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

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

UAV base station deployment method and system based on global optimal artificial bee colony algorithm

technical field

The present application relates to the field of wireless network technology, in particular to a method and system for deploying an Unmanned Aerial Vehicle Base Station (UAV-BS) based on a Global Optimal Artificial Bee Colony (GOABC, Global Optimal Artificial Bee Colony) algorithm.

Background technique

The statements in this section merely mention the background art related to the present application and do not necessarily constitute prior art.

When a disaster occurs, the ground communication equipment is damaged and a large area of signal blind spots is caused. UAV-BS can overcome the terrain limitations and quickly deploy an emergency communication network, which plays an important role in the rescue, recovery and reconstruction work in the disaster area. Secondly, in large crowded places, such as stadiums, concerts and other places, too many people often have the problem of weak network signal. Unmanned Aerial Vehicle (UAV, Unmanned Aerial Vehicle) can be used as a temporary air base station to improve the area. Network signal strength. Therefore, it is an urgent task to study how to deploy UAV-BS to improve signal strength and coverage.

SUMMARY OF THE INVENTION

In order to solve the deficiencies of the prior art, the present application provides a UAV base station deployment method and system based on the global optimal artificial bee colony algorithm, which effectively improves the overall network throughput in the area.

In the first aspect, the present application provides a UAV base station deployment method based on a globally optimal artificial bee colony algorithm;

The UAV base station deployment method based on the global optimal artificial bee colony algorithm includes:

constructing a device-to-device (D2D, Device-to-Device) network, where several user terminals (UE, User Equipment) are distributed on the D2D network;

On the basis of the D2D network, the network coverage problem of the UAV-BS of the unmanned aerial vehicle base station is constructed as an objective function and constraints; the objective function is to maximize the throughput of the overall network and the number of end-user UEs of the overall network; wherein , the overall network includes not only the D2D network for communication between user terminals UE, but also the network for communication between the user terminal UE and the unmanned aerial vehicle base station UAV-BS;

Through the global optimal artificial bee colony algorithm, the objective function is solved to obtain the coordinate position of the UAV base station deployment; based on the obtained coordinate position of the UAV base station deployment, the deployment of the UAV base station is completed.

In the second aspect, the present application provides a UAV base station deployment system based on a globally optimal artificial bee colony algorithm;

UAV base station deployment system based on global optimal artificial bee colony algorithm, including:

The D2D network building module is configured to: construct a D2D network on which a number of user terminals UE are distributed;

The objective function building module is configured to: on the basis of the D2D network, construct the network coverage problem of the UAV base station UAV-BS into an objective function and constraints; the objective function is: maximizing the throughput of the overall network and the number of end-user UEs in the overall network; wherein, the overall network includes both the D2D network for communication between user terminals UE and the network for communication between the user terminal UE and the UAV base station UAV-BS;

The output module is configured to: solve the objective function through the global optimal artificial bee colony algorithm to obtain the coordinate position of the UAV base station deployment; based on the obtained coordinate position of the UAV base station deployment, complete the UAV base station deployment Deployment of base stations.

In a third aspect, the present application also provides an electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and one or more of the above The computer program is stored in the memory, and when the electronic device runs, the processor executes one or more computer programs stored in the memory, so that the electronic device performs the method described in the first aspect above.

In a fourth aspect, the present application further provides a computer-readable storage medium for storing computer instructions, and when the computer instructions are executed by a processor, the method described in the first aspect is completed.

In a fifth aspect, the present application also provides a computer program (product), including a computer program, which when run on one or more processors, is used to implement the method of any one of the foregoing first aspects.

Compared with the prior art, the beneficial effects of the present application are:

(1) In order to allow more UEs to participate in network communication, the present application introduces the multi-hop D2D technology and constructs a D2D-based network model.

(2) Based on the D2D network model, the network coverage problem of UAV-BS is constructed as an optimization model. Under the constraints of UAV-BS capacity and Signal to Interference plus Noise Ratio (SINR, Signal to Interference plus Noise Ratio), this application considers the communication between UE and UAV-BS, and also considers D2D communication between UEs, and finally The optimization problem is formulated to maximize an objective function based on the overall network throughput and the number of UE communications.

(3) This application proposes a heuristic GOABC algorithm. Based on the ABC algorithm, the algorithm improves the search method of the optimal nectar source, updates each dimension of the nectar source, and increases the factor of the global optimal nectar source, so that the bees can search for a better nectar source. Improved convergence. The present application uses the GOABC algorithm to maximize the objective function based on the overall network throughput and the number of UE communications in the UAV-BS deployment model, and optimizes the deployment location of the UAV-BS.

Description of drawings

The accompanying drawings that form a part of the present application are used to provide further understanding of the present application, and the schematic embodiments and descriptions of the present application are used to explain the present application and do not constitute improper limitations on the present application.

FIG. 1 is a flow chart of the method of the first embodiment.

2 is a schematic diagram of UAV-BS deployment without considering D2D communication according to the first embodiment;

3 is a schematic diagram of UAV-BS deployment considering D2D communication according to the first embodiment;

Fig. 4(a)-Fig. 4(c) are the artificial bee colony (ABC, Artificial Bee Colony) algorithm, GOABC algorithm, Particle Swarm Optimization (PSO, Particle Swarm Optimization) algorithm and gray wolf-particle swarm according to the first embodiment The target value comparison chart of the optimization (GWOPSO, Gray Wolf Particle Swarm Optimization) algorithm;

Figures 5(a)-5(c) are comparison diagrams of target values considering D2D communication and not considering D2D communication according to the first embodiment.

Detailed ways

It should be noted that the following detailed description is exemplary and intended to provide further explanation of the application. Unless otherwise defined, 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 should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it is to be understood that the terms "including" and "having" and any conjugations thereof are intended to cover the non-exclusive A process, method, system, product or device comprising, for example, a series of steps or units is not necessarily limited to those steps or units expressly listed, but may include those steps or units not expressly listed or for such processes, methods, Other steps or units inherent to the product or equipment.

Embodiments of the invention and features of the embodiments may be combined with each other without conflict.

Example 1

This embodiment provides a UAV base station deployment method based on the global optimal artificial bee colony algorithm;

As shown in Figure 1, the UAV base station deployment method based on the global optimal artificial bee colony algorithm includes:

S101: Construct a D2D network, where several user terminals UE are distributed on the D2D network;

S102: On the basis of the D2D network, construct the network coverage problem of the UAV base station UAV-BS into an objective function and constraints; the objective function is to maximize the throughput of the overall network and the number of end-user UEs in the overall network ; Wherein, the overall network includes both a D2D network for communication between user terminals UE, and a network for communication between user terminals UE and UAV-BS;

S103: Solve the objective function through the global optimal artificial bee colony algorithm to obtain the coordinate position of the UAV base station deployment; complete the deployment of the UAV base station based on the obtained coordinate position of the UAV base station deployment.

Further, the S101: construct a D2D network on which a fixed number of user terminals UE are distributed, and consider random distribution, normal distribution and exponential distribution of user terminals UE; the specific steps include:

When a certain user terminal UE cannot establish a connection with the UAV-BS, it can establish a D2D connection with a nearby user terminal UE that satisfies the SINR threshold and has the largest SINR.

If the UE in its vicinity is idle, the idle UE is used as a relay node and attempts to establish a D2D connection with the UE in the vicinity of the relay node until a non-idle UE meeting the SINR condition is found, otherwise, the D2D link is abandoned.

Further, the objective function is equal to the weighted summation result of the throughput of the overall network and the number of terminal user communications in the overall network, and the weight is a parameter that weighs the overall network throughput and the number of user terminals UEs.

Further, the number of end user communications in the overall network is equal to the sum of the number of end users communicating directly with the UAV base station UAV-BS and the number of end users communicating through 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 UAV base station UAV-BS and the network throughput of the end user communicating through the D2D network.

Further, the network throughput of each end user is equal to its own channel capacity.

Further, the constraints are:

The parameter weighing the overall network throughput and the number of user terminals UE is greater than zero and less than 1;

The network throughput of the drone base station UAV-BS is less than or equal to its capacity;

The signal-to-interference-plus-noise ratio SINR of the user terminal UE participating in the communication between the user terminal UE and the UAV base station UAV-BS is greater than or equal to the set threshold;

In the D2D network, the signal-to-interference-plus-noise ratio SINR of the user terminal UE that has a communication relationship is greater than or equal to a set threshold.

Further, the SINR, in the communication process between the user terminal UE and the UAV base station UAV-BS, refers to the ratio of the received power and the interference power to the channel noise of the user terminal within the coverage area of the UAV base station UAV-BS.

