CN115696352B - 6G unmanned aerial vehicle base station site planning method and system based on circle coverage power optimization - Google Patents
6G unmanned aerial vehicle base station site planning method and system based on circle coverage power optimization Download PDFInfo
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Abstract
The invention discloses a 6G unmanned aerial vehicle base station site planning method and system based on circle coverage power optimization, wherein the method comprises the following steps: setting network bandwidth, carrier frequency and UAV-BS number which can be controlled by a flight control center at the same time according to the planning; calculating the coverage radius of each UAV-BS in the current scene according to the relation between the number of small circles and the radius under the circle coverage strategy fitted by the circle coverage model; calculating the transmitting power of a single UAV-BS under a circle coverage model to find the minimum UAV-BS deployment number meeting the power limit; solving the minimum value of the transmitting power of the unmanned aerial vehicle communication network system and the optimal solution of the UAV-BS deployment number, carrying out rounding operation on the UAV-BS optimal deployment number, recalculating the transmitting power of the system, comparing the minimum value, updating and outputting the minimum number of unmanned aerial vehicle base stations and the small circle position under the corresponding circle coverage strategy as a station address plan; the invention ensures that users can access the network at any position in the target area, and realizes the ubiquitous network requirement of 6G communication.
Description
Technical Field
The invention belongs to the technical field of planning of unmanned aerial vehicle base stations of space-based core communication equipment in a 6G space-earth integrated network scene, and particularly relates to an unmanned aerial vehicle base station site planning method for modeling geometrical circle coverage to ensure full coverage of ground users.
Background
The communication characteristics of mobile communication are greatly changed from the initial development to the fifth generation communication network (The Fifth Generation Mobile Communication, 5G), the 1G generation can only make a call, the signal quality is poor, the 2G generation is called a word generation to a 3G generation, the data transmission is improved to a degree from 4G to a video generation, and then the 5G opening communication technology and the Internet technology are fused, so that a plurality of novel wireless access technologies and evolution technologies are developed, but a great number of shortages still exist for increasingly abundant demands of people. The 11 months 2019 establishes a 6G research and development expert work group in Beijing, and marks the formal beginning of the 6G key technology research and development work in China.
Compared with the 5G age, the 6G is changed in the whole architecture of the communication network, so that various application scenes and communication requirements appearing in the future are met. Currently, base stations in 5G networks are mostly deployed on land, and such static, single-dimensional communication networks may not meet the communication requirements of users when facing special communication scenarios or sudden communication network paralysis. The 6G communication network is an unprecedented full-dimensional full-coverage ultra-flexible compact network, and combines a traditional ground network, an aerial network, a satellite constellation network and an underwater network to realize the global coverage of the air, the ground and the sea. In particular, the variety and number of air vehicles, such as hot air balloons, air craft, unmanned aerial vehicles (Unmanned aerial vehicle, UAV), and the like, which are highly mobile and convenient to deploy, are increasing, and the establishment of a low-altitude network of air flight base stations as communication nodes for supplementing a static network architecture will play an important role in the 6G era.
Compared with other communication, the unmanned aerial vehicle communication network has the advantages of strong controllability, high flexibility and the like, can be used for emergency communication scenes such as fire detection, emergency rescue and the like, and can also provide effective communication services for high-density business scenes such as important conferences, large-scale events and the like. Meanwhile, in recent years, unmanned aerial vehicle technology is also rapidly developed, and the unmanned aerial vehicle communication is changed from the military field to the public civil field with great improvement in the aspects of manufacturing cost, operation controllability and body size, and is widely applied to the aspects of urban traffic, water conservancy management, battlefield reconnaissance, forestry management and the like. Therefore, unmanned aerial vehicle communication has huge application market and development potential.
Unmanned aerial vehicle communication development is not separated from research on unmanned aerial vehicle communication system models and performance aspects thereof. In the field of communications, unmanned aerial vehicles can either be used as aerial users or can be equipped with aerial base stations as relays or base stations. The unmanned aerial vehicle is used as an aerial user in the Internet of things on a large scale, and collects sensing data and the like from ground equipment; the unmanned aerial vehicle-mounted base station is used as a relay in an ultra-dense scene, and can strengthen signals of mobile users and provide long-distance communication; the UAV-BS has more data volume and emergency communication direction for offloading the ground network. Unmanned aerial vehicle communication becomes low-altitude network composition preferred in the aspect of emergency communication by virtue of the advantages of convenience and less environmental influence. But simultaneously, the unmanned aerial vehicle has the characteristics of small volume, low battery capacity and limited duration, and the unmanned aerial vehicle network is also provided with the problems of limited communication service time and low energy efficiency. In addition, unmanned aerial vehicles have limited fly heights and limited payload capacity, which limits UAV-BS coverage. Therefore, reasonable position deployment and site planning technical research by comprehensively considering limiting factors of unmanned aerial vehicles and communication requirements of different scenes has profound influence on 6G communication technical development.
