Unmanned aerial vehicle base station deployment method and apparatus, electronic device, and storage medium

Abstract

The application provides a UAV base station deployment method and device, electronic equipment and storage medium, the minimum number of UAVs capable of providing services and the initial positions of the UAVs are obtained by solving a UAV base station deployment optimization model, the UAV base station deployment optimization model comprises an optimization objective and a constraint condition; the attractive and repulsive relationships between objects are quantified according to the position information between the UAVs, other UAVs, users and obstacles within a preset range, and the virtual forces acting on the UAVs are obtained; the UAVs providing services are selected from the UAVs capable of providing services based on the virtual forces acting on the UAVs, the target positions and transmission powers of each UAV providing services are determined by solving in a local area as a search space, the target positions of the UAVs can be quickly calculated by local search solving, services are quickly provided on demand on the basis of preventing the risk of collision, on-demand coverage of user terminals is achieved, and thus an optimal deployment scheme is obtained.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method, apparatus, electronic device, and storage medium for deploying unmanned aerial vehicle (UAV) base stations. Background Technology

[0002] In emergency scenarios in mountainous and forested areas, situations such as damaged communication infrastructure are common. Disruptions to communication networks hinder information transmission between the disaster area and the outside world, making it impossible to accurately convey the disaster situation and implement targeted relief plans. Therefore, building a robust and reliable communication system in emergency scenarios is crucial for improving the efficiency of rescue missions and restoring social order. Drones can respond quickly in emergency scenarios, unaffected by harsh environments such as collapsed buildings or dense forests. Unlike traditional ground base stations, information transmission between drone base stations and ground user terminals is less obstructed. Although drone base stations overcome geographical limitations to some extent, current drone-assisted emergency rescue still faces severe challenges such as limited service range and severe obstruction in forested environments. How to efficiently deploy drone base stations in mountainous and forested scenarios to meet the communication coverage needs of ground user terminals remains a challenging problem in drone research and development.

[0003] Deploying drones plays a crucial role in improving network performance and has been extensively studied. Existing research often uses heuristic algorithms to address drone deployment issues; however, these algorithms are computationally complex and struggle to meet the efficient and reliable communication requirements of emergency communications. Furthermore, dense forest environments are complex, with significant tree obstruction leading to more severe and irregular signal attenuation. This renders existing urban and rural channel models unusable, and newly constructed channels fail to consider the limitations of drone resources, such as their endurance and deployment costs, resulting in unreliable communication performance for drone networks. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for deploying drone base stations, which addresses the shortcomings of traditional drone base station deployment methods that struggle to meet the high-efficiency and reliable communication needs in emergency communications, and the difficulty in guaranteeing the communication performance of drone networks due to the lack of consideration for the limitations of drone resources.

[0005] This invention provides a method for deploying a drone base station, comprising:

[0006] Solving the drone base station deployment optimization model yields the minimum number of drones capable of providing services and the initial positions of the drones. The drone base station deployment optimization model includes optimization objectives and constraints.

[0007] Based on the positional information of the drone and other drones, users, and obstacles within a preset range, the attraction and repulsion relationships between each object are quantified to obtain the virtual force acting on the drone.

[0008] Based on the virtual forces acting on the drone, drones capable of providing services are selected from those that can provide services. The search space is a local area to determine the target location and transmission power of each drone providing services.

[0009] According to a method for deploying a drone base station provided by the present invention, the virtual force includes the gravitational force of the user terminal on the drone and the repulsive force between drones. Based on the positional information of the drone and other drones, users, and obstacles within a preset range, the attraction and repulsion relationships between the objects are quantified to obtain the virtual force acting on the drone, including:

[0010] The user terminal's gravitational modeling of the drone is as follows

[0011]

[0012] Where K a It is the gravitational coefficient, d n,k It is a drone n The distance between user k and user k; It is the SNR threshold at which a user can generate gravitational pull on a drone, when γ n,k Exceeding the threshold And drones n The actual transmit power is less than the maximum transmit power P. n At that time, gravity produce;

[0013] drones m and drones n The repulsive force between (m≠n) is modeled as follows:

[0014]

[0015] Where K r d is the repulsion coefficient. n,m For drones m With drones n The distance between them; when d n,m Smaller than the predefined range Furthermore, the actual transmission power of the drone is less than the maximum transmission power P. n At that time, a repulsive force is generated and propels the drone. n Stay away from drones m .

[0016] According to a method for deploying a drone base station provided by the present invention, the virtual force includes the repulsive force of an obstacle on the drone, and the repulsive force of the obstacle on the drone is modeled as follows:

[0017]

[0018] in It is the safe distance between the drone and the obstacle, d o,n It is a drone n The actual distance between the target area and the obstacle, which is the boundary of the target area.

