CN113923675A - Aerial base station deployment method for improving communication performance of ground user - Google Patents

Aerial base station deployment method for improving communication performance of ground user Download PDF

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CN113923675A
CN113923675A CN202111209135.5A CN202111209135A CN113923675A CN 113923675 A CN113923675 A CN 113923675A CN 202111209135 A CN202111209135 A CN 202111209135A CN 113923675 A CN113923675 A CN 113923675A
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unmanned aerial
aerial vehicle
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CN113923675B (en
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刘玲玲
王爱民
孙庚�
吴静
李家辉
梁爽
郑婷婷
李琛泽
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Jilin University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

The invention discloses an aerial base station deployment method for improving communication performance of ground users, which comprises the following steps: initializing the position of a ground node, and determining the numerical values of a paired neighbor population and an alternative neighbor population; dividing a multi-objective optimization problem into a plurality of sub-problems by a Chebyshev decomposition method, and initializing an objective function value of each sub-problem; calculating objective function values of all unmanned aerial vehicles at different positions; step four, updating the population by using the copy operation of the mixed solution to obtain a new solution, and performing border crossing processing on the new solution; step five, calculating the objective function value of the new solution after processing, and updating the reference point when the objective function value of the new solution is smaller than the objective function value of the reference point; and step six, comparing the new solution with the objective function value of the individual in the substitute neighborhood of one individual, and reserving the unmanned aerial vehicle individual with a small function value.

Description

Aerial base station deployment method for improving communication performance of ground user
Technical Field
The invention relates to an aerial base station deployment method for improving communication performance of a ground user, and belongs to the field of communication.
Background
The air base station is a station composed of one or more devices with functions of sensing, receiving, transmitting and the like, and aims to provide internet service for remote areas with unsmooth networks in the world, and hot air balloons, unmanned planes and the like can be used as the air base station. In various communication systems, large-scale data transmission needs to be carried out between terminal users, and an unmanned aerial vehicle can be used as an aerial base station due to the advantages of flexible deployment, strong maneuverability, strong operability and the like, provides services for ground users in different environments, and provides favorable conditions for communication between terminals, so that the unmanned aerial vehicle is used as the aerial base station in the research.
However, in real-world research, the use of a drone to communicate with multiple ground users consumes a lot of energy of the drone, the continuous flight time of the drone is also a problem to be optimized, and it is not practical for the drone to be fixed at one location, so the purpose of using multiple drones to communicate with corresponding users by mobile deployment to the appropriate locations is achieved herein. In the scene of communication between ground users and an unmanned aerial vehicle, it is assumed that one unmanned aerial vehicle can communicate with a plurality of users at the same time, one user can communicate with one unmanned aerial vehicle only, the unmanned aerial vehicle can enable channels among the users to interfere with each other when communicating with the users at the same time, and then information received by the users is interfered, so that a time division multiple access protocol is considered to be used in the scene, and interference among the same channels is reduced.
Throughput is one of indexes for measuring system communication performance, and energy consumption is essential in the moving process and the communication process of the unmanned aerial vehicle. Based on the two aspects, the aerial base station deployment method for improving the communication performance of the ground users is provided, the minimum throughput between the unmanned aerial vehicle and the users in aerial base station deployment is established, the throughput of the system is improved, and the total energy consumption is reduced.
Disclosure of Invention
The invention designs and develops an aerial base station deployment method for improving the communication performance of ground users, realizes communication by optimizing deployment positions, transmitting power and an incidence matrix with users by using a plurality of unmanned aerial vehicles, changes the traditional method for communicating with all users by using one unmanned aerial vehicle, reduces the requirements on the service life and the communication performance of a single unmanned aerial vehicle, and improves the communication performance of ground users.
The technical scheme provided by the invention is as follows:
an aerial base station deployment method for improving communication performance of ground users comprises the following steps:
initializing the position of a ground node, generating the position, power and an incidence matrix of an unmanned aerial vehicle, generating a group of weight vectors, calculating Euclidean distances between each weight vector and other weight vectors, and determining the nearest weight vector;
dividing a multi-objective optimization problem into a plurality of sub-problems by a Chebyshev decomposition method, and initializing an objective function value of each sub-problem;
calculating objective function values of all unmanned aerial vehicles at different positions;
step four, updating the population by using the copy operation of the mixed solution to obtain a new solution, and performing border crossing processing on the new solution;
step five, calculating the objective function value of the new solution after processing, and updating the reference point when the objective function value of the new solution is smaller than the objective function value of the reference point;
step six, comparing the objective function value of the new solution with the objective function value of an individual in the population, and reserving the individual parameter of the unmanned aerial vehicle with a small function value;
step seven, when the obtained solution meets the iteration termination condition, outputting the optimal solution of the objective function;
and when the iteration termination condition is not met, taking the solutions as the current solution to re-iterate and execute the steps from four to six.