The UAV-BS site planning problem is studied to improve the effectiveness and coverage performance of the unmanned aerial vehicle communication network as much as possible through position deployment planning. On the one hand, the unmanned aerial vehicle flies or hovers to influence the channel, and when the unmanned aerial vehicle approaches the ground, the path loss of the signal can be reduced, but the indirect signal can be increased to aggravate multipath effect and small-scale fading. On the other hand, the limited power of the airborne base station leads to limited coverage of the UAV-BS, the problem of interference is caused by too many unmanned aerial vehicles deployed, and the user requirements cannot be met if the number of unmanned aerial vehicles is too small.
At present, related technologies are still to be developed, most site planning technologies are still aimed at a ground network, base stations are deployed on the ground network, the coverage area is limited, and the available communication service capacity can not meet the dense service request brought by a 6G heaven-earth integrated architecture. Meanwhile, the current site planning also has the problems of high coverage area repetition rate of the base station, serious interference and serious power loss, and cannot achieve green energy conservation well. In addition, current site planning is mostly based on a long-term target area geographic traffic mode, the redeployment flexibility is poor, and the ubiquitous connection requirement of the 6G network cannot be guaranteed.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a 6G UAV-BS site planning method based on circle coverage power optimization, which utilizes a circle coverage model to carry out unmanned aerial vehicle site planning so as to ensure that a space-based wireless network can realize full coverage of ground users, utilizes a least square method to carry out fitting circle coverage result to put forward a system transmitting power function as a site planning performance index, and finally utilizes Newton iteration to solve a planning scheme which meets coverage constraint and comprises optimal UAV-BS deployment number, site information and minimum system power.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: A6G unmanned aerial vehicle base station site planning method based on circle coverage power optimization comprises the following steps:
s1, measuring and calculating the size of a target area according to planning requirements, setting network bandwidth, carrier frequency and the number m of UAV-BS (unmanned aerial vehicle-base station) which can be controlled by a flight control center at the same time according to service requirements, and carrying out channel modeling and network energy consumption modeling between the UAV-BS and ground users to obtain a circle coverage model;
s2, according to a circle coverage model, fitting the relation between the number of small circles and the radius under a circle coverage strategy by using a bivariate power function, and calculating the coverage radius of each UAV-BS under the current scene;
s3, calculating the transmitting power of the single UAV-BS under the circle coverage model to find the minimum UAV-BS deployment number meeting the power limit;
s4, setting Newton iteration initial values as the minimum UAV-BS deployment number and the system error eta, carrying out Newton iteration calculation by adopting a Lagrangian function, and judging whether Newton iteration calculation results meet |n or not i+1 -n i If the number is smaller than eta, stopping iteration and outputting the optimal deployment number n of the UAV-BS opt ＝n i+1 Otherwise, returning to continue Newton iterative computation;
s5, rounding the optimal deployment number of the UAV-BS obtained in the S4, recalculating the system transmitting power, comparing the system transmitting power with the minimum value, updating and outputting the minimum number of the unmanned aerial vehicle base stations and the small circle positions under the corresponding circle coverage strategy as a station address plan.
In S1, channel modeling and network energy consumption modeling are carried out between an unmanned aerial vehicle base station and a ground user, and a channel model between an UAV-BS and the ground user is as follows:
PL(d,f)＝PL Fs (f)+10αlg(d)+ξ
wherein f is carrier frequency, c is light speed, d is signal transmission distance, alpha is attenuation index, alpha is more than or equal to 2, xi is shadow fading term, obeying mean value is 0, variance is sigma 2 Is law using free space propagation at a first term above and a f-dependent reference distance of 1mThe second term is calculated as the logarithmic relationship of d to path loss,where r is the horizontal distance between the projection of the UAV-BS on the ground and the target user, and H is the unmanned aerial vehicle flight height.
In the circular coverage model described in S1, the channel multiplexing is performed between the design center UVA-BS and other UVA-BSs by using a frequency division multiple access method, the communication link multiplexing between the UAV-BS and the user uses a time division multiple access method to involve channels, site planning is performed considering n UVA-BSs, and the system communication energy consumption is the sum of the total transmission power of the UVA-BS to the ground and the communication power between the UVA-BSs, that is
P ugi (d) Representing the transmit power, P, required by the ith UAV-BS when full coverage of a ground user with n UAV-BSs uu And allocating power to channels between the central UVA-BS and other UVA-BSs, wherein d is a signal transmission distance.
According to a channel model between the UAV-BS and the ground user, system communication energy consumption and minimum transmitting power required by each unmanned aerial vehicle for service of the ground user under full coverage, the transmitting power of the unmanned aerial vehicle communication system under the full coverage condition is ensured:
pn is noise power, gamma is SNR of ground user, d n To the maximum distance of each unmanned aerial vehicle connection when n unmanned aerial vehicles are deployed, P uu And distributing power for channels between the central UVA-BS and other UVA-BSs, wherein n is the number of unmanned aerial vehicles.
