Method and apparatus for joint trajectory resource configuration in UAV heterogenous wireless networks

Abstract

A method for joint trajectory and resource configuration in unmanned aerial vehicle (UAV) heterogeneous wireless networks, comprising acquiring initial three-dimensional trajectory information and initial communication resource configuration corresponding to a target UAV, determining an initial energy efficiency based on the initial three-dimensional trajectory information and the initial communication resource configuration, performing an iterative optimization process using a UAV configuration model comprising an inner-loop model and an outer-loop model to obtain operational configuration data, wherein the iterative optimization process comprises determining optimized three- dimensional trajectory information and optimized communication resource configuration using the inner-loop model, determining an optimized energy efficiency using the outer-loop model, and determining the optimized three-dimensional trajectory information and the optimized communication resource configuration as the operational configuration data when convergence is achieved, and controlling the target UAV according to the operational configuration data.

Description

METHOD AND APPARATUS FOR JOINT TRAJECTORY RESOURCE CONFIGURATION IN UAV HETEROGENOUS WIRELESS NETWORKSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to Chinese Patent Application No. ZL 202510062051.5 (Application No. 202510062051.5), filed January 15, 2025, which is incorporated herein by reference in its entirety.FIELD

[0002] This application relates to the field of communication technologies, and in particular, to a method and apparatus for joint trajectory and resource configuration in Unmanned Aerial Vehicle (UAV) heterogeneous wireless networks.BACKGROUND

[0003] In modern network communications, due to the inadequacy of ground stations in network coverage, UAVs are typically employed to extend the coverage range. When utilizing UAVs to provide network services to users, it is necessary to configure relevant data of the UAVs to control their flight trajectories. In related technologies, UAV configuration is usually determined based on the area to be covered, thereby planning the UAVs operating trajectory within a two-dimensional plane, so that the UAV can provide network services to users within the area to be covered. However, the aforementioned UAV configuration method suffers from the problem of low configuration accuracy.SUMMARY

[0004] In one aspect, the present disclosure relates to a method for joint trajectory and resource configuration in unmanned aerial vehicle (UAV) heterogeneous wireless networks, comprising acquiring initial three-dimensional trajectory information and initial communication resource configuration corresponding to a target UAV, determining an initial energy efficiency based on the initial three-dimensional trajectory information and the initial communication resource configuration, performing an iterative optimization process using a UAV configuration model comprising an inner-loop model and an outer-loop model to obtain operational configuration data, wherein the iterative optimization process comprises determining optimized three-dimensional trajectory information and optimized communication resource configuration using the inner-loop model, determining an optimized energy efficiency using the outer-loop model, and determining the optimized three-dimensional trajectory information and the optimized communication resource configuration as the operational configuration data when convergence is achieved, and controlling the target UAV according to the operational configuration data.

[0005] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, the initial communication resource configuration comprises an initial user scheduling configuration, an initial transmission power configuration, and an initial transmission bandwidth configuration.

[0006] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, the user scheduling configuration defines user devices connected in communication with the target UAV within a preset period, the user devices comprising real-time user devices that maintain continuous communication connection with the target UAV and non-real-time user devices that maintain communication connection with the target UAV within specific time slots.

[0007] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, determining the optimized three-dimensional trajectory information and the optimized communication resource configuration using the inner-loop model comprises performing a second iterative optimization process that alternately optimizes the three-dimensional trajectory information and communication resource configuration until the convergence.

[0008] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, the second iterative optimization process comprises sequentially optimizing user scheduling configuration, the three-dimensional trajectory information, transmission bandwidth configuration, and transmission power configuration using respective sub-problem models within the inner-loop model.

[0009] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, the initial three-dimensional trajectory information comprises position coordinates and altitude information for the target UAV within a preset time period.

[0010] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, determining the initial energy efficiency comprises calculating a ratio of total system throughput to total system power consumption, wherein the total system power consumption includes propulsion energy consumption of the target UAV.

[0011] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, the UAV configuration model optimizes the three-dimensional trajectory information and communication resource configuration subject to maximum UAV velocity, maximum UAV acceleration, altitude limits, and communication coverage requirements.

[0012] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, the target UAV employs Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) techniques for managing communication with multiple user devices simultaneously.

[0013] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, controlling the target UAV comprises directing the target UAV to follow the optimized three-dimensional trajectory while applying the optimized communication resource configuration to serve user devices according to the operational configuration data.

[0014] In one aspect, the present disclosure relates to a system for joint trajectory and resource configuration in unmanned aerial vehicle (UAV) heterogeneous wireless networks, comprising a target UAV, and a computing device configured to acquire initial three-dimensional trajectory information and initial communication resource configuration corresponding to the target UAV, determine an initial energy efficiency based on the initial three-dimensional trajectory information and the initial communication resource configuration, perform an iterative optimization process using a UAV configuration model comprising an inner-loop model and an outer-loop model to obtain operational configuration data, wherein the iterative optimization process comprises determining optimized three-dimensional trajectory information and optimized communication resource configuration using the inner-loop model, determining an optimized energy efficiency using the outerloop model, and determining the optimized three-dimensional trajectory information and the optimized communication resource configuration as the operational configuration data when convergence is achieved, and control the target UAV according to the operational configuration data.

[0015] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, the computing device is further configured such that the initial communication resource configuration comprises an initial user scheduling configuration, an initial transmission power configuration, and an initial transmission bandwidth configuration.

[0016] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, the computing device is further configured such that the user scheduling configuration characterizes user devices connected in communication with the target UAV within a preset period, the user devices comprising real-time user devices that maintain continuous communication connection with the target UAV and non-real-time user devices that maintain communication connection with the target UAV within specific time slots.

[0017] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, the computing device is further configured to determine the optimized three-dimensional trajectory information and the optimized communication resource configuration using the inner-loop model by performing a second iterative optimization process that alternately optimizes trajectory information and communication resource configuration until the convergence.

[0018] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, the computing device is further configured such that the second iterative optimization process comprises sequentially optimizing user scheduling configuration, the three-dimensionaltrajectory information, transmission bandwidth configuration, and transmission power configuration using respective sub-problem models within the inner-loop model.

[0019] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, the computing device is further configured such that the initial three-dimensional trajectory information comprises position coordinates and altitude information for the target UAV within a preset time period.

[0020] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, the computing device is further configured to determine the initial energy efficiency by calculating a ratio of total system throughput to total system power consumption, wherein the total system power consumption includes propulsion energy consumption of the target UAV.

[0021] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, the computing device is further configured such that the UAV configuration model optimizes the three-dimensional trajectory information and communication resource configuration subject to constraints comprising maximum UAV velocity, maximum UAV acceleration, altitude limits, and communication coverage requirements.

[0022] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, the target UAV is further configured to employ Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) techniques for managing communication with multiple user devices simultaneously.

[0023] In embodiments of this aspect, the disclosure according to any one of the above example embodiments, the target UAV is further configured to follow the optimized three-dimensional trajectory while applying the optimized communication resource configuration to serve user devices according to the operational configuration data.BRIEF DESCRIPTION OF THE DRAWINGS

[0024] So that the way the above-recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, briefly summarized above, may be made by reference to example embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only example embodiments of this disclosure and are therefore not to be considered limiting of its scope, for the disclosure may admit to other equally effective example embodiments.

[0025] Fig. 1 shows an application environment diagram of a method for joint trajectory and resource configuration in UAV heterogeneous wireless networks, according to an aspect of the disclosure.

[0026] Fig. 2 shows a diagram of an application scenario after movement of a target UAV, according to an aspect of the disclosure.

[0027] Fig. 3 shows a schematic flowchart of a method for joint trajectory and resource configuration in UAV heterogeneous wireless networks, according to an aspect of the disclosure.

[0028] Fig. 4 shows a schematic flowchart of a method optimization for UAV trajectory and communication resource allocation, according to an aspect of the disclosure.

[0029] Fig. 5 shows a schematic flowchart for obtaining intermediate trajectory information and intermediate communication resource configuration, according to an aspect of the disclosure.

[0030] Fig. 6 shows a structural block diagram of an apparatus for joint trajectory and resource configuration in UAV heterogeneous wireless networks, according to an aspect of the disclosure.

[0031] Fig. 7 shows an internal structural diagram of a computer device, according to an aspect of the disclosure.DETAILED DESCRIPTION

[0032] The present disclosure relates to a method and apparatus for joint trajectory and resource configuration in UAV heterogeneous wireless networks that addresses the limitations of existing UAV configuration approaches by considering energy efficiency optimization in three-dimensional trajectory planning.

[0033] With the rapid development of beyond 5G (B5G) and 6G communication technology, user demand for higher data rates and sharing / utilizing massive amounts of information continues to grow. In modern communication networks, due to the inadequacy of ground stations in network coverage, it has become common to extend the coverage range.

