A multi-uav on-demand deployment and spectrum allocation joint optimization method and system

Abstract

The application discloses a kind of multi-unmanned aerial vehicle on-demand deployment and spectrum allocation joint optimization method and system, it is related to unmanned aerial vehicle communication technical field, this method includes: according to the minimum unmanned aerial vehicle quantity when meeting target area task completion rate based on estimation and simulated annealing algorithm is determined;Using spectrum allocation algorithm based on interference cancellation and channel multiplexing, the correlation optimization algorithm of unmanned aerial vehicle service ground user based on local iterative optimization, the unmanned aerial vehicle deployment position optimization algorithm based on particle swarm, the sub-problem after solving the multi-unmanned aerial vehicle on-demand deployment and spectrum allocation joint optimization problem model meeting the minimum unmanned aerial vehicle quantity using block coordinate descent method decomposition, further obtain optimal matrix set, reach the data transmission performance of unmanned aerial vehicle assisted ground communication is improved, while, ensure the fairness of frequency used by ground user in unmanned aerial vehicle assisted ground communication.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a method and system for joint optimization of on-demand deployment and spectrum allocation of multiple UAVs in an UAV-assisted ground communication network. Background Technology

[0002] Existing terrestrial communication networks require fixed infrastructure support, and once damaged by natural disasters or other causes, they are difficult to restore quickly, leading to network paralysis. Unmanned Aerial Vehicles (UAVs), due to their high mobility, flexibility, and low cost, are widely used in military reconnaissance, communication relay, and search and rescue operations, making them well-suited as emergency aerial base stations for rapidly restoring communication in disaster areas and assisting ground users in conducting communication services. Location deployment is a crucial issue to be addressed in UAV-assisted communication applications, and can be mainly categorized into static deployment, mobile deployment, and multi-hop relay deployment. To better provide communication services to ground users, the deployment location of UAVs must meet the communication needs of all ground users. A single UAV often cannot meet the needs of large-scale or wide-area communication services; multi-UAV systems effectively improve system performance by distributing large-scale communication tasks among multiple UAVs to complete collaboratively.

[0003] Existing research largely focuses on scenarios with a fixed number of drones to reduce the complexity of drone mission planning algorithms, but it cannot adapt to scenarios where the number of drones is uncertain. Some studies have made preliminary explorations into scenarios where the number of drones is uncertain. Some studies have utilized K-means clustering and particle swarm optimization algorithms to study the deployment problem in scenarios where the number of drones is uncertain, effectively reducing the number of drones performing tasks. Some studies have studied the problem of minimizing the number of drones completing tasks within a specific monitoring area, proposing an asymptotically optimal deployment algorithm that effectively reduces the number of drones completing tasks within the task area. Some studies have studied minimizing the number of drones and their deployment problem when drones serve as airborne base stations for cellular ground users to meet ground user coverage requirements. Some studies have studied the speed of drone data collection, using the fewest drones to complete the data collection task within a specified time. However, these works have given little consideration to the spectrum allocation problem necessary for drone information transmission, especially in the context of increasingly scarce spectrum resources. How to reuse spectrum resources to improve the spectrum resource efficiency of drone information transmission is a problem worthy of further research. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for joint optimization of on-demand deployment and spectrum allocation of multiple UAVs, so as to ensure that auxiliary ground communication tasks are performed with the minimum number of UAVs, and effectively improve the data transmission rate and frequency fairness of ground users when UAVs assist ground communication tasks.

[0005] To achieve the above objectives, this invention provides a joint optimization method for on-demand deployment and spectrum allocation of multiple UAVs, comprising: (1) obtaining input parameters corresponding to the target area; the input parameters include a ground user location matrix, the number of available channels, and the range of UAV-assisted ground communication; the ground user location matrix includes ground user location information and ground user number information; (2) determining the minimum number of UAVs required to meet the target area task completion rate based on the input parameters and a UAV number determination algorithm based on pre-estimation and simulated annealing; (3) solving the spectrum allocation subproblem of UAV-assisted communication using a spectrum allocation algorithm based on interference cancellation and channel reuse to obtain the channel allocation matrix of UAV-assisted ground users corresponding to the current iteration number, solving the correlation optimization subproblem of UAV-assisted ground users using a correlation optimization algorithm based on local iterative optimization to obtain the correlation matrix of UAV-assisted ground users corresponding to the current iteration number, and solving the UAV deployment location optimization subproblem of UAV-assisted ground users using a particle swarm optimization algorithm to obtain the UAV deployment location optimization subproblem of UAV-assisted ground users corresponding to the current iteration number. The human-machine deployment location matrix; wherein, the spectrum allocation subproblem of the UAV-assisted communication, the correlation optimization subproblem of the UAV serving ground users and the UAV deployment location optimization subproblem are obtained by decomposing the multi-UAV on-demand deployment and spectrum allocation joint optimization problem model that satisfies the minimum number of UAVs using the block coordinate descent method; (4) Calculate the data transmission rate of the UAV-assisted ground communication corresponding to the current iteration number based on the channel allocation matrix of the UAV serving ground users, the correlation matrix of the UAV serving ground users and the UAV deployment location matrix corresponding to the current iteration number; (5) When the data transmission rate difference is less than the set threshold, determine the channel allocation matrix of the UAV serving ground users, the correlation matrix of the UAV serving ground users and the UAV deployment location matrix corresponding to the current iteration number as the optimal matrix set, and output the optimal matrix set and the minimum number of UAVs; the data transmission rate difference is the difference between the data transmission rate of the UAV-assisted ground communication corresponding to the current iteration number and the data transmission rate of the UAV-assisted ground communication corresponding to the previous iteration number.

