CN113507304B - Intelligent reflector-assisted unmanned aerial vehicle safety communication method - Google Patents

Intelligent reflector-assisted unmanned aerial vehicle safety communication method Download PDF

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CN113507304B
CN113507304B CN202110840400.3A CN202110840400A CN113507304B CN 113507304 B CN113507304 B CN 113507304B CN 202110840400 A CN202110840400 A CN 202110840400A CN 113507304 B CN113507304 B CN 113507304B
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unmanned aerial
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CN113507304A (en
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逄小玮
赵楠
刘明骞
邹德岳
陈炳才
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Dalian University of Technology
Xidian University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/04013—Intelligent reflective surfaces
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/02—Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition

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Abstract

An intelligent reflector-assisted unmanned aerial vehicle safety communication method belongs to the field of unmanned aerial vehicle communication safety. The unmanned aerial vehicle sends the confidential information to a legal user on the ground, and an eavesdropper in the vicinity of the legal user wants to eavesdrop the information of the legal user. In order to ensure the safe transmission of private information, an intelligent reflecting surface is installed on the outer vertical surface of a building to help an unmanned aerial vehicle and a ground legal user to realize safe communication. By utilizing the maneuverability of the unmanned aerial vehicle, the multi-antenna beam forming gain and the passive reflection and phase-adjustable characteristics of the intelligent reflecting surface, the communication quality of a ground legal user can be enhanced, and malicious eavesdropping can be destroyed. According to the invention, the flight path of the unmanned aerial vehicle, the emission beam forming and the phase shift design of the reflecting surface are jointly optimized, so that the safety transmission rate is maximized, and information leakage is prevented as far as possible.

Description

Intelligent reflector-assisted unmanned aerial vehicle safety communication method
Technical Field
The invention belongs to the field of unmanned aerial vehicle communication safety, and relates to a method for realizing safe information transmission to a ground user by an intelligent reflecting surface auxiliary flying unmanned aerial vehicle.
Background
With the continuous development of scientific technology and the continuous reduction of manufacturing cost, unmanned aerial vehicles gradually expand from the military field to the commercial and civil fields, and begin to enter the production and life of people. The unmanned aerial vehicle has the advantages of small size, easiness in operation, high flexibility, strong adaptability and the like, and is particularly suitable for sudden application scenes needing rapid deployment. Under most circumstances, the unmanned aerial vehicle can establish a line-of-sight communication link with the ground, so that the unmanned aerial vehicle can be used as an aerial communication platform to make up the deficiency of ground communication, and provide communication service with higher cost performance for users. Although drones play an important role in the field of communications, drone communication systems also face many new challenges. On one hand, the ground line-of-sight communication channel of the unmanned aerial vehicle is easily blocked; on the other hand, the openness of the wireless channel and the wireless channel with the main view distance of the unmanned aerial vehicle make the unmanned aerial vehicle communication system face a great security threat, and sensitive and private information is inevitably collected, processed or sent in the process of realizing communication through the unmanned aerial vehicle.
Currently, in the sixth generation of mobile communication research, an intelligent reflecting surface with the capability of manipulating a wireless propagation environment becomes a hot spot of active research, and the intelligent reflecting surface is expected to improve the channel condition of unmanned aerial vehicle communication and enhance the safety of transmission information. The intelligent reflecting surface consists of a large number of passive reflecting elements, is controlled by a controller, and can adjust the reflecting angle and the reflecting intensity, thereby realizing the control of the intensity and the phase of the reflected signal. The intelligent reflecting surface has the advantages of low power consumption, easy installation, strong compatibility and the like, and does not have self-interference or generate transmitting power consumption. Therefore, the intelligent reflecting surface can be utilized to assist the unmanned aerial vehicle in reconstructing a communication link, the problem that the communication distance is limited and the shielding is easy to happen is solved, and the coverage range of a communication system is further improved. On the other hand, to prevent the drone communication from being attacked and intercepted, traditional means such as beam forming and artificial noise addition do not always work, for example, in a scenario where an eavesdropper is around a legitimate user and the channel correlation is strong. In this case, the intelligent reflecting surface can intelligently control the reflected signal, so as to enhance the receiving power of a legal user and weaken the receiving power of an eavesdropper, thereby improving the safety of the system.
Most of the existing researches aim at intelligent reflecting surface assisted land communication, and less schemes for expanding the coverage and safety performance of an air-ground communication network are provided. By utilizing the flexible mobility of the unmanned aerial vehicle and the phase adjustability of the intelligent reflecting surface, the track of the unmanned aerial vehicle and the passive beam forming of the reflecting surface can be designed in a combined manner, and a high-speed safe air-ground wireless communication network is realized.
In the invention, a new method for realizing safe transmission rate maximization by using an intelligent reflecting surface to assist an unmanned aerial vehicle is provided, and the specific scheme is shown in fig. 1. The drone sends confidential information to a secure user on the groundAn eavesdropper near the full user wants to eavesdrop on the information of the legitimate user. The direct communication link between the drone and the ground node is blocked due to the complex terrain environment. In order to ensure the safe transmission of private information, an intelligent reflecting surface is installed on the outer facade of the building to help reflect the signal of the unmanned aerial vehicle. The number of the antennas installed on the unmanned aerial vehicle is NuAbove ground HuThe altitude plane flies with a period T. For convenience in representation and design, the whole flight process is divided into N time slots, and the trajectory of the unmanned aerial vehicle in the whole flight process is represented as a discrete vector. By jointly optimizing the unmanned aerial vehicle trajectory, the transmit beam forming and the phase shift matrix of the reflecting surface, the average safety rate of the user is maximized.
Disclosure of Invention
In order to solve the problem of blocking of a communication link of the unmanned aerial vehicle and guarantee safe transmission of private information, the invention provides a method for realizing safe ground communication by using an intelligent reflecting surface to assist a multi-antenna flying unmanned aerial vehicle. By utilizing the self maneuverability of the unmanned aerial vehicle, the multi-antenna beam forming gain and the passive reflection and phase-adjustable characteristics of the intelligent reflecting surface, the communication quality of a ground legal user can be enhanced and malicious eavesdropping can be destroyed. In an air-ground communication network with an unmanned aerial vehicle as a transmitting end and eavesdroppers around a ground legal user, the maximization of the safe transmission rate can be realized by planning the flight trajectory of the multi-antenna unmanned aerial vehicle, jointly optimizing the transmitting beam forming and the reflecting beam forming, and the information leakage is prevented as far as possible.
In order to solve the technical problems, the invention adopts the following technical scheme:
an intelligent reflector-assisted unmanned aerial vehicle secure communication method utilizes an intelligent reflector to enhance the received signal power of an expected user, and destroys the intelligent reflector at an eavesdropping end to ensure the secure communication of the ground-to-ground communication confidentiality of an unmanned aerial vehicle. The method improves the privacy speed to the maximum extent by jointly optimizing the emission beam forming and the flight trajectory of the multi-antenna unmanned aerial vehicle and the passive beam forming of the intelligent reflecting surface. The method comprises the following concrete steps:
firstly, establishing a network and channel model:
(1) and determining a network topological structure and establishing a three-dimensional Cartesian coordinate system. Wherein the horizontal coordinates of the legal user and the eavesdropper are respectively Cl＝[xl,yl]TAnd Ce＝[xe,ye]TAnd (4) showing. The first element of the reflecting surface is taken as a reference point, and the horizontal coordinate is CI＝[xI,yI]TAnd height is HI. The total flight process of the unmanned aerial vehicle is divided into N time slots, and the coordinate projected to the ground in the nth time slot is recorded as q [ N ]]＝[x[n]，y[n]]TN is 1, 2. The initial position and the final position of the unmanned aerial vehicle during flight are fixed, q respectivelyIAnd q isF. The unmanned aerial vehicle is constrained by speed and the like in the flying process, so that the following movement constraint conditions are met.
Figure BDA0003178602280000021
Wherein, VmIs the maximum flying speed of the unmanned plane, T is the total flying period of the unmanned plane, N is the total time slot number, therefore (V)mT)/N represents the maximum distance that the drone can move in each slot.
(2) The intelligent reflecting surface is a planar array formed by a large number of reconfigurable passive elements, contains M reflecting elements, and is connected with the unmanned aerial vehicle through a software controller. Phase shift matrix for intelligent reflector in nth time slot
Figure BDA0003178602280000022
The representation is a diagonal matrix. The elements in diag () are diagonal elements of the matrix, where
Figure BDA0003178602280000023
Indicating the phase change caused by the mth reflection element in the nth time slot, and j represents an imaginary unit.
(3) A direct link from the unmanned aerial vehicle to a ground node is blocked by ground buildings, trees and the like, and a large number of scattering links exist; thus, from drone to legitimate usersAnd the eavesdropper's channel follows rayleigh fading. Direct communication channel vectors from the unmanned aerial vehicle to a legal user and an eavesdropper are respectively expressed as
Figure BDA0003178602280000031
And
Figure BDA0003178602280000032
because the unmanned aerial vehicle is in the air, the vertical height of the intelligent reflecting surface on the building is higher, and the unmanned aerial vehicle and the intelligent reflecting surface have a sight distance communication channel, the channel matrix from the unmanned aerial vehicle to the reflecting surface is represented as HUI[n]. The channel between the reflecting surface and the terrestrial user contains both direct and multiple scattering paths and is therefore subject to the leis fading model. The channel vectors from the reflecting surface to the secure user and the eavesdropper are denoted hI,l[n]And hI,e[n]。
(4) In slot n, the transmit precoding vector of the drone is denoted w n. By using the channel vectors represented in the reflecting surface phase shift matrixes Φ [ n ] in (2) and (3), the reachable rates of a legal user and an eavesdropper in any time slot are respectively calculated as follows:
Figure BDA0003178602280000033
Figure BDA0003178602280000034
wherein the content of the first and second substances,
Figure BDA0003178602280000035
and
Figure BDA0003178602280000036
respectively representing the power of additive white gaussian noise at the receiving end of a legal user and an eavesdropping user.
Therefore, the average achievable safe rate for N slots is expressed as:
Figure BDA0003178602280000037
wherein, [ x ]]+
Figure BDA00031786022800000312
Is a non-smooth operator. In fact RL[n]-RE[n]Always guaranteeing a non-negative value, because if it is a negative value, R can be set to zero by adjusting the transmission powerL[n]-RE[n]0. That is, RL[n]-RE[n]More than or equal to 0 can always meet the requirement. Thus the non-smoothing operator [ ·]+The optimal solution to the problem is not affected and so is omitted in the subsequent problem solving.
Secondly, constructing an optimization problem and designing an algorithm to solve:
in order to ensure the communication safety of a legal user and fully exert the potential of combining the unmanned aerial vehicle and the intelligent reflecting surface, the average safety rate of the user can be maximized by constructing and solving a joint optimization problem. Wherein the optimization variables include a set of flight trajectories of the unmanned aerial vehicle
Figure BDA0003178602280000038
Transmit beamforming vector set
Figure BDA0003178602280000039
And phase shift matrix set of intelligent reflective surfaces
Figure BDA00031786022800000310
The security rate maximization optimization problem established in the network is shown as (P1):
(P1):
Figure BDA00031786022800000311
Figure BDA0003178602280000041
Figure BDA0003178602280000042
Figure BDA0003178602280000043
Figure BDA0003178602280000044
wherein, PuFor the maximum transmit power of the drone, m represents the index of the reflection element and n represents the number of slots.
The constraint conditions comprise actual flight constraint of the unmanned aerial vehicle, the phase shift range of the reflection element is 0-2 pi, and the maximum available transmission power limit of the unmanned aerial vehicle. Although the constraints are all convex constraints, the optimization variables in the objective function are coupled to each other and non-convex, making this problem difficult to solve. Based on the idea of a block coordinate descent method, the complex non-convex problem can be decomposed into three sub-optimization problems, and the sub-problems are alternately solved by adopting an iterative algorithm.
And step three, respectively processing and solving three sub-optimization problems:
(1) fixing unmanned aerial vehicle trajectory and reflection phase shift, optimizing transmit beam forming
When the unmanned aerial vehicle track and the reflecting surface phase shift matrix are fixed, the optimal unmanned aerial vehicle transmitting beam forming vector w [ n ] needs to be obtained by solving the following optimization problem:
(P2):
Figure BDA0003178602280000045
wherein, wH[n]Representative vector w [ n ]]The conjugate of the transposed vector of (a),
Figure BDA0003178602280000046
and
Figure BDA0003178602280000047
representing the valid channels of a legitimate user and an eavesdropper, respectively. To solve the problem (P2), goOne step introduction of two auxiliary matrices
Figure BDA0003178602280000048
And
Figure BDA0003178602280000049
and make mathematical transformation
Figure BDA00031786022800000410
And
Figure BDA00031786022800000411
by utilizing the existing optimization knowledge and solution method, the optimal transmitting beam forming vector w of the problem (P2) can be directly obtainedopt[n]The closed expression of (1). Definition matrix
Figure BDA00031786022800000412
Wherein
Figure BDA00031786022800000413
Is dimension NuThe identity matrix of (2). Then, the optimal transmit beamforming vector expression can be obtained as
Figure BDA00031786022800000414
Wherein v ismax[n]Is a matrix A [ n ]]The feature vector corresponding to the maximum feature value of (1).
(2) Optimization of reflecting surface phase shift matrix
Obtaining the optimal transmit beam forming solution w at the flight track of the fixed unmanned aerial vehicle and by adopting the methodopt[n]On the basis of (1), a subproblem of optimizing the reflected beam forming is equivalent to maximizing the ratio of the signal-to-noise ratio of a legitimate user to the signal-to-noise ratio of an eavesdropper. Introducing auxiliary variable uH[n]＝[θ1[n],...,θm[n],...,θM[n]]Each term θm[n]Representing the mth diagonal element of the reflecting surface phase shift matrix. For convenience of presentation, define
Figure BDA0003178602280000051
Figure BDA0003178602280000052
Solving for optimal reflecting surface phase shift is equivalent to optimizing u [ n ]]It can be expressed as the following sub-optimization problem form:
(P3):
Figure BDA0003178602280000053
the problem belongs to a fractional programming problem, so that the fractional programming problem can be equivalently converted into a series of sub-optimization problems with parameters by adopting a Dinkelbach algorithm. Introducing a parameter variable etarThe upper right-hand corner r represents the r-th iteration, so the problem (P3) can be translated into the following band parameter ηrThe sub-problems of (1):
(P4):
Figure BDA0003178602280000054
however, the objective function of the problem is still non-convex, and in order to reduce the solving complexity of the optimization problem, the optimization problem can be minimized by approximately solving the upper bound function of the objective function. EtarIs a non-negative parameter, and the initial value eta is updated along with the iteration number r 00. Each etarAnd the corresponding subproblems can obtain closed expressions of phase shift elements until the algorithm converges, and finally, the phase shift matrix of the reflecting surface is output.
(3) Unmanned aerial vehicle flight trajectory optimization
The transmit beamforming and intelligent reflecting surface phase shift matrices may be obtained by solving sub-problems (P2) and (P4), respectively. According to the thought of block optimization, the two variables are fixed next, and the unmanned aerial vehicle track variable Q is optimized. This optimization sub-problem can be expressed as:
(P5):
Figure BDA0003178602280000055
the constraints of this optimization problem are convex, but the objective function is non-convex and non-concave and cannot be solved directly. The continuous convex approximation technology can be adopted to convert the objective function intoAn affine function and a plurality of convex constraints. Introducing auxiliary variables
Figure BDA0003178602280000056
And
Figure BDA0003178602280000057
the objective function in the problem (P5) can be translated into the following objectives and new constraints:
Figure BDA0003178602280000061
wherein the content of the first and second substances,
Figure BDA0003178602280000062
the received signal power strength of a legitimate user and an eavesdropper, respectively. Their lower and upper bound functions can be obtained by relaxation techniques and first order taylor series expansions as functions instead of them. Since both the upper and lower bound functions are affine functions, the constraint condition is a functional expression that satisfies the concavity and convexity requirement. The final transformed trajectory optimization is a convex optimization problem that can be solved using existing convex optimization tools such as CVX.
Fourthly, designing an overall optimization algorithm:
the invention provides a two-layer iterative algorithm to solve the problem of joint optimization of security rate maximization. In the third step, three sub-optimization problems are described and solved respectively, and in order to obtain the optimal solution of three variables in the original optimization problem (P1), an overall optimization algorithm is designed. The algorithm comprises an inner layer iteration and an outer layer iteration: the outer layer iteration is based on the idea of block coordinate reduction, and three sub-problems in the third step are solved in a blocking mode, namely the other two variables are fixed, one variable is optimized, and the optimization is carried out alternately in an iteration mode; the inner layer iteration is carried out when two sub-problems of a reflecting surface phase shift matrix and an unmanned aerial vehicle track are solved. Specifically, when solving two sub-problems of reflector phase shift matrix design and unmanned aerial vehicle trajectory optimization, an expression of an optimal solution cannot be directly obtained, and a group of approximate sub-optimization problems need to be solved. And continuously updating the solutions of the reflecting surface phase shift matrix and the unmanned aerial vehicle track by adopting inner layer iteration until the algorithm converges. Since the objective function of the original problem (P1) is bounded, and each solved subproblem can gradually approximate to the optimal solution of the original problem through iteration, the overall algorithm finally converges to a finite value.
The intelligent reflection surface has the advantages of high energy efficiency, easiness in installation and capability of reshaping a wireless transmission environment, and the unmanned aerial vehicle is assisted to realize safe transmission to the ground. And (3) realizing a transmission scheme for maximizing the safe speed by jointly designing the flight path of the unmanned aerial vehicle, the transmitting beam forming and the reflecting beam forming. The invention provides a new method for enhancing the communication security performance of the unmanned aerial vehicle by using the intelligent reflecting surface, and provides important theoretical and technical reference for the combination of the reflecting surface and the unmanned aerial vehicle and the improvement of the privacy of network legal users.
Drawings
Fig. 1 is a schematic diagram of an intelligent reflector assisted unmanned aerial vehicle secure communication network.
Fig. 2 shows a network topology structure, the number of elements of different reflection surface elements and a flight trajectory diagram of the unmanned aerial vehicle in different periods.
Fig. 3 simulation verifies the convergence of the proposed iterative algorithm.
Fig. 4 compares the average lawful rate and the eavesdropping rate of two optimization schemes under different transmitting powers and transmitting antennas.
Fig. 5 shows a comparison of the safety rate performance of the two optimization schemes under different transmission powers and antenna numbers.
FIG. 6 impact of the flight cycle on the safe rate for different optimization schemes.
Fig. 7 shows that the safety rate is affected by the number of reflecting elements in the case of different numbers of transmitting antennas.
FIG. 8 influence of intelligent reflector placement on network security rates.
Detailed Description
The technical scheme is further explained by combining the drawings and the specific implementation mode, and specific simulation result analysis is given.
Firstly, establishing a network model, and determining a network topology structure:
and establishing a three-dimensional Cartesian coordinate system by taking the ground projection position of the first element of the intelligent reflecting surface as an origin. Suppose the horizontal coordinate of a legitimate user is Cl＝[20,30]Tm, an eavesdropper randomly generates nearby legitimate users. The height of the reflecting surface is HI30m, unmanned aerial vehicle flying height is Hu100 m. The starting position and the ending position of the unmanned aerial vehicle are q respectivelyI＝[-300,80]Tm and qF＝[300,80]Tm, maximum flying speed Vm20m/s, noise power of
Figure BDA0003178602280000071
When not specifically stated, the transmission power P is adopted by defaultu20dBm, N is used for the number of antennas and the number of reflection elements u16 and M64.
And step two, respectively processing and solving three sub-optimization problems:
according to the idea of block coordinate descending, an original complex non-convex problem is decomposed into three sub-optimization problems, and the sub-problems are alternately solved by adopting an iterative algorithm. When one variable is optimized, the other two variables are fixed, namely three sub-optimization problems of (P2), (P4) and (P6) are solved respectively.
When the transmit beam forming and the intelligent reflecting surface phase shift matrix are fixed, the flight path of the unmanned aerial vehicle is optimized. The flight trajectories of drones with or without reflector assistance and at different periods are plotted in fig. 2, and an example of a network topology is shown. It can be seen that the eavesdropper is near the legitimate user and closer to the reflective surface. It can be seen from the figure that T-30 s is the minimum period for the drone to fly from the initial position to the end, in which case the drone can only fly straight at maximum speed. When there is no reflector assistance in the network (M ═ 0), nobody can get away from the eavesdropper to avoid eavesdropping. In the presence of reflector assistance, the flight trajectory of the drone at T50 s is similar to a parabola with the legal user coordinates as the vertex. And when T is 100s, the unmanned aerial vehicle flies to the position between the user and the reflecting surface from the starting point in a straight line, and then reaches the end point after wandering near the user for a period of time. This flight path can help establish a better direct path between the user and the drone, and the reflected signal can also be enhanced with the aid of the reflecting surface.
Thirdly, designing an overall optimization algorithm:
the invention provides a two-layer iterative algorithm to solve the problem of joint optimization of security rate maximization. The convergence result of the overall iterative optimization algorithm is shown in fig. 3. In the embodiment shown in fig. 3, the parameters T-40 s and N-40 are set, and the cases of M-64 and M-100 were respectively simulated and tested. It can be observed that the algorithm can converge quickly, not more than 10 times. In addition, the average safe rate that can be achieved by M-100 is significantly higher than the safe rate after M-64 finally converges.
The purpose of maximizing the safety rate can be achieved by solving the three subproblems and adopting an overall optimization algorithm. To further demonstrate the advantage of this scheme in improving network security performance, fig. 4 and 5 compare the network rate performance of the maximum safe rate scheme and the maximum legal rate scheme, respectively at N u32 and NuSimulation experiments were performed under 16 conditions. As can be seen from fig. 4, since the goal of the latter is to maximize the legitimate rate, the legitimate user rate finally achieved is higher than the scheme that maximizes the safe rate, but the time difference is small when the power does not exceed 20 dBm. In contrast, the gap in the eavesdropping rate is more pronounced, maximizing the security rate allows the eavesdropping rate to approach zero, whereas maximizing the eavesdropping rate for the legal rate scheme is higher and varies with the transmission power PuIs increased. The results in fig. 5 show that the average safe rate of the maximized safe rate scheme is higher than the maximized legal rate scheme, and the larger the transmission power, the larger the gap in the safe rates. By combining the comparison results of the two schemes in fig. 4 and 5, it is verified that the method provided by the present invention can effectively suppress eavesdropping while ensuring a higher legal rate, thereby achieving the purpose of maximizing a security rate.
Fourthly, the influence of the flight period of the unmanned aerial vehicle, the number of the transmitting antennas and the reflecting elements and the installation position of the reflecting surface on the network safety performance is simulated and analyzed:
fig. 6 compares the safe rate with the flight period T of the drone for several different scenarios. Data show that the average safe speed of the uniform-speed linear flight scheme of the unmanned aerial vehicle hardly changes along with the flight period. Since the rate at which the drone serves users is also uniform during this period, there is little change after averaging. The average safe rate of the other two schemes increases with the increase of the flight period, and because the track of the unmanned aerial vehicle is optimized, the unmanned aerial vehicle has more time to stay between a legal user and the reflecting surface to provide better service. In addition, the safety performance of the optimization scheme provided by the invention is obviously superior to that of the scheme without the assistance of a reflecting surface and without optimizing the track of the unmanned aerial vehicle.
Next, fig. 7 further analyzes the effect of the number of transmit antennas and reflective elements on the average security rate. Assuming that the total element M of the intelligent reflecting surface is MxMzWherein M isx＝MzThe number of elements along the X-axis and Z-axis directions, respectively. As can be seen from the graph, the increased number of transmitting antennas can greatly improve the security rate, which is consistent with the theoretical analysis. The greater the number of antennas, the better the beamforming effect and the better the performance that can be achieved. As the number of reflective elements increases, the security rate also increases. This is because there are more phase shift elements that can be optimized, the reconstructed valid user equivalent channel quality is better, and the received signal strength at the valid user will be enhanced.
Finally, FIG. 8 analyzes the effect of intelligent reflective surface mounting at different locations on the average safe rate. The height of the reflecting surface is fixed and moves along the X axis, namely the Y coordinate is 0 and X all the timeIAre constantly changing. The results show that the position of the reflecting surface installation can influence the average safety rate which can be reached by the network, but the position does not monotonically increase or decrease along with the change of the coordinate of the reflecting surface. In the range of-100 m to 100m, the optimum reflecting surface abscissa is circled in the figure. At Pu＝30dBm，NuWhen equal to 16, xI-100m is the optimal position; at Pu＝20dBm,N u32 or NuSet at 16, xIA higher average safe rate can be achieved at 50 m. The research result enlightens that in practical engineering application, an intelligent reflecting surface is deployed according to practical conditions to achieve the best effect.
It should be noted that the above-mentioned examples only express the embodiments of the present invention, but do not limit the present invention, and the present invention is not limited to the above-mentioned examples. Modifications and alterations may be made by those skilled in the art without departing from the spirit of the invention, and these are within the scope of the invention.

Claims (1)

1. The utility model provides an unmanned aerial vehicle safety communication method assisted by intelligent reflector, which is characterized by comprising the following steps:
firstly, establishing a network and channel model:
step 1.1, determining a network topological structure and establishing a three-dimensional Cartesian coordinate system; wherein the horizontal coordinates of the legal user and the eavesdropper are respectively Cl＝[xl,yl]TAnd Ce＝[xe,ye]TRepresents; the first element of the reflecting surface is taken as a reference point, and the horizontal coordinate is CI＝[xI,yI]THeight of HI(ii) a The total flight process of the unmanned aerial vehicle is divided into N time slots, and the coordinate projected to the ground in the nth time slot is recorded as q [ N ]]＝[x[n],y[n]]TN is 1,2,. N; the initial position and the final position of the unmanned aerial vehicle during flight are fixed, q respectivelyIAnd q isF(ii) a The unmanned aerial vehicle is constrained in the flight process and needs to meet the following movement constraint conditions;
Figure FDA0003577134400000011
wherein, VmIs the maximum flying speed of the unmanned plane, T is the total flying period of the unmanned plane, N is the total time slot number, therefore (V)mT)/N represents the maximum distance that the unmanned aerial vehicle can move in each time slot;
step 1.2, the intelligent reflecting surface is a planar array consisting of a large number of reconfigurable passive elements, contains M reflecting elements, and is connected with the unmanned aerial vehicle through a software controller; for phase-shift matrices of intelligent reflectors in the nth time slot
Figure FDA0003577134400000012
Representing, is a diagonal matrix; the elements in diag () are diagonal elements of the matrix, where
Figure FDA0003577134400000013
Representing the phase change of the mth reflection element caused in the nth time slot, wherein j represents an imaginary number unit;
step 1.3 the channels from the drone to the legitimate users and the eavesdropper follow Rayleigh fading, and the direct communication channel vectors from the drone to the legitimate users and the eavesdropper are respectively expressed as
Figure FDA0003577134400000014
And
Figure FDA0003577134400000015
because the unmanned aerial vehicle is in the air, the vertical height of the intelligent reflecting surface on the building is higher, and the unmanned aerial vehicle and the intelligent reflecting surface have a sight distance communication channel, the channel matrix from the unmanned aerial vehicle to the reflecting surface is represented as HUI[n](ii) a The channel between the reflecting surface and the ground user comprises direct and multiple scattering paths, and therefore is subject to a rice fading model; the channel vectors from the reflecting surface to the secure user and the eavesdropper are denoted hI,l[n]And hI,e[n]；
Step 1.4, when a time slot n is used, the transmitting precoding vector of the unmanned aerial vehicle is represented as w [ n ]; by using the reflecting surface phase shift matrix Φ [ n ] in step 1.2 and the channel vector represented in step 1.3, the reachable rates of a legitimate user and an eavesdropper at any time slot are respectively calculated as:
Figure FDA0003577134400000016
Figure FDA0003577134400000021
wherein the content of the first and second substances,
Figure FDA0003577134400000022
and
Figure FDA0003577134400000023
respectively representing the power of additive white Gaussian noise at the receiving ends of a legal user and an eavesdropping user;
therefore, the average achievable safe rate for N slots is expressed as:
Figure FDA0003577134400000024
wherein the content of the first and second substances,
Figure FDA0003577134400000025
a non-smooth operator; in fact RL[n]-RE[n]Always guaranteeing non-negative values, if a negative value is obtained, then adjusting the transmission power to zero to make RL[n]-RE[n]When R is equal to 0, R is always guaranteedL[n]-RE[n]≥0；
Secondly, constructing an optimization problem and designing an algorithm to solve:
the average security rate of the user is maximized by constructing and solving a joint optimization problem, and the communication security of a legal user is ensured; wherein the optimization variables include a set of flight trajectories of the unmanned aerial vehicle
Figure FDA0003577134400000026
Transmit beamforming vector set
Figure FDA0003577134400000027
And phase shift matrix set of intelligent reflective surfaces
Figure FDA0003577134400000028
The security rate maximization optimization problem established in the network is shown as (P1):
Figure FDA0003577134400000029
Figure FDA00035771344000000210
Figure FDA00035771344000000211
Figure FDA00035771344000000212
Figure FDA00035771344000000213
wherein, PuThe maximum transmitting power of the unmanned aerial vehicle is shown, m represents the index of a reflecting element, and n represents the number of time slots;
the constraint conditions comprise actual flight constraint of the unmanned aerial vehicle, the phase shift range of the reflection element is 0-2 pi, and the maximum available transmission power limit of the unmanned aerial vehicle; optimizing variables in the objective function are mutually coupled and are non-convex, the non-convex problem is decomposed into three sub-optimization problems based on the idea of a block coordinate descent method, and the sub-problems are alternately solved by adopting an iterative algorithm;
and step three, respectively processing and solving three sub-optimization problems:
step 3.1 fix unmanned aerial vehicle orbit and reflection phase shift, optimize transmission beam forming
