CN115665800A - Joint optimization method for intelligent campus unmanned aerial vehicle track task unloading cache and RIS - Google Patents

Joint optimization method for intelligent campus unmanned aerial vehicle track task unloading cache and RIS Download PDF

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CN115665800A
CN115665800A CN202211288468.6A CN202211288468A CN115665800A CN 115665800 A CN115665800 A CN 115665800A CN 202211288468 A CN202211288468 A CN 202211288468A CN 115665800 A CN115665800 A CN 115665800A
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aerial vehicle
unmanned aerial
intelligent
terminal
reflecting surface
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黄丽娟
陶江
吉薇羲
肖杨
徐璐
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Sichuan Tianfu New Area Luhu Primary School Chengdu Hamilton Luhu Primary School
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Sichuan Tianfu New Area Luhu Primary School Chengdu Hamilton Luhu Primary School
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Abstract

The invention relates to a combined optimization method for unloading cache and RIS (intelligent reflecting surface) of a locus task of an unmanned aerial vehicle in a smart campus, which belongs to the technical field of unmanned aerial vehicle air-ground communication and comprises the following steps: a1, establishing a communication model to collect information, setting a target optimization function and constraint conditions and initializing; a2, converting the non-convex problem into a plurality of sub-problems which are convex optimization problems; a3, performing multiple conversion and simplification on the optimization problem; a4, optimizing a signal gain function, and aligning the intelligent reflection surface with the phase of the unmanned aerial vehicle; a5, solving the transformed convex optimization problem through a linear programming tool; and A6, repeating the steps A3-A5 until the algorithm converges to the specified precision. The unmanned aerial vehicle three-dimensional trajectory and intelligent reflecting surface phase shift optimization method is based on a successive convex approximation method, combines an intelligent reflecting surface technology, and jointly optimizes the three-dimensional trajectory and the intelligent reflecting surface phase shift of the unmanned aerial vehicle, so that the energy consumption of the unmanned aerial vehicle is minimized, meanwhile, the gain of a wireless communication network is maximized, and the communication and calculation energy efficiency of the unmanned aerial vehicle and a ground terminal is improved.

Description

Joint optimization method for intelligent campus unmanned aerial vehicle track task unloading cache and RIS
Technical Field
The invention relates to the technical field of unmanned aerial vehicle air-ground communication, in particular to a combined optimization method for intelligent campus unmanned aerial vehicle track task unloading caching and RIS.
Background
The wireless communication supported by the unmanned aerial vehicle is a research hotspot in recent years, and the unmanned aerial vehicle has high mobility, so that the unmanned aerial vehicle can flexibly work in a three-dimensional space and attract the air-ground wireless communication by adjusting the horizontal and vertical positions of the unmanned aerial vehicle at the same time; due to high mobility, the wireless network assisted by the unmanned aerial vehicle is particularly suitable for on-demand deployment in emergency situations, wherein the unmanned aerial vehicle is mainly used as an air temporary base station or an access point to transmit or receive data to a ground terminal, but in practical campus environment application, a communication link between the unmanned aerial vehicle and the terminal is most likely to be blocked by a regional obstacle, so that signal attenuation is caused, and the data transmission rate is reduced.
The above problem can be solved by an intelligent reflective surface (RIS), which is a meta-surface equipped with integrated electronic circuits, which can be programmed to change the input electromagnetic field in a customizable way, each surface unit being realized by a reflective array, the communication link blocked between the drone and the ground terminal can be re-established by the transit of the intelligent reflective surface on the building, effectively utilizing the energy and spectral efficiency of the cellular system, helping the drone to overcome the signal blocking problem of the air-to-ground wireless communication; however, under the assistance of the intelligent reflection surface, two sections of communication links between the ground terminal and the unmanned aerial vehicle are influenced by the movement of the unmanned aerial vehicle, and three-dimensional track caching is difficult to realize; in addition, the phase shift of the intelligent reflecting surface needs to be calculated and determined in real time according to the current communication link condition, and the traditional algorithm has large calculation amount and poor real-time performance, so that the quality of the communication link is influenced; finally, the energy of the unmanned aerial vehicle is limited, and how to simultaneously consider the overall propulsion energy minimization and the high system energy efficiency of the unmanned aerial vehicle is also considered to be solved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a combined optimization method for unloading cache and RIS of a locus task of an intelligent campus unmanned aerial vehicle, and solves the problems that a ground terminal and an unmanned aerial vehicle communication link are influenced by the movement of the unmanned aerial vehicle and other problems exist under the assistance of an intelligent reflection surface at present.
The purpose of the invention is realized by the following technical scheme: a combined optimization method for unloading cache and RIS of intelligent campus unmanned aerial vehicle track tasks comprises the following steps:
a1, collecting corresponding information in a current area, importing the information into an established unmanned aerial vehicle-terminal communication model, setting a target optimization function and constraint conditions, and initializing;
a2, converting a non-convex problem into an optimization problem with a plurality of sub-problems as convex by using a successive convex approximation method;
a3, converting the optimization problem into a task unloading and caching problem, setting a horizontal track as a variable and fixing other parameters, further converting the problem into a problem that the variable is the horizontal track, setting phase offset as the variable and fixing other parameters, and simplifying the problem that the variable is the horizontal track;
a4, optimizing a signal gain function by taking the passive offset of the intelligent reflecting surface as a reference condition, and setting a new phase offset to realize the phase alignment of the intelligent reflecting surface and the unmanned aerial vehicle;
a5, solving the transformed convex optimization problem through a linear programming tool;
and A6, repeating the steps A3-A5, and continuously updating the optimization variables by using an optimization iterative algorithm until the algorithm converges to the specified precision.
2. The joint optimization method for intelligent campus unmanned aerial vehicle trajectory task offloading caching and RIS according to claim 1, characterized in that: the step A1 specifically includes:
a11, establishing an unmanned aerial vehicle-terminal communication model under the assistance of an intelligent reflecting surface;
a12, collecting information of the unmanned aerial vehicle, the intelligent reflection surface and the ground terminal in the current area and importing the information into a communication model;
and A13, establishing an optimization problem, and defining an objective optimization function and a parameter constraint condition.
The unmanned aerial vehicle-terminal communication model established under the assistance of the intelligent reflecting surface comprises:
a111, using the spatial coordinates
Figure BDA0003900340080000021
And time slot duration
Figure BDA0003900340080000022
Characterizing a path of the drone, wherein,
Figure BDA0003900340080000023
indicating the horizontal position of the drone at the nth slot,
Figure BDA0003900340080000024
n denotes all of the time slots and,
Figure BDA0003900340080000025
indicating the vertical position of the unmanned plane in the nth time slot;
a112, setting a three-dimensional area of unmanned aerial vehicle and terminal communication under the assistance of an intelligent reflection surface, uniformly dividing the area into a plurality of cells, wherein the horizontal coordinate of the center of the ith cell is
Figure BDA0003900340080000026
Wherein,
Figure BDA0003900340080000027
set of abscissa representing horizontal center of all cells, and set
Figure BDA0003900340080000028
And
Figure BDA0003900340080000029
take-off and landing for preset unmanned aerial vehiclesA falling water flat center;
a113, set the propulsive energy of the unmanned rotorcraft to
Figure BDA00039003400800000210
Wherein,
Figure BDA00039003400800000211
for horizontal flight speed of unmanned aerial vehicle, P 0 At constant wing power, P 1 For hover induced power, P 2 For constant falling or rising power, U tip Is the bucket wing velocity, v 0 For average rotor induced speed at hover, d 0 For the fuselage drag ratio, S is the rotor solidity, ρ is the air density, and G is the rotor disk area;
a114, number M of reflecting units according to each uniform planar array on the intelligent reflecting surface c ×M r Uniform planar array column spacing d c Distance d between rice and line r And calculating the channel gain between the unmanned aerial vehicle and the intelligent reflecting surface at the nth time slot
Figure BDA0003900340080000031
