CN112261623A - Unmanned aerial vehicle base station deployment method and system based on global optimal artificial bee colony algorithm - Google Patents

Unmanned aerial vehicle base station deployment method and system based on global optimal artificial bee colony algorithm Download PDF

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CN112261623A
CN112261623A CN202010961131.1A CN202010961131A CN112261623A CN 112261623 A CN112261623 A CN 112261623A CN 202010961131 A CN202010961131 A CN 202010961131A CN 112261623 A CN112261623 A CN 112261623A
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陆佃杰
李佳流源
张桂娟
吕蕾
吕晨
田杰
艾鑫伟
刘弘
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Shandong Normal University
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Abstract

The invention discloses an unmanned aerial vehicle base station deployment method and system based on a global optimal artificial bee colony algorithm, which comprises the following steps: constructing a D2D network, wherein a plurality of user terminals UE are distributed on the D2D network; on the basis of a D2D network, constructing a network coverage problem of an unmanned aerial vehicle base station UAV-BS into an objective function and a constraint condition; the objective function is: maximizing the throughput of the overall network and the number of end Users (UE) of the overall network; wherein, the overall network comprises a D2D network for communication between user terminals UE and a network for communication between the user terminals UE and the unmanned aerial vehicle base station UAV-BS; solving the objective function through a global optimal artificial bee colony algorithm to obtain a coordinate position of unmanned aerial vehicle base station deployment; and finishing the deployment of the unmanned aerial vehicle base station based on the obtained coordinate position of the unmanned aerial vehicle base station deployment.

Description

Unmanned aerial vehicle base station deployment method and system based on global optimal artificial bee colony algorithm
Technical Field
The application relates to the technical field of wireless networks, in particular to a method and a system for deploying Unmanned Aerial Vehicle Base stations (UAV-BS) based on a Global Optimal Artificial Bee Colony (GOABC) algorithm.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
After a disaster occurs, the ground communication equipment is damaged to cause a large-area signal blind area, the UAV-BS can overcome terrain limitation and rapidly deploy an emergency communication network, and plays an important role in rescue and restoration reconstruction work in the disaster area. Secondly, in a large place with dense people, such as a stadium, a concert, etc., the problem of weak network signals often exists when the number of people is too large, and an Unmanned Aerial Vehicle (UAV) can be used as a temporary Aerial base station to improve the network signal strength in an area. Therefore, it is an urgent task to be solved by people to develop how to deploy UAV-BS to improve signal strength and coverage.
Disclosure of Invention
In order to overcome the defects of the prior art, the application provides an unmanned aerial vehicle base station deployment method and system based on a global optimal artificial bee colony algorithm; the overall network throughput in the area is effectively improved.
In a first aspect, the application provides an unmanned aerial vehicle base station deployment method based on a global optimal artificial bee colony algorithm;
an unmanned aerial vehicle base station deployment method based on a global optimal artificial bee colony algorithm comprises the following steps:
constructing a Device-to-Device (D2D) network, wherein a plurality of User terminals (UE) are distributed on the D2D network;
on the basis of a D2D network, constructing a network coverage problem of an unmanned aerial vehicle base station UAV-BS into an objective function and a constraint condition; the objective function is: maximizing the throughput of the overall network and the number of end Users (UE) of the overall network; wherein, the overall network comprises a D2D network for communication between user terminals UE and a network for communication between the user terminals UE and the unmanned aerial vehicle base station UAV-BS;
solving the objective function through a global optimal artificial bee colony algorithm to obtain a coordinate position of unmanned aerial vehicle base station deployment; and finishing the deployment of the unmanned aerial vehicle base station based on the obtained coordinate position of the unmanned aerial vehicle base station deployment.
In a second aspect, the application provides an unmanned aerial vehicle base station deployment system based on a global optimal artificial bee colony algorithm;
unmanned aerial vehicle base station deployment system based on global optimal artificial bee colony algorithm includes:
a D2D network construction module configured to: constructing a D2D network, wherein a plurality of user terminals UE are distributed on the D2D network;
an objective function construction module configured to: on the basis of a D2D network, constructing a network coverage problem of an unmanned aerial vehicle base station UAV-BS into an objective function and a constraint condition; the objective function is: maximizing the throughput of the overall network and the number of end Users (UE) of the overall network; wherein, the overall network comprises a D2D network for communication between user terminals UE and a network for communication between the user terminals UE and the unmanned aerial vehicle base station UAV-BS;
an output module configured to: solving the objective function through a global optimal artificial bee colony algorithm to obtain a coordinate position of unmanned aerial vehicle base station deployment; and finishing the deployment of the unmanned aerial vehicle base station based on the obtained coordinate position of the unmanned aerial vehicle base station deployment.
In a third aspect, the present application further provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first aspect.
In a fourth aspect, the present application also provides a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
In a fifth aspect, the present application also provides a computer program (product) comprising a computer program for implementing the method of any of the preceding first aspects when run on one or more processors.
Compared with the prior art, the beneficial effects of this application are:
(1) in order to enable more UEs to participate in network communication, the multi-hop D2D technology is introduced and a D2D-based network model is constructed.
(2) On the basis of a D2D network model, the network coverage problem of the UAV-BS is constructed into an optimization model. Under the constraints of UAV-BS capacity and Signal to Interference plus Noise Ratio (SINR), the method considers the communication between the UE and the UAV-BS, and also considers the D2D communication between the UE, and finally makes the optimization problem to be the maximum objective function based on the total network throughput and the UE communication quantity.
(3) The application provides a heuristic GOABC algorithm. The algorithm improves the searching mode of the optimal honey source on the basis of the ABC algorithm, updates each dimension of the honey source, increases the factors of the global optimal honey source, enables bees to search the more optimal honey source, and improves the convergence on the basis of the ABC algorithm. The UAV-BS deployment position is optimized by adopting a GOABC algorithm to maximize an objective function based on the total network throughput and the UE communication quantity in a UAV-BS deployment model.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow chart of the method of the first embodiment.
FIG. 2 is a schematic diagram of a UAV-BS deployment without regard to D2D communications for the first embodiment;
FIG. 3 is a schematic diagram of a UAV-BS deployment considering D2D communications of the first embodiment;
fig. 4(a) -4 (c) are target value comparison graphs of Artificial Bee Colony (ABC, Artificial Bee Colony) algorithm, GOABC algorithm, Particle Swarm Optimization (PSO) algorithm, and Wolf-Particle Swarm Optimization (GWOPSO, Gray Wolf Particle Swarm Optimization) algorithm of the first embodiment;
fig. 5(a) -5 (c) are graphs comparing target values for the first embodiment with consideration of D2D communication and without consideration of D2D communication.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
The embodiment provides an unmanned aerial vehicle base station deployment method based on a global optimal artificial bee colony algorithm;
as shown in fig. 1, the unmanned aerial vehicle base station deployment method based on the global optimal artificial bee colony algorithm includes:
s101: constructing a D2D network, wherein a plurality of user terminals UE are distributed on the D2D network;
s102: on the basis of a D2D network, constructing a network coverage problem of an unmanned aerial vehicle base station UAV-BS into an objective function and a constraint condition; the objective function is: maximizing the throughput of the overall network and the number of end Users (UE) of the overall network; wherein, the overall network comprises a D2D network for communication between user terminals UE and a network for communication between the user terminals UE and the unmanned aerial vehicle base station UAV-BS;
s103: solving the objective function through a global optimal artificial bee colony algorithm to obtain a coordinate position of unmanned aerial vehicle base station deployment; and finishing the deployment of the unmanned aerial vehicle base station based on the obtained coordinate position of the unmanned aerial vehicle base station deployment.
Further, the step S101: constructing a D2D network, wherein a fixed number of user terminals UE are distributed on the D2D network, and random distribution, normal distribution and exponential distribution of the user terminals UE are considered; the method comprises the following specific steps:
when a certain user terminal UE cannot establish a connection with the UAV-BS, it may establish a D2D connection with a nearby user terminal UE that satisfies the SINR threshold and has the largest SINR.
If the UE in the vicinity of the idle UE is idle, the idle UE is used as a relay node and the D2D connection is established with the UE in the vicinity of the relay node until the non-idle UE meeting the SINR condition is found, otherwise, the D2D link is abandoned.
Further, the objective function is equal to the result of weighted summation of throughput of the overall network and the number of end user communications of the overall network, and the weight is a parameter for balancing the overall network throughput and the number of user terminal UEs.
Further, the total network has a number of end users communicating equal to the sum of the number of end users communicating directly with the drone base stations UAV-BS and the number of end users communicating over the D2D network.
Further, the throughput of the overall network is equal to the sum of the network throughput of the end user communicating directly with the drone base station UAV-BS and the network throughput of the end user communicating over the D2D network.
Further, the network throughput of each end user is equal to its own channel capacity.
Further, the constraint conditions are:
the parameter for balancing the total network throughput and the number of the user terminals UE is larger than zero and smaller than 1;
the network throughput of the UAV base station UAV-BS is less than or equal to the capacity of the UAV base station UAV-BS;
the signal to interference plus noise ratio SINR of the user terminal UE participating in the communication between the user terminal UE and the unmanned aerial vehicle base station UAV-BS is greater than or equal to a set threshold value;
in the D2D network, the signal to interference plus noise ratio SINR of the user terminal UE having a communication relationship is equal to or greater than a set threshold.
Further, the SINR refers to the ratio of the received power and the interference power of the user terminal within the coverage area of the UAV-BS to the channel noise during the communication between the user terminal UE and the UAV-BS.
Further, the SINR refers to the ratio of the received power and the interference power of the current UE receiving the UE of the previous hop to the channel noise in the D2D network.
Further, the received power refers to the power received by the user terminal from the UAV-BS during the communication between the user terminal UE and the UAV-BS.
Further, the received power, in the D2D network, refers to the power received by the current user terminal UE from its previous hop user terminal UE.
In a network formed by UAV-BS, there are situations where coverage is not available for some remote UEs or resource spectrum is scarce, so the present application establishes a D2D connection between UEs to assist communication to solve these problems. According to the method, the shared spectrum between the UAV-BS and the D2D network is considered, UE which is not communicated with the UAV-BS directly establishes D2D communication with UE meeting SINR conditions or establishes D2D communication in a multi-hop relay technology mode by multiplexing spectrum resources of the UE which is communicated with the UAV-BS, the QoS (quality of service) obtained by the UE at the edge of a scene is improved, and meanwhile the total network throughput and the UE communication quantity in the scene are also improved.
As shown in fig. 2 and 3, the present application creates a three-dimensional scene to simulate the scene communication situation. Several UAV-BSs (UAV-BSs) and User Equipments (UEs) are distributed in the scene, and there are three ways of communication, respectively, a link of the UAV-BS to a cloud server (U2S communication), a link of the UAV-BS to a UE (U2D communication), a link of a UE to a UE (D2D communication). There are m UAVs-BSs and p UEs in the scene, some UEs belong to U2D communication only (e.g., UE1 and UE2), some UEs belong to D2D communication only (e.g., UE5, UE7 …, UE12), some UEs belong to both U2D communication and D2D communication (UE3, UE4), and some UEs do not belong to any communication link (e.g., UE 6). Compared with fig. 2, D2D communication is added in fig. 3, so that some UEs which do not establish communication with the UAV-BS can establish communication with nearby UEs by means of D2D, and network throughput and UE coverage amount in the scene are improved.
The UAV-BS deployment model is divided into three steps. First, the network throughput and the number of UE communications for all UEs capable of communicating directly with the UAV-BS are calculated. Second, the network throughput and the number of communications for all UEs that can establish D2D communications are calculated. Third, a target value based on the network throughput and the number of communications of the UE in the scenario is calculated.
Next, the present embodiment introduces the received power, SINR, UE communication number and UE network throughput in the UAV-BS deployment model as follows.
