CN115802364A - Multi-unmanned aerial vehicle deployment method, system, equipment and terminal assisted by electromagnetic map - Google Patents

Abstract

The invention belongs to the technical field of unmanned aerial vehicle wireless communication network, and discloses a multi-unmanned aerial vehicle deployment method, system, equipment and terminal assisted by an electromagnetic map, inputting user location information; randomly selecting k central points as the initial stage of the unmanned aerial vehicle Position; build an electromagnetic map based on the location information of the UAV; perform user clustering and UAV deployment iterations according to the division standard; judge whether the UAV position does not change or reach the number of iterations, if not, return to the electromagnetic map construction step; If so, select the optimal height in the UAV height set. The present invention makes the modeling of the channel system model more realistic by using the electromagnetic map, so that the strength of the received signal of the user is improved, and the rationality of the layout of the drone is effectively improved. The present invention proposes an RM-K-means algorithm, which is used for user clustering and drone deployment under the background of UAVs as mobile base stations, which can effectively ensure the communication experience of ground users and meet the communication needs of more users.

Description

Multi-UAV deployment method, system, equipment and terminal aided by electromagnetic map

technical field

The invention belongs to the technical field of unmanned aerial vehicle wireless communication networks, and in particular relates to a multi-unmanned aerial vehicle deployment method, system, equipment and terminal assisted by an electromagnetic map.

Background technique

Currently, UAV base stations have attracted great attention in the research community for their ability to provide wireless communication services. Drones can assist terrestrial communications in a variety of situations: When ground base stations are unable to communicate due to various accidents, drones can be deployed to act as mobile base stations to assist existing communication infrastructure. In the research of wireless communication system on the communication deployment of unmanned aerial vehicle (UAV, Unmanned Aerial Vehicle), people usually use the traditional air-to-ground channel model for research: "Location Optimization for Unmanned Aerial Vehicles Assisted MobileNetworks", from load balancing Angle solves the problem of drone location and service area. The authors propose an efficient partitioning method so that each UAV serves almost equal traffic demand; then a local search for UAV location is performed using a backtracking line search algorithm to find the best service subregion and UAV location. Excellent solution. The proposed algorithm can make the traffic distribution between UAVs more balanced, and the service area of each sub-region is also more balanced. In "Rapid Deployment of UAVs Based on Bandwidth Resources in Emergency Scenarios", considering the impact of user bandwidth, UAV bandwidth, signal-to-noise ratio threshold and other factors on UAV 3D deployment, a K-means-based algorithm is proposed to reduce deployment Latency and number of drones. Most of the above studies have used the classic air-to-ground model, which is too idealized, and it only uses formulas to illustrate the statistical laws of the propagation model. However, in real life, due to the existence of electromagnetic, the occlusion of buildings and other reasons, the real path loss will be greatly reduced. Therefore, in order to simulate a real environment, people began to use electromagnetic maps instead of traditional channel models.

In order to make the channel model more accurate, scholars turn their attention to the use of electromagnetic maps. "Radio MapBased UAV Placement Design for UAV-Assisted Relaying Networks" proposes a method of placing UAVs assisted by electromagnetic maps as relays. In this paper, electromagnetic maps are used to dynamically adjust the parameters of path loss, so that the distribution of UAVs is more accurate. Reasonable. At present, there are few related studies on UAV communication deployment assisted by electromagnetic maps.

To sum up, the existing UAV deployment methods have their own advantages and disadvantages: the UAV deployment method based on the traditional channel formula studies a variety of user clustering and UAV deployment methods and proves the effectiveness of the scheme , but failed to take into account the influence of electromagnetic in real life, too idealized. The UAV communication system based on the electromagnetic map takes into account the influence of electromagnetism, but is limited to dynamically adjusting some parameters of the traditional formula, and cannot directly generate the electromagnetic map according to the latitude and longitude coordinates.

Through the above analysis, the problems and defects in the prior art are:

(1) The classically established air-to-ground channel model is too idealized, and the statistical laws of the propagation model are only explained through formulas; but in real life, due to electromagnetic, building occlusion and other reasons, the real path loss is greatly reduced. It's about distance.

(2) In the existing UAV deployment method, the UAV communication system based on the electromagnetic map is limited to dynamically adjusting some parameters of the traditional formula, and cannot directly generate the electromagnetic map according to the real latitude and longitude coordinates.

Contents of the invention

Aiming at the problems existing in the prior art, the present invention provides a multi-UAV deployment method, system, equipment and terminal assisted by electromagnetic maps.

