CN111970713A - Unmanned aerial vehicle base station deployment method and three-dimensional parameter setting for dense urban area - Google Patents

Unmanned aerial vehicle base station deployment method and three-dimensional parameter setting for dense urban area Download PDF

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CN111970713A
CN111970713A CN202010820050.XA CN202010820050A CN111970713A CN 111970713 A CN111970713 A CN 111970713A CN 202010820050 A CN202010820050 A CN 202010820050A CN 111970713 A CN111970713 A CN 111970713A
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张鸿涛
刘江徽
何元
唐文斐
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Beijing University of Posts and Telecommunications
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    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/24—Cell structures
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
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Abstract

Due to the serious shielding effect of dense urban buildings and the large traffic pressure of the base station, the user experience is poor. Therefore, the embodiment of the invention researches an unmanned aerial vehicle base station deployment method and three-dimensional parameter setting for dense urban areas. The method comprises the following specific steps: firstly, simulating a target dense urban area, and carrying out horizontal triangulation by taking the horizontal center of a building as a vertex; secondly, finding out the position with the largest horizontal LoS angle inside the triangle, and placing a UAV at the position; then calculating the coverage range and the placeable height range of the UAV according to the channel state; then, considering the shielding of the building, and selecting the height with the largest number of covered users by using an optimization strategy; and finally, screening the UAVs according to the principle of more service user numbers and less overlapped user numbers until the required coverage rate is reached. According to the method, triangulation is adopted for modeling, the shielding effect of the building is considered, deployment resources are saved, and meanwhile, a method for setting the UAV three-dimensional deployment parameters in the dense urban area can be obtained.

Description

Unmanned aerial vehicle base station deployment method and three-dimensional parameter setting for dense urban area
Technical Field
The invention relates to the technical field of wireless communication, in particular to research on unmanned aerial vehicle base station deployment in dense urban scenes.
Background
In recent years, with the progress and development of science and technology, the mobile data traffic consumed by people for sharing, exchanging, entertaining and transmitting data is increasing, and the service quality of some areas is poor, especially in some areas with dense population and dense buildings. And more high buildings are pulled out from more and more places, and in order to meet the communication requirement of the places, base stations need to be rearranged. However, the cost of building a ground base station is high, and the adjustability is not large, so that it is a good solution to provide wireless coverage service by using an Unmanned Aerial Vehicle (UAV) as an air mobile base station.
UAV base stations have several advantages over traditional ground base stations as follows: it is easier to establish a Line of Sight (LoS) communication link; the deployment can be fast and flexibly, the scheduling can be carried out according to the requirement, and a better position can be found under the condition of poor communication quality; the UAV can be controlled to move, artificially controlled and the adjacent station interference is reduced.
UAVs are well-competent for emergency or time-constrained communications tasks because of their low cost of manufacture and rapid and convenient deployment. In particular, in view of the high mobility of UAVs, it can establish LoS communication links in most cases to improve quality of service compared to traditional fixed ground base stations. In addition, the UAV also has high maneuverability, which enables the UAV to adjust the posture to adapt to the current environmental change through controllable movement, which opens up a new road for improving the communication service quality. For example, when the unmanned aerial vehicle perceives that the communication link is not good, the unmanned aerial vehicle can adjust its position, reduce packet loss at a reduced rate, and increase the transmission rate when a position with high communication quality is to be found. These obvious advantages make drone-assisted wireless communication a promising point of research for future wireless systems.
The unmanned aerial vehicle deployment problem has the characteristics of diversity, and the target for different problem researches is also very different. But whatever drone deployment issue typically includes non-convex constraints, binary discrete variable constraints, continuous or discrete time variables, and non-convex objective functions. Therefore, it is an open topic and very challenging to solve the deployment optimization problem.
In fact, the deployment problem of drones, studied at present, whether solved or not, is substantially of NP-Hard type in its temporal complexity. The basic research idea for the problems is to convert the complex non-convex problem into an extremum covering problem similar to that which can be solved by convex optimization and linear programming or an indiscriminate imitation problem with an existing conclusion which are researched by some proper assumptions. Generally, the conversion method is also influenced by research scenes, and different mathematical methods and theoretical formulas are used according to different scenes, such as some heuristic algorithms or problem decoupling methods. On the other hand, referring to research scenes, most of the research does not consider the influence of building occlusion on UAV deployment in dense urban scenes, which is also a key problem in UAV deployment.
Disclosure of Invention
The invention mainly considers UAV base station deployment and three-dimensional parameter setting under the coverage rate reaching standard in dense urban area scene, and the concrete expression is as follows: decoupling the 3-dimensional deployment problem of the UAVs from the horizontal dimension and height dimension deployment, firstly carrying out plane triangulation on a building scene, and presetting one UAV in each triangle; then maximizing a horizontal LoS angle in a two-dimensional plane to obtain an optimal horizontal covering position; then optimizing the UAV height based on a greedy strategy; and finally screening a minimum number of UAVs based on a sorting algorithm according to the coverage rate requirement.
Furthermore, the minimum number UAVs with the coverage rate up to the standard are deployed in the dense urban area scene, the whole calculation process is completed on a back-end computer, the participation of a ground base station is not needed, and the method has good independence; after the computer completes the deployment calculation, the UAV is controlled to go to the deployment place manually. In addition, the deployment scheme can recalculate the position of the UAV according to the position change of the user, and has flexibility.
The scheme for deploying the UAVs and setting the three-dimensional parameters in the dense urban area comprises the following steps:
and 200, acquiring parameters of the target dense urban building, and performing horizontal triangulation by taking the horizontal center of the building as a vertex to obtain a plurality of triangles.
The UAV three-dimensional deployment is decoupled into two steps of two-dimensional position determination and altitude optimization. And (3) constructing a triangular subdivision chart by taking the horizontal center of the building as a vertex to obtain a plurality of triangular results as shown in the upper left corner part of the graph in the attached figure 1, and entering the next step.
Step 210, find the position inside each triangle where the horizontal LoS angle is the largest, and place a UAV on this position.
As shown in FIG. 2, UAVs are deployed inside each triangle after triangulation of the building, defining horizontal LoS angles
Figure BDA0002634130390000031
Comprises the following steps: the sum of the angles of the UAV and the connecting lines of the three building boundaries, i.e.
Figure BDA0002634130390000032
Defining horizontal coverage as PhorcovThe probability of the user being within the horizontal LoS angle. For a UAV, its horizontal coverage rate PhorcovThe larger its horizontal coverage. And if the horizontal coverage is larger, the number of UAVs required to cover an area under the required coverage is smaller. Thus, how to make video coverage P of each UAVhorcovThe most critical is.
According to horizontal coverage rate PhorcovDefinition of (2), horizontal LoS Angle
Figure BDA0002634130390000033
The larger, PhorcovThe larger the horizontal coverage can be simply calculated as
Figure BDA0002634130390000034
In contrast, a heuristic method such as Particle Swarm Optimization (PSO) is used to obtain two-dimensional points that maximize the horizontal LoS angle.
After the position in each triangle at which the horizontal LoS angle is maximized is obtained, the next step is performed.
Step 220, calculating the coverage area and the placeable height range of each UAV under the building-free condition according to the channel state.
The method considers that the conditions covered by the user by the UAV are: the user accepts a signal-to-noise ratio SNR that is greater than a defined signal-to-noise ratio threshold y. According to the following SNR formula
Figure BDA0002634130390000041
Wherein, P is UAV transmitting power;
n is the number of UAV k service users;
K0is the unit road loss of the reference point;
PLoSand PNLoSProbability of LoS and non line of Sight (NLoS) links;
μLoSand muNLoSAdditional path loss for LoS and NLoS links;
dkiis the distance between UAV k and user i;
σ2is the noise power.
D can be deducedkiThen after the limits of UAV altitude are obtained through a series of transformations of scaling, and the UAV coverage and placeable altitude range are obtained, the next step is performed.
And step 230, setting traversal precision, traversing the height of the UAV by using a greedy strategy, and selecting the height which covers the largest number of users.