[0006] To achieve the above objectives, the present invention also provides a joint optimization system for on-demand deployment and spectrum allocation of multiple unmanned aerial vehicles (UAVs), comprising: an input parameter acquisition module for acquiring input parameters corresponding to a target area; the input parameters include a ground user location matrix, the number of available channels, and the range of UAV-assisted ground communication; the ground user location matrix includes ground user location information and ground user number information; a minimum number of UAVs determination module for determining the minimum number of UAVs required to meet the target area task completion rate based on the input parameters and a UAV number determination algorithm based on pre-estimation and simulated annealing; and a matrix calculation module for solving the spectrum allocation subproblem of UAV-assisted communication using a spectrum allocation algorithm based on interference cancellation and channel reuse to obtain the channel allocation matrix of UAVs serving ground users corresponding to the current iteration number, solving the association relationship optimization subproblem of UAVs serving ground users using a local iterative optimization algorithm to obtain the association relationship matrix of UAVs serving ground users corresponding to the current iteration number, and solving the UAV deployment location optimization subproblem using a particle swarm optimization algorithm to obtain the current iteration number. The system includes a UAV deployment location matrix corresponding to the number of UAVs; wherein the spectrum allocation subproblem of UAV-assisted communication, the correlation optimization subproblem of UAV service ground users, and the UAV deployment location optimization subproblem are obtained by decomposing the joint optimization problem model of multi-UAV on-demand deployment and spectrum allocation that satisfies the minimum number of UAVs using the block coordinate descent method; a data transmission rate calculation module is used to calculate the data transmission rate of UAV-assisted ground communication corresponding to the current iteration number based on the channel allocation matrix of UAV service ground users, the correlation matrix of UAV service ground users, and the UAV deployment location matrix corresponding to the current iteration number; a result output module is used to determine the channel allocation matrix of UAV service ground users, the correlation matrix of UAV service ground users, and the UAV deployment location matrix corresponding to the current iteration number as the optimal matrix set when the data transmission rate difference is less than a set threshold, and output the optimal matrix set and the minimum number of UAVs; the data transmission rate difference is the difference between the data transmission rate of UAV-assisted ground communication corresponding to the current iteration number and the data transmission rate of UAV-assisted ground communication corresponding to the previous iteration number.

[0007] According to specific embodiments provided by the present invention, the technical effects disclosed in the present invention are as follows: Addressing the resource efficiency and fairness issues faced in UAV-assisted ground communication, the present invention studies a joint optimization method for on-demand deployment of UAVs and spectrum allocation. It utilizes the block coordinate descent method to reduce the complexity of the joint optimization problem and proposes a spectrum allocation algorithm based on interference cancellation and channel reuse, an optimization algorithm for the association between UAV services and ground users based on local iterative optimization, and a UAV deployment location optimization algorithm based on particle swarm optimization to improve the data transmission performance of UAV-assisted ground communication and ensure the fairness of frequency usage by ground users in UAV-assisted ground communication. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating the method for joint optimization of on-demand deployment and spectrum allocation of multiple UAVs provided in an embodiment of the present invention.

[0010] Figure 2 This is a schematic diagram of the communication network structure when an unmanned aerial vehicle (UAV) assists a ground user, provided in an embodiment of the present invention.

[0011] Figure 3 A schematic diagram of the algorithm for determining the number of drones based on pre-estimation and simulated annealing provided in an embodiment of the present invention;

[0012] Figure 4 A comparison of simulation results of the minimum number of UAVs required by the algorithm for determining the minimum number of UAVs based on pre-estimation and simulated annealing provided in this embodiment of the invention and the spiral cluster deployment algorithm proposed in the prior art.

[0013] Figure 5 shows the optimization results of UAV deployment location and the communication interference relationship between UAVs provided in the embodiment of the present invention; Figure 5(a) shows the optimization results of the initial deployment and the communication interference relationship between UAVs; Figure 5(b) shows the optimization results of the optimized deployment and the communication interference relationship between UAVs.

[0014] Figure 6 This is a diagram showing the channel allocation results of an unmanned aerial vehicle (UAV) communication system optimized by a spectrum allocation algorithm based on interference cancellation and channel multiplexing, as provided in an embodiment of the present invention.

[0015] Figure 7 A comparison chart of data transmission rates for UAV-assisted communication provided in this embodiment of the invention;

[0016] Figure 8 The convergence performance diagram of the joint optimization algorithm when 300 ground users are distributed in different ranges, as provided in the embodiments of the present invention;

[0017] Figure 9 The figure shows a comparison of the data transmission performance of UAV-assisted ground communication under different strategies provided in the embodiments of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Example 1: To address the cost issues and frequency fairness issues for ground users in UAV-assisted ground communication tasks, this example provides a joint optimization method for on-demand deployment and spectrum allocation of multiple UAVs serving ground users, based on the principle of shared spectrum resources. First, an algorithm for determining the number of UAVs based on pre-estimation and simulated annealing is used to achieve on-demand deployment when UAVs perform assisted ground communication tasks. Then, the joint optimization problem is decomposed into three sub-problems using block coordinate descent: spectrum allocation for UAV-assisted communication, optimization of the association relationship between UAVs serving ground users, and optimization of UAV deployment locations. Iterative optimization is then performed. To solve these sub-problems, this example provides a spectrum allocation algorithm based on interference cancellation and channel reuse, an algorithm for optimizing the association relationship between UAVs serving ground users based on local iterative optimization, and a UAV deployment location optimization algorithm based on particle swarm optimization to improve data transmission performance during UAV-assisted ground communication tasks and ensure fairness in frequency allocation for ground users. Simulation results show that, compared with existing strategies, this invention can ensure that assisted ground communication tasks are performed with the fewest possible UAVs and effectively improve data transmission rates and fairness in frequency allocation for ground users during UAV-assisted ground communication tasks.

[0020] The main contributions of this embodiment can be summarized as follows: (1) A hierarchical solution for joint optimization of on-demand deployment and spectrum allocation of multiple UAVs is proposed to solve the problems of cost minimization and frequency fairness for ground users in UAVs performing auxiliary ground communication tasks. In the first stage, an optimization algorithm based on pre-estimation and simulated annealing is used to solve the minimum number requirement for on-demand deployment of UAVs, so as to reduce the cost of UAVs performing auxiliary ground communication tasks. In the second stage, the Block Coordinate Descent (BCD) method is used to decompose the joint optimization problem of on-demand deployment and spectrum allocation of UAVs into sub-problems such as spectrum allocation for UAV-assisted communication, optimization of the correlation between UAVs serving ground users, and optimization of UAV deployment location, and iterative optimization is performed to reduce the complexity of the joint optimization problem. (2) A spectrum allocation algorithm based on interference cancellation and channel reuse is proposed to solve the problem of frequency fairness for ground users in UAV-assisted ground communication tasks. The algorithm first calculates and analyzes the interference relationship between UAVs, and allocates a channel for UAV-assisted communication without interference. Then, it determines the channel reuse priority based on the data transmission rate of UAV-assisted communication, and allocates channels to UAVs with lower data transmission rates based on the channel reuse priority, thus allocating more communication time to their corresponding ground users. This achieves the goal of maximizing the minimum data transmission rate when UAV-assisted ground communication is performed, effectively improving spectrum utilization efficiency and fairness. (3) A local iterative optimization algorithm for the association relationship between UAVs and ground users is proposed to solve the nonlinear integer programming problem of the association relationship between multiple UAVs and ground users. The algorithm first extracts ground users in the common coverage and edge areas of multiple UAVs based on UAV data and deployment location changes, calculates their data transmission rates and sorts them. Then, it prioritizes allocating ground users in the common coverage of multiple UAVs to UAVs with higher data transmission rates, dynamically adjusting the association relationship between UAVs and ground users to improve the minimum data transmission rate when UAVs serve ground users.

[0021] like Figure 1As shown, the multi-UAV on-demand deployment and spectrum allocation joint optimization method provided in this embodiment includes step 100: obtaining input parameters corresponding to the target area; the input parameters include a ground user location matrix, the number of available channels, and the range of UAV-assisted ground communication (e.g., R0); the ground user location matrix includes ground user location information and ground user quantity information. Step 200: determining the minimum number of UAVs required to meet the target area task completion rate based on the input parameters and a UAV quantity determination algorithm based on pre-estimation and simulated annealing. Step 300: Solve the spectrum allocation subproblem of UAV-assisted communication using a spectrum allocation algorithm based on interference cancellation and channel multiplexing to obtain the channel allocation matrix for UAVs serving ground users corresponding to the current iteration number. Solve the correlation optimization subproblem for UAVs serving ground users using a correlation optimization algorithm based on local iterative optimization to obtain the correlation matrix for UAVs serving ground users corresponding to the current iteration number. Solve the UAV deployment location optimization subproblem using a particle swarm optimization algorithm to obtain the UAV deployment location matrix for the current iteration number. The spectrum allocation subproblem, the correlation optimization subproblem for UAVs serving ground users, and the UAV deployment location optimization subproblem are obtained by decomposing the multi-UAV on-demand deployment and spectrum allocation joint optimization problem model that satisfies the minimum number of UAVs using the block coordinate descent method. Step 400: Calculate the data transmission rate of UAV-assisted ground communication corresponding to the current iteration number based on the channel allocation matrix, the correlation matrix, and the deployment location matrix for UAVs serving ground users corresponding to the current iteration number. Step 500: When the data transmission rate difference is less than a set threshold, determine the optimal matrix set as the channel allocation matrix, association matrix, and deployment location matrix of the UAV serving ground users corresponding to the current iteration number, and output the optimal matrix set and the minimum number of UAVs. The data transmission rate difference is the difference between the data transmission rate of UAV-assisted ground communication corresponding to the current iteration number and the data transmission rate of UAV-assisted ground communication corresponding to the previous iteration number. Specifically, when the data transmission rate difference is greater than or equal to the set threshold, increment the iteration number by 1, and determine the channel allocation matrix, association matrix, and deployment location matrix of the UAV serving ground users corresponding to the current iteration number as the channel allocation matrix, association matrix, and deployment location matrix of the UAV serving ground users corresponding to the previous iteration number, then return to step 300.

[0022] Before implementing the joint optimization method for on-demand deployment and spectrum allocation of multiple UAVs provided in this embodiment of the invention, it is necessary to first determine the communication network when UAVs assist ground users and the joint optimization problem model for on-demand deployment and spectrum allocation of multiple UAVs constructed based on the communication network when UAVs assist ground users. For example... Figure 2 As shown, M rotary-wing UAVs are deployed at an altitude of H to provide communication services to K fixed IoT ground users (hereinafter referred to as ground users). Each UAV provides communication services to ground users within its coverage area. To avoid mutual interference caused by multiple UAVs sharing the spectrum, it is assumed that the spectrum resources among the multiple UAVs are used in the form of Orthogonal Frequency Division Multiple Access (OFDMA), and spectrum reuse is used between UAVs outside the coverage area and ground users to improve spectrum efficiency. Therefore, multi-UAV assisted ground communication will generate a spectrum allocation strategy based on the interference relationship between the UAVs.

[0023] In this embodiment, step 300 specifically includes: 1) Based on the association matrix of UAV service ground users and the UAV deployment location matrix corresponding to the previous iteration number, solving the spectrum allocation sub-problem of UAV-assisted communication using a spectrum allocation algorithm based on interference cancellation and channel multiplexing to obtain the channel allocation matrix of UAV service ground users corresponding to the current iteration number; 2) Based on the channel allocation matrix of UAV service ground users and the UAV deployment location matrix corresponding to the previous iteration number, solving the association optimization sub-problem of UAV service ground users using a local iterative optimization algorithm to obtain the association matrix of UAV service ground users corresponding to the current iteration number; 3) Based on the channel allocation matrix of UAV service ground users and the association matrix of UAV service ground users corresponding to the previous iteration number, solving the UAV deployment location optimization sub-problem using a particle swarm optimization algorithm to obtain the UAV deployment location matrix corresponding to the current iteration number; wherein, based on the channel allocation matrix of UAV service ground users corresponding to the current iteration number, the time ratio of UAV service ground users corresponding to the current iteration number is calculated.

[0024] In this embodiment, step 400 specifically includes: calculating the data transmission rate of UAV-assisted ground communication corresponding to the current iteration number based on the channel allocation matrix of UAV serving ground users, the association matrix of UAV serving ground users, the UAV deployment location matrix, and the time ratio of UAV serving ground users corresponding to the current iteration number.

[0025] Thanks to the information transmission gain brought by the takeoff of the drone, the line-of-sight (LoS) link between the drone and the ground user is predominant in non-urban environments such as rural areas. Therefore, the air-to-ground channel and the air-to-air channel can be equivalently modeled as a free-space path loss model, and the channel gain coefficient h between the drone m and the ground user k is... m,k It can be represented as Where β0 represents the channel power gain at a reference distance of 1 meter, d m,k This represents the distance between the drone m and the ground user k.