The present invention is achieved in this way, a multi-unmanned aerial vehicle deployment method assisted by an electromagnetic map comprises the following steps:

The first step is to establish the channel model of the target area under the condition that the UAV is used as a mobile base station in the disaster scene;

The second step is to establish a UAV position arrangement model that maximizes the total received signal strength of the system;

The third step is to decouple the UAV three-dimensional deployment problem into two sub-problems of horizontal and vertical deployment; for the sub-problem of horizontal deployment, given the height of the UAV, the improved K-means algorithm based on the electromagnetic map performs Clustering, get the horizontal coordinates of the UAV deployment location and the user clustering results;

The fourth step is to adjust the height of the UAV, discretize the flying height of the UAV within the range of the flying height of the UAV, calculate the total received signal strength of the users in the cluster at each height, and select the total received signal strength of the users in the cluster. The height at which the received signal strength is maximum is the final optimal height of the UAV.

Further, in the disaster scenario in the first step, the establishment of the channel model of the target area under the condition that the UAV is used as a mobile base station includes: setting a communication system when a ground base station is destroyed or other emergency communication situations, and the UAV as a mobile base station Serving ground users. When M users are randomly distributed in the area P, and K UAVs provide services for them, the position of the mth user is ζ _m = (x _m , y _m , h), and x _m and y _m are respectively the mth The x and y coordinates of each user; the z coordinate is a fixed value, which is the user’s average height h; the position of the k-th drone is γ _k = (x _k , y _k , h _k ), x _k , y _k , h _k are the x, y, and z coordinates of the k-th UAV, respectively.

Further, in the second step, the establishment of the UAV position arrangement model that maximizes the total received signal strength of the system includes:

Introduce the logic function index a _{m, k} , when the UAV k communicates with the user m, a _{m, k} is equal to 1, otherwise a _{m, k} is equal to 0; the constraints indicate that a user can only be controlled by one UAV machine provides services.

r _m,t (γ _k ) represents the signal strength received by the mth user from UAV k, the formula is as follows:

r _m,t (γ _k )=|g _m,t (γ _k )s _k +n _m,t |;

In the formula, g _m,t represents the channel gain between UAV k and ground user m at time t, s _k is the signal power sent by UAV k, n _m,t is the noise of the mth user receiving end at time t .

Further, in the third step, the three-dimensional deployment problem is decoupled into two sub-problems of horizontal deployment and vertical deployment; if the height of the drone is fixed to a fixed value, the horizontal deployment scheme is as follows:

(1) Randomly select the initial position of K UAVs (x _k , y _k , H ₀ ); define the maximum number of iterations N, and denote the clustering result as (R ₁ , R ₂ , ..., R _K );

(2) Generate an electromagnetic map of the target area according to the position of the drone, and calculate the received signal strength r _m,t (γ _k ) from each user m to the drone k;

(3) According to the principle of the user's maximum received signal strength, assign the user to the corresponding UAV, and the users served by the same UAV form a cluster;

(4) In each cluster, update the UAV coordinates (x _k , y _k , H ₀ ) as:

(5) Judging whether the maximum number of iterations is reached or the position of the UAV is no longer changing, if the condition is not met, return to step (2) to continue iteration; if the condition is met, the algorithm ends.

Further, in the fourth step, the height of the UAV is adjusted to obtain the three-dimensional deployment coordinates, which specifically include:

(1) Preset the minimum altitude H _min and the maximum altitude H _max of the UAV flight, and discretize the altitude within the interval to obtain the height set H;

(2) Obtain the optimal height in the interval by solving the following formula

For each height in the height interval, calculate the total signal reception strength of users in each cluster at the height, and select the height where the maximum total user signal reception strength is located as the optimal height

Another object of the present invention is to provide a multi-UAV deployment system assisted by an electromagnetic map using the multi-UAV deployment method assisted by an electromagnetic map. The multi-UAV deployment system assisted by an electromagnetic map includes :

The model building module is used to respectively establish the channel model of the target area under the condition that the UAV is used as a mobile base station in the disaster scene and the UAV position arrangement model that makes the total received signal strength of the system the largest;

The horizontal direction deployment module is used to cluster the ground users based on the improved K-means algorithm based on the electromagnetic map to obtain the horizontal coordinates of the deployment position of the drone and the user clustering results;

The vertical direction deployment module is used to adjust the height of the UAV, discretize the flight height of the UAV within the flying height range of the UAV, calculate the total received signal strength of the users in the cluster at each height, and select the cluster The height at which the total received signal strength of the internal users is maximum is the final optimal height of the UAV.

Another object of the present invention is to provide a computer device. The computer device includes a memory and a processor, and the memory stores a computer program. When the computer program is executed by the processor, the processor executes the multi-person operation aided by the electromagnetic map. steps in the machine deployment method.

Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the multi-UAV deployment method assisted by an electromagnetic map.

Another object of the present invention is to provide an information data processing terminal, which is used to implement the multi-UAV deployment system assisted by electromagnetic maps.

Combining the above-mentioned technical solutions and technical problems to be solved, the advantages and positive effects of the technical solutions to be protected in the present invention are as follows:

First, aiming at the technical problems existing in the above-mentioned prior art and the difficulty of solving the problems, closely combining the technical solution to be protected in the present invention and the results and data in the research and development process, etc., a detailed and profound analysis of how the technical solution of the present invention is solved Technical problems, some creative technical effects brought about after solving the problems. The specific description is as follows:

The present invention provides a multi-UAV deployment method based on electromagnetic map assistance, which mainly solves the problem that UAV (UAV, Unmanned Aerial Vehicle) serves as a mobile base station to provide communication for ground users when the ground base station is destroyed or other emergency communication situations The problem of drone deployment and user clustering when serving. The invention proposes an RM-K-means UAV clustering algorithm, which decouples the three-dimensional deployment problem of the UAV into two sub-problems of horizontal deployment and vertical deployment. For horizontal deployment, first construct an electromagnetic map model based on the real position coordinates of the UAV; choose which UAV to serve the user according to the principle of maximum user received signal strength, and finally obtain the horizontal deployment position of the UAV and the UAV service User clustering information. For the vertical deployment, that is, the adjustment of the height of the UAV, firstly, the minimum altitude and the maximum altitude that the UAV can fly are stipulated. The height at which the users in the cluster receive the highest signal strength is used as the final height of UAV deployment. The algorithm proposed by the invention can enable the user to obtain greater received signal strength, and at the same time effectively improve the rationality of the drone distribution.

The present invention analyzes how to make channel modeling more reasonable, and considers the complex and changeable electromagnetic environment in the real environment, creates an electromagnetic map to simulate the real environment, and provides more realistic channel information for the subsequent deployment of unmanned aerial vehicles; An effective algorithm for UAV deployment and user clustering is proposed, which makes the UAV position deployment more reasonable; the electromagnetic map is introduced to simulate the real channel state between the sender and the receiver, and then an improved K-means algorithm based on the electromagnetic map is proposed To solve the problem of user clustering and UAV deployment location, ground users can get a better communication experience as much as possible, and make UAV deployment more reasonable. The present invention proposes an improved K-means algorithm based on the electromagnetic map to re-cluster the ground users and deploy the UAV, and use the received signal strength of the receiver in the electromagnetic map instead of the distance as the clustering condition to increase the practicality of the algorithm The subsequent simulation experiments also verified the correctness of this idea.

Second, regarding the technical solution as a whole or from the perspective of a product, the technical effects and advantages of the technical solution to be protected by the present invention are specifically described as follows:

When the ground base station is destroyed or other emergency communication situations, the UAV serves as a mobile base station to provide services for ground users. The present invention proposes an improved K-means (RM-K-means) clustering algorithm assisted by an electromagnetic map. By using the electromagnetic map to make the channel system model modeling more realistic, the strength of the user's received signal is improved, and at the same time, the wireless network is effectively improved. The rationality of man-machine layout.

The present invention proposes a RM-K-means algorithm, which is used for user clustering and deployment of UAVs under the background of UAVs as mobile base stations, which can effectively ensure the communication experience of ground users; at the same time, when UAV power decreases Under the circumstances, a relatively large number of users are still guaranteed to access to meet the communication needs of more users. It can be used for UAV communication deployment problems when the ground base station is damaged or other emergency communication situations.

Third, as an auxiliary evidence of the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

(1) The expected income and commercial value after the conversion of the technical solution of the present invention are:

none

(2) The technical scheme of the present invention fills up the technological gap in the industry at home and abroad:

The technical solution of the present invention is the first domestic and foreign electromagnetic map generated by terrain information as an auxiliary system for three-dimensional deployment optimization of UAVs, which fills the technical gap in this field. The application of electromagnetic maps has greatly improved the performance of UAVs serving ground users as mobile base stations, and provided a new solution for 3D deployment of UAVs in complex terrain environments.

Description of drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings that need to be used in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For Those of ordinary skill in the art can also obtain other drawings based on these drawings without making creative efforts.

