CN109286913B - Energy consumption optimization method of unmanned aerial vehicle mobile edge computing system based on cellular network connection - Google Patents
Energy consumption optimization method of unmanned aerial vehicle mobile edge computing system based on cellular network connection Download PDFInfo
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Abstract
An energy consumption optimization method of an unmanned aerial vehicle mobile edge computing system based on cellular network connection aims at minimizing data processing energy consumption and flight energy consumption of an unmanned aerial vehicle node, takes flight condition constraints of the unmanned aerial vehicle node and ground cellular network communication base station energy consumption constraints into consideration, and establishes a model to carry out combined optimization on parameters such as data flow split of the unmanned aerial vehicle node and flight paths, speeds and accelerations of the unmanned aerial vehicle node. The invention has the beneficial effects that: the problem of current unmanned aerial vehicle removes the energy consumption optimization of edge computing system is solved, the energy consumption of unmanned aerial vehicle node is reduced.
Description
Technical Field
The invention relates to the technical field of wireless communication and cloud computing, in particular to an energy consumption optimization method of an unmanned mobile edge computing system based on cellular network connection.
Background
With the rapid development and continuous maturation of Unmanned Aerial Vehicle (UAV) technology, wide development opportunities are brought to related industries based on an unmanned aerial Vehicle air platform, such as air road supervision, agriculture and forestry area monitoring, unmanned aerial Vehicle logistics distribution, air relay emergency communication, hot spot area unmanned aerial Vehicle base station load distribution and the like. Meanwhile, with the rapid development of information technologies such as wireless communication and image signal processing, the application of the mobile internet to unmanned aerial vehicle platforms, such as real-time high-definition image return, target identification, virtual reality and the like, is more and more extensive by carrying an advanced data processing module, a radio frequency communication module, an audio and video sensor and the like on the unmanned aerial vehicle. However, with the popularization of these mobile applications, higher demands are also made on the computing resources, energy resources, storage resources, and the like of the drone. Especially for calculation-sensitive mobile applications, a large amount of data information needs to be processed and calculated in real time, so that energy resources of the unmanned aerial vehicle platform are greatly consumed, and excessive hardware computing resources are occupied. For the unmanned aerial vehicle platform with limited physical size, the battery energy and the operation resources of the unmanned aerial vehicle platform are very limited, and most of the unmanned aerial vehicles mainly rely on a self-contained power module or an oil tank to supply power at present. Besides the energy consumption of the drone in data processing, another part of the drone is mainly due to the energy consumption of the drone in the flight or hovering phase. How to fully utilize effective energy resources for data processing and flight of the unmanned aerial vehicle under the condition that the energy of the unmanned aerial vehicle is limited is a key problem of future unmanned aerial vehicle application.
In order to solve the problem of energy and resource consumption of energy-limited unmanned aerial vehicle nodes in information processing, various major research institutions and researchers have proposed a Mobile Cloud Computing System (Mobile Cloud Computing System), that is, an unmanned aerial vehicle node transmits part of data processing tasks to a remote Cloud resource pool in a wireless transmission mode to perform data load distribution, so as to reduce the local data processing energy consumption of the unmanned aerial vehicle node. In order to further reduce transmission delay and path loss from the unmanned aerial vehicle node to the remote cloud resource pool, save energy consumption of the unmanned aerial vehicle node, and ensure System service quality, researchers have proposed a Mobile Edge Computing System (Mobile Edge Computing System), that is, data processing nodes are deployed in a close-range area of the unmanned aerial vehicle node, so as to perform load distribution on a data processing task of the unmanned aerial vehicle node. However, for the unmanned aerial vehicle node, due to the flexible flying path, the unmanned aerial vehicle node has a random distribution characteristic in geographic position. In order to achieve better edge computing node coverage, the mobile edge computing system must deploy a large number of edge computing nodes to shorten the distance between the mobile edge computing system and the unmanned aerial vehicle node, thereby completing load sharing in a short distance area.
