# Research on Performance Optimization Method of UAV Communication Link in Emergency Communication Environment

Authors: Ju Li; Fengjun Shang
Journal: Security and Communication Networks
 (2022)

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## Abstract

Aiming at the problem of long average delay of traditional UAV emergency communication, a performance optimization method of UAV communication link based on emergency communication environment is proposed. The target tasks in the emergency communication channel are divided into different target task groups. According to the distance between the target tasks in the target task group, the symmetric matrix is constructed, all elements in the symmetric matrix are classified, and the classification results are calculated by ant colony algorithm to construct the emergency communication task scheduling model of UAV. Combined with the emergency communication task scheduling model and the demand, priority, time window, and communication ability of UAV emergency scheduling task, the time delay control of UAV emergency communication is realized with the goal of maximizing the efficiency of UAV task execution, maximizing the completion rate of UAV task execution and minimizing the risk of UAV delay. The experimental results show that the average delay of the three methods also increases. When the system load is 50%, the average delay of common methods is 23 ms, the average delay of traditional methods is 76 ms, the average delay of the method proposed in this paper is 26 ms, and the common average delay is the shortest. When the system load is 99%, the common methods cannot realize emergency communication scheduling due to system load overshoot, and the corresponding average delay cannot be considered, indicating that the common methods are unstable. The average delay of the method proposed in this paper is stable under different system loads and always remains below 40 ms. It is proved that compared with the traditional methods, the method proposed in this paper takes less time for UAV emergency communication and has less average delay under different system loads, so it has better application value.


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## Body

## 1. Introduction

Natural disasters are usually unpredictable and inevitable, but they can be minimized by effective means. After the occurrence of natural disasters, on the one hand, the communication demand of the people in the disaster area has increased sharply, resulting in the overload operation of the original communication system; on the other hand, natural disasters can lead to power outages, traffic disruptions, the collapse of buildings and communication bases, the complete collapse of the communications system, and the inability to provide full communication services to victim region [1]. Keeping communications open is a prerequisite for disaster relief efforts, but repairing and rebuilding local communications infrastructure is time consuming and difficult to operate. Therefore, setting up a temporary emergency communication system is the best option. In general, emergency communication systems should be fast, convenient, low cost, large capacity, and wide-ranging. On-site assessments of aircraft networks are important for disaster monitoring, disaster relief, and postdisaster recovery. A network of drones helps disaster response commanders identify, process, and understand key elements of a major natural disaster or other emergency [2]. In addition, the network has attracted the attention of researchers due to its advantages such as low construction cost, low site requirements, easy to use, fast, and scalable. Therefore, an in-depth study of the multi-pilot wireless emergency network is of practical importance and application perspective. Game theory is a theory and method of studying the phenomena of competition and cooperation. This is not only a new branch of modern mathematics, but also an important topic of functional research [3].Two-layer wireless network as shown in Figure1 is routing layer mesh network and drone network. The first consists of a K router and some end users, and belongs to the traditional wireless router network. The latter consists of a U-shaped unmanned aerial vehicle (UAV) and a remote base station (e.g., a mobile communication vehicle) [4]. Drones transmit data from routers or other unmanned aerial vehicles and enable communication between end users inside and outside the disaster zone. The two-tier network can be easily and quickly installed in disaster zones, shelters, and ground rescue centers, providing emergency communication services to terminals. The research scope is that under the condition of limited router energy, UAV collects data in the fastest way, which is the key point to judge the network performance. For UAVs, we can regard UAVs as players in the game. There are eight kinds of executable actions in flight: east, west, south, north, southeast, northeast, southwest, and northwest. The flying direction of UAVs in a certain position is actually 360 degrees around itself, but we only consider the positions in 8 directions [5]. Then, these eight directions east, west, south, north, southeast, northeast, southwest, and northwest are the strategy set of UAV as a participant. In this case, it becomes a game between UAV and UAV, and their benefit is the router data collected by UAV. At the same time, routers have energy constraints. UAVs should choose the most beneficial flight path under the condition of limiting energy, that is, the strategy with the greatest benefit, so as to achieve the overall optimization, that is, find the Nash equilibrium point. As a participant, UAVs can take the form of cooperative game to achieve the overall optimization [6].Figure 1
Two-layer wireless network.


