CN114173304A - Method for balancing throughput and time delay of three-dimensional unmanned aerial vehicle communication network - Google Patents

Method for balancing throughput and time delay of three-dimensional unmanned aerial vehicle communication network Download PDF

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CN114173304A
CN114173304A CN202111510187.6A CN202111510187A CN114173304A CN 114173304 A CN114173304 A CN 114173304A CN 202111510187 A CN202111510187 A CN 202111510187A CN 114173304 A CN114173304 A CN 114173304A
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CN114173304B (en
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龚珏
詹成
徐常元
廖婧睿
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Southwest University
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/42—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for mass transport vehicles, e.g. buses, trains or aircraft
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention relates to the technical field of unmanned aerial vehicle communication, and particularly discloses a method for balancing throughput and time delay of a three-dimensional unmanned aerial vehicle communication network, which comprises the following steps: determining an original optimization problem by jointly optimizing the trajectory, communication time and rate distribution of the three-dimensional unmanned aerial vehicle by taking the weighted sum of the user throughput and the required minimum rate of the maximized three-dimensional unmanned aerial vehicle communication system as an optimization target; giving the height of the unmanned aerial vehicle, simplifying an original optimization problem into a first sub-problem, and converting the first sub-problem into a first convex optimization problem; given the two-dimensional trajectory and resource allocation of any unmanned aerial vehicle, simplifying the original optimization problem into a second sub-problem, and converting the second sub-problem into a second convex optimization problem; and solving the first convex optimization problem and the second convex optimization problem to obtain a suboptimal solution of the original optimization problem. Simulation results show that the method realizes the compromise balance among throughput, time delay and highly-related angle-distance on the three-dimensional unmanned aerial vehicle communication network.

Description

Method for balancing throughput and time delay of three-dimensional unmanned aerial vehicle communication network
Technical Field
The invention relates to the technical field of unmanned aerial vehicle communication, in particular to a method for balancing throughput and time delay of a three-dimensional unmanned aerial vehicle communication network.
Background
Due to the characteristics of mobility, low cost and easy deployment, Unmanned Aerial Vehicles (UAVs) are expected to be an aerial wireless platform and widely used in the ultra-five (B5G) and sixth (6G) generation wireless communication networks. Specifically, when a Ground Base Station (GBS) is paralyzed due to a natural disaster, the unmanned aerial vehicle can be used as an Air Base Station (ABS) for backup transmission, or as an auxiliary communication platform for supplementing and unloading hotspot base station network data to the ground base station. Unlike conventional terrestrial wireless communication systems, unmanned aerial vehicles, due to their operability and flexibility, are more likely to establish a line-of-sight (LoS) communication link, which helps to improve the performance of the communication system.
On the other hand, multimedia services such as video and the like are more and more popular, such as virtual reality, network games and the like, and huge data volume brings huge pressure to the service capacity of the existing wireless network, resulting in low quality of service (QoS) of users. Therefore, B5G and 6G networks are expected to provide different quality of service guarantees for media applications on various networks in network design. For example, when a user downloads a file with a time delay requirement, others may play a High Definition (HD) movie at the same time, which requires a minimum play rate limit. Therefore, these different types of services should be optimized simultaneously, maximizing the system throughput while meeting the heterogeneous latency requirements of different applications. Intuitively, for applications that support drone communications, a drone may serve a particular user by getting a better channel quality near it to improve throughput. But this situation can cause the drone to be far from other users, degrading the channel quality for those users and violating their latency requirements. Thus, there is a fundamental trade-off between throughput and latency requirements for the user, as the mobility of the drone may be limited due to latency requirements. At present, work is carried out to study the balance between throughput and access delay in unmanned aerial vehicle communication, and the unmanned aerial vehicle flies along a fixed straight line and communicates with users in a cyclic multiple access mode. There are also working studies on unmanned aerial vehicle communication systems that consider time delays, maximizing the minimum of the average throughput of all users by optimizing unmanned aerial vehicle trajectories and power and bandwidth allocation. Still further work has separated latency-demanding transmissions into two categories, namely latency-limited transmissions and latency-tolerant transmissions, and maximized system throughput based thereon. However, these efforts only consider the drone flying in a straight line or flying horizontally, and neglect that the drone can move freely in three-dimensional (3D) space.
Compared to ground communication, since the positions of drones are located in 3D free space, modeling of the air-to-ground (A2G) communication channel is more challenging, as A2G communication contains more model parameters. Obstacles (like buildings and trees etc.) can block the transmission signal or reduce its power, the effect of which is the main difficulty to be solved in the A2G channel. Generally, a line-of-sight (LoS) channel and a non-line-of-sight (NLoS) channel are two channel states common in A2G channels, and each state is characterized by a different model. At present, work proposes a probability LoS channel model, and uses a function of elevation angles of an unmanned aerial vehicle and a ground user to depict the occurrence probability of a LoS/NLoS channel state. Intuitively, by increasing the altitude of the drone, the LoS probability increases as the drone-ground elevation angle increases. However, if the drone altitude increases, the distance between the drone and the user will increase, which will result in greater path loss, embodying the channel gain angle-distance tradeoff associated with altitude. On the other hand, when considering that the unmanned aerial vehicle flies in a three-dimensional space, the larger the height of the unmanned aerial vehicle, the larger and approximately the same elevation angle between the unmanned aerial vehicle and all users, and thus, fairer communication between the unmanned aerial vehicle and all users can be achieved while achieving similar time delay. However, increasing the drone height results in an increase in the distance between the drone and the user, thereby causing a reduction in the user's throughput. In fact, the effect of drone altitude on throughput and latency is unknown, which has not been studied in previous work.
Disclosure of Invention
The invention provides a method for balancing throughput and time delay of a three-dimensional unmanned aerial vehicle communication network, which solves the technical problems that: how to achieve a compromise balance between throughput, latency, and height-dependent angle-distance over a three-dimensional drone communication network.
In order to solve the technical problems, the invention provides a method for balancing throughput and time delay of a three-dimensional unmanned aerial vehicle communication network, which comprises the following steps:
s1, determining an original optimization problem by jointly optimizing the three-dimensional unmanned aerial vehicle track, the communication time and the rate distribution by taking the weighted sum of the user throughput and the required minimum rate of the three-dimensional unmanned aerial vehicle communication system as an optimization target;
s2, setting the height of the unmanned aerial vehicle, simplifying the original optimization problem into a first sub-problem, and converting the first sub-problem into a first convex optimization problem based on a differential convex function optimization framework and successive convex approximation;
s3, given the two-dimensional track and resource allocation of any unmanned aerial vehicle, simplifying the original optimization problem into a second sub-problem, and converting the second sub-problem into a second convex optimization problem based on a difference convex function optimization framework and successive convex approximation;
s4, solving the first convex optimization problem and the second convex optimization problem based on the iterative algorithm and the convex optimization solver to obtain a suboptimal solution of the original optimization problem.
Further, in step S1, the three-dimensional drone communication system includes 1 drone and K ground users, where the drone serves as an aerial base station for providing services to the K ground users; the original optimization problem is described as:
(P1):
Figure BDA0003404936540000031
Figure BDA0003404936540000032
Figure BDA0003404936540000033
Figure BDA0003404936540000034
Figure BDA0003404936540000035
Figure BDA0003404936540000036
Figure BDA0003404936540000037
q[1]＝q[N],z[1]＝z[N], (8)
Figure BDA0003404936540000038
Figure BDA0003404936540000039
wherein equations (9) and (10) represent the speed limit of the drone, q [ n ]]、z[n]Respectively representing the horizontal position and height of the drone at time slot n,
Figure BDA00034049365400000310
respectively representing the maximum speed of the unmanned aerial vehicle in the horizontal and vertical directions, and dividing the total time T into N equal-length time slots deltatI.e. T ═ N δt(ii) a Equation (8) represents that the drone serves the ground users periodically; formula (7) represents that the unmanned aerial vehicle in any time slot has the minimum flying height HminAnd a maximum flying height HmaxFly in the air; equation (6) represents a ground user skDefinition of elevation angle to drone, wkRepresenting terrestrial users skA set of K ground users as
Figure BDA00034049365400000311
In the formula (5), rkRepresenting terrestrial users skMinimum rate of (x)k[n]Unmanned aerial vehicle and ground user s when representing time slot nkThe time allocation of communication satisfies the condition of the formula (2),
Figure BDA0003404936540000041
unmanned aerial vehicle and ground user s when representing time slot nkApproximate desired rate of communication, from desired rate
Figure BDA0003404936540000042
Approximation is carried out; formula (3) represents that the unmanned aerial vehicle communicates with at most one ground user at each moment; in the formula (4), the reaction mixture is,
Figure BDA0003404936540000043
representing unmanned aerial vehicle to ground user skM represents a weighting coefficient, m is 0. ltoreq. m.ltoreq.1, mDk+(1-m)rkN denotes a terrestrial user skμ represents the minimum of the weighted sum of the user throughput and the required rate of the three-dimensional unmanned aerial vehicle communication system;
Figure BDA0003404936540000044
further, unmanned aerial vehicle and ground user s in time slot nkThe distance between
Figure BDA0003404936540000045
Ground user skAnd LoS channel probability between drones
Figure BDA0003404936540000046
Denoted as terrestrial users skAngle of elevation theta to unmanned aerial vehiclek[n]Function of (c):
Figure BDA0003404936540000047
wherein B isa＜0，Bb＞0，Bd＞0，Bc＝1-BdThe values of the four constants are determined by specific environments; at time slot n, unmanned aerial vehicle and ground user skChannel power gain beta in betweenk[n]By probability
Figure BDA0003404936540000048
Expressed as LoS communication link expressions
Figure BDA0003404936540000049
By probability
Figure BDA00034049365400000410
Expressed as NLoS communication link expression
Figure BDA00034049365400000411
Wherein beta is0Is the channel power gain at a reference distance of 1m, epsilon represents an additional attenuation factor due to NLoS communication link propagation, epsilon < 1; the path loss exponent of the LoS channel link and the NLoS channel link are respectively expressed as alphaLAnd alphaN。
Further, unmanned aerial vehicle and ground user s in time slot nkAchievable rate in between
Figure BDA00034049365400000412
Where B denotes the signal bandwidth, P denotes the transmit power of the drone, σ2Representing the noise power;
Figure BDA00034049365400000413
further, the step S2 specifically includes the steps of:
s21, given the unmanned aerial vehicle height Z, simplifying the original optimization problem (P1) into a first sub-problem:
(P2):
Figure BDA0003404936540000051
s.t.(2)-(6),(10),
q[1]＝q[N] (11)
s22, introducing relaxation variable Y ═ Yk[n]},Θ＝{θk[n]The question (P2) is further converted into:
(P3):
Figure BDA0003404936540000052
s.t.(2)-(4),(10),(11),
Figure BDA0003404936540000053
Figure BDA0003404936540000054
Figure BDA0003404936540000055
s23, converting the non-convex constrained expressions (4), (12) to (14) into convex constraints based on the differential convex function optimization framework, and further converting the problem (P3) into a standard convex optimization problem (P4).
Further, the step S23 specifically includes the steps of:
s231, converting the non-convex term x in the formula (13)k[n]yk[n]Rewrite as a difference convex function:
Figure BDA0003404936540000056
s232, item pairing
Figure BDA0003404936540000057
Approximation with a first order Taylor expansion yields:
Figure BDA0003404936540000058
wherein Q isk[n]lbIs about xk[n]And yk[n]A joint concave function of (a);
s233, for the formula (14), given qk[n]lWhen passing through
Figure BDA0003404936540000059
The function applies a first order Taylor expansion to approximate, yielding:
Figure BDA00034049365400000510
wherein,
Figure BDA00034049365400000511
s234, for the formulae (4) and (12), q is givenk[n]lAnd thetak[n]lTo, for
Figure BDA00034049365400000512
The function is approximated with a first order Taylor expansion to yield:
Figure BDA0003404936540000061
wherein,
Figure BDA0003404936540000062
Figure BDA0003404936540000063
Figure BDA0003404936540000064
Figure BDA0003404936540000065
s235, using derived lower limit Qk[n]lb、vk[n]lb、
Figure BDA0003404936540000066
Replacing equations (15) - (17), further transforming the problem (P3) into a standard first convex optimization problem:
(P4):
Figure BDA0003404936540000067
s.t.(2),(3),(10),(11),
Figure BDA0003404936540000068
Figure BDA0003404936540000069
Figure BDA00034049365400000610
Figure BDA00034049365400000611
in all first-order Taylor expansions, a parameter is indicated with a superscript l to the value corresponding to the parameter at the i-th iteration.
