CN114489120B - Unmanned aerial vehicle deployment and tracking control method for mobile network - Google Patents
Unmanned aerial vehicle deployment and tracking control method for mobile network Download PDFInfo
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Abstract
The invention discloses a mobile network-oriented unmanned aerial vehicle deployment and tracking control method, which comprises the following steps: step S1: obtaining the coordinates of the users in the target area and numbering the coordinates; step S2: performing unmanned aerial vehicle position deployment on a target area by using an improved HHO algorithm; step S3: the unmanned aerial vehicle calculates that the unmanned aerial vehicle receives the virtual force from a user or other unmanned aerial vehicles; step S4: the unmanned plane moves according to the received virtual force in a specific time interval; step S5: removing the unmanned aerial vehicle which can enter a dormant state; step S6: repeating the steps S3-S5 until all unmanned aerial vehicles enter a dormant state or the maximum iteration number is reached. By adopting the technical scheme of the invention, the unmanned aerial vehicle base station can be controlled to provide continuous and reliable network service for users in hot spots, and the pressure of a ground cellular network is relieved.
Description
Technical Field
The invention belongs to the field of wireless communication, and particularly relates to an unmanned aerial vehicle deployment and tracking control method for a mobile network.
Background
Currently, mobile communication technology is about to completely enter the 5G era. Our lives will apply higher transmission rates, lower delays and richer communication ecology, but at the same time the huge data traffic also causes the existing terrestrial cellular networks to be difficult to cope with the proliferation of hot users, resulting in overload of the terrestrial base stations. Meanwhile, mobility and distribution of users in the hot spot areas are uneven, and huge pressure is brought to hot spot users served by the ground network. However, unmanned aerial vehicles have attracted increasing attention as air base stations due to their strong line-of-sight connection links, flexible deployment, and low cost of deployment.
However, since the conventional unmanned aerial vehicle deployment scheme does not consider or rarely considers the mobility of the user, many unmanned aerial vehicle movement algorithms need to know the movement direction of the user in advance, and it is difficult to continuously and effectively serve the ground mobile user.
The harris eagle Optimization algorithm (HHO) is a group intelligent Optimization algorithm proposed by Heidari et al in 2019, which has the advantages of strong global search capability and fewer parameters to be adjusted. The method is often used for solving the optimal solution of the optimization problem, but in the optimization problem of the unmanned aerial vehicle auxiliary ground cellular network, the number of unmanned aerial vehicles input by operators is often more than one, so that the ground cellular network has larger pressure.
Therefore, aiming at the technical problems in the prior art, the invention provides a technical scheme for overcoming the defects in the prior art.
Disclosure of Invention
In view of the above-mentioned technical problems, the present invention is configured to provide a mobile network-oriented unmanned aerial vehicle deployment and tracking control method, by introducing a penalty value of an improved HHO algorithm, the algorithm can effectively provide a set of preferred solutions in the unmanned aerial vehicle deployment problem, and can control an unmanned aerial vehicle base station to provide continuous and reliable network service for users in a hot spot area, thereby alleviating the pressure of a ground cellular network.
In order to solve the technical problems in the prior art, the technical scheme of the invention is as follows:
A unmanned aerial vehicle deployment and tracking control method facing a mobile network comprises the following steps:
step S1: obtaining the coordinates of the users in the target area and numbering the coordinates;
Step S2: performing unmanned aerial vehicle position deployment on a target area by using an improved HHO algorithm;
Step S3: the unmanned aerial vehicle calculates that the unmanned aerial vehicle receives the virtual force from a user or other unmanned aerial vehicles;
step S4: the unmanned plane moves according to the received virtual force in a specific time interval;
Step S5: removing the unmanned aerial vehicle which can enter a dormant state;
Step S6: repeating the steps S3-S5 until all unmanned aerial vehicles enter a dormant state or reach the maximum iteration times;
Further, the improved HHO algorithm is used for performing unmanned aerial vehicle location deployment on the target area, and specifically includes:
step S21: importing user coordinates, determining the size of a Harris eagle population, the maximum iteration times, the maximum number of unmanned aerial vehicles to be put into, setting the upper and lower limits of solving space, the maximum service range of the unmanned aerial vehicles, and initializing the contribution degree of users;
step S22: generating a random number q epsilon [0,1], and determining the distribution mode of the harris eagle according to the size of q;
step S23: calculating the fitness of all individuals in the Harris eagle population;
Step S24: setting the position of the Harris hawk with the maximum fitness as the position of the prey;
Step S25: four attack strategies are executed according to escape energy of the prey and the distance between each harris eagle and the prey;
step S26: executing the steps S22 to S25 until the maximum iteration times, and outputting the position of the optimal solution;
Step S27: recording the position of the optimal solution, giving corresponding punishment values to the contribution degree of the user according to the position of the optimal solution from the user, and executing the steps S21 to S26 until the maximum number of unmanned aerial vehicles is to be put into;
Further, the unmanned aerial vehicle calculates that the unmanned aerial vehicle receives virtual force from a user or other unmanned aerial vehicles, and the virtual force specifically comprises:
the unmanned plane is attracted by the direction of the movement trend of the user;
The unmanned aerial vehicle is attracted by users;
The unmanned aerial vehicle receives the repulsive force of other unmanned aerial vehicles;
Further, the unmanned aerial vehicle moves according to the received virtual force in a specific time interval, and the specific contents include:
Assuming that the length of each time period is T, we equally divide each time period into n time intervals of the same length. Three time intervals are taken out from n time intervals, the three virtual forces according to claim 3 are executed respectively, the unmanned aerial vehicle moves according to the virtual force corresponding to the time interval, and the unmanned aerial vehicle is in a static service state in other time intervals.
