Reconfigurable Intelligent Surface-Assisted Localization Method, System, Device, Medium and Product

Abstract

The present invention provides a method, system, device, medium and product for reconfigurable intelligent surface assisted positioning. The method includes: receiving reflection signals of a plurality of selected reconfigurable intelligent surfaces corresponding to a user; for each selected reconfigurable intelligent surface corresponding to the user, determining a local positioning result of the user according to the reflection signal of the selected reconfigurable intelligent surface and the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle; fusing all local positioning results of the user to obtain a global positioning result of the user; if the global positioning result of the user meets the accuracy requirement, using the global positioning result of the user as the final positioning result of the user; otherwise, performing optimization processing and returning to the step of receiving the reflection signals of each selected reconfigurable intelligent surface. The solution of the present application can reduce the power consumption and cost of the positioning system while improving the positioning accuracy.

Description

Technical Field

[0001] The present invention relates to the field of wireless positioning and sensing technologies, and in particular, to a reconfigurable intelligent surface-assisted positioning method, system, device, medium, and product. Background Art

[0002] With the development of 6G, many location-based services have emerged, such as high-precision navigation, user monitoring, and autonomous driving, and the demand for positioning has further increased. To meet the high-precision positioning requirements, the positioning method based on electromagnetic waves is one of the promising technologies. Using electromagnetic waves for positioning is less affected by the external environment and has a low privacy risk, so it has received extensive attention.

[0003] Existing high-precision positioning systems are mostly based on multi-antenna base stations. Its working principle is that the user emits electromagnetic waves, and the base station uses the received signals to analyze information such as the time delay and angle of arrival of the signals to calculate the user's position. Existing high-precision positioning systems are mostly based on multi-antenna base stations. Limited by power consumption and cost, the number of its antennas is often low, which limits its positioning accuracy. At the same time, to improve the coverage rate and diversity gain, multiple base stations need to be deployed.

[0004] Therefore, how to achieve higher-precision positioning on the premise of low cost is an urgent problem to be solved. Summary of the Invention

[0005] The present invention provides a reconfigurable intelligent surface-assisted positioning method, system, device, medium, and product to solve the defects in the prior art that the power consumption and cost of the positioning system are relatively high and the positioning accuracy is relatively poor, and to achieve the reduction of the power consumption and cost of the positioning system while improving the positioning accuracy.

[0006] The present invention provides a reconfigurable intelligent surface-assisted positioning method, which is applied to a base station in a reconfigurable intelligent surface-assisted positioning system. The reconfigurable intelligent surface-assisted positioning system includes a base station and a plurality of reconfigurable intelligent surfaces. The method includes:

[0007] Receiving the reflected signals of a plurality of selected reconfigurable intelligent surfaces corresponding to a user, where the reflected signals of the selected reconfigurable intelligent surfaces are the signals obtained by reflecting the signals sent by the user by the selected reconfigurable intelligent surfaces;

[0008] For each selected reconfigurable intelligent surface corresponding to the user, determining the local positioning result of the user according to the reflected signal of the selected reconfigurable intelligent surface and the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle;

[0009] Fusing the local positioning results of the user to obtain the global positioning result of the user;

[0010] Determine whether the global positioning result of the user meets the accuracy requirements; wherein, the accuracy requirements include that the positioning accuracy of the global positioning result of the user is less than a preset threshold or the number of positioning cycles reaches the maximum number of iterations;

[0011] If the global positioning result of the user meets the accuracy requirements, then use the global positioning result of the user as the final positioning result of the user; otherwise, perform optimization processing and return to the step of receiving the reflected signals of each selected reconfigurable intelligent surface; wherein, the optimization processing includes: performing reconfigurable intelligent surface selection optimization, updating the selected reconfigurable intelligent surface, and according to the updated selected reconfigurable intelligent surface and the global positioning result of the user, updating the phase shift configuration of each selected reconfigurable intelligent surface for the next positioning cycle.

[0012] According to a reconfigurable intelligent surface assisted positioning method provided by the present invention, after receiving the reflected signals of a plurality of selected reconfigurable intelligent surfaces corresponding to the user, the method further includes:

[0013] Determine the field type where the user is located according to the signal characteristics of the reflected signals of the selected reconfigurable intelligent surface; wherein, the signal characteristics include at least one of the following: phase, amplitude, time delay; the field type includes the near field of the reconfigurable intelligent surface and the far field of the reconfigurable intelligent surface.

[0014] According to a reconfigurable intelligent surface assisted positioning method provided by the present invention, for each selected reconfigurable intelligent surface corresponding to the user, determining the local positioning result of the user according to the reflected signal of the selected reconfigurable intelligent surface and the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle includes:

[0015] Establish a local coordinate system corresponding to each selected reconfigurable intelligent surface, and sample the near field and far field of the selected reconfigurable intelligent surface respectively to obtain a plurality of sampling points;

[0016] Based on the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle, construct an atomic channel corresponding to each sampling point; the atomic channel corresponding to the sampling point characterizes the propagation characteristics of the signal from the user to the reconfigurable intelligent surface and then to the base station;

[0017] Apply the orthogonal matching pursuit algorithm to match the reflected signal of the selected reconfigurable intelligent surface and the atomic channel corresponding to each sampling point to determine the local positioning result of the user in the local coordinate system of the selected reconfigurable intelligent surface.

[0018] According to a reconfigurable intelligent surface assisted positioning method provided by the present invention, the step of fusing all local positioning results of the user to obtain the global positioning result of the user includes:

[0019] Determine that the fusion loss is the sum of the squares of the relative distances between all local positioning results of the user and the global positioning result of the user;

[0020] For each selected reconfigurable intelligent surface, if the user is in the near field of the selected reconfigurable intelligent surface, the relative distance between the global positioning result of the user and the local positioning result is the Euclidean distance between the global positioning result of the user and the local positioning result;

[0021] For each selected reconfigurable intelligent surface, if the user is in the far field of the selected reconfigurable intelligent surface, the relative distance between the global positioning result of the user and the local positioning result is the minimum distance between the global positioning result of the user and the far-field ray;

[0022] Transform the fusion loss minimization problem into a quadratic programming problem and solve to obtain the global positioning result of the user.

[0023] According to a reconfigurable intelligent surface assisted positioning method provided by the present invention, perform reconfigurable intelligent surface selection optimization, update the selected reconfigurable intelligent surface, and based on the updated selected reconfigurable intelligent surface and the global positioning result of the user, update the phase shift configuration of each selected reconfigurable intelligent surface for the next positioning cycle, including:

[0024] Select a predetermined number of reconfigurable intelligent surfaces as the selected reconfigurable intelligent surfaces in the order of decreasing channel gain;

[0025] Based on the updated selected reconfigurable intelligent surface and the global positioning result of the user, optimize the phase shift configuration of each selected reconfigurable intelligent surface based on the alternating direction multiplier method, and update the phase shift configuration of each selected reconfigurable intelligent surface for the next positioning cycle.

