CN111064501B - Resource optimization method based on unmanned aerial vehicle double-relay communication system - Google Patents

Resource optimization method based on unmanned aerial vehicle double-relay communication system Download PDF

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CN111064501B
CN111064501B CN201911336700.7A CN201911336700A CN111064501B CN 111064501 B CN111064501 B CN 111064501B CN 201911336700 A CN201911336700 A CN 201911336700A CN 111064501 B CN111064501 B CN 111064501B
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unmanned aerial
aerial vehicle
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CN111064501A (en
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张广驰
陈娇
崔苗
林凡
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Guangdong University of Technology
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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
    • 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
    • H04W52/243—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account interferences
    • 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
    • H04W52/38—TPC being performed in particular situations
    • H04W52/46—TPC being performed in particular situations in multi hop networks, e.g. wireless relay networks
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    • 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 invention discloses a resource optimization method based on an unmanned aerial vehicle double-relay communication system, which comprises the steps of establishing the unmanned aerial vehicle double-relay communication system for information transmission in a half-duplex mode, enabling two unmanned aerial vehicle relay nodes to work in a half-duplex decoding and forwarding mode and alternately sending information to a destination terminal, carrying out power distribution and optimal deployment of positions of double unmanned aerial vehicles on the basis, improving the effectiveness of the multi-unmanned aerial vehicle half-duplex communication mode, and maximizing the transmission rate under the condition of reducing interference as much as possible. The method solves the problem that the existing multi-unmanned aerial vehicle communication system has interference and cannot reasonably utilize communication resources.

Description

Resource optimization method based on unmanned aerial vehicle double-relay communication system
Technical Field
The invention relates to the technical field of unmanned aerial vehicle communication, in particular to a resource optimization method based on an unmanned aerial vehicle double-relay communication system.
Background
In recent years, due to the characteristics of high flexibility, high mobility, low cost and the like, the application range of the unmanned aerial vehicle is wider and wider, for example, information transmission and cargo transmission are carried out in communication obstacle areas, monitoring and information transmission are carried out in the military field, and aerial photography is more and more popular in the civil field. In the field of wireless communication, one of the main cases is unmanned aerial vehicle relay, and an unmanned aerial vehicle is deployed as a relay in the sky to provide wireless connection between remote users without a reliable direct communication link. In the unmanned aerial vehicle relay communication system, the deployment and the resource allocation of the track and the position of the unmanned aerial vehicle are reasonably designed, the throughput of the system can be maximized, and the transmission performance is improved.
In recent years, the unmanned aerial vehicle wireless relay technology is fully developed, and compared with the traditional static relay technology, the coverage range of the unmanned aerial vehicle relay-assisted wireless transmission technology is wider, meanwhile, the reliability of a communication system is remarkably improved, and the communication capacity of the system is increased. However, in the current research field of unmanned aerial vehicles, multiple unmanned aerial vehicles communicate alternately compared with a single unmanned aerial vehicle, so that the timeliness of the whole communication system can be improved, however, most of the multiple unmanned aerial vehicles concentrate on full-duplex information transmission, that is, the information is transmitted and received simultaneously for the unmanned aerial vehicles, and the problem of serious interference exists. For example, in a patent number "CN 201810083086.7" named "full duplex mobile relay system and its path optimization method", the relay node of the unmanned aerial vehicle forwards the data in a full duplex decoding manner, and meanwhile, power distribution is not performed in the text, so that the communication resources cannot be reasonably utilized.
Disclosure of Invention
The invention provides a resource optimization method based on an unmanned aerial vehicle double-relay communication system, which aims to solve the problems that the existing multi-unmanned aerial vehicle communication system has interference and cannot reasonably utilize communication resources.
In order to achieve the above purpose, the technical means adopted is as follows:
the resource optimization method based on the unmanned aerial vehicle dual-relay communication system comprises the following steps:
s1, establishing an unmanned aerial vehicle double-relay communication system, wherein the unmanned aerial vehicle double-relay communication system comprises a source end, a destination end and two unmanned aerial vehicles serving as relay nodes; in the unmanned aerial vehicle dual-relay communication system, the unmanned aerial vehicles transmit information received from a source end to a destination end in turn; the optimization target in the unmanned aerial vehicle double-relay communication system is that the system throughput is maximized by jointly and alternately optimizing the transmitting power of a source end and an unmanned aerial vehicle and the relay position of the unmanned aerial vehicle;
in the step, by establishing an unmanned aerial vehicle double-relay communication system for information transmission in a half-duplex mode, two unmanned aerial vehicle relay nodes work in a half-duplex decoding and forwarding mode and alternately send information to a destination terminal, and on the basis, power distribution and optimal deployment of positions of double unmanned aerial vehicles are carried out.
