阅读说明：本技术 一种智能反射表面辅助下的无人机数据与能量传输方法 (Unmanned aerial vehicle data and energy transmission method assisted by intelligent reflection surface ) 是由 杨鲲 符钰婧 梅海波 于 2021-11-12 设计创作，主要内容包括：本发明涉及一种智能反射表面辅助下的无人机数据与能量传输方法,涉及无人机通信技术领域,包括将智能反射表面附着在建筑物上,无人机通过智能反射表面与视距链路被阻挡的地面设备进行数据与能量的传输；建立无人机与地面设备的上行数据传输模型和下行能量传输模型,并根据地面设备数据传输的需求设置地面设备收集能量最大化的优化问题；采用块坐标下降和连续凸优化方法将优化问题分解为三个子问题,并对每个子问题分别求解,最后通过迭代算法将各个子问题的解进行迭代得到最终解。本发明在智能反射表面辅助下的无人机与地面设备的调度方案,给出了优化的数据与能量传输的时隙分配方案,在保证地面设备数据的传输要求下,增加了收集到的能量。(The invention relates to an unmanned aerial vehicle data and energy transmission method under the assistance of an intelligent reflection surface, which relates to the technical field of unmanned aerial vehicle communication and comprises the steps that the intelligent reflection surface is attached to a building, and the unmanned aerial vehicle transmits data and energy through the intelligent reflection surface and ground equipment with a blocked line-of-sight link; establishing an uplink data transmission model and a downlink energy transmission model of the unmanned aerial vehicle and ground equipment, and setting an optimization problem of maximum energy collection of the ground equipment according to the data transmission requirement of the ground equipment; and decomposing the optimization problem into three subproblems by adopting a block coordinate descent and continuous convex optimization method, respectively solving each subproblem, and finally iterating the solution of each subproblem through an iterative algorithm to obtain a final solution. The invention provides an optimized time slot allocation scheme for data and energy transmission by adopting a scheduling scheme of the unmanned aerial vehicle and the ground equipment under the assistance of the intelligent reflecting surface, and increases the collected energy under the condition of ensuring the transmission requirement of the ground equipment data.)

1. The utility model provides an unmanned aerial vehicle data and energy transmission method under assistance of intelligent reflection surface which characterized in that: the data and energy transmission method comprises the following steps:

s1, attaching the intelligent reflection surface to a building around the ground equipment, and transmitting data and energy by the unmanned aerial vehicle through the intelligent reflection surface and the ground equipment with the blocked line-of-sight link;

s2, establishing an uplink data transmission model and a downlink energy transmission model of the unmanned aerial vehicle and the ground equipment, and setting an optimization problem of maximum energy collection of the ground equipment according to the data transmission requirement of the ground equipment;

s3, decomposing the optimization problem into three sub-problems by adopting a block coordinate descent and continuous convex optimization method, respectively solving each sub-problem, and finally iterating the solution of each sub-problem through an iterative algorithm to obtain a final solution.

2. The method of claim 1, wherein the method comprises: the method for decomposing the optimization problem into three subproblems by adopting a block coordinate descent and continuous convex optimization method, solving each subproblem respectively, and finally iterating the solution of each subproblem through an iterative algorithm to obtain a final solution comprises the following steps:

a1, time slot distribution variable for initializing data and energy transmissionFlight trajectory of unmanned aerial vehiclePhase shift matrix of intelligent reflective surface；

A2 at xi_r，Q_r，Θ_rAt a certain timing, the optimization problem is converted into oneLinear programming problem for optimizing variables and solving by using linear programming methodThe final dispatch A of the unmanned aerial vehicle and the ground equipment is obtained by solution_r+1Wherein r represents the number of iterations;

a3 at A_r+1，Ξ_r，Θ_rAt a certain time, the optimization variables of the optimization problem only have the track of the unmanned aerial vehicle, the track optimization problem of the unmanned aerial vehicle is solved by using a continuous convex optimization method, and the optimized track Q is obtained_r+1；

A4 according to the optimized track Q_r+1And optimal phase shift formulaObtaining the optimal phase shift matrix theta of the intelligent reflecting surface_r+1；

A5 at A_r+1，Q_r+1，Θ_r+1On a certain timing, the optimization problem is converted into a xi part_rIn order to optimize the linear programming problem of the variables, the optimal time slot assignment xi for data and energy transmission is obtained by solving the linear programming method_r+1；

A6, according to A_r+1，Q_r+1，Θ_r+1，Ξ_r+1Solving to obtain the maximum collected energy E of the current iteration_r+1Setting the iteration precision， For a predetermined iteration accuracy, ifUpdating the iteration number r = r +1, and returning to the step A2; otherwise, the process is finished directly.

3. The method of claim 1, wherein the method comprises: the unmanned aerial vehicle carries out data and energy transmission through intelligent reflection surface and the ground equipment that the stadia link is blockked includes:

discretizing the flight track of the unmanned aerial vehicle into M points, dividing the whole flight period T into M time slots with equal length, wherein the length of each time slot is delta tau;

in each time slot of the unmanned aerial vehicle in the flight process, the unmanned aerial vehicle firstly transmits wireless energy to the ground equipment, then receives data transmitted by the ground equipment, and usesIndicating the proportion of time slots used by the drone for charging the ground equipment at each time slot, whereinRepresenting a set of surface devices, K representing the total number of surface devices,representing the set of all time slots in the flight period of the unmanned aerial vehicle, wherein M represents the total number of the time slots;

in each time slot, the unmanned aerial vehicle selects at most one ground device for data and energy transmission, and uses a_k[t]Representing the dispatch of the drone with the ground equipment, a_k[t]=1 represents that the drone selects data and energy transmission with the kth ground equipment in the tth time slot;

the position coordinate of the unmanned plane in each time slot is [ q [ t ]],Z_U]^TWherein q [ t ]]=[x[t],y[t]]As a horizontal coordinate, Z_UIs the flying height of the unmanned plane.

4. The method of claim 3, wherein the method comprises: setting a path loss model for charging energy for kth ground equipment by the unmanned aerial vehicle at the t time slot asWhereinIs shown asPath loss from the intelligent reflective surface to the kth surface equipment,indicating that at the t-th time slot, from drone to dronePath loss of intelligent reflecting surface theta^kl[t]Indicating the phase shift matrix of the ith intelligent reflective surface for the kth land facility at the tth time slot,andrespectively representThe distance between the intelligent reflecting surface and the kth ground equipment and the distance between the intelligent reflecting surface and the unmanned aerial vehicle at the t-th time slot,represents from the firstThe cosine of the signal departure angle of the intelligent reflective surface to the kth surface unit,indicating that the time slot is from drone to tthAngle of arrival of intelligent reflective surfaceD is the antenna spacing, λ is the carrier wavelength, and ρ is the path loss for a reference distance of 1 m.

5. The method of claim 3, wherein the method comprises: the unmanned plane passes through the first slot at the t time slotChannel gain settings for each intelligent reflective surface to collect data transmitted by the kth surface unit，Indicating from the kth ground equipment to the kthThe channel gain of the intelligent reflecting surface,indicating from the kth ground equipment to the kthCosine of the angle of arrival of the signal at the intelligent reflective surface,indicates at the t-th time slot, from the t-thChannel gain, theta, from the intelligent reflective surface to the drone^kl[t]Is indicated at the t-th time slot, thAn intelligent counterThe phase shift matrix of the emitting surface for the kth terrestrial device,indicating that the signal is transmitted from the t-th time slotThe cosine value of the departure angle of the individual intelligent reflective surface to the unmanned aerial vehicle.

6. The method of claim 5, wherein the method comprises: for the kth terrestrial device, the achievable rate at the tth time slot isIn which P is_kRepresenting the transmitted power of the kth ground device, the energy that can be collected over the entire flight period isWhere σ is²Representing noise power, eta energy conversion efficiency, P_UWhich represents the power of the wireless energy transmission,and (4) a path loss model for charging the kth ground equipment by the unmanned aerial vehicle at the t time slot.

7. The method of claim 1, wherein the method comprises: expressing the phase shift of each reflective element of the intelligent reflective surface asWhereinIntelligent indicating reflection meterThe set of the faces is then selected,a set of reflective elements representing each of the intelligent reflective surfaces, having coordinates ofWhereinIs as followsThe horizontal coordinates of the intelligent reflecting surface,which represents the horizontal abscissa and represents the horizontal abscissa,the horizontal ordinate of the vertical axis,is the height of the intelligent reflective surface; the intelligent reflecting surface adopts a uniform linear arrayThe phase shift matrix of the intelligent reflecting surface to the kth ground equipment in the t time slot isWhereinRepresenting a diagonal matrix with a as diagonal element.