[0026] According to a reconfigurable intelligent surface assisted positioning method provided by the present invention, after receiving the reflected signals of multiple selected reconfigurable intelligent surfaces corresponding to the user, the method further includes:

[0027] Distinguish the reflected signals of different selected reconfigurable intelligent surfaces through digital beamforming.

[0028] The present invention also provides a reconfigurable intelligent surface assisted positioning system, the system includes a base station and multiple reconfigurable intelligent surfaces; the base station includes:

[0029] A receiving module, configured to receive the reflected signals of multiple selected reconfigurable intelligent surfaces corresponding to the user, and the reflected signals of the selected reconfigurable intelligent surfaces are the signals obtained by reflecting the signals sent by the user;

[0030] A local positioning module, for each selected reconfigurable intelligent surface corresponding to the user, determines the local positioning result of the user according to the reflection signal of the selected reconfigurable intelligent surface and the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle;

[0031] A fusion module, which is used to fuse all the local positioning results of the user to obtain the global positioning result of the user;

[0032] A judgment module, which is used to judge whether the global positioning result of the user meets the accuracy requirement; wherein, the accuracy requirement includes that the positioning accuracy of the global positioning result of the user is less than a preset threshold or the number of positioning cycles reaches the maximum number of iterations;

[0033] A processing module, if the global positioning result of the user meets the accuracy requirement, then uses the global positioning result of the user as the final positioning result of the user; otherwise, performs optimization processing and returns to the step of receiving the reflection signal of each selected reconfigurable intelligent surface; wherein, the optimization processing includes: performing optimization on the selection of reconfigurable intelligent surfaces, updating the selected reconfigurable intelligent surfaces, and according to the updated selected reconfigurable intelligent surfaces and the global positioning result of the user, updating the phase shift configuration of each selected reconfigurable intelligent surface in the next positioning cycle.

[0034] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and running on the processor, and when the processor executes the computer program, it implements the reconfigurable intelligent surface assisted positioning method as described in any one of the above.

[0035] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the reconfigurable intelligent surface assisted positioning method as described in any one of the above.

[0036] The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the reconfigurable intelligent surface assisted positioning method as described in any one of the above.

[0037] The reconfigurable intelligent surface assisted positioning method, system, device, medium and product provided by the present invention. The reconfigurable intelligent surface assisted positioning system includes multiple reconfigurable intelligent surfaces. The reconfigurable intelligent metasurface has the characteristics of low power consumption and low cost, and can achieve channel customization by changing the signal phase shift. Therefore, by deploying multiple large-scale reconfigurable intelligent metasurface arrays, the system coverage rate and diversity gain are improved, and the power consumption and cost of the positioning system can be reduced. By receiving the reflected signals of multiple selected reconfigurable intelligent surfaces, multipath signals can be utilized to improve the positioning accuracy. Further, according to the reflected signals of the selected reconfigurable intelligent surfaces and the phase shift configuration of the selected reconfigurable intelligent surfaces in the current positioning cycle, the local positioning result of the user is determined. By considering the phase shift configuration of the reconfigurable intelligent surfaces in the current positioning cycle, the signal propagation path can be more accurately modeled. Further, all the local positioning results of the user are fused to obtain the global positioning result of the user. Fusing multiple local positioning results can reduce the error caused by a single reconfigurable intelligent surface and improve the robustness and accuracy of the positioning. Further, when the global positioning result does not meet the accuracy requirement, an optimization process is performed, which can effectively improve the positioning accuracy. Therefore, the solution of the present application can reduce the power consumption and cost of the positioning system while improving the positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0039] Figure 1 It is a schematic flow chart of the reconfigurable intelligent surface assisted positioning method provided by the present invention.

[0040] Figure 2 It is a schematic architecture diagram of the reconfigurable intelligent surface assisted positioning system provided by the present invention.

[0041] Figure 3 It is a schematic structural diagram of the base station provided by the present invention.

[0042] Figure 4 It is a schematic structural diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] To make the objectives, technical solutions and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without making creative efforts fall within the scope of protection of the present invention.

[0044] It should be noted that the brief description of terms in this application is only for facilitating the understanding of the following described embodiments, rather than intending to limit the embodiments of this application. Unless otherwise specified, these terms should be understood in their ordinary and common meanings.

[0045] In this application, terms such as "first" and "second" in the specification, claims and the above-mentioned drawings are used to distinguish similar or the same kind of objects or entities, and do not necessarily mean to limit a specific order or sequence, unless otherwise indicated. It should be understood that such terms can be interchanged under appropriate circumstances, for example, it is possible to implement in an order other than those given in the illustration or description of the embodiments of this application.

[0046] In addition, the terms "comprising" and "having" and any variations thereof are intended to cover but not exclude inclusion. For example, a product or device comprising a series of components does not necessarily have to be limited to those components clearly listed, but may include other components not clearly listed or inherent to these products or devices. The term "module" used in this application refers to any known or later developed hardware, software, firmware, artificial intelligence, fuzzy logic or a combination of hardware or / and software code that can perform the functions related to the element.

[0047] The following will specifically describe the technical solutions of this application and how the technical solutions of this application solve the above technical problems in detail. These several specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The following combines Figure 1 and Figure 2 to describe the reconfigurable intelligent surface assisted positioning method of the present invention.

[0048] In practical applications, the execution subject of the reconfigurable intelligent surface assisted positioning method can be a reconfigurable intelligent surface assisted positioning device. There are various implementation methods for the reconfigurable intelligent surface assisted positioning device. For example, it can be implemented through a computer program, such as an application software, etc.; or, for example, a chip, etc. It can also be implemented as a medium storing relevant computer programs, such as a USB flash drive, a cloud disk, etc.; or, it can also be implemented through an entity device integrated or installed with relevant computer programs, such as a server, an intelligent device, a base station, etc.

[0049] Exemplarily, the base station is used as the execution entity of the reconfigurable intelligent surface assisted positioning method for specific description. Specifically, the reconfigurable intelligent surface assisted positioning method is applied to the base station in the reconfigurable intelligent surface assisted positioning system, and the reconfigurable intelligent surface assisted positioning system includes a base station and multiple reconfigurable intelligent surfaces.

[0050] It can be understood that the reconfigurable intelligent surface assisted positioning system includes multiple reconfigurable intelligent surfaces. The reconfigurable intelligent metasurface has the characteristics of low power consumption and low cost, and can achieve channel customization by changing the signal phase shift. Therefore, by deploying multiple large-scale reconfigurable intelligent metasurface arrays, the power consumption and cost of the positioning system can be reduced.

[0051] Exemplarily, the reconfigurable intelligent surface assisted positioning system includes a base station, M reconfigurable intelligent metasurfaces, and K single-antenna users with unknown positions. The base station is equipped with Q antennas, and each reconfigurable intelligent surface includes N reflection units with adjustable phase shifts. The positions of the base station and RIS are known, and the positions of the users are unknown.

[0052] Figure 1 is a schematic flow chart of the reconfigurable intelligent surface assisted positioning method provided by the present invention. As Figure 1 shown, the method includes the following steps 101 to step 106.

