CN110113106B - Wireless communication method for laser energy supply unmanned aerial vehicle with multiple base stations and multiple laser transmitters - Google Patents

Wireless communication method for laser energy supply unmanned aerial vehicle with multiple base stations and multiple laser transmitters Download PDF

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CN110113106B
CN110113106B CN201910310376.5A CN201910310376A CN110113106B CN 110113106 B CN110113106 B CN 110113106B CN 201910310376 A CN201910310376 A CN 201910310376A CN 110113106 B CN110113106 B CN 110113106B
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车越岭
欧阳颉
伍楷舜
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Shenzhen University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
    • H04B10/50—Transmitters
    • H04B10/501—Structural aspects
    • H04B10/503—Laser transmitters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
    • H04B10/80—Optical aspects relating to the use of optical transmission for specific applications, not provided for in groups H04B10/03 - H04B10/70, e.g. optical power feeding or optical transmission through water
    • H04B10/806—Arrangements for feeding power
    • H04B10/807—Optical power feeding, i.e. transmitting power using an optical signal
    • 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
    • H04B7/18506—Communications with or from aircraft, i.e. aeronautical mobile service

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Abstract

The invention discloses a wireless communication method for a laser energy supply unmanned aerial vehicle with multiple base stations and multiple laser transmitters, which realizes maximization of the minimum throughput of downlink communication between the unmanned aerial vehicle and each ground base station and meets the communication requirement of a system when the laser energy constraint condition is met under the condition of multiple laser transmitters and multiple base stations by jointly optimizing the track, the related speed, the transmission power and the transmission mode of the unmanned aerial vehicle.

Description

Wireless communication method for laser energy supply unmanned aerial vehicle with multiple base stations and multiple laser transmitters
Technical Field
The invention relates to the technical field of unmanned aerial vehicle communication, in particular to a laser energy supply unmanned aerial vehicle wireless communication method with multiple base stations and multiple laser transmitters.
Background
The unmanned aerial vehicle is used as auxiliary communication to greatly improve the ground wireless communication range and the network capacity. The wide application and popularization of the unmanned aerial vehicle can be promoted only by improving effective and reliable communication. However, the short endurance is a common fault for drones. The insufficient cruising ability means that the task execution is greatly limited and the maximum advantage is not exerted. In order to solve the problem, the prior art proposes that the laser is used for charging the unmanned aerial vehicle, and meanwhile, the unmanned aerial vehicle communicates with a downlink base station, and the unmanned aerial vehicle does not need to return to the base station or a charging station at a fixed position, and can acquire energy in the process of executing a task, namely, in the process of flying.
Therefore, how to maximize the minimum throughput of downlink communication between the drone and each ground base station to meet the communication performance requirement of the system when the laser energy constraint condition is met under the condition of multiple laser transmitters and multiple base stations is a problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a wireless communication method for a laser-powered unmanned aerial vehicle with multiple base stations and multiple laser transmitters, which achieves maximization of the minimum throughput of downlink communication between the unmanned aerial vehicle and each ground base station and meets the communication requirements of a system when laser energy constraint conditions are met under the condition of multiple laser transmitters and multiple base stations.
In order to achieve the purpose, the invention adopts the following technical scheme:
the utility model provides a many laser emitter's of many base stations laser energy supply unmanned aerial vehicle wireless communication method, is applicable to many laser emitter's of many base stations laser energy supply unmanned aerial vehicle wireless communication system, unmanned aerial vehicle wireless communication system includes: the system comprises an unmanned aerial vehicle, K ground base stations and M ground laser transmitters; the unmanned aerial vehicle acquires energy from a laser beam emitted by a ground laser emitter and communicates with a ground base station in a radio frequency range, wherein M is more than or equal to 1, and K is more than or equal to 1;
s1: based on the laser energy supply unmanned aerial vehicle wireless communication system with multiple base stations and multiple laser transmitters, when the unmanned aerial vehicle is subjected to downlink communication, the problem of the minimum throughput of all K ground base stations is maximized to establish an optimization target P2:
Figure GDA0002617452690000021
s.t.q(0)＝q0，
q(T)＝qF，
Figure GDA0002617452690000022
Figure GDA0002617452690000023
Figure GDA0002617452690000024
Figure GDA0002617452690000025
Pd(pk(t)，ρk(t))+Pf(q(t))≤Ph(q(t))
wherein q is0And q isFRespectively the initial and final position, V, of the dronemaxIs unmannedMaximum speed of flight of the aircraft, q (t), unmanned aerial vehicle trajectory, pk(t) is downlink transmission power, pk(t) is transmission mode, uk＝{xk，ykThe coordinates are horizontal position coordinates of the ground base station; h is the flying height of the unmanned aerial vehicle from the ground, is a fixed value, and is more than 0; pdTotal energy consumption for downlink communications by the drone on T; pfThe energy consumption of the forward flight power of the unmanned aerial vehicle in the whole time range T is reduced; phThe total energy obtained from all laser transmitters in the T time period is obtained for the unmanned aerial vehicle; gamma is the signal to noise ratio;
s2: and solving the optimization target P2 by using a joint optimization iteration method to obtain the flight path and the power distribution value of the unmanned aerial vehicle.