Considering that existing cellular communication networks are already mature, the base stations in the cells have great advantages in terms of computing resources, energy supply, coverage area, and the number of stations. The cellular network communication base station is used as an edge computing node, data transmission is carried out on the unmanned aerial vehicle node in the coverage range of the cellular network communication base station, data processing is carried out on the base station, and the unmanned aerial vehicle mobile edge computing system based on cellular network connection is formed and is an efficient data load distribution mode. It is worth noting that the unmanned aerial vehicle node needs to send a data signal to the base station in the data load splitting process, and the process consumes energy of the unmanned aerial vehicle node. Therefore, whether more data are shunted to the base station or left in the unmanned aerial vehicle node for local processing is a complex compromise optimization problem. On the other hand, in the flight process of the unmanned aerial vehicle node, the wireless channel condition between the unmanned aerial vehicle node and the base station also changes along with the flight path, the unmanned aerial vehicle node is more suitable for transmitting data under the good channel condition, and the unmanned aerial vehicle node is more prone to transmitting less data when the channel condition is poor. Therefore, from the perspective of the energy consumption of the nodes of the unmanned aerial vehicle, how to optimize parameters such as data information bit distribution between the nodes of the unmanned aerial vehicle and the base station, the flight path of the unmanned aerial vehicle, the flight speed and the acceleration at each flight moment of the nodes of the unmanned aerial vehicle has very important practical significance.
Disclosure of Invention
The invention aims to solve the technical problem of providing an energy consumption optimization method of an unmanned aerial vehicle mobile edge computing system based on cellular network connection, solving the energy consumption optimization problem of the existing unmanned aerial vehicle mobile edge computing system and reducing the energy consumption of unmanned aerial vehicle nodes.
The technical scheme adopted by the invention for solving the technical problems is as follows: an energy consumption optimization method for an unmanned aerial vehicle mobile edge computing system based on cellular network connection is disclosed, wherein the unmanned aerial vehicle mobile edge computing system comprises an unmanned aerial vehicle node and a ground cellular network communication base station and is free ofThe method comprises the steps that a human-computer node has a certain amount of data bits to be processed, an unmanned machine node flies according to a specified path, speed and acceleration, at each flying moment, the unmanned machine node sends part of data to be processed to a ground cellular network communication base station, the ground cellular network communication base station carries out operation processing on the data, a three-dimensional space rectangular coordinate system (x, y and z) is established, the z-axis coordinate represents height position information of a space, and the coordinate w of the ground cellular network communication base station is equal to (x, y and z)w,yw,H1)TWherein, whereinTRepresenting matrix/vector transfer, wherein an unmanned aerial vehicle node has L information bits of data to be processed, rho L information bits are locally calculated at the unmanned aerial vehicle node, and (1-rho) L information bits are sequentially transmitted to a ground cellular network communication base station in the flight process of the unmanned aerial vehicle node in a load distribution mode, and the ground cellular network communication base station processes the distributed data, wherein rho is more than or equal to 0 and less than or equal to 1 represents an information bit distribution factor used for balancing the data volume proportion of local calculation and load distribution of the unmanned aerial vehicle node; unmanned aerial vehicle node is with fixed height H in three-dimensional space2Flying, the single flight time is T, the time period is divided into N +1 time slots, and each time slot has a width δ, that is, T ═ δ (N + 1); the flight parameters of the nth time slot unmanned aerial vehicle node comprise: unmanned aerial vehicle position coordinate q [ n ]]＝(x[n],y[n],H2)TUnmanned aerial vehicle flight velocity vector v [ n ]]＝(vx[n],vy[n],0)TUnmanned plane acceleration vector a [ n ]]＝(ax[n],ay[n],0)TThe energy consumption optimization method comprises the following steps:
(1) according to the data information bit operation processing energy consumption definition, establishing an energy consumption model E when the unmanned aerial vehicle node processes the data bits within the time length Tcomp；
(2) Establishing a flight energy consumption model E of the unmanned aerial vehicle node in single flight time Tfly；
(3) And (3) establishing a mathematical model about the unmanned aerial vehicle node flight parameters and the information bit distribution parameters and solving the mathematical model by taking the total energy consumption of the unmanned aerial vehicle nodes as an optimization target and considering the unmanned aerial vehicle node flight condition constraints and the base station energy consumption constraints based on the data processing energy consumption and flight energy consumption models of the unmanned aerial vehicle nodes in the step (1) and the step (2).
Energy consumption model E of unmanned aerial vehicle node processing data bits in time length T in step (1) of the inventioncompComprises the following steps:
wherein G represents a hardware computing power constant of the drone node.
The flight energy consumption model E of the unmanned aerial vehicle node established in the step (2) in the single flight time T is establishedflyComprises the following steps:
wherein, c1And c2Is a positive constant factor related to the unmanned aerial vehicle node weight, wing area, air density and the like, g represents the gravity acceleration,
the kinetic energy variation that represents the unmanned aerial vehicle node, if the start and stop speed parameter of unmanned aerial vehicle node is fixed, then Δ p is fixed quantity, and m represents unmanned aerial vehicle node total weight, and supposing that unmanned aerial vehicle node start and stop speed is the same, then Δ p equals 0.
The mathematical model about the node flight parameters and the information bit distribution parameters of the unmanned aerial vehicle established in the step (3) is as follows:
C7:0≤ρ≤1
wherein the content of the first and second substances,
qIindicates the starting position of the unmanned plane node, qFIndicating unmanned plane node termination position, VmaxIndicates the maximum flight speed of the unmanned plane node, amaxRepresenting the maximum acceleration of the unmanned plane node; l isu[n]The number of bit data sent to the base station by the unmanned aerial vehicle node in the nth time slot is represented, the data processing delay is 1 time slot, and the base station processing L of the (n +1) th time slotb[n+1]The number of bits of data is one,
is indicative of a flight parameter of the aircraft,
representing a data bit allocation parameter, B representing a channel bandwidth, P representing a data transmission power of the drone node, and being a fixed value,σ2representing the power of additive complex Gaussian white noise; d [ n ]]Indicates the distance between the unmanned aerial vehicle node and the base station in the nth time slot, beta0The maximum energy constraint value of the terrestrial cellular network communication base station for processing data information is E1totalC2 represents data transmission bit constraint of the unmanned aerial vehicle node in the nth time slot under the condition that the channel bandwidth is B, C3 represents bit cascade constraint, that is, the number of information bits processed by the ground cellular network communication base station at each flight time does not exceed the number of information bits transmitted to the unmanned aerial vehicle node, C4 represents the total number of information bits transmitted to the ground cellular network communication base station by the unmanned aerial vehicle node, C5 represents the total number of information bits of the sub-streams processed by the ground cellular network communication base station, C6 represents relationship constraint among the flight parameters of the unmanned aerial vehicle, and C7 and C8 represent feasible domain boundary constraint conditions of the optimization parameters.
The invention has the beneficial effects that: the method provided by the invention aims at minimizing the data processing energy consumption and flight energy consumption of the unmanned aerial vehicle node, considers the flight condition constraint and the base station energy consumption constraint of the unmanned aerial vehicle node, and performs combined optimization on the data split flow of the unmanned aerial vehicle node and the parameters such as the flight path, speed and acceleration of the unmanned aerial vehicle node so as to obtain the flight parameters and the data load split flow which minimize the energy consumption of the unmanned aerial vehicle.
Drawings
FIG. 1 is a system model diagram of the method of the present invention;
fig. 2 is a node flight path of the unmanned aerial vehicle obtained by solving the single flight time T of the simulation experiment in 50 seconds;
fig. 3 is a change curve of the node flight speed and acceleration of the unmanned aerial vehicle obtained by the solution of the method of the present invention under the condition that the single flight time T is 50 seconds in the simulation experiment;
fig. 4 is a data information bit distributed and transmitted by the node of the unmanned aerial vehicle at each moment and a data information bit change curve processed by the base station for ground cellular network communication, which are obtained by the solution of the method of the present invention, under the condition that the single flight time T is 50 seconds in the simulation experiment;
fig. 5 is a data information bit allocation factor (1- ρ) variation curve of the unmanned aerial vehicle node obtained by the method of the present invention under different base station energy constraint value conditions in a simulation experiment.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The energy consumption optimization method of the unmanned aerial vehicle mobile edge computing system based on cellular network connection comprises an unmanned aerial vehicle node and a ground cellular network communication base station, wherein the unmanned aerial vehicle node has a certain amount of data bits to be processed, the unmanned aerial vehicle node flies according to a specified path, speed and acceleration, at each flying moment, the unmanned aerial vehicle node sends part of data to be processed to the ground cellular network communication base station, and the ground cellular network communication base station performs operation processing on the data, and specifically comprises the following steps:
(1) establishing a three-dimensional rectangular coordinate system (x, y, z), wherein the z-axis coordinate represents height position information of the space, and the coordinate w of the ground cellular network communication base station is equal to (x)w,yw,H1)TWherein, whereinTRepresenting matrix/vector transfer, wherein an unmanned aerial vehicle node is provided with L data information bits to be processed, rho L information bits are locally calculated at the unmanned aerial vehicle node, and (1-rho) L information bits are sequentially transmitted to a base station in the flight process of the unmanned aerial vehicle node in a load distribution mode, and the base station processes the distributed data, wherein rho is more than or equal to 0 and less than or equal to 1 and is used for balancing the data volume proportion of local calculation and load distribution of the unmanned aerial vehicle node; unmanned aerial vehicle node is with fixed height H in three-dimensional space2Flying, the single flight time is T, the time segment is divided into N +1 time slots, and each time slot has a width δ, that is, T ═ δ (N + 1); the flight parameters of the nth time slot unmanned aerial vehicle node comprise: unmanned aerial vehicle position coordinate q [ n ]]＝(x[n],y[n],H2)TUnmanned aerial vehicle flight velocity vector v [ n ]]＝(vx[n],vy[n],0)TUnmanned aerial vehicle acceleration vector a [ n ]]＝(ax[n],ay[n],0)TThe three satisfy the following relation:
wherein the content of the first and second substances,
qIindicates the starting position of the unmanned plane node, qFIndicating the end position of the unmanned plane node, VmaxIndicates the maximum flight speed of the unmanned plane node, amaxRepresenting the maximum acceleration of the unmanned plane node; assuming that the slot width is sufficiently small, the three flight parameters of the drone can be described by a set of parameters of each slot, i.e. a set of position points of the flight path
Set of flight speeds
Set of accelerations
Suppose that the UAV node sends L to the base station in the nth time slotu[n]The data processing delay of each bit is 1 time slot, and the base station processes L in the (n +1) th time slotb[n+1]The bit data and the bit cascade relation are satisfied, as shown below
Wherein the content of the first and second substances,
assuming that a wireless channel between the unmanned aerial vehicle node and the base station is a direct-view path, the wireless channel from the unmanned aerial vehicle node to the base station in the nth time slot obeys a free space path loss model, namely
Wherein, d [ n ]]Indicates the distance between the unmanned aerial vehicle node and the base station in the nth time slot, beta0Represents the channel gain reference value when the distance is 1m and the signal transmission power is 1W, | | | -, represents the Euclidean norm.
(2) According to the Shannon channel capacity formula of the information theory, in the nth time slot, under the condition that the channel bandwidth is B, the data transmission bit L of the unmanned aerial vehicle nodeu[n]Satisfies the following relation:
wherein B represents the channel bandwidth, P represents the data transmission power of the UAV node and is a fixed value, and σ represents2Representing the power of additive complex Gaussian white noise;
(3) according to the definition of the energy consumption of the data information bit arithmetic processing, for a given data amount to be processed with M bits and processing time delta, the energy E consumed when the information bits are processed is calculated as follows:
k is a constant and is determined by the hardware computing capacity of the node; energy consumption E of the drone node in processing data bits within a time length TcompIs composed of
Wherein, G represents the hardware computing capacity constant of the unmanned aerial vehicle node in the system.
(4) Defining flight energy consumption E of unmanned aerial vehicle node in single flight timeflyAnd then:
wherein, c1And c2Is a positive constant factor related to the unmanned aerial vehicle node weight, wing area, air density and the like, g represents the gravity acceleration,
the kinetic energy variation of representing the unmanned aerial vehicle node, if the start and stop speed parameter of unmanned aerial vehicle node is fixed, then Δ p is fixed volume, and m represents unmanned aerial vehicle node total weight, assumes that unmanned aerial vehicle node start and stop speed is the same, then Δ p is 0, can ignore.
(5) Based on the data processing energy consumption and flight energy consumption model of the unmanned aerial vehicle node in the step (3) and the step (4), taking the total energy consumption of the unmanned aerial vehicle node as an optimization target, considering the unmanned aerial vehicle node flight condition constraint and the base station energy consumption constraint, establishing a mathematical model related to the unmanned aerial vehicle node flight parameter and the information bit distribution parameter, and solving the model to obtain an optimal energy consumption optimization scheme, wherein the mathematical model is shown as follows:
C7:0≤ρ≤1
wherein the content of the first and second substances,
is indicative of a flight parameter of the aircraft,
representing data bit allocation parameters, C1 representing a maximum energy constraint value E for the base station to process the data informationtotalC2 represents the data transmission bit constraint in step (2), C3 represents the bit cascade constraint, that is, the number of information bits processed by the base station in each flight time does not exceed the number of information bits transmitted to the base station by the drone node, C4 represents the total number of information bits transmitted to the base station by the drone node, C5 represents the total number of information bits of the stream processed by the base station, C6 represents the relationship constraint between the flight parameters of the drone, and C7 and C8 represent the feasible region boundary constraint conditions of the optimization parameters.
