CN113347593B - Relay selection method of unmanned aerial vehicle - Google Patents

Relay selection method of unmanned aerial vehicle Download PDF

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CN113347593B
CN113347593B CN202110199223.5A CN202110199223A CN113347593B CN 113347593 B CN113347593 B CN 113347593B CN 202110199223 A CN202110199223 A CN 202110199223A CN 113347593 B CN113347593 B CN 113347593B
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transmission
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aerial vehicle
unmanned aerial
user
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CN113347593A (en
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刘占军
谭新
梁承超
张娇
王改新
陈前斌
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/18502—Airborne stations
    • H04B7/18504—Aircraft used as relay or high altitude atmospheric platform
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a relay selection method of an unmanned aerial vehicle, and belongs to the field of unmanned aerial vehicle communication. The method specifically comprises the following steps: acquiring a position set of a user and a base station by using a satellite positioning system; defining three transmission modes according to a task transmission path and a calculation position, and constructing a network life model; fixing task transmission power and a transmission mode, converting the service life problem into a position optimization problem and solving the problem by adopting a convex optimization method; fixing the position and the transmission mode of the unmanned aerial vehicle, converting the service life problem into a power optimization problem, and performing convex approximation and solving by adopting a first-order Taylor expansion aiming at non-convexity of the problem; fixing the position and transmission power of the unmanned aerial vehicle, converting the service life problem into a mode optimization problem and solving the problem by adopting a linear programming method; and solving the optimal unmanned aerial vehicle position, the task sending power and the transmission mode task sending by adopting a block coordinate iterative algorithm. The invention reduces the energy loss of user transmission and prolongs the overall service life of the network.

Description

Relay selection method of unmanned aerial vehicle
Technical Field
The invention belongs to the field of unmanned aerial vehicle communication, and particularly relates to an unmanned aerial vehicle relay selection method.
Background
The unmanned aerial vehicle carries an information processing unit and a task processor, and an efficient and reliable communication data transmission link can be established quickly and conveniently by using the unmanned aerial vehicle as a relay transmission platform, so that the relay transmission technology based on the UAV receives wide attention of scholars at home and abroad. The current edge computing and unmanned aerial vehicle communication technology is quite mature, and unmanned aerial vehicle controls information transmission as an edge server and can effectively improve the performance of unmanned aerial vehicle communication, improves the accuracy and the transmission rate of information transmission, and therefore it is an inevitable development result to design an unmanned aerial vehicle relay control strategy.
The unmanned aerial vehicle carries an information processing unit and a task processor, and an efficient and reliable communication data transmission link can be established quickly and conveniently by using the unmanned aerial vehicle as a relay transmission platform, so that the relay transmission technology based on the UAV receives wide attention of scholars at home and abroad. Among them, some researchers have proposed a centralized algorithm for positioning drones to maximize the throughput of software-defined disaster area drone communication networks. The software-defined network controller may maintain up-to-date information about the network topology as well as data rate requirements and flow paths, which the proposed algorithm uses to determine the position of the drone, thereby maximizing overall throughput. There are also trainees studying an energy-efficient drone communication by designing the drone trajectory path. The UAV is scheduled to communicate with the user by assigning a binary decision variable assuming that the UAV is flying at a fixed altitude, a model of the UAV's path to the user is derived based on line-of-sight and non-line-of-sight communication links, and the flight path, launch power and speed of the UAV are jointly optimized. multi-UAV enabled wireless communication systems are also contemplated by scholars in which multiple UAV-mounted airborne base stations are used to serve a group of users on the ground. To achieve fair performance among users, the minimum throughput of all terrestrial users is maximized in downlink communications by optimizing scheduling of multi-user communications and combining trajectory and power control of drones.