Preferably, step S2 specifically includes:
s21: discretizing the optimization target to obtain a discretized optimization target P2';
s22: decomposing the discretized optimization target P2' into a given track optimization sub-problem P2.1-1 and a given power distribution optimization sub-problem P2.1-2;
s23: and alternately and iteratively solving the given track optimization power distribution subproblem P2.1-1 and the given power distribution optimization track subproblem P2.1-2 to obtain the flight track and the power distribution value of the unmanned aerial vehicle.
Preferably, in step S21, the discretized optimization target is P2':
Figure GDA0002617452690000031
Figure GDA0002617452690000032
Figure GDA0002617452690000037
Figure GDA0002617452690000038
Figure GDA0002617452690000039
Figure GDA0002617452690000033
Figure GDA0002617452690000034
Figure GDA0002617452690000035
Figure GDA0002617452690000036
where ρ isk[n]For discretized transmission mode, pk[n]For the discretized downlink transmission power, R represents the minimum throughput of K terrestrial base stations, q [ n ]]For the discretized trajectory, v [ n ]]For the speed of the unmanned aerial vehicle after discretization,tfor each time slot, η is the radio link efficiency, P0And PiTwo constants, U, representing blade profile power and induced power in the hovering statetipIs the tip speed of the rotor blade, d0And s represent fuselage drag ratio and rotor solidity, respectively; ρ and A represent air density and rotor disk area, respectively; d is the size of the initial laser beam, and Delta theta is the transmission angle; c ═ ω a χ, ω ∈ (0, 1) is the efficiency of laser energy acquisition, a is the area of the receiving telescope or the collection lens; χ is the optical efficiency of the transmission received; α is the attenuation coefficient of the medium.
Preferably, in step S22, the trajectory optimization power allocation sub-problem is given as P2.1-1:
Figure GDA0002617452690000041
Figure GDA0002617452690000042
Figure GDA00026174526900000412
Figure GDA0002617452690000043
Figure GDA0002617452690000044
wherein each time slot is timedtIs subdivided into K sub-slots, then
Figure GDA0002617452690000049
Communication time between the unmanned aerial vehicle and the kth ground base station in the nth time slot is set; k ground base stations in any time slot
Figure GDA00026174526900000410
Internal shared timetThus there are
Figure GDA0002617452690000045
Figure GDA00026174526900000411
Pf is the forward flight power energy consumption of the unmanned aerial vehicle in the whole time range T; phThe total energy obtained from all laser transmitters in the T time period is obtained for the unmanned aerial vehicle;
the sub-problem of the given power distribution optimization trajectory is P2.1-2:
Figure GDA0002617452690000046
Figure GDA00026174526900000413
Figure GDA0002617452690000047
Figure GDA0002617452690000048
Figure GDA0002617452690000055
Figure GDA0002617452690000056
Δn≤tVmax，
Figure GDA0002617452690000051
Figure GDA0002617452690000052
τn≥0；
wherein, taunAnd
Figure GDA0002617452690000053
are all relaxation variables; rlb(q[n]) Lower bound for minimum throughput, PdTotal energy consumption for downlink communication for the drone during T time; delta n is the distance of the unmanned aerial vehicle flying in the time slot n; tau isinAnd
Figure GDA0002617452690000054
to give toRelaxation variables of fixed initial values, v0Is the average rotor induced speed at hang-up; w is am＝{xm，ymAnd is the horizontal position coordinate of the laser emitter m.
Preferably, step S23 specifically includes:
s1: initializing an unmanned aerial vehicle track;
s2: obtaining a power distribution value of the unmanned aerial vehicle according to the track of the unmanned aerial vehicle and the P2.1-1, thereby obtaining downlink transmission power;
s3: and solving the trajectory and the relaxation variable of the unmanned aerial vehicle according to the downlink transmission power, the preset continuous convex programming initial value and the P2.1-2, taking the trajectory and the relaxation variable of the unmanned aerial vehicle as new continuous convex programming initial values, and circularly executing S3 until convergence or the maximum iteration number is reached. The convergence means that the target value is not changed and the maximum number of iterations is preset.
