CN115696352A - 6G unmanned aerial vehicle base station site planning method and system based on circular coverage power optimization - Google Patents

6G unmanned aerial vehicle base station site planning method and system based on circular coverage power optimization Download PDF

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CN115696352A
CN115696352A CN202210631321.6A CN202210631321A CN115696352A CN 115696352 A CN115696352 A CN 115696352A CN 202210631321 A CN202210631321 A CN 202210631321A CN 115696352 A CN115696352 A CN 115696352A
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coverage
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CN115696352B (en
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刘鑫一
申玉洁
孟芸
王威
侯俊
狄陈琪
刘岩
邢艳超
黄子娇
石蒙蒙
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Changan University
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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
    • H04W16/18—Network planning tools
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract

The invention discloses a 6G unmanned aerial vehicle base station site planning method and a system based on circular coverage power optimization, wherein the method comprises the following steps: setting network bandwidth, carrier frequency and the number of UAV-BS (unmanned aerial vehicle-base station) which can be simultaneously controlled by a flight control center 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 circular 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, rounding the UAV-BS optimal deployment number, recalculating the transmitting power of the system, comparing to obtain the minimum value, updating and outputting the minimum number of the unmanned aerial vehicle base stations and the positions of the small circles under the corresponding circle coverage strategy as station address planning; 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

6G unmanned aerial vehicle base station site planning method and system based on circular coverage power optimization
Technical Field
The invention belongs to the technical field of planning of unmanned aerial vehicle base stations of space-base core communication equipment in a 6G space-ground integrated network scene, and particularly relates to an unmanned aerial vehicle base station site planning method for ensuring full coverage of ground users by modeling through geometric circle coverage.
Background
Mobile Communication has been greatly changed from The first development to The Fifth Generation Communication network (5G), the 1G era can only make calls and has poor signal quality, the 2G era is called "text era" to 3G era, data transmission is promoted from 4G to enter "video era" to The fused era of 5G open Communication technology and internet technology, and a plurality of novel wireless access technologies and evolution technologies appear, but there are still a lot of deficiencies for The increasingly abundant demands of people. A6G research and development expert working group is established in Beijing in 11 months in 2019, and marks the formal start of the research and development work of the 6G key technology in China.
Compared with the 5G era, the 6G communication network has the advantages that the overall architecture of the communication network is changed, and accordingly various application scenes and communication requirements appearing in the future are met. At present, base stations in a 5G network are mostly deployed on the land, and such a static and single-dimensional communication network may not meet the communication requirements of users when facing a special communication scene or a 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 air network, a satellite constellation network and an underwater network to realize the air-sky-earth-sea integrated global coverage. In particular, the types and the number of air vehicles which are highly mobile and convenient to deploy, such as hot air balloons, air craft, unmanned Aerial Vehicles (UAVs), and the like, are increasing, and establishing an air flight base station as a low-altitude network of a communication node for supplementing a static network architecture will play an important role in the 6G era.
Compared with other communications, 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 service for high-density service scenes such as important conferences, large-scale events and the like. Meanwhile, in recent years, the technology of the unmanned aerial vehicle is also rapidly developed, and the manufacturing cost, the operation controllability and the size of the body of the unmanned aerial vehicle are greatly improved, so that the communication of the unmanned aerial vehicle is turned from the military field to the public civil field, and the unmanned aerial vehicle can be 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.
The development of unmanned aerial vehicle communication is not independent of the research on unmanned aerial vehicle communication system models and performances thereof. In the communication field, the unmanned aerial vehicle can be used as an aerial user and can also be used as a relay or a base station by carrying an aerial base station. The unmanned aerial vehicle is used in the Internet of things as an aerial user in a large scale, and collects sensing data and the like from ground equipment; the unmanned aerial vehicle carrying base station is used as a relay in an ultra-dense scene, can strengthen signals of mobile users and provides long-distance communication; the UAV-BS is more used to off-load the data volume and emergency communication directions of the ground network. Unmanned aerial vehicle communication becomes low latitude network composition first-choice in the aspect of emergency communication with the help of its advantage convenient, that receive the environmental impact less. But the characteristics of small size, low battery capacity and limited endurance time of the unmanned aerial vehicle also bring the problems of limited communication service time and low energy efficiency to the unmanned aerial vehicle network. In addition, the limited flying height and the limited carrying capacity of the unmanned aerial vehicle limit the coverage range of the UAV-BS. Therefore, reasonable position deployment and site planning technical research are carried out by comprehensively considering the limiting factors of the unmanned aerial vehicle and the communication requirements of different scenes, and the 6G communication technical development is deeply influenced.
The research of the UAV-BS site planning problem aims to improve the effectiveness and the coverage performance of an unmanned aerial vehicle communication network as much as possible through position deployment planning. On one hand, the channel is affected by the flying or hovering of the drone, which can reduce the path loss of the signal when the drone is close to the ground, but it is also possible to increase the non-direct signal to exacerbate the multipath effect and the small-scale fading. On the other hand, the coverage area of the UAV-BS is limited due to the limited power of the airborne base station, the problem of interference is caused by too many unmanned aerial vehicles deployed, and the user requirements cannot be met if the number of the unmanned aerial vehicles is too few.
At present, related technologies are still to be developed, most of the site planning technologies are still directed at ground networks, base stations are deployed in the ground networks, coverage is limited, and the available communication service capacity can not meet the intensive service request brought by a 6G day-ground integrated architecture. Meanwhile, the current station address planning also has the problems of high repetition rate of the coverage area of the base station, serious interference and serious power loss, and can not achieve green energy conservation well. In addition, the current site planning is mostly based on a long-term geographic traffic mode of a target area, 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 circular coverage power optimization, which is characterized in that a circular coverage model is used for planning the UAV site to ensure that a space-based wireless network can realize full coverage on a ground user, meanwhile, a least square method is used for fitting a circular coverage result, a system transmitting power function is provided as a site planning performance index, and finally, newton iteration is used for solving a planning scheme which meets coverage constraint and comprises the optimal UAV-BS deployment number, site information and minimum system power.
In order to achieve the purpose, the invention adopts the technical scheme that: A6G unmanned aerial vehicle base station site planning method based on circular 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 UAVs-BSs which can be simultaneously controlled by a flight control center according to service requirements, and carrying out channel modeling and network energy consumption modeling between the UAVs-BSs and ground users to obtain a circular coverage model;
s2, according to the circle coverage model, utilizing a binomial power function to fit the relation between the number of small circles and the radius under the circle coverage strategy, and calculating the coverage radius of each UAV-BS under the current scene;
s3, calculating the transmitting power of a single UAV-BS under the circular coverage model, and searching 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, performing Newton iteration calculation by adopting a Lagrangian function, and judging whether the Newton iteration calculation result meets | n | i+1 -n i If the eta is less than the 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 S5, rounding the optimal deployment number of the UAV-BS obtained in the S4, recalculating the system transmitting power, comparing to obtain the minimum value, updating and outputting the minimum number of the UAV base stations and the corresponding small circle position under the 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 a 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 The Friis law using free space propagation at a reference distance of 1m from the first term of the above equation
Figure BDA0003679980300000041
Calculated, the second term is the logarithmic relation of d to path loss,
Figure BDA0003679980300000042
where r is the projection of UAV-BS onto the ground andand H is the flying height of the unmanned aerial vehicle.
S1, in the circular coverage model, a frequency division multiple access method is used for channel multiplexing between a central UVA-BS and other UVA-BSs, a time division multiple access method is used for communication link multiplexing between a UAV-BS and a user to relate to a channel, n UVA-BSs are considered for site planning, and system communication energy consumption is the sum of total ground transmitting power of the UVA-BS and communication power between the UVA-BSs, namely
Figure BDA0003679980300000043
P ugi (d) Represents the required transmit power, P, of the ith UAV-BS when the ground user is fully covered by n UAV-BSs uu Allocating power for channels between the central UVA-BS and other UVA-BSs, and d is signal transmission distance.
Calculating to obtain the transmitting power of the unmanned aerial vehicle communication system under the condition of ensuring full coverage according to a channel model between the UAV-BS and the ground user, the system communication energy consumption and the minimum transmitting power required by each unmanned aerial vehicle for the ground user service under the full coverage:
Figure BDA0003679980300000044
pn is the noise power, γ is the SNR of the terrestrial users, d n Maximum distance, P, that each drone can connect when deploying n drones uu And distributing power for channels between the central UVA-BS and other UVA-BSs, wherein n is the number of the unmanned aerial vehicles.
In S2, according to the circle coverage model, the number M and the radius r of the small circles under the circle coverage strategy are fitted by utilizing a binomial power function 0 When the coverage radius of each UAV-BS in the current scene is calculated,
the UAV-BS site planning is regarded as the problem that a small circle covers a large circle, an equal circle with a fixed size is placed in a given circular area to be fully covered, the radius of each small circle is reduced along with the increase of the number of the circles, and a fitting function of the relation between the number of the small circles and the radius of the small circles under a circle covering strategy is as follows:
r 0 (n)＝ax -b +c
under the condition that a, b and c are constants and the confidence coefficient is 95%, the confidence interval of a, b and c is { (1.725, 1.851); (-0.8055, -0.7105); (0.06404, 0.1085) }, take (1.788, 0.758, 0.08626); deploying n unmanned aerial vehicles, wherein the farthest distance that each unmanned aerial vehicle can be connected is as follows:
Figure BDA0003679980300000051
h is the flying height of the unmanned aerial vehicle;
the maximum radius of the projection circle covered by each UAV-BS is as follows:
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 condition of ensuring full coverage according to the UAV-BS coverage radius based on the circular coverage model;
the optimization problem of the minimum communication energy consumption required by the UAV-BS in the full coverage of 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 Represents the maximum power, S, that UAV-BS can transmit in communication with a ground user c (N) the area which can be covered by planning and deploying N unmanned aerial vehicles, S is the total area of a target area, the constraint represents that within N UAVs-BSs are deployed under the condition that the transmitting power of each unmanned aerial vehicle is limited to realize the full coverage of the target area, and the transmitting power of a single UAV-BS under a circular coverage model is obtained by combining the recalculation of the transmitting power of the unmanned aerial vehicle communication system and the solution of an optimization problem to search the minimum deploying number of the UAVs-BSs which meet the power limit.
And S5, performing Newton iteration to obtain the optimal number, performing upward and downward rounding, calculating the system transmitting power required by the coverage target area under the corresponding circle coverage strategy, comparing the calculation results of the two, and selecting the UAV-BS site planning corresponding to a smaller value to obtain the optimal integer solution of the UAV-BS deployment number, the transmitting power of the minimum unmanned aerial vehicle communication network system 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 circular coverage power optimization, which comprises a model building 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 building 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 UAVs-BSs which can be simultaneously controlled by the flight control center according to service requirements, and carrying out channel modeling and network energy consumption modeling between the UAVs-BSs 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 utilizing a binomial power function to fit the relation between the number of small circles and the radius under a circle coverage strategy according to a circle coverage model;
the minimum UAV-BS deployment number calculation module is used for calculating the emission power of a single UAV-BS under the circular coverage model and searching 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, performing Newton iteration calculation by adopting a Lagrangian function, and judging whether the Newton iteration calculation result meets | n i+1 -n i If the eta is less than the 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 rounding the optimal deployment number of the UAV-BS, recalculating the system transmitting power, comparing to obtain the minimum value, updating and outputting the minimum number of the unmanned aerial vehicle base stations and the positions of the small circles under the corresponding circle coverage strategy as station address planning.
The invention also provides computer equipment which comprises a processor and a memory, wherein the memory is used for storing the computer executable program, the processor reads the computer executable program from the memory and executes the computer executable program, and when the processor executes the computer executable program, the 6G unmanned aerial vehicle base station site planning method based on circular coverage power optimization can be realized.
Meanwhile, a computer readable storage medium is provided, in which a computer program is stored, and when the computer program is executed by a processor, the 6G unmanned aerial vehicle base station site planning method based on circular 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 the network, and realizes the ubiquitous connection network requirement promoted by 6G communication; the circular coverage model is utilized to realize the full coverage requirement, so that the coverage repetition area can be reduced to the maximum extent, and the communication interference between the UAV-BS and the base station is reduced; the optimal unmanned aerial vehicle deployment number and the minimum system transmission power are searched from the perspective of the unmanned aerial vehicle communication network system transmission power, and an optimal station planning scheme is given by comparing the optimal station planning number with the circular 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 capacity 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 used 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 the present invention.
Fig. 2 is a schematic diagram of a circle overlay.
FIG. 3 is a flow chart of the steps of the planning method of the present invention.
FIG. 4 is a schematic diagram showing an example planning result of a 6G UAV-BS site planning method based on circular coverage power optimization.
Fig. 5 shows the effect of the flying height of the drone on the transmission power of the system.
Fig. 6 is a graph of the effect of the attenuation exponent on the system transmit power.
FIG. 7 is a graph of the effect of target zone radius on deployment scenario.
Fig. 8 is a comparison of the performance of the present invention and the conventional site planning method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
A6G unmanned aerial vehicle base station site planning method based on circular coverage power optimization comprises the following steps:
step 1: setting network basic parameters
The purpose of the station address planning is to realize the coverage of a target area and simultaneously meet the service requirements of customers by reasonably planning the number of base stations and position information. Before planning the station site, 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 number of UAVs-BSs which can be simultaneously controlled by the flight control center according to the service requirement. Fig. 1 shows a schematic diagram of a site planning scenario.
According to the parameters, channel modeling and network energy consumption modeling are carried out between an unmanned aerial vehicle base station (UAV-BS) and a ground user.
The UAV-BS and the ground user can be influenced by multipath propagation and signal fading in the communication process, namely the UAV-BS and the ground communication link comprise a non-line-of-sight communication link, and the propagation of signals in free space is considered to meet a shadow fading model. The channel model between the UAV-BS and the ground user 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 greater than or equal to 2, xi is shadow fading term, obedience mean value is 0, variance is sigma 2 Normal distribution of (c). Fris's law using free space propagation at a first term of the above equation with a reference distance of 1m relative to f
Figure BDA0003679980300000081
And calculating to obtain. The second term giving d and path lossThe relation of the logarithm is obtained by the method,
Figure BDA0003679980300000082
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, namely a central UVA-BS communication link and other UVA-BS communication links, and a UVA-BS ground communication link. In the invention, the channel multiplexing is carried out between the central UVA-BS and other UVA-BSs by using a frequency division multiple access method so as to avoid serious co-channel interference, and the communication link multiplexing between the UAV-BS and a user adopts a time division multiple access method to relate to a channel. Considering n UVA-BSs for site planning, the system communication energy consumption is the sum P of the total UVA-BS ground transmitting power and the UVA-BS communication power sum I.e. by
Figure BDA0003679980300000091
P ugi (d) Represents the required transmit power of the ith UAV-BS when the ground user is fully covered by n UAV-BSs. P uu Power is allocated for channels between the central UVA-BS and other UVA-BSs. Assuming that all UAVs-BSs are indifferent gyroplanes and carry similar small-sized base stations, the gyroplanes have the same coverage capability, and the transmitting power required by the signal propagation distance d is P ug (d)。
According to the purpose of station address planning, UAV-BS deployment needs to ensure that the received Signal-to-Noise ratio (SNR) of any position user in a target area is greater than an SNR threshold value gamma th That is, the deployment of the unmanned aerial vehicle must meet the requirements of users to realize 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 the ground user service under full coverage is P ug (d n )
Figure BDA0003679980300000092
d n The maximum distance each drone can connect when deploying n drones. The joint formulas (1), (2) and (4) can ensure that the transmission power of the unmanned aerial vehicle communication system under the condition of full coverage is as follows:
Figure BDA0003679980300000093
step 2: obtaining coverage radius of each UAV-BS under current scene
According to the invention, according to a circular coverage model, a quadratic power function is utilized to fit the relation between the number of small circles and the radius under a circular coverage strategy, and the coverage radius of each UAV-BS under the current scene is calculated.
According to the emission power of the unmanned aerial vehicle communication system under the condition of ensuring full coverage, namely according to the formula (5), the system emission power is influenced by the signal-to-noise ratio threshold, the communication distance between the UAV-BS and the user and the number of the UAV-BS.