S2, fitting the number M and the radius r of small circles under a circle coverage strategy by using a bivariate power function according to a circle coverage model 0 In calculating the coverage radius of each UAV-BS in the current scenario,
considering UAV-BS site planning as a problem that small circles cover large circles, placing equal circles with fixed sizes in a given circular area to fully cover, wherein the radius of each small circle is reduced along with the increase of the number of the circles, and the fitting function of the relation between the number of the small circles and the radius of the small circles under a circle coverage strategy is as follows:
r 0 (n)＝ax -b +c
wherein a, b, c are constants, and under the condition that the confidence coefficient is 95%, the confidence intervals of a, b, c are { (1.725,1.851); (-0.8055, -0.7105); (0.06404,0.1085), fetch (1.788,0.758,0.08626); deploying n unmanned aerial vehiclesThe furthest distance that every unmanned aerial vehicle can connect is:h is the flight height of the unmanned aerial vehicle;
the projected circle covered by each UAV-BS has a maximum radius of:
r(n)＝R c r 0 (n)
R c is the radius of the great circle.
S3, recalculating the transmitting power of the unmanned aerial vehicle communication system under the full coverage condition according to the UAV-BS coverage radius based on the circle coverage model;
the minimum communication energy consumption optimization problem required when the UAV-BS is fully covered on the ground is as follows:
P1：n opt ＝arg min{P sum (n)}
s.t.
0＜P ug ＜P max
S c (n)-S≥0
n≤N
wherein P is max Representing the maximum power that UAV-BS can transmit for communicating with ground users, S c And (N) planning to deploy areas which can be covered by N unmanned aerial vehicles, wherein S is the total area of a target area, the constraint represents that the UAV-BS which is deployed within N under the condition that the transmission power of each unmanned aerial vehicle is limited realizes the full coverage of the target area, and the minimum UAV-BS deployment number which meets the power limit is searched by combining the recalculated UAV communication system transmission power and the optimization problem to calculate the transmission power of a single UAV-BS under a round coverage model.
And S5, solving the obtained optimal number for Newton iteration, simultaneously rounding upwards and downwards, calculating the system transmitting power required by covering the target area under the corresponding circle coverage strategy, comparing the calculation results of the optimal number and the system transmitting power, and selecting a UAV-BS station address plan corresponding to a smaller value to obtain an optimal integer solution of the UAV-BS deployment number, the minimum UAV communication network system transmitting power and the UAV-BS position under the corresponding circle coverage strategy.
On the other hand, the invention provides a 6G unmanned aerial vehicle base station site planning system based on circle coverage power optimization, which comprises a model construction module, a coverage radius calculation module of a UAV-BS, a minimum UAV-BS deployment number calculation module and an optimization solving module;
the model construction module is used for measuring and calculating the size of a target area according to planning requirements, setting network bandwidth, carrier frequency and the number m of UAV-BS which can be controlled by the flight control center at the same time according to service requirements, and carrying out channel modeling and network energy consumption modeling between the UAV-BS and ground users to obtain a circular coverage model;
the coverage radius calculation module of the UAV-BS is used for calculating the coverage radius of each UAV-BS in the current scene by fitting the relation between the number of small circles under the circle coverage strategy and the radius by using a two-term power function according to the circle coverage model;
the minimum UAV-BS deployment number calculation module is used for calculating the transmitting power of the single UAV-BS under the round coverage model to find the minimum UAV-BS deployment number meeting the power limit;
the optimization solving module is used for setting Newton iteration initial values as the minimum UAV-BS deployment number and system errors, carrying out Newton iteration calculation by adopting Lagrangian functions, and judging whether Newton iteration calculation results meet |n or not i+1 -n i If the number is smaller than eta, stopping iteration and outputting the optimal deployment number n of the UAV-BS opt ＝n i+1 Otherwise, returning to continue Newton iterative computation; and (3) rounding the optimal deployment number of the UAV-BS, recalculating the system transmitting power, comparing the system transmitting power to obtain a minimum value, updating and outputting the minimum number of the unmanned aerial vehicle base stations and the small circle position under the corresponding circle coverage strategy as a station address plan.
The invention also provides computer equipment, which comprises a processor and a memory, wherein the memory is used for storing a computer executable program, the processor reads the computer executable program from the memory and executes the computer executable program, and the method for planning the base station site of the 6G unmanned aerial vehicle based on the circular coverage power optimization can be realized when the processor executes the computer executable program.
Meanwhile, a computer readable storage medium is provided, and a computer program is stored in the computer readable storage medium, and when the computer program is executed by a processor, the method for planning the base station site of the 6G unmanned aerial vehicle based on the circle coverage power optimization can be realized.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention takes full coverage as a basic requirement, can ensure that users at any position in a target area can access a network, and realizes the ubiquitous network connection requirement advocated by 6G communication; the full coverage requirement can be realized by using the round coverage model, so that the coverage repeated area can be reduced to the greatest extent, and the communication interference between the UAV-BS and the base station is reduced; the optimal deployment number of unmanned aerial vehicles and the minimum system transmitting power are searched from the perspective of the transmitting power of the unmanned aerial vehicle communication network system, and an optimal station planning scheme is given by comparing the minimum system transmitting power with a round coverage model, so that green energy-saving communication can be realized; the discrete circle coverage model is subjected to curve fitting and then solved by considering the calculation capability of the flight control center, so that the calculation complexity is effectively reduced, the calculation resources of the flight control center are saved, the planning requirement is taken as input, the service requirements of a plurality of different scenes can be met, and the method has good universality.