However, the current research focuses mainly on the problems of optimal relay arrangement, flight path, network performance optimization, and the like, and the research on the power allocation algorithm is relatively few. The unmanned aerial vehicle is limited in energy due to self volume, the power is used as an important resource of the unmanned aerial vehicle communication system, the distribution problem of the unmanned aerial vehicle directly influences the performance of each taken link, and the optimal distribution of the power can be realized through a power distribution algorithm, so that the performance of the unmanned aerial vehicle communication system can be effectively improved.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. The method has the advantages of reducing energy consumption of users, reducing task transmission time delay and improving reliability and throughput of information transmission. The relay selection method of the unmanned aerial vehicle reduces the energy loss of user transmission and prolongs the overall service life of the network. The technical scheme of the invention is as follows:
a relay selection method of an unmanned aerial vehicle comprises the following steps:
s1: acquiring a position set of a user and a base station by using a satellite positioning system;
s2: defining three transmission modes according to the task transmission path and the calculation position, comprising the following steps: the user sends the task to the unmanned aerial vehicle for calculation, the user sends the task to the unmanned aerial vehicle and the unmanned aerial vehicle forwards the task to the base station for calculation, and the user sends the task to the base station for calculation to construct a network life model;
s3: decomposing the network life problem into a position optimization problem, a power optimization problem and a mode optimization problem by respectively fixing two variables in the position, the transmission power and the transmission mode of the unmanned aerial vehicle, and carrying out corresponding solution; when the transmission power and the transmission mode of the task are fixed, converting the service life problem into a position optimization problem and solving the problem by adopting a convex optimization method;
s4: when the position and the transmission mode of the unmanned aerial vehicle are fixed, converting the service life problem into a power optimization problem, and performing convex approximation by adopting a first-order Taylor expansion formula aiming at non-convexity of the problem and solving the convexity;
s5: when the position and the transmission power of the unmanned aerial vehicle are fixed, converting the service life problem into a mode optimization problem and solving the problem by adopting a linear programming method;
s6: and solving the optimal unmanned aerial vehicle position, task sending power and transmission mode task sending by adopting a block coordinate iterative algorithm.
Further, step S1 defines three transmission modes according to the task transmission path and the calculated position, and specifically includes:
remaining life L of a single usersExpressed as:
Figure BDA0002947490810000021
wherein L issRepresenting the residual life of the user S belonging to S, wherein S represents the user set and has the value range of [0,1]，siThe ith task, N, sent on behalf of user ssIndicating the total number of tasks transmitted by subscriber s, EsRepresenting the initial total energy of the user s,
Figure BDA0002947490810000031
representing the energy consumed by user s to send the ith task,
Figure BDA0002947490810000032
a mode selection variable indicating that user s sends the ith task, where k e 1,2,3 indicates mode 1, mode 2 and mode 3,
Figure BDA0002947490810000033
the value range of (a) is {0,1},
Figure BDA0002947490810000034
indicating that user s sends the ith task using the kth transmission mode, and that the other two transmission modes are not selected,
Figure BDA0002947490810000035
it means that the k-th transmission mode is not selected.
Further, the network life model is built by adding the residual life of all users in the network. The method specifically comprises the following steps:
the remaining lifetime problem for the user network is expressed as:
Figure BDA0002947490810000036
s.t.C1:χuav∈D
Figure BDA0002947490810000037
Figure BDA0002947490810000038
Figure BDA0002947490810000039
Figure BDA00029474908100000310
wherein, χuav＝(xuav,yuav,zuav) Representing the operating coordinates, x, of the droneuav,yuav,zuavRespectively represent x-axis, y-axis and z-axis coordinates in a three-dimensional coordinate system,
Figure BDA00029474908100000311
denotes the transmission power, P, of task i in the k-th transmission modemaxDenotes the maximum transmission power, RminWhich indicates the minimum transmission rate of the data,
Figure BDA00029474908100000312
representing the transmission rate of user s in the k-th transmission mode, and constraint C1 representing the flight range of the drone in the fixed set of positions D; constraints C2 and C3 indicate that the user only selects one transmission mode for task transmission; constraint C4 indicates that the transmission power of the user cannot exceed a maximum value Pmax(ii) a The constraint C5 indicates that the transmission rate of the task cannot be less than the minimum transmission rate RminThat is, the task transmission rate must be large enough to ensure reliable transmission of the task;
converting the residual service life problem of the user network into the task sending energy consumption minimum problem for solving, wherein the method comprises the following steps:
Figure BDA0002947490810000041
s.t.C1:χuav∈D
Figure BDA0002947490810000042
Figure BDA0002947490810000043
Figure BDA0002947490810000044
Figure BDA0002947490810000045
wherein the content of the first and second substances,
Figure BDA0002947490810000046
further, in step S3, when the transmission power and the transmission mode of the task are fixed, the lifetime problem is converted into a position optimization problem and solved by using a convex optimization method, which specifically includes:
the position optimization model is represented as:
Figure BDA0002947490810000047
s.t.C1:Hmin≤zuav≤Hmax
C2:xuav,yuav∈[-X,X]
Hmin、Hmaxthe maximum height and the minimum height of unmanned aerial vehicle flight are respectively represented, X represents the coordinate range of the horizontal plane motion of the unmanned aerial vehicle, wherein, the mode selection variable and the transmitting power in the problem are given in advance, and only the position of the unmanned aerial vehicle is a variable, so the problem is a convex optimization problem, and the optimal position is directly solved by applying CVX toolkit simulation in MATLAB.
