A multi-antenna unmanned aerial vehicle communication network robust resource scheduling method and related device

Abstract

The application discloses a kind of multi-antenna unmanned plane communication network robust resource scheduling method and related device, comprising: the basic parameters of unmanned plane communication network are initialized, each ground user is arranged in ascending order according to the horizontal distance between ground user and unmanned plane ground projection point, according to channel gain difference, the user with unmanned plane ground projection point horizontal distance is close and the user with unmanned plane ground projection point horizontal distance is far away is paired;Determine high-probability confidence region;With the minimum unmanned plane total transmit power as target, construct the mathematical model P1 of joint optimization beam forming and power allocation;Solve the mathematical model P1, obtain unmanned plane robust beam forming and power allocation coefficient, according to unmanned plane robust beam forming and power allocation coefficient, multi-antenna unmanned plane communication network robust resource scheduling, the method and related device can effectively improve the reliability and network utility of multi-antenna unmanned plane network.

Description

Technical Field

[0001] This invention belongs to the field of communication technology and relates to a robust resource scheduling method and related apparatus for multi-antenna unmanned aerial vehicle (UAV) communication networks. Background Technology

[0002] With their high mobility, flexible deployment, and low communication overhead, unmanned aerial vehicles (UAVs) have shown broad application prospects in military, public safety, and civilian fields. Especially in scenarios with high traffic demands or damaged ground infrastructure, UAVs, acting as aerial base stations to provide line-of-sight communication services to ground equipment, have become a crucial solution. To further improve system throughput and compensate for severe path loss in air-to-ground links, multi-antenna technology has been widely applied to UAV communication systems, with related research covering beamforming, user scheduling, and resource allocation. However, traditional multi-antenna spatial division multiple access (SDMA) technology mainly relies on spatial domain degrees of freedom to distinguish users. In densely distributed user scenarios or highly correlated line-of-sight scenarios, relying solely on the spatial domain is insufficient to effectively suppress inter-user interference, thus limiting the system's access capacity. Non-orthogonal multiple access (NOAMI) technology, through serial interference cancellation, allows multiple users to simultaneously access a sub-channel, making it a promising technology in multi-antenna UAV communication networks.

[0003] While existing research has extensively explored resource management in UAV networks, most of it relies on ideal channel state information. In real-world scenarios, UAVs inevitably experience body jitter due to airflow and rotor rotation, causing rapid fluctuations in the antenna array's pointing. Because the channel response is strongly coupled to the array pointing, this jitter drastically shortens the channel coherence time, resulting in inherent lag in channel estimation and significantly increasing the overall channel state information error. Since it's impossible to accurately track rapid channel changes, traditional resource allocation schemes based on the assumption of an instantaneously perfect channel are insufficient to guarantee reliable communication services. To address channel uncertainty, some current work has proposed methods based on distribution assumptions. While these methods improve system robustness to some extent, in reality, the distribution of channel state information is usually dynamically changing. Using a known distribution to schedule resources can lead to overly conservative or ineffective allocation strategies. Therefore, there is an urgent need for a data-driven uncertainty set modeling method that does not rely on prior distribution assumptions to effectively improve the reliability and network utility of multi-antenna UAV networks. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a robust resource scheduling method and related apparatus for multi-antenna UAV communication networks. This method and related apparatus can effectively improve the reliability and network utility of multi-antenna UAV networks.

[0005] To achieve the above objectives, this invention discloses a robust resource scheduling method for multi-antenna unmanned aerial vehicle (UAV) communication networks, comprising: Initialize the basic parameters of the UAV communication network, sort the ground users in ascending order according to the horizontal distance between the ground user and the UAV ground projection point, and pair users who are closer to the UAV ground projection point and users who are farther from the UAV ground projection point according to the channel gain difference. Uncertain channel state information between UAVs and ground users is sampled. Based on the collected sample data, an ellipsoidal uncertainty set is constructed by estimating the sample mean and covariance matrix, and the size is calibrated using the quantile method to obtain a high-probability confidence region. To minimize the total transmit power of the UAV, a mathematical model P1 is constructed that jointly optimizes beamforming and power allocation. Solve the mathematical model P1 to obtain the robust beamforming and power allocation coefficients of the UAV, and perform robust resource scheduling of the multi-antenna UAV communication network based on the robust beamforming and power allocation coefficients of the UAV.

