Unmanned aerial vehicle direction modulation design method and device based on intelligent reflecting surface assistance and medium

Abstract

The invention belongs to the technical field of unmanned aerial vehicle communication, and discloses an unmanned aerial vehicle direction modulation design method and device based on intelligent reflecting surface assistance and a medium, and the method comprises the steps: determining an original problem; and determining the optimal position of the unmanned aerial vehicle base station on the x-y plane, and solving based on the optimal position to obtain a weight coefficient meeting all conditions of the original problem. According to the method, the position of the unmanned aerial vehicle base station on the x-y plane is determined by adopting a new method different from a traditional exhaustive search method so as to maximize the channel gain, the near-optimal solution meeting the condition can be quickly solved, the solving efficiency is greatly improved, and the accuracy close to that of the exhaustive search method is achieved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle communication technology, and more specifically, to a method, device and medium for designing directional modulation of an unmanned aerial vehicle based on the assistance of an intelligent reflective surface. Background Art

[0002] In recent years, UAV-assisted millimeter-wave communication has been widely studied due to its high reliability, high flexibility and huge bandwidth availability. The performance of UAV-assisted millimeter-wave communication systems can be greatly improved through reasonable resource allocation and optimized UAV deployment. The literature (Xiao, Z., Dong, H., Bai, L., Wu, DO, & Xia, XG (2019). Unmanned aerial vehicle base station (UAV-BS) deployment with millimeter-wave beamforming. IEEE Internet of Things Journal, 7 (2), 1336-1349.) maximizes the achievable transmission rate for all users by finding the optimal relative position between the UAV base station and the target user and the optimal beamforming vector.

[0003] However, due to the high carrier frequency of millimeter wave communication, millimeter waves suffer more significant path loss compared to path attenuation in low frequency bands. On the other hand, the short wavelength and high directivity of millimeter waves make it difficult for millimeter wave signals to pass through obstacles and are easily blocked. In order to overcome the blocking effect in millimeter wave systems, thereby improving the coverage of millimeter wave signals and maintaining the robustness of millimeter wave systems, intelligent reflecting surface (IRS) assisted communication has become a solution to enhance coverage when the line-of-sight (LoS) link in millimeter wave systems is severely blocked by obstacles. A large number of passive reflective elements are integrated on the surface of the IRS, each of which receives a signal from the transmitter and then independently reflects the incident signal by controlling the amplitude and / or phase to achieve passive beamforming. By cleverly changing the phase shift of the passive elements, the reflected signal can be added at the desired receiving end to create an effective virtual LoS link while achieving directional signal enhancement. The literature (You, C., Zheng, B., Mei, W., & Zhang, R. (2022). How to deploy intelligent reflecting surfaces in wireless network: BS-side, user-side, or both sides? Journal of Communications and Information Networks, 7(1), 1-10.) deeply compares the differences between two traditional IRS deployment strategies in IRS-assisted communication systems including millimeter wave communication in various communication performance indicators, and proposes a new deployment strategy to optimize the performance of IRS-assisted communication systems, which also provides useful insights in practical beamforming design. Considering the physical layer security of the communication system, directional modulation technology has also been introduced into the IRS-assisted communication system. Directional modulation technology can transmit digitally modulated signals to a predetermined desired direction and distort the signal constellation in other directions, thereby achieving the physical layer security of the communication system. The literature (Babakhani, A., Rutledge, DB, & Hajimiri, A. (2009). Near-field direct antenna modulation. IEEE Microwave Magazine, 10(1), 36-46.) first proposed directional modulation technology.The literature (Zhang, B., & Liu, W. (2018, July). Antenna array based positional modulation with a two-ray multi-path model. In 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM) (pp. 203-207). IEEE.) first added directional modulation technology to the IRS auxiliary communication system, mainly using the multipath effect to generate a given modulation pattern at the desired location, and realizing the distortion of the signal constellation around the desired location.

[0004] The existing research model of IRS-assisted communication system is mainly the reflection model between ground base station-IRS-user. There has not been enough research on the reflection model between aerial base station-IRS-user, and there has not been enough attention to the potential physical layer security risks. In addition, based on cost and drone endurance issues, the drone transmitter antenna array adopts an analog beamforming array. In the analog beamforming array, all antennas share an RF chain and have a phase shifter equipped with a power amplifier. At the same time, all power amplifiers are set to have the same scale factor, so the beamforming vector w must satisfy the constant modulus constraint. In addition, since the dimension of the beamforming vector w is very high and each element of w has a constant modulus constraint, this leads to the non-convexity of the problem. Therefore, solving this high-dimensional problem will not be computationally feasible using existing convex optimization tools. Summary of the invention

[0005] The present invention is provided to solve the above problems existing in the prior art. Therefore, a method, device and medium for designing directional modulation of unmanned aerial vehicles based on the assistance of intelligent reflective surfaces are needed to solve potential physical layer security risks and achieve the solution of high-latitude problems.

