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 PDFInfo
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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
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 asAndwherein 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:
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)
wherein p iss,maxAnd p1,maxAnd p2,maxRepresenting the peak power of the source and the first drone and the second drone respectively,andandrespectively 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:
and similarly, obtaining the information transmission rate from the first unmanned aerial vehicle to the destination:
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:
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:
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:
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:
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (4a)
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:
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:
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:
and similarly, obtaining the information transmission rate from the first unmanned aerial vehicle to the destination:
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:
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:
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:
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (7e)
introducing relaxation variables A and B:
an initial first optimization model P2 is obtained by converting an optimization target P1 of the unmanned aerial vehicle dual-relay communication system:
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)
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:
by means of an iterative method of solving for,is p2[n]The r iteration, orderAnd pairPerforming a first order Taylor expansion, obtained by globally estimating the property using the first order Taylor expansion of the concave functionUpper bound of (2)To p2[n]A first order Taylor expansion yields the following equation:
Similarly, for the transmission rate from the source to the second drone, the following equation is obtained:
obtained by the equations (15) and (19), respectivelyAndupper bound ofAndthus, P2 is transformed into the final first optimization model P3:
s.t.A(n)≤log2(1+Ps,1[n]γs1) n=1 (23)
(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:
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:
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:
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:
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:
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:
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 andthus the optimization variables are Q, eta1、η2The second optimization model P4 is transformed into the following formula:
N2K +1, K being a positive integer (38)
(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:
when N is {3, 5., N-2}, N is 2K +1, and K is a positive integer; (44)
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:
wherein N is 2K +1, K is positive integer (46)
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, definePosition of the drone relay representing r iterations, for local pointsRespectively 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)There are the following formulas:
wherein:
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)There are the following formulas:
wherein:
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)There are the following formulas:
wherein:
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 obtainLower boundary of (1)There is the following expression:
wherein:
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 obtainLower boundary of (1)There is the following expression:
wherein:
and q1-q2||2Is non-concave for equation (51), so it is also necessary to right of the inequality (51), at pointAnda first order Taylor expansion is performed, resulting in the following lower bound for this equation:
from the above it is known that the problem of P6 translates approximately into the following:
wherein N is 2K +1, K is positive integer (69)
(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 trajectoryr is 0 and error threshold e is 10-2;
S42, relaying the unmanned aerial vehicleAndsubstituting 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
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 solutionAndand obtaining an objective function value
S44, making r equal to r + 1;
s45, ifObtaining optimal power allocation And unmanned aerial vehicle relayIs best positionAndcompleting 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 asAndwherein 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:
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)
wherein p iss,maxAnd p1,maxAnd p2,maxRepresenting the peak power of the source and the first drone and the second drone respectively,andandrespectively 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:
and similarly, obtaining the information transmission rate from the first unmanned aerial vehicle to the destination:
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:
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:
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:
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:
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (4a)
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:
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:
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:
and similarly, obtaining the information transmission rate from the first unmanned aerial vehicle to the destination:
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:
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:
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:
wherein N is {2, 4., N-1}, N is 2K +1, and K is a positive integer; (7e)
introducing relaxation variables A and B:
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:
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)
(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:
by means of an iterative method of solving for,is p2[n]The r iteration, orderAnd pairPerforming a first order Taylor expansion to obtainUpper bound of (2)To p2[n]A first order Taylor expansion yields the following equation:
Similarly, for the transmission rate from the source to the second drone, the following equation is obtained:
Wherein P is1 r[n]Is p1[n]The number r of iterations is then repeated,
obtained by the equations (15) and (19), respectivelyAndupper bound ofAndthus, P2 is transformed into the final first optimization model P3:
s.t.A(n)≤log2(1+Ps,1[n]γs1)n=1 (23)
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:
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:
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:
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:
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:
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:
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 andthus the optimization variables are Q, eta1、η2The second optimization model P4 is transformed into the following formula:
N2K +1, K being a positive integer (38)
(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:
when N is {3, 5., N-2}, N is 2K +1, and K is a positive integer; (44)
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:
wherein N is 2K +1, K is positive integer (46)
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, definePosition of the drone relay representing r iterations, for local pointsRespectively 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)There are the following formulas:
wherein:
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)There are the following formulas:
wherein:
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)There are the following formulas:
wherein:
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 obtainLower boundary of (1)There is the following expression:
wherein:
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 obtainLower boundary of (1)There is the following expression:
wherein:
and q1-q2||2Is non-concave for equation (51), so it is also necessary to right of the inequality (51), at pointAnda first order Taylor expansion is performed, resulting in the following lower bound for this equation:
from the above it is known that the problem of P6 translates approximately into the following:
wherein N is 2K +1, K is positive integer (69)
(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 trajectoryr is 0 and error threshold e is 10-2;
S42, relaying the unmanned aerial vehicleAndsubstituting 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
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 vehicleAndand obtaining an objective function value
S44, making r equal to r + 1;
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