Drawings
Fig. 1 is a schematic view of a scenario of the present invention.
Fig. 2 is a schematic view of circle coverage.
FIG. 3 is a flowchart illustrating steps performed in the method of planning according to the present invention.
FIG. 4 is a schematic diagram of example planning results using a circle coverage power optimization based 6G UAV-BS site planning method.
Fig. 5 is an illustration of the effect of drone flight altitude on system transmit power.
Fig. 6 is an illustration of the effect of the decay index on the system transmit power.
FIG. 7 is an illustration of the effect of target zone radius on deployment scenario.
Fig. 8 is a comparison of the performance of the present invention with a conventional site planning method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clear and clear, the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
A6G unmanned aerial vehicle base station site planning method based on circle coverage power optimization comprises the following steps:
step 1: setting basic parameters of network
The purpose of site planning is to realize coverage of a target area by reasonably planning the number of base stations and position information and simultaneously meet the service requirements of clients. Before site planning is carried out, the invention also needs to measure and calculate the size of the target area according to the planning requirement for modeling, and sets the network bandwidth, the carrier frequency and the UAV-BS number which can be controlled by the flight control center at the same time according to the service requirement. Fig. 1 shows a site planning scenario.
And according to the parameters, carrying out channel modeling and network energy consumption modeling between an unmanned aerial vehicle base station (UAV-BS) and ground users.
The UAV-BS is subject to multipath propagation and signal fading during communication with ground users, i.e., UAV-BS's ground-to-ground communication links include non-line-of-sight communication links, where the propagation of signals in free space is considered to satisfy a shadow fading model. The channel model between the UAV-BS and the ground users can be expressed as:
PL(d,f)＝PL Fs (f)+10αlg(d)+ξ (1)
wherein f is carrier frequency, c is light speed, d is signal transmission distance, alpha is attenuation index, alpha is more than or equal to 2, xi is shadow fading term, obeying mean value is 0, variance is sigma 2 Is a normal distribution of (c). Fris's law using free space propagation at a first term above and a f-dependent reference distance of 1mThe calculation is available. The second term gives the logarithmic relation of d and path loss, +.>Where r is the horizontal distance between the projection of the UAV-BS on the ground and the target user.
Meanwhile, the unmanned aerial vehicle communication network comprises two communication links,communication links of the central UVA-BS with other UVA-BSs and communication links of the UVA-BS to ground. In the invention, a frequency division multiple access method is used for multiplexing channels between a design center UVA-BS and other UVA-BSs so as to avoid serious co-channel interference, and a time division multiple access method is used for multiplexing communication links between the UAV-BS and users so as to relate to the channels. Site planning is carried out by considering n UVA-BSs, and the system communication energy consumption is the sum P of the total transmission power of the UVA-BSs to the ground and the communication power between the UVA-BSs sum I.e.
P ugi (d) The transmit power required by the ith UAV-BS when the ground user is fully covered with n UAV-BSs is represented. P (P) uu The channels between the central UVA-BS and other UVA-BSs are allocated power. Assuming that all UAV-BS have no difference rotor unmanned aerial vehicle and are provided with the same type of small base station, the UAV-BS has the same covering capacity, and the transmitting power required by the signal propagation distance d is P ug (d)。
From the purpose of site planning, UAV-BS deployment needs to ensure that the received signal-to-noise ratio (Signal Noise Ration, SNR) of users at any location within the target area is greater than the SNR threshold γ th I.e. unmanned deployment must meet user requirements to achieve full coverage. The SNR for the terrestrial users is:
γ＝P ug (d)-Pn-PL(d) (3)
where Pn is the noise power.
The minimum transmitting power required by each unmanned aerial vehicle for serving the ground user under full coverage is P ug (d n )
d n For the maximum distance each drone can connect when deploying n drones. The joint formulas (1), (2) and (4) can ensure that the transmitting power of the unmanned aerial vehicle communication system under the full-coverage condition is as follows:
step 2: acquiring coverage radius of each UAV-BS in current scene
According to the circle coverage model, the coverage radius of each UAV-BS in the current scene is calculated by fitting the relation between the number of small circles and the radius under the circle coverage strategy through a bivariate power function.
According to the transmitting power of the unmanned aerial vehicle communication system under the full coverage guaranteeing condition, namely according to the formula (5), the transmitting power of the system is influenced by the signal-to-noise ratio threshold, the communication distance between the UAV-BS and the users and the number of the UAV-BS.