Reception power: received power Pr,ijRefers to the power received by UE i from UAV-BS j,
Figure BDA0002680576660000081
to the kth communication to establish D2D
Figure BDA0002680576660000082
From its last oneNode point
Figure BDA0002680576660000083
Received power, and l ∈ {1,2, …, V ∈ }k}，VkThe total number of hops for D2D communication where the kth UE is located.
Pr,ij＝Pt,U-Lij(1)
Figure BDA0002680576660000084
Wherein P ist,UIs the transmission power, P, of the UAV-BSt,DIs the transmit power of the UE. L isijIs the transmission loss between UAV-BS j and UE i,
Figure BDA0002680576660000085
is that
Figure BDA0002680576660000086
And
Figure BDA0002680576660000087
the transmission loss therebetween. L isijUpper limit of Lu,ijAnd a lower limit Ll,ij，
Figure BDA0002680576660000088
Upper limit of (2)
Figure BDA0002680576660000089
And lower limit
Figure BDA00026805766600000810
The calculation is as follows:
Figure BDA00026805766600000811
Figure BDA00026805766600000812
Figure BDA00026805766600000813
Figure BDA00026805766600000814
Figure BDA00026805766600000815
Figure BDA00026805766600000816
in the above formula, dijIs the euclidean distance of UAV-BS j and UE i,
Figure BDA00026805766600000817
is that
Figure BDA00026805766600000818
And
Figure BDA0002680576660000091
the euclidean distance between them. (x)j,yj,hj) Is the coordinate of UAV-BS j, (x)i,yi,hp) Is the coordinates of UE i. L isjIs the free space path loss of UAV-BS j,
Figure BDA0002680576660000092
is that
Figure BDA0002680576660000093
Free space path loss.
Figure BDA0002680576660000094
Figure BDA0002680576660000095
Wherein h isjIs the flight altitude, h, of UAV-BS j above groundpIs the height of the UE above the ground.
Figure BDA0002680576660000096
Where λ is the wavelength, c is the wave speed, and f is the frequency.
Figure BDA0002680576660000097
Figure BDA0002680576660000098
In the formulae (12) and (13), RjAnd
Figure BDA0002680576660000099
for measuring UAV-BS j and
Figure BDA00026805766600000910
a threshold value of the transmission loss radius of (a).
SINR: in U2D communication, γinRefers to the ratio of the received power and interference power of UE i within the coverage of UAV-BS n to the channel noise. In the D2D communication, if the communication is multi-hop D2D communication, the present application adopts a decode-and-forward relay protocol (DF), and the relay UE decodes and re-encodes the received signal and sends the decoded signal to the next-hop UE.
Figure BDA00026805766600000911
Means that
Figure BDA00026805766600000912
Received from
Figure BDA00026805766600000913
Received power and interference power of and channel noiseThe ratio of the sounds.
Figure BDA00026805766600000914
Figure BDA00026805766600000915
Wherein P isr,inIs the maximum received power in all UAV-BSs that UE i receives. The UAV-BS where UE i receives the maximum received power is referred to herein as UAV-BS n.
Figure BDA00026805766600000916
Is that
Figure BDA00026805766600000917
From reception of maximum received power
Figure BDA00026805766600000918
The received power. σ is the constant channel noise. I isinIs the interference power received by UE i, is the sum of the received powers received by UE i from the other m-1 UAV-BSs.
Figure BDA0002680576660000101
Is that
Figure BDA0002680576660000102
The received interference power is
Figure BDA0002680576660000103
Sum of received power from all UAV-BSs.