However, because the model involves many factors and variables, has a complex form, and is difficult to optimize in an iterative process, a set of sub-optimal solutions of the optimization problem can be obtained by using a first-order taylor series expansion and continuous convex approximation method. As follows.
(6) Since the objective function and the constraint condition C2 of the optimization problem in step (5) are non-convex, a continuous convex approximation method can be used to convert them; introducing relaxation variable sets
The unmanned aerial vehicle in the nth time slotThe flying energy consumption of a point is expressed in the form
And adds a new constraint C9, as shown below
Wherein the content of the first and second substances,
(7) the | | v [ n ] in the constraint condition C9 in the step (6) is processed]||2At local point { vl[n]Performing Taylor series expansion to obtain | v [ n |)]||2Lower bound of (D), as shown below
Wherein l represents the l-th iteration; using the lower bound flb(v[n]) Converting the constraint C9 into the following form
(8) Aiming at the constraint condition C2 in the step (5), introducing a relaxation variable set
Restated C2 as follows
Wherein the content of the first and second substances,
representing a reference signal-to-noise ratio(ii) a And introduces a new constraint C11, as shown below
(9) Subjecting the mixture obtained in step (8)
At local point yl[n]The first order Taylor series expansion is performed to obtain the lower bound, as shown below
Wherein, S [ n ]]＝log2(y[n]+Pγ0)，
Thus, constraint C2 is converted to a new constraint C12, as shown below
(10) Based on flight energy consumption in step (6)
And new constraint conditions C10, C11 and C12 in the steps (7), (8) and (9), and converting the optimization problem in the step (5) into a convex optimization problem
s.t.C1,C3-C8,C10,C11,C12
And a standard convex optimization method (such as an interior point method) can be adopted to solve to obtain a suboptimal solution of the original problem.
Simulation experiment
Setting simulation parameters: reference signal-to-noise ratio gamma0＝5×103System bandwidth B1 MHz, base station height H120m, unmanned aerial vehicle node altitude H2100m, the coordinates of the starting and stopping positions of the nodes of the unmanned aerial vehicle are q respectivelyI＝(-500,-500,100)TAnd q isF＝(500,-500,100)TMaximum flying speed V of unmanned aerial vehicle nodemaxMaximum acceleration a of 50m/smax＝5m/s2The maximum energy constraint value of the base station is Etotal＝6×103J, the total data bits to be processed of the unmanned plane nodes are 1Mbits, and the flight energy consumption coefficient c of the unmanned plane nodes10.002 and c270.698, the drone node computing power constant G10-11The time slot length delta is 0.5s, and the signal transmission power of the unmanned aerial vehicle node is fixed to be P2W.
Fig. 2-5 show the solved flight parameters and data information bit allocation of the nodes of the unmanned aerial vehicle. Fig. 2 is a flight path diagram of the node of the unmanned aerial vehicle obtained by the present invention when the flight time T of the node of the unmanned aerial vehicle is 50 s; fig. 3 shows the flight speed and acceleration variation trend of the node of the unmanned aerial vehicle obtained by the invention when the flight time T of the node of the unmanned aerial vehicle is 50 s; fig. 4 shows the change of the data transmission quantity of the node of the unmanned aerial vehicle and the data calculation quantity of the base station at each flight moment obtained by the invention when the flight time T of the node of the unmanned aerial vehicle is 50 s; fig. 5 shows a trend of the data information bit allocation factor (1- ρ) of the drone node obtained by the present invention along with a change of the base station energy constraint value when the time of flight T of the drone node is 50 s.