Further, the SINR, in the D2D network, refers to the ratio of the received power and the interference power to the channel noise of the current user terminal UE when it receives the previous hop user terminal.

Further, the received power refers to the power received by the user terminal from the UAV base station UAV-BS during the communication process between the user terminal UE and the UAV base station 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 composed of UAV-BS, it is inevitable that some distant UEs cannot be covered or the resource spectrum is scarce. Therefore, the present application establishes a D2D connection between UEs to assist communication to solve these problems. This application considers the shared spectrum between the UAV-BS and the D2D network. The UE that does not communicate with the UAV-BS directly establishes D2D communication with the UE that meets the SINR condition by multiplexing the spectrum resources of the UE that communicates with the UAV-BS or communicates with the UE through multiple The D2D communication is established by means of the skip relay technology, which improves the quality of service (QoS) obtained by the UE at the edge of the scene, and also improves the overall network throughput and the number of UE communications in the scene.

As shown in FIG. 2 and FIG. 3 , the present application establishes a three-dimensional scene to simulate the communication situation of the scene. There are several UAV-BS (UAV-BS) and user equipment (UE) distributed in the scene, and there are three communication methods, namely the link between UAV-BS and cloud server (U2S communication), the link between UAV-BS and UE ( U2D communication), UE-UE link (D2D communication). There are m UAV-BSs and p UEs in the scene, some UEs only belong to U2D communication (such as UE1 and UE2), some UEs only belong to D2D communication (such as UE5, UE7...UE12), and some UEs belong to both U2D communication It also belongs to D2D communication (UE3, UE4), and some UEs do not belong to any communication link (eg UE6). Compared with FIG. 2 , D2D communication is added in FIG. 3 , so that some UEs that have not established communication with the UAV-BS can establish communication with nearby UEs through D2D, which improves the network throughput and the number of UE coverage in the scene.

The UAV-BS deployment model is divided into three steps. First, calculate the network throughput and the number of UE communications of all UEs that can communicate directly with the UAV-BS. Second, calculate the network throughput and communication quantity of all UEs that can establish D2D communication. Third, calculate the target value based on the network throughput of the UE and the number of communications in the scenario.

Next, in this embodiment, the received power, SINR, UE communication quantity and UE network throughput in the UAV-BS deployment model are introduced as follows.

指第k个要建立D2D通信的

从它的上一节点

接收的功率，且l∈{1,2,…,V_k}，V_k为第k个UE所处的D2D通信的总跳数。Received power: Received power Received power _Pr,ij refers to the power received by UE i from UAV-BS j,

Refers to the kth device to establish D2D communication

from its previous node

received power, and l∈{1,2,...,V _k }, where V _k is the total number of hops of D2D communication where the kth UE is located.

P _r,ij =P _t,U -L _ij (1)

是

与

之间的传输损耗。L_ij的上限L_u,ij和下限L_l,ij，

的上限

和下限

计算如下：where P _{t, U} is the transmit power of the UAV-BS, and P _{t, D} is the transmit power of the UE. L _ij is the transmission loss between UAV-BS j and UE i,

Yes

and

transmission loss between them. Upper limit _Lu,ij and lower limit L _{l,ij of} L ij,

upper limit of

and lower bound

The calculation is as follows:

是

和

之间的欧几里得距离。(x_j,y_j,h_j)是UAV-BS j的坐标，(x_i,y_i,h_p)是UE i的坐标。L_j是UAV-BS j的自由空间路径损耗，

是

的自由空间路径损耗。In the above formula, d _ij is the Euclidean distance between UAV-BS j and UE i,

Yes

and

Euclidean distance between . (x _j , y _j , h _j ) are the coordinates of UAV-BS j, and (x _i , y _i , h _p ) are the coordinates of UE i. L _j is the free space path loss of UAV-BS j,

Yes

free space path loss.

where h _j is the flying height of UAV-BS j above the ground, and h _p is the height of the UE above the ground.

where λ is the wavelength, c is the wave speed, and f is the frequency.

分别用于测量UAV-BS j和

的传输损耗半径的阈值。In formulas (12) and (13), R _j and

are used to measure UAV-BS j and

The threshold of the transmission loss radius.