S2, giving an initial position of the unmanned aerial vehicle, wherein an optimization target in the unmanned aerial vehicle double-relay communication system is to maximize system throughput by jointly and alternately optimizing the transmitting power of a source end and the unmanned aerial vehicle and the relay position of the unmanned aerial vehicle; considering how to allocate power in this step to maximize the throughput of the system, the objective of the first optimization model is to optimize the end-to-end throughput, where the optimization variables are power and position, the power includes the source end and the transmission power of the two drones, and the position is the coordinates of the two drones; converting the first optimization model into a convex optimization problem, and solving the convex optimization problem through an interior point method or a CVX tool kit;
s3, setting the transmitting power of a source end and the unmanned aerial vehicle, and establishing a second optimization model for carrying out unmanned aerial vehicle relay position optimization on the unmanned aerial vehicle double-relay communication system; in the step, after power distribution is carried out, the optimal position point of the relay of the unmanned aerial vehicle is continuously optimized; likewise, the second optimization model is converted into a convex optimization problem, so that the solution is carried out by an interior point method or a CVX tool kit;
and S4, initializing the unmanned aerial vehicle double-relay communication system, and solving based on the first optimization model and the second optimization model and the optimization target of the unmanned aerial vehicle double-relay communication system to obtain the optimal source end, the emission power of the unmanned aerial vehicle and the relay position of the unmanned aerial vehicle. In this step, utilize unmanned aerial vehicle two relay communication system to jointly optimize unmanned aerial vehicle's power distribution and best unmanned aerial vehicle relay position, compare in single unmanned aerial vehicle communication, many unmanned aerial vehicles alternate communication improves the effectivity, and half-duplex communication mode maximizes transmission rate under the condition of minimize interference.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the method solves the problem that the existing multi-unmanned aerial vehicle communication system has interference and cannot reasonably utilize communication resources. The multi-unmanned aerial vehicle half-duplex communication mode improves the effectiveness, and maximizes the transmission rate under the condition of reducing the interference as much as possible.
Drawings
FIG. 1 is a general flow diagram of the process of the present invention.
Fig. 2 is a schematic diagram of a dual-relay communication system of an unmanned aerial vehicle according to the present invention.
Fig. 3 is a schematic diagram of information forwarding in an unmanned aerial vehicle dual-relay communication system.
Fig. 4 is a flowchart of step S4 of the method of the present invention.
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 resource optimization method based on the unmanned aerial vehicle dual-relay communication system comprises the following steps:
s1, establishing an unmanned aerial vehicle double-relay communication system, as shown in FIG. 2, the unmanned aerial vehicle double-relay communication system is as follows: the unmanned aerial vehicle alternately forwards information received from the source end to the destination end; setting the total flight time of the unmanned aerial vehicle as T, and dividing the T into N time slots; FIG. 3 shows the information forwarding situation of the system, W [ i ]]Indicates the information transmission condition of the ith time slot, i ═ 1,2]Wherein N is 2K +1, and K is a positive integer; corresponding to the lower part, S represents a source end, and D represents a destination end; r1 and R2 denote a first drone relay and a second drone relay, respectively; e.g. when N ═ 1, S-R1Indicating that the source peer sends a message to the first drone, when N is 2, S-R2Indicating that the source end sends information, R, to the second drone1-D denotes the first drone relay sending a message to the destination, S-R when N is 31Indicating that the source end sends information, R, to the first drone2D indicates that the second drone transmits information to the destination, proceeding in sequence until the nth slot, R2-D represents the second drone sending a message to the destination; let the flying heights of the two unmanned aerial vehicles respectively be z1And z2All receive the maximum height ZmaxAnd a minimum height ZminLimiting; setting the coordinates of the source end and the destination end as
Figure BDA0002331127990000031
And
Figure BDA0002331127990000032
wherein ws＝[0,0]TAnd wd＝[L,0]T(ii) a The position of the first drone is represented as a sequence q1＝[x1,y1,z1]TThe second unmanned position is q2＝[x2,y2,z2]TAnd setting each communication channel as a line-of-sight channel, wherein the channel power gain conforms to a free space path loss model:
the channel power gain from the source to the first drone is expressed as
Figure BDA0002331127990000033
The channel power gain from the source to the second drone is expressed as
Figure BDA0002331127990000034
The channel power gain between two drones is expressed as
Figure BDA0002331127990000035
Channel power increase of first drone and destinationThe benefits are expressed as
Figure BDA0002331127990000041
The second drone has a channel power gain to the destination of
Figure BDA0002331127990000042
Wherein beta is0Denotes the channel power gain at a reference distance of 1m, ds,r(1)And ds,r(2)Respectively represents the distance between the source end and the first unmanned aerial vehicle and the second unmanned aerial vehicle, dr(1),r(2)Indicating the distance between two drones, dr(1),fAnd dr(2),fRespectively representing the distance from the first unmanned aerial vehicle to the destination end and the distance from the second unmanned aerial vehicle to the destination end; let ps,1And ps,2Representing the transmission power from the source to the first drone and the transmission power from the source to the second drone, p, respectively1Indicating the transmission power, p, of the first drone relay2Representing the transmit power of the second drone relay; p is a radical ofs,1、ps,2、p1、p2Constrained by the peak power and the average power, namely:
0≤ps,1≤ps,max (1a)
0≤ps,2≤ps,max (1b)
0≤p1≤p1,max (1c)
0≤p2≤p2,max (1b)
Figure BDA0002331127990000043
Figure BDA0002331127990000044
Figure BDA0002331127990000045
Figure BDA0002331127990000046
wherein p iss,maxAnd p1,maxAnd p2,maxRepresenting the peak power of the source and the first drone and the second drone respectively,
Figure BDA0002331127990000047
and
Figure BDA0002331127990000048
and
Figure BDA0002331127990000049
respectively representing the average power of the source end, the first unmanned aerial vehicle and the second unmanned aerial vehicle;