[0006] Furthermore, the process of solving the mathematical model P1 to obtain the robust beamforming and power allocation coefficients of the UAV is as follows: The S-Procedure method is used to transform the non-convex quadratic constraints with uncertain channel information in mathematical model P1 into bilinear matrix inequalities, and to transform the beamforming vector in mathematical model P1 into a bilinear matrix inequality. The quadratic objective is transformed into a question about The linear objective is obtained, resulting in problem P2 with rank-one constraints; The rank-one constraint in problem P2 is addressed using differential convex function programming, and within the block coordinate descent framework, problem P2 is decomposed into solving blocks of auxiliary variables. Subproblem SP1 and solution of beamforming matrix block Subproblem SP2; Based on the DC-BCD algorithm, the robust beamforming and power allocation coefficients of the UAV are obtained by alternately solving subproblems SP1 and SP2 and iteratively updating until convergence.

[0007] Furthermore, the process of solving the mathematical model P1 to obtain the robust beamforming and power allocation coefficients of the UAV is as follows: The S-Procedure method is used to transform the non-convex quadratic constraints with uncertain channel information in mathematical model P1 into bilinear matrix inequalities, and to transform the beamforming vector in mathematical model P1 into a bilinear matrix inequality. The quadratic objective is transformed into a question about The linear objective is obtained, resulting in problem P2 with rank-one constraints; A semidefinite relaxation technique is used to handle the rank-one constraint in problem P2, and within the block coordinate descent framework, problem P2 is decomposed into blocks of auxiliary variables to be solved. Subproblem SP1 and solution of beamforming matrix block Subproblem SP3; Based on the SDR-BCD algorithm, the robust beamforming and power allocation coefficients of the UAV are obtained by iteratively solving subproblems SP1 and SP3 alternately and updating until convergence.

[0008] Furthermore, the mathematical model P1 is:

[0009] , ,

[0010] , ,

[0011]

[0012] ,

[0013] , ,

[0014] in, Indicates the first Beamforming vectors of users in the group, Indicates the drone has reached the Group User channel vector, where Representing central users or peripheral users, Indicates noise. Indicates the first Users in the group The signal-to-interference-plus-noise ratio threshold. Indicates the first In the group User power allocation factor, Indicates the first Power allocated to users in the group express The user's ellipsoidal indeterminate set.

[0015] Furthermore, the question P2 is:

[0016] ,

[0017] ,

[0018]

[0019] ,

[0020] , ,

[0021] ,

[0022] ,

[0023] ,

[0024] ,

[0025] in, , .

[0026] Furthermore, the subproblem SP1 is:

[0027] ,

[0028] ,

[0029] ,

[0030] , ,

[0031] in, This refers to the slack variables introduced in order to find a set of solutions that satisfy all constraints with maximum margin; Construct a fixed variable block Solving matrix blocks Subproblem SP2 in the 1st The calculation formula for the next iteration is:

[0032] ,

[0033] ,

[0034] ,

[0035] ,

[0036] in, express A subgradient of the spectral norm, Represents the beamforming matrix The eigenvector corresponding to the largest eigenvalue.

[0037] Furthermore, the sub-problem SP3 is:

[0038] ,

[0039] ,

[0040] ,

[0041] , .