Since the building is modeled in a rectangular parallelepiped shape, the building coverage is difficult to calculate accurately, so the following model is adopted for simplification, as shown in fig. 3. The method includes the steps that the length and the width of a building are simplified, the height dimension of the building is reserved, if a user is located in an area outside a horizontal LoS angle and the height angle of the user is larger than theta, the user is located in a shadow area of the building, a communication link is an NLoS link, and the additional path loss of the NLoS link is muNLoSOtherwise, the communication link is a LoS link and the additional path loss is muLoS。
The search precision Δ h is an adjustable parameter, and the value of the search precision Δ h affects the selection of the optimal height of the UAV, and the smaller Δ h, the more accurate the selection of the optimal height is, but the too small Δ h may cause the too large search cost and affect the efficiency. To solve this problem, in each UAV altitude search, the search accuracy depends on the user density served by each UAV, and it is ensured that the number of covered users increases or decreases by one or no change for each increase of the UAV altitude by Δ h. Therefore, the optimal height cannot be omitted, and the height with the largest number of covered users is finally selected to enter the next step.
And 240, screening the UAVs according to the principle that the number of the service users is large and the number of the overlapped users is small until the required coverage rate is reached.
In order to meet the actual deployment requirement, the overlapping rate of coverage users between each UAV must be controlled within a certain range, which not only reduces the resource waste, but also reduces the interference between different UAVs. Therefore, the number of overlapping users per UAV must first be calculated. And then, sequencing all the UAVs according to the number of the covered users from large to small, sequentially selecting the UAVs according to the size of the overlapping rate, and updating the coverage rate until the coverage rate reaches the required coverage rate.
One of them needs to be noted that, if all the UAVs are traversed, the coverage rate has not reached the required coverage rate, the process goes back to step 230, adjusts the search precision Δ h, reselects the UAV altitude, and resumes the subsequent steps.
Advantageous effects
The decoupling deployment method of the UAV in the dense urban area can relieve the problems of high flow pressure and low user service quality in the dense urban area, simultaneously adopts triangulation to carry out modeling, considers the shielding effect of buildings, maximizes the coverage area of each UAV, minimizes the number of overlapped users to reduce interference, uses the least number of UAVs to complete the coverage of a certain coverage rate, and can obtain guiding results of deploying UAV base stations in the dense urban area.
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In order to clearly and clearly explain the technical steps of the present invention, all the drawings used in the description of the present invention will be briefly described below. It should be noted that the drawings described below are only exemplary words of the present invention, and those skilled in the art can still obtain other drawings in other different scenarios according to the drawings.
FIG. 1 is a dynamic process diagram of UAV three-dimensional decoupling deployment in dense urban scenes of the present invention;
FIG. 2 is a horizontal LoS angle definition diagram of the present invention;
FIG. 3 is a schematic view of the height adjustment user coverage of the present invention;
FIG. 4 is a flow chart of the UAV decoupling deployment of the present invention;
FIG. 5 is a plot of the UAV deployment average height of the present invention as a function of the building average height;
figure 6 is a plot of UAV deployment average height versus user building density ratio for the present invention.
Detailed Description
The steps and processes of the present invention are described in detail below with reference to the drawings in the present application, and it is obvious that the example described in the present application is only an example application scenario of the present invention, and other results based on the present disclosure without substantial changes are all within the protection scope of the present invention.
FIG. 1 is an exemplary scenario of the present invention illustrating a step-wise variation of the implementation of the present invention. In each step, a rectangle and a cube respectively represent a horizontal plane and a solid plane of a building, a small green dot represents a user, and a small black-edge circle represents a UAV. The whole process is completed on a background computer of a wireless coverage service provider, and each UAV is connected with a ground base station and a satellite through a return link or can be a tethered unmanned aerial vehicle so as to be accessed into a core network.
The invention uses the network scene as a UAV single-layer network, does not consider the coverage and interference of the ground base station, can adopt a frequency division multiplexing mode as the ground base station, and further improves the network capacity by planning the position. The mechanism implementation of the present invention is performed by a wireless overlay service provider backend computer.
A UAV decoupling deployment step based on dense urban area triangulation under the condition of standard coverage:
200, acquiring target dense urban building parameters, generating a simulated building scene through computer drawing, and decoupling UAV three-dimensional deployment into two steps of two-dimensional plane and height optimization. The triangular subdivision diagram is constructed with the building horizontal center as the vertex, resulting in the triangular result shown in the upper left diagram of fig. 1.
Step 210, deploying the UAV inside each triangle after triangulation of the building, wherein the specific position satisfies a condition of maximizing the LoS angle.
In the example, a particle swarm optimization method is adopted to obtain a two-dimensional point position which maximizes the horizontal LoS angle. The actual above method is not limited to PSO, but other heuristic algorithms are possible.
Will level the coverage rate PhorcovThe set of UAVs is considered a population, and each UAV is considered an individual in the population, as a fitness function. The ith UAV spatial position is represented as (x)i1,xi2,xi3) The best position (position with the largest coverage) where the history passes is denoted as Pi＝(pi1,pi2,pi3). Best position P experienced by all UAVs in the populationgAnd (4) showing. The update of the speed of each individual is based on its own historical experience and the experience of other UAVs within the population. Velocity of UAVi is Vi＝(vi1,vi2,vi3) And (4) showing. For each generation of UAV, its d-dimensional position and velocity (1 ≦ d ≦ 3) are changed according to the following equation:
vid＝w×vid+c1×rand()×(pid-xid)+c2×rand()×(pgd-xid) (3)
xid＝xid+vid (4)
wherein, c1,c2Is a constant;
and rand () generates a random number between 0-1.
According to the above ideas, the individual positions are initialized randomly, and after a certain number of iterations, the optimal UAV two-dimensional point location in each building triangle can be obtained, as shown in the upper right diagram of fig. 1.
And step 220, calculating the coverage area and the placeable height of the UAVs inside the triangulated triangles.
The distance condition that the user i is covered by the unmanned aerial vehicle k can be obtained by testing the channel condition, bringing related parameters into the channel condition by using the formula (2), performing item shifting transformation and other operations
Figure BDA0002634130390000071
Similarly, it is still based on the principle that the received SNR of the user is larger than the threshold value
L＝PLoS(θki)μLoS+PNLoS(θki)μNLoS (6)
Then by SNRkiThe following inequality can be obtained for ≧ γ:
PiK0 -1dki -αL-1≥γσ2 (7)
continuing to deduce, we get:
Figure BDA0002634130390000081
substituting the expression (6) of L into the inequality (8), and carrying out appropriate transformation to obtain:
Figure BDA0002634130390000082
after phase shifting to obtain
Figure BDA0002634130390000083
Wherein the content of the first and second substances,
Figure BDA0002634130390000084
further obtaining:
Figure BDA0002634130390000085
wherein the content of the first and second substances,
Figure BDA0002634130390000086
therefore, h can be calculated from equation (11)k≥dkisin(eM+ C), and h because the hypotenuse of the right triangle is longestk≤dkiIn combination with formula (6), it is known that:
Figure BDA0002634130390000087
in summary, the obtained UAV may be placed in a height range:
dkisin(eM+C)≤hk≤T1/α (13)
wherein the content of the first and second substances,
Figure BDA0002634130390000091
step 230, find the altitude that maximizes the number of users covered by each UAV.
And setting the height searching precision delta h of each UAV according to the distance between the two nearest users in each triangle. And with the precision as a search step length, traversing each UAV height in the UAV placeable height range, and recording the number of covered users at each height. After the traversal is completed, the height corresponding to the maximum number of covered users is selected as the optimal height of the UAV, and the figure is the lower right diagram of the attached figure 1.
At step 240, the optimal UAV three-dimensional locations within all of the building triangles have been obtained. The question that remains is how to select the appropriate number and location of UAVs. For this problem, in order to meet the actual deployment requirement, the overlapping rate of coverage users between each UAV must be controlled within a certain range, which not only reduces the resource waste, but also reduces the interference between different UAVs.
Firstly, calculating the overlapping rate of each UAV (the proportion of the number of users overlapping with other UAVs); then, sequencing all UAVs according to the number of covered users from large to small; then, sequentially selecting the UAVs according to the size of the overlapping rate, and updating the coverage rate until the coverage rate reaches the required coverage rate; and finally, obtaining the number and three-dimensional position of the UAVs required in the target scene, such as the left lower graph of the attached drawing 1.
The simulation results are shown in fig. 5 and 6.