## 2. Literature Review

It is found that the probability density function distribution of angle of department (AOD) and angle of arrival (AOA) in multipath channel can be accurately used to analyze and evaluate the performance of wireless communication system. So far, many research teams at home and abroad have proposed a variety of geometric channel models for mobile communication environment. Wang et al. proposed geometric channel models in two-dimensional (2D) space, such as circular scattering region geometric channel model, elliptical scattering region geometric channel model, and asymmetric scattering region geometric channel model, in which the signal transmission is assumed to be on the horizontal plane, while ignoring the influence of vertical angle on transmission characteristics [7]; therefore, the 2D channel model cannot accurately reflect the actual mobile communication environment. Later, many researchers proposed 3D geometric channel model. In 2010, Zhao and Gao proposed a 3D geometric channel model for the mobile communication environment of outdoor macro cell. It is assumed that the base station (BS) end is equipped with directional antenna, so the geometric scattering area presents an irregular shape [8]. In addition, Zhang proposed a 3D indoor microcell geometric channel model, in which it is assumed that the scatterers are evenly distributed in the hemispherical scattering region with BS as the spherical center [9]. Recently, Wu et al. proposed a semi-ellipsoid geometric channel model for M2M mobile communication environment. The author mainly studied the influence of the distribution of buildings near BS and MR on the channel transmission characteristics. In the above geometric scattering channel model, it is mostly assumed that the signal sent by the transmitting end reaches the receiving end after a single reflection, but the situation that the signal reaches the receiving end after multiple reflections is not analyzed. Therefore, in order to describe the urban street mobile communication environment, we hope to propose a virtual scattering channel model to describe the situation of multiple transmission paths [10]. In view of the above technical difficulties, Farooqi et al. proposed the concept of effective street width [11]. Later, Chen et al. proposed a virtual geometric scattering channel model, in which the transmission path under two and three reflections is equivalent to the transmission path under one reflection [12]. Liu et al. proposed a channel model under polarization through experimental measurements in Tokyo and Yokohama [13]. Liu et al. proposed a broadband MIMO multi antenna geometric channel model, but did not analyze the influence of the relative motion speed and direction of MT and MR on the channel transmission characteristics [14]; at the same time, the channel model in Liu et al. still focuses on the situation that the signal reaches the receiver after a single reflection [15]. Ding proposed a classical elliptic channel model for the wireless communication environment of urban street macro cell, only considering the line of sight (LOS) and single reflection path, and did not analyze the impact of multiple reflection path on transmission characteristics [6]. Chu et al. pointed out that the path loss model can effectively describe the 5g communication environment through experimental measurement in the urban street environment [16]. Dong et al. proposed a new vehicle geometric channel model, in which the double loop model is used to describe the distribution of moving vehicles near MT and MR, and the elliptical model is used to describe the distribution of stationary buildings on the roadside; at the same time, the transmission signal described by the author reaches Mr through Los, single reflection, and two reflections [17]. For the 60 GHz urban street mobile communication environment, Lv et al. proposed a nondeterministic geometric channel model to describe the vehicle communication environment; in this paper, the author only considers the situation that the transmitted signal reaches the receiving end after single and twice reflection, and cannot effectively describe the situation that the signal is reflected multiple times in the actual mobile communication environment. In addition, combined with the above discussion, it can be found that the analysis of transmission characteristics by channel model in the past is still in the stage of time domain analysis, and the influence of Mr motion on Doppler frequency shift in vehicle mobile communication environment has not been studied [18].Based on the current research, this paper proposes a performance optimization method of UAV communication link based on emergency communication environment. The target tasks in the emergency communication channel are divided into different target task groups. According to the distance between the target tasks in the target task group, the symmetric matrix is constructed, all elements in the symmetric matrix are classified, and the classification results are calculated by ant colony algorithm to construct the emergency communication task scheduling model of UAV. Combined with the emergency communication task scheduling model and the demand, priority, time window, and communication ability of UAV emergency scheduling task, the time delay control of UAV emergency communication is realized with the goal of maximizing the efficiency of UAV task execution, maximizing the completion rate of UAV task execution, and minimizing the risk of UAV delay.


