Method and system for optimizing IOT network assisted by mobile antenna unmanned aerial vehicle

Abstract

The invention relates to the technical field of mobile communication, and particularly discloses an optimization method and system for an IOT network assisted by a movable antenna unmanned aerial vehicle, which maximizes long-term uplink throughput under physical constraints and energy constraints by jointly optimizing the trajectory of the unmanned aerial vehicle and the discrete orientation of a movable antenna surface, and improves the network performance. And a future ground equipment motion track or a channel state does not need to be predicted. According to the method and the system, an optimization problem is decomposed into time slot level sub-problems, the position of the unmanned aerial vehicle is optimized through a discretization strategy of structure perception, and the orientation of a movable antenna surface is configured by adopting a projection-free conditional gradient method, so that the overall optimization problem is solved step by step. A simulation result shows that compared with a reference scheme, the method and the system provided by the invention have the advantages that the uplink throughput is remarkably improved in a dynamic IoT environment, and meanwhile, the long-term energy consumption of the UAV is always controlled within a budget range.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technologies, and in particular to an optimization method and system for an IoT network assisted by a movable antenna drone. Background Art

[0002] Large-scale wireless data collection is playing an increasingly important role in emerging Internet of Things (IoT) applications such as smart agriculture, environmental monitoring, and disaster response. These applications typically rely on a large number of distributed ground devices (GDs) to transmit data to remote controllers in dynamic, infrastructure-constrained environments. In this context, achieving high-throughput and energy-efficient uplink communications presents numerous technical challenges. Unmanned aerial vehicles (UAVs) have been widely recognized as a highly effective data collection solution due to their flexible maneuverability and ability to establish line-of-sight communication. Through carefully designed flight trajectories, UAVs can dynamically adjust their positions to maintain strong signal links, enabling efficient data collection over large areas. Previous studies have demonstrated that trajectory optimization can significantly improve the coverage and communication throughput of UAV-assisted networks.

[0003] Despite the numerous advantages of UAVs in communication, most current UAV communication systems still rely on fixed-position antennas (FPAs), which have significant limitations in spatial directivity and adaptability to GD locations. This limitation weakens their ability to maintain strong channel connections, particularly in scenarios where GDs are sparsely distributed or frequently changing. To address this, mobile antenna (MA) technology has recently been proposed, allowing antenna units to adjust their position or orientation within a confined area. For example, some studies optimize transmit beamforming and antenna position to maximize the confidentiality rate; others optimize the positions of transmitting and receiving MAs and the covariance matrix of the transmitted signal to characterize the capacity of MA-supported point-to-point MIMO communication systems; some studies use MAs to enhance spatial diversity and improve channel gain by orienting the antenna array toward active GDs without requiring the UAV to fly directly over them; and some studies optimize MA positions based on improved antenna pattern models and iterative algorithms to save UAV energy and improve system performance. However, to simplify system modeling, most existing studies assume static GDs, failing to account for the system complexity introduced by dynamic GD movement.

[0004] Introducing MA in a dynamic environment brings new challenges. Since the GD moves randomly and its future position is unpredictable, the UAV needs to frequently adjust its position to maintain the communication link, which will result in continuous consumption of flight energy, and the energy consumption is uncertain over time. Although the MA configuration can reduce the UAV's large maneuvering needs to a certain extent, the system still faces a fundamental trade-off between communication quality and energy consumption: improving communication quality often means more movement overhead, which accelerates the UAV's energy consumption. In addition, the movement trajectory of the GD and the energy consumption process of the UAV are both uncertain and can only be observed after the action is executed. In such an uncertain environment, designing an online control strategy that can dynamically balance communication performance and long-term energy sustainability remains an important research issue that has not been fully explored. Summary of the Invention

[0005] The present invention provides an optimization method and system for an IoT network assisted by a movable antenna drone, and solves the technical problem of how to dynamically balance the communication performance and long-term energy sustainability of the IoT network assisted by a movable antenna drone.

[0006] To solve the above technical problems, the present invention provides an optimization method for an IoT network assisted by a movable antenna drone, comprising the following steps:

[0007] Construct a communication model for IoT networks assisted by mobile antenna drones;

[0008] Based on the communication model, with the goal of maximizing the total uplink throughput of the network and the physical and energy constraints of the network as constraints, an optimization problem is constructed to optimize the trajectory of the UAV and the orientation of the movable antenna surface;

[0009] Solve the optimization problem to obtain the trajectory of the UAV and the orientation of the movable antenna surface.

[0010] Furthermore, the communication model includes a drone and multiple ground devices that move randomly within a server; the drone has a movable antenna surface with predefined multiple unit orientation vectors, and the movable antenna surface includes multiple antenna units arranged in an array; the drone maintains a fixed altitude H0 during the entire collection mission; in a dynamic environment where the ground devices move randomly over time, the drone completes the collection of uplink data by adjusting its flight trajectory and the orientation of the movable antenna surface.

[0011] Furthermore, the physical constraints of the network include UAV displacement constraints, movable antenna surface orientation constraints, and movable antenna surface selection constraints; the UAV displacement constraint means that the displacement between the UAV positions in two time slots is not greater than the product of the maximum flight speed of the UAV and the duration of each time slot; the movable antenna surface orientation constraint means that only one movable antenna surface orientation is selected in each time slot; the movable antenna surface selection constraint means that when a direction is selected, the binary selection vector of the direction is 1, otherwise it is 0; the energy constraint is that the average flight energy consumption of the UAV during the mission is not greater than its long-term average flight energy consumption budget

[0012] Furthermore, solving the optimization problem specifically includes the steps of:

[0013] A virtual energy queue H(t) is introduced to transform the optimization problem into a time slot-by-time slot equivalent problem.

[0014] Initialize virtual energy queue H(0)=0;

[0015] The equivalent problem of each time slot is solved time slot by time slot to obtain the trajectory of the UAV and the orientation of the movable antenna surface in each time slot.

[0016] Furthermore, in each time slot t, solving the equivalent problem of each time slot t specifically includes the following steps:

[0017] Observe the current queue state H(t);

[0018] Solve the equivalent problem at time slot t to obtain the trajectory of the UAV and the orientation of the movable antenna surface at time slot t;

[0019] Update the virtual queue for the next time slot E f (t) is the energy consumed by the UAV in time slot t, max{} represents the maximum value function, and t+1 represents the next time slot.

[0020] Furthermore, the objective function of the equivalent problem for time slot t is: min means minimization, s uav (t), g(t) represent the trajectory of the UAV and the orientation of the movable antenna surface at time slot t, respectively. C(g(t)) represents the total uplink throughput of the network at time slot t. V ≥ 0 is the control parameter. The constraints of the equivalent problem at time slot t are converted into physical constraints for time slot t.

[0021] Furthermore, solving the equivalent problem of time slot t specifically includes the following steps:

[0022] Fix g(t) and transform the equivalent problem of time slot t into solving s uav The first sub-problem of (t);

[0023] Fixed uav (t), transforming the equivalent problem of time slot t into the second subproblem of solving g(t);

[0024] The first subproblem and the second subproblem are solved alternately and iteratively until the relative decrease in the objective function value is lower than a threshold ∈.

[0025] Furthermore, solving the second sub-problem specifically includes the following steps:

[0026] The second sub-problem is transformed into a linear problem. The objective function of the linear problem is:

[0027] The constraints are the movable antenna surface orientation constraint and movable antenna surface selection constraint for time slot t, C(g (r) (t)) T represents the transpose of C(g(t)) in each iteration r, represents the gradient operation;

[0028] The conditional gradient method is used to solve the linear problem and the relaxed solution g is obtained. * (t), where the current iteration point is updated by convex combination.

[0029] Furthermore, the total uplink throughput of the network in time slot t is p is the transmission power of each ground device, σ 2 is the noise power, I K and I N They are K×K and N×N unit matrices respectively, K is the number of ground equipment, N is the number of antennas on the movable antenna surface, diag() is a function to construct a diagonal matrix, H(s sur (t)) is the multiple access channel matrix from all ground equipment to the movable antenna surface at time slot t.

[0030] The present invention also provides an optimization system for an IoT network assisted by a movable antenna drone, the key of which is that it includes a communication model construction module, an optimization problem construction module and an optimization problem solving module; the communication model construction module is used to construct a communication model of the IoT network assisted by a movable antenna drone; the optimization problem construction module is used to construct an optimization problem for optimizing the trajectory of the drone and the orientation of the movable antenna surface based on the communication model, with the goal of maximizing the total uplink throughput of the network and the physical constraints and energy constraints of the network as constraints; the optimization problem solving module is used to solve the optimization problem and obtain the trajectory of the drone and the orientation of the movable antenna surface.

[0031] The present invention provides a method and system for optimizing IoT networks assisted by mobile antenna drones. By jointly optimizing the drone trajectory and the discrete orientation of the mobile antenna surface, the method maximizes long-term uplink throughput under physical and energy constraints, without having to predict the future trajectory of ground equipment or channel state. The method and system decompose the optimization problem into time-slot subproblems, optimizes the drone position through a structure-aware discretization strategy, and configures the orientation of the mobile antenna surface using a projection-free conditional gradient method, thereby gradually solving the overall optimization problem. Simulation results show that compared with the baseline solution, the proposed method and system significantly improve uplink throughput in a dynamic IoT environment, while ensuring that the UAV's long-term energy consumption remains within the budget. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a communication model diagram of an IoT network assisted by a movable antenna drone provided by an embodiment of the present invention;

[0033] Figure 2 Schematic diagram of the optimized UAV flight trajectory provided by an embodiment of the present invention;

[0034] Figure 3 Schematic diagram of the optimized MA surface position provided by an embodiment of the present invention;

[0035] Figure 4 is a graph showing the relationship between the total throughput and T for the four comparative solutions provided in the embodiments of the present invention;

[0036] Figure 5 The total throughput of the four comparison solutions provided by the embodiment of the present invention is The relationship diagram between

[0037] Figure 6 4 is a diagram showing the relationship between AQL and V for four comparative solutions provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following describes the embodiments of the present invention in detail with reference to the accompanying drawings. The embodiments are provided for illustrative purposes only and are not to be construed as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of protection of the present invention. Many changes may be made to the present invention without departing from the spirit and scope of the present invention.

[0039] The embodiment of the present invention provides a method for optimizing an IoT network assisted by a movable antenna drone, comprising the steps of:

[0040] S1. Build a communication model for an IoT network assisted by a mobile antenna drone.

[0041] S2. Based on the communication model, with the goal of maximizing the total uplink throughput of the network and the physical and energy constraints of the network as constraints, an optimization problem is constructed to optimize the trajectory of the UAV and the orientation of the MA surface;

[0042] S3. Solve the optimization problem and obtain the trajectory of the UAV and the orientation of the MA surface.

[0043] (1) Step S1

[0044] The data collection scenarios of the mobile antenna UAV-assisted IoT network (i.e., MA UAV-assisted IoT network) are as follows: Figure 1 As shown in the figure, the MA surface is mounted on a rigid rod extending from the UAV and always remains perpendicular to the rod. The surface uses a uniform planar array (UPA) containing N ≥ 1 antenna elements. To avoid excessive energy consumption in vertical maneuvers, the UAV maintains a fixed altitude H0 throughout the entire acquisition mission. The total mission duration is divided into T equal time slots, with time slot index t∈{1,2,…,T} and a time slot duration of τ. The UAV uses a fixed altitude strategy to avoid frequent vertical movements, because vertical maneuvers usually result in higher propulsion energy consumption compared to horizontal flight.

[0045] The horizontal position of the UAV at time slot t is recorded as Then the three-dimensional coordinates of the UAV can be expressed as in[] T In order to characterize the optional discrete orientations of the MA surface, a set of M unit orientation vectors {q1,...,q M}, where the mth unit is oriented towards the vector Using binary selection vectors To indicate the orientation of the MA plane selected in time slot t: if and only if the mth orientation is selected, then [g(t)] m =1, otherwise 0. Constraint Ensure that each time slot selects exactly one direction. The actual direction of the MA plane is determined by Given, the corresponding MA surface position is Where L is the length of the rod connecting the UAV body and the MA surface.

[0046] There are K GDs in the system that move randomly in the service area, and their trajectories follow the Gauss–Markov process. The horizontal position of the kth GD at time slot t is updated as k∈{1,2,...,K}, α∈[0,1] is the memory factor, is the mean velocity vector, is a zero-mean Gaussian perturbation, and all GDs transmit data to the UAV independently in each time slot.

[0047] Considering the general multipath fading environment between GD and UAV, at time slot t, all uplink channels from GD to MA plane constitute the multiple access channel matrix where h k (s sur (t)) represents the channel vector from the kth GD to the N antennas on the MA plane. Each channel vector is modeled as where η k represents the scalar channel coefficient between the kth GD and the UAV reference point, and It also includes the effective antenna gain and array steering vector of the kth GD, which are expressed as: in Indicates the corresponding effective antenna gain in dBi. and represents the elevation and azimuth of the signal direction in its local Cartesian coordinate system relative to the center of the MA plane, λ is the carrier wavelength, and f k (t) is the direction vector, r m,n [s sur (t)] indicates that when the MA surface position is s sur The position of the nth antenna at time (t). []H represents the conjugate transpose operation.

[0048] In this embodiment, the total uplink throughput is used as the performance indicator of the network, reflecting the overall data collection capability of the UAV for all GDs. Under the given MA configuration g(t), the total uplink throughput of the system in time slot t can be expressed as Where p is the transmission power of each GD, σ 2 is the noise power, I K and I N They are K×K and N×N identity matrices respectively, and diag() is a function for constructing diagonal matrices. The UAV flight speed is affected by the maximum speed v max Constraints: In each time slot t≥1, the displacement between the UAV positions in two adjacent time slots must satisfy the constraint ||s uav (t)-s uav (t-1)||≤v max τ. The flight speed of the UAV The propulsion power required to maintain this flight speed is modeled as: Where P0 and P i are blade type energy consumption and induced energy consumption, U tip is the rotor tip speed, v0 represents the average induced rotor speed, d0 and s0 represent the fuselage drag ratio and rotor solidity, σ0 and A represent the air density and rotor disc area, respectively. The energy consumed in time slot t is expressed as E f (t) = Puav (t)τ. In order to ensure energy sustainability, the average flight energy consumption constraint is introduced. The long-term average flight energy consumption is required to be To satisfy in is the average energy budget of the UAV.

[0049] (2) Step S2

[0050] In a dynamic environment where the GD moves randomly over time, the UAV must adjust its flight trajectory and the orientation of the MA surface to efficiently complete the uplink data collection. The goal is to maximize the total uplink throughput during the entire mission while meeting physical and energy constraints. denote the trajectory of the UAV and the orientation of the MA surface respectively. The joint optimization problem can be expressed as:

[0051]

[0052] Constraint (1) ensures that only one MA orientation is selected in each time slot, while constraint (2) stipulates the binary nature of the selection decision. Constraint (4) is used to limit the long-term average flight energy budget of the UAV.

[0053] (3) Step S3

[0054] Due to the random movement of the GD, the channel state and the corresponding flight energy consumption vary over time and are unpredictable. This results in the actual instantaneous energy consumption being observable only in retrospect, while the long-term energy consumption constraint (i.e., Constraint 4) must be satisfied in advance. Therefore, this problem falls into the category of stochastic optimization with long-term constraints. Even in a completely known environment, the existence of binary decision variables and the non-convexity of the throughput function make the problem essentially a mixed-integer non-convex optimization problem, making it difficult to solve directly.

[0055] To address the challenges of stochastic dynamics and long-term energy constraints in problem (P1), this paper proposes an online optimization framework. The core idea is to transform the original constraint problem into an equivalent "drift plus penalty" minimization problem, which enables online decision-making without relying on future system information.

[0056] In order to deal with the long-term average energy constraint in problem (P1), this example introduces a virtual energy queue H(t), whose update rule is:

[0057]

[0058] The initial state is set to H(0)=0, and max{} represents the maximum value function.

[0059] This queue simulates the cumulative deviation between the UAV’s actual energy consumption and the target average budget. In this scenario, Ef (t) is the arrival rate of the queue, which represents the energy consumption of each time slot, As the service rate, corresponds to the average energy consumption allowed in each time slot. If the queue remains stable in time, the system implicitly satisfies the long-term energy constraint. This relationship is formalized by the following lemma:

[0060] Lemma 1: If the virtual queue H(t) is mean rate stable, that is Then constraint (4) is satisfied.

[0061] Proof: According to the queue update rule, there is Sum the values from t = 0 to T-1 and divide by T, and we get Take the expectation and let lim t→∞ , if H(t) is stable, then the left side is finite, thus proving that the constraint holds.

[0062] In order to quantify the stability of the virtual energy queue, a quadratic Lyapunov function is defined It measures the severity of queue congestion. Smaller J(H(t)) indicates shorter queue backlogs, which better meet long-term energy constraints. To maintain queue stability, J(H(t)) should remain bounded in all time slots. The conditional Lyapunov drift is defined as the expected change of the Lyapunov function within a time slot, expressed as In order to jointly enforce energy constraints and maximize communication performance, a drift-plus-penalty framework is adopted, which is achieved by minimizing the following expression: Where V ≥ 0 is a control parameter used to balance queue stability and system throughput.

[0063] However, since this expression depends on the queue evolution and the future system state, direct minimization is not feasible. By applying the inequality (max{ab,0}+c) to Equation (5) 2 ≤a 2 +b 2 +c 2 +2a(cb), we get the upper bound: Since the first term is a second-order moment and is bounded, replacing it with a finite constant Φ yields Substituting this upper bound into the drift plus penalty function, we get Minimizing this upper bound yields a feasible slot-by-slot control rule that balances energy efficiency and uplink throughput. Therefore, the original problem (P1) is transformed into an equivalent problem (P2) for slot-by-slot t:

[0064]

[0065]

[0066] ||s uav (t)-s uav (t-1)||≤v max τ. (8)

[0067] Through this transformation, the original long-term energy constraint is implicitly constrained by the virtual queue, and only the instantaneous constraint is explicitly retained. This greatly simplifies the online optimization process and enables adaptive decision-making in dynamic environments. The detailed algorithm steps are given in Algorithm 1 shown in Table 1.

[0068] Table 1

[0069]

[0070] As shown in Table 1, solving the problem (P1) includes the following steps:

[0071] 11. Initialize the virtual energy queue H(0)=0;

[0072] 12. Enter the first time slot t=1;

[0073] 13. Observe the current queue status H(t);

[0074] 14. Solve problem (P2) to get the optimized {s uav (t),g(t)};

[0075] 15. Update the virtual queue H(t+1) according to formula (5);

[0076] 16. Enter the next time slot;

[0077] 17. Execute steps 13 to 16 time slot by time slot until the last time slot is completed.

[0078] Output time slot 1 to T {s uav (t), g(t)} is the optimal solution for each time slot of problem (P1).

[0079] Although problem (P2) is inherently nonconvex due to the inclusion of binary choice variables and a nonlinear objective function, its structural properties allow for an efficient iterative solution. By decoupling the optimization of UAV trajectories and MA orientation, the present invention transforms the original joint optimization problem into a series of tractable subproblems. This decomposition not only significantly reduces computational complexity but also enables the UAVs to adjust their movements and antenna configurations in a coordinated manner.

[0080] A. UAV trajectory design sub-problem

[0081] Discrete selection vector on a given MA surface Under the premise of , by optimizing the UAV trajectory, the communication efficiency is improved while minimizing the flight energy consumption. The corresponding sub-problem is represented by the trajectory variable {s uav (t)}, which can be expressed as:

[0082]

[0083] st||s uav (t)-s uav (t-1)||≤v max τ. (9)

[0084] Due to the coupling relationship between trajectory and communication rate, the problem is non-convex and difficult to solve. To this end, this paper proposes a structure-aware discretization strategy to transform the continuous search space into a finite set of candidates while retaining feasibility and diversity. At each time slot t, the continuous reachable area of the UAV is defined. s is At any point in the pace Uniformly grid the plane to construct a finite set of candidate points Therefore, the UAV only needs to Select the point that minimizes the objective function, that is:

[0085]

[0086] The grid resolution is taken as a ratio of the maximum flight distance: d pace =ρv max τ,ρ∈(0,1), taking into account both search accuracy and computational complexity.

[0087] B. Discrete orientation sub-problem of MA surface

[0088] Given the current position of the UAV, the MA plane must choose the best orientation to maximize the uplink throughput. This decision is modeled as a subproblem of (P2):

[0089]

[0090] st(6),(7).

[0091] First, relax the binary constraint and let g(t)∈[0,1] M , we get the following continuous optimization problem:

[0092]

[0093]

[0094] The present invention uses the conditional gradient method to solve the relaxed problem (P5). This method is particularly suitable for optimization problems on convex constraint domains and can avoid explicit projection steps. Specifically, in each iteration r, the objective function is linearized at the current iteration point to guide the update of the search direction. To estimate the gradient The present invention adopts the approximate method of forward difference. Specifically, for each orientation index m, its partial derivative is estimated as:

[0095]

[0096] where ε is a small positive scalar. The forward difference approximation is consistent with the first-order definition of the directional derivative when ε→0, but in practice, a finite but small ε is used for efficient computation. This first-order approximation provides a computationally efficient method for estimating the directional derivative, approximating the gradient of the objective function without the need for an analytical expression. Using the estimated gradient, the present invention further solves the following linear problem:

[0097]

[0098] st(11),(12).

[0099] The solution to problem (P6) is expressed as As a direction-finding vector, it represents the fastest-growing feasible direction in the linearized objective function. Therefore, the current iteration point is updated by convex combination:

[0100]

[0101] Among them, ζ∈[0,1] is the iteration step size. After convergence, the maximum value is selected towards the index m * For the relaxed solution g * (t) is discretized, that is:

[0102]

[0103] Then configure the MA surface as q m* ,The specific solution of the MA face orientation selection problem (P4) is shown in Algorithm 2 shown in Table 2.

[0104] Table 2

[0105]

[0106] As shown in Table 2, solving the sub-problem (P4) specifically includes the following steps:

[0107] 21. Initialize the iteration round r←0, g (r) (t) = g (0)(t) (initial value), convergence threshold δ>0, step size ζ;

[0108] 22. Enter the first iteration of r = 1;

[0109] 23. Estimate the gradient using the forward finite difference method according to formula (13)

[0110] 24. Solve subproblem (P6) and get the direction vector

[0111] 25. Update g according to formula (14) (r+1) (t);

[0112] 26. Enter the next iteration, i.e. r←r+1;

[0113] 27. Iterate steps 23 to 26 until ||g is satisfied (r) (t)-g (r-1) (t)||<δ, we get g * (t);

[0114] 28. Based on g * (t), and the final heading index m is obtained according to formula (15) * .

[0115] Then, the present invention proposes a unified optimization framework for jointly optimizing the UAV flight trajectory subproblem and the discrete orientation of the MA surface in problem (P2). This framework adopts an alternating optimization strategy, solving the two subproblems (P3) and (P4) in sequence in each iteration. The specific process is detailed in Algorithm 3 shown in Table 3. The objective function value of problem (P2) at the first iteration in Algorithm 3 is 0 (l) , the convergence of the algorithm can be proved by observing that its objective value does not increase in each iteration. When , solving the UAV trajectory subproblem (P3) can obtain the new trajectory S (l+1) , and meet Then, on the fixed trajectory S (r) In this case, solve the orientation subproblem (P4) to obtain the new orientation vector and target value So the inequality chain Established. Since the target value of problem (P2) has a lower bound and is determined by the limited transmission power, limited antenna gain and compact feasible domain. Therefore, the sequence {O (l)}It is monotonically non-increasing and has a lower bound, so it converges to a finite value.

[0116] Table 3

[0117]

[0118] As shown in Table 3, solving problem (P2) specifically includes the following steps:

[0119] 31. Initialize the iteration round l←0, convergence threshold∈>0, UAV trajectory Facing vector

[0120] 32. Enter the first iteration with l=1;

[0121] 33. In the given In this case, solve subproblem (P3) according to formula (10) and obtain the new UAV trajectory

[0122] 34. In the given In this case, use Algorithm 2 to solve subproblem (P4) and get the new MA face orientation

[0123] 35. Enter the next iteration, i.e. r←r+1;

[0124] 36. Iterate steps 33 to 35 until the target value decreases by less than ∈, and output the current As the optimal solution to problem (P2).

[0125] It should be noted that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This embodiment is not limited here.

[0126] Based on the above method, an embodiment of the present invention also provides an optimization system for an IoT network assisted by a movable antenna drone, including a communication model construction module, an optimization problem construction module and an optimization problem solving module; the communication model construction module is used to construct a communication model of the IoT network assisted by a movable antenna drone; the optimization problem construction module is used to construct an optimization problem for optimizing the trajectory of the drone and the orientation of the movable antenna surface based on the communication model, with the goal of maximizing the total uplink throughput of the network and the physical constraints and energy constraints of the network as constraints; the optimization problem solving module is used to solve the optimization problem and obtain the trajectory of the drone and the orientation of the movable antenna surface.

[0127] The embodiments described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0128] Computer programs for implementing the methods and systems of the present invention can be written in any combination of one or more programming languages and stored in a computer-readable storage medium. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0129] Computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, compact disk read-only memories (CD ROM), optical storage devices, magnetic storage devices, or any combination of the foregoing.

[0130] The performance of the proposed method and system is evaluated by numerical simulation. Unless otherwise specified, the system parameters are configured as follows: the number of GDs is set to K = 4, and the 2 Random movement within the area; UAV flies at a fixed altitude H0 = 100m, the mission is divided into T = 30 time slots, each time slot duration τ = 1s; UAV horizontal speed limit v max=30m / s; MA plane uses 2×2 uniform planar array, the number of antennas N=4 and supports M=12 discrete orientation directions. These orientation directions are determined by elevation angle. and azimuth Combine and distribute evenly in the lower hemisphere, each unit vector is represented by q m =[sinθ m cosφ m ,sinθ m sinφ m ,cosθ m ] T ; UAV flight energy consumption parameters: P0 = 7.9856W, P i =88.628W, U tip =120m / s, v0=4.03m / s, d0=0.6, s0=0.05, ρ0=1.225kg / m 3 A=0.503m 2 Average flight energy budget

[0131] Figure 2 The movement trajectories of the GDs and the optimized UAV flight trajectory are shown. The UAV's trajectory drifts to the right overall on a two-dimensional plane, accompanied by vertical fluctuations. This motion closely matches the collective movement trend of all GDs, demonstrating that the UAV can adaptively maintain efficient communication performance while avoiding unnecessary displacement.

[0132] Figure 3 The three-dimensional view of the discrete orientation decision of the MA surface at time slots 6, 21 and 24 is given. The snowflake-shaped arrows represent the normal vector direction of the MA surface, and the blue quadrilateral area marks the GD gathering area under the corresponding time slot. Figure 3 As shown in the figure, the UAV strategically maintains a favorable spatial position relative to the GDs, while the MA surface continuously reorients itself to align with the GD group. This spatial-directional coordination improves directional gain, reduces the need for the UAV to hover directly above the GDs, and thus forms a more energy-efficient flight trajectory. It also highlights the system advantage of incorporating MA flexibility while maintaining high communication quality.

[0133] The proposed method is further compared with three benchmark schemes: 1) a fixed trajectory design scheme, in which the UAV flies along a preset path around the geometric center of the GD region, and only the MA orientation is optimized; 2) a fixed antenna position scheme, in which the MA orientation is fixed, and only the UAV trajectory is optimized; and 3) a minimum energy consumption design scheme, in which the UAV follows a conservative flight path to minimize energy consumption, and only the MA orientation is optimized. Figure 4 and Figure 5 The results of different time slot numbers T and different average energy budgets are given respectively. The total throughput comparison results under these two settings. Under these two settings, the proposed scheme always outperforms other baseline schemes. As T increases, the performance gap becomes more significant, further highlighting the advantages of jointly optimizing trajectory and orientation in the long term. The system performance has also shown a trend of gradual improvement, which is mainly due to the strong maneuverability and higher flexibility brought by the dynamic flight of UAV. Figure 6 The relationship between the Lyapunov control parameter V and the average queue length (Average Queue Length, AQL) is presented. As expected, larger values of V lead to longer queues, indicating that the system prioritizes improving throughput at the expense of increasing data backlog. This trend reveals a trade-off between energy efficiency and communication performance. These results verify that the additional spatial flexibility provided by MA reconfiguration enables efficient link alignment and reduces the UAV's flight burden. The Lyapunov-based framework can adapt online to GD movement and system changes in dynamic environments, effectively balancing trajectory control, antenna orientation, and energy consumption.

[0134] In summary, in order to cope with GD mobility and channel uncertainty, an embodiment of the present invention proposes a Lyapunov-based online optimization framework to jointly optimize the UAV trajectory and MA face orientation. By decomposing the original long-term stochastic optimization problem into sub-problems that can be solved in each time slot, online control is achieved without the need to predict future system information. At the same time, the present invention introduces a structure-aware trajectory discretization strategy to reduce the continuous search complexity of UAV positioning, and adopts a projection-free conditional gradient method to efficiently solve the MA face orientation selection problem. Simulation results show that, under the premise of meeting long-term energy constraints, the proposed method significantly outperforms multiple benchmark schemes in uplink throughput. The research results fully demonstrate the effectiveness of combining UAV mobility with reconfigurable antenna systems in achieving adaptive and energy-efficient IoT data acquisition.

[0135] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. The optimization method of the IOT network assisted by the movable antenna drone is characterized in that: Including steps: Construct a communication model for IoT networks assisted by mobile antenna drones; Based on the communication model, with the goal of maximizing the total uplink throughput of the network and the physical and energy constraints of the network as constraints, an optimization problem is constructed to optimize the trajectory of the UAV and the orientation of the movable antenna surface; Solve the optimization problem to obtain the trajectory of the UAV and the orientation of the movable antenna surface.

2. The method for optimizing an IoT network assisted by a movable antenna drone according to claim 1, characterized in that: The communication model includes a drone and multiple ground devices that move randomly within a server; the drone has a movable antenna surface with predefined multiple unit orientation vectors, and the movable antenna surface includes multiple antenna units arranged in an array; the drone maintains a fixed altitude H0 throughout the entire collection mission; in a dynamic environment where the ground devices move randomly over time, the drone completes the collection of uplink data by adjusting its flight trajectory and the orientation of the movable antenna surface.

3. The method for optimizing an IoT network assisted by a movable antenna drone according to claim 2, characterized in that: The physical constraints of the network include UAV displacement constraints, movable antenna surface orientation constraints, and movable antenna surface selection constraints. The UAV displacement constraint means that the displacement between the UAV positions in two time slots is not greater than the product of the UAV's maximum flight speed and the duration of each time slot. The movable antenna surface orientation constraint means that only one movable antenna surface orientation is selected in each time slot. The movable antenna surface selection constraint means that when a direction is selected, the binary selection vector of the direction is 1, otherwise it is 0. The energy constraint means that the average flight energy consumption of the UAV during the mission is not greater than its long-term average flight energy consumption budget.

4. The method for optimizing an IOT network assisted by a movable antenna drone according to claim 3, wherein: Solving the optimization problem specifically includes the following steps: A virtual energy queue H(t) is introduced to transform the optimization problem into a time slot-by-time slot equivalent problem. Initialize virtual energy queue H(0)=0; The equivalent problem of each time slot is solved time slot by time slot to obtain the trajectory of the UAV and the orientation of the movable antenna surface in each time slot.

5. The method for optimizing an IOT network assisted by a movable antenna drone according to claim 4, characterized in that: In each time slot t, solving the equivalent problem of each time slot t specifically includes the following steps: Observe the current queue state H(t); Solve the equivalent problem at time slot t to obtain the trajectory of the UAV and the orientation of the movable antenna surface at time slot t; Update the virtual queue for the next time slot E f (t) is the energy consumed by the UAV in time slot t, max{} represents the maximum value function, and t+1 represents the next time slot.

6. The method for optimizing an IOT network assisted by a movable antenna drone according to claim 5, characterized in that: The objective function of the equivalent problem for time slot t is: min means minimization, s uav (t), g(t) represent the trajectory of the UAV and the orientation of the movable antenna surface at time slot t, respectively. C(g(t)) represents the total uplink throughput of the network at time slot t. V ≥ 0 is the control parameter. The constraints of the equivalent problem at time slot t are converted into physical constraints for time slot t.

7. The method for optimizing an IOT network assisted by a movable antenna drone according to claim 6, characterized in that: Solving the equivalent problem of time slot t specifically includes the following steps: Fix g(t) and transform the equivalent problem of time slot t into solving s uav The first sub-problem of (t); Fixed uav (t), transforming the equivalent problem of time slot t into the second subproblem of solving g(t); The first subproblem and the second subproblem are solved alternately and iteratively until the relative decrease in the objective function value is lower than a threshold ∈.

8. The method for optimizing an IOT network assisted by a movable antenna drone according to claim 7, wherein: Solving the second sub-problem specifically includes the following steps: The second sub-problem is transformed into a linear problem. The objective function of the linear problem is: The constraints are the movable antenna surface orientation constraint and the movable antenna surface selection constraint for time slot t. represents the transpose of C(g(t)) in each iteration r, represents the gradient operation; The conditional gradient method is used to solve the linear problem and the relaxed solution g is obtained. * (t), where the current iteration point is updated by convex combination.

9. The method for optimizing an IoT network assisted by a movable antenna drone according to any one of claims 1 to 8, characterized in that: The total uplink throughput of the network in time slot t is p is the transmission power of each ground device, σ 2 is the noise power, I K and I N They are K×K and N×N unit matrices respectively, K is the number of ground equipment, N is the number of antennas on the movable antenna surface, diag() is a function to construct a diagonal matrix, H(s sur (t)) is the multiple access channel matrix from all ground equipment to the movable antenna surface at time slot t.

10. A mobile antenna drone-assisted IoT network optimization system, characterized in that: It includes a communication model construction module, an optimization problem construction module and an optimization problem solving module; the communication model construction module is used to construct a communication model of an IoT network assisted by a movable antenna drone; the optimization problem construction module is used to construct an optimization problem for optimizing the drone's trajectory and the orientation of the movable antenna surface based on the communication model, with the goal of maximizing the network's total uplink throughput and the network's physical constraints and energy constraints as constraints; the optimization problem solving module is used to solve the optimization problem and obtain the drone's trajectory and the orientation of the movable antenna surface.

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