Further, step S4 fixes unmanned aerial vehicle position and transmission mode, converts the life problem into a power optimization problem, specifically includes: aiming at the non-convexity of the problem, a first-order Taylor expansion is adopted to carry out convex approximation and solve;
the power optimization model is represented as:
Figure BDA0002947490810000048
Figure BDA0002947490810000049
Figure BDA00029474908100000410
wherein, the objective function in the problem is a non-convex function, so the problem is a non-convex problem, and the solution is carried out by adopting a continuous convex approximation mode;
order to
Figure BDA0002947490810000051
Because the first order Taylor expansion of any convex function is the lower bound of the function at any point, the problem is addressed at a given point
Figure BDA0002947490810000052
The lower bound of (c) is obtained by solving the following problem:
Figure BDA0002947490810000053
Figure BDA0002947490810000054
indicating a certain fixed point, UrIs represented at a fixed point
Figure BDA0002947490810000055
Lower bound of a function of wherein
Figure BDA0002947490810000056
To represent
Figure BDA0002947490810000057
High order infinity of (D)1Expressed as:
Figure BDA0002947490810000058
when any point is given
Figure BDA0002947490810000059
And lower limit UrThe problem can be approximately optimized as:
Figure BDA00029474908100000510
Figure BDA00029474908100000511
Figure BDA00029474908100000512
D1and U both represent intermediate functions, and because the objective function is a convex function and the limiting conditions are linear constraints, the problem is a convex problem, and a standard convex optimization solution method including CVX toolkit simulation in MATLAB is adopted for solving.
Further, the step S5 is to fix the position and the transmission power of the unmanned aerial vehicle, convert the life problem into a mode optimization problem and solve the problem by using a linear programming method, and specifically includes;
the model optimization model is represented as:
Figure BDA0002947490810000061
Figure BDA0002947490810000062
Figure BDA0002947490810000063
Figure BDA0002947490810000064
because y is the objective function when the position and task transmit power of the drone have been givenkAnd
Figure BDA0002947490810000065
are all fixed values, only
Figure BDA0002947490810000066
Is variable and all three constraint conditions are linear constraints, so the problem is a linear programming problem and is solved by adopting a plurality of (such as Lagrange multiplier method, simplex method and the like) standard optimization methods.
Further, in the step S6, a block coordinate iteration algorithm is used to iteratively solve the three sub-problems, so as to obtain an optimal unmanned aerial vehicle position, task sending power and a transmission mode for task sending. The basic idea of the block coordinate iterative algorithm is to solve a local optimal solution of another variable by circularly fixing the two variables, and circularly iterate the solved local optimal solution until an iteration error is smaller than a threshold value.
