Positioning error tolerant unmanned aerial vehicle communication deployment method and system

Abstract

The invention discloses a positioning error tolerant unmanned aerial vehicle communication deployment method and system, and belongs to the technical field of wireless communication, and the method comprises the steps: quantifying the spatial positions of an unmanned aerial vehicle and a ground user in a three-dimensional rectangular coordinate system; the channel capacity of each link is quantitatively analyzed based on the Shannon theorem, and the user rate reliability is introduced into a network average transmission rate maximization framework in a probability constraint form. Further, the Bernstein inequality is utilized to convert the probability guarantee constraint which is difficult to directly solve into a deterministic linear matrix inequality related to the positioning error covariance, and a solvable deterministic problem is constructed. Then, an iterative algorithm based on continuous convex approximation is designed, and a non-convex part is linearized through first-order Taylor expansion, so that the model is rapidly converged. According to the method, the high transmission rate can be stably supported within the preset error range, and the disturbance rejection capability and the spectrum utilization efficiency of the system are effectively enhanced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless communication, and particularly relates to a positioning error tolerant unmanned aerial vehicle communication deployment method and system. BACKGROUND

[0002] With the development of 5G / 6G networks, unmanned aerial vehicle communication is widely used in emergency communication, edge computing and other fields due to its flexible deployment capability. However, the positioning module of the unmanned aerial vehicle often has certain errors in actual flight, which causes the node position to deviate, affects the estimation of the link channel gain, and further causes the deployment strategy and power allocation to be invalid, and the communication performance to decrease.

[0003] In the prior art, unmanned aerial vehicle deployment schemes are mainly divided into three categories: Geometric center method: the unmanned aerial vehicle is deployed at the geometric center (such as the center of the minimum coverage circle) of the user group, but the positioning error is completely ignored, and when the actual position deviates, the link interruption probability significantly increases.

[0004] Random optimization method: error modeling is performed by using random programming, a large number of Monte Carlo sampling is required, the calculation complexity exponentially increases with the number of users, and real-time solution cannot be achieved.

[0005] Conservative robust method: a "super conservative" deployment is designed based on the worst error boundary, which guarantees reliability but sacrifices more than 30% of the average rate.

[0006] Positioning errors cause random fluctuations in channel gain, making the rate constraint a probabilistic event, which cannot be handled by traditional deterministic optimization. Probability constraints require multidimensional integration, which has no closed-form solution; real-time requirement: the unmanned aerial vehicle needs to adjust the position online, but the random optimization calculation takes more than a minute, which cannot meet the real-time requirement of flight control. Ignoring the statistical characteristics (such as Gaussian distribution) of the error leads to an overly conservative scheme; without using convex optimization tools, the scheme falls into local optimization or cannot be solved in non-convex problems. SUMMARY

[0007] The technical problem to be solved by the application is to provide a positioning error tolerant unmanned aerial vehicle communication deployment method and system to solve the technical problem that the existing unmanned aerial vehicle communication deployment method assumes absolute accuracy of positioning, ignores the channel gain distortion caused by positioning errors in actual flight, and causes deployment position failure and transmission rate decrease.

[0008] The application adopts the following technical scheme: A positioning error tolerant unmanned aerial vehicle communication deployment method comprises the following steps: S1, a communication model comprising one unmanned aerial vehicle and K ground users is constructed, a probability distribution model of the position deviation of the unmanned aerial vehicle is established, and the channel capacity of each link is calculated based on the channel power gain; S2, based on the communication model constructed in step S1, constructing an optimization problem containing user transmission rate probability constraints with the goal of maximizing network average transmission rate; S3, using Bernstein inequality to convert the probability constraints obtained in step S2 into deterministic linear matrix inequality constraints related to positioning error covariance; S4, based on the deterministic optimization problem obtained in step S3, designing a continuous convex approximation iterative algorithm to solve the optimal deployment position of the unmanned aerial vehicle by linearizing the non-convex constraints through first-order Taylor expansion.

[0009] Preferably, in step S1, the probability distribution model of the position deviation of the unmanned aerial vehicle is

[0010] wherein, is the predicted position of the unmanned aerial vehicle, and the error conforms to an independent Gaussian random distribution , is the standard deviation of the error, is a third-order unit matrix.

[0011] Preferably, the channel capacity of the link of any user is :

[0012] wherein, is the received power at the reference distance, is the transmission power allocated by the unmanned aerial vehicle to the user k , is the power of the Gaussian white noise, is the channel power gain from the unmanned aerial vehicle to the user.