Preferably, the solution of the objective function of the aerial base station deployment method is:
Figure BDA0003308176150000031
x, Y, Z respectively represents the three-dimensional coordinates of unmanned aerial vehicle deployment, P represents the transmitting power of the unmanned aerial vehicle, A represents the communication relationship between the unmanned aerial vehicle and the ground user, and when the u-th unmanned aerial vehicle communicates with the n-th user, A represents the communication relationship between the unmanned aerial vehicle and the ground usern＝u。
It is preferable that the first and second liquid crystal layers are formed of,
the process of initializing a ground node location includes a method of mixing an initialization solution:
x＝rand(N,nVar)·*(vMax-vMin)+vMin；
h＝rand(N,1)·*(H-L)+L；
Pt＝mod(Pt+b-(a/(2*π)),1)+PL+(PH-PL)*r；
wherein, X represents the coordinate of unmanned aerial vehicle in the horizontal plane, and nVar is 2, and vMin and vMax represent unmanned aerial vehicle minimum and maximum value on the horizontal direction, and H represents unmanned aerial vehicle's height, and H represents unmanned aerial vehicle's height maximum, and L represents unmanned aerial vehicle's height minimum, and mod represents the mould in the mathematics and divides the operation, and P dividestFor the current power of the unmanned plane, a and b are constants, PLIs the lower power limit, PHR is a random distribution of 0-1 as the upper power limitAnd (4) counting.
Preferably, the second step includes:
the unmanned aerial vehicle communicating with the user is searched through the incidence relation matrix, and when the unmanned aerial vehicle communicates with the user through a communication link, the communication between the remaining other unmanned aerial vehicles and the user is considered as an interference signal.
Preferably, the calculating the objective function value of the drone at the initial position in the third step includes:
Figure BDA0003308176150000032
in the formula,
f1representing a first objective function that maximizes a minimum throughput between the drone and a ground user;
f2representing a second objective function, maximizing the sum of the throughputs of all the drones;
f3representing a third objective function to minimize the total energy consumed by the drone; the specific expressions of the three functions are as follows:
f1＝max Rmin；
Figure BDA0003308176150000041
Figure BDA0003308176150000042
in the formula, RminFor minimum throughput, R, between unmanned aerial vehicle and ground useruThroughput for drone u;
calculating the energy consumption of the unmanned aerial vehicle in the horizontal direction:
Em＝Phor(Vhor)thor；
wherein,
Figure BDA0003308176150000043
Figure BDA0003308176150000044
Figure BDA0003308176150000045
in the formula, thorFor the movement time of the unmanned aerial vehicle in the horizontal direction, LhorFor the distance of movement of the unmanned aerial vehicle in the horizontal direction, VhorThe moving speed of the unmanned aerial vehicle in the horizontal direction is obtained;
the model of the pushing power consumption of the unmanned aerial vehicle in the horizontal direction is as follows:
Figure BDA0003308176150000046
the energy consumed by the unmanned aerial vehicle when the unmanned aerial vehicle is in communication with the ground user during hovering is as follows:
Figure BDA0003308176150000047
the communication time between the unmanned aerial vehicle and the ground user is as follows:
Figure BDA0003308176150000048
the energy consumed by the unmanned aerial vehicle when moving in the non-horizontal direction is as follows:
Figure BDA0003308176150000051
preferably, the fourth step includes:
the discrete solution space and the continuous solution space are separated to update the solution and carry out border-crossing processing on the updated solution, and the method comprises the following steps:
the updating method of the continuous solution comprises the following steps:
Figure BDA0003308176150000052
Lstepsize＝α*step·(xi-best)；
Figure BDA0003308176150000053
Figure BDA0003308176150000054
xnew＝xi+Lstepsize*randn(size(xi))；
λi＝unifrnd(-γ,1+γ,size(xi))；
Figure BDA0003308176150000055
where α and β are weighting factors, best is the first solution selected in estimating the pareto frontier, randn is a normally distributed random matrix obeying a mean of 0 and a variance of 1, and xiIs a continuous variable representing the position coordinates or power of the drone, g is a constant, xnewFor flying a newly generated population according to Levy, xfinalFor the final new solution, γ is the lower bound in the unifrnd function, λiIs a weight factor, τ is a distribution function;
for the discrete solution part, the update method of the incidence matrix of the unmanned aerial vehicle and the ground user is as follows:
μj＝randi(U,[1,N])；
in the formula, U is unmanned aerial vehicle's number, and N is the number of ground node.
