Intelligent reconfigurable metasurface assisted positioning method and device

Abstract

The invention discloses an intelligent reconfigurable metasurface assisted positioning method which is used for a wireless communication system, the system comprises one or more RISs, each RIS comprises a plurality of reflection units, and one or more communication terminals located at a target position are included in an RIS radiation area, and the method comprises the following steps: determining the target position according to a CSI signal; determining a target position error; and the phase modulation vector of the RIS reflection unit is adjusted, so that the target position error is minimum. The invention further comprises a device for implementing the method. According to the invention, the problem of low optimization efficiency of RIS phase modulation is solved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method and device for intelligent reconfigurable metasurface assisted positioning. Background Art

[0002] Reflecting intelligent surface (RIS) is considered an important technology for the development of 6G and has received widespread attention from researchers. RIS improves the spatial channel by changing the phase and reflectivity of each reflective unit. The improvement of the spatial channel reduces the loss caused by the communication and positioning process, and improves the communication speed and positioning accuracy. By dynamically adjusting the reflection characteristics of a large number of passive reflective units, RIS can optimize beamforming, reduce multipath interference, adapt to highly mobile vehicles, improve energy efficiency, enhance safety, and effectively manage high-density connections. This technology is expected to provide more efficient, reliable and secure communication connections in urban environments and high-density connection scenarios, promote the development of V2X and the Internet of Things, and make it more suitable for future communication needs.

[0003] In the future 6G network, the importance of positioning technology is becoming more and more prominent, especially in the development of future intelligent agents, collaborative positioning becomes crucial. At present, various positioning technologies such as arrival time, arrival direction, and received signal strength are available. CSI positioning stands out for its large bandwidth and provides a large amount of positioning-related information. Therefore, CSI positioning is a key technology for future positioning methods. In the case of GPS signal blocking, the position of each sensor cannot be obtained through satellites. Therefore, anchor points need to be deployed to locate the target. However, deploying a large number of nodes as anchor points requires a lot of hardware and human resources, which is not conducive to the advancement of environmental protection.

[0004] Three key challenges need to be addressed within the intelligent reconfigurable metasurface collaborative positioning system based on channel information. First, using RIS as an anchor introduces high complexity in system analysis. Since each reflection unit in RIS is able to reflect the signal, the received signal size of the target is high. Second, the phase modulation of RIS presents a non-deterministic polynomial time problem. In practical applications, RIS phase modulation is discrete, and this modulation affects the accuracy of the collaborative positioning system. It is worth noting that the optimization of RIS phase modulation constitutes a non-convex integer programming problem. Finally, faced with the complex RIS phase modulation problem, many algorithms such as alternating optimization algorithms and genetic algorithms require step-by-step iterations and therefore cannot be parallelized. It takes a lot of time for iterative optimization. Summary of the invention

[0005] The present application proposes an intelligent reconfigurable metasurface assisted positioning method and device to solve the problem of low optimization efficiency of RIS phase modulation.

[0006] The embodiment of the present application proposes an intelligent reconfigurable metasurface assisted positioning method for a wireless communication system, wherein the system includes one or more RISs, each RIS includes multiple reflection units, and includes one or more communication terminals located at a target position within the RIS radiation area. The method includes the following steps: Determine the target location based on the CSI signal; Determine target position error; Adjust the phase modulation vector of the RIS reflector unit to minimize the target position error.

[0007] In one embodiment, the method for determining the target position error is to obtain the partial derivative of the coordinates of each target node with respect to the logarithm of the effective channel information to obtain a Fisher information matrix, use the Fisher information matrix to represent the position uncertainty of multiple targets, and then use the Fisher information matrix to calculate the sum of the variances of the position errors of the multiple targets as the target position error.

[0008] In one embodiment, the quantity representing the target position error is the square of the target position error, or the sum of the squares of the optimal estimation error lower bound of the unbiased estimation of the target position.

[0009] In one embodiment, the minimum target position error is equivalent to the maximum trace of the Fisher information matrix.

[0010] Preferably, the step of determining the target position error comprises: taking the partial derivative of the coordinates of each target node on the logarithm of the effective channel information to obtain a Fisher information matrix; in one embodiment, the step of adjusting the phase modulation vector of the RIS reflector unit to minimize the target position error comprises: Update the phase modulation, iteratively calculate the cumulative gradient of the trace of the Fisher information matrix of each reflector unit as the RIS phase modulation changes, and update the phase learning rate of each reflector unit in RIS until the phase modulation scheme with the minimum target position error is output.