[0034] UAVs, due to their low cost, fast deployment speed, fully controllable mobility, strong line-of-sight (LoS) communication advantages, and flexible path-finding capabilities, are used to extend network communication coverage. Therefore, when utilizing UAVs to provide network services to users, it may be beneficial to configure the UAVs to control their flight trajectories.

[0035] In related technologies, UAV configuration may be typically determined based on the area to be covered, thereby planning the UAVs operating trajectory within a two-dimensional plane so that the UAV can provide network services to users within the target area. However, two-dimensional trajectories do not consider constraints such as UAV coverage range and obstacle avoidance. Existing techniques optimizing the UAVs three-dimensional trajectory through configuration fail to consider the energy efficiency issues arising from the UAVs three-dimensional movement. Therefore, the aforementioned methods for joint trajectory and resource configuration in UAV heterogeneous wireless networks suffer from the problem of configuration accuracy.

[0036] In view of this, this disclosure provides a method and apparatus for joint trajectory and resource configuration in UAV heterogeneous wireless networks. By acquiring initial trajectory information andinitial communication resource configuration corresponding to a target UAV, determining the initial energy efficiency corresponding to the target UAV based on this information, and then performing a first iterative optimization process on the initial trajectory information, the initial communication resource configuration, and the initial energy efficiency according to a preset UAV configuration model, operational configuration data for the target UAV may be obtained. The UAV configuration model may be used to optimize the initial trajectory information and the initial communication resource configuration based on the initial energy efficiency. In this way, when configuring the operation of the target UAV, the energy efficiency generated during the UAV's operation is considered. The initial trajectory information and communication resource configuration are iteratively optimized through the preset UAV configuration model to obtain the final operational configuration data. This avoids the problem of inaccurate configuration in traditional techniques that determine UAV configuration and plan its operating trajectory solely within a two-dimensional plane based on the area to be covered. The technical solution provided by this disclosure considers the energy efficiency problem generated during the target UAV's operation to optimize trajectory information and communication resource configuration, resulting in more accurate operational configuration data and more optimized energy efficiency.

[0037] The solution described in this disclosure may include communication network formed by one UAV and K mobile ground users, where the users have varying real-time requirements. The K ground users may include Ki real-time users and Kz non-real-time users, where real-time users may require continuous communication connection with the UAV throughout the service period, while non-real-time users may maintain communication connection within specific time slots determined through optimization. The three-dimensional trajectory optimization approach provided by this disclosure may offer advantages over traditional two-dimensional methods by enabling the UAV to adjust its altitude dynamically, thereby optimizing coverage patterns, improving line-of-sight communication links, avoiding obstacles, and adapting to varying user distributions and mobility patterns while simultaneously considering energy consumption constraints.

[0038] The method for joint trajectory and resource configuration in UAV heterogeneous wireless networks provided by the examples of this disclosure can be applied in an application environment 100 as shown in Fig. 1. The target UAV 102 can communicate with multiple users 104 (e.g. mobile user devices). The users 104 can include real-time users requiring continuous communication connection with the target UAV, or non-real-time users maintaining communication connection with the target UAV during specific time slots. Terminal devices (e.g. mobile user devices) corresponding to the users 104 can include, but are not limited to, various personal computers, laptops, smartphones, tablets, loT devices, and portable wearable devices. loT devices can include smart speakers, smart TVs, smart air conditioners, smart vehicle devices, projectors, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can include Virtual Reality (VR) devices, Augmented Reality (AR) devices, smart glasses, etc.

[0039] Real-time users may include applications and services that require continuous, uninterrupted communication with the target UAV to maintain operational functionality. Examples of real-time users may include emergency response personnel conducting search and rescue operations, where constant communication may be beneficial for coordination and safety updates, autonomous vehicles requiring continuous navigation and traffic management data, industrial loT sensors monitoring critical infrastructure such as power grids or manufacturing processes, and live video streaming applications for surveillance or broadcasting purposes. Non-real-time users, on the other hand, may include applications that can tolerate communication interruptions and may operate effectively with periodic or scheduled data exchanges. Examples of non-real-time users may include weather monitoring stations that transmit periodic environmental data, smart agriculture sensors collecting soil moisture and temperature readings at regular intervals, software update services for connected devices, file transfer applications for data backup or synchronization, and email or messaging services that can buffer communications during temporary disconnections. The distinction between these user types may allow the UAV to prioritize resource allocation, ensuring that time-sensitive applications maintain service levels while efficiently serving delay-tolerant applications during available time slots.

[0040] Refer now to Fig. 2, a diagram of an actual application scenario after movement of a target UAV is shown. Fig. 2 illustrates a three-dimensional operational environment 200 where a UAV 202 provides communication services to multiple ground users 204 and 206 distributed across a geographical area 208 in a dynamic mobility scenario. The diagram shows the UAV positioned at an elevated altitude above the ground plane, with its coverage area extending downward to serve users distributed across the surface below. The UAV's trajectory path through three-dimensional space demonstrates how the UAV may move to maintain optimal communication coverage with mobile users, including both real-time users that require continuous communication connection and non-real-time users that maintain communication connection during specific time slots. The spatial arrangement illustrates the UAV's capability to adjust its position in three dimensions, including horizontal movement and altitude changes, to optimize communication quality and energy efficiency while serving the distributed user base. The coverage pattern extends from the UAV's position to encompass the service area on the ground, showing the wireless communication links between the UAV and the users, representing a dynamic environment where the UAV may continuously adapt its trajectory and resource allocation to serve mobile users while managing energy consumption and maintaining communication quality constraints.

[0041] In an example, as shown in Fig. 3, a method 300 for joint trajectory and resource configuration in UAV heterogeneous wireless networks may be provided. Taking the method applied to the targetUAV in Fig. 1 as an example, it may include the following steps 301 and 302. The steps in Fig. 3 include acquisition step 301 for obtaining initial trajectory information and communication resource configuration and determining initial energy efficiency, and optimization step 302 for performing iterative optimization to obtain operational configuration data for the target UAV.

[0042] In step 301, the system may acquire initial trajectory information and initial communication resource configuration corresponding to a target UAV, and determine an initial energy efficiency corresponding to the target UAV based on the initial trajectory information and the initial communication resource configuration.

[0043] The target UAV can be a UAV awaiting configuration. The target UAV can acquire the initial trajectory information and initial communication resource configuration. In the examples of this disclosure, the trajectory information can include three-dimensional trajectory information corresponding to the target UAV within a preset time period. The initial trajectory information may be trajectory information initialized according to certain preset rules. The communication resource configuration can characterize relevant communication configurations when the target UAV provides network communication services to users within its coverage, such as user scheduling configuration, transmission power configuration, and transmission bandwidth configuration. The user scheduling configuration can characterize users connected to the target UAV during the preset time. Users may include real-time users and non-real-time users. Real-time users may be users maintaining continuous communication connection with the target UAV. Non-real-time users may be users maintaining communication connection with the target UAV within preset time slots. The transmission power configuration may represent the transmit power allocated by the target UAV to each user. The transmission bandwidth configuration may represent the bandwidth allocated by the target UAV to each user. The initial communication resource configuration may be the communication resource configuration initialized according to certain preset rules.

[0044] Optionally, the target UAV can generate the initial trajectory information and initial communication resource configuration according to preset rules, or alternatively, the target UAV can take the result of historical configuration as the current initial trajectory and initial communication resource configuration.

[0045] Regarding trajectory information, it can be denoted as Q = {qt, 0 < t < T] where qt= (xt, yt, hf), 0-T is the preset time period, and (xt, yt,t) are the position coordinates of the target UAV at time t . xtis the coordinate of the target UAV on the x-axis at time t , ytis the coordinate on the y-axis at time t , htis the altitude coordinate of the target UAV at time t.

[0046] If the communication resource configuration includes user scheduling configuration, transmission power configuration, and transmission bandwidth configuration, then the user scheduling configuration can be denoted as X = {xtk, 0 < t < T,Vk 6 K], where t represents the time slot index,k represents the user identifier, K is the set of all users, xt,k represents whether the communication state between the target UAV and the user is connected at time t (xtk= 1 if connected, xtk= 0 otherwise). The transmission power configuration can be denoted as P = {Ptk, 0 < t < T, k G K , Pt fcrepresents the power transmitted by the target UAV to user k at time t, correspondingly, the transmission bandwidth configuration can be denoted as S = Stk, 0 < t < T, k 6 K , where Stkrepresents the bandwidth allocated by the target UAV to user k at time t. To efficiently manage multiple users simultaneously, the target UAV may jointly employ Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) techniques, allowing for flexible resource allocation across both time and frequency domains while avoiding interference between users.

[0047] Based on the above initial communication resource configuration and initial trajectory information, the corresponding initial energy efficiency can be determined. In one possible implementation, the target UAV can input the initial communication resource configuration and initial trajectory information into a pre-trained network model to obtain the initial energy efficiency output by the model. In another possible implementation, the target UAV can obtain a formula related to calculating energy efficiency and substitute the initial communication resource configuration and initial trajectory information into the formula to obtain the initial energy efficiency.

[0048] In the examples of this disclosure, energy efficiency is used to characterize the amount of information that can be transmitted per unit energy of the target UAV. It can be denoted by the following formula:where EE is the energy efficiency, E is the total system power consumption, R is the total system throughput.

[0049] The calculation formula for total system throughput can refer to the following formula:where,s* represents the data transmission rate between the target UAV and user k at time t. It can be calculated with reference to the following formula:where yt kis the signal-to-noise ratio (SNR) received by user k at time t. The communication channel between the UAV and ground users may be characterized using the free-space propagation model, which may be particularly suitable for UAV communications due to the strong line-of-sight (LoS) links that UAVs can typically establish with ground users. This model may account for the path loss based on thedistance between the UAV and each user, enabling accurate calculation of the received signal strength and corresponding data transmission rates.

[0050] The calculation formula for total system power consumption E can refer to the following formula:E - j (4)where Petis the propulsion energy consumption of the target UAV at time t. Its calculation formula can refer to the following formula:where c is a first parameter related to the wing area of the target UAV, c2is a second parameter related to the air density and weight of the target UAV, g is the gravitational acceleration (standard value 9.8 m / s2), vtis the velocity of the target UAV at time t, and atis the acceleration of the target UAV at time t.

[0051] Substituting the initial user scheduling configuration, initial transmission power configuration, and initial transmission bandwidth configuration into the above formulas, the initial energy efficiency corresponding to the target UAV can be determined.

[0052] In step 302, the system may perform a first iterative optimization process on the initial trajectory information, the initial communication resource configuration, and the initial energy efficiency according to a preset UAV configuration model to obtain operational configuration data for the target UAV. The UAV configuration model may be used to optimize the initial trajectory information and the initial communication resource configuration based on the initial energy efficiency.

[0053] In one possible implementation, the target UAV can directly input the initial trajectory information, initial communication resource configuration, and initial energy efficiency into a pretrained iterative optimization model for iterative optimization processing to obtain the operational configuration data output by the model. In another possible implementation, after obtaining the initial trajectory information, initial communication resource configuration, and initial energy efficiency, the target UAV can take the energy efficiency as the objective function and continuously optimize the initial trajectory information and initial communication resource configuration according to preset constraints to obtain the trajectory information and communication resource configuration corresponding to the optimized energy efficiency. The target UAV can take the trajectory information and communication resource configuration corresponding to the optimized energy efficiency as the operationalconfiguration data of the target UAV. This operational configuration data can be used to control the operation of the target UAV.

[0054] In implementations, the operational configuration data may be transmitted to the target UAV through various communication channels, including ground-to-air data links, satellite communications, or cellular networks. The target UAV may store the operational configuration data in onboard memory and execute the optimized trajectory using its flight control systems while simultaneously applying the communication resource configuration through its radio frequency subsystems. During operation, the UAV may periodically report its actual position and communication status back to the computing device to enable real-time monitoring and potential re-optimization if significant deviations occur. In cases where communication is temporarily lost, the UAV may continue following the last received operational configuration data until communication is restored, at which point updated optimization may be performed based on the current system state.

[0055] In the above example, when configuring the operation of the target UAV, the energy efficiency generated during the UAV's operation is considered. The initial trajectory information and communication resource configuration are iteratively optimized through the preset UAV configuration model to obtain the final operational configuration data. This approach may avoid the problem of inaccurate configuration in traditional techniques that determine UAV configuration and plan its operating trajectory solely within a two-dimensional plane based on the area to be covered. The technical solution provided by this disclosure considers the energy efficiency problem generated during the target UAV's operation to optimize trajectory information and communication resource configuration, resulting in more accurate operational configuration data and more optimized energy efficiency.

[0056] In an example, the process performs the first iterative optimization process on the initial trajectory information, initial communication resource configuration, and initial energy efficiency according to the preset UAV configuration model to obtain the operational configuration data for the target UAV. In this example, the UAV configuration model includes a dual-loop optimization solution including an inner-loop model and an outer-loop model.

[0057] The dual-loop optimization process may be implemented in multiple operational scenarios to enhance the flexibility and adaptability of UAV operations. In a pre-flight planning scenario, the optimization process may be executed to generate initial operational configuration data before the UAV begins its mission. During this pre-flight phase, the system may have access to complete information about the service area, expected user distributions, and mission requirements, allowing for thorough optimization of both trajectory and communication resource allocation. The resulting operational configuration data may serve as a baseline mission plan that maximizes energy efficiency while meeting all service requirements.

[0058] Additionally, the dual-loop optimization process may be performed during flight operations to provide dynamic adjustments and real-time adaptability. During in-flight execution, the optimization process may be triggered periodically or in response to changing conditions such as user mobility, varying communication demands, environmental factors, or deviations from expected energy consumption patterns. In such scenarios, the optimization process may utilize current UAV state information and updated user requirements as new initial conditions, allowing the system to recalculate optimal trajectory adjustments and communication resource reallocation. This in-flight optimization capability may enable the UAV to maintain optimal energy efficiency and service quality even when operating conditions differ from the original mission plan, thereby enhancing the robustness and practical applicability of the disclosed method.

[0059] As shown in method 400 in Fig. 4, step 302 can include steps 401 to 403. Step 401 may determine intermediate trajectory information and intermediate communication resource configuration using the inner-loop model to optimize the UAV's operational parameters, while step 403 may evaluate whether the iterative optimization process has converged based on the resulting energy efficiency and designate the intermediate results as final operational configuration data if convergence is achieved. Throughout the iterative optimization process, the values generated during each iteration are referred to as 'intermediate' results, as they represent progressive refinements toward the optimal solution. Upon convergence of the dual-loop optimization process, these intermediate trajectory information and intermediate communication resource configuration values represent the final optimized solution and become the operational configuration data for the target UAV. Thus, the terms 'intermediate' and 'optimized' may be used interchangeably when referring to the final converged values that satisfy the convergence criteria. The convergence criteria for the dual-loop optimization process may be determined using multiple approaches. For the outer-loop Dinkelbach algorithm, convergence may be achieved when the absolute difference between consecutive energy efficiency values falls below a predetermined threshold or when the maximum number of iterations is reached. For the inner-loop BCD algorithm, convergence may be detected when the objective function value changes by less than a threshold between consecutive iterations, indicating that further optimization provides diminishing returns. In some implementations, the system may employ adaptive threshold values that adjust based on the complexity of the optimization problem or available computational resources. The convergence detection may also consider the rate of change in trajectory coordinates and communication resource allocations to ensure stable operational configuration data.

[0060] Specifically, in step 401 the system may determine intermediate trajectory information and intermediate communication resource configuration based on the initial trajectory information, the initial communication resource configuration, and an inner-loop model included in the UAV configuration model.

[0061] In the examples of this disclosure, the target UAV can determine the objective function based on energy efficiency, take various trajectory information and communication resource configurations as sub-problems, and obtain the UAV configuration model based on the constraint conditions corresponding to each sub-problem. The UAV configuration model may be formulated as a multivariable optimization problem that jointly optimizes user scheduling, UAV trajectory, transmission power allocation, and bandwidth allocation to maximize energy efficiency while satisfying various operational constraints.

[0062] After establishing the UAV configuration model, the target UAV can decompose it into an inner-loop model and an outer-loop model. The outer-loop model can take the energy efficiency as the objective function and the trajectory information and communication resource configuration as parameters to be optimized. The inner-loop model may include sub-problem models corresponding to the trajectory information and communication resource configuration. In this example, the target UAV can first perform iterative optimization on the initial trajectory information and initial communication resource configuration according to the inner-loop model to obtain the intermediate trajectory information and intermediate communication resource configuration.

[0063] In step 402, the system may determine the intermediate energy efficiency based on the intermediate trajectory information, the intermediate communication resource configuration, and the outer-loop model included in the UAV configuration model.

[0064] After obtaining the intermediate trajectory information and intermediate communication resource configuration, the target UAV can determine the intermediate energy efficiency corresponding to the current first iterative optimization process based on the intermediate trajectory information, intermediate communication resource configuration, and the objective function (i.e., energy efficiency) set by the outer-loop model.

[0065] In step 403, the system may determine whether the first iterative optimization process has ended based on the intermediate energy efficiency. If the first iterative optimization process has ended, determine the intermediate trajectory information and the intermediate communication resource configuration as the operational configuration data.

[0066] After determining the intermediate energy efficiency corresponding to the current first iterative optimization process, the target UAV can determine the corresponding objective function value based on the intermediate energy efficiency to determine if the first iterative optimization process has ended. In one possible implementation, the target UAV can detect whether the objective function value has reached a preset threshold to determine if the process has ended. Additionally, the target UAV can detect whether the number of iterations corresponding to the current first iterative optimization process has reached a preset target to determine if the process has ended.

[0067] Optionally, if the first iterative optimization process has ended, the target UAV can determine the intermediate trajectory information and intermediate communication resource configuration as the operational configuration data. Alternatively, if the first iterative optimization process has ended, the target UAV can continue the first iterative optimization process on the intermediate trajectory information, intermediate communication resource configuration, and intermediate energy efficiency according to the UAV configuration model until the operational configuration data is obtained.

[0068] In one possible implementation, taking the communication resource configuration including user scheduling configuration, transmission power configuration, and transmission bandwidth configuration as an example, the constraints corresponding to the user scheduling configuration can include the following conditions:< < < <<where dt kis the horizontal distance between the target UAV and the user k at time t, Rctis the coverage radius of the target UAV relative to the ground at time t, T is a very large parameter,is the number of real-time users, and K2is the number of non-real-time users.

[0069] Constraints corresponding to the transmission power configuration and transmission bandwidth configuration can include the following conditions:> < > > < < < >>where, Pmaand smaxrepresent the maximum transmit power and total bandwidth of the UAV, respectively.

[0070] Since energy efficiency is the objective function, the data transmission rate Rt kalso is constrained. The constraint is as follows:where Rthkis the minimum data transmission rate that can be achieved between the target UAV and the user, determined according to the actual application scenario.

[0071] In the examples of this disclosure, the target UAVs own operational capabilities also impose limitations. Therefore, there are corresponding constraints on velocity and acceleration:where Vmaxrepresents the maximum speed of the target UAV, a^ax represents the maximum acceleration of the target UAV.

[0072] It is understandable that constraints on the target UAVs flight altitude are also necessary. Therefore, the constraint corresponding to flight altitude is:where hminis the minimum altitude,the maximum altitude, and htis the altitude of the target UAV at time t.

[0073] For some application scenarios, the starting point and ending point of the target UAV are the same. Therefore, constraints on the UAVs starting and ending points may be as follows:where q0is the starting point of the target UAV and qTis the ending point. In summary, the target UAV can obtain the UAV configuration model:Ate £ £< < << << < <" < < < <> > < <

[0074] For ease of solving, time discretization can be used. The preset time period [0, T] may be divided into N equal time slots. The length of the time slot may be sufficiently small so that the position of the target UAV within each time slot can be considered approximately constant. Therefore, the UAVs trajectory information can be represented by an N-dimensional sequence qn= (xn,yn, h^n = 1, N. The UAV configuration model can also be updated to the following form:<< & &< >"<><& <>>

[0075] Correspondingly, the user scheduling configuration can be represented as X = xn / c, Vn 6 N,k G K, the transmission power configuration as P = {pn fe, Vn G N, k G K, the transmission bandwidth configuration as S = {sn>fe, Vn G N,k G K, and the trajectory information as Q = {qn, Vn G N.

[0076] In this example, it can be seen that trajectory information, user scheduling configuration, transmission power configuration, and transmission bandwidth configuration form a multi-variable optimization problem with non-convex constraints. The problem may be non-convex due to the fractional form of the energy efficiency objective function and the coupling between optimization variables. To facilitate computation, an approach combining the Dinkelbach iterative algorithm and the block coordinate descent (BCD) algorithm may be adopted, decomposing the problem into the inner-loop model and the outer-loop model.

[0077] In this example, since the energy efficiency is in fractional form, the Dinkelbach method is used to transform the energy efficiency into a subtractive form to enable iterative solution:where £ is the system's energy efficiency, and it is known that the function F is monotonically decreasing with respect to s. Let E* be the energy efficiency obtained when the trajectory information, user scheduling configuration, transmission power configuration, and transmission bandwidth configuration all achieve their optimal solutions, so we have:

[0078] When E = s*, F(s) = 0. Through the above analysis, it is found that solving for the maximum energy efficiency is equivalent to finding the intersection point of the auxiliary function F(s) with the abscissa axis. Therefore, the Dinkelbach algorithm can be used for solving. Thus, the objective function can be F(s).

[0079] Therefore, the UAV configuration model can be updated to the following form:<<&< <> >

[0080] The UAV configuration model may include an outer-loop model with the objective function F(s) and optimization parameters trajectory information Q, user scheduling configuration X, transmission power configuration P, and transmission bandwidth configuration S. For one first iterative optimization process, the target UAV can obtain the intermediate trajectory information, intermediate user scheduling configuration, intermediate transmission power configuration, and intermediate transmission bandwidth configuration output by the inner-loop model. The target UAV can obtain the corresponding intermediate energy efficiency based on this intermediate information. Substituting the intermediate energy efficiency into the objective function yields the corresponding objective functionvalue. The target UAV can preset an objective function threshold and a maximum number of iterations. It then detects whether the objective function value corresponding to the current first iterative optimization process reaches the preset threshold, and whether the iteration count corresponding to the current first iterative optimization process exceeds the maximum number of iterations. If the objective function value reaches the preset threshold, or the iteration count exceeds the maximum number, the target UAV determines that the first iterative optimization process has ended and takes the intermediate trajectory information and intermediate communication resource configuration as the operational configuration data.

[0081] In an implementation, the above iterative solving process can be algorithmized to obtain the algorithm corresponding to the outer-loop model. The specific content can refer to Table 1 :Algorithm 1 Dinkelbach Algorithm<&end whilewhere Xois the initial user scheduling configuration, Qois the initial trajectory information, Sois the initial transmission bandwidth configuration, Pois the initial transmission power configuration, I is the iteration count for the first iterative optimization process (outer loop), £0is the initial energy efficiency, T] is the preset objective function threshold, and lmaxis the maximum number of iterations.

[0082] For the inner-loop model, the target UAV can decompose the problem into four sub-problem models corresponding to user scheduling information, trajectory information, transmission power configuration, and transmission bandwidth configuration using the BCD algorithm. The BCD algorithm may alternately optimize each block of variables while keeping the others fixed, which enables the solution of the complex multi-variable optimization problem. Subsequently, the target UAV cancontinuously optimize the initial user scheduling configuration, initial trajectory information, initial transmission bandwidth configuration, and initial transmission power configuration according to the four sub-problem models included in the inner-loop model to obtain the intermediate user scheduling configuration, intermediate trajectory information, intermediate transmission bandwidth configuration, and intermediate transmission power configuration.

[0083] In an example, based on the method shown in Fig. 4, this example relates to the process of determining the intermediate trajectory information and intermediate communication resource configuration based on the initial trajectory information, initial communication resource configuration, and the inner-loop model. Step 401 can include performing a second iterative optimization process on the initial communication resource configuration and the initial trajectory information using the inner-loop model to obtain the intermediate trajectory information and the intermediate communication resource configuration.

[0084] In this example, the target UAV may use the inner-loop model to perform the second iterative optimization process on the initial communication resource configuration and the initial trajectory information to obtain the intermediate trajectory information and intermediate communication resource configuration. For the second iterative optimization process, in one possible implementation, referring to Fig. 5, this process 500 can include steps 501, 502, 503 and 504. The steps in Fig. 5 may include optimization step 501 for obtaining optimized trajectory information and communication resource configuration, determination step 502 for checking if the second iterative optimization process has ended, assignment step 503 for setting optimized values as intermediate values when convergence is achieved, and continuation step 504 for further optimization when convergence has not been reached.

[0085] In step 501, the system may optimize the initial trajectory information and initial communication resource configuration according to the inner-loop model to obtain optimized trajectory information and optimized communication resource configuration.

[0086] In step 502, the system may determine whether the second iterative optimization process has ended based on the optimized trajectory information and the optimized communication resource configuration.

[0087] In step 503, if the second iterative optimization process has ended, the system may take the optimized trajectory information as the intermediate trajectory information and the optimized communication resource configuration as the intermediate communication resource configuration.

[0088] In step 504, if the second iterative optimization process has not ended, the system may continue to optimize the optimized trajectory information and the optimized communication resource configuration according to the inner-loop model until the intermediate communication resource configuration is obtained.

[0089] For each second iterative optimization process, the target UAV may first optimize the initial trajectory information and initial communication resource configuration according to the inner-loop model to obtain optimized trajectory information and optimized communication resource configuration.