It is preferable that the first and second liquid crystal layers are formed of,
in the fifth step, when the new solution x is generatedfinalWhen the objective function value of (1) is smaller than the value of the reference point, the reference point x is updated to xfinal，fx＝ffinal。
The invention has the following beneficial effects: in a communication model between a ground user and an aerial base station, a multi-target joint optimization model for improving the minimum throughput between an unmanned aerial vehicle and the ground user in the aerial base station assisted by multiple unmanned aerial vehicles and improving the system throughput and reducing the total energy consumption of the unmanned aerial vehicles is established, the model is solved by utilizing an evolutionary algorithm to design an ideal deployment position of the unmanned aerial vehicles, transmitting power and an incidence relation communicated with the ground user, the ideal deployment position of the unmanned aerial vehicles is utilized, the unmanned aerial vehicles can be communicated with the multiple users at the same time, the throughput is improved, the unmanned aerial vehicles do not need to move positions for many times, and the moving energy consumption is reduced.
In addition, in order to improve throughput, when multiple unmanned aerial vehicles are used for communicating with ground users at the same time, the same-channel interference exists, so that the time division multiple access protocol is used for weakening the interference, the multiple unmanned aerial vehicles are used for realizing communication by optimizing the deployment positions, the transmitting power and the incidence matrixes of the multiple unmanned aerial vehicles and the users, the traditional method for communicating with all users by using one unmanned aerial vehicle is changed, the requirements on the service life and the communication performance of a single unmanned aerial vehicle are reduced, and the communication performance of the ground users is also improved.
Drawings
Fig. 1 is a schematic structural diagram of a flow chart of an air base station deployment method for improving communication performance of a ground user according to the present invention.
Fig. 2 is a schematic view of the deployment of an airborne base station for communication between an unmanned aerial vehicle and a ground user according to the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in fig. 1-2, the invention provides an air base station deployment method for improving communication performance of ground users, which uses multiple unmanned aerial vehicles to realize communication by optimizing deployment positions, transmission power and an incidence matrix with users, changes the traditional method of using one unmanned aerial vehicle to communicate with all users, reduces the requirements on the service life and communication performance of a single unmanned aerial vehicle, and improves the communication performance of ground users.
Initializing the position of a ground node, and generating the position, power and incidence matrix of the unmanned aerial vehicle; generating a set of weight vectors, for each of the weight vectors, calculating the euclidean distance of each vector from all other vectors, and determining the set of weight vectors closest to each vector;
dividing a multi-objective optimization problem into a plurality of sub-problems by a Chebyshev decomposition method, and initializing an objective function value of each sub-problem;
calculating objective function values of all unmanned aerial vehicles at different positions;
update population using a hybrid solution replication operation: i.e. from one individual x in the populationiRandomly selecting two individuals in the pairing neighborhood, and then creating a new solution according to the two individuals; carrying out border crossing processing on the new solution;
calculating the objective function value of the new solution after processing, and updating the reference point when the objective function value of the new solution is smaller than the objective function value of the reference point;
the new solution is summed with xiThe target function values of the individuals in the substitute neighborhood are compared, and the parameters of the unmanned aerial vehicle individuals with small function values, including the coordinates of x, y and z, power and the incidence matrix of the ground users, are reserved;
when the obtained new solution meets the iteration termination condition, outputting the optimal solution of the objective function;
and when the iteration termination condition is not met, taking the solutions as the current solution to re-iterate and execute the steps from four to six.