[0013] Preferably, in step S2, the optimization problem is modeled as:

[0014] wherein, is the average transmission rate of the user, is the predicted position of the unmanned aerial vehicle, is the total number of users, is the user set, is the transmission rate of the user k , is the probability condition, is the transmission power of the unmanned aerial vehicle, is the channel gain, is the environmental white noise power, is the user rate requirement, is the outage probability of meeting the user rate requirement.

[0015] Preferably, the user transmission rate probability constraint is expressed as:

[0016] wherein, denotes the user transmission rate requirement, denotes the outage probability of actual transmission rate failing to meet the requirement threshold.

[0017] Preferably, in step S3, a slack variable is introduced to replace , which is the lower bound of , with the constraint , so as to obtain the original probability constraint; then the optimization problem is converted into a deterministic optimization problem.

[0018] Preferably, the optimization problem is specifically as follows:

[0019] wherein, is the lower bound of the user average transmission rate, is a newly introduced slack variable, is the predicted position of the UAV, is the total number of users, is the user set, is the lower bound of the single-user transmission rate, is the position standard error matrix, is the outage probability of meeting the single-user rate requirement, and is a newly introduced slack variable, is the calculated scalar of the user position, is the calculated vector of the user position, is the three-dimensional unit matrix.

[0020] Preferably, step S4 is specifically as follows: a slack variable is introduced, and is obtained; the first constraint of the original problem is converted into a concave constraint, a continuous convex approximation is used, a first-order Taylor expansion is performed on to obtain a convex constraint, and the final optimized UAV deployment position is obtained by solving the problem through continuous iteration.

[0021] Preferably, the convex constraint is as follows:

[0022] wherein, is the trace of the position standard error matrix, is the outage probability of meeting the single-user rate requirement, and is a newly introduced slack variable, is a predicted position of the UAV, is a real position of the UAV, is a transmit power of a given UAV, is a channel attenuation factor per distance, is a base station transmit power, is a channel gain from the base station to a user, is an ambient white noise power, is a newly introduced slack variable, is a specific i-th user, k is a set of users.

[0023] In a second aspect, an embodiment of the present application provides a positioning error tolerant UAV communication deployment system, comprising: a construction module, which constructs a communication model containing one UAV and K ground users, establishes a probability distribution model of the UAV position deviation, and calculates the channel capacity of each link based on the channel power gain; a constraint module, which, based on the constructed communication model, constructs an optimization problem containing user transmission rate probability constraints with the goal of maximizing the network average transmission rate; a conversion module, which converts the obtained probability constraints into deterministic linear matrix inequality constraints related to the positioning error covariance by using Bernstein inequality; a deployment module, which, based on the obtained deterministic optimization problem, designs a continuous convex approximation iterative algorithm, linearizes the non-convex constraints by first-order Taylor expansion, and solves the optimal deployment position of the UAV.

[0024] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the positioning error tolerant UAV communication deployment method described above when executing the computer program.

[0025] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium comprising a computer program, and the computer program implements the steps of the positioning error tolerant UAV communication deployment method described above when executed by a processor.

[0026] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the positioning error tolerant UAV communication deployment method described above when executing the computer program.

[0027] ​In a sixth aspect, an embodiment of the present application provides an electronic device comprising a computer program, which, when executed by the electronic device, implements the steps of the positioning error tolerant UAV communication deployment method described above.

[0028] Compared with the prior art, the present application has at least the following beneficial effects: A positioning error tolerant UAV communication deployment method models the positioning error as a probability distribution, first introduces a probability constraint in rate optimization, converts it into a deterministic linear matrix inequality through Bernstein inequality, and designs a continuous convex approximation algorithm for efficient solution, the probability constraint quantifies the influence of positioning error on rate, ensures that more than 95% of the rate reliability is still met within a preset error range (such as σ≤5m), Bernstein inequality converts the complex probability problem into a linear matrix form that can be solved by convex optimization, avoiding the high computational overhead of traditional Monte Carlo simulation, the continuous convex approximation linearizes the non-convex constraint, and the iterative algorithm converges within 10 times (accuracy 0.01), with an efficiency improvement of 80% compared with random search.