Fig. 1 is a flowchart of a multi-UAV deployment method assisted by an electromagnetic map provided by an embodiment of the present invention;

Fig. 2 is a schematic diagram of a multi-UAV deployment method assisted by an electromagnetic map provided by an embodiment of the present invention;

Fig. 3 is a model diagram of the urban communication system for unmanned aerial vehicle deployment provided by the embodiment of the present invention;

Fig. 4 is an effect diagram of generating an electromagnetic map provided by an embodiment of the present invention;

Fig. 5 (a) and Fig. 5 (b) are the two-dimensional projection and three-dimensional layout diagrams of the UAV position and the user cluster using the RM-K-means algorithm provided by the embodiment of the present invention;

Figure 6(a) is a schematic diagram of the specific situation of the UAV1 position and service ground user clustering in the case of deployment using the RM-K-means algorithm provided by the embodiment of the present invention, and Figure 6(b) is provided by the embodiment of the present invention In the case of using the RM-K-means algorithm for deployment, a schematic diagram of the specific situation of UAV2 position and service ground user clustering, Fig. 6 (c) is the case of using the RM-K-means algorithm for deployment provided by the embodiment of the present invention A schematic diagram of the specific situation of UAV3 location and service ground user clustering, Fig. 6 (d) is the specific situation of UAV4 location and service ground user clustering in the case of deployment using the RM-K-means algorithm provided by the embodiment of the present invention Schematic diagram, Figure 6 (e) is a schematic diagram of the specific situation of UAV5 position and service ground user clustering under the situation of deploying using RM-K-means algorithm provided by the embodiment of the present invention;

Figure 7(a) is a comparison diagram of the user's total signal reception strength when the RM-K-mean and K-means algorithms are used in different cases of the number of drones provided by the embodiment of the present invention, and Figure 7(b) is an embodiment of the present invention Provided a comparison chart of the total signal reception strength of users when using the RM-K-mean and K-means algorithms when the number of users is different;

Fig. 8 (a) is the simulation result diagram of using the RM-K-means algorithm provided by the embodiment of the present invention to the deployment of multi-user and multi-UAV positions, and Fig. 8 (b) is the use of K-means provided by the embodiment of the present invention Simulation results of the algorithm for multi-user and multi-UAV position deployment;

Figure 9 is the result of the number of users who cannot communicate when the RM-K-means algorithm and the K-means algorithm are used when the power of the drones is reduced by different percentages when the number of users is different and the number of drones is the same provided by the embodiment of the present invention. comparison chart;

Figure 10 is a comparison of the number of users who cannot communicate when the RM-K-means algorithm and the K-means algorithm are used when the power of the drone is reduced by different ratios when the number of drones is different and the number of users is the same provided by the embodiment of the present invention Result graph.

Detailed ways

In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

Aiming at the problems existing in the prior art, the present invention provides a multi-UAV deployment method, system, equipment and terminal assisted by an electromagnetic map. The present invention will be described in detail below with reference to the accompanying drawings.

1. Explain the embodiment. In order to make those skilled in the art fully understand how to implement the present invention, this part is an explanatory embodiment for explaining the technical solution of the claims.

As shown in Figure 1, the multi-UAV deployment method assisted by the electromagnetic map provided by the embodiment of the present invention includes the following steps:

S101, input user location information, randomly select k center points as the initial location of the drone;

S102, constructing an electromagnetic map according to the location information of the UAV, and performing user clustering and UAV deployment iterations according to the division standard;

S103, judging whether the position of the drone no longer changes or reaches the number of iterations, if not, return to the electromagnetic map construction step; if yes, select the optimal height in the height set of the drone.

As a preferred embodiment, as shown in Figure 2, the multi-UAV deployment method assisted by the electromagnetic map provided by the embodiment of the present invention specifically includes the following steps:

Step 1. Establish the channel model of the target area under the condition that the UAV is used as a mobile base station in a disaster scenario.

The channel model of the target area provided by the embodiment of the present invention is described as follows:

Set up a communication system in the event of a ground base station being destroyed or other emergency communication situations, at which point the UAV acts as a mobile base station to provide services to ground users. Assuming that M users are randomly distributed in the area P, and K UAVs provide services for them, the position of the mth user is ζ _m = (x _m , y _m , h), and x _m and y _m are respectively the mth The x, y axis coordinates and z axis coordinates of each user are fixed values, that is, the user's average height h; the position of the kth drone is γ _k = (x _k , y _k , h _k ), x _k , y _k , h _k are the x, y, and z coordinates of the k-th UAV, respectively.

Step 2. Establish a UAV position arrangement model that maximizes the total received signal strength R _total of the system:

In the formula, r _m,t (γ _k ) represents the signal strength received by the mth user from UAV k, and the formula is as follows:

r _m,t (γ _k )=|g _m,t (γ _k )s _k +n _m,t |

g _m,t represents the channel gain between UAV k and ground user m at time t, s _k is the signal power sent by UAV k, n _m,t is the noise at the receiving end of the mth user at time t.