[0053] Step 101: Receive the reflected signals of multiple selected reconfigurable intelligent surfaces corresponding to the user. The reflected signals of the selected reconfigurable intelligent surfaces are the signals obtained by reflecting the signals sent by the user by the selected reconfigurable intelligent surfaces.

[0054] Step 102: For each selected reconfigurable intelligent surface corresponding to the user, determine the local positioning result of the user according to the reflected signal of the selected reconfigurable intelligent surface and the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle.

[0055] Step 103: Fuse all the local positioning results of the user to obtain the global positioning result of the user.

[0056] Step 104: Determine whether the global positioning result of the user meets the accuracy requirements.

[0057] Among them, the accuracy requirements include that the positioning accuracy of the global positioning result of the user is less than the preset threshold or the number of positioning cycles reaches the maximum number of iterations.

[0058] Step 105: If the global positioning result of the user meets the accuracy requirements, then use the global positioning result of the user as the final positioning result of the user.

[0059] Step 106: If the global positioning result of the user meets the accuracy requirement, perform optimization processing and return to execute Step 101.

[0060] Among them, the optimization processing includes: performing reconfigurable intelligent surface selection optimization, updating the selected reconfigurable intelligent surface, and updating the phase shift configuration of each selected reconfigurable intelligent surface for the next positioning cycle according to the updated selected reconfigurable intelligent surface and the user's global positioning result.

[0061] In one example, the maximum number of iterations C can be set, that is, the maximum number of cycles for the above steps to iterate. Each cycle consists of three stages, namely the transmission stage (including Step 101), the positioning stage (including Steps 102 to 103), and the optimization stage (including Steps 104 to 106).

[0062] Specifically, in the transmission stage, the user emits a signal, which is reflected by the selected reconfigurable intelligent surface and then received by the base station. Denote the signal received by the base station after being reflected by the m-th reconfigurable intelligent surface in the c-th cycle as .

[0063] In the positioning stage, the base station estimates the user's position according to the received signals reflected by the selected reconfigurable intelligent surface of each user, and denote the estimation result as .

[0064] In the optimization stage, the base station first optimizes the set of selected reconfigurable intelligent surfaces of each user to update the selected reconfigurable intelligent surface, and then updates the phase shift configuration of each selected reconfigurable intelligent surface for the next positioning cycle according to the updated selected reconfigurable intelligent surface and the user's global positioning result.

[0065] Specifically, Step 101 includes: receiving the reflected signals of multiple selected reconfigurable intelligent surfaces corresponding to the user, and the reflected signals of the selected reconfigurable intelligent surfaces are the signals obtained by reflecting the signals sent by the user by the selected reconfigurable intelligent surfaces.

[0066] Among them, the reconfigurable intelligent surface (RIS) is a wireless communication technology that optimizes the propagation of wireless signals by dynamically adjusting the electromagnetic characteristics of the surface. The RIS consists of a large number of adjustable reflection units, and these units can independently change the phase and amplitude of the signal, so as to realize the customization of the signal propagation path.

[0067] Specifically, according to user requirements and specific optimization rules, a set of reconfigurable intelligent surfaces can be selected from multiple reconfigurable intelligent surfaces in the reconfigurable intelligent surface assisted positioning system. The set of reconfigurable intelligent surfaces includes multiple selected reconfigurable intelligent surfaces. It can be understood that the selected reconfigurable intelligent surfaces receive the signals sent by the user, and then the base station receives the reflected signals that have passed through the selected reconfigurable intelligent surfaces.

[0068] In practical applications, the user can send signals through an intelligent device. For example, the intelligent device can be a smart phone 21, a laptop computer 24, a tablet computer, a smart watch, a computer 25, a server 26, etc. Figure 2 It is a schematic diagram of the architecture of the reconfigurable intelligent surface assisted positioning system provided by the present invention. As Figure 2 shown, the user sends a signal through the smart phone 21, the selected reconfigurable intelligent surface 22 receives the signal sent by the user, and then the base station 23 receives the reflected signal that has passed through the selected reconfigurable intelligent surface 22.

[0069] It should be noted that the number of selected reconfigurable intelligent surfaces is multiple. The user sends signals to multiple reconfigurable intelligent surfaces, each reconfigurable intelligent surface receives the signal, and then the base station receives the reflected signals that have passed through multiple selected reconfigurable intelligent surfaces.

[0070] In practice, the base station can simultaneously receive the reflected signals of multiple selected reconfigurable intelligent surfaces corresponding to the user. Optionally, for the method of the base station to distinguish the reflected signals of different selected reconfigurable intelligent surfaces, in a possible implementation manner, after the above step 101, the method further includes:

[0071] Distinguish the reflected signals of different selected reconfigurable intelligent surfaces through digital beamforming.

[0072] It can be understood that by receiving the reflected signals of multiple selected reconfigurable intelligent surfaces, multi-path signals can be utilized to improve the positioning accuracy.

[0073] It should be noted that in practice, the reconfigurable intelligent surface assisted positioning system can include multiple base stations and support positioning of multiple users. To avoid multi-user interference, frequency division is used between different users.

[0074] For the scenario of multiple users, the above positioning steps are executed separately for each user. Specifically, for each user, the reflected signals of multiple selected reconfigurable intelligent surfaces corresponding to the user are received. Further, for each selected reconfigurable intelligent surface corresponding to the user, the local positioning result of the user is determined. Further, all the local positioning results of the user are fused to obtain the global positioning result of the user. Further, it is judged whether the global positioning result of the user meets the accuracy requirement; if the global positioning result of the user meets the accuracy requirement, the global positioning result of the user is used as the final positioning result of the user; otherwise, optimization processing is performed, and the step of receiving the reflected signals of each selected reconfigurable intelligent surface is returned.

[0075] Exemplarily, the channel through which the k-th user reaches the base station after being reflected by the m-th RIS can be expressed as:

[0076]

[0077] where is the phase shift vector of the m-th RIS, is the phase shift of its n-th unit. is the position of the k-th user relative to the m-th RIS. represents the channel between the m-th RIS and the base station, is the channel from the k-th user to the m-th RIS.

[0078] As the reconfigurable intelligent metasurface array expands, its near-field region expands. The near-field refers to the region where the traditional plane wave model cannot accurately describe the signal propagation characteristics, and the spherical wave model needs to be used to model the signal. Correspondingly, the far-field refers to the region where the plane wave model is applicable. In the actual positioning environment, it may include both the near-field and far-field of the array, so it is necessary to achieve positioning in the hybrid near-far field. Existing positioning schemes based on multiple reconfigurable intelligent metasurfaces mainly target their far-field. If applied in the hybrid field, it will lead to a reduction in accuracy. The scheme of this application can be applied in the hybrid field, improving the accuracy and reliability of hybrid field positioning.