Further, the unmanned aerial vehicle capable of entering the sleep state after being removed comprises the following specific contents:
the unmanned aerial vehicle attempts to unload all users connected with the unmanned aerial vehicle to the ground base station, if the unmanned aerial vehicle is successfully unloaded, the unmanned aerial vehicle is dormant, and if the unmanned aerial vehicle cannot be unloaded, the unmanned aerial vehicle is continuously in a working state.
Further, importing user coordinates, determining the size of the Harris eagle population, the maximum iteration times, the maximum number of unmanned aerial vehicles to be put into, setting the upper and lower limits of solving space, and initializing the user contribution degree; the concrete contents include:
let the set m= {1,2,3,..m } represent all users on the ground, the set e= {1,2,3,.. the maximum iteration number is t max, the maximum service range of the unmanned aerial vehicle is R s, and the maximum number of unmanned aerial vehicles is u. The initial contribution degree of each user is 1, the contribution degree of all users is represented by a set xi, the xi is a1×m-dimensional matrix, the element value represents the user contribution degree of the users, the psi represents an m×e-dimensional matrix, and the matrix elements are all composed of 0 or 1. For the following K e M, matrix ψ k,i =1 if the distance between harris eagle i and user k is smaller than R s, and ψ k,i =0 if the distance is larger than R s.
Further, the generated random number q epsilon [0,1] determines the distribution mode of the harris eagle according to the q; the concrete contents include:
Wherein X (t) is the position of the harris eagle at the time t, X (t+1) is the position of the harris eagle at the time t+1, q and r 1,r2,r3,r4 are random numbers in [0,1 ]. ub, lb are the upper and lower limits of the solution space, respectively, X rand is a random position in the solution space, X rabbit is the position of the prey at time t, and X ave is the average position of all harris eagles at time t.
Further, the calculating the fitness of each unmanned aerial vehicle in the harris eagle population comprises the following specific steps:
let A be the set of all Harris eagle individual fitness, then the formula of calculation of A is:
A＝ξ·ψ
For the harris eagle individual i, the fitness value is:
fitness(i)＝A(i)
Furthermore, according to the escape energy of the prey and the distance between each harris eagle and the prey, four attack strategies are executed by the harris eagle, and the escape energy is calculated in the following way:
E＝2E0(1-t/tmax)
Wherein E is the escape energy of the prey and E 0 is a random number between [ -1,1 ]. r is the distance between the harris eagle and the prey, and the distance threshold r s can be set by itself.
For each iteration, the attack strategy for the harris eagle is:
(1) When r is larger than or equal to r s and |E| is larger than or equal to 0.5:
X(t+1)＝Xrabbit(t)-X(t)-E|J·Xrabbit(t)-X(t)|
Where J is the jump distance j=2× (1-rand) during running of the prey, rand is a random number between 0, 1.
(2) When r is larger than or equal to r s and |E| < 0.5:
X(t+1)＝Xrabbit(t)-E|Xrabbit(t)-X(t)|
(3) When r < r s and |E| is not less than 0.5:
Wherein the specific functional form of the Y and Z functions is as follows:
Y＝Xrabbit(t)-E|J·Xrabbit(t)-X(t)|
Z＝Y+S×LF(D)
for the Z function, S is a random vector of dimension 1×d, i.e., s= randn (1, D), and the LF function is a Levy flight function, expressed in detail as:
(4) When r < r s and |E| < 0.5:
Wherein the specific functional form of the Y and Z functions is as follows:
Y＝Xrabbit(t)-E}J·Xrabbit(t)-Xm(t)|
Z＝Y+S×LF(D)
Further, the position of the optimal solution is recorded, corresponding penalty values are given according to the position of the optimal solution from the user, the steps 1 to 6 are executed until the maximum number of unmanned aerial vehicles is to be put into, the penalty value calculation mode can be flexibly adjusted according to the algorithm requirement, and a feasible setting scheme is provided:
For the following k∈M
Where d k denotes the distance of user k from the optimal solution. The optimal solution output by each algorithm is recorded until the maximum number U of unmanned aerial vehicles is to be input, and the solution set formed by all solutions is U= {1,2,3, & gt, U }, namely the optimal deployment site of the U-frame unmanned aerial vehicle.