## 3. Time Delay Control Method of UAV Emergency Communication under Emergencies

### 3.1. Task Scheduling Model of UAV Emergency Communication

Using the longest distance method in the clustering method, the target tasks in the UAV emergency communication channel under emergencies are divided into several different target task groups. According to the distance between the target tasks in each target task group, a symmetric matrix is constructed, all elements in the matrix are classified, the classification results are calculated by ant colony algorithm, and then the UAV emergency communication task scheduling model is constructed according to the calculation results.The longest distance method is used to merge the two categories with the longest distance between tasks. Assuming that there are multiple initial target tasksG1,G2,…,Gm in the UAV emergency communication channel in an expressway emergency, and dm,m−1 is the distance between the m-th target task Gm and the (m − 1) th target task Gm−1, there are(1)dm,m−1=Gm−Gm−1.The clustering steps of the longest distance method are as follows:(i)
Calculate the distance betweenm target tasks and two target tasks in UAV emergency communication, di,j,i=j+1,j=1,2,…,m−1, and obtain the symmetry matrix D0=di,j.(ii)
Select the largest and smallest elements inD (0), record them as dmax and dmin, respectively, and combine dmax and dmin to obtain the combined result, which is recorded as dr.(iii)
The distancedrk of ant kk=1,2,…,u from task d to new task dr is(2)drk=dr−maxdi,j.The elements on the remaining rows and columns after removingdmax and dmin from D (0) form a new task according to step (2), which is recorded as dr′. The matrix obtained from the corresponding new row p′ and new column q′ is recorded as D (1).(iv)
Repeat steps (2) and (3) forD (1) to obtain D (2) and so on, until all target tasks in the UAV emergency communication task are integrated.After clustering, the UAV task targets are divided into several target task groups, which reduces the difficulty of UAV task execution and reduces the time of UAV task execution.The ant colony algorithm is used to solve the classification results:(i)
Input the coordinates of the geographical location of each target task in the UAV emergency communication channel into the program, and calculate the distance matrix between the emergency and the UAV take-off and landing field. In order to ensure that the denominator of the heuristic function is not equal to 0, correct the 0 element of the diagonal of the matrix, and correct the 0 element on the diagonal to 10−3 according to the magnitude of the data [19].(ii)
Set the maximum number of iterationsietrmax so that the initial value of the number of iterations is ietr = 0, and optimize the parameters corresponding to the initial value through iterations to speed up the task execution of UAV.(iii)
Set the ant colony size asu, and put u ants on n target tasks at random.(iv)
For each antk, select the destination f to go in the next stage through the transfer probability formula, and read the destination f into the taboo table tabuk.(v)
Judge whether the taboo table is full. If not, return to step ④; if it is full, proceed to step ⑥.(vi)
The pheromone in the population is updated globally, and steps ③∼⑤ are repeated until the maximum number of iterationsietrmax or a certain accuracy is met.(vii)
Output results. Through ant colony algorithm, the emergency communication tasks are divided into corresponding regions, and the UAV emergency communication tasks are scheduled according to the regional geographical environment. Therefore, the UAV emergency communication task scheduling model is constructed, which lays a foundation for the subsequent implementation of emergency communication delay control method [20].


### 3.2. Realization of Time Delay Control Method for UAV Emergency Communication

Consider the task requirements, priority, time window, geographical environment where the task is performed, and the UAV emergency communication capability of UAV emergency communication. Taking the maximization of task revenue efficiency, the maximization of UAV task completion rate and the minimization of UAV emergency communication transmission risk as the goal, combined with the clustering results of the longest distance method in3.1, the time delay control of UAV emergency communication is realized.SetMi as the UAV emergency communication task, and limit the set task execution time to Tsi=tsi,tei; that is, the start limit time of the task is tsi and the end limit time is tei; set t as the total execution time of emergency communication dispatching; the total time for UAV to perform tasks is ti,j; tpi,pj represents the actual time of UAV ground transfer, and pi and pj are the two points to determine the UAV ground transfer distance; Fi represents the task execution variable. When Fi≠0, it indicates that the task is being executed, and when Fi=0, it indicates that there is no task at present; the selection decision variables are described by Gi,j. Based on the above parameter setting, the UAV emergency communication delay control is carried out. The specific control process is as follows:(1)
Enter the task. The problem model is used to describe the communication task. The emergency communication data of a UAV are executed only once within the set time to describe the communication task. The process is expressed as(3)Tx=MiGi,jTsi.(2)
Enter the time constraints for UAV to perform emergency communication tasks. The start timetpi of UAV emergency communication task shall be less than the task start limit time, tpi<tsi. The time tpj of UAV ending the task shall not be greater than the task termination limit time, tpj<tei.(3)
Input the constraints of UAV task transition time in UAV emergency communication task scheduling model. The constraints on the transition time of the mission are mainly the time relationship between the UAVRj executing the mission Mi and the previous mission Mk; that is, the execution start time of Mi cannot be greater than the time tMk,tMi transferred to Mi after the UAV executing Mk, and enough waiting time is reserved to reduce the risk of the UAV executing the mission.(4)
Enter the matching constraints of UAV emergency communication task. Due to the risk of time delay when the UAV performs its mission, it is necessary to consider that in the specified operation timeti,j, the parameters of the UAV remote sensing system are matched with geographic information D1 and meteorological information Qx as constraints to obtain its risk probability:(4)PMi,Rj=Qx+D1.Maximize the efficiency of UAV emergency communication mission. The purpose of UAV emergency communication flight mapping is to obtain the maximum mapping results in the shortest time in case of highway emergencies. The result of multiplying the priority, timeliness, and range of UAV tasks is the final benefit of the mapping task. The benefit efficiencyO1 obtained from the final benefit is(5)O1=∑i=1mFiriPMiLi+VT,where ri is the priority of UAV emergency communication task; Li is the operation voyage for performing the task; and V is the normalized constant in the unit range [21]. Normalization processing Fi, ∀Fi=1, that is, when the emergency communication task Mi is assigned for execution, the task completion timeliness PMi can be set as(6)PMi=13,0<tMi<13,tMi,13≤tMi<1,where tMi is the execution time of task Mi.In equation (6), the timeliness increases with the reduction of completion time within the set time.In order to maximize the ability of UAV to perform tasks, it is necessary to increase the calculation of emergency communication tasks to measure the completion rate of UAV’s task scheduling. Therefore, the total completion rate of completed tasksO2 is(7)O2=∑i=1mFim.To minimize the risk of UAV in emergency communication scheduling, many influencing factors need to be considered, including task execution risk and task scheduling risk. Reducing the risk can ensure the UAV to complete the task according to the expected time and ensure the stability of scheduling [22]. The delay risk O3 of UAV emergency communication is set as(8)O3=∑PMi,Rj+Fx,where Fx is the task scheduling efficiency.(9)Fx=∑ri,j+si,j+∑i=1mRjm,where ∑ri,j+si,j is the risk of road section, ri,j is the hazard intensity of emergencies between adjacent nodes i and j in the road network, and si,j represents the sensitivity of road sections between adjacent nodes i and j.The comprehensive evaluation objective 0 of UAV emergency communication is the weighted sum of the above UAV task revenue efficiency maximization, UAV task completion rate maximization, and UAV emergency communication delay risk minimization objectives:(10)O=λO1+μO2−ωO3,where λ is the weight to maximize the revenue efficiency of emergency communication task; μ is the weight to maximize the completion rate of emergency communication tasks; and ω is the weight of minimizing the delay risk of emergency communication.UAV emergency communication delay controlK(z) can be expressed as(11)Kz=OPMi,RjFxPi+PjPMiTx.Based on the above model, the time delay control of UAV emergency communication is realized.


## 3.1. Task Scheduling Model of UAV Emergency Communication

Using the longest distance method in the clustering method, the target tasks in the UAV emergency communication channel under emergencies are divided into several different target task groups. According to the distance between the target tasks in each target task group, a symmetric matrix is constructed, all elements in the matrix are classified, the classification results are calculated by ant colony algorithm, and then the UAV emergency communication task scheduling model is constructed according to the calculation results.The longest distance method is used to merge the two categories with the longest distance between tasks. Assuming that there are multiple initial target tasksG1,G2,…,Gm in the UAV emergency communication channel in an expressway emergency, and dm,m−1 is the distance between the m-th target task Gm and the (m − 1) th target task Gm−1, there are(1)dm,m−1=Gm−Gm−1.The clustering steps of the longest distance method are as follows:(i)
Calculate the distance betweenm target tasks and two target tasks in UAV emergency communication, di,j,i=j+1,j=1,2,…,m−1, and obtain the symmetry matrix D0=di,j.(ii)
Select the largest and smallest elements inD (0), record them as dmax and dmin, respectively, and combine dmax and dmin to obtain the combined result, which is recorded as dr.(iii)
The distancedrk of ant kk=1,2,…,u from task d to new task dr is(2)drk=dr−maxdi,j.The elements on the remaining rows and columns after removingdmax and dmin from D (0) form a new task according to step (2), which is recorded as dr′. The matrix obtained from the corresponding new row p′ and new column q′ is recorded as D (1).(iv)
Repeat steps (2) and (3) forD (1) to obtain D (2) and so on, until all target tasks in the UAV emergency communication task are integrated.After clustering, the UAV task targets are divided into several target task groups, which reduces the difficulty of UAV task execution and reduces the time of UAV task execution.The ant colony algorithm is used to solve the classification results:(i)
Input the coordinates of the geographical location of each target task in the UAV emergency communication channel into the program, and calculate the distance matrix between the emergency and the UAV take-off and landing field. In order to ensure that the denominator of the heuristic function is not equal to 0, correct the 0 element of the diagonal of the matrix, and correct the 0 element on the diagonal to 10−3 according to the magnitude of the data [19].(ii)
Set the maximum number of iterationsietrmax so that the initial value of the number of iterations is ietr = 0, and optimize the parameters corresponding to the initial value through iterations to speed up the task execution of UAV.(iii)
Set the ant colony size asu, and put u ants on n target tasks at random.(iv)
For each antk, select the destination f to go in the next stage through the transfer probability formula, and read the destination f into the taboo table tabuk.(v)
Judge whether the taboo table is full. If not, return to step ④; if it is full, proceed to step ⑥.(vi)
The pheromone in the population is updated globally, and steps ③∼⑤ are repeated until the maximum number of iterationsietrmax or a certain accuracy is met.(vii)
Output results. Through ant colony algorithm, the emergency communication tasks are divided into corresponding regions, and the UAV emergency communication tasks are scheduled according to the regional geographical environment. Therefore, the UAV emergency communication task scheduling model is constructed, which lays a foundation for the subsequent implementation of emergency communication delay control method [20].