[0079] Specifically, when the user is in the near-field of the m-th RIS, the spherical wave model is used to model the signal received by the RIS. At this time, the channel from the k-th user to the m-th RIS can be modeled as:

[0080]

[0081] where is the channel gain, is the near-field range of the m-th RIS, is the steering vector in the near-field, and its mathematical form can be expressed as:

[0082]

[0083] wherein, is the distance from the k-th user to the n-th unit of the m-th RIS.

[0084] Correspondingly, when the user is in the far field of the m-th RIS, the wavefront can be approximated as a plane, so the received signal can be modeled by a plane wave model, and the channel from the k-th user to the m-th RIS can be expressed as:

[0085]

[0086] wherein, represents the far-field range of the m-th RIS, is the far-field steering vector, which is given by:

[0087] ,

[0088]

[0089] wherein, is the position of the n-th RIS unit in the local coordinate system of the m-th RIS.

[0090] The solution of the present application can achieve the positioning of the hybrid near and far fields, that is, the user may be in the near field or far field of each selected reconfigurable intelligent surface, and it is unknown whether each user is in the near field or far field.

[0091] Optionally, in a possible implementation manner, after the above step 101, the method further includes:

[0092] Determine the field type where the user is located according to the signal characteristics of the reflected signal of the selected reconfigurable intelligent surface; wherein, the signal characteristics include at least one of the following: phase, amplitude, time delay; the field type includes the near field of the reconfigurable intelligent surface and the far field of the reconfigurable intelligent surface.

[0093] In this embodiment, the method for determining whether the user is in the near field or far field of the reconfigurable intelligent surface is not specifically limited. Exemplarily, if the phase and amplitude of the reflected signal change significantly with distance, it is determined that the user is in the near field. If the phase change of the reflected signal is mainly related to the angle and the amplitude change is small, it is determined that the user is in the far field. If the time delay of the reflected signal is less than a preset threshold, it is determined that the user is in the near field; otherwise, it is determined that the user is in the far field.

[0094] It should be noted that in practice, the field type where the user is located can be determined according to one signal characteristic, or can be determined by combining multiple signal characteristics, which is not limited herein.

[0095] It should be noted that the above signal characteristics are only examples, and the signal characteristics can be increased or decreased according to actual needs, and no specific limitation is made here.

[0096] Step 102 includes: for each selected reconfigurable intelligent surface corresponding to a user, determine the local positioning result of the user according to the reflected signal of the selected reconfigurable intelligent surface and the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle.

[0097] Among them, the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle refers to the current phase shift state of the reflection units in the selected reconfigurable intelligent surface. These phase shift states directly affect the reflection characteristics of the signal, thereby affecting the positioning accuracy and the signal propagation path. Specifically, the current phase shift state of the reflection unit is a matrix or vector representing the phase shift value of each reflection unit.

[0098] Optionally, in a possible implementation manner, the above step 102 includes:

[0099] Establish a local coordinate system corresponding to each selected reconfigurable intelligent surface, and sample the near field and far field of the selected reconfigurable intelligent surface respectively to obtain a plurality of sampling points;

[0100] Based on the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle, construct an atomic channel corresponding to each sampling point; the atomic channel corresponding to the sampling point characterizes the propagation characteristics of the signal from the user to the reconfigurable intelligent surface and then to the base station.

[0101] Apply the orthogonal matching pursuit algorithm to match the reflected signal of the selected reconfigurable intelligent surface and the atomic channel corresponding to each sampling point, and determine the local positioning result of the user in the local coordinate system of the selected reconfigurable intelligent surface.

[0102] Specifically, model the local positioning problem, and define the positioning loss as the norm of the residual between the received signal and the signal reconstructed using the estimated position. The local positioning problem can be modeled as:

[0103]

[0104]

[0105] Among them, is the digital beamforming matrix from the base station to the m-th RIS, is the narrowband signal transmitted by the k-th user, is the set of users who select the m-th RIS for positioning in the i-th cycle, is the signal received by the base station after being reflected by the m-th RIS in the i-th cycle, is the entire search area.

[0106] Furthermore, to solve the above local positioning problem, a positioning algorithm based on orthogonal matching pursuit is proposed. Specifically, first, a local coordinate system is established for each RIS, and then the near field and far field of each RIS are sampled. Among them, the angles and distances in the near field are sampled, and only the angles in the far field are sampled. The sampling intervals of the angles and distances are respectively . Exemplarily, the sampling results are where and The expressions are as follows:

[0107]

[0108]

[0109] where and are the numbers of sampling points in the near field and far field respectively. The steering vectors corresponding to each sampling point form the atomic channel , and The expressions are as follows:

[0110]

[0111]

[0112] where is the steering vector of the near field point , is the steering vector of the far field point .

[0113] Furthermore, the above local positioning problem can be approximated as:

[0114]

[0115]

[0116] where is the amplitude of the atomic channel . Since only the direct path from the user to the selected RIS is considered, has only one non-zero element. Therefore The orthogonal matching pursuit algorithm can be used to solve it to obtain the local positioning result of the user in the local coordinate system of the selected reconfigurable intelligent surface.

[0117] Specifically, step 103 includes: fusing all the local positioning results of the user to obtain the global positioning result of the user.

[0118] Optionally, in a possible implementation, step 103 above includes:

[0119] Determine that the fusion loss is the sum of the squares of the relative distances between all local positioning results of the user and the global positioning result of the user;

[0120] For each selected reconfigurable intelligent surface, if the user is in the near field of the selected reconfigurable intelligent surface, the relative distance between the user's global positioning result and the local positioning result is the Euclidean distance between the user's global positioning result and the local positioning result;

[0121] For each selected reconfigurable intelligent surface, if the user is in the far field of the selected reconfigurable intelligent surface, the relative distance between the user's global positioning result and the local positioning result is the minimum distance between the user's global positioning result and the far-field ray;

[0122] Transform the fusion loss minimization problem into a quadratic programming problem, and solve to obtain the user's global positioning result.

[0123] Specifically, define the fusion loss as the sum of the squares of the relative distances between all local positioning results and the global positioning result. Since the forms of the positioning results are different when the local positioning results are near-field points and far-field points, the relative distances for the near-field and far-field cases need to be calculated separately.

[0124] Specifically, for the case where the user is in the near field of the reconfigurable intelligent surface, the relative distance for the near-field positioning result is defined as:

[0125]

[0126] where, is the global positioning result of the k-th user, is the local positioning result obtained from the received signal reflected by the m-th RIS.

[0127] Specifically, for the case where the user is in the far field of the reconfigurable intelligent surface, since the far-field positioning result only contains angle information and corresponds to a ray in three-dimensional space. Define the far-field relative distance as the minimum distance from the global positioning result to this ray, given by the following formula:

[0128]

[0129] where, is the vector from the center of the m-th RIS pointing to the global positioning result, is the unit vector parallel to the local estimation direction.

[0130] Therefore, the result fusion problem can be modeled as:

[0131]

[0132]

[0133] wherein, is the RIS set selected by the k-th user. Specifically, for the near field, , and for the far field, .