8. The method of claim 2 for data and energy transfer for unmanned aerial vehicle assisted by smart reflective surfaceThe method is characterized in that: the wireless energy signals reflected by the N reflection elements of the intelligent reflection surface have the same phase, i.e. the wireless energy signals are reflected by the N reflection elements(ii) a For each reflector, when the phase angle of each transmitted signal is set to 0, the maximum collected energy can be obtained, and thus the optimal phase shift can be expressed as。

9. The method of claim 2, wherein the method comprises: the optimization problem of maximizing the collected energy of the ground equipment is as follows:and setting a constraint condition:、、、、、and；

whereinRepresenting the energy collected by all surface equipment, D_kIndicating the data transmission requirement, P, of the kth ground facility_kRepresents the transmit power of the kth terrestrial device,represents the residual energy requirement, V, of the kth ground facility_maxMaximum flying speed for unmanned aerial vehicle，，，The scheduling variable sets of the unmanned aerial vehicle and the ground equipment, the flight trajectory of the unmanned aerial vehicle, the phase shift matrix of the intelligent reflecting surface and the variable sets of the time slot allocation of data and energy transmission are respectively represented.

10. The method of claim 9, wherein the method comprises: said at A_r+1，Ξ_r，Θ_rAt a certain time, the optimization variables of the optimization problem only have the track of the unmanned aerial vehicle, the track optimization problem of the unmanned aerial vehicle is solved by using a continuous convex optimization method, and the optimized track Q is obtained_r+1The method comprises the following steps:

a31, settingFor unmanned aerial vehicles at the r-th iterationTrace, q^r[t]Horizontal coordinate at the r-th iteration, settingWhere ρ is the path loss at a reference distance of 1m, N is the number of reflective elements per intelligent reflective surface,is shown asThe distance between the intelligent reflecting surface and the kth ground equipment is obtained by a first-order Taylor expansionThe lower bound of (a) is,representing the energy collected by the kth ground equipment through the l intelligent reflecting surface at the t time slot;

a32, utilizing lower boundAndand converting the problem of solving the trajectory optimization problem of the unmanned aerial vehicle into a problemThe constraint condition is that,、andwhereinThe optimization objective function and constraints of the optimization problem are all related to the trajectory q [ t ]]The optimization problem is converted into a convex problem which can be solved by a standard convex optimization solver to obtain an optimized trajectory Q_r+1。

Technical Field

The invention relates to the technical field of unmanned aerial vehicle communication, in particular to an unmanned aerial vehicle data and energy transmission method assisted by an intelligent reflection surface.

Background

Unmanned aerial vehicles play an increasingly important role in modern wireless networks, and as a wireless communication platform for air flight, unmanned aerial vehicles can serve as an air base station, a data collection platform, a mobile relay, a mobile edge computing cloud and the like. Unlike conventional base stations, due to their high mobility and operational flexibility, drones can avoid obstacles by flying higher up, establishing line-of-sight transmission links with communication nodes. By jointly optimizing the flight trajectory and resource allocation of the unmanned aerial vehicle, the system performance of the existing communication system can be greatly improved.

However, due to the randomness and unpredictability of the wireless propagation environment, the quality of the received signal may be uncontrollably degraded. Based on this problem, the intelligent reflective surface technology, which is emerging in the sixth generation mobile communication, can enhance the strength of signals in wireless networks. An Intelligent Reflective Surface (IRS) is a two-dimensional large-scale array Surface made of passive electromagnetic materials. Each passive reflector element may be configured to phase shift an incident signal under the intelligent controller of the intelligent reflective surface such that the reflected signal is coherent or destructive to achieve a predetermined reception goal. Due to its low power consumption and low cost, the smart reflective surface can be widely deployed in existing wireless communication environments.

In some urban areas, the line-of-sight link of the drone with the ground equipment may be severely blocked, and the movement of the drone makes the signal propagation environment more complex. In addition, the large path loss caused by the long distance between the unmanned aerial vehicle and the ground equipment is also one of the reasons for poor signal quality. At present, data transmission is mainly considered for unmanned aerial vehicle communication under the assistance of an intelligent reflection surface, and energy transmission is not involved. Nor do they consider the more practical case of multiple devices and multiple intelligent reflective surfaces.

Disclosure of Invention

The invention aims to overcome the defects of the prior art, provides a data and energy transmission method of an unmanned aerial vehicle assisted by an intelligent reflection surface, and can solve the problem of poor signal quality caused by the blockage of a sight distance link between the unmanned aerial vehicle and ground equipment, and solve the problem of insufficient energy supply of the equipment because the unmanned aerial vehicle can charge the ground equipment.

The purpose of the invention is realized by the following technical scheme: a method of data and energy transfer for a drone assisted by an intelligent reflective surface, the method comprising:

s1, attaching the intelligent reflection surface to a building around the ground equipment, and transmitting data and energy by the unmanned aerial vehicle through the intelligent reflection surface and the ground equipment with the blocked line-of-sight link;

s2, establishing an uplink data transmission model and a downlink energy transmission model of the unmanned aerial vehicle and the ground equipment, and setting an optimization problem of maximum energy collection of the ground equipment according to the data transmission requirement of the ground equipment;

s3, decomposing the optimization problem into three sub-problems by adopting a block coordinate descent and continuous convex optimization method, respectively solving each sub-problem, and finally iterating the solution of each sub-problem through an iterative algorithm to obtain a final solution.

The method for decomposing the optimization problem into three subproblems by adopting a block coordinate descent and continuous convex optimization method, solving each subproblem respectively, and finally iterating the solution of each subproblem through an iterative algorithm to obtain a final solution comprises the following steps:

a1, time slot distribution variable for initializing data and energy transmissionFlight trajectory of unmanned aerial vehiclePhase shift matrix of intelligent reflective surface；

A2 at xi_r，Q_r，Θ_rAt a certain time, the optimization problem is converted into a scheduling variable only using the unmanned aerial vehicle and the ground equipmentTo optimize the linear programming problem of the variables, and the final dispatch A of the unmanned aerial vehicle and the ground equipment is obtained by solving the linear programming method_r+1Wherein r represents the number of iterations;

a3 at A_r+1，Ξ_r，Θ_rAt a certain time, the optimization variables of the optimization problem only have the track of the unmanned aerial vehicle, the track optimization problem of the unmanned aerial vehicle is solved by using a continuous convex optimization method, and the optimized track Q is obtained_r+1；

A4 according to the optimized track Q_r+1And optimal phase shift formulaObtaining the optimal phase shift matrix theta of the intelligent reflecting surface_r+1；

A5 at A_r+1，Q_r+1，Θ_r+1At a certain time, the preference question is converted into a xi part_rIn order to optimize the linear programming problem of the variables, the optimal time slot assignment xi for data and energy transmission is obtained by solving the linear programming method_r+1；

A6, according to A_r+1，Q_r+1，Θ_r+1，Ξ_r+1Solving to obtain the maximum collected energy E of the current iteration_r+1Setting the iteration precision，For a predetermined iteration accuracy, ifUpdating the iteration number r = r +1, and returning to the step A2; otherwise, the process is finished directly.

The unmanned aerial vehicle carries out data and energy transmission through intelligent reflection surface and the ground equipment that the stadia link is blockked includes:

discretizing the flight track of the unmanned aerial vehicle into M points, dividing the whole flight period T into M time slots with equal length, wherein the length of each time slot is delta tau;

in each time slot of the unmanned aerial vehicle in the flight process, the unmanned aerial vehicle firstly transmits wireless energy to the ground equipment, then receives data transmitted by the ground equipment, and usesIndicating the proportion of time slots used by the drone for charging the ground equipment at each time slot, whereinA collection of surface devices is represented as,representing the set of all time slots within the flight period of the unmanned aerial vehicle;

in each time slot, the unmanned aerial vehicle selects at most one ground device for data and energy transmission, and uses a_k[t]Representing the dispatch of the drone with the ground equipment, a_k[t]=1 represents that the drone selects data and energy transmission with the kth ground equipment in the tth time slot;

the position coordinate of the unmanned plane in each time slot is [ q [ t ]],Z_U]^TWherein q [ t ]]=[x[t],y[t]]As a horizontal coordinate, Z_UThe flight height of the unmanned aerial vehicle is represented, and the solution of the track of the unmanned aerial vehicle is realized.

Setting a path loss model for charging energy for kth ground equipment by the unmanned aerial vehicle at the t time slot asWhereinIs shown asPath loss from the intelligent reflective surface to the kth surface equipment,indicating that at the t-th time slot, from drone to dronePath loss of intelligent reflecting surface theta^kl[t]Indicating the phase shift matrix of the ith intelligent reflective surface for the kth land facility at the tth time slot,andrespectively representThe distance between the intelligent reflecting surface and the kth ground equipment and the distance between the intelligent reflecting surface and the unmanned aerial vehicle at the t-th time slot,represents from the firstThe cosine of the signal departure angle of the intelligent reflective surface to the kth surface unit,indicating that the time slot is from drone to tthThe cosine of the arrival angle of the intelligent reflection surface, d is the antenna spacing, lambda is the carrier wavelength, and rho is the path loss when the reference distance is 1 m.

The unmanned plane passes through the first slot at the t time slotChannel gain settings for each intelligent reflective surface to collect data transmitted by the kth surface unit，Indicating from the kth ground equipment to the kthThe channel gain of the intelligent reflecting surface,indicating from the kth ground equipment to the kthCosine of the angle of arrival of the signal at the intelligent reflective surface,indicates at the t-th time slot, from the t-thChannel gain, theta, from the intelligent reflective surface to the drone^kl[t]Is indicated at the t-th time slot, thThe phase shift matrix for each intelligent reflective surface for the kth terrestrial device,indicating that the signal is transmitted from the t-th time slotThe cosine value of the departure angle of the individual intelligent reflective surface to the unmanned aerial vehicle.

For the kth terrestrial device, the achievable rate at the tth time slot isIn which P is_kRepresenting the transmitted power of the kth ground equipment, over the entire flight periodThe energy which can be collected isWhere σ is²Representing noise power, eta energy conversion efficiency, P_UWhich represents the power of the wireless energy transmission,and (4) a path loss model for charging the kth ground equipment by the unmanned aerial vehicle at the t time slot.

Expressing the phase shift of each reflective element of the intelligent reflective surface asWhereinA collection of intelligent reflective surfaces is represented,a set of reflective elements representing each of the intelligent reflective surfaces, having coordinates ofWherein，Which represents the horizontal abscissa and represents the horizontal abscissa,horizontal ordinate. The intelligent reflecting surface adopts a uniform linear arrayThe phase shift matrix of the intelligent reflecting surface to the kth ground equipment in the t time slot isWhereinAnd (4) representing a diagonal matrix taking a as a diagonal element to realize the model establishment of the optimization problem.

The wireless energy signal reflected by the N reflection elements of the smart reflective surface should have the same phase, i.e. the phase of the wireless energy signal is determined by the phase of the wireless energy signal(ii) a For each reflector, when the phase angle of each transmitted signal is set to 0, the maximum collected energy can be obtained, and thus the optimal phase shift can be expressed as。

The optimization problem of maximizing the collected energy of the ground equipment is set as follows:and setting a constraint condition:、、、、、and；

whereinRepresenting the energy collected by all surface equipment, D_kIndicating the data transmission requirement, P, of the kth ground facility_kRepresents the transmit power of the kth terrestrial device,represents the residual energy requirement, V, of the kth ground facility_maxIs the maximum flying speed of the unmanned aerial vehicle,，，，the scheduling variable sets of the unmanned aerial vehicle and the ground equipment, the flight trajectory of the unmanned aerial vehicle, the phase shift matrix of the intelligent reflecting surface and the variable sets of the time slot allocation of data and energy transmission are respectively represented.

In A_r+1，Ξ_r，Θ_rAt a certain time, the optimization variables of the optimization problem only have the track of the unmanned aerial vehicle, the track optimization problem of the unmanned aerial vehicle is solved by using a continuous convex optimization method, and the optimized track Q is obtained_r+1The method comprises the following steps:

a31, settingIs the trajectory of the drone at the r-th iteration, q^r[t]Horizontal coordinate at the r-th iteration, settingWhere ρ is the path loss at a reference distance of 1m and N is the number of reflective elements per intelligent reflective surface，Is shown asThe distance between the intelligent reflecting surface and the kth ground equipment is obtained by a first-order Taylor expansionThe lower bound of (a), wherein,representing the energy collected by the kth ground equipment through the l intelligent reflecting surface at the t time slot;

a32, utilizing lower boundAndand converting the problem of solving the trajectory optimization problem of the unmanned aerial vehicle into a problemThe constraint condition is that,、andwhereinThe optimization objective function and constraints of the optimization problem are all related to the trajectory q [ t ]]The optimization problem is converted into a convex problem which can be solved by a standard convex optimization solver to obtain an optimized trajectory Q_r+1。

The invention has the following advantages: an unmanned aerial vehicle data and energy transmission method under the assistance of an intelligent reflection surface, wherein the unmanned aerial vehicle utilizes the intelligent reflection surface to transmit data and energy, and when a line of sight link is blocked, the quality of signals is obviously improved; the scheduling scheme of the unmanned aerial vehicle and the ground equipment under the assistance of the intelligent reflecting surface is designed, an optimized time slot allocation scheme for data and energy transmission is given, and the collected energy is increased under the condition of ensuring the transmission requirement of the ground equipment data; the energy collected by the ground equipment through the intelligent reflecting surface is further improved through the track optimization of the unmanned aerial vehicle; the algorithm is suitable for the unmanned aerial vehicle data and energy transmission system under the assistance of the intelligent surfaces with different flight times and different flight heights.

Drawings

FIG. 1 is a data and power transmission schematic of the present invention;

FIG. 2 is a flow chart of the algorithm of the present invention;

FIG. 3 is a simulation graph comparing the performance of the proposed algorithm and other basic algorithms in collecting energy under different flight times

FIG. 4 is a comparative simulation plot of the proposed algorithm in terms of energy harvesting under intelligent reflective surfaces of different numbers of reflective elements;

FIG. 5 is a data simulation plot of the effect of different fly heights and different data transmission requirements on the amount of energy collected over time of flight.

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, generally described and illustrated in the figures herein, can 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 relates to an unmanned aerial vehicle data and energy transmission method assisted by an intelligent reflection surface. The problem of maximization of energy collection of ground equipment is solved by jointly designing scheduling of the unmanned aerial vehicle and the ground equipment, time slot allocation of data and energy transmission and flight path of the unmanned aerial vehicle and a phase shift matrix of an intelligent reflection surface; the validity of the proposed algorithm is verified using data simulation. The method specifically comprises the following steps:

as shown in fig. 1, the drone selects at most one device per timeslot for data and energy transmission, using a_k[t]To represent the dispatch of the drone with the ground equipment, a_k[t]=1 represents that the kth ground device and the unmanned aerial vehicle perform data and energy transmission in the t-th time slot; by usingAnd the allocation proportion of the timeslot when the unmanned aerial vehicle charges the kth device in the t-th timeslot is shown.

S1, firstly, giving a model of the system, the unmanned aerial vehicle transmits information and energy to the ground equipment by using the intelligent reflection surface.

S2, establishing an uplink data transmission model and a downlink energy transmission model of the unmanned aerial vehicle and the ground equipment under the intelligent reflection surface.

S3, under the condition of meeting the data transmission requirement of the ground equipment, the optimization problem of the maximization of the collected energy of the ground equipment is provided.

S4, an energy maximization algorithm is provided to solve the optimization problem by using the block coordinate descent and continuous convex optimization technology.

As shown in fig. 2, the specific operation steps are as follows:

a1, time slot distribution variable for initializing data and energy transmissionFlight trajectory of unmanned aerial vehiclePhase shift matrix of intelligent reflective surfaceLet iteration number r = 0;

a2 for a given xi_r，Q_r，Θ_rThe optimization problem becomes a linear programming problem only taking A as an optimization variable,in order to optimize the problem of linear programming of variables,

，

，

，

，

solving the optimal scheduling variable A of the unmanned aerial vehicle and the ground equipment by using a convex optimization toolkit_r+1；

A3, for a given A_r+1，Ξ_r，Θ_rThe optimization variables of the optimization problem are only the track of the unmanned aerial vehicle,

，

，

，

converting the non-convex part in the problem into a convex function related to a track variable Q by using a continuous convex optimization technology, wherein the specific conversion steps are as follows:

a31, definitionFor the trajectory of the drone at the r-th iteration, orderWhere ρ is the path loss at a reference distance of 1m, N is the number of reflective elements per intelligent reflective surface,is shown asThe distance of the intelligent reflective surface from the kth floor facility,

then it can be obtained by a first order Taylor expansionThe lower bound of (c) is as follows:，

thus, the energy collected by all surface equipment can be expressed asThis is a concave function with respect to the flight trajectory Q. In the same way, defineApplying a first order Taylor expansion toCan obtain，

， 。

A32, therefore, utilizing the lower boundAndthe problem of solving the trajectory optimization of the drone can be converted into the following problem,

，

，

，

whereinAt this time, the optimization objective function and constraint of the problem are all about the trajectory q [ t ]]The problem is converted into a convex problem which can be solved by a standard convex optimization solver to obtain an optimized trajectory Q_r+1；

A4, for each time slot, the drone can only provide the service of charging and data collection for one ground device at most. At this time, in order to maximize the energy collected by all the ground equipment, the wireless energy signals reflected by the N reflection elements of the intelligent reflection surface should have the same phase, i.e., the same phase(ii) a For each reflector, when the phase angle of each transmitted signal is set to 0, the maximum collected energy can be obtained, and thus the optimal phase shift can be expressed as(ii) a Will Q_r+1Substituting the expression to obtain the optimal phase shift matrix theta of the intelligent reflecting surface_r+1；

A5, for a given A_r+1，Q_r+1，Θ_r+1The optimization problem becomes a more than xi_rIn order to optimize the problem of linear programming of variables,

，

，

，

solving optimal timeslot assignment xi for data and energy transmission using convex optimization toolkit_r+1；

A6, use of A_r+1，Q_r+1，Θ_r+1，Ξ_r+1Determining the maximum energy E collected for the current iteration_r+1Let us order，For a set iteration accuracy, ifAnd updating the iteration number r = r +1, returning to the step A2, and otherwise, directly ending.

In the simulation process of the invention, the flying height of the unmanned aerial vehicle is assumed to be unchanged in the whole flying period, the initial position and the final position of the unmanned aerial vehicle are the same, and the initial track of the unmanned aerial vehicle passes above each ground device. The path loss value is set to ρ = -30dB, and the noise power is set to σ²= -110dBm, the maximum speed of the unmanned aerial vehicle is set to 0.5m/s, the method simulates 6 pieces of ground equipment and 6 intelligent reflection surfaces with 100 reflection elements, and the specific process is as follows:

as shown in fig. 3, the proposed algorithm compares the performance of the proposed algorithm in collecting energy with other basic algorithms for different flight times. Other basic algorithms are implemented as follows: the initial trajectory represents that the trajectory of the unmanned aerial vehicle is not optimized in the whole iteration process, and the initial trajectory is kept unchanged all the time; the fixed charging time slot allocation means that each time slot, and the charging time of the ground equipment by the unmanned aerial vehicle is kept unchanged in the whole flight period; the genetic algorithm means that the scheduling of the unmanned aerial vehicle and the ground equipment is solved in iteration by using the genetic algorithm. From fig. 3, it can be seen that the algorithm proposed by the present invention is superior to other algorithms in energy harvesting. With the increase of the flight period, the energy collected by the ground equipment is continuously increased, and more energy can be collected by optimizing the flight track of the unmanned aerial vehicle, aligning the phase of the reflected signal, optimizing the scheduling of the unmanned aerial vehicle and the ground equipment and optimizing the time slot ratio of energy transmission.

FIG. 4 is a comparative simulation plot of the proposed algorithm in terms of energy harvesting under intelligent reflective surfaces of different numbers of reflector elements; it can be seen from fig. 4 that the invention has a large increase in the collected energy with the number of reflector elements, since the intelligent reflective surface will have an N when the phases of all the reflected signals are aligned²Gain of (i.e.Meanwhile, the proposed algorithm is superior to other basic algorithms in energy collection.

FIG. 5 is a data simulation graph of the effect of different flight heights and different data transmission requirements on the amount of energy collected under time-of-flight variations. It can be seen from fig. 5 that for the same flight altitude, the less data transmission requirements, the more energy is collected. Furthermore, the ground devices may collect more energy when the flying height of the drone is lower.

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.