And calculating a channel gain between the kth terminal and the intelligent reflective surface
Figure BDA0003900340080000032
Where ξ is the channel loss at a distance of 1 meter,
Figure BDA0003900340080000033
the distance between the intelligent reflecting surface of the nth time slot and the unmanned aerial vehicle is obtained; z is a radical of R And w R Which respectively represent the vertical and horizontal position of the first element of the intelligent reflective surface, lambda is the carrier wavelength,
Figure BDA0003900340080000034
and
Figure BDA0003900340080000035
respectively representing intelligent reflective surface waterThe cosine and sine values of the angle of arrival of the flat signal,
Figure BDA0003900340080000036
a sine value representing the arrival angle of the vertical signal of the intelligent reflection surface;
Figure BDA0003900340080000037
indicating the distance between the kth terminal and the intelligent reflective surface,
Figure BDA0003900340080000038
and
Figure BDA0003900340080000039
respectively representing cosine value and sine value of k terminal horizontal signal emission angle,
Figure BDA00039003400800000310
a sine value representing the k terminal vertical signal emission angle;
and the channel gain of the k terminal of the overall process is expressed as
Figure BDA00039003400800000311
Wherein, theta n The method comprises the steps of obtaining an intelligent reflection surface reflection phase coefficient matrix;
a115, calculating the blocking probability of the link between the unmanned aerial vehicle and the k ground terminal in the nth time slot
Figure BDA00039003400800000312
Wherein,
Figure BDA00039003400800000313
the distance between the unmanned aerial vehicle and the ground is represented, a and b represent variables which change along with the change of the communication environment, and the average channel gain achieved by the kth terminal is represented as
Figure BDA00039003400800000314
Signal rate of
Figure BDA00039003400800000315
Wherein P is the fixed transmitting power of the unmanned aerial vehicle, B is the bandwidth, sigma is the noise variable, c k,n = {0,1} represents whether or not the k-th terminal is scheduled.
In the step A12, unmanned aerial vehicle L, H, T information, intelligent reflection surface theta information and ground terminal C information in the current area of the mobile phone are imported into a communication model; wherein,
Figure BDA00039003400800000316
representing a set of horizontal positions of the drone,
Figure BDA00039003400800000317
a set of vertical positions of the drone is represented,
Figure BDA0003900340080000041
represents the duration of each flight slot of the drone, Θ = { Θ n N ∈ N } represents an intelligent reflection surface reflection phase coefficient matrix, C = { C = } k,n And N ∈ N } represents a terrestrial terminal scheduling scheme.
The invention has the following advantages: a smart campus unmanned aerial vehicle track task unloading caching and RIS combined optimization method is characterized in that balance of traditional optimization algorithms on computational complexity and precision is achieved based on a convex optimization method, an intelligent reflection surface technology is combined, three-dimensional tracks of an unmanned aerial vehicle and cache optimization technology and intelligent reflection surface phase shift are jointly optimized, wireless communication network gain is maximized while energy consumption of the unmanned aerial vehicle is minimized, and communication energy efficiency of the unmanned aerial vehicle and a ground terminal is improved.
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FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, as generally described and illustrated in the figures herein, could be arranged and designed in a wide variety of different configurations. Thus, the detailed description of the embodiments of the present application provided below in connection with the appended drawings is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application. The invention is further described below with reference to the accompanying drawings.
The invention provides an intelligent reflecting surface phase shift and unmanned aerial vehicle path planning method which effectively balances computational complexity and computational accuracy, maximizes wireless communication network gain while minimizing unmanned aerial vehicle energy consumption, and consists of three parts of system model establishment, model transformation and solution, as shown in figure 1, the method specifically comprises the following steps:
s1, establishing an unmanned aerial vehicle-terminal communication model under the assistance of an intelligent reflecting surface;
the method specifically comprises the following steps: in a three-dimensional area of unmanned aerial vehicle and terminal communication under the assistance of intelligent reflection surface, this area is evenly divided into a plurality of cells, and the horizontal coordinate of the central of ith cell is
Figure BDA0003900340080000042
In the formula
Figure BDA0003900340080000043
Set of abscissa, x, referring to horizontal center of all cells s And y s Refers to the horizontal distance between two adjacent cells in the x and y directions.
Figure BDA0003900340080000044
Refers to the horizontal position of the unmanned plane at the nth time slot, wherein
Figure BDA0003900340080000045
Where N refers to all slots. Is provided with
Figure BDA0003900340080000046
And
Figure BDA0003900340080000047
the horizontal center for the takeoff and landing of the unmanned aerial vehicle is set in advance.
Figure BDA0003900340080000048
Refers to the vertical position of the drone at the nth slot. Hence spatial coordinates
Figure BDA0003900340080000049
And time slot duration
Figure BDA00039003400800000410
The path plan of the drone can be characterized.
Establishing an energy consumption model according to the horizontal flight speed of the unmanned aerial vehicle
Figure BDA0003900340080000051
Constant power P of blade wing 0 Hovering induced power P 1 Constant falling or rising power P 2 Speed of moving blade tip Average rotor induced velocity v at hover 0 Body resistance ratio d 0 Rotor solidity S, air density rho and rotor disc area G, the propulsive energy of calculating rotor unmanned aerial vehicle is:
Figure BDA0003900340080000052
establishing a communication model between the intelligent reflecting surface and the unmanned aerial vehicle, and according to the number M of reflecting units of each uniform planar array on the intelligent reflecting surface c ×M r Uniform planar array of column spacing d c Distance d between rice and line r And m, calculating the channel gain between the unmanned aerial vehicle at the nth time slot and the intelligent reflecting surface:
Figure BDA0003900340080000053
xi in the formula refers to channel loss when the distance is 1 meter, and the nth time slot intelligent reflecting surface and the unmanned aerial vehicleThe distance between is expressed as
Figure BDA0003900340080000054
z R And w R Which respectively indicate the position of the first element of the intelligent reflective surface in the vertical and horizontal directions, lambda refers to the carrier wavelength,
Figure BDA0003900340080000055
and
Figure BDA0003900340080000056
respectively denote cosine and sine values of the angle of arrival of the horizontal signal of the intelligent reflecting surface,
Figure BDA0003900340080000057
refers to the sine of the angle of arrival of the vertical signal at the intelligent reflective surface.
Establishing a communication model between the intelligent reflecting surface and the ground terminal, and calculating the channel gain between the kth terminal and the intelligent reflecting surface:
Figure BDA0003900340080000058
wherein the distance between the kth terminal and the intelligent reflection surface
Figure BDA0003900340080000059
And
Figure BDA00039003400800000510
respectively refers to a cosine value and a sine value of a k terminal horizontal signal emission angle,
Figure BDA00039003400800000511
refers to the sine of the k-th terminal vertical signal transmission angle. Further, the channel gain of the k-th terminal of the overall process may be expressed as
Figure BDA00039003400800000512
Wherein T represents the transpose of the matrix, in which
Figure BDA00039003400800000513
Is an intelligent reflective surface reflection phase coefficient matrix and
Figure BDA00039003400800000514
establishing a communication link model of the unmanned aerial vehicle and the ground terminal under the assistance of the intelligent reflecting surface, and calculating the blocking probability of the link between the unmanned aerial vehicle and the kth ground terminal at the nth time slot
Figure BDA0003900340080000061
In the formula
Figure BDA0003900340080000062
a and b are variables that change as the communication environment changes. Further, the average channel gain achievable by the kth terminal is expressed as
Figure BDA0003900340080000063
Channel rate of
Figure BDA0003900340080000064
Wherein, P is the fixed transmitting power of the unmanned aerial vehicle, B is the bandwidth, sigma is the noise variable, c k,n = {0,1} indicates whether the kth terminal is scheduled, and the same slot intelligent reflective surface serves at most one terminal.