[0042] This invention discloses a robust resource scheduling system for multi-antenna unmanned aerial vehicle (UAV) communication networks, comprising: The initialization module is used to initialize the basic parameters of the UAV communication network, sort the ground users in ascending order according to the horizontal distance between the ground user and the UAV ground projection point, and pair users who are closer to the UAV ground projection point and users who are farther from the UAV ground projection point according to the channel gain difference. The calibration module is used to sample uncertain channel state information of UAVs and ground users. Based on the collected sample data, it constructs an ellipsoidal uncertainty set by estimating the sample mean and covariance matrix, and uses the quantile method to perform size calibration to obtain a high probability confidence region. The solver module is used to construct a mathematical model P1 that jointly optimizes beamforming and power allocation with the goal of minimizing the total transmit power of the UAV. The scheduling module is used to solve the mathematical model P1 to obtain the robust beamforming and power allocation coefficients of the UAV, and to perform robust resource scheduling of the multi-antenna UAV communication network based on the robust beamforming and power allocation coefficients of the UAV.

[0043] This invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the robust resource scheduling method for the multi-antenna unmanned aerial vehicle communication network.

[0044] This invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the robust resource scheduling method for a multi-antenna unmanned aerial vehicle (UAV) communication network.

[0045] The present invention has the following beneficial effects: The robust resource scheduling method and related apparatus for multi-antenna UAV communication networks described in this invention, in practical operation, model the problem of maximizing network utility under uncertain channel state information as an optimization problem with uncertain parameters, and characterizes the uncertainty of channel state information using an ellipsoidal ensemble learning method. To ensure user service quality, with the goal of minimizing the total UAV transmit power, a mathematical model P1 for jointly optimizing beamforming and power allocation is constructed. Solving the mathematical model P1 yields the robust beamforming and power allocation coefficients for the UAVs, and robust resource scheduling of the multi-antenna UAV communication network is performed based on these coefficients. Simulation results show that, compared with deterministic methods, the two robust algorithms proposed in this invention exhibit superior system performance and can effectively improve the reliability and network utility of multi-antenna UAV networks. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram illustrating the scenario in which the present invention is used; Figure 2 This is a diagram illustrating the user pairing situation of the present invention; Figure 3 This is a schematic diagram of the overall process of the present invention; Figure 4 This is a schematic diagram of the DC-BCD algorithm in this invention; Figure 5This is a schematic diagram of the SDR-BCD algorithm in this invention; Figure 6 This is a cumulative SINR distribution map of a UAV link provided in an embodiment of the present invention; Figure 7 Another cumulative distribution map of UAV link SINR provided for an embodiment of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0050] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0051] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0052] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0053] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0055] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0056] Example 1 The robust resource scheduling method for multi-antenna UAV communication networks described in this invention includes the following steps: 1) Initialize the basic parameters of the UAV communication network, sort the ground users in ascending order according to the horizontal distance between the ground users and the UAV ground projection point, and pair users who are closer to the UAV ground projection point and users who are farther from the UAV ground projection point according to the channel gain difference. The specific operation of step 1) is as follows: 1a) Let This indicates the number of antennas equipped on the drone. This indicates the maximum transmission power of the drone. Indicates the number of ground users. Indicates the first Users, among whom , Indicates the location coordinates of the drone. Indicates the first individual users Position coordinates; 1b) Assume the number of users Users will be divided into Groups, each group has For multiple access systems with more than two users, the complexity of serial interference cancellation and the risk of error propagation increase significantly. In the matching scenario, calculate user Horizontal distance between the vertical projection of the drone for:

[0057] 1c) Based on the horizontal distance of the UAV's ground projection point for all Users are sorted in ascending order, that is... The closer ones User Name Called the central user, denoted as , ,the remaining, User Name These are called edge users, denoted as , To ensure that users within the group have an effective channel gain difference, the first individual users With the marginal users To perform pairing, thereby obtaining A non-orthogonal multiple access user pair; 1d) Let Indicates the drone has reached the Group User channel vector, where Representing central users or peripheral users, Indicates noise. Indicates the first Users in the group The signal-to-interference-plus-noise ratio threshold. Indicates the first In the group User power allocation factor, Indicates the first Beamforming vectors of users in the group, Indicates the first The power allocated to users in the group.