Further, the step S3 specifically includes the steps of:
s31, given the two-dimensional trajectory and resource allocation { Q, X, R } of any unmanned aerial vehicle, simplifying the original optimization problem (P1) into a second subproblem:
(P5):
Figure BDA00034049365400000612
s.t.(4)-(7),(9),
z[1]＝z[N] (22)
s32, further equating the second sub-problem (P5) as:
(P6):
Figure BDA0003404936540000071
s.t.(4),(5),(7),(9),(14),(22)；
s33, given thetak[n]lAnd z [ n ]]lTo, for
Figure BDA0003404936540000072
Approximation is solved by applying first order Taylor expansion to obtain:
Figure BDA0003404936540000073
wherein,
Figure BDA0003404936540000074
Figure BDA0003404936540000075
Figure BDA0003404936540000076
Figure BDA0003404936540000077
s34, further approximating the problem (P6) as a standard second convex optimization problem based on equation (23):
(P7):
Figure BDA0003404936540000078
s.t.
Figure BDA0003404936540000079
Figure BDA00034049365400000710
(7),(9),(14),(22)。
further, the step S4 specifically includes the steps of:
s41, solving the first subproblem based on the iterative algorithm and the convex optimization solver, and obtaining { x ] when the target value of the first subproblem converges to the predefined precisionk[n],yk[n],q[n]Target value of }
Figure BDA00034049365400000711
The method specifically comprises the following steps:
s411, initialization
Figure BDA0003404936540000081
Setting the iteration number l as 0;
s412, giving local points
Figure BDA0003404936540000082
Solving a first convex optimization problem (P4) using a convex optimization solver to obtain a current optimal solution
Figure BDA0003404936540000083
S413, updating the local point of the ith iteration:
Figure BDA0003404936540000084
s414, updating l + 1;
s415, until the target value of the first subproblem (P2) converges to the predefined precision, outputting the current optimal solution
Figure BDA0003404936540000085
As an optimal solution to solve the first sub-problem (P2);
s42, solving the second sub-problem (P5) based on steps similar to steps S411 to S415, obtaining the second sub-problem (P5)Z [ n ] when the target value converges to a predefined accuracy]Target value z of^[n]。
Further, the step S4 specifically includes the steps of:
s401, initialization
Figure BDA0003404936540000086
Setting the iteration number l as 0;
s402, giving local points
Figure BDA0003404936540000087
The same steps as the steps S412 to S413 are carried out to solve the first convex optimization problem (P4) to obtain
Figure BDA0003404936540000088
S403, give
Figure BDA0003404936540000089
Similar to steps S412-S413, the second convex optimization problem (P7) is solved to obtain { zl+1[n]}；
S404, updating l + 1;
s405, until the target value of the optimization problem (P1) converges to the predefined precision, outputting the current optimal solution
Figure BDA00034049365400000810
As a sub-optimal solution to solve the original optimization problem (P1).
The invention provides a method for balancing throughput and time delay of a three-dimensional unmanned aerial vehicle communication network, which has the following effects:
1. in order to realize the balance between throughput and time delay in a three-dimensional unmanned aerial vehicle communication network, minimum rate required by users is introduced, the minimum value of the weighted sum of the throughput and the required rate of each user is maximized by jointly optimizing the track of the three-dimensional unmanned aerial vehicle and the communication time and rate distribution, and an optimization problem is constructed by considering an elevation-based LoS probability channel model and the fairness among all users;
2. the optimization problem can not be directly solved, the optimization problem is further decomposed into two sub-problems, and a Successive Convex Approximation (SCA) and difference convex function optimization framework is adopted to solve the sub-problems in each iteration, so that the suboptimal solution of the optimization problem is obtained;
3. simulation results show that the method is superior to each reference scheme, and the balance of throughput, delay and angle-distance correlation with height on the three-dimensional unmanned aerial vehicle communication network is achieved.
Drawings
Fig. 1 is a flowchart illustrating steps of a method for balancing throughput and delay in a communication network of a three-dimensional unmanned aerial vehicle according to an embodiment of the present invention;
FIG. 2 is an optimized trajectory diagram of the UAV in the simulation provided by the embodiment of the present invention;
fig. 3 is a communication time allocation diagram of the unmanned aerial vehicle to different ground users in the simulation provided by the embodiment of the present invention;
FIG. 4 is a graph of throughput versus minimum rate required in simulations provided by an embodiment of the present invention;
FIG. 5 is a graph comparing the mean distribution time and the sum of the max-min weights for the solution presented herein and the baseline solutions in a simulation provided by an embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, which are given solely for the purpose of illustration and are not to be construed as limitations of the invention, including the drawings which are incorporated herein by reference and for illustration only and are not to be construed as limitations of the invention, since many variations thereof are possible without departing from the spirit and scope of the invention.
The embodiment of the invention provides a method for balancing throughput and time delay of a three-dimensional unmanned aerial vehicle communication network, which comprises the following steps of:
s1, determining an original optimization problem by jointly optimizing the three-dimensional unmanned aerial vehicle track, the communication time and the rate distribution by taking the weighted sum of the user throughput and the required minimum rate of the three-dimensional unmanned aerial vehicle communication system as an optimization target;
s2, setting the height of the unmanned aerial vehicle, simplifying the original optimization problem into a first sub-problem, and converting the first sub-problem into a first convex optimization problem based on a differential convex function optimization framework and successive convex approximation;
s3, given the two-dimensional track and resource allocation of any unmanned aerial vehicle, simplifying the original optimization problem into a second sub-problem, and converting the second sub-problem into a second convex optimization problem based on a difference convex function optimization framework and successive convex approximation;
s4, solving the first convex optimization problem and the second convex optimization problem based on the iterative algorithm and the convex optimization solver to obtain a suboptimal solution of the original optimization problem.
In step S1, a three-dimensional (3D) drone communication system (i.e., a three-dimensional drone communication network) includes 1 drone and K ground users, where the drone serves the K Ground Users (GUs) as an Air Base Station (ABS). Wherein, K terrestrial user sets can be expressed as
Figure BDA0003404936540000101
Ground user sk(abbreviated as user s)k) For horizontal position of
Figure BDA0003404936540000102
And (4) showing. To simplify the analysis, the example divides the total time T into N equal-length time slots δtI.e. T ═ N δt. At any time slot n, the horizontal position of the unmanned aerial vehicle can be approximated
Figure BDA0003404936540000103
To indicate. This example defines z [ n ]]For the altitude of the drone at time slot n, then Hmin≤z[n]≤Hmax,
Figure BDA0003404936540000104
Wherein HminAnd HmaxThe minimum and maximum flying heights of the drone. Thus, the drone 3D position at slot n may be represented as (q n)],z[n]). This example also assumes q [1 ]]＝q[N]And z [1 ]]＝z[N]So that the drone can periodically serve the ground users. Due to the speed limitation of unmanned aerial vehicles, there are
Figure BDA0003404936540000105
2≤n≤N，
Figure BDA0003404936540000106
The maximum speed of the drone in the horizontal and vertical directions, respectively. Unmanned aerial vehicle and user skThe distance between the two in the time slot n can be determined by
Figure BDA0003404936540000107
And (4) showing.
In general, the A2G channel includes a size-scale fading coefficient depending on whether the A2G channel is a LoS channel or an NLoS channel. Although each slot may contain multiple small-scale fading blocks, its impact can be averaged by using sufficiently long channel coding. In time slot n, user skAnd LoS channel probability between UAVs
Figure BDA0003404936540000108
Can be expressed as the elevation angle theta of the user to the dronek[n]Wherein the elevation angle is defined as
Figure BDA0003404936540000109
In particular, the present invention relates to a method for producing,
Figure BDA00034049365400001010
wherein B isa＜0，Bb＞0，Bd＞0，Bc＝1-BdThe values of the four constants are determined by the specific environment. At time slot n, drone and user skChannel power gain beta in betweenk[n]By probability
Figure BDA0003404936540000111
Expressed as LoS communication link expressions
Figure BDA0003404936540000112
By probability
Figure BDA0003404936540000113
Expressed as NLoS communication link expression
Figure BDA0003404936540000114
Wherein beta is0Is the channel power gain at a reference distance of 1m, epsilon represents an additional attenuation factor due to NLoS communication link propagation, epsilon < 1. Further, the path loss exponents of the LoS channel link and the NLoS channel link may be expressed as α, respectivelyLAnd alphaN。
Defining a variable xk[n]Unmanned aerial vehicle and user s representing time slot nkTime allocation of communication, 0 ≦ xk[n]Less than or equal to 1. The example assumes that the drone communicates with at most one user at each moment in time, then
Figure BDA0003404936540000115
Definition P denotes the transmit power of the drone, then the drone and the user skThe achievable rate in between can be expressed as
Figure BDA0003404936540000116
Where B denotes the signal bandwidth and σ2Representing the noise power. From the above expression, R can be seenk[n]Dependent on betak[n]And βk[n]Depending on whether the link is a LoS communication link. Deriving the expected rate from the LoS probabilistic model
Figure BDA0003404936540000117
And deriving an expression of the approximation
Figure BDA0003404936540000118
Namely:
Figure BDA0003404936540000119
wherein the intermediate variable
Figure BDA00034049365400001110
For this example
Figure BDA00034049365400001111
And carrying out unmanned aerial vehicle track design. Thus, the drone is directed to the ground user skCan be expressed as
Figure BDA00034049365400001112
Service applications, e.g. video applications, taking into account user delay constraints, per user skHaving a minimum rate rkIs required to be
Figure BDA00034049365400001113
Wherein r iskCan be interpreted as a user skAnd playing the video stream at a playing speed, wherein if the lowest playing speed is met, the video playing is not blocked. Therefore, the temperature of the molten metal is controlled,
Figure BDA00034049365400001114
representative user skThe latency requirement at each instant. Intuitively, greater throughput and less latency may be achieved for each terrestrial user skProviding better quality of service (QoS) guarantees. The object of the invention is therefore to provide for each user skMaximizing D simultaneouslykAnd rk. To realize DkAnd rkThe example will be DkAnd rkAssociated with a weighting factor m, 0 ≦ m ≦ 1, and maximizing the weighted sum mDk+(1-m)rkAnd N is added. Wherein let rkMultiply by N to rkThe size of N and the size of the throughput may be of comparable order. Definition of
Figure BDA00034049365400001115
In order to achieve fairness among all ground users, the present example maximizes the minimum value of the weighted sum of all ground users by optimizing the 3D trajectory of the drone and the communication time and rate allocation. Based on the above settings, the optimization problem can be written as:
(P1):
Figure BDA0003404936540000121
Figure BDA00034049365400001214
Figure BDA0003404936540000122
Figure BDA0003404936540000123
Figure BDA0003404936540000124
Figure BDA0003404936540000125
Figure BDA0003404936540000126
q[1]＝q[N],z[1]＝z[N], (8)
Figure BDA0003404936540000127
Figure BDA0003404936540000128
among them, the non-convex constraint in (4) - (6) makes the problem (P1) a non-convex optimization problem with coupled variables and complex rate expressions, and solving the optimal solution is very difficult. This example obtains a sub-optimal solution to the problem (P1) through steps S2-S4 to efficiently solve the problem (P1).
Firstly, the problem (P1) is simplified into a first sub-problem and converted into a first convex optimization problem through the step S2, which specifically includes the steps of:
s21, given the unmanned aerial vehicle height Z, simplifying the original optimization problem (P1) into a first sub-problem:
(P2):
Figure BDA0003404936540000129
s.t.(2)-(6),(10),
q[1]＝q[N] (11)
s22, introducing relaxation variable Y ═ Yk[n]},Θ＝{θk[n]The question (P2) is further converted into:
(P3):
Figure BDA00034049365400001210
s.t.(2)-(4),(10),(11),
Figure BDA00034049365400001211
Figure BDA00034049365400001212
Figure BDA00034049365400001213
it can be seen that equations (12) and (14) must hold true in the optimal solution of (P3). Otherwise, θ can always be increasedk[n]And yk[n]Until the equation is established, and without changing the target value, the optimal solution still satisfies the other constraints. Therefore, the problem (P3) is equivalent to the problem (P2). However, since (4), (12) - (14) are non-convex constraints, the problem (P3) remains a non-convex optimization problem, and the non-convex constraint in the problem (P3) is converted into a convex constraint.
