Wireless positioning method based on intelligent metasurface multipath suppression

Abstract

This invention discloses a wireless positioning method based on intelligent metasurface multipath suppression, belonging to the field of wireless communication technology. Addressing the problem of decreased positioning accuracy due to non-line-of-sight (NLS) disturbances in multipath environments, this invention utilizes the adjustable electromagnetic reflection coefficient of intelligent metasurfaces to actively reshape the wireless channel, achieving physical-layer destructive interference suppression of NLS paths. The method includes: deriving closed-form solution conditions for complete NLS path suppression based on orthogonal decomposition of the base station array response using discrete Fourier transform; constructing an optimization problem considering amplitude constraints, jointly adjusting the reflection coefficient of metasurface units to minimize NLS disturbance power; optionally, improving suppression accuracy through iterative channel estimation and metasurface configuration; and finally, achieving user positioning based on the purified line-of-sight dominant channel. This invention can effectively reduce the impact of multipath interference on positioning accuracy in complex propagation environments.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and specifically relates to a wireless positioning method based on intelligent metasurface multipath suppression. Background Technology

[0002] In the field of wireless communication and sensing, high-precision user positioning is a key capability of sixth-generation mobile communication networks. However, in complex communication environments such as cities or indoors, the widespread obstruction of obstacles can cause severe non-line-of-sight propagation and multipath effects, thereby interfering with traditional positioning methods based on line-of-sight path parameters (such as angle of arrival and time of arrival), resulting in significant deviations in channel parameter estimation results and a sharp decline in positioning accuracy.

[0003] To address multipath interference, existing solutions primarily focus on receiver-side post-processing techniques, such as identifying and eliminating non-line-of-sight measurements. These methods are inherently passive and cannot eliminate the root cause of interference at the physical propagation level. In recent years, smart metasurface technology has provided a new approach to actively modulating wireless channels. However, how to fundamentally, actively, and effectively suppress non-line-of-sight interference in complex multipath environments to obtain a clean channel approaching that of a line-of-sight environment, thus laying the foundation for high-precision and robust wireless positioning, remains an unsolved problem. Summary of the Invention

[0004] This invention addresses the problem of large channel parameter estimation deviations and low positioning accuracy caused by non-line-of-sight interference in multipath-rich environments, which is a challenge faced by existing wireless positioning technologies. It proposes a wireless positioning method based on intelligent metasurface multipath suppression. This method aims to fundamentally suppress non-line-of-sight components by leveraging the active reshaping characteristics of the physical channel through intelligent metasurfaces, thereby improving the accuracy and robustness of wireless positioning in complex environments.

[0005] This invention provides a wireless positioning method based on intelligent metasurface multipath suppression, comprising the following steps:

[0006] Step S1: Obtain initial channel state information using the base station, and separate the user-base station non-line-of-sight path channel component and the user-smart metasurface-base station cascaded channel component from the initial channel state information; construct a discrete Fourier transform orthogonal basis matrix based on the number of base station antennas, and project the user-base station non-line-of-sight path channel component and the user-smart metasurface-base station cascaded channel component onto the orthogonal basis matrix to obtain projection coefficients;

[0007] Step S2: Based on the projection coefficient, the reflection coefficient of the smart metasurface is adjusted so that the user-smart metasurface-base station cascaded channel component and the user-base station non-line-of-sight path channel component achieve destructive interference on the orthogonal basis matrix, thereby suppressing the non-line-of-sight path component and purifying the physical propagation channel.

[0008] Step S3: High-precision positioning is achieved using the purified line-of-sight path.

[0009] Optionally, in one embodiment of the present invention, step S1 specifically includes:

[0010] The base station receives the uplink pilot signal sent by the user equipment and estimates the initial channel state information between the base station and the user equipment based on the signal. It constructs a discrete Fourier transform orthogonal basis matrix based on the number of base station antennas and projects the user-base station non-line-of-sight path channel component and the user-smart metasurface-base station cascaded channel component separated from the initial channel onto the orthogonal basis matrix. The projection coefficients of the above two channel components on the orthogonal basis matrix are calculated.

[0011] Optionally, in one embodiment of the present invention, step S2 specifically includes:

[0012] Based on the accuracy of the acquired initial channel state information and the hardware constraints of the smart metasurface, a closed-loop solution or an optimized algorithm is selected to configure the smart metasurface: when the estimation error of the initial channel state information is lower than a preset threshold and the amplitude adjustment range of the smart metasurface reflection unit meets the complete suppression condition, the closed-loop solution is executed; otherwise, the optimized algorithm is executed.

[0013] Optionally, in one embodiment of the present invention, the closed-form solution specifically includes:

[0014] Based on the projection coefficients, a closed-form solution for complete suppression of non-line-of-sight paths without hardware constraints is obtained. The closed-form condition is: for any orthogonal basis vector, the sum of the projection components of the user-smart metasurface-base station reflection path and the projection components of the user-base station non-line-of-sight path is zero. The optimal reflection configuration of the smart metasurface is obtained according to the closed-form solution.

[0015] Optionally, in one embodiment of the present invention, the optimization algorithm specifically includes:

[0016] Considering the physical constraints of the intelligent metasurface reflective unit, an optimization problem is constructed with the objective of minimizing the interference power residual. The objective function of the optimization problem is configured to minimize the sum of squared magnitudes of the projection coefficient residuals on all orthogonal bases. The projection coefficient residuals are the sum of the projection coefficients of the user-base station non-line-of-sight path channel components and the projection coefficients of the user-intelligent metasurface-base station cascaded channel components after weighting by the reflection coefficients. The physical constraints include the amplitude gain constraint and phase constraint of the reflective unit. The optimal reflection configuration of the intelligent metasurface is obtained by solving the optimization problem based on the projection gradient descent method.

[0017] Optionally, in one embodiment of the present invention, step S2 further includes an iterative optimization process based on the channel estimation accuracy, specifically:

[0018] Based on the optimal reflection configuration of the smart metasurface obtained from the above solution, the wireless propagation channel is physically reshaped; the base station receives the uplink pilot signal sent by the user equipment again, performs further channel estimation based on the reshaped channel, and calculates the difference metric between the current channel estimate and the previous channel estimate; if the difference metric is less than a preset threshold, step S3 is executed; if it is greater than or equal to the threshold, the current channel estimate is updated, and a new round of multipath suppression is performed.

[0019] Optionally, in one embodiment of the present invention, step S3 specifically includes:

[0020] The base station calculates the distance estimate between the user and the base station based on the received signal strength, and uses a beam scanning algorithm to estimate the angle of arrival of the signal; the distance estimate and the angle estimate are fused together, and the user's two-dimensional position coordinates are calculated through geometric relationships.

[0021] The present invention, by adopting the above technical solution, can produce the following effects:

[0022] This invention fundamentally purifies the channel environment by actively constructing a cascaded channel that cancels out non-line-of-sight multipath signals. Utilizing the channel reshaping capability and controllable reflection coefficient amplitude of intelligent metasurfaces, a more thorough multipath suppression is achieved at the physical level. By establishing an optimization problem based on the actual hardware constraints of intelligent metasurfaces, both theoretical performance and engineering feasibility are considered. Furthermore, through an iterative feedback mechanism, channel estimation is re-performed and the difference metric is calculated after each RIS configuration, effectively overcoming the impact of initial channel estimation errors and significantly improving positioning accuracy and robustness in complex multipath environments. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of channel path propagation for a wireless positioning method based on intelligent metasurface multipath suppression provided in an embodiment of the present invention;

[0024] Figure 2This is a flowchart of an embodiment of the present invention;

[0025] Figure 3 This is a cumulative distribution function graph of the angle estimation before and after the error configuration of the smart metasurface in an embodiment of the present invention;

[0026] Figure 4 This is a graph showing the cumulative distribution function of the distance estimation error before and after configuring the smart metasurface in an embodiment of the present invention. Detailed Implementation

[0027] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein 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 accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0028] like Figure 1 As shown, the wireless positioning method based on intelligent metasurface multipath suppression provided by the present invention includes the following steps:

[0029] Step S1: Obtain initial channel state information using the base station;

[0030] Step S2: Based on the acquired channel state information, configure intelligent metasurfaces to eliminate non-line-of-sight paths and purify the physical propagation channel;

[0031] Step S3: High-precision positioning is achieved using the purified line-of-sight path.