The coverage area radiated by the UAV-BS carrying omnidirectional antennas towards the ground can be modeled as a Circle, and then the coverage Problem can be regarded as a type of geometric Problem, similar to the Circular Packing Problem (CPP) in the location Problem. The CPP problem falls into two categories, one is the round packing problem, packing a certain number of circles in a container, each circle having the largest radius (each circle need not be the same). Another is the problem of circle coverage, i.e. how large an area of a container can be covered completely by a given circle. The shape of the container may be "simple" circle, square, rectangle or consist of a combination of wires and arc segments. Henry Friedman collated the best results of the small circle coverage big circle problem from 1983 to 2018. In order to achieve full coverage of the ground user by the UAV-BS, the UAV-BS site planning is regarded as the problem that a small circle covers a large circle. I.e. placing a constant size of an equal circle for a given circular area for full coverage.
Figure 2 shows the optimal placement of 5 equicircles 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 required number of completely covering 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 strategy of placing small circles for each value of M, M being the number of small circles. It is difficult to find a general placement strategy that is optimal for any M, and for each value of M, a wrapping strategy needs to be provided.
TABLE 1 Small circular radius r 0 Relation between number of small circles
Figure BDA0003679980300000101
In order to save ACC computing resources and make the scheme more universal, the invention uses nonlinear least squares to perform curve fitting on the data, and adopts a binomial power function to perform approximation, and the fitting function of the relation between the number of small circles and the radius of the small circle under the circle covering strategy is given as follows:
r 0 (n)＝ax -b +c
under the condition that a, b and c are constants and the confidence coefficient is 95%, the confidence interval of a, b and c is { (1.725, 1.851); (-0.8055, -0.7105); (0.06404, 0.1085) }, take (1.788, 0.758, 0.08626). Then deploy n unmanned aerial vehicles, the farthest distance that each unmanned aerial vehicle can be connected is:
Figure BDA0003679980300000111
the maximum radius of the projection circle covered by each UAV-BS is as follows:
r(n)＝R c r 0 (n) (6)
and step 3: and calculating the transmitting power of a single UAV-BS under the circular coverage model, and calculating to obtain 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 condition of ensuring full coverage according to the coverage radius of the UAV-BS
Figure BDA0003679980300000112
Figure BDA0003679980300000113
The minimum communication energy consumption optimization problem required when considering the full coverage of the UAV-BS to the ground 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. the c (n) is the area that can be covered by planning and deploying n unmanned aerial vehicles, and S is the total area of the target area. The constraint represents deployment of within N UAV-BSs to achieve full coverage of the target area under the condition that the transmit power of each drone is limited.
And 4, step 4: and 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 by utilizing Newton iteration.
And (3) converting the problem into an unconstrained optimization problem by using a Lagrange multiplier method so as to solve a solution meeting inequality constraints. Constructing a lagrangian function of the problem described in step 3 as follows:
Figure BDA0003679980300000121
wherein k is 1 ,k 2 ,k 3 ,k 4 Is a non-negative lagrange factor.
The (Karush-Kuhn-Tucker, KKT) condition in the optimization theory is satisfied with a local optimal solution for the above formula. Namely that
Figure BDA0003679980300000122
Solving the above formula by Newton iteration, and recording the ith iteration value as n i I +1 iterations of a value of
Figure BDA0003679980300000123
Figure BDA0003679980300000124
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 the content of the first and second substances,
Figure BDA0003679980300000125
then, n meeting the problem P1 can be obtained by solving the above formula, and the minimum total system transmitting power required by covering the target area by adopting the base station address planning strategy provided by the invention can be obtained by substituting the formula (9).
And 5: and updating the optimal integer solution of the UAV-BS deployment number, the minimum unmanned aerial vehicle communication network system transmitting power and the UAV-BS position under the corresponding circle coverage strategy.
And 4, considering that the number of the UAVs-BSs is an integer value, simultaneously rounding up and down for the obtained optimal number, calculating the system transmitting power required by the coverage of the target area under the corresponding circular coverage strategy, comparing the calculation results of the two, and selecting the UAV-BS site planning corresponding to a smaller value to obtain the UAV-BS deployment number optimal integer solution, the minimum UAV communication network system transmitting power and the UAV-BS position under the corresponding circular coverage strategy.
By way of example, the 6G UAV-BS site planning method based on circular coverage power optimization of the present invention is applicable to a 6G air-space-ground integrated wireless network scenario, as shown in fig. 1, the scenario includes: the unmanned aerial vehicle comprises a coverage target area without any base station, a flight control center with certain aircraft control and basic computing capacity, and a plurality of unmanned aerial vehicles carrying aerial base stations.
A 6G UAV-BS site planning method based on circular coverage power optimization, as shown in fig. 3, the method comprising the steps of:
step 1, measuring and calculating the size of a target area according to planning requirements to carry out modeling, and setting network bandwidth and carrier frequency according to business requirements, wherein the number of UAVs-BSs which can be simultaneously controlled by a flight control center.
And 2, according to the circular coverage model, utilizing a binomial power function to fit the relation between the number of small circles and the radius under the circular coverage strategy, and calculating the coverage radius of each UAV-BS in the current scene.
And 3, calculating the transmitting power of a single UAV-BS under the circular coverage model to search the minimum UAV-BS deployment number meeting the power limit.
And 4, setting Newton iteration initial values as the minimum UAV-BS deployment number and the system error limit obtained in the step 3.
Step 5, according to Lagrange function, newton iteration is carried out to calculate the i +1 th iteration value n i+1 。
Figure BDA0003679980300000131
Step 6, judging whether the result obtained in the step 5 meets | n i+1 -n i If | < eta, terminating iteration and outputting the optimal UAV-BS deployment number n if satisfied opt ＝n i+1 Otherwise, returning to the step 5 to continue the iterative computation.
And 7, performing ceil and floor operation on the optimal deployment number of the UAV-BS obtained in the step 6, recalculating the system transmitting power, comparing to obtain a minimum value, updating and outputting the minimum base station number of the UAVs and the position of a small circle under a corresponding circle coverage strategy as a station address plan.
One can carry out
The flight control center comprises 50 rotor unmanned aerial vehicles carrying base stations, and a target area is a circular scene with the radius of 1000 m. The flying height of the unmanned aerial vehicle 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 threshold value of the receiving signal to noise ratio 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 circular coverage power optimization. Single unmanned aerial vehicle coverage under circle coverage model satisfies table 1
TABLE 2 Single unmanned aerial vehicle coverage under circular coverage model
Figure BDA0003679980300000141
Considering the limited battery capacity of the un-extension equipment, the UAV-BS transmission power limitation results in the 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, with the station planning method provided by the present invention, the number of the optimal unmanned aerial vehicles deployed in the target area with a radius of 1000m is 20, and the position planning is performed with the system minimum transmission power of 43.537W as shown in fig. 3.
The specific implementation of the 6G UAV-BS site planning method based on circular coverage power optimization provided by the invention is based on specific planning requirements, and FIG. 4 reflects the influence of different flight heights on the planning result; FIG. 5 reflects the impact of attenuation index changes on system power in different planning scenarios; FIG. 6 reflects the variation of deployment scenario for different target area radii.
The 6G UAV-BS site planning method based on circular coverage power optimization aims to ensure that the system transmitting power is reduced while the target area is fully covered. Fig. 7 analyzes the system transmitting power of the method and the traditional hexagonal circumscribed circle deployment method, and through simulation comparison, fig. 8 is a performance comparison of the method and the traditional station address planning method, it can be seen that the method can significantly reduce the system transmitting power, thereby prolonging the network service time and effectively solving the problem of UAV-BS station address planning.
On the other hand, the invention also provides a 6G unmanned aerial vehicle base station site planning system based on circular coverage power optimization, which comprises a model building 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 building 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 UAVs (unmanned aerial vehicles) -BSs (base stations) which can be controlled by the flight control center at the same time according to service requirements, and building 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 utilizing a binomial power function to fit the relation between the number of small circles and the radius under a circle coverage strategy according to a circle coverage model;
the minimum UAV-BS deployment number calculation module is used for calculating the transmitting power of a single UAV-BS under the circular coverage model and searching 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 error limit, performing Newton iteration calculation for the (i + 1) th iteration calculation by adopting a Lagrange function, and judging whether the Newton iteration calculation result meets the | n | i+1 -n i If the eta is less than the eta, terminating iteration and outputting the optimal UAV-BS deployment number n if the eta is less than the eta opt ＝n i+1 Otherwise, returning to continue Newton iterative computation; and rounding the optimal deployment number of the UAV-BS, recalculating the system transmitting power, comparing to obtain the minimum value, updating and outputting the minimum number of the unmanned aerial vehicle base stations and the positions of the small circles under the corresponding circle coverage strategy as station address planning.
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 when the processor executes part or all of the computer executable programs, the 6G unmanned aerial vehicle base station site planning method based on circular coverage power optimization can be realized.
In another aspect, the present invention provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for planning a station address of a 6G drone base station based on circular coverage power optimization according to the present invention can be implemented.
The computer equipment can adopt 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 of the invention can be an internal storage unit of a notebook computer, a desktop computer or a workstation, such as a memory and a hard disk; external memory 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: a Read Only Memory (ROM), a Random Access Memory (RAM), a Solid State Drive (SSD), or an optical disc. The Random Access Memory may include a Resistance Random Access Memory (ReRAM) and a Dynamic Random Access Memory (DRAM).

Claims (10)

1. A6G unmanned aerial vehicle base station site planning method based on circular coverage power optimization is characterized by comprising 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-BSs which can be simultaneously controlled by a flight control center according to service requirements, and carrying out channel modeling and network energy consumption modeling on the UAV-BS and a ground user to obtain a circular coverage model;
s2, according to the circle coverage model, utilizing a binomial power function to fit the relation between the number of small circles and the radius under the circle coverage strategy, and calculating the coverage radius of each UAV-BS under the current scene;
s3, calculating the transmitting power of a single UAV-BS under the circular coverage model, and searching 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, performing Newton iteration calculation by adopting a Lagrangian function, and judging whether the Newton iteration calculation result meets | n | i+1 -n i If the eta is less than the 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 S5, rounding the optimal deployment number of the UAV-BS obtained in the S4, recalculating the system transmitting power, comparing to obtain the minimum value, updating and outputting the minimum number of the UAV base stations and the corresponding small circle position under the circle coverage strategy as a station address plan.
2. The method for 6G UAV base station site planning based on circular coverage power optimization of claim 1, wherein in S1, channel modeling and network energy consumption modeling are performed between the UAV base station and the ground user, and the channel model between the 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 The Friis law using free space propagation at a reference distance of 1m from the first term of the above equation
Figure FDA0003679980290000011
Calculated, the second term is the logarithmic relation of d to path loss,
Figure FDA0003679980290000021
where r is the horizontal distance between the UAV-BS projection on the ground and the target user, and H is the UAV flight altitude.
3. The 6G UAV base station site planning method based on circular coverage power optimization of claim 1, wherein in the circular coverage model S1, channel multiplexing is performed between a design center UVA-BS and other UVA-BSs by using a frequency division multiple access method, communication link multiplexing between the UAV-BS and a user adopts a time division multiple access method to relate to channels, site planning is performed by considering n UVA-BSs, and system communication energy consumption is sum of total ground emission power of the UVA-BSs and communication power between the UVA-BSs, namely sum of total ground emission power of the UVA-BSs and the communication power between the UVA-BSs
Figure FDA0003679980290000022
P ugi (d) Represents the required transmit power, P, of the ith UAV-BS when the ground user is fully covered by n UAV-BSs uu Allocating power for channels between the central UVA-BS and other UVA-BSs, and d is signal transmission distance.
4. The method of claim 3, wherein the UAV communication system transmit power under guaranteed full coverage is calculated according to a channel model between UAV-BS and ground user, system communication energy consumption, and minimum transmit power required by each UAV to serve ground user under full coverage:
Figure FDA0003679980290000023
pn is the noise power, γ is the SNR of the terrestrial users, d n Maximum distance, P, that each drone can connect when deploying n drones uu And distributing power for channels between the central UVA-BS and other UVA-BSs, wherein n is the number of the unmanned aerial vehicles.