S23, converting the non-convex constrained expressions (4), (12) to (14) into convex constraints based on a differential convex function optimization framework, and further converting the problem (P3) into a standard convex optimization problem (P4); the step S23 specifically includes the steps of:
s231, converting the non-convex term x in the formula (13)k[n]yk[n]Rewrite as a difference convex function:
Figure BDA0003404936540000131
s232, item pairing
Figure BDA0003404936540000132
Approximation with a first order Taylor expansion yields:
Figure BDA0003404936540000133
wherein Q isk[n]lbIs about xk[n]And yk[n]In the first-order Taylor expansion process, a parameter with a superscript l represents a value corresponding to the parameter in the first iteration, and the parameter in the first-order Taylor expansion process is represented as the same;
s233, for equation (14), verification is possible
Figure BDA0003404936540000134
Is a convex function for x > 0; therefore, given qk[n]lWhen passing through
Figure BDA0003404936540000135
The function applies a first order Taylor expansion to approximate, yielding:
Figure BDA0003404936540000136
wherein,
Figure BDA0003404936540000137
s234, for (4) and (12), the function can be verified
Figure BDA0003404936540000138
Is a convex function combining x and y; thus, for equations (4) and (12), q is givenk[n]lAnd thetak[n]lTo, for
Figure BDA0003404936540000139
The function is approximated with a first order Taylor expansion to yield:
Figure BDA0003404936540000141
wherein,
Figure BDA0003404936540000142
Figure BDA0003404936540000143
Figure BDA0003404936540000144
Figure BDA0003404936540000145
s235, using derived lower limit Qk[n]lb、vk[n]lb、
Figure BDA0003404936540000146
Replacing equations (15) - (17), further converting the problem (P3) to a standard first convex optimization problem (P4):
(P4):
Figure BDA0003404936540000147
s.t.(2),(3),(10),(11),
Figure BDA0003404936540000148
Figure BDA0003404936540000149
Figure BDA00034049365400001410
Figure BDA00034049365400001411
it can be verified (P4) that it is a standard convex optimization problem that can be solved by a standard convex optimization solver, such as CVX. The problem (P2) can thus be solved by a differential convex function (d.c.) optimization framework and SCA method, the details of which are reflected in step S4, the complexity of which can be represented by O ((NK)3.5log (1/k)) indicates that k is the solution accuracy.
Then, the problem (P1) is simplified into a second sub-problem and converted into a second convex optimization problem through step S3, which specifically includes the steps of:
s31, given the two-dimensional trajectory and resource allocation { Q, X, R } of any unmanned aerial vehicle, simplifying the original optimization problem (P1) into a second subproblem:
(P5):
Figure BDA0003404936540000151
s.t.(4)-(7),(9),
z[1]＝z[N] (22)
s32, further equating the second sub-problem (P5) as:
(P6):
Figure BDA0003404936540000152
s.t.(4),(5),(7),(9),(14),(22)；
s33, given thetak[n]lAnd z [ n ]]lTo, for
Figure BDA0003404936540000153
Approximation is solved by applying first order Taylor expansion to obtain:
Figure BDA0003404936540000154
wherein,
Figure BDA0003404936540000155
Figure BDA0003404936540000156
Figure BDA0003404936540000157
Figure BDA0003404936540000158
s34, further approximating the problem (P6) as a standard second convex optimization problem based on equation (23):
(P7):
Figure BDA0003404936540000159
s.t.
Figure BDA00034049365400001510
Figure BDA00034049365400001511
(7),(9),(14),(22)。
it can be verified that the problem (P7) is a standard convex optimization problem that can be solved efficiently by CVX. Thus, the iterative algorithm for solving the problem (P5) is similar to that for solving the problem (P2), by taking the value of θ into accountk[n]lAnd z [ n ]]lThe SCA method is applied to iterative solution under the condition of (1).
Next, step S4 is adopted to solve the two sub-problems, which specifically includes the steps of:
s41, solving the first subproblem based on the iterative algorithm and the convex optimization solver, and obtaining { x ] when the target value of the first subproblem converges to the predefined precisionk[n],yk[n],q[n]Target value of }
Figure BDA0003404936540000161
The method specifically comprises the following steps:
s411, initialization
Figure BDA0003404936540000162
Setting the iteration number l as 0;
s412, giving local points
Figure BDA0003404936540000163
Solving a first convex optimization problem (P4) using a convex optimization solver to obtain a current optimal solution
Figure BDA0003404936540000164
S413, updating the local point of the ith iteration:
Figure BDA0003404936540000165
s414, updating l + 1;
s415, until the target value of the first subproblem (P2) converges to the predefined precision, outputting the current optimal solution
Figure BDA0003404936540000166
As an optimal solution to solve the first sub-problem (P2);
s42, solving the second sub-problem (P5) based on the steps similar to steps S411 to S415, and obtaining z [ n ] when the target value of the second sub-problem (P5) converges to the predefined precision]Target value z of^[n]。
Generally, the process of solving the problem P1 in step S4 includes the steps of:
s401, initialization
Figure BDA0003404936540000167
Setting the iteration number l as 0;
s402, giving local points
Figure BDA0003404936540000168
The same steps as the steps S412 to S413 are carried out to solve the first convex optimization problem (P4) to obtain
Figure BDA0003404936540000169
S403, give
Figure BDA00034049365400001610
Similar to steps S412-S413, the second convex optimization problem (P7) is solved to obtain { zl+1[n]}；
S404, updating l + 1;
s405, until the target value of the optimization problem (P1) converges to the predefined precision, outputting the current optimal solution
Figure BDA00034049365400001611
As a sub-optimal solution to solve the original optimization problem (P1).
To evaluate the effectiveness of the optimization proposed in this example, simulation experiments were conducted below. Assume that K terrestrial users are randomly distributed in an 800m × 800m square area and K equals 5. The parameters in the communication model may be set to Ba＝-0.4568，Bb＝0.0470，Bc＝-0.63，Bd-1.63. The lowest flying height and the highest flying height of the unmanned aerial vehicle are respectively Hmin＝50m，Hmax100 m. Suppose that the maximum horizontal and vertical flight speeds of the drone are respectively
Figure BDA0003404936540000171
And
Figure BDA0003404936540000172
the initial trajectory of the drone may be set to height HminA 50m circular trajectory with its center at the geometric center of all terrestrial user coordinates. The communication parameter is set as P ═ 0.1W, beta0＝-60dB，B＝1MHz，σ2＝-110dBm，αL＝2.5。
Fig. 2 shows the optimized trajectories of drones under different weighting factors m when T is 60 s. Fig. 2(a) depicts the flight trajectory of a drone moving in three-dimensional space, and fig. 2(b) and 2(c) depict the trajectory of a drone and the change in altitude of a drone, respectively, projected on a two-dimensional plane. As can be seen from fig. 2, the optimized trajectory of the drone changes with the change of m values, from which the conclusion can be drawn: when the value of m is close to 1, the influence of the maximized throughput of each ground user on the target value is larger, so that the unmanned aerial vehicle can get close to each ground user when flying, and better channel quality is obtained. In addition, when the drone is close to the ground user, the flying height of the drone is reduced, which can further reduce the distance between the drone and the ground user, improving the communication efficiency, as shown in fig. 2 (c). Interestingly, the drone frequently flies up and down and tends to stay at a higher altitude during the flight between two adjacent users until it lowers again after approaching the ground user. The main reason why the drone flies higher after leaving the user is that the LoS channel probability between the drone and the ground user depends on the elevation angle, and the higher the altitude, the higher the LoS channel probability. Thus, the main factors affecting the channel quality differ at different times. When the unmanned aerial vehicle is close to the ground user, the influence of the reduced distance on the communication quality is larger than the influence of the elevation angle, so that the height of the unmanned aerial vehicle is reduced. Conversely, when the drone leaves the ground user, increasing the elevation angle between the drone and the user has a more significant effect on increasing the quality of the communication, so the drone height increases. Another reason for the increase of the height of the unmanned aerial vehicle is to ensure fairness of the unmanned aerial vehicle for data transmission between users, and the unmanned aerial vehicle can increase the elevation angle of each user by increasing the height thereof, so that the elevation angles of each user are not greatly different.
On the other hand, when m is close to 0, the minimum rate increase required for each terrestrial user has a greater influence on the system performance than the throughput increase of the system. The 2D drone trajectory shrinks, remaining higher, as shown in fig. 2(b) and 2 (c). In other words, the drone tends to hover over all ground users, and thus away from all ground users. The reason is that the required rate per terrestrial user should be large in each time slot where m is small. If the drone is far from a particular ground user, the demand rate for that ground user may not be achieved.
In fig. 3, when m is 0.9, 5 terrestrial users (S)1To S5) Communication time allocation of (1). In this case, maximizing system throughput has a more significant impact on increasing communication quality than maximizing the minimum required rate of the user, so drones tend to communicate with terrestrial users more often than they are close enough to the terrestrial users and have better channel quality, to improve communication efficiency.
Figure 4 depicts the trade-off between throughput and minimum rate required by the user for different values of m. As can be seen from fig. 2, when m is larger and close to 1, the throughput of the drone increases although the minimum demand rate is reduced. In practical applications, m can be selected appropriately according to specific requirements to balance the trade-off between throughput and latency. To demonstrate the superiority of the joint optimization design, this example compares the target values of the proposed solution with the following:
(1) a two-dimensional trajectory fixed reference scheme, wherein the unmanned aerial vehicle is kept at the geometric centers of all ground users, and only the height is optimized;
(2) altitude fixed reference scheme, in which the drone is held at altitude HmaxFlying;
(3) the average distribution reference scheme is used for averagely distributing communication time to all ground users;
(4) static reference scheme, wherein unmanned aerial vehicle keeps all the time in all ground user's geometric centre, and the height is fixed as Hmax。
Fig. 5 is a minimum value of a weighted sum of throughput of the system and a minimum required rate of the user at different time periods T when m is 0.9. It can be observed that the weighted sum increases as expected as the time T increases. Compared with other reference schemes, the minimum value of the weighted sum of the scheme provided by the embodiment is the largest, and the data communication between the unmanned aerial vehicle and the ground user is facilitated. The performance gap between the proposed solution and the average allocation benchmark indicates the benefit of airtime allocation. In the embodiment, the additional gain caused by the maneuverability of the unmanned aerial vehicle in the three-dimensional space is demonstrated by comparing the two-dimensional track fixed reference scheme, the height fixed reference scheme and the static reference scheme, and the effectiveness of information transmission is improved.
To sum up, the embodiment of the invention researches the balance between throughput and time delay in a three-dimensional unmanned aerial vehicle communication network, introduces the minimum required rate of each ground user, maximizes the minimum of the weighted sum of the throughput and the required rate of each user by jointly optimizing the unmanned aerial vehicle track, communication time and rate distribution in a 3D space, obtains a joint design optimization problem, and models the optimization problem into a non-convex optimization problem. Finally, obtaining a suboptimal solution through an iterative algorithm based on coordinate descent, difference convex optimization and an SCA method. Simulation results illustrate the significant gain of the solution proposed by the present invention and reveal the fundamental trade-off between throughput and latency in a three-dimensional unmanned aerial vehicle communication network.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (9)

1. A method for balancing throughput and time delay of a three-dimensional unmanned aerial vehicle communication network is characterized by comprising the following steps:
s1, determining an original optimization problem by jointly optimizing the three-dimensional unmanned aerial vehicle track, the communication time and the rate distribution by taking the weighted sum of the user throughput and the required minimum rate of the three-dimensional unmanned aerial vehicle communication system as an optimization target;
s2, setting the height of the unmanned aerial vehicle, simplifying the original optimization problem into a first sub-problem, and converting the first sub-problem into a first convex optimization problem based on a differential convex function optimization framework and successive convex approximation;
s3, given the two-dimensional track and resource allocation of any unmanned aerial vehicle, simplifying the original optimization problem into a second sub-problem, and converting the second sub-problem into a second convex optimization problem based on a difference convex function optimization framework and successive convex approximation;
s4, solving the first convex optimization problem and the second convex optimization problem based on the iterative algorithm and the convex optimization solver to obtain a suboptimal solution of the original optimization problem.
2. The method of claim 1, wherein the method comprises the following steps: in step S1, the three-dimensional drone communication system includes 1 drone and K ground users, where the drone serves as an aerial base station to provide services for the K ground users; the original optimization problem is described as:
(P1):
Figure FDA0003404936530000011
q[1]＝q[N],z[1]＝z[N], (8)
Figure FDA00034049365300000212
Figure FDA00034049365300000213
wherein equations (9) and (10) represent the speed limit of the drone, q [ n ]]、z[n]Respectively representing the horizontal position and height of the drone at time slot n,
Figure FDA0003404936530000021
individual watchShowing the maximum speed of the unmanned aerial vehicle in the horizontal and vertical directions, and dividing the total time T into N equal-length time slots deltatI.e. T ═ N δt(ii) a Equation (8) represents that the drone serves the ground users periodically; formula (7) represents that the unmanned aerial vehicle in any time slot has the minimum flying height HminAnd a maximum flying height HmaxFly in the air; equation (6) represents a ground user skDefinition of elevation angle to drone, wkRepresenting terrestrial users skA set of K ground users as
Figure FDA0003404936530000022
In the formula (5), rkRepresenting terrestrial users skMinimum rate of (x)k[n]Unmanned aerial vehicle and ground user s when representing time slot nkThe time allocation of communication satisfies the condition of the formula (2),
Figure FDA0003404936530000023
unmanned aerial vehicle and ground user s when representing time slot nkApproximate desired rate of communication, from desired rate
Figure FDA0003404936530000024
Approximation is carried out; formula (3) represents that the unmanned aerial vehicle communicates with at most one ground user at each moment; in the formula (4), the reaction mixture is,
Figure FDA0003404936530000025
representing unmanned aerial vehicle to ground user skM represents a weighting coefficient, m is 0. ltoreq. m.ltoreq.1, mDk+(1-m)rkN denotes a terrestrial user skMu represents the minimum value of the weighted sum of the user throughput and the required rate of the three-dimensional unmanned aerial vehicle communication system;
Figure FDA0003404936530000026
3. the method of claim 2, wherein the method comprises the following steps:
unmanned aerial vehicle and ground user s in time slot nkThe distance between
Figure FDA0003404936530000027
Ground user skAnd LoS channel probability between drones
Figure FDA0003404936530000028
Denoted as terrestrial users skAngle of elevation theta to unmanned aerial vehiclek[n]Function of (c):
Figure FDA0003404936530000029
wherein B isa＜0，Bb＞0，Bd＞0，Bc＝1-BdThe values of the four constants are determined by specific environments; at time slot n, unmanned aerial vehicle and ground user skChannel power gain beta in betweenk[n]By probability
Figure FDA00034049365300000210
Expressed as LoS communication link expressions
Figure FDA00034049365300000214
By probability
Figure FDA00034049365300000211
Expressed as NLoS communication link expression
Figure FDA0003404936530000031
Wherein beta is0Is the channel power gain at a reference distance of 1m, epsilon represents an additional attenuation factor due to NLoS communication link propagation, epsilon < 1; the path loss exponent of the LoS channel link and the NLoS channel link are respectively expressed as alphaLAnd alphaN。
4. The method of claim 3, wherein the method comprises the following steps:
unmanned aerial vehicle and ground user s in time slot nkAchievable rate in between
Figure FDA0003404936530000032
Where B denotes the signal bandwidth, P denotes the transmit power of the drone, σ2Representing the noise power;
Figure FDA0003404936530000033
5. the method for trading off throughput and latency of a communication network of a three-dimensional unmanned aerial vehicle according to claim 4, wherein the step S2 specifically comprises the steps of:
s21, given the unmanned aerial vehicle height Z, simplifying the original optimization problem (P1) into a first sub-problem:
(P2):
Figure FDA0003404936530000034
s.t.(2)-(6),(10),
q[1]＝q[N] (11)
s22, introducing relaxation variable Y ═ Yk[n]},Θ＝{θk[n]The question (P2) is further converted into:
(P3):
Figure FDA0003404936530000035
s.t.(2)-(4),(10),(11),
Figure FDA0003404936530000036
Figure FDA0003404936530000037
Figure FDA0003404936530000038
s23, converting the non-convex constrained expressions (4), (12) to (14) into convex constraints based on the differential convex function optimization framework, and further converting the problem (P3) into a standard convex optimization problem (P4).
6. The method for trading off throughput and latency of a communication network of a three-dimensional unmanned aerial vehicle according to claim 5, wherein the step S23 specifically comprises the steps of:
s231, converting the non-convex term x in the formula (13)k[n]yk[n]Rewrite as a difference convex function:
Figure FDA0003404936530000041
s232, item pairing
Figure FDA0003404936530000042
Approximation with a first order Taylor expansion yields:
Figure FDA0003404936530000043
wherein Q isk[n]lbIs about xk[n]And yk[n]A joint concave function of (a);
s233, for the formula (14), given qk[n]lWhen passing through
Figure FDA0003404936530000044
The function applies a first order Taylor expansion to approximate, yielding:
Figure FDA0003404936530000045
wherein,
Figure FDA0003404936530000046
s234, for the formulae (4) and (12), q is givenk[n]lAnd thetak[n]lTo, for
Figure FDA0003404936530000047
The function is approximated with a first order Taylor expansion to yield:
Figure FDA0003404936530000048
wherein,
Figure FDA0003404936530000049
Figure FDA00034049365300000410
Figure FDA00034049365300000411
Figure FDA0003404936530000051
s235, using derived lower limit Qk[n]lb、vk[n]lb、
Figure FDA0003404936530000052
Replacing equations (15) - (17), further transforming the problem (P3) into a standard first convex optimization problem:
(P4):
Figure FDA0003404936530000053
s.t.(2),(3),(10),(11),
Figure FDA0003404936530000054
Figure FDA0003404936530000055
Figure FDA0003404936530000056
Figure FDA0003404936530000057
in all first-order Taylor expansions, a parameter is indicated with a superscript l to the value corresponding to the parameter at the i-th iteration.
7. The method for trading off throughput and latency of a communication network of a three-dimensional unmanned aerial vehicle according to claim 6, wherein the step S3 specifically comprises the steps of:
s31, given the two-dimensional trajectory and resource allocation { Q, X, R } of any unmanned aerial vehicle, simplifying the original optimization problem (P1) into a second subproblem:
(P5):
Figure FDA0003404936530000058
s.t.(4)-(7),(9),
z[1]＝z[N]
(22)
s32, further equating the second sub-problem (P5) as:
(P6):
Figure FDA0003404936530000059
s.t.(4),(5),(7),(9),(14),(22)；
s33, given thetak[n]lAnd z [ n ]]lTo, for
Figure FDA00034049365300000510
Approximation is solved by applying first order Taylor expansion to obtain:
Figure FDA0003404936530000061
wherein,
Figure FDA0003404936530000062
Figure FDA0003404936530000063
Figure FDA0003404936530000064
Figure FDA0003404936530000065
s34, further approximating the problem (P6) as a standard second convex optimization problem based on equation (23):
(P7):
Figure FDA0003404936530000066
Figure FDA0003404936530000067
(7),(9),(14),(22)。
8. the method for trading off throughput and latency of a communication network of a three-dimensional unmanned aerial vehicle according to claim 7, wherein the step S4 specifically includes the steps of:
s41, solving the first subproblem based on the iterative algorithm and the convex optimization solver, and obtaining { x ] when the target value of the first subproblem converges to the predefined precisionk[n],yk[n],q[n]Target value of }
Figure FDA0003404936530000068
The method specifically comprises the following steps:
s411, initialization
Figure FDA0003404936530000069
Setting the iteration number l as 0;
s412, giving local points
Figure FDA00034049365300000610
Solving a first convex optimization problem (P4) using a convex optimization solver to obtain a current optimal solution
Figure FDA00034049365300000611
S413, updating the local point of the ith iteration:
Figure FDA0003404936530000071
s414, updating l + 1;
s415, until the target value of the first subproblem (P2) converges to the predefined precision, outputting the current optimal solution
Figure FDA0003404936530000072
As an optimal solution to solve the first sub-problem (P2);
s42, solving the second sub-problem (P5) based on steps similar to steps S411 to S415, and obtaining the purpose of the second sub-problem (P5)Z [ n ] when the scalar converges to a predefined precision]Target value z of^[n]。
9. The method for trading off throughput and latency of a communication network of a three-dimensional unmanned aerial vehicle according to claim 8, wherein the step S4 specifically includes the steps of:
s401, initialization
Figure FDA0003404936530000073
Setting the iteration number l as 0;
s402, giving local points
Figure FDA0003404936530000074
The same steps as the steps S412 to S413 are carried out to solve the first convex optimization problem (P4) to obtain
Figure FDA0003404936530000075
S403, give
Figure FDA0003404936530000076
Similar to steps S412-S413, the second convex optimization problem (P7) is solved to obtain { zl+1[n]}；
S404, updating l + 1;
s405, until the target value of the optimization problem (P1) converges to the predefined precision, outputting the current optimal solution
Figure FDA0003404936530000077
As a sub-optimal solution to solve the original optimization problem (P1).
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115276747A (en) * 2022-07-20 2022-11-01 华北电力大学（保定） Position and beam vector joint optimization method in unmanned aerial vehicle-assisted wireless communication system

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017019595A1 (en) * 2015-07-27 2017-02-02 Genghiscomm Holdings, LLC Airborne relays in cooperative-mimo systems
CN110856191A (en) * 2019-10-24 2020-02-28 广东工业大学 Unmanned aerial vehicle track optimization method based on wireless communication
WO2021062913A1 (en) * 2019-09-30 2021-04-08 华南理工大学 Unmanned aerial vehicle three-dimensional trajectory design method based on wireless energy transmission network
CN112911537A (en) * 2021-02-20 2021-06-04 广东工业大学 Method for minimizing task time of multi-unmanned aerial vehicle information acquisition system
CN113490176A (en) * 2021-06-01 2021-10-08 武汉理工大学 Unmanned aerial vehicle communication anti-interference method based on three-dimensional track power optimization
CN113489531A (en) * 2021-08-13 2021-10-08 华侨大学 Unmanned aerial vehicle cooperative communication method for joint optimization of power and three-dimensional trajectory

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017019595A1 (en) * 2015-07-27 2017-02-02 Genghiscomm Holdings, LLC Airborne relays in cooperative-mimo systems
WO2021062913A1 (en) * 2019-09-30 2021-04-08 华南理工大学 Unmanned aerial vehicle three-dimensional trajectory design method based on wireless energy transmission network
CN110856191A (en) * 2019-10-24 2020-02-28 广东工业大学 Unmanned aerial vehicle track optimization method based on wireless communication
CN112911537A (en) * 2021-02-20 2021-06-04 广东工业大学 Method for minimizing task time of multi-unmanned aerial vehicle information acquisition system
CN113490176A (en) * 2021-06-01 2021-10-08 武汉理工大学 Unmanned aerial vehicle communication anti-interference method based on three-dimensional track power optimization
CN113489531A (en) * 2021-08-13 2021-10-08 华侨大学 Unmanned aerial vehicle cooperative communication method for joint optimization of power and three-dimensional trajectory

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
张涛;张君;唐洪莹;李宝清;袁晓兵;: "有限能量下的无人机辅助无线供电通信网络资源分配研究", 工业控制计算机, no. 07, 25 July 2020 (2020-07-25) *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115276747A (en) * 2022-07-20 2022-11-01 华北电力大学（保定） Position and beam vector joint optimization method in unmanned aerial vehicle-assisted wireless communication system
CN115276747B (en) * 2022-07-20 2024-05-03 华北电力大学（保定） Position and beam vector joint optimization method in unmanned aerial vehicle auxiliary wireless communication system

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