[0032] like Figure 2 As shown, the signal between the user and the base station is interfered with by multipath signals generated by scattering from obstacles, which seriously affects the user's positioning accuracy. By constructing a cascaded path of user-smart metasurface-base station, interference cancellation of non-line-of-sight paths is achieved. The base station is equipped with... The RIS is equipped with one antenna. There is one reflector element, and the user has a single antenna. The composite channel between the base station and the user can be represented as:

[0033]

[0034] in, This is the reflection coefficient matrix of the smart metasurface, where the reflection coefficient matrix contains... It is the natural logarithm. It is the imaginary unit. and They are the first The reflection phase and amplitude of each unit. , and Let represent the channels from the base station to the smart metasurface, from the smart metasurface to the user, and from the base station to the user, respectively. Under the Rayleigh fading model, It can be represented as:

[0035]

[0036] in, This represents the large-scale path loss between the user and the base station. Indicates the distance between UE and BS. Represents Rice factor. and These are the line-of-sight and non-line-of-sight components, It is the total number of paths for non-line-of-sight components. This represents the small-scale fading of each non-line-of-sight path. This represents the array response vector of the uniform linear array at the base station. and Representing sight distance and the first The horizontal angle of arrival (AoA) of the non-line-of-sight component. Similarly, It can be represented as:

[0037]

[0038] Most of the parameters are defined the same as above. The similarity. In particular, the line-of-sight and non-line-of-sight components are respectively and , The array response vector represents the uniform planar array of smart metasurfaces. and These represent the vertical AOD (angle of departure) and horizontal AOD of the line-of-sight component, respectively. This represents the horizontal AOA. To simplify the expression, the line-of-sight component and the non-line-of-sight component can be combined into one. ,in, Represents the line-of-sight component coefficient; , This represents the non-line-of-sight component coefficients. Similarly, It can be represented as:

[0039]

[0040] in, Represents the line-of-sight component coefficient. , Represents the non-line-of-sight component coefficient.

[0041] Based on the above technical background explanation, such as Figure 2 As shown in the example, the wireless positioning method based on intelligent metasurface multipath suppression provided by this invention utilizes the adjustable electromagnetic reflection coefficient of intelligent metasurfaces to achieve physical layer destructive interference suppression of non-line-of-sight paths through cascaded paths, thereby improving positioning accuracy. The method specifically includes the following steps:

[0042] Step S1: The base station receives the uplink pilot signal transmitted by the user equipment and estimates the initial channel between the base station and the user equipment based on this signal. Then, based on... Build Discrete Fourier transform orthogonal basis vectors .therefore, It can be represented as The non-line-of-sight path channel components of the user-base station and the cascaded channel components of the user-smart metasurface-base station in the initial channel are projected onto the orthogonal basis, and the projection coefficients on the orthogonal basis matrix of the above two channel components are calculated, i.e. and .

[0043] Step S2: Based on the accuracy of the acquired initial channel state information and the hardware constraints of the smart metasurface, select either a closed-loop solution or an optimized algorithm to configure the smart metasurface: when the estimation error of the initial channel state information is lower than a preset threshold and the amplitude adjustment range of the smart metasurface reflection unit meets the complete suppression condition, execute the closed-loop solution; otherwise, execute the optimized algorithm.

