CN111970711A - Unmanned aerial vehicle dynamic deployment method under space boundary constraint - Google Patents
Unmanned aerial vehicle dynamic deployment method under space boundary constraint Download PDFInfo
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04B7/00—Radio transmission systems, i.e. using radiation field
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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
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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
- H04W16/26—Cell enhancers or enhancement, e.g. for tunnels, building shadow
Abstract
The invention provides an unmanned aerial vehicle dynamic deployment method under space boundary constraint, in the method, a base station of an unmanned aerial vehicle is deployed to quickly cover blind spots and hot spot areas, the covered areas are called target areas, and the initial deployment of the unmanned aerial vehicle is determined according to parameters of the target areas; in the initial state, the unmanned aerial vehicles are randomly distributed in the cylindrical area, and then the position is dynamically updated according to the random walk model so as to meet the dynamic flow demand of the target area; the method comprises the steps of establishing a large-scale time statistics performance index model, calculating throughput of a user in a period of time based on steady-state distribution of the unmanned aerial vehicles, and determining the optimal number and the optimal deployment space of the unmanned aerial vehicles according to the change relation of the throughput in the period of time with the number and the deployment height of the unmanned aerial vehicles under the environment parameters of different target areas.
Description
Technical Field
The present invention relates to the field of wireless communication technologies, and in particular, to a dynamic deployment method of an Unmanned Aerial Vehicle (UAV) under spatial boundary constraints in an Unmanned Aerial Vehicle (UAV) base station network in a 5th Generation (5G) mobile communication.
Background
Due to the high dynamic characteristics of unmanned aerial vehicles, deployment on demand is possible, and the field of wireless communication has taken unmanned aerial vehicle base stations as an important solution for temporarily covering scenes. With the continuous improvement of unmanned aerial vehicle technology, unmanned aerial vehicle base stations play an increasingly important role in military, public and civil applications. Drones may be utilized in various ways to enhance cellular communications, for example, dedicated drones may be used as airborne wireless access points or relay nodes to further improve ground communications, enable greater service coverage and flexible spatial network architecture, such UAV applications being referred to as UAV assisted cellular communications.
Despite the many benefits of drone mobility, drone communications still suffer from practical limitations, such as limited deployment areas and safety issues, limited battery power, transmission instability and discontinuities, and the like.
When the unmanned aerial vehicle base station is used for supplementing blind spots and hot spot areas, the deployment of the unmanned aerial vehicle is restricted by the space of the blind spots and the hot spot areas, specifically, the deployment position of the unmanned aerial vehicle is restricted due to the obstruction of buildings and terrain in the blind spots or the hot spot areas; the unmanned aerial vehicle transmission has the advantage of line-of-sight transmission, the signal attenuation from the base station to the service user is reduced, but when the unmanned aerial vehicle distribution area is too wide, the coverage area is larger than blind spots and hot spots needing to be covered, the line-of-sight transmission of the unmanned aerial vehicle base station can generate huge interference on ground users, and therefore the distribution area of the unmanned aerial vehicle is restrained; in order to meet the dynamic data requirements of blind spots and hot spot areas, the unmanned aerial vehicle base station has certain mobility, so that the dynamic deployment of the unmanned aerial vehicle is restricted by user distribution. In summary, the deployment range and deployment method of the unmanned aerial vehicle need to be designed based on the actual blind-complementing/heat-complementing scene.
In addition, the high dynamics of the drone base station, although making the network more flexible and adjustable, also increases the instability, discontinuity and unreliability of the transmission. Due to the change of the transmission position of the unmanned aerial vehicle, the signal intensity and the interference topology at the user position change all at the moment, and the coverage performance and the transmission rate at the user position have time-varying property, the large-scale time performance of the network needs to be modeled, and the deployment of the unmanned aerial vehicle is optimized according to the large-scale time performance of the network.
Disclosure of Invention
The invention provides a dynamic deployment method of an unmanned aerial vehicle under space boundary constraint, wherein an unmanned aerial vehicle base station is used as a supplement of a ground network to quickly cover blind spots and hot spot areas. Specifically, constrained by the spatial boundary of the target area, the base stations of the drones are deployed in a cylindrical area above the target area, and the number of drones is related to the data demand of the target area; in the initial state, the unmanned aerial vehicles are randomly distributed in the cylindrical area, then the unmanned aerial vehicles are dynamically deployed according to a certain time interval, the unmanned aerial vehicle base station can move in the vertical direction and can also move in the horizontal direction, in the vertical direction, the unmanned aerial vehicles can randomly select a speed to move upwards or downwards, in the horizontal direction, the unmanned aerial vehicles can move at 0 to 2 pi, an angle is randomly selected to move at the randomly selected speed, and when the unmanned aerial vehicles move to the boundary of the cylindrical area, the turning-back movement can occur; based on the steady-state distribution of the unmanned aerial vehicles, the large-scale time performance of a typical user is obtained, and the optimal deployment of the unmanned aerial vehicle base station is determined according to the variation relation of the large-scale time performance along with the number of the unmanned aerial vehicles and the deployment space.
The deployment method of the unmanned aerial vehicle base station group comprises the following steps:
The unmanned aerial vehicle base station is deployed in a three-dimensional cylindrical space above a target area, the height of the cylindrical area is H-H, the bottom of the cylindrical area is a circular area with the circle center of which the radius is R, wherein H is the average height of buildings in the target area, H is a height variable, the upper limit of the deployed height of the unmanned aerial vehicle is determined, O corresponds to the geometric center of the target area, and the value of R is determined by the radius value corresponding to the smallest circle covering the target area;
the number of unmanned aerial vehicle base stations should meet the requirement of user data flow in a target area, and the relational expression is as follows:
wherein the content of the first and second substances,
is the total user data demand in the target area,
the lower limit value of the number of base stations of the unmanned aerial vehicle can be periodically updated according to the requirement for the maximum data volume which can be borne by a single unmanned aerial vehicle.
Unmanned base station with period TsUpdating deployment location, TsThe configuration of (a) depends on the actual requirements; each TsAt the starting moment, the unmanned aerial vehicle base station can select an updating position according to three types of movement in the vertical direction and the horizontal direction;
for vertical movement, the drone chooses to move up or down at a movement speed v; for horizontal movement, the drone chooses (0,2 π)]Moving in any direction within the range at a moving speed v; the moving speed v is (0, v)max]The range is selected at random, and when the unmanned aerial vehicle moves to the boundary of the cylindrical area, turning back can occur.
And selecting the unmanned aerial vehicle base station closest to the user in the target area as a service base station, and obtaining the probability distribution of the transmission distance of the service station based on the unmanned aerial vehicle steady-state distribution and the closest station association strategy, wherein other base stations are interference base stations.
And based on the acquired channel parameters of the target area, the lower height bound h and the bottom radius R of the cylindrical space distributed by the unmanned aerial vehicles and the lower number bound N of the unmanned aerial vehiclesLFurther, a period of time throughput at the user is calculated by the following formula:
where n denotes the number of time slots, which represents the time length of the measurement, p(n,t)Representing the joint event probability, which is calculated as:
P(n,t)＝P{SIR1≥T,...,SIRt≥T,SIRt+1<T,...,SIRn<T} (3)
wherein the SIRiRepresenting the signal-to-interference ratio, p, of a typical user in time slot i(n,t)Representing the probability that the signal-to-interference ratio of T consecutive slots at a typical user is greater than the reception threshold T and the remaining n-T slots are less than the threshold T, SIRiIs calculated as follows:
wherein l and g are respectively large scale fading and small scale fading of the unmanned aerial vehicle network, x1Representing the serving drone and phi the drone base station set.
And step 230, determining the deployment quantity and the distribution space of the unmanned aerial vehicles according to the variation relation of the throughput along with the deployment parameters of the unmanned aerial vehicles in a period of time.
Determining the number N of the unmanned aerial vehicles with the maximum throughput in a period of time according to the change relation of the throughput in a period of time along with the number of the unmanned aerial vehicles0And the number of the finally deployed unmanned aerial vehicles is max { NL,N0}；
And determining the height H with the maximum throughput for a period of time according to the variation relation of the throughput for a period of time to the upper bound H of the deployment height of the unmanned aerial vehicle, and finally determining the distribution space of the unmanned aerial vehicle base station.