Fig. 5 is a graph of the average UAV height as a function of the average building height, where η is the required coverage and γ is the minimum snr required for correct decoding at the user end. As can be seen from the figure, as the average building height varies, the optimal average UAV height increases and then decreases, there is a maximum, and the maximum decreases as γ increases. This is because increasing building height would result in a decrease in LoS link probability and therefore the UAV would increase to increase LoS link probability, but UAV elevation results in an increase in user-UAV distance, a decrease in UAV service radius, a decrease in coverage, and thus a maximum UAV height. While an increase in γ results in a decrease in gain obtained by an increase in UAV altitude, the maximum value decreases as γ increases.
Fig. 6 is a graph of UAV average height as a function of user building density ratio, where α represents the road loss index. As can be seen from the figure, there is also a maximum value for the UAV optimal average height under different α conditions, and the maximum value decreases as α increases. This is because the increase in building density also results in a decrease in LoS link probability and the UAV will rise to increase LoS link probability, but the UAV rising results in an increase in user-UAV distance, a decrease in UAV service radius, and a decrease in coverage, so there is a maximum for UAV optimal altitude. While an increase in α results in a decrease in gain obtained by an increase in UAV altitude, the maximum value decreases as α increases.

Claims (7)

1. An unmanned aerial vehicle base station deployment method and three-dimensional parameter setting for dense urban areas are characterized by comprising the following steps: decoupling the 3-dimensional deployment problem of the UAV from horizontal and height dimensional deployment, and firstly performing plane triangulation on a target urban scene by taking the horizontal center of a building as a vertex; then presetting a UAV in each subdivision triangle, wherein the horizontal position of the UAV is obtained by maximizing a horizontal LoS angle in a two-dimensional plane by utilizing a heuristic algorithm and is used as the optimal two-dimensional position of the UAV; then, for each UAV, calculating the coverage distance and the placeable height range of the UAV according to the distribution of surrounding users and buildings; traversing the placement height of each UAV within the range of the placeable height, simultaneously calculating the number of covered users at each height, and taking the height with the highest number of covered users as the optimal height of the UAV; and finally, sequencing all the UAVs according to the number of covered users from large to small, simultaneously calculating the number of overlapped users between two adjacent UAVs, and sequentially selecting the UAVs according to the priority principle of more covered users and less overlapped users until the required coverage rate is achieved in the target urban scene, wherein the obtained result is the least number of UAVs.
2. The triangulation method according to claim 1, wherein the target urban scene is horizontally triangulated with the horizontal center of the building as the vertex, and the process further comprises: and obtaining the building parameters of the target urban scene, and determining the boundary of the urban scene and the number of triangles obtained by subdivision.
3. The UAV two-dimensional position determination method of claim 1, wherein finding a two-dimensional position inside each subdivision triangle that maximizes the horizontal LoS angle; the process uses a heuristic algorithm to improve the search efficiency, where building boundary parameters are used, resulting in a two-dimensional position of the best UAV in each triangle.
4. The method of claim 1, wherein the coverage and placeable altitude range of the UAV are calculated according to the current channel status under the building-free condition, and if the coverage and corresponding altitude range are obtained by narrowing the coverage or changing the UAV altitude if the coverage and corresponding altitude range are not satisfied.
5. The UAV altitude determining method of claim 1, wherein when traversing the placeable altitude of the UAV, it is necessary to set an accuracy of the traversal, which affects the number of UAV service users at each altitude, and for this purpose, an accuracy setting rule is set: when the height is increased by one precision, the change of the number of the UAV service users is at most 1; the accuracy setting principle is the same for each UAV, but the accuracy setting values may be different.
6. The UAV screening method of claim 1, wherein all UAVs are screened according to coverage requirements, and the screening criteria are: the UAVs with a large number of covered users and a small number of overlapped users are preferentially selected, the proportion of the overlapped users of each UAV must be smaller than a certain threshold, and the termination condition is that the coverage requirement is met.
7. The method of claim 6, wherein if the coverage requirement is not finally met, returning to the method of claim 5, changing the traversal accuracy, and repeating the following steps; if the coverage requirement can be reached, the final UAV three-dimensional deployment result is obtained, including the three-dimensional position information of each UAV, the number of UAVs, and the coverage.
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