[0029] Further, the positioning error adopts a three-dimensional Gaussian distribution, and the channel gain is based on a free space path loss model, the Gaussian distribution is the optimal statistical description of the positioning error, the covariance matrix simplifies the calculation, and the path loss model clearly quantifies the relationship between link capacity and distance, providing a physical layer basis for the rate probability constraint.

[0030] Further, the probability constraint defines the rate outage probability, which is converted through a slack variable and Bernstein inequality, the slack variable associates the lower bound of the rate with the error, Bernstein inequality derives a closed-form solution, avoiding complex integration, and the linear matrix inequality can be directly solved by convex optimization tools such as CVX, reducing the computational complexity.

[0031] Further, Taylor expansion linearizes the concave constraint, ensuring that each iteration produces a feasible solution, and the objective function monotonically increases until convergence, reducing the calculation time by 50% compared with global optimization algorithms.

[0032] Further, the model construction→ optimization modeling→ constraint conversion→ position solving form a closed loop, the outputs of each module serve as the inputs of the next module, and the system can be embedded into the UAV flight control system to receive positioning data in real time and output the error-resistant deployment coordinates.

[0033] It can be understood that the beneficial effects of the above-mentioned second aspect to sixth aspect can be referred to the related description in the first aspect, which will not be repeated here.

[0034] In summary, the present application models the error influence through probability constraint, guarantees 95% of the user rate requirement when σ=5m; Bernstein inequality + continuous convex approximation converts the non-convex problem into low complexity convex optimization, with an 80% improvement in convergence speed; and the disturbance-resistant deployment increases the average transmission rate by 30% compared with the traditional method.

[0035] The technical solutions of the present application will be further described in detail below with the aid of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 Flowchart of the present application; Figure 2 Scenario diagram provided by the present application; Figure 3 Comparison diagram of the optimized UAV position under the same scenario between the method (Robust location) provided by the present application and the non-error-tolerant UAV deployment method (Non-robust location); Figure 4 Comparison diagram of the UAV transmission rate distribution under the same scenario between the method (Robust optimization) provided by the present application and the non-error-tolerant UAV deployment method (Non-robust optimization); Figure 5 Schematic diagram of a computer device provided by an embodiment of the present application; Figure 6 Block diagram of a chip provided by the present application according to an embodiment.

[0037] Among them, 60. Computer device; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access storage unit; 6202. Cache storage unit; 6203. Read-only storage unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be described clearly and completely below with the aid of the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0039] In the description of the present application, it should be understood that the terms "include" and "contain" indicate the existence of described features, whole, steps, operations, elements and / or components, but do not exclude the existence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.

[0040] It should also be understood that the terms used in the specification and the following claims are for the purpose of describing particular embodiments and are not intended to be limiting, as the specific scope of the invention is disclosed in the attached claims. As used in this specification and the appended claims, the singular forms "a," "an" and "the" encompass both singular and plural referents, unless the context clearly dictates otherwise.

[0041] It should also be further understood that the term "and / or" as used in the specification and in the claims, means any one of the items, or combinations of items, listed are possible and includes all possible combinations, for example A and / or B can mean A alone, B alone, or A and B together. In addition, the character " / " in the present invention generally represents a "or" relationship between the objects before and after it.

[0042] It should be understood that, although the terms first, second, third, etc. can be used herein to describe various ranges or elements, these ranges or elements should not be limited by these terms. These terms are only used to distinguish one range or element from another. For example, a first range could be termed a second range without departing from the scope of the embodiments.

[0043] The word "if" as used herein means "when" or "upon" or "in response to a determination" or "in response to a detection," depending on the context. Similarly, the phrase "if it is determined" or "if [a stated condition or event] is detected" can mean "when it is determined" or "in response to a determination" or "when [a stated condition or event] is detected" or "in response to a detection [of a stated condition or event]," depending on the context.

[0044] Various structural diagrams according to the disclosed embodiments of the present application are shown in the accompanying drawings. These diagrams are not drawn to scale, in which certain details are shown in a somewhat exaggerated manner for purposes of clarity and understanding, and certain other details are omitted. The shapes and relative sizes of the various regions, layers, and the relative positions of the regions and layers shown in the drawings are shown for the purpose of example only and can deviate in actual devices due to manufacturing tolerances or technical limitations, and regions / layers with different shapes, sizes, and relative positions can be designed by those skilled in the art according to actual needs.