5. The 6G unmanned aerial vehicle base station site planning method based on circular coverage power optimization of claim 1, wherein in S2, according to a circular coverage model, a binomial power function is used for fitting the number M and the radius r of small circles under a circular coverage strategy 0 When the coverage radius of each UAV-BS in the current scene is calculated,
the UAV-BS site planning is regarded as the problem that a small circle covers a large circle, an equal circle with a fixed size is placed in a given circular area to be fully covered, the radius of each small circle is reduced along with the increase of the number of the circles, and a fitting function of the relation between the number of the small circles and the radius of the small circles under a circle covering strategy is as follows:
r 0 (n)＝ax -b +c
under the condition that a, b and c are constants and the confidence degrees are 95%, the confidence intervals of a, b and c are { (1.725, 1.851); (-0.8055, -0.7105); (0.06404, 0.1085) }, take (1.788, 0.758, 0.08626); deploying n unmanned aerial vehicles, wherein the farthest distance that each unmanned aerial vehicle can be connected is as follows:
Figure FDA0003679980290000031
h is the flying height of the unmanned aerial vehicle;
the maximum radius of the projection circle covered by each UAV-BS is as follows:
r(n)＝R c r 0 (n)
R c is the radius of the great circle.
6. The 6G UAV base station site planning method based on circle coverage power optimization of claim 1, wherein in S3, based on the circle coverage model, the UAV communication system transmitting power under the condition of ensuring full coverage is recalculated according to the UAV-BS coverage radius;
the optimization problem of the minimum communication energy consumption required by the UAV-BS in the full coverage of 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, S, that UAV-BS can transmit in communication with a ground user c (N) the area which can be covered by planning and deploying N unmanned aerial vehicles, S is the total area of the target area, the constraint represents that within N UAVs-BSs are deployed under the condition that the transmitting power of each unmanned aerial vehicle is limited to realize the full coverage of the target area, and the recalculation is combinedAnd resolving the obtained transmitting power and optimization problem of the unmanned aerial vehicle communication system to obtain the transmitting power of a single UAV-BS under the circular coverage model, and searching for the minimum UAV-BS deployment number meeting the power limit.
7. The 6G unmanned aerial vehicle base station site planning method based on circle coverage power optimization of claim 1, wherein in S5, for the optimal number obtained by Newton iteration solution, upward and downward rounding is performed at the same time, system transmission power required by coverage of a target area under a corresponding circle coverage strategy is calculated, the calculation results of the two are compared, UAV-BS site planning corresponding to a smaller value is selected, and the UAV-BS deployment number optimal integer solution, the minimum unmanned aerial vehicle communication network system transmission power and the UAV-BS position under the corresponding circle coverage strategy are obtained.
8. A6G unmanned aerial vehicle base station site planning system based on circular coverage power optimization is characterized by comprising a model building module, a UAV-BS coverage radius calculation module, a minimum UAV-BS deployment number calculation module and an optimization solving module;
the model building 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-BSs which can be simultaneously controlled by the flight control center according to service requirements, and carrying out channel modeling and network energy consumption modeling between the UAV-BSs 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 utilizing a binomial power function to fit the relation between the number and the radius of the small circles under the circle coverage strategy according to the circle coverage model;
the minimum UAV-BS deployment number calculation module is used for calculating the emission power of a single UAV-BS under the circular coverage model and searching 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, performing Newton iteration calculation by adopting a Lagrangian function, and judging whether the Newton iteration calculation result meets | n i+1 -n i If | < η, terminate the stack if satisfiedGeneration and output of optimal deployment number n of UAV-BS opt ＝n i+1 Otherwise, returning to continue Newton iterative computation; and rounding the optimal deployment number of the UAV-BS, recalculating the system transmitting power, comparing to obtain the minimum value, updating and outputting the minimum number of the unmanned aerial vehicle base stations and the positions of the small circles under the corresponding circle coverage strategy as station address planning.
9. A computer device, 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 processor can implement the 6G drone base station site planning method based on circular coverage power optimization according to any one of claims 1 to 7 when executing 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 6G drone base station site based on circular coverage power optimization according to any one of claims 1 to 7 is implemented.
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