[0011] In one embodiment, the method of adjusting the phase modulation vector of the RIS reflector unit to minimize the target position error includes at least one of the following: an AO algorithm, a GA algorithm, and an AG-PO algorithm.

[0012] In one embodiment, the following steps are also included: the AG-PO algorithm accumulates variables by the square of the gradient, modifies the learning rate of the reflection unit, and the new learning rate is obtained by dividing the initial learning rate by the accumulated gradient.

[0013] The embodiment of the present application further proposes an intelligent reconfigurable metasurface assisted positioning device, which is used to implement the method described in any embodiment of the present application, including: An acquisition module, used for acquiring CIS from a target location; A determination module, used to determine a parameter representing a target position error; The optimization module is used to change the phase modulation vector of the RIS reflection unit so as to minimize the target position error.

[0014] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: In order to solve the three main challenges brought by the intelligent reconfigurable metasurface collaborative positioning system based on channel information, the present application proposes corresponding methods, devices and wireless communication systems. In the best embodiment of the present application, the Fisher information matrix (FIM) of the intelligent reconfigurable metasurface collaborative positioning system based on channel information is determined. The squared position error bound (SPEB) is used as a scalar measure of positioning accuracy. In order to optimize SPEB, the AG-PO algorithm is introduced. The AG-PO algorithm effectively solves the non-convex integer programming problem related to RIS phase modulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A wireless communication system for the application scenario of this application; Figure 2 This is a flow chart of an embodiment of the method of this application; Figure 3 The improved AG-PO method process of this application; Figure 4 This is a block diagram of an embodiment of the device of the present application; Figure 5 This is a block diagram of another embodiment of the device of the present application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application is clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0017] The present invention proposes an intelligent reconfigurable metasurface collaborative positioning method, device and wireless communication system based on channel state information (CSI), wherein RIS acts as an anchor node in the collaborative positioning.

[0018] In the intelligent reconfigurable metasurface collaborative positioning system based on channel information, the target node is able to locate through signals from RIS and other target nodes. The use of RIS as an anchor node reduces the square of the position error of collaborative positioning. Therefore, the intelligent reconfigurable metasurface collaborative positioning system based on channel information achieves excellent positioning accuracy with lower hardware and maintenance costs. This approach uses RIS as an anchor node in a collaborative positioning system, surpassing the traditional system that uses nodes as anchor points.

[0019] The device of the present application can be widely used in Internet of Things (IoT) and Vehicle to Everything (V2X) systems.

[0020] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0021] Figure 1 A wireless communication system for the application scenario of this application. Collaborative positioning networks utilize communication and positioning between targets to greatly reduce dependence on multiple anchor points and reduce resource consumption. Collaborative positioning improves the accuracy, robustness, and real-time performance of positioning through information sharing between devices. Future intelligent agents will need to obtain precise location information in complex environments, and collaborative positioning plays a vital role in realizing application scenarios such as smart cities, autonomous driving, and smart Internet of Things by integrating multi-source data, improving system robustness, and promoting multi-agent collaboration.

[0022] Phase modulation of the RIS reflection unit reduces the fading of the channel. The frequency domain channel state information received by the target contains the position data. In addition, the RIS contains many individual reflection units. Each reflection unit reflects and modulates the signal independently. Therefore, the RIS acts as an anchor, achieving excellent positioning accuracy with low hardware and maintenance costs.

[0023] For example, constructing an intelligent reconfigurable metasurface collaborative positioning system based on channel information, such as Figure 1 As shown. Vehicles with unknown positions are randomly placed as targets. Four RIS are placed around the square area A. The four RIS have the same number of reflectors. The coordinates of the reference reflectors of the four RIS are (500,0,5), (500,1000,5), (0,500,5) and (1000,500,5). It is assumed that all anchors and targets are arranged in a completely open environment. Therefore, the environmental attenuation coefficient n=2. The spacing of the reflectors in each RIS is , where the transmission frequency of the intelligent reconfigurable metasurface collaborative positioning system for channel information is 2.4GHz, c is the speed of light.