[0026] Assume S K S represents the minimum received signal strength that all ground users can correctly demodulate. U Represents the minimum received signal strength that all UAVs can correctly demodulate, and the maximum distance for normal communication between UAV m and ground users. Where, p m Let H be the transmission power of the UAV communication system. When the UAV flies at altitude H, the radius of the area covered by the UAV (m) is:

[0027] The maximum interference distance of drone m to other drones is:

[0028] Assuming the available spectrum is divided into a series of C channels of equal bandwidth B and mutually orthogonal, and each UAV communication system can be allocated multiple channels, then the constraint relationship can be expressed as follows: Among them, w m，c The allocation relationship between the UAV m communication system and channel c, when w m,c When w = 1, channel c is assigned to UAV m, when w m,c When = 0, channel c is not allocated to UAV m. To be more realistic, multiple UAVs can share the multiplexed spectrum, meaning each channel can be allocated to M UAVs. Therefore, the channel allocation relationship for UAVs serving ground users can be expressed as:

[0029] Based on the channel allocation and interference relationships in UAV-assisted ground communication missions, when the distance between UAVs is greater than d... U Multiple drones can only reuse the same channel if they are in a state where interference occurs. Therefore, the distance d between drone m and drone n is... Um,Un Distance d from interference U Relationship α Um,Un It can be represented as: For all α Um,Un UAVs m and n, whose values ​​are equal to 1, cannot be assigned to the same channel to prevent mutual interference. Therefore, the relationship between UAVs serving ground users and being assigned to the same channel can be expressed as: wUm,c and w Un,c These represent the allocation relationship between the communication systems of UAVs m and n and channel c, respectively.

[0030] The correlation between UAV m and ground user k communication can be represented as follows: To simplify the analysis, within the ground user cluster serving the UAV, it is assumed that the transmit power of the UAV communication system is evenly distributed across its allocated channels, i.e. Where, p m,c This represents the transmit power of the communication system of UAV m when channel c is assigned to UAV m.

[0031] When a drone provides communication services to only one ground user, its achievable data transmission rate can be expressed as: Among them, h m,k Let σ be the channel gain coefficient between the UAV m and the ground user k. 2 This represents the power spectral density of ambient noise.

[0032] Considering the limited number of available channels, when UAVs provide communication services to ground users, they employ Time Division Multiple Access (TDMA) technology. Therefore, the actual achievable data transmission rate for each ground user can be expressed as: Where, τ k This represents the proportion of communication time allocated to ground user k within a time period, 0 ≤ τ k ≤1. And there is Among them, J m The number of ground users serving the drone m.

[0033] In practical applications, to reduce the cost of drone missions, the goal is to achieve the highest mission completion rate with the fewest possible drones through mission planning. Simultaneously, to ensure fairness, factors such as the number of drones, deployment locations, the relationship between drones and ground users, spectrum allocation, and communication service time are optimized to maximize the minimum data transmission rate for all ground users. Therefore, the optimization problem can be modeled as follows:

[0034]

[0035] Where M represents the minimum number of drones, C represents the number of available channels, and Q = {q1,...,q} M} represents the drone deployment location matrix, q m ∈R 2×1 The horizontal coordinate q of drone m M Let q1 represent the horizontal coordinate of UAV M, and q1 represent the horizontal coordinate of UAV 1; w = {w1,...,w M} represents the channel allocation matrix for UAV services to ground users, w M Let w1 represent the channel allocation vector for UAV M to assist ground user communication, and w1 represent the channel allocation vector for UAV 1 to assist ground user communication; γ = {γ1,...,γ} M} represents the relationship matrix between unmanned aerial vehicle (UAV) services and ground users, γ M Let γ1 represent the association vector of UAV M assisting ground user communication, and let C1 represent the association vector of UAV 1 assisting ground user communication. C1 represents the minimum received signal strength limit for ground users; C2 is the constraint on the UAV communication interference state; C3 is the association constraint of UAV serving ground users. C4-C7 are channel allocation constraints for UAV-assisted communication; each channel can be allocated to multiple UAVs, but UAVs with interference relationships cannot share the same channel; C8 is the constraint on the data transmission time allocation ratio for the same UAV serving multiple ground users; and C9 is the constraint on minimizing the number of UAVs.

[0036] In this embodiment, step 200 specifically includes: 1) determining the minimum number of drones based on the ground user location matrix; 2) transforming the subproblem of determining the minimum number of drones to meet the target area task completion rate into a discrete M-center problem; 3) solving the discrete M-center problem using the simulated annealing algorithm based on the initially determined minimum number of drones to obtain the minimum number of drones to meet the target area task completion rate.

[0037] The detailed process of the above steps is as follows: In order to provide communication services to all ground users, each ground user must be within the communication coverage of at least one UAV. Therefore, the subproblem of determining the minimum number of UAVs required to achieve the mission completion rate can be expressed as follows: Where, q k q represents the horizontal coordinate of ground user k. m ∈R 2×1 Let m represent the horizontal coordinate of the drone. It is not difficult to see that the drone number determination subproblem given by equation (15) is a Geometric Disk Cover (GDC) problem, that is, covering all points in a given radius area using the minimum number of disks. Therefore, this embodiment of the invention provides a drone number determination algorithm based on pre-estimation and simulated annealing. First, based on the distribution of ground users (i.e., ground user location information and ground user number information), the demand when the number of drones is M is pre-estimated, transforming the problem in equation (15) into a discrete M-center problem, that is, covering all ground users with M identical disks with the smallest possible radius, expressed as: d mGiven the coverage radius of the m-th UAV, this is a typical NP-hard problem. We then use simulated annealing to solve for the minimum radius in equation (15) until we find a minimum radius less than or equal to the maximum coverage radius r of the m-th UAV. m The number of drones, when the drones have the same transmission power, means that all drones have the same maximum coverage radius, denoted by R0 instead of r. m The specific process is as follows: Figure 3 As shown.

[0038] Given the number of UAVs and their deployment locations, as well as the relationship between the UAVs and the ground users they serve, the spectrum allocation optimization subproblem for UAV-assisted ground communication can be transformed into formula (17).

[0039] Proposition 1: When the achievable data transmission rate of UAV services to ground users reaches its maximum, the amount of data transmitted by the UAV-assisted ground communication system within a time period is equal.

[0040] Proof: According to equation (11), it is easy to see that when the number of UAVs and their deployment locations are fixed, the achievable data transmission rate for UAV-assisted ground user communication is determined. From equation (17), it can be seen that the achievable data transmission rate for UAV-assisted ground communication is determined by both channel resources and time resources. First, the same transmission time is allocated to ground users served by the UAVs, channel allocation is implemented, and the maximum achievable data transmission rate is calculated based on the channel allocation. Simultaneously, a larger proportion τ of transmission time is allocated to ground users with lower data transmission rates. k This increases the amount of data transmitted, ultimately ensuring that the amount of data transmitted by the drone within a given time period is constant. Therefore, when the achievable data transmission rate for drones serving ground users reaches its maximum, the amount of data transmitted by the drone-assisted ground communication system within a given time period is constant.

[0041]

[0042] Based on equations (12) and (13) and Proposition 1, the time ratio constraint for UAV services to ground users is expressed as follows: To solve the spectrum allocation problem of UAV-assisted communication, a spectrum allocation algorithm based on interference cancellation and channel reuse is proposed. This algorithm first calculates the mutual interference relationship between UAVs according to equation (7), allocates a channel to each UAV-assisted communication system without interference, and removes this channel from the set of UAV-assisted communication channels to be allocated where mutual interference exists. If the set of channels to be allocated for UAV m corresponding to the lowest data transmission rate of the UAV serving ground users is not empty, then a channel c is allocated to it. Simultaneously, channel c is removed from the set of channels to be allocated for other UAV-assisted ground communication systems that have mutual interference with UAV m. This process is repeated until all UAV-assisted ground communication channels to be allocated are empty. The specific flowchart of the proposed spectrum allocation algorithm based on interference cancellation and channel reuse is shown in Table 1.

[0043] Table 1. Flowchart of spectrum allocation algorithm based on interference cancellation and channel reuse

[0044]

[0045]

[0046] Given the number of UAVs and their deployment locations, as well as the spectrum allocation results for UAV-assisted ground communication, the subproblem of optimizing the relationship between UAV services and ground users can be transformed into equation (19).

[0047] Due to the correlation between drone services and ground users γ m,k It is a 0-1 integer variable, and the correlation determination problem given by equation (19) is a nonlinear integer programming problem. To simplify the solution process, this paper proposes an association relationship optimization algorithm for UAV services to ground users based on local iterative optimization. The algorithm first calculates and sorts the data transmission rates of ground users in areas covered by multiple UAVs and edge areas. Then, it prioritizes assigning ground users covered by multiple UAVs to UAVs with larger data transmission rates until the minimum transmission rate of UAV services to ground users can no longer be increased. The specific process of the proposed UAV service to ground user association relationship optimization algorithm is shown in Table 2.

[0048]

[0049] Table 2. Flowchart of the algorithm for optimizing the relationship between UAV services and ground users based on iterative optimization.

[0050]

[0051]

[0052] Given the spectrum allocation results for UAV-assisted ground communication and the correlation between UAV services and ground users, the UAV deployment location optimization problem can be transformed into formula (20).

[0053]

[0054] The deployment location of UAVs is affected by the distribution of ground users, the mutual interference between UAVs, and the proportion of communication time, resulting in a complex coupling relationship. This paper uses the particle swarm optimization algorithm to optimize the location of each UAV one by one. Based on the existing UAV locations, it searches for deployment locations where each UAV has a higher data transmission rate for assisting ground communication. The particle velocity update formula is Equation (21).

[0055]

[0056] Where ρ represents the particle inertia factor, v k-1 Let p represent the velocity of the particle in the (k-1)th iteration, c1 and c2 represent the individual and social acceleration constants respectively, r1 and r2 represent two random numbers in the range [0,1], and p best and g best These represent the historical best position of the particle itself and the historical best position of the particle swarm, respectively. The specific process of the particle swarm-based UAV deployment location optimization algorithm is shown in Table 3.

[0057] Table 3. Flowchart of the UAV Deployment Location Optimization Algorithm Based on Particle Swarm Optimization

[0058]

[0059] This invention proposes a joint optimization algorithm for on-demand deployment and spectrum allocation of unmanned aerial vehicles (UAVs) based on the block coordinate descent method. First, the minimum number of UAVs required is determined. Then, based on the acquired number of UAVs, the algorithm solves for spectrum allocation for UAV-assisted ground communication, optimization of the association between UAVs and ground users, and optimization of UAV deployment locations. Iterative optimization is then performed to find the optimal solution step by step. The specific flowchart of the joint optimization algorithm is shown in Table 4.

[0060] Table 4. Flowchart of the Joint Optimization Algorithm for UAV Deployment and Spectrum Allocation Based on Block Coordinate Descent Method

[0061]

[0062]

[0063] To verify the performance and feasibility of the method protected in the embodiments of the present invention, the main parameters used in the simulation process are set as shown in Table 5.

[0064] Table 5 Simulation Parameter Table

[0065]

[0066]

[0067] Figure 4 A comparison is presented between an algorithm for determining the minimum number of UAVs based on pre-estimation and simulated annealing and a proposed spiral clustering deployment algorithm. Figure 4 It is evident that both algorithms can calculate the minimum number of drones required to achieve full coverage of ground users based on their distribution range. Because the proposed algorithm for determining the minimum number of drones utilizes the exploratory capability of simulated annealing, it effectively avoids getting trapped in local optima and iteratively optimizes drone deployment locations to obtain fewer drones to assist ground user communication. Regardless of changes in the distribution range of ground users, the proposed algorithm consistently outperforms existing algorithms.

[0068] To verify the performance of the joint optimization algorithm protected in this embodiment of the invention, 400 ground users were randomly and uniformly deployed within a 12km×12km area. Figure 5 shows the optimization results of the UAV deployment locations and the communication interference relationship between UAVs. In Figure 5, ground users within a circle are served by the same UAV. The numbers next to the UAV deployment locations represent UAV numbers, and the solid lines between UAVs indicate communication interference relationships between them, meaning they cannot reuse the same channel. As shown in Figure 5, each ground user is covered by at least one UAV, achieving the goal of serving all ground users. In the initial deployment, since the optimization of the relationship between channel allocation and UAV service to ground users was not considered, there was a lot of mutual interference between UAVs, resulting in a low channel reuse rate for the UAV-assisted communication system, as shown in Figure 5(a). After optimizing the UAV deployment locations using the proposed algorithm, mutual interference between UAVs was reduced, thereby improving the channel reuse rate for UAV-assisted communication. At the same time, the relationship matrix of UAV service to ground users was also optimized, allowing UAVs with less interference to serve more ground users, promoting fairness in UAV service to ground users, as shown in Figure 5(b). Furthermore, Figure 5 further illustrates that the proposed algorithm can optimize and adjust the deployment location of UAVs based on the distribution of ground users, effectively reducing mutual interference between UAVs while ensuring coverage of all ground users, and optimizing the correlation between UAV services and ground users. In the figure, solid circles represent ground users, solid pentagrams represent deployment locations, dashed lines represent coverage areas, and solid lines represent interference relationships.

