CN108616302B - Unmanned aerial vehicle multiple coverage model under power control and deployment method - Google Patents

Unmanned aerial vehicle multiple coverage model under power control and deployment method Download PDF

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CN108616302B
CN108616302B CN201810396881.1A CN201810396881A CN108616302B CN 108616302 B CN108616302 B CN 108616302B CN 201810396881 A CN201810396881 A CN 201810396881A CN 108616302 B CN108616302 B CN 108616302B
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CN108616302A (en
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王金龙
陈瑾
阮朗
任国春
徐煜华
杨旸
陈学强
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Army Engineering University of PLA
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/18502—Airborne stations
    • H04B7/18504—Aircraft used as relay or high altitude atmospheric platform
    • 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
    • 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
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
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Abstract

The invention discloses an unmanned aerial vehicle multiple coverage model under power control and a deployment method. The model is as follows: in a communication coverage scenario in the drone network, for any mission area, the probability that it can be covered by the drone swarm network is determined jointly by the drones that can detect the area. The method comprises the following steps: firstly, constructing a multi-coverage deployment model of the unmanned aerial vehicle, wherein participants are all unmanned aerial vehicles in an unmanned aerial vehicle cluster network; then each unmanned aerial vehicle constructs a state set of the unmanned aerial vehicle, and other unmanned aerial vehicles are divided into neighbor unmanned aerial vehicles and non-neighbor unmanned aerial vehicles; then calculating the optimal coverage under the maximum transmission power to obtain an optimal coverage strategy; and finally, under the optimal coverage strategy, performing power control under the current coverage deployment to obtain the optimal power strategy. The invention can accurately depict the coverage capability of the unmanned aerial vehicle on the ground in the unmanned aerial vehicle cluster network, and realizes the minimization of power overhead on the premise of meeting the communication requirement.

Description

Unmanned aerial vehicle multiple coverage model under power control and deployment method
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to an unmanned aerial vehicle multi-coverage model under power control and a deployment method.
Background
Unmanned aerial vehicle technology has developed rapidly in the last decade. Features of drones include small volume, high mobility, and low communication overhead, which make drones widely used for civilian and military applications. However, with diversification and specialization of task requirements, a single unmanned aerial vehicle cannot meet the requirements of complex tasks, so that an unmanned aerial vehicle cluster needs to be built to form a wireless sensing network for working, and cooperative communication in the unmanned aerial vehicle cluster network becomes the key for unmanned aerial vehicle technology development.
Studies have shown that drone Communication technology can often ultimately translate into practical applications within The area coverage, and therefore, area coverage is one of The most fundamental problems in drone swarm networks (references y.chen, h.zhang, and m.xu, "The coverage protocol in UAV network: a survey," in international conference on company-ting, Communication and Networking Technologies, pp.1-5,2014.). The unmanned aerial vehicle cooperative control coverage can better complete tasks such as detection, communication, mapping and the like, and the ratio of the coverage area to the task area is a measurement mode of the coverage capability of the unmanned aerial vehicle cluster network.
The drone coverage problem also presents many challenges such as mobility, battery life, connectivity and barriers, etc. This wherein, battery life is not enough is an important shortcoming of unmanned aerial vehicle, when the battery can't support unmanned aerial vehicle to accomplish the task in the regulation time, can lead to the task failure even be the serious consequence of unmanned aerial vehicle's crash. Therefore, by researching the efficient cooperative coverage of the unmanned aerial vehicles in the unmanned aerial vehicle cluster network, the environment adaptation capability of the unmanned aerial vehicles can be improved, the communication and flight expenses are reduced, and serious consequences are avoided.
For the unmanned aerial vehicle coverage problem, a game theoretical formula under cooperative search and monitoring of multiple unmanned aerial vehicles is proposed, a concept of an action set is proposed, and a potential energy game is introduced as a theoretical support (reference document p.li and h.dual, "a potential volume adaptive to multiple UAV cooperative search and theoretical support," in Aerospace Science & Technology, vol.68, 2017.); a coverage probability function determined by antenna gain and height at a given drone and ground unit is proposed and theoretically derived (references m.mozaffari, w.saad, m.bennis, and m.debbah, "effective delivery of Multiple Unmanned antenna for optical Wireless coverage," in ieee communications Letters, vol.20, pp.1647-1650,2016.). Most of works do not emphasize the cooperative operation capability among unmanned aerial vehicles, the cooperative characteristic of multiple unmanned aerial vehicles is not well utilized, and the definition of coverage is too direct and simple, so that the coverage scene is often not practical enough.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle multi-coverage model and a power control deployment learning algorithm under power control, which can reduce the acquisition overhead of frequency spectrum resources and improve the utilization rate of frequency spectrum.
The technical solution for realizing the purpose of the invention is as follows: in a communication coverage scene in an unmanned aerial vehicle cluster network, for any task area, the probability of coverage of the unmanned aerial vehicle network is jointly determined by unmanned aerial vehicles capable of detecting the area; by unmanned aerial vehicle deployment and transmission power control, the aim of minimizing the total transmission power is achieved on the premise of meeting communication requirements.