According to the technical scheme, compared with the prior art, the invention discloses the laser energy supply unmanned aerial vehicle wireless communication method with the multiple base stations and the multiple laser transmitters.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a wireless communication method of a laser powered unmanned aerial vehicle with multiple base stations and multiple laser transmitters, which is provided by the invention;
fig. 2 is a trajectory plan of a rotary-wing drone provided by the present invention;
fig. 3(a) is a trajectory diagram of the drone at different times T provided by the present invention;
fig. 3(b) is a diagram comparing the downlink transmission power of the drone at different times T according to the present invention;
FIG. 4 is a graph comparing throughput provided by the present invention over time;
fig. 5(a) is a track diagram of the unmanned aerial vehicle when the number of ground base stations increases;
fig. 5(b) is a graph illustrating the throughput variation of each base station when the number of terrestrial base stations provided by the present invention is increased;
fig. 6 is a schematic diagram of a communication system model of a multi-base-station multi-laser transmitter provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to the attached drawings 1 and 6, the embodiment of the invention discloses a wireless communication method for a laser energy supply unmanned aerial vehicle with multiple base stations and multiple laser transmitters, which is suitable for a wireless communication system for the laser energy supply unmanned aerial vehicle with multiple base stations and multiple laser transmitters, wherein the wireless communication system for the unmanned aerial vehicle comprises: the system comprises an unmanned aerial vehicle, K ground base stations and M ground laser transmitters; the unmanned aerial vehicle acquires energy from a laser beam emitted by a ground laser emitter and communicates with a ground base station in a radio frequency range, wherein M is more than or equal to 1, and K is more than or equal to 1;
s1: based on the laser energy supply unmanned aerial vehicle wireless communication system with multiple base stations and multiple laser transmitters, when the unmanned aerial vehicle is subjected to downlink communication, the problem of the minimum throughput of all K ground base stations is maximized to establish an optimization target P2:
Figure GDA0002617452690000071
s.t.q(0)＝q0，
q(T)＝qF，
Figure GDA0002617452690000072
Figure GDA0002617452690000073
Figure GDA0002617452690000074
Figure GDA0002617452690000075
Pd(pk(t)，ρk(t))+Pf(q(t))≤Ph(q(t))
wherein q is0And q isFRespectively the initial and final position, V, of the dronemaxIs the maximum speed of flight of the drone, q (t) is the trajectory of the drone, pk(t) is downlink transmission power, pk(t) is transmission mode, uk＝{xk，ykThe coordinates are horizontal position coordinates of the ground base station; h is the flying height of the unmanned aerial vehicle from the ground, is a fixed value, and is more than 0; pdTotal energy consumption for downlink communications by the drone on T; pfThe energy consumption of the forward flight power of the unmanned aerial vehicle in the whole time range T is reduced; phThe total energy obtained from all laser transmitters in the T time period is obtained for the unmanned aerial vehicle; gamma is the signal to noise ratio;
the first and second constraints are constraints on the initial and final positions of the drone; the third is the maximum speed constraint, the fourth is the drone downlink transmit power constraint, the fifth and sixth are drone downlink communication modes; the last one is a laser energy acquisition constraint condition;
s2: and solving the optimization target P2 by using a joint optimization iteration method to obtain the flight path and the power distribution value of the unmanned aerial vehicle, and maximizing the minimum throughput of downlink communication between the unmanned aerial vehicle and each ground base station when the laser energy constraint condition is met, so that the communication requirement of the system is met.
The technical solution of the present invention is further discussed below with reference to specific technical details.
1.1 System model
Referring to fig. 6, the drone acquires energy from laser beams emitted by the ground laser emitter with M being greater than or equal to 1, and communicates with the ground base stations with K being greater than or equal to 1 at a radio frequency band. By using
Figure GDA0002617452690000081
And
Figure GDA0002617452690000082
respectively, a set of laser transmitters and base stations. Each terrestrial laser transmitter
Figure GDA0002617452690000083
Ground base station
Figure GDA0002617452690000084
Have their fixed positions, respectively expressed as x in 3D euclidean coordinatesm，y m0, and { xk，yk0} in which
Figure GDA0002617452690000085
And uk＝{xk，ykDefined as the horizontal position coordinates of the laser transmitter m and the ground base station k. Also, it is studied for a specific limited period of time
Figure GDA0002617452690000086
And (4) the following steps. Assuming that the flying height of the unmanned aerial vehicle from the ground is a fixed value H > 0, the coordinates of the unmanned aerial vehicle changing along with the time t are (x (t), y (t), and H). Then q (t) { x (t), y (t) } denotes the coordinates of the drone in the horizontal position, the distance of the drone from the laser emitter m being
Figure GDA0002617452690000087
A distance to ground base station k of
Figure GDA0002617452690000088
1.1.1 radio frequency communication Link
At the moment, the unmanned aerial vehicle can only communicate with one ground base station at each moment, and the time division multiple access technology is considered to be adopted. At any time t, with the symbol ρk(t) e {0, 1} to represent information transmission. When rhokAnd (t) is 1, and indicates that the unmanned aerial vehicle communicates with the ground base station k. Thus is provided with
Figure GDA0002617452690000089
At time t, the channel gain to the kth terrestrial base station is
Figure GDA00026174526900000810
When the instantaneous downlink throughput (bps/Hz) is
Figure GDA00026174526900000811
Wherein p isk(t) ≧ 0 is the downlink transmit power when the drone communicates with ground base station k. Thus, for each base station
Figure GDA00026174526900000812
The cumulative throughput (bits/Hz) over the entire period T is
Figure GDA00026174526900000813
1.1.2 energy consumption of unmanned aerial vehicle
The total energy consumption of downlink communications by a drone on T may be expressed as
Figure GDA0002617452690000091
Wherein 0 < eta < 1 is the radio frequency link efficiency.
The flight energy consumption of the unmanned aerial vehicle needs to ensure that the unmanned aerial vehicle flies and moves in the air. Generally, the energy consumption model is related to speed and acceleration, such as the fixed wing drone energy consumption model of the previous section. While a rotorcraft is often hovering at some point in assisting wireless communication applications, dispatch time is relatively small for the entire flight time, and therefore energy consumption due to acceleration is ignored here.
The speed of the unmanned aerial vehicle is v (T), and the forward flight power energy consumption of the rotor unmanned aerial vehicle in the whole time range T can be expressed as
Figure GDA0002617452690000092
Wherein P is0And PiTwo constants, U, representing blade profile power and induced power in the hovering statetipRepresenting the tip speed, v, of the rotor blade0Is the average rotor induced speed at suspension, d0And s represent fuselage drag ratio and rotor solidity, respectively, and ρ and a represent air density and rotor disk area, respectively. If v (t) is 0, that is to say, the unmanned aerial vehicle is in the hovering state, the energy consumption at this time is Pf＝P0+PiThis is a finite value depending on the relevant parameters of aircraft weight, air density and rotor disk area. As the speed increases, the energy consumption decreases first and then increases, and the hover state is generally not the most energy consuming one.
The total energy consumption of the drone may be expressed as
Pc＝Pd+Pf.
1.1.3 laser energy link
Assuming that the transmitting power of the laser transmitter m is constant
Figure GDA0002617452690000093
Then at time t, the laser signal intensity received by the drone from laser emitter m is
Figure GDA0002617452690000094
Also assuming that the efficiency of laser energy harvesting is ω e (0, 1), then the laser energy harvested by the drone from laser emitter m at time t is ω e (0, 1)
Figure GDA0002617452690000101
Let C be ω a χ, and the total energy obtained by the drone from all the laser transmitters during the T period be
Figure GDA0002617452690000102
1.1.4 objective function and its constraints
For an unmanned aerial vehicle wireless communication system with multiple laser transmitters and multiple base stations, the aim is to jointly optimize the trajectory { q (t) }, the related speed { v (t) }andthe transmission power { p } of the unmanned aerial vehiclek(t) }, and its transmission mode { ρ }k(t), when downlink communication is carried out on the unmanned aerial vehicle, the minimum throughput of all K ground base stations is maximized. However, the speed and trajectory of the drone are related as follows:
Figure GDA0002617452690000103
thus, the entire problem can be expressed in a mathematical model as:
Figure GDA0002617452690000104
s.t.q(0)＝q0，
q(T)＝qF，
Figure GDA0002617452690000105
Figure GDA0002617452690000106
Figure GDA0002617452690000107
Figure GDA0002617452690000108
Pd(pk(t)，ρk(t))+Pf(q(t))≤Ph(q(t)).
wherein q is0And q isFRespectively the initial and final position, V, of the dronemaxIs the maximum speed at which the drone is flying. The objective function is to maximize the cumulative minimum throughput of each ground base station over T time, and the first and second constraints are the initial and final positions of the drone, which is necessary in practical drone application scenarios. The third is the maximum speed constraint, the fourth is the drone downlink transmit power constraint, the fifth and sixth are the drone downlink communication modes, i.e. the constraints on which base station to choose to communicate with, the last being the aforementioned laser energy acquisition constraint.
Problem (P2) variable drone trajectory q (t) to be optimized, downlink transmission power Pk(t), transmission mode ρk(t) are all continuous functions with respect to time. Therefore (P1) contains an infinite number of optimization variables. In addition, the laser energy acquisition model and the flight energy consumption model in (P2) are complex, plus the objective function and the laser energy acquisition constraint are both non-convex, and the binary constraint ρk(t) value problem. Therefore, (P2) is difficult to solve.
1.2 solving algorithm of MTMS
The objective function is to maximize the minimum throughput of all terrestrial base stations, where a variable R is added to represent the minimum throughput of K terrestrial base stations, and thus has
Figure GDA0002617452690000111
Firstly, discretizing time, namely discretizing a limited time range T into N +1 equal time slots, wherein the time of each time slot ist. With t ═ ntN is 0, 1. So the speed v [ n ] of the unmanned plane after dispersion]And the track q [ n ]]Can be simply considered as
Figure GDA0002617452690000112
And also rhok[n]And pk[n]N is 1. Therefore (P2) can be expressed as
Figure GDA0002617452690000113
Figure GDA0002617452690000114
q[0]＝q0，
q[N]＝qF，
v[n]≤Vmax，n＝1，...，N，
Figure GDA0002617452690000115
Figure GDA0002617452690000116
Figure GDA0002617452690000117
Figure GDA0002617452690000121
And finally, decomposing the discretized (P2') into two subproblems (P2.1) given track optimized power distribution and (P2.2) given track optimized power distribution to solve, and finally solving by using a joint optimization iterative algorithm.