[0058] 2) Sample the uncertain channel state information of UAVs and ground users. Based on the collected sample data, construct an ellipsoidal uncertainty set by estimating the sample mean and covariance matrix, and use the quantile method to perform size calibration to obtain a high probability confidence region. The specific operation of step 2) is as follows: 2a) Uncertain road condition information for UAVs and ground users conduct Secondary sampling, pattern sampling results to construct a sample set ; 2b) Determine the uncertain set of the ellipsoid for:

[0059] in, Denotes the center of the ellipsoidal uncertainty set. The sample covariance matrix representing the shape parameters of the ellipsoid. Indicates the size of the ellipsoidal uncertainty set; 2c) Shape learning; The ellipsoidal uncertainty set is obtained by calculating the sample mean. center for:

[0060] 2d) Calculate the sample covariance matrix for:

[0061] 2e) Size calibration to reduce the uncertainty set of the ellipsoid Able to The probability satisfies The specific process is as follows: set up Represents the transition from the random space containing the channel samples to the real number field. The mapping, for all channels mapped to the real number field. The point will quantile is defined as Then we have:

[0062] 2f) For In the sample dataset function value on Sort in ascending order to get: .set up express of The upper limit of the quantile, then the uncertainty set The size is:

[0063] 2g) Calculate the shape matrix:

[0064] in, for The Cholesky decomposition can transform an uncertain set into a solvable affine form.

[0065] 3) To minimize the total transmit power of the UAV, a mathematical model P1 is constructed to jointly optimize beamforming and power allocation; The specific operation of step 3) is as follows: Model the multi-antenna UAV communication system as an optimization problem P1:

[0066] , ,

[0067] , ,

[0068]

[0069] ,

[0070] , , .

[0071] 4) The S-Procedure method is used to transform the non-convex quadratic constraint with uncertain channel information into a bilinear matrix inequality, thereby resolving the issue regarding the beamforming vector in problem P1. The quadratic objective is transformed into a question about The linear objective is obtained, resulting in problem P2 with rank-one constraints; The specific operation of step 4) is as follows: 4a) Introduce non-negative scalar auxiliary variables Homogeneity is achieved for channel uncertainty constraints:

[0072]

[0073] 4b) The signal-to-interference-plus-noise ratio (SIR) constraint for edge users is:

[0074] 4c) Constructing auxiliary vectors At the same time, set to satisfy At this point, the expression in step 4b) can be transformed into:

[0075]

[0076] 4d) It can be represented as:

[0077] in, , yes An identity matrix of 3D; 4e) According to the S-procedure lemma, for step 4c), and step 4d) Scalars exist , making This is established, thus enabling the first [channel] with imperfect channel state information to [be established]. The signal-to-interference-plus-noise ratio (SIR) constraint for edge users is transformed into a tractable linear matrix inequality, i.e.

[0078] in, ; 4f) Similarly, the first one with imperfect CSI The signal-to-interference-plus-noise ratio constraint for the group center user can be expressed as:

[0079] in, , ; 4g) Regarding question P1 The quadratic objective is transformed into a question about The linear objective is then given by problem P2:

[0080] ,

[0081] ,

[0082]

[0083] ,

[0084] , ,

[0085] ,

[0086] ,

[0087] ,

[0088] , .

[0089] 5) Difference-of-Convex (DC) programming is used to handle the rank-one constraint in problem P2, and within the framework of Block Coordinate Descent (BCD), problem P2 is decomposed into solving blocks of auxiliary variables. Subproblem SP1 and solution of beamforming matrix block Subproblem SP2; The specific operation of step 5) is as follows: 5a) For positive semidefinite matrices The necessary and sufficient condition for its rank to be one is that its trace norm is equal to its spectral norm, that is:

[0090] 5b) Due to the positive semi-definite matrix If the trace equals the trace norm, then It can be represented as:

[0091] 5c) Due to The trace and spectral norm are both convex functions, therefore the rank-one constraint is applied. It can be equivalently represented as the difference between two convex functions, i.e., the rank-one constraint. The DC function can be expressed as:

[0092] 5d) To address the trace minimization problem in the objective function of problem P2. Using a reweighted minimization method, the approximation is achieved by updating the weights in each iteration. The approximate objective function is then:

[0093] in, Represents the regularization parameter; 5e) To effectively solve problem P2, a Block Coordinate Descent (BCD) framework is adopted, dividing all optimization variables into beamforming matrix blocks. and auxiliary variable blocks By alternately optimizing one variable block in each iteration while fixing the other, problem P2 is decomposed into two more manageable subproblems, SP1 and SP2. Subproblem SP1 is as follows:

[0094] ,

[0095] ,

[0096] ,

[0097] , ,

[0098] in, This refers to the slack variables introduced in order to find a set of solutions that satisfy all constraints with maximum margin; 5f) Constructing a fixed variable block Solving matrix blocks Subproblem SP2 in the The calculation formula for the next iteration is:

[0099] ,

[0100] ,

[0101] ,

[0102] ,

[0103] in, express A subgradient of the spectral norm, Represents the beamforming matrix The eigenvector corresponding to the largest eigenvalue.

[0104] 6) Design the DC-BCD algorithm, and iteratively update the subproblems SP1 and SP2 by alternately solving them until convergence, so as to obtain the robust beamforming and power allocation coefficients of the UAV; The specific operation of step 6) is as follows: 6a) Initialize the beamforming matrix Initialize function value Initialize the convergence threshold Initialize the number of iterations ; 6b) Calculation ; 6c) Solve SP1 to obtain the distribution coefficients. and auxiliary variables ; 6d) Solve SP2 to obtain ; 6e) Update ; 6f) Update ; 6g) if If not, proceed to step 6b); otherwise, let ; 6h) If Then we perform eigenvalue decomposition. Output beamforming vector Otherwise, Gaussian randomization is used. Generate approximate beam vector Output .

[0105] 7) Semidefinite relaxation (SDR) is used to handle the rank-one constraint in problem P2, and within the framework of block coordinate descent (BCD), problem P2 is decomposed into blocks of auxiliary variables to be solved. Subproblem SP1 and solving the beamforming matrix block Subproblem SP3; The specific operation of step 7) is as follows: 7a) The Lagrange dual form of problem P2 after relaxing the rank-one constraint is:

[0106] in, Represents the dual variable corresponding to the constraint in problem P2; 7b) The objective function after dualization is expressed as:

[0107] 7c) Order , and Let the optimal dual solution of problem P2 in SDR form be represented by the matrix. for: ; 7d) It must be The solution. To obtain the lower bound dual optimal value, we need to... .

[0108] at this time The optimal value is 0, that is ,because and Then we have:

[0109] 7e) The equality holds if and only if ,but It has at most one zero eigenvalue, therefore When problem P2 is feasible, then we have ; 7f) In summary, it has been proven that the optimal solution to problem P2 satisfies the rank-one condition. Therefore, subproblem SP3 is:

[0110] ,

[0111] ,

[0112] ,

[0113] , .

[0114] 8) Design the SDR-BCD algorithm, which iteratively updates the subproblems SP1 and SP3 by alternately solving them until convergence, thereby obtaining the robust beamforming and power allocation coefficients of the UAV.

[0115] The specific operation of step 8) is as follows: 8a) Initialize the beamforming matrix Initialize function value Initialize the convergence threshold Initialize the number of iterations ; 8b) Solve for SP1 to obtain the distribution coefficients. and auxiliary variables ; 8c) Solve SP2 to obtain ; 8d) Update ; 8e) Update ; 8f) If If not, proceed to step 6b); otherwise, let ; 8g) Eigenvalue decomposition Output beamforming vector .

[0116] Example 2 The robust resource scheduling system for multi-antenna UAV communication networks described in this invention includes: The initialization module is used to initialize the basic parameters of the UAV communication network, sort the ground users in ascending order according to the horizontal distance between the ground user and the UAV ground projection point, and pair users who are closer to the UAV ground projection point and users who are farther from the UAV ground projection point according to the channel gain difference. The calibration module is used to sample uncertain channel state information of UAVs and ground users. Based on the collected sample data, it constructs an ellipsoidal uncertainty set by estimating the sample mean and covariance matrix, and uses the quantile method to perform size calibration to obtain a high probability confidence region. The solver module is used to construct a mathematical model P1 that jointly optimizes beamforming and power allocation with the goal of minimizing the total transmit power of the UAV. The scheduling module is used to solve the mathematical model P1 to obtain the robust beamforming and power allocation coefficients of the UAV, and to perform robust resource scheduling of the multi-antenna UAV communication network based on the robust beamforming and power allocation coefficients of the UAV.

[0117] The module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0118] Example 3 A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a robust resource scheduling method for a multi-antenna unmanned aerial vehicle (UAV) communication network. For example, the method includes: initializing basic parameters of the UAV communication network; arranging ground users in ascending order based on their horizontal distance from the UAV's ground projection point; pairing users closer to the UAV's ground projection point with users farther away from the UAV's ground projection point based on channel gain differences; sampling uncertain channel state information between the UAV and ground users; constructing an ellipsoidal uncertainty set based on the collected sample data using sample mean estimation and covariance matrix, and performing size calibration using the quantile method to obtain a high-probability confidence region; constructing a mathematical model P1 for joint optimization of beamforming and power allocation with the objective of minimizing the total UAV transmit power; solving the mathematical model P1 to obtain robust beamforming and power allocation coefficients for the UAV; and performing robust resource scheduling for the multi-antenna UAV communication network based on the robust beamforming and power allocation coefficients. The memory may include main memory, such as high-speed random access memory (RAM), or non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry-standard architecture bus, a peripheral component interconnection standard bus, or an extended industry-standard architecture bus. The bus can be categorized as an address bus, data bus, or control bus. The memory stores programs; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0119] Example 4 A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a robust resource scheduling method for a multi-antenna UAV communication network. For example, the method includes: initializing basic parameters of the UAV communication network; arranging ground users in ascending order based on their horizontal distance from the UAV's ground projection point; pairing users closer to the UAV's ground projection point with users farther away based on channel gain differences; sampling uncertain channel state information between the UAV and ground users; constructing an ellipsoidal uncertainty set based on the collected sample data using sample mean estimation and covariance matrix, and performing size calibration using the quantile method to obtain a high-probability confidence region; constructing a mathematical model P1 for joint optimization of beamforming and power allocation with the objective of minimizing the total UAV transmit power; solving the mathematical model P1 to obtain robust beamforming and power allocation coefficients for the UAV; and performing robust resource scheduling for the multi-antenna UAV communication network based on these coefficients. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0120] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0122] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0123] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0124] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0125] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

[0126] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A robust resource scheduling method for multi-antenna unmanned aerial vehicle (UAV) communication networks, characterized in that, include: Initialize the basic parameters of the UAV communication network, sort the ground users in ascending order according to the horizontal distance between the ground user and the UAV ground projection point, and pair users who are closer to the UAV ground projection point and users who are farther from the UAV ground projection point according to the channel gain difference. Uncertain channel state information between UAVs and ground users is sampled. Based on the collected sample data, an ellipsoidal uncertainty set is constructed by estimating the sample mean and covariance matrix, and the size is calibrated using the quantile method to obtain a high-probability confidence region. To minimize the total transmit power of the UAV, a mathematical model P1 is constructed that jointly optimizes beamforming and power allocation. Solve the mathematical model P1 to obtain the robust beamforming and power allocation coefficients of the UAV, and perform robust resource scheduling of the multi-antenna UAV communication network based on the robust beamforming and power allocation coefficients of the UAV.

2. The robust resource scheduling method for multi-antenna UAV communication networks according to claim 1, characterized in that, The process of solving the mathematical model P1 to obtain the robust beamforming and power allocation coefficients of the UAV is as follows: The S-Procedure method is used to transform the non-convex quadratic constraints with uncertain channel information in mathematical model P1 into bilinear matrix inequalities, and to transform the beamforming vector in mathematical model P1 into a bilinear matrix inequality. The quadratic objective is transformed into a question about The linear objective is obtained, resulting in problem P2 with rank-one constraints; The rank-one constraint in problem P2 is addressed using differential convex function programming, and within the block coordinate descent framework, problem P2 is decomposed into solving blocks of auxiliary variables. Subproblem SP1 and solution of beamforming matrix block Subproblem SP2; Based on the DC-BCD algorithm, the robust beamforming and power allocation coefficients of the UAV are obtained by alternately solving subproblems SP1 and SP2 and iteratively updating until convergence.