[0006] According to a first aspect of the present invention, a method for designing directional modulation of a UAV based on the assistance of an intelligent reflective surface is provided, the method comprising:

[0007] Identify the original problem;

[0008] Determine the optimal position of the drone base station on the xy plane, and solve based on the optimal position to obtain a weight coefficient that satisfies all conditions of the original problem;

[0009] The original problem is expressed as:

[0010]

[0011] Where R is the information transmission rate received by the user, Qk is the expected response of symbol k, including the expected amplitude and phase, β is the maximum error between the actual response of symbol k and the expected response of symbol k, w k =[w k,1 ,w k,2 ,…,w k,n ] T and are the weight coefficients of the uniform array and IRS equipped by the UAV, respectively, k,n is the phase shift caused by UPA to the signal, is the phase shift caused by IRS to the signal, M is the number of rows and columns of the reflection unit on IRS, [·] T and[·] H are transpose and Hermitian transpose, respectively, v m is the phase shift value range of the mth reflection unit in the IRS, b is the number of bits used to control the phase on the reflection unit, a, and are the steering vectors of the UAV-user channel, the steering vector of the UAV-IRS channel and the steering vector of the IRS-user channel, respectively. min is the minimum x-coordinate value in the established three-dimensional rectangular coordinate system, x0 is the x-axis coordinate of the drone, and x max is the maximum value of the x coordinate in the established three-dimensional rectangular coordinate system, and y min is the maximum value of the y coordinate in the established three-dimensional rectangular coordinate system, y0 is the y-axis coordinate of the drone, and y max is the maximum value of the y coordinate in the established three-dimensional rectangular coordinate system, K is the base number of the multi-base phase shift keying;

[0012] θ1 is the pitch angle from the drone to the user, θ2 is the azimuth angle from the drone to the user, and j is n is e is a natural constant, N is the number of rows and columns of antenna elements on the UPA;

[0013] x is the x-axis coordinate of the target user, y is the y-axis coordinate of the target user, and h0 is the height of the drone;

[0014] ψ1 is the pitch angle in the UAV-IRS direction, ψ2 is the azimuth angle in the UAV-IRS direction;

[0015] x s is the x-axis coordinate of IRS, y s is the y-axis coordinate of IRS, h s is the height of IRS;

[0016] ξ1 is the elevation angle in the IRS-user direction, ξ2 is the azimuth angle in the IRS-user direction;

[0017]

[0018] R is represented by:

[0019]

[0020] Where p is the transmission power of the drone base station, δ 2 is Gaussian white noise, h1 is the channel gain of the UAV-user path, and h2 is the channel gain of the UAV-IRS-user path, which are expressed as:

[0021] h1=λ1a

[0022]

[0023] Where λ1 and λ2 are the channel gain coefficients of the UAV-user channel and the UAV-IRS-user channel, respectively, expressed as:

[0024]

[0025] Where α is the channel fading index, c is the electromagnetic wave propagation speed, f is the carrier frequency, d1 is the straight-line distance between the UAV and the user, d2 and d3 are the straight-line distances between the UAV and the IRS and between the IRS and the user, respectively.

[0026] According to a second aspect of the present invention, a device for designing direction modulation of a UAV based on the assistance of an intelligent reflective surface is provided, the device comprising:

[0027] A problem determination unit is configured to determine an original problem; the original problem is expressed as:

[0028]

[0029] Where R is the information transmission rate received by the user, Q k is the expected response of symbol k, including the expected amplitude and phase, β is the maximum error between the actual response of symbol k and the expected response of symbol k, w k =[w k,1 ,w k,2 ,...,w k,n ] T and are the weight coefficients of the uniform array and IRS equipped by the UAV, respectively, k,n is the phase shift caused by UPA to the signal, is the phase shift caused by IRS to the signal, M is the number of rows and columns of the reflection unit on IRS, [·] T and[·] H are transpose and Hermitian transpose, respectively, v m is the phase shift value range of the mth reflection unit in the IRS, b is the number of bits used to control the phase on the reflection unit, a, and are the steering vectors of the UAV-user channel, the steering vector of the UAV-IRS channel and the steering vector of the IRS-user channel, respectively. min is the minimum x-coordinate value in the established three-dimensional rectangular coordinate system, x0 is the x-axis coordinate of the drone, and x max is the maximum value of the x coordinate in the established three-dimensional rectangular coordinate system, and y min is the maximum value of the y coordinate in the established three-dimensional rectangular coordinate system, y0 is the y-axis coordinate of the drone, and y max is the maximum value of the y coordinate in the established three-dimensional rectangular coordinate system, K is the base number of the multi-base phase shift keying;

[0030] θ1 is the pitch angle from the drone to the user, θ2 is the azimuth angle from the drone to the user, and j is n is e is a natural constant, N is the number of rows and columns of antenna elements on the UPA;

[0031] x is the x-axis coordinate of the target user, y is the y-axis coordinate of the target user, and h0 is the height of the drone;

[0032] ψ1 is the pitch angle in the UAV-IRS direction, ψ2 is the azimuth angle in the UAV-IRS direction;

[0033] x s is the x-axis coordinate of IRS, y s is the y-axis coordinate of IRS, h s is the height of IRS;

[0034] ξ1 is the elevation angle in the IRS-user direction, ξ2 is the azimuth angle in the IRS-user direction;

[0035]

[0036] R is represented by:

[0037]

[0038] Where p is the transmission power of the drone base station, δ 2is Gaussian white noise, h1 is the channel gain of the UAV-user path, and h2 is the channel gain of the UAV-IRS-user path, which are expressed as:

[0039] h1=λ1a

[0040]

[0041] Where λ1 and λ2 are the channel gain coefficients of the UAV-user channel and the UAV-IRS-user channel, respectively, expressed as:

[0042]

[0043] Where α is the channel fading index, c is the electromagnetic wave propagation speed, f is the carrier frequency, d1 is the straight-line distance between the UAV and the user, d2 and d3 are the straight-line distances between the UAV and the IRS and between the IRS and the user, respectively.

[0044] The problem solving unit is configured to determine the optimal position of the drone base station on the xy plane, and solve based on the optimal position to obtain a weight coefficient that satisfies all conditions of the original problem.

[0045] According to a third aspect of the present invention, there is provided a readable storage medium storing one or more programs, wherein the one or more programs can be executed by one or more processors to implement the method as described above.

[0046] The present invention has at least the following beneficial effects:

[0047] The present invention optimizes the weight coefficient of the drone antenna array, disrupts the signal phase in the non-desired direction while ensuring a sufficiently large information transmission rate, and reduces the probability of information leakage in the non-desired direction. At the same time, a new method different from the traditional exhaustive search method is used to determine the position of the drone base station on the xy plane to maximize the channel gain. The present invention can quickly obtain a near-optimal solution that meets the conditions, greatly improving the solution efficiency and having an accuracy close to that of the exhaustive search method. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of a communication system between a UAV and a ground user based on IRS assistance in the prior art is shown;

[0049] Figure 2 A flow chart of a method for designing a direction modulation of a UAV based on the assistance of an intelligent reflective surface according to an embodiment of the present invention is shown;

[0050] Figure 3A flowchart of solving an original problem according to an embodiment of the present invention is shown;

[0051] Figure 4 The symbol 00 three-dimensional beam pattern of the UAV directional modulation design based on the assistance of the intelligent reflective surface according to an embodiment of the present invention is shown;

[0052] Figure 5 The symbol 01 three-dimensional beam diagram of the UAV directional modulation design based on the assistance of the intelligent reflective surface according to an embodiment of the present invention is shown;

[0053] Figure 6 The three-dimensional beam pattern of symbol 11 according to the UAV directional modulation design based on the assistance of the intelligent reflective surface according to an embodiment of the present invention is shown;

[0054] Figure 7 A three-dimensional beam diagram of symbol 10 designed for directional modulation of a UAV assisted by an intelligent reflective surface according to an embodiment of the present invention is shown;

[0055] Figure 8 The pitch beam pattern of the unmanned aerial vehicle directional modulation design based on the assistance of the intelligent reflective surface according to an embodiment of the present invention is shown;

[0056] Fig. 9 The directional surface beam pattern of the UAV directional modulation design based on the assistance of the intelligent reflective surface according to an embodiment of the present invention is shown;

[0057] Fig.10 The pitch plane phase diagram of the UAV directional modulation design based on the assistance of the intelligent reflective surface according to an embodiment of the present invention is shown;

[0058] Fig.11 The directional surface phase diagram of the UAV directional modulation design based on the assistance of the intelligent reflective surface according to an embodiment of the present invention is shown;

[0059] Fig.12 A structural diagram of a UAV direction modulation design device based on the assistance of an intelligent reflective surface according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0060] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and specific embodiments, but are not intended to limit the present invention. For the various steps described herein, if there is no necessity for a causal relationship between each other, the order in which they are described as examples herein should not be regarded as a limitation, and those skilled in the art should know that they can be adjusted in order, as long as the logic between them is not destroyed, resulting in the inability to implement the entire process.

[0061] The embodiment of the present invention provides a single-reflection-surface-assisted single-user scenario considering millimeter-wave downlink, such as Figure 1 As shown in the figure, the user scenario is a UAV-ground user communication system based on IRS assistance, in which the UAV base station equipped with an N*N element uniform planar array serves the ground single antenna user, and uses this uniform planar array to realize multi-phase phase shift keying directional modulation under K-ary signal in the desired direction. The UAV has two paths for information transmission to the target user, one is directly transmitted to the target user, which is defined as the UAV-user path, and the other is transmitted to the user through reflection of the IRS equipped with M*M reflection units, which is defined as the UAV-IRS-user path. Both paths can be regarded as LoS links. In order to obtain the position and geometric relationship of the UAV / user / IRS, a three-dimensional rectangular coordinate system is first established, in which the x-axis range is [x min ,x max ], the y-axis range is [y min ,y max ], the z-axis range is unlimited, where the target user is located at a certain position in the xy plane with coordinates (x,y,0), and the drone base station and IRS are located at (x0,y0,h0) and (x s ,y s ,h s ), where h0 is the height of the drone, h s is the height of the IRS. In this scenario, the target user position and the IRS position are both known, the height of the drone is known, and its position on the xy plane is unknown.

[0062] The uniform planar array model equipped by the drone base station uses an analog beamforming array, in which all antennas share a radio frequency chain, each antenna uses an isotropic antenna, and has a phase shifter with a power amplifier. In order to avoid grating lobes and make full use of space, the antenna array is placed at half-wavelength intervals. Since all power amplifiers have the same scale factor, the constant modulus constraint must be met: where w n is the weight coefficient of antenna n.

[0063] The embodiment of the present invention provides a method for designing a directional modulation of a UAV based on the assistance of an intelligent reflective surface. The method can be applied to the application scenario described above to realize the directional modulation of the UAV. Specifically, Figure 2 As shown in FIG. 1 , it is a flow chart of the method. The UAV direction modulation design method based on intelligent reflective surface assistance includes the following steps:

[0064] S1. Identify the original problem.

[0065] In this embodiment, according to Figure 1 For the communication system shown, the original problem is expressed as:

[0066]

[0067] Where R is the information transmission rate received by the user, Q k is the expected response of symbol k, including the expected amplitude and phase, β is the maximum error between the actual response of symbol k and the expected response of symbol k, w k =[w k,1 ,w k,2 ,...,w k,n ] T and are the weight coefficients of the uniform array and IRS equipped by the UAV, respectively, k,n is the phase shift caused by UPA on the signal, is the phase shift caused by IRS to the signal, M is the number of rows and columns of the reflection unit on IRS, [·] T and[·] H are transpose and Hermitian transpose, respectively, v m is the phase shift value range of the mth reflection unit in the IRS, b is the number of bits used to control the phase on the reflection unit, a, and are the steering vectors of the UAV-user channel, the steering vector of the UAV-IRS channel and the steering vector of the IRS-user channel, respectively. min is the minimum x-coordinate value in the established three-dimensional rectangular coordinate system, x0 is the x-axis coordinate of the drone, and x max is the maximum value of the x coordinate in the established three-dimensional rectangular coordinate system, and y min is the maximum value of the y coordinate in the established three-dimensional rectangular coordinate system, y0 is the y-axis coordinate of the drone, and y max is the maximum value of the y coordinate in the established three-dimensional rectangular coordinate system, K is the base number of the multi-base phase shift keying;

[0068] θ1 is the pitch angle from the drone to the user, θ2 is the azimuth angle from the drone to the user, and j is n is e is a natural constant, N is the number of rows and columns of antenna elements on the UPA;

[0069] x is the x-axis coordinate of the target user, y is the y-axis coordinate of the target user, and h0 is the height of the drone;

[0070] ψ1 is the pitch angle in the UAV-IRS direction, ψ2 is the azimuth angle in the UAV-IRS direction;

[0071] x s is the x-axis coordinate of IRS, ys is the y-axis coordinate of IRS, h s is the height of IRS;

[0072] ξ1 is the elevation angle in the IRS-user direction, ξ2 is the azimuth angle in the IRS-user direction;

[0073]

[0074] R is represented by:

[0075]

[0076] Where p is the transmission power of the drone base station, δ 2 is Gaussian white noise, h1 is the channel gain of the UAV-user path, and h2 is the channel gain of the UAV-IRS-user path, which are expressed as:

[0077] h1=λ1a

[0078]

[0079] Where λ1 and λ2 are the channel gain coefficients of the UAV-user channel and the UAV-IRS-user channel, respectively, expressed as:

[0080]

[0081] Where α is the channel fading index, c is the electromagnetic wave propagation speed, f is the carrier frequency, d1 is the straight-line distance between the UAV and the user, d2 and d3 are the straight-line distances between the UAV and the IRS and between the IRS and the user, respectively.

[0082] S2. Determine the optimal position of the drone base station on the xy plane, and solve based on the optimal position to obtain the weight coefficient that satisfies all the conditions of the original problem.

[0083] Since the original problem (1) is a non-convex problem, it is difficult to solve it with conventional methods. This embodiment will solve this problem step by step. First, a new method different from the traditional exhaustive search method is used to determine the position of the drone base station on the xy plane to maximize the channel gain. This method can quickly find a near-optimal solution that meets the conditions, greatly improving the solution efficiency and having an accuracy close to that of the exhaustive search method. Then, based on the obtained optimal position of the drone base station, the artificial bee colony algorithm is used to solve the weight coefficient w that meets all the conditions of problem (1). k and

[0084] In some implementations, such as Figure 3As shown, it is a flowchart for solving the original problem. Step S2 specifically includes:

[0085] S21. Convert the information transmission rate R received by the user to obtain the first problem.

[0086] In this embodiment, the specific implementation process of step S21 is as follows:

[0087] First, the information transmission rate R received by the user is converted and R is re-expressed as

[0088]

[0089] Among them, T1 is the T1 parameter, T2 is the T2 parameter; T1 is T2 is

[0090] The lower bound of (6) is:

[0091]

[0092] By maximizing R L We get a nearly optimal solution. For ease of presentation, we will maximize part, The maximization method of is the same. The problem is expressed as:

[0093]

[0094] (8) The upper bound of This upper bound can be minimized to obtain a near-optimal solution, so the first problem can be expressed as

[0095]

[0096] Among them, R represents the near-optimal solution for information transmission rate.

[0097] S22. Solve the first problem to obtain a near-optimal solution, and based on the optimal solution, transform the original problem into a second problem; wherein the near-optimal solution is the optimal position of the UAV on the xy plane.

[0098] In this embodiment, the specific implementation process of step S22 is as follows:

[0099] Solve the first problem (9). First, take the derivative of x0 and y0 respectively:

[0100]

[0101] Taking the derivative again, we get:

[0102]

[0103] The Hessian matrix of R is:

[0104]

[0105] According to the Cauchy-Schwarz inequality, it can be proved that det(H R )>0 and Therefore H R is a positive definite matrix, which proves that (9) is a convex problem and has a global optimal solution. It can be concluded that

[0106]

[0107] Usually in the LoS link of the millimeter wave system, the channel fading exponent α = 2, so the final near-optimal solution is The original problem (1) can be transformed into the second problem:

[0108]

[0109]

[0110] S23. Based on the set constraints, transform the second problem into the third problem.

[0111] Since problem (14) has many constraints, it is still very difficult to solve it directly, so it is necessary to transform problem (14).

[0112] In this embodiment, the specific implementation process of step S23 is as follows:

[0113] First, we reformulate R as

[0114]

[0115] After finding the optimal location of the drone base station, the only unknown quantity in R is and w k , in order to satisfy the constraints Problem (14) can be transformed into

[0116]

[0117] In order to solve the constant modulus constraint problem, w k Transformation, where w k It can be expressed as Thus (16) can be transformed into

[0118]

[0119] S24. Solve the third problem to obtain a weight coefficient that satisfies all conditions of the original problem.

[0120] In this embodiment, the specific implementation process of step S24 is as follows:

[0121] S241, this step will use the artificial bee colony algorithm to solve problem (17). The artificial bee colony algorithm has a strong ability to find local optimal solutions and global optimal solutions, and has a fast convergence speed. Therefore, the artificial bee colony algorithm is very suitable for solving optimization problems with high-dimensional variables in (17). First, initialize a matrix Φ as the initial search position, where each row represents a potential feasible solution The number of columns is the dimension of the optimization variable, that is, the number of elements in the antenna array of the drone transmitter. The jth element of the i-th row of the matrix Φ is initialized as

[0122]

[0123] where i=1,2,...,N S ,j=1,2,...,N 2 , express The lower bound of express The upper bound of φ i,j ∈[0,1] is a random number, N S is the number of rows of Φ, N is the number of rows and columns of antenna elements on the UPA, and the solution vector of the i-th row of the matrix Φ is The fitness function value F i for:

[0124]

[0125] in As the i-th objective function value, we transform the i-th row of Φ, i.e., the potential feasible solution Substituting into formula (17) we can get the objective function value It can be seen from the formula that The smaller the value of , the larger the fitness function value of the solution. Randomly generate new The random number γ is within the set precision range (-Γ, Γ), then The updated calculation formula is

[0126]

[0127] in represents the i-th row of the matrix Φ, represents the a-th row of the matrix Φ, which is taken from any row of Φ except the i-th row, where a=1,2,...,N s And a≠i. If The fitness value of is greater than that of the previous iteration. The fitness value of Will replace the previous iteration's Otherwise, keep the last iteration

[0128] S242, repeat step S241 until the maximum number of iterations is reached, and the algorithm ends. Then calculate each The fitness value of , according to the fitness function value, the optimal solution vector can be found from it.

[0129] After solving the third problem in step S24 to obtain a weight coefficient that satisfies all conditions of the original problem, the method further includes the following steps:

[0130] S25, according to the phase shift value range constraint of the IRS reflection unit m=1,2,...,M 2 , substitute the phase in the constraint into In each element of , and list all possible phase shift combinations accordingly, repeat steps S241 and S242 for each combination listed, and save the optimal solution vector obtained from steps S241 and S242 and its fitness function, and finally find the unique optimal solution vector by comparing the sizes of their fitness functions and thereby derive the optimal IRS weight coefficient.

[0131] In this embodiment, the positions of the IRS and the target user are randomly selected within the range, and the corresponding amplitude response diagram and phase response diagram are obtained by using the UAV directional modulation design method based on the assistance of the intelligent reflector as described above, and orthogonal phase shift keying (QPSK), that is, the system model of K=4 is adopted, and the phase angles of the four symbols in the target direction are equal to 45°, 135°, -45° and -135°.

[0132] In this simulation comparison, the number of antennas is set to N*N=16, the number of IRS reflectors is set to M*M=4, the drone height is set to h0=200m, and the IRS height is set to h s =100m, UAV transmission power p = 40dbm, Gaussian white noise δ 2 =-100dbm, carrier frequency f=28GHz, channel fading index α=2, and the number of bits b for controlling the phase on the reflection unit of the IRS=2. In addition, the ranges of the elevation angles and azimuth angles shown in all the images of this embodiment are the ranges of the transmitting end. Figures 4 to 7The three-dimensional beam diagrams of the four symbols obtained by the method proposed in the present invention based on the UAV directional modulation design assisted by the intelligent reflective surface. From these four figures, it can be seen that the main lobe of the beam of each symbol points in the direction of the target user, and the amplitude of the side lobe is relatively low.

[0133] like Figure 8 and Fig. 9 As shown in the figure, the two-dimensional beam patterns of the four symbols in the two observation directions of the elevation plane and the azimuth plane are displayed. Figures 4 to 7 , it can be observed more intuitively that the main lobe has a significantly higher amplitude in the target direction, and the side lobe amplitude is relatively low. Fig.10 and Fig.11 In the phase diagram comparison shown, in both the elevation and azimuth observation directions, the target direction phases satisfy the expected constellation points, while the phases in other directions are chaotic, thus proving that the method proposed in the present invention achieves directional modulation.

[0134] The embodiment of the present invention also provides a UAV direction modulation design device based on intelligent reflective surface assistance, such as Fig.12 As shown, the device 120 includes:

[0135] The problem determination unit 121 is configured to determine the original problem; the original problem is expressed as:

[0136]

[0137] Where R is the information transmission rate received by the user, Q k is the expected response of symbol k, including the expected amplitude and phase, β is the maximum error between the actual response of symbol k and the expected response of symbol k, w k =[w k,1 ,w k,2 ,...,w k,n ] T and are the weight coefficients of the uniform array and IRS equipped by the UAV, respectively, k,n is the phase shift caused by UPA to the signal, is the phase shift caused by IRS to the signal, M is the number of rows and columns of the reflection unit on IRS, [·] T and[·] H are transpose and Hermitian transpose, respectively, v m is the phase shift value range of the mth reflection unit in the IRS, b is the number of bits used to control the phase on the reflection unit, a, and are the steering vectors of the UAV-user channel, the steering vector of the UAV-IRS channel and the steering vector of the IRS-user channel, respectively. minis the minimum x-coordinate value in the established three-dimensional rectangular coordinate system, x0 is the x-axis coordinate of the drone, and x max is the maximum value of the x coordinate in the established three-dimensional rectangular coordinate system, and y min is the maximum value of the y coordinate in the established three-dimensional rectangular coordinate system, y0 is the y-axis coordinate of the drone, and y max is the maximum value of the y coordinate in the established three-dimensional rectangular coordinate system, K is the base number of the multi-base phase shift keying;

[0138] θ1 is the pitch angle from the drone to the user, θ2 is the azimuth angle from the drone to the user, and j is n is e is a natural constant, N is the number of rows and columns of antenna elements on the UPA;

[0139] x is the x-axis coordinate of the target user, y is the y-axis coordinate of the target user, and h0 is the height of the drone;

[0140] ψ1 is the pitch angle in the UAV-IRS direction, ψ2 is the azimuth angle in the UAV-IRS direction;

[0141] x s is the x-axis coordinate of IRS, y s is the y-axis coordinate of IRS, h s is the height of IRS;

[0142] ξ1 is the elevation angle in the IRS-user direction, ξ2 is the azimuth angle in the IRS-user direction;

[0143]

[0144] R is represented by:

[0145]

[0146] Where p is the transmission power of the drone base station, δ 2 is Gaussian white noise, h1 is the channel gain of the UAV-user path, and h2 is the channel gain of the UAV-IRS-user path, which are expressed as:

[0147] h1=λ1a

[0148]

[0149] Where λ1 and λ2 are the channel gain coefficients of the UAV-user channel and the UAV-IRS-user channel, respectively, expressed as:

[0150]

[0151] Where α is the channel fading index, c is the electromagnetic wave propagation speed, f is the carrier frequency, d1 is the straight-line distance between the UAV and the user, d2 and d3 are the straight-line distances between the UAV and the IRS and between the IRS and the user, respectively.

[0152] The problem solving unit 122 is configured to determine the optimal position of the drone base station on the xy plane, and solve based on the optimal position to obtain a weight coefficient that satisfies all conditions of the original problem.

[0153] In some embodiments, the problem solving unit is further configured to:

[0154] Transform the information transmission rate R received by the user to obtain the first problem;

[0155] Solving the first problem to obtain a near-optimal solution, and based on the optimal solution, transforming the original problem into a second problem; wherein the near-optimal solution is an optimal position of the drone on the xy plane;

[0156] Based on set constraints, transform the second problem into a third problem;

[0157] The third problem is solved to obtain a weight coefficient that satisfies all conditions of the original problem.

[0158] It should be noted that the various device structures described in this embodiment belong to the same technical concept as the previously described method, and achieve the same technical effect through the same principle, which will not be repeated here.

[0159] An embodiment of the present invention further provides a readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the method described in any of the above embodiments.

[0160] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present invention with equivalent elements, modifications, omissions, combinations (e.g., various embodiments intersecting schemes), adaptations or changes. The elements in the claims will be interpreted broadly based on the language adopted in the claims, and are not limited to the examples described in this specification or during the implementation of this application, and the examples will be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered as examples only, and the true scope and spirit are indicated by the following claims and the full scope of their equivalents.

[0161] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of them) can be used in combination with each other. For example, those of ordinary skill in the art can use other embodiments when reading the above description. In addition, in the above-mentioned specific embodiments, various features can be grouped together to simplify the present invention. This should not be interpreted as a feature of an invention that is not claimed for protection being necessary for any claim. On the contrary, the subject matter of the present invention may be less than all the features of the embodiments of a specific invention. Thus, the following claims are incorporated into the specific embodiments as examples or embodiments, wherein each claim is independently used as a separate embodiment, and it is considered that these embodiments can be combined with each other in various combinations or arrangements. The scope of the present invention should be determined with reference to the full scope of the equivalent forms of the attached claims and these claims.

Claims

1. A design method for directional modulation of unmanned aerial vehicles based on intelligent reflective surface assistance, characterized in that: The method comprises: Identify the original problem; Determine the optimal position of the drone base station on the xy plane, and solve based on the optimal position to obtain a weight coefficient that satisfies all conditions of the original problem; The original problem is expressed as: maxR x min ≤x0≤x max ,and min ≤y0≤y max Where R is the information transmission rate received by the user, Q k is the expected response of symbol k, including the expected amplitude and phase, β is the maximum error between the actual response of symbol k and the expected response of symbol k, w k =[w k,1 ,w k,2 ,...,w k,n ] T and are the weight coefficients of the uniform array and IRS equipped by the UAV, respectively, k,n is the phase shift caused by UPA to the signal, is the phase shift caused by IRS to the signal, M is the number of rows and columns of the reflection unit on IRS, [·] T and[·] H are transpose and Hermitian transpose, respectively, v m is the phase shift value range of the mth reflection unit in the IRS, b is the number of bits used to control the phase on the reflection unit, a, and are the steering vectors of the UAV-user channel, the steering vector of the UAV-IRS channel and the steering vector of the IRS-user channel, respectively. min is the minimum x-coordinate value in the established three-dimensional rectangular coordinate system, x0 is the x-axis coordinate of the drone, and x max is the maximum value of the x coordinate in the established three-dimensional rectangular coordinate system, and y min is the maximum value of the y coordinate in the established three-dimensional rectangular coordinate system, y0 is the y-axis coordinate of the drone, and y max is the maximum value of the y coordinate in the established three-dimensional rectangular coordinate system, K is the base number of the multi-base phase shift keying; θ1 is the pitch angle from the drone to the user, θ2 is the azimuth angle from the drone to the user, and j is n is e is a natural constant, N is the number of rows and columns of antenna elements on the UPA; x is the x-axis coordinate of the target user, y is the y-axis coordinate of the target user, and h0 is the height of the drone; ψ1 is the pitch angle in the UAV-IRS direction, ψ2 is the azimuth angle in the UAV-IRS direction; x s is the x-axis coordinate of IRS, y s is the y-axis coordinate of IRS, h s is the height of IRS; ξ1 is the elevation angle in the IRS-user direction, ξ2 is the azimuth angle in the IRS-user direction; R is represented by: Where p is the transmission power of the drone base station, δ 2 is Gaussian white noise, h1 is the channel gain of the UAV-user path, and h2 is the channel gain of the UAV-IRS-user path, which are expressed as: h1=λ1a Where λ1 and λ2 are the channel gain coefficients of the UAV-user channel and the UAV-IRS-user channel, respectively, expressed as: Where α is the channel fading index, c is the electromagnetic wave propagation speed, f is the carrier frequency, d1 is the straight-line distance between the UAV and the user, d2 and d3 are the straight-line distances between the UAV and the IRS and between the IRS and the user, respectively.

2. The method according to claim 1, characterized in that Determine the optimal position of the drone base station on the xy plane, and solve based on the optimal position to obtain the weight coefficient w that satisfies all the conditions of the original problem k and Specifically include: Transform the information transmission rate R received by the user to obtain the first problem; Solving the first problem to obtain a near-optimal solution, and based on the optimal solution, transforming the original problem into a second problem; wherein the near-optimal solution is an optimal position of the drone on the xy plane; Based on set constraints, transform the second problem into a third problem; The third problem is solved to obtain a weight coefficient that satisfies all conditions of the original problem.

3. The method according to claim 2, characterized in that The information transmission rate R received by the user is converted to obtain the first problem, including: Transform the information transmission rate R received by the user and re-express R as make Then formula (5) can be reformulated as Among them, T1 is the T1 parameter, T2 is the T2 parameter; The lower bound R of formula (6) is L for: By maximizing R L Get a near-optimal solution, maximizing The problem is expressed as: Question (8) The upper bound of By minimizing the upper bound To obtain a near-optimal solution; to obtain a near-optimal solution as the first problem, the first problem is expressed as: Among them, R represents the near-optimal solution for information transmission rate.

4. The method according to claim 3, characterized in that Solving the first problem to obtain a near-optimal solution, and based on the optimal solution, transforming the original problem into a second problem, including: Based on the first problem, derive x0 and y0 respectively Taking the derivative of x0 and y0 again, we get The Hessian matrix of R is expressed as make get: When α = 2, the near-optimal solution is The original problem is transformed into a second problem, which is expressed as: max R 5. The method according to claim 4, characterized in that Based on the set constraints, the second problem is transformed into a third problem, including: Re-express R as: When determining the optimal location of the drone base station, the constraints are set as follows: The second problem is transformed into an intermediate problem, which is expressed as: For w k Transform to solve the constant modulus constraint problem, where w k Expressed as The intermediate problem (16) is transformed into the third problem, which is expressed as:

6. The method according to claim 5, characterized in that Solving the third problem to obtain a weight coefficient that satisfies all conditions of the original problem includes: Initialize the matrix Φ as the initial search position. Each row in the matrix Φ represents a potential feasible solution. The number of columns is the dimension of the optimization variable, and the dimension of the optimization variable is the number of elements of the antenna array of the UAV transmitter; wherein the jth element of the i-th row of the matrix Φ is initialized to: where i=1,2,…,N S ,j=1,2,…,N 2 , express The lower bound of express The upper bound of φ i,j ∈[0,1] is a random number, N S is the number of rows of Φ, N is the number of rows and columns of antenna elements on the UPA, and the solution vector of the i-th row of the matrix Φ is The fitness function value F i for: in is the value of the i-th objective function, and the solution vector Substituting into the third problem (17) we get the objective function value The smaller the value of , the larger the fitness function value of the solution; By random number γ according to each Randomly generate new The random number γ is within the set accuracy range (-Γ, Γ). The updated calculation formula is in represents the updated solution vector of row i, represents the a-th row solution vector of the matrix Φ, a≠i; if The fitness value of is greater than that of the previous iteration. The fitness value of Will replace the previous iteration's Otherwise, keep the last iteration Repeat the iteration until the maximum number of iterations is reached, and calculate each The fitness value of the optimal solution vector is determined according to the fitness function value.

7. The method according to claim 6, characterized in that After solving the third problem to obtain a weight coefficient that satisfies all conditions of the original problem, the method further includes: According to the phase shift value range constraints of the IRS reflection unit m=1,2,…,M 2 , substitute the phase in the phase shift value range constraint into In each element of , all possible phase shift combinations are listed accordingly. The third problem is solved repeatedly for each phase shift combination listed, and the optimal solution vector and the corresponding fitness function from each phase shift combination are saved. Finally, the unique optimal solution vector is found by comparing the sizes of the fitness functions of various phase shift combinations to obtain the optimal IRS weight coefficient.

8. A UAV direction modulation design device based on intelligent reflective surface assistance, characterized in that: The device comprises: A problem determination unit is configured to determine an original problem; the original problem is expressed as: x min ≤x0≤x max ,and min ≤y0≤y max Where R is the information transmission rate received by the user, Q k is the expected response of symbol k, including the expected amplitude and phase, β is the maximum error between the actual response of symbol k and the expected response of symbol k, w k =[w k,1 ,w k,2 ,…,w k,n ] T and are the weight coefficients of the uniform array and IRS equipped by the UAV, respectively, k,n is the phase shift caused by UPA to the signal, is the phase shift caused by IRS to the signal, M is the number of rows and columns of the reflection unit on IRS, [·] T and[·] H are transpose and Hermitian transpose, respectively, v m is the phase shift value range of the mth reflection unit in the IRS, b is the number of bits used to control the phase on the reflection unit, a, and are the steering vectors of the UAV-user channel, the steering vector of the UAV-IRS channel and the steering vector of the IRS-user channel, respectively. min is the minimum x-coordinate value in the established three-dimensional rectangular coordinate system, x0 is the x-axis coordinate of the drone, and x max is the maximum value of the x coordinate in the established three-dimensional rectangular coordinate system, and y min is the maximum value of the y coordinate in the established three-dimensional rectangular coordinate system, y0 is the y-axis coordinate of the drone, and y max is the maximum value of the y coordinate in the established three-dimensional rectangular coordinate system, K is the base number of the multi-base phase shift keying; θ1 is the pitch angle from the drone to the user, θ2 is the azimuth angle from the drone to the user, and j is n is e is a natural constant, N is the number of rows and columns of antenna elements on the UPA; x is the x-axis coordinate of the target user, y is the y-axis coordinate of the target user, and h0 is the height of the drone; ψ1 is the pitch angle in the UAV-IRS direction, ψ2 is the azimuth angle in the UAV-IRS direction; x s is the x-axis coordinate of IRS, y s is the y-axis coordinate of IRS, h s is the height of IRS; ξ1 is the elevation angle in the IRS-user direction, ξ2 is the azimuth angle in the IRS-user direction; R is represented by: Where p is the transmission power of the drone base station, δ 2 is Gaussian white noise, h1 is the channel gain of the UAV-user path, and h2 is the channel gain of the UAV-IRS-user path, which are expressed as: h1=λ1a Where λ1 and λ2 are the channel gain coefficients of the UAV-user channel and the UAV-IRS-user channel, respectively, expressed as: Where α is the channel fading index, c is the electromagnetic wave propagation speed, f is the carrier frequency, d1 is the straight-line distance between the UAV and the user, d2 and d3 are the straight-line distances between the UAV and the IRS and between the IRS and the user, respectively. The problem solving unit is configured to determine the optimal position of the drone base station on the xy plane, and solve based on the optimal position to obtain a weight coefficient that satisfies all conditions of the original problem.

9. The device according to claim 8, characterized in that The problem solving unit is further configured to: Transform the information transmission rate R received by the user to obtain the first problem; Solving the first problem to obtain a near-optimal solution, and based on the optimal solution, transforming the original problem into a second problem; wherein the near-optimal solution is an optimal position of the drone on the xy plane; Based on set constraints, transform the second problem into a third problem; The third problem is solved to obtain a weight coefficient that satisfies all conditions of the original problem. 10 . A non-transitory computer-readable storage medium storing instructions, which, when executed by a processor, perform the method according to claim 1 .

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