A method for deploying multiple coverage areas of an unmanned aerial vehicle under power control comprises the following steps:
step 1, modeling a problem of unmanned aerial vehicle multiple coverage deployment under power control into a potential energy game model, wherein participants of a game are all unmanned aerial vehicles with task requirements in an unmanned aerial vehicle network;
step 2, each unmanned aerial vehicle constructs a state set of the unmanned aerial vehicle, wherein the state set comprises direction, action and power, a local mutual profit game model is introduced, other unmanned aerial vehicles are divided into neighbor unmanned aerial vehicles and non-neighbor unmanned aerial vehicles according to whether detection ranges are overlapped, and a local coverage utility function is defined;
step 3, calculating the optimal coverage under the maximum transmission power to obtain an optimal coverage strategy, which is as follows:
(3.1) initialization: setting the state of each unmanned aerial vehicle N belonging to N as an initial state, and setting the iteration times;
(3.2) detecting: all unmanned aerial vehicles in the unmanned aerial vehicle cluster network carry out information interaction, the iterative state selection strategy of the unmanned aerial vehicle is updated, an unmanned aerial vehicle n is randomly selected for operation, neighbor users and local task areas of the unmanned aerial vehicle are selected, a local coverage utility function value under a prediction action and a current action is calculated, and a coverage strategy which can bring the optimal local coverage utility function value is selected according to probability;
(3.3) convergence: the step (3.2) is circulated until the action strategy selection of all the unmanned aerial vehicles converges or reaches the set iteration times, and the detection learning is finished to obtain the optimal coverage strategy;
step 4, under the optimal coverage strategy, performing power control under the current coverage deployment to obtain the optimal power strategy, which is specifically as follows:
(4.1) initialization: deploying each unmanned aerial vehicle by using the obtained optimal coverage strategy obtained in the step (3.3);
(4.2) detection: all unmanned aerial vehicles in the unmanned aerial vehicle cluster network carry out information interaction, the iterative power selection strategy of the unmanned aerial vehicles is updated, an unmanned aerial vehicle n is randomly selected for operation, the local coverage utility function value of the randomly selected power strategy is calculated, and the power selection strategy which can bring the optimal local coverage utility function value is selected according to the probability;
(4.3) convergence: and (4.2) the step is circulated until the power strategy selection of all the unmanned aerial vehicles realizes convergence or reaches the set iteration times, and the algorithm is ended.
Further, in the step 1, the problem of multiple coverage deployment of the unmanned aerial vehicle under power control is modeled into a potential energy game model, which specifically includes:
the game model is defined as:
Figure BDA0001644772780000031
n ═ {1,2 …, N } represents the drone sequence number, I represents the mission area; snIndicating the state of the drone, including position and transmission power, Sn＝(xn,yn,hn,pn,n∈N)；xn,yn,hnRespectively representing the abscissa, ordinate and altitude, p, of the drone nnRepresenting the operating power of drone n, AnRepresenting the action policy set, U, of drone niRepresenting the set of coverage utilities for region i.
Further, the defining the local coverage utility function in step 2 specifically includes:
gridding the whole area, the coverage utility function u of each grid iiThe following were used:
Figure BDA0001644772780000032
wherein σiRepresenting the service requirement of the area i, and carrying out normalization processing;
Figure BDA0001644772780000033
representing the coverage success rate of the unmanned plane n to the area; u. ofi,N(sn,s-n) Representing the coverage utility of the unmanned aerial vehicle group N to the area i;
calculating to obtain a coverage utility function U of the whole area:
Figure BDA0001644772780000034
wherein I represents a task area.