The invention has the following advantages and beneficial effects:
on the basis of fully considering adverse effects caused by network paralysis due to limited service life of a user, the invention designs an unmanned aerial vehicle relay selection method, and a satellite positioning system is utilized to obtain a position set of the user and a base station; defining three transmission modes according to a task transmission path and a calculation position, and constructing a network life model; fixing task transmission power and a transmission mode, converting the service life problem into a position optimization problem and solving the problem by adopting a convex optimization method; fixing the position and the transmission mode of the unmanned aerial vehicle, converting the service life problem into a power optimization problem, and performing convex approximation and solving by adopting a first-order Taylor expansion aiming at non-convexity of the problem; fixing the position and transmission power of the unmanned aerial vehicle, converting the service life problem into a mode optimization problem and solving the problem by adopting a linear programming method; and solving the optimal unmanned aerial vehicle position, task sending power and transmission mode task sending by adopting a block coordinate iterative algorithm. The invention provides an unmanned aerial vehicle relay selection method aiming at the problem of network paralysis caused by limited service life of a user in an unmanned aerial vehicle relay network, and the method can better provide an information transmission path for the user, reduce the energy consumption of the user, reduce the time delay of task transmission and improve the reliability and throughput of information transmission. The energy loss of user transmission is reduced, and the overall service life of the network is prolonged.
Drawings
FIG. 1 is a diagram of a task transmission scenario in accordance with a preferred embodiment of the present invention;
FIG. 2 is a flow chart of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
a relay selection method of an unmanned aerial vehicle comprises the following steps:
s1: acquiring a position set of a user and a base station by using a satellite positioning system;
s2: defining three transmission modes according to the task transmission path and the calculation position, comprising the following steps: the user sends the task to the unmanned aerial vehicle for calculation, the user sends the task to the unmanned aerial vehicle and the unmanned aerial vehicle forwards the task to the base station for calculation, and the user sends the task to the base station for calculation to construct a network life model;
s3: fixing task transmission power and a transmission mode, converting the service life problem into a position optimization problem and solving the problem by adopting a convex optimization method;
s4: fixing the position and the transmission mode of the unmanned aerial vehicle, converting the service life problem into a power optimization problem, and performing convex approximation and solving by adopting a first-order Taylor expansion aiming at non-convexity of the problem;
s5: fixing the position and transmission power of the unmanned aerial vehicle, converting the service life problem into a mode optimization problem and solving the problem by adopting a linear programming method;
s6: and solving the optimal unmanned aerial vehicle position, task sending power and transmission mode task sending by adopting a block coordinate iterative algorithm.
As shown in the flowchart 1 of the present invention, three transmission modes are defined according to a task transmission path and a calculation position, and a network life model is constructed. The method specifically comprises the following steps:
remaining life L of a single usersExpressed as:
Figure BDA0002947490810000071
wherein L issRepresenting the residual service life of the user S belonging to S, and the value range is [0,1]。siRepresenting the ith task sent by user s. N is a radical ofsRepresenting the total number of tasks transmitted by subscriber s. EsRepresenting the initial total energy of user s.
Figure BDA0002947490810000081
Representing the energy consumed by user s to send the ith task.
Figure BDA0002947490810000082
A mode selection variable indicating that user s sends the ith task, where k ∈ {1,2,3} indicates mode 1, mode 2, and mode 3.
Figure BDA0002947490810000083
The value range of (a) is {0,1},
Figure BDA0002947490810000084
indicating that the user s sends the ith task using the kth transmission mode and that the other two transmission modes are not selected.
Figure BDA0002947490810000085
It means that the k-th transmission mode is not selected.
And building a network life model by adding the residual lives of all users in the network. The method specifically comprises the following steps:
constructing the residual life of the multi-user network according to the residual life of the single user:
Figure BDA0002947490810000086
s.t.C1:χuav∈D
Figure BDA0002947490810000087
Figure BDA0002947490810000088
Figure BDA0002947490810000089
Figure BDA00029474908100000810
wherein, χuav＝(xuav,yuav,zuav) Representing the operating coordinates, x, of the droneuav,yuav,zuavRespectively represent x-axis, y-axis and z-axis coordinates in a three-dimensional coordinate system,
Figure BDA00029474908100000811
denotes the transmission power, P, of task i in the k-th transmission modemaxDenotes the maximum transmission power, RminWhich indicates the minimum transmission rate of the data,
Figure BDA00029474908100000812
indicating the transmission rate of user s in the kth transmission mode. Constraint C1 represents that the flight range of the drone is in the fixed set of positions D. Constraints C2 and C3 indicate that the user can only select one transmission mode for task transmission. Constraint C4 indicates that the transmission power of a user cannot exceed a maximum value Pmax. Constraint C5 denotes the transmission rate of the taskCan not be less than the minimum transmission rate RminI.e. the task transmission rate must be large enough to guarantee reliable transmission of the task.