Further, the unmanned aerial vehicle is attracted by the direction of the movement trend of the user, and the specific steps are as follows:
Firstly, the unmanned plane senses and distances all users within the range of R s -epsilon of the unmanned plane at time t 0 through a sensor carried by the unmanned plane, the number of the users is recorded as m s1, and the vector sums are solved simultaneously
The user counted at the time T 0 forms a plurality of direction vectors by calculating the range of the distance R s and the distance R s once every time T, and simultaneously solves the sum of the vectorsThen calculate/>And/>Vector sum/>Then, all users within R s -epsilon range of the unmanned plane are perceived again, the number of the users is recorded as m s1, and the vector sum/>, are solved simultaneouslyI.e. update m s1 and/>
Unmanned aerial vehicle receives user's direction of movement trend's appealThe following equation can be used to determine the value:
wherein T is the length of one movement period of the unmanned aerial vehicle, R s is the maximum service range of the unmanned aerial vehicle, a 1 is a perception parameter, and the unmanned aerial vehicle can be set by itself. Epsilon is a distance parameter introduced by preventing an edge user from separating from the safe distance of the unmanned aerial vehicle in a T period, can be set by the user, and is generally epsilon=T×v assuming that the moving speed of the user is v.
Further, the unmanned aerial vehicle is attracted by a user, and the method comprises the following specific steps:
All users in the range of the unmanned plane R s are perceived by the unmanned plane to form a plurality of direction vectors, the number of the users is recorded as m s2, and the vector sums are solved simultaneously
Unmanned aerial vehicle receives user's appealThe following equation can be used to determine the value:
a 2 is an attractive force parameter, and can be set by itself:
Further, the unmanned aerial vehicle receives the repulsion of other unmanned aerial vehicles, and its concrete step does:
the unmanned aerial vehicle perceives all unmanned aerial vehicles within the range of R s between the unmanned aerial vehicle and the unmanned aerial vehicle. For unmanned aerial vehicle with distance smaller than R s, the unmanned aerial vehicle is assumed to be relative to own direction vector The repulsive force suffered by the unmanned aerial vehicle is/>
Wherein R opt is the safety distance between two unmanned aerial vehicles.
Compared with the prior art, the technical scheme of the invention has the following advantages:
1. the invention provides a pre-deployment scheme of an improved HHO algorithm, and the algorithm can more output a group of reasonable deployment positions by setting a punishment value.
2. The invention designs the virtual force model with the user movement trend perception capability, and can convert the user movement trend, the distance between the user and the unmanned aerial vehicle and the distance between the unmanned aerial vehicles into corresponding virtual force to guide the unmanned aerial vehicles to move. Continuous and effective service is provided for user tracking, an optimal service place is determined, unmanned aerial vehicles are prevented from colliding, and as shown in fig. 8, the algorithm is not input into unmanned aerial vehicles in the application process, and network coverage rate is improved to different degrees in the hot spot dissipation process of a target area compared with the process of deploying unmanned aerial vehicles only in advance.
3. The invention designs a multi-virtual force time-sharing unmanned plane motion algorithm. The traditional virtual force hybrid calculation scheme may cause repulsive force between unmanned aerial vehicles, so as to influence judgment on the movement trend of the user. The application of the algorithm can obviously improve the sensitivity of the unmanned aerial vehicle to the movement trend of the user. The unmanned aerial vehicle can serve users moving at a higher speed or prolong the moving period of the unmanned aerial vehicle so as to save energy consumption. As shown in FIG. 9, compared with the virtual force mixing operation in the application process, the algorithm is not easy to lose the target hot spot, and can better serve users in the hot spot area.
Drawings
FIG. 1 is a flow chart of the steps performed in the present invention;
FIG. 2 is a flowchart of steps for implementing the modified HHO algorithm;
FIG. 3 is a schematic illustration of an attractive force of a drone subject to a direction of a user's movement trend;
FIG. 4 is a schematic illustration of the attraction of a user to a drone;
Fig. 5 is a schematic diagram of a repulsive force of another unmanned aerial vehicle to the unmanned aerial vehicle;
fig. 6 is a schematic diagram of unmanned plane movement time interval division;
FIG. 7 is a pre-deployment effect of a drone after simulation using MATLAB;
FIG. 8 is a graph of the change in coverage of the target area network using the unmanned aerial vehicle tracking motion algorithm versus without the unmanned aerial vehicle tracking motion algorithm;
FIG. 9 is a graph of network coverage variation using a unmanned aerial vehicle time-sharing motion algorithm with virtual force mixing operation;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a mobile network-oriented unmanned aerial vehicle deployment and tracking movement method which provides continuous and effective service for users in hot spots.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, a deployment and tracking motion scheme of an unmanned aerial vehicle facing a mobile network specifically includes the following steps:
step 1: obtaining the coordinates of the users in the target area and numbering the coordinates;
step 2: performing unmanned aerial vehicle position deployment on a target area by using an improved HHO algorithm;
Step 3: the unmanned aerial vehicle calculates that the unmanned aerial vehicle receives the virtual force from a user or other unmanned aerial vehicles;
step 4: the unmanned plane moves according to the received virtual force in a specific time interval;
step 5: removing the unmanned aerial vehicle which can enter a dormant state;
step 6: repeating the steps 3-5 until all unmanned aerial vehicles enter a dormant state or reach the maximum iteration times;
The working principle of the method is described in detail as follows:
step 1: obtaining the coordinates of the users in the target area and numbering the coordinates;
Firstly, determining an area where the unmanned aerial vehicle is to be deployed, and then obtaining the position of a user in the area through various modes, such as the acquisition of a user pre-backup or pre-release reconnaissance unmanned aerial vehicle, and the acquisition can also be provided by fixed Base Stations (BSs) on the ground.
Step 2: using an improved HHO algorithm to perform unmanned aerial vehicle position deployment on a target area;
as shown in fig. 2, the improved HHO algorithm for unmanned location deployment of a target area includes the following processes:
(1) Importing user coordinates, determining the size of a Harris eagle population, the maximum iteration times, the maximum number of unmanned aerial vehicles to be put into, setting the upper and lower limits of solving space, the maximum service range of the unmanned aerial vehicles, and initializing the contribution degree of users;
Let the set m= {1,2,3,..m } represent all users on the ground, the set e= {1,2,3,.. the maximum iteration number is t max, the maximum service range of the unmanned aerial vehicle is R s, and the maximum number of unmanned aerial vehicles is u. The initial contribution degree of each user is 1, the contribution degree of all users is represented by a set with xi, xi is a matrix with 1×m dimension, the contribution degree of each element value user is represented by a matrix with m×e dimension, and matrix elements are all composed of 0 or 1. For the following K e M, matrix ψ k,i =1 if the distance between harris eagle i and user k is smaller than R s, and ψ k,i =0 if the distance is larger than R s.
(2) Generating a random number q epsilon [0,1], and determining the distribution mode of the harris eagle according to the size of q;
Assuming that X (t) is the position of the harris eagle at time t, X (t+1) is the position of the harris eagle at time t+1, q, and r 1,r2,r3,r4 are random numbers within [0,1 ]. ub, lb are the upper and lower limits of the solution space, respectively, X rand is a random position in the solution space, X rabbit is the position of the prey at time t, and X ave is the average position of all harris eagles at time t. Then for each iteration all harris eagles are updated with the position by equation 1:
(3) Calculating the fitness of all individuals in the Harris eagle population;
assuming that a is the set of fitness of all harris eagles, a can be calculated by the matrix according to the formula 2, and each iteration, a can be updated once.
A=ζ·ψ formula (2)
For convenience of the following description, for the harris eagle individual i, we will express the fitness value of i as fitness (i), which is the value of fitness (i) =a (i).
(4) Setting the position of the Harris hawk with the maximum fitness as the position of the prey;
To obtain the optimal deployment location, we define the location of the hawk with the greatest fitness as the location of the prey, and other hawks develop around it by different strategies to see if it is a better deployment point.
(5) Four attack strategies are executed according to escape energy of the prey and the distance between each harris eagle and the prey;
assuming E is the escaping energy of the prey, it determines whether the Harris eagle is in the global search phase or the local development phase, and E 0 is a random number between [ -1,1 ]. The escape energy can be calculated by the formula (3):
e=2e 0(1-t/tmax) (equation 3)
Wherein E is the escape energy of the prey and E 0 is a random number between [ -1, 1].
For each iteration, the harris eagle will perform four different attack strategies depending on the escape energy of the prey to the distance between them:
(1) Gently enclose:
When r is larger than or equal to r s and |E| is larger than or equal to 0.5
R is the distance between the harris eagle and the prey, and the distance threshold R s is equal to R s. J is the jump distance during the running of the prey, gas j=2× (1-rand), rand is a random number between [0,1 ]. The position update of harris eagle is found by equation 4:
x (t+1) =x rabbit(t)-X(t)-E|J·Xrabbit (t) -X (t) | (formula 4)
(2) Strong and hard enclosing:
When r is greater than or equal to r s and |E| < 0.5, the position update of the harris eagle can be found by equation 5:
X (t+1) =x rabbit(t)-E|Xrabbit (t) -X (t) | (formula 5)
(3) Progressive rapid dive gentle tapping:
when r < r s and |E| is not less than 0.5, the position update of the harris eagle can be found by the formula 6:
In formula 6, the specific functional forms of the Y and Z functions are formula 7 and formula 8, respectively:
Y=x rabbit(t)-E|J·Xrabbit (t) -X (t) | (formula 7)
Z=y+s×lf (D) (formula 8)
For the Z function, S is a random vector of dimension 1×d, i.e., s= randn (1, D), the LF function is a Levy flight function, and the specific expression is formula 9:
(4) Strong and hard enclosure of progressive rapid dive:
when r < rs and |E| < 0.5, the position update of the harris eagle can be found by equation 10:
Wherein the specific functional forms of the Y and Z functions are formula 11 and formula 12, respectively:
y=x rabbit(t)-E|J·Xrabbit(t)-Xm (t) | (formula 11)
Z=y+s×lf (D) (formula 12)
LF (D) can be found by equation 9.
(6) Performing (2) to (5) up to a maximum number of iterations, and outputting a position of an optimal solution:
The position of the output optimal solution is an optimal deployment site of the unmanned aerial vehicle.
(7) Recording the position of the optimal solution, giving corresponding punishment values to the contribution degree of the user according to the position of the optimal solution from the user, and executing the steps 1 to 6 until the maximum number of unmanned aerial vehicles to be put into is reached;
because unmanned aerial vehicle deployment is carried out on a large-area, a plurality of unmanned aerial vehicles can be deployed, but if algorithms are directly executed for many times, all solutions can appear in almost the same position, and in order to enable unmanned aerial vehicles to be distributed as far as possible, communication interference among unmanned aerial vehicles is reduced, and a punishment value for users is introduced.
Let d k denote the distance of user k from the optimal solution. The optimal solution output by each algorithm is recorded until the maximum number U of unmanned aerial vehicles is to be input, and the solution set formed by all solutions is U= {1,2,3, & gt, U }, namely the optimal deployment site of the U-frame unmanned aerial vehicle. The penalty value can be flexibly adjusted according to the algorithm requirement, and we present a feasible setting scheme, wherein the penalty value is calculated by the formula 13:
For the following k∈M
The user contribution matrix ζ of the user is then updated
Step 3: the unmanned aerial vehicle calculates that the unmanned aerial vehicle receives the virtual force from a user or other unmanned aerial vehicles;
the unmanned aerial vehicle receives three virtual forces from different sources when the unmanned aerial vehicle serves the user in the air, wherein the virtual forces are respectively used for guiding the unmanned aerial vehicle to track the user Virtual force/>, which keeps the position of the drone as far as possible in the centre of the hotspotPrevent that too close distance between unmanned aerial vehicle from leading to unmanned aerial vehicle coverage overlap area too big waste of resources that causes to prevent virtual power/>, the collision of unmanned aerial vehicleTheir detailed description is as follows:
(1) The unmanned plane is attracted by the direction of the movement trend of the user;
Firstly, the unmanned plane senses and distances all users within the range of R s -epsilon of the unmanned plane at time t 0 through a sensor carried by the unmanned plane, the number of the users is recorded as m s1, and the vector sums are solved simultaneously
The user counted at the time T 0 forms a plurality of direction vectors by calculating the range of the distance R s and the distance R s once every time T, and simultaneously solves the sum of the vectorsThen calculate/>And/>Vector sum/>Then, all users within R s -epsilon range of the unmanned plane are perceived again, the number of the users is recorded as m s1, and the vector sum/>, are solved simultaneouslyI.e. update m s1 and/>
As shown in fig. 3, the unmanned aerial vehicle is attracted by the direction of the movement trend of the userThe value can be obtained by the formula 14:
a 1 is a perception parameter, which can be set by itself and recommended to take 2.
(3) The unmanned aerial vehicle is attracted by users;
All users in the range of the unmanned plane R s are perceived by the unmanned plane to form a plurality of direction vectors, the number of the users is recorded as m s2, and the vector sums are solved simultaneously
As shown in fig. 4, the drone is subject to the attraction of the userThe equation 15 can be used to determine:
a 2 is an attractive force parameter, which can be set by itself and is recommended to be 0.2.
(4) The unmanned aerial vehicle receives the repulsive force of other unmanned aerial vehicles;
As shown in fig. 5, it is assumed that R opt is the safe distance between two unmanned aerial vehicles. The unmanned aerial vehicle perceives all unmanned aerial vehicles within the range of R s between the unmanned aerial vehicle and the unmanned aerial vehicle. For unmanned aerial vehicle with distance smaller than R s, the unmanned aerial vehicle is assumed to be relative to own direction vector The repulsive force suffered by the unmanned aerial vehicle is/>Can be calculated by equation 16.
A 3 is an attractive force parameter, can be set by self, and needs to be far larger than a 1 and a 2, and is recommended to be 1000.
Step 4: the unmanned plane moves according to the received virtual force in a specific time interval;
Because the repulsive force guaranteeing the distance between the unmanned aerial vehicles is too strong, the unmanned aerial vehicle is extremely sensitive to the perception of the movement trend of the user, the perception capability of the unmanned aerial vehicle to the movement trend of the user can be greatly reduced due to the fact that the traditional virtual force mixing and resultant force solving method, and therefore the unmanned aerial vehicle can move only according to the received specific virtual force in a specific time interval
As shown in fig. 6, assuming that the length of each motion cycle of the unmanned aerial vehicle is T, we equally divide each time cycle into n time intervals of the same length. Three time intervals are taken out from n time intervals, the three virtual forces according to claim 3 are executed respectively, the unmanned aerial vehicle moves according to the virtual force corresponding to the time interval, and the unmanned aerial vehicle is in a static service state in other time intervals.
Step 5: removing the unmanned aerial vehicle which can enter a dormant state;
the unmanned aerial vehicle attempts to unload all users connected with the unmanned aerial vehicle to the ground base station, if the unmanned aerial vehicle is successfully unloaded, the unmanned aerial vehicle is dormant, and if the unmanned aerial vehicle cannot be unloaded, the unmanned aerial vehicle is continuously in a working state.
Step 6: repeating the steps 3-5 until all unmanned aerial vehicles enter a dormant state or reach the longest service time;
The following describes in detail the conditions for determining whether the user is connected to the unmanned aerial vehicle:
both the drone and the base station have the largest number of service users, depending on the type of drone and base station. When the unmanned aerial vehicle and the base station are fully loaded, communication service cannot be provided for redundant users.
(1) When the distance between the unmanned aerial vehicle or the fixed base station and the user is smaller than R s and the SINR value between the unmanned aerial vehicle or the fixed base station and the user is larger than the threshold value lambda th, the unmanned aerial vehicle or the fixed base station accords with the connection condition with the user. The SINR value is calculated in a manner related to the frequency band allocation of the operator, and Λ th depends on the minimum communication quality requirement of the communication operator.
(2) If the user meets the connection conditions with a plurality of unmanned aerial vehicles and the ground base station at the same time, the user can preferentially select to be connected with the fixed base station.
(3) If the user meets the connection conditions with a plurality of base stations at the same time, the user can preferentially select to connect with the fixed base stations with fewer connection users in order to obtain better communication quality.
(4) If the user accords with the connection condition with a plurality of unmanned aerial vehicle simultaneously, in order to reduce unmanned aerial vehicle's input, the user can the preference be connected with the unmanned aerial vehicle that the number of connected users is more.
The percentage of all users within the served user's area is represented using the user coverage C.
The manner in which the virtual force experienced by the drone is translated into velocity is described in detail below:
Since the drone is always accelerating under the condition of being always subjected to virtual force, but is affected by the performance of the drone, the movement speed of the drone has a maximum value, it is specified that the virtual force to which the drone is subjected will be converted into virtual force according to equation 17. Wherein V is the speed of the unmanned aerial vehicle, V max is the maximum speed of the unmanned aerial vehicle, Is a virtual force experienced by the drone.
Compared with the prior art, the technical scheme of the invention has the following advantages:
1. The invention provides a pre-deployment scheme of an improved HHO algorithm, which enables the algorithm to output a group of reasonable deployment positions by setting a punishment value, and enables a limited number of unmanned aerial vehicles to serve as many hot users as possible, as shown in fig. 7, wherein the punishment position of the unmanned aerial vehicles is shown by the algorithm when the number of users is 500.
2. The invention designs the virtual force model with the user movement trend perception capability, and can convert the user movement trend, the distance between the user and the unmanned aerial vehicle and the distance between the unmanned aerial vehicles into corresponding virtual force to guide the unmanned aerial vehicles to move. Continuous and effective service is provided for user tracking, an optimal service place is determined, unmanned aerial vehicles are prevented from colliding, and as shown in fig. 8, the algorithm is not input into unmanned aerial vehicles in the application process, and network coverage rate is improved to different degrees in the hot spot dissipation process of a target area compared with the process of deploying unmanned aerial vehicles only in advance.
3. The invention designs a multi-virtual force time-sharing unmanned plane motion algorithm. The traditional virtual force hybrid calculation scheme may cause repulsive force between unmanned aerial vehicles, so as to influence judgment on the movement trend of the user. The application of the algorithm can obviously improve the sensitivity of the unmanned aerial vehicle to the movement trend of the user. The unmanned aerial vehicle can serve users moving at a higher speed or prolong the moving period of the unmanned aerial vehicle so as to save energy consumption. As shown in FIG. 9, compared with the virtual force mixing operation in the application process, the algorithm is not easy to lose the target hot spot, and can better serve users in the hot spot area.
The embodiments described herein are provided to assist the reader in understanding the principles of the present invention and should be construed as protecting the invention. The scope is not limited to such specific statements and embodiments. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention, for example, to modify the calculation mode of the penalty value, shall be included in the scope of the claims of the present invention.
Claims (5)
1. The unmanned aerial vehicle deployment and tracking control method for the mobile network is characterized by comprising the following steps of:
step S1: obtaining the coordinates of the users in the target area and numbering the coordinates;
Step S2: using an improved HHO algorithm to perform unmanned aerial vehicle position deployment on a target area;
Step S3: the unmanned aerial vehicle calculates that the unmanned aerial vehicle receives the virtual force from a user or other unmanned aerial vehicles;
step S4: the unmanned plane moves according to the received virtual force in a specific time interval;
Step S5: removing the unmanned aerial vehicle which can enter a dormant state;
Step S6: repeating the steps S3 to S5 until all unmanned aerial vehicles enter a dormant state or reach the maximum iteration times;
the step S2 specifically includes the following steps:
step S21: importing user coordinates, determining the size of a Harris eagle population, the maximum iteration times, the maximum number of unmanned aerial vehicles to be put into, setting the upper and lower limits of solving space, the maximum service range of the unmanned aerial vehicles, and initializing the contribution degree of users;
step S22: generating a random number q epsilon [0,1], and determining the distribution mode of the harris eagle according to the size of q;
step S23: calculating the fitness of all individuals in the Harris eagle population;
Step S24: setting the position of the Harris hawk with the maximum fitness as the position of the prey;
Step S25: four attack strategies are executed according to escape energy of the prey and the distance between each harris eagle and the prey;
step S26: executing the steps S22 to S25 until the maximum iteration times, and outputting the position of the optimal solution;
Step S27: recording the position of the optimal solution, giving corresponding punishment values to the contribution degree of the user according to the position of the optimal solution from the user, and executing the steps S21 to S26 until the maximum number of unmanned aerial vehicles is to be put into;
in step S21, the step of determining the position of the object,
Setting a set M= {1,2,3, & gt, M } represents all users on the ground, and the set represents E= {1,2,3, & gt, E } represents all Harris hawks, the maximum iteration number is t max, the maximum service range of the unmanned aerial vehicle is R s, and the maximum number of unmanned aerial vehicles is u; the initial contribution degree of each user is 1, the aggregate of the contribution degrees of all users is represented by xi, the xi is a matrix with 1×m dimension, each element value represents the user contribution degree of the user, the phi represents a matrix with m×e dimension, and the matrix elements are all composed of 0 or 1; for the followingIf the distance between harris eagle i and user k is less than R s, matrix ψ k,i =1, if the distance is greater than R s, then ψ k,i =0;
in step S22, the following formula is used to determine the distribution mode of harris eagles according to the q size:
Wherein X (t) is the position of the harris eagle at the time t, X (t+1) is the position of the harris eagle at the time t+1, q and r 1,r2,r3,r4 are random numbers in [0,1 ]; ub and lb are the upper and lower limits of the solution space respectively, X rand is a random position in the solution space, X rabbit is the position of the prey at time t, and X ave is the average position of all Harris hawks at time t;
The specific steps of the step S23 are as follows:
let A be the set of all Harris eagle individual fitness, then the formula of calculation of A is:
A＝ξ·ψ
both xi and ψ are set matrixes, and for the harris eagle individual i, the fitness value is as follows:
fitness(i)＝A(i)；
in step S25, the hunting energy is calculated by:
E＝2E0(1-t/tmax)
Wherein E is the escape energy of the prey, E 0 is a random number between [ -1,1 ]; r is the distance between the harris eagle and the prey, and the distance threshold r s can be set by itself;
for each iteration, the attack strategy for the harris eagle is:
(1) When r is larger than or equal to r s and |E| is larger than or equal to 0.5:
X(t+1)＝Xrabbit(t)-X(t)-E|J·Xrabbit(t)-X(t)|
wherein J is a jump distance j=2× (1-rand) during running of the prey, rand is a random number between [0,1 ];
(2) When r is larger than or equal to r s and |E| < 0.5:
X(t+1)＝Xrabbit(t)-E|Xrabbit(t)-X(t)|
(3) When r < r s and |E| is not less than 0.5:
Wherein the specific functional form of the Y and Z functions is as follows:
Y＝Xrabbit(t)-E|J·Xrabbit(t)-X(t)|
Z＝Y+S×LF(D)
for the Z function, S is a random vector of dimension 1×d, i.e., s= randn (1, D), and the LF function is a Levy flight function, expressed in detail as:
(4) When r < r s and |E| < 0.5
Wherein the specific functional form of the Y and Z functions is as follows:
Y＝Xrabbit(t)-E|J·Xrabbit(t)-Xm(t)|
Z＝Y+S×LF(D)；
In step S27, the position of the optimal solution is recorded, corresponding penalty values are given according to the position of the optimal solution from the user, and steps S1 to S6 are executed until the maximum number of unmanned aerial vehicles to be put into is reached, wherein the penalty value is calculated in the following manner:
The penalty value can be flexibly adjusted according to the algorithm requirement, and a feasible setting scheme is provided here:
For the following
Wherein d k represents the distance of user k from the optimal solution; the optimal solution output by each algorithm is recorded until the maximum number U of unmanned aerial vehicles is to be input, and the solution set formed by all solutions is U= {1,2,3, & gt, U }, namely the optimal deployment site of the U-frame unmanned aerial vehicle.
