CN111328122A - Power distribution and flight route optimization method for multi-unmanned-aerial-vehicle alternate relay communication - Google Patents

Power distribution and flight route optimization method for multi-unmanned-aerial-vehicle alternate relay communication Download PDF

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CN111328122A
CN111328122A CN202010087027.4A CN202010087027A CN111328122A CN 111328122 A CN111328122 A CN 111328122A CN 202010087027 A CN202010087027 A CN 202010087027A CN 111328122 A CN111328122 A CN 111328122A
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power
drone
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CN111328122B (en
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张广驰
欧晓琪
崔苗
林凡
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Guangdong University of Technology
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    • 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
    • H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • 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
    • 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
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/241—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account channel quality metrics, e.g. SIR, SNR, CIR, Eb/lo
    • 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
    • H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
    • H04W52/267—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
    • 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
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/36—TPC using constraints in the total amount of available transmission power with a discrete range or set of values, e.g. step size, ramping or offsets
    • H04W52/367—Power values between minimum and maximum limits, e.g. dynamic range
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
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Abstract

The invention provides a power distribution and flight route optimization method for multi-unmanned aerial vehicle alternative relay communication, which comprises the following steps: establishing a multi-unmanned-aerial-vehicle alternative relay communication model; initializing and setting iteration times, initial power of an unmanned aerial vehicle relay, an initial track and an error threshold; substituting the initial power and the track of the unmanned aerial vehicle into a preset power optimization constraint condition to obtain an optimal solution of the predicted source end transmitting power and the transmitting power of the unmanned aerial vehicle relay and a first objective function value; when the increment of the first objective function value meets an error threshold, substituting the optimal solution data and the track of the unmanned aerial vehicle relay into a preset track optimization constraint condition to obtain a predicted optimal solution of the track of the unmanned aerial vehicle relay and a second objective function value; and when the increment of the second objective function value meets the error threshold, further judging whether the increment of the second objective function value meets the error threshold by taking the second objective function value as the target throughput, if so, outputting, and otherwise, continuing iteration.

Description

Power distribution and flight route optimization method for multi-unmanned-aerial-vehicle alternate relay communication
Technical Field
The invention relates to the technical field of unmanned aerial vehicle communication, in particular to a power distribution and flight route optimization method for multi-unmanned aerial vehicle alternate relay communication.
Background
In recent years, unmanned aerial vehicles are increasingly used in wireless communication, news television, logistics distribution and the like. In some disaster areas, the communication infrastructure is damaged, and the unmanned aerial vehicle relay can quickly establish a communication system to help the disaster-stricken masses to recover communication. For a three-point cooperative communication system with fixed source and target nodes, a communication system which works in a full-duplex mode through relay of a single unmanned aerial vehicle exists. Due to the restriction of a communication model and information causal constraints, when the unmanned aerial vehicle relay is far away from a source node and close to a target node, the throughput of the system can be rapidly reduced. In order to improve system throughput, a communication model established by multi-hop unmanned aerial vehicle multi-hop aerial relay for assisting communication from a fixed source end to a target node is mainly adopted at present, however, the model is also restricted by information cause and effect constraints, and each unmanned aerial vehicle relay can only forward information received from a previous relay, so that information transmitted to a target end is greatly lost relative to information sent by the source end.
Disclosure of Invention
The invention provides a power distribution and flight route optimization method for multi-unmanned aerial vehicle alternative relay communication, aiming at overcoming the defects of low system throughput and large transmission information loss in the prior art.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a power distribution and flight route optimization method for multi-unmanned aerial vehicle alternate relay communication comprises the following steps:
s1: establishing a multi-unmanned-aerial-vehicle alternative relay communication model, wherein the model comprises a fixed source end, a fixed target node and M unmanned-aerial-vehicle relays, and M is a positive integer;
s2: setting the iteration number gamma to be 0 by initialization, and setting the initial power of the relay of the unmanned aerial vehicle
Figure BDA0002382420480000011
And an initial trajectory
Figure BDA0002382420480000012
And an error threshold epsilon;
s3: the initial power of the unmanned aerial vehicle
Figure BDA0002382420480000013
And track
Figure BDA0002382420480000014
Substituting the preset power optimization constraint condition to obtain the predicted optimal solution of the source end emission power
Figure BDA0002382420480000015
Optimal solution to transmit power relayed by drone
Figure BDA0002382420480000016
And obtaining a first objective function value
Figure BDA0002382420480000017
S4: judging the first objective function value
Figure BDA0002382420480000021
If the increment of (a) is less than or equal to the error threshold epsilon, if yes, executing a step S5, if no, setting the iteration number to gamma +1, and jumping to execute a step S3;
s5: optimal solution of the predicted source emission power
Figure BDA0002382420480000022
Predicted transmit power for drone relaysOf (2) an optimal solution
Figure BDA0002382420480000023
And the trajectory relayed by the drone
Figure BDA0002382420480000024
Substituting the preset track optimization constraint condition to obtain the predicted optimal solution of the track of the unmanned aerial vehicle relay
Figure BDA0002382420480000025
And obtaining a second objective function value
Figure BDA0002382420480000026
S6: judging the second objective function value
Figure BDA0002382420480000027
If the increment of (a) is less than or equal to the error threshold epsilon, if yes, executing a step S7, if no, setting the iteration number to gamma +1, and jumping to execute a step S5;
s7: the second objective function value
Figure BDA0002382420480000028
As target throughput RrThen judging the target throughput RγIs less than or equal to the error threshold epsilon, and if not, the predicted optimal solution of the unmanned aerial vehicle relay trajectory is determined
Figure BDA0002382420480000029
And initial power of the drone
Figure BDA00023824204800000210
As an input, the step S3 is executed after γ +1 is set; if yes, outputting the predicted source end emission power
Figure BDA00023824204800000211
Predicted transmit power for drone relays
Figure BDA00023824204800000212
Predicted trajectory of drone relay
Figure BDA00023824204800000213
And the target throughput Rγ。
In the technical scheme, the problem is solved by adopting an alternative maximization method, an optimization variable is divided into a transmission power distribution variable and a variable of a relay flight path of the unmanned aerial vehicle, initial power and an initial track which are initially set are respectively substituted into a preset power optimization constraint condition and a track optimization constraint condition, namely, the power distribution is optimized by giving the initial track, then the power obtained by optimization is brought to a track optimization part, finally the obtained track optimization result is brought to the power optimization part, and iteration is carried out according to a preset error threshold or iteration times until a target value is converged to obtain the maximum value of throughput.
Preferably, in the multi-unmanned-aerial-vehicle alternating relay communication model, the source end sequentially sends information with the duration of Nt to each unmanned aerial vehicle relay; the mth unmanned aerial vehicle relay receives the information and then forwards the information to the destination end, and meanwhile, the source end sends the information to the (M +1) th unmanned aerial vehicle relay until the mth unmanned aerial vehicle relay sends the received information to the destination end; wherein, M is 1, 2.
Preferably, the information transmission between the source, drone relay and target node is in the form of broadcasting.
Preferably, in step S3, the expression formula of the power optimization constraint is as follows:
Figure BDA00023824204800000214
Figure BDA00023824204800000215
Figure BDA0002382420480000031
Figure BDA0002382420480000032
Figure BDA0002382420480000033
Figure BDA0002382420480000034
Figure BDA0002382420480000035
Figure BDA0002382420480000036
Figure BDA0002382420480000037
Figure BDA0002382420480000038
Figure BDA0002382420480000039
wherein, ηm＝[η1,...,ηM]Represents an introduced relaxation variable; n denotes the total flight length of the drone relay, i.e. N ═ m +1) Nt；
Figure BDA00023824204800000310
Representing the transmit power of the mth drone relay,
Figure BDA00023824204800000311
represents the mean transmitted power, P, of the mth drone relaym,maxMaximum power constraint for the mth drone relay;
Figure BDA00023824204800000312
which represents the transmit power of the source side,
Figure BDA00023824204800000313
representing the average transmitted power, P, of the sources,maxMaximum power constraint for source end; h ism,m-1[n]Represents the channel power gain, h, of the mth drone relay to the m-1 drone relays,m[n]Representing the channel power gain from the source to the mth drone relay;
Figure BDA00023824204800000314
representing an upper bound of the achievable rate of the source to the mth drone relay; gamma rays,m[n]Represents the signal-to-noise ratio, gamma, of the sourcem,d[n]Representing the signal-to-noise ratio, gamma, of the drone0Is referred to the signal-to-noise ratio, and
Figure BDA00023824204800000315
β0denotes the channel power gain, σ, at a reference distance of 1 meter2Representing the additive white gaussian noise power at the receiving end.
Preferably, the power optimization constraints in step S3 are solved using the cvx toolkit.
Preferably, the expression formula of the trajectory optimization constraint in the step S5 is as follows:
Figure BDA00023824204800000316
Figure BDA0002382420480000041
Figure BDA0002382420480000042
Figure BDA0002382420480000043
Sm,m-1[n]≤-||qm[n]-qm-1[n]||2+2(qm[n]-qm-1[n])T(qm[n]-qm-1[n])，
m＝2,...,M
Figure BDA00023824204800000412
wherein the content of the first and second substances,
Figure BDA0002382420480000044
Figure BDA0002382420480000045
Figure BDA0002382420480000046
Figure BDA0002382420480000047
Figure BDA0002382420480000048
Figure BDA0002382420480000049
Figure BDA00023824204800000410
Figure BDA00023824204800000411
wherein, tm＝[t1,...,tm]Represents an introduced relaxation variable; rs(Ss,m[n]) Representing the communication rate of the source, its upper bound
Figure BDA0002382420480000051
For the second purposeValue of standard function
Figure BDA0002382420480000052
Ss,m[n]Slack variable, S, representing the distance of the source to the mth dronem,m-1[n]Slack variable, S, representing the distance between adjacent dronesm,d[n]A slack variable representing the mth drone to terminal distance; q. q.sm[n]A trajectory relayed for the mth drone; h is the flight height of unmanned aerial vehicle relay.
Preferably, the trajectory optimization constraint condition in step S5 is calculated by an interior point method to obtain a predicted optimal solution of the trajectory of the drone relay
Figure BDA0002382420480000053
Preferably, the error threshold e is initially set to 10 in step S2-2。
Compared with the prior art, the technical scheme of the invention has the beneficial effects that: the multi-unmanned-aerial-vehicle alternate relay communication system is established by utilizing the multi-unmanned-aerial-vehicle relay, so that the defects of unmanned-aerial-vehicle relay in such communication scenes are overcome, the throughput of the system is effectively improved, and the information loss caused by forwarding is avoided; by designing the flight track of the relay of the unmanned aerial vehicle, the advantage of flexibility of the unmanned aerial vehicle is fully utilized, and the throughput of the relay system is optimized; and through power distribution, the interference of the mth link to the (m +1) th link is reduced, and the throughput is improved.
Drawings
Fig. 1 is a flowchart of a power distribution and flight route optimization method for multi-drone alternate relay communication according to the present invention.
Fig. 2 is a schematic structural diagram of the multi-drone alternate relay communication system of the embodiment.
Fig. 3 is a graph comparing throughput performance at different times for different schemes.
Fig. 4 is a graph comparing end-to-end throughput for different schemes and different average powers P.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The present embodiment provides a power distribution and flight route optimization method for multi-drone alternative relay communication, and as shown in fig. 1, the method is a flowchart of the power distribution and flight route optimization method for multi-drone alternative relay communication according to the present embodiment.
The method for power distribution and flight route optimization of multi-unmanned-aerial-vehicle alternate relay communication provided by the embodiment comprises the following steps:
s1: the method comprises the steps of establishing a multi-unmanned-aerial-vehicle alternative relay communication model, wherein the multi-unmanned-aerial-vehicle alternative relay communication model comprises a fixed source end, a fixed target node and M unmanned-aerial-vehicle relays, wherein M is a positive integer.
The multi-unmanned-aerial-vehicle alternate relay communication model in the embodiment is shown in fig. 2, a direct communication link exists between two nodes, a barrier seriously influences communication, and information transmission among a source end, an unmanned aerial vehicle relay and a target node adopts a broadcasting mode.
In the multi-unmanned-aerial-vehicle alternate relay communication model of the embodiment, a source end sequentially sends information with the duration of Nt to each unmanned aerial vehicle relay; the mth unmanned aerial vehicle relays and receives the information and forwards the information with the received time length of Nt to the destination end, meanwhile, the source end sends the information with the time length of Nt to the (M +1) th unmanned aerial vehicle relay, the Mth unmanned aerial vehicle relay sends the received information to the destination end, and the source end does not transmit power in the last Nt time slots; wherein, M is 1, 2.
S2: setting the iteration number gamma to be 0 by initialization, and setting the initial power of the relay of the unmanned aerial vehicle
Figure BDA0002382420480000061
And initiallyTrack of
Figure BDA0002382420480000062
And an error threshold epsilon. In the present embodiment, the error threshold ε is set to 10-2。
S3: the initial power of the unmanned aerial vehicle
Figure BDA0002382420480000063
And track
Figure BDA0002382420480000064
Substituting the preset power optimization constraint condition to obtain the predicted optimal solution of the source end emission power
Figure BDA0002382420480000065
Optimal solution to transmit power relayed by drone
Figure BDA0002382420480000066
And obtaining a first objective function value
Figure BDA0002382420480000067
S4: judging the first objective function value
Figure BDA0002382420480000068
If the increment of (c) is less than or equal to the error threshold epsilon, if yes, executing step S5, if no, setting the iteration number to gamma +1, and jumping to execute step S3.
S5: optimal solution of the predicted source emission power
Figure BDA0002382420480000069
Predicted optimal solution for transmit power of drone relays
Figure BDA00023824204800000610
And the trajectory relayed by the drone
Figure BDA00023824204800000611
Substitution intoObtaining the predicted optimal solution of the unmanned aerial vehicle relay track by using the preset track optimization constraint condition
Figure BDA00023824204800000612
And obtaining a second objective function value
Figure BDA00023824204800000613
S6: judging the second objective function value
Figure BDA00023824204800000614
If the increment of (a) is less than or equal to the error threshold epsilon, if yes, executing a step S7, if no, setting the iteration number to gamma +1, and jumping to execute a step S5; s7: the second objective function value
Figure BDA00023824204800000615
As target throughput RrThen judging the target throughput RγIs less than or equal to the error threshold epsilon, and if not, the predicted optimal solution of the unmanned aerial vehicle relay trajectory is determined
Figure BDA00023824204800000616
And initial power of the drone
Figure BDA00023824204800000617
As an input, the step S3 is executed after γ +1 is set; if yes, outputting the predicted source end emission power
Figure BDA00023824204800000618
Predicted transmit power for drone relays
Figure BDA00023824204800000619
Predicted trajectory of drone relay
Figure BDA00023824204800000620
And the target throughput Rγ。
In this embodiment, it is assumed that M unmanned aerial vehicles are in the air as air mobile relays, a source end sends information with a time length of Nt to each unmanned aerial vehicle relay in turn, and each unmanned aerial vehicle relay receives the information and then forwards data with the time length of Nt to a destination end, that is, the source end first transmits information to the first unmanned aerial vehicle within a first Nt time; within a second Nt, the first drone forwards information to the target node, while the source peer transmits information to the second drone; in the third Nt, the second unmanned aerial vehicle forwards information to the target node, and meanwhile, the source end sends information to the third unmanned aerial vehicle; and the data are alternately sent down until the last unmanned machine finishes transmitting the data to the destination end.
Wherein, the position coordinates of the source end and the destination end are assumed to be [ W ] respectivelys T,0]TAnd [ W ]d T,0]TWherein W iss T＝[xs,ys]THorizontal coordinate, W, representing the sourced＝[xd,td]TRepresenting the horizontal coordinate of the destination; assuming that all drone relays maintain the same height H-100 meters, the coordinates of the mth drone at time t may be represented as [ q [ q ] ]m(t)T,H]TWherein M ∈ { 1.,. M }, T is more than or equal to 0 and less than or equal to T, qm(t)＝[xm(t),ym(t)]TThe horizontal coordinate of the mth drone is represented, and T represents the flight time of the drone. In this embodiment, the flight time T of the unmanned aerial vehicle is evenly divided into lengths dtN time slots, i.e. T ═ N × dt。
Thus, the trajectory of the mth drone is approximately denoted qm(n)＝[xm(n),ym(n)]TN ∈ { 1.,. N }, the starting position of the mth drone is q0,m＝[x0,m,y0,m]TThe end position is denoted by qF,m＝[xF,m,yF,m]。vmaxRepresents the maximum flight speed of the drone, and thus the maximum speed at which the drone can fly per time slot may be represented as V ═ Vmax*dtSetting the initial trajectory of the unmanned aerial vehicle to fly straight from the initial positionWhen the line reaches the end position, the expression formula of the motion constraint of the unmanned aerial vehicle is as follows:
Figure BDA0002382420480000071
Figure BDA0002382420480000072
Figure BDA0002382420480000073
the expression formula of the collision avoidance constraint between the drones is as follows:
Figure BDA0002382420480000074
wherein d isminRepresenting the minimum safe distance between two drones.
At the nth moment, the distance from the source end to the mth drone relay is represented as
Figure BDA0002382420480000075
At the nth moment, the distance from the relay of the mth unmanned aerial vehicle to the destination end is expressed as
Figure BDA0002382420480000076
At the nth time, the distance from the mth drone relay to the M-1 drone relay (M ═ 2.. M) is represented as
Figure BDA0002382420480000077
In this embodiment, it is assumed that communication links from the source end to the drone relay and from the drone relay to the destination end are both regarded as line of sight (LOS) models, and the channel power gain follows a free space path LOSs model, so the channel power gain from the source end to the first drone relay at the nth time can be expressed as
Figure BDA0002382420480000081
β therein0Is shown at a reference distance d0Channel power gain of 1. Similarly, at time n, the channel power gain from the mth drone relay to the destination and from the mth drone relay to the m-1 drone relay is expressed as
Figure BDA0002382420480000082
Figure BDA0002382420480000083
In this example, psAnd pmThe power that represents source end transmission and the power that unmanned aerial vehicle relayed the transmission respectively, for better carrying out power distribution, be provided with average power constraint in this embodiment, its expression is as follows:
Figure BDA0002382420480000084
0≤ps≤Ps,max(12)
and a maximum power constraint, expressed as follows:
Figure BDA0002382420480000085
0≤pm≤Pm,max(14)
wherein
Figure BDA0002382420480000086
And
Figure BDA0002382420480000087
respectively representing the average transmission power, P, of the source terminal transmission and the mth UAV relays,maxRepresenting the emission peak, P, of the sourcem,maxRepresenting the transmission peak of the mth drone relay. Without loss of the generality of the method,
Figure BDA0002382420480000088
and
Figure BDA0002382420480000089
at the nth moment, the reachable rate R from the source end to the first unmanned aerial vehicles,1[n]Can be expressed as
Figure BDA00023824204800000810
Wherein σ2Represents the Additive White Gaussian Noise (AWGN) power of the receiving end,
Figure BDA00023824204800000811
is a reference signal-to-noise ratio.
Reachable rate R relayed from first unmanned aerial vehicle to destination at nth moment1,d[n]Can be expressed as
Figure BDA0002382420480000091
Likewise, the achievable rate R relayed from the mth drone to the destination1,d[n]Can be expressed as
Figure BDA0002382420480000092
However, when data is transmitted from the source end to the mth drone (m > 1), at this time, the mth drone is transmitting data to the destination end, and since information transmission is transmitted in a broadcast manner, the mth drone receiving the data transmitted from the source end also receives interference information from the mth drone. Thus, the achievable rate R from the source to the mth drones,m[n]Can be expressed as
Figure BDA0002382420480000093
Because the system model has M drones, there are M links from the source to the destination. The data of each link is transmitted from the source end to the destination end by a drone, so the delay of each link is Nt slots. And the source end continuously and alternately sends data to the unmanned aerial vehicle in a relay mode until the Mth unmanned aerial vehicle finishes sending, so that the source end does not transmit power in the last Nt time slots. For the mth drone relay (M1.,. M-1), the time delay from the mth drone relay to the destination is M × Nt time slots, after the mth drone finishes sending data, the link has finished communication, and the mth drone should not transmit power in the rest time. Therefore, the power constraints of the source and drone relays also include the following representation
Figure BDA0002382420480000094
Figure BDA0002382420480000095
Figure BDA0002382420480000096
For each link, the minimum of the reachable rate of the source-to-drone relay and the reachable rate of the drone relay into the destination represents the throughput of that link. Thus, the average throughput from source to destination R can be expressed as
Figure BDA0002382420480000101
Wherein, in order to ensure the communication time of each hop to be consistent, the time of each hop is set as NtThe total flight length of the unmanned aerial vehicle relay is N ═ N (m +1) Nt。Rs1dThe average throughput of the first path is expressed by the following expression:
Figure BDA0002382420480000102
Rsmdthe average throughput of the mth path is expressed by the following expression:
Figure BDA0002382420480000103
the goal of this embodiment is to maximize the end-to-end throughput R from the source end to the destination end, and its constraints include power constraints, i.e., equations (11) to (14), (19) to (21), and motion constraints (1) to (3), and collision avoidance constraint (4).
The objective function is set as follows:
Figure BDA0002382420480000104
since the objective function is non-concave and the constraints (3), (4) are non-convex pairs. Therefore, the embodiment adopts an alternating maximization method, power allocation is optimized by giving an initial trajectory, then the optimized power is brought to the trajectory optimization part, and finally the obtained trajectory is brought to the power optimization part, and iteration is continuously carried out until the target value is converged.
According to the objective function of the formula (25), the power optimization constraint condition is obtained through division, and the expression formula is as follows:
Figure BDA0002382420480000105
Figure BDA0002382420480000106
Figure BDA0002382420480000107
Figure BDA0002382420480000111
Figure BDA0002382420480000112
Figure BDA0002382420480000113
Figure BDA0002382420480000114
Figure BDA0002382420480000115
Figure BDA0002382420480000116
Figure BDA0002382420480000117
wherein, ηm＝[η1,...,ηM]Represents an introduced relaxation variable; n denotes the total flight length of the drone relay, i.e. N ═ m +1) Nt；
Figure BDA0002382420480000118
Representing the transmit power of the mth drone relay,
Figure BDA0002382420480000119
represents the mean transmitted power, P, of the mth drone relaym,maxMaximum power constraint for the mth drone relay;
Figure BDA00023824204800001110
which represents the transmit power of the source side,
Figure BDA00023824204800001111
representing the average transmitted power, P, of the sources,maxMaximum power constraint for source end; h ism,m-1[n]Indicates the m-th nobodyChannel power gain, h, from airborne relay to m-1 th unmanned aerial vehicle relays,m[n]Representing the channel power gain from the source to the mth drone relay;
Figure BDA00023824204800001112
representing an upper bound of the achievable rate of the source to the mth drone relay; gamma rays，m[n]Represents the signal-to-noise ratio, gamma, of the sourcem,d[n]Represents the signal-to-noise ratio, gamma, of the mth drone0Is referred to the signal-to-noise ratio, and
Figure BDA00023824204800001113
β0denotes the channel power gain, σ, at a reference distance of 1 meter2Representing the additive white gaussian noise power at the receiving end.
According to the objective function of the formula (25), the track optimization constraint condition is obtained through division, and the expression formula is as follows:
Figure BDA00023824204800001114
Figure BDA00023824204800001115
Figure BDA00023824204800001116
Figure BDA0002382420480000121
Sm,m-1[n]≤-||qm[n]-qm-1[n]||2+2(qm[n]-qm-1[n])T(qm[n]-qm-1[n])，
m＝2,...,M
Figure BDA0002382420480000122
wherein the content of the first and second substances,
Figure BDA00023824204800001212
Figure BDA0002382420480000123
Figure BDA0002382420480000124
Figure BDA0002382420480000125
Figure BDA0002382420480000126
Figure BDA0002382420480000127
Figure BDA0002382420480000128
Figure BDA0002382420480000129
wherein, tm＝[t1,...,tm]Represents an introduced relaxation variable; rs(Ss，m[n]) Representing the communication rate of the source, its upper bound
Figure BDA00023824204800001210
Is the second objective function value
Figure BDA00023824204800001211
Ss，m[n]Slack variable, S, representing the source-to-mth drone distancem,m-1[n]Slack variable, S, representing the distance between adjacent dronesm,d[n]Indicating the relaxation of the mth drone to the terminal; q. q.sm[n]A trajectory relayed for the mth drone; h is the flight altitude of unmanned aerial vehicle relayIn this embodiment, the flying height H of the drone relay is set to 100 meters.
In the specific implementation process, the power optimization constraint condition is solved by adopting a cvx toolkit, and the track optimization constraint condition is calculated by adopting an interior point method to obtain the predicted optimal solution of the track of the unmanned aerial vehicle relay
Figure BDA0002382420480000131
According to the power distribution and flight route optimization method for multi-unmanned-aerial-vehicle alternate relay communication, a multi-unmanned-aerial-vehicle alternate relay communication system is established by utilizing multi-unmanned-aerial-vehicle relays, so that the defects of unmanned-aerial-vehicle relays in such communication scenes are overcome, the throughput of the system is effectively improved, and information loss caused by forwarding is avoided; by designing the flight track of the relay of the unmanned aerial vehicle, the advantage of flexibility of the unmanned aerial vehicle is fully utilized, and the throughput of the relay system is optimized; and through power distribution, the interference of the mth link to the (m +1) th link is reduced, and the throughput is improved.
To better illustrate the optimization effect of the present embodiment, the present embodiment uses a simulation model to represent the average power of the two UAV relays UAV1 and UAV2
Figure BDA0002382420480000132
In the case, the end-to-end throughput of different schemes and time-of-flight T are compared. As shown in fig. 3, a graph of throughput performance at different times for different schemes. Wherein, the contrast scheme used comprises:
1. power and track combined optimization scheme for spectrum efficiency unmanned aerial vehicle substitution: the communication model is the optimization method described in this embodiment, and combines power optimization and flight route design;
2. heuristic unmanned aerial vehicle substituted joint optimization trajectory and power: the scheme is the same as the optimization method of the embodiment in that the positions and the communication functions of a source end and a terminal are consistent, and the unmanned aerial vehicle is used as an alternate relay; the difference is that the time of unmanned aerial vehicle communication is different, and the concrete performance is shown as the following table:
1:Nt Nt+1:2Nt 2Nt+1:3Nt 3Nt+1:4Nt
UAV1 S→UAV1 UAV1→D
UAV2 S→UAV2 UAV2→D
3. joint trajectory and power optimization of a single drone: only one drone participates in the communication;
4. no optimization scheme (initial trajectory and average power): on the basis of the optimization method in the embodiment, the communication speed of the system is calculated by adopting an initial track and an average power;
5. optimizing a power scheme under an initial trajectory replaced by a spectrum efficiency unmanned aerial vehicle: on the basis of the optimization method described in this embodiment, an initial trajectory and an optimization method for optimizing power are adopted to maximize the communication rate of the system;
6. optimizing trajectories at average power for spectral efficiency drone substitution: on the basis of the optimization method described in this embodiment, an optimization method of optimizing the flight trajectory by using the average power is adopted, and the communication rate of the system is maximized.
As can be seen, in addition to the initial scheme (initial trajectory and average power only) and the power allocation only (initial trajectory) scheme, the throughput of the other schemes increases with time. This result is expected because the trajectory of the initial solution and the power allocation only solution is a straight line from the source end to the destination end, and because there is no trajectory optimization, the longer T is, the longer the communication time between the drone relay and the source end and the destination end is, and the communication performance is reduced accordingly. The optimization proposed in this embodiment has a higher throughput than either of the schemes.
In addition, the present embodiment uses a simulation model to represent the end-to-end throughput of two drone relay UAVs 1 and UAVs 2 at time T-84 s for different scenarios and different average powers P. As shown in fig. 4, which is a graph comparing end-to-end throughput for different schemes and different average powers P. As can be seen from the figure, the end-to-end throughput of each scheme increases with the increase of the average power P, and the optimization scheme proposed by the present embodiment is larger than the end-to-end throughput of other schemes. Obviously, the optimization scheme provided by the embodiment can effectively overcome the problems of low system throughput and large transmission information loss to be solved by the invention.
The same or similar reference numerals correspond to the same or similar parts;
the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (8)

1. A power distribution and flight route optimization method for multi-unmanned aerial vehicle alternate relay communication is characterized by comprising the following steps:
s1: establishing a multi-unmanned-aerial-vehicle alternative relay communication model, wherein the model comprises a fixed source end, a fixed target node and M unmanned-aerial-vehicle relays, and M is a positive integer;
s2: setting the iteration number gamma to be 0 by initialization, and setting the initial power P of the relay of the unmanned aerial vehicle1 γAnd an initial trajectory
Figure FDA0002382420470000011
And an error threshold epsilon;
s3: the initial power P of the unmanned aerial vehicle1 γAnd track
Figure FDA0002382420470000012
Substituting the preset power optimization constraint condition to obtain the predicted optimal solution of the source end emission power
Figure FDA0002382420470000013
Optimal solution to transmit power relayed by drone
Figure FDA0002382420470000014
And obtaining a first objective function value
Figure FDA0002382420470000015
S4: judging the first objective function value
Figure FDA0002382420470000016
Is less than or equal to the error threshold epsilon, if so, execution is performedStep S5, if no, setting the iteration number to γ +1, and jumping to step S3;
s5: optimal solution of the predicted source emission power
Figure FDA0002382420470000017
Predicted optimal solution for transmit power of drone relays
Figure FDA0002382420470000018
And the trajectory relayed by the drone
Figure FDA0002382420470000019
Substituting the preset track optimization constraint condition to obtain the predicted optimal solution of the track of the unmanned aerial vehicle relay
Figure FDA00023824204700000110
And obtaining a second objective function value
Figure FDA00023824204700000111
S6: judging the second objective function value
Figure FDA00023824204700000112
If the increment of (a) is less than or equal to the error threshold epsilon, if yes, executing a step S7, if no, setting the iteration number to gamma +1, and jumping to execute a step S5;
s7: the second objective function value
Figure FDA00023824204700000113
As target throughput RrThen judging the target throughput RγIs less than or equal to the error threshold epsilon, and if not, the predicted optimal solution of the unmanned aerial vehicle relay trajectory is determined
Figure FDA00023824204700000114
And the unmanned aerial vehicleInitial power P of1 γAs an input, the step S3 is executed after γ +1 is set; if yes, outputting the predicted source end emission power
Figure FDA00023824204700000115
Predicted transmit power for drone relays
Figure FDA00023824204700000116
Predicted trajectory of drone relay
Figure FDA00023824204700000117
And the target throughput Rγ。
2. The power distribution and flight path optimization method of claim 1, wherein: in the multi-unmanned-aerial-vehicle alternating relay communication model, the source end sequentially sends information with the duration of Nt to each unmanned aerial vehicle relay; the mth unmanned aerial vehicle relay receives the information and then forwards the information to the destination end, and meanwhile, the source end sends the information to the (M +1) th unmanned aerial vehicle relay until the mth unmanned aerial vehicle relay sends the received information to the destination end; wherein, M is 1, 2.
3. The power distribution and flight path optimization method of claim 2, wherein: and the information transmission among the source end, the unmanned aerial vehicle relay and the target node adopts a broadcasting form.
4. The power distribution and flight path optimization method of claim 2, wherein: in the step S3, the expression formula of the power optimization constraint condition is as follows:
Figure FDA0002382420470000021
Figure FDA0002382420470000022
Figure FDA0002382420470000023
Figure FDA0002382420470000024
Figure FDA0002382420470000025
Figure FDA0002382420470000026
Figure FDA0002382420470000027
Figure FDA0002382420470000028
Figure FDA0002382420470000029
Figure FDA00023824204700000210
Figure FDA00023824204700000211
wherein, ηm＝[η1,...,ηM]Represents an introduced relaxation variable; n denotes the total flight length of the drone relay, i.e. N ═ m +1) Nt；
Figure FDA00023824204700000212
Representing the transmit power of the mth drone relay,
Figure FDA00023824204700000213
represents the mean transmitted power, P, of the mth drone relaym,maxMaximum power constraint for the mth drone relay;
Figure FDA00023824204700000214
which represents the transmit power of the source side,
Figure FDA00023824204700000215
representing the average transmitted power, P, of the sources,maxMaximum power constraint for source end; h ism,m-1[n]Represents the channel power gain, h, of the mth drone relay to the m-1 drone relays,m[n]Representing the channel power gain from the source to the mth drone relay;
Figure FDA00023824204700000216
representing an upper bound of the achievable rate of the source to the mth drone relay; gamma rays，m[n]Represents the signal-to-noise ratio, gamma, of the sourcem，d[n]Represents the signal-to-noise ratio, gamma, of each drone0Is referred to the signal-to-noise ratio, and
Figure FDA0002382420470000031
β0denotes the channel power gain, σ, at a reference distance of 1 meter2Representing the additive white gaussian noise power at the receiving end.
5. The method of claim 4, wherein: the power optimization constraints in step S3 are solved using the cvx toolkit.
6. The method of claim 4, wherein: the expression formula of the trajectory optimization constraint in the step S5 is as follows:
Figure FDA0002382420470000032
Figure FDA0002382420470000033
Figure FDA0002382420470000034
Figure FDA0002382420470000035
Sm,m-1[n]≤-||qm[n]-qm-1[n]||2+2(qm[n]-qm-1[n])T(qm[n]-qm-1[n])，
m＝2,...,M
Figure FDA0002382420470000036
wherein the content of the first and second substances,
Figure FDA0002382420470000037
Figure FDA0002382420470000038
Figure FDA0002382420470000039
Figure FDA00023824204700000310
wherein, tm＝[t1,...,tm]Represents an introduced relaxation variable; rs(Ss,m[n]) Representing the communication rate of the source, its upper bound
Figure FDA00023824204700000311
Is the second objective function value
Figure FDA00023824204700000312
Ss，m[n]Slack variable, S, representing the distance of the source to the mth dronem,m-1[n]Slack variable, S, representing the distance between two adjacent dronesm,d[n]A slack variable representing the mth drone to terminal distance; q. q.sm[n]A trajectory relayed for the mth drone; h is the flight height of unmanned aerial vehicle relay.
7. The power distribution and flight path optimization method of claim 6, wherein: and calculating the predicted optimal solution of the unmanned aerial vehicle relay track by adopting an interior point method under the track optimization constraint condition in the step S5
Figure FDA0002382420470000041
8. The method for power distribution and flight path optimization according to any one of claims 1 to 7, wherein: in the step S2, the error threshold e is initially set to 10-2。
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