[0045] The application provides a positioning error tolerant unmanned aerial vehicle communication deployment method, a robust optimization model containing a probability constraint is constructed for link performance fluctuation caused by unmanned aerial vehicle position uncertainty, three-dimensional coordinates of the unmanned aerial vehicle are taken as decision variables, and a probability constraint which is difficult to directly solve is converted into an equivalent deterministic linear matrix inequality by means of Bernstein inequality; subsequently, a continuous convex approximation method is introduced, a non-convex problem is linearized gradually, and a convex problem is solved efficiently to accelerate convergence. Finally, within a preset positioning error range, the deployment scheme can stably support high transmission rate, and significantly enhances system anti-disturbance performance and spectrum utilization.

[0046] Referring to Figure 1 , the application is a positioning error tolerant unmanned aerial vehicle communication deployment method, comprising the following steps: S1, a communication model containing one unmanned aerial vehicle and K a user is constructed, a probability distribution model of unmanned aerial vehicle position deviation is established, and channel capacity of the model is determined; Referring to Figure 2 , the communication model is constructed, and the channel capacity is determined, specifically: A rectangular coordinate system is constructed, and the position of the user in the rectangular coordinate system is k , The uncertainty model of the unmanned aerial vehicle position is modeled as , wherein the expected position of the unmanned aerial vehicle is , and the error conforms to an independent Gaussian random distribution , is the standard deviation of the error, is a third-order unit matrix.

[0047] Considering free space loss, the channel power gain of the unmanned aerial vehicle to the user is represented as , wherein

[0048] is the received power at a reference distance.

[0049] wherein represents the transmission power of the unmanned aerial vehicle allocated to the user k , and represents the power of Gaussian white noise.

[0050] S2, based on the communication model constructed in step S1, a network average transmission rate maximization optimization problem containing a probability constraint is constructed; The network average transmission rate maximization optimization problem containing the probability constraint is constructed, specifically: S201, due to the position deviation, the transmission rate introduces uncertainty. In order to ensure that the robustness meets the user transmission rate requirement, the corresponding probability constraint is formulated, which is expressed as:

[0051] Wherein, The user transmission rate requirement is expressed as: The actual transmission rate fails to meet the requirement threshold, and the interruption probability is expressed as:

[0052] S202, further, the deployment position of the unmanned aerial vehicle and the power control are considered. The probability constraint of the network average transmission rate maximization optimization problem is modeled as:

[0053] Wherein, the objective function is the network average transmission rate, and the constraint is the probability constraint of the minimum requirement of the transmission rate.

[0054] S3, based on the optimization problem constructed in step S2, the probability constraint is converted into a deterministic constraint related to the positioning error by Bernstein inequality; The probability constraint is converted into a deterministic constraint by Bernstein inequality, which is: S301, introduce slack variable is the lower bound of and replace with it, then the constraint is obtained. Further, since the probability constraint of the transmission rate is expressed as:

[0055] The form of complies with Bernstein inequality, and the original probability constraint is expressed as:

[0056] Wherein

[0057] Is a newly introduced slack variable.

[0058] S302, further, the optimization problem is converted into a deterministic optimization problem, which is expressed as:

[0059] S4, based on the deterministic optimization problem established in step S3, an algorithm based on continuous convex approximation is designed to optimize the deployment position of the unmanned aerial vehicle.

[0060] Based on the algorithm based on continuous convex approximation, the deployment position of the unmanned aerial vehicle is optimized, which is: Introduce slack variable , let

[0061] The first constraint of the original problem is converted to:

[0062] For the concave constraint, a continuous convex approximation is used, and a first-order Taylor expansion thereof at The convex constraint is obtained as:

[0063] Therefore, the first constraint of the original problem is converted to the convex constraint described above, and note that the remaining constraints are also convex, so that the final optimized UAV deployment position can be obtained by solving the problem through continuous iteration.

[0064] In another embodiment of the present application, a positioning error tolerant UAV communication deployment system is provided, which can be used to implement the positioning error tolerant UAV communication deployment method described above. Specifically, the positioning error tolerant UAV communication deployment system includes a construction module, a constraint module, a conversion module, and a deployment module.

[0065] The construction module constructs a communication model containing one UAV and K ground users, establishes a probability distribution model of the UAV position deviation, and calculates the channel capacity of each link based on the channel power gain. The constraint module, based on the constructed communication model, constructs an optimization problem containing user transmission rate probability constraints with the goal of maximizing the network average transmission rate. The conversion module uses the Bernstein inequality to convert the obtained probability constraints into deterministic linear matrix inequality constraints related to the positioning error covariance. The deployment module, based on the obtained deterministic optimization problem, designs a continuous convex approximation iterative algorithm, linearizes the non-convex constraints through first-order Taylor expansion, and solves the optimal deployment position of the UAV.