Figure BDA0002680576660000104
Figure BDA0002680576660000105
Figure BDA0002680576660000106
And
Figure BDA0002680576660000107
SINR of the direct link (S-D) between them is:
Figure BDA0002680576660000108
since the multi-hop D2D relay communication adopts the DF relay mode, when V isk>1 hour, source node
Figure BDA0002680576660000109
With the destination node
Figure BDA00026805766600001010
The total SINR of the multi-hop relay links between the two is:
Figure BDA00026805766600001011
due to source nodes in multi-hop D2D communication
Figure BDA00026805766600001012
To the destination node
Figure BDA00026805766600001013
Finally adopting maximum combining ratio (MRC) combining principle for received signals of S-D direct transmission link and DF relay link, and finally obtaining destination node
Figure BDA00026805766600001014
The SINR of (1) is as follows:
Figure BDA00026805766600001015
UE communication quantity: UE communication number NtotalRefers to the direct and in the sceneUE number N of UAV-BS communicationsijPlus the number of UEs communicating via D2D
Figure BDA00026805766600001016
The SINR of the UE participating in UAV-BS communication must not be less than the set threshold θ, and the SINR of the UE participating in UAV-BS communication must not be less than the set threshold τ, and the formula is as follows:
Figure BDA00026805766600001017
Figure BDA00026805766600001018
Figure BDA00026805766600001019
UE network throughput: network throughput refers to the maximum data rate that is actually transmitted during a network transmission. The channel capacity is the maximum information rate that the channel can transmit without error. The present application assumes that the network throughput per UE is equal to the channel capacity per UE. The shannon formula is used to calculate the channel capacity per UE:
Cin＝W*log2(1+γin) (24)
Figure BDA0002680576660000111
wherein C isinIs the throughput of UE i to UAV-BS n,
Figure BDA0002680576660000112
is that
Figure BDA0002680576660000113
To
Figure BDA0002680576660000114
Throughput of (1), W isChannel bandwidth in Hz. Throughput per UAV-BS TjThe calculation formula of (c) can be defined as follows:
Figure BDA0002680576660000115
network throughput T within a scenetotalThe calculation formula of (c) can be defined as follows:
Figure BDA0002680576660000116
an objective function: the objective of the present application is to find the three-dimensional coordinates of a set of UAV-BSs, maximizing network throughput and UE communication volume within the scene under certain weights.
Figure BDA0002680576660000117
Alpha is a parameter for balancing the total network throughput and the UE communication quantity, the network throughput of each UAV-BS is equal to the throughput of the UE directly communicating with the UAV-BS plus the network throughputs of g UEs communicating through D2D on the premise of not being lower than an SINR threshold value, and the network throughput of the UAV-BS is not larger than the capacity T of the UAV-BSmaxThe SINR of the UE participating in U2D communication should not be less than the set threshold θ, and the SINR of the UE participating in D2D communication should not be less than the set threshold τ.
The corresponding position P of the UAV-BS that maximizes network throughput can be obtained by:
Figure BDA0002680576660000121
further, the step S103: solving the objective function through a global optimal artificial bee colony algorithm to obtain a coordinate position of unmanned aerial vehicle base station deployment; the method comprises the following specific steps:
s1031: initializing parameters, and randomly generating a plurality of solutions in a specified scene, wherein each solution comprises three-dimensional coordinates of a set number of UAV-BSs;
s1032: recording the maximum fitness value and the solution with the maximum fitness value in all solutions;
s1033: leading bees: searching a new solution based on the solution near the current unmanned aerial vehicle base station UAV-BS and the solution with the maximum fitness value, and if the new solution has a larger fitness value, recording a target value of the new solution and replacing the current solution with the new solution;
s1034: following the bee stage: searching a new solution based on a solution near the current solution and the global optimal solution according to probability by adopting a roulette wheel, and if the new solution has a larger fitness value, recording the fitness value of the new solution and replacing the current solution with the new solution;
s1035: and (3) a bee scouting stage: if the search frequency exceeds the specified frequency, randomly generating a new solution to replace the current solution in the specified scene;
s1036: and judging whether the total iteration times are reached, if so, outputting the recorded maximum fitness value and a corresponding set of UAV-BS coordinates, and otherwise, re-executing S1032 to S1036.
In the S1031, the artificial bee colony algorithm generates I initial solution sets S, and each initial solution set SiLocation containing J UAV-BSs
Figure BDA0002680576660000122
Wherein the content of the first and second substances,
Figure BDA0002680576660000123
denotes the j (th)th(J ∈ 1,2, …, J) positions of UAV-BS, the formula for generating each UAV-BS position is as follows:
Figure BDA0002680576660000124
d is {1,2,3}, J is {1,2, …, J }, d is a dimension, rand is a random number between [0,1], and ub and lb are the upper and lower limits of the search space of the solution, respectively.