[0134] Furthermore, considering a rectangular area, the solution to the above problem can be given by quadratic programming. Based on the fusion loss, through the quadratic programming solution method, the global positioning result of the user is obtained. That is, the problem of minimizing the fusion loss is transformed into a quadratic programming problem, and the global positioning result of the user is solved.

[0135] Step 104 includes: determining whether the global positioning result of the user meets the accuracy requirement; wherein, the accuracy requirement includes that the positioning accuracy of the global positioning result of the user is less than a preset threshold or the number of positioning cycles reaches the maximum number of iterations.

[0136] Combined with the above description, the maximum number of iterations C is predefined. In practical applications, when the positioning accuracy of the global positioning result of the user is less than the preset threshold or reaches the maximum number of iterations, it indicates that the global positioning result meets the accuracy requirement.

[0137] wherein, the positioning accuracy can be the error between the global positioning result of the user and the actual position. This error can be measured by the Euclidean distance. The preset threshold is the upper limit of the positioning accuracy set by the system. If the error of the global positioning result is less than this threshold, it is considered to meet the accuracy requirement. The maximum number of iterations is the upper limit of the number of iterative optimizations set by the system. If the number of iterations in the above steps reaches this number, the iterative process will terminate regardless of whether the positioning accuracy meets the requirement.

[0138] In practical applications, after obtaining the global positioning result of the user, the positioning accuracy is calculated. For example, the error between the global positioning result of the user and the actual position is calculated. In one example, if the actual position is unknown, other reference points or historical data can be used to estimate the error. Optionally, the Cramer-Rao bound of the global positioning result of the user can be calculated as the estimated error. Specifically, if the error is less than the preset threshold, it is considered that the global positioning result meets the accuracy requirement; if the error is greater than or equal to the preset threshold, it is considered that the global positioning result does not meet the accuracy requirement.

[0139] Specifically, if the number of iterations has not reached the maximum number of iterations, the optimization process can continue to be executed to improve the positioning accuracy. Correspondingly, if the number of iterations reaches the maximum number of iterations, the iterative process is terminated, and the current global positioning result is used as the final result.

[0140] It can be understood that if the global positioning result of the user satisfies at least one of the two conditions that the positioning accuracy is less than the preset threshold or the maximum number of iterations is reached, it can be determined that the global positioning result of the user meets the accuracy requirements.

[0141] It should be noted that the present application does not specifically limit the method for determining whether the global positioning result meets the accuracy requirements. Optionally, other parameters can be set according to actual needs to determine whether the global positioning result meets the accuracy requirements.

[0142] Optionally, only judge whether the global positioning result meets the accuracy requirements by the positioning accuracy, that is, if the positioning accuracy of the user's global positioning result is less than the preset threshold, it is determined that the global positioning result meets the accuracy requirements; otherwise, it is determined that the global positioning result does not meet the accuracy requirements and further optimization is performed.

[0143] Optionally, only judge whether the global positioning result meets the accuracy requirements by the maximum number of iterations, that is, if the number of iterations reaches the maximum number of iterations, it is determined that the global positioning result meets the accuracy requirements; otherwise, it is determined that the global positioning result does not meet the accuracy requirements and further optimization is performed.

[0144] Step 105 includes: if the global positioning result of the user meets the accuracy requirements, then use the global positioning result of the user as the final positioning result of the user;

[0145] Step 106 includes: if the global positioning result of the user meets the accuracy requirements, then perform optimization processing and return to execute Step 101.

[0146] Among them, the optimization processing includes: performing reconfigurable intelligent surface selection optimization, updating the selected reconfigurable intelligent surface, and updating the phase shift configuration of each selected reconfigurable intelligent surface in the next positioning cycle according to the updated selected reconfigurable intelligent surface and the global positioning result of the user.

[0147] Specifically, when the global positioning result of the user does not meet the accuracy requirements, first optimize each RIS set selected by the user, and then optimize the RIS phase shift according to the updated selected reconfigurable intelligent surface and the global positioning result of the user.

[0148] Optionally, in a possible implementation manner, the above-mentioned performing reconfigurable intelligent surface selection optimization, updating the selected reconfigurable intelligent surface, and updating the phase shift configuration of each selected reconfigurable intelligent surface in the next positioning cycle according to the updated selected reconfigurable intelligent surface and the global positioning result of the user includes:

[0149] Select a predetermined number of reconfigurable intelligent surfaces as the selected reconfigurable intelligent surfaces in the order of decreasing channel gain;

[0150] Based on the updated global positioning results of the selected reconfigurable intelligent surfaces and the user, the phase shift configuration of each selected reconfigurable intelligent surface is optimized using the Alternating Direction Method of Multipliers (ADMM), and the phase shift configuration for the next positioning cycle of each selected reconfigurable intelligent surface is updated.

[0151] First, optimize the RIS selection. Specifically, for each user, select L RISs with the highest channel gains to assist in their positioning. Since the channel gain is negatively correlated with the distance between the user and the RIS, for each user, select L RISs that are closest to this user for their positioning.

[0152] Furthermore, optimize the RIS phase shift based on the updated global positioning results of the selected reconfigurable intelligent surfaces and the user to update the phase shift configuration for the next positioning cycle of each selected reconfigurable intelligent surface.

[0153] Specifically, select the Cramer-Rao bound as the optimization objective. At the same time, to reduce the interference between RISs, it is necessary to reduce the sidelobe amplitude of the RIS pointing to other RISs. The RIS phase shift optimization problem can be modeled as:

[0154]

[0155]

[0156]

[0157] where, is the phase shift of the m-th RIS in the (c + 1)-th cycle, and each element satisfies the constraint of modulus 1. is 's Fisher information matrix, affected by . is the penalty factor used to balance the influence of the maximum allowable sidelobe amplitude . The second constraint is the sidelobe suppression constraint to ensure that the radiation at the angle is less than . Assume that the RIS is in the far-field range of other RISs, is the angle of other RISs relative to the m-th RIS.

[0158] Furthermore, for the above RIS phase shift optimization problem, design an algorithm based on the Alternating Direction Method of Multipliers. Among them, the Alternating Direction Method of Multipliers is an iterative algorithm for solving optimization problems, especially suitable for large-scale optimization problems and distributed optimization scenarios. It decomposes the original problem into multiple sub-problems and alternately solves these sub-problems to gradually approach the global optimal solution.

[0159] Specifically, introduce an auxiliary variable , where The expression is as follows:

[0160] .

[0161] The augmented Lagrangian function of the above RIS phase shift optimization problem can be given by the following formula:

[0162]

[0163]

[0164] where is the penalty factor, is the dual variable, and it is required that .

[0165] Subsequently, the variable is iteratively optimized. Hereinafter, will be abbreviated as .