[0019] According to a method for deploying a drone base station provided by the present invention, the step of selecting drones capable of providing services from among the drones that are capable of providing services based on the virtual forces acting on the drones, and solving the problem using a local area as the search space to determine the target location and transmission power of each drone providing services, includes:

[0020] Construct a drone-user association matrix corresponding to a local area, and calculate the transmission rate based on the drone-user association matrix and the virtual force;

[0021] The transmission power of each drone is fixed, and the drone positions are updated to maximize the transmission rate, resulting in an updated set of drone positions.

[0022] The position of each UAV is fixed, and the transmission power of the UAV is updated to maximize the transmission rate, resulting in an updated transmission power allocation scheme.

[0023] When the rate of change of the transmission rate is lower than a preset threshold, a deployment scheme is obtained, which includes the drone providing the service, the drone deployment location, and the drone transmission power.

[0024] According to a method for deploying unmanned aerial vehicle (UAV) base stations provided by the present invention, the construction of the UAV-user association matrix includes:

[0025] Establish a user terminal alliance associated with each drone, where each user is associated with the drone that can provide the maximum received signal-to-noise ratio;

[0026] Select user terminals that can be transferred to the alliance based on preset transfer criteria;

[0027] Each drone selects the user terminal that generates the highest estimated utility from the user terminals of the transferable alliance, and updates the user terminal alliance. The estimated utility is determined based on the drone preference relationship.

[0028] The UAV-user association matrix is ​​obtained when the increase in the total estimated utility generated by all user terminal alliances is less than a preset utility threshold.

[0029] According to a method for deploying a drone base station provided by the present invention, the step of calculating the transmission rate based on the drone-user association matrix and the virtual force includes:

[0030] The virtual force of each drone is obtained based on the drone-user association matrix;

[0031] The resultant force on each drone is calculated based on the virtual force of each drone, and the resultant force is a function of the drone's position and transmission power;

[0032] The maximum speed that the drone can achieve is calculated based on the combined force.

[0033] The drone's position and power are updated based on the maximum speed to calculate the transmission rate based on the updated drone position and power.

[0034] According to a method for deploying a drone base station provided by the present invention, the optimization objective includes:

[0035] Maximize transmission rate, minimize flight energy consumption, and minimize total flight path;

[0036] The constraints include: Boolean constraints for establishing communication connections between the UAV and the user, constraints that each user can be served by at most one UAV, constraints on the UAV's transmission power, constraints on the signal-to-noise ratio for all users, and constraints on ensuring that the UAV is deployed within a specified area.

[0037] The present invention also provides a drone base station deployment device, comprising:

[0038] The solution module is used to solve the UAV base station deployment optimization model to obtain the minimum number of UAVs that can provide services and the initial positions of the UAVs. The UAV base station deployment optimization model includes optimization objectives and constraints.

[0039] The quantization module is used to quantify the attraction and repulsion relationships between objects based on the positional information of the drone and other drones, users, and obstacles within a preset range, and to obtain the virtual force acting on the drone.

[0040] The output module is used to filter out the drones that can provide services from the drones that are capable of providing services based on the virtual forces acting on the drone, and to solve the problem in a local area as the search space to determine the target position and transmission power of each drone that provides services.

[0041] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the unmanned aerial vehicle base station deployment method described in any of the preceding claims.

[0042] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the unmanned aerial vehicle base station deployment method described in any of the preceding claims.

[0043] The present invention provides a method, apparatus, electronic device, and storage medium for deploying drone base stations. By solving a drone base station deployment optimization model, the minimum number of drones capable of providing services and the initial positions of the drones are obtained. The drone base station deployment optimization model includes optimization objectives and constraints. Based on the positional information of the drones and other drones, users, and obstacles within a preset range, the attraction and repulsion relationships between objects are quantified to obtain the virtual forces acting on the drones. Based on the virtual forces acting on the drones, drones capable of providing services are selected from those that can provide services. The search space is used to determine the target position and transmission power of each drone providing services. Through local search, the target position of the drones can be quickly calculated. This allows for rapid on-demand service provision while preventing the risk of collisions between drones or between drones and obstacles, achieving on-demand coverage of user terminals and thus obtaining the optimal deployment scheme. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is one of the flowcharts illustrating the drone base station deployment method provided by the present invention;

[0046] Figure 2 This is the second flowchart illustrating the drone base station deployment method provided by the present invention;

[0047] Figure 3 This is the third flowchart illustrating the drone base station deployment method provided by the present invention;

[0048] Figure 4 This is the fourth flowchart illustrating the drone base station deployment method provided by the present invention;

[0049] Figure 5 This is a schematic diagram of virtual force provided by the present invention;

[0050] Figure 6 This is a schematic diagram illustrating the deployment, power allocation, and user association process of the drone provided by the present invention;

[0051] Figure 7 This is a convergence comparison chart of the algorithm proposed in this invention and its benchmark algorithm.

[0052] Figure 8This is a comparison chart of transmission rates under different numbers of users provided by the present invention;

[0053] Figure 9 This is a comparison chart of runtime under different numbers of users provided by the present invention;

[0054] Figure 10 This is a schematic diagram of the structure of the UAV base station deployment device provided by the present invention;

[0055] Figure 11 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0057] Figure 1 A flowchart of the drone base station deployment method provided in the embodiments of the present invention is shown below. Figure 1 As shown, the drone base station deployment method provided in this embodiment of the invention includes:

[0058] Step 101: Solve the UAV base station deployment optimization model to obtain the minimum number of UAVs that can provide services and the initial positions of the UAVs. The UAV base station deployment optimization model includes optimization objectives and constraints.

[0059] In this embodiment of the invention, the optimization objectives include, but are not limited to: maximizing the transmission rate, minimizing flight energy consumption, and minimizing the total flight path.

[0060] The constraints include, but are not limited to: Boolean constraints for establishing communication connections between the drone and the user, constraints that each user can be served by at most one drone, drone transmission power constraints, constraints that satisfy the signal-to-noise ratio of all users, and constraints that ensure the deployment of drones within a specified area.

[0061] Step 102: Based on the positional information of the drone and other drones, users, and obstacles within the preset range, quantify the attraction and repulsion relationships between the objects to obtain the virtual force acting on the drone;

[0062] Step 103: Based on the virtual force acting on the drone, select the drones that can provide services from the drones that are capable of providing services, and solve the problem using a local area as the search space to determine the target position and transmission power of each drone that provides services.

[0063] In this embodiment of the invention, each drone quantifies the attraction and repulsion relationships based on the position information of other drones, users, and obstacles within a certain range, obtains virtual forces, and solves the problem using a local area as the search space, avoiding the waste of search time by global search. In a 10km×10km deployment area, the drone deployment results are obtained in seconds, achieving the goal of rapid deployment.

[0064] In another embodiment of the invention, communication coverage services are provided to ground terminals in forest emergency scenarios. N drones are deployed to form an aerial backbone network and establish communication connections with K ground-based disaster-affected user terminals. The drones use different frequency bands to communicate with the connected user terminals. (Definition set) and Let L represent the drone and the user group, respectively. Assume the disaster area is of length L. x Width is L y The rectangular region. The coordinates of the drone n are u. n =(x n ,y n ), where 0≤x n ≤L x , 0≤y n ≤L y ,gather This is a set of UAV coordinates. Let p represent the set of transmitted power, where p n,k Indicates drone n Transmission power to user k. Introducing a matrix. This represents the communication connection between the drone and the user, where the binary variable c n,k This represents the connection between drone n and user k. Specifically, when drone n establishes a communication connection with user k, c n,k =1, otherwise, c n,k =0. The UAV uses Frequency Division Multiple Access (FDMA) technology, and the available bandwidth of UAV n is B. n Distributed equally There are [number] users. Therefore, the bandwidth occupied by user k is [amount].

[0065] Due to the presence of vegetation in the forest area, the transmission path loss between drone n and user k is given by the following formula:

[0066]

[0067] Reference distance, d n,k Let α be the distance between drone n and user k, and α be the path loss exponent. The free-space path loss between drone n and user k is described. This is additional loss to the forest area. Specifically, and They can be represented as follows:

[0068]

[0069]

[0070] In the formula, c is the speed of light, and θ n,k Let f be the elevation angle correction factor, and f be the carrier frequency; in the equation, A, B, C, E, and G are empirical parameters for forest area channels. (UAV) n The channel gain between user k and user k can be expressed as

[0071]

[0072] Among them, g n,k For small-scale fading gain.

[0073] Therefore, from drones n The signal-to-interference-plus-noise ratio (SNR) received by UE k can be expressed as:

[0074]

[0075] drones n The transmission rate between user k and user k can be written as

[0076] R n,k =b k log2(1+γ n,k )

[0077] Where, σ 2 This represents the variance of additive white Gaussian noise (AWGN).

[0078] To provide communication services to all ground users, this invention proposes a joint optimization problem involving UAV deployment, power allocation, and user association. In this embodiment, the optimization objective is maximizing the transmission rate. The optimization problem that satisfies user reception signal-to-noise ratio and transmission power constraints while maximizing the total transmission rate can be modeled as follows:

[0079]

[0080] stc n,k ∈{0,1} (a)

[0081]

[0082]

[0083]

[0084]

[0085] In this optimization problem, (a) represents the Boolean constraint for establishing a communication connection between the UAV and the user, (b) represents that each user can be served by at most one UAV, and (c) represents the UAV's transmit power constraint, where P n Indicates drone n (d) indicates that the SNR requirements of all users are met. (e) ensures that the UAV is deployed within the designated area.