Further, the step 3 of calculating the optimal coverage under the maximized transmission power to obtain an optimal coverage strategy specifically includes:
setting the minimum communication requirement to be tau, and setting the optimal coverage deployment strategy P1 to be:
Figure BDA0001644772780000035
wherein, PmaxIndicating that all unmanned planes in the unmanned plane cluster carry the maximum transmission power;
pair type (3) whole area by using local mutual interest game modelOptimizing a coverage utility function of the domain to obtain a function of the local coverage utility of the unmanned aerial vehicle along with the position change
Figure BDA0001644772780000036
Figure BDA0001644772780000041
Wherein In,covRefers to the total coverage area of all drones overlapping with the drone n detection range, J1nRefers to the set of all drones overlapping with the detection range of drone n, InThe method refers to an area within an unmanned aerial vehicle n detection range;
therefore, P1 translates to a location state strategy under locally optimal coverage
Figure BDA0001644772780000042
Figure BDA0001644772780000043
Further, in the step 4, under the optimal coverage strategy, power control under the current coverage deployment is performed to obtain an optimal power strategy, which specifically includes:
constructing a function G1 of coverage efficiency as a function of power at the maximum coverage deployment location:
Figure BDA0001644772780000044
wherein the content of the first and second substances,
Figure BDA0001644772780000045
is shown in the maximum coverage position soptThe coverage utility of the unmanned aerial vehicle which is changed with the carrier transmission power of the unmanned aerial vehicle n; formula (7) shows that when the coverage utility of the unmanned aerial vehicle group meets the communication requirement tau, the coverage efficiency is the ratio of the total coverage utility to the total power overhead, and when the communication requirement is not met, the communication is regarded as failure, and the coverage efficiency is 0;
therefore, the optimal power allocation strategy at the maximum coverage location is expressed as:
P2:popt＝arg max G1 (8)
using a local mutual profit game model optimization formula (7), obtaining a function of the local coverage utility of the unmanned aerial vehicle along with the power change:
Figure BDA0001644772780000046
wherein, InRepresenting a detectable task area of drone n; formula (9) shows that the ratio of the total coverage utility to the total power consumption of the unmanned aerial vehicle group in the n detection range of the unmanned aerial vehicle changes with the power to obtain the optimal power strategy
Figure BDA0001644772780000047
Figure BDA0001644772780000051
Figure BDA0001644772780000052
Further, the updating of the iterative state selection policy of the drone in step (3.2) is specifically as follows:
for the selected drone j, its current state s is calculated using equation (5)jAnd predicting action s'jLocal coverage utility value in state
Figure BDA0001644772780000053
And
Figure BDA0001644772780000054
and then the unmanned aerial vehicle updates the state selection strategy s of the next iteration through a formula (11)j(t+1)：
Figure BDA0001644772780000055
Wherein beta > 0 is BohrZeeman learning coefficient, P(s)j(t+1)＝sj(t)) is the probability that the current state remains unchanged when the drone j selects for the t +1 th time, P(s)j(t+1)＝s'j(t)) is the probability that the predicted action was selected at the t +1 th selection of drone j.
Further, the step (4.2) of updating the iterative power selection policy of the drone is specifically as follows:
neighbor drones other than drone n repeat the previous power selection pk(t+1)＝pk(t),k∈JnFor the selected drone n, it first calculates its current transmission power p, using equation (8)nAnd predicting transmission power
Figure BDA0001644772780000056
Local coverage energy efficiency of
Figure BDA0001644772780000057
And
Figure BDA0001644772780000058
the drone then updates the power selection policy according to equation (12):
Figure BDA0001644772780000059
compared with the prior art, the invention has the remarkable advantages that: (1) on the basis of combining a plurality of unmanned aerial vehicles with relevance, a multi-coverage probability function is constructed, and a real unmanned aerial vehicle cluster network communication scene is more accurately described; (2) on the basis of potential energy game modeling, a local mutual profit model is introduced according to an actual unmanned aerial vehicle detection range, and the position adjustment and power selection calculation convergence of the unmanned aerial vehicle group is faster by constructing a local coverage utility function, so that the calculation cost is reduced, and the task efficiency is improved.
Drawings
Fig. 1 is a schematic structural diagram of a multiple coverage deployment model of an unmanned aerial vehicle under power control of the invention.
FIG. 2 is a graph showing the importance density distribution of the set task areas in example 1 of the present invention.
Fig. 3 is a diagram of maximum coverage utility deployment at maximum transmission power carried by 8 drones in example 1 of the present invention.
Fig. 4 is a graph of the convergence curve (10 times) of the total utility of the full-network coverage of 8 unmanned planes under the MUECD-SAP algorithm in example 1 of the invention.
Fig. 5 is an optimal diagram of power selection of 8 drones in the current coverage utility deployment in example 1 of the present invention.
Fig. 6 is a graph showing the convergence of the number of the 8 drones selected for each power under the MUECD-SAP algorithm in example 1 of the present invention.
Fig. 7 is a graph showing the convergence of the total transmission power of the drone swarm under the MUECD-SAP algorithm for different numbers of drones in example 1 of the present invention.
FIG. 8 is a graph of the convergence of the total coverage energy efficiency of a drone swarm for the MUECD-SAP algorithm described for different numbers of drones in example 1 of the present invention.
Detailed Description
Unmanned aerial vehicles in the unmanned aerial vehicle cluster communication network can be divided into neighboring unmanned aerial vehicles and non-neighboring unmanned aerial vehicles according to whether the detection ranges are overlapped or not. For a ground unit area i, assume that the probability of coverage of drone j on it is
Figure BDA0001644772780000061
Considering that in the unmanned aerial vehicle cluster network, the probability that any task area can be covered by the unmanned aerial vehicle network is jointly determined by the unmanned aerial vehicles capable of detecting the area, and assuming that the unmanned aerial vehicles with the detection ranges covering the ground unit area i are unmanned aerial vehicles 1,2 and 3, the coverage probability of the area under the comprehensive action of the multiple unmanned aerial vehicles is
Figure BDA0001644772780000062
The formula indicates that the more drones that can detect zone i, the higher the probability that they will be successfully covered. By means of deployment of the unmanned aerial vehicle and control of transmission power, the goal of minimizing total transmission power on the premise of meeting communication requirements is to be achieved.
With reference to fig. 1, the multiple coverage model of the unmanned aerial vehicle under power control of the present invention is characterized as follows: in a communication coverage scene in the unmanned aerial vehicle cluster network, for any task area, the probability of coverage of the unmanned aerial vehicle network is jointly determined by the unmanned aerial vehicles which can detect the area; by unmanned aerial vehicle deployment and transmission power control, the aim of minimizing the total transmission power is achieved on the premise of meeting communication requirements.
The invention discloses a deployment method of multiple coverage of an unmanned aerial vehicle under power control, which comprises the following steps:
step 1, modeling a problem of unmanned aerial vehicle multiple coverage deployment under power control into a potential energy game model, wherein participants of a game are all unmanned aerial vehicles with task requirements in an unmanned aerial vehicle network;
step 2, each unmanned aerial vehicle constructs a state set of the unmanned aerial vehicle, wherein the state set comprises direction, action and power, a local mutual profit game model is introduced, other unmanned aerial vehicles are divided into neighbor unmanned aerial vehicles and non-neighbor unmanned aerial vehicles according to whether detection ranges are overlapped, and a local coverage utility function is defined;
step 3, calculating the optimal coverage under the maximum transmission power to obtain an optimal coverage strategy, which is as follows:
(3.1) initialization: setting the state of each unmanned aerial vehicle N belonging to N as an initial state, and setting the iteration times;
(3.2) detecting: all unmanned aerial vehicles in the unmanned aerial vehicle cluster network carry out information interaction, the iterative state selection strategy of the unmanned aerial vehicle is updated, an unmanned aerial vehicle n is randomly selected for operation, neighbor users and local task areas of the unmanned aerial vehicle are selected, a local coverage utility function value under a prediction action and a current action is calculated, and a coverage strategy which can bring the optimal local coverage utility function value is selected according to probability;
(3.3) convergence: the step (3.2) is circulated until the action strategy selection of all the unmanned aerial vehicles converges or reaches the set iteration times, and the detection learning is finished to obtain the optimal coverage strategy;
step 4, under the optimal coverage strategy, performing power control under the current coverage deployment to obtain the optimal power strategy, which is specifically as follows:
(4.1) initialization: deploying each unmanned aerial vehicle by using the obtained optimal coverage strategy obtained in the step (3.3);
(4.2) detection: all unmanned aerial vehicles in the unmanned aerial vehicle cluster network carry out information interaction, the iterative power selection strategy of the unmanned aerial vehicles is updated, an unmanned aerial vehicle n is randomly selected for operation, the local coverage utility function value of the randomly selected power strategy is calculated, and the power selection strategy which can bring the optimal local coverage utility function value is selected according to the probability;
(4.3) convergence: and (4.2) the step is circulated until the power strategy selection of all the unmanned aerial vehicles realizes convergence or reaches the set iteration times, and the algorithm is ended.
The invention is implemented as follows:
step 1, modeling the unmanned aerial vehicle coverage deployment problem into a potential energy game model, wherein the game model is defined as:
Figure BDA0001644772780000071
n ═ {1,2 …, N } represents the drone sequence number, I represents the mission area; snIndicating the state of the drone, including position and transmission power, Sn＝(xn,yn,hn,pn,n∈N)；xn,yn,hnRespectively representing the abscissa, ordinate and altitude, p, of the drone nnRepresenting the operating power of drone n, AnRepresenting the action policy set, U, of drone niRepresenting the set of coverage utilities for region i.
Secondly, gridding the area and calculating the coverage utility u of each grid iiThe following were used:
Figure BDA0001644772780000081
wherein σiRepresenting the service requirement of the area i, and carrying out normalization processing;
Figure BDA0001644772780000082
representing the coverage success rate of the unmanned plane n to the area; u. ofi,N(sn,s-n) Representing the coverage utility of the unmanned aerial vehicle group N to the area i;
from this, we calculate the coverage utility over the whole area:
Figure BDA0001644772780000083
wherein I represents a task area.
By utilizing a local mutual profit game model, functions of the local coverage utility of the unmanned aerial vehicle along with the change of the position and the local coverage energy efficiency of the unmanned aerial vehicle along with the change of the transmission power are respectively expressed as follows:
Figure BDA0001644772780000084
Figure BDA0001644772780000085
wherein In,covRefers to the total coverage area of all drones overlapping with the drone n detection range, J1nRefers to the set of all drones overlapping with the detection range of drone n, InRefers to the area within the investigation range of the unmanned plane n.
Thus, the objective function is expressed as follows:
Figure BDA0001644772780000086
Figure BDA0001644772780000087
Figure BDA0001644772780000088
thirdly, executing the space adaptive learning-based unmanned aerial vehicle multi-coverage efficient deployment algorithm (MUECD-SAP) in the step 4, wherein the unmanned aerial vehicle conducts exploration learning and executes strategy selection according to the local coverage utility value until the strategy selection of all the unmanned aerial vehicles achieves convergence or reaches the set iteration times, and the method specifically comprises the following steps:
maximizing the optimal coverage of the unmanned aerial vehicle under the carrier transmission power:
(1) all unmanned aerial vehicles carry out information interaction;
(2) randomly selecting one unmanned aerial vehicle j for operation in each iteration;
(3) unmanned j selects a predicted action, s'j(t), j ∈ N, and t is the iteration number.
For the selected drone j, its current state s is calculated using equation (4)jAnd predicted action State s'jLocal coverage utility value of
Figure BDA0001644772780000091
And
Figure BDA0001644772780000092
then the unmanned plane updates the state selection strategy s of the next iteration of the unmanned plane through a formula (9)j(t+1)：
Figure BDA0001644772780000093
Wherein β > 0 is Boltzmann learning coefficient, P(s)j(t+1)＝sj(t)) represents the probability that the current state remains unchanged, P(s), for the t +1 th selection of drone jj(t+1)＝s'j(t)) the probability of the predicted action is selected at the time of the t +1 th selection of drone j.
(4) And (3) looping until the strategy selection converges or a set iteration number is reached. The current coverage deployment state is saved and entered into step 2.
② power control under current coverage deployment (spatial adaptive learning):
(1) all unmanned aerial vehicles carry out information interaction;
(2) randomly selecting one unmanned aerial vehicle n for operation in each iteration, and setting t as the iteration number;
(3) neighbors other than drone nPower selection p before drone repetitionk(t+1)＝pk(t),k∈Jn
For the selected drone n, it first calculates its current transmission power p, using equation (5)nAnd predicting transmission power
Figure BDA0001644772780000094
Local coverage energy efficiency of
Figure BDA0001644772780000095
And
Figure BDA0001644772780000096
the drone then updates its power selection strategy according to equation (10):
Figure BDA0001644772780000097
(4) looping (3) until the power strategy selection converges or a set iteration number is reached;
and the unmanned aerial vehicle group performs power distribution according to the convergence results of the first step and the second step and flies to the respective appointed task areas.
Example 1
One embodiment of the invention is described below: matlab software is adopted for system simulation, and generality is not influenced by parameter setting; considering an unmanned aerial vehicle cluster communication network in an urban environment, on the premise of knowing task area information, dividing an area into 50 × 50 area blocks, wherein the unit distance is 200m, the flying height of an unmanned aerial vehicle is uniformly set to be 500m, fig. 2 shows a task area example in the unmanned aerial vehicle cluster network, wherein (15,35), (35,15) (× 200m) is set as a central point of a special area, namely, the closer to the central point, the higher the importance of the task area is, optimization calculation is performed on a target, and the importance of a total task area is normalized; the carrier frequency carried by the unmanned aerial vehicle is set to be 2000MHZ, and other parameters are set to be muLoS＝1dB，μNLoS＝20dB，α＝0.6，γ＝0.11；σLoS(θj)＝k1exp(-k2θj)，σNLoS(θj)＝g1exp(-g2θj) Wherein (k)1,k2)＝(10.39,0.05),(g1,g2) Not (29.06, 0.03). The learning coefficient β changes depending on the actual utility value and increases as the number of iterations increases, so that the result gradually deviates from the learning detection process toward convergence.
Considering the space adaptive learning-based unmanned aerial vehicle multi-coverage efficient deployment algorithm (MUECD-SAP), the specific implementation process is specifically operated until the power strategy selection converges or a set iteration number is reached, and the unmanned aerial vehicle cluster performs power distribution and flies to the respectively specified task areas according to the result converged in the steps 1 and 2 in the specific implementation process.
And (3) simulation result analysis:
fig. 3 is a diagram of maximum coverage utility deployment considering 8 drones carrying maximum transmission power. The colors of the task areas in the graph correspond to the coverage success probability under the action of the multiple unmanned aerial vehicles. And the optimal coverage calculation under the maximum transmission power is carried out by utilizing a linear logarithm learning algorithm. The deployment of the unmanned aerial vehicle in the figure corresponds well to the distribution characteristics of the importance in the task area of figure 2, and the effectiveness and the authenticity of the algorithm are proved.
Fig. 4 is a convergence curve (10 times) considering the total utility of full-network coverage of 8 drones under the MUECD-SAP algorithm. Multiple simulation results avoid the contingency of algorithm results. The first step curve of the graph shows that the whole network coverage utility can be optimized and converged when the unmanned aerial vehicle maximizes carrier transmission power by calculating the local coverage utility of the unmanned aerial vehicle, and also proves that the optimal coverage deployment of the unmanned aerial vehicle cluster in the graph 3 is practical and reliable, the second step curve of the graph shows the convergence of the unmanned aerial vehicle under a power control algorithm, and the final convergence result meets the minimum communication requirement and also proves the reliability of the algorithm.
Fig. 5 is a schematic diagram of power control coverage deployment under the MUECD-SAP algorithm considering 8 unmanned aerial vehicles. According to the step 2, the power selection and distribution are carried out after the unmanned aerial vehicle group determines the coverage deployment position. The figure shows that the unmanned aerial vehicle cluster performs optimal power distribution, and the total power is minimized on the premise of meeting the communication requirement, so that the energy efficiency is optimal.
Fig. 6 is a convergence curve of the number of drones at each power selection under the spatial adaptive learning algorithm. The diagram shows that the power energy efficiency of the whole network can be optimized and converged by calculating the local energy efficiency of the unmanned aerial vehicle, and also proves that the power distribution deployment of the unmanned aerial vehicle cluster in the diagram 5 is practical and reliable.
Fig. 7 and 8 are convergence curves of the total transmission power and the total coverage energy efficiency of the drone swarm under the MUECD-SAP algorithm under consideration of different numbers of drones. The diagram shows that the power and energy efficiency of the whole network can be optimized and converged by calculating the local transmission power and the energy efficiency of the unmanned aerial vehicle, and also proves that the power distribution deployment of the unmanned aerial vehicle cluster in the diagram 5 is practical and reliable, the rationality of the energy efficiency concept and the convergence of the algorithm are further verified, and the authenticity and the rationality of the proposed model are proved.

Claims (7)

1. A method for deploying multiple coverage areas of an unmanned aerial vehicle under power control is characterized by comprising the following steps:
step 1, modeling a problem of unmanned aerial vehicle multiple coverage deployment under power control into a potential energy game model, wherein participants of a game are all unmanned aerial vehicles with task requirements in an unmanned aerial vehicle network;
step 2, each unmanned aerial vehicle constructs a state set of the unmanned aerial vehicle, wherein the state set comprises direction, action and power, a local mutual profit game model is introduced, other unmanned aerial vehicles are divided into neighbor unmanned aerial vehicles and non-neighbor unmanned aerial vehicles according to whether detection ranges are overlapped, and a local coverage utility function is defined;
step 3, calculating the optimal coverage under the maximum transmission power to obtain an optimal coverage strategy, which is as follows:
(3.1) initialization: setting the state of each unmanned aerial vehicle N belonging to N as an initial state, and setting the iteration times;
(3.2) detecting: all unmanned aerial vehicles in the unmanned aerial vehicle cluster network carry out information interaction, the iterative state selection strategy of the unmanned aerial vehicle is updated, an unmanned aerial vehicle n is randomly selected for operation, neighbor users and local task areas of the unmanned aerial vehicle are selected, a local coverage utility function value under a prediction action and a current action is randomly selected by the unmanned aerial vehicle, and a coverage strategy which can bring the optimal local coverage utility function value is selected according to probability;
(3.3) convergence: the step (3.2) is circulated until the action strategy selection of all the unmanned aerial vehicles converges or reaches the set iteration times, and the detection learning is finished to obtain the optimal coverage strategy;
step 4, under the optimal coverage strategy, performing power control under the current coverage deployment to obtain the optimal power strategy, which is specifically as follows:
(4.1) initialization: deploying each unmanned aerial vehicle by using the optimal coverage strategy obtained in the step (3.3);
(4.2) detection: all unmanned aerial vehicles in the unmanned aerial vehicle cluster network carry out information interaction, the iterative power selection strategy of the unmanned aerial vehicles is updated, an unmanned aerial vehicle n is randomly selected for operation, the local coverage utility function value of the randomly selected power strategy is calculated, and the power selection strategy which can bring the optimal local coverage utility function value is selected according to the probability;
(4.3) convergence: and (4.2) circulating the step until the power strategy selection of all the unmanned aerial vehicles realizes convergence or reaches the set iteration number.
2. The method for deploying multiple coverage areas of an unmanned aerial vehicle under power control as claimed in claim 1, wherein the problem of deploying multiple coverage areas of the unmanned aerial vehicle under power control is modeled as a potential energy game model in step 1, and the method specifically comprises the following steps:
the game model is defined as:
Figure FDA0002645703210000011
n ═ {1,2 …, N } represents the drone sequence number, I represents the mission area; snIndicating the state of the drone, including position and transmission power, Sn＝(xn,yn,hn,pn,n∈N)；xn,yn,hnRespectively representing the crosswebs of unmanned aerial vehicle nCoordinate, ordinate and height, pnRepresenting the operating power of drone n, AnRepresenting the action policy set, U, of drone niRepresenting the set of coverage utilities for region i.
3. The method for deploying multiple coverage areas of an unmanned aerial vehicle under power control as claimed in claim 1, wherein the step 2 of defining the local coverage utility function specifically comprises:
gridding the whole area, the coverage utility function u of each grid iiThe following were used:
Figure FDA0002645703210000021
wherein σiRepresenting the service requirement of the area i, and carrying out normalization processing;
Figure FDA0002645703210000022
representing the coverage success rate of the unmanned plane n to the area; u. ofi,N(sn,s-n) Representing the coverage utility of the unmanned aerial vehicle group N to the area i;
calculating to obtain a coverage utility function U of the whole area:
Figure FDA0002645703210000023
wherein I represents a task area.
4. The method for deploying multiple coverage areas of an unmanned aerial vehicle under power control as claimed in claim 1, wherein the step 3 calculates an optimal coverage area under the maximum transmission power to obtain an optimal coverage strategy, specifically:
setting the minimum communication requirement to be tau, and setting the optimal coverage deployment strategy P1 to be:
Figure FDA0002645703210000024
wherein，PmaxIndicating that all unmanned planes in the unmanned plane cluster carry the maximum transmission power;
optimizing the coverage utility function of the whole region of the formula (3) by using a local mutual interest game model to obtain the function of the local coverage utility of the unmanned aerial vehicle along with the position change
Figure FDA0002645703210000025
Figure FDA0002645703210000026
Wherein In,covRefers to the total coverage area of all drones overlapping with the drone n detection range, J1nRefers to the set of all drones overlapping with the detection range of drone n, InThe method refers to an area within an unmanned aerial vehicle n detection range;
therefore, P1 translates to a location state strategy under locally optimal coverage
Figure FDA0002645703210000027
Figure FDA0002645703210000031
5. The method for deploying multiple coverage areas of an unmanned aerial vehicle under power control as claimed in claim 1, wherein in the step 4, power control under current coverage deployment is performed under an optimal coverage strategy to obtain an optimal power strategy, specifically:
constructing a function G1 of coverage efficiency as a function of power at the maximum coverage deployment location:
Figure FDA0002645703210000032
wherein the content of the first and second substances,
Figure FDA0002645703210000033
is shown in the maximum coverage position soptThe coverage utility of the unmanned aerial vehicle which is changed with the carrier transmission power of the unmanned aerial vehicle n; formula (7) shows that when the coverage utility of the unmanned aerial vehicle group meets the communication requirement tau, the coverage efficiency is the ratio of the total coverage utility to the total power overhead, and when the communication requirement is not met, the communication is regarded as failure, and the coverage efficiency is 0;
therefore, the optimal power allocation strategy at the maximum coverage location is expressed as:
P2：popt＝arg max G1 (8)
using a local mutual profit game model optimization formula (7), obtaining a function of the local coverage utility of the unmanned aerial vehicle along with the power change:
Figure FDA0002645703210000034
wherein, InRepresenting a detectable task area of drone n; formula (9) shows that the ratio of the total coverage utility to the total power consumption of the unmanned aerial vehicle group in the n detection range of the unmanned aerial vehicle changes with the power to obtain the optimal power strategy
Figure FDA0002645703210000035
Figure FDA0002645703210000036
Figure FDA0002645703210000037
6. The method for multiple coverage deployment of drones under power control as claimed in claim 4, wherein the step (3.2) of updating the iterative drone state selection policy is as follows:
for the selected drone j, its current state s is calculated using equation (5)jAnd predicting action s'jLocal coverage utility value in state
Figure FDA0002645703210000041
And
Figure FDA0002645703210000042
and then the unmanned aerial vehicle updates the state selection strategy s of the next iteration through a formula (11)j(t+1)：
Figure FDA0002645703210000043
Wherein, beta>0 is Boltzmann learning coefficient, P(s)j(t+1)＝sj(t)) is the probability that the current state remains unchanged when the drone j selects for the t +1 th time, P(s)j(t+1)＝s'j(t)) is the probability that the predicted action was selected at the t +1 th selection of drone j.