1.2.1 given trajectory optimizing Power distribution
Then, a track q [ n ] is given]In case of optimization of pk[n]And ρk[n]To maximize downlink throughput of the drone. Under such conditions, (P2') can therefore be simplified to (P2.1) such an optimization problem.
Figure GDA0002617452690000122
Figure GDA0002617452690000123
Figure GDA0002617452690000124
Figure GDA0002617452690000125
Figure GDA0002617452690000126
Figure GDA0002617452690000127
Binary selection variable p for downlink communication transmission modek[n]The following processing can be performed: k ground base stations in any time slot
Figure GDA0002617452690000128
Internal shared timetI.e. each time slot is further divided into K sub-slots. For the sub-slot k in the slot n, that is, when the unmanned aerial vehicle communicates with the kth ground base station at the nth time, the communication time is
Figure GDA0002617452690000129
Therefore, obtaining a constraint condition has
Figure GDA00026174526900001210
At this time due to variable
Figure GDA00026174526900001211
And pk[n]The problem is still non-convex. By a variable
Figure GDA00026174526900001212
(P2.1) can be expressed as this convex optimization problem of (P2.1-1) below:
Figure GDA00026174526900001213
Figure GDA0002617452690000131
Figure GDA0002617452690000132
Figure GDA0002617452690000133
Figure GDA0002617452690000134
therefore, (P2.1-1) can be solved.
Now that the above problem is already a convex optimization problem, it is considered that a problem is actually solved if it is transformed into a convex optimization problem, because there are many methods in the prior art to solve the convex optimization problem, such as the CVX toolkit in MATLAB, which is the most common. However, it should be noted here that it is challenging to determine whether a certain problem belongs to the convex optimization problem or to identify and convert the problem into the convex optimization problem.
1.2.2 given Power Allocation optimization trajectory
And optimizing the locus q [ n ] of the unmanned aerial vehicle under the condition of giving power distribution]. Let Δ n | | | q [ n |)]-q[n-1]1, 2, N, then
Figure GDA0002617452690000135
N is 1, 2. The flight energy consumption model of the drone may be expressed as
Figure GDA0002617452690000136
Under such conditions, (P2') can therefore be simplified to (P2.2) such an optimization problem.
Figure GDA0002617452690000137
Figure GDA0002617452690000138
q[0]＝q0， (4-2)
q[N]＝qF， (4-3)
Δn≤tVmax， (4-4)
Pd+Pf(q[n])≤Ph(q[n]).
The constraint (4-1) of (P1.2) and the laser energy harvesting constraint remain non-convex. For unmanned aerial vehicle flight energy consumption model Pf(q[n]) First and third terms of which for q [ n ]]In the case of a convex function, it is,then the second term and the laser acquire the model Ph(q[n]) Is a non-convex function. To solve this problem, it is possible to increase two relaxation variables τnAnd
Figure GDA0002617452690000141
order to
Figure GDA0002617452690000142
Figure GDA0002617452690000143
(4-6) formula is equivalent to
Figure GDA0002617452690000144
So (P2.2) can be again finished into
Figure GDA0002617452690000145
Figure GDA0002617452690000146
Figure GDA0002617452690000147
Figure GDA0002617452690000148
τn≥0， (4-10)
Figure GDA0002617452690000149
(4-1),(4-2),(4-3),(4-4).
Constraints (4-9) and (4-11) are conditions obtained by replacing the equal signs of (4-5) and (4-7) with unequal signs, but this does not affect the equivalence of (P2.2) and (P2.2-1). The laser energy capture constraints of (P2.2-1) at this time, (4-1) and (4-11) remain non-convex. The same can be solved by continuous convex programming. For example, for the laser energy acquisition constraint, the left side of the inequality is for q [ n ]]And τnConvex function of (1), right side of inequality
Figure GDA0002617452690000151
Also relates to
Figure GDA0002617452690000152
A convex function of (a). Then the right side of the inequality is subjected to a first order taylor expansion to obtain a global minimum as follows:
Figure GDA0002617452690000153
when in use
Figure GDA0002617452690000154
Then, the equation holds.
Figure GDA0002617452690000155
Represents a function pair
Figure GDA0002617452690000156
The value of its derivative is determined, therefore
Figure GDA0002617452690000157
Figure GDA0002617452690000158
Similarly, for constraints (4-11), the lower bound on the right is
Figure GDA0002617452690000159
τinAnd q isi[n]Is the corresponding τ from the ith iterationnAnd q [ n ]]The value of (c). When tau isn＝τinThen, the equation holds.
In addition, for constraint (4-1), its left side still gets a lower bound of from Taylor expansion
Figure GDA00026174526900001510
Wherein
Figure GDA00026174526900001511
Figure GDA00026174526900001512
Rlb(q[n]) Is about q [ n ]]A concave function of (a). For any given qi[n]Can obtain the value of
Figure GDA00026174526900001513
Wherein when q [ n ]]＝qi[n]Then, the equation holds. And at this time
Figure GDA00026174526900001514
And Rlb(q[n]) Respectively is
Figure GDA0002617452690000161
Figure GDA0002617452690000162
When q [ n ]]＝qi[n]When it comes to
Figure GDA0002617452690000163
And Rlb(q[n]) The gradient of (a) is the same.
To this end, (P2.2-1) can be approximated as the following convex optimization problem:
Figure GDA0002617452690000164
s.t.Rlb(q[n])≥R，
Figure GDA0002617452690000165
Figure GDA0002617452690000166
(4-2),(4-3),(4-4),(4-8),(4-9),(4-10).
therefore, (P2.2-2) can be solved.
1.2.3 Joint optimization iterative Algorithm
In conjunction with the above two parts, the problem (P2) can be solved by alternately and iteratively solving (P2.1-1) and (P2.1-2). The entire process of the joint optimization iterative algorithm for solving (P2) is summarized as follows.
Value of input trajectory { q0[n]H, let i equal to 0; in the experimental simulation result, the initial track of the uniform-speed flight straight line from the starting point to the end point is adopted.
According to the value of the unmanned aerial vehicle track, solving (P2.1-1) to obtain
Figure GDA0002617452690000167
Thereby obtaining a downlink transmission power;
based on the downlink transmission power and the problem P2.1-2, will be preset
Figure GDA0002617452690000168
Is used as a given known initial value of the continuous convex programming, and the unmanned aerial vehicle track { q is solvedi+1[n]And relaxation variables
Figure GDA0002617452690000171
And the obtained unmanned aerial vehicle track { qi+1[n]And relaxation variables
Figure GDA0002617452690000172
As a new continuous convex programming initial value, the loop executes S3 until convergence or a maximum number of iterations is reached.
The present invention is further discussed below in conjunction with specific experimental results.
To the unmanned aerial vehicle wireless communication system of laser energy supply, unmanned aerial vehicle liftoff fixed height is 100 m. The parameters of the rf communication link and the laser acquisition model are set as shown in table 1.
Table 1 laser acquisition model and rf link parameter settings
Figure GDA0002617452690000173
The flight energy consumption model parameter settings of the rotorcraft are shown in table 2.
TABLE 2
Figure GDA0002617452690000174
To the unmanned aerial vehicle wireless communication system model of many laser emitter polybase stations, it is the unmanned aerial vehicle of rotor to supply energy based on wireless laser, so unmanned aerial vehicle can hover at a certain point. To illustrate the problem more intuitively, in a first step, a simple case was studied: m2 ground laser transmitters and K1 ground base station. And the emission power of all laser emitters is
Figure GDA0002617452690000181
The initial and final positions of the drone are q, respectively0(0, 500) and qF＝(1000, 500), time of flight T is 100 s. The initial trajectory of the drone is from q0To qFIs the straight line of uniform flying.
Fig. 2 is a UAV trajectory using a joint optimization iterative algorithm, and the proposed design is also compared to two reference schemes, i.e., a trajectory with maximized laser energy collection and a trajectory with maximized information transmission throughput. For maximum laser energy collection, aiming to collect as much laser energy as possible, the drone will fly at the maximum allowable speed on the path, hovering at the red circle point where the total energy is maximized, i.e. in the figure. For information transfer throughput maximization, the drone may also fly at maximum speed and hover over the ground base station to maximize downlink information transfer throughput. There is no energy constraint in both reference schemes. In fact, the drone cannot obtain enough energy whether the throughput of the transmitted information is maximized or the energy obtained is maximized. And for the proposed track, the initial track is optimized, and it can be found that the unmanned aerial vehicle can fly at the two laser transmitters and the ground base station in a self-adaptive manner, so that the downlink transmission throughput is maximized while the laser energy acquisition constraint is satisfied.
When there are time-limited ranges of T100 s and T400 s, respectively, fig. 3(a) shows the proposed trajectory of the drone and fig. 3(b) shows the variation of the drone downlink transmit power. The red circle represents the position of the drone at this time. It can be observed that the drone will acquire sufficient energy and then communicate with the ground base station. For T100 s, the flight time of the drone is very limited, and it takes much time to fly on the flight path from the initial position to the final position. For the period t-40 s to 60s, the drone starts communicating with the ground base station. When t is 50s, the unmanned aerial vehicle flies to the point nearest to the ground base station and is allocated with a maximum transmitting power. And because T is relatively large, for T ═ 400s, the drone will also obtain sufficient energy and perform downlink communication, but at this time the drone will effectively balance the time allocation between laser energy harvesting and radio frequency wireless communication. For the period t 144s and 256s, the drone begins communicating with the ground base station. And t 179s to 221s, the drone may hover over the ground base station and maintain the same maximum transmit power during this period.