[0090] In one possible implementation, the target UAV may input the initial trajectory information and initial communication resource configuration into the inner-loop model to obtain the optimized trajectory information and optimized communication resource configuration output by the model.

[0091] In another possible implementation, the inner-loop model can include sub-problem models established for the trajectory information and different communication resource configurations. In this example, taking the UAV configuration model of formula (16) as an example, and the initial communication resource configuration including initial user scheduling configuration, initial transmission power configuration, and initial transmission bandwidth configuration, the subproblem models can include a first sub-problem model corresponding to user scheduling configuration, a second sub-problem model corresponding to trajectory information, a third subproblem model corresponding to transmission bandwidth configuration, and a fourth sub-problem model corresponding to transmission power configuration.

[0092] In this example, the first sub-problem model (User Scheduling) can refer to the following formula:Ate&&> < >

[0093] The second sub-problem model (Trajectory Q) can refer to the following formula:& >""<

[0094] It can be seen that since the trajectory information includes multiple parameters to be optimized (three-dimensional coordinate information), the current objective function is non-convex. Moreover, the current sub-problem model may also have non-convex constraint Cl. To transform the non-convex problem into a convex one for solving, this example introduces relaxation and successive convex approximation (SCA) techniques. The basic idea of SCA is to replace the non-convex objective and constraints of the original problem with their upper or lower bounds, thereby obtaining a local optimal (suboptimal) solution for the original non-convex problem.

[0095] First, based on first-order and second-order Taylor expansions, the approximate relationships between velocity, acceleration, and position can be obtained:<

[0096] Since the original propulsion energy consumption function is non-convex, to solve this problem, relaxation variables {on} are introduced. The propulsion energy consumption can be rewritten as:

[0097] Now the propulsion energy consumption is convex with respect to {an,vn,on}, but this introduces a non-convex constraint, which can be referred to in the following formula:

[0098] Therefore, assuming the UAV's velocity obtained in the r-th iteration is Vr= {v^ n G I }:- ibNow J (vn) is a linear function with respect to vnand is convex. Therefore, the original non-convex constraint can be written as:> <

[0099] Then, to address the non-concavity of Rnkand the non-convexity of constraint condition Cl, successive convex optimization is used to handle it. Let the distance between the target UAV and user k at time slot n be ln kLet the square of the distance obtained in the r-th iteration be U^k= Z^fc2. Now the data transmission rate Rn kof the target UAV with user k at time slot n is convex with respect to un feTherefore, its lower bound using the first-order Taylor expansion is:

[0100] Therefore, the objective function can be rewritten as F' = Riow— EP' . The second subproblem model can be reformulated as follows:Max F‘yV,< <<>>< & <

[0101] The third sub-problem model can refer to the following formula:< <>

[0102] The fourth sub-problem model can refer to the following formula:> >>

[0103] Based on the above sub-problem models, the process of optimizing the initial trajectory information and initial communication resource configuration according to the inner-loop model to obtain optimized trajectory information and optimized communication resource configuration can include obtaining an optimized user scheduling configuration based on the first sub-problem model included in the inner-loop model, the initial trajectory information, the initial transmission power configuration, and the initial transmission bandwidth configuration, obtaining the optimized trajectory information based on the second sub-problem model included in the inner-loop model, the optimized user scheduling configuration, the initial transmission power configuration, and the initial transmission bandwidth configuration, obtaining an optimized transmission bandwidth configuration based on the third sub-problem model included in the inner-loop model, the optimized user scheduling configuration, the optimized trajectory information, and the initial transmission power configuration, obtaining an optimized transmission power configuration based on the fourth sub-problem model included in the inner-loop model, the optimized user scheduling configuration, the optimized trajectory information, and the optimized transmission bandwidth configuration. This sequential optimization approach may ensure that each sub-problem is solved optimally given the current values of the other variables.

[0104] In one possible implementation, the above process is coded into the algorithm corresponding to the inner-loop model. The specific content can refer to Table 2:Algorithm 2 UAV Dynamic Deployment and Communication Resource Optimization for Energy Efficiency Maximization <" >>>>&<>43end whilewhere r is the iteration count for the second iterative optimization process, rmaxis the maximum number of iterations for the second iterative optimization process, is a preset threshold value for convergence detection. When the statistical value calculated from the objective function values of two adjacent second iterative optimization processes satisfies the preset threshold, or when the iteration count of the second iterative optimization process reaches the maximum iteration count, the target UAV can determine that the second iterative optimization process has ended and obtain the intermediate trajectory information and intermediate communication resource configuration. The dual-loop iterative approach may exhibit favorable convergence properties, with the outer-loop Dinkelbach algorithm typically converging within a limited number of iterations due to its superlinear convergence rate, while the inner-loop BCD algorithm may converge to a local optimum for each sub-problem, ensuring computational tractability even for complex multi-variable optimization scenarios.

[0105] In an example, an exemplary method for joint trajectory and resource configuration in UAV heterogeneous wireless networks is provided. This method can be applied to the implementation environment shown in Fig. 1.

[0106] In step (a) the system may acquire initial trajectory information and initial communication resource configuration corresponding to a target UAV, and determine an initial energy efficiency corresponding to the target UAV based on the initial trajectory information and the initial communication resource configuration. The initial communication resource configuration includes initial user scheduling configuration, initial transmission power configuration, and initial transmission bandwidth configuration.

[0107] In step (b) the system may obtain an optimized user scheduling configuration based on a first sub-problem model included in an inner-loop model, the initial trajectory information, the initial transmission power configuration, and the initial transmission bandwidth configuration.

[0108] In step (c) the system may obtain optimized trajectory information based on a second subproblem model included in the inner-loop model, the optimized user scheduling configuration, the initial transmission power configuration, and the initial transmission bandwidth configuration.

[0109] In step (d) the system may obtain an optimized transmission bandwidth configuration based on a third subproblem model included in the inner-loop model, the optimized user scheduling configuration, the optimized trajectory information, and the initial transmission power configuration.

[0110] In step (e) the system may obtain an optimized transmission power configuration based on a fourth subproblem model included in the inner-loop model, the optimized user scheduling configuration, the optimized trajectory information, and the optimized transmission bandwidth configuration.

[0111] In step (f) the system may determine whether a second iterative optimization process has ended based on the optimized trajectory information and the optimized communication resource configuration.

[0112] In step (g) if the second iterative optimization process has ended, the system may take the optimized trajectory information as intermediate trajectory information and the optimized communication resource configuration as intermediate communication resource configuration.

[0113] In step (h) if the second iterative optimization process has not ended, the system may continue to optimize the optimized trajectory information and the optimized communication resource configuration according to the inner-loop model until the intermediate communication resource configuration is obtained.

[0114] In step (i), the system may determine an intermediate energy efficiency based on the intermediate trajectory information, the intermediate communication resource configuration, and an outer-loop model.

[0115] In step (j), the system may determine whether a first iterative optimization process has ended based on the intermediate energy efficiency. If the first iterative optimization process has ended, the system may determine the intermediate trajectory information and the intermediate communication resource configuration as operational configuration data. The UAV configuration model may be used to optimize the initial trajectory information and the initial communication resource configuration based on the initial energy efficiency.

[0116] In step (k), if the first iterative optimization process has not ended, the system may continue the first iterative optimization process on the intermediate trajectory information, the intermediate communication resource configuration, and the intermediate energy efficiency according to the UAV configuration model until the operational configuration data may be obtained.

[0117] It should be understood that although the steps in the flowcharts related to the various examples described above are shown in sequence as indicated by arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict sequence limitation on the execution of these steps, they can be performed in other orders. Moreover, at least some of the steps in the flowcharts related to the above examples can include multiple sub-steps or stages, which are not necessarily executed at the same time but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, they can be performed alternately or interleaved with other steps or at least some sub-steps or stages of other steps.

[0118] Based on the same inventive concept, examples of this disclosure also provide an apparatus for joint trajectory and resource configuration in UAV heterogeneous wireless networks used to implement the method described above. The problem-solving implementation scheme provided by this apparatus is similar to that described in the method. Therefore, specific limitations in one or more apparatus examples provided below can be found in the limitations for the method described above and will not be repeated here.

[0119] In an example, as shown in Fig. 6, an apparatus 600 for joint trajectory and resource configuration in UAV heterogeneous wireless networks may be provided, which may include an acquisition module 601 and a configuration module 602, wherein the acquisition module 601 may be configured to acquire initial trajectory information and initial communication resource configuration corresponding to a target UAV, and determine an initial energy efficiency corresponding to the target UAV based on the initial trajectory information and the initial communication resource configuration, the configuration module 602 may be configured to determine intermediate trajectory information and intermediate communication resource configuration based on the initial trajectory information, the initial communication resource configuration, and an inner-loop model, determine an intermediate energy efficiency based on the intermediate trajectory information, the intermediate communication resource configuration, and an outer-loop model, and, as a first iteration end unit, determine whether afirst iterative optimization process has ended based on the intermediate energy efficiency, and if the first iterative optimization process has ended, determine the intermediate trajectory information and the intermediate communication resource configuration as operational configuration data.