Advantageous effects
The invention provides a dynamic deployment method of an unmanned aerial vehicle base station considering space boundary constraint effect aiming at the condition that the unmanned aerial vehicle base station is used as ground supplement. Utilize unmanned aerial vehicle basic station to carry out dynamic cover, can realize shunting for ground basic station, provide interim service for ground user.
The position and the range of an unmanned aerial vehicle deployment area are determined according to the position and the size of a target area, the number of unmanned aerial vehicles is determined according to the data demand, and the unmanned aerial vehicle base station deployment is guided to be completed according to the condition of the target area in an actual network; meanwhile, the space boundary constraint of the target area is considered, namely the space boundary constraint is influenced by the terrain, shelters (buildings and trees) and the like of the target area, the deployment height range of the unmanned aerial vehicle is limited, and the buildings have important influence on the unmanned aerial vehicle deployment space limitation, so that the invention points out that the average height of the buildings in the target area needs to be obtained to determine the lower limit of the deployment height of the unmanned aerial vehicle, and the design is favorable for improving the communication quality of the unmanned aerial vehicle network.
A three-dimensional mobile model is introduced, and an unmanned aerial vehicle position updating method is provided, so that the dynamic flow demand of a target area is met, and the network performance of a user is improved.
Due to the fact that the instantaneous performance of the user has time-varying property due to the change of the transmission distance and the change of the link quality caused by the high dynamic characteristic of the unmanned aerial vehicle, and the network communication quality can not be accurately represented, the method introduces large-scale time performance indexes, calculates the throughput of the user in a period of time, and effectively solves the problem that the instantaneous performance indexes are invalid. And guiding the optimal deployment of the unmanned aerial vehicle base station in the actual network according to the variation relation of the throughput along with the unmanned aerial vehicle deployment parameters in a period of time.
Drawings
Fig. 1 is a schematic diagram of a network model of drone base station deployment under the spatial constraints of the present invention;
FIG. 2 is a flow chart of an algorithm implementation of the present invention;
FIG. 3 is a diagram of a network steady-state distribution function of a three-dimensional mobile unmanned aerial vehicle;
FIG. 4 is a graph of throughput as a function of deployment radius over time;
Detailed Description
The invention considers the boundary constraint of a coverage area and provides a three-dimensional dynamic deployment method of an unmanned aerial vehicle base station, wherein an unmanned aerial vehicle network model is shown as an attached figure 1, the limitation of building distribution on the deployment position of the unmanned aerial vehicle is considered, the unmanned aerial vehicle is deployed in a cylindrical area, the lower plane height of the cylinder corresponds to the average height of the building, and the bottom surface of the cylinder is a circular area covering a target area; and the unmanned aerial vehicle base station moves in the cylindrical area by a three-dimensional random walk model to realize the dynamic coverage of the target area. Due to the fact that the mobility of the unmanned aerial vehicle causes time-varying of transmission distance and transmission links, throughput is introduced for a period of time, network communication quality of the unmanned aerial vehicle is obtained by measuring large-scale time performance of a user, and optimal deployment of an unmanned aerial vehicle base station is determined according to the variation relation of the throughput along with deployment parameters of the unmanned aerial vehicle.
The algorithm flow of this case is shown in fig. 2, and the specific implementation steps are as follows:
step 300, acquiring target area parameters: geographic location, area size, average height of building, user traffic demand. The unmanned aerial vehicle base station is deployed in a three-dimensional cylindrical space on a target area, the height of the cylindrical area is H-H, the bottom of the cylindrical area is a circle area with the circle center of which is O and the radius of which is R, wherein H is the average height of buildings in the target area, H is a height variable, the upper limit of the deployed height of the unmanned aerial vehicle is determined, O corresponds to the geometric center of the target area, and the value of R is determined by the radius value corresponding to the minimum circle covering the target area. The minimum number of drones is determined by the ratio of the total data demand in the area and the amount of data that can be carried by a single drone.
And 310, dynamically deploying the unmanned aerial vehicle base station according to the three-dimensional random walk model.
In the initial state, the unmanned aerial vehicles are randomly distributed in the cylindrical area, and in order to realize the dynamic coverage of the target area, the positions of the unmanned aerial vehicles are dynamically updated according to a random walk model.
Unmanned base station with period TsUpdating deployment location, TsThe configuration of (a) depends on the actual requirements; each TsAt the starting moment, the unmanned aerial vehicle base station can select an updating position according to three types of movement in the vertical direction and the horizontal direction;
for vertical movement, the drone chooses to move up or down at a movement speed v; for horizontal movement, the drone chooses (0,2 π)]Within the range ofIn either direction, moving at a moving speed v; the moving speed v is (0, v)max]The range is selected at random, and when the unmanned aerial vehicle moves to the boundary of the cylindrical area, turning back can occur.
For different implementation scenes, the unmanned aerial vehicle has different position updating periods, different motion directions and different speed selections, specifically, when the unmanned aerial vehicle selects vertical motion, the building density of the current position at the projection position of the target area is judged, if the building density is high and the average height is high, the unmanned aerial vehicle moves upwards, and if not, the direction is optional; when the unmanned aerial vehicle moves in the horizontal direction, the movement direction can be selected according to user distribution, so that the unmanned aerial vehicle is more intensively deployed in a user gathering area; period TsAnd the velocity v may be configured depending on the variation of the user distribution, when the user distribution in the target area does not vary much, TsAnd v may take a large value, T when the distribution of users in the target area is largely changedsAnd v may take a smaller value;
and step 320, measuring the large-scale H (H > -H) time performance of the user and reporting to the unmanned aerial vehicle base station.
Within a period of time, based on a certain unmanned aerial vehicle height upper limit and the number N (N) of unmanned aerial vehicles>＝NL) Configuring an unmanned aerial vehicle base station, optionally, informing a user to measure the throughput for a period of time and reporting a base station measurement result by the base station through a periodic measurement signal; or the ground is deployed and receives a plurality of devices, the throughput measurement is carried out for a period of time, and the measurement result is reported to the unmanned aerial vehicle base station periodically.
And step 330, determining the deployment quantity and the distribution space of the unmanned aerial vehicles according to the variation relation of the throughput along with the deployment parameters of the unmanned aerial vehicles in a period of time.
And determining the optimal deployment height and deployment quantity by the unmanned aerial vehicle according to the measurement result of the user or the receiver.
The simulation results are shown in fig. 3 and 4.
Fig. 3 shows a probability density function of the distance from the drone to the user when the drone reaches a steady distribution after moving in a random walk model in the three-dimensional cylindrical domain. As shown, different deployment radii and different upper altitude limits affect the steady state distribution of the drones, in particular, larger deployment radii and higher upper altitude limits result in larger transmission distances.
Figure 4 shows throughput as a function of deployment radius over time. The trend of the curves in fig. 4 is a result of the combination of various factors. As the deployment radius increases, the average distance of the serving drone and the interfering drone from the user is greater. Initially, the signal attenuation at the user is less than the attenuation of the interference, which results in an increase in the signal-to-interference ratio and thus an increase in throughput over time. As the deployment radius continues to increase, the attenuation of the signal will gradually become greater than the attenuation of the interference, which will result in a decrease in the signal-to-interference ratio, resulting in a period of throughput. In actual deployment, the deployment radius is determined by the size of the target area, so that the deployment height of the target area can be determined by the throughput value in a period of time when the deployment radius is determined. Further, n represents the length of time taken to measure throughput over a period of time, and the larger the value of n, the longer the length of time taken.