[0166] The first sub-problem refers to the step of updating the RIS phase shift configuration in the alternating direction method of multipliers. Specifically, the objective of the first sub-problem is to minimize the augmented Lagrangian function , while satisfying the constraint condition of . The first sub-problem can be expressed as:

[0167]

[0168]

[0169] The second sub-problem refers to the step of updating the variables and in the alternating direction method of multipliers. Specifically, the objective of the second sub-problem is to minimize the augmented Lagrangian function , while satisfying the constraint condition of . The second sub-problem can be expressed as:

[0170]

[0171]

[0172]

[0173] The following is the solution to the first sub-problem:

[0174] Define and , the first sub - problem above can be rewritten as:

[0175]

[0176]

[0177] where, , . Considering the above constraints, the method of complex circular manifold is adopted to solve the first sub - problem. Specifically, in the first step, the Euclidean gradient of the above objective function is calculated. In the second step, the Euclidean gradient is projected onto the tangent plane of the complex circular manifold to obtain the Riemannian gradient. In the third step, move from the existing RIS phase shift in the direction of the Riemannian gradient and project it onto the complex circular manifold to obtain the updated point.

[0178] Next, solve the second sub - problem:

[0179] Define , and ignoring the irrelevant terms, the second sub - problem can be rewritten as:

[0180]

[0181]

[0182] where, The solution of can be obtained by the following formula

[0183]

[0184] where, if , then , otherwise . can be given by the following formula

[0185]

[0186] The reconfigurable intelligent surface assisted positioning method provided in this embodiment, the reconfigurable intelligent surface assisted positioning system includes multiple reconfigurable intelligent surfaces. The reconfigurable intelligent metasurface has the characteristics of low power consumption and low cost, and can achieve channel customization by changing the signal phase shift. Therefore, by deploying multiple large-scale reconfigurable intelligent metasurface arrays, the power consumption and cost of the positioning system can be reduced. By receiving the reflected signals of multiple selected reconfigurable intelligent surfaces, multipath signals can be utilized to improve the positioning accuracy. Further, according to the reflected signals of the selected reconfigurable intelligent surfaces and the phase shift configuration of the selected reconfigurable intelligent surfaces in the current positioning cycle, the local positioning result of the user is determined. By considering the phase shift configuration of the reconfigurable intelligent surfaces in the current positioning cycle, the signal propagation path can be more accurately modeled. Further, all local positioning results of the user are fused to obtain the global positioning result of the user. Fusing multiple local positioning results can reduce the error caused by a single reconfigurable intelligent surface and improve the robustness and accuracy of positioning. Further, when the global positioning result does not meet the accuracy requirements, optimization processing is performed, which can effectively improve the positioning accuracy. Therefore, the solution of this application can reduce the power consumption and cost of the positioning system while improving the positioning accuracy.

[0187] The reconfigurable intelligent surface assisted positioning system provided by the present invention will be described below. The reconfigurable intelligent surface assisted positioning system described below can be correspondingly referred to the reconfigurable intelligent surface assisted positioning method described above.

[0188] Specifically, the above reconfigurable intelligent surface assisted positioning method is applied to the base station in the reconfigurable intelligent surface assisted positioning system. The reconfigurable intelligent surface assisted positioning system includes a base station and multiple reconfigurable intelligent surfaces.

[0189] It can be understood that the reconfigurable intelligent surface assisted positioning system includes multiple reconfigurable intelligent surfaces. The reconfigurable intelligent metasurface has the characteristics of low power consumption and low cost, and can achieve channel customization by changing the signal phase shift. Therefore, by deploying multiple large-scale reconfigurable intelligent metasurface arrays, the power consumption and cost of the positioning system can be reduced.

[0190] Exemplarily, the reconfigurable intelligent surface assisted positioning system includes a base station, M reconfigurable intelligent metasurfaces, and K single-antenna users with unknown positions. The base station is equipped with Q antennas, and each reconfigurable intelligent metasurface includes N reflection units with adjustable phase shifts. The positions of the base station and RIS are known, and the user positions are unknown.

[0191] As Figure 2 shown, the user sends a signal through the smartphone 21, the selected reconfigurable intelligent surface 22 receives the signal sent by the user, and then the base station 23 receives the reflected signal that has passed through the selected reconfigurable intelligent surface 22.

[0192] Figure 3 is a schematic structural diagram of the base station provided by the present invention. As Figure 3 shown, the base station includes: a receiving module 31, a local positioning module 32, a fusion module 33, a judgment module 34, and a processing module 35.

[0193] The above-mentioned receiving module 31 is used to receive the reflected signals of multiple selected reconfigurable intelligent surfaces corresponding to the user. The reflected signals of the selected reconfigurable intelligent surfaces are the signals obtained by reflecting the signals sent by the user by the selected reconfigurable intelligent surfaces.

[0194] The above-mentioned local positioning module 32 is used to determine the local positioning result of the user for each selected reconfigurable intelligent surface corresponding to the user according to the reflected signal of the selected reconfigurable intelligent surface and the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle.

[0195] The above-mentioned fusion module 33 is used to fuse the results of all local positioning results of the user to obtain the global positioning result of the user.

[0196] The above-mentioned judgment module 34 is used to judge whether the global positioning result of the user meets the accuracy requirements; wherein, the accuracy requirements include that the positioning accuracy of the global positioning result of the user is less than a preset threshold or the number of positioning cycles reaches the maximum number of iterations;

[0197] The above-mentioned processing module 35 is used to, if the global positioning result of the user meets the accuracy requirements, use the global positioning result of the user as the final positioning result of the user; if the global positioning result of the user meets the accuracy requirements, perform optimization processing and return to the step of receiving the reflected signals of each selected reconfigurable intelligent surface. Among them, the optimization processing includes: performing optimization on the selection of reconfigurable intelligent surfaces, updating the selected reconfigurable intelligent surfaces, and updating the phase shift configuration of each selected reconfigurable intelligent surface in the next positioning cycle according to the updated selected reconfigurable intelligent surfaces and the global positioning result of the user.

[0198] In a possible implementation manner, the above-mentioned receiving module 31 is further used for:

[0199] Distinguishing the reflected signals of different selected reconfigurable intelligent surfaces through digital beamforming.

[0200] In a possible implementation manner, the above-mentioned base station further includes:

[0201] A determination module, configured to determine the field type where the user is located according to the signal characteristics of the reflected signal of the selected reconfigurable intelligent surface; wherein, the signal characteristics include at least one of the following: phase, amplitude, time delay; the field type includes the near field of the reconfigurable intelligent surface and the far field of the reconfigurable intelligent surface.

[0202] In a possible implementation manner, the above-mentioned local positioning module 32 is specifically configured to:

[0203] Establish a local coordinate system corresponding to each selected reconfigurable intelligent surface, and sample the near field and far field of the selected reconfigurable intelligent surface respectively to obtain a plurality of sampling points;

[0204] Based on the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning period, construct an atomic channel corresponding to each sampling point; the atomic channel corresponding to the sampling point characterizes the propagation characteristics of the signal from the user to the reconfigurable intelligent surface and then to the base station;

[0205] Apply the orthogonal matching pursuit algorithm to match the reflected signal of the selected reconfigurable intelligent surface with the atomic channel corresponding to each sampling point, and determine the local positioning result of the user in the local coordinate system of the selected reconfigurable intelligent surface.