[0024] When positioning, the phase modulation of the RIS reflection unit and the Doppler effect caused by the target motion are fully considered. In a dynamic three-dimensional space, each target exhibits a continuously changing motion state, which poses a challenge to the system. Each RIS reflection unit is considered to be an anchor responsible for reflecting and phase modulating the signal from the target. In an embodiment of the present application, each target is accurately positioned by analyzing the frequency domain CSI of the signals from the RIS and other targets.

[0025] Figure 2 This is a flow chart of an embodiment of the method of this application.

[0026] The embodiment of the present application proposes an intelligent reconfigurable metasurface assisted positioning method for a wireless communication system, wherein the system includes one or more RISs, each RIS includes multiple reflection units, and includes one or more communication terminals located at a target position within the RIS radiation area. The method includes the following steps: Step 110: Determine the target position according to the CSI signal.

[0027] In the embodiments of the present application, the target position is determined by collaborative positioning. Preferably, RIS can be used as an anchor point instead of a large number of nodes. The collaborative positioning network provides a more efficient positioning system. In the collaborative positioning network, each target is located by signals from other targets and anchor points, while helping adjacent targets to be located.

[0028] Step 120: Determine the target position error.

[0029] In one embodiment, the quantity representing the target position error is the square of the target position error, or the sum of the squares of the optimal estimation error lower bound of the unbiased estimation of the target position.

[0030] In one embodiment, the method for determining the target position error is to use a Fisher information matrix to represent the position uncertainty of multiple targets, and then calculate the sum of the variances of the position errors of the multiple targets through the Fisher information matrix as the target position error.

[0031] FIM and SPEB of intelligent reconfigurable metasurface collaborative positioning system to calculate and obtain channel information.

[0032] In the intelligent reconfigurable metasurface collaborative positioning system with channel information, there are RIS, yes The FIM of intelligent reconfigurable metasurface collaborative positioning system based on channel information The dimension is The real matrix of .

[0033] Fisher information refers to the variance of the derivative of the log-likelihood function with respect to the overall distribution parameter. The following describes how to construct Fisher information in this case and the relationship between Fisher information and the performance of RIS.

[0034] In this case, the Fisher information matrix is ​​specifically a method for measuring the amount of information about the location information (i.e., the coordinates of the target node) carried by a sample of effective channel information. In this case, the Fisher information matrix can be obtained by calculating the score function, that is, taking the partial derivative of the coordinates of each target node for the logarithm of the effective channel information. The larger the variance of the score function, the larger the overall absolute value of the Fisher information matrix, the more convex the shape of the log-likelihood function, and the more information about the location information (i.e., the coordinates of the target node) contained in the effective channel information. The smaller the variance of the score function, the smaller the overall absolute value of the Fisher information matrix, the more convex the shape of the log-likelihood function, and the less information about the location information (i.e., the coordinates of the target node) contained in the effective channel information. At the physical level, the more information about the location information (i.e., the coordinates of the target node) contained in the effective channel information, the more obvious and drastic the change in the effective channel information will be as the coordinates of the target node change, so it is easy to extract the precise coordinates of the target node from the effective channel information. Therefore, the Fisher information matrix is ​​an indicator to measure the positioning effect of the system. We can improve the positioning accuracy of the system by changing the parameters of RIS, which is manifested by increasing the overall absolute value of the Fisher information matrix.

[0035]

[0036] in For FIM The dimensions of x rows and y columns are The real submatrix of , which is represented as the Fisher information submatrix of the system estimating the coordinates of the x-th target and the y-th target using the effective channel information. The larger the absolute value, the more convex the shape of the log-likelihood function, and the more information about the x-th and y-th target coordinates contained in the effective channel information. On the physical level, the more information about the x-th and y-th target coordinates contained in the effective channel information, the more obvious and dramatic the change in the effective channel information is as the x-th and y-th target coordinates change. Therefore, it is easier to extract the precise coordinates of the x-th and y-th targets from the effective channel information. The larger the FIM, the smaller the uncertainty in parameter estimation. Vector is the coordinate vector of all target nodes, k The coordinates of the target are . is the effective channel information vector, where k The effective channel information of each target For the The channel frequency domain amplitude of each target, effective channel information is the observation quantity of the system. In this case, the Fisher information matrix can be obtained by calculating the score function, that is, taking the partial derivative of the coordinates of each target node with respect to the logarithm of the effective channel information.

