Near-field array related information positioning method and device based on intelligent reflecting surface, and medium

Abstract

The invention discloses a near-field array related information positioning method and device based on an intelligent reflecting surface, and a medium. The method comprises the following steps: inputting the number and position of access point antennas, the number and position of intelligent reflecting surface units, and a pilot frequency sequence signal sent by a positioned user; according to channel information between the access point and the intelligent reflection surface, joint design is carried out on receiving beam forming of the access point and a fixed reflection coefficient of an intelligent reflection surface unit; a near-field codebook of the intelligent reflecting surface and a layered near-field codebook beam training method are designed to carry out low-training-overhead searching; acquiring stacked receiving signals of the access point by using a receiving signal set obtained by codebook training, and estimating a near-field array covariance matrix of the intelligent reflecting surface; extracting special elements in a covariance matrix of the near-field array of the intelligent reflecting surface, decoupling and respectively estimating information in the near-field array so as to estimate the position of a user. According to the invention, the positioning work can be completed in a single access point scene, and the near-field positioning performance is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless positioning detection, and in particular, to a positioning method, device, and medium for near-field array-related information based on an intelligent reflecting surface. Background Art

[0002] Intelligent reflecting surface is a key technology that has received extensive attention in the sixth-generation wireless communication era. It controls a large number of low-cost passive reflecting elements to appropriately adjust signal reflection, dynamically configure the signal propagation environment in a wireless network, and thus improve the performance of communication and sensing systems. In particular, by establishing a virtual line-of-sight link for the transceiver whose direct link is blocked, the intelligent reflecting surface can greatly improve the channel quality of a wireless positioning system.

[0003] Traditional wireless positioning is achieved by multiple active sensing nodes measuring geometric information in channel state information, such as received signal strength, time of arrival, time difference of arrival, and angle of arrival. However, this requires a sufficient number of data sets to ensure reliability. Using passive intelligent reflecting surfaces instead of some active nodes can avoid complex coordination and interference management and reduce overall power consumption. Therefore, intelligent reflecting surfaces show great potential in providing additional degrees of freedom for wireless positioning.

[0004] However, an intelligent reflecting surface usually has a large number of reflecting elements, and the plane of its passive reflecting units has a large size, such that typical indoor scenarios fall within its Fresnel region, making the traditional far-field propagation model inaccurate. When considering a more accurate near-field channel model that includes both angle-of-arrival and distance information, the parameters included in the near-field array response vector are already sufficient to directly determine the position of the target, which makes a single-node positioning system possible.

[0005] In summary, based on the emerging intelligent reflecting surface technology and a more accurate near-field channel model, existing wireless positioning methods need further improvement and perfection. Summary of the Invention

[0006] To at least partly solve one of the technical problems existing in the prior art, an object of the present invention is to provide a positioning method, device, and medium for near-field array-related information based on an intelligent reflecting surface, which can simply and feasibly estimate user position information with high precision and low training overhead by using the covariance matrix of the near-field array response vector of the intelligent reflecting surface.

[0007] The first technical solution adopted by the present invention is:

[0008] A positioning method for near-field array-related information based on an intelligent reflecting surface, comprising the following steps:

[0009] S1. Input the number and positions of access point antennas, the number and positions of intelligent reflecting surface units, and the pilot sequence signal sent by the user to be located;

[0010] S2. Jointly design the receive beamforming of the access point and the fixed reflection coefficients of the intelligent reflecting surface units according to the channel information between the access point and the intelligent reflecting surface;

[0011] S3. Design the near-field codebook of the intelligent reflecting surface and the hierarchical near-field codebook beam training method for low-training-overhead search;

[0012] S4. Use the set of received signals obtained from codebook training to obtain the stacked received signals of the access point, and use the least squares method to estimate the near-field array covariance matrix of the intelligent reflecting surface;

[0013] S5. Extract the special elements in the near-field array covariance matrix of the intelligent reflecting surface, decouple and separately estimate the elevation angle, azimuth angle, and distance information in the near-field array, and then estimate the user position.

[0014] Further, step S1 includes:

[0015] Assume that at time block t, the user to be located sends a string of mean-normalized pilot sequence signals \(x = [x 1 ,...,x m ,...,x M T , and the superscript \((\cdot) T is defined as the transpose operation. Input the number of access point antennas \(N A and position \(p A , the number of intelligent reflecting surface units \(N I and position \(p I , and set the initial reflection coefficient matrix \(\Theta t of the intelligent reflecting surface.

[0016] Further, the mathematical modeling of the intelligent reflecting surface is as follows:

[0017] Assume that there is an access point and an intelligent reflecting surface in an indoor environment, which have \(N A antennas and \(N I units respectively, and the receiving antennas and passive reflecting units are arranged in the form of a uniform planar array. For the convenience of explanation, assume that the number of passive reflecting units of the intelligent reflecting surface is odd. Then, taking the center point of the intelligent reflecting surface as the origin and the plane where the intelligent reflecting surface is located as the y-z plane, a three-dimensional Cartesian coordinate system is established. The receiving antennas distributed along the x-axis and y-axis are \(N 1 and \(N 2 respectively, and \(N A = N 1 ×N 2 ​; The passive reflection units distributed along the y-axis and z-axis are N y and N z , and N I = N y × N z ; At time block t, the reflection coefficient matrix set by the intelligent reflecting surface is expressed as:

[0018]

[0019] where and are the amplitude and phase limits of the reflection coefficient of the nth passive reflection unit. diag(·) is defined as the vector diagonal matrix operation, |·| is defined as the modulus value, and ∠(·) is defined as the argument.

[0020] Furthermore, the step S2 includes:

[0021] Assume that the intelligent reflecting surface is close to the side of the user to be located; Since the positions of the access point and the intelligent reflecting surface are fixed, the channel state information between the two is regarded as a known far-field channel:

[0022]

[0023] where α AI is defined as the complex path gain, and are the far-field array response vectors of the receiving antenna plane and the passive reflection unit plane, and their dimensions are N A and N I , and are the corresponding arrival angle pairs and departure angle pairs respectively;

[0024] The channel state information between the intelligent reflecting surface and the user is an unknown near-field channel:

[0025]

[0026] where α IU is defined as the complex path gain, is defined as the near-field array response vector of the passive reflection unit plane with a dimension of N I , is the arrival angle pair, d represents the distance from the user to the center reference point of the intelligent reflecting surface, represents the distance difference between the user to the center reference point of the intelligent reflecting surface and to the nth I passive reflection unit of the intelligent reflecting surface; For the row index column index and the passive reflection unit number n I , the relational expression

[0027] At time block \(t\), the signal received by the access point can be expressed as \(y\) t =[y t,1 ,…,y t,m ,…,y t,M T , where \(P\) represents the antenna transmission power of the user, \(n\) t,m represents an additive white Gaussian noise vector with zero mean and variance \(\sigma\) 2 , and \(f\) represents the normalized receive beamforming vector of the access point, with dimension \(N\) A ;

[0028] Define \(\Theta\) t as the product of a fixed reflection coefficient matrix \(\Theta\) A and a time-varying reflection coefficient matrix , where The superscript \((\cdot)\) * is defined as the conjugate operation; then, by designing the signal received by the access point is re-expressed as:

[0029]

[0030] where represents the total complex path gain, represents an all-ones vector with dimension \(N\) x , and \(n\) t,m =f T n t,m .

[0031] Furthermore, step S3 includes:

[0032] According to the calculation result of S2, the optimal is designed as:

[0033]

[0034] Since the user location \(p\) U is unknown, it is impossible to directly know Therefore, a near-field codebook and a hierarchical near-field codebook beam training method are designed to perform a search with low training overhead;

[0035] Assume that the hierarchical near-field codebook has \(L\) layers of sub-codebooks. Using the Fresnel approximation, approximate as:

[0036]

[0037] where and \(\omega = \sin\varphi\)、 ​And γ = 1 / d are improved parameters for more scientific sampling; assuming that the search range of each dimension of the l-th layer sub-codebook (l = 1,..., L) is The number of samples for each dimension is Then the S l set of sampling points of the l-th layer sub-codebook is:

[0038]

[0039] According to this set of sampling points, the l-th layer sub-codebook is expressed as where ⊙ represents the Hadamard product.

[0040] Furthermore, the hierarchical near-field codebook beam training method includes:

[0041] First, determine the number of samples for each dimension of the L-layer sub-codebook and the search range of each dimension of the first layer sub-codebook

[0042] Secondly, for l = 1,..., L, enter the following loop:

[0043] First, obtain the l-th layer sub-codebook W l ;

[0044] Secondly, for s l = 1,..., S l , let Enter the following loop:

[0045] According to equation Get Deposit into the set

[0046] End the loop

[0047] Then, search in to obtain the best index

[0048] Finally, update the search range of each dimension:

[0049] End the loop

[0050] Finally, obtain the set

[0051] Furthermore, the step S4 includes:

[0052] According to the calculation result of step S3, if the training cost number τ of the last L - J + 1 layer sub-codebooks is τ = S J +...+ S L satisfies τ ≥ NI , the received signals in are stacked as:

[0053] Y = [y 1 ,..., y τ T

[0054] = GX + N,

[0055] where represents a full column rank stacked matrix that collects channel gains in τ beam trainings, Θ 1 ,..., Θ τ are the corresponding reflection coefficient matrices of y 1 ,..., y τ respectively, and N represents the stacked signal matrix; using the least squares method, X is estimated as:

[0056] X = (G H G) -1 G H Y

[0057] Ignoring noise, the covariance matrix of the near - field array response vector from the user to the intelligent reflecting surface is:

[0058] R = |α| 2 b(ω, ν, γ)b H (ω, ν, γ)

[0059] where the superscript (·) H is defined as the conjugate transpose operation; in actual operation, R is estimated through X:

[0060] R = XX H / M

[0061] The (n I1 , n I2 ) - th element in R shows the correlation information of the corresponding two passive reflecting elements of the intelligent reflecting surface, denoted as (n I1 , n I2 = 1,..., N I ):

[0062]

[0063] where and respectively represent the row index and column index of the n I1 - th and n I2 - th passive reflecting elements, and satisfy the relational expressions and

[0064] Further, ​

[0065] Furthermore, step S5 includes:

[0066] According to the calculation result of step S4, for any two intelligent reflecting surface passive reflecting units satisfying the relationship n y2 = n y1 , n z2 = -n z1 (i.e., n I2 = n I1 - 2n z1 ), the relevant information of the intelligent reflecting surface passive reflecting unit is as follows:

[0067]

[0068] Construct a vector that contains N I elements in R that satisfy this relationship:

[0069]

[0070] Let represent the estimated value of c ω , denote as the argument vector of

[0071]

[0072] and design the vector:

[0073] For any two intelligent reflecting surface passive reflecting units satisfying the relationship n y2 = -n y1 , n z2 = -n z1 (i.e., n I2 = N I + 1 - 2n I1 ), the relevant information of the intelligent reflecting surface passive reflecting unit is as follows:

[0074]

[0075] Construct a vector that contains N I elements in R that satisfy this relationship:

[0076]

[0077] Obviously, there is So, let represent the estimated value of c ν , and design the vector:

[0078]

[0079] Then, using the least squares method, is estimated as:

[0080] Construct the far-field array response vector In this way, the estimated covariance matrix R = ΛRΛ H is transformed into being only related to the distance parameter, where is the estimated value of; the (n I1 , n I2 )-th element in R is expressed as:

[0081]

[0082] Let Q γ = ∠(R), and design the matrix U γ , where the (n I1 , n I2 )-th element is expressed as:

[0083]

[0084] Using the least squares method, d is estimated as:

[0085] Finally, based on and the user's position is estimated as:

[0086]

[0087] Thus, the positioning of the user is achieved.

[0088] The second technical solution adopted by the present invention is:

[0089] An electronic device, the electronic device includes a processor and a memory, and at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement a positioning method of near-field array related information based on an intelligent reflecting surface as described above.

[0090] The third technical solution adopted by the present invention is:

[0091] A computer-readable storage medium, and at least one instruction, at least one program, a code set or an instruction set is stored in the storage medium, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement a positioning method of near-field array related information based on an intelligent reflecting surface as described above.

[0092] The fourth technical solution adopted by the present invention is as follows:

[0093] A computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to enable the computer device to execute the above-mentioned method.

[0094] The present invention has the following advantages and effects compared with the prior art:

[0095] 1) A positioning method for near-field array-related information based on an intelligent reflecting surface proposed by the present invention, compared with traditional technologies, this method avoids the problems of high energy consumption, synchronization, and interference of multiple active nodes, greatly reducing costs and energy consumption, and significantly improving the positioning effect.

[0096] 2) A positioning method for near-field array-related information based on an intelligent reflecting surface proposed by the present invention only requires a relatively low beam training overhead to solve the problem of insufficient matrix rank in the least squares estimation, and compared with existing methods, this method greatly improves the received signal-to-noise ratio at the access point, thus significantly improving the positioning effect.

[0097] 3) A positioning method for near-field array-related information based on an intelligent reflecting surface proposed by the present invention is a closed-form solution, which does not require iterative calculations, greatly reducing the computational complexity and having a low requirement for hardware performance. Description of the Drawings

[0098] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following introduces the accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings in the following introduction are only for conveniently and clearly expressing some embodiments of the technical solutions in the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative efforts.

[0099] Figure 1 It is a model diagram of an intelligent reflecting surface-assisted uplink indoor positioning system in an embodiment of the present invention;

[0100] Figure 2 It is a flowchart of a positioning method for near-field array-related information based on an intelligent reflecting surface of the present invention;

[0101] Figure 3 It is a performance comparison simulation diagram in Embodiment 1 of the present invention;

[0102] Figure 4It is another performance comparison simulation diagram in Embodiment 1 of the present invention. Detailed implementation manners

[0103] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention, and should not be construed as a limitation to the present invention. For the step numbers in the following embodiments, they are only set for the convenience of explanation and illustration, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0104] In the description of the present invention, it should be understood that for the orientation description, such as the orientation or positional relationship indicated by up, down, front, back, left, right, etc. is based on the orientation or positional relationship shown in the drawings, and it is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention.

[0105] In the description of the present invention, the meaning of several is one or more, the meaning of multiple is two or more, greater than, less than, exceeding, etc. are understood as not including the present number, and above, below, within, etc. are understood as including the present number. If there is a description of first and second, it is only for the purpose of distinguishing technical features and should not be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features.

[0106] In the description of the present invention, unless otherwise clearly defined, words such as setting, installation, connection, etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above words in the present invention in combination with the specific content of the technical solution.

[0107] Please refer to Figure 1 , Figure 1 is the model diagram of the intelligent reflecting surface-assisted uplink indoor positioning system in all embodiments of the present invention. The wireless positioning system includes 1 single-antenna user, 1 intelligent reflecting surface with N I =N y ×N z passive reflecting units, 1 access point with N A =N 1 ×N 2 receiving antennas, and 1 intelligent reflecting surface controller. The controller adjusts the reflection coefficients of the passive reflecting units of the intelligent reflecting surface. The access point receives the pilot sequence signal sent by the user for positioning.

[0108] To illustrate the technical advancement of the method of the present invention, on the MATLAB platform, the positioning method of the present invention based on the intelligent reflecting surface for near-field array-related information is compared with other codebook designs in different embodiments in terms of the normalized received signal strength of the system. Among them, other designs include: 1) Single-layer near-field codebook: The reflection phase shift of the intelligent reflecting surface is searched in the hierarchical near-field codebook in the special case of L = 1; 2) Discrete Fourier transform codebook method: The reflection phase shift of the intelligent reflecting surface is searched in the codebook based on the discrete Fourier transform to maximize the normalized received signal strength.

[0109] In addition, on the MATLAB platform, the positioning method of the present invention based on the intelligent reflecting surface for near-field array-related information is compared with other positioning algorithms in different embodiments in terms of the mean square error of position estimation. Among them, other designs include: 1) The proposed algorithm (in the case of random reflection coefficients): The reflection phase shift of the intelligent reflecting surface when the access point receives the signal is randomly generated uniformly in the interval [0, 2π), and the positioning method uses the algorithm proposed by the present invention; 2) Cramer-Rao bound-based positioning algorithm: The reflection phase shift of the intelligent reflecting surface is set by minimizing the Cramer-Rao bound, and the maximum likelihood estimation method is used to estimate the position; 3) Grid search-based positioning algorithm: Based on the subspace, the method of grid search for spectral peaks is used to estimate the position.

[0110] Embodiment 1

[0111] In this Embodiment 1, the specific parameter settings are as follows:

[0112] Taking the center point of the intelligent reflecting surface as the origin, a three-dimensional Cartesian coordinate system is established with the plane where the intelligent reflecting surface is located as the y-z plane. In each simulation of the system, the position of the single-antenna user is randomly generated uniformly in the cuboid space where x ∈ [1m, 5m], y ∈ [-3m, 3m], and z ∈ [-1m, 1m]. At the same time, it is assumed that the carrier wavelength λ of the user's transmitted signal is 0.015m. In addition, it is set that the element intervals of the receiving antenna / passive reflection unit of the access point / intelligent reflecting surface are all Δ 1 = Δ 2 = Δ y = Δ z = 0.5λ = 0.0075m. The noise power of the access point is σ 2 = -120dBm. For each independent link, the reference path gain at 1m is α = -30dB. If not otherwise specified, the transmit power of the user is set to P = 0dBm. The performance evaluation of the system is based on the average value of 100 simulations.

[0113] The following will be combined with Figure 1 and Figure 2, specifically describe the process steps of a positioning method for near-field array-related information based on an intelligent reflecting surface disclosed in Embodiment 1.

[0114] In this Embodiment 1, step S1 is specifically implemented as follows:

[0115] Assume that at time block t, the user to be located sends a string of mean-normalized pilot sequence signals x = 1 10 (i.e., M = 10), representing a all-ones vector of dimension N x and the number of access point antennas N A = 3×3 and position p A = [2m, -10m, 0] T , the number of intelligent reflecting surface units N I = 21×21 and position p I = [0, 0, 0] T , the superscript (·) T is defined as the transpose operation, and the initial reflection coefficient matrix Θ of the intelligent reflecting surface is set t , and the reflection coefficient matrix of the intelligent reflecting surface is expressed as: where and are the amplitude and phase limits of the reflection coefficient of the nth passive reflection unit, diag(·) is defined as the vector diagonal matrix operation, |·| is defined as the modulus value, and ∠(·) is defined as the argument.

[0116] In this Embodiment 1, step S2 is specifically implemented as follows:

[0117] Assume that the intelligent reflecting surface is close to the user side to be located. Since the positions of the access point and the intelligent reflecting surface are fixed, the channel state information between them can be regarded as a known far-field channel:

[0118]

[0119] where, α AI is defined as the complex path gain, and are the far-field array response vectors of the receiving antenna plane and the passive reflection unit plane, respectively, and their dimensions are N A and N I , and are the corresponding arrival angle pairs and departure angle pairs, respectively. Taking as an example, it can be expressed as:

[0120]

[0121] where, e y and e zis the one-dimensional far-field steering vector. For convenience of representation, let and κ z = 2πΔ z sinφ I / λ represent the constant phase differences between the signals on two adjacent passive reflecting elements along the y-axis and the z-axis respectively, where Δ y and Δ z represent the corresponding passive reflecting element spacings respectively. denotes the Kronecker product, and j represents the imaginary unit.

[0122] The channel state information between the intelligent reflecting surface and the user is an unknown near-field channel:

[0123]

[0124] where α IU is defined as the complex path gain, is defined as the near-field array response vector of the passive reflecting element plane with dimension N I , is the pair of angles of arrival, d represents the distance from the user to the center reference point of the intelligent reflecting surface, represents the distance difference from the user to the center reference point of the intelligent reflecting surface and to the n I th passive reflecting element of the intelligent reflecting surface. For the row index column index and the passive reflecting element serial number n I , the relational expression

[0125] is satisfied. Thus, at time block t, the signal received by the access point can be expressed as y t = [y t,1 ,..., y t,m ,..., y t,M T , where P represents the antenna transmit power of the user, n t,m represents the additive Gaussian white noise vector with mean zero and variance σ 2 , and f represents the normalized receive beamforming vector of the access point with dimension N A .

[0126] Define Θ t as the product of a fixed reflection coefficient matrix Θ A and a time-varying reflection coefficient matrix , where The superscript (·) * is defined as the conjugate operation. Then, by designing the signal received by the access point can be re-expressed as:​

[0127]

[0128] Among them, represents the total complex path gain, n t,m = f T n t,m .

[0129] In this Embodiment 1, step S3 is specifically implemented as follows:

[0130] According to the calculation result of S2, the optimal should be designed as:

[0131]

[0132] Since the user location p U is unknown and it is impossible to directly know Therefore, a near - field codebook and a hierarchical near - field codebook beam training method are designed to perform a search with low training overhead. Assume that the hierarchical near - field codebook has L layers of sub - codebooks. Using the Fresnel approximation, is approximated as:

[0133]

[0134] Among them, while ω = sinφ, and γ = 1 / d are improved parameters for more scientific sampling. Assume that the search range of each dimension of the l - th layer sub - codebook (l = 1,..., L) is and the number of samples in each dimension is Then the set of the S l -th sampling points of the l - th layer sub - codebook is:

[0135]

[0136] According to this set of sampling points, the l - th layer sub - codebook is represented as Among them, ⊙ represents the Hadamard product.

[0137] Design the following hierarchical near - field codebook beam training method:

[0138] First, determine the number of samples in each dimension of the L = 3 - layer sub - codebook and the search range of each dimension of the first - layer sub - codebook

[0139] Secondly, for l = 1,..., L, enter the following loop:

[0140] First, obtain the l - th layer sub - codebook W l ;

[0141] Secondly, for s l = 1, ..., S l , let enter the following loop:

[0142] According to equation obtain store in the set

[0143] end the loop

[0144] Then, search in to obtain the best index

[0145] Finally, update the search range of each dimension:

[0146] end the loop

[0147] Finally, obtain the set

[0148] In this Embodiment 1, step S4 is specifically implemented as follows:

[0149] According to the calculation result of S3, if the training cost number τ of the last L - J + 1 - layer sub - codebook satisfies τ = S J +...+S L and τ ≥ N I , then stack the received signals in as:

[0150] Y = [y 1 ,..., y τ T

[0151] = GX + N,

[0152] where represents the full - column - rank stacked matrix for collecting channel gains in τ beam trainings, Θ 1 ,..., Θ τ are the respective reflection coefficient matrices corresponding to y 1 ,…, y τ , and N represents the stacked signal matrix. Using the least - squares method, X is estimated as:

[0153] X = (G H G) -1 G H Y.

[0154] Ignoring the noise, the covariance matrix of the near - field array response vector from the user to the intelligent reflecting surface is:

[0155] R = |α|​2 b(ω, ν, γ)b H (ω, ν, γ).

[0156] Among them, the superscript (·) H is defined as the conjugate transpose operation. In actual operation, we estimate R through X: R = XX H / M.

[0157] The (n I1 , n I2 )-th element in R represents the correlation information of the corresponding two passive reflecting elements of the intelligent reflecting surface, denoted as (n I1 , n I2 = 1, …, N I ):

[0158]

[0159] Among them and respectively represent the row index and column index of the n I1 -th and n I2 -th passive reflecting elements, and satisfy the relational expressions and

[0160] Furthermore

[0161] In this Embodiment 1, step S5 is specifically implemented as follows:

[0162] According to the calculation result of S4, for any two intelligent reflecting surface passive reflecting elements that satisfy the relationship n y2 = n y1 , n z2 = -n z1 (i.e., n I2 = n I1 - 2n z1 ), the correlation information is:

[0163]

[0164] Construct a vector that contains N I elements in R that satisfy this relationship:

[0165]

[0166] Let represent the estimated value of c ω , denote 's argument vector, and design the vector:

[0167]

[0168] Then, using the least squares method, φ is estimated as:

[0169] For any two that satisfy the relationship n y2 = -n y1 , n z2 = -n z1 (i.e., n I2 = N I + 1 - 2n I1 ), the relevant information of the passive reflection unit of the intelligent reflecting surface is:

[0170]

[0171] Construct a vector that contains N I elements in R that satisfy this relationship:

[0172]

[0173] Obviously, there is So, let represent the estimated value of c ν , and design the vector:

[0174]

[0175] Then, using the least squares method, is estimated as:

[0176] Similar to construct the far - field array response vector In this way, the estimated covariance matrix R = ΛRΛ H is transformed into being only related to the distance parameter, where is 's estimated value. The (n I1 , n I2 ) - th element in R is expressed as:

[0177]

[0178] Let Q γ = ∠(R), and design the matrix U γ , where the (n I1 , n I2 ) - th element is expressed as:

[0179]

[0180] Then, using the least squares method, d is estimated as:

[0181] Finally, based on and the position of the user is estimated as:

[0182]

[0183] Thus, the positioning of the user is achieved.

[0184] As Figure 3 shown, Figure 3 the relationship between the normalized received signal strength and the beam training overhead under different codebook designs in the intelligent reflecting surface assisted uplink indoor positioning system is plotted. The normalized received signal strength of the method of the embodiment of the present invention is significantly higher than that of the single-layer near-field codebook and the discrete Fourier transform codebook. In addition, when the beam training overhead is 768, the method in this embodiment approaches the perfect beamforming normalized received signal strength, while the discrete Fourier transform codebook is only 0.65 of the normalized received signal strength, and the single-layer near-field codebook is only 0.05 of the normalized received signal strength.

[0185] As Figure 4 shown, Figure 4 the relationship between the mean square error of position estimation and the user transmission power under the positioning algorithm design in the intelligent reflecting surface assisted uplink indoor positioning system is plotted. The mean square error of position estimation of the method of the embodiment of the present invention is significantly lower than that of the positioning algorithm based on the Cramer-Rao bound and the positioning algorithm based on grid search. In addition, in the case of beam training, the mean square error of position estimation of the algorithm proposed in the present invention is about 9 dB lower than that in the case of random reflection coefficients, reflecting the great improvement in positioning performance by the well-designed intelligent reflecting surface phase shift coefficient. Moreover, when the user transmission power exceeds 0 dBm, even without beam training, the performance of the positioning algorithm proposed in the present invention is better than that of the positioning algorithm based on the Cramer-Rao bound and the positioning algorithm based on grid search.

[0186] In summary, the positioning method based on the near-field array related information of the intelligent reflecting surface proposed by the present invention can complete the positioning work in a single access point scenario and significantly improve the near-field positioning performance.

[0187] Embodiment 2

[0188] The embodiment of the present invention also provides an electronic device, the electronic device includes a processor and a memory, and at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement a positioning method based on the near-field array related information of the intelligent reflecting surface as Figure 2 shown.

[0189] It can be understood that the memory may include a Random Access Memory (RAM), and may also include a Read-Only Memory. Optionally, the memory includes a non-transitory computer-readable storage medium. The memory can be used to store instructions, programs, code, code sets or instruction sets. The memory may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function, instructions for implementing the above various method embodiments, etc.; the data storage area can store data created according to the use of the server, etc.

[0190] The processor may include one or more processing cores. The processor connects various parts within the entire server using various interfaces and lines. By running or executing instructions, programs, code sets or instruction sets stored in the memory, and by calling data stored in the memory, it executes various functions of the server and processes data. Optionally, the processor may be implemented in at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor may integrate a combination of one or several of a Central Processing Unit (CPU) and a modem, etc. Among them, the CPU mainly processes the operating system and application programs, etc.; the modem is used for processing wireless communication. It can be understood that the above modem may not be integrated into the processor and can be implemented separately through a single chip.

[0191] Since this electronic device is an electronic device corresponding to a positioning method related to near-field array information based on an intelligent reflecting surface in an embodiment of the present invention, and the principle of how this electronic device solves problems is similar to that of this method, the implementation of this electronic device can refer to the implementation process of the above method embodiment, and the repeated parts will not be elaborated again.

[0192] Embodiment 3

[0193] An embodiment of the present invention also provides a computer-readable storage medium, in which at least one instruction, at least one segment of program, code set or instruction set is stored, and the at least one instruction, the at least one segment of program, the code set or instruction set is loaded and executed by a processor to implement Figure 2 a positioning method related to near-field array information based on an intelligent reflecting surface as shown.

[0194] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. This program can be stored in a computer-readable storage medium, which includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disc memories, tape memories, or any other medium that can be used to carry or store data and is computer-readable.

[0195] Since this storage medium is the storage medium corresponding to a positioning method based on the near-field array related information of the intelligent reflecting surface in the embodiments of the present invention, and the principle of solving problems by this storage medium is similar to that of this method, the implementation of this storage medium can refer to the implementation process of the above method embodiments, and the repeated parts will not be described again.

[0196] Embodiment 4

[0197] In some possible implementation manners, each aspect of the method in the embodiments of the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a computer device, the program code is used to cause the computer device to execute the steps of a positioning method based on the near-field array related information of the intelligent reflecting surface according to various exemplary implementation manners described above in this specification. Among them, the executable computer program code or "code" for executing each embodiment can be written in a high-level programming language such as C, C++, C#, Smalltalk, Java, JavaScript, Visual Basic, structured query language (e.g., Transact-SQL), Perl, or written in various other programming languages.

[0198] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0199] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0200] The above embodiments are only for illustrating the technical concept and features of the present invention, and the purpose is to enable those of ordinary skill in the art to understand the content of the present invention and implement it accordingly, and it cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the essence of the content of the present invention should be covered within the protection scope of the present invention.

Claims

1. A positioning method based on near-field array related information of a smart reflective surface, characterized in that: The following steps are involved: S1, input the number and position of access point antennas, the number and position of smart reflector units, and the pilot sequence signal sent by the located user; S2. Jointly designing the receiving beamforming of the access point and the fixed reflection coefficient of the smart reflective surface unit according to the channel information between the access point and the smart reflective surface; S3, designing a near-field codebook for smart reflectors and a layered near-field codebook beam training method to perform low-training-overhead searches; S4. Using the received signal set obtained by codebook training, obtain the stacked received signal of the access point, and use the least squares method to estimate the near-field array covariance matrix of the smart reflector; S5. Extract special elements in the covariance matrix of the near-field array of the smart reflective surface, decouple and estimate the pitch angle, azimuth angle, and distance information in the near-field array respectively, and then estimate the user position.

2. A positioning method based on near-field array related information of a smart reflective surface according to claim 1, characterized in that: The step S1 comprises: Assume that at time block t, the located user sends a mean-normalized pilot sequence signal x = [x1, ..., x m ,...,x M ] T , superscript (·) T Defined as a transpose operation, the input access point antenna number N A and position p A 、Number of intelligent reflective surface units I and position p I , and set the initial reflection coefficient matrix Θ of the smart reflection surface t .

3. The method for positioning information related to a near-field array based on a smart reflective surface according to claim 2, characterized in that: The mathematical modeling of the smart reflective surface is as follows: Assume that there is an access point and a smart reflective surface in an indoor environment, each of which has N A antennas and N I units, and the receiving antennas and passive reflection units are arranged in the form of a uniform planar array; for the convenience of explanation, it is assumed that the number of passive reflection units of the intelligent reflection surface is an odd number, and a three-dimensional Cartesian coordinate system is established with the center point of the intelligent reflection surface as the origin and the plane where the intelligent reflection surface is located as the yz plane. The receiving antennas distributed along the x-axis and y-axis are N1 and N2 respectively. And N A =N1×N2; the passive reflection units distributed along the y-axis and z-axis are N y and N z , and N I =N y ×N z ; At time block t, the reflection coefficient matrix set by the smart reflection surface is expressed as: in and is the reflection coefficient amplitude and phase limit of the nth passive reflection unit, diag(·) is defined as the vector diagonal matrix operation, |·| is defined as the modulus value, and ∠(·) is defined as the argument angle.

4. The method for positioning information related to a near-field array based on a smart reflective surface according to claim 1, characterized in that: The step S2 comprises: Assume that the smart reflective surface is close to the user being located; since the positions of the access point and the smart reflective surface are fixed, the channel state information between the two is considered as a known far-field channel: Among them, α AI Defined as the complex path gain, a A (φ A ,θ A ) and a I (φ I ,θ I ) is the far-field array response vector of the receiving antenna plane and the passive reflector plane, and its dimension is N A and N I , (φ A ,θ A ) and (φ I ,θ I ) are the corresponding arrival angle pairs and departure angle pairs respectively; The channel state information between the smart reflective surface and the user is an unknown near-field channel: Among them, α IU Defined as the complex path gain, b(φ,θ,d) is defined as the dimension N I The near-field array response vector of the passive reflector plane, (φ,θ) is the arrival angle pair, d represents the distance from the user to the central reference point of the smart reflector, Represents the distance from the user to the central reference point of the smart reflective surface and to the nth reference point of the smart reflective surface I The distance difference of the passive reflection units; the row index of the passive reflection unit Column Index and passive reflector unit number n I , satisfying the relation At time block t, the signal received by the access point can be expressed as y t =[y t,1 ,...,y t,m ,...,y t,M ] T ,in, P represents the user's antenna transmission power, n t,m The mean is zero and the variance is σ 2 The additive white Gaussian noise vector of f represents the normalized receive beamforming vector of the access point, and the dimension is N A ; t Defined as a fixed reflection coefficient matrix Θ A and a time-varying reflection coefficient matrix The product of Superscript (·) * is defined as a conjugate operation; then, by designing The signal received by the access point is then re-expressed as: in, represents the total complex path gain, Indicates the dimension is N x A vector of all 1s, n t,m =f T n t,m 。 5. The method for positioning information related to a near-field array based on a smart reflective surface according to claim 1, characterized in that: The step S3 comprises: According to the S2 calculation results, the best Designed for: Since the user's location p U It is unknown and b(φ,θ,d) cannot be directly known, so a near-field codebook and a layered near-field codebook beam training method are designed to perform a search with low training cost; Assuming that the layered near-field codebook has L layers of sub-codebooks, using the Fresnel approximation, b(φ,θ,d) is approximated as: in, ω = sinφ, ν = sinθ and γ = 1 / d are improved parameters for more scientific sampling; Assume that the search range of each dimension of the l-th layer sub-codebook (l = 1, ..., L) is The number of samples in each dimension is Then the S of the l-th layer sub-codebook is l The set of sampling points is: According to this sampling point set, the l-th layer sub-codebook is expressed as in, ⊙ represents the Hadamard product.

6. The method for positioning information related to a near-field array based on a smart reflective surface according to claim 5, characterized in that: The hierarchical near-field codebook beam training method comprises: First, determine the number of samples in each dimension of the L-layer sub-codebook and the search range of each dimension of the first layer sub-codebook Secondly, for l=1,...,L, enter the following loop: First, get the l-th layer subcodebook W l ; Secondly, for s l =1,...,S l ,make Enter the following loop: According to the formula get Save to collection End the loop Then, in Search for the best index Finally, update the search scope for each dimension: End the loop Finally, we get the set 7. The method for positioning information related to a near-field array based on a smart reflective surface according to claim 1, characterized in that: The step S4 comprises: According to the calculation result of step S3, if the training overhead of the last L-J+1 layer sub-codebook is τ=S J +...+S L Satisfy τ ≥ N I , then The received signal stack in is: Y=[y1,...,y τ ] T =GX+N, Where X = αb(φ,θ,d)x T , G = [θ1a I (φ I ,θ I ),...,Θ τ a I (φ I ,θ I )] T represents the full column rank stacked matrix collecting channel gains in τ beam training, Θ1,...,Θ τ for y1,...,y τ The corresponding reflection coefficient matrix, N represents the stacked signal matrix; using the least squares method, X is estimated as: X=(G H G) -1 G H Y Ignoring noise, the covariance matrix of the near-field array response vector from the user to the smart reflector is: R=|α| 2 b(ω,ν,γ)b H (oh,n,c) Among them, the superscript (·) H It is defined as the conjugate transpose operation; in practice, R is estimated by X: R=XX H / M R I1 ,n I2 ) elements represent the relevant information of the corresponding two intelligent reflector passive reflector units, expressed as (n I1 ,n I2 =1,...,N I ): in and Each represents the nth I1 and n I2 The row index and column index of the passive reflector unit satisfy the relationship and 8. The method for positioning information related to a near-field array based on a smart reflective surface according to claim 1, characterized in that: The step S5 comprises: According to the calculation result of step S4, for any two satisfying the relationship n y2 =n y1 , n z2 =-n z1 The relevant information of the intelligent reflector passive reflector unit is: Construct a vector containing N I The elements in R that satisfy this relationship are: make Represents c ω The estimated value of express The argument vector of , and design the vector: Using the least squares method, φ is estimated as: For any two n that satisfy the relation y2 =-n y1 , n z2 =-n z1 The relevant information of the intelligent reflector passive reflector unit is: Construct a vector containing N I The elements in R that satisfy this relationship are: Obviously, there are So, let Represents c ν The estimated value of And design the vector: Then using the least squares method, θ is estimated as: Construct the far-field array response vector a(φ,θ), so that the estimated covariance matrix R = ΛRΛ H Transformed to be related only to the distance parameter, where is Λ=diag(a * (φ,θ)) estimated value; the (nth I1 ,n I2 ) elements are represented as: Let Q γ =∠(R), and design the matrix U γ , where the first (n I1 ,n I2 ) elements are represented as: Using the least squares method, d is estimated as: Finally, based on and The user's location is estimated as: This enables user positioning.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 8.

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