[0206] In a possible implementation manner, the above-mentioned fusion module 33 is specifically configured to:

[0207] Determine that the fusion loss is the sum of the squares of the relative distances between all local positioning results of the user and the global positioning result of the user;

[0208] For each selected reconfigurable intelligent surface, if the user is in the near field of the selected reconfigurable intelligent surface, the relative distance between the global positioning result of the user and the local positioning result is the Euclidean distance between the global positioning result of the user and the local positioning result;

[0209] For each selected reconfigurable intelligent surface, if the relative distance between the global positioning result of the user and the local positioning result is the minimum distance between the global positioning result of the user and the far-field ray;

[0210] Convert the fusion loss minimization problem into a quadratic programming problem, and solve to obtain the global positioning result of the user.

[0211] In a possible implementation manner, when the above-mentioned processing module 35 is used to perform reconfigurable intelligent surface selection optimization, update the selected reconfigurable intelligent surface, and update the phase shift configuration of each selected reconfigurable intelligent surface in the next positioning period according to the updated selected reconfigurable intelligent surface and the global positioning result of the user, it is specifically configured to:

[0212] Select a predetermined number of reconfigurable intelligent surfaces as the selected reconfigurable intelligent surfaces in the order of decreasing channel gain;

[0213] Based on the updated selected reconfigurable intelligent surface and the global positioning result of the user, optimize the phase shift configuration of each selected reconfigurable intelligent surface based on the alternating direction multiplier method, and update the phase shift configuration of each selected reconfigurable intelligent surface in the next positioning period.

[0214] The reconfigurable intelligent surface assisted positioning system provided by the present invention includes multiple reconfigurable intelligent surfaces. The reconfigurable intelligent metasurface has the characteristics of low power consumption and low cost, and can achieve channel customization by changing the signal phase shift. Therefore, by deploying multiple large-scale reconfigurable intelligent metasurface arrays, the power consumption and cost of the positioning system can be reduced. By receiving the reflected signals of multiple selected reconfigurable intelligent surfaces, multipath signals can be utilized to improve the positioning accuracy. Further, according to the reflected signals of the selected reconfigurable intelligent surfaces and the phase shift configuration of the selected reconfigurable intelligent surfaces in the current positioning cycle, the local positioning result of the user is determined. By considering the phase shift configuration of the reconfigurable intelligent surfaces in the current positioning cycle, the signal propagation path can be more accurately modeled. Further, all the local positioning results of the user are fused to obtain the global positioning result of the user. Fusing multiple local positioning results can reduce the error caused by a single reconfigurable intelligent surface and improve the robustness and accuracy of the positioning. Further, when the global positioning result does not meet the accuracy requirements, an optimization process is executed, which can effectively improve the positioning accuracy. Therefore, the solution of the present application can reduce the power consumption and cost of the positioning system while improving the positioning accuracy.

[0215] Figure 4 is a schematic structural diagram of the electronic device provided by the present invention, as Figure 4As shown in the figure, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communication bus 440. Among them, the processor 410, the communications interface 420, and the memory 430 complete communication with each other through the communication bus 440. The processor 410 may call the logical instructions in the memory 430 to execute a reconfigurable intelligent surface assisted positioning method, which includes: receiving the reflected signals of a plurality of selected reconfigurable intelligent surfaces corresponding to the user, and the reflected signals of the selected reconfigurable intelligent surfaces are the signals obtained by reflecting the signals sent by the user by the selected reconfigurable intelligent surfaces. For each selected reconfigurable intelligent surface corresponding to the user, determine the local positioning result of the user according to the reflected signal of the selected reconfigurable intelligent surface and the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle. Fuse all the local positioning results of the user to obtain the global positioning result of the user. Determine whether the global positioning result of the user meets the accuracy requirements; where the accuracy requirements include that the positioning accuracy of the global positioning result of the user is less than a preset threshold or the number of positioning cycles reaches the maximum number of iterations; if the global positioning result of the user meets the accuracy requirements, then use the global positioning result of the user as the final positioning result of the user; if the global positioning result of the user meets the accuracy requirements, then perform optimization processing and return to the step of receiving the reflected signals of each selected reconfigurable intelligent surface. Among them, the optimization processing includes: performing optimization on the selection of reconfigurable intelligent surfaces, updating the selected reconfigurable intelligent surfaces, and according to the updated selected reconfigurable intelligent surfaces and the global positioning result of the user, updating the phase shift configuration of each selected reconfigurable intelligent surface in the next positioning cycle.

[0216] In addition, when the logical instructions in the above-mentioned memory 430 can be implemented in the form of software functional units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0217] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the reconfigurable intelligent surface assisted positioning method provided by the above-mentioned various methods. The method includes: receiving the reflection signals of a plurality of selected reconfigurable intelligent surfaces corresponding to the user, where the reflection signals of the selected reconfigurable intelligent surfaces are the signals obtained by reflecting the signals sent by the user by the selected reconfigurable intelligent surfaces. For each selected reconfigurable intelligent surface corresponding to the user, determine the local positioning result of the user according to the reflection signal of the selected reconfigurable intelligent surface and the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle. Perform result fusion on all the local positioning results of the user to obtain the global positioning result of the user. Determine whether the global positioning result of the user meets the accuracy requirement; where the accuracy requirement includes that the positioning accuracy of the global positioning result of the user is less than a preset threshold or the number of positioning cycles reaches the maximum number of iterations; if the global positioning result of the user meets the accuracy requirement, then use the global positioning result of the user as the final positioning result of the user; if the global positioning result of the user meets the accuracy requirement, then perform optimization processing and return to the step of receiving the reflection signals of each selected reconfigurable intelligent surface. Among them, the optimization processing includes: performing optimization on the selection of reconfigurable intelligent surfaces, updating the selected reconfigurable intelligent surfaces, and updating the phase shift configuration of each selected reconfigurable intelligent surface in the next positioning cycle according to the updated selected reconfigurable intelligent surfaces and the global positioning result of the user.

[0218] In another aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the reconfigurable intelligent surface assisted positioning method provided by the above-mentioned various methods. The method includes: receiving the reflected signals of a plurality of selected reconfigurable intelligent surfaces corresponding to a user, where the reflected signals of the selected reconfigurable intelligent surfaces are the signals obtained by reflecting the signals sent by the user by the selected reconfigurable intelligent surfaces. For each selected reconfigurable intelligent surface corresponding to the user, determine the local positioning result of the user according to the reflected signal of the selected reconfigurable intelligent surface and the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle. Perform result fusion on all the local positioning results of the user to obtain the global positioning result of the user. Determine whether the global positioning result of the user meets the accuracy requirement; where the accuracy requirement includes that the positioning accuracy of the global positioning result of the user is less than a preset threshold or the number of positioning cycles reaches the maximum number of iterations; if the global positioning result of the user meets the accuracy requirement, then use the global positioning result of the user as the final positioning result of the user; if the global positioning result of the user meets the accuracy requirement, then perform optimization processing and return to the step of receiving the reflected signals of each selected reconfigurable intelligent surface. Among them, the optimization processing includes: performing optimization on the selection of reconfigurable intelligent surfaces, updating the selected reconfigurable intelligent surfaces, and according to the updated selected reconfigurable intelligent surfaces and the global positioning result of the user, updating the phase shift configuration of each selected reconfigurable intelligent surface in the next positioning cycle.