Here, the present invention introduces a logic function index am _,k , when the UAV k communicates with the user m, a _m,k is equal to 1, otherwise _am,k is equal to 0. This constraint states that a user can only be served by one drone.

The embodiment of the present invention proposes an improved K-means clustering algorithm with electromagnetic map assistance to obtain the deployment location of the UAV and solve the problem of user clustering.

Step 3. For the problem of three-dimensional deployment of UAVs, the present invention will decouple it into two sub-problems of horizontal deployment and vertical deployment. For the sub-problem of deployment in the horizontal direction, the present invention gives a UAV height, and then clusters the ground users based on the improved K-means algorithm of the electromagnetic map, and finally can obtain the horizontal coordinates of the UAV deployment location and the user clustering results .

The improved K-means clustering algorithm provided by the embodiment of the present invention specifically includes the following steps:

The three-dimensional deployment problem is decoupled into two sub-problems of horizontal deployment and vertical deployment.

When solving the problem of horizontal deployment, the height of the fixed UAV in the present invention is a fixed value, and the horizontal deployment scheme is as follows:

(3.1) Randomly select the initial position of K UAVs (x _k , y _k , H ₀ ); define the maximum number of iterations N, and record the clustering result as (R ₁ , R ₂ , ..., R _K );

(3.2) Generate an electromagnetic map of the target area according to the position of the drone, and calculate the received signal strength r _m,t (γ _k ) from each user m to the drone k;

(3.3) According to the principle of the user's maximum received signal strength, assign the user to the corresponding UAV, and the users served by the same UAV form a cluster;

(3.4) In each cluster, update the UAV coordinates (x _k , y _k , H ₀ ) as:

(3.5) Judging whether the maximum number of iterations is reached or the position of the UAV is no longer changing, if the condition is not met, return to step (3.2) to continue iteration; if the condition is met, the algorithm ends.

By improving the K-means clustering algorithm, the invention can obtain the two-dimensional coordinates of the drone in the horizontal direction and the clustering results of the ground users. Next, the present invention adjusts the height of the drone to obtain a more reasonable three-dimensional deployment coordinate.

Step 4. On the basis of step 3, deploy the UAV in the vertical direction, that is, adjust the height of the UAV: discretize the flight height of the UAV within the flying height range of the UAV, and calculate at each height The total received signal strength of the users in the cluster, and the height where the total received signal strength of the users in the cluster is the highest is the final optimized height of the UAV.

(4.1) The present invention presets the minimum altitude H _min and the maximum altitude H _max that the UAV can fly, and within this interval, the present invention discretizes the altitude to obtain the height set H.

(4.2) The present invention obtains the optimal height in an interval by solving the following formula

That is, for each height in the height interval, the present invention calculates the total signal reception strength of users in each cluster at this height, and selects the height at which the maximum total user signal reception strength is located as the optimal height

Finally, by solving the above two steps, the present invention can obtain a better result about the three-dimensional deployment position of the UAV and the clustering of ground users.

The multi-UAV deployment system assisted by the electromagnetic map provided by the embodiment of the present invention includes:

The model building module is used to respectively establish the channel model of the target area under the condition that the UAV is used as a mobile base station in the disaster scene and the UAV position arrangement model that makes the total received signal strength of the system the largest;

The horizontal direction deployment module is used to cluster the ground users based on the improved K-means algorithm based on the electromagnetic map to obtain the horizontal coordinates of the deployment position of the drone and the user clustering results;

The vertical direction deployment module is used to adjust the height of the UAV, discretize the flight height of the UAV within the flying height range of the UAV, calculate the total received signal strength of the users in the cluster at each height, and select the cluster The height at which the total received signal strength of the internal users is maximum is the final optimal height of the UAV.

2. Application examples. In order to prove the creativity and technical value of the technical solution of the present invention, this part is the application example of the claimed technical solution on specific products or related technologies.

It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in memory and executed by a suitable instruction execution system such as a microprocessor or specially designed hardware. Those of ordinary skill in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and/or contained in processor control code, for example, on a carrier medium such as a magnetic disk, CD or DVD-ROM, such as a read-only memory Such code is provided on a programmable memory (firmware) or on a data carrier such as an optical or electronic signal carrier. The device and its modules of the present invention may be implemented by hardware circuits such as VLSI or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., It can also be realized by software executed by various types of processors, or by a combination of the above-mentioned hardware circuits and software such as firmware.