The method for solving the problem of the residual service life of the user network converted into the task sending energy consumption minimum problem comprises the following steps:
Figure BDA0002947490810000091
s.t.C1:χuav∈D
Figure BDA0002947490810000092
Figure BDA0002947490810000093
Figure BDA0002947490810000094
Figure BDA0002947490810000095
wherein the content of the first and second substances,
Figure BDA0002947490810000096
by fixing two variables in the position, the transmission power and the transmission mode of the unmanned aerial vehicle respectively, the network life problem is decomposed into a position optimization problem, a power optimization problem and a mode optimization problem, and corresponding solution is carried out. The method comprises the following steps:
a, fixing task transmission power and a transmission mode, converting a service life problem into a position optimization problem and solving the problem by adopting a convex optimization method. The method specifically comprises the following steps:
the position optimization model is represented as:
Figure BDA0002947490810000097
s.t.C1:Hmin≤zuav≤Hmax
C2:xuav,yuav∈[-X,X]
Hmin、Hmaxrespectively represent the highest height and the lowest height of the unmanned aerial vehicle flight, and X represents the coordinate range of the horizontal plane motion of the unmanned aerial vehicle. The mode selection variable and the transmitting power in the problem are given in advance, and only the position of the unmanned aerial vehicle is a variable, so that the problem is a convex optimization problem, and the optimal position is directly solved by applying CVX toolkit simulation in MATLAB.
b, fixing the position and the transmission mode of the unmanned aerial vehicle, converting the service life problem into a power optimization problem, and performing convex approximation and solving by adopting a first-order Taylor expansion aiming at the non-convexity of the problem. The method specifically comprises the following steps:
the power optimization model is represented as:
Figure BDA0002947490810000101
Figure BDA0002947490810000102
Figure BDA0002947490810000103
wherein the objective function in the problem is a non-convex function, and thus the problem is a non-convex problem. Aiming at the problem that the problem is non-convex and can not be solved by adopting the traditional optimization method, the continuous convex approximation method is adopted for solving.
Order to
Figure BDA0002947490810000104
Since the first order Taylor expansion of any convex function is the lower bound of the function at any point, the problem is addressed at a given point
Figure BDA0002947490810000105
The lower bound of (c) is obtained by solving the following problem:
Figure BDA0002947490810000106
Figure BDA0002947490810000107
indicating a certain fixed point, UrIs represented at a fixed point
Figure BDA0002947490810000108
Lower bound of a function of wherein
Figure BDA0002947490810000109
To represent
Figure BDA00029474908100001010
High order infinity of (D)1Expressed as:
Figure BDA00029474908100001011
when any point is given
Figure BDA00029474908100001012
And lower limit UrThe problem is approximately optimized as:
Figure BDA00029474908100001013
Figure BDA00029474908100001014
Figure BDA00029474908100001015
because the objective function is a convex function and the limiting conditions are all linear constraints, the problem is a convex problem and is solved by adopting a standard convex optimization solution method including CVX toolkit simulation in MATLAB.
And c, fixing the position and the transmission power of the unmanned aerial vehicle, converting the service life problem into a mode optimization problem and solving the problem by adopting a linear programming method. Specifically comprises the following steps of;
the model optimization model is represented as:
Figure BDA0002947490810000111
Figure BDA0002947490810000112
Figure BDA0002947490810000113
Figure BDA0002947490810000114
because y is the objective function when the position and task transmit power of the drone have been givenkAnd
Figure BDA0002947490810000116
are all fixed values, only
Figure BDA0002947490810000115
Is variable and all three constraint conditions are linear constraints, so the problem is a linear programming problem and is solved by adopting a plurality of (such as Lagrange multiplier method, simplex method and the like) standard optimization methods.
And (3) carrying out iterative solution on the three subproblems by adopting a block coordinate iterative algorithm to obtain the optimal unmanned aerial vehicle position, the task sending power and the transmission mode for task sending. The basic idea of the block coordinate iterative algorithm is to solve a local optimal solution of another variable by circularly fixing the two variables, and circularly iterate the solved local optimal solution until an iteration error is smaller than a threshold value.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.

Claims (2)

1. A relay selection method of an unmanned aerial vehicle is characterized by comprising the following steps:
s1: acquiring a position set of a user and a base station by using a satellite positioning system;
s2: defining three transmission modes according to the task transmission path and the calculation position, comprising the following steps: the user sends the task to the unmanned aerial vehicle for calculation, the user sends the task to the unmanned aerial vehicle and the unmanned aerial vehicle forwards the task to the base station for calculation, and the user sends the task to the base station for calculation to construct a network life model;
s3: the network service life problem is decomposed into a position optimization problem, a power optimization problem and a mode optimization problem by respectively fixing two variables in the position, the transmission power and the transmission mode of the unmanned aerial vehicle, and corresponding solution is carried out; when the transmission power and the transmission mode of the task are fixed, converting the service life problem into a position optimization problem and solving the problem by adopting a convex optimization method;
s4: when the position and the transmission mode of the unmanned aerial vehicle are fixed, converting the service life problem into a power optimization problem, and performing convex approximation and solving by adopting a first-order Taylor expansion aiming at the problem non-convexity;
s5: when the position and the transmission power of the unmanned aerial vehicle are fixed, converting the service life problem into a mode optimization problem and solving the problem by adopting a linear programming method;
s6: obtaining the optimal unmanned aerial vehicle position, task sending power and transmission mode task sending by adopting a block coordinate iterative algorithm;
step S1 defines three transmission modes according to the task transmission path and the calculated position, and specifically includes:
remaining life L of a single usersExpressed as:
Figure FDA0003528161910000011
wherein L issRepresenting the residual life of the user S belonging to S, wherein S represents the user set and has the value range of [0,1]，siThe ith task, N, sent on behalf of user ssIndicating the total number of tasks transmitted by subscriber s, EsRepresenting the initial total energy of the user s,
Figure FDA0003528161910000012
representing the energy consumed by user s to send the ith task,
Figure FDA0003528161910000013
a mode selection variable indicating that user s sends the ith task, where k e {1,2,3} indicates mode 1, mode 2, and mode 3, the three transmission modes are L0, L1, L2,
Figure FDA0003528161910000014
the value range of (a) is {0,1},
Figure FDA0003528161910000015
representing user s hairWhen sending the ith task, the kth transmission mode is adopted, and the other two transmission modes are not selected,
Figure FDA0003528161910000016
it means that the kth transmission mode is not selected;
the method for constructing the network life model by adding the residual lives of all users in the network specifically comprises the following steps:
the remaining lifetime problem for the user network is expressed as:
Figure FDA0003528161910000021
s.t.C1:χuav∈D
C2:
Figure FDA0003528161910000022
C3:
Figure FDA0003528161910000023
C4:
Figure FDA0003528161910000024
C5:
Figure FDA0003528161910000025
wherein, χuav＝(xuav,yuav,zuav) Representing the operating coordinates, x, of the droneuav,yuav,zuavRespectively represent x-axis, y-axis and z-axis coordinates in a three-dimensional coordinate system,
Figure FDA0003528161910000026
denotes the transmission power, P, of task i in the k-th transmission modemaxDenotes the maximum transmission power, RminWhich indicates the minimum transmission rate of the data,
Figure FDA0003528161910000027
representing the transmission rate of user s in the k-th transmission mode, and constraint C1 representing the flight range of the drone in the fixed set of positions D; constraints C2 and C3 indicate that the user only selects one transmission mode for task transmission; constraint C4 indicates that the transmission power of the user cannot exceed a maximum value Pmax(ii) a The constraint C5 indicates that the transmission rate of the task cannot be less than the minimum transmission rate RminThat is, the task transmission rate must be large enough to ensure reliable transmission of the task;
converting the residual service life problem of the user network into the task sending energy consumption minimum problem for solving, wherein the method comprises the following steps:
Figure FDA0003528161910000028
s.t.C1:χuav∈D
C2:
Figure FDA0003528161910000029
C3:
Figure FDA00035281619100000210
C4:
Figure FDA00035281619100000211
C5:
Figure FDA00035281619100000212
wherein the content of the first and second substances,
Figure FDA00035281619100000213
in step S3, when the transmission power and the transmission mode of the task are fixed, the lifetime problem is converted into a location optimization problem and solved by a convex optimization method, which specifically includes:
the position optimization model is represented as:
Figure FDA0003528161910000031
s.t.C1:Hmin≤zuav≤Hmax
C2:xuav,yuav∈[-X,X]
Hmin、Hmaxrespectively representing the highest height and the lowest height of the unmanned aerial vehicle in flight, and X represents the coordinate range of horizontal plane motion of the unmanned aerial vehicle, wherein a mode selection variable and transmission power in the problem are given in advance, and only the position of the unmanned aerial vehicle is a variable, so that the problem is a convex optimization problem, and the optimal position is directly solved by using CVX toolkit simulation in MATLAB;
step S4 fixes unmanned aerial vehicle position and transmission mode, converts the life problem into the power optimization problem, specifically includes: aiming at the non-convexity of the problem, performing convex approximation by adopting a first-order Taylor expansion formula and solving;
the power optimization model is represented as:
Figure FDA0003528161910000032
s.t.C1:
Figure FDA0003528161910000033
C2:
Figure FDA0003528161910000034
wherein, the objective function in the problem is a non-convex function, so the problem is a non-convex problem, and the solution is carried out by adopting a continuous convex approximation mode;
order to
Figure FDA0003528161910000035
Because the first order Taylor expansion of any convex function is the lower bound of the function at any point, the problem is addressed at a given point
Figure FDA0003528161910000036
The lower bound of (c) is obtained by solving the following problem:
Figure FDA0003528161910000037
Figure FDA0003528161910000041
indicating a certain fixed point, UrIs represented at a fixed point
Figure FDA0003528161910000042
Lower bound of a function of wherein
Figure FDA0003528161910000043
To represent
Figure FDA0003528161910000044
High order infinity of (D)1Expressed as:
Figure FDA0003528161910000045
when any point is given
Figure FDA0003528161910000046
And lower limit UrThe problem can be approximately optimized as:
Figure FDA0003528161910000047
s.t.C1:
Figure FDA0003528161910000048
C2:
Figure FDA0003528161910000049
D1and U both represent intermediate functions, because the objective function is a convex function and the limiting conditions are linear constraints, the problem is a convex problem, and a standard convex optimization solving method including CVX toolkit simulation in MATLAB is adopted for solving;
the step S5 of fixing the position and the transmission power of the unmanned aerial vehicle, converting the service life problem into a mode optimization problem and solving the problem by adopting a linear programming method specifically comprises the following steps;
the model optimization model is represented as:
Figure FDA00035281619100000410
s.t.C1:
Figure FDA00035281619100000411
C2:
Figure FDA00035281619100000412
C3:
Figure FDA00035281619100000413
because y is the objective function when the position and task transmit power of the drone have been givenkAnd
Figure FDA00035281619100000414
are all fixed values, only
Figure FDA00035281619100000415
Are variables and all three constraints are linearThe beam, and therefore the problem, is a linear programming problem that is solved using standard optimization methods, including lagrange multiplier or simplex.
2. The relay selection method for the unmanned aerial vehicle as claimed in claim 1, wherein in step S6, a block coordinate iterative algorithm is used to solve the three sub-problems iteratively, so as to obtain an optimal unmanned aerial vehicle position, a task transmission power and a transmission mode for task transmission; the basic idea of the block coordinate iterative algorithm is to solve a local optimal solution of another variable by circularly fixing the two variables, and circularly iterate the solved local optimal solution until an iteration error is smaller than a threshold value.
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