A method and related apparatus for robust resource scheduling of a UAV-assisted cellular network

Abstract

This invention discloses a robust resource scheduling method and related apparatus for UAV-assisted cellular networks, comprising: 1) initializing the basic parameters of the UAV-assisted cellular network and sampling uncertain channel state information between the UAV and ground users to obtain uncertain channel state information samples; 2) constructing a polyhedral model with a high-probability region based on the uncertain channel state information samples; 3) constructing a two-stage robust optimization model, wherein the first stage is the main problem MP and the second stage is the subproblem SP; 4) linearizing the subproblem SP to obtain a mixed-integer linear programming problem SP. MILP 5) Design a solution to the mixed-integer linear programming problem SP. MILP The branch and bound algorithm; 6) Control the drone users and base station users according to the optimal transmit power of the drone users and base station users. This method and related devices can effectively improve the reliability and network utility of the drone-assisted 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 unmanned aerial vehicle-assisted cellular networks. Background Technology

[0002] Due to their low cost and high mobility, drones are often used as aerial base stations to address coverage blind spots in cellular networks. By combining drones with ground base stations to provide services to users, drone-assisted cellular networks have become one of the key technologies for meeting the demand for seamless mobile communication coverage. However, with the explosive growth in the number of mobile terminals, drone-assisted cellular networks face a severe spectrum scarcity problem and can no longer meet the demand for large-scale access. Non-orthogonal multiple access (NOAMI) technology, through serial interference cancellation, allows multiple users to access a sub-channel simultaneously, thus becoming a very promising technology in drone-assisted cellular networks.

[0003] While existing research has extensively explored resource management in UAV networks, most of this work is based on ideal channel state information. In real-world scenarios, factors such as channel estimation errors and feedback delays lead to significant uncertainties in channel state information, making it difficult for strategies based on ideal models to meet users' quality of service (QoS) requirements. To address channel uncertainty, some current works have proposed methods based on distribution assumptions. Although this approach improves 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.

[0004] Furthermore, existing methods also have limitations in addressing the variable coupling challenges introduced by uncertain channel state information. They typically rely on transforming a non-convex problem into a series of convex subproblems for iterative solutions. When dealing with the strong nonlinear coupling between decision variables and uncertain parameters, treating all variables as a whole at the same level for iterative solutions makes it difficult to effectively handle the complex asymmetric coupling structures between decision variables and uncertain parameters in mixed scenarios. Based on these considerations, designing a robust resource scheduling method for UAV-assisted cellular networks that can meet the needs of large-scale access becomes crucial. Summary of the Invention

[0005] 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 unmanned aerial vehicle (UAV) assisted cellular networks. This method and related apparatus can effectively improve the reliability and network utility of UAV assisted networks.

[0006] To achieve the above objectives, this invention discloses a robust resource scheduling method for unmanned aerial vehicles (UAVs) assisted cellular networks, comprising the following steps: 1) Initialize the basic parameters of the UAV-assisted cellular network, sample the uncertain channel state information between the UAV and ground users, and obtain uncertain channel state information samples; 2) Based on the uncertain channel state information samples, construct a polyhedral model with a high-probability region; 3) Construct a two-stage robust optimization model, where the first stage is the main problem MP and the second stage is the sub-problem SP; 4) Linearize the subproblem SP to obtain the mixed-integer linear programming problem SP. MILP ; 5) Design a solution to the mixed-integer linear programming problem SP. MILP Branch and bound algorithm; 6) The outer layer solves the main problem MP and generates Benders cut, while the inner layer uses the branch and bound algorithm to solve the mixed-integer linear programming problem SP. MILP The optimal transmission power of drone users and base station users is obtained, and the drone users and base station users are controlled based on the optimal transmission power of drone users and base station users.

[0007] Furthermore, the specific operation of step 1) is as follows: 1a) Initialize the number of drone users and number of base station users In this context, drones and base stations share the same spectrum resources, which are divided into... The sub-channel, in the... On the sub-channel, the base station is the first Each base station user service, drones for Individual drone user services; 1b) Initialize UAV transmit power Base station transmission power Additive white Gaussian noise power Initialize the drone assigned to the first Power of individual drone users Initialize the base station assigned to the first Power of each base station user ; 1c) Initialize the channel gain of the UAV communication link, base station communication link, interference link between the base station and the UAV user, and interference link between the base station and other cellular areas. , , and Initialize small-scale channel fading in UAV communication links Initialize the maximum tolerable interference level for users in other cellular areas. Initialize the weighting factor of energy consumption ; Initialize signal-to-interference-plus-noise ratio threshold , Sub-channel Upper The lower limit of the signal-to-interference-plus-noise ratio that a drone user must achieve to maintain a normal communication link; 1d) For uncertain path state information conduct The second sampling, the sample set is , where vector Indicates the first Channel state information collected this time.

[0008] Furthermore, the specific operation of step 2) is as follows: 2a) Determine the uncertain set G of the polyhedron:

[0009] in, This is the UAV channel state vector. Denotes the center of an uncertain polyhedron set. In a set of polyhedra In the process of drones reaching users The mean of the channel state information, Indicates drone to user The maximum deviation between the true channel state value and the center value of the polyhedron set. The deviation variable represents the degree of deviation between the actual value of the channel state information and the center. For the boundary of an uncertain polyhedral set; 2b) Calculate the sample mean of the channel sampling:

[0010] 2c) Obtain the result without deviation from the variable The range of other variables in the following case:

[0011] 2d) Calculate from random space arrive Transformation mapping data:

[0012] in, Indicates from the sample set Mapped to A point in space; 2e) Sort the transformation mapping data obtained in step 2d). ,set up express of The upper bound of the quantile is the uncertainty set. The size is:

[0013] in, This represents the maximum tolerable probability of interruption in the drone's communication link.

[0014] Furthermore, the specific operation of step 3) is as follows: 3a) Construct the main problem MP:

[0015] , ,

[0016] ,

[0017] ,

[0018] ,

[0019] in, Indicates channel bandwidth. This represents the optimal solution to the subproblem SP; 3b) Constructing the subproblem SP:

[0020] , ,

[0021] , ,

[0022] , ,

[0023] , ,

[0024] , ,

[0025] , , .

[0026] Furthermore, the specific operation of step 4) is as follows: 4a) Employing duality theory and introducing dual variables , and The inner min problem of subproblem SP is transformed into a max problem, where, Representing constraints dual variables, Representing constraints dual variables, Representing constraints The dual variable; 4b) Adopt large M Method guarantees auxiliary variables Able to accurately represent deviation variables and dual variables The product of these terms yields the mixed-integer linear programming problem SP corresponding to the subproblem SP. MILP :

[0027] , ,

[0028] , ,

[0029] , ,

[0030] , ,

[0031]

[0032] , ,

[0033] , ,

[0034] , , .

[0035] Furthermore, the specific operation of step 5) is as follows: 5a) Initialize an empty list of nodes L; 5b) Solving by ignoring constraints SP, a mixed-integer linear programming problem MILP The objective function value of the problem is obtained. ; 5c) When constraints ,but ,in, This represents the objective function value of the subproblem SP; otherwise, a non-integer deviation variable is chosen. Create node P1 in node list L: ignoring constraints SP problem MILP Add constraints based on Create node P2 in node list L: ignoring constraints SP, a mixed-integer linear programming problem MILP Add constraints based on ; 5d) Select and remove a node P from the node list L, and solve the problem corresponding to node P; 5e) When Then proceed to step 5d), where, This represents the objective function value of the problem corresponding to node P; 5f) When all Then update ,make ,in, Indicates deviation variable The optimal solution is found; otherwise, a non-integer deviation variable is selected. Create node P in node list L. new1 : Ignoring constraints SP problem MILP Add constraints based on Create node P in node list L. new2 : Ignoring constraints SP problem MILP Add constraints based on ; 5g) If the node list L is not empty, jump to step 5d); otherwise, output ,in, This represents the optimal solution to the subproblem SP.