because the source end sends information to two unmanned aerial vehicles in turn, and the unmanned aerial vehicles send information to the destination end in turn at the same time, the source end and the unmanned aerial vehicle relay also have the following power constraints:
Ps,2[1]＝0 (2a)
P1[N]＝0 (2b)
when N ═ 2, 4., N-1}, N ═ 2K +1, K is a positive integer, the following constraints apply:
Ps,1[n]＝0，P2[n]＝0 (2c)
when N is {3, 5., N-2}, N is 2K +1, and K is a positive integer, the following constraints apply:
Ps,2[n]＝0，P1[n]＝0 (2d)
first, gamma is defined0＝β0/σ2，σ2Is Gaussian white noise power, gamma0For reference to the signal-to-noise ratio, the following formula is substituted, thus obtaining the information transmission rate of the source-to-first drone link when n is 1:
Figure BDA0002331127990000051
and similarly, obtaining the information transmission rate from the first unmanned aerial vehicle to the destination:
Figure BDA0002331127990000052
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (3b)
transmission rate of the second drone to the destination:
Figure BDA0002331127990000053
wherein N is {3, 5., N }, N is 2K +1, and K is a positive integer; (3c)
when n is greater than or equal to 2, that is, from the second time slot, when the source end sends information to the drone, the source end is interfered by sending information from another drone to the destination end, so that the information transmission rate of the link from the source end to the first drone is:
Figure BDA0002331127990000061
wherein N is {3, 5., N-2}, N is 2K +1, and K is a positive integer; (3d) similarly, the information transmission rate from the source end to the second unmanned aerial vehicle is as follows:
Figure BDA0002331127990000062
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (3e)
the throughput of the two source-to-drone end links is represented as 1/2 of the minimum value between the source-to-drone transmission rate and the drone-to-destination transmission rate, respectively; the throughputs of the first link and the second link are thus written as the following expressions:
Figure BDA0002331127990000063
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (4a)
Figure BDA0002331127990000064
where N is {3, 5., N }, N is 2K +1, and K is a positive integer. (4b)
The optimization target in the unmanned aerial vehicle double-relay communication system is that the system throughput is maximized by jointly and alternately optimizing the transmitting power of a source end and an unmanned aerial vehicle and the relay position of the unmanned aerial vehicle; the optimization objective is specifically expressed as:
Figure BDA0002331127990000065
wherein N is 2K +1, K is a positive integer,
Figure BDA0002331127990000071
s.t.(1a)-(1h)(2a)-(2d)。
s2, setting an initial position of the unmanned aerial vehicle, and establishing a first optimization model for optimizing the transmitting power of a source end and the unmanned aerial vehicle of the unmanned aerial vehicle dual-relay communication system; the method comprises the following specific steps:
defining:
Figure BDA0002331127990000072
Figure BDA0002331127990000073
Figure BDA0002331127990000074
Figure BDA0002331127990000075
Figure BDA0002331127990000076
it follows that when n is 1, the information transmission rate from the source to the first drone link is further reduced to the following equation:
Figure BDA0002331127990000077
and similarly, obtaining the information transmission rate from the first unmanned aerial vehicle to the destination:
Figure BDA0002331127990000078
Figure BDA0002331127990000081
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (7b)
transmission rate of the second drone to the destination:
Figure BDA0002331127990000082
where N is {3, 5., N }, N is 2K +1, and K is a positive integer (7c)
When n is greater than or equal to 2, that is, from the second time slot, when the source end sends information to the drone, interference from another drone sending information to the destination end is caused, so that the information transmission rate from the source end to the first drone link is as follows:
Figure BDA0002331127990000083
wherein N is {3, 5., N-2}, N is 2K +1, and K is a positive integer; (7d)
the information transmission rate from the source end to the second unmanned aerial vehicle is as follows:
Figure BDA0002331127990000091
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (7e)
introducing relaxation variables A and B:
Figure BDA0002331127990000092
Figure BDA0002331127990000093
an initial first optimization model P2 is obtained by converting an optimization target P1 of the unmanned aerial vehicle dual-relay communication system:
Figure BDA0002331127990000094
wherein N is 2K +1, K is positive integer (8)
s.t.A(n)≤Rsr(1)[n]＝log2(1+Ps，1[n]γs1) n＝1 (9)
Figure BDA0002331127990000095
Figure BDA0002331127990000096
Figure BDA0002331127990000097
Figure BDA0002331127990000101
It can be shown by inverse method that there is always an optimal solution such that inequalities (9) - (13) take equal signs, and the optimization objective function is converted into P2 to solve, it is obvious that inequality constraints (9), (11), (12) are relative to Ps，1,P1,P2It is a convex problem that can be solved directly by the CVX toolkit, however, it is not a convex problem for the inequality constraints (10), (13). The right formula of the inequality constraint (10) can be written as:
Figure BDA0002331127990000102
by means of an iterative method of solving for,
Figure BDA0002331127990000103
is p2[n]The r iteration, order
Figure BDA0002331127990000104
And pair
Figure BDA0002331127990000105
Performing a first order Taylor expansion, obtained by globally estimating the property using the first order Taylor expansion of the concave function
Figure BDA0002331127990000106
Upper bound of (2)
Figure BDA0002331127990000107
To p2[n]A first order Taylor expansion yields the following equation:
Figure BDA0002331127990000108
wherein
Figure BDA00023311279900001017
Figure BDA00023311279900001010
Similarly, for the transmission rate from the source to the second drone, the following equation is obtained:
Figure BDA00023311279900001011
here let
Figure BDA00023311279900001012
By making p pairs1[n]First order Taylor expansion to obtain
Figure BDA00023311279900001013
Upper bound of (2)
Figure BDA00023311279900001014
Figure BDA00023311279900001015
Wherein
Figure BDA00023311279900001016
Is p1[n]The number r of iterations is then repeated,
Figure BDA0002331127990000111
Figure BDA0002331127990000112
obtained by the equations (15) and (19), respectively
Figure BDA0002331127990000113
And
Figure BDA0002331127990000114
upper bound of
Figure BDA0002331127990000115
And
Figure BDA0002331127990000116
thus, P2 is transformed into the final first optimization model P3:
Figure BDA0002331127990000117
wherein N is 2K +1, K is a positive integer,
Figure BDA0002331127990000118
s.t.A(n)≤log2(1+Ps，1[n]γs1) n＝1 (23)
Figure BDA0002331127990000119
Figure BDA00023311279900001110
Figure BDA00023311279900001111
Figure BDA00023311279900001112
(a)-1(h) 2(a)-2(d)。
the final first optimization model P3 is transformed into a convex optimization problem that can be solved by interior point method or CVX toolkit.
S3, setting the transmitting power of a source end and the unmanned aerial vehicle, and establishing a second optimization model for carrying out unmanned aerial vehicle relay position optimization on the unmanned aerial vehicle double-relay communication system; the method comprises the following specific steps:
establishing an initial second optimization model P4:
Figure BDA00023311279900001113
wherein N is 2K +1, K is a positive integer,
Figure BDA00023311279900001114
s.t.(1a)-(1h) (2a)-(2d)
due to gamma0＝β0/σ2Further defined as follows:
γ01[n]＝Ps,1[n]γ0 (29)
γ02[n]＝Ps,2[n]γ0 (30)
γ1[n]＝P1[n]γ0 (31)
γ2[n]＝P2[n]γ0 (32)
when n is 1, obtaining that the information transmission rate from the source end to the first unmanned aerial vehicle is:
Figure BDA0002331127990000121
when n is larger than or equal to 2, interference exists, and the information transmission rate from the source end to the first unmanned aerial vehicle is as follows:
Figure BDA0002331127990000122
wherein N is {3, 5., N-2}, N is 2K +1, and K is a positive integer; (34) the information transmission rate between the source terminal and the second unmanned aerial vehicle is as follows:
Figure BDA0002331127990000123
Figure BDA0002331127990000131
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (35)
similarly, the information transmission rate from the first unmanned aerial vehicle to the destination end is as follows:
Figure BDA0002331127990000132
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (36)
the information transmission rate from the second unmanned aerial vehicle to the destination end is as follows:
Figure BDA0002331127990000133
where N is {3, 5., N }, N is 2K +1, and K is a positive integer. (37)
Since the objective function is a non-convex problem, to solve this problem, a relaxation variable η is introduced1And η2And is and
Figure BDA0002331127990000134
thus the optimization variables are Q, eta1、η2The second optimization model P4 is transformed into the following formula:
Figure BDA0002331127990000141
N2K +1, K being a positive integer (38)
Figure BDA0002331127990000142
Figure BDA0002331127990000143
Figure BDA0002331127990000144
Figure BDA0002331127990000145
Figure BDA0002331127990000146
(1a)-(1h) (2a)-(2d)
Constraints (39) - (43) with respect to q1And q is2Non-convex, introducing a relaxation variable S12Let S12＝||q1-q2||2The definition is as follows:
Figure BDA0002331127990000147
when N is {3, 5., N-2}, N is 2K +1, and K is a positive integer; (44)
Figure BDA0002331127990000148
when N is {2, 4., N-1}, N is 2K +1, K is a positive integer (45)
Then P5 is converted to the following formula:
Figure BDA0002331127990000151
wherein N is 2K +1, K is positive integer (46)
Figure BDA0002331127990000152
Figure BDA0002331127990000153
Figure BDA0002331127990000154
Figure BDA0002331127990000155
Figure BDA0002331127990000156
S12≤||q1-q2||2 (52)
(1a)-(1h) (2a)-(2d)
Using the continuous convex optimization technique, the right formula for constraints (47), (49), (50) is referred to as q1And q is2Is non-concave; about q1-ws||2And q1-wd||2And q2-wd||2Is a convex function, define
Figure BDA0002331127990000157
Position of the drone relay representing r iterations, for local points
Figure BDA0002331127990000158
Respectively represent q1And q is2For the nth iteration, any convex function is the global lower bound of the first-order taylor expansion at any point:
when n is 1, for | | | q1-ws||2Is subjected to a first order Taylor expansion to obtain Rsr(1)[n]Lower boundary of (1)
Figure BDA0002331127990000159
There are the following formulas:
Figure BDA00023311279900001510
wherein:
Figure BDA00023311279900001511
Figure BDA0002331127990000161
when N is {2, 4.. N-1}, N is 2K +1, K is a positive integer, by pairing with | | q1-wd||2Performing a first order Taylor expansion to obtain Rr(1)f[n]Lower boundary of (1)
Figure BDA0002331127990000162
There are the following formulas:
Figure BDA0002331127990000163
wherein:
Figure BDA0002331127990000164
Figure BDA0002331127990000165
when N is {3,5,., N }, N is 2K +1, K is a positive integer, by pairing | | q2-wd||2Performing a first order Taylor expansion to obtain Rr(2)f[n]Lower boundary of (1)
Figure BDA0002331127990000166
There are the following formulas:
Figure BDA0002331127990000167
wherein:
Figure BDA0002331127990000168
Figure BDA0002331127990000169
the concave lower boundaries of the right formulae of the formulae (47), (49) and (50) are obtained from the above, and with the inequalities (48) and (51), | q1-ws||2And q2-ws||2And q1-q2||2P6 is also a non-convex problem if it is also non-concave relative to the right equation and if it is not the equation for equation (52); the following equation is solved from the property that the binary first order Taylor expansion of the convex function is estimated globally:
when N is {3, 5., N-2}, N is 2K +1, and K is a positive integer, the following formula | | | q1-ws||2And q1-q2||2Performing binary function first-order Taylor expansion to obtain
Figure BDA00023311279900001610
Lower boundary of (1)
Figure BDA00023311279900001611
There is the following expression:
Figure BDA0002331127990000171
wherein:
Figure BDA0002331127990000172
Figure BDA0002331127990000173
when N is {2,4, · N-1}, N is 2K +1, and K is a positive integer, the following formula | | | q2-ws||2And q1-q2||2Performing first-order Taylor expansion of binary function to obtain
Figure BDA0002331127990000174
Lower boundary of (1)
Figure BDA0002331127990000175
There is the following expression:
Figure BDA0002331127990000176
wherein:
Figure BDA0002331127990000177
Figure BDA0002331127990000181
and q1-q2||2Is non-concave for equation (51), so it is also necessary to right of the inequality (51), at point
Figure BDA0002331127990000182
And
Figure BDA0002331127990000183
a first order Taylor expansion is performed, resulting in the following lower bound for this equation:
Figure BDA0002331127990000184
from the above it is known that the problem of P6 translates approximately into the following:
Figure BDA0002331127990000185
wherein N is 2K +1, K is positive integer (69)
Figure BDA0002331127990000186
Figure BDA0002331127990000187
Figure BDA0002331127990000188
Figure BDA0002331127990000189
Figure BDA00023311279900001810
Figure BDA00023311279900001811
(1a)-(1h) (2a)-(2d)。
The final second optimization model P7 is transformed into a convex optimization problem that can be solved by interior point method or CVX toolkit.
S4, initializing the unmanned aerial vehicle double-relay communication system, and solving based on the first optimization model and the second optimization model and an optimization target of the unmanned aerial vehicle double-relay communication system to obtain the optimal source end, the emission power of the unmanned aerial vehicle and the relay position of the unmanned aerial vehicle; as shown in fig. 4, the method specifically includes the following steps:
s41, initialization: setting an initial unmanned aerial vehicle flight trajectory
Figure BDA0002331127990000191
r is 0 and error threshold e is 10-2；
S42, relaying the unmanned aerial vehicle
Figure BDA0002331127990000192
And
Figure BDA0002331127990000193
substituting the optimal solution into a final first optimization model P3 to obtain optimal solutions of the transmitting power of the source end and the unmanned aerial vehicle respectively
Figure BDA0002331127990000194
Figure BDA0002331127990000195
S43, substituting the obtained source end and the emission power of the unmanned aerial vehicle into the final second optimization model P7 to obtain an optimal solution
Figure BDA0002331127990000196
And
Figure BDA0002331127990000197
and obtaining an objective function value
Figure BDA0002331127990000198
S44, making r equal to r + 1;
s45, if
Figure BDA0002331127990000199
Obtaining optimal power allocation
Figure BDA00023311279900001910
Figure BDA00023311279900001911
And unmanned aerial vehicle relayIs best position
Figure BDA00023311279900001912
And
Figure BDA00023311279900001913
completing resource optimization of the unmanned aerial vehicle dual-relay communication system; otherwise, steps S42-S44 are repeated.
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 resource optimization method based on an unmanned aerial vehicle dual-relay communication system is characterized by comprising the following steps:
s1, establishing an unmanned aerial vehicle double-relay communication system for information transmission in a half-duplex mode, wherein the unmanned aerial vehicle double-relay communication system comprises a source end, a destination end and two unmanned aerial vehicles serving as relay nodes; in the unmanned aerial vehicle dual-relay communication system, the unmanned aerial vehicles transmit information received from a source end to a destination end in turn; the optimization target in the unmanned aerial vehicle double-relay communication system is that the system throughput is maximized by jointly and alternately optimizing the transmitting power of a source end and an unmanned aerial vehicle and the relay position of the unmanned aerial vehicle;
s2, setting an initial position of the unmanned aerial vehicle, and establishing a first optimization model for optimizing the transmitting power of a source end and the unmanned aerial vehicle of the unmanned aerial vehicle dual-relay communication system;
s3, setting the transmitting power of a source end and the unmanned aerial vehicle, and establishing a second optimization model for carrying out unmanned aerial vehicle relay position optimization on the unmanned aerial vehicle double-relay communication system;
s4, initializing the unmanned aerial vehicle double-relay communication system, and solving based on the first optimization model and the second optimization model and an optimization target of the unmanned aerial vehicle double-relay communication system to obtain the optimal source end, the emission power of the unmanned aerial vehicle and the relay position of the unmanned aerial vehicle;
the unmanned aerial vehicle dual-relay communication system in step S1 specifically includes:
setting the total flight time of the unmanned aerial vehicle as T, and dividing the T into N time slots; definition W [ i ]]Indicates the information transmission condition of the ith time slot, i ═ 1,2]Wherein N is 2K +1, and K is a positive integer; s represents a source end, and D represents a destination end; r1 and R2 denote a first drone relay and a second drone relay, respectively; let the flying heights of the two unmanned aerial vehicles respectively be z1And z2All receive the maximum height ZmaxAnd a minimum height ZminLimiting; setting the coordinates of the source end and the destination end as
Figure FDA0003196905180000011
And
Figure FDA0003196905180000012
wherein ws＝[0,0]TAnd wd＝[L,0]T(ii) a The position of the first drone is represented as a sequence q1＝[x1,y1,z1]TThe second unmanned position is q2＝[x2,y2,z2]TAnd setting each communication channel as a line-of-sight channel, wherein the channel power gain conforms to a free space path loss model:
the channel power gain from the source to the first drone is expressed as
Figure FDA0003196905180000013
The channel power gain from the source to the second drone is expressed as
Figure FDA0003196905180000014
The channel power gain between two drones is expressed as
Figure FDA0003196905180000015
The channel power gain of the first drone and the destination is expressed as
Figure FDA0003196905180000021
The second drone has a channel power gain to the destination of
Figure FDA0003196905180000022
Wherein beta is0Denotes the channel power gain at a reference distance of 1m, ds,r(1)And ds,r(2)Respectively represents the distance between the source end and the first unmanned aerial vehicle and the second unmanned aerial vehicle, dr(1),r(2)Indicating the distance between two drones, dr(1),fAnd dr(2),fRespectively representing the distance from the first unmanned aerial vehicle to the destination end and the distance from the second unmanned aerial vehicle to the destination end; let ps,1And ps,2Representing the transmission power from the source to the first drone and the transmission power from the source to the second drone, p, respectively1Indicating the transmission power, p, of the first drone relay2Representing the transmit power of the second drone relay; p is a radical ofs,1、ps,2、p1、p2Constrained by the peak power and the average power, namely:
0≤ps,1≤ps,max (1a)
0≤ps,2≤ps,max (1b)
0≤p1≤p1,max (1c)
0≤p2≤p2,max (1b)
Figure FDA0003196905180000023
Figure FDA0003196905180000024
Figure FDA0003196905180000025
Figure FDA0003196905180000026
wherein p iss,maxAnd p1,maxAnd p2,maxRepresenting the peak power of the source and the first drone and the second drone respectively,
Figure FDA0003196905180000027
and
Figure FDA0003196905180000028
and
Figure FDA0003196905180000029
respectively representing the average power of the source end, the first unmanned aerial vehicle and the second unmanned aerial vehicle;
because the source end sends information to two unmanned aerial vehicles in turn, and the unmanned aerial vehicles send information to the destination end in turn at the same time, the source end and the unmanned aerial vehicle relay also have the following power constraints:
Ps,2[1]＝0 (2a)
P1[N]＝0 (2b)
when N ═ 2, 4., N-1}, N ═ 2K +1, K is a positive integer, the following constraints apply:
Ps,1[n]＝0，P2[n]＝0 (2c)
when N is {3, 5., N-2}, N is 2K +1, and K is a positive integer, the following constraints apply:
Ps,2[n]＝0，P1[n]＝0 (2d)
first, gamma is defined0＝β0/σ2，σ2Is Gaussian white noise power, gamma0For reference to the signal-to-noise ratio, the following formula is substituted, thus obtaining the information transmission rate of the source-to-first drone link when n is 1:
Figure FDA0003196905180000031
and similarly, obtaining the information transmission rate from the first unmanned aerial vehicle to the destination:
Figure FDA0003196905180000032
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (3b)
transmission rate of the second drone to the destination:
Figure FDA0003196905180000033
wherein N is {3, 5., N }, N is 2K +1, and K is a positive integer; (3c)
when n is greater than or equal to 2, that is, from the second time slot, when the source end sends information to the drone, the source end is interfered by sending information from another drone to the destination end, so that the information transmission rate of the link from the source end to the first drone is:
Figure FDA0003196905180000041
wherein N is {3, 5., N-2}, N is 2K +1, and K is a positive integer; (3d)
similarly, the information transmission rate from the source end to the second unmanned aerial vehicle is:
Figure FDA0003196905180000042
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (3e)
the throughput of the two source-to-drone end links is represented as 1/2 of the minimum value between the source-to-drone transmission rate and the drone-to-destination transmission rate, respectively; the throughputs of the first link and the second link are thus written as the following expressions:
Figure FDA0003196905180000043
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (4a)
Figure FDA0003196905180000044
where N is {3, 5., N }, N is 2K +1, and K is a positive integer. (4b)
2. The method for optimizing resources based on dual-relay communication system of unmanned aerial vehicle as claimed in claim 1, wherein the optimization objective in the dual-relay communication system of unmanned aerial vehicle in step S1 is:
Figure FDA0003196905180000045
wherein N is 2K +1, K is a positive integer,
Figure FDA0003196905180000051
s.t. (1a)-(1h) (2a)-(2d)。
3. the method for optimizing resources based on the dual-relay communication system of the unmanned aerial vehicle according to claim 2, wherein the step S2 specifically includes:
defining:
Figure FDA0003196905180000052
Figure FDA0003196905180000053
Figure FDA0003196905180000054
Figure FDA0003196905180000055
Figure FDA0003196905180000056
it follows that when n is 1, the information transmission rate from the source to the first drone link is further reduced to the following equation:
Figure FDA0003196905180000057
and similarly, obtaining the information transmission rate from the first unmanned aerial vehicle to the destination:
Figure FDA0003196905180000058
Figure FDA0003196905180000061
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (7b)
transmission rate of the second drone to the destination:
Figure FDA0003196905180000062
where N is {3, 5., N }, N is 2K +1, and K is a positive integer (7c)
When n is greater than or equal to 2, that is, from the second time slot, when the source end sends information to the drone, interference from another drone sending information to the destination end is caused, so that the information transmission rate from the source end to the first drone link is as follows:
Figure FDA0003196905180000063
wherein N is {3, 5., N-2}, N is 2K +1, and K is a positive integer; (7d)
the information transmission rate from the source end to the second unmanned aerial vehicle is as follows:
Figure FDA0003196905180000071
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (7e)
introducing relaxation variables A and B:
Figure FDA0003196905180000072
Figure FDA0003196905180000073
from the aboveThe optimization target P1 of the unmanned aerial vehicle double-relay communication system is converted to obtain an initial first optimization model P2:
Figure FDA0003196905180000074
wherein N is 2K +1, K is positive integer (8)
s.t.A(n)≤Rsr(1)[n]＝log2(1+Ps，1[n]γs1)n＝1 (9)
Figure FDA0003196905180000075
Figure FDA0003196905180000076
Figure FDA0003196905180000077
Figure FDA0003196905180000081
(1a)-(1h) (2a)-(2d)。
4. The method for optimizing resources based on UAV dual-relay communication system as claimed in claim 3, wherein the step S2 further comprises transforming the initial first optimization model P2 into a final first optimization model P3 of convex optimization problem, specifically:
the right formula of the inequality constraint (10) is written as:
Figure FDA0003196905180000082
by means of an iterative method of solving for,
Figure FDA0003196905180000083
is p2[n]The r iteration, order
Figure FDA0003196905180000084
And pair
Figure FDA0003196905180000085
Performing a first order Taylor expansion to obtain
Figure FDA0003196905180000086
Upper bound of (2)
Figure FDA0003196905180000087
To p2[n]A first order Taylor expansion yields the following equation:
Figure FDA0003196905180000088
wherein
Figure FDA0003196905180000089
Figure FDA00031969051800000810
Similarly, for the transmission rate from the source to the second drone, the following equation is obtained:
Figure FDA00031969051800000811
here let
Figure FDA00031969051800000812
By making p pairs1[n]First order TaylorIs unfolded to obtain
Figure FDA00031969051800000813
Upper bound of (2)
Figure FDA00031969051800000814
Figure FDA00031969051800000815
Wherein P is1 r[n]Is p1[n]The number r of iterations is then repeated,
Figure FDA0003196905180000091
Figure FDA0003196905180000092
obtained by the equations (15) and (19), respectively
Figure FDA0003196905180000093
And
Figure FDA0003196905180000094
upper bound of
Figure FDA0003196905180000095
And
Figure FDA0003196905180000096
thus, P2 is transformed into the final first optimization model P3:
Figure FDA0003196905180000097
wherein N is 2K +1, K is a positive integer,
Figure FDA0003196905180000098
s.t.A(n)≤log2(1+Ps，1[n]γs1)n＝1 (23)
Figure FDA0003196905180000099
Figure FDA00031969051800000910
Figure FDA00031969051800000911
Figure FDA00031969051800000912
1(a)-1(h) 2(a)-2(d)。
5. the method for optimizing resources based on the dual-relay communication system of unmanned aerial vehicle according to claim 4, wherein the step S3 specifically comprises:
initial second optimization model P4:
Figure FDA00031969051800000913
wherein N is 2K +1, K is a positive integer,
Figure FDA00031969051800000914
s.t. (1a)-(1h) (2a)-(2d)
due to gamma0＝β0/σ2Further defined as follows:
γ01[n]＝Ps，1[n]γ0 (29)
γ02[n]＝Ps，2[n]γ0 (30)
γ1[n]＝P1[n]γ0 (31)
γ2[n]＝P2[n]γ0 (32)
when n is 1, obtaining that the information transmission rate from the source end to the first unmanned aerial vehicle is:
Figure FDA0003196905180000101
when n is larger than or equal to 2, interference exists, and the information transmission rate from the source end to the first unmanned aerial vehicle is as follows:
Figure FDA0003196905180000102
wherein N is {3, 5., N-2}, N is 2K +1, and K is a positive integer; (34)
the information transmission rate from the source end to the second unmanned aerial vehicle is as follows:
Figure FDA0003196905180000103
Figure FDA0003196905180000111
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (35)
similarly, the information transmission rate from the first unmanned aerial vehicle to the destination end is as follows:
Figure FDA0003196905180000112
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (36)
the information transmission rate from the second unmanned aerial vehicle to the destination end is as follows:
Figure FDA0003196905180000113
where N is {3, 5., N }, N is 2K +1, and K is a positive integer. (37)
6. The method for optimizing resources based on UAV dual-relay communication system as claimed in claim 5, wherein the step S3 further comprises transforming the initial second optimization model P4 into a second optimization model P7 of convex optimization problem, specifically:
introducing a relaxation variable eta1And η2And is and
Figure FDA0003196905180000114
thus the optimization variables are Q, eta1、η2The second optimization model P4 is transformed into the following formula:
Figure FDA0003196905180000121
N2K +1, K being a positive integer (38)
Figure FDA0003196905180000122
Figure FDA0003196905180000123
Figure FDA0003196905180000124
Figure FDA0003196905180000125
Figure FDA0003196905180000126
(1a)-(1h) (2a)-(2d)
Constraints (39) - (43) with respect to q1And q is2Non-convex, introducing a relaxation variable S12Let S12＝||q1-q2||2The definition is as follows:
Figure FDA0003196905180000127
when N is {3, 5., N-2}, N is 2K +1, and K is a positive integer; (44)
Figure FDA0003196905180000128
when N is {2, 4., N-1}, N is 2K +1, K is a positive integer (45)
Then P5 is converted to the following formula:
Figure FDA0003196905180000131
wherein N is 2K +1, K is positive integer (46)
Figure FDA0003196905180000132
Figure FDA0003196905180000133
Figure FDA0003196905180000134
Figure FDA0003196905180000135
Figure FDA0003196905180000136
S12≤||q1-q2||2 (52)
(1a)-(1h) (2a)-(2d)
Using the continuous convex optimization technique, the right formula for constraints (47), (49), (50) is referred to as q1And q is2Is non-concave; about q1-ws||2And q1-wd||2And q2-wd||2Is a convex function, define
Figure FDA0003196905180000137
Position of the drone relay representing r iterations, for local points
Figure FDA0003196905180000138
Respectively represent q1And q is2For the nth iteration, any convex function is the global lower bound of the first-order taylor expansion at any point:
when n is 1, for | | | q1-ws||2Is subjected to a first order Taylor expansion to obtain Rsr(1)[n]Lower boundary of (1)
Figure FDA0003196905180000139
There are the following formulas:
Figure FDA00031969051800001310
wherein:
Figure FDA0003196905180000141
Figure FDA0003196905180000142
when N is {2, 4.. N-1}, N is 2K +1, K is a positive integer, by pairing with | | q1-wd||2Performing a first order Taylor expansion to obtain Rr(1)f[n]Lower boundary of (1)
Figure FDA0003196905180000143
There are the following formulas:
Figure FDA0003196905180000144
wherein:
Figure FDA0003196905180000145
when N is {3,5,., N }, N is 2K +1, K is a positive integer, by pairing | | q2-wd||2Performing a first order Taylor expansion to obtain Rr(2)f[n]Lower boundary of (1)
Figure FDA0003196905180000146
There are the following formulas:
Figure FDA0003196905180000147
wherein:
Figure FDA0003196905180000148
Figure FDA0003196905180000149
the concave lower boundaries of the right formulae of the formulae (47), (49) and (50) are obtained from the above, and with the inequalities (48) and (51), | q1-ws||2And q2-ws||2And q1-q2||2P6 is also a non-convex problem if it is also non-concave relative to the right equation and if it is not the equation for equation (52); the following equation is solved from the property that the binary first order Taylor expansion of the convex function is estimated globally:
when N is {3, 5., N-2}, N is 2K +1, and K is a positive integer, the following formula | | | q1-ws||2And q1-q2||2Performing binary function first-order Taylor expansion to obtain
Figure FDA0003196905180000151
Lower boundary of (1)
Figure FDA0003196905180000152
There is the following expression:
Figure FDA0003196905180000153
wherein:
Figure FDA0003196905180000154
Figure FDA0003196905180000155
when N is {2,4, · N-1}, N is 2K +1, and K is a positive integer, the following formula | | | q2-ws||2And q1-q2||2Performing first-order Taylor expansion of binary function to obtain
Figure FDA0003196905180000156
Lower boundary of (1)
Figure FDA0003196905180000157
There is the following expression:
Figure FDA0003196905180000158
wherein:
Figure FDA0003196905180000161
Figure FDA0003196905180000162
and q1-q2||2Is non-concave for equation (51), so it is also necessary to right of the inequality (51), at point
Figure FDA0003196905180000163
And
Figure FDA0003196905180000164
a first order Taylor expansion is performed, resulting in the following lower bound for this equation:
Figure FDA0003196905180000165
from the above it is known that the problem of P6 translates approximately into the following:
Figure FDA0003196905180000166
wherein N is 2K +1, K is positive integer (69)
Figure FDA0003196905180000167
Figure FDA0003196905180000168
Figure FDA0003196905180000169
Figure FDA00031969051800001610
Figure FDA00031969051800001611
Figure FDA00031969051800001612
(1a)-(1h) (2a)-(2d)。
7. The method of claim 6, wherein in step S4, the final first optimization model P3 and the final second optimization model P7 are solved by using an interior point method or a CVX toolkit, so that the transmit power of the source end and the drone and the relay position of the drone are jointly and alternately optimized based on the first optimization model and the second optimization model, and the optimal transmit power of the source end and the drone and the relay position of the drone are obtained.
8. The method for optimizing resources based on the dual-relay communication system of unmanned aerial vehicle according to claim 7, wherein the specific step of step S4 includes:
s41, initialization: setting an initial unmanned aerial vehicle flight trajectory
Figure FDA0003196905180000171
r is 0 and error threshold e is 10-2；
S42, relaying the unmanned aerial vehicle
Figure FDA0003196905180000172
And
Figure FDA0003196905180000173
substituting the optimal solution into a final first optimization model P3 to obtain optimal solutions of the transmitting power of the source end and the unmanned aerial vehicle respectively
Figure FDA0003196905180000174
Figure FDA0003196905180000175
S43, substituting the obtained source end and the emission power of the unmanned aerial vehicle into the final second optimization model P7 to obtain the optimal solution of the relay position of the unmanned aerial vehicle
Figure FDA0003196905180000176
And
Figure FDA0003196905180000177
and obtaining an objective function value
Figure FDA0003196905180000178
S44, making r equal to r + 1;
s45, if
Figure FDA0003196905180000179
Obtaining optimal power allocation
Figure FDA00031969051800001710
Figure FDA00031969051800001711
And optimal position of drone relay
Figure FDA00031969051800001712
And
Figure FDA00031969051800001713
completing resource optimization of the unmanned aerial vehicle dual-relay communication system; otherwise, steps S42-S44 are repeated.
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