The method specifically comprises the following steps:
step 1: for a communication scenario with nPop ground users and N drones, candidate solutions are formed by the optimization variables, namely deployment positions, transmission powers of the drones and communication incidence matrixes of the ground users. The method comprises the steps of firstly randomly initializing to generate nPop ground users, then generating a group of candidate solutions of the unmanned aerial vehicle by using the following mixed solution initialization method, and then initializing a group of parametersTest point z is min (f)i(x) | x ∈ ψ) and a set of weight vectors, and calculating the euclidean distance of one vector from all other vectors and determining a set of weight vectors closest thereto; then determining the size T of the paired neighbor populationmAnd size T of the surrogate neighbor populationpThe solution of the objective function of the air base station deployment method is composed as follows:
Figure BDA0003308176150000071
x, Y, Z respectively represents the three-dimensional coordinates of unmanned aerial vehicle deployment, P represents the transmitting power of the unmanned aerial vehicle, A represents the communication relationship between the unmanned aerial vehicle and the ground user, and when the u-th unmanned aerial vehicle communicates with the n-th user, A represents the communication relationship between the unmanned aerial vehicle and the ground usernU, a method of hybrid initialization solution:
x＝rand(N,nVar)·*(vMax-vMin)+vMin；
h＝rand(N,1)·*(H-L)+L；
Pt＝mod(Pt+b-(a/(2*π)),1)+PL+(PH-PL)*r；
wherein, X represents the coordinate of unmanned aerial vehicle in the horizontal plane, and nVar is 2, and vMin and vMax represent unmanned aerial vehicle minimum and maximum value on the horizontal direction, and H represents unmanned aerial vehicle's height, and H represents unmanned aerial vehicle's height maximum, and L represents unmanned aerial vehicle's height minimum, and mod represents the mould in the mathematics and divides the operation, and P dividestFor the current power of the unmanned plane, a and b are constants, PLIs the lower power limit, PHR is a random number distributed between 0 and 1 as the upper power limit.
Determining the size, T, of a paired neighbor population1＝max(ceil(0.15*N),2)；
Tm＝min(max(T12),15), N is the number of drones;
determining the size of the replacement neighbor: t isp＝min(max(T1,2),15)；
Step 2: dividing a multi-objective optimization problem into a plurality of subproblems according to a Chebyshev decomposition method, initializing an objective function value of each subproblem, firstly judging whether a certain unmanned aerial vehicle is communicated with a certain user or not through an incidence relation matrix, if the unmanned aerial vehicle is communicated with the user through a communication link, and considering the communication between all the remaining unmanned aerial vehicles and the user as interference signals which are equivalent to Gaussian white noise;
and step 3: then, calculating an objective function value of the unmanned aerial vehicle at the initial position by using the following formula;
Figure BDA0003308176150000081
in the formula,
f1representing a first objective function that maximizes a minimum throughput between the drone and a ground user;
f2representing a second objective function, maximizing the sum of the throughputs of all the drones;
f3representing a third objective function to minimize the total energy consumed by the drone; the specific expressions of the three functions are as follows:
f1＝max Rmin；
Figure BDA0003308176150000082
Figure BDA0003308176150000083
in the formula, RminFor minimum throughput, R, between unmanned aerial vehicle and ground useruThroughput for drone u;
wherein,
Figure BDA0003308176150000091
according to the shannon theorem, throughput can be expressed in communications as the transmission rate of channel information, which is defined by the channel signal-to-noise ratioAnd the performance of the proposed method can be quantified well, as determined by the bandwidth. Therefore, in the aerial base station deployment method for improving the communication performance of the ground users, in order to improve the performance of the ground users, the minimum throughput R between the unmanned aerial vehicle and the ground users is designedmin(expression below) and throughput R of all dronesuAs our optimization objective, as described above f1，f2
Rmin＝min{R1,R2,...,RN}；
Em＝Phor(Vhor)thor；