In S1032, for the problem of finding the maximum target value, the new fitness calculation formula proposed herein is as follows:
Figure BDA0002680576660000131
and bringing the initial solution into an objective function, calculating the fitness value of the solution, and recording the current maximum fitness value and the solution with the maximum fitness value.
In S1033, the guidance bee searches whether there is a better solution based on the nearby solution and the solution with the largest fitness value, and when a new solution is searched
Figure BDA0002680576660000132
Then, calculate its fitness value, the search formula is as follows:
Figure BDA0002680576660000133
k ≠ 1,2, …,3J is the coordinate value index of the current solution, l ∈ {1,2, …, I }, I ≠ l is the index of the randomly selected new solution,
Figure BDA0002680576660000134
is [ -1,1 [ ]]A random number in between, rand is a [0,1]]A random number in between, and a random number,
Figure BDA0002680576660000135
is the solution with the largest fitness value. And if the fitness value of the new solution is larger than that of the current solution, replacing the current solution with the new solution.
In the step S1034: the probability of selecting a solution by the following bees is in direct proportion to the quality of the solution, and the solution found by each leading bee is further optimized. And each following bee follows the leading bee by adopting a roulette method, if the solution of the leading bee is selected, the following bee searches a new solution according to the solution of the leading bee, and the larger the adaptability value of the solution is, the higher the selection probability of the following bee is. The probability formula proposed herein is as follows:
Figure BDA0002680576660000136
fitifor the fitness value of the current solution,
Figure BDA0002680576660000137
is the sum of the fitness values of all solutions. Subsequently, a [0,1] is generated]Random number in between, if randi<piThen the following bee selects a lead bee, searches for a new solution using equation (32), and calculates its fitness value. If randi≥piThen the follower bee does not search for a new solution. And if the fitness value of the new solution is larger than that of the current solution, replacing the current solution with the new solution set.
In the S1035: if a certain solution SiWithout improvement over a predetermined number of iterations, the corresponding lead bee abandons the solution, turning into a scout bee to re-find a new solution using equation (30).
In S1036, the iteration of the algorithm is stopped when the iteration number reaches the maximum iteration number maximum that is pre-explained, and the robustness of the program can be improved by running the algorithm for multiple times.
Deploying UAV-BS is usually an NP-hard problem, which is mainly approximated by heuristic algorithms. The update mode of the solution of the ABC algorithm is that only one certain dimension is selected in the neighborhood for update, so the quality of the solution is not high. The GOABC algorithm provided by the application considers all dimensions of the solution in updating, and adds the influence of the global optimal solution, so that the convergence of the algorithm is better.
The method proposed herein was analyzed and compared with other methods, as follows:
two comparison experiments are analyzed together, namely an ABC algorithm, a GOABC algorithm, a PSO algorithm and a GWOPSO algorithm, and whether D2D communication is considered by a model or not is analyzed. The target value in the experiment is the median of the target values obtained after running the algorithm several times. Experimental results show that the UAV-BS deployment model provided by the application combined with the GOABC algorithm can enable the overall network throughput and the UE communication quantity in a scene to be larger, and the UAV-BS deployment model is a more optimized UAV-BS deployment method. The experimental parameters are shown in table 1.
