Joint beam forming and power distribution method and system based on unmanned aerial vehicle

Abstract

The invention discloses a joint beam forming and power distribution method and system based on an unmanned aerial vehicle, and relates to the technical field of joint beam forming and power distribution, and the method comprises the steps: collecting a first channel matrix under the condition of statistical information in a state with the statistical information; and when the unmanned aerial vehicle is in a state without statistical information, the second channel matrix without the statistical information is acquired, so that the unmanned aerial vehicle is ensured to be suitable for two environments with the statistical information and without the statistical information. Outputting a passive beam forming matrix, an active analog beam forming matrix, an active digital beam forming matrix and a power distribution matrix based on the first channel matrix or the second channel matrix; and optimizing system performance and spectrum efficiency according to the passive beam forming matrix, the active analog beam forming matrix, the active digital beam forming matrix and the power distribution matrix. By optimizing the joint beam forming design, the influence of the jitter of the unmanned aerial vehicle on the passive beam disturbance is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of joint beamforming and power allocation, and particularly to a method and system for joint beamforming and power allocation based on an unmanned aerial vehicle (UAV). Background Art

[0002] With the development of technology, the joint beamforming scheme of the UAV Internet of Things system consists of two parts: active beamforming and passive beamforming. Among them, active beamforming is realized by an active antenna array, and passive beamforming is realized by a passive antenna array.

[0003] Currently, the research on beamforming mainly focuses on the situation of active beam perturbation. However, since the passive beamforming of a UAV equipped with a Reconfigurable Intelligent Surface (RIS) is different from the active beamforming of a base station, for a UAV carrying an RIS, the existing anti-perturbation schemes are not applicable to solve the problem of passive beam perturbation and cannot reduce the impact of UAV jitter on passive beam perturbation. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art, and the present invention provides a method and system for joint beamforming and power allocation based on a UAV.

[0005] An embodiment of the present invention provides a method for joint beamforming and power allocation based on a UAV, including: the UAV carries an RIS and determines the presence or absence of statistical information based on historical data of monitoring beam jitter on the passive RIS array; when in a state with statistical information, collecting a first channel matrix under the condition of having statistical information; when in a state without statistical information, collecting a second channel matrix under the condition of not having statistical information; outputting a passive beamforming matrix, an active analog beamforming matrix, an active digital beamforming matrix, and a power allocation matrix based on the first channel matrix or the second channel matrix; and optimizing the system performance and spectral efficiency according to the passive beamforming matrix, the active analog beamforming matrix, the active digital beamforming matrix, and the power allocation matrix.

[0006] An embodiment of the present invention provides a system for joint beamforming and power allocation based on a UAV. The system for joint beamforming and power allocation based on a UAV is applied to the above-mentioned method for joint beamforming and power allocation based on a UAV, and the system for joint beamforming and power allocation based on a UAV includes:

[0007] A statistical information module, which is used for the UAV carrying an RIS to determine the presence or absence of statistical information based on historical data of monitoring beam jitter on the passive RIS array;

[0008] The first channel matrix module is used to collect the first channel matrix under the condition of having statistical information when in the state of having statistical information;

[0009] The second channel matrix module is used to collect the second channel matrix under the condition of having no statistical information when in the state of having no statistical information;

[0010] The matrix module is used to output a passive beamforming matrix, an active analog beamforming matrix, an active digital beamforming matrix, and a power allocation matrix based on the first channel matrix or the second channel matrix;

[0011] The optimization module is used to optimize the system performance and spectral efficiency according to the passive beamforming matrix, the active analog beamforming matrix, the active digital beamforming matrix, and the power allocation matrix.

[0012] Compared with the prior art, the beneficial effects of the present invention are:

[0013] In the embodiment of the present invention, by the method in the embodiment of the present invention, the drone carries the RIS, and judges the presence or absence of statistical information based on the historical data of monitoring the beam jitter on the passive RIS array; when in the state of having statistical information, it collects the first channel matrix under the condition of having statistical information; when in the state of having no statistical information, it collects the second channel matrix under the condition of having no statistical information, introduces the first channel matrix or the second channel matrix, and is compatible with the scenarios of having or not having statistical information, ensuring that the drone is applicable to both environments with and without statistical information.