## 3.2. Realization of Time Delay Control Method for UAV Emergency Communication

Consider the task requirements, priority, time window, geographical environment where the task is performed, and the UAV emergency communication capability of UAV emergency communication. Taking the maximization of task revenue efficiency, the maximization of UAV task completion rate and the minimization of UAV emergency communication transmission risk as the goal, combined with the clustering results of the longest distance method in3.1, the time delay control of UAV emergency communication is realized.SetMi as the UAV emergency communication task, and limit the set task execution time to Tsi=tsi,tei; that is, the start limit time of the task is tsi and the end limit time is tei; set t as the total execution time of emergency communication dispatching; the total time for UAV to perform tasks is ti,j; tpi,pj represents the actual time of UAV ground transfer, and pi and pj are the two points to determine the UAV ground transfer distance; Fi represents the task execution variable. When Fi≠0, it indicates that the task is being executed, and when Fi=0, it indicates that there is no task at present; the selection decision variables are described by Gi,j. Based on the above parameter setting, the UAV emergency communication delay control is carried out. The specific control process is as follows:(1)
Enter the task. The problem model is used to describe the communication task. The emergency communication data of a UAV are executed only once within the set time to describe the communication task. The process is expressed as(3)Tx=MiGi,jTsi.(2)
Enter the time constraints for UAV to perform emergency communication tasks. The start timetpi of UAV emergency communication task shall be less than the task start limit time, tpi<tsi. The time tpj of UAV ending the task shall not be greater than the task termination limit time, tpj<tei.(3)
Input the constraints of UAV task transition time in UAV emergency communication task scheduling model. The constraints on the transition time of the mission are mainly the time relationship between the UAVRj executing the mission Mi and the previous mission Mk; that is, the execution start time of Mi cannot be greater than the time tMk,tMi transferred to Mi after the UAV executing Mk, and enough waiting time is reserved to reduce the risk of the UAV executing the mission.(4)
Enter the matching constraints of UAV emergency communication task. Due to the risk of time delay when the UAV performs its mission, it is necessary to consider that in the specified operation timeti,j, the parameters of the UAV remote sensing system are matched with geographic information D1 and meteorological information Qx as constraints to obtain its risk probability:(4)PMi,Rj=Qx+D1.Maximize the efficiency of UAV emergency communication mission. The purpose of UAV emergency communication flight mapping is to obtain the maximum mapping results in the shortest time in case of highway emergencies. The result of multiplying the priority, timeliness, and range of UAV tasks is the final benefit of the mapping task. The benefit efficiencyO1 obtained from the final benefit is(5)O1=∑i=1mFiriPMiLi+VT,where ri is the priority of UAV emergency communication task; Li is the operation voyage for performing the task; and V is the normalized constant in the unit range [21]. Normalization processing Fi, ∀Fi=1, that is, when the emergency communication task Mi is assigned for execution, the task completion timeliness PMi can be set as(6)PMi=13,0<tMi<13,tMi,13≤tMi<1,where tMi is the execution time of task Mi.In equation (6), the timeliness increases with the reduction of completion time within the set time.In order to maximize the ability of UAV to perform tasks, it is necessary to increase the calculation of emergency communication tasks to measure the completion rate of UAV’s task scheduling. Therefore, the total completion rate of completed tasksO2 is(7)O2=∑i=1mFim.To minimize the risk of UAV in emergency communication scheduling, many influencing factors need to be considered, including task execution risk and task scheduling risk. Reducing the risk can ensure the UAV to complete the task according to the expected time and ensure the stability of scheduling [22]. The delay risk O3 of UAV emergency communication is set as(8)O3=∑PMi,Rj+Fx,where Fx is the task scheduling efficiency.(9)Fx=∑ri,j+si,j+∑i=1mRjm,where ∑ri,j+si,j is the risk of road section, ri,j is the hazard intensity of emergencies between adjacent nodes i and j in the road network, and si,j represents the sensitivity of road sections between adjacent nodes i and j.The comprehensive evaluation objective 0 of UAV emergency communication is the weighted sum of the above UAV task revenue efficiency maximization, UAV task completion rate maximization, and UAV emergency communication delay risk minimization objectives:(10)O=λO1+μO2−ωO3,where λ is the weight to maximize the revenue efficiency of emergency communication task; μ is the weight to maximize the completion rate of emergency communication tasks; and ω is the weight of minimizing the delay risk of emergency communication.UAV emergency communication delay controlK(z) can be expressed as(11)Kz=OPMi,RjFxPi+PjPMiTx.Based on the above model, the time delay control of UAV emergency communication is realized.