[0044] A preferred approach to this step is to solve for the closed-form solution under ideal, unconstrained conditions. In the ideal case, without considering the amplitude limitations of the smart metasurface hardware, to achieve complete suppression of the non-line-of-sight component, the following conditions must be met:

[0045]

[0046] Since both components are expanded on the same set of orthogonal bases in step S1, this condition is equivalent to requiring the sum of the projection coefficients corresponding to each set of basis vectors to be zero. The non-line-of-sight path projection coefficients obtained in step S1 are then aggregated into a vector. The projection coefficients related to the cascaded channels are aggregated into a matrix. Therefore, a system of equations can be constructed. ,in This is the vector obtained by vectorizing the reflection coefficient matrix of the intelligent metasurface. Using the least squares criterion and matrix inversion theory, a closed-form solution for the reflection coefficient vector of the intelligent metasurface can be derived:

[0047]

[0048] in, The pseudo-inverse of the matrix is ​​represented. The optimal reflection configuration of the intelligent metasurface under ideal conditions is obtained from the closed-form solution.

[0049] Another preferred approach to this step is to consider the amplitude constraints of the smart metasurface reflective unit and construct an optimization problem aimed at minimizing the interference power residual. Utilizing the norm-preserving property of orthogonal transformation, the problem of minimizing the residual channel vector norm is transformed into minimizing the sum of squared magnitudes of the projection coefficient residuals on all orthogonal bases. Here, the projection coefficient residual is defined as the sum of the projection coefficients of the user-base station non-line-of-sight path channel components and the projection coefficients of the user-smart metasurface-base station cascaded channel components after weighting by the reflection coefficients. The final optimization problem is as follows:

[0050]

[0051] The optimal reflection configuration of an intelligent metasurface that satisfies physical constraints is obtained by solving the optimization problem using the projection gradient descent method.

[0052] Based on the optimal reflection configuration of the smart metasurface obtained from the above solution, the wireless propagation channel is physically reshaped; the base station receives the uplink pilot signal sent by the user equipment again, performs further channel estimation based on the reshaped channel, and calculates the difference metric between the current channel estimate and the previous channel estimate; if the difference metric is less than a preset threshold, step S3 is executed; if it is greater than or equal to the threshold, the current channel estimate is updated, and a new round of multipath suppression is performed.

[0053] Step S3: The base station uses the classic logarithmic distance path loss model. Utilizing the received signal strength Calculate the distance estimate between the user and the base station And the beam scanning algorithm is used to estimate the angle of arrival of the signal. By fusing the distance and angle estimates, the user's two-dimensional position coordinates are calculated using geometric relationships, i.e.:

[0054]

[0055] in, These are the two-dimensional coordinates of the base station. These are the user's estimated coordinates.

[0056] To verify the effectiveness of the proposed method, simulation experiments were conducted under a typical multipath propagation environment. In the experimental scenario, the base station was equipped with M=64 antennas, the smart metasurface was configured with N=256 reflective elements, and the user equipment had a single antenna. Multiple non-line-of-sight paths were considered in the simulation, and the positioning performance before and after configuring the smart metasurface was compared and analyzed.

[0057] Figure 3The graph shows the cumulative distribution function curve of the angle estimation error, with the horizontal axis representing the angle estimation error (unit: °) and the vertical axis representing the cumulative distribution function value. The graph contains two curves: the dashed line represents the cumulative distribution characteristic of the angle estimation error before configuring the smart metasurface, and the solid line represents the cumulative distribution characteristic of the angle estimation error after configuring the smart metasurface. The curve trends clearly show that, for the same cumulative distribution function value, the angle estimation error after configuring the smart metasurface is much smaller than before. Furthermore, as the angle estimation error increases, the curve after configuration is always to the left of the curve before configuration, indicating that the method proposed in this invention effectively reduces the angle estimation error and significantly improves the accuracy of angle estimation.

[0058] Figure 4 The graph shows the cumulative distribution function curve of the distance estimation error, with the horizontal axis representing the distance estimation error (in meters) and the vertical axis representing the cumulative distribution function value. The graph also includes two curves: the dashed line represents the cumulative distribution characteristic of the distance estimation error before configuring the smart metasurface, and the solid line represents the cumulative distribution characteristic of the distance estimation error after configuring the smart metasurface. The curve trend shows that the curve after configuring the smart metasurface is generally to the left of the curve before configuration. For any cumulative distribution function value, the distance estimation error after configuration is lower than before configuration, and the curve rises more steeply in the low error range. This indicates that the multipath suppression method of this invention significantly reduces the deviation in distