CN116132944A - Topology and power joint control method in unmanned aerial vehicle communication network - Google Patents

Topology and power joint control method in unmanned aerial vehicle communication network Download PDF

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CN116132944A
CN116132944A CN202310112814.3A CN202310112814A CN116132944A CN 116132944 A CN116132944 A CN 116132944A CN 202310112814 A CN202310112814 A CN 202310112814A CN 116132944 A CN116132944 A CN 116132944A
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unmanned aerial
aerial vehicle
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王海超
方贵
宋绯
李国鑫
官新荣
徐承龙
贾倩
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Army Engineering University of PLA
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/08—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on transmission power
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
    • 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
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/28—TPC being performed according to specific parameters using user profile, e.g. mobile speed, priority or network state, e.g. standby, idle or non transmission
    • H04W52/283—Power depending on the position of the mobile
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract

The topology and power joint control method in the unmanned aerial vehicle communication network comprises the steps of firstly obtaining information such as unmanned aerial vehicle position, communication requirement, power limitation and the like, and describing the functions of all elements and the relation between the elements; fixing the position of the unmanned aerial vehicle and optimizing the power of the unmanned aerial vehicle; judging whether the target value changes, if so, fixing the unmanned aerial vehicle power and optimizing the unmanned aerial vehicle position, and if not, outputting the unmanned aerial vehicle position and the unmanned aerial vehicle power; after the unmanned aerial vehicle power is fixed and the unmanned aerial vehicle position is optimized, judging whether the target value is changed, and if so, executing the unmanned aerial vehicle position fixing again and the unmanned aerial vehicle power optimizing again until the target value is unchanged; if not, outputting the unmanned aerial vehicle position and the unmanned aerial vehicle power. Taking factors such as unmanned aerial vehicle group formation requirements, internal mutual interference, communication constraint, power limitation and the like into consideration, the control method can be suitable for a scene of large-scale unmanned aerial vehicle communication; communication throughput can be greatly improved; and can extend to unmanned aerial vehicle communication and control integration scene.

Description

Topology and power joint control method in unmanned aerial vehicle communication network
Technical Field
The invention belongs to the field of wireless communication and unmanned aerial vehicle communication, and particularly relates to a topology and power joint control model and method in an unmanned aerial vehicle communication network.
Background
An unmanned aerial vehicle (Unmanned Aerial Vehicle, UAV) is a short form of aircraft that is driven by a power system, unmanned on board, and reusable. Unmanned aircraft, being an "unmanned on-board, system-manned", has natural advantages in performing boring, harsher and dangerous tasks. As a product of military operation, the unmanned aerial vehicle has evolved from an initial simple control and rough guidance aircraft into a novel strength integrating attack and defense and investigation, and plays an important role in various tasks such as relay communication, reconnaissance and early warning, tracking and positioning, special combat, accurate guidance, information countermeasure, battlefield search and rescue and the like. As a bridge connecting the tie of the unmanned aerial vehicle system and the control center and the cooperation of the unmanned aerial vehicle group, unmanned aerial vehicle communication plays a vital role.
Current research on drone communications has focused primarily on a single drone as a base station, relay, or energy source to assist ground communications. While less research on unmanned aerial vehicle group communications is involved, the main research includes: unmanned aerial vehicle cluster track planning problem, unmanned aerial vehicle cluster cooperative control and formation, unmanned aerial vehicle cluster task reliability model and the like in a complex environment. However, unmanned aerial vehicle group communication faces challenges such as limited spectrum resources, serious internal interference, dynamic environment change and the like, and the existing method cannot be directly applied.
Disclosure of Invention
Aiming at the problem of data transmission in an unmanned aerial vehicle group communication network, the invention provides a data transmission method based on network topology and transmission power joint control by considering factors such as unmanned aerial vehicle group formation requirement, internal mutual interference, communication constraint, power limitation and the like.