7. The method for multiple coverage deployment of drones under power control as claimed in claim 5, wherein the step (4.2) of updating the iterative power selection strategy of drones is as follows:
neighbor drones other than drone n repeat the previous power selection pk(t+1)＝pk(t),k∈Jn，For the selected drone n, it first calculates its selected current transmission power p using equation (9)nAnd predicting transmission power
Figure FDA0002645703210000044
Local coverage energy efficiency of
Figure FDA0002645703210000045
And
Figure FDA0002645703210000046
the drone then updates the power selection policy according to equation (12):
Figure FDA0002645703210000047
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Families Citing this family (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109445946B (en) * 2018-11-01 2023-03-31 西北工业大学 Unmanned aerial vehicle cloud task deployment method and device
CN109803344B (en) 2018-12-28 2019-10-11 北京邮电大学 A kind of unmanned plane network topology and routing joint mapping method
CN109660292B (en) * 2019-01-23 2021-01-19 南京邮电大学 Flight height design method of wireless communication system based on multi-unmanned aerial vehicle coverage
CN110069074B (en) * 2019-04-17 2020-08-18 南京航空航天大学 Unmanned aerial vehicle collaborative flight path planning method based on multi-target three-point positioning
CN110412999B (en) * 2019-06-20 2022-12-09 合肥工业大学 Game intelligent decision method and system for multi-unmanned aerial vehicle task allocation in confrontation environment
CN110312265B (en) * 2019-06-24 2021-01-01 清华大学 Power distribution method and system for unmanned aerial vehicle formation communication coverage
US11083026B2 (en) 2019-09-25 2021-08-03 Nokia Technologies Oy Determining coverage availability estimates of mobile non-terrestrial access node
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CN111711960A (en) * 2020-01-16 2020-09-25 中国人民解放军陆军工程大学 Energy efficiency perception unmanned aerial vehicle cluster three-dimensional deployment method
KR102262835B1 (en) * 2020-01-28 2021-06-08 연세대학교 산학협력단 Drone control apparatus and method for efficient military drone operations
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CN112580537B (en) * 2020-12-23 2022-06-21 中国人民解放军国防科技大学 Deep reinforcement learning method for multi-unmanned aerial vehicle system to continuously cover specific area
CN112929866B (en) * 2021-01-20 2024-05-28 河北工程大学 Unmanned aerial vehicle deployment method for adaptively optimizing network coverage of urban disaster area
CN113608548A (en) * 2021-07-23 2021-11-05 中国科学院地理科学与资源研究所 Unmanned aerial vehicle emergency processing method and system, storage medium and electronic equipment
CN113825142B (en) * 2021-09-27 2022-10-11 南京航空航天大学 Intelligent optimization method for cooperative task area coverage of unmanned cluster system
CN114200964B (en) * 2022-02-17 2022-04-26 南京信息工程大学 Unmanned aerial vehicle cluster cooperative reconnaissance coverage distributed autonomous optimization method
CN114501551B (en) * 2022-04-02 2022-07-01 中国人民解放军军事科学院国防科技创新研究院 Multi-user distributed heterogeneous network selection strategy method based on ordered potential game

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102811443B (en) * 2012-07-27 2014-11-19 南京邮电大学 Interference management method based on spectrum allocation and power control in family base station system
WO2015130794A1 (en) * 2014-02-25 2015-09-03 Lockheed Martin Corporation Single platform doppler geolocation
CN105700555A (en) * 2016-03-14 2016-06-22 北京航空航天大学 Potential game-based multi-unmanned aerial vehicle cooperative search method
CN106059840A (en) * 2016-08-02 2016-10-26 北京邮电大学 Power allocation method and device for cognitive radio system
CN106197432A (en) * 2016-08-30 2016-12-07 北京航空航天大学 A kind of UAV Landing method based on FastSLAM algorithm
US9622133B1 (en) * 2015-10-23 2017-04-11 The Florida International University Board Of Trustees Interference and mobility management in UAV-assisted wireless networks
CN107037828A (en) * 2017-03-24 2017-08-11 西安电子科技大学 The single step optimization method of unmanned plane region overlay based on particle cluster algorithm
CN107463094A (en) * 2017-07-13 2017-12-12 江西洪都航空工业集团有限责任公司 A kind of multiple no-manned plane air battle dynamic game method under uncertain information

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9881021B2 (en) * 2014-05-20 2018-01-30 Verizon Patent And Licensing Inc. Utilization of third party networks and third party unmanned aerial vehicle platforms
CN106406346B (en) * 2016-11-01 2019-04-16 北京理工大学 A kind of multiple no-manned plane collaboration rapid Cover search path planning method

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102811443B (en) * 2012-07-27 2014-11-19 南京邮电大学 Interference management method based on spectrum allocation and power control in family base station system
WO2015130794A1 (en) * 2014-02-25 2015-09-03 Lockheed Martin Corporation Single platform doppler geolocation
US9622133B1 (en) * 2015-10-23 2017-04-11 The Florida International University Board Of Trustees Interference and mobility management in UAV-assisted wireless networks
CN105700555A (en) * 2016-03-14 2016-06-22 北京航空航天大学 Potential game-based multi-unmanned aerial vehicle cooperative search method
CN105700555B (en) * 2016-03-14 2018-04-27 北京航空航天大学 A kind of multiple no-manned plane collaboratively searching method based on gesture game
CN106059840A (en) * 2016-08-02 2016-10-26 北京邮电大学 Power allocation method and device for cognitive radio system
CN106197432A (en) * 2016-08-30 2016-12-07 北京航空航天大学 A kind of UAV Landing method based on FastSLAM algorithm
CN107037828A (en) * 2017-03-24 2017-08-11 西安电子科技大学 The single step optimization method of unmanned plane region overlay based on particle cluster algorithm
CN107463094A (en) * 2017-07-13 2017-12-12 江西洪都航空工业集团有限责任公司 A kind of multiple no-manned plane air battle dynamic game method under uncertain information

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Computation offloading game for an UAV network in mobile edge computing;Mohamed-Ayoub Messous;《2017 IEEE International conference on communications》;20170731;全文 *
多无人机协同任务规划技术研究;邓启波;《中国博士学位论文全文数据库》;20150430;全文 *

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