The UAV-BS onboard omni-directional antenna radiation coverage area to the ground can be modeled as a circle, then the coverage problem can be seen as a class of geometric problems, similar to the disk placement problem in the location problem (Circle Packing Problem, CPP). CPP problems fall into two categories, a round packaging problem, where a number of circles are packaged in a container, each circle having a maximum radius (each circle need not be identical). Another is the circle coverage problem, i.e. how large the area of a container that can be covered by a given circle can be. The shape of the container may be "simple" circles, squares, rectangles or consist of a combination of lines and arcs. Henry Friedman has consolidated the best results of the small circle coverage large circle problem from 1983 to 2018. In order to achieve full coverage of the UAV-BS to ground users, the invention treats UAV-BS site planning as a problem of small circle coverage large circle. I.e. a given circular area is placed with full coverage of equal circles of fixed size.
Figure 2 shows the optimal placement of 5 equal circles within a larger circle. Further, assuming that the radius of the large circle is Rc, the following table 1 is satisfied between the radius of the small circle and the number required to completely cover the large circle. As can be seen from table 1, the radius of each circle decreases as the number of circles increases. There is a specific small circle placement strategy for each value of M, which is the number of small circles. It is difficult to find a general placement strategy that is optimal for any M, and a packaging strategy needs to be provided for each value of M.
TABLE 1 Small circle halfDiameter r 0 Relationship with the number of small circles
In order to save the computing resources of ACC and make the scheme more universal, the invention utilizes nonlinear least square to perform curve fitting on the data, adopts a two-term power function to approach, and gives the following fitting function of the relationship between the number of small circles and the radius of the small circles under a circle coverage strategy:
r 0 (n)＝ax -b +c
wherein a, b, c are constants, and under the condition that the confidence coefficient is 95%, the confidence intervals of a, b, c are { (1.725,1.851); (-0.8055, -0.7105); (0.06404,0.1085), fetch (1.788,0.758,0.08626). Then n unmanned aerial vehicles are deployed, and the furthest distance that each unmanned aerial vehicle can connect is:
the projected circle covered by each UAV-BS has a maximum radius of:
r(n)＝R c r 0 (n) (6)
step 3: and calculating the transmitting power of the single UAV-BS under the circle coverage model, and calculating the minimum UAV-BS deployment number meeting the power limit.
Based on the model in the step 2, recalculating the transmitting power of the unmanned aerial vehicle communication system under the full coverage condition according to the obtained UAV-BS coverage radius to ensure that the transmitting power is
The minimum communication energy consumption optimization problem required when considering the full coverage of the ground by the UAV-BS can be expressed as:
P1：n opt ＝arg min{P sum (n)}
s.t.
0＜P ug ＜P max (9)
S c (n)-S≥0
n≤N
wherein P is max Representing the maximum power that the UAV-BS can transmit to communicate with the ground user. S is S c And (n) planning to deploy the coverage area of n unmanned aerial vehicles, wherein S is the total area of the target area. The constraint represents deployment of up to N UAV-BSs to achieve full coverage of the target area with limited transmit power per drone.
Step 4: and solving the minimum transmitting power value of the unmanned aerial vehicle communication network system and the optimal solution of the UAV-BS deployment number by utilizing Newton iteration.
The minimum system transmitting power optimization problem obtained in the step 3 is a convex problem, and the invention utilizes the Lagrange multiplier method to convert the problem into an unconstrained optimization problem so as to solve a solution meeting inequality constraint. Constructing a Lagrangian function of the problem described in step 3 as follows:
wherein k is 1 ,k 2 ,k 3 ,k 4 Is a non-negative lagrangian factor.
The (Karush-Kuhn-Tucker, KKT) condition in the optimization theory is satisfied for the above formula with a locally optimal solution. I.e.
Solving the above by Newton iteration, and marking the ith iteration value as n i The i+1 iteration value is
L(n i )＝(A-k 1 +k 2 )D(n i )-2k 3 r 0 (n i )r 0 '(n i )+P uu +k 4
Wherein,
then n conforming to the problem P1 can be obtained by solving the above formula, and the minimum system total transmitting power required by covering the target area by adopting the base station site planning strategy provided by the invention can be obtained by taking the formula (9).
Step 5: and updating the UAV-BS position under the UAV-BS deployment number optimal integer solution, the minimum UAV communication network system transmitting power and the corresponding circle coverage strategy.
And 4, the optimal solution obtained in the step is not necessarily an integer solution, the number of UAV-BS is considered to be an integer value, and for the obtained optimal number, the system transmitting power required by covering the target area under the corresponding circle coverage strategy is simultaneously rounded up and down, the calculation results of the two are compared, the UAV-BS station address planning corresponding to a smaller value is selected, and the UAV-BS deployment number optimal integer solution, the minimum UAV communication network system transmitting power and the UAV-BS position under the corresponding circle coverage strategy are obtained.
The 6G UAV-BS site planning method based on circle coverage power optimization is applicable to a 6G space-earth integrated wireless network scene by way of example, and as shown in figure 1, the scene comprises: the unmanned aerial vehicle comprises a coverage target area without any base station, a flight control center with a certain aircraft control and basic calculation capability, and a plurality of unmanned aerial vehicles carrying air base stations.
A method for 6G UAV-BS site planning based on circle coverage power optimization, as shown in fig. 3, the method comprising the steps of:
and step 1, measuring and calculating the size of a target area according to planning requirements for modeling, setting network bandwidth and carrier frequency according to service requirements, and controlling the number of UAV-BS (unmanned aerial vehicle-base station) simultaneously by a flight control center.
And 2, according to the circle coverage model, fitting the relation between the number of small circles and the radius under the circle coverage strategy by using a bivariate power function, and calculating the coverage radius of each UAV-BS under the current scene.
And 3, calculating the transmitting power of the single UAV-BS under the circle coverage model to find the minimum UAV-BS deployment number meeting the power limit.
And 4, setting the Newton iteration initial value as the minimum UAV-BS deployment number and the systematic error limit obtained in the step 3.
Step 5, newton iterative calculation of the (i+1) th iterative value n according to Lagrangian function i+1 。
Step 6, judging whether the result obtained in the step 5 meets |n i+1 -n i If the number is smaller than eta, stopping iteration and outputting the optimal UAV-BS deployment number n opt ＝n i+1 Otherwise, returning to the step 5 to continue the iterative computation.
And 7, performing ceil and floor operation on the result UAV-BS optimal deployment number obtained in the step 6, recalculating system transmitting power, comparing the system transmitting power to obtain a minimum value, updating and outputting the minimum unmanned aerial vehicle base station number and the small circle position under the corresponding circle coverage strategy as a station address plan.
One possible embodiment
The flight control center comprises 50 rotor unmanned aerial vehicles carrying base stations, and the target area is a circular scene with the radius of 1000 m. The unmanned aerial vehicle flight altitude is 100m, the maximum transmitting power of a single UAV-BS is 5W, the channel attenuation index alpha=4, the channel noise is-174 dbm/Hz, and the receiving signal-to-noise ratio threshold is 5dB.
Aiming at the planning requirements, the unmanned aerial vehicle network planning deployment is carried out by using the 6G UAV-BS site planning method based on the circle coverage power optimization. Single unmanned aerial vehicle coverage under circle coverage model satisfies table 1
Table 2 single unmanned aerial vehicle coverage under round coverage model
Considering that the non-extension equipment battery capacity is limited, the UAV-BS transmit power limitation results in a UAV-BS maximum coverage radius of 397.7560m, so the portions shown in the first and second rows of table 2 are not alternatives.
In summary, by using the site planning method provided by the invention, the optimal number of unmanned aerial vehicles deployed in the target area with the radius of 1000m is 20, and the position planning with the minimum system transmitting power of 43.537W is shown in fig. 3.
The implementation of the 6G UAV-BS site planning method based on circle coverage power optimization is based on specific planning requirements, and the influence of different flight heights on the planning result is reflected in FIG. 4; FIG. 5 shows the effect of decay exponential changes on system power for different planning scenarios; fig. 6 reflects the variation of deployment scenario at different target area radii.
The invention provides a 6G UAV-BS station planning method based on circle coverage power optimization, which aims to reduce system transmitting power while ensuring full coverage of a target area. Fig. 7 is a diagram showing the system transmitting power of the method and the conventional hexagonal circumscribed circle deployment method, and fig. 8 is a diagram showing the performance of the method and the conventional station address planning method, wherein the simulation comparison shows that the system transmitting power can be remarkably reduced, so that the network service time can be prolonged, and the problem of UAV-BS station address planning can be effectively solved.
On the other hand, the invention also provides a 6G unmanned aerial vehicle base station site planning system based on circle coverage power optimization, which comprises a model construction module, a coverage radius calculation module of the UAV-BS, a minimum UAV-BS deployment number calculation module and an optimization solving module; the model construction module is used for measuring and calculating the size of a target area according to planning requirements, setting network bandwidth, carrier frequency and UAV-BS number m which can be controlled by the flight control center at the same time according to service requirements, and constructing a circle coverage model;
the coverage radius calculation module of the UAV-BS is used for calculating the coverage radius of each UAV-BS in the current scene by fitting the relation between the number of small circles under the circle coverage strategy and the radius by using a two-term power function according to the circle coverage model;
the minimum UAV-BS deployment number calculation module is used for calculating the transmitting power of the single UAV-BS under the round coverage model to find the minimum UAV-BS deployment number meeting the power limit;
the optimization solving module is used for setting the Newton iteration initial value as the minimum UAV-BS deployment number and the system error limit, carrying out Newton iteration calculation (i+1) th iteration calculation by adopting a Lagrangian function, and judging whether the Newton iteration calculation result meets |n or not i+1 -n i If the number is smaller than eta, stopping iteration and outputting the optimal UAV-BS deployment number n opt ＝n i+1 Otherwise, returning to continue Newton iterative computation; and (3) rounding the optimal deployment number of the UAV-BS, recalculating the system transmitting power, comparing the system transmitting power to obtain a minimum value, updating and outputting the minimum number of the unmanned aerial vehicle base stations and the small circle position under the corresponding circle coverage strategy as a station address plan.
The invention also provides computer equipment, which comprises a processor and a memory, wherein the memory is used for storing computer executable programs, the processor reads part or all of the computer executable programs from the memory and executes the computer executable programs, and the method for planning the base station site of the 6G unmanned aerial vehicle based on the circular coverage power optimization can be realized when the processor executes part or all of the computer executable programs.
On the other hand, the invention provides a computer readable storage medium, and a computer program is stored in the computer readable storage medium, and when the computer program is executed by a processor, the method for planning the base station site of the 6G unmanned aerial vehicle based on the circle coverage power optimization can be realized.
The computer device may be a notebook computer, a desktop computer, or a workstation.
The processor may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), or an off-the-shelf programmable gate array (FPGA).
The memory can be an internal memory unit of a notebook computer, a desktop computer or a workstation, such as a memory and a hard disk; external storage units such as removable hard disks, flash memory cards may also be used.
Computer readable storage media may include computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. The computer readable storage medium may include: read Only Memory (ROM), random access Memory (RAM, random Access Memory), solid state disk (SSD, solid State Drives), or optical disk, etc. The random access memory may include resistive random access memory (ReRAM, resistance Random Access Memory) and dynamic random access memory (DRAM, dynamic Random Access Memory), among others.
Claims (10)
1. The 6G unmanned aerial vehicle base station site planning method based on the round coverage power optimization is characterized by comprising the following steps of:
s1, measuring and calculating the size of a target area according to planning requirements, setting network bandwidth, carrier frequency and the number m of UAV-BS (unmanned aerial vehicle-base station) which can be controlled by a flight control center at the same time according to service requirements, and carrying out channel modeling and network energy consumption modeling between the UAV-BS and ground users to obtain a circle coverage model;
s2, according to a circle coverage model, fitting the relation between the number of small circles and the radius under a circle coverage strategy by using a bivariate power function, and calculating the coverage radius of each UAV-BS under the current scene; fitting the number of small circles under a circle coverage strategy by using a bivariate power function according to a circle coverage modelMRadius and radiusIn calculating the coverage radius of each UAV-BS in the current scenario,
considering UAV-BS site planning as a problem that small circles cover large circles, placing equal circles with fixed sizes in a given circular area to fully cover, wherein the radius of each small circle is reduced along with the increase of the number of the circles, and the fitting function of the relation between the number of the small circles and the radius of the small circles under a circle coverage strategy is as follows:
wherein the method comprises the steps ofIs a constant;
s3, calculating the transmitting power of the single UAV-BS under the circle coverage model to find the minimum UAV-BS deployment number meeting the power limit;
s4, setting Newton iteration initial values as the minimum UAV-BS deployment number and system errorsNewton iterative computation is carried out by adopting Lagrangian function, and whether Newton iterative computation results meet +.>Terminating the iteration and outputting the optimal deployment number of the UAV-BS if the optimal deployment number is satisfied>Otherwise, returning to continue Newton iterative computation;
s5, rounding the optimal deployment number of the UAV-BS obtained in the S4, recalculating the system transmitting power, comparing the system transmitting power with the minimum value, updating and outputting the minimum number of the unmanned aerial vehicle base stations and the small circle positions under the corresponding circle coverage strategy as a station address plan.
2. The method for planning a station site of a 6G unmanned aerial vehicle based on circle coverage power optimization according to claim 1, wherein in S1, channel modeling and network energy consumption modeling are performed between the unmanned aerial vehicle base station and a ground user, and a channel model between a UAV-BS and the ground user is as follows:
wherein the method comprises the steps ofFor carrier frequency +.>For the speed of light->For signal transmission distance, ++>Is an attenuation index>，/>Is a shadow fading term, obeys to have a mean value of 0 and a variance of +.>Is a normal distribution of the above formula first item and +.>Fris's law using free space propagation at a relevant reference distance of 1m +.>Calculated, the second term is +>Logarithmic relation to path loss, +.>Wherein->For projection of UAV-BS on the ground and target userThe horizontal distance between the two plates,His the flying height of the unmanned aerial vehicle.
3. The method for planning base station site of 6G unmanned aerial vehicle based on round coverage power optimization according to claim 1, wherein in the round coverage model of S1, the channel multiplexing between the design center UVA-BS and other UVA-BSs is performed by using a frequency division multiple access method, the communication link multiplexing between the UAV-BS and the user adopts a time division multiple access method to relate to the channel, and the consideration is given toSite planning is carried out by each UVA-BS, and the system communication energy consumption is the sum of the total transmission power of the UVA-BS to the ground and the communication power between the UVA-BSs, namely
Representation +.>The individual UAV-BS is the +.>Transmit power required by the individual UAV-BS, < >>Allocating power for channels between the central UVA-BS and other UVA-BSs, +.>Is the signal transmission distance.
4. The method for planning the base station site of the 6G unmanned aerial vehicle based on the optimization of the round coverage power according to claim 3, wherein the transmission power of the unmanned aerial vehicle communication system under the full coverage condition is ensured according to a channel model between the UAV-BS and the ground user, the system communication energy consumption and the minimum transmission power required by each unmanned aerial vehicle to serve the ground user under the full coverage:
is noise power +.>For SNR of terrestrial users, +.>For deployment->Maximum distance of each unmanned aerial vehicle connection when the unmanned aerial vehicle is in use, +.>And distributing power for channels between the central UVA-BS and other UVA-BSs, wherein n is the number of unmanned aerial vehicles.