The calculation and caching model has the following characteristics:
Figure BDA0003900340080000065
Figure BDA0003900340080000066
Figure BDA0003900340080000067
Figure BDA0003900340080000068
Figure BDA0003900340080000069
wherein, the formula (1) represents the uninstallation delay, and the formulas (2) and (3) represent the energy consumption of unmanned aerial vehicle and ground terminal, and the formulas (4) and (5) are the delay and the energy consumption after utilizing the buffer mechanism to simplify.
S2, collecting information of the unmanned aerial vehicle, the intelligent reflection surface and the ground terminal in the current area, and importing a communication model:
and collecting information of the unmanned aerial vehicle L, H, T, information of the intelligent reflection surface theta and information of the ground terminal C in the current area, and importing the information into a communication model. Wherein
Figure BDA00039003400800000610
Indicating a set of horizontal positions of the drone,
Figure BDA00039003400800000611
indicating a set of vertical positions of the drone,
Figure BDA00039003400800000612
indicating unmanned aerial vehicle per flight slot duration, Θ = { Θ n N ∈ N } indicates the intelligent reflection surface reflection phase coefficient matrix, C = { C = k,n N belongs to N and indicates the ground terminal scheduling scheme;
s3, establishing an optimization problem, and defining a target optimization function and a parameter constraint condition;
Figure BDA0003900340080000071
Figure BDA0003900340080000072
Figure BDA0003900340080000073
Figure BDA0003900340080000074
Figure BDA0003900340080000075
q 1 ＝q N+1 ；(6.5)
Figure BDA0003900340080000076
Figure BDA0003900340080000077
wherein s.t. represents that the constraint is on a certain condition, and the formula (6) is an optimization problem, and is used for optimizing the energy consumption of the unmanned aerial vehicle and the energy consumption of the ground terminal to the minimum; equation (6.1) represents the (0,1) variable whether the ground terminal offloads the task to the drone, and the (0,1) variable whether the drone caches the task; formula (6.2) represents the capacity limit of the unmanned aerial vehicle cache; formula (6.3) represents the calculated power limit of the edge of the drone; equation (6.4) indicates that the task delay of the ground terminal must be lower than the maximum allowed delay; formula (6.5) represents that in one unmanned aerial vehicle task, the unmanned aerial vehicle needs to fly back to the original point; equation (6.6) represents the speed limit of the drone; equation (6.7) represents the scheduling constraint for the drone serving ground terminals, i.e., one slot can only be scheduled to serve one ground terminal.
S4, initializing the state of the communication scene of the unmanned aerial vehicle assisted by the intelligent reflecting surface and the terminal;
s5, converting the non-convex problem into an optimization problem with a plurality of sub-problems as convex by using a successive convex approximation method;
s6, converting the original optimization problem into a task unloading and caching problem by using a convex optimization tool box according to the formulas (1) to (5) and the physical significance of the formulas; based on the problem P, there is an existing task offloading and caching mechanism that translates into problems P1 and P2:
Figure BDA0003900340080000078
Figure BDA0003900340080000079
s7, setting the horizontal track as a variable, fixing other parameters, and converting the original problem into the problem of the variable;
Figure BDA0003900340080000081
Figure BDA0003900340080000082
where (9.1) indicates that the data transmission rate must be greater than a threshold, based on the translation problem P3, we morph the transmission rate as:
Figure BDA0003900340080000083
Figure BDA0003900340080000084
Figure BDA0003900340080000085
the derivation is performed by the basic communication transmission rate and the shannon formula, and further simplification is performed, so that the problem P4 is obtained:
Figure BDA0003900340080000086
s.t. equations (6.5), (6.6) and
Figure BDA0003900340080000087
s8, setting the phase offset as a variable and fixing other parameters to further simplify the original problem;
Figure BDA0003900340080000088
s.t. equation (10.1) and
Figure BDA0003900340080000089
where Φ is the phase offset matrix, i.e. one sub-problem of the original problem is decomposed into sub-problems with respect to Φ as the optimization variable. This results in a problem P5, which is a variant to introduce a problem P6, and the data rate expression is again simplified using the basic communication transmission rate and shannon's formula:
Figure BDA00039003400800000810
s9, considering the passive offset of the intelligent reflecting surface, and optimizing a channel gain function; based on problem P5, get
Problem P6:
Figure BDA0003900340080000091
s.t. (6.7) and
Figure BDA0003900340080000092
that is, the combination of the communication rates of the unmanned aerial vehicle and the ground terminal cannot be lower than a series of threshold values, which is a discrete combination problem, namely a knapsack problem. From this, it can be seen that the problem P6 is a set of multi-knapsack problems that can be solved by relaxing.