[0036] Furthermore, the specific operation of step 6) is as follows: 6a) Initialize the lower bound Upper Realm Number of iterations and convergence deviation ; 6b) Solve the main problem MP to obtain... and ,in, Indicates the first The solution obtained in the nth iteration The power of each base station user Indicates the first The objective function value of the main problem MP is obtained from the next iteration; 6c) Update the Nether ; 6d) Jump to step 5), Substitute into the mixed integer linear programming problem SP MILP Solve for the solution; if it is solvable, then obtain the first... The dual variables obtained from the second iteration , , Update the upper boundary Update the cutting plane constraints If the problem is unsolvable, a new cutting plane is generated using the dual information of the subproblem SP and added to the main problem MP. Indicates the first The objective function value of the subproblem SP obtained from the next iteration; 6e) Number of update iterations ; 6f) When If yes, then jump to step 6b); otherwise, output step 6b. The optimal solution for each base station user and sub-channel Upper The optimal solution for a drone user .

[0037] This invention discloses a robust resource scheduling system for cellular networks assisted by unmanned aerial vehicles (UAVs), comprising the following steps: The initialization module is used to initialize the basic parameters of the UAV-assisted cellular network and sample the uncertain channel state information between the UAV and ground users to obtain uncertain channel state information samples. The first construction module is used to construct a polyhedral model with a high-probability region based on the uncertain channel state information sample. The second building module is used to build a two-stage robust optimization model, where the first stage is the main problem MP and the second stage is the sub-problem SP. The linearization module is used to linearize the subproblem SP to obtain the mixed-integer linear programming problem SP. MILP ; The design module is used to design solutions to the mixed-integer linear programming problem SP. MILP Branch and bound algorithm; The solution module is used to solve the main problem MP in the outer layer and generate Benders cut, and to solve the mixed integer linear programming problem SP in the inner layer using the branch and bound algorithm. MILP The optimal transmission power of drone users and base station users is obtained, and the drone users and base station users are controlled based on the optimal transmission power of drone users and base station users.

[0038] The present invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the UAV-assisted cellular network robust resource scheduling method.

[0039] The present invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the UAV-assisted cellular network robust resource scheduling method.

[0040] The present invention has the following beneficial effects: The robust resource scheduling method and related apparatus for UAV-assisted cellular networks described in this invention, in practical operation, model the network utility maximization problem under uncertain channel state information as an optimization problem with uncertain parameters, and characterizes the uncertainty of channel state information using a polyhedron set learning method. Subsequently, considering the difference in uncertainty of channel state information between base station users and UAV users, the original robust resource scheduling problem is transformed into a two-stage robust optimization problem consisting of a main problem of base station power allocation and a sub-problem of UAV power allocation. Based on the polyhedron set under channel state information, the sub-problem with uncertain parameters is transformed into a deterministic mixed-integer linear programming problem, which is solved using the branch and bound method. Based on this, a robust resource scheduling algorithm for UAV-assisted cellular networks based on the Benders dual cutting plane method is designed. Simulation results demonstrate that, compared with non-robust resource allocation methods, the proposed two-stage robust optimization method effectively improves the reliability and network utility of UAV-assisted networks. Attached Figure Description

[0041] 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.

[0042] Figure 1This is a schematic diagram illustrating the scenario in which the present invention is used; Figure 2 This is a schematic diagram of the overall process of the present invention; Figure 3 This is a system structure block diagram of the present invention; Figure 4 This is a schematic diagram of the two-stage robust algorithm in this invention; Figure 5 This is a cumulative SINR distribution map of the UAV link implemented by this invention; Figure 6 This is another SINR cumulative distribution map of the UAV link implemented in this invention. Detailed Implementation

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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)."

[0049] 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.

[0050] 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.