[0086] In emergency communication scenarios in forest areas, due to the complex environment of dense forest areas and the large number of trees blocking the signal, the signal attenuation is more severe and irregular, making the existing urban and rural channel models unusable directly. Furthermore, the newly built channels do not take into account the limitations of drone resources, such as drone endurance and deployment costs, making it difficult to guarantee the communication performance of drone networking.

[0087] The UAV base station deployment method provided in this invention obtains the minimum number of UAVs capable of providing services and the initial positions of the UAVs by solving a UAV base station deployment optimization model. The UAV base station deployment optimization model includes optimization objectives and constraints. Based on the positional information of the UAVs and other UAVs, users, and obstacles within a preset range, the attraction and repulsion relationships between each object are quantified to obtain the virtual forces acting on the UAVs. Based on the virtual forces acting on the UAVs, UAVs capable of providing services are selected from those that can provide services. The search space is used to determine the target position and transmission power of each UAV providing services. By solving the local search problem, the target position of the UAVs can be quickly calculated. Thus, while preventing the risk of collisions between UAVs and between UAVs and obstacles, services can be provided quickly on demand, achieving on-demand coverage of user terminals and obtaining the optimal deployment scheme.

[0088] Based on any of the above embodiments, such as Figure 2 As shown, solving the drone base station deployment optimization model yields the minimum number of drones capable of providing services and the initial positions of the drones, including:

[0089] Step 201: Randomly select multiple user terminals from the user terminal set, and use the location of each user terminal as the cluster center of the initial UAV cluster;

[0090] Step 202: Calculate the distance between each user terminal and the cluster center, and assign the user terminal to the cluster corresponding to the nearest cluster center;

[0091] Step 203: Update the cluster center position by calculating the geometric center of the user terminals in each cluster until the cluster center position no longer changes;

[0092] Step 204: If the current drone deployment can meet the communication needs of all user terminals, then determine the minimum number of drones to be deployed based on the current number of drones, and take the current cluster center position as the initial deployment position of the drones.

[0093] Step 205: If the current drone deployment cannot meet the communication needs of all user terminals, add a drone to the current communication network and repeat the above steps until the communication needs of all user terminals are met.

[0094] In this embodiment of the invention, the K-Means algorithm is used to determine the minimum number of drones required to cover all user terminals. It is assumed that the drone's transmit power is evenly distributed among connected users. First, the number of clusters is initialized. And set t=0. Next, let... Let represent the nth cluster in the t-th iteration. From the set Random selection 1 user, and use their location as the initial cluster centroid. This is a set used to represent the location of cluster centers. It calculates the distance between each user terminal and the cluster center, and assigns the user to the cluster corresponding to the nearest cluster center. In, it can be represented as

[0095]

[0096] The cluster center position is updated by calculating the geometric center of each user in each cluster.

[0097]

[0098] Update each cluster based on the above formula. This continues until the cluster center position no longer changes. The cluster center is the location of the drone, i.e. This represents the coordinates of the nth drone. Check if the current drone deployment can meet the communication needs of all user terminals. If so, determine the minimum number of drones to deploy and obtain the initial deployment location. Otherwise, adding a drone to the existing communication network, i.e. Repeat the above steps until the communication needs of all user terminals are met. Algorithm details are shown in Table 1.

[0099] Table 1. K-Means-based drone initialization and deployment algorithm

[0100]

[0101]

[0102] Based on any of the above embodiments, such as Figure 3 As shown, drones providing services are selected from those capable of providing services based on the virtual forces acting on them, and the initial positions of the drones are optimized to determine the target position and transmission power of each drone providing services, including:

[0103] Step 301: Construct a drone-user association matrix, obtain virtual forces between users, drones and obstacles based on the drone-user association matrix, and calculate the transmission rate based on the virtual forces;

[0104] In this embodiment of the invention, constructing the drone-user association matrix includes:

[0105] Step 3011: Establish a user terminal alliance associated with each drone, where each user is associated with the drone that can provide the maximum received signal-to-noise ratio;

[0106] Step 3012: Select user terminals of the transferable alliance based on preset transfer conditions;

[0107] Step 3013: Each UAV selects the user terminal that can generate the highest estimated utility from the user terminals of the transferable alliance, and updates the user terminal alliance. The estimated utility is determined according to the UAV preference relationship.

