An unmanned aerial vehicle backscattering communication method based on general-purpose integrated and related device

Abstract

This invention discloses a UAV backscatter communication method and related device based on the integration of sensing, communication, and energy. The invention uses a UAV equipped with a uniform planar array antenna as a dual-function access point in the air, transmitting communication and sensing multiplexed signals to ground-based IoT devices and potential sensing targets. Ground-based IoT devices can choose to collect energy from the signal or modulate their own information onto the transmitted signal and reflect it back to the UAV, thereby completing energy transmission and information collection tasks respectively. By using an alternating optimization algorithm to optimize the UAV's flexible mobility, beamforming spatial control capabilities, and device scheduling capabilities, the average total throughput of the device backscatter return link can be maximized while meeting the sensing performance and energy collection requirements of IoT devices. This achieves deep synergy between sensing, communication, and energy supply, thereby realizing resource reuse and transmission performance balance across multiple tasks.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a backscatter communication method and related apparatus for unmanned aerial vehicles (UAVs) based on the integration of communication, sensing, and energy. Background Technology

[0002] With the development from 5G to 6G communication systems, large-scale IoT services such as smart cities, smart factories, and autonomous driving place higher demands on device energy supply, communication media, and network costs. Wireless power supply technology provides energy for low-power IoT devices, with wireless power supply communication networks being a typical application. In this network, devices can harvest energy using a radio frequency source in the downlink and actively transmit information in the uplink when needed. However, traditional wireless power supply communication networks rely on a harvest-then-transmit working mode, which limits emergency data transmission. Backscatter communication, as a low-power communication mode, allows backscatter tags to consume power down to the nanowatt level. Backscatter devices can transmit information by passively modulating and reflecting instantaneous incident signals, eliminating the need for dedicated energy harvesting time and thus extending communication duration. Therefore, combining backscatter communication with wireless power supply technology can overcome the technical shortcomings of traditional wireless power supply communication networks and is an important way to achieve sustainable IoT networks.

[0003] In addition, the promotion of new application scenarios has also exposed the dual challenges faced by mobile communication systems: limited spectrum resources and insufficient environmental perception capabilities. Meanwhile, the integrated sensing and communication technology proposed by 6G communication systems achieves synergy between communication and sensing functions through hardware sharing and spectrum reuse, effectively improving resource utilization efficiency. Deeply integrating sensing with backscatter communication and energy transmission to build an integrated sensing-energy system can provide comprehensive services for IoT devices, combining sensing, communication, and power supply. However, in practical deployments, ground-based fixed base stations face severe obstruction and dual path loss, resulting in extremely weak backscatter signals and limited coverage. Simultaneously, energy transmission efficiency and sensing performance are significantly reduced due to non-line-of-sight propagation, making it difficult to achieve deep synergy among sensing, communication, and power supply functions, thus affecting resource reuse and transmission performance. Summary of the Invention

[0004] This invention provides a UAV backscatter communication method and related device based on integrated sensing, communication and power supply, which solves the technical problem that ground fixed base stations face severe obstruction and dual path loss in existing deployment applications, resulting in extremely weak backscatter signals and limited coverage. At the same time, energy transmission efficiency and sensing performance are also greatly reduced due to non-line-of-sight propagation, making it difficult to achieve deep coordination of sensing, communication and power supply functions, thus affecting the resource reuse and transmission performance of the mission.

[0005] This invention provides a backscatter communication method for unmanned aerial vehicles (UAVs) based on the integration of sensing and energy, the method comprising:

[0006] A system model integrating sensing, backscatter communication, and energy transfer is constructed; the system model includes a full-duplex UAV equipped with a uniform planar array, multiple IoT devices, and multiple sensing targets;

[0007] Based on the system model, the channel gain vector between the full-duplex UAV and the IoT device is calculated and the beam pattern gain at the sensing target is determined. The energy harvesting amount and backscatter communication spectrum efficiency of the IoT device are determined according to the channel gain vector.

[0008] Based on the energy harvesting amount, the backscatter communication spectral efficiency, and the beam pattern gain, a joint optimization problem is established under the conditions of sensing and energy harvesting constraints, with the objective of maximizing the average total throughput of the backscatter backhaul link of the system model.

[0009] The joint optimization problem is decomposed into multiple sub-problems using an alternating optimization algorithm, and the sub-problems are solved iteratively using convex optimization techniques to output the optimal equipment scheduling strategy, beamforming design parameters, and UAV flight trajectory.

[0010] Optionally, the step of calculating the channel gain vector between the full-duplex UAV and the IoT device based on the system model and determining the beam pattern gain at the sensing target, and determining the energy harvesting amount and backscatter communication spectral efficiency of the IoT device based on the channel gain vector, includes:

[0011] Based on the line-of-sight wireless transmission model, the channel gain vector between the full-duplex UAV and the IoT device is calculated according to the position information between the full-duplex UAV and the IoT device in the system model.

[0012] Based on the channel gain vector, determine the energy harvesting amount and signal-to-noise ratio of the corresponding IoT device; determine the backscatter communication spectral efficiency of the corresponding IoT device based on the signal-to-noise ratio;

[0013] Calculate the beam pattern gain between the full-duplex UAV and the sensing target in the system model to determine the sensing performance at the corresponding sensing target.

[0014] Optionally, in the system model, the mission cycle of the full-duplex UAV is discretized into multiple time slots, and each time slot is divided into K+1 sub-time slots using a time-division multiple access protocol; wherein, the first sub-time slot Energy harvesting for all IoT devices; in the (k+1)th sub-slot Within this context, only the k-th IoT device performs backscatter communication, while the other IoT devices continue energy harvesting; among them, .

[0015] Optionally, the joint optimization problem is expressed as follows:

[0016]

[0017] In the formula: For the mission cycle of the drone, The number of time slots divided into the task cycle. The total number of IoT devices. In time slot Internal allocation to the first The sub-time slot length for backscatter communication of an IoT device In time slot The horizontal position coordinates of the internal UAV In time slot Internal drones for the first Transmit beamforming vector of an IoT device In time slot The covariance matrix of the dedicated sensing signals emitted by the UAV. In time slot The internal drone received from the first Backscatter communication spectrum efficiency of an IoT device; In time slot Inner Energy harvesting capacity of an IoT device For each IoT device in the cycle Internal energy loss ; In time slot The drone transmits signals to the target. Beam pattern gain at the location, In time slot The interior is located in a horizontal position. The drone and the horizontal position The distance between perceived targets. The beam pattern gain threshold. , ; This is the maximum transmit power of the drone; This is the fixed starting horizontal position for the drone. This is the fixed end horizontal position of the drone. The maximum flight speed of the drone. The length of each time slot, .

[0018] Optionally, the step of employing an alternating optimization algorithm to decompose the joint optimization problem into multiple sub-problems, and iteratively solving the sub-problems using convex optimization techniques to output the optimal equipment scheduling strategy, beamforming design parameters, and UAV flight trajectory includes:

[0019] The joint optimization problem is decomposed into an equipment scheduling subproblem, a beamforming optimization subproblem, and a UAV trajectory subproblem based on the alternating optimization algorithm, and the optimization variables of the joint optimization problem are initialized. The optimization variables include the initial equipment scheduling, beamforming parameters, and UAV flight position.

[0020] The equipment scheduling subproblem is solved using convex optimization techniques based on the initial beamforming parameters and the UAV flight position to optimize the initial equipment scheduling and obtain the updated equipment scheduling.

[0021] Introducing a first auxiliary variable, the beamforming optimization subproblem is transformed into a beamforming convex problem using a semidefinite relaxation algorithm; convex optimization techniques are then employed to solve the beamforming convex problem based on the initial UAV flight position and the updated equipment scheduling to optimize the initial beamforming parameters and obtain the updated beamforming parameters.

[0022] A second auxiliary variable is introduced to transform the non-convex terms in the UAV trajectory subproblem into convex terms, resulting in a convex trajectory problem. Convex optimization techniques are then used to solve the convex trajectory problem based on the updated equipment scheduling and beamforming parameters to optimize the initial UAV flight position, thus obtaining the updated UAV flight position.

[0023] If the preset iteration conditions are not met, the updated equipment scheduling, beamforming parameters, and UAV flight position are used as the initial equipment scheduling, beamforming parameters, and UAV flight position. The process then jumps to the step of using convex optimization techniques to solve the equipment scheduling subproblem based on the initial beamforming parameters and UAV flight position to optimize the initial equipment scheduling and obtain the updated equipment scheduling. If the conditions are met, the optimal equipment scheduling strategy, beamforming design parameters, and UAV flight trajectory are generated based on the updated equipment scheduling, beamforming parameters, and UAV flight position.

[0024] Optionally, the energy harvesting amount is expressed as follows:

[0025]

[0026] In the formula: For energy conversion efficiency, In time slot The interior is located in a horizontal position. The drone at the location and located in the horizontal position The first Channel gain vector between IoT devices In time slot Signals transmitted by the drone; unit reference distance Channel power gain under, In time slot The drone pointed to the first The guidance vector of an IoT device For the first Horizontal position of an IoT device In time slot Internal drones and the first The transmit beamforming vector of an IoT device, where H is the flight altitude of the drone;

[0027] The spectral efficiency of the backscatter communication is expressed as follows:

[0028]

[0029] In the formula: In time slot The internal drone received from the first Signal-to-noise ratio of individual IoT devices;

[0030] The signal-to-noise ratio is expressed as follows:

[0031]

[0032] In the formula: For the first Backscattering coefficient of an IoT device For drones and the first Two-way communication between IoT devices , For the data of this device; It is the combined noise that includes receiver noise and residual noise; It is the Frobenius norm; This represents the combined noise power of the receiver noise and the residual.

[0033] The beam pattern gain is represented as follows:

[0034]

[0035] In the formula: In time slot The interior is located in a horizontal position. The drone at the location and located in the horizontal position Perceived target at the location Beam pattern gain; In time slot Inner horizontal position The drone is pointing horizontally. The guidance vector of the perceived target at the location.

[0036] This invention also provides a UAV backscatter communication device based on the integration of sensing and communication, the device comprising:

[0037] The system model building module is used to build a system model that integrates sensing, backscatter communication, and energy transfer; the system model includes a full-duplex UAV equipped with a uniform planar array, multiple IoT devices, and multiple sensing targets;

[0038] The system performance calculation module is used to calculate the channel gain vector between the full-duplex UAV and the IoT device based on the system model and determine the beam pattern gain at the sensing target. Based on the channel gain vector, the module determines the energy harvesting amount and backscatter communication spectrum efficiency of the IoT device.

[0039] The joint optimization problem establishment module is used to establish a joint optimization problem with the objective of maximizing the average total throughput of the backscatter backhaul link of the system model, based on the energy harvesting amount, the backscatter communication spectral efficiency, and the beam pattern gain, under the conditions of sensing and energy harvesting constraints.

[0040] The strategy optimization module is used to decompose the joint optimization problem into multiple sub-problems using an alternating optimization algorithm, and to iteratively solve the sub-problems using convex optimization techniques, outputting the optimal equipment scheduling strategy, beamforming design parameters, and UAV flight trajectory.

[0041] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, characterized in that the processor executes the computer program to implement the steps of the UAV backscatter communication method as described above.

[0042] This invention also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor, implements the steps of the UAV backscatter communication method described above.

[0043] This invention also provides a computer program product, including a computer program or instructions, characterized in that, when the computer program or instructions are executed by a processor, they implement the steps of the UAV backscatter communication method as described above.

[0044] As can be seen from the above technical solutions, the present invention has the following advantages:

[0045] This invention provides a UAV backscatter communication method and related devices based on integrated sensing, sensing, and energy transfer. The method includes: constructing a system model integrating sensing, backscatter communication, and energy transfer; the system model includes a full-duplex UAV equipped with a uniform planar array, multiple IoT devices, and multiple sensing targets; based on the system model, calculating the channel gain vector between the full-duplex UAV and the IoT devices and determining the beam pattern gain at the sensing targets; determining the energy harvesting amount and backscatter communication spectral efficiency of the IoT devices based on the channel gain vector; based on the energy harvesting amount, backscatter communication spectral efficiency, and beam pattern gain, establishing a joint optimization problem with the objective of maximizing the average total throughput of the backscatter backhaul link of the system model under the constraints of sensing and energy harvesting; using an alternating optimization algorithm, decomposing the joint optimization problem into multiple sub-problems, and iteratively solving the sub-problems using convex optimization techniques to output the optimal device scheduling strategy, beamforming design parameters, and UAV flight trajectory.

[0046] In this invention, a UAV equipped with a uniform planar array antenna serves as an aerial dual-function access point, transmitting communication and sensing multiplexed signals to ground-based IoT devices and potential sensing targets. Ground-based IoT devices can choose to collect energy from the signal or modulate their own information onto the transmitted signal and reflect it back to the UAV, thereby completing energy transmission and information collection tasks respectively. By optimizing the UAV's flexible mobility, beamforming spatial control capabilities, and device scheduling capabilities through an alternating optimization algorithm, the average total throughput of the device backscatter return link can be maximized while meeting the sensing performance and energy collection requirements of IoT devices. This achieves deep synergy between sensing, communication, and power supply, thereby realizing resource reuse and transmission performance balance for multiple tasks. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 A flowchart illustrating the steps of a UAV backscatter communication method based on the integration of sensing and energy in an embodiment of the present invention;

[0049] Figure 2 A structural block diagram of a system model for an unmanned aerial vehicle-assisted integrated sensing, backscatter communication, and energy transfer provided in an embodiment of the present invention;

[0050] Figure 3 This is a trajectory comparison diagram between the proposed solution and the non-perceptual reference solution provided in this embodiment of the invention;

[0051] Figure 4 The spatial beamforming gain distribution of the uniform planar array antenna in the 14th time slot provided in this embodiment of the invention;

[0052] Figure 5 The average total throughput of the backscattering backhaul link of this scheme and other benchmark schemes provided in this embodiment of the invention varies with the number of antennas;

[0053] Figure 6 This is a structural block diagram of a UAV backscatter communication device based on the integration of sensing and energy, provided for an embodiment of the present invention. Detailed Implementation

[0054] This invention provides a UAV backscatter communication method and related device based on the integration of sensing, communication and power supply. It is used to solve the technical problem that ground fixed base stations face severe obstruction and dual path loss, resulting in extremely weak backscatter signals and limited coverage. At the same time, energy transmission efficiency and sensing performance are also greatly reduced due to non-line-of-sight propagation, making it difficult to achieve deep coordination of sensing, communication and power supply functions, thus affecting the resource reuse and transmission performance of the mission.

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] It should be noted that, in the optional embodiments of the present invention, the data related to object information, etc., requires the permission or consent of the object when the embodiments of the present invention are applied to specific products or technologies. Furthermore, the collection, use, and processing of the relevant data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of the present invention involve data related to an object, it needs to be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.

[0057] Please see Figure 1This invention provides a backscatter communication method for unmanned aerial vehicles (UAVs) based on the integration of sensing and energy, the method comprising:

[0058] Step 101: Construct a system model integrating sensing, backscatter communication and energy transfer; the system model includes a full-duplex UAV equipped with a uniform planar array, multiple IoT devices and multiple sensing targets.

[0059] Specifically, please refer to Figure 2 The system model constructed in this embodiment includes an aircraft equipped with a total number of antennas. Full-duplex UAVs with uniform planar arrays IoT devices and One potential target to be detected; among them, the drone is on the ground. While providing wireless power transfer and backscatter return link information collection services, the IoT device also monitors the area of ​​interest. The system senses potential targets. After building the system model, it is necessary to initialize the system parameters of the system model, which include the location of the drone, the location of the IoT device, the location of the sensed target, and the signals emitted by the drone.

[0060] For ease of calculation, the coordinates are defined in a three-dimensional Cartesian coordinate system, where the positions of the IoT device and the potential target are fixed and known; IoT device The coordinates are Its horizontal coordinate is ,Target The coordinates are Its horizontal coordinate is During a duration of During the mission, the drone flew at a fixed altitude. Flight, throughout the entire cycle The duration is of Given equal time slots, and making reasonable assumptions, in a single time slot Internally, the drone's position can be considered approximately constant, and the drone operates within sub-time slots. horizontal position .make and These represent the starting and ending horizontal coordinates of the drone, respectively.

[0061] Specifically, this embodiment employs a time-division multiple access (TDMA) transmission protocol and a full-duplex operating mode to achieve orderly communication and energy transfer between IoT devices and drones: uplink transmissions between each IoT device and the drone share the same frequency band, but signal interference is avoided through time-division. Specifically, the drone continuously transmits signals at any given time, with at most one IoT device performing backscatter communication at any given moment, while all other IoT devices synchronously acquire energy from the signals transmitted by the drone. We will define each time slot... Divided into Each time slot, set This refers to the duration of dedicated downlink power transmission; all IoT devices only perform energy harvesting. Internet of Things (IoT) devices The duration of backscatter return link reflection communication is determined, while other IoT devices perform energy harvesting. Because a time-division multiple access (TDMA) transmission protocol is used, device scheduling must meet certain requirements. ,in .

[0062] In the time slot Signals emitted by the drone It can be represented as:

[0063] (1)

[0064] In the formula: Represented as in time slot Internal drones send data to IoT devices Information signals, Indicates in time slot Internal drones and the first Transmit beamforming vector of each IoT device Indicates time slot Dedicated sensor signal at the location; assumed communication signal It is an independent, cyclic, symmetric complex Gaussian random variable with zero mean and unit variance, i.e. And dedicated sensor signals It is a matrix with zero mean and covariance. Independently generated random vectors; considering Used for multi-beam transmission, its covariance matrix It has the general rank property, that is, the rank satisfies ,and .

[0065] Step 102: Based on the system model, calculate the channel gain vector between the full-duplex UAV and the IoT device and determine the beam pattern gain at the sensing target. Based on the channel gain vector, determine the energy harvesting amount and backscatter communication spectrum efficiency of the IoT device.

[0066] In this embodiment, based on the system model, the channel gain vector between the full-duplex UAV and the IoT device is calculated, and the beam pattern gain at the sensing target is determined to ensure sensing performance. The energy harvesting amount and backscatter communication spectrum efficiency of the IoT device are determined according to the channel gain vector. This quantitatively correlates the UAV's maneuverability with the performance indicators of sensing, communication, and power supply functions, providing a unified analytical basis for subsequent joint optimization. This enables the UAV to adjust its flight trajectory while taking into account the needs of multiple tasks, achieving a balance between resource reuse and transmission rate and efficiency.

[0067] In one specific implementation, step 102 may include the following steps:

[0068] S11. Based on the line-of-sight wireless transmission model, the channel gain vector between the full-duplex UAV and the IoT device is calculated according to the location information between the full-duplex UAV and the IoT device in the system model.

[0069] S12. Based on the channel gain vector, determine the energy harvesting amount and signal-to-noise ratio of the corresponding IoT device; determine the backscatter communication spectral efficiency of the corresponding IoT device based on the signal-to-noise ratio.

[0070] S13. Calculate the beam pattern gain between the full-duplex UAV and the sensing target in the system model to determine the sensing performance at the corresponding sensing target.

[0071] In this specific embodiment, the positional information between the full-duplex UAV and the IoT device includes the total number of array elements, the spacing between adjacent antenna elements in the array, the horizontal position of the UAV and the IoT device, and the elevation angle relationship between the UAV and the IoT device. It is assumed that the uniform planar array is parallel to the ground and can be along... shaft and Axis splitting, total number of arrays satisfies (in , They are respectively axis, (Number of antennas in the axial direction); similarly, the spacing between adjacent antenna elements is set to satisfy... , Represents the signal wavelength; assuming it is in the time slot The drone reached the The departure angle of the IoT device is The azimuth angle away from is In this situation, the drone points to the first... Guide vector of an IoT device It can be represented as:

[0072] (2)

[0073] In practical applications, the wireless channel between drones and IoT devices is primarily based on line-of-sight propagation, and its channel modeling employs a free-space path loss model; therefore, in time slots Drones and the Channel gain vector between IoT devices It can be represented as:

[0074] (3)

[0075] In the formula: where, The reference channel gain per unit distance. In time slot The interior is located in a horizontal position. The drone at the location and located in the horizontal position The distance between IoT devices in a given location.

[0076] The duration for which an IoT device absorbs energy within a time slot is , For energy conversion efficiency, in time slots Internal IoT devices Energy harvesting amount for:

[0077] (4)

[0078] Assuming the Doppler frequency shift of the IoT device and the drone is constant within a time slot and can be well compensated, the channel between the drone and the IoT device is reciprocal, and the drone receives signals from the IoT device. Backscattered signal Represented as:

[0079] (5)

[0080] In the formula: For the first Backscattering coefficient of an IoT device For time slots Inner Data from individual IoT devices, and meeting the requirements Channel , This is the self-interference channel between the transmitting and receiving ends of the UAV antenna. It is the noise at the antenna receiver, and it satisfies... .

[0081] To enable full-duplex drones to simultaneously perform the dual tasks of transmitting power / sensing signals and receiving weak backscattered signals in practical deployments, and to ensure the engineering feasibility of the integrated sensing and power system, it is necessary to eliminate the effects of self-interference. Specifically, the received signal after eliminating self-interference... It can be represented as:

[0082] (6)

[0083] In the formula: This is the combined noise, which includes receiver noise and residual noise.

[0084] As can be seen from equation (6), after eliminating self-interference, the interference experienced by each IoT device comes only from noise. Based on the expansion of the Frobenius norm, the drone in the time slot... Received from the Signal-to-noise ratio of individual IoT devices It can be represented as:

[0085] (7)

[0086] exist At that time, according to Shannon's formula, the number of IoT devices that a drone can receive... In the time slot Spectral efficiency of backscatter communication (bps / Hz) for:

[0087] (8)

[0088] We use the transmitted beam pattern gain towards the target as a sensing performance indicator. By utilizing integrated sensing technology, the UAV can reuse the transmitted signal to sense the target and obtain sensing information between the full-duplex UAV and the target. This sensing information includes the transmitted signal, the steering vectors of the UAV and the target, and therefore the direction of the beam towards the target. Transmit beam pattern gain for:

[0089] (9)

[0090] In the formula: In time slot Inner horizontal position The drone is pointing horizontally. The guidance vector of the perceived target at the location.

[0091] Step 103: Based on the energy harvesting amount, backscatter communication spectral efficiency, and beam pattern gain, establish a joint optimization problem with the objective of maximizing the average total throughput of the backscatter backhaul link in the system model, under the constraints of sensing and energy harvesting.

[0092] It should be noted that the objective of this invention is to maximize the average total throughput of the system's average backscatter return link reflections by optimizing the variables of the joint optimization problem under the constraints of sensing and energy harvesting. Among them, the sensing and energy harvesting constraints include energy harvesting constraints, sensing constraints, equipment scheduling constraints, power constraints, and UAV flight constraints; the optimization variables include equipment scheduling. Beamforming parameters , and drone flight position .

[0093] The joint optimization problem (P1) is represented as follows:

[0094] (10)

[0095] In the formula: For each IoT device in the cycle Internal energy loss The beam pattern gain threshold. At maximum transmission power, This is the fixed starting horizontal position for the drone. This is the fixed end horizontal position of the drone. This is the maximum flight speed of the drone.

[0096] Under energy harvesting constraints, the total energy harvested by each IoT device throughout the entire mission cycle shall not be less than the minimum energy requirement. Under sensing constraints, within each time slot, the beam pattern gain at each sensing target is not lower than a preset threshold. Under equipment scheduling constraints, the sum of the durations of all sub-time slots within each time slot equals the time slot length; under power constraints, the total transmit power of the UAV within each time slot does not exceed the maximum transmit power. Under drone flight constraints, drones must start from a designated origin. Fly to the designated destination And the displacement between adjacent time slots does not exceed the maximum flight speed. The product of the time slot length and the time slot length.

[0097] Step 104: Using an alternating optimization algorithm, the joint optimization problem is decomposed into multiple sub-problems, and the sub-problems are solved iteratively using convex optimization techniques to output the optimal equipment scheduling strategy, beamforming design parameters, and UAV flight trajectory.

[0098] It should be noted that the joint optimization problem (P1) is a non-convex problem. To overcome this problem, this embodiment decomposes the joint optimization problem into multiple sub-problems based on the alternating optimization algorithm, and iteratively solves the sub-problems through convex optimization techniques to output the optimal equipment scheduling strategy, beamforming design parameters and UAV flight trajectory, thereby achieving resource reuse and a balance between transmission rate and efficiency for multiple tasks.

[0099] In one specific implementation, step 104 may include the following steps:

[0100] S21. Based on the alternating optimization algorithm, the joint optimization problem is decomposed into equipment scheduling sub-problem, beamforming optimization sub-problem and UAV trajectory sub-problem, and the optimization variables of the joint optimization problem are initialized; the optimization variables include the initial equipment scheduling, beamforming parameters and UAV flight position;

[0101] S22. Using convex optimization techniques, the equipment scheduling subproblem is solved based on the initial beamforming parameters and the UAV flight position to optimize the initial equipment scheduling and obtain the updated equipment scheduling.

[0102] S23. Introduce the first auxiliary variable and use the semidefinite relaxation algorithm to transform the beamforming optimization subproblem into a beamforming convex problem; use convex optimization technology to solve the beamforming convex problem based on the initial UAV flight position and the updated equipment scheduling to optimize the initial beamforming parameters and obtain the updated beamforming parameters.

[0103] S24. Introduce a second auxiliary variable to transform the non-convex terms in the UAV trajectory subproblem into convex terms, resulting in a convex trajectory problem. Use convex optimization techniques to solve the convex trajectory problem based on the updated equipment scheduling and beamforming parameters to optimize the initial UAV flight position and obtain the updated UAV flight position.

[0104] S25. Determine whether the preset iteration conditions are met. If not, use the updated equipment scheduling, beamforming parameters, and UAV flight position as the initial equipment scheduling, beamforming parameters, and UAV flight position, and jump to execute the step of using convex optimization technology to solve the equipment scheduling subproblem based on the initial beamforming parameters and UAV flight position to optimize the initial equipment scheduling and obtain the updated equipment scheduling. If the conditions are met, generate the optimal equipment scheduling strategy, beamforming design parameters, and UAV flight trajectory based on the updated equipment scheduling, beamforming parameters, and UAV flight position.

[0105] In this specific embodiment, a collaborative algorithm combining alternating optimization and semidefinite relaxation is used to solve the joint optimization model of UAV trajectory equipment scheduling and beamforming. This results in a joint optimization scheme for UAV trajectory equipment scheduling and beamforming oriented towards integrated sensing, backscatter communication and wireless energy transmission. Under the premise of ensuring sensing performance and energy harvesting requirements, the scheme maximizes the average total throughput of the backscatter backhaul link.

[0106] Specifically, given the initial beamforming parameters , and drone flight position The equipment scheduling subproblem (P2) is represented as follows:

[0107] (11)

[0108] Since the objective function of the equipment scheduling subproblem (P2) is about A linear function, whose constraints are also... Because of the linear constraints, we can use convex optimization techniques (such as CVX) to effectively obtain the optimal solution to this subproblem and get the updated equipment scheduling.

[0109] At the initial drone flight position and updated device scheduling The beamforming optimization subproblem (P3) is represented as follows:

[0110] (12)

[0111] The beamforming optimization subproblem (P3) is a quadratic constrained quadratic programming problem. We introduce a first auxiliary variable. as well as ,in ,and The beamforming optimization subproblem (P3) can be transformed into the following form:

[0112] (13)

[0113] in, .

[0114] Problem (P4) is strongly nonconvex due to the rank-one constraint. To address this difficulty, we introduce a positive semidefinite relaxation to ease this property. Removing the rank-one constraint yields (SDR.P4). It can be seen that after introducing the positive semidefinite relaxation, (SDR.P4) becomes a convex problem with respect to beamforming variables, which can be optimally solved using CVX. Let... and This is the optimal solution for (SDR.P4). Through channel normalization, the following reconstructed solution can be constructed:

[0115] (14)

[0116] in The constructed reconstructed solution can achieve the same optimal objective function value as the optimization solution of the beamforming convex problem (SDR.P4), while satisfying the rank-one constraint.

[0117] Due to the non-convex dependence of the guidance vector on the UAV trajectory, solving the UAV trajectory subproblem is difficult. To simplify this UAV trajectory subproblem, we use the first... The drone trajectory obtained in the second iteration is used to approximate the first... The guiding vector in the next iteration and .

[0118] Updated device scheduling and beamforming parameters , Next, the In the next iteration, the UAV trajectory subproblem (P5) can be represented as:

[0119] (15)

[0120] in,

[0121] (16)

[0122] (17)

[0123] (18)

[0124] (19)

[0125] (20)

[0126] ,(twenty one)

[0127] In the UAV trajectory subproblem (P5), a second auxiliary variable is introduced. and The objective function and the first and second constraints are all about It is convex, but regarding It is non-convex, therefore, we can perform calculations on the objective function, energy harvesting function, and beam directional gain respectively. exist The first-order Taylor expansion at the given point transforms the non-convex terms in the UAV trajectory subproblem into convex terms:

[0128] ,(twenty two)

[0129] ,(twenty three)

[0130] (twenty four)

[0131] in, , They are respectively exist The function value and first derivative at point; , They are respectively exist The function value and first derivative at point; , They are respectively exist The function value and first derivative at point .

[0132] Based on the above processing, the UAV trajectory subproblem (P5) can be transformed into a trajectory convex problem (P6), which can be effectively optimized and solved using CVX. The trajectory convex problem (P6) is represented as follows:

[0133] (25)

[0134] Therefore, the three sub-problems of the joint optimization problem (P1) based on the global alternating optimization algorithm can be alternately optimized. The alternating optimization process can be further summarized into an optimization algorithm based on UAV trajectory, equipment scheduling, and beamforming. The complete algorithm is summarized as follows:

[0135] 1) Initialize optimization variables , , , Number of iterations and the maximum number of iterations ;

[0136] 2) Given , and drone flight position Update optimization variables through subproblem (P2) ;

[0137] 3) Given , The optimization variables are updated through the subproblem (SDR.P4) and its reconstructed solution. , ;

[0138] 4) Given , , Update optimization variables through subproblem (P6)

[0139] 5) ;

[0140] 6) Repeat steps 2-5 above until the preset iteration condition is met; the preset iteration condition is that the objective function value of the joint optimization problem (P1) converges or... ;

[0141] 7) After completing the above steps, output the optimal equipment scheduling strategy. Optimal beamforming design parameters and and the optimal drone flight trajectory .

[0142] This invention uses a full-duplex UAV equipped with a uniform planar array as a platform to overcome the limitations of ground-based integrated sensing and energy systems caused by obstruction. It constructs a system model that integrates sensing, backscatter communication, and energy transmission. By employing a time-division multiple access protocol, time slots are divided into energy transmission and backscatter return link reflection communication sub-time slots, achieving deep multiplexing of the three functions of sensing, backscatter communication, and energy transmission. The resource utilization rate is much higher than that of dual-function schemes. The alternating optimization algorithm used takes into account both the solution efficiency and the feasibility of non-convex problems. Under the premise of meeting the sensing performance and energy harvesting requirements of IoT devices, it maximizes the average total throughput of the device's backscatter return link, effectively improving the resource reuse and transmission efficiency of the task.

[0143] To further verify the technical effects of the present invention, the embodiments of the present invention also provide corresponding simulation experiments. Please refer to [link / reference]. Figures 3-5 .

[0144] Let's consider a The region with the number of IoT devices Perceive target number The number of drone antennas is Beam pattern gain threshold The energy consumption threshold is Maximum flight speed of the drone Flight altitude is and maximum launch energy Additionally, we set the noise power of each antenna receiver to... and reference distance The channel power gain at that location is set to Furthermore, we set the backscattering coefficient. Energy conversion efficiency of IoT devices Flight time Number of time slots To compare performance and verify the effectiveness of the proposed joint optimization scheme based on UAV trajectory, equipment scheduling, and beamforming, the following three benchmark schemes are considered:

[0145] Joint optimization scheme for equipment scheduling and beamforming of straight-line flight trajectory: The UAV adopts uniform straight-line flight with a speed of Fly from the starting point to the ending point, and solve problems (P2) and (P4) based on this trajectory to dynamically optimize equipment scheduling, communication and beamforming.

[0146] Joint optimization scheme for equipment scheduling and flight trajectory of constant beamforming: UAVs dynamically optimize equipment scheduling and flight trajectory by solving problems (P2) and (P6).

[0147] Unconstrained approach: The UAV optimizes its trajectory, equipment scheduling, and beamforming by solving problem (P1) while ignoring the constraints of perception.

[0148] Figure 3 The drone trajectories of the proposed design scheme and the baseline scheme without perception constraints were compared. As can be seen from the figures, both schemes employ an arc-shaped flight trajectory as close as possible to the device, which achieves a higher backscatter communication transmission rate. The difference lies in the fact that the flight trajectory of the proposed scheme exhibits a significant characteristic of closely approaching the perceived target. This phenomenon reveals the important influence of perception constraints on drone trajectory optimization.

[0149] Figure 4 The figure shows the spatial beamforming gain distribution of a UAV using a uniform planar array antenna in the 14th time slot. As can be seen from the figure, the beamforming scheme designed in this invention will select to generate two directional high-gain main lobes to provide communication services to multiple users during optimization, thereby improving resource reuse efficiency.

[0150] Figure 5 Revealed the number of antennas The relationship with the average backscatter communication rate. Due to the additional degrees of freedom and array gain brought by the extra antennas, the backscatter communication rate of the four schemes increases with the increase of the number of antennas, but the increase varies. The scheme proposed in this invention is significantly better than the "joint optimization scheme of equipment scheduling and beamforming for straight flight trajectory" and the "joint optimization of equipment scheduling and flight trajectory for constant beamforming". In addition, the rate difference between the scheme proposed in this invention and the "unconstrained baseline scheme" intuitively reflects the cost of backscatter communication rate for sensing performance.

[0151] The following describes the UAV backscatter communication device based on the integration of sensing and energy provided in the embodiments of this application. The UAV backscatter communication device based on the integration of sensing and energy described below can be referred to in correspondence with the UAV backscatter communication method based on the integration of sensing and energy described above.

[0152] Please see Figure 6 This invention also provides a UAV backscatter communication device based on the integration of sensing and communication, the device comprising:

[0153] System model building module 201 is used to build a system model integrating sensing, backscatter communication and energy transfer; the system model includes a full-duplex UAV equipped with a uniform planar array, multiple IoT devices and multiple sensing targets;

[0154] The system performance calculation module 202 is used to calculate the channel gain vector between the full-duplex UAV and the IoT device based on the system model and determine the beam pattern gain at the sensing target. Based on the channel gain vector, the energy harvesting amount and backscatter communication spectrum efficiency of the IoT device are determined.

[0155] The joint optimization problem establishment module 203 is used to establish a joint optimization problem based on energy harvesting amount, backscatter communication spectral efficiency and beam pattern gain, under the condition of satisfying sensing and energy harvesting constraints, with the objective of maximizing the average total throughput of the backscatter backhaul link of the system model;

[0156] The strategy optimization module 204 is used to decompose the joint optimization problem into multiple sub-problems using an alternating optimization algorithm, and to iteratively solve the sub-problems using convex optimization techniques, outputting the optimal equipment scheduling strategy, beamforming design parameters and UAV flight trajectory.

[0157] System performance calculation module 202 includes:

[0158] The channel gain vector determination unit is used to calculate the channel gain vector between the full-duplex UAV and the IoT device based on the line-of-sight wireless transmission model and the position information between the full-duplex UAV and the IoT device in the system model.

[0159] A communication transmission unit is used to determine the energy harvesting amount and signal-to-noise ratio of the corresponding IoT device based on the channel gain vector; and to determine the backscatter communication spectral efficiency of the corresponding IoT device based on the signal-to-noise ratio.

[0160] The sensing unit is used to calculate the beam pattern gain between the full-duplex UAV and the sensing target in the system model to determine the sensing performance at the corresponding sensing target.

[0161] Strategy optimization module 204 includes:

[0162] The decomposition unit is used to decompose the joint optimization problem into a device scheduling subproblem, a beamforming optimization subproblem, and a UAV trajectory subproblem based on an alternating optimization algorithm, and to initialize the optimization variables of the joint optimization problem; the optimization variables include the initial device scheduling, beamforming parameters, and UAV flight position;

[0163] The equipment scheduling subproblem solving unit is used to solve the equipment scheduling subproblem using convex optimization techniques based on the initial beamforming parameters and the UAV flight position to optimize the initial equipment scheduling and obtain the updated equipment scheduling.

[0164] The beamforming optimization subproblem solving unit is used to introduce a first auxiliary variable and use a semidefinite relaxation algorithm to transform the beamforming optimization subproblem into a beamforming convex problem; convex optimization techniques are used to solve the beamforming convex problem based on the initial UAV flight position and the updated equipment scheduling to optimize the initial beamforming parameters and obtain the updated beamforming parameters.

[0165] The UAV trajectory subproblem solving unit introduces a second auxiliary variable to transform the non-convex terms in the UAV trajectory subproblem into convex terms, resulting in a trajectory convex problem. Convex optimization techniques are used to solve the trajectory convex problem based on the updated equipment scheduling and beamforming parameters to optimize the initial UAV flight position, resulting in the updated UAV flight position.

[0166] The optimal strategy output unit is used to determine whether the preset iteration conditions are met. If not, the updated equipment scheduling, beamforming parameters, and UAV flight position are used as the initial equipment scheduling, beamforming parameters, and UAV flight position, and the process jumps to execute the step of using convex optimization technology to solve the equipment scheduling sub-problem based on the initial beamforming parameters and UAV flight position to optimize the initial equipment scheduling and obtain the updated equipment scheduling. If the conditions are met, the optimal equipment scheduling strategy, beamforming design parameters, and UAV flight trajectory are generated based on the updated equipment scheduling, beamforming parameters, and UAV flight position.

[0167] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, characterized in that the processor executes the computer program to implement the steps of the UAV backscatter communication method as described above.

[0168] This invention also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor, implements the steps of the UAV backscatter communication method described above.

[0169] This invention also provides a computer program product, including a computer program or instructions, characterized in that, when the computer program or instructions are executed by a processor, they implement the steps of the UAV backscatter communication method as described above.

[0170] The terms "first," "second," etc., used in this application's specification and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0171] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0172] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0173] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0174] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0175] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A backscatter communication method for unmanned aerial vehicles (UAVs) based on the integration of sensing and energy, characterized in that, The method includes: A system model integrating sensing, backscatter communication, and energy transfer is constructed; the system model includes a full-duplex UAV equipped with a uniform planar array, multiple IoT devices, and multiple sensing targets; Based on the system model, the channel gain vector between the full-duplex UAV and the IoT device is calculated and the beam pattern gain at the sensing target is determined. The energy harvesting amount and backscatter communication spectrum efficiency of the IoT device are determined according to the channel gain vector. Based on the energy harvesting amount, the backscatter communication spectral efficiency, and the beam pattern gain, a joint optimization problem is established under the conditions of sensing and energy harvesting constraints, with the objective of maximizing the average total throughput of the backscatter backhaul link of the system model. The joint optimization problem is decomposed into multiple sub-problems using an alternating optimization algorithm, and the sub-problems are solved iteratively using convex optimization techniques to output the optimal equipment scheduling strategy, beamforming design parameters, and UAV flight trajectory.

2. The UAV backscatter communication method according to claim 1, characterized in that, The steps of calculating the channel gain vector between the full-duplex UAV and the IoT device based on the system model and determining the beam pattern gain at the sensing target, and determining the energy harvesting amount and backscatter communication spectral efficiency of the IoT device based on the channel gain vector, include: Based on the line-of-sight wireless transmission model, the channel gain vector between the full-duplex UAV and the IoT device is calculated according to the position information between the full-duplex UAV and the IoT device in the system model. Based on the channel gain vector, determine the energy harvesting amount and signal-to-noise ratio of the corresponding IoT device; determine the backscatter communication spectral efficiency of the corresponding IoT device based on the signal-to-noise ratio; Calculate the beam pattern gain between the full-duplex UAV and the sensing target in the system model to determine the sensing performance at the corresponding sensing target.

3. The UAV backscatter communication method according to claim 1, characterized in that, In the system model, the mission cycle T of the full-duplex UAV is discretized into N time slots, and each time slot is divided into K+1 sub-time slots using a time-division multiple access protocol; wherein, the first sub-time slot Energy harvesting for all IoT devices; in the (k+1)th sub-slot Within this context, only the k-th IoT device performs backscatter communication, while the other IoT devices continue energy harvesting; among them, .

4. The UAV backscatter communication method according to claim 3, characterized in that, The joint optimization problem is expressed as follows: In the formula: For the mission cycle of the drone, The number of time slots divided into the task cycle. The total number of IoT devices. In time slot Internal allocation to the first The sub-time slot length for backscatter communication of an IoT device In time slot The horizontal position coordinates of the internal UAV In time slot Internal drones for the first Transmit beamforming vector of an IoT device In time slot The covariance matrix of the dedicated sensing signals emitted by the UAV. In time slot The internal drone received from the first Backscatter communication spectrum efficiency of an IoT device; In time slot Inner Energy harvesting capacity of an IoT device For each IoT device in the cycle Internal energy loss ; In time slot The drone transmits signals to the target. Beam pattern gain at the location, In time slot The interior is located in a horizontal position. The drone and the horizontal position The distance between perceived targets. The beam pattern gain threshold. , ; This is the maximum transmit power of the drone; This is the fixed starting horizontal position for the drone. This is the fixed end horizontal position of the drone. The maximum flight speed of the drone. The length of each time slot, .

5. The UAV backscatter communication method according to claim 4, characterized in that, The steps of employing an alternating optimization algorithm to decompose the joint optimization problem into multiple sub-problems, and iteratively solving the sub-problems using convex optimization techniques to output the optimal equipment scheduling strategy, beamforming design parameters, and UAV flight trajectory include: The joint optimization problem is decomposed into an equipment scheduling subproblem, a beamforming optimization subproblem, and a UAV trajectory subproblem based on the alternating optimization algorithm, and the optimization variables of the joint optimization problem are initialized. The optimization variables include the initial equipment scheduling, beamforming parameters, and UAV flight position. The equipment scheduling subproblem is solved using convex optimization techniques based on the initial beamforming parameters and the UAV flight position to optimize the initial equipment scheduling and obtain the updated equipment scheduling. Introducing a first auxiliary variable, the beamforming optimization subproblem is transformed into a beamforming convex problem using a semidefinite relaxation algorithm; convex optimization techniques are then employed to solve the beamforming convex problem based on the initial UAV flight position and the updated equipment scheduling to optimize the initial beamforming parameters and obtain the updated beamforming parameters. A second auxiliary variable is introduced to transform the non-convex terms in the UAV trajectory subproblem into convex terms, resulting in a convex trajectory problem. Convex optimization techniques are then used to solve the convex trajectory problem based on the updated equipment scheduling and beamforming parameters to optimize the initial UAV flight position, thus obtaining the updated UAV flight position. If the preset iteration conditions are not met, the updated equipment scheduling, beamforming parameters, and UAV flight position are used as the initial equipment scheduling, beamforming parameters, and UAV flight position. The process then jumps to the step of using convex optimization techniques to solve the equipment scheduling subproblem based on the initial beamforming parameters and UAV flight position to optimize the initial equipment scheduling and obtain the updated equipment scheduling. If the conditions are met, the optimal equipment scheduling strategy, beamforming design parameters, and UAV flight trajectory are generated based on the updated equipment scheduling, beamforming parameters, and UAV flight position.

6. The UAV backscatter communication method according to claim 2, characterized in that, The energy harvesting amount is expressed as follows: In the formula: For energy conversion efficiency, In time slot The interior is located in a horizontal position. The drone at the location and located in the horizontal position The first Channel gain vector between IoT devices In time slot Signals transmitted by the drone; unit reference distance Channel power gain under, In time slot The drone pointed to the first The guidance vector of an IoT device For the first Horizontal position of an IoT device In time slot Internal drones and the first The transmit beamforming vector of an IoT device, where H is the flight altitude of the drone; The spectral efficiency of the backscatter communication is expressed as follows: In the formula: In time slot The internal drone received from the first Signal-to-noise ratio of individual IoT devices; The signal-to-noise ratio is expressed as follows: In the formula: For the first Backscattering coefficient of an IoT device For drones and the first Two-way communication between IoT devices , For equipment Data; It is the combined noise that includes receiver noise and residual noise; It is the Frobenius norm; This represents the combined noise power of the receiver noise and the residual. The beam pattern gain is represented as follows: In the formula: In time slot The interior is located in a horizontal position. The drone at the location and located in the horizontal position Perceived target at the location Beam pattern gain; In time slot Inner horizontal position The drone is pointing horizontally. The guidance vector of the perceived target at the location.

7. A UAV backscatter communication device based on the integration of sensing and electrical energy, characterized in that, The device includes: The system model building module is used to build a system model that integrates sensing, backscatter communication, and energy transfer; the system model includes a full-duplex UAV equipped with a uniform planar array, multiple IoT devices, and multiple sensing targets; The system performance calculation module is used to calculate the channel gain vector between the full-duplex UAV and the IoT device based on the system model and determine the beam pattern gain at the sensing target. Based on the channel gain vector, the module determines the energy harvesting amount and backscatter communication spectrum efficiency of the IoT device. The joint optimization problem establishment module is used to establish a joint optimization problem with the objective of maximizing the average total throughput of the backscatter backhaul link of the system model, based on the energy harvesting amount, the backscatter communication spectral efficiency, and the beam pattern gain, under the conditions of sensing and energy harvesting constraints. The strategy optimization module is used to decompose the joint optimization problem into multiple sub-problems using an alternating optimization algorithm, and to iteratively solve the sub-problems using convex optimization techniques, outputting the optimal equipment scheduling strategy, beamforming design parameters, and UAV flight trajectory.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the UAV backscatter communication method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by the processor, they implement the steps of the UAV backscatter communication method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the processor, they implement the steps of the UAV backscatter communication method as described in any one of claims 1-6.

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