In the aerial base station deployment method, the communication and the movement of the aerial base station bring energy consumption certainly, the greater the energy consumption is, the more unfavorable the deployment of the aerial base station is, therefore, the total energy consumption of the unmanned aerial vehicle is limited, and the objective function f is given3. In addition, the movement of the unmanned aerial vehicle is divided into horizontal and vertical directions, and we assume that the unmanned aerial vehicle first moves in the horizontal direction and then moves in the vertical direction (the vertical direction is divided into upward movement and downward movement), and the movement time t in the horizontal directionhorEqual to the distance L of movement in the horizontal directionhorVelocity VhorThe vertical direction is similar to the horizontal direction, when the unmanned aerial vehicle moves in the horizontal direction, E is usedm＝Phor(Vhor)thorThe energy consumption in the horizontal direction is calculated. And the energy in the vertical direction is given below in e (t). The distance, speed and time relation expressions of the three directions are as follows:
Figure BDA0003308176150000092
Figure BDA0003308176150000093
Figure BDA0003308176150000094
the model of the pushing power consumption of the unmanned aerial vehicle in the horizontal direction is as follows:
Figure BDA0003308176150000095
the energy consumed by the unmanned aerial vehicle when the unmanned aerial vehicle is in communication with the ground user during hovering is as follows:
Figure BDA0003308176150000096
expression Ehc＝...Representing the energy consumed by the drone when hovering in communication with a ground user, including the communication energy and the energy required to keep the drone airborne, the power at which the drone communicates with the user being Pc，PhRepresenting the power consumed by the drone to keep it airborne, i.e. at a speed of 0. T ishovRepresents the communication time of the drone with the ground user (also equal to the time the drone hovers in the air), i.e. the time required to transmit the Q bits of data;
the communication time between the unmanned aerial vehicle and the ground user is as follows:
Figure BDA0003308176150000101
the energy consumed by the unmanned aerial vehicle when moving in the non-horizontal direction is as follows:
Figure BDA0003308176150000102
Ph＝P0+Pi；
wherein R isuDenotes the communication throughput of drone u, B denotes the channel bandwidth when the drone communicates with the user, PuIndicating the transmit power, h, of the drone communicating with the useru，nRepresenting channel work between the u-th drone and the corresponding n-th ground userRate gain, PrRepresenting the transmission power, σ, of other drones2Representing the Gaussian white noise power, P, at a ground userhor，Pup，PdescIndicating the transmitted power, V, of the drone in the horizontal direction, ascending or descendinghor，Vup，VdescThe distribution represents the flight speed, L, of the drone at level, ascent and descenthor，Lup，LdescRespectively representing the unmanned plane t in the horizontal, ascending and descending processeshor，tup，tdescRespectively representing the time spent by the drone in flight in the horizontal direction, during ascent and descent, P0And PiRepresenting the blade profile power and the induced power in the hovering state, UtipIs the tip speed of the rotor blade, V represents the horizontal movement speed of the drone, V0Representing the average rotor induced speed during the hover of the drone. d0And s represent fuselage resistance ratio and rotor solidity, respectively. Further, r and a are air density and rotor disk area. v (T) is the instantaneous speed of the drone at time T, T is the duration of the whole flight of the drone, mUAVGross weight of unmanned aerial vehicle, g is acceleration of gravity, PhThe method is used for maintaining the transmitting power of the unmanned aerial vehicle in the air when the speed of the unmanned aerial vehicle is 0, namely the unmanned aerial vehicle is hovering, and mainly depends on physical factors such as the weight of the aircraft, the air density and the area of a rotor disc, PcThe transmission power is the transmission power of the unmanned aerial vehicle during communication, and Q is the communication traffic between the unmanned aerial vehicle and a ground user.
And 4, step 4: randomly electing two individuals x from a pairing neighborhood of unmanned aerial vehicles1And x2The method comprises the following steps of updating by using the following formula to obtain a new solution, wherein the purpose is to enhance the exploration capability of the algorithm and improve the convergence rate of the solution. The updating method of the continuous solution combines the Laevir flight with the cross operation of the genetic algorithm, and accelerates the exploration speed of the solution:
Lstepsize＝α*step·(xi-best)；
Figure BDA0003308176150000111
Figure BDA0003308176150000112
xnew＝xi+Lstepsize*randn(size(xi))；
λi＝unifrnd(-y，1+y，size(xi))
Figure BDA0003308176150000113
where α and β are weighting factors, best is the first solution selected in estimating the pareto frontier, randn is a normally distributed random matrix obeying a mean of 0 and a variance of 1, and xiIs a continuous variable representing the position coordinates or power of the drone, g is a constant, xnewFor flying a newly generated population according to Levy, xfinalFor the final new solution, γ is the lower bound in the unifrnd function, λiτ is the distribution function for the weighting factor.