2. The mobile network oriented unmanned aerial vehicle deployment and tracking control method of claim 1, wherein: in step S3, the unmanned aerial vehicle calculates that the unmanned aerial vehicle receives a virtual force from a user or other unmanned aerial vehicles, where the virtual force at least includes:
the unmanned plane is attracted by the direction of the movement trend of the user;
The unmanned aerial vehicle is attracted by users;
The unmanned aerial vehicle receives the repulsion of other unmanned aerial vehicles.
3. The mobile network oriented unmanned aerial vehicle deployment and tracking control method of claim 2, wherein: in step S4, the unmanned aerial vehicle moves according to the received virtual force in a specific time interval, and further includes:
assuming that the length of each time period is T, equally dividing each time period into n sections of time intervals with the same length; three time intervals are taken out from the n time intervals, and the following virtual forces are respectively executed:
the unmanned plane is attracted by the direction of the movement trend of the user;
The unmanned aerial vehicle is attracted by users;
The unmanned aerial vehicle receives the repulsive force of other unmanned aerial vehicles;
the unmanned aerial vehicle moves according to the virtual force corresponding to the time interval, and the unmanned aerial vehicle is in a static service state in other time intervals.
4. The mobile network oriented unmanned aerial vehicle deployment and tracking control method of claim 1, wherein: step S5 further comprises:
and unloading all users connected with the unmanned aerial vehicle to the ground base station by the unmanned aerial vehicle, if the unmanned aerial vehicle is successfully unloaded, the unmanned aerial vehicle is dormant, and if the unmanned aerial vehicle cannot be unloaded, the unmanned aerial vehicle is continuously in a working state.
5. The unmanned aerial vehicle deployment and tracking control method for mobile networks according to claim 1, wherein the unmanned aerial vehicle is attracted by a direction of a user movement trend, and the method comprises the following specific steps:
firstly, at time t 0, the unmanned plane senses all users within the range of R s -s from the unmanned plane through a sensor carried by the unmanned plane, the number of the users is recorded as m s1, and the vector sums are solved simultaneously
The user counted at the time T 0 forms a plurality of direction vectors by calculating the range of the distance R s and the distance R s once every time T, and simultaneously solves the sum of the vectorsThen calculate/>And/>Vector sum/>Then, all users within R s -epsilon range of the unmanned plane are perceived again, the number of the users is recorded as m s1, and the vector sum/>, are solved simultaneouslyI.e. update m s1 and/>
Unmanned aerial vehicle receives user's direction of movement trend's appealThe following equation can be used to determine the value:
Wherein T is the length of one movement period of the unmanned aerial vehicle, R s is the maximum service range of the unmanned aerial vehicle, a 1 is a perception parameter and can be set by itself; epsilon is a distance parameter introduced by preventing an edge user from being separated from the safe distance of the unmanned aerial vehicle in a T period, can be set by the user, and is generally epsilon=T×v on the assumption that the moving speed of the user is v;
the unmanned aerial vehicle receives user's appeal, and its concrete step does:
All users in the range of the unmanned plane R s are perceived by the unmanned plane to form a plurality of direction vectors, the number of the users is recorded as m s2, and the vector sums are solved simultaneously
Unmanned aerial vehicle receives user's appealThe following equation can be used to determine the value:
a 2 is an attractive force parameter;
the unmanned aerial vehicle receives the repulsion of other unmanned aerial vehicles, and specific steps are:
The unmanned aerial vehicle perceives all unmanned aerial vehicles within the range of R s of the unmanned aerial vehicle and the distance; for unmanned aerial vehicle with distance smaller than R s, the unmanned aerial vehicle is assumed to be relative to own direction vector The repulsive force suffered by the unmanned aerial vehicle is/>
Wherein R opt is the safety distance between two unmanned aerial vehicles.
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