[0120] In an example, the configuration module 602 may include a first iteration not-end unit, configured to, if the first iterative optimization process has not ended, continue the first iterative optimization process on the intermediate trajectory information, the intermediate communication resource configuration, and the intermediate energy efficiency according to the UAV configuration model until the operational configuration data is obtained.

[0121] In an example, the inner-loop unit may be configured to perform a second iterative optimization process on the initial communication resource configuration and the initial trajectory information using the inner-loop model to obtain the intermediate trajectory information and the intermediate communication resource configuration.

[0122] In an example, the inner-loop unit may be applicable to optimize the initial trajectory information and the initial communication resource configuration according to the inner-loop model to obtain optimized trajectory information and optimized communication resource configuration, determine whether the second iterative optimization process has ended based on the optimized trajectory information and the optimized communication resource configuration, if the second iterative optimization process has ended, take the optimized trajectory information as the intermediate trajectory information and the optimized communication resource configuration as the intermediate communication resource configuration, if the second iterative optimization process has not ended, continue to optimize the optimized trajectory information and the optimized communication resource configuration according to the inner-loop model until the intermediate communication resource configuration is obtained.

[0123] In an example, the initial communication resource configuration may include initial user scheduling configuration, initial transmission power configuration, and initial transmission bandwidth configuration. The inner-loop unit may be applicable to obtain an optimized user scheduling configuration based on a first sub-problem model included in the inner-loop model, the initial trajectory information, the initial transmission power configuration, and the initial transmission bandwidth configuration, obtain the optimized trajectory information based on a second sub-problem model included in the inner-loop model, the optimized user scheduling configuration, the initial transmission power configuration, and the initial transmission bandwidth configuration, obtain an optimized transmission bandwidth configuration based on a third sub-problem model included in the inner-loop model, the optimized user scheduling configuration, the optimized trajectory information, and the initial transmission power configuration, obtain an optimized transmission power configuration based on a fourth sub-problem model included in the inner-loop model, the optimized user schedulingconfiguration, the optimized trajectory information, and the optimized transmission bandwidth configuration.

[0124] In an example, the user scheduling configuration characterizes users communicating with the target UAV during a preset time period. As described above, the users may include real-time users and non-real-time users. The real-time users are users maintaining continuous communication connection with the target UAV throughout the service period. The non-real-time users are users maintaining communication connection with the target UAV within some time slots determined through optimization allowing for more flexible resource allocation.

[0125] The various modules in the aforementioned apparatus for joint trajectory and resource configuration in UAV heterogeneous wireless networks can be implemented entirely or partially through software, hardware, or a combination thereof. The above modules can be embedded in the form of hardware in or independent of a processor in a computer device, or stored in the form of software in a memory of a computer device to facilitate the processor calling and executing operations corresponding to the above modules.

[0126] In an exemplary example, a computer device may be provided. This computer device can be the target UAV or a remote server device having the internal structure shown in Fig. 7. The computer device 700 may include a processor 702, a memory 704, storage media 706, system bus 708, an input / output interface (I / O) 710, and a communication interface 712. The processor, memory, and input / output interface are connected via a system bus. The communication interface may be connected to the system bus via the input / output interface. The processor of the computer device may be used to provide computing and control capabilities. The memory of the computer device may include non-volatile storage media and internal memory. The non-volatile storage media store an operating system, computer programs, and a database. The internal memory may provide an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device may be used to store UAV configuration data. The input / output interface of the computer device may be used for exchanging information between the processor and external devices. The communication interface of the computer device may be used for network connection and communication with external terminals. When the computer program is executed by the processor, it may implement a method for joint trajectory and resource configuration in UAV heterogeneous wireless networks.

[0127] Those skilled in the art can understand that the structure shown in Fig. 7 is a block diagram related to the solution of this disclosure and does not constitute a limitation on the computer device to which the solution of this disclosure is applied. A specific computer device may include more or fewer components than shown in the figure, or combine some components, or have a different arrangement of components.

[0128] In an exemplary example, a computer device may be provided which may include a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it implements the following steps: acquiring initial trajectory information and initial communication resource configuration corresponding to a target UAV, and determining an initial energy efficiency corresponding to the target UAV based on the initial trajectory information and the initial communication resource configuration, determining intermediate trajectory information and intermediate communication resource configuration based on the initial trajectory information, the initial communication resource configuration, and an inner-loop model included in a UAV configuration model, determining an intermediate energy efficiency based on the intermediate trajectory information, the intermediate communication resource configuration, and an outer-loop model included in the UAV configuration model, determining whether a first iterative optimization process has ended based on the intermediate energy efficiency, if the first iterative optimization process has ended, determining the intermediate trajectory information and the intermediate communication resource configuration as operational configuration data, wherein, the UAV configuration model may be used to optimize the initial trajectory information and the initial communication resource configuration based on the initial energy efficiency.

[0129] In an example, a computer-readable storage medium may be provided, having a computer program stored thereon. When the computer program is executed by a processor, it may implement the following steps acquiring initial trajectory information and initial communication resource configuration corresponding to a target UAV, and determining an initial energy efficiency corresponding to the target UAV based on the initial trajectory information and the initial communication resource configuration, determining intermediate trajectory information and intermediate communication resource configuration based on the initial trajectory information, the initial communication resource configuration, and an inner-loop model included in a UAV configuration model, determining an intermediate energy efficiency based on the intermediate trajectory information, the intermediate communication resource configuration, and an outer-loop model included in the UAV configuration model, determining whether a first iterative optimization process has ended based on the intermediate energy efficiency, if the first iterative optimization process has ended, determining the intermediate trajectory information and the intermediate communication resource configuration as operational configuration data, wherein, the UAV configuration model may be used to optimize the initial trajectory information and the initial communication resource configuration based on the initial energy efficiency.

[0130] In an example, a computer program product is provided, may include a computer program. When the computer program is executed by a processor, it implements the following steps acquiring initial trajectory information and initial communication resource configuration corresponding to a target UAV, and determining an initial energy efficiency corresponding to the target UAV based on the initial trajectory information and the initial communication resource configuration, determining intermediatetrajectory information and intermediate communication resource configuration based on the initial trajectory information, the initial communication resource configuration, and an inner-loop model included in a UAV configuration model, determining an intermediate energy efficiency based on the intermediate trajectory information, the intermediate communication resource configuration, and an outer-loop model included in the UAV configuration model, determining whether a first iterative optimization process has ended based on the intermediate energy efficiency, if the first iterative optimization process has ended, determining the intermediate trajectory information and the intermediate communication resource configuration as operational configuration data, wherein, the UAV configuration model may be used to optimize the initial trajectory information and the initial communication resource configuration based on the initial energy efficiency.

[0131] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all authorized by users or fully authorized by all parties, and the collection, use, and processing of relevant data to comply with relevant regulations. Based on the above, it is beneficial to address the technical problem mentioned above by providing a method and apparatus for joint trajectory and resource configuration in UAV heterogeneous wireless networks that can improve planning accuracy.

[0132] In an aspect, the disclosure provides a method for joint trajectory and resource configuration in UAV heterogeneous wireless networks, may include acquiring initial trajectory information and initial communication resource configuration corresponding to a target UAV, and determining an initial energy efficiency corresponding to the target UAV based on the initial trajectory information and the initial communication resource configuration, determining intermediate trajectory information and intermediate communication resource configuration based on the initial trajectory information, the initial communication resource configuration, and an inner-loop model included in a UAV configuration model, determining an intermediate energy efficiency based on the intermediate trajectory information, the intermediate communication resource configuration, and an outer-loop model included in the UAV configuration model, determining whether a first iterative optimization process has ended based on the intermediate energy efficiency, if the first iterative optimization process has ended, determining the intermediate trajectory information and the intermediate communication resource configuration as operational configuration data, wherein, the UAV configuration model is used to optimize the initial trajectory information and the initial communication resource configuration based on the initial energy efficiency.

[0133] In an aspect, the UAV configuration model includes an inner-loop model and an outer-loop model. The step of performing a first iterative optimization process on the initial trajectory information, the initial communication resource configuration, and the initial energy efficiency according to a presetUAV configuration model to obtain operational configuration data for the target UAV may include , determining intermediate trajectory information and intermediate communication resource configuration based on the initial trajectory information, the initial communication resource configuration, and the inner-loop model, determining an intermediate energy efficiency based on the intermediate trajectory information, the intermediate communication resource configuration, and the outer-loop model, determining whether the first iterative optimization process has ended based on the intermediate energy efficiency, if the first iterative optimization process has ended, determining the intermediate trajectory information and the intermediate communication resource configuration as the operational configuration data.

[0134] In an aspect, the method further may include if the first iterative optimization process has not ended, continuing the first iterative optimization process on the intermediate trajectory information, the intermediate communication resource configuration, and the intermediate energy efficiency according to the UAV configuration model until the operational configuration data is obtained.

[0135] In an aspect, determining the intermediate trajectory information and the intermediate communication resource configuration based on the initial trajectory information, the initial communication resource configuration, and the inner-loop model which may include, performing a second iterative optimization process on the initial communication resource configuration and the initial trajectory information using the inner-loop model to obtain the intermediate trajectory information and the intermediate communication resource configuration. In an example, performing the second iterative optimization process on the initial communication resource configuration and the initial trajectory information using the inner-loop model to obtain the intermediate trajectory information and the intermediate communication resource configuration which may include, optimizing the initial trajectory information and the initial communication resource configuration according to the inner-loop model to obtain optimized trajectory information and optimized communication resource configuration, determining whether the second iterative optimization process has ended based on the optimized trajectory information and the optimized communication resource configuration, if the second iterative optimization process has ended, taking the optimized trajectory information as the intermediate trajectory information and the optimized communication resource configuration as the intermediate communication resource configuration, if the second iterative optimization process has not ended, continuing to optimize the optimized trajectory information and the optimized communication resource configuration according to the inner-loop model until the intermediate communication resource configuration is obtained.

[0136] In an aspect, the initial communication resource configuration includes initial user scheduling configuration, initial transmission power configuration, and initial transmission bandwidth configuration. Optimizing the initial trajectory information and the initial communication resourceconfiguration according to the inner-loop model to obtain the optimized trajectory information and the optimized communication resource configuration which may include obtaining an optimized user scheduling configuration based on a first sub-problem model included in the inner-loop model, the initial trajectory information, the initial transmission power configuration, and the initial transmission bandwidth configuration, obtaining the optimized trajectory information based on a second sub-problem model included in the inner-loop model, the optimized user scheduling configuration, the initial transmission power configuration, and the initial transmission bandwidth configuration, obtaining an optimized transmission bandwidth configuration based on a third sub-problem model included in the inner-loop model, the optimized user scheduling configuration, the optimized trajectory information, and the initial transmission power configuration, obtaining an optimized transmission power configuration based on a fourth sub-problem model included in the inner-loop model, the optimized user scheduling configuration, the optimized trajectory information, and the optimized transmission bandwidth configuration.

[0137] In an aspect, the disclosure also provides an apparatus for joint trajectory and resource configuration in UAV heterogeneous wireless networks, which may include, an acquisition module, configured to acquire initial trajectory information and initial communication resource configuration corresponding to a target UAV, and determine an initial energy efficiency corresponding to the target UAV based on the initial trajectory information and the initial communication resource configuration, a configuration module, comprising an inner-loop unit, configured to determine intermediate trajectory information and intermediate communication resource configuration based on the initial trajectory information, the initial communication resource configuration, and an inner-loop model included in a UAV configuration model, an outer-loop unit, configured to determine an intermediate energy efficiency based on the intermediate trajectory information, the intermediate communication resource configuration, and an outer-loop model included in the UAV configuration model, a first iteration end unit, configured to determine whether a first iterative optimization process has ended based on the intermediate energy efficiency, and if the first iterative optimization process has ended, determine the intermediate trajectory information and the intermediate communication resource configuration as operational configuration data, wherein, the UAV configuration model is used to optimize the initial trajectory information and the initial communication resource configuration based on the initial energy efficiency.

[0138] In an aspect, the disclosure provides a computer device comprising a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method according to the first aspect described above are implemented.

[0139] In an aspect, an example of this application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method according to the first aspect described above are implemented.

[0140] In an aspect, the disclosure also provides a computer program product. The computer program product may include a computer program. When the computer program is executed by a processor, the steps of the method according to the first aspect described above are implemented.

[0141] The method and apparatus for joint trajectory and resource configuration in UAV heterogeneous wireless networks described above acquire initial trajectory information and initial communication resource configuration corresponding to a target UAV, determine the initial energy efficiency corresponding to the target UAV based on this information, and then perform a first iterative optimization process on the initial trajectory information, the initial communication resource configuration, and the initial energy efficiency according to a preset UAV configuration model to obtain operational configuration data for the target UAV. The UAV configuration model is used to optimize the initial trajectory information and the initial communication resource configuration based on the initial energy efficiency. In this way, when configuring the operation of the target UAV, the energy efficiency generated during the UAV's operation is considered. The initial trajectory information and communication resource configuration are iteratively optimized through the preset UAV configuration model to obtain the final operational configuration data. This avoids the problem of inaccurate configuration in traditional techniques that determine UAV configuration and plan its operating trajectory solely within a two-dimensional plane based on the area to be covered. The technical solution provided by this disclosure considers the energy efficiency problem generated during the target UAV's operation to optimize trajectory information and communication resource configuration, resulting in more accurate operational configuration data and more optimized energy efficiency.

[0142] In an aspect, the disclosure relates to a method for joint configuration of trajectory and radio resources in a heterogeneous wireless network for a UAV, which may include obtaining initial trajectory information and an initial communication-resource configuration corresponding to a target UAV, and determining, based on the initial trajectory information and the initial communication-resource configuration, an initial energy efficiency of the target UAV, determining, based on the initial trajectory information, the initial communication-resource configuration, and an inner-loop model included in a UAV configuration model, intermediate trajectory information and an intermediate communicationresource configuration, determining, based on the intermediate trajectory information, the intermediate communication-resource configuration, and an outer-loop model included in the UAV configuration model, an intermediate energy efficiency, determining, according to the intermediate energy efficiency, whether a first iterative optimization process has ended, if the first iterative optimization process has ended, determining the intermediate trajectory information and the intermediate communication-resource configuration as operational configuration data, wherein the UAV configuration model may be configured to optimize the initial trajectory information and the initial communication-resource configuration based on the initial energy efficiency, wherein determining the intermediate trajectory information and the intermediate communication-resource configuration based on the initial trajectory information, the initial communication-resource configuration, and the inner-loop model of the UAV configuration model may include performing, using the inner-loop model, a second iterative optimization process on at least the initial communication-resource configuration to obtain the intermediate trajectory information and the intermediate communication-resource configuration.

[0143] The method further may include if the first iterative optimization process has not ended, continuing, according to the UAV configuration model, to perform the first iterative optimization process on the intermediate trajectory information, the intermediate communication-resource configuration, and the intermediate energy efficiency until the operational configuration data is obtained.

[0144] The method wherein performing the second iterative optimization process using the inner-loop model on the initial communication-resource configuration to obtain the intermediate trajectory information and the intermediate communication-resource configuration may include optimizing, according to the inner-loop model, the initial trajectory information and the initial communicationresource configuration to obtain optimized trajectory information and an optimized communicationresource configuration, determining, based on the optimized trajectory information and the optimized communication-resource configuration, whether the second iterative optimization process has ended, if the second iterative optimization process has ended, setting the optimized trajectory information as the intermediate trajectory information and the optimized communication-resource configuration as the intermediate communication-resource configuration, and if the second iterative optimization process has not ended, continuing, according to the inner-loop model, to optimize the optimized trajectory information and the optimized communication-resource configuration until the intermediate communication-resource configuration is obtained.

[0145] The method wherein the initial communication-resource configuration may include an initial user-scheduling configuration, an initial transmit-power configuration, and an initial transmissionbandwidth configuration, and optimizing the initial trajectory information and the initial communication-resource configuration according to the inner-loop model to obtain the optimized trajectory information and the optimized communication-resource configuration may include, obtaining, according to a first sub-problem model included in the inner-loop model, the initial trajectory information, the initial transmit-power configuration, and the initial transmission-bandwidth configuration, an optimized user-scheduling configuration, obtaining, according to a second subproblem model included in the inner-loop model, the optimized user-scheduling configuration, the initialtransmit-power configuration, and the initial transmission-bandwidth configuration, the optimized trajectory information, obtaining, according to a third sub-problem model included in the inner-loop model, the optimized user-scheduling configuration, the optimized trajectory information, and the initial transmit-power configuration, an optimized transmission-bandwidth configuration, and obtaining, according to a fourth sub-problem model included in the inner-loop model, the optimized userscheduling configuration, the optimized trajectory information, and the optimized transmissionbandwidth configuration, an optimized transmit-power configuration.

[0146] The method wherein the user-scheduling configuration characterizes users connected in communication with the target UAV within a preset period, the users which may include real-time users and non-real-time users, the real-time users being users that maintain a communication connection with the target UAV on a persistent basis, and the non-real-time users being users that maintain a communication connection with the target UAV within a preset timeslot.

[0147] The method wherein the intermediate energy efficiency is used to determine a corresponding objective-function value, and the objective-function value is used to determine whether the first iterative optimization process has ended.

[0148] In an aspect, the disclosure relates to a device for joint configuration of trajectory and radio resources in a heterogeneous wireless network for a UAV, which may include, an acquisition module, configured to obtain initial trajectory information and an initial communication-resource configuration corresponding to a target UAV, and to determine, based on the initial trajectory information and the initial communication-resource configuration, an initial energy efficiency of the target UAV, a configuration module, configured to determine, based on the initial trajectory information, the initial communication-resource configuration, and an inner-loop model included in a UAV configuration model, intermediate trajectory information and an intermediate communication-resource configuration, to determine, based on the intermediate trajectory information, the intermediate communicationresource configuration, and an outer-loop model included in the UAV configuration model, an intermediate energy efficiency, and to determine, according to the claims intermediate energy efficiency, whether a first iterative optimization process has ended, if the first iterative optimization process has ended, to determine the intermediate trajectory information and the intermediate communicationresource configuration as operational configuration data, wherein the UAV configuration model may be configured to optimize the initial trajectory information and the initial communication-resource configuration based on the initial energy efficiency, an inner-loop unit, specifically configured to perform, using the inner-loop model, a second iterative optimization process on at least the initial communication-resource configuration to obtain the intermediate trajectory information and the intermediate communication-resource configuration.

[0149] In an aspect, the disclosure relates to a computer device which may include a memory and a processor, the memory storing a computer program, wherein the processor executes the computer program to implement the steps of the method according to the method above.

[0150] In an aspect, the disclosure relates to a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method according to the method above.

[0151] In an aspect, the disclosure relates to a computer program product which may include a computer program, wherein the computer program, when executed by a processor, implements the steps of the method according to the method above.

[0152] While the foregoing is directed to example embodiments described herein, other and further example embodiments may be devised without departing from the basic scope thereof. For example, aspects of the present disclosure may be implemented in hardware or software or a combination of hardware and software. One example embodiment described herein may be implemented as a program product for use with a computer system. The program(s) of the program product defines functions of the example embodiments (including the methods described herein) and can be contained on a variety of computer-readable storage media. Illustrative computer-readable storage media include, but are not limited to: (i) non-writable storage media (e.g., read-only memory (ROM) devices within a computer, such as CD-ROM disks readably by a CD-ROM drive, flash memory, ROM chips, or any type of solid-state non-volatile memory) on which information is permanently stored; and (ii) writable storage media (e.g., floppy disks within a diskette drive or hard-disk drive or any type of solid-state random-access memory) on which alterable information is stored. Such computer-readable storage media, when carrying computer-readable instructions that direct the functions of the disclosed example embodiments, are example embodiments of the present disclosure.

[0153] It will be appreciated by those skilled in the art that the preceding examples are exemplary and not limiting. It is intended that all permutations, enhancements, equivalents, and improvements thereto are apparent to those skilled in the art upon a reading of the specification and a study of the drawings are included within the true spirit and scope of the present disclosure. It is therefore intended that the following appended claims include all such modifications, permutations, and equivalents as fall within the true spirit and scope of these teachings.

Claims

1. CLAIMSWhat is claimed is:

1. A method for joint trajectory and resource configuration in unmanned aerial vehicle (UAV) heterogeneous wireless networks, comprising:acquiring initial three-dimensional trajectory information and initial communication resource configuration corresponding to a target UAV ;determining an initial energy efficiency based on the initial three-dimensional trajectory information and the initial communication resource configuration;performing an iterative optimization process using a UAV configuration model comprising an inner-loop model and an outer-loop model to obtain operational configuration data, wherein the iterative optimization process comprises:determining optimized three-dimensional trajectory information and optimized communication resource configuration using the inner-loop model,determining an optimized energy efficiency using the outer-loop model, anddetermining the optimized three-dimensional trajectory information and the optimized communication resource configuration as the operational configuration data when convergence is achieved; andcontrolling the target UAV according to the operational configuration data.

2. The method of claim 1, wherein the initial communication resource configuration comprises an initial user scheduling configuration, an initial transmission power configuration, and an initial transmission bandwidth configuration.

3. The method of claim 2, wherein the user scheduling configuration defines user devices connected in communication with the target UAV within a preset period, the user devices comprising real-time user devices that maintain continuous communication connection with the target UAV and non-real-time user devices that maintain communication connection with the target UAV within specific time slots.

4. The method of claim 1, wherein determining the optimized three-dimensional trajectory information and the optimized communication resource configuration using the inner-loop model comprises performing a second iterative optimization process that alternately optimizes the three-dimensional trajectory information and communication resource configuration until the convergence.

5. The method of claim 4, wherein the second iterative optimization process comprises sequentially optimizing user scheduling configuration, the three-dimensional trajectory information, transmission bandwidth configuration, and transmission power configuration using respective sub-problem models within the inner-loop model.

6. The method of claim 1, wherein the initial three-dimensional trajectory information comprises position coordinates and altitude information for the target UAV within a preset time period.

7. The method of claim 1, wherein determining the initial energy efficiency comprises calculating a ratio of total system throughput to total system power consumption, wherein the total system power consumption includes propulsion energy consumption of the target UAV.

8. The method of claim 1, wherein the UAV configuration model optimizes the three-dimensional trajectory information and communication resource configuration subject to maximum UAV velocity, maximum UAV acceleration, altitude limits, and communication coverage requirements.

9. The method of claim 1, wherein the target UAV employs Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) techniques for managing communication with multiple user devices simultaneously.

10. The method of claim 1, wherein controlling the target UAV comprises directing the target UAV to follow the optimized three-dimensional trajectory while applying the optimized communication resource configuration to serve user devices according to the operational configuration data.

11. A system for joint trajectory and resource configuration in unmanned aerial vehicle (UAV) heterogeneous wireless networks, comprising:a target UAV ; anda computing device configured to:acquire initial three-dimensional trajectory information and initial communication resource configuration corresponding to the target UAV ;determine an initial energy efficiency based on the initial three-dimensional trajectory information and the initial communication resource configuration;perform an iterative optimization process using a UAV configuration model comprising an inner-loop model and an outer-loop model to obtain operational configuration data, wherein the iterative optimization process comprises:determining optimized three-dimensional trajectory information and optimized communication resource configuration using the inner-loop model,determining an optimized energy efficiency using the outer-loop model, and determining the optimized three-dimensional trajectory information and the optimized communication resource configuration as the operational configuration data when convergence is achieved; andcontrol the target UAV according to the operational configuration data.

12. The system of claim 11, wherein the computing device is further configured such that the initial communication resource configuration comprises an initial user scheduling configuration, an initial transmission power configuration, and an initial transmission bandwidth configuration.

13. The system of claim 12, wherein the computing device is further configured such that the user scheduling configuration characterizes user devices connected in communication with the target UAV within a preset period, the user devices comprising real-time user devices that maintain continuous communication connection with the target UAV and non-real-time user devices that maintain communication connection with the target UAV within specific time slots.

14. The system of claim 11, wherein the computing device is further configured to determine the optimized three-dimensional trajectory information and the optimized communication resource configuration using the inner-loop model by performing a second iterative optimization process that alternately optimizes trajectory information and communication resource configuration until the convergence.

15. The system of claim 14, wherein the computing device is further configured such that the second iterative optimization process comprises sequentially optimizing user scheduling configuration, the three-dimensional trajectory information, transmission bandwidth configuration, and transmission power configuration using respective sub-problem models within the inner-loop model.

16. The system of claim 11, wherein the computing device is further configured such that the initial three-dimensional trajectory information comprises position coordinates and altitude information for the target UAV within a preset time period.

17. The system of claim 11, wherein the computing device is further configured to determine the initial energy efficiency by calculating a ratio of total system throughput to total system power consumption, wherein the total system power consumption includes propulsion energy consumption of the target UAV.

18. The system of claim 11, wherein the computing device is further configured such that the UAV configuration model optimizes the three-dimensional trajectory information and communication resource configuration subject to constraints comprising maximum UAV velocity, maximum UAV acceleration, altitude limits, and communication coverage requirements.

19. The system of claim 11, wherein the target UAV is further configured to employ Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) techniques for managing communication with multiple user devices simultaneously.

20. The system of claim 11 , wherein the target UAV is further configured to follow the optimized three-dimensional trajectory while applying the optimized communication resource configuration to serve user devices according to the operational configuration data.

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