Wherein the first three expressions are all xnewAll the important components are used for solving xnewThe intermediate process of (1). In connection with the calculation of step four, the purpose of the whole expression is to generate the final solution xfinalFrom the expression, the final solution x is seenfinalRelating to two parameters, i.e. λ and xnewλ is according to the expression λi＝unifrnd(-γ，1+y，size(xi) Is generated, represents xfinalWeight factor of xnewIs a discrete solution of a newly generated population according to Levy flight, namely an x coordinate, a y coordinate, a z coordinate and power of an unmanned aerial vehicle, and obviously the solutions have different physical meanings, the upper limit and the lower limit of the solutions are also different, and the x isnewAccording to xnew＝xi+LstepsizeRank (xi)) yields that step is used to generate random steps obeying the Lewy distributionLength, LstepsizeA probability density function representing the distribution function of the levey flight.
For the discrete solution part, the update method of the incidence matrix of the unmanned aerial vehicle and the ground user is as follows:
μj＝randi(U，[1，N]).
in the formula, U is unmanned aerial vehicle's number, and N is the number of ground node.
And 5: calculating an objective function of the processed solution, if a new solution x is generatedfinalWhen the objective function value is smaller than the value of the reference point, the reference point is updated to x ═ xfinal，fx＝ffinal；
Step 6: comparing the new solution obtained in the step 4 with the objective function value of the individual in the substitute neighborhood of one individual, and reserving the unmanned aerial vehicle individual with a smaller function value;
and 7: the iteration termination condition is whether the maximum iteration number It is reachedmax(ii) a And if the final hovering position, the transmitting power and a solution formed by the incidence relation matrix of the user of each unmanned aerial vehicle are output according with the iteration termination condition, and if not, the steps 4 to 6 are executed in an iterative manner.
In the ground user and aerial base station communication model, the minimum throughput between the unmanned aerial vehicle and the ground user is improved in the aerial base station assisted by multiple unmanned aerial vehicles, the multi-objective joint optimization model for improving the system throughput and reducing the total energy consumption of the unmanned aerial vehicle is established, the model is solved by utilizing the ideal deployment position of the unmanned aerial vehicle, the unmanned aerial vehicle can simultaneously communicate with multiple users, the throughput is improved, the unmanned aerial vehicle does not need to move the position for multiple times, and the mobile energy consumption is reduced.
In addition, in order to improve throughput, when multiple unmanned aerial vehicles are used for communicating with ground users at the same time, the same-channel interference exists, so that the time division multiple access protocol is used for weakening the interference, the multiple unmanned aerial vehicles are used for realizing communication by optimizing the deployment positions, the transmitting power and the incidence matrixes of the multiple unmanned aerial vehicles and the users, the traditional method for communicating with all users by using one unmanned aerial vehicle is changed, the requirements on the service life and the communication performance of a single unmanned aerial vehicle are reduced, and the communication performance of the ground users is also improved.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.

Claims (7)

1. An aerial base station deployment method for improving communication performance of a ground user is characterized by comprising the following steps:
initializing the position of a ground node, generating the position, power and an incidence matrix of an unmanned aerial vehicle, generating a group of weight vectors, calculating Euclidean distances between each weight vector and other weight vectors, and determining the nearest weight vector;
dividing a multi-objective optimization problem into a plurality of sub-problems by a Chebyshev decomposition method, and initializing an objective function value of each sub-problem;
calculating objective function values of all unmanned aerial vehicles at different positions;
step four, updating the population by using the copy operation of the mixed solution to obtain a new solution, and performing border crossing processing on the new solution;
step five, calculating the objective function value of the new solution after processing, and updating the reference point when the objective function value of the new solution is smaller than the objective function value of the reference point;
step six, comparing the objective function value of the new solution with the objective function value of an individual in the population, and reserving the individual unmanned aerial vehicle with a small function value;
step seven, when the obtained solution meets the iteration termination condition, outputting the optimal solution of the objective function;
and when the iteration termination condition is not met, taking the solutions as the current solution to re-iterate and execute the steps from four to six.