是指

收到来自

的接收功率和干扰功率与信道噪声之比。SINR: In U2D communication, γ _in refers to the ratio of received power and interference power to channel noise of UE i within the coverage of UAV-BS n. In D2D communication, if it is multi-hop D2D communication, the present application adopts the Decode and Forward Relay Protocol (DF), and the relay UE decodes and re-encodes the received signal and sends it to the next-hop UE.

Refers to

received from

The ratio of received power and interference power to channel noise.

是

从接收到最大接收功率的

接收的功率。σ是恒定的信道噪声。I_in是UE i接收到的干扰功率，是UE i从其他m-1个UAV-BS接收到的接收功率之和。

是

接收到的干扰功率，是

从所有UAV-BS接收到的接收功率之和。where P _r,in is the maximum received power among all UAV-BSs received by UE i. In this application, the UAV-BS that UE i receives the maximum received power is referred to as UAV-BS n.

Yes

from the received maximum received power

received power. σ is the constant channel noise. I _in is the interference power received by UE i, and is the sum of the received powers received by UE i from other m-1 UAV-BSs.

Yes

The received interference power, is

Sum of received power received from all UAV-BSs.

与

之间的直传链路(S-D)的SINR为：

and

The SINR of the direct link (SD) between is:

与目的节点

之间的多跳中继链路总的SINR为：Since the multi-hop D2D relay communication adopts the DF relay mode, when V _k > 1, the source node

with destination node

The total SINR of the multi-hop relay link between them is:

到目的节点

对于S-D直传链路和DF中继链路的接收信号最终采取最大合并比(MRC)合并原则，则最终目的节点

的SINR为：Since the source node in multi-hop D2D communication

to the destination node

The maximum combining ratio (MRC) combining principle is adopted for the received signals of the SD direct link and DF relay link, and the final destination node

The SINR is:

参与UAV-BS通信的UE的SINR必须不小于设定的阈值θ，参与UAV-BS通信的UE的SINR必须不小于设定的阈值τ，公式如下：Number of UE communications: The number of UE communications N _total refers to the number N _ij of UEs that communicate directly with the UAV-BS in the scenario plus the number of UEs communicating through D2D

The SINR of the UE participating in the UAV-BS communication must be no less than the set threshold θ, and the SINR of the UE participating in the UAV-BS communication must be no less than the set threshold τ. The formula is as follows:

UE network throughput: Network throughput refers to the maximum data rate actually transmitted during network transmission. Channel capacity is the maximum information rate that a channel can transmit without errors. This application assumes that the network throughput of each UE is equal to the channel capacity of each UE. Shannon's formula is used to calculate the channel capacity per UE:

C _in =W*log ₂ (1+γ _in ) (24)

是

到

的吞吐量，W是以Hz为单位的信道带宽。每个UAV-BS的吞吐量T_j的计算公式可以定义如下：where C _in is the throughput of UE i to UAV-BS n,

Yes

arrive

The throughput, W is the channel bandwidth in Hz. The formula for calculating the throughput T _j of each UAV-BS can be defined as follows:

The calculation formula of the network throughput T _total in the scenario can be defined as follows:

Objective function: The objective of this application is to find the three-dimensional coordinates of a set of UAV-BSs to maximize the network throughput and the number of UE communications within the scene under a certain weight.

α is a parameter that weighs the overall network throughput and the number of UE communications. On the premise that it is not lower than the SINR threshold, the network throughput of each UAV-BS is equal to the throughput of the UE directly communicating with the UAV-BS plus a total of g The network throughput of the UE through D2D communication, and the network throughput of the UAV-BS shall not be greater than its capacity T _max , the SINR of the UE participating in the U2D communication shall not be less than the set threshold θ, and the SINR of the UE participating in the D2D communication shall not be less than the set threshold θ a certain threshold τ.

The corresponding position P of the UAV-BS that maximizes the network throughput can be obtained in the following ways:

Further, the S103: solve the objective function through the global optimal artificial bee colony algorithm, and obtain the coordinate position of the deployment of the UAV base station; the specific steps include:

S1031: parameter initialization, randomly generating a number of solutions in a specified scene, each solution including a set number of three-dimensional coordinates of the UAV-BS;

S1032: Record the solution with the largest fitness value and the solution with the largest fitness value among all solutions;

S1033: Lead bee stage: search for a new solution based on the solution near the current UAV base station UAV-BS and the solution with the largest fitness value, if the new solution has a larger fitness value, record its target value and Replace the current solution with a new solution;

S1034: Follow the bee stage: use roulette to search for a new solution based on the solution near the current solution and the global optimal solution according to the probability, if the new solution has a larger fitness value, record its fitness value and use the current replace the solution with a new solution;

S1035: Scout bee stage: if the number of searches exceeds the specified number of times, a new solution is randomly generated in the specified scene to replace the current solution;

S1036: Determine whether the total number of iterations has been reached, and if so, output the recorded maximum fitness value and a corresponding set of UAV-BS coordinates, otherwise perform S1032 to S1036 again.

其中，

表示第j^th(j∈1,2,…,J)个UAV-BS的位置，生成每个UAV-BS位置的公式如下：In described S1031, the artificial bee colony algorithm generates 1 initial solution sets S, and each initial solution set S _i includes the position of J UAV-BSs

in,

Representing the position of the jth ( ^j∈1,2 ,…,J) UAV-BS, the formula for generating each UAV-BS position is as follows:

d={1,2,3}, j={1,2,...,J}, d is the dimension, rand is a random number between [0,1], ub and lb are the upper limit of the search space of the solution, respectively and lower limit.

In the S1032, for the problem of finding the maximum target value in this paper, the new fitness calculation formula proposed in this paper is as follows:

Bring the initial solution into the objective function, calculate the fitness value of the solution, and record the current maximum fitness value and the solution with the largest fitness value.

时，计算它的适应度值，搜索公式如下：In the S1033, the leading bee searches for a better solution based on the nearby solution and the solution with the largest fitness value, and when a new solution is found

, calculate its fitness value, the search formula is as follows:

是[-1,1]之间的一个随机数，rand是一个[0,1]之间的随机数，

是适应度值最大的解。若新解的适应度值大于当前解，则新解替换掉当解。k={1,2,…,3J} is the index of the coordinate value of the current solution, l∈{1,2,…,I}, i≠l is the index of the randomly selected new solution,

is a random number between [-1,1], rand is a random number between [0,1],

is the solution with the largest fitness value. If the fitness value of the new solution is greater than the current solution, the new solution replaces the current solution.

In the S1034: the probability that the follower bee selects the solution is proportional to the quality of the solution, which further optimizes the solution found by each lead bee. Each follower bee uses the roulette method to follow the leader bee. If the solution where the leader bee is located is selected, the follower bee will search for a new solution according to the solution where the leader bee is located. bigger. The probability formula proposed in this paper is as follows:

为所有解的适应度值之和。随后，产生一个[0,1]之间的随机数，若rand_i<p_i，则跟随蜂选择了一只引领蜂，使用公式(32)搜索一个新解，并计算它的适应度值。若rand_i≥p_i，则跟随蜂不搜索新解。若新解的适应度值大于当前解，则用新解集替换掉当前解。fit _i is the fitness value of the current solution,

is the sum of the fitness values of all solutions. Then, a random number between [0, 1] is generated. If rand _i < p _i , the follower bee selects a leader bee, uses formula (32) to search for a new solution, and calculates its fitness value. If rand _i ≥ p _i , the follower bee does not search for new solutions. If the fitness value of the new solution is greater than the current solution, the current solution is replaced with the new solution set.

In the S1035: if a certain solution S _i is not improved in a predetermined number of iterations, the corresponding lead bee abandons the solution and turns into a scout bee to search for a new solution using formula (30).

In the S1036, the algorithm stops when the number of iterations reaches the pre-interpreted maximum number of iterations maxiteration, and running the algorithm multiple times can improve the robustness of the program.

Deploying UAV-BS is usually an NP-hard problem, which is mostly approximated by heuristics. Since the update method of the solution of the ABC algorithm is to select only one dimension in the neighborhood to update, the quality of the solution is not high. The GOABC algorithm proposed in this application considers all the dimensions of the solution when updating the solution, and adds the influence of the global optimal solution, so that the convergence of the algorithm is better.

The method proposed in this paper is analyzed and compared with other methods. The specific process is as follows:

This application analyzes a total of two comparative experiments, namely ABC algorithm, GOABC algorithm, PSO algorithm and GWOPSO algorithm, whether the model considers D2D communication. The target value in the experiment is the median of the target values obtained after running the algorithm multiple times. The experimental results show that the UAV-BS deployment model proposed in this application combined with the GOABC algorithm can make the overall network throughput and the number of UE communications in the scene larger, and is a more optimized UAV-BS deployment method. The experimental parameters are shown in Table 1.

Table 1 Model experimental parameters

(1) Comparison of four algorithms

In Fig. 4(a)-Fig. 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 simulates a bird in a flock by designing a massless particle. The basic idea of PSO algorithm is to find the optimal solution through cooperation and information sharing among individuals in the group. The GWOPSO algorithm is an optimization search method that simulates the prey activities of gray wolves, and it is also a hybrid optimization algorithm combining the gray wolf optimization (GWO) algorithm and the PSO algorithm. This application can clearly see that among the three distributions, the GOABC algorithm can achieve the largest target value, followed by the GWOPSO algorithm. Experimental data show that the GOABC algorithm outperforms the GWOPSO algorithm by approximately 4.5%, 5.2%, and 7.3% in reaching the maximum target value in random, normal, and exponential distributions, respectively. This application can conclude that the GOABC algorithm has advantages over the other three heuristics. (2) Comparison of whether D2D communication is considered: In Figure 5(a)-Figure 5(c), in order to test the gap between the model proposed in this application considering D2D communication and the model not considering D2D communication, this application will two All the same models except for D2D communication are compared. It can be seen from this application that among the three distributions, the model considering D2D communication generally has a larger target value than the model not considering D2D communication, and the advantage of normal distribution is the most obvious. Experimental data show that in random, normal and exponential distributions, the model considering D2D communication is approximately 6.3%, 3.8% and 4% higher than the model without D2D communication in reaching the maximum target value. It can be concluded from this application that the model considering D2D communication is better than the model not considering D2D communication.

Embodiment 2

This embodiment provides a UAV base station deployment system based on the global optimal artificial bee colony algorithm;

UAV base station deployment system based on global optimal artificial bee colony algorithm, including:

The D2D network building module is configured to: construct a D2D network on which a number of user terminals UE are distributed;

The objective function building module is configured to: on the basis of the D2D network, construct the network coverage problem of the UAV base station UAV-BS into an objective function and constraints; the objective function is: maximizing the throughput of the overall network and the number of end-user UEs in the overall network; wherein, the overall network includes both the D2D network for communication between user terminals UE and the network for communication between the user terminal UE and the UAV base station UAV-BS;

The output module is configured to: solve the objective function through the global optimal artificial bee colony algorithm to obtain the coordinate position of the UAV base station deployment; based on the obtained coordinate position of the UAV base station deployment, complete the UAV base station deployment Deployment of base stations.

It should be noted here that the above-mentioned D2D network building module, objective function building module and output module correspond to steps S101 to S103 in the first embodiment, and the examples and application scenarios realized by the above-mentioned modules and corresponding steps are the same, but not limited to The content disclosed in the first embodiment above. It should be noted that the above modules may be executed in a computer system such as a set of computer-executable instructions as part of the system.

The description of each embodiment in the foregoing embodiments has its own emphasis. For the part that is not described in detail in a certain embodiment, reference may be made to the relevant description of other embodiments.

The proposed system can be implemented in other ways. For example, the system embodiments described above are only illustrative. For example, the division of the above modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into Another system, or some features can be ignored, or not implemented.

Embodiment 3

This embodiment also provides an electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are Stored in the memory, when the electronic device runs, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in 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 array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory may include read-only memory and random access memory and 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 the implementation process, each step of the above-mentioned method can be completed by a hardware integrated logic circuit in a processor or an instruction in the form of software.

The method in the first embodiment can be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor. The software modules may be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other storage media mature in the art. The storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware. To avoid repetition, detailed description is omitted here.

Those of ordinary skill in the art can realize that the unit, that is, the algorithm step of each example described in conjunction with this embodiment, can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

Embodiment 4

This embodiment also provides a computer-readable storage medium for storing computer instructions, and when the computer instructions are executed by a processor, the method described in the first embodiment is completed.

