objective function. Table 1 shows the cluster head UAV allocation matrix. and member drone allocation matrix It is the initial value set, for and It can be applied to the random generation of initial individuals during the initial population construction process in genetic algorithms.

[0061] (2) Solve the channel allocation optimization objective function using the genetic algorithm to obtain the optimal channel allocation solution for the cluster leader UAV and member UAVs in each UAV group. and

[0062] (3) Find the optimal channel allocation solution for the cluster leader drone and member drones in each drone group. and Substitute this into the objective function for spectrum resource allocation, and let the transmission power variable of the member UAV... Using the currently set initial values, the objective function for optimizing the transmission power of the cluster-head UAV is derived. r1 represents the iteration number of the joint optimization algorithm, initially set to...

[0063] (4) Solve the objective function for optimizing the transmission power of the cluster head UAV using the convex optimization method, and obtain the optimal solution for the transmission power of the cluster head UAV in each UAV group.

[0064] (5) Find the optimal channel allocation solution for the cluster leader drone in each current drone group. and optimal solution for transmission power And the optimal solution for channel allocation for member drones Substituting these values ​​into the spectrum resource allocation objective function yields the member UAV transmission power optimization objective function.

[0065] (6) Solve the objective function for optimizing the transmission power of the member UAVs using the convex optimization method to obtain the optimal solution for the transmission power of the member UAVs in each UAV group.

[0066] (7) Determine whether the iteration termination condition is met; the iteration termination condition is that the difference between the current spectrum resource allocation objective function value and the spectrum resource allocation objective function value of the previous iteration is less than the error precision. Or the current iteration number is greater than the corresponding maximum iteration number r1 > r1 max .

[0067] (8) If not, let the initial values ​​of the transmission power variables of the cluster head UAV and the member UAVs in the spectrum resource allocation objective function be the optimal solutions for transmission power allocation of the cluster head UAV and each member UAV in the current UAV group, respectively, and return to step "and bring the initial values ​​into the spectrum resource allocation objective function".

[0068] (9) If so, output the optimal channel allocation solution for the cluster head UAV in each UAV group. Optimal solution for transmission power allocation Optimal solution for channel allocation for each member UAV Optimal solution for transmission power allocation

[0069] In another exemplary embodiment of this application, for channel allocation optimization, the optimization of channel allocation is considered first, therefore, let the transmit power matrix P in equation (5) be... G P N Treated as a fixed variable, the channel allocation matrix θ G θ N If we consider them as decision variables, then the MINLP problem in equation (5) is transformed into an integer programming problem, therefore equation (5) can be rewritten as:

[0070]

[0071] in,

[0072]

[0073] Equation (6) is an integer non-convex optimization problem, which is difficult to solve directly. Therefore, this invention proposes a channel allocation algorithm based on genetic algorithm. This algorithm can optimize the channel allocation of both the cluster head UAV and the member UAVs at the same time, and solves the optimization problem of equation (6) well. Table 2 below shows the solution steps of the genetic algorithm.

[0074] Table 2 Channel Allocation Algorithm Based on Genetic Algorithm

[0075]

[0076]

[0077] Based on Table 2, step (2) specifically includes:

[0078] (2-1) Generate an initial UAV channel allocation matrix that satisfies equation (6) for the optimization problem. and member UAV channel allocation matrix An initial population is randomly generated. Each individual in the initial population includes the channel allocation matrix of the cluster leader UAV and the channel allocation matrices of the member UAVs in all UAV groups; the randomly generated channel allocation matrices of the cluster leader UAV and the member UAVs satisfy the channel allocation constraints.

[0079] Regarding gene encoding, channel allocation can be represented using binary encoding. The channel allocation matrix θ for the cluster-head UAV... G Gene straightening is performed, that is, the G×M matrix is ​​transformed into a 1×GM row vector, and the channel allocation matrix θ of the member UAV is applied. N Gene straightening is performed by converting the G×N×M matrix into a 1×GNM row vector, and representing each element of the vector as either 1 or 0. The two vectors generated in the above operation are simultaneously saved as an individual in the population in the form of a structure, and it is checked whether the individual satisfies the constraint of equation (6). If it does not satisfy the constraint, the constraint is processed to complete the initialization of the individual. Then, all NIND individuals in the population are initialized in the above manner.