2. The method of claim 1, wherein the solution of the objective function of the method of deploying the aerial base station is:
Figure FDA0003308176140000011
wherein, X, Y, Z sub-table represents the three-dimensional coordinate of unmanned aerial vehicle deployment, P represents the transmission power of unmanned aerial vehicle, A represents the communication relation of unmanned aerial vehicle and ground user, when the u unmanned aerial vehicle communicates with the nth user, then A representsn＝u。
3. The method of claim 2, wherein the base station deployment further comprises a step of, after the step of determining the location of the mobile station,
the process of initializing a ground node location includes a method of mixing an initialization solution:
x＝rand(N,nVar)·*(vMax-vMin)+vMin；
h＝rand(N,1)·*(H-L)+L；
Pt＝mod(Pt+b-(a/(2*π)),1)+PL+(PH-PL)*r；
wherein, X represents the coordinate of unmanned aerial vehicle in the horizontal plane, and nVar is 2, and vMin and vMax represent unmanned aerial vehicle minimum and maximum value on the horizontal direction, and H represents unmanned aerial vehicle's height, and H represents unmanned aerial vehicle's height maximum, and L represents unmanned aerial vehicle's height minimum, and mod represents the mould in the mathematics and divides the operation, and P dividestFor the current power of the unmanned plane, a and b are constants, PLIs the lower power limit, PHR is a random number distributed between 0 and 1 as the upper power limit.
4. The method for deploying an aerial base station for improving the communication performance of the ground user according to claim 3, wherein the second step comprises:
the unmanned aerial vehicle communicating with the user is searched through the incidence relation matrix, and when the unmanned aerial vehicle communicates with the user through a communication link, the communication between the remaining other unmanned aerial vehicles and the user is considered as an interference signal.
5. The method of claim 4, wherein the step three of calculating the objective function of the drone at the initial position comprises:
Figure FDA0003308176140000021
in the formula,
f1representing a first objective function that maximizes a minimum throughput between the drone and a ground user;
f2representing a second objective function, maximizing the sum of the throughputs of all the drones;
f3representing a third objective function to minimize the total energy consumed by the drone; the specific expressions of the three functions are as follows:
f1＝max Rmin；
Figure FDA0003308176140000022
Figure FDA0003308176140000023
in the formula, RminFor minimum throughput, R, between unmanned aerial vehicle and ground useruThroughput for drone u;
calculating the energy consumption of the unmanned aerial vehicle in the horizontal direction:
Em＝Phor(Vhor)thor；
wherein,
Figure FDA0003308176140000031
Figure FDA0003308176140000032
Figure FDA0003308176140000033
in the formula, thorFor the movement time of the unmanned aerial vehicle in the horizontal direction, LhorFor the distance of movement of the unmanned aerial vehicle in the horizontal direction, VhorThe speed of the unmanned aerial vehicle moving in the horizontal direction;
the model of the pushing power consumption of the unmanned aerial vehicle in the horizontal direction is as follows:
Figure FDA0003308176140000034
the energy consumed by the unmanned aerial vehicle when the unmanned aerial vehicle is in communication with the ground user during hovering is as follows:
Figure FDA0003308176140000035
the communication time between the unmanned aerial vehicle and the ground user is as follows:
Figure FDA0003308176140000036
the energy consumed by the unmanned aerial vehicle when moving in the non-horizontal direction is as follows:
Figure FDA0003308176140000037
6. the method for deploying an airborne base station to improve ground user communication performance according to claim 5, wherein the fourth step comprises:
the discrete solution space and the continuous solution space are separated to update the solution and carry out border-crossing processing on the updated solution, and the method comprises the following steps:
the updating method of the continuous solution comprises the following steps:
L′evy(β)＝Lstepsize；
Lstepsize＝α*step·(xi-best)；
Figure FDA0003308176140000041
Figure FDA0003308176140000042
xnew＝xi+Lstepsize*randn(size(xi))；
λi＝unifrnd(-γ,1+γ,size(xi))；
Figure FDA0003308176140000043
where α and β are weighting factors, best is the first solution selected in estimating the pareto frontier, randn is a normally distributed random matrix obeying a mean of 0 and a variance of 1, and xiIs a continuous variable representing the position coordinates or power of the drone, g is a constant, xnewFor flying a newly generated population according to Levy, xfinalFor the final new solution, γ is the lower bound in the unifrnd function, λiIs a weight factor, τ is a distribution function;
for the discrete solution part, the update method of the incidence matrix of the unmanned aerial vehicle and the ground user is as follows:
μj＝randi(U,[1,N])；
in the formula, U is unmanned aerial vehicle's number, and N is the number of ground node.
7. The method of claim 6, wherein the base station deployment further comprises a step of, after the step of determining the location of the mobile station,
in the fifth step, when the new solution x is generatedfinalWhen the objective function value is smaller than the value of the reference point, updating the reference point x to xfinal，fx＝ffinal。
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