When the unmanned aerial vehicle track and the reflecting surface phase shift matrix are fixed, the optimal unmanned aerial vehicle transmitting beam forming vector w [ n ] needs to be obtained by solving the following optimization problem:
Figure FDA0003577134400000031
wherein, wH[n]Representative vector w [ n ]]The conjugate of the transposed vector of (a),
Figure FDA0003577134400000032
and
Figure FDA0003577134400000033
effective channels representing a legitimate user and an eavesdropper, respectively;
to solve the problem (P2), two auxiliary matrices are introduced
Figure FDA0003577134400000034
And
Figure FDA0003577134400000035
and make a change
Figure FDA0003577134400000036
And
Figure FDA0003577134400000037
directly obtaining a problem (P2) optimized transmit beamforming vector wopt[n]The closed expression of (1); definition matrix
Figure FDA0003577134400000038
Wherein
Figure FDA0003577134400000039
Is dimension NuThe identity matrix of (1); the optimal transmit beamforming vector expression is finally obtained as
Figure FDA00035771344000000310
Wherein v ismax[n]Is a matrix A [ n ]]The feature vector corresponding to the maximum feature value of (1);
step 3.2 optimization of the reflecting surface phase-shift matrix
At fixed unmanned aerial vehicle flight path and adopt above-mentioned to try out optimum to send outBeam forming solution wopt[n]On the basis, the sub-problem of the reflected beam forming is optimized by maximizing the ratio of the signal-to-noise ratio of a legal user to the signal-to-noise ratio of an eavesdropper; introducing auxiliary variable uH[n]＝[θ1[n],...,θm[n],...,θM[n]]Each term θm[n]Representing the mth diagonal element of the reflecting surface phase shift matrix;
definition of
Figure FDA00035771344000000311
Figure FDA00035771344000000312
Solving for optimal reflecting surface phase shift is equivalent to optimizing u [ n ]]Expressed as the following sub-optimization problem form:
Figure FDA00035771344000000313
equivalently converting the fractional planning problem into a series of sub-optimization problems with parameters by adopting a Dinkelbach algorithm; introducing a parameter variable etarThe upper right-hand corner r represents the r-th iteration, so the problem (P3) translates into the following band parameter ηrThe sub-problems of (1):
Figure FDA00035771344000000314
when the problem is solved, an upper bound function of an objective function can be approximately solved, and the optimization problem is minimized; etarIs a non-negative parameter, and the initial value eta is updated along with the iteration number r00; each etarThe corresponding subproblems can obtain closed expressions of phase shift elements until the algorithm converges, and finally, a phase shift matrix of the reflecting surface is output;
step 3.3 unmanned aerial vehicle flight trajectory optimization
The transmitting beam forming and the intelligent reflecting surface phase shift matrix are obtained by solving sub-problems (P2) and (P4) respectively; according to the thought of block optimization, fixing the two variables and optimizing the unmanned aerial vehicle track variable Q; this optimization sub-problem is represented as:
Figure FDA0003577134400000041
converting the target function into an affine function and a plurality of convex constraint conditions by adopting a continuous convex approximation technology; introducing auxiliary variables
Figure FDA0003577134400000042
And
Figure FDA0003577134400000043
the objective function in the problem (P5) is translated into the following objectives and new constraints:
Figure FDA0003577134400000044
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003577134400000045
respectively spreading the received signal power intensities of a legal user and an eavesdropper to obtain a lower bound function and an upper bound function of the legal user and the eavesdropper, and using the lower bound function and the upper bound function as functions for replacing the legal user and the eavesdropper; the final track optimization is a convex optimization problem, and a convex optimization tool is adopted for solving;
fourthly, designing an overall optimization algorithm:
in the third step, three sub-optimization problems are respectively described and solved, and in order to obtain the optimal solution of three variables in the original optimization problem (P1), an overall optimization algorithm is designed; the algorithm comprises an inner layer iteration and an outer layer iteration: the outer layer iteration is based on the idea of block coordinate reduction, and three sub-problems in the third step are solved in a blocking mode, namely the other two variables are fixed, one variable is optimized, and the optimization is carried out alternately in an iteration mode; the inner layer iteration is carried out when two sub-problems of a reflecting surface phase shift matrix and an unmanned aerial vehicle track are solved; specifically, the method comprises the following steps: when solving two sub-problems of reflector phase shift matrix design and unmanned aerial vehicle track optimization, an expression of an optimal solution cannot be directly obtained, and a group of approximate sub-optimization problems need to be solved; continuously updating the solutions of the reflecting surface phase shift matrix and the unmanned aerial vehicle track by adopting inner layer iteration until the algorithm converges; because the objective function of the original problem (P1) is bounded, each solved subproblem can gradually approximate to the optimal solution of the original problem through iteration, and the overall algorithm finally converges to a finite value.
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