[0051] Example 1 refer to Figure 1 and Figure 2 The UAV-assisted robust resource scheduling method for cellular networks according to the present invention includes the following steps: 1) Initialize the basic parameters of the UAV-assisted cellular network and sample the uncertain channel state information between the UAV and ground users; The specific operation of step 1) is as follows: 1a) Initialize the number of drone users and number of base station users In this context, drones and base stations share the same spectrum resources, which are divided into... Sub-channels. In the... On the sub-channel, the base station is the first Each base station user service, drones for Individual drone user services; 1b) Initialize UAV transmit power Base station transmission power Additive white Gaussian noise power Initialize the drone assigned to the first Power of individual drone users Initialize the base station assigned to the first Power of each base station user ; 1c) Initialize the channel gain of the UAV communication link, base station communication link, interference link between the base station and the UAV user, and interference link between the base station and other cellular areas. , , , Initialize small-scale channel fading in UAV communication links Initialize the maximum tolerable interference level for users in other cellular areas. Initialize the weighting factor of energy consumption Initialize the signal-to-interference-plus-noise ratio threshold It represents the sub-channel Upper The lower limit of the signal-to-interference-plus-noise ratio that a drone user must achieve to maintain a normal communication link; 1d) For uncertain path state information conduct The second sampling, the sample set is , where vector Indicates the first Channel state information collected this time; 2) Based on the collected uncertain channel state information samples, construct a polyhedral model with high-probability regions; The specific operation of step 2) is as follows: 2a) Determine the uncertain set of the polyhedron:

[0052] in, This is the UAV channel state vector. It represents the center of the polyhedral uncertain set, i.e., the sample mean of the channel sampling; In a set of polyhedra In the process of drones reaching users The mean of the channel state information; Indicates drone to user The maximum deviation between the true value of the channel state and the center value of the polyhedron set; This represents the deviation variable, used to indicate the degree of deviation between the actual value of the channel state information and the center. For the boundary of an uncertain polyhedral set; 2b) Calculate the sample mean of the channel sampling. :

[0053] 2c) Obtain the result without deviation from the variable The range of the remaining variables under the given conditions, i.e.: ; 2d) Calculate from random space arrive Transformation mapping data:

[0054] in, Indicates from the sample set Mapped to A point in space; 2e) Sort the transformation mapping data obtained in step 2d). ,make express of The upper bound of the quantile is the uncertainty set. The size is:

[0055] in, Represents an uncertain set The coverage area, i.e., the maximum tolerable probability of interruption of the drone communication link.

[0056] 3) Construct a two-stage robust optimization model, where the first stage is the main problem MP and the second stage is the sub-problem SP; The specific operation of step 3) is as follows: 3a) The main problem MP is:

[0057] , ,

[0058] ,

[0059] ,

[0060] ,

[0061] in, Indicates channel bandwidth, auxiliary variable This represents the optimal solution to the subproblem SP; 3b) Subproblem SP is:

[0062] , ,

[0063] , ,

[0064] , ,

[0065] , ,

[0066] , ,

[0067] , , .

[0068] 4) Linearize the subproblem SP to obtain the corresponding mixed-integer linear programming problem SP. MILP ; The specific operation of step 4) is as follows: 4a) Employing duality theory and introducing dual variables , and The inner min problem of subproblem SP is transformed into a max problem, resulting in problem SP. MAX :

[0069]

[0070] , ,

[0071] , ,

[0072] , ,

[0073] , ,

[0074] in, Representing constraints dual variables, Representing constraints dual variables, Representing constraints The dual variable; 4b) Adopt large M Method to ensure auxiliary variables Able to accurately represent deviation variables and dual variables The product of these terms yields the mixed-integer linear programming problem SP corresponding to the subproblem SP. MILP :

[0075] , ,

[0076] , ,

[0077] , ,

[0078] , ,

[0079]

[0080] , ,

[0081] , ,

[0082] , , .

[0083] 5) Design a solution to the mixed-integer linear programming problem SP MILP Branch and bound algorithm; The specific operation of step 5) is as follows: 5a) Initialize an empty list of nodes L; 5b) Solving by ignoring constraints SP, a mixed-integer linear programming problem MILP The objective function value of the problem is obtained. ; 5c) When constraints Then we get ,in, Represent the objective function value of the subproblem SP; otherwise, choose a non-integer deviation variable. Create node P1 in node list L: ignoring constraints SP problem MILP Add constraints based on Create node P2 in node list L: ignoring constraints SP, a mixed-integer linear programming problem MILP Add constraints based on ; 5d) Select and remove a node P from the node list L, and solve the problem corresponding to node P; 5e) When Then proceed to step 5d), where, This represents the objective function value of the problem corresponding to node P; 5f) When all Then update ,make ,in, Indicates deviation variable The optimal solution is found; otherwise, a non-integer deviation variable is selected. Create node P in node list L. new1 : Ignoring constraints SP problem MILP Add constraints based on Create node P in node list L. new2 : Ignoring constraints SP problem MILP Add constraints based on ; 5g) If the node list L is not empty, jump to step 5d); otherwise, output ,in, This represents the optimal solution to the subproblem SP.