[0069] Figure 6The proposed spectrum allocation algorithm based on interference cancellation and channel multiplexing optimizes the channel allocation results for the UAV communication system. The algorithm optimizes the channel allocation matrix for UAVs with lower data transmission rates serving ground users based on the mutual interference relationship between UAVs. When there is no mutual interference between UAVs, the UAV can be allocated all channels, such as... Figure 6 In this algorithm, UAV No. 2 is assigned all channels. When there is interference between UAVs, they cannot reuse the same channel. For example, UAVs No. 3 and No. 5 are close together and interfere with each other; therefore, they cannot share the same channel. Thus, the proposed algorithm can allocate channels to all UAVs without interference based on the inter-UAV interference matrix.

[0070] Figure 7 The data transmission rates of UAV-assisted communication are compared. From... Figure 7 It can be seen that the proposed algorithm can improve the minimum transmission rate and fairness of UAV-assisted ground communication by adjusting the channel allocation coefficient, UAV deployment location and the correlation between UAV service ground users. At the same time, by eliminating interference between UAVs and channel reuse, more channels are allocated to UAVs with lower data transmission rates, thereby increasing the minimum data transmission rate of UAV-assisted ground communication by 20.61%.

[0071] Figure 8 The convergence performance of the joint optimization algorithm is presented when 300 ground users are distributed at different ranges. From Figure 8 It is known that when the distribution range of ground users exceeds a certain value, the joint optimization algorithm can converge after 2 to 3 iterations. However, when the distribution range of ground users is below this value, the joint optimization algorithm requires multiple iterations to converge. Furthermore, the wider the distribution range of ground users, the higher the minimum data transmission rate for UAV-assisted ground communication. This is because the number of ground users is fixed, and their distribution range is wider. Since the range of UAV-assisted ground communication is fixed and finite, the more UAVs are needed, and the greater the distance between UAVs, the less mutual interference caused by channel multiplexing in UAV-assisted ground communication. Moreover, as the number of UAVs increases, each UAV serves fewer ground users, thus resulting in a higher minimum transmission rate for UAVs serving ground users.

[0072] Figure 9This paper presents a comparison of data transmission performance for UAV-assisted ground communication under different strategies. The "fixed deployment location" strategy involves the UAV hovering at its initial location to assist ground user communication, optimizing the spectrum allocation for UAV-assisted communication and the correlation between UAV services to ground users. The "channel equalization" strategy, under the constraint of communication interference between UAVs, evenly distributes spectrum resources among all UAV communication systems to maximize the minimum number of channels obtained by UAV-assisted communication and optimize the correlation between UAV services to ground users and UAV deployment locations. The "fixed correlation" strategy determines the correlation between UAV services to ground users based on the minimum distance between the UAV and the ground user, optimizing the spectrum allocation for UAV-assisted communication and UAV deployment locations. Figure 9 As can be seen, the proposed algorithm achieves better performance than the existing strategies of fixed deployment location, channel equalization, and fixed association, and is unaffected by the distribution range of ground users. Compared with the "fixed association" strategy, the minimum data transmission rate of UAV-assisted ground communication optimized by the proposed algorithm is improved by 8.3%-25.2%. Furthermore, the minimum data transmission rate of UAV-assisted ground communication increases with the increase in the distribution range of ground users, further demonstrating... Figure 8 This conclusion is drawn because the more dispersed the ground users are and the greater the distance between drones, the less mutual interference caused by channel reuse and the higher the spectrum reuse rate.

[0073] Unmanned aerial vehicles (UAVs) are widely used in both military and civilian fields due to their high flexibility and maneuverability, and will continue to develop rapidly in the future. As a supplement and enhancement to terrestrial communication networks, UAV-assisted ground communication will play an important role in applications such as emergency communication and blind spot coverage in the field. This invention addresses the resource efficiency and fairness issues faced in UAV-assisted ground communication by studying a joint optimization method for on-demand deployment of UAVs and spectrum allocation. It utilizes the block coordinate descent method to reduce the complexity of the joint optimization problem and proposes a spectrum allocation algorithm based on interference cancellation and channel multiplexing, an optimization algorithm for the association between UAVs and ground users based on local iterative optimization, and a UAV deployment location optimization algorithm based on particle swarm optimization to improve the data transmission performance of UAV-assisted ground communication and ensure the fairness of frequency use for ground users. Simulation results show that, compared with existing strategies, the proposed algorithm can ensure that the auxiliary ground communication task is performed with the fewest UAVs and effectively improve the data transmission rate and the fairness of frequency use for ground users in UAV-assisted ground communication.

[0074] Example 2: To execute the method corresponding to Example 1 above and achieve the corresponding functions and technical effects, a multi-UAV on-demand deployment and spectrum allocation joint optimization system is provided below, including: an input parameter acquisition module, used to acquire input parameters corresponding to the target area; the input parameters include a ground user location matrix, the number of available channels, and the range of UAV-assisted ground communication; the ground user location matrix includes ground user location information and ground user number information; a minimum UAV number determination module, used to determine the minimum number of UAVs required to meet the target area task completion rate based on the input parameters and a UAV number determination algorithm based on pre-estimation and simulated annealing; a matrix calculation module, used to solve the spectrum allocation subproblem of UAV-assisted communication using a spectrum allocation algorithm based on interference cancellation and channel reuse to obtain the channel allocation matrix of UAV-serving ground users corresponding to the current iteration number, use the association relationship optimization algorithm of UAV-serving ground users based on local iterative optimization to solve the association relationship optimization subproblem of UAV-serving ground users to obtain the association relationship matrix of UAV-serving ground users corresponding to the current iteration number, and use the UAV deployment location optimization algorithm based on particle swarm optimization to solve the UAV deployment location... The optimization subproblem yields the UAV deployment location matrix corresponding to the current iteration number. The spectrum allocation subproblem for UAV-assisted communication, the correlation optimization subproblem for UAV-serving ground users, and the UAV deployment location optimization subproblem are obtained by decomposing the joint optimization problem model of multi-UAV on-demand deployment and spectrum allocation to satisfy the minimum number of UAVs using the block coordinate descent method. A data transmission rate calculation module calculates the data transmission rate of UAV-assisted ground communication corresponding to the current iteration number based on the channel allocation matrix, correlation matrix, and deployment location matrix for UAV-serving ground users. A result output module determines the optimal matrix set of the channel allocation matrix, correlation matrix, and deployment location matrix for UAV-serving ground users corresponding to the current iteration number when the data transmission rate difference is less than a set threshold, and outputs the optimal matrix set and the minimum number of UAVs. The data transmission rate difference is the difference between the data transmission rate of UAV-assisted ground communication corresponding to the current iteration number and the data transmission rate of UAV-assisted ground communication corresponding to the previous iteration number.

[0075] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. Specific examples have been used to illustrate the principles and implementation methods of the invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of the invention; furthermore, those skilled in the art will recognize that, based on the ideas of the invention, there will be changes in specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the invention.

Claims

1. A method for joint optimization of on-demand deployment and spectrum allocation of multiple unmanned aerial vehicles (UAVs), characterized in that, include: Obtain the input parameters corresponding to the target area; the input parameters include the ground user location matrix, the number of available channels, and the range of UAV-assisted ground communication. The ground user location matrix includes ground user location information and ground user quantity information; Based on the input parameters and an algorithm for determining the number of drones using pre-estimated and simulated annealing methods, the minimum number of drones required to achieve the target area mission completion rate is determined. Specifically, this includes: Based on the ground user location matrix, the minimum number of drones is initially determined; The subproblem of determining the minimum number of drones required to achieve the target area task completion rate is transformed into a discrete M-central problem; M represents the minimum number of drones. Based on the initially determined minimum number of drones, the discrete M-center problem is solved using the simulated annealing algorithm to obtain the minimum number of drones required to satisfy the target area task completion rate. The subproblem of determining the minimum number of drones required to satisfy the task completion rate is expressed as: ； Where, q k q represents the horizontal coordinate of ground user k. m ∈R 2×1 Represents the horizontal coordinate of drone m; γ m,k This indicates the relationship between unmanned aerial vehicle (UAV) services and ground users; r K This represents the communication range of ground user k assisted by the drone, i.e., the coverage radius threshold. The spectrum allocation subproblem of UAV-assisted communication is solved using a spectrum allocation algorithm based on interference cancellation and channel multiplexing, yielding the channel allocation matrix for UAVs serving ground users at the current iteration number. The correlation optimization subproblem for UAVs serving ground users is solved using a local iterative optimization algorithm, yielding the correlation matrix for UAVs serving ground users at the current iteration number. Finally, the UAV deployment location optimization subproblem is solved using a particle swarm optimization algorithm, yielding the UAV deployment location matrix for the current iteration number. The spectrum allocation subproblem, the correlation optimization subproblem for UAVs serving ground users, and the UAV deployment location optimization subproblem are obtained by decomposing the joint optimization problem model of multi-UAV on-demand deployment and spectrum allocation that satisfies the minimum number of UAVs using the block coordinate descent method. Based on the channel allocation matrix, the correlation matrix, and the deployment location matrix for UAVs serving ground users at the current iteration number, the data transmission rate of UAV-assisted ground communication at the current iteration number is calculated. The channel multiplexing priority is determined based on the data transmission rate of UAV-assisted communication, and channels are allocated to UAVs with lower data transmission rates based on the channel multiplexing priority, so that more communication time is allocated to their corresponding ground users. When the data transmission rate difference is less than a set threshold, the channel allocation matrix, the association matrix, and the drone deployment location matrix corresponding to the current iteration number are determined as the optimal matrix set, and the optimal matrix set and the minimum number of drones are output. The data transmission rate difference is the difference between the data transmission rate of drone-assisted ground communication corresponding to the current iteration number and the data transmission rate of drone-assisted ground communication corresponding to the previous iteration number.

2. The method for joint optimization of on-demand deployment and spectrum allocation of multiple UAVs according to claim 1, characterized in that, Before retrieving the input parameters corresponding to the target region, the process also includes: Determine the communication network when using drones to assist ground users; Based on the communication network when UAVs assist ground users, a joint optimization problem model for on-demand deployment and spectrum allocation of multiple UAVs is constructed. The joint optimization model for on-demand deployment and spectrum allocation of multiple UAVs is as follows: Where M represents the minimum number of drones, and Q = {q1,...,q} M } represents the drone deployment location matrix, q M Let q1 represent the horizontal coordinate of UAV M, and q1 represent the horizontal coordinate of UAV 1; w = {w1,...,w M } represents the channel allocation matrix for UAV services to ground users, w M Let w1 represent the channel allocation vector for UAV M to assist ground user communication, and w1 represent the channel allocation vector for UAV 1 to assist ground user communication; γ = {γ1,...,γ} M } represents the relationship matrix between unmanned aerial vehicle (UAV) services and ground users, γ M Let τ represent the association vector for UAV M to assist ground user communication, and γ1 represent the association vector for UAV 1 to assist ground user communication; k This represents the proportion of communication time allocated to ground user k within a time period, 0 ≤ τ k ≤1; w m，c The diagram illustrates the allocation relationship between the UAV m communication system and channel c; C represents the number of available channels; B represents the channel bandwidth; p m,c σ represents the transmit power of the communication system of UAV m when channel c is assigned to UAV m; 2 h represents the power spectral density of ambient noise. m,k This represents the channel gain coefficient between the UAV m and the ground user k; C1 represents the constraint condition for the communication distance between the UAV and the ground user, indicating that it cannot be less than the minimum received signal strength limit of the ground user; d m,k β0 represents the distance from the UAV m to the ground user k; β0 represents the channel power gain at a reference distance of 1 meter; S K D represents the minimum received signal strength that all ground users can correctly demodulate; m p represents the maximum communication distance between the drone m and the ground user for normal communication. m This refers to the transmission power of the UAV's m-communication system. C2 represents the constraint condition for the value of the UAV communication interference state; α Um,Un dU represents the distance between drone m and drone n. m U n Distance d from interference U Relationship; C3 represents the relational constraints between UAV services and ground users; γ m，k This indicates the relationship between the communication between the UAV m and the ground user k. C4-C7 are the channel allocation constraints for UAV-assisted communication. Each channel can be allocated to multiple UAVs, and UAVs that are interfering with each other cannot share the same channel. Um,c and w Un,c Let m and n represent the communication systems of UAVs m and n, respectively, and their corresponding channel c. C8 represents the constraint on the data transmission time allocation ratio for the same UAV serving multiple ground users; J m The number of ground users serving the drone m; C9 is the constraint that minimizes the number of drones.

3. The method for joint optimization of on-demand deployment and spectrum allocation of multiple UAVs according to claim 1, characterized in that, The spectrum allocation subproblem of UAV-assisted communication is solved using a spectrum allocation algorithm based on interference cancellation and channel multiplexing. This yields the channel allocation matrix for UAV-served ground users corresponding to the current iteration number, specifically including: Based on the association matrix of UAV services to ground users and the UAV deployment location matrix corresponding to the previous iteration number, the spectrum allocation subproblem of UAV-assisted communication is solved using a spectrum allocation algorithm based on interference cancellation and channel reuse, and the channel allocation matrix of UAV services to ground users corresponding to the current iteration number is obtained.