[0219] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0220] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solutions in essence or the parts that contribute to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0221] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A reconfigurable intelligent surface assisted positioning method, characterized in that, A base station applied to a reconfigurable intelligent surface assisted positioning system, the reconfigurable intelligent surface assisted positioning system including a base station and a plurality of reconfigurable intelligent surfaces; the method includes: Receiving reflected signals of a plurality of selected reconfigurable intelligent surfaces corresponding to a user, where the reflected signals of the selected reconfigurable intelligent surfaces are signals obtained by reflecting signals sent by the user by the selected reconfigurable intelligent surfaces; For each selected reconfigurable intelligent surface corresponding to the user, determining a local positioning result of the user according to the reflected signal of the selected reconfigurable intelligent surface and the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle; Performing result fusion on all local positioning results of the user to obtain a global positioning result of the user; Determining whether the global positioning result of the user meets the accuracy requirement; where the accuracy requirement includes that the positioning accuracy of the global positioning result of the user is less than a preset threshold or the number of positioning cycles reaches the maximum number of iterations; If the global positioning result of the user meets the accuracy requirement, then using the global positioning result of the user as the final positioning result of the user; otherwise, performing an optimization process and returning to the step of receiving the reflected signals of a plurality of selected reconfigurable intelligent surfaces corresponding to the user; where the optimization process includes: performing optimization on the selection of reconfigurable intelligent surfaces, updating the selected reconfigurable intelligent surfaces, and updating the phase shift configuration of each selected reconfigurable intelligent surface in the next positioning cycle according to the updated selected reconfigurable intelligent surfaces and the global positioning result of the user.

2. The reconfigurable intelligent surface assisted positioning method according to claim 1, wherein After receiving the reflected signals of a plurality of selected reconfigurable intelligent surfaces corresponding to the user, the method further includes: Determining the field type where the user is located according to the signal characteristics of the reflected signals of the selected reconfigurable intelligent surfaces; where the signal characteristics include at least one of the following: phase, amplitude, time delay; the field type includes the near field of the reconfigurable intelligent surface and the far field of the reconfigurable intelligent surface.

3. The reconfigurable intelligent surface assisted positioning method according to claim 2, wherein The step of, for each selected reconfigurable intelligent surface corresponding to the user, determining a local positioning result of the user according to the reflected signal of the selected reconfigurable intelligent surface and the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle, includes: Establishing a local coordinate system corresponding to each selected reconfigurable intelligent surface, and respectively sampling the near field and the far field of the selected reconfigurable intelligent surface to obtain a plurality of sampling points; Based on the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle, constructing an atomic channel corresponding to each sampling point; the atomic channel corresponding to the sampling point characterizes the propagation characteristics of the signal from the user to the reconfigurable intelligent surface and then to the base station; Applying the orthogonal matching pursuit algorithm to match the reflected signal of the selected reconfigurable intelligent surface and the atomic channel corresponding to each sampling point to determine the local positioning result of the user in the local coordinate system of the selected reconfigurable intelligent surface.

4. The reconfigurable intelligent surface assisted positioning method according to claim 2, wherein The step of performing result fusion on all local positioning results of the user to obtain a global positioning result of the user, includes: Determining that the fusion loss is the sum of the squares of the relative distances between all local positioning results of the user and the global positioning result of the user; For each selected reconfigurable intelligent surface, if the user is in the near field of the selected reconfigurable intelligent surface, the relative distance between the user's global positioning result and the local positioning result is the Euclidean distance between the user's global positioning result and the local positioning result; For each selected reconfigurable intelligent surface, if the user is in the far field of the selected reconfigurable intelligent surface, the relative distance between the user's global positioning result and the local positioning result is the minimum distance between the user's global positioning result and the far-field ray; Transform the fusion loss minimization problem into a quadratic programming problem and solve it to obtain the user's global positioning result.

5. The reconfigurable intelligent surface assisted positioning method according to claim 1, wherein Perform reconfigurable intelligent surface selection optimization, update the selected reconfigurable intelligent surfaces, and update the phase shift configuration of each selected reconfigurable intelligent surface for the next positioning cycle according to the updated selected reconfigurable intelligent surfaces and the user's global positioning result, including: Select a predetermined number of reconfigurable intelligent surfaces as the selected reconfigurable intelligent surfaces in the order of decreasing channel gain; Based on the updated selected reconfigurable intelligent surfaces and the user's global positioning result, optimize the phase shift configuration of each selected reconfigurable intelligent surface using the alternating direction method of multipliers, and update the phase shift configuration of each selected reconfigurable intelligent surface for the next positioning cycle.

6. The reconfigurable intelligent surface assisted positioning method according to any one of claims 1-5, characterized in that, After receiving the reflected signals of the multiple selected reconfigurable intelligent surfaces corresponding to the user, the method further includes: Distinguish the reflected signals of different selected reconfigurable intelligent surfaces through digital beamforming.

7. A reconfigurable intelligent surface assisted positioning system, characterized in that, The system includes a base station and multiple reconfigurable intelligent surfaces; the base station includes: A receiving module, configured to receive the reflected signals of the multiple selected reconfigurable intelligent surfaces corresponding to the user, where the reflected signals of the selected reconfigurable intelligent surfaces are the signals obtained by reflecting the signals sent by the user by the selected reconfigurable intelligent surfaces; A local positioning module, configured to, for each selected reconfigurable intelligent surface corresponding to the user, determine the local positioning result of the user according to the reflected signal of the selected reconfigurable intelligent surface and the phase shift configuration of the selected reconfigurable intelligent surface in the current positioning cycle; A fusion module, configured to fuse all the local positioning results of the user to obtain the global positioning result of the user; A judgment module, configured to judge whether the global positioning result of the user meets the accuracy requirement; where the accuracy requirement includes that the positioning accuracy of the global positioning result of the user is less than a preset threshold or reaches the maximum number of iterations of the positioning cycle; A processing module, configured to, if the global positioning result of the user meets the accuracy requirement, use the global positioning result of the user as the final positioning result of the user; otherwise, perform optimization processing and return to the step of receiving the reflected signals of the multiple selected reconfigurable intelligent surfaces corresponding to the user; where the optimization processing includes: performing reconfigurable intelligent surface selection optimization, updating the selected reconfigurable intelligent surfaces, and updating the phase shift configuration of each selected reconfigurable intelligent surface for the next positioning cycle according to the updated selected reconfigurable intelligent surfaces and the user's global positioning result.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the reconfigurable intelligent surface assisted positioning method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the reconfigurable intelligent surface assisted positioning method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the reconfigurable intelligent surface assisted positioning method according to any one of claims 1 to 6.

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