[0084] 6) Design a robust power allocation algorithm based on Benders' dual cutting plane method: The outer layer solves the main problem MP and generates Benders cuts, while the inner layer uses the branch and bound algorithm from step 5) to solve the mixed-integer linear programming model SP. MILP The process is iterated until convergence, and the optimal transmit power for drone users and base station users is calculated.

[0085] The specific operation of step 6) is as follows: 6a) Initialize the lower bound Upper Realm Number of iterations and convergence deviation ; 6b) Solve the main problem MP to obtain... and ,in, Indicates the first The solution obtained in the nth iteration The power of each base station user Indicates the first The objective function value of the main problem MP is obtained from the next iteration; 6c) Update the Nether ; 6d) Jump to step 5), Substitute into the mixed integer linear programming problem SP MILP Solve for the solution; if it is solvable, then obtain the first... The dual variables obtained from the second iteration , , Update the upper boundary Update the cutting plane constraints And add it to the main problem MP; if it is unsolvable, then use the dual information of the subproblem SP to generate a new cutting plane and add it to the main problem MP, where Indicates the first The objective function value of the subproblem SP obtained from the next iteration; 6e) Number of update iterations ; 6f) When If yes, then jump to step 6b); otherwise, output step 6b. The optimal solution for each base station user and sub-channel Upper The optimal solution for a drone user .

[0086] Example 2 The UAV-assisted cellular network robust resource scheduling system of the present invention includes the following steps: The initialization module is used to initialize the basic parameters of the UAV-assisted cellular network and sample the uncertain channel state information between the UAV and ground users to obtain uncertain channel state information samples. The first construction module is used to construct a polyhedral model with a high-probability region based on the uncertain channel state information sample. The second building module is used to build a two-stage robust optimization model, where the first stage is the main problem MP and the second stage is the sub-problem SP. The linearization module is used to linearize the subproblem SP to obtain the mixed-integer linear programming problem SP. MILP ; The design module is used to design solutions to the mixed-integer linear programming problem SP. MILP Branch and bound algorithm; The solution module is used to solve the main problem MP in the outer layer and generate Benders cut, and to solve the mixed integer linear programming problem SP in the inner layer using the branch and bound algorithm. MILP The optimal transmission power of drone users and base station users is obtained, and the drone users and base station users are controlled based on the optimal transmission power of drone users and base station users.

[0087] 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.

[0088] 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 the robust resource scheduling method for a drone-assisted cellular network. For example, the method includes: initializing basic parameters of the drone-assisted cellular network; sampling uncertain channel state information between the drone and ground users to obtain uncertain channel state information samples; constructing a polyhedral model with a high-probability region based on the uncertain channel state information samples; constructing a two-stage robust optimization model, wherein the first stage is the main problem MP and the second stage is a subproblem SP; and linearizing the subproblem SP to obtain a mixed-integer linear programming problem SP. MILP Design a solution to the mixed-integer linear programming problem SP. MILP The branch and bound algorithm is used; the outer layer solves the main problem MP and generates Benders cut, while the inner layer uses the branch and bound algorithm to solve the mixed integer linear programming problem SP.MILP The system obtains the optimal transmission power for both the drone user and the base station user, and controls the drone user and base station user based on these optimal transmission powers. The memory may include main memory, such as high-speed random access memory, or it may also include non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which can 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, and control bus. The memory stores programs, specifically 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.

[0089] Example 4 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 UAV-assisted cellular network. For example, the method includes: initializing basic parameters of the UAV-assisted cellular network; sampling uncertain channel state information between the UAV and ground users to obtain uncertain channel state information samples; constructing a polyhedral model with a high-probability region based on the uncertain channel state information samples; constructing a two-stage robust optimization model, wherein the first stage is the main problem MP and the second stage is a subproblem SP; and linearizing the subproblem SP to obtain a mixed-integer linear programming problem SP. MILP Design a solution to the mixed-integer linear programming problem SP. MILP The branch and bound algorithm is used; the outer layer solves the main problem MP and generates Benders cut, while the inner layer uses the branch and bound algorithm to solve the mixed integer linear programming problem SP. MILP The system obtains the optimal transmission power for both the drone user and the base station user, and controls the drone user and base station user based on these optimal transmission powers. 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 method for robust resource scheduling in a UAV-assisted cellular network, the method comprising: Includes the following steps: 1) Initialize the basic parameters of the UAV-assisted cellular network, sample the uncertain channel state information between the UAV and ground users, and obtain uncertain channel state information samples; 2) Based on the uncertain channel state information samples, construct a polyhedral model with a high-probability region; 3) Construct a two-stage robust optimization model, where the first stage is the main problem MP and the second stage is the sub-problem SP; 4) Linearization of the subproblem SP to obtain the mixed integer linear programming problem SP MILP ; 5) designing a branch-and-bound algorithm to solve said mixed integer linear programming problem SP MILP ; 6) outer layer solves the main problem MP and generates Benders cuts, inner layer solves the mixed integer linear programming problem SP using the branch and bound algorithm MILP Obtaining optimal transmission power of the UAV user and the base station user, and controlling the UAV user and the base station user according to the optimal transmission power of the UAV user and the base station user. 2.The UAV-assisted cellular network robust resource scheduling method of claim 1, wherein, The specific operation of step 1) is as follows: 1a) initializing the number of drone users and the number of base station users where the drones and the base stations share the same spectrum resources, the spectrum resources are divided into sub-channels, on the th sub-channel, the base station serves the th base station user, and the drone serves the th drone user; 1b) initializing the drone transmit power , the base station transmit power , and the additive white Gaussian noise power 1c) initializing the power allocated by the drone to the jth drone user 1d) initializing the power allocated by the base station to the jth base station user 1e) initializing the power allocated by the drone to the jth base station user 1f) initializing the power allocated by the base station to the jth drone user ; 1 c) initializing the channel gains of the drone communication link, the base station communication link, the interference link between the base station and the drone user, the interference link between the base station and the other cellular area , , and , initializing small-scale channel fading in the drone communication link , initializing the maximum tolerable interference level of the other cellular area user , initializing the weight factor of the energy consumption ; Initializing a signal-to-interference-and-noise ratio threshold , Indicating a subchannel The first Signal-to-interference-and-noise ratio floor that a drone user must achieve in order to maintain a normal communication link 1d) on uncertain channel state information performing sub-sampling, the set of samples being where the vector represents the channel state information collected at the th sub-collection.

3. The robust resource scheduling method for UAV-assisted cellular networks according to claim 2, characterized in that, The specific operation of step 2) is as follows: 2a) Determine the uncertain set G of the polyhedron: in, This is the UAV channel state vector. Denotes the center of an uncertain polyhedron. In a set of polyhedra In the process of drones reaching users The mean of the channel state information, Indicates drone to user The maximum deviation between the true channel state value and the center value of the polyhedron set. The deviation variable represents the degree of deviation between the actual value of the channel state information and the center. For the boundary of an uncertain polyhedral set; 2b) Calculate the sample mean of the channel sampling: 2c) Obtain the result without deviation from the variable The range of other variables in the case of: 2d) Calculate from random space arrive Transformation mapping data: in, Indicates from the sample set Mapped to A point in space; 2e) Sort the transformation mapping data obtained in step 2d). ,set up express of The upper bound of the quantile is the uncertainty set. The size is: in, This represents the maximum tolerable probability of interruption in the drone's communication link.