4. The method for joint optimization of on-demand deployment and spectrum allocation of multiple UAVs according to claim 3, characterized in that, Also includes: Based on the channel allocation matrix for UAV services to ground users corresponding to the current iteration number, calculate the time ratio of UAV services to ground users corresponding to the current iteration number.

5. The method for joint optimization of on-demand deployment and spectrum allocation of multiple UAVs according to claim 1, characterized in that, The association relationship optimization algorithm for UAV services to ground users based on local iterative optimization is used to solve the sub-problem of UAV service to ground users association relationship optimization, and the association relationship matrix of UAV service to ground users corresponding to the current iteration number is obtained, specifically including: Based on the channel allocation matrix and UAV deployment location matrix of the UAV service ground users corresponding to the previous iteration number, the association relationship optimization sub-problem of the UAV service ground users is solved using the association relationship optimization algorithm based on local iterative optimization, and the association relationship matrix of the UAV service ground users corresponding to the current iteration number is obtained.

6. The method for joint optimization of on-demand deployment and spectrum allocation of multiple UAVs according to claim 1, characterized in that, The UAV deployment location optimization subproblem is solved using a particle swarm optimization algorithm to obtain the UAV deployment location matrix corresponding to the current iteration number, specifically including: Based on the channel allocation matrix and the correlation matrix of UAV services to ground users corresponding to the previous iteration number, the UAV deployment location optimization subproblem is solved using a particle swarm optimization algorithm to obtain the UAV deployment location matrix corresponding to the current iteration number.

7. The method for joint optimization of on-demand deployment and spectrum allocation of multiple UAVs according to claim 4, characterized in that, Based on the channel allocation matrix, the association matrix, and the UAV deployment location matrix corresponding to the current iteration number, the data transmission rate of UAV-assisted ground communication corresponding to the current iteration number is calculated, specifically including: Based on the channel allocation matrix, correlation matrix, deployment location matrix, and time ratio of UAV services to ground users corresponding to the current iteration number, calculate the data transmission rate of UAV-assisted ground communication corresponding to the current iteration number.

8. The method for joint optimization of on-demand deployment and spectrum allocation of multiple UAVs according to claim 1, characterized in that, Also includes: When the data transmission rate difference is greater than or equal to a set threshold, the iteration count is incremented by 1. The channel allocation matrix, association matrix, and deployment location matrix of the UAV serving ground users corresponding to the current iteration count are determined as the channel allocation matrix, association matrix, and deployment location matrix of the UAV serving ground users corresponding to the previous iteration count. The return step uses a spectrum allocation algorithm based on interference cancellation and channel multiplexing to solve the spectrum allocation subproblem of UAV-assisted communication, obtaining the channel allocation matrix of the UAV serving ground users corresponding to the current iteration count. It then uses a local iterative optimization algorithm for the association matrix of the UAV serving ground users to solve the association matrix of the UAV serving ground users, obtaining the association matrix of the UAV serving ground users corresponding to the current iteration count. Finally, it uses a particle swarm optimization algorithm for the UAV deployment location to solve the UAV deployment location optimization subproblem, obtaining the UAV deployment location matrix of the current iteration count.

9. A joint optimization system for on-demand deployment and spectrum allocation of multiple unmanned aerial vehicles (UAVs), characterized in that, include: The input parameter acquisition module is used to acquire input parameters corresponding to the target area; the input parameters include the ground user location matrix, the number of available channels, and the range of UAV-assisted ground communication; the ground user location matrix includes ground user location information and ground user number information. The minimum number of drones required to determine the target area mission completion rate is determined based on input parameters and a drone number determination algorithm based on pre-estimation and simulated annealing. Specifically, this includes: Based on the ground user location matrix, the minimum number of drones is initially determined; The subproblem of determining the minimum number of drones required to achieve the target area task completion rate is transformed into a discrete M-central problem; M represents the minimum number of drones. Based on the initially determined minimum number of drones, the discrete M-center problem is solved using the simulated annealing algorithm to obtain the minimum number of drones required to satisfy the target area task completion rate. The subproblem of determining the minimum number of drones required to satisfy the task completion rate is expressed as: ； Where, q k q represents the horizontal coordinate of ground user k. m ∈R 2×1 Represents the horizontal coordinate of drone m; γ m,k This indicates the relationship between unmanned aerial vehicle (UAV) services and ground users; r K This represents the communication range of ground user k assisted by the drone, i.e., the coverage radius threshold. The matrix calculation module is used to solve the spectrum allocation subproblem of UAV-assisted communication using a spectrum allocation algorithm based on interference cancellation and channel multiplexing, to obtain the channel allocation matrix of UAVs serving ground users corresponding to the current iteration number; to solve the correlation optimization subproblem of UAVs serving ground users using a correlation optimization algorithm based on local iterative optimization, to obtain the correlation matrix of UAVs serving ground users corresponding to the current iteration number; and to solve the UAV deployment location optimization subproblem using a UAV deployment location optimization algorithm based on particle swarm optimization, to obtain the UAV deployment location matrix corresponding to the current iteration number. The spectrum allocation subproblem of UAV-assisted communication, the correlation optimization subproblem of UAVs serving ground users, and the UAV deployment location optimization subproblem are obtained by decomposing the joint optimization problem model of multi-UAV on-demand deployment and spectrum allocation that satisfies the minimum number of UAVs using the block coordinate descent method. The data transmission rate calculation module is used to calculate the data transmission rate of UAV-assisted ground communication corresponding to the current iteration number based on the channel allocation matrix of UAV service ground users, the correlation matrix of UAV service ground users, and the UAV deployment location matrix corresponding to the current iteration number. The channel multiplexing priority is determined based on the data transmission rate of UAV-assisted communication, and channels are allocated to UAVs with lower data transmission rates based on the channel multiplexing priority, so that more communication time is allocated to their corresponding ground users. The result output module is used to determine the optimal matrix set as the channel allocation matrix, the association matrix, and the drone deployment location matrix corresponding to the current iteration number when the data transmission rate difference is less than a set threshold, and output the optimal matrix set and the minimum number of drones; the data transmission rate difference is the difference between the data transmission rate of drone-assisted ground communication corresponding to the current iteration number and the data transmission rate of drone-assisted ground communication corresponding to the previous iteration number.

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