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 PDFInfo
- Publication number
- 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
- Authority
- CN
- China
- Prior art keywords
- aerial vehicle
- unmanned aerial
- intelligent
- terminal
- reflecting surface
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 46
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000004891 communication Methods 0.000 claims abstract description 43
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 9
- 230000010363 phase shift Effects 0.000 claims abstract description 6
- 239000011159 matrix material Substances 0.000 claims description 8
- 230000008859 change Effects 0.000 claims description 5
- 230000000903 blocking effect Effects 0.000 claims description 4
- 235000007164 Oryza sativa Nutrition 0.000 claims description 3
- 229910052739 hydrogen Inorganic materials 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 230000001141 propulsive effect Effects 0.000 claims description 3
- 235000009566 rice Nutrition 0.000 claims description 3
- 230000000630 rising effect Effects 0.000 claims description 3
- 240000007594 Oryza sativa Species 0.000 claims 1
- 238000005265 energy consumption Methods 0.000 abstract description 8
- 238000004364 calculation method Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000006243 chemical reaction Methods 0.000 abstract description 2
- 230000005540 biological transmission Effects 0.000 description 5
- 241000209094 Oryza Species 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001413 cellular effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 230000005672 electromagnetic field Effects 0.000 description 1
- 230000002040 relaxant effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- 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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
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
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 coordinatesAnd time slot durationCharacterizing a path of the drone, wherein,indicating the horizontal position of the drone at the nth slot,n denotes all of the time slots and,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 isWherein,set of abscissa representing horizontal center of all cells, and setAndtake-off and landing for preset unmanned aerial vehiclesA falling water flat center;
a113, set the propulsive energy of the unmanned rotorcraft toWherein,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 slotAnd calculating a channel gain between the kth terminal and the intelligent reflective surface
Where ξ is the channel loss at a distance of 1 meter,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,andrespectively representing intelligent reflective surface waterThe cosine and sine values of the angle of arrival of the flat signal,a sine value representing the arrival angle of the vertical signal of the intelligent reflection surface;indicating the distance between the kth terminal and the intelligent reflective surface,andrespectively representing cosine value and sine value of k terminal horizontal signal emission angle,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 asWherein, 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 slotWherein,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 asSignal rate ofWherein 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,representing a set of horizontal positions of the drone,a set of vertical positions of the drone is represented,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.
Drawings
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 isIn the formulaSet 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.Refers to the horizontal position of the unmanned plane at the nth time slot, whereinWhere N refers to all slots. Is provided withAndthe horizontal center for the takeoff and landing of the unmanned aerial vehicle is set in advance.Refers to the vertical position of the drone at the nth slot. Hence spatial coordinatesAnd time slot durationThe path plan of the drone can be characterized.
Establishing an energy consumption model according to the horizontal flight speed of the unmanned aerial vehicleConstant 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:
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:
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 asz 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,andrespectively denote cosine and sine values of the angle of arrival of the horizontal signal of the intelligent reflecting surface,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:
wherein the distance between the kth terminal and the intelligent reflection surfaceAndrespectively refers to a cosine value and a sine value of a k terminal horizontal signal emission angle,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 asWherein T represents the transpose of the matrix, in whichIs an intelligent reflective surface reflection phase coefficient matrix and
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 slotIn the formulaa and b are variables that change as the communication environment changes. Further, the average channel gain achievable by the kth terminal is expressed asChannel rate ofWherein, 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:
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. WhereinIndicating a set of horizontal positions of the drone,indicating a set of vertical positions of the drone,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;
q 1 =q N+1 ;(6.5)
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:
s7, setting the horizontal track as a variable, fixing other parameters, and converting the original problem into the problem of the variable;
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:
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:
s8, setting the phase offset as a variable and fixing other parameters to further simplify the original problem;
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:
s9, considering the passive offset of the intelligent reflecting surface, and optimizing a channel gain function; based on problem P5, get
Problem P6:
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;
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 coordinatesAnd time slot durationCharacterizing a path of the drone, wherein,indicating the horizontal position of the drone at the nth slot,n denotes all of the time slots and,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 isWherein,set of abscissa representing horizontal center of all cells, and setAndtaking off and landing horizontal centers for the preset unmanned aerial vehicle;
a113, set the propulsive energy of the unmanned rotorcraft toWherein,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 slotj represents a complex number representing the phase shift and the channel gain between the kth terminal and the intelligent reflective surface is calculated
Where ξ is the channel loss at a distance of 1 meter,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,andrespectively representing cosine and sine values of the angle of arrival of the horizontal signal at the intelligent reflecting surface,a sine value representing the arrival angle of the vertical signal of the intelligent reflection surface;representing the distance between the kth terminal and the intelligent reflective surface,andrespectively representing cosine value and sine value of k terminal horizontal signal emission angle,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 asWherein, 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 slotWherein,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 asSignal rate ofWherein 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,representing a set of horizontal positions of the drone,a set of vertical positions of the drone is represented,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.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202211288468.6A CN115665800A (en) | 2022-10-20 | 2022-10-20 | Joint optimization method for intelligent campus unmanned aerial vehicle track task unloading cache and RIS |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202211288468.6A CN115665800A (en) | 2022-10-20 | 2022-10-20 | Joint optimization method for intelligent campus unmanned aerial vehicle track task unloading cache and RIS |
Publications (1)
Publication Number | Publication Date |
---|---|
CN115665800A true CN115665800A (en) | 2023-01-31 |
Family
ID=84989741
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202211288468.6A Pending CN115665800A (en) | 2022-10-20 | 2022-10-20 | Joint optimization method for intelligent campus unmanned aerial vehicle track task unloading cache and RIS |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN115665800A (en) |
-
2022
- 2022-10-20 CN CN202211288468.6A patent/CN115665800A/en active Pending
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2023015659A1 (en) | Design method for high-energy-efficiency unmanned aerial vehicle communication system assisted by intelligent reflecting surface | |
Zhang et al. | Energy-efficient trajectory optimization for UAV-assisted IoT networks | |
CN111682895B (en) | Cache-based unmanned aerial vehicle relay auxiliary internet-of-vehicles transmission optimization method | |
CN110730031B (en) | Unmanned aerial vehicle track and resource allocation joint optimization method for multi-carrier communication | |
CN111552313B (en) | Multi-unmanned aerial vehicle path planning method based on edge calculation dynamic task arrival | |
CN109511134B (en) | Unmanned aerial vehicle auxiliary wireless communication system load distribution method based on optimal energy efficiency | |
Fan et al. | RIS-assisted UAV for fresh data collection in 3D urban environments: A deep reinforcement learning approach | |
CN109839955B (en) | Trajectory optimization method for wireless communication between unmanned aerial vehicle and multiple ground terminals | |
CN113784314B (en) | Unmanned aerial vehicle data and energy transmission method assisted by intelligent reflection surface | |
CN114980169A (en) | Unmanned aerial vehicle auxiliary ground communication method based on combined optimization of track and phase | |
CN115499921A (en) | Three-dimensional trajectory design and resource scheduling optimization method for complex unmanned aerial vehicle network | |
CN114158010B (en) | Unmanned aerial vehicle communication system and resource allocation strategy prediction method based on neural network | |
Xu et al. | Joint power and trajectory optimization for IRS-aided master-auxiliary-UAV-powered IoT networks | |
Wang et al. | Trajectory optimization and power allocation scheme based on DRL in energy efficient UAV‐aided communication networks | |
CN114035610B (en) | Unmanned intelligent cluster joint track design method | |
CN114257298B (en) | Intelligent reflecting surface phase shift and unmanned aerial vehicle path planning method | |
Sun et al. | Three-dimensional trajectory design for energy-efficient UAV-assisted data collection | |
CN117119489A (en) | Deployment and resource optimization method of wireless energy supply network based on multi-unmanned aerial vehicle assistance | |
CN115665800A (en) | Joint optimization method for intelligent campus unmanned aerial vehicle track task unloading cache and RIS | |
CN113741530B (en) | Data acquisition method based on intelligent perception of multiple unmanned aerial vehicles | |
Lyu et al. | Resource Allocation in UAV‐Assisted Wireless Powered Communication Networks for Urban Monitoring | |
Zhang et al. | Learning-based trajectory design and time allocation in UAV-supported wireless powered NOMA-IoT networks | |
CN114489146B (en) | Scheduling and trajectory planning method for multi-unmanned aerial vehicle data collection system | |
CN115441925B (en) | Wireless computing system based on air-ground communication network | |
CN116878520B (en) | Unmanned aerial vehicle path planning method |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination |