Backscattering node differentiated uav location and beam joint optimization method

Abstract

The application discloses a kind of reverse scattering nodes (Tag) differentiation UAV position and beam joint optimization method, comprising: unmanned aerial vehicle receives the data sequence uploaded by each Tag in its current hovering position, and extract cache state information from it;Combining the cache data amount in each Tag cache state information and maximum backscattering communication rate, the priority weight of each Tag is calculated;Based on the priority weight of each Tag, the receiving beam, transmitting beam and hovering position of the unmanned aerial vehicle are jointly optimized to maximize the system weighted channel capacity as the goal;Unmanned aerial vehicle activates each Tag using the optimized transmitting beam, and receives the environmental perception data uploaded by each Tag through backscattering mode.The application can perceive and utilize the differentiated needs of different Tags, improve the rationality of resource allocation and the overall efficiency of the system, effectively reduce the data transmission delay of high-weight Tags, and improve the system information freshness performance.

Description

Technical Field

[0001] This invention relates to the field of wireless communication Internet of Things (IoT) technology, specifically to a method, device, and storage medium for optimizing the location and beam of unmanned aerial vehicles (UAVs) based on backscatter node (Tag) differentiation and weighted capacity maximization, which can realize resource scheduling and channel optimization of UAV-assisted backscatter communication systems. Background Technology

[0002] In 6G Ambient IoT (A-IoT), passive sensors (tags) utilize energy harvesting (EH) technology to collect energy from downlink radio frequency signals, using it as their power source. They then modulate the information they need to transmit onto this radio frequency signal, which is then sent to the uplink channel via a backscatterer. To ensure good transmission conditions for tags in different locations, unmanned aerial vehicles (UAVs) can be used as mobile relays between tags and base stations (BSs), providing energy to the tags and collecting and forwarding environmental perception information uploaded by multiple tags.

[0003] To achieve efficient and reliable data collection, existing research mainly focuses on optimizing the trajectory and beamforming of UAVs. Traditional methods often employ polling or fixed time slot scheduling strategies, with the core optimization objective typically being to maximize system throughput or minimize data collection completion time.

[0004] In recent years, the introduction of optimization theory (such as convex optimization and block coordinate descent) and machine learning methods has been used to jointly design the flight trajectory, transmit beam and receive beam of UAVs, thereby improving system performance. However, existing methods still face significant challenges when facing real-world IoT scenarios with differentiated data urgency.

[0005] For example, in UAV-assisted backscatter communication data collection, existing methods mainly suffer from the following technical bottlenecks: 1) Ignoring the inherent differences in data: Existing research usually assumes that all tagged data are equally important and that the scheduling goal is to optimize global capacity or efficiency. However, in practical applications (such as disaster monitoring and industrial sensing), the data generated by different sensors have fundamental differences in indicators such as buffer length and maximum backscatter communication rate. A unified scheduling strategy cannot guarantee the priority and reliable transmission of high-value and high-urgency data, and it is difficult to meet the differentiated quality of service (QoS) requirements. 2) Rigid scheduling mechanism, resource allocation is disconnected from data characteristics: Most existing solutions adopt a single activation and transmission mode and fail to dynamically adjust the resource allocation strategy according to the urgency of the tag data. This results in low-value data occupying a large amount of communication resources when channel conditions or energy are limited, while the transmission of critical data is delayed or interrupted, and the system utility is not maximized. 3) Multi-dimensional joint optimization is highly complex and lacks effective target guidance: The trajectory of the UAV, multibeamforming and scheduling are tightly coupled, forming a high-dimensional, non-convex optimization problem. Although frameworks such as Block Coordinate Descent (BCD) can be used to solve it, traditional optimization models with "total capacity" or "worst capacity" as the objective lack a fine characterization of the weights of differentiated data, resulting in limited improvement in the overall benefits of optimization results in practical applications (such as emergency response efficiency). 4) In addition, current standards or proposals (such as 3GPP-related discussions) do not explicitly require backscatter tags to report real-time buffer status (BSR) information in the uplink sequence. Therefore, the system cannot directly and routinely obtain the immediate transmission requirements of each tag, which limits the implementation and effectiveness of differentiated scheduling strategies.