[0080] (2-2) Using the channel allocation optimization objective function as the fitness function, calculate the fitness value of each individual in the current population. The fitness function represents the adaptability of an individual in the population and is used to evaluate the quality of an individual. In this algorithm, it is the optimization objective η of problem (6).

[0081] (2-3) Based on the fitness value, the roulette wheel selection algorithm is used to select individuals in the current population to obtain the selected individuals.

[0082] In gene selection, the selection strategy is used to select the next population based on the fitness value of an individual. It is necessary to pay attention not only to individuals with high fitness values, but also to individuals with low fitness values. Therefore, this invention adopts the roulette wheel selection method. The selection basis of this method is the size of the individual's fitness value. If the individual's fitness value is low, it is very likely to be discarded. If the individual's fitness value is high, it has a high probability of being selected.

[0083] (2-4) Perform gene crossover on individuals in the current population to obtain crossover individuals, and perform gene mutation on individuals in the current population to obtain mutated individuals.

[0084] Regarding gene crossover, parental individuals are randomly paired and crossovered. In this invention, a single-point crossover method is used, that is, based on a given crossover probability p. cAt the intersection, genes are exchanged between the gene vectors to generate new encoding vectors. Regarding gene mutation, to maintain population diversity and prevent the population from getting trapped in local optima, a mutation probability p is used... m Inverting the values ​​of one or more mutation points in a gene produces a new encoding vector.

[0085] (2-5) Select the individuals with the highest fitness values ​​in the current population (Q), and combine the individuals with the highest fitness values ​​(Q), the selected individuals, the crossover individuals, and the mutated individuals to form a new population, i.e., the next generation population; the value of Q is the total number of individuals in the population minus the number of selected individuals, the number of crossover individuals, and the number of mutated individuals.

[0086] For gene recombination, after the above selection, crossover, and mutation operations, Nsel individuals are obtained. An elite retention strategy is adopted, selecting the top NIND-Nsel individuals with the highest fitness values ​​from the parent population and adding them to the offspring population, thus improving the algorithm's convergence speed. NIND-Nsel = Q.

[0087] The individuals obtained after the above operations are subjected to constraint processing. For individuals that do not meet the constraints, gene correction and gene screening are performed to make them meet the constraints of equation (6) to obtain the next generation population.

[0088] (2-6) Determine whether the current iteration number has reached the corresponding maximum iteration number.

[0089] (2-7) If not, then let the next generation population be the current population and return to the step "Calculate the fitness value of each individual in the current population using the channel allocation optimization objective function as the fitness function".

[0090] (2-8) If so, then the channel allocation matrix of the cluster head UAV and the channel allocation matrix of the member UAV corresponding to the individual with the smallest fitness value in the next generation population are selected as the optimal solution for channel allocation of the cluster head UAV and member UAV in each UAV group.

[0091] In another exemplary embodiment of this application, regarding transmission power optimization, considering transmission power optimization, therefore let the channel allocation matrix θ in equation (5) be... G θ N Treating it as a fixed variable, the transmission power matrix P G P N If we consider them as decision variables, then the MINLP problem in equation (5) is transformed into a non-integer programming problem, and then combined with the group throughput formula... Therefore, equation (5) can be rewritten as:

[0092]

[0093] Among them, st

[0094]

[0095] in, This indicates the maximum transmission power of the cluster-head drone; This indicates the maximum transmission power of the member drone.

[0096] At this point, the optimization problem of this invention is transformed into a non-convex optimization subproblem, and the equation contains two continuous variables P. G and P N Directly solving this problem is too complex. Therefore, we consider solving the power optimization of the cluster head UAV and the power optimization of the member UAVs separately. First, we fix the transmission power of all member UAVs to optimize the transmission power of the cluster head UAV. Then, we fix the transmission power of all cluster head UAVs to optimize the transmission power of the member UAVs. Finally, we can iterate to obtain the suboptimal solution.