[0014] Therefore, a passive beamforming matrix, an active analog beamforming matrix, an active digital beamforming matrix, and a power allocation matrix are output based on the first channel matrix or the second channel matrix; the system performance and spectral efficiency are optimized according to the passive beamforming matrix, the active analog beamforming matrix, the active digital beamforming matrix, and the power allocation matrix. By optimizing the joint beamforming design, the influence of the drone jitter on the passive beam perturbation is effectively reduced, the computational complexity of the system is greatly reduced, and the stability and robustness of the system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic flowchart of the method for joint beamforming and power allocation based on a drone in an embodiment of the present invention;

[0016] Figure 2 is a schematic flowchart of step S11 in the method for joint beamforming and power allocation based on a drone in an embodiment of the present invention;

[0017] Figure 3 is a schematic flowchart of step S12 in the method for joint beamforming and power allocation based on a drone in an embodiment of the present invention;

[0018] Figure 4 It is a schematic flowchart of step S13 in the method for joint beamforming and power allocation based on an unmanned aerial vehicle (UAV) in an embodiment of the present invention;

[0019] Figure 5 It is a schematic flowchart of step S14 in the method for joint beamforming and power allocation based on an unmanned aerial vehicle (UAV) in an embodiment of the present invention;

[0020] Figure 6 It is a schematic flowchart of step S15 in the method for joint beamforming and power allocation based on an unmanned aerial vehicle (UAV) in an embodiment of the present invention;

[0021] Figure 7 It is a schematic diagram of the structural composition of the system for joint beamforming and power allocation based on an unmanned aerial vehicle (UAV) in an embodiment of the present invention. Detailed implementation manners

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.

[0023] Please refer to Figures 1 to 7 , a method for joint beamforming and power allocation based on an unmanned aerial vehicle (UAV), which is applied to a scenario of joint beamforming and power allocation based on an unmanned aerial vehicle (UAV); the method for joint beamforming and power allocation based on an unmanned aerial vehicle (UAV) includes:

[0024] Step S11: The UAV carries a reconfigurable intelligent surface (RIS), and determines the presence or absence of statistical information based on historical data of monitoring beam jitter on the passive RIS array;

[0025] Step S12: When in a state with statistical information, collect a first channel matrix under the condition of having statistical information. Here, the first channel matrix is the channel matrix between the base station and the user, between the base station and the UAV, and between the UAV and the user;

[0026] Step S13: When in a state without statistical information, collect a second channel matrix under the condition of having no statistical information. Here, the second channel matrix is the channel matrix between the base station and the UAV, between the UAV and the user, and the rearranged channel matrix between the base station and the user;

[0027] Step S14: Output a passive beamforming matrix, an active analog beamforming matrix, an active digital beamforming matrix, and a power allocation matrix based on the first channel matrix or the second channel matrix;

[0028] Step S15: Optimize the system performance and spectral efficiency according to the passive beamforming matrix, the active analog beamforming matrix, the active digital beamforming matrix, and the power allocation matrix;

[0029] Refer to Figure 2, in step S11, the drone carries the RIS and determines the presence or absence of statistical information based on the historical data of beam jitter on the passive RIS array.

[0030] In the specific implementation process of the present invention, step S11 specifically includes S111 to S113, which are described in detail as follows:

[0031] S111: The drone is equipped with a passive RIS array, and the passive RIS array is a uniform planar array (N x *N y ) composed of passive reflection units, where N x and N y are both not less than 1;

[0032] S112: The ground base station is equipped with an active antenna array, and the active antenna array is a uniform planar array (M x *M y ) composed of active antenna units, where M x and M y are both not less than 1;

[0033] S113: Continuously monitor the passive RIS array and determine the presence or absence of statistical information based on the historical data of beam jitter on the passive RIS array.

[0034] In the embodiment of the present application, the drone is equipped with a passive RIS array, and the passive RIS array is a uniform planar array (N x *N y ) composed of passive reflection units, where N x and N y are both not less than 1; the ground base station is equipped with an active antenna array, and the active antenna array is a uniform planar array (M x *M y ) composed of active antenna units, where M x and M y are both not less than 1. In the above embodiment, continuously monitoring the passive RIS array means that the drone is equipped with a sensor for sensing the jitter of the drone. If the amount of historical data of the drone jitter collected exceeds a given threshold, it can be considered that the statistical information of the beam jitter generated on the drone RIS is calculated through the existing amount of historical data, so as to determine the presence or absence of statistical information.

[0035] At this time, this design is applicable to a RIS-assisted UAV IoT system affected by wind disturbances. It is required that the UAVs are equipped with passive RIS arrays and the ground base stations are equipped with active antenna arrays. The passive RIS array and the active antenna array are introduced to achieve further control of the passive RIS array and the active antenna array. At the same time, each user is equipped with a single antenna, but it is required that the number of radio frequency links at the base station is the same as the number of users, and for a uniform planar array, the distance between adjacent units is half of the carrier wavelength.

[0036] Therefore, the passive RIS array is monitored in real time, and based on the historical data of beam jitter on the monitored passive RIS array, the presence or absence of statistical information is judged, and the scenario with statistical information and the scenario without statistical information are introduced.

[0037] At this time, the design of joint beamforming in this invention considers two cases: with statistical information and without statistical information. At the same time, for the case with statistical information, the sub-problems of joint beamforming and power allocation are designed and solved respectively to obtain the analog beamforming matrix of active beamforming, the digital beamforming matrix of active beamforming, the passive beamforming matrix, and the power allocation matrix.

[0038] For the optimal design in the case without statistical information, according to whether there is a line-of-sight channel between the base station and the user, the users are divided into two categories, and then the channel matrix between the base station and the users is rearranged according to the user category.

[0039] According to the rearranged matrix above, the sub-problems of joint beamforming and power allocation are redesigned and solved to obtain the active analog beamforming matrix, the active digital beamforming matrix, the passive beamforming matrix, and the power allocation matrix.

[0040] Reference Figure 3 , in step S12, when in the state with statistical information, the first channel matrix under statistical information is collected. Here, the first channel matrix is the channel matrix between the base station and the users, between the base station and the UAVs, and between the UAVs and the users.

[0041] In the specific implementation process of this invention, the specific steps are as follows:

[0042] S121: When in the state with statistical information, the transmitting end sends a known pilot signal, and the receiving end uses the least mean square error method to estimate the channel matrix between the base station and the users, the channel matrix between the base station and the UAVs, and the channel matrix between the UAVs and the users in the system.

[0043] S122: Determine the passive beamforming matrix under statistical information based on the channel matrix between the base station and the users, the channel matrix between the base station and the UAVs, and the channel matrix between the UAVs and the users.

[0044] S123: Calculate the analog beamforming matrix of the active beamforming based on the channel matrix between the base station and the user, the channel matrix between the base station and the UAV, the channel matrix between the UAV and the user, and the obtained passive beamforming matrix.

[0045] S124: On the basis of knowing the passive beamforming matrix and the analog beamforming matrix of the active beamforming, introduce the Lagrange multiplier method to obtain a new objective sub-function, and introduce a gain control factor to solve the digital beamforming matrix of the active beamforming with statistical information.

[0046] S125: Based on the obtained first joint beamforming matrix and the corresponding signal-to-interference-plus-noise ratio, match the corresponding first power allocation matrix. Here, the first joint beamforming matrix refers to the passive beamforming matrix and the active beamforming matrix with statistical information.

[0047] In the embodiment of the present application, when in the state of having statistical information, the transmitting end sends a known pilot signal, and the receiving end uses the minimum mean square error method to estimate the channel matrix between the base station and the user, the channel matrix between the base station and the UAV, and the channel matrix between the UAV and the user in the system. The minimum mean square error method is introduced, and the accuracy of the channel matrix between the base station and the user, the channel matrix between the base station and the UAV, and the channel matrix between the UAV and the user in the system is ensured.

[0048] At this time, through the pilot-assisted channel estimation method, the transmitting end (base station or UAV) sends a known pilot signal, and the receiving end (user or UAV) uses the minimum mean square error (MMSE) method to estimate the channel matrix between the base station and the user (AH D ), the channel matrix between the base station and the UAV (H BU ), and the channel matrix between the UAV and the user (H UU ). Introduce the ergodic SMSE design joint beamforming sub-problem based on statistical information.

[0049] Furthermore, determine the passive beamforming matrix with statistical information based on the channel matrix between the base station and the user, the channel matrix between the base station and the UAV, and the channel matrix between the UAV and the user, ensuring the accuracy of the passive beamforming matrix with statistical information.

[0050] At this time, on the basis of the obtained channel matrices, simplify the objective function and find the extreme value to calculate the passive beamforming matrix (v) with statistical information.

[0051]

[0052] where tr[.] represents the matrix trace operation, and E represents

[0053] the expected value.

[0054]

[0055] At this time, represents the diagonal matrix operation.

[0056] Furthermore, based on the channel matrix between the base station and the user, the channel matrix between the base station and the UAV, the channel matrix between the UAV and the user, and the above-obtained passive beamforming matrix, the analog beamforming matrix of the active beamforming is calculated, ensuring the accuracy of the analog beamforming matrix of the active beamforming.

[0057] At this time, according to the Matched Filter (MF) criterion, the analog beamforming matrix (F) of the active beamforming is calculated.

[0058]

[0059] At this time, represents the angle extraction operation.

[0060] Therefore, based on the known passive beamforming matrix and the analog beamforming matrix of the active beamforming, a new objective sub-function is obtained by introducing the Lagrange multiplier method, and the digital beamforming matrix of the active beamforming with statistical information is solved by introducing the gain control factor; based on the obtained first joint beamforming matrix and the corresponding signal-to-interference-plus-noise ratio, the corresponding first power allocation matrix is matched, and the first joint beamforming matrix and the first power allocation matrix are output.

[0061] At this time, based on the known passive beamforming matrix and the analog beamforming matrix of the active beamforming, the design problem of the digital beamforming matrix of the active beamforming is solved by introducing the Lagrange multiplier method, a new objective sub-function L(W, λ) is obtained, and the digital beamforming matrix of the active beamforming with statistical information is solved by introducing the gain control factor ρ.

[0062]

[0063]

[0064] The first power allocation matrix (P) obtains a new objective sub-function on the basis of satisfying the Signal-to-Interference-plus-Noise Ratio (SINR) The target sub-function is equivalently replaced with a new target sub-function, which is χ. Meanwhile, a new constraint is introduced. The target sub-function χ at this time is approximated as a quasi-convex optimization problem in the form of second-order cone programming, and the desired first power allocation matrix is obtained by solving.

[0065]

[0066] Reference Figure 4 , in step S13, when in a state without statistical information, the second channel matrix under no statistical information is collected;

[0067] In the specific implementation process of the present invention, the specific steps are as follows:

[0068] S131: When in a state without statistical information, the channel is divided into a line-of-sight channel and a non-line-of-sight channel, and the channel matrices of the base station and the user are rearranged;

[0069] S132: According to the channel matrix between the base station and the unmanned aerial vehicle, the channel matrix between the unmanned aerial vehicle and the user, and the rearranged channel matrix between the base station and the user, by reasonably designing the active analog beamforming matrix, the active digital beamforming matrix under the line-of-sight channel, and the active digital wave number forming matrix under the non-line-of-sight channel, the passive beamforming matrix and the active analog beamforming matrix under the non-line-of-sight channel are obtained;

[0070] S133: Based on the second joint beamforming matrix and the corresponding signal-to-interference-plus-noise ratio, the corresponding second power allocation matrix is matched.

[0071] The second joint beamforming matrix in the above embodiment includes a passive beamforming matrix, an active analog beamforming matrix and an active digital beamforming matrix under the line-of-sight channel, and an active digital wave number forming matrix and an active analog beamforming matrix under the non-line-of-sight channel. In the embodiment of the present application, when in a state without statistical information, the channel is divided into a line-of-sight channel and a non-line-of-sight channel, the channel matrices of the base station and the user are rearranged, and the channel matrix between the base station and the unmanned aerial vehicle, the channel matrix between the unmanned aerial vehicle and the user, and the rearranged channel matrix between the base station and the user are output.

[0072] At this time, a minimum traversal SMSE design joint beamforming sub-problem without statistical information is introduced. According to the above division of the channel into two categories of line-of-sight channel and non-line-of-sight channel and one-user category, the channel matrices of the base station and the user are rearranged (AH D ). The analog beamforming matrix of the active beamforming is (F), the digital beamforming matrix of the active beamforming is (W), the channel matrix between the base station and the unmanned aerial vehicle is H BU , and the channel matrix between the unmanned aerial vehicle and the user is H UU .

[0073]

[0074] F = [F L , F NL ;

[0075] W = [W L , W NL ;

[0076] Furthermore, based on the channel matrix between the base station and the UAV (H BU ), the channel matrix between the UAV and the user (H UU ), and the rearranged channel matrix between the base station and the user (AH D ), by reasonably designing the active analog beamforming matrix (F L ) and the active digital beamforming matrix (W L ) in the line-of-sight channel and the active digital wavenumber forming matrix (W NL ) in the non-line-of-sight channel, the passive beamforming matrix (v) and the active analog beamforming matrix (F NL ) in the non-line-of-sight channel are obtained, and the passive beamforming matrix (v), the analog beamforming matrix (F) of active beamforming, and the digital beamforming matrix (W) of active beamforming are output.

[0077] At this time, according to the channel matrix between the base station and the user (AH D ) rearranged in the previous step, the active analog beamforming matrix (F L ) and the active digital beamforming matrix (W L ) in the line-of-sight channel and the active digital wavenumber forming matrix (W NL ) in the non-line-of-sight channel are reasonably designed, and by using the channel matrix between the base station and the UAV (H BU ) and the channel matrix between the UAV and the user (H UU ), the active analog beamforming matrix (F NL ) in the non-line-of-sight channel is obtained, and the passive beamforming matrix (v), the analog beamforming matrix (F) of active beamforming, and the digital beamforming matrix (W) of active beamforming are obtained.

[0078]

[0079]

[0080] Ones(x, y) represents a matrix with all elements being 1 in the dimension of x×y.

[0081] Therefore, based on the second joint beamforming matrix and the corresponding signal-to-interference-plus-noise ratio, the corresponding second power allocation matrix is matched, and the second joint beamforming matrix and the second power allocation matrix are output.

[0082] At this time, similar to the steps in the joint beamforming sub-problem design of the minimum traversal SMSE based on statistical information, the power allocation matrix (P) obtains a new objective sub-function on the basis of satisfying the signal-to-interference-plus-noise ratio. The objective sub-function is equivalently replaced with a new objective sub-function, the new objective sub-function is χ, and a new constraint is introduced. The objective sub-function χ at this time is approximated as a quasiconvex optimization problem in the form of second-order cone programming, and the desired power allocation matrix is obtained by solving.

[0083] Different from the steps in the joint beamforming sub-problem design of the minimum traversal SMSE method based on statistical information, in this step, due to the lack of statistical information, the in the newly introduced constraint needs to be replaced with AH D +H UU VH BU .

[0084]

[0085] Reference Figure 5 , in step S14, based on the first channel matrix or the second channel matrix, a passive beamforming matrix, an active analog beamforming matrix, an active digital beamforming matrix, and a power allocation matrix are output;

[0086] In the specific implementation process of the present invention, the specific steps are as follows:

[0087] S141: Selectively collect the first channel matrix or the second channel matrix;

[0088] S142: Based on the first channel matrix or the second channel matrix, output a passive beamforming matrix, an active analog beamforming matrix, an active digital beamforming matrix, and a power allocation matrix.

[0089] In the embodiment of the present application, the first channel matrix or the second channel matrix is selectively collected, and a passive beamforming matrix, an active analog beamforming matrix, an active digital beamforming matrix, and a power allocation matrix are output based on the first channel matrix or the second channel matrix.

[0090] Based on the RIS-assisted UAV Internet of Things system, the present invention proposes a heuristic design scheme to design a joint beamforming matrix and a power allocation matrix, aiming to minimize the ergodic system mean square error and optimize the signal-to-interference-plus-noise ratio, thereby significantly improving the system performance. By decoupling the joint beamforming design and the power allocation design, the present invention realizes the independent optimization of both. Specifically, the joint beamforming matrix is optimized by using the design method of minimizing the ergodic SMSE, while the power allocation matrix is effectively solved by the max-min signal-to-interference-plus-noise ratio optimization method.

[0091] Reference Figure 6 , in step S15, the system performance and spectral efficiency are optimized according to the passive beamforming matrix, the active analog beamforming matrix, the active digital beamforming matrix, and the power allocation matrix;

[0092] In the specific implementation process of the present invention, the specific steps are as follows:

[0093] S151: Collect the first channel matrix or the second channel matrix;

[0094] S152: Determine the joint beamforming matrix and the power allocation matrix according to the collected first channel matrix or second channel matrix;

[0095] S153: Determine the corresponding power allocation logic according to the joint beamforming matrix and the current flight parameters of the UAV, and optimize the system performance and spectral efficiency through the power allocation logic.

[0096] In the embodiment of the present application, the first channel matrix or the second channel matrix is collected; based on the collected channel matrix, the joint beamforming matrix and the power allocation matrix are determined, which takes into account the overall consideration of the passive beamforming matrix, the active analog beamforming matrix, the active digital beamforming matrix, and the power allocation matrix, ensures the joint beamforming and power allocation, and improves the accuracy of the joint beamforming matrix and the power allocation matrix.

[0097] At this time, since the UAV is often affected by jitter in the air, resulting in perturbations in the passive beam. The present invention effectively reduces the influence of UAV jitter on the passive beam perturbation through optimizing the joint beamforming design, and greatly reduces the computational complexity of the system, improving the stability and robustness of the system. Through the max-min signal-to-interference-plus-noise ratio optimization problem, the power allocation matrix is further optimized, which improves the overall signal-to-interference-plus-noise ratio while ensuring the system coverage and communication reliability, thereby enhancing the communication efficiency and reliability of the UAV Internet of Things system.

[0098] In addition, the present invention is applicable to both environments with and without statistical information. In the environment with statistical information, the algorithm designs the joint beamforming matrix and power allocation matrix and obtains a sub-optimal solution. In the environment without statistical information, through the user classification strategy, the channel matrix is rearranged, and the desired joint beamforming matrix and power allocation matrix are designed.

[0099] Therefore, according to the obtained joint beamforming matrix and the current flight parameters of the UAV, the corresponding power allocation logic is determined, and the system performance and spectral efficiency are optimized through the power allocation logic. Based on the RIS-assisted UAV IoT system, the present invention explores a heuristic decoupling scheme according to minimizing the sum of the ergodic mean square errors of joint beamforming and power allocation, decoupling the design of joint beamforming, i.e., active beamforming and passive beamforming, and the design of power allocation.

[0100] In an embodiment of the present application, this embodiment relates to a specific UAV IoT system architecture, and on this basis, the working process of the joint beamforming design is proposed. Assumptions:

[0101] 1. System composition: The UAV IoT system consists of a single base station, multiple user devices, and a single UAV carrying RIS. Among them, the base station and the UAV carrying RIS are configured with multiple antennas, and the user devices are configured with single antennas. Uniform planar array technology is used for the multiple antennas in the system.

[0102] 2. Channel and RF link settings: In this system, the number of RF links M RF is equal to the number of users K, the number of base station antennas is M x M y the number of antennas of the UAV carrying RIS is N x N y the number of user antennas is 1, and the adjacent antenna spacing is all λ / 2 (half of the carrier wavelength). The maximum transmit power of the system is P total the channel matrix between the base station and the users is the channel matrix between the base station and the UAV is the channel matrix between the UAV and the users is At the same time, considering that there may be obstacles between the base station and the users, a diagonal matrix A ∈ ò K×K is used to represent the presence of obstacles at a certain moment.

[0103] 3. Joint beamforming and power allocation: The analog beamforming matrix of active beamforming is The digital beamforming matrix of active beamforming is The power allocation matrix is P ∈ ò K×K and the passive beamforming matrix is The k-th element in the power allocation diagonal matrix is P k，k , s ∈ á K×1 represents the user data vector transmitted by the base station, the threshold of the signal-to-interference-plus-noise ratio is γ0, and the received noise vector is composed of an additive white Gaussian noise vector is denoted, and the noise variance is i = 1, 2. Among them, represents the noise variance caused during the transmission from the base station to the UAV, represents the sum of the noise variances caused during the transmissions from the base station to the user and from the UAV to the user.

[0104] 4. UAV attitude: The pitch angle of the UAV in three-dimensional space is θ and the azimuth angle is φ.

[0105] 5. Channel matrix and user classification: With statistical information, there is no classification for the channel matrix - no user category division. In the absence of statistical information, the channel between the base station and the user is divided into two types: the line-of-sight channel and the non-line-of-sight channel - User category division: The set of users in the line-of-sight channel is u, and the set of users in the non-line-of-sight channel is Therefore, under the above user categories, the rearranged channel matrix can be expressed as:

[0106] The channel matrix between the base station and the user is AH D , where,

[0107] The analog beamforming matrix of active beamforming is F = [F L , F NL , where,

[0108] The digital beamforming matrix of active beamforming is W = [W L , W NL , where w = [W L , W NL W L ∈ á K×|u| , W NL ∈ á K×|u| .

[0109] The power allocation matrix is P = [P L , 0 NL ; 0 L , P NL , where, P L ∈ ò |u|×|u| , 0 NL ∈ ò |u|×|u| ; 0 L ∈ ò |u|×|u| , PNL ∈ò |u|×|u| 。

[0110] According to the flowchart, the method is implemented through the following steps:

[0111] (1) There is statistical information

[0112] Step 1: Initialize the channel matrix

[0113] Step 2: Considering the case where the UAV does not jitter, the objective function can be simplified to a new form For the simplified objective function Take the derivative with respect to the passive beamforming matrix V WO That is And because V = V WO , finally, the optimal passive beamforming matrix v can be solved.

[0114]

[0115] Step 3: Given the probability distribution functions of Dθ and Dφ, an optimization solution related to the channel matrix can be obtained According to the matched filter rule, when is a diagonal matrix, the optimal solution F of the analog beamforming matrix for active beamforming can be obtained

[0116]

[0117] Step 4: Based on the foregoing derivation results, combining the obtained optimal passive beamforming matrix v and the analog beamforming matrix F of active beamforming, further update the objective function By introducing the Lagrange multiplier method, optimize the objective function under the constraint conditions. The updated objective function can be expressed as L(W, λ) under the constraint conditions of the Lagrange multiplier method. Since the system

[0118]

[0119] needs to satisfy specific constraint conditions during the optimization process, take the partial derivative of w when solving this objective function and set it equal to zero to obtain the following equation W can be obtained H , W H is the conjugate matrix of w. At the same time, is the current w. Considering the influence of signal gain, introduce the gain control factor ρ for correction to optimize the digital beamforming matrix of active beamforming, The optimal solution w of the digital beamforming matrix of active beamforming can be obtained.

[0120] Step 5: Considering the direct limitation that the joint beamforming design is not subject to the SINR constraint, the SINR constraint is considered when designing the power allocation to ensure that the system can meet the SINR requirements of each user. To this end, auxiliary variables are introduced to optimize the objective sub-function and ensure that the SINR constraint is satisfied through the power allocation scheme. The optimization process is as follows:

[0121] ① The original objective sub-function is By introducing the auxiliary variable χ, the objective sub-function is replaced with χ. This step introduces a new constraint condition, expressed as:

[0122]

[0123] r represents the minimum required rate for data transmission. This constraint equivalently converts the minimum SINR constraint of each user into the minimum rate constraint of the user, which indirectly satisfies the SINR requirement.

[0124] ② To further simplify the optimization problem, expand and combine to obtain Then perform a linear estimation on to get where ζ k is the maximum eigenvalue of the matrix Re(b k，k )Re(b k，k ) T +Im(b k，k )Im(b k，k ), and γ T is the corresponding eigenvector. k

[0125] ③ Through the above processing, the objective sub-function χ can be approximated as a quasiconvex optimization problem in the form of second-order cone programming. Further update the objective sub-function χ, and the constraint is updated to

[0126] ④ To solve the above objective sub-function, fixing χ can give a feasibility problem, denoted as p. This problem is a typical quadratic cone programming feasibility problem, which is solved by the CVX software. The optimal solution is achieved by the binary search method. This optimal solution is the optimal power allocation scheme, and the power allocation matrix P can be obtained.

[0127] (2) Without statistical information

[0128] Step 1: The rearranged channel matrix

[0129]

[0130] a k ​∈ {0, 1}, |u| is the number of line-of-sight channel users, is the number of non-line-of-sight channel users.

[0131] Step 2: To satisfy the objective function minimization, satisfying specific conditions holds. F can be obtained L , W L .

[0132]

[0133]

[0134] Step 3: Divide the change in the pitch angle Dθ and the change in the azimuth angle Dφ of the UAV into N x and N y equal parts respectively, and an updated is obtained, which represents the impact of UAV jitter on passive beamforming. As N x and N y increase, the matching error decreases. According to V WO represents the passive beamforming matrix without passive beam perturbation, and the passive beamforming matrix v can be obtained.

[0135]

[0136] Step 4: Set the digital beamforming matrix for non-line-of-sight channel active beamforming and satisfy specific conditions the k'-th row is F NL is designed to minimize F can be obtained NL . Combining the above-known F L , W L , F and w can be calculated.

[0137]

[0138] Step 5: Considering that the joint beamforming design is not directly restricted by the signal-to-interference-plus-noise ratio constraint, so the signal-to-interference-plus-noise ratio constraint is considered when designing the power allocation to ensure that the system can meet the signal-to-interference-plus-noise ratio requirements of each user. To this end, the objective sub-function is optimized by introducing auxiliary variables, and it is ensured that the signal-to-interference-plus-noise ratio constraint is satisfied through the power allocation scheme. The optimization process is as follows:

[0139] ① The original objective sub-function is By introducing the auxiliary variable χ, the objective sub-function is replaced by χ. This step introduces a new constraint condition, which is expressed as:

[0140]

[0141] r represents the minimum rate required for data transmission. This constraint equivalently transforms the minimum constraint of the signal-to-interference-plus-noise ratio (SINR) of each user into the minimum constraint of the user rate, which indirectly satisfies the SINR requirement.

[0142] ② To further simplify the optimization problem, expand AH D +H UU VH BU FW k′ P k′，k′ and combine them. We can get Then, perform linear estimation on to obtain where ζ k is the maximum eigenvalue of the matrix Re(b k，k )Re(b k，k ) T +Im(b k，k )Im(b k，k ) T and γ k is the corresponding eigenvector.

[0143] ③ Through the above processing, the objective sub-function χ can be approximated as a quasiconvex optimization problem in the form of a second-order cone programming. Further update the objective sub-function χ, and the constraints are updated to

[0144] ④ To solve the above objective sub-function, fixing χ can give a feasibility problem, denoted as p. This problem is a typical quadratic cone programming feasibility problem, which is solved by the CVX software. The optimal solution is achieved by the binary search method, and this optimal solution is the optimal power allocation scheme. The power allocation matrix P can be obtained. Therefore, the results of the joint beamforming design algorithm are v, F, w, and P.

[0145] Please refer to Figure 7 , Figure 7 which is a schematic diagram of the structure composition of the UAV-based joint beamforming and power allocation system in an embodiment of the present invention; the UAV-based joint beamforming and power allocation system includes:

[0146] A statistical information module 21, which is used for the UAV carrying the RIS to judge the presence or absence of statistical information based on the historical data of monitoring the beam jitter on the passive RIS array;

[0147] A first channel matrix module 22, which is used to collect the first channel matrix under the condition of having statistical information when in the state of having statistical information;

[0148] The second channel matrix module 23 is configured to collect the second channel matrix under the condition of no statistical information when in the state of no statistical information;

[0149] The matrix module 24 is configured to output a passive beamforming matrix, an active analog beamforming matrix, an active digital beamforming matrix, and a power allocation matrix based on the first channel matrix or the second channel matrix;

[0150] The optimization module 25 is configured to optimize the system performance and spectral efficiency according to the passive beamforming matrix, the active analog beamforming matrix, the active digital beamforming matrix, and the power allocation matrix.

[0151] For any combination of the technical features of the above embodiments, for the sake of brevity of description, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

Claims

1. A method for joint beamforming and power allocation based on unmanned aerial vehicles, characterized in that, Including: The drone carries the RIS and determines the presence or absence of statistical information based on the historical data of beam jitter monitored on the passive RIS array; When in the state of having statistical information, collect the first channel matrix under the condition of having statistical information; When in the state of having no statistical information, collect the second channel matrix under the condition of having no statistical information; Output the passive beamforming matrix, active analog beamforming matrix, active digital beamforming matrix and power allocation matrix based on the first channel matrix or the second channel matrix; Optimize the system performance and spectral efficiency according to the passive beamforming matrix, active analog beamforming matrix, active digital beamforming matrix and power allocation matrix.

2. The method for joint beamforming and power allocation based on an unmanned aerial vehicle according to claim 1, wherein The drone carries the RIS and determines the presence or absence of statistical information based on the historical data of beam jitter monitored on the passive RIS array, including: The drone is equipped with a passive RIS array, and the passive RIS array is a uniform planar array (N x *N y ) composed of passive reflection units, where N x and N y are both not less than 1; The ground base station is equipped with an active antenna array, and the active antenna array is a uniform planar array (M x *M y ) composed of active antenna elements, where M x and M y are both not less than 1; Real-time monitor the passive RIS array and determine the presence or absence of statistical information based on the historical data of beam jitter on the passive RIS array.

3. The method for joint beamforming and power allocation based on an unmanned aerial vehicle according to claim 1, wherein The step of collecting the first channel matrix under the condition of having statistical information when in the state of having statistical information, including: When in the state of having statistical information, the transmitter sends a known pilot signal, and the receiver uses the minimum mean square error method to estimate the channel matrix between the base station and the user, the channel matrix between the base station and the drone, and the channel matrix between the drone and the user in the system; Determine the passive beamforming matrix under the condition of having statistical information based on the channel matrix between the base station and the user, the channel matrix between the base station and the drone, and the channel matrix between the drone and the user; Based on the channel matrix between the base station and the user, the channel matrix between the base station and the drone, the channel matrix between the drone and the user, and the obtained passive beamforming matrix above, calculate the analog beamforming matrix of the active beamforming; On the basis of knowing the passive beamforming matrix and the analog beamforming matrix of the active beamforming, introduce the Lagrange multiplier method to obtain a new objective sub-function, and introduce a gain control factor to solve the digital beamforming matrix of the active beamforming under the condition of having statistical information; Match the corresponding first power allocation matrix based on the obtained first joint beamforming matrix and the corresponding signal-to-interference-plus-noise ratio.

4. The method for joint beamforming and power allocation based on an unmanned aerial vehicle according to claim 3, wherein The step of collecting the first channel matrix under the condition of having statistical information when in the state of having statistical information, further including: Simplify the objective function for the passive beamforming matrix with statistical information And calculate the extreme value to obtain the passive beamforming matrix with statistical information; Where tr[.] represents the matrix trace operation and E represents the expected value; For the analog beamforming matrix (F) of the active beamforming, 5. The method for joint beamforming and power allocation based on an unmanned aerial vehicle according to claim 1, wherein The step of collecting the second channel matrix under the condition of having no statistical information when in the state of having no statistical information, including: When in the state of having no statistical information, divide the channel into a line-of-sight channel and a non-line-of-sight channel, and rearrange the channel matrix between the base station and the user.

6. The method for joint beamforming and power allocation based on an unmanned aerial vehicle according to claim 5, wherein The step of collecting the second channel matrix under the condition of having no statistical information when in the state of having no statistical information, further including: According to the channel matrix between the base station and the drone, the channel matrix between the drone and the user, and the rearranged channel matrix between the base station and the user, design the active analog beamforming matrix, active digital beamforming matrix in the line-of-sight channel and the active digital beamforming matrix in the non-line-of-sight channel to obtain the passive beamforming matrix and the active analog beamforming matrix in the non-line-of-sight channel; Match the corresponding second power allocation matrix based on the second combined beamforming matrix and the corresponding signal-to-interference-plus-noise ratio.

7. The method for joint beamforming and power allocation based on an unmanned aerial vehicle according to claim 1, wherein Outputting a passive beamforming matrix, an active analog beamforming matrix, an active digital beamforming matrix, and a power allocation matrix based on the first channel matrix or the second channel matrix includes: Selectively collect the first channel matrix or the second channel matrix; Output a passive beamforming matrix, an active analog beamforming matrix, an active digital beamforming matrix, and a power allocation matrix based on the first channel matrix or the second channel matrix.

8. A drone-based joint beamforming and power allocation system, characterized in that, The unmanned aerial vehicle (UAV)-based joint beamforming and power allocation system is applied to the UAV-based joint beamforming and power allocation method according to any one of claims 1 to 7. The UAV-based joint beamforming and power allocation system includes: A statistical information module for the UAV carrying the RIS to determine the presence or absence of statistical information based on historical data monitoring beam jitter on the passive RIS array; A first channel matrix module for collecting the first channel matrix under the condition of having statistical information when in a state of having statistical information; A second channel matrix module for collecting the second channel matrix under the condition of having no statistical information when in a state of having no statistical information; A matrix module for outputting a passive beamforming matrix, an active analog beamforming matrix, an active digital beamforming matrix, and a power allocation matrix based on the first channel matrix or the second channel matrix; An optimization module for optimizing the system performance and spectral efficiency according to the passive beamforming matrix, the active analog beamforming matrix, the active digital beamforming matrix, and the power allocation matrix.

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