5. The method for planning the base station site of the 6G unmanned aerial vehicle based on the round coverage power optimization according to claim 1, wherein under the condition that the confidence in S2 is 95%,the confidence interval of (2) is { (1.725,1.851); (-0.8055, -0.7105); (0.06404,0.1085), fetch (1.788,0.758,0.08626); deployment->The furthest distance that each unmanned aerial vehicle can be connected is: />，HThe flying height of the unmanned aerial vehicle;
the projected circle covered by each UAV-BS has a maximum radius of:
is the radius of the great circle.
6. The 6G unmanned aerial vehicle base station site planning method based on circle coverage power optimization of claim 1, wherein in S3, based on the circle coverage model, the unmanned aerial vehicle communication system transmitting power under the full coverage condition is recalculated according to the UAV-BS coverage radius;
the minimum communication energy consumption optimization problem required when the UAV-BS is fully covered on the ground is as follows:
wherein the method comprises the steps ofRepresenting the maximum power that UAV-BS can transmit for communication with ground users, +.>For planning deployment->Area covered by the personal unmanned aerial vehicle, < >>For the total area of the target area, the constraint represents deployment under the condition that the transmitting power of each unmanned aerial vehicle is limitedNThe UAV-BS within each achieves full coverage of the target area,and combining the recalculated emission power of the unmanned aerial vehicle communication system and the optimization problem to calculate the emission power of the single UAV-BS under the round coverage model, and searching the minimum UAV-BS deployment number meeting the power limit.
7. The method for planning the base station site of the 6G unmanned aerial vehicle based on the circle coverage power optimization according to claim 1, wherein in the S5, the optimal number obtained by Newton iteration solution is obtained, meanwhile, system transmitting power required by covering a target area under a corresponding circle coverage strategy is rounded up and down, the calculated results of the two are compared, UAV-BS site planning corresponding to a smaller value is selected, and UAV-BS deployment number optimal integer solution, minimum UAV communication network system transmitting power and UAV-BS positions under a corresponding circle coverage strategy are obtained.
8. The 6G unmanned aerial vehicle base station site planning system based on circle coverage power optimization is characterized by comprising a model construction module, a coverage radius calculation module of a UAV-BS, a minimum UAV-BS deployment number calculation module and an optimization solving module;
the model construction module is used for measuring and calculating the size of a target area according to planning requirements, setting network bandwidth, carrier frequency and the number m of UAV-BS which can be controlled by the flight control center at the same time according to service requirements, and carrying out channel modeling and network energy consumption modeling between the UAV-BS and ground users to obtain a circular coverage model;
the coverage radius calculation module of the UAV-BS is used for calculating the coverage radius of each UAV-BS in the current scene by fitting the relation between the number of small circles under the circle coverage strategy and the radius by using a two-term power function according to the circle coverage model; fitting the number of small circles under a circle coverage strategy by using a bivariate power function according to a circle coverage modelMRadius and radiusIn calculating the coverage radius of each UAV-BS in the current scenario,
considering UAV-BS site planning as a problem that small circles cover large circles, placing equal circles with fixed sizes in a given circular area to fully cover, wherein the radius of each small circle is reduced along with the increase of the number of the circles, and the fitting function of the relation between the number of the small circles and the radius of the small circles under a circle coverage strategy is as follows:
wherein the method comprises the steps ofIs a constant value, and is used for the treatment of the skin,
the minimum UAV-BS deployment number calculation module is used for calculating the transmitting power of the single UAV-BS under the round coverage model to find the minimum UAV-BS deployment number meeting the power limit;
the optimization solving module is used for setting Newton iteration initial values as the minimum UAV-BS deployment number and the system error, carrying out Newton iteration calculation by adopting Lagrangian functions, and judging whether Newton iteration calculation results meet the requirementTerminating the iteration and outputting the optimal deployment number of the UAV-BS if the optimal deployment number is satisfied>Otherwise, returning to continue Newton iterative computation; and (3) rounding the optimal deployment number of the UAV-BS, recalculating the system transmitting power, comparing the system transmitting power to obtain a minimum value, updating and outputting the minimum number of the unmanned aerial vehicle base stations and the small circle position under the corresponding circle coverage strategy as a station address plan.
9. The computer device is characterized by comprising a processor and a memory, wherein the memory is used for storing a computer executable program, the processor reads part or all of the computer executable program from the memory and executes the computer executable program, and the method for planning the base station site of the 6G unmanned aerial vehicle based on the circular coverage power optimization can be realized when the processor executes part or all of the computer executable program.
10. A computer readable storage medium, wherein a computer program is stored in the computer readable storage medium, and when the computer program is executed by a processor, the method for planning a base station site of a 6G unmanned aerial vehicle based on circle coverage power optimization according to any one of claims 1 to 7 can be realized.
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