[0097] (a) Optimize the transmission power of cluster head UAVs

[0098] When the transmission power of the fixed-member drone is constant, the variable is P. G The power optimization subproblem is still a difficult non-convex optimization subproblem to solve. This invention uses geometric programming to transform the problem into a convex optimization problem.

[0099] Theorem 1: By using logarithmic transformation The non-convex optimization problem in equation (7) can be transformed into a convex optimization problem.

[0100] Proof: In geometric programming, log2(1+x) can be approximated as log2(x) under high signal-to-noise ratio conditions, using the logarithmic transformation in Theorem 1. Then constraint C3 in equation (7) can be converted to:

[0101]

[0102] It can be seen from equation (8) that the constraint is about The affine function can be either convex or concave. Similarly, constraint C4 in equation (7) can be transformed into:

[0103]

[0104] Equation (9) shows that it is a function of the form log-sum-exp, which is convex. Inverting it makes it a concave function, so constraint C4 in equation (7) is a convex constraint. Therefore, the original problem corresponding to equation (5) is transformed into:

[0105]

[0106] in,

[0107]

[0108] At this point, the power optimization subproblem of the cluster-head UAV is transformed into a convex optimization problem, and the optimal solution to this problem can be obtained by applying the CVX solver. Then according to available

[0109] (b) Optimize the transmission power of member drones

[0110] When the transmission power of the fixed cluster-head UAV is used, the variable is P. N This power optimization subproblem remains a difficult non-convex optimization subproblem to solve. Therefore, we employ the same convex optimization method as described above, using logarithmic transformation. This problem can be transformed into a convex optimization problem. Similarly, the original problem can be transformed into:

[0111]

[0112] in,

[0113]

[0114] At this point, the subproblem of optimizing the transmission power of the member drones is transformed into a convex optimization problem, and the optimal solution to this problem can be obtained by applying the CVX solver. Then according to available

[0115] This embodiment has the following advantages:

[0116] (1) To address the low spectrum resource utilization of UAV swarms in multi-task scenarios, the UAV swarm is divided into multiple groups, and a model for allocating spectrum resources according to UAV groups is constructed. Considering spectrum reuse and co-channel interference, spectrum resources are allocated to both the cluster leader UAV and member UAVs of each UAV group. A joint spectrum resource optimization algorithm is proposed, which can decompose the above mixed integer nonlinear programming problem into multiple sub-problems for solving, and finally obtain a relatively optimal solution to the original problem.

[0117] (2) For the channel allocation optimization subproblem of the cluster leader UAV and member UAVs, a genetic algorithm was adopted, which can simultaneously optimize the channel allocation of the cluster leader UAV and member UAVs. An elite retention strategy was introduced, which reduced the complexity of the problem and improved the convergence speed of the algorithm. For the non-convex power optimization subproblem of the cluster leader UAV and member UAVs, a step-by-step solution was adopted. The non-convex problem was transformed into a convex problem using a convex optimization method, and the CVX solver was applied for the solution. Finally, the original optimization problem was divided into three iterative optimization steps using the block coordinate descent method, which reduced the complexity of the problem.

[0118] The following is a simulation verification process of the allocation method of the present invention:

[0119] To make the parameter settings more reasonable, some simulation parameters of this invention refer to the values ​​in the prior art. The specific simulation parameter settings are shown in Table 3.

[0120] Table 3 Simulation parameter settings

[0121]

[0122]

[0123] Figure 4 The convergence curves of the joint spectrum resource optimization algorithm under different scenarios are presented. The results show that the maximum-minimum group throughput, i.e., the objective function value of this paper, continuously increases during the iteration process. Furthermore, the iteration results indicate that as the number of groups, member drones, and channels increases, the number of iterations required for convergence also increases, as does the time required for one iteration. This is because increasing the number of groups, member drones, and channels increases the complexity of the algorithm.