S10, setting a new phase offset to achieve phase alignment of the intelligent reflecting surface and the unmanned aerial vehicle;
Figure BDA0003900340080000093
Figure BDA0003900340080000094
Figure BDA0003900340080000095
Figure BDA0003900340080000096
equations (14) - (17) represent the new phase alignment form and the lower bound of the channel rate after phase alignment.
S11, solving the convex optimization problem after conversion in the step S11 by using a linear programming tool box;
and S12, repeating the steps S6 to S11 for a fixed number of times, and continuously updating the optimization variables by using an optimization iterative algorithm until the algorithm converges to the specified precision.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (4)

1. A combined optimization method for unloading cache and RIS (intelligent reflecting surface) of intelligent campus unmanned aerial vehicle track tasks is characterized by comprising the following steps: the joint optimization method comprises the following steps:
a1, collecting corresponding information in a current area, importing the information into an established unmanned aerial vehicle-terminal communication model, setting a target optimization function and constraint conditions, and initializing;
a2, converting a non-convex problem into an optimization problem with a plurality of sub-problems as convex by using a successive convex approximation method;
a3, converting the optimization problem into a task unloading and caching problem, setting a horizontal track as a variable and fixing other parameters, further converting the problem into a problem that the variable is the horizontal track, setting phase offset as the variable and fixing other parameters, and simplifying the problem that the variable is the horizontal track;
a4, optimizing a signal gain function by taking the passive offset of the intelligent reflecting surface as a reference condition, and setting a new phase offset to realize the phase alignment of the intelligent reflecting surface and the unmanned aerial vehicle;
a5, solving the transformed convex optimization problem through a linear programming tool;
and A6, repeating the steps A3-A5, and continuously updating the optimized variables by using an optimization iterative algorithm until the algorithm converges to the specified precision.
2. The joint optimization method for intelligent campus unmanned aerial vehicle trajectory task offloading caching and RIS according to claim 1, characterized in that: the step A1 specifically includes:
a11, establishing an unmanned aerial vehicle-terminal communication model under the assistance of an intelligent reflecting surface;
a12, collecting information of the unmanned aerial vehicle, the intelligent reflection surface and the ground terminal in the current area and importing the information into a communication model;
and A13, establishing an optimization problem, and defining an objective optimization function and a parameter constraint condition.
3. The joint optimization method for intelligent campus unmanned aerial vehicle trajectory task offloading caching and RIS according to claim 2, characterized in that: the unmanned aerial vehicle-terminal communication model established under the assistance of the intelligent reflecting surface comprises the following steps:
a111, using the spatial coordinates
Figure FDA0003900340070000011
And time slot duration
Figure FDA0003900340070000012
Characterizing a path of the drone, wherein,
Figure FDA0003900340070000013
indicating the horizontal position of the drone at the nth slot,
Figure FDA0003900340070000014
n denotes all of the time slots and,
Figure FDA0003900340070000015
indicating the vertical position of the unmanned plane in the nth time slot;
a112, setting a three-dimensional area of unmanned aerial vehicle and terminal communication under the assistance of an intelligent reflection surface, uniformly dividing the area into a plurality of cells, wherein the horizontal coordinate of the center of the ith cell is
Figure FDA0003900340070000016
Wherein,
Figure FDA0003900340070000017
set of abscissa representing horizontal center of all cells, and set
Figure FDA0003900340070000018
And
Figure FDA0003900340070000019
taking off and landing horizontal centers for the preset unmanned aerial vehicle;
a113, set the propulsive energy of the unmanned rotorcraft to
Figure FDA0003900340070000021
Wherein,
Figure FDA0003900340070000022
for horizontal flight speed of unmanned aerial vehicle, P 0 At constant blade power, P 1 For hover induced power, P 2 For constant falling or rising power, U tip Is the bucket wing velocity, v 0 For average rotor induced speed at hover, d 0 For the fuselage drag ratio, S is the rotor solidity, ρ is the air density, and G is the rotor disk area;
a114, number M of reflecting units according to each uniform planar array on the intelligent reflecting surface c ×M r Uniform planar array column spacing d c Distance d between rice and row r And calculating the channel gain between the unmanned aerial vehicle and the intelligent reflecting surface at the nth time slot
Figure FDA0003900340070000023
j represents a complex number representing the phase shift and the channel gain between the kth terminal and the intelligent reflective surface is calculated
Figure FDA0003900340070000024
Where ξ is the channel loss at a distance of 1 meter,
Figure FDA0003900340070000025
the distance between the intelligent reflecting surface of the nth time slot and the unmanned aerial vehicle is obtained; z is a radical of R And w R Which respectively represent the vertical and horizontal position of the first element of the intelligent reflective surface, lambda is the carrier wavelength,
Figure FDA0003900340070000026
and
Figure FDA0003900340070000027
respectively representing cosine and sine values of the angle of arrival of the horizontal signal at the intelligent reflecting surface,
Figure FDA0003900340070000028
a sine value representing the arrival angle of the vertical signal of the intelligent reflection surface;
Figure FDA0003900340070000029
representing the distance between the kth terminal and the intelligent reflective surface,
Figure FDA00039003400700000210
and
Figure FDA00039003400700000211
respectively representing cosine value and sine value of k terminal horizontal signal emission angle,
Figure FDA00039003400700000212
a sine value representing the k-th terminal vertical signal emission angle;
and the channel gain of the k terminal of the overall process is expressed as
Figure FDA00039003400700000213
Wherein, theta n The method comprises the steps of obtaining an intelligent reflection surface reflection phase coefficient matrix;
a115, calculating the blocking probability of the link between the unmanned aerial vehicle and the kth ground terminal in the nth time slot
Figure FDA0003900340070000031
Wherein,
Figure FDA0003900340070000032
the distance between the unmanned plane and the ground is represented, wherein a and b represent variables changing along with the change of the communication environment, and the average channel gain achieved by the k terminal is represented as
Figure FDA0003900340070000033
Signal rate of
Figure FDA0003900340070000034
Wherein P is the fixed transmitting power of the unmanned aerial vehicle, B is the bandwidth, sigma is the noise variable, c k,n = {0,1} represents whether or not the k-th terminal is scheduled.
4. The joint optimization method for intelligent campus unmanned aerial vehicle trajectory task offloading caching and RIS according to claim 2, characterized in that: in the step A12, unmanned aerial vehicle L, H, T information, intelligent reflection surface theta information and ground terminal C information in the current area of the mobile phone are imported into a communication model; wherein,
Figure FDA0003900340070000035
representing a set of horizontal positions of the drone,
Figure FDA0003900340070000036
a set of vertical positions of the drone is represented,
Figure FDA0003900340070000037
represents the duration of each flight slot of the drone, Θ = { Θ n N ∈ N } represents an intelligent reflection surface reflection phase coefficient matrix, C = { C = } k,n And N ∈ N } represents a terrestrial terminal scheduling scheme.
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