[0037] The trace of the inverse matrix of FIM is SPEB. By the definition of the Cramer-Rao lower bound, the difference between the covariance matrix of the unbiased estimate of the target position in the system of the present application and the inverse matrix of FIM is a positive definite matrix. Therefore, the inverse matrix of FIM can be approximated as the lower limit of the covariance matrix of the unbiased estimate of the target position. SPEB is expressed as the sum of the variances of the optimal estimation error lower limit of the unbiased estimate of the target position, that is, the sum of the variances of the optimal estimation error lower limit of the target node coordinates estimated by the system using effective channel information. Please note that SPEB is a scalar. Therefore, using SPEB as the best indicator for positioning is closer to actual performance analysis. Use SPEB as a quantitative standard for positioning accuracy.

[0038] Step 130: Adjust the phase modulation vector of the RIS reflector unit to minimize the target position error.

[0039] Preferably, the phase modulation amount of the RIS reflection unit is updated, the cumulative gradient of each reflection unit is iteratively calculated, and the learning rate is updated until a phase modulation scheme with the minimum target position error is output.

[0040] In one embodiment, the minimum target position error is equivalent to the maximum trace of the Fisher information matrix. ,in is the matrix trace operation. When the effective channel information is larger, the target node coordinates estimated by the system are more reliable, which is manifested as the smaller the square of the position error SPEB. It can be found that SPEB needs to invert the matrix, resulting in an algorithm complexity as high as Therefore, it is complicated to solve the phase modulation scheme of the smart reconfigurable metasurface using SPEB as the loss function. According to the relaxation of the inequality, the trace of FIM can be used The positioning accuracy of equivalent SPEB is quantified, that is, minimizing SPEB is equivalent to maximizing the trace of FIM. Through analysis, the algorithm complexity of the trace of FIM is In the following, the trace of FIM is used as the objective function of the optimization algorithm.

[0041] In one embodiment, the method of adjusting the phase modulation vector of the RIS reflector unit to minimize the target position error includes at least one of the following: an AO algorithm, a GA algorithm, and an AG-PO algorithm.

[0042] Preferably, the phase modulation scheme of the intelligent reconfigurable metasurface is obtained by using the AG-PO algorithm. In one embodiment, the following steps are also included: the AG-PO algorithm accumulates variables by the square of the gradient, modifies the learning rate of the reflection unit, and the new learning rate is obtained by dividing the initial learning rate by the accumulated sum of the gradients.

[0043] Figure 3 This is the improved AG-PO method process of the present application. This application proposes an Adagrad gradient descent based phase optimization (AG-PO) algorithm. Compared with the alternating optimization (AO) algorithm and the genetic algorithm (GA), the proposed AG-PO algorithm has lower complexity, effectively improves the positioning accuracy, and can perform parallel operations. The low complexity and effectiveness of the phase optimization of the AG-PO algorithm are verified by simulation. The squared position error bound (SPEB) of the AG-PO algorithm is reduced by 44.17% and 44.02% respectively, which is better than the AO and GA algorithms.

[0044] The proposed AG-PO algorithm can realize parallel computing. Figure 2 It is indicated that the AG-PO algorithm can be divided into four parts: updating the cumulative gradient of the trace of FIM as the RIS phase modulation changes, updating the phase learning rate of each reflection unit in RIS, and updating the phase modulation and selection and output.

[0045] Step 200: At the beginning of the AG-PO algorithm, input the initial learning rate, error, and number of iterations. Then, iterative calculation is performed according to steps 210 to 230. By updating the cumulative gradient of the trace of the FIM as the RIS phase modulation changes, the phase learning rate of each reflection unit in the RIS is dynamically adjusted based on the previous gradient change. By updating the phase learning rate of each reflection unit in the RIS, the phase of each reflection unit in the RIS is dynamically adjusted, thereby improving the positioning performance of the system.

[0046] Step 210: Update the cumulative sum of gradients in parallel.

[0047] It consists of two sub-parts: (1) Parallel computing in The slope function of the RIS phase modulation vector of the iteration on the trace of the FIM.

[0048] Each element in the RIS phase modulation vector represents the phase of each reflector in the RIS. By adjusting the phase of each reflector in the RIS, we can improve the positioning accuracy of the system, which is manifested by increasing the overall absolute value of the Fisher information matrix.

[0049] The trace of FIM is specifically an indicator that measures the amount of coordinate information of all target nodes carried by the effective channel information samples. The larger the trace of FIM is, the easier it is to extract the precise coordinates of the target nodes from the effective channel information.