Figure 4 shows the cumulative downlink throughput in bps/Hz versus the finite time of flight T. Here also compared to two references for the initial trajectory and the hover-maximized throughput trajectory. At the position of q0To qFIn the initial trajectory of the straight flight path, the drones distribute equal power P ═ P (P)h-Pf) In the hover maximize throughput trajectory, the drone hovers over a fixed point to maximize throughput without the initial and final position constraints. In practice, if the time range T or the maximum drone speed VmaxLarge enough, these two constraints can be ignored. Through the one-dimensional search, a hover position that satisfies the laser energy constraints of the drone and maximizes downlink throughput may be found to be (500, 531). Compared with two benchmarks, the throughput size after algorithm optimization first increases significantly and then tends to be flat over time. The performance is much better than the two comparison benchmarks, which also verifies the validity of the proposed algorithm.
All the previous discussions are the trade-offs of trajectories and communications between multiple laser sources and a single ground base station by the drone, which in turn increases the number of ground base stations K. Fig. 5(a) shows the initial trajectory and the optimized trajectory when the ground base station K is 4. In this design, the flight time T of the drone is 300 s. It can be seen that for some remote ground base stations, in order to balance flight energy consumption and communication performance, the drone will not pass right above the ground base station, but will choose to communicate at the appropriate location and transmit power level. Fig. 5-6(b) show the variation of the throughput of each ground base station as a function of the time of flight t. The drone does not communicate with a ground base station for a continuous period of time, for example for the base station 3 the drone communicates with it for around 50s and 190s, which is also the relatively closest position of the drone to the base station 3. And between two communications with the base station 3, the drone also has information transmission with the base station 1 and the base station 2. Finally, all terrestrial base stations achieve the same throughput. This is consistent with maximizing the minimum throughput for all base stations in the objective function.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (2)

1. The utility model provides a many laser emitter's of multistation laser energy supply unmanned aerial vehicle wireless communication method which characterized in that, is applicable to many laser emitter's of multistation laser energy supply unmanned aerial vehicle wireless communication system, unmanned aerial vehicle wireless communication system includes: the system comprises an unmanned aerial vehicle, K ground base stations and M ground laser transmitters; the unmanned aerial vehicle acquires energy from a laser beam emitted by a ground laser emitter and communicates with a ground base station in a radio frequency range, wherein M is more than or equal to 1, and K is more than or equal to 1;
s1: based on the laser energy supply unmanned aerial vehicle wireless communication system with multiple base stations and multiple laser transmitters, when the unmanned aerial vehicle is subjected to downlink communication, the problem of the minimum throughput of all K ground base stations is maximized to establish an optimization target P2:
Figure FDA0002681316230000011
s.t.q(0)＝q0，
q(T)＝qF，
Figure FDA0002681316230000012
Figure FDA0002681316230000013
Figure FDA0002681316230000014
Figure FDA0002681316230000015
Pd(pk(t)，ρk(t))+Pf(q(t))≤Ph(q(t))
wherein q is0And q isFRespectively the initial and final position, V, of the dronemaxIs the maximum speed of flight of the drone, q (t) is the trajectory of the drone, pk(t) is downlink transmission power, pk(t) is transmission mode, uk＝{xk，ykThe coordinates are horizontal position coordinates of the ground base station; h is the flying height of the unmanned aerial vehicle from the ground, is a fixed value, and is more than 0; pdTotal energy consumption for downlink communications for the drone over a T period of time; pfThe energy consumption of the forward flight power of the unmanned aerial vehicle in the whole time range T is reduced; phThe total energy obtained from all laser transmitters in the T time period is obtained for the unmanned aerial vehicle; gamma is the signal to noise ratio;
s2: solving the optimization target P2 by using a joint optimization iteration method to obtain the flight path and the power distribution value of the unmanned aerial vehicle;
step S2 specifically includes:
s21: discretizing the optimization target to obtain a discretized optimization target P2';
s22: decomposing the discretized optimization target P2' into a given track optimization sub-problem P2.1-1 and a given power distribution optimization sub-problem P2.1-2;
s23: alternately and iteratively solving a given track optimization power distribution subproblem P2.1-1 and a given power distribution optimization track subproblem P2.1-2 to obtain the flight track and the power distribution value of the unmanned aerial vehicle;
in step S21, the discretized optimization target is P2':
Figure FDA0002681316230000021
Figure FDA0002681316230000022
Figure FDA0002681316230000027
Figure FDA0002681316230000028