[0066] The application provides a terminal device, which comprises a processor and a memory, the memory is used for storing a computer program, the computer program comprises program instructions, and the processor is used for executing the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components and the like, which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement a corresponding method flow or a corresponding function; the processor in the embodiment of the application can be used for the operation of the positioning error tolerant unmanned aerial vehicle communication deployment method, comprising: A communication model comprising one unmanned aerial vehicle and K ground users is constructed, a probability distribution model of the position deviation of the unmanned aerial vehicle is established, and the channel capacity of each link is calculated based on the channel power gain; based on the constructed communication model, an optimization problem comprising a user transmission rate probability constraint and taking the maximization of the network average transmission rate as the target is constructed; the probability constraint obtained is converted into a deterministic linear matrix inequality constraint related to the positioning error covariance by using the Bernstein inequality; based on the obtained deterministic optimization problem, a continuous convex approximation iterative algorithm is designed, the non-convex constraint is linearized by first-order Taylor expansion, and the optimal deployment position of the unmanned aerial vehicle is solved.

[0067] Please refer to Figure 5 , the terminal device is a computer device, the computer device 60 of the embodiment comprises a processor 61, a memory 62 and a computer program 63 stored in the memory 62 and capable of running on the processor 61, and the computer program 63 realizes the method for estimating the concentration of radioactive iodine species in the post-accident containment when executed by the processor 61, and details are not repeated here. Alternatively, the computer program 63 realizes the functions of each model / unit in the positioning error tolerant unmanned aerial vehicle communication deployment system when executed by the processor 61, and details are not repeated here.

[0068] The computer device 60 can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The computer device 60 can include, but is not limited to, a processor 61, a memory 62. Those skilled in the art can understand that Figure 5 The computer device 60 is only an example and does not constitute a limitation on the computer device 60, and can include more or fewer components than shown, or combine certain components, or different components, for example, the computer device can also include an input / output device, a network access device, a bus, and the like.

[0069] The processor 61 can be a central processing unit (CPU), and can also be other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0070] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or a memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like.

[0071] Further, the memory 62 can include both an internal storage unit and an external storage device of the computer device 60. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0072] Please refer to Figure 6 The terminal device is an electronic device 600, and the electronic device 600 is in the form of a general computing device. The components of the electronic device can include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.

[0073] The storage unit stores program codes which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present application described in the method part of the present specification. For example, the processing unit 610 can perform the steps as shown in Figure 1

[0074] The storage unit 620 can include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 6201 and / or a cache memory 6202, and can further include a read-only memory (ROM) 6203.

[0075] The storage unit 620 can further include a program / utility 6204 having a set of programs / modules 6205, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or a combination thereof, can include implementation of a network environment.

[0076] The bus 630 can represent one or more of several types of bus structures, including a storage unit bus or bus controller, a peripheral bus, a graphics acceleration port, a processing unit bus, or a local bus using any of a variety of bus architectures.

[0077] The electronic device 600 can also communicate with one or more external devices 700 such as a keyboard or pointing device, a Bluetooth device, etc.; other devices that enable a user to interact with the electronic device 600; and / or one or more devices that enable the electronic device 600 to communicate with one or more other computing devices. Such communication can be via the input / output interface 650. The electronic device 600 can also communicate with one or more networks, such as a local area network, a wide area network, and / or the public switched telephone network, such as the Internet, via the network adapter 660. The network adapter 660 can be any of a plurality of types of network adapters known in the art, such as a modem, a cable modem, a DSL modem, a LAN adapter, a wireless adapter, etc. The network adapter 660 can include an internal rule, and can communicate with the other modules of the electronic device 600 via the bus 630. It will be appreciated that, although not shown, other hardware and / or software modules can be used in connection with the electronic device 600, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0078] Example 4 ​The present application further provides a storage medium, specifically a computer readable storage medium, which is a memory device in the terminal device, and is used to store programs and data. It can be understood that the computer readable storage medium herein can include the built-in storage medium in the terminal device, and of course can include the expansion storage medium supported by the terminal device, and can be any tangible medium containing or storing programs, which can be used by or in combination with an instruction execution system, device or apparatus. The computer readable storage medium provides a storage space, which stores the operating system of the terminal. Moreover, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that more specific examples of the computer readable storage medium include an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0079] The computer readable storage medium further includes a data signal carried in baseband or propagated as a carrier wave, in which readable program codes are borne. Such a propagated data signal can take various forms, including but not limited to electro-magnetic signal, optical signal or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, which can send, propagate or transmit programs for use by or in combination with an instruction execution system, device or apparatus. The program codes contained in the readable storage medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, radio frequency, etc., or any suitable combination of the above.

[0080] The program codes for executing the operation of the present application can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages. The program codes can be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case involving a remote computing device, the remote computing device can be connected to the user computing device through any kind of network, including local area network or wide area network, or can be connected to an external computing device (for example, connected through the Internet by using an Internet service provider).

[0081] The one or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the positioning error tolerant UAV communication deployment method in the above embodiments; the one or more instructions stored in the computer readable storage medium are loaded and executed by the processor to implement the following steps: A communication model including one UAV and K ground users is constructed, a probability distribution model of the position deviation of the UAV is established, and the channel capacity of each link is calculated based on the channel power gain; based on the constructed communication model, an optimization problem including a user transmission rate probability constraint is constructed with the maximum network average transmission rate as the target; the Bernstein inequality is used to convert the obtained probability constraint into a deterministic linear matrix inequality constraint related to the positioning error covariance; based on the obtained deterministic optimization problem, a continuous convex approximation iterative algorithm is designed, the non-convex constraint is linearized by first-order Taylor expansion, and the optimal deployment position of the UAV is solved.

[0082] The database involved in each embodiment provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, and the like, without being limited thereto. The processor involved in each embodiment provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, and the like, without being limited thereto.

[0083] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by a person of ordinary skill in the art without creative labor on the basis of the embodiments in the present application belong to the scope of protection of the present application.

[0084] The positioning error tolerant UAV communication deployment method and the existing mechanism based on the same network parameters are simulated in the experiment to verify the superiority of the method. The specific steps are as follows: the same network parameters are that the user height is 1.5 m, the UAV height is 50 m, the UAV power is 1 W, the noise is-100 dBm, the reference channel gain is-40 dB, the user rate requirement is 6 bps / Hz, and the convergence accuracy is 0.01.

[0085] Please refer to Figure 3For the same scene, the positioning error tolerant unmanned aerial vehicle communication deployment method of the application is more in the middle of the user distribution than other solutions, please refer to Figure 4 For the same scene, the positioning error tolerant unmanned aerial vehicle communication deployment method of the application is more in the middle of the user distribution than other solutions, please refer to

[0086] In summary, the positioning error tolerant unmanned aerial vehicle communication deployment method and system of the application firstly quantifies the spatial position of the unmanned aerial vehicle and the ground user in the three-dimensional rectangular coordinate system accurately, and establishes a link channel capacity expression combined with the positioning deviation model of Gaussian distribution. Based on Shannon formula, the reliability of the link capacity under different deviation conditions is analyzed. An optimization framework containing probability coverage constraint is designed, and the probability constraint is converted into a deterministic linear matrix inequality by using Bernstein inequality. On this basis, a continuous convex approximation iterative algorithm is introduced to quickly obtain the approximate optimal deployment position. The method can stably provide high transmission rate within the preset error range, and effectively enhance the system disturbance resistance.

[0087] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The above integrated unit can be realized in the form of hardware or software function unit. In addition, the specific name of each functional unit and module is only for easy distinction, and does not limit the protection scope of the application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0088] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0089] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.

[0090] In the embodiments of the present application, it should be understood that the disclosed apparatus / terminal and method can be implemented in other manners. For example, the embodiments of the apparatus / terminal described above are merely schematic, and the division of the modules or units is merely logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0091] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0092] In addition, each functional unit in the various embodiments of the present application can be integrated into a processing unit, or each unit can be a physically independent unit, or two or more units can be integrated into a unit. The integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0093] The integrated module / unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. When the processor executes the computer program, the steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable file or some intermediate form. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the computer readable medium can include appropriate contents according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0094] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps in one or more flow or blocks

[0095] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps in one or more flow or blocks

[0096] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps in one or more flow or blocks

[0097] The above merely provides the technical idea of the present application, and cannot be used to limit the protection scope of the present application. Any modification made according to the technical idea of the present application, and on the basis of the technical solutions, falls within the protection scope of the claims of the present application.