3. The robust resource scheduling method for multi-antenna UAV communication networks according to claim 1, characterized in that, The process of solving the mathematical model P1 to obtain the robust beamforming and power allocation coefficients of the UAV is as follows: The S-Procedure method is used to transform the non-convex quadratic constraints with uncertain channel information in mathematical model P1 into bilinear matrix inequalities, and to transform the beamforming vector in mathematical model P1 into a bilinear matrix inequality. The quadratic objective is transformed into a question about The linear objective is obtained, resulting in problem P2 with rank-one constraints; A semidefinite relaxation technique is used to handle the rank-one constraint in problem P2, and within the block coordinate descent framework, problem P2 is decomposed into blocks of auxiliary variables to be solved. Subproblem SP1 and solution of beamforming matrix block Subproblem SP3; Based on the SDR-BCD algorithm, the robust beamforming and power allocation coefficients of the UAV are obtained by iteratively solving subproblems SP1 and SP3 alternately and updating until convergence.

4. The robust resource scheduling method for multi-antenna UAV communication networks according to claim 2, characterized in that, The mathematical model P1 is: ， ， ， ， ， ， ， in, Indicates the first Beamforming vectors of users in the group, Indicates the drone has reached the Group User channel vector, where Representing central users or peripheral users, Indicates noise. Indicates the first Users in the group The signal-to-interference-plus-noise ratio threshold. Indicates the first In the group User power allocation factor, Indicates the first Power allocated to users in the group express The user's ellipsoidal indeterminate set.

5. The robust resource scheduling method for multi-antenna UAV communication networks according to claim 4, characterized in that, The question P2 is: ， ， ， ， ， ， ， ， ， in, , .

6. The robust resource scheduling method for multi-antenna UAV communication networks according to claim 4, characterized in that, The subproblem SP1 is: ， ， ， ， ， in, This refers to the slack variables introduced in order to find a set of solutions that satisfy all constraints with maximum margin; Construct a fixed variable block Solving matrix blocks Subproblem SP2 in the The calculation formula for the next iteration is: ， ， ， ， in, express A subgradient of the spectral norm, Represents the beamforming matrix The eigenvector corresponding to the largest eigenvalue.

7. The robust resource scheduling method for multi-antenna UAV communication networks according to claim 4, characterized in that, The subproblem SP3 is: ， ， ， ， 。 8. A robust resource scheduling system for a multi-antenna unmanned aerial vehicle (UAV) communication network, characterized in that, include: The initialization module is used to initialize the basic parameters of the UAV communication network, sort the ground users in ascending order according to the horizontal distance between the ground user and the UAV ground projection point, and pair users who are closer to the UAV ground projection point and users who are farther from the UAV ground projection point according to the channel gain difference. The calibration module is used to sample uncertain channel state information of UAVs and ground users. Based on the collected sample data, it constructs an ellipsoidal uncertainty set by estimating the sample mean and covariance matrix, and uses the quantile method to perform size calibration to obtain a high probability confidence region. The solver module is used to construct a mathematical model P1 that jointly optimizes beamforming and power allocation with the goal of minimizing the total transmit power of the UAV. The scheduling module is used to solve the mathematical model P1 to obtain the robust beamforming and power allocation coefficients of the UAV, and to perform robust resource scheduling of the multi-antenna UAV communication network based on the robust beamforming and power allocation coefficients of the UAV.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the robust resource scheduling method for multi-antenna unmanned aerial vehicle communication networks as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the robust resource scheduling method for multi-antenna unmanned aerial vehicle communication networks as described in any one of claims 1-7.

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