Figure FDA0002681316230000029
Figure FDA0002681316230000023
Figure FDA0002681316230000024
Figure FDA0002681316230000025
Figure FDA0002681316230000026
where ρ isk[n]For discretized transmission mode, pk[n]For the discretized downlink transmission power, R represents the minimum throughput of K terrestrial base stations, q [ n ]]For the discretized trajectory, v [ n ]]For the speed of the unmanned aerial vehicle after discretization,tfor each time slot, η is the radio link efficiency, P0And PiTwo constants, U, representing blade profile power and induced power in the hovering statetipIs the tip speed of the rotor blade, d0And s represent fuselage drag ratio and rotor solidity, respectively; ρ and A represent air density and rotor disk area, respectively; d is the size of the initial laser beam; Δ θ is the angle of transmission; c ═ ω a χ, ω ∈ (0, 1) is the efficiency of laser energy acquisition, a is the area of the receiving telescope or the collection lens; χ is the optical efficiency of the transmission received; α is the attenuation coefficient of the medium;
Figure FDA0002681316230000031
is the emission power of the laser emitter;
in step S22, the trajectory optimization power allocation sub-problem is given as P2.1-1:
Figure FDA0002681316230000032
Figure FDA0002681316230000033
Figure FDA0002681316230000034
Figure FDA0002681316230000035
Figure FDA0002681316230000036
wherein each time slot is timedtIs subdivided into K sub-slots, then
Figure FDA0002681316230000037
Communication time between the unmanned aerial vehicle and the kth ground base station in the nth time slot is set; k ground base stations in any time slot
Figure FDA0002681316230000038
Internal shared timetThus there are
Figure FDA0002681316230000039
PfThe energy consumption of the forward flight power of the unmanned aerial vehicle in the whole time range T is reduced; phThe total energy obtained from all laser transmitters in the T time period is obtained for the unmanned aerial vehicle;
the sub-problem of the given power distribution optimization trajectory is P2.1-2:
Figure FDA00026813162300000310
Figure FDA00026813162300000312
Figure FDA00026813162300000311
Figure FDA0002681316230000041
Figure FDA00026813162300000413
Figure FDA00026813162300000414
Δn≤tVmax，
Figure FDA0002681316230000042
Figure FDA0002681316230000043
τn≥0；
wherein, taunAnd
Figure FDA0002681316230000044
are all relaxation variables; rlb(q[n]) Lower bound for minimum throughput, PdTotal energy consumption for downlink communication for the drone during T time; delta n is the distance of the unmanned aerial vehicle flying in the time slot n; tau isinAnd
Figure FDA0002681316230000045
for a given initial value of the relaxation variable, v0Is the average rotor induced speed at hang-up;
Figure FDA0002681316230000046
is the horizontal position coordinate of the laser transmitter m;
Figure FDA0002681316230000047
is about
Figure FDA0002681316230000048
A convex function of (d);
Figure FDA0002681316230000049
represents a function pair
Figure FDA00026813162300000410
Calculating the value of the derivative; q. q.si[n]Is the corresponding q [ n ] obtained in the ith iteration]A value of (d);
Figure FDA00026813162300000411
Figure FDA00026813162300000412
2. the method for wireless communication between a laser-powered unmanned aerial vehicle with multiple base stations and multiple laser transmitters as claimed in claim 1, wherein step S23 specifically comprises:
s1: initializing an unmanned aerial vehicle track;
s2: obtaining a power distribution value of the unmanned aerial vehicle according to the track of the unmanned aerial vehicle and the P2.1-1, thereby obtaining downlink transmission power;
s3: and solving the trajectory and the relaxation variable of the unmanned aerial vehicle according to the downlink transmission power, the preset continuous convex programming initial value and the P2.1-2, taking the trajectory and the relaxation variable of the unmanned aerial vehicle as new continuous convex programming initial values, and circularly executing S3 until convergence or the maximum iteration number is reached.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106230134A (en) * 2016-08-31 2016-12-14 安徽中科自动化股份有限公司 A kind of novel unmanned plane laser transmits without system line energy
CN107894712A (en) * 2017-10-13 2018-04-10 深圳大学 A kind of energy distributing method of laser power supply unmanned plane track optimizing and power of communications
CN108768497A (en) * 2018-04-27 2018-11-06 郑州航空工业管理学院 Unmanned plane assists wireless sense network and its node scheduling and flight Parameter design method
CN108880662A (en) * 2018-07-16 2018-11-23 深圳大学 A kind of optimization method of wireless messages and energy transmission based on unmanned plane

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106230134A (en) * 2016-08-31 2016-12-14 安徽中科自动化股份有限公司 A kind of novel unmanned plane laser transmits without system line energy
CN107894712A (en) * 2017-10-13 2018-04-10 深圳大学 A kind of energy distributing method of laser power supply unmanned plane track optimizing and power of communications
CN108768497A (en) * 2018-04-27 2018-11-06 郑州航空工业管理学院 Unmanned plane assists wireless sense network and its node scheduling and flight Parameter design method
CN108880662A (en) * 2018-07-16 2018-11-23 深圳大学 A kind of optimization method of wireless messages and energy transmission based on unmanned plane

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