Claims

1. A positioning error tolerant unmanned aerial vehicle communication deployment method, characterized in that, The method comprises the following steps: S1, constructing a communication model comprising one unmanned aerial vehicle and K ground users, establishing a probability distribution model of the unmanned aerial vehicle position deviation, and calculating the channel capacity of each link based on the channel power gain; S2, based on the communication model constructed in step S1, constructing an optimization problem with the maximum network average transmission rate as the target and containing user transmission rate probability constraints; S3, using Bernstein inequality to convert the probability constraints obtained in step S2 into deterministic linear matrix inequality constraints related to the positioning error covariance; S4, based on the deterministic optimization problem obtained in step S3, designing a continuous convex approximation iterative algorithm, linearizing the non-convex constraints through first-order Taylor expansion, and solving the optimal deployment position of the unmanned aerial vehicle.

2. The positioning error tolerant drone communication deployment method of claim 1, wherein, In step S1, the probability distribution model of the unmanned aerial vehicle position deviation is wherein, is the predicted position of the UAV, and the error conforms to an independent Gaussian random distribution , is the standard deviation of the error, is a third-order identity matrix.

3. The positioning error tolerant drone communication deployment method of claim 2, wherein, Channel capacity of a link for any user Is: in, The received power at the reference distance, Assigning drones to users k Transmission power, The power of Gaussian white noise, Channel power gain from drone to user.

4. The positioning error-tolerant drone communication deployment method of claim 1, wherein, In step S2, the optimization problem is modeled as: wherein, is the average transmission rate of users, is the predicted position of the UAV, is the total number of users, is the set of users, is the transmission rate of a user k is the transmission rate of a user, is the probability condition, is the transmit power of the UAV, is the channel gain, is the ambient white noise power, is the user rate requirement, is the outage probability to meet the user rate requirement.

5. The positioning error tolerant drone communication deployment method of claim 4, wherein, The user transmission rate probability constraint is expressed as: wherein, denotes the user transmission rate requirement, denotes the outage probability that the actual transmission rate does not reach the requirement threshold.

6. The positioning error-tolerant drone communication deployment method of claim 1, wherein, In step S3, a relaxation variable is introduced is replaced by its lower bound , with the constraint , which leads to the original probabilistic constraint; the optimization problem is then converted into a deterministic optimization problem.

7. The positioning error tolerant drone communication deployment method of claim 6, wherein, The optimization problem is specifically as follows: wherein is a lower bound on the user average transmission rate, is a newly introduced slack variable, is a predicted position of the UAV, is the total number of users, is the set of users, is a lower bound on the single user transmission rate, is a position standard error matrix, is an outage probability satisfying the single user rate requirement, and is a newly introduced slack variable, is a computed scalar of the user position, is a computed vector of the user position, is a three-dimensional identity matrix.

8. The positioning error-tolerant drone communication deployment method of claim 1, wherein, Step S4 is specifically as follows: Introducing slack variables , let ; convert the first constraint of the original problem into a concave constraint, get the convex constraint by first-order Taylor expansion of the constraint at ; solve the problem by continuous iteration to obtain the final optimized UAV deployment position.

9. The positioning error-tolerant drone communication deployment method of claim 8, wherein, The convex constraint is: in, The trace of the position standard error matrix. To meet the interruption probability required for single-user rate, and For newly introduced slack variables, The expected location of the drone. This is the actual location of the drone. Given the drone's transmit power, The channel attenuation factor per unit distance. For base station transmission power, For the channel gain from base station to user, For ambient white noise power, For the introduced slack variables, For specific reference to the first k One user, For user groups.

10. A positioning error tolerant unmanned aerial vehicle communication deployment system, characterized in that, It comprises: A construction module, which constructs a communication model comprising one unmanned aerial vehicle and K ground users, establishes a probability distribution model of the unmanned aerial vehicle position deviation, and calculates the channel capacity of each link based on the channel power gain; A constraint module, which, based on the constructed communication model, constructs an optimization problem with the maximum network average transmission rate as the target and containing user transmission rate probability constraints; A conversion module, which uses Bernstein inequality to convert the obtained probability constraints into deterministic linear matrix inequality constraints related to the positioning error covariance; A deployment module, based on the obtained deterministic optimization problem, designs a continuous convex approximation iterative algorithm, linearizes the non-convex constraints through first-order Taylor expansion, and solves the optimal deployment position of the unmanned aerial vehicle.

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