4. The robust resource scheduling method for UAV-assisted cellular networks according to claim 3, characterized in that, The specific operation of step 3) is as follows: 3a) Construct the main problem MP: ， ， ， ， ， in, Indicates channel bandwidth. This represents the optimal solution to the subproblem SP; 3b) Constructing the subproblem SP: ， ， ， ， ， ， ， ， ， ， ， ， 。 5. The robust resource scheduling method for unmanned aerial vehicles (UAVs) assisted cellular networks according to claim 4, characterized in that, The specific operation of step 4) is as follows: 4a) Employing duality theory and introducing dual variables , and The inner min problem of subproblem SP is transformed into a max problem, where, Representing constraints dual variables, Representing constraints dual variables, Representing constraints The dual variable; 4b) Adopt large M Method guarantees auxiliary variables Able to accurately represent deviation variables and dual variables The product of these terms yields the mixed-integer linear programming problem SP corresponding to the subproblem SP. MILP : ， ， ， ， ， ， ， ， ， ， ， ， ， ， 。 6. The robust resource scheduling method for unmanned aerial vehicles (UAVs) assisted cellular networks according to claim 5, characterized in that, The specific operation of step 5) is as follows: 5a) Initialize an empty list of nodes L; 5b) Solving by ignoring constraints SP, a mixed-integer linear programming problem MILP The objective function value of the problem is obtained. ; 5c) When constraints ,but ,in, This represents the objective function value of the subproblem SP; otherwise, a non-integer deviation variable is chosen. Create node P1 in node list L: ignoring constraints SP problem MILP Add constraints based on Create node P2 in node list L: ignoring constraints SP, a mixed-integer linear programming problem MILP Add constraints based on ; 5d) Select and remove a node P from the node list L, and solve the problem corresponding to node P; 5e) When Then proceed to step 5d), where, This represents the objective function value of the problem corresponding to node P; 5f) When all Then update ,make ,in, Indicates deviation variable The optimal solution is found; otherwise, a non-integer deviation variable is selected. Create node P in node list L. new1 : Ignoring constraints SP problem MILP Add constraints based on Create node P in node list L. new2 : Ignoring constraints SP problem MILP Add constraints based on ; 5g) If the node list L is not empty, jump to step 5d); otherwise, output ,in, This represents the optimal solution to the subproblem SP.

7. The robust resource scheduling method for unmanned aerial vehicles (UAVs) assisted cellular networks according to claim 1, characterized in that, The specific operation of step 6) is as follows: 6a) Initialize the lower bound Upper Realm Number of iterations and convergence deviation ; 6b) Solve the main problem MP to obtain... and ,in, Indicates the first The solution obtained in the nth iteration The power of each base station user Indicates the first The objective function value of the main problem MP is obtained from the next iteration; 6c) Update the Nether ; 6d) Jump to step 5), Substitute into the mixed integer linear programming problem SP MILP Solve for the solution; if it is solvable, then obtain the first... The dual variables obtained from the second iteration , , Update the upper boundary Update the cutting plane constraints If the problem is unsolvable, a new cutting plane is generated using the dual information of the subproblem SP and added to the main problem MP. Indicates the first The objective function value of the subproblem SP obtained from the next iteration; 6e) Number of update iterations ; 6f) When If yes, then jump to step 6b); otherwise, output step 6b. The optimal solution for each base station user and sub-channel Upper The optimal solution for a drone user .

8. A robust resource scheduling system for cellular networks assisted by unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: The initialization module is used to initialize the basic parameters of the UAV-assisted cellular network and sample the uncertain channel state information between the UAV and ground users to obtain uncertain channel state information samples. The first construction module is used to construct a polyhedral model with a high-probability region based on the uncertain channel state information sample. The second building module is used to build a two-stage robust optimization model, where the first stage is the main problem MP and the second stage is the sub-problem SP. a linearization module for linearizing the sub-problem SP to obtain a mixed integer linear programming problem SP MILP ; a design module for designing a branch and bound algorithm for solving the mixed integer linear programming problem SP MILP ​ a solving module configured to solve the main problem MP and generate Benders cuts in an outer layer, and solve the mixed integer linear programming problem SP in an inner layer using the branch and bound algorithm MILP Obtaining optimal transmission power of the UAV user and the base station user, and controlling the UAV user and the base station user according to the optimal transmission power of the UAV user and the base station user.

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 unmanned aerial vehicle-assisted cellular 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 unmanned aerial vehicle-assisted cellular networks as described in any one of claims 1-7.

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