[0124] Figure 5 This paper illustrates the relationship between the maximum and minimum group throughput and the number of available channels for different numbers of member drones. The drone group size is 4, and the number of available channels increases from 4 to 10. The comparison shows that with the same number of group and member drones, the group throughput increases as the number of available channels increases. This is because with the number of leader drones and member drones remaining constant, an increase in available channels reduces inter-group co-channel interference, thus improving group throughput. Furthermore, an increase in the number of member drones in a group also affects group throughput. When the number of available channels is low, an increase in the number of member drones leads to stronger inter-group co-channel interference, thus reducing the minimum group throughput. However, when the number of available channels is high, the interference from an increase in member drones does not have a significant impact; on the contrary, it may even increase the group's throughput.

[0125] Figure 6This diagram illustrates the relationship between the maximum and minimum group throughput for different numbers of member drones, with 10 available channels. It shows that the more groups there are, the lower the minimum group throughput. This is because, with the number of available channels remaining constant, an increase in the number of groups increases inter-channel interference, thus reducing the minimum group throughput. Furthermore, fewer groups mean a relatively more available channel count, which in turn increases the minimum group throughput with more member drones. Conversely, more groups mean a relatively fewer available channel count, which in turn decreases the minimum group throughput with more member drones.

[0126] To further verify the effectiveness of the proposed algorithm in multi-task scenarios of UAV swarms, a simulation comparison method was adopted. The performance of the proposed algorithm was analyzed by comparing it with a random allocation algorithm, benchmark 1, and benchmark 2. Benchmark 1 only optimizes the channel allocation of the cluster leader UAV and member UAVs, while benchmark 2 only optimizes the transmit power of the cluster leader UAV and member UAVs. Figure 7 The performance comparison of four schemes is shown when the number of available channels is 10 and the number of member drones is 3. The algorithm proposed in this invention is superior to the other three schemes. This is because the algorithm proposed in this invention can obtain a relatively optimal channel and power selection scheme by jointly optimizing the channel allocation and transmission power of the cluster leader drone and member drones. Furthermore, the algorithm proposed in this invention has a better optimization effect as the number of drone groups increases.

[0127] This application also provides an application scenario in which the above-described multi-task-oriented UAV swarm spectrum resource allocation method is applied. Specifically, the multi-task-oriented UAV swarm spectrum resource allocation method provided in this embodiment can be applied in large-scale post-disaster reconstruction scenarios. The scenario includes stages such as task delivery, task execution, centralized data processing, and post-disaster reconstruction. The multi-task-oriented UAV swarm spectrum resource allocation method provided in this embodiment pertains to the stage of executing reconnaissance tasks.

[0128] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 8As shown, the computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores spectrum resource allocation data for multi-task UAV swarms. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a multi-task UAV swarm spectrum resource allocation method.

[0129] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0130] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0131] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0132] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0133] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0134] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0135] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0136] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0137] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for allocating spectrum resources in a multi-tasking UAV swarm, characterized in that, The method for allocating spectrum resources for multi-task UAV swarms includes: The system acquires the number of drone groups within the target task area, the number of member drones in each drone group, the number of channels, and the location information of each drone. Each drone group includes a leader drone and several member drones. Each drone group performs one task within the target task area. For each UAV group, the first transmission throughput between the cluster leader UAV and the ground control station is calculated based on the channel allocation and transmission power of the cluster leader UAV, and the second transmission throughput between the member UAV and the cluster leader UAV within the UAV group is calculated based on the channel allocation and transmission power of the member UAVs. For each drone group, the smaller of the first transmission throughput and the second transmission throughput in the group is taken as the transmission throughput of the drone group, and a spectrum resource allocation objective function is constructed with the goal of maximizing the minimum transmission throughput of all drone groups. The spectrum resource joint optimization algorithm is applied to solve the spectrum resource allocation objective function, yielding the optimal channel allocation and transmission power allocation solutions for the cluster leader UAV and for each member UAV in each UAV group. The spectrum resource joint optimization algorithm includes a genetic algorithm and a convex optimization method. The genetic algorithm is used to solve for the optimal channel allocation solution for the cluster leader UAV and member UAVs, and the convex optimization method is used to solve for the optimal transmission power allocation solution for the cluster leader UAV and member UAVs.

2. The method for allocating spectrum resources for multi-task UAV swarms according to claim 1, characterized in that, The expression for the first transmission throughput is: in, In the formula, R g,m This represents the throughput of data transmitted from the cluster-head UAV g to the ground control station via channel m. The value is either 1 or 0, indicating whether the cluster leader drone g of the g-th drone group uses channel m; g,m B0 represents the signal-to-interference-plus-noise ratio (SIR) of the cluster-head UAV g using channel m for transmission; B0 is the channel bandwidth. and d represents the transmission power of the cluster leader UAV g and the nth member UAV within the j-th UAV group using channel m, respectively; g,O and Let g and n represent the distances from the cluster leader UAV g to the ground control station O, respectively, and the distances from the nth member UAV in the j-th UAV group to the ground control station O. The value is 1 or 0, which indicates whether member drone n in the j-th drone group uses channel m; α is the path loss exponent; δ0 is the unit noise power.

3. The method for allocating spectrum resources for multi-task UAV swarms according to claim 2, characterized in that, The expression for the second transmission throughput is: in, In the formula, This represents the throughput of data transmitted by member drone n within the g-th group using channel m; This represents the signal-to-interference-plus-noise ratio (SINR) of drone n, a member of the g-th group, transmitted using channel m. The value is 1 or 0, which respectively indicate whether member drone n in the g-th drone group uses channel m; This represents the transmission power of drone n, a member of the g-th drone group; This represents the transmission power of the j-th drone cluster leader drone; This represents the transmission power of other member drones i that are different from member drone n within the g-th drone group; d represents the distance from member drone n in drone swarm g to the head drone of that swarm; j,g and These represent the distances from cluster-head UAV j to cluster-head UAV g, respectively. This represents the distance from member drone i in drone swarm g to the cluster leader drone g; The value is 1 or 0, indicating whether the cluster head UAV j uses channel m, respectively; The value is 1 or 0, which respectively indicate whether member drone i in the g-th drone group uses channel m.

4. The method for allocating spectrum resources for multi-task UAV swarms according to claim 3, characterized in that, The expression for the objective function of spectrum resource allocation is: in, The constraints are as follows: In the formula, the superscripts G and N of the parameters refer to the cluster leader UAV and the member UAVs, respectively; the subscripts of the matrix [] and the summation symbol ∑, G, N and M shown at the top, represent the total number of UAV groups, the total number of member UAVs in each UAV group and the total number of channels, respectively. This indicates the maximum transmission power of the cluster-head drone; This indicates the maximum transmission power of the member drone.

5. The method for allocating spectrum resources for multi-task UAV swarms according to claim 1, characterized in that, The spectrum resource joint optimization algorithm is applied to solve the spectrum resource allocation objective function, yielding the optimal channel allocation and transmission power allocation solutions for the cluster leader UAV and for each member UAV in each UAV group. Specifically, this includes: Initial values ​​are set for the transmission power variables of the cluster leader UAV and the member UAVs, and these initial values ​​are substituted into the spectrum resource allocation objective function to obtain the channel allocation optimization objective function. The genetic algorithm is used to solve the channel allocation optimization objective function to obtain the optimal channel allocation solution for the cluster leader UAV and member UAVs in each UAV group; The optimal channel allocation solution for the cluster leader drone and member drones in each drone group is substituted into the spectrum resource allocation objective function, and the transmission power variable of the member drones is set to the currently set initial value to obtain the cluster leader drone transmission power optimization objective function. The objective function for optimizing the transmission power of the cluster-head UAV is solved using the convex optimization method, and the optimal solution for the transmission power of the cluster-head UAV in each UAV group is obtained. The optimal channel allocation solution and optimal transmission power solution of the cluster leader UAV and the optimal channel allocation solution of the member UAVs in each UAV group are substituted into the spectrum resource allocation objective function to obtain the member UAV transmission power optimization objective function. The objective function for optimizing the transmission power of the member UAVs is solved using the convex optimization method, and the optimal solution for the transmission power of the member UAVs in each UAV group is obtained. Determine whether the iteration termination condition is met; the iteration termination condition is that the difference between the current spectrum resource allocation objective function value and the spectrum resource allocation objective function value of the previous iteration is less than the error precision or the current iteration number is greater than the corresponding maximum iteration number; If not, then set the initial values ​​of the transmission power variables of the cluster head UAV and the member UAVs in the spectrum resource allocation objective function to be the optimal solutions for transmission power allocation of the cluster head UAV and each member UAV in the current UAV group, respectively, and return to step "and substitute the initial values ​​into the spectrum resource allocation objective function"; If so, output the optimal channel allocation and transmission power allocation solutions for the cluster leader UAV and for each member UAV in each UAV group.