TABLE 1 model Experimental parameters
Figure BDA0002680576660000141
Figure BDA0002680576660000151
(1) Comparison of four algorithms
In fig. 4(a) -4 (c), in order to test the effect of the GOABC algorithm proposed by the present application, the present application compares it with other three heuristic algorithms. The PSO algorithm is an evolutionary algorithm that models birds in a flock of birds by designing a particle without mass. The basic idea of the PSO algorithm is to find the optimal solution through collaboration and information sharing among individuals in a population. The GWPSO algorithm is an optimization searching method for simulating the prey activities of the sirius, and is also a hybrid optimization algorithm combining the sirius optimization (GWO) algorithm and the PSO algorithm. It can be clearly seen from the present application that in the three distributions, the GOABC algorithm can reach the maximum target value, followed by the GWOPSO algorithm. Experimental data show that the GOABC algorithm is approximately 4.5%, 5.2%, and 7.3% higher than the GWOPSO algorithm, respectively, when reaching the maximum target value in random distribution, normal distribution, and exponential distribution. The application can conclude that the GOABC algorithm has more advantages than the other three heuristic algorithms. (2) Whether D2D communication contrast is considered: in fig. 5(a) -5 (c), in order to test how much the difference between the model proposed by the present application considering D2D communication and the model not considering D2D communication is, the present application compares two otherwise identical models except for D2D communication. The present application can see that of the three distributions, the model considering D2D communication is generally larger than the model considering D2D communication for a larger target value, where the advantage of normal distribution is most evident. Experimental data show that in the random distribution, normal distribution, and exponential distribution, the model that accounts for D2D communication is approximately 6.3%, 3.8%, and 4% higher than the model that does not account for D2D communication when the maximum target value is reached. The present application may conclude that models that consider D2D communication work better than models that do not consider D2D communication.
Example two
The embodiment provides an unmanned aerial vehicle base station deployment system based on a global optimal artificial bee colony algorithm;
unmanned aerial vehicle base station deployment system based on global optimal artificial bee colony algorithm includes:
a D2D network construction module configured to: constructing a D2D network, wherein a plurality of user terminals UE are distributed on the D2D network;
an objective function construction module configured to: on the basis of a D2D network, constructing a network coverage problem of an unmanned aerial vehicle base station UAV-BS into an objective function and a constraint condition; the objective function is: maximizing the throughput of the overall network and the number of end Users (UE) of the overall network; wherein, the overall network comprises a D2D network for communication between user terminals UE and a network for communication between the user terminals UE and the unmanned aerial vehicle base station UAV-BS;
an output module configured to: solving the objective function through a global optimal artificial bee colony algorithm to obtain a coordinate position of unmanned aerial vehicle base station deployment; and finishing the deployment of the unmanned aerial vehicle base station based on the obtained coordinate position of the unmanned aerial vehicle base station deployment.
It should be noted here that the D2D network building module, the objective function building module and the output module correspond to steps S101 to S103 in the first embodiment, and the modules are the same as the corresponding steps in the implementation example and application scenarios, but are not limited to the disclosure in the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In the foregoing embodiments, the descriptions of the embodiments have different emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules is merely a logical functional division, and in actual implementation, there may be other divisions, for example, multiple modules may be combined or integrated into another system, or some features may be omitted, or not executed.
EXAMPLE III
The present embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, a processor is connected with the memory, the one or more computer programs are stored in the memory, and when the electronic device runs, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first embodiment.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Example four
The present embodiments also provide a computer-readable storage medium for storing computer instructions, which when executed by a processor, perform the method of the first embodiment.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (10)

1. An unmanned aerial vehicle base station deployment method based on a global optimal artificial bee colony algorithm is characterized by comprising the following steps:
constructing a D2D network, wherein a plurality of user terminals UE are distributed on the D2D network;
on the basis of a D2D network, constructing a network coverage problem of an unmanned aerial vehicle base station UAV-BS into an objective function and a constraint condition; the objective function is: maximizing the throughput of the overall network and the number of end Users (UE) of the overall network; wherein, the overall network comprises a D2D network for communication between user terminals UE and a network for communication between the user terminals UE and the unmanned aerial vehicle base station UAV-BS;
solving the objective function through a global optimal artificial bee colony algorithm to obtain a coordinate position of unmanned aerial vehicle base station deployment; and finishing the deployment of the unmanned aerial vehicle base station based on the obtained coordinate position of the unmanned aerial vehicle base station deployment.
2. The method of claim 1, wherein the objective function is equal to a weighted sum of the throughput of the overall network and the number of end-user communications of the overall network, the weights being parameters that trade off the overall network throughput against the number of user terminals UE.