A topology and power joint control method in an unmanned aerial vehicle communication network comprises the following steps:
step 1: the roles of the elements and the relations between the elements in the topology and power combined control method are described;
step 2: establishing an unmanned aerial vehicle topology and power joint control model by considering unmanned aerial vehicle group formation requirements, internal mutual interference, communication constraint and power limitation;
step 3: optimizing network topology under the condition of fixed power and judging whether the condition is met;
step 4: the power optimization problem under the network topology fixation and judging whether the condition is satisfied;
step 5: and jointly optimizing topology and power, and judging whether the conditions are met.
The network topology and transmission power combined control method can be suitable for the scene of large-scale unmanned aerial vehicle communication, is designed for large-scale unmanned aerial vehicle groups, can obtain good system performance within acceptable complexity, solves the problem that the traditional unmanned aerial vehicle optimization method only considers the condition of a pair of unmanned aerial vehicle communication pairs, can better adapt to the requirement of future unmanned aerial vehicle group communication, and provides a larger supporting effect for both civil field and military field; conventional optimization algorithms typically perform power optimization only, without taking into account the effect of the spatial locations of the transmitter and receiver on the channel transmission quality. The joint control method provided by the invention considers the integration of the network topology (spatial position) and the transmission power, can more fully utilize the mobility of the unmanned aerial vehicle, spatially multiplexes spectrum resources, and can greatly improve the communication throughput; the invention starts from the multidisciplinary intersection of information and communication engineering and control engineering, combines power and flight formation, is beneficial to improving the capability of the unmanned aerial vehicle group to execute complex tasks, and can be expanded to unmanned aerial vehicle communication and control fusion scenes.
Drawings
Fig. 1 is a flow chart of a method for controlling the topology and power of a unmanned aerial vehicle in a combined manner.
Fig. 2 is a schematic diagram of a system scenario in an embodiment of the present invention.
Fig. 3 is an algorithm convergence scenario in an embodiment of the present invention.
Fig. 4 is a positional diagram of a group of robots in accordance with the present invention.
Fig. 5 is a graph of throughput as a function of the radius of the unmanned aerial vehicle population activity.
Detailed Description
A topology and power joint control method in an unmanned aerial vehicle communication network comprises the following steps:
step 1: the roles of the elements and the relations between the elements in the topology and power combined control method are described;
step 2: establishing an unmanned aerial vehicle topology and power joint control model by considering unmanned aerial vehicle group formation requirements, internal mutual interference, communication constraint and power limitation;
step 3: optimizing network topology under the condition of fixed power and judging whether the condition is met;
step 4: the power optimization problem under the network topology fixation and judging whether the condition is satisfied;
step 5: and jointly optimizing topology and power, and judging whether the conditions are met.
As shown in fig. 1, a flow diagram of a topology and power joint control method in an unmanned aerial vehicle communication network of the invention is that firstly, relevant parameters are input, information such as unmanned aerial vehicle position, communication demand, power limitation and the like is obtained, and the actions of each element and the relation between the elements are described; fixing the position of the unmanned aerial vehicle and optimizing the power of the unmanned aerial vehicle; judging whether the target value changes, if so, fixing the unmanned aerial vehicle power and optimizing the unmanned aerial vehicle position, and if not, outputting the unmanned aerial vehicle position and the unmanned aerial vehicle power; after the unmanned aerial vehicle power is fixed and the unmanned aerial vehicle position is optimized, judging whether the target value is changed, and if so, executing the unmanned aerial vehicle position fixing again and the unmanned aerial vehicle power optimizing again until the target value is unchanged; if not, outputting the unmanned aerial vehicle position and the unmanned aerial vehicle power.
In the topology and power combined control method of the invention, the actions of each element and the relation between the elements are described, and the description comprises the following contents:
considering that N unmanned aerial vehicles need to transmit information due to tasks, and the unmanned aerial vehicle group needs to keep a certain formation in the advancing process, and under the condition of limited frequency spectrum resources, all unmanned aerial vehicle communication pairs work on the same frequency band; the unmanned aerial vehicles can be interfered by other unmanned aerial vehicles in the unmanned aerial vehicle group when in two-to-two communication; therefore, the maximum transmission rate of the unmanned aerial vehicle group communication network is realized by optimally designing the space position and the power of the unmanned aerial vehicle.
In the step 2, an unmanned plane topology and power joint control model is established, which comprises the following contents:
when N unmanned aerial vehicle pairs communicate simultaneously, the unmanned aerial vehicle transmitter can generate interference to other unmanned aerial vehicle receivers; let the positions of the unmanned aerial vehicle transmitter and unmanned aerial vehicle receiver be q respectively n and wn Communication rate R between the two n Is that
Figure BDA0004077499130000031
Where W is the bandwidth of the channel,
Figure BDA0004077499130000036
for the n-th unmanned aerial vehicle receiver to receive the signal intensity, I n Is the interference of other unmanned aerial vehicles in the unmanned aerial vehicle group to the nth unmanned aerial vehicle communication pair, sigma 2 Is Gaussian white noise; because the aerial unmanned aerial vehicle communication has little shielding, the signal intensity and the interference that the nth unmanned aerial vehicle receiver received are respectively:
Figure BDA0004077499130000032
/>
Figure BDA0004077499130000033
wherein pn 、p m G is the transmitting power of the n and m unmanned aerial vehicle transmitters nn 、g mn For communication link and interfering link channel gains ρ 0 For reference distance d 0 The channel gain at the position, alpha is a path loss index, and the alpha is the Euclidean distance of the a vector; for a group of unmanned aerial vehicles, the total maximum information transmission rate is
Figure BDA0004077499130000034
Because the unmanned aerial vehicle has limited airborne energy, for any unmanned aerial vehicle, the transmitting power of the unmanned aerial vehicle cannot exceed the maximum power of the unmanned aerial vehicle, namely
Figure BDA0004077499130000035
Assume that each drone is at q n + In areas of centre, r radius, i.e. movable
|q n -q n + |≤r T (36)
|w n -w n + |≤r R (37)
wherein rT 、r R For the radius of activity of the unmanned aerial vehicle transmitter and receiver,
Figure BDA0004077499130000041
is the initial position of the nth unmanned aerial vehicle receiver.
Unmanned aerial vehicle transmitting position q n ＝(x T (n),y T (n), H (n)), receiver position w n ＝(x R (n),y R (n), H (n)) coordinates are in meters; considering that the altitude of flight of two unmanned aerial vehicles is the same, definition
Figure BDA0004077499130000042
Figure BDA0004077499130000043
Equations (6) and (7) are embodied as
Figure BDA0004077499130000044
Figure BDA0004077499130000045
In order to ensure the communication requirement between unmanned aerial vehicles, the minimum signal-to-interference and noise ratio is considered to be gamma, and the method comprises the following steps of
Figure BDA0004077499130000046
Definition p min and pmax The minimum and maximum transmit powers of the drone, respectively. The aim is to achieve the maximum transmission rate of the unmanned aerial vehicle group communication network by optimally designing the spatial position and the power of the unmanned aerial vehicle, and therefore, the problems are converted into
Figure BDA0004077499130000047
The network topology optimization under the power fixation in the step 3, namely fixing the position of the unmanned aerial vehicle and optimizing the power of the unmanned aerial vehicle, specifically comprises,
when the unmanned aerial vehicle position is given, namely, the variable in the optimization problem is the unmanned aerial vehicle transmitter transmitting power p n In a given unmanned plane position { x [ n ]],y[n]When the transmit power optimization problem is written as:
Figure BDA0004077499130000051
since the objective function is a non-convex function, the objective function is written as the difference form of two functions:
R(p)＝L(p)-H(p) (43)
wherein R (p) is the system throughput, L (p) and H (p) are intermediate variables introduced for convenience of expression, and there are
Figure BDA0004077499130000052
Given point p 0 Since both L (p) and H (p) are concave functions, the objective function is written as the difference between the two concave functions, then there is the following theorem:
theorem 1: given point p k A series of non-decreasing solutions { p } are obtained by solving the following problems continuously k+1 }:
Figure BDA0004077499130000053
wherein
Figure BDA0004077499130000055
The nth element of (2) is
Figure BDA0004077499130000054
wherein gnk and gmk Channel gains between the nth and m unmanned aerial vehicle transmitters and the kth unmanned aerial vehicle receiver, respectively.
Since L (p) and H (p) are concave functions, there are
Figure BDA0004077499130000056
Further obtaining R (p) =L (p) -H (p). Ltoreq.R (p, p'); thus, R (p, p k ) Is a tight lower limit of the function R (p); and due to p k+1 Is the question (15) at the kth best solution, then there is
Figure BDA0004077499130000061
This shows that by solving the problem (15), a series of non-decreasing values are obtained;
assuming an initial power p 1 Calculating the maximum throughput and the power p of the unmanned aerial vehicle group at the moment k+1 Will p k+1 Value update to p 0 The value continues to be calculated until
Figure BDA0004077499130000062
Where ε is a set threshold. As shown by the algorithm in table I.
Table I Power optimization algorithm
Figure BDA0004077499130000063
The power optimization problem under the network topology fixation in the step 4, namely fixing the power of the unmanned aerial vehicle and optimizing the position of the unmanned aerial vehicle, specifically comprises the following steps:
when the transmission power P of the unmanned aerial vehicle n Given, the problem of positional optimization of the drone is as follows:
Figure BDA0004077499130000064
by introducing intermediate variables S [ n ] and G [ n ], one can obtain
Figure BDA0004077499130000065
Figure BDA0004077499130000066
Then
Figure BDA0004077499130000071
By introducing the variable u m [n]Order-making
U m [n]≤||q m -w n || 2 (52)
Then the formula (21) is
Figure BDA0004077499130000072
Conversion of the objective function into
Figure BDA0004077499130000073
Since the objective function is a convex function, the Taylor formula is used to obtain the value of the initial value (S k [n],G k [n])
Figure BDA0004077499130000074
wherein ,
Figure BDA0004077499130000075
since the constraint of equation (22) is not a convex function, the following is done, at a feasible location (x q [n] k ,y q [n] k) and (xw [n] k ,y w [n] k ) From Taylor's formula
Figure BDA0004077499130000076
Figure BDA0004077499130000077
wherein xq [n] k and yq [n] k X is the abscissa and ordinate, x, respectively, of the position of the unmanned aerial vehicle transmitter at the kth iteration w [n] k and yw [n] k Unmanned aerial vehicle reception at the kth iteration, respectivelyThe abscissa and ordinate of the machine position, x' [ n ]]And y' [ n ]]Is an intermediate variable introduced to simplify the presentation.
Therefore, equation (22) is converted into
U m [n]≤x`[n]+y`[n] (58)
For constraint C6, by introducing redundant variables
Figure BDA0004077499130000081
Further, is->
Figure BDA0004077499130000082
I.e.
Figure BDA0004077499130000083
In conclusion, the formula (18) is converted into
Figure BDA0004077499130000084
The topology control algorithm is shown in table II.
Table II topology control algorithm
Figure BDA0004077499130000085
/>
Step 5, the topology and the power are jointly optimized, and whether the condition is met or not is judged; the method specifically comprises the following steps:
s51: initializing a drone swarm position q 1 and w1 ；
S52: given unmanned aerial vehicle location group q 1 and w1 Solving the power optimization problem to obtain an optimal solution p k and Rk ；
S53: updating the transmit power p 1 ＝p k And an optimal solution R k ；
S54: given unmanned plane power p k Solving the position optimization problem to obtain an optimal solution q k and wk ；
S55: updating unmanned aerial vehicle group position q k and wk ；
S56: judging whether the stopping condition is met, and stopping if the stopping condition is met; otherwise, step 2 is performed.
The topology and power joint control method in the unmanned aerial vehicle communication network is characterized in that an optimization algorithm is shown in a table III.
Table III Power and topology Joint optimization algorithm
Figure BDA0004077499130000091
The effectiveness of the algorithm provided by the invention is evaluated through simulating the unmanned aerial vehicle group communication scene. The main simulation parameters were set as follows: as shown in fig. 2, assume that four pairs of unmanned aerial vehicle communication pairs communicate in the same frequency band, unmanned aerial vehicle maximum transmit power p max =5w, minimum transmit power p of unmanned aerial vehicle min =1w, channel gain at reference ρ 0 -20dB, path loss factor α=2, bandwidth b=1 MHz, unmanned plane radius of motion r=40m, noise power spectral density σ 2 = -169dBm/Hz. Consider that the unmanned aerial vehicle group is distributed in 200 x 200m 2 Is defined in the region of the substrate.
Fig. 3 shows the convergence behaviour of the power optimization, position optimization and joint optimization algorithm. From the figure, it can be seen that the three proposed algorithms can reach a convergence state through 4-5 iterations, thereby verifying the convergence of the proposed algorithms. Meanwhile, compared with available power and position combined optimization algorithm, the throughput improvement is greatly improved.
Fig. 4 shows the spatial positions of the various unmanned aerial vehicles in the unmanned aerial vehicle group, and from the perspective of network topology, it can be seen that two pairs of communicating unmanned aerial vehicles are close to each other, and the communication pairs of unmanned aerial vehicles which interfere with each other are relatively far away from each other, and the throughput of the unmanned aerial vehicle group is maximized under the formation.
As shown in fig. 5, as the radius of the unmanned aerial vehicle increases, the throughput of the unmanned aerial vehicle group increases, for the following reasons: when the unmanned aerial vehicle has a large radius of movement, the range of deviation thereof is also increasing. The greater the approach distance between two drone communication pairs, the greater the drone swarm throughput.

Claims (6)

1. The topology and power joint control method in the unmanned aerial vehicle communication network is characterized by comprising the following steps:
step 1: the roles of the elements and the relations between the elements in the topology and power combined control method are described;
step 2: establishing an unmanned aerial vehicle topology and power joint control model by considering unmanned aerial vehicle group formation requirements, internal mutual interference, communication constraint and power limitation;
step 3: optimizing network topology under the condition of fixed power and judging whether the condition is met;
step 4: the power optimization problem under the network topology fixation and judging whether the condition is satisfied;
step 5: and jointly optimizing topology and power, and judging whether the conditions are met.
2. The method for controlling topology and power in a communication network of unmanned aerial vehicle according to claim 1, wherein the actions of each element in the method for controlling topology and power and the relationships between them are described in step 1, and the content of the description includes:
considering that N unmanned aerial vehicles need to transmit information due to tasks, and the unmanned aerial vehicle group needs to keep a certain formation in the advancing process, and under the condition of limited frequency spectrum resources, all unmanned aerial vehicle communication pairs work on the same frequency band; the unmanned aerial vehicles can be interfered by other unmanned aerial vehicles in the unmanned aerial vehicle group when in two-to-two communication; therefore, the maximum transmission rate of the unmanned aerial vehicle group communication network is realized by optimally designing the space position and the power of the unmanned aerial vehicle.
3. The method for controlling topology and power combination in a communication network of an unmanned aerial vehicle according to claim 2, wherein the establishing of the unmanned aerial vehicle topology and power combination control model in step 2 comprises the following steps:
among N unmanned personsWhen the pair of unmanned aerial vehicles communicate simultaneously, the unmanned aerial vehicle transmitter can generate interference to other unmanned aerial vehicle receivers; let the positions of the unmanned aerial vehicle transmitter and unmanned aerial vehicle receiver be q respectively n and wn Communication rate R between the two n Is that
Figure FDA0004077499120000011
Where W is the bandwidth of the channel,
Figure FDA0004077499120000012
for the n-th unmanned aerial vehicle receiver to receive the signal intensity, I n Is the interference of other unmanned aerial vehicles in the unmanned aerial vehicle group to the nth unmanned aerial vehicle communication pair, sigma 2 Is Gaussian white noise; because the aerial unmanned aerial vehicle communication has little shielding, the signal intensity and the interference that the nth unmanned aerial vehicle receiver received are respectively:
P r n ＝P n g nn ＝P n ρ 0 ||q n -w n || -α (2)
Figure FDA0004077499120000013
wherein pn 、p m G is the transmitting power of the n and m unmanned aerial vehicle transmitters nn 、g mn Channel gains for communication link and interfering link, q m Is the position of the m-th unmanned aerial vehicle transmitter, ρ 0 For reference distance d 0 The channel gain at the position, alpha is a path loss index, and the alpha is the Euclidean distance of the a vector; for a group of unmanned aerial vehicles, the total maximum information transmission rate is
Figure FDA0004077499120000021
Because the unmanned aerial vehicle has limited airborne energy, for any unmanned aerial vehicle, the transmitting power of the unmanned aerial vehicle cannot exceed the maximum power of the unmanned aerial vehicle, namely
Figure FDA0004077499120000022
Assume that each drone is at q n + In areas of centre, r radius, i.e. movable
|q n -q n + |≤r T (6)
|w n -w n + |≤r R (7)
wherein rT 、r R For the radius of activity of the unmanned aerial vehicle transmitter and receiver,
Figure FDA0004077499120000023
the initial position of the n-th unmanned aerial vehicle receiver;
unmanned aerial vehicle transmitting position q n ＝(x T (n),y T (n), H (n)), receiver position w n ＝(x R (n),y R (n), H (n)) coordinates are in meters; considering that the altitude of flight of two unmanned aerial vehicles is the same, definition
Figure FDA0004077499120000024
Figure FDA0004077499120000025
Equations (6) and (7) are embodied as
Figure FDA0004077499120000026
Figure FDA0004077499120000027
In order to ensure the communication requirement between unmanned aerial vehicles, the minimum signal-to-interference and noise ratio is considered to be gamma, and the method comprises the following steps of
Figure FDA0004077499120000028
Definition p min and pmax Minimum and maximum transmit power of the unmanned aerial vehicle respectively; the aim is to achieve the maximum transmission rate of the unmanned aerial vehicle group communication network by optimally designing the spatial position and the power of the unmanned aerial vehicle, and therefore, the problems are converted into
Figure FDA0004077499120000031
4. The method for controlling topology and power in an unmanned aerial vehicle communication network according to claim 3, wherein the network topology under the power fixation in step 3 is optimized, and whether the condition is satisfied is determined; specifically comprises the steps of fixing the position of the unmanned aerial vehicle to solve the power of the unmanned aerial vehicle,
when the unmanned aerial vehicle position is given, namely, the variable in the optimization problem is the unmanned aerial vehicle transmitter transmitting power p n In a given unmanned plane position { x [ n ]],y[n]When the transmit power optimization problem is written as:
Figure FDA0004077499120000032
since the objective function is a non-convex function, the objective function is written as the difference form of two functions:
R(p)＝L(p)-H(p) (13)
wherein R (p) is the system throughput, L (p) and H (p) are intermediate variables introduced for convenience of expression, and there are
Figure FDA0004077499120000033
Given point p 0 Since both L (p) and H (p) are concave functions, the objective function is written as the difference between the two concave functions, then there is the following theorem:
theorem 1: given point p k A series of non-decreasing solutions { p } are obtained by solving the following problems continuously k+1 }:
Figure FDA0004077499120000034
wherein p'＝pk ,
Figure FDA0004077499120000047
The nth element of (2) is
Figure FDA0004077499120000041
wherein gnk and gmk Channel gains between the nth and m unmanned aerial vehicle transmitters and the kth unmanned aerial vehicle receiver, respectively;
since L (p) and H (p) are concave functions, there are
Figure FDA0004077499120000042
Further obtaining R (p) =L (p) -H (p). Ltoreq.R (p, p'); thus, R (p, p k ) Is a tight lower limit of the function R (p); and due to p k+1 Is the question (15) at the kth best solution, then there is
Figure FDA0004077499120000043
This shows that by solving the problem (15), a series of non-decreasing values are obtained;
assuming an initial power p 1 Calculating the maximum throughput and the power p of the unmanned aerial vehicle group at the moment k+1 Will p k+1 Value update to p 0 The value continues to be calculated until
Figure FDA0004077499120000044
Where ε is a set threshold.
5. The method for controlling topology and power combination in a communication network of an unmanned aerial vehicle according to claim 4, wherein the step 4 of solving the unmanned aerial vehicle position by using the fixed unmanned aerial vehicle power specifically comprises:
when the transmission power P of the unmanned aerial vehicle n Given, the problem of positional optimization of the drone is as follows:
Figure FDA0004077499120000045
by introducing intermediate variables S [ n ] and G [ n ], one can obtain
Figure FDA0004077499120000046
Then
Figure FDA0004077499120000051
Figure FDA0004077499120000052
By introducing the variable u m [n]Order-making
U m [n]≤||q m -w n || 2 (22)
Then the formula (21) is
Figure FDA0004077499120000053
Conversion of the objective function into
Figure FDA0004077499120000054
Since the objective function is a convex function, the Taylor formula is used to obtain the value of the initial value (S k [n],G k [n])
Figure FDA0004077499120000055
wherein ,
Figure FDA0004077499120000056
since the constraint of equation (22) is not a convex function, the following is done, at a feasible location (x q [n] k ,y q [n] k) and (xw [n] k ,y w [n] k ) From Taylor's formula
Figure FDA0004077499120000057
Figure FDA0004077499120000058
wherein xq [n] k and yq [n] k X is the abscissa and ordinate, x, respectively, of the position of the unmanned aerial vehicle transmitter at the kth iteration w [n] k and yw [n] k Respectively the abscissa and the ordinate, x' n of the position of the receiver of the unmanned aerial vehicle at the kth iteration]And y' [ n ]]To simplify the presentation for intermediate variables introduced;
therefore, equation (22) is converted into
U m [n]≤x`[n]+y`[n] (28)
For constraint C6, by introducing redundant variables
Figure FDA0004077499120000061
Further, is->
Figure FDA0004077499120000062
I.e.
Figure FDA0004077499120000063
In conclusion, the formula (18) is converted into
Figure FDA0004077499120000064
6. The method for controlling topology and power jointly in a communication network of an unmanned aerial vehicle according to claim 5, wherein the topology and power are jointly optimized in step 5, and whether the condition is satisfied is determined; the method specifically comprises the following steps:
s51: initializing a drone swarm position q 1 and w1 ；
S52: given unmanned aerial vehicle location group q 1 and w1 Solving the power optimization problem to obtain an optimal solution p k and Rk ；
S53: updating the transmit power p 1 ＝p k And an optimal solution R k ；
S54: given unmanned plane power p k Solving the position optimization problem to obtain an optimal solution q k and wk ；
S55: updating unmanned aerial vehicle group position q k and wk ；
S56: judging whether the stopping condition is met, and stopping if the stopping condition is met; otherwise, step 2 is performed.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116367291A (en) * 2023-06-01 2023-06-30 四川腾盾科技有限公司 Unmanned aerial vehicle interference avoidance group topology optimization method based on self-adaptive power control
CN116954266A (en) * 2023-09-21 2023-10-27 北京航空航天大学 Communication constraint unmanned aerial vehicle formation robust control method and system

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116367291A (en) * 2023-06-01 2023-06-30 四川腾盾科技有限公司 Unmanned aerial vehicle interference avoidance group topology optimization method based on self-adaptive power control
CN116367291B (en) * 2023-06-01 2023-08-18 四川腾盾科技有限公司 Unmanned aerial vehicle interference avoidance group topology optimization method based on self-adaptive power control
CN116954266A (en) * 2023-09-21 2023-10-27 北京航空航天大学 Communication constraint unmanned aerial vehicle formation robust control method and system
CN116954266B (en) * 2023-09-21 2023-12-08 北京航空航天大学 Communication constraint unmanned aerial vehicle formation robust control method and system

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