6. The method for allocating spectrum resources for multi-task UAV swarms according to claim 5, characterized in that, The genetic algorithm is used to solve the channel allocation optimization objective function to obtain the optimal channel allocation solution for the cluster leader UAV and member UAVs in each UAV group, specifically including: Construct an initial population; each individual in the initial population includes the channel allocation matrix of the cluster leader UAV and the channel allocation matrix of the member UAVs in all UAV groups; the randomly generated channel allocation matrix of the cluster leader UAV and the channel allocation matrix of the member UAVs satisfy the channel allocation constraint conditions; Using the channel allocation optimization objective function as the fitness function, calculate the fitness value of each individual in the current population; Based on the fitness value, the roulette wheel selection algorithm is used to select individuals from the current population to obtain the selected individuals; Perform gene crossover on individuals in the current population to obtain crossover individuals, and perform gene mutation on individuals in the current population to obtain mutated individuals; Select the individuals with the highest fitness values ​​in the current population (ranked in the top Q), and combine these individuals, the selected individuals, the crossover individuals, and the mutated individuals to form a new population, i.e., the next generation population. The value of Q is the total number of individuals in the population minus the number of selected individuals, the number of crossover individuals, and the number of mutated individuals. Determine if the current iteration count has reached the corresponding maximum iteration count; If not, then set the next generation population as the current population and return to the step "Calculate the fitness value of each individual in the current population using the channel allocation optimization objective function as the fitness function"; If so, the channel allocation matrix of the cluster leader UAV and the channel allocation matrix of the member UAVs corresponding to the individual with the smallest fitness value in the next generation population are selected as the optimal solution for channel allocation of the cluster leader UAV and member UAVs in each UAV group.

7. The method for allocating spectrum resources for multi-task UAV swarms according to claim 5, characterized in that, The objective function for optimizing the transmission power of the cluster leader UAV is solved using convex optimization methods, yielding the optimal solution for the transmission power of the cluster leader UAV in each UAV group. Specifically, this includes: Logarithmically transforming the transmission power variable of the cluster-head UAV in the objective function for optimizing the transmission power of the cluster-head UAV, we obtain an objective function for optimizing the transmission power of the cluster-head UAV that satisfies convex optimization; the expression of the objective function for optimizing the transmission power of the cluster-head UAV that satisfies convex optimization is as follows: The constraints are as follows: The CVX solver is used to solve the objective function for optimizing the transmission power of the cluster-head UAV that satisfies convex optimization, and the optimal solution for the transmission power of the cluster-head UAV in each UAV group is obtained.

8. The method for allocating spectrum resources for multi-task UAV swarms according to claim 5, characterized in that, The objective function for optimizing the transmission power of the member UAVs is solved using convex optimization methods, yielding the optimal solution for the transmission power of the member UAVs in each UAV group. Specifically, this includes: Logarithmically transforming the transmission power variables of the member UAVs in the objective function for optimizing the transmission power of the member UAVs yields an objective function for optimizing the transmission power of the member UAVs that satisfies convex optimization. The expression of the objective function for optimizing the transmission power of the member UAVs that satisfies convex optimization is as follows: The constraints are as follows: The CVX solver is used to solve the objective function for optimizing the transmission power of member UAVs that satisfies convex optimization, and the optimal solution for the transmission power of member UAVs in each UAV group is obtained.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the multi-task-oriented UAV swarm spectrum resource allocation method according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the multi-task-oriented UAV swarm spectrum resource allocation method according to any one of claims 1-8.

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