3. The method of claim 2, wherein the total network has a number of end user communications equal to the sum of the number of end users communicating directly with the drone base station UAV-BS and the number of end users communicating over the D2D network.
4. The method of claim 2, wherein the throughput of the overall network is equal to the sum of the network throughput of end users communicating directly with the drone base station UAV-BS and the network throughput of end users communicating over the D2D network.
5. The method of claim 1, wherein the constraints are:
the parameter for balancing the total network throughput and the number of the user terminals UE is larger than zero and smaller than 1;
the network throughput of the UAV base station UAV-BS is less than or equal to the capacity of the UAV base station UAV-BS;
the signal to interference plus noise ratio SINR of the user terminal UE participating in the communication between the user terminal UE and the unmanned aerial vehicle base station UAV-BS is greater than or equal to a set threshold value;
in the D2D network, the signal to interference plus noise ratio SINR of the user terminal UE having a communication relationship is equal to or greater than a set threshold.
6. The method of claim 1, wherein the SINR is the ratio of the received power and the interference power of the UE within the coverage area of the UAV-BS to the channel noise during the communication between the UE and the UAV-BS;
alternatively, the first and second electrodes may be,
the SINR is a ratio of the received power of the current UE receiving the previous hop UE and the ratio of the interference power to the channel noise in the D2D network;
alternatively, the first and second electrodes may be,
the received power refers to power received by the user terminal from the unmanned aerial vehicle base station UAV-BS in the communication process of the user terminal UE and the unmanned aerial vehicle base station UAV-BS;
alternatively, the first and second electrodes may be,
the received power, in the D2D network, refers to the power received by the current UE from its previous hop UE.
7. The method of claim 1, wherein the objective function is solved by a global optimal artificial bee colony algorithm to obtain a coordinate position of the unmanned aerial vehicle base station deployment; the method comprises the following specific steps:
initializing parameters, and randomly generating a plurality of solutions in a specified scene, wherein each solution comprises three-dimensional coordinates of a set number of UAV-BSs;
a recording step: recording the maximum fitness value and the solution with the maximum fitness value in all solutions;
leading bees: searching a new solution based on the solution near the current unmanned aerial vehicle base station UAV-BS and the solution with the maximum fitness value, and if the new solution has a larger fitness value, recording a target value of the new solution and replacing the current solution with the new solution;
following the bee stage: searching a new solution based on a solution near the current solution and the global optimal solution according to probability by adopting a roulette wheel, and if the new solution has a larger fitness value, recording the fitness value of the new solution and replacing the current solution with the new solution;
and (3) a bee scouting stage: if the search frequency exceeds the specified frequency, randomly generating a new solution to replace the current solution in the specified scene;
a judging step: and judging whether the total iteration times are reached, if so, outputting the recorded maximum fitness value and a corresponding set of UAV-BS coordinates, and otherwise, re-executing the recording step to the judging step.
8. Unmanned aerial vehicle base station deployment system based on global optimal artificial bee colony algorithm, characterized by includes:
a D2D network construction module configured to: constructing a D2D network, wherein a plurality of user terminals UE are distributed on the D2D network;
an objective function construction module configured to: on the basis of a D2D network, constructing a network coverage problem of an unmanned aerial vehicle base station UAV-BS into an objective function and a constraint condition; the objective function is: maximizing the throughput of the overall network and the number of end Users (UE) of the overall network; wherein, the overall network comprises a D2D network for communication between user terminals UE and a network for communication between the user terminals UE and the unmanned aerial vehicle base station UAV-BS;
an output module configured to: solving the objective function through a global optimal artificial bee colony algorithm to obtain a coordinate position of unmanned aerial vehicle base station deployment; and finishing the deployment of the unmanned aerial vehicle base station based on the obtained coordinate position of the unmanned aerial vehicle base station deployment.
9. An electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs being stored in the memory, the processor executing the one or more computer programs stored in the memory when the electronic device is running, to cause the electronic device to perform the method of any of the preceding claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
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