[0006] To address this, this application proposes a joint optimization method for UAV position and beam differentiation based on backscatter nodes. This method can sense data priority and use it as a guide for intelligent resource scheduling and joint optimization. This new method aims to maximize the weighted capacity for high-value data in complex UAV backscatter communication systems, thereby improving the overall efficiency and practicality of the system in differentiated service scenarios and solving the aforementioned technical problems. Summary of the Invention

[0007] The main objective of this invention is to provide a method for joint optimization of UAV location and beam with differentiated backscattering nodes, in order to solve the technical problems mentioned in the background art, such as low resource utilization efficiency, high latency of some tags, and insufficient timeliness of system information caused by ignoring tag differences when allocating resources and optimizing channels in existing UAV-assisted backscattering communication systems.

[0008] The present invention solves the above-mentioned technical problems by adopting the following technical solutions: A method for joint optimization of UAV position and beam with backscattering node differentiation, executed by computer equipment, includes the following steps: Step S1. The drone receives the data sequence uploaded by each tag at its current hovering position and extracts the cache state information of each tag from the data; Step S2. Combine the cached data volume and maximum backscatter communication rate in the known cached status information of each tag to calculate the priority weight of each tag. At this time, by fusing and normalizing the cached data volume and maximum backscatter communication rate, the optimized weight of each tag is obtained, which is used to reflect the transmission priority of the tag. Step S3. Based on the priority weight of each tag, with the goal of maximizing the system weighted channel capacity, jointly optimize the UAV's receiving beam, transmitting beam, and hovering position. The optimization variables include: the UAV's receiving beam facing each tag, the UAV's unified transmitting beam, and the UAV's hovering position. Step S4. Tag Data Upload and Dynamic Optimization: After beam and position optimization, the UAV uses the optimized transmit beam to send energy or activation signals to each tag, activating each tag and receiving environmental perception data uploaded by each tag via backscatter. The environmental perception data contains the tag's cached state information, which is used to re-execute steps S1 to S3 in the next optimization cycle to achieve dynamic optimization of system resources. Therefore, during the upload process, the tag carries cached state information (Tag-BSR) in the data sequence. The UAV receives and separates the data of each tag and forwards it to the base station. In step S3, to reduce the complexity of the problem solution, the optimization problem is decoupled into three sub-problems. During the joint optimization process, the Block Coordinate Descent (BCD) method is used to iteratively solve the receive beam optimization, transmit beam optimization, and hovering position, and optimize the corresponding sub-problems. The optimization problem is defined as follows: ; in, For the first The priority weight of each tag For the first Backscatter communication rate of each tag, The position of the UAV in space. For the transmit beam vector of the UAV (Unmanned Aerial Vehicle), For UAVs to the first The receiving beam vector used by each tag The total number of sets of Tags. This represents the flight range constraints for unmanned aerial vehicles (UAVs). This is represented as a Tag activation constraint. It is the first downlink received signal-to-noise ratio of each tag The minimum threshold for the downlink received signal-to-noise ratio of the tag, i.e. At that time, the first The tag cannot be activated and data cannot be uploaded. This is represented as a transmit power constraint. This is the maximum transmit power of the UAV (Unmanned Aerial Vehicle). Represented as a unit vector received beam; The optimization problem is decoupled into three subproblems: Subproblem P1: Receive beam optimization Under the condition of fixed UAV transmission beam and hovering position, optimize the UAV's receiving beam facing each tag to improve the UAV's received signal-to-interference-plus-noise ratio for each tag signal; Subproblem P2: Transmit beam optimization By optimizing the UAV's transmit beam under the condition of fixed UAV hovering position and receive beam, the system's weighted channel capacity can be further improved. Sub-problem P3: UAV hovering position optimization Under the condition of fixed UAV transmit and receive beams, the hovering position of the UAV is optimized by finding the UAV position that maximizes the weighted channel capacity of the system through gradient iteration method. By iteratively updating the above three sub-problems, the optimal combination of UAV transmit beam, receive beam, and hovering position can be gradually obtained; Through the above steps, this method can realize the perception and utilization of tag differentiation requirements in UAV-assisted backscatter communication systems, enabling tags with higher transmission requirements or higher communication capabilities to obtain better channel conditions, thereby improving system resource utilization efficiency, reducing tag data transmission latency, and effectively improving system information freshness performance.

[0009] Preferably, the calculation process for obtaining the maximum backscatter communication rate in step S2 includes: Based on the prior known position of each tag, and according to the free space propagation model, the relationship between the UAV and the first tag is obtained. Channel vectors of each tag The calculation formula is as follows: ; in, and These are the components of line-of-sight Loss (LoS) and non-line-of-sight NLoS, respectively. The Rice factor is used to quantify the power ratio of the line-of-sight (LoS) and non-line-of-sight (NLoS) components in a wireless channel. The UAV sends an activation signal to the Tag via a beam. The Tag uses backscatter modulation to modulate its own data onto the incident signal and reflects it back to the UAV. At this point, there is a UAV-to-Tag connection. Backscattered signal received by each tag The expression is

[0010] in, It is the initial radio frequency excitation signal emitted by the drone. It is the first The backscattering coefficient of each tag, It is the first The data carried by each tag It is Gaussian noise; Based on Shannon's formula, the calculation of the first... The backscatter communication rate of each tag is: ; in, For the first Uplink signal-to-interference-plus-noise ratio of each tag Represents additive white Gaussian noise The standard deviation of , its square This is the variance of the noise.

[0011] Preferably, the priority weight calculation process for the Tag includes: For the Each tag, based on its cached data volume and maximum backscatter communication rate Calculate the first The weight value of each tag ,have: ; Then, the weight values ​​of all tags are normalized to obtain the first... The final priority weight of each tag is: ; in, It is a hyperparameter that controls the weight difference.

[0012] Preferably, the optimization calculation process for the receiving beam sub-problem in step S3 is as follows: Sending beams from a fixed drone and hovering position Under the condition of optimization for the first The receiving beam of each tag To maximize the signal-to-interference-plus-noise ratio of the tag. ; The beam reception subproblem can now be expressed in the generalized Rayleigh quotient form, with an existing subproblem expressed as: ; Its optimal solution for: ; in, For drones and the first Channel vectors between tags It is the first The backscattering coefficient of each tag, Characterizing the noise covariance matrix at the receiver, This represents the identity matrix, whose dimensions correspond to the number of receiving antennas.

[0013] Preferably, the optimization calculation process for the transmitted beam sub-problem in step S3 is as follows: Fixed drone hovering position and receiving beam Under the condition of optimizing the transmission beam To maximize the system's weighted capacity ; At this point, auxiliary variables are introduced. The continuous convex approximation (SCA) and semidefinite relaxation (SDR) methods are applied to transform the original non-convex problem into a convex optimization problem for solution. Then, Gaussian randomization is used to extract the optimal solution. Recover the transmitted beam vector The existence of subproblems can be expressed as: ; in, Indicates the first The lower bound of the uplink communication rate of a tag In iterative optimization algorithms, auxiliary variables are... Taylor's expansion point:

[0014]

[0015] ; in, Indicates transmit power constraint. Represents the trace of a matrix. This indicates that the Tag activation constraint is in place. For the first The downlink average received noise power of each tag.

[0016] Preferably, the optimization calculation process for the hovering position subproblem in step S3 is as follows: Sending beams from a fixed drone and receiving beam Optimize the hovering position of the drone under the given conditions. To maximize the system's weighted capacity ; This subproblem is solved using gradient descent, where the objective function is relative to the position. gradient Obtained by differentiation using the chain rule.

[0017] Preferably, the iterative process based on the block coordinate descent method in step S3 specifically includes: Step S31. Initialize the drone's hovering position Transmit beam and receiving beam ; Step S32. Fix and Update the received beam based on the optimization calculation of the received beam subproblem. ; Step S33. Fix and The transmitted beam is updated based on the optimization calculation of the transmitted beam subproblem. ; Step S34. Fix and Update the hover position based on the optimized calculation of the hover position subproblem. ; Step S35: Determine whether the weighted capacity has converged based on the preset convergence condition. If the convergence condition is not met, return to step S32 to continue the iteration.

[0018] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0019] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0020] As can be seen from the above technical solution, the present invention provides a method for joint optimization of UAV position and beam with differentiated backscattering nodes. Compared with the prior art, the present invention has the following advantages: 1. This invention introduces differentiated weights based on the amount of data cached by the tag and the maximum backscatter communication rate during the optimization process. This allows the system to fully consider the differentiated characteristics between tags, enabling the system to allocate resources according to the actual transmission requirements and communication capabilities of each tag. This solves the resource waste problem caused by the uniform optimization strategy for tags in traditional methods, and ultimately improves the overall communication resource utilization efficiency of the system.

[0021] 2. By utilizing tag cache state information to construct optimized weights, this invention enables tags with larger cache data volumes to obtain better channel conditions and transmission opportunities, thereby reducing their data queuing latency. This allows for priority in ensuring the data transmission of high-demand tags, guaranteeing that data from high-demand tags can be uploaded in a timely manner, and thus improving the overall fairness of the system's service.

[0022] 3. By incorporating Tag caching demand information into the joint optimization process of UAV location and beam, this invention enables the system to prioritize the processing of tags with high information update requirements, thereby effectively reducing system information transmission latency and improving the system's Age of Information (AoI) performance.

[0023] 4. This invention optimizes the transmit beam, receive beam, and hovering position of the UAV by jointly optimizing them and using the block coordinate descent (BCD) method for iterative solution. This enables the system to obtain better channel conditions, thereby further improving the signal-to-interference-plus-noise ratio and overall communication capacity of the backscatter communication link based on joint optimization, and ultimately improving the system's communication performance.

[0024] 5. The present invention can be implemented based on the cached state information carried in the Tag data sequence during the optimization process, without relying on complex additional signaling interactions. At the same time, the decomposition solution strategy reduces the computational complexity of the optimization problem. Therefore, it has good engineering feasibility and scalability, and can be applied to backscatter IoT scenarios of different scales.

[0025] It should be understood that the descriptions in this section are not intended to identify key or essential features of embodiments of the invention, nor are they intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Of course, implementing any product of the invention does not necessarily require achieving all of the advantages described above simultaneously. Attached Figure Description

[0026] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram of the data processing flow architecture of the method of the present invention; Figure 2 This is a schematic diagram illustrating a usage scenario of the method of the present invention; Figure 3 This is an explanatory diagram of the Tag environmental information in this invention. Detailed Implementation

[0027] 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 a part of the embodiments of the present invention, and not all of them. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. 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.

[0028] For details in the embodiments, please refer to Figures 1 to 3 .

[0029] In ambient IoT scenarios, different tags typically exhibit significant differences in buffered data volume and maximum backscatter communication rate. Existing methods, when optimizing UAV location and beam, usually prioritize maximizing the overall system channel capacity, neglecting the actual transmission requirements and hardware capability differences of each tag. This may lead to the following problems: (1) Tags with large cached data cannot get priority transmission opportunities, resulting in increased data transmission latency; (2) Tags with low maximum backscatter communication rates are allocated excessive channel resources, resulting in a waste of system resources; (3) The overall system’s AoI level is difficult to control effectively.

[0030] To solve the above problems, such as Figure 1 As shown, this invention proposes a joint optimization method for UAV position and beam based on the differentiated weights of backscatter nodes. By comprehensively considering the buffer data volume of each tag and the maximum backscatter communication rate information, priority weights are generated for each tag. With the goal of maximizing the weighted channel capacity of each tag, the transmitting beam, receiving beam, and hovering position of the UAV are jointly optimized, thereby achieving reasonable allocation and efficient utilization of channel resources.

[0031] Specifically, the joint optimization method for UAV position and beam based on differential weights of backscattering nodes includes the following steps: Step 1: System Model Establishment First, a model of a UAV-assisted backscatter communication system is established.

[0032] like Figure 2 As shown, deployment within the target monitoring area A tag (Tag), carried by the UAV. An antenna is used to hover in the air and collect data from the tags. The position of each tag is known prior to the previous one. The position of each tag is The location of the UAV in space is According to the free-space propagation model, we can obtain the relationship between UAV and the first... Channel vector between tags : ; in: and These are the line-of-sight (LoS) and non-line-of-sight (NLoS) components, respectively. Rice factor here The power ratio of the Loss to NLoS components in a wireless channel is used to quantify the power of the non-line-of-sight (LoS) component. This ratio is typically large in UAV scenarios, therefore the non-line-of-sight component... It can be ignored; then: ; In the formula: Indicates the distance between the UAV and the Tag. It is the path loss index; It is the channel power gain at a unit reference distance (e.g., 1 meter); It is a tag The unit steering vector of the UAV's angle of arrival. This represents the angle of arrival from the UAV (Unmanned Aerial Vehicle) antenna array towards the k-th tag; In wireless communication, the angle of arrival is usually defined as the angle between the incident signal wavefront and the normal direction of the receiving antenna array.

[0033] The UAV then sends an activation signal to the Tag via its transmission beam. The Tag uses backscatter modulation to modulate its own data onto the incident signal and reflects it back to the UAV. Let the UAV's transmission beam vector be: UAV for the first The receiving beam vector used for each tag (denoted as Tag-k) is .

[0034] Under the above conditions, the expression for the backscattered signal received by the UAV can be obtained as follows: ; The UAV receives the following signal for Tag-k: ; in: It is the initial radio frequency excitation signal emitted by the drone. It is the backscattering coefficient of Tag-k; It is the data carried by Tag-k; It is Gaussian noise. Representing a dimension as The zero vector, It refers to the number of antennas. Characterizing the noise covariance matrix at the receiver, This represents the identity matrix, whose dimensions correspond to the number of receiving antennas.

[0035] Furthermore, the uplink signal-to-interference-plus-noise ratio (SINR) of Tag-k can be derived. for: ; for The transpose of the matrix, for The conjugate transpose of the matrix. Represents additive white Gaussian noise The standard deviation of , its square This is the variance of the noise. According to Shannon's formula, the backscatter communication rate of Tag-k can be obtained as: .

[0036] Step 2: Tag Differentiation Weight Calculation In A-IoT scenarios, different tags have different cached data volumes and maximum backscatter communication rates. To achieve differentiated resource allocation, a tag weighting mechanism can be introduced. Therefore, the specific calculation process includes: Step L21: Obtaining Tag Cache Information During system operation, each tag carries its own cached state information (Tag-BSR) when uploading data, where the cached data size of Tag-k is set to... .

[0037] Step L22: Weight Calculation The system has prior knowledge of the maximum backscatter communication rate of each tag. The tag weight function is constructed based on the tag cache data volume and the maximum communication rate information as follows: ; After normalization, the result is: ; The priority weight of each tag in the system optimization process can be obtained through the above steps, where: It is a hyperparameter that controls the weight difference.

[0038] In this embodiment, by introducing differentiated weights based on the amount of cached data in the tag and the maximum backscatter communication rate during the optimization process, the differentiated characteristics between tags can be fully considered. This allows the system to allocate resources according to the actual transmission needs and communication capabilities of each tag, thus avoiding the resource waste caused by the uniform optimization strategy for tags in traditional methods. Ultimately, this improves the overall communication resource utilization efficiency of the system. In addition, by using tag cache state information to construct optimization weights, tags with larger cached data can obtain better channel conditions and transmission opportunities, thereby reducing their data queuing latency. This prioritizes the data transmission of high-demand tags, ensuring that data from high-demand tags can be uploaded in a timely manner, thereby improving the fairness of the overall system service.

[0039] Step 3: Problem Formulation and Solution of Joint Optimization After obtaining the weights of each tag, the transmit beam, receive beam, and hovering position of the UAV are jointly optimized with the goal of maximizing the weighted channel capacity of the system.

[0040] In the specific implementation process, the original optimization problem is described as follows: ; in: It is the downlink receive signal-to-noise ratio (SNR) of Tag-k. Its lowest threshold, i.e. At that time, Tag-k cannot be activated and data cannot be uploaded; This is the maximum transmit power of the UAV; This represents the area where the UAV is hovering. It represents the set of all tags.

[0041] Because the UAV position, transmitting beam, and receiving beam variables are coupled with each other in this optimization problem, it is a non-convex optimization problem and is difficult to solve directly. Therefore, to further reduce the complexity of solving the problem, the block coordinate descent (BCD) method can be used in this embodiment to decompose and solve the original optimization problem.

[0042] Specifically, the original optimization problem is divided into three sub-problems.

[0043] Sub-problem 1: Receive beam optimization Optimize the receiving beam under the conditions of fixed UAV transmit beam and UAV hovering position: ; make , ,rewrite ,have: ; Here It is a generalized Rayleigh quotient form, receiving beam. The optimal solution can be written directly: .

[0044] Sub-problem 2: Transmit beam optimization Optimize the UAV transmit beam under the condition of fixed UAV hovering position and receive beam: First Expanded to: ; make , It is the Taylor expansion point of the objective function in P2.1. During BCD alternating optimization, the optimal value obtained in the previous optimization is used as... ; Applying the continuous convex approximation SCA, and replacing the denominator with its first-order Taylor upper bound, we obtain:

[0045]

[0046] ; The original problem This can be reduced to a subproblem solvable by CVX: ; in: It is the downlink average received noise power of Tag-k; Finally, in use Iteratively find Optimal solution Then, Gaussian randomization was used to obtain... optimal value First, Singular value decomposition Then, a series of Gaussian random vectors are generated: ,make Take the middle order The largest solution is denoted as .

[0047] Sub-problem 3: UAV positioning optimization Optimizing the hovering position of a UAV under fixed transmit and receive beams can be achieved using a gradient iteration method. ; make , ,but ; for For UAV position Find the partial derivative: ; Furthermore, it is deduced that and It was observed that both of these are composed of shapes like The structure consists of [the following] components, so we first derive [the structure]. :

[0048]

[0049]

[0050]

[0051] ; in, Represent any complex vector, It is the imaginary unit. Wavenumber, relative to signal wavelength The relationship is , This indicates the spacing between adjacent elements in an antenna array. This represents the angle of arrival (AoA) from the UAV antenna array towards the k-th tag. It is a tag The unit steering vector of the UAV's angle of arrival. It refers to the number of antennas. This indicates the drone's flight altitude.

[0052] Thus, through multiple layers of chain rules, the complete... and It can all be written out now.

[0053] Step 4: Iterative optimization process Further reference Figure 1 The above three sub-problems are solved by alternating iterations, and the specific process is as follows: Step L51: Initialize the UAV hovering position, transmit beam, and receive beam; Step L52: Solve for the receiving beam under the conditions of fixed UAV position and transmitting beam; Step L53: Solve for the transmit beam under the conditions of fixed UAV position and receive beam; Step L54: Update the UAV position under the condition of fixed transmit and receive beams; Step L55: Determine whether the algorithm meets the convergence condition. If it does, output the optimization result; otherwise, return to step L52 to continue the iteration.

[0054] Furthermore, by incorporating Tag cache requirement information into the joint optimization process of UAV location and beam, the above operation enables the system to prioritize Tag with high information update requirements, thereby effectively reducing system information transmission latency and improving the system's Age of Information (AoI) performance. Moreover, the optimization process can be achieved based on the cache state information carried in the Tag data sequence without relying on complex additional signaling interactions. At the same time, the decomposition solution strategy reduces the computational complexity of the optimization problem. Therefore, this operation method also has good engineering feasibility and scalability, and can be applied to backscatter IoT scenarios of different scales.

[0055] In summary, by jointly optimizing the UAV's transmit beam, receive beam, and hovering position, and using the block coordinate descent (BCD) method for iterative solution, the system can obtain better channel conditions. This allows for further improvement of the signal-to-interference-plus-noise ratio and overall communication capacity of the backscatter communication link based on joint optimization, ultimately enhancing the system's communication performance.

[0056] Step 6: System Operation and Dynamic Optimization like Figure 2 As shown, after obtaining the optimized UAV position and beam parameters, the UAV sends an activation signal to each tag, and each tag uploads environmental data through backscattering.

[0057] like Figure 3 As shown, each tag carries cache status information when uploading data. The UAV recalculates the tag weight based on the latest cache information and re-executes the above joint optimization process in the next optimization cycle, thereby achieving dynamic optimization of system resources.

[0058] To illustrate the implementation process and technical effects of the present invention in more detail, this embodiment provides a set of proposed experimental parameters and demonstrates the calculation process and results under the above-mentioned joint optimization framework.

[0059] Specifically, in this embodiment, the joint optimization process and effect analysis based on experimental data are as follows: (I) Experimental Scenario and Parameter Settings In a drone-enabled backscatter communication system, there exists A ground tag (Tag). The drone is at a fixed altitude. Hovering provides the service. Key simulation parameters of the system are shown in Table 1 below.

[0060] Table 1: Comparison Table of Experimental Data for System Simulation Parameters

[0061] (II) Optimize the calculation process and intermediate results Based on the above parameters, the joint optimization algorithm proposed in this invention was executed, and the final experimental results are shown in Table 2 below.

[0062] Table 2: Comparison of Intermediate Results Data in the Calculation Process

[0063] By comparing the data in Table 2, the technical principles and effects of this method can be clearly understood: the execution scheme disclosed by this method achieves the highest weighted capacity because joint optimization enables the UAV's position... and transmit beamforming Mutual matching and synergistic effect, the optimized position at this time Placing the tag closer to the higher-weighted tag improves the median gain of the channel.

[0064] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0065] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0066] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the backscattering node-differentiated UAV position and beam joint optimization methods in the above embodiments.

[0067] It is understood that the system provided in the embodiments of the present invention corresponds to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0068] This application also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus. Memory, used to store computer programs; When the processor executes the program stored in the memory, it implements the above-mentioned method for joint optimization of UAV position and beam with differentiated backscattering nodes.

[0069] The communication bus mentioned in the aforementioned electronic device can be a bus for UAV position and beam co-optimization method with differentiated backscattering nodes based on peripheral component interconnection standards, or a bus for UAV position and beam co-optimization method with differentiated backscattering nodes based on extended industry standard structures, etc. This communication bus can be divided into address bus, data bus, control bus, etc.

[0070] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0071] The memory may include a random access memory-based UAV position and beam joint optimization method with differentiated backscattering nodes, or a non-volatile memory-based UAV position and beam joint optimization method with differentiated backscattering nodes, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0072] The aforementioned processor can be a general-purpose processor, including a central processing unit (CPU) method for co-optimizing the UAV position and beam based on the backscattering node, a network processor method for co-optimizing the UAV position and beam based on the backscattering node, etc.; it can also be a digital signal processor method for co-optimizing the UAV position and beam based on the backscattering node, an application-specific integrated circuit method for co-optimizing the UAV position and beam based on the backscattering node, a field-programmable gate array method for co-optimizing the UAV position and beam based on the backscattering node, or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0073] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, an optical medium, or a semiconductor medium, etc.

[0074] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0075] Furthermore, it should be noted that if any directional indication (such as up, down, left, right, front, back, etc.) is involved in the embodiments of the present invention, the directional indication is only used to explain the relative positional relationship and movement of each component in a specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0076] Furthermore, those skilled in the art should understand that in the actual use of the embodiments of this application, there may be preset thresholds used as the basis for judging the corresponding technical solutions. These thresholds are conventional technical means commonly used in the field to implement functions such as state judgment, condition recognition, and control logic switching. The specific values, setting basis, value selection methods, determination methods, and adjustment rules of the thresholds involved in this technical solution are all conventional technical choices that can be reasonably determined by those skilled in the art based on conventional technical factors such as actual application scenarios, system working states, characteristics of the detection object, hardware performance parameters, and functional requirements, through conventional experiments, calibrations, and debugging. The specific setting and adjustment of the aforementioned thresholds will not cause this technical solution to be unimplementable as a whole, nor will it affect the realization of the core concept and the achievement of the technical effects of this technical solution.

[0077] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, in the embodiments of this invention, "multiple" refers to two or more. Moreover, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

Claims

1. A method for joint optimization of UAV position and beam with differentiated backscattering nodes, characterized in that, include: Step S1. The drone receives the data sequences uploaded by each tag at its current hovering position and extracts the cached state information from them; Step S2. Calculate the priority weight of each tag by combining the cached data volume in the cached status information of each tag and the maximum backscatter communication rate; Step S3. Based on the priority weight of each tag, with the goal of maximizing the system weighted channel capacity, jointly optimize the UAV's receiving beam, transmitting beam, and hovering position; Step S4. The UAV activates each tag using the optimized transmit beam and receives environmental perception data uploaded by each tag via backscatter. In step S3, the joint optimization uses a block coordinate descent method to iteratively solve the receive beam optimization, transmit beam optimization, and hovering position, and optimize the corresponding sub-problems. The optimization problem is defined as follows: ； in, For the first The priority weight of each tag For the first Backscatter communication rate of each tag, The position of the drone in space, This is the transmit beam vector of the UAV. For drones to the first The receiving beam vector used by each tag The total number of sets of Tags. This represents the flight range constraints for the drone. This is represented as a Tag activation constraint. It is the first downlink received signal-to-noise ratio of each tag This represents the minimum threshold for the downlink received signal-to-noise ratio of the Tag. This is represented as a transmit power constraint. This is the maximum transmission power of the drone. This is represented as a unit vector receiving beam.

2. The method for joint optimization of UAV position and beam with backscattering node differentiation as described in claim 1, characterized in that, The calculation process for obtaining the maximum backscatter communication rate in step S2 includes: Based on the prior known position of each tag, and according to the free space propagation model, the relationship between the UAV and the first tag is obtained. Channel vectors of each tag The calculation formula is as follows: ； in, and These are the components of line-of-sight and non-line-of-sight, respectively. The Rice factor is used to quantify the power ratio of line-of-sight to non-line-of-sight components in a wireless channel. The drone sends an activation signal to the tag by transmitting a beam. The tag uses backscatter modulation to modulate its own data onto the incident signal and reflects it back to the drone. At this point, there is a drone-to-tag interaction. Backscattered signal received by each tag The existence expression is: ； in, It is the initial radio frequency excitation signal emitted by the drone. It is the first The backscattering coefficient of each tag, It is the first The data carried by each tag It is Gaussian noise; Based on Shannon's formula, the calculation of the first... The backscatter communication rate of each tag is: ； in, For the first Uplink signal-to-interference-plus-noise ratio of each tag Represents additive white Gaussian noise The standard deviation of , its square This is the variance of the noise.

3. The method for joint optimization of UAV position and beam with backscattering node differentiation as described in claim 1, characterized in that, The priority weight calculation process for the Tag includes: For the Each tag, based on its cached data volume and maximum backscatter communication rate Calculate the first The weight value of each tag ,have: ; Then, the weight values ​​of all tags are normalized to obtain the first... The final priority weight of each tag is: ； in, It is a hyperparameter that controls the weight difference.

4. The method for joint optimization of UAV position and beam with backscattering node differentiation as described in claim 1, characterized in that, The optimization calculation process for the receiving beam sub-problem in step S3 is as follows: Sending beams from a fixed drone and hovering position Under the condition of optimization for the first The receiving beam of each tag To maximize the signal-to-interference-plus-noise ratio of the tag. ; The beam reception subproblem can now be expressed in the generalized Rayleigh quotient form, with an existing subproblem expressed as: ； Its optimal solution for: ； in, For drones and the first Channel vectors between tags It is the first The backscattering coefficient of each tag, Characterizing the noise covariance matrix at the receiver, This represents the identity matrix, whose dimensions correspond to the number of receiving antennas.

5. The method for joint optimization of UAV position and beam with backscattering node differentiation as described in claim 1, characterized in that, The optimization calculation process for the beamform problem in step S3 is as follows: Fixed drone hovering position and receiving beam Under the condition of optimizing the transmission beam To maximize the system's weighted capacity ; At this point, auxiliary variables are introduced. The original non-convex problem is transformed into a convex optimization problem by applying the continuous convex approximation and semi-definite relaxation method. Then, the optimal solution is obtained using the Gaussian randomization method. Recover the transmitted beam vector The existence of subproblems can be expressed as: ； in, Indicates the first The lower bound of the uplink communication rate of a tag Indicates transmit power constraint. Represents the trace of a matrix. This indicates that the Tag activation constraint is in place. For the first The downlink average received noise power of each tag.

6. The method for joint optimization of UAV position and beam with backscattering node differentiation as described in claim 1, characterized in that, The optimization calculation process for the hovering position subproblem in step S3 is as follows: Sending beams from a fixed drone and receiving beam Optimize the hovering position of the drone under the given conditions. To maximize the system's weighted capacity ; This subproblem is solved using gradient descent, where the objective function is relative to the position. gradient Obtained by differentiation using the chain rule.

7. The method for joint optimization of UAV position and beam with backscattering node differentiation as described in claim 1, characterized in that, The iterative process based on the block coordinate descent method in step S3 specifically includes: Step S31. Initialize the drone's hovering position Transmit beam and receiving beam ; Step S32. Fix and Update the received beam based on the optimization calculation of the received beam subproblem. ; Step S33. Fix and The transmitted beam is updated based on the optimization calculation of the transmitted beam subproblem. ; Step S34. Fix and Update the hover position based on the optimized calculation of the hover position subproblem. ; Step S35: Determine whether the weighted capacity has converged based on the preset convergence condition. If the convergence condition is not met, return to step S32 to continue the iteration.

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