[0108] Step 3014: When the increase in the total estimated utility generated by all user terminal alliances is less than the preset utility threshold, the UAV-user association matrix is ​​obtained.

[0109] In this embodiment of the invention, a user association matrix is ​​determined by maximizing the total transmission rate using a federation game theory approach. The corresponding optimization problem is expressed as:

[0110]

[0111] stc n,k ∈{0,1} (a)

[0112]

[0113]

[0114] Alliance Δ n Let be defined as the set of users associated with drone n. Define the coalition game as... Where Θ={θ1,…,θ N} is a union set, U = {U(θ1), ..., U(θ)} N )} is the set of benefits for each alliance, where

[0115]

[0116] The benefit to user k associated with drone n can be expressed as

[0117]

[0118] Therefore, the binary variable c n,k It can be represented as

[0119]

[0120] Drones can establish preference relationships based on estimated benefits, and drone n can rank users according to these preference relationships. Preference relationship k > n l means that drone n is more inclined to user k than user l, that is

[0121] If some users migrate from a high-load federation to a low-load federation, the total utility... This will increase because when more users connect to a drone, the available power per user decreases, thus reducing performance. When user k is moved from the consortium θ... m Transfer to Alliance θ n At this time, the following transfer conditions must be met:

[0122]

[0123] Where θ m′ =θ m \{k},θ n′ =θ n The condition ∪{k} guarantees that transmission increases the overall utility of the network.

[0124] The optimal user association is as follows: First, let s = 0 and initialize the association to Θ. s Each user is associated with a drone that can provide the highest received signal-to-noise ratio. Users who can be transferred to another coalition are selected based on transfer criteria. Then, each drone selects the user with the highest estimated utility, resulting in a new coalition partition Θ. (s+1) Repeat this process until the increase in total utility U(Θ) falls below a predefined threshold ε.

[0125] Table 2 User Associations Based on Alliance Game Theory

[0126]

[0127]

[0128] Step 302: Fix the transmission power of each UAV, update the UAV position to maximize the transmission rate, and obtain the updated UAV position set;

[0129] Step 303: Fix the position of each UAV and update the transmission power of the UAV to maximize the transmission rate, thereby obtaining the updated transmission power allocation scheme;

[0130] Step 304: When the rate of change of the transmission rate is lower than a preset threshold, a deployment scheme is obtained. The deployment scheme includes the drone providing the service, the drone deployment location, and the drone transmission power.

[0131] Based on any of the above embodiments, the virtual force algorithm is a method based on artificial potential fields. It posits that there are "virtual forces" of attraction or repulsion between drones, between drones and ground users, and between drones and obstacles. Each drone is displaced under the combined effect of these virtual forces, preventing excessive concentration of drones or collisions with obstacles. Therefore, this invention proposes a drone deployment method based on a forest area scenario. This method deploys drones and adjusts the transmission power of the communication link to achieve on-demand coverage for users, improving the overall transmission rate while ensuring the signal-to-noise ratio requirements of each ground user. The optimization problem is solved iteratively by combining alliance game theory and virtual force methods. Figure 4 As shown, virtual forces between users, drones, and obstacles are obtained based on the drone-user correlation matrix, and the transmission rate is calculated based on these virtual forces, including:

[0132] Step 401: Model the virtual forces acting on the drones to predict the virtual forces acting on each drone from the user, other drones, and obstacles.

[0133] Step 402: Obtain the virtual force of each drone based on the drone-user association matrix;

[0134] Step 403: Calculate the net force on each drone based on the virtual force of each drone. The net force is a function of the drone's position and transmission power.

[0135] Step 404: Calculate the maximum speed that the drone can achieve based on the resultant force;

[0136] Step 405: Update the drone's position and power based on the maximum speed, and calculate the transmission rate based on the updated drone position and power.

[0137] In embodiments of the present invention, such as Figure 5As shown, consider the existence of three virtual forces: 1) the gravitational force exerted by the user on the drone. 2) Repulsive forces between drones 3) The repulsive force of obstacles on the drone The user's gravitational modeling of the drone is as follows

[0138]

[0139] Where K a It is the gravitational coefficient, d n,k It is the distance between drone n and user k; It is the SNR threshold at which a user can generate gravitational pull on a drone, when γ n,k Exceeding the threshold Furthermore, the actual transmission power of UAV n is less than the maximum transmission power P. n At that time, gravity produce;

[0140] The repulsive force between drones: The repulsive force between drone m and drone n (m≠n) is modeled as follows:

[0141]

[0142] Where K r d is the repulsion coefficient. n,m Let d be the distance between drone m and drone n; when d n,m Smaller than the predefined range Furthermore, the actual transmission power of the drone is less than the maximum transmission power P. n At that time, a repulsive force is generated and pushes drone n away from drone m.