Claims (1)
1. An energy consumption optimization method of an unmanned aerial vehicle mobile edge computing system based on cellular networking is characterized by comprising the following steps: the unmanned aerial vehicle mobile edge computing system comprises an unmanned aerial vehicle node and a ground cellular network communication base station, wherein the unmanned aerial vehicle node has a certain amount of data bits to be processed, the unmanned aerial vehicle node flies according to a specified path, speed and acceleration, at each flying moment, the unmanned aerial vehicle node sends part of data to be processed to the ground cellular network communication base station, the ground cellular network communication base station performs operation processing on the data, and a three-dimensional space is establishedA rectangular coordinate system (x, y, z), wherein the z-axis coordinate represents height position information of the space, and the coordinate w of the ground cellular network communication base station is (x ═ x)w,yw,H1)TWherein, whereinTRepresenting matrix/vector transposition, wherein an unmanned aerial vehicle node is provided with L information bits of data to be processed, rho L information bits are locally calculated in the unmanned aerial vehicle node, and (1-rho) L information bits are sequentially transmitted to a ground cellular network communication base station in the flight process of the unmanned aerial vehicle node in a load distribution mode, and the ground cellular network communication base station processes the distributed data, wherein rho is more than or equal to 0 and less than or equal to 1 represents an information bit distribution factor and is used for balancing the data volume proportion of local calculation and load distribution of the unmanned aerial vehicle node; unmanned aerial vehicle node is with fixed height H in three-dimensional space2Flying, the single flight time is T, the time period is divided into N +1 time slots, and each time slot has a width δ, that is, T ═ δ (N + 1); the flight parameters of the nth time slot unmanned aerial vehicle node comprise: unmanned aerial vehicle position coordinate q [ n ]]＝(x[n],y[n],H2)TUnmanned aerial vehicle flight velocity vector v [ n ]]＝(vx[n],vy[n],0)TUnmanned aerial vehicle acceleration vector a [ n ]]＝(ax[n],ay[n],0)TThe energy consumption optimization method comprises the following steps:
(1) according to the data information bit operation processing energy consumption definition, establishing an energy consumption model E when the unmanned aerial vehicle node processes the data bits in the time length Tcomp；
(2) Establishing a flight energy consumption model E of the unmanned aerial vehicle node in single flight time Tfly；
(3) Based on the data processing energy consumption and flight energy consumption models of the nodes of the unmanned aerial vehicle in the step (1) and the step (2), taking the total energy consumption of the nodes of the unmanned aerial vehicle as an optimization target, considering the flight condition constraint and the base station energy consumption constraint of the nodes of the unmanned aerial vehicle, establishing a mathematical model about the flight parameters and the information bit distribution parameters of the nodes of the unmanned aerial vehicle, and solving the mathematical model;
energy consumption model E of the UAV node processing data bits in the time length T in the step (1)compComprises the following steps:
wherein G represents a hardware computing power constant of the unmanned aerial vehicle node;
the flight energy consumption model E of the unmanned aerial vehicle node established in the step (2) in single flight time TflyComprises the following steps:
wherein, c1And c2Is a positive constant factor related to the unmanned aerial vehicle node weight, wing area, air density and the like, g represents the gravity acceleration,
the method comprises the steps that kinetic energy variation of unmanned aerial vehicle nodes is represented, if starting and stopping speed parameters of the unmanned aerial vehicle nodes are fixed, delta p is a fixed quantity, m represents the total weight of the unmanned aerial vehicle nodes, and if the starting and stopping speeds of the unmanned aerial vehicle nodes are the same, delta p is equal to 0;
the mathematical model about the node flight parameters and the information bit distribution parameters of the unmanned aerial vehicle established in the step (3) is as follows:
C7:0≤ρ≤1
wherein the content of the first and second substances,
qIindicates the starting position of the unmanned plane node, qFIndicating unmanned plane node termination position, VmaxIndicates the maximum flight speed of the unmanned plane node, amaxRepresenting the maximum acceleration of the unmanned plane node; l isu[n]The number of bit data sent to the base station by the unmanned aerial vehicle node in the nth time slot is represented, the data processing delay is 1 time slot, and the base station processing L of the (n +1) th time slotb[n+1]The number of bits of data is one,
is indicative of a flight parameter of the aircraft,
denotes a data bit allocation parameter, B denotes a channel bandwidth, P denotes a data transmission power of the drone node and is a fixed value, σ2Representing the power of additive complex Gaussian white noise; d [ n ]]Indicates the distance between the unmanned aerial vehicle node and the base station in the nth time slot, beta0Represents a signal with a distance of 1mThe reference value of the channel gain when the transmission power is 1W, | | · | -represents the Euclidean norm, and C1 represents that the maximum energy constraint value used by the terrestrial cellular network communication base station for processing data information is EtotalC2 represents data transmission bit constraint of the unmanned aerial vehicle node in the nth time slot under the condition that the channel bandwidth is B, C3 represents bit cascade constraint, that is, the number of information bits processed by the ground cellular network communication base station at each flight time does not exceed the number of information bits transmitted to the unmanned aerial vehicle node, C4 represents the total number of information bits transmitted to the ground cellular network communication base station by the unmanned aerial vehicle node, C5 represents the total number of information bits of the stream processed by the ground cellular network communication base station, C6 represents relationship constraint among the flight parameters of the unmanned aerial vehicle, and C7 and C8 represent feasible domain boundary constraint conditions of the optimization parameters.
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