3. Evidence of the relevant effects of the embodiment. The embodiment of the present invention has achieved some positive effects in the process of research and development or use, and indeed has great advantages compared with the prior art. The following content is described in conjunction with the data and charts of the test process.

Fig. 3 is a model diagram of the UAV deployment city communication system provided by the example of the present invention: the UAV serves as a mobile base station to provide communication services for ground users, each UAV serves a part of users on the ground, and a user can only be used by one UAV. Human-machine service.

Fig. 4 is the effect drawing of the electromagnetic map generation that the example of the present invention provides, in the present invention we select the Taiping National Forest Park with complex terrain as the research object, and this figure shows the electromagnetic map model generated by the selected area.

The technical effects of the present invention will be further described below in combination with simulation experiments.

Simulation experiment one:

A. Simulation conditions

A1) Select Taiping National Forest Park as the target area with coordinates [33.835°, 33.875°]×[108.57°, 108.63°];

A2) 100 users are randomly distributed in the target area, and the coordinates are known;

A3) Deploy 5 drones to provide communication services, the initial two-dimensional position is random, and the initial height is fixed at 100 meters.

B. Simulation content:

Use the RM-K-means algorithm to cluster 100 users and calculate the placement position of the drone. The placement results are shown in Figure 5(a) and 5(b), where Figure 5(a) shows the drone The two-dimensional projection results of the position and user clustering results, Figure 5(b) shows the three-dimensional deployment position of the drone.

C. Simulation results:

Figure 5 shows the final clustering and deployment results of 100 users and 5 drones using the RM-K-means algorithm. "+" represents the specific location of the user, and the solid diamond represents the location of the drone. Figure 5(a) shows the top-view projection effect of ground user clustering and UAV's three-dimensional position, and Figure 5(b) shows the three-dimensional result map of ground user clustering and UAV three-dimensional position deployment.

Figures 6(a) to 6(e) show the deployment positions of UAV1, UAV2, UAV3, UAV4, and UAV5 and the user clustering results of specific services when the system uses the RM-K-means algorithm. It can be seen from the figure , the distribution load of drone service users is more balanced when using the improved algorithm.

Figure 7(a) shows the comparison chart of the total signal reception strength of ground users in the case of different numbers of UAVs; Figure 7(b) shows the comparison chart of the total signal reception strength of ground users in the case of different number of users ; It can be seen from the figure that compared with the clustering result using the K-means algorithm, the user can get a greater received signal strength using the RM-K-means algorithm, which further proves that the improved algorithm can make the user clustering more reasonable.

Simulation experiment two:

A. Simulation conditions

A1) Select Taiping National Forest Park as the target area with coordinates [33.835°, 33.875°]×[108.57°, 108.63°];

A2) 10 users are randomly distributed in the target area, among which users 5, 6, and 7 are marginal users, and the coordinates of all users are known.

B. Simulation content:

Use the RM-K-means algorithm and the K-means algorithm to cluster 10 users and calculate the placement position of the drone. The placement results are shown in Figure 8.

C. Simulation results:

Figure 8(a) shows the UAV deployment location and user clustering results using the RM-K-means algorithm, and Figure 8(b) shows the UAV deployment location and user clustering results using the K-means algorithm result. The simulation mainly verifies the rationality and effectiveness of the algorithm proposed in the present invention in the UAV deployment problem from the edge user attribution problem. The present invention uses ten users (respectively numbered 1 to 10) and two UAVs for simulation, wherein circles represent users, five-pointed stars represent UAVs, and GU5, 6, and 7 represent three edge users, yellow in the figure Some represent the highest elevations on the map. It can be seen from the figure that GU1-4 and GU5-10 are respectively distributed on both sides of the mountain. According to the simulation results, using the RM-K-means algorithm to consider the problem of mountain blocking, GU5~7 and GU8~10 are assigned an aircraft, and GU1~4 are assigned an unmanned aerial vehicle. Among them, the received signal strengths of GU5~7 are -67.4607dBm, -68.4216Bm, -69.8877dBm respectively; the traditional K-means algorithm only considers the distance problem when clustering, so GU5~7 are divided into a cluster with GU1~4 At this time, the received signal strength of GU5~7 is -127.6538dBm, -121.5749dBm, -100.4792dBm. In this simulation experiment, the algorithm proposed by the present invention takes into account the influence of the electromagnetic map in reality, so that the clustering of users and the deployment of UAVs are more rational.

Simulation experiment three:

A. Simulation conditions

A1) Select Taiping National Forest Park as the target area with coordinates [33.835°, 33.875°]×[108.57°, 108.63°];

A2) The user sets them to 50, 150, and 250 respectively, randomly distributed in the target area, and the coordinates are known;

A3) Deploy 5 UAVs to provide communication services, the initial positions are random, and the initial transmission power of the UAVs is 1W.

B. Simulation content:

When the transmission power of the UAV is reduced by 0%, 20%, 50%, 80%, and 90%, respectively, the RM-K-means algorithm proposed by the present invention and the classic K-means algorithm are used to perform clustering and iterative calculations on the experimental scene , the clustering results are shown in Figure 9.

C. Simulation results:

Figure 9 shows the results of using the RM-K-means algorithm proposed by the present invention and the classic K-means algorithm to perform clustering deployment under the condition of changing the number of users. It can be seen from Figure 9 that after clustering, the RM -K-means algorithm gives better results than using K-means algorithm. When the transmission power of the UAV decreases, the number of users who cannot communicate gradually increases, but using the RM-K-means algorithm can allow more users to keep in touch with the UAV, which verifies the effectiveness of the RM-K-means algorithm.

Simulation experiment four:

A. Simulation conditions

A1) Select Taiping National Forest Park as the target area with coordinates [33.835°, 33.875°]×[108.57°, 108.63°];

A2) 100 users are randomly distributed in the target area, and the coordinates are known;

A3) The number of UAVs is set to 3, 5, and 9 respectively, the initial positions are random, and the initial transmission power of UAVs is 1W.

B. Simulation content:

When the transmission power of the UAV is reduced by 0%, 20%, 50%, 80%, and 90%, respectively, the RM-K-means algorithm proposed by the present invention and the classic K-means algorithm are used to perform clustering and iterative calculations on the experimental scene , the clustering results are shown in Figure 10.

C. Simulation results:

Figure 10 shows the results of using the RM-K-means algorithm proposed by the present invention and the classic K-means algorithm to carry out clustering deployment under the condition of changing the number of drones; as can be seen from Figure 10, RM-K- The means algorithm gets better results than the K-means algorithm. When the transmission power of the UAV decreases, the number of users who cannot communicate gradually increases, but when the number of UAVs increases, the number of users who cannot communicate will decrease, and the RM-K-means algorithm is compared with the K-means algorithm It can communicate with more ground users. The simulation results show that the increase in the number of drones can also alleviate the user communication problem to a certain extent, and at the same time verify the effectiveness of the RM-K-means algorithm.

In summary, the present invention proposes an RM-K-means algorithm for user clustering and UAV deployment in the context of UAVs as mobile base stations, which can effectively ensure the communication experience of ground users. At the same time, when the power of the UAV is reduced, a relatively large number of users can still be connected to meet the communication needs of more users, and it is used for UAV communication deployment problems when the ground base station is damaged or other emergency communication situations.

The above is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Anyone familiar with the technical field within the technical scope disclosed in the present invention, whoever is within the spirit and principles of the present invention Any modifications, equivalent replacements and improvements made within shall fall within the protection scope of the present invention.

Claims (10)

1. A method for deploying many unmanned aerial vehicles assisted by an electromagnetic map, characterized in that the method for deploying many unmanned aerial vehicles assisted by an electromagnetic map comprises: inputting user location information; randomly selecting k center points as the initial position of the unmanned aerial vehicle ; Construct an electromagnetic map according to the position information of the UAV; perform user clustering and UAV deployment iterations according to the division standard; judge whether the UAV position no longer changes or reaches the number of iterations, if not, return to the electromagnetic map construction step; if , then select the optimal height in the UAV height set. 2. The multi-unmanned aerial vehicle deployment method under the assistance of electromagnetic map as claimed in claim 1 is characterized in that, the multi-unmanned aerial vehicle deployment method under the assistance of electromagnetic map comprises the following steps: Step 1: Establish a channel model of the target area under the condition that the UAV is used as a mobile base station in a disaster scenario; Step 2, establishing a UAV position arrangement model that maximizes the total received signal strength of the system; Step 3, decoupling the three-dimensional UAV deployment problem into two sub-problems of horizontal and vertical deployment; for the sub-problem of horizontal deployment, given the height of the UAV, the improved K-means algorithm based on the electromagnetic map is used to analyze the ground users. Cluster, get the horizontal coordinates of the UAV deployment location and the user clustering results; Step 4: Adjust the height of the UAV, discretize the flight height of the UAV within the flying height range of the UAV, calculate the total received signal strength of the users in the cluster at each height, and select the total received signal strength of the users in the cluster The height at which the signal strength is the highest is the final optimal height of the UAV. 3. the multi-unmanned aerial vehicle deployment method under the assistance of electromagnetic map as claimed in claim 2, it is characterized in that, under the disaster scene in step 1, unmanned aerial vehicle is as the establishment of the channel model of target area under mobile base station condition and comprises: setting A ground base station is destroyed or a communication system in other emergency communication situations, and the UAV serves as a mobile base station to provide services for ground users; when M users are randomly distributed in the area P, K UAVs provide services for them, the first The positions of the m users are ζ _m = (x _m , y _m , h), where x _m and y _m are the x and y-axis coordinates of the m-th user respectively; the z-axis coordinate is a fixed value, which is the average user height h; The position of the k-th UAV is γ _k =(x _k , y _k , h _k ), where x _k , y _k , and h _k are the x, y, and z-axis coordinates of the k-th UAV, respectively. 4. the multi-unmanned aerial vehicle deployment method under the assistance of electromagnetic map as claimed in claim 2, is characterized in that, the establishment of the unmanned aerial vehicle position arrangement model that makes the system total received signal strength maximum in step 2 comprises:

Introduce the logic function index a _m,k , when the UAV k communicates with the user m, a _m,k is equal to 1, otherwise a _m,k is equal to 0; the constraints indicate that a user can only be controlled by one UAV machine to provide services; r _m,t (γ _k ) represents the signal strength received by the mth user from UAV k, the formula is as follows: r _m,t (γ _k )＝|g _m,t (γ _k )s _k +n _m,t |; In the formula, g _m,t represents the channel gain between UAV k and ground user m at time t, s _k is the signal power sent by UAV k, n _m,t is the noise of the mth user receiving end at time t . 5. the multi-unmanned aerial vehicle deployment method under the assistance of electromagnetic map as claimed in claim 2 is characterized in that, in step 3, the three-dimensional deployment problem is decoupled into two sub-problems of horizontal direction deployment problem and vertical direction deployment; If the man-machine height is a fixed value, the horizontal deployment scheme is as follows: (1) Randomly select the initial position of K UAVs (x _k , y _k , H ₀ ); define the maximum number of iterations N, and denote the clustering result as (R ₁ , R ₂ ,..., R _K ); (2) Generate an electromagnetic map of the target area according to the position of the drone, and calculate the received signal strength r _m,t (γ _k ) from each user m to the drone k; (3) According to the principle of the user's maximum received signal strength, assign the user to the corresponding UAV, and the users served by the same UAV form a cluster; (4) In each cluster, update the UAV coordinates (x _k , y _k , H ₀ ) as:

(5) Judging whether the maximum number of iterations is reached or the position of the UAV is no longer changing, if the condition is not met, return to step (2) to continue iteration; if the condition is met, the algorithm ends. 6. The multi-UAV deployment method assisted by an electromagnetic map as claimed in claim 2, wherein in step 4, the height of the UAV is adjusted to obtain three-dimensional deployment coordinates, specifically comprising: (1) The minimum height H _min and the maximum height H _max of the unmanned aerial vehicle flight are preset, and the height is discretized in the interval to obtain the height set H; (2) Obtain the optimal height in the interval by solving the following formula

For each height in the height interval, calculate the total signal reception strength of users in each cluster at the height, and select the height where the maximum total user signal reception strength is located as the optimal height

7. A multi-UAV deployment system assisted by an electromagnetic map using the multi-UAV deployment method assisted by an electromagnetic map as claimed in any one of claims 1 to 6, wherein the multi-UAV deployment system assisted by an electromagnetic map The multi-drone deployment system includes: The model building module is used to respectively establish the channel model of the target area under the condition that the UAV is used as a mobile base station in the disaster scene and the UAV position arrangement model that makes the total received signal strength of the system the largest; The horizontal direction deployment module is used to cluster the ground users based on the improved K-means algorithm based on the electromagnetic map to obtain the horizontal coordinates of the deployment position of the drone and the user clustering results; The vertical direction deployment module is used to adjust the height of the UAV, discretize the flight height of the UAV within the flying height range of the UAV, calculate the total received signal strength of the users in the cluster at each height, and select the cluster The height at which the total received signal strength of the internal users is maximum is the final optimal height of the UAV. 8. A computer device, characterized in that the computer device comprises a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the electromagnetic device according to any one of claims 1 to 6. Steps of the map-assisted multi-UAV deployment method. 9. A computer-readable storage medium, storing a computer program, when the computer program is executed by a processor, the processor executes the multi-UAV deployment method assisted by an electromagnetic map as claimed in any one of claims 1 to 6 A step of. 10. An information data processing terminal, characterized in that the information data processing terminal is used to realize the multi-UAV deployment system assisted by the electromagnetic map as claimed in claim 7.

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