Claims (5)
1. A method for dynamically deploying unmanned aerial vehicles under space boundary constraint is characterized by comprising the steps of deploying an unmanned aerial vehicle base station to quickly cover blind spots and hot spot areas, wherein the covered areas are called target areas, and determining the initial deployment of the unmanned aerial vehicles according to parameters of the target areas; in an initial state, the unmanned aerial vehicles are randomly distributed in a deployment area, and then the positions are dynamically updated according to a random walk model so as to meet the dynamic flow demand of a target area; the method comprises the steps of establishing a large-scale time statistics performance index model, calculating throughput of a user in a period of time based on steady-state distribution of the unmanned aerial vehicles, and determining the optimal number and the optimal deployment space of the unmanned aerial vehicles according to the change relation of the throughput in the period of time with the number and the deployment height of the unmanned aerial vehicles under the environment parameters of different target areas.
2. The method according to claim 1, wherein the initial deployment position and the movement space of the unmanned aerial vehicles and the number of the unmanned aerial vehicles are determined according to the geographical position, the geographical environment and the user traffic demand of the target area, and specifically:
the unmanned aerial vehicle base station is deployed in a three-dimensional cylindrical space above a target area, the height of the cylindrical area is H-H, the bottom of the cylindrical area is a circle area with the circle center of which is O and the radius of which is R, wherein H is the average height of buildings in the target area, H is a height variable, the upper limit of the deployed height of the unmanned aerial vehicle is determined, O corresponds to the geometric center of the target area, and the value of R is determined by the radius value corresponding to the minimum circle covering the target area;
the number of unmanned aerial vehicle base stations should meet the requirement of user data flow in a target area, and the relational expression is as follows:
wherein the content of the first and second substances,
the total user data demand in the target area,
the lower limit value of the number of base stations of the unmanned aerial vehicle can be periodically updated according to the requirement for the maximum data volume which can be borne by a single unmanned aerial vehicle.
3. The method according to claim 1, wherein in the initial state, the drones are randomly distributed in the cylindrical area, and in order to achieve dynamic coverage of the target area, the position of the drones is dynamically updated according to a random walk model, which specifically includes:
unmanned base station with period TSUpdating deployment location, TSThe configuration of (a) depends on the actual requirements; each TSAt the starting moment, the unmanned aerial vehicle base station can move in the vertical direction and move in the horizontal direction to select an updating position;
for vertical movement, the drone chooses to move up or down at a movement speed v; for horizontal movement, the drone chooses (0,2 π)]Moving in any direction within the range at a moving speed v; the moving speed v is (0, v)max]The range is randomly selected, and when the unmanned aerial vehicle moves to the boundary of the cylindrical area, rebound can occur.
4. The method of claim 1, wherein a user in the target area selects a nearest drone base station as a serving base station, other base stations are interfering base stations, a user in the target area is randomly selected as a typical user, a time-period throughput calculation model is established to capture large-scale time performance at the typical user, and the time-period throughput calculation formula is:
where n denotes the number of time slots, which represents the time length of the measurement, p(n,t)Representing the joint event probability, which is calculated as:
P(n,t)＝P{SIR1≥T,...,SIRt≥T,SIRt+1<T,...,SIRn<T}
wherein the SIRiRepresenting the signal-to-interference ratio, p, of a typical user in time slot i(n,t)Representing the probability that the signal-to-interference ratio for T consecutive time slots at a typical user is greater than the receive threshold T and the remaining n-T time slots are less than the threshold T.
5. The method of claim 1 or 4, wherein the method is based on the obtained channel parameters of the target area, the lower bound h of the height of the cylindrical space in which the drones are distributed, the radius R of the bottom surface, and the lower bound N of the number of dronesLAnd calculating the throughput of a typical user within a period of time according to the steady-state distribution of the unmanned aerial vehicle, and determining the optimal deployment of the unmanned aerial vehicle according to the variation relation of the value along with the deployment parameters, wherein the steady-state distribution of the unmanned aerial vehicle specifically comprises the following steps:
determining the number N of the unmanned aerial vehicles with the maximum throughput in a period of time according to the change relation of the throughput in a period of time along with the number of the unmanned aerial vehicles0And the number of the finally deployed unmanned aerial vehicles is max { NL,N0}；
And determining the height H with the maximum throughput for a period of time according to the variation relation of the throughput for a period of time to the upper bound H of the deployment height of the unmanned aerial vehicle, and finally determining the distribution space of the unmanned aerial vehicle base station.
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