[0143] According to a method for deploying a drone base station provided by the present invention, the virtual forces acting on the drone include the repulsive force exerted by obstacles on the drone, and the repulsive force exerted by obstacles on the drone is modeled as follows:

[0144]

[0145] in It is the safe distance between the drone and the obstacle, d o,n It is the actual distance between the drone n and the obstacle, which is the boundary of the target area.

[0146] The boundary of the target area is used as an obstacle to ensure that the drone does not go beyond the area during its movement.

[0147] The net force acting on drone n can be expressed as:

[0148]

[0149] The resultant force is a function of the position and transmission power of the drone n, and satisfies the constraints. According to Newton's second law of motion, the change in velocity over time Δt is controlled by the resultant force. Assume the virtual mass of the drone n is m. n =1, then we have

[0150]

[0151] The change in velocity is positively correlated with the resultant force, but an infinitely large resultant force would lead to an infinitely large change in velocity, which would violate dynamics. Therefore, this invention maps the change in velocity to a finite range. To achieve this mapping, the arctangent function arctan() is introduced, so the velocity can be expressed as...

[0152]

[0153] Where v max The maximum achievable speed for the drone is defined as the velocity. This velocity incorporates all constraints within the resultant force, propelling the drone closer to the user while optimizing power allocation to maximize the SNR received by the user, thereby increasing the overall transmission rate. Therefore, the primary objective of the virtual force algorithm proposed in this invention is to maximize the transmission rate by equivalently updating the velocity. When the velocity variation is small, the drone reaches the optimal position and power allocation, thus achieving the maximum transmission rate.

[0154] After achieving an initial deployment that meets coverage requirements, further joint optimization of drone deployment, transmission power, and user association will maximize transmission rates. In the r-th iteration, the power allocation for each drone is fixed as follows: Based on the drone's current location Update drone location Get the updated drone location set Next, the position of the fixed drone is Update the drone's transmit power:

[0155]

[0156] Next, using and Obtain the drone-user association matrix

[0157] Finally, the optimal drone deployment scheme is obtained when the increase in network transmission rate is lower than a predefined threshold ε. and power distribution and drone user association matrix Table 3 summarizes the details of the joint optimization of UAV location, power allocation, and user association.

[0158] Table 3 Joint Optimization Algorithm for UAV Deployment, Power Allocation, and User Association

[0159]

[0160]

[0161] In some embodiments of the present invention, the above method was simulated and verified, with ground users distributed within a 10km × 10km area. The flight altitude of the UAVs was set to h = 200m, and each UAV had the same maximum transmission power, i.e., P1 = ... = P n =38dBm, bandwidth B1 for each drone = ... = B N =2MHz, the minimum SNR threshold required for a user to be served is The carrier power is f = 1.4 GHz, and the noise power is σ. 2 = -130dBm. The empirical parameters for the forest channel are A = 0.25, B = 0.39, C = 0.25, E = 0, G = 0.05. The path loss exponent is α = 3.5, and the standard deviation of shadow fading is σ. x =6dB, reference distance is d0=1m. Gravitational coefficient is K. a =1000, repulsion coefficient is K r =300, convergence threshold is ε=10 -4 .

[0162] Figure 6 The diagram illustrates the drone deployment, power allocation, and user association process of the algorithm proposed in this invention, with 100 users. (a) through (c) represent increasing the number of drones to ensure coverage for all users. It can be seen that 10 drones can provide communication services to all users. After determining the number of drones, the virtual force method proposed in this invention further optimizes drone location, power allocation, and user association to further improve the transmission rate. Compared to (c), in (d), due to the joint optimization of deployment location, power allocation, and user association, the total transmission rate increases from 118.64.38 bit / s / Hz to 123.69 bit / s / Hz.

[0163] Figure 7The convergence of the proposed algorithm was investigated. Four algorithms were used as benchmarks: a virtual force-based deployment-only algorithm (VFDOA), which improves the transmission rate by adjusting the location of drones with fixed transmit power and assumes that users establish communication connections with the nearest drone; a virtual force-based joint deployment and power allocation (VFJDPA) algorithm, which uses the virtual force algorithm to jointly optimize drone location and power allocation, where users establish communication connections with the drone providing the maximum received signal-to-noise ratio; a genetic algorithm-based joint deployment, power allocation, and user association algorithm (GAJDPAUA), which uses a genetic algorithm to iteratively optimize drone location and transmit power with transmission rate as the optimization objective and uses a federated game approach to optimize user connections; and a particle swarm optimization (PSO)-based joint deployment, power allocation, and user association algorithm. The association (PSOJDPAUA) uses a particle swarm optimization algorithm to iteratively optimize the UAV's position and transmit power with transmission rate as the optimization objective, and employs a federated game theory approach to optimize user connectivity. For example... Figure 8 As shown, all five algorithms converge to the maximum transmission rate after a finite number of iterations. Among them, PSOJDPAUA and GAJDPAUA converge slowly due to their high computational complexity, which indicates that the virtual force method is superior to the heuristic algorithm in terms of computational complexity.