The above descriptions are only preferred embodiments of the present application, and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims (10)

1. The UAV base station deployment method based on the global optimal artificial bee colony algorithm, is characterized in that, comprises: constructing a D2D network on which several user terminals UE are distributed; On the basis of the D2D network, the network coverage problem of the UAV-BS of the unmanned aerial vehicle base station is constructed as an objective function and constraints; the objective function is to maximize the throughput of the overall network and the number of end-user UEs of the overall network; wherein , the overall network includes not only the D2D network for communication between user terminals UE, but also the network for communication between the user terminal UE and the unmanned aerial vehicle base station UAV-BS; Through the global optimal artificial bee colony algorithm, the objective function is solved to obtain the coordinate position of the UAV base station deployment; based on the obtained coordinate position of the UAV base station deployment, the deployment of the UAV base station is completed. 2. The method according to claim 1, wherein the objective function is equal to the weighted summation result of the throughput of the overall network and the communication quantity of the end users of the overall network, and the weight is to weigh the overall network throughput and the user terminal UE. number of parameters. 3. The method of claim 2, wherein the number of end-user communications in the overall network is equal to the number of end-users communicating directly with the UAV base station UAV-BS and the number of end-users communicating through the D2D network Sum. 4. The method according to claim 2, wherein the throughput of the overall network is equal to the network throughput of the end user communicating directly with the UAV base station UAV-BS and the end user communicating through the D2D network The sum of the network throughput. 5. The method of claim 1, wherein the constraints are: The parameter weighing the overall network throughput and the number of user terminals UE is greater than zero and less than 1; The network throughput of the drone base station UAV-BS is less than or equal to its capacity; The signal-to-interference-plus-noise ratio SINR of the user terminal UE participating in the communication between the user terminal UE and the UAV base station UAV-BS is greater than or equal to the set threshold; In the D2D network, the signal-to-interference-plus-noise ratio SINR of the user terminal UE that has a communication relationship is greater than or equal to a set threshold. 6. The method of claim 1, wherein the signal-to-interference-plus-noise ratio (SINR), in the communication process between the user terminal UE and the unmanned aerial vehicle base station UAV-BS, means that the user terminal is at the unmanned aerial vehicle base station. Ratios of received power and interference power to channel noise within the coverage area of the UAV-BS; or, The signal-to-interference-plus-noise ratio SINR, in the D2D network, refers to the ratio of the received power and the interference power to the channel noise of the current user terminal UE when it receives the previous hop user terminal; or, The received power refers to the power received by the user terminal from the UAV base station UAV-BS during the communication process between the user terminal UE and the UAV base station UAV-BS; or, 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. 7. The method according to claim 1, wherein the objective function is solved by a global optimal artificial bee colony algorithm to obtain the coordinate position of the UAV base station deployment; Concrete steps include: The parameters are initialized, and several solutions are randomly generated in the specified scene, and each solution includes the three-dimensional coordinates of a set number of UAV-BSs; Recording step: record the solution with the largest fitness value and the solution with the largest fitness value among all solutions; Leading bee stage: Search for a new solution based on the solution near the current UAV-BS and the solution with the largest fitness value. If the new solution has a larger fitness value, record its target value and use the current replace the solution with a new solution; Follow the bee stage: use roulette to search for a new solution based on the solution near the current solution and the global optimal solution according to the probability, if the new solution has a larger fitness value, record its fitness value and replace the current solution into a new solution; Scout bee stage: if the number of searches exceeds the specified number, a new solution will be randomly generated to replace the current solution in the specified scene; Judging step: Judging whether the total number of iterations has been reached, and if so, output the recorded maximum fitness value and a corresponding set of UAV-BS coordinates, otherwise re-execute the recording step to the judging step. 8. The UAV base station deployment system based on the global optimal artificial bee colony algorithm is characterized in that, including: A D2D network building module, which is configured to: build a D2D network on which several user terminals UE are distributed; An objective function building module, which is configured to: on the basis of the D2D network, construct the network coverage problem of the UAV base station UAV-BS into an objective function and constraints; the objective function is: maximizing the throughput of the overall network and the number of end-user UEs in the overall network; wherein, the overall network includes both the D2D network for communication between user terminals UE, and the network for communication between the user terminal UE and the UAV base station UAV-BS; The output module is configured to: solve the objective function through the global optimal artificial bee colony algorithm to obtain the coordinate position of the UAV base station deployment; based on the obtained coordinate position of the UAV base station deployment, complete the UAV base station deployment Deployment of base stations. 9. An electronic device, characterized in that it comprises: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are Stored in a memory, when the electronic device is running, the processor executes one or more computer programs stored in the memory to cause the electronic device to perform the method of any one of claims 1-7 above. 10 . A computer-readable storage medium, characterized in that it is used for storing computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1-7 is completed. 11 .

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