## 4. Simulation Experiment

In order to verify the effectiveness of the time delay control method of UAV emergency communication under expressway emergencies, simulation experiments are carried out. The simulation experiment environment is 12 core Intel (R) Xeon(R) CPUE5－2620, memory 64 GB, programming language Python 2.7, and CNN training and learning using TensorFlow－0.9.0 deep learning framework [23].Compare the communication time between the method proposed in this paper and the traditional method, and the comparison results are shown in Figures2 and 3.Figure 2
Communication duration of traditional methods.Figure 3
Communication duration of the method in this paper.As can be seen from Figures2 and 3, when the communication flow is 2.88 gb, the communication time of the traditional method is 80 s longer than that of the method proposed in this paper. Compared with the communication time under other different communication flows, the execution time of the two methods is the closest at 2.88 GB. With the gradual increase of communication traffic, the communication time of the traditional method is gradually larger than that of the method proposed in this paper. The main reason is that part of the nonblocking transmission and reception time of the traditional method is wasted, which prolongs the communication time. The communication time of the method proposed in this paper has been stable below 100 s, which verifies the feasibility of applying the method proposed in this paper to UAV emergency communication [24].In order to further verify the effectiveness of the proposed method, compare the average delay of the three methods when the system load is 50%, 90%, and 99%, respectively. The comparison results are shown in Figures4–6.Figure 4
Average time delay of traditional methods.Figure 5
Average delay of common methods.Figure 6
Average time delay of the proposed method.It can be seen from Figures4 to 6 that with the increase of system load, the average delay in Figure 5 of the three methods also increases. When the system load is 50%, the average delay of common methods is 23 ms, the average delay of traditional methods is 76 ms, the average delay of the method proposed in this paper is 26 ms, and the common average delay is the shortest. When the system load is 99%, the common methods cannot realize emergency communication scheduling due to system load overshoot, and the corresponding average delay cannot be considered, indicating that the common methods are unstable. The average delay of the method proposed in this paper is relatively stable under different system loads, which is always kept below 40 ms, and has practical application value [25, 26].


## 5. Conclusion

Based on the improvement of some previous methods in UAV communication link, this paper designs and expands new algorithm ideas. The classification results are calculated by ant colony algorithm, and the emergency communication task scheduling model of UAV is constructed. Combined with the emergency communication task scheduling model and the demand, priority, time window, and communication ability of UAV emergency scheduling task, the time delay control of UAV emergency communication is realized with the goal of maximizing the efficiency of UAV task execution, maximizing the completion rate of UAV task execution, and minimizing the risk of UAV delay. In addition, simulation experiments verify the performance of the new method. Compared with some previous ideas, the new method has higher recognition rate, good robustness, and relatively small amount of calculation. It is proved that compared with the traditional method, the time required for UAV emergency communication is shorter and the average delay under different system loads is smaller, which has better application value.



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*Source: 5098730-2022-05-19.xml*