[0050] (2) By accumulating the slope function, calculate the The gradient accumulation sum of each reflection unit on each RIS is calculated in each iteration. Since the phase of each reflection unit in RIS is relatively independent, the slope function and the cumulative sum are calculated in parallel. The purpose of calculating the cumulative sum is to achieve adaptive learning rate changes and avoid the problem of algorithm gradient explosion or slow convergence.

[0051] Step 220: Update the learning rate in parallel.

[0052] The learning rate in each dimension is adjusted according to the gradient value of the independent variable in each dimension.

[0053] In one embodiment of the present application, the specific adjustment principle is: when the gradient value is a positive number, the phase of the learning rate of the reflection unit multiplied by the gradient is increased; when the gradient is less than zero, the phase of the learning rate of the reflection unit multiplied by the gradient is reduced. This avoids the problem that a unified learning rate is difficult to adapt to all dimensions. The method of changing the learning rate is as follows: The AG-PO algorithm accumulates variables by the square of the gradient to modify the learning rate of the reflection unit. The new learning rate is obtained by dividing the initial learning rate by the cumulative sum of the gradients. The learning rate converts large gradients into large updates and small gradients into small updates. As the number of iterations increases, the learning rate gradually decreases, showing adaptive behavior. At the same time, the degree of attenuation of each dimension of the gradient is different, indicating that the learning rate of each reflection unit in RIS is calculated independently and in parallel.

[0054] Step 230: Update phase modulation in parallel.

[0055] A new phase modulation scheme can be obtained by adding the product of the learning rate and the slope function to the phase modulation scheme of the previous iteration. When the slope is positive, the phase of RIS increases; if the slope is negative, the phase of RIS decreases. In practical applications, the phase of RIS is discrete to reduce resource consumption. Therefore, the phase modulation is discretized according to the number of bits of RIS to use a limited phase to represent all RIS phase modulations.

[0056] Step 240: Select and output the phase modulation scheme with the minimum SPEB.

[0057] In iteration After that, the latest phase modulation may not be the optimal result in the algorithm optimization process. In the iteration, the phase modulation with the smallest SPEB is selected as the optimal phase modulation of RIS.

[0058] In the best embodiment of the present application, attention is paid to the influence of the phase modulation of RIS as an anchor on the positioning accuracy of the intelligent reconfigurable supersurface collaborative positioning system of channel information, and an in-depth analysis is performed in the form of FIM. In the embodiment of the present application, FIM is based on a fully connected network topology and captures the information of each node in the intelligent reconfigurable supersurface collaborative positioning system of channel information. Through FIM, the positioning performance is quantified by quantizing it into a scalar through SPEB. This indicator can more accurately evaluate the performance of the system under different conditions. The intelligent reconfigurable supersurface collaborative positioning system of channel information of the present application can capture the dynamic position changes of targets in complex environments. Then, in order to solve the positioning optimization problem in the intelligent reconfigurable supersurface collaborative positioning system of channel information, the AG-PO algorithm is proposed. Because the localization optimization problem of the intelligent reconfigurable supersurface collaborative positioning system of channel information is a non-convex integer programming problem. To meet this challenge, the proposed AG-PO algorithm has faster convergence speed and higher accuracy than the AO algorithm and GA, and can also realize parallel computing. Through the Adagrad gradient descent algorithm, the AG-PO algorithm can adjust the learning rate during the optimization process, so as to more effectively adapt to the complexity of the problem and improve the convergence speed and accuracy of the optimization results. The parallel computing characteristics of the AG-PO algorithm make it more competitive in processing large-scale data and high-dimensional problems. Therefore, the AG-PO algorithm performs well in solving the positioning optimization problem of the intelligent reconfigurable metasurface cooperative positioning system with channel information.

[0059] It should be noted that the execution subject of each step of the method provided in the embodiment of the present application can be the same device, or the method can be executed by different devices. For example, the execution subject of step 110 and step 120 can be device 1, and the execution subject of step 130 can be device 2; for another example, the execution subject of step 110 can be device 1, and the execution subject of steps 120 and 130 can be device 2; and so on.

[0060] Figure 4 The embodiment of the present application also proposes an intelligent reconfigurable metasurface assisted positioning device, which is used to implement the method described in any embodiment of the present application, including: The acquisition module 410 is used to acquire the CIS from the target location, for example, to implement the function of step 110 of the embodiment of the present application.