[0164] The transmission rate and running time of the algorithm were compared under different numbers of users. For example... Figure 9 As shown, under the joint optimization of UAV deployment, power allocation, and user association, the proposed algorithm achieves performance similar to PSOJDPAUA and GAJDPAUA, further improving network speed, and outperforming VFDOA (which only considers deployment) and VFJDPA (which only optimizes deployment location and power allocation). Furthermore, the proposed algorithm significantly reduces computation time. Therefore, the proposed algorithm demonstrates better performance in both network performance and computational cost.

[0165] The UAV base station deployment method provided in this invention addresses the issues of rapid and reliable establishment of transmission links in emergency communications and the limited resources available to UAVs. Based on a forest-specific channel model, it optimizes user connections, transmission power allocation, and deployment location for each UAV while meeting the communication needs of ground users, thereby improving network throughput. Specifically, this invention transforms the optimal deployment problem into an on-demand coverage problem. First, it calculates the minimum number of UAVs required to provide user coverage under a fixed UAV base station transmission power. Then, it adjusts the deployment location and transmission power of the UAVs using a virtual force method and optimizes user association using a federated game theory approach. This further enhances the communication performance of the UAV network, maximizing the transmission rate within a specified area while ensuring the user's signal-to-noise ratio requirements.

[0166] The drone base station deployment device provided by the present invention is described below. The drone base station deployment device described below can be referred to in correspondence with the drone base station deployment method described above.

[0167] Figure 10 This is a schematic diagram of a drone base station deployment device provided in an embodiment of the present invention, such as... Figure 10 As shown, the drone base station deployment device provided in this embodiment of the invention includes:

[0168] Module 1001 is used to build an optimization model for UAV base station deployment with the goal of maximizing the total transmission rate and the constraints of signal-to-noise ratio and transmission power.

[0169] The solution module 1002 is used to solve the UAV base station deployment optimization model to obtain the minimum number of UAVs that can provide services and the initial position of the UAVs;

[0170] The output module 1003 is used to obtain the virtual force between the user, the drone and the obstacle based on the minimum number of drones that can provide services and the initial position of the drone, so as to output a deployment plan according to the virtual force.

[0171] The UAV base station deployment device provided in this embodiment of the invention constructs an optimization model for UAV base station deployment with the goal of maximizing the total transmission rate and the constraints of signal-to-noise ratio and transmission power. The optimization model is solved to obtain the minimum number of UAVs capable of providing services and the initial positions of the UAVs. Based on the virtual forces acting on the UAVs, UAVs capable of providing services are selected from the available UAVs, and the initial positions of the UAVs are optimized to determine the target position and transmission power of each providing service UAV. By deploying UAVs and adjusting the transmission power of the communication link, on-demand coverage for users is achieved, increasing the total transmission rate while ensuring the signal-to-noise ratio requirements of each ground user. The optimal deployment scheme is obtained by iteratively solving the optimization problem through virtual forces.

[0172] Figure 11 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 11 As shown, the electronic device may include a processor 1110, a communications interface 1120, a memory 1130, and a communication bus 1140. The processor 1110, communications interface 1120, and memory 1130 communicate with each other via the communication bus 1140. The processor 1110 can call logical instructions in the memory 1130 to execute a UAV base station deployment method. This method includes: constructing a UAV base station deployment optimization model with the goal of maximizing the total transmission rate and signal-to-noise ratio and transmission power as constraints; solving the UAV base station deployment optimization model to obtain the minimum number of UAVs capable of providing services and the initial positions of the UAVs; selecting UAVs capable of providing services from the UAVs based on the virtual forces acting on the UAVs, and optimizing the initial positions of the UAVs to determine the target position and transmission power of each UAV providing services.

[0173] On the other hand, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a method for deploying unmanned aerial vehicle (UAV) base stations provided by the methods described above. The method includes: constructing an optimization model for UAV base station deployment with the goal of maximizing the total transmission rate and with signal-to-noise ratio and transmission power as constraints; solving the optimization model to obtain the minimum number of UAVs capable of providing services and the initial positions of the UAVs; selecting UAVs capable of providing services from the UAVs based on the virtual forces acting on the UAVs, and optimizing the initial positions of the UAVs to determine the target position and transmission power of each UAV providing services.

[0174] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0175] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for deploying a drone base station, characterized in that, include: Solving the drone base station deployment optimization model yields the minimum number of drones capable of providing services and the initial positions of the drones. The drone base station deployment optimization model includes optimization objectives and constraints. The optimization objectives include: maximizing the transmission rate, minimizing flight energy consumption, and minimizing the total flight path; the constraints include: Boolean constraints for establishing communication connections between the UAV and the user, constraints that each user can be served by at most one UAV, constraints on the UAV's transmission power, constraints on satisfying the signal-to-noise ratio for all users, and constraints on ensuring that the UAV is deployed within a specified area. Based on the positional information of the drone and other drones, users, and obstacles within a preset range, the attraction and repulsion relationships between each object are quantified to obtain the virtual force acting on the drone. Based on the virtual forces acting on the drone, drones that can provide services are selected from those that are capable of providing services. The search space is a local area to determine the target location and transmission power of each drone that provides services. The process of selecting service-providing drones from among those capable of providing services based on the virtual forces acting on the drone, and solving the problem using a local area as the search space to determine the target location and transmission power of each service-providing drone, includes: Construct a drone-user association matrix, and calculate the transmission rate based on the drone-user association matrix and the virtual force; The transmission power of each drone is fixed, and the drone positions are updated to maximize the transmission rate, resulting in an updated set of drone positions. The position of each drone is fixed, and the transmission power of the drones is updated to maximize the transmission rate, resulting in an updated transmission power allocation scheme. When the rate of change of the transmission rate is lower than a preset threshold, a deployment scheme is obtained, which includes the drone providing the service, the drone deployment location, and the drone transmission power.

2. The method for deploying a drone base station according to claim 1, characterized in that, The virtual forces include the gravitational force exerted by the user terminal on the drone and the repulsive force between drones. Based on the positional information of the drone and other drones, users, and obstacles within a preset range, the attraction and repulsion relationships between objects are quantified to obtain the virtual forces acting on the drone, including: The user terminal's gravitational modeling of the drone is as follows ； in It is the gravitational coefficient. It is a drone and users The distance between them; It is the SNR threshold at which a user can receive gravitational pull from a drone. Exceeding the threshold And drones The actual transmit power is less than the maximum transmit power. At that time, gravity produce; drones and drones Modeling the repulsive force between them as ； in, , The repulsion coefficient is... For drones With drones The distance between; when Smaller than the predefined range Furthermore, the actual transmission power of the drone is less than its maximum transmission power. At that time, a repulsive force is generated and propels the drone. Stay away from drones .

3. The method for deploying a drone base station according to claim 2, characterized in that, The virtual force includes the repulsive force of obstacles on the drone, which is modeled as follows: ； in It is the safe distance between the drone and the obstacle. It is a drone The actual distance between the target area and the obstacle, which is the boundary of the target area.

4. The method for deploying a drone base station according to claim 1, characterized in that, The construction of the drone-user association matrix includes: Establish a user terminal alliance associated with each drone, where each user is associated with the drone that can provide the maximum received signal-to-noise ratio; Select user terminals that can be transferred to the alliance based on preset transfer criteria; Each drone selects the user terminal that generates the highest estimated utility from the user terminals of the transferable alliance, and updates the user terminal alliance. The estimated utility is determined based on the drone preference relationship. The UAV-user association matrix is ​​obtained when the increase in the total estimated utility generated by all user terminal alliances is less than a preset utility threshold.

5. The method for deploying a drone base station according to claim 3, characterized in that, The calculation of transmission rate based on the UAV-user association matrix and the virtual force includes: The virtual force of each drone is obtained based on the drone-user association matrix; The resultant force on each drone is calculated based on the virtual force of each drone, and the resultant force is a function of the drone's position and transmission power; The maximum speed that the drone can achieve is calculated based on the combined force. The drone's position and power are updated based on the maximum speed to calculate the transmission rate based on the updated drone position and power.

6. A drone base station deployment apparatus, applicable to the drone base station deployment method as described in any one of claims 1 to 5, characterized in that, include: The solution module is used to solve the UAV base station deployment optimization model to obtain the minimum number of UAVs that can provide services and the initial positions of the UAVs. The UAV base station deployment optimization model includes optimization objectives and constraints. The quantization module is used to quantify the attraction and repulsion relationships between objects based on the positional information of the drone and other drones, users, and obstacles within a preset range, and to obtain the virtual force acting on the drone. The output module is used to filter out the drones that can provide services from the drones that are capable of providing services based on the virtual forces acting on the drone, and to solve the problem in a local area as the search space to determine the target position and transmission power of each drone that provides services.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the UAV base station deployment method as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the drone base station deployment method as described in any one of claims 1 to 5.

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