[0061] The determination module 420 is used to determine the parameters representing the target position error, for example, to implement the function of step 120 of the embodiment of the present application.

[0062] The optimization module 430 is used to change the phase modulation vector of the RIS reflector unit to minimize the target position error, for example, to implement the function of step 130 of the embodiment of the present application, and the functions of steps 200 to 240.

[0063] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] Therefore, the present application also proposes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in any embodiment of the present application is implemented.

[0065] Furthermore, the present application also proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present application when executing the computer program.

[0066] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0067] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0068] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0069] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory. The memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0070] Figure 5 600 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 600 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiment of the present application. It includes: one or more processors 620; a storage device 610, which is used to store one or more programs. When the one or more programs are run by the one or more processors 620, the one or more processors 620 implement the method provided in the embodiment of the present application.

[0071] The electronic device 600 further includes an input device 630 and an output device 640; the processor 620, storage device 610, input device 630 and output device 640 in the electronic device can be connected via a bus or other means, and the figure takes the connection via a bus 650 as an example.

[0072] The storage device 610, as a computer-readable storage medium, can be used to store software programs, computer executable programs, and module units, such as program instructions corresponding to the method for determining the cloud bottom height in the embodiment of the present application. The storage device 610 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function; the storage data area may store data created according to the use of the terminal, etc. In addition, the storage device 610 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the storage device 610 may further include a memory remotely arranged relative to the processor 620, and these remote memories may be connected via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0073] The input device 630 may be used to receive input numbers, character information or voice information, and generate key signal input related to user settings and function control of the electronic device. The output device 640 may include electronic devices such as a display screen and a speaker.

[0074] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0075] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. An intelligent reconfigurable metasurface assisted positioning method for a wireless communication system, wherein the system comprises one or more RIS, each RIS comprises a plurality of reflection units, and the RIS radiation area comprises one or more communication terminals located at a target position, characterized in that: The following steps are involved: Determine the target location based on the CSI signal; Determine target position error; Adjust the phase modulation vector of the RIS reflector unit to minimize the target position error.

2. The intelligent reconfigurable metasurface assisted positioning method according to claim 1, characterized in that: The method for determining the target position error is to obtain the partial derivative of the coordinates of each target node for the logarithm of the effective channel information to obtain a Fisher information matrix, use the Fisher information matrix to represent the position uncertainty of multiple targets, and then calculate the variance sum of the position errors of the multiple targets through the Fisher information matrix as the target position error.

3. The intelligent reconfigurable metasurface assisted positioning method according to claim 1, characterized in that: The quantity representing the target position error is the square of the target position error, or the sum of the squares of the optimal estimate error lower bound of the unbiased estimate of the target position.

4. The intelligent reconfigurable metasurface assisted positioning method according to claim 1, characterized in that: The minimum target position error is equivalent to the maximum trace of the Fisher information matrix.

5. The intelligent reconfigurable metasurface assisted positioning method according to claim 1, characterized in that: Determining the target position error includes: taking the partial derivative of the coordinates of each target node on the logarithm of the effective channel information to obtain a Fisher information matrix; The step of adjusting the phase modulation vector of the RIS reflector unit to minimize the target position error includes: Update the phase modulation, iteratively calculate the cumulative gradient of the trace of the Fisher information matrix of each reflector unit as the RIS phase modulation changes, and update the phase learning rate of each reflector unit in RIS until the phase modulation scheme with the minimum target position error is output.

6. The intelligent reconfigurable metasurface assisted positioning method according to claim 1, characterized in that: The method of adjusting the phase modulation vector of the RIS reflector unit to minimize the target position error includes at least one of the following: AO algorithm, GA algorithm, AG-PO algorithm.

7. The intelligent reconfigurable metasurface assisted positioning method according to claim 1, characterized in that: The following steps are involved: The AG-PO algorithm accumulates variables by the square of the gradient and modifies the learning rate of the reflection unit. The new learning rate is obtained by dividing the initial learning rate by the accumulated gradient.

8. An intelligent reconfigurable metasurface assisted positioning device, used to implement the method described in any one of claims 1 to 7, characterized in that: include: An acquisition module, used for acquiring CIS from a target location; A determination module, used to determine a parameter representing a target position error; The optimization module is used to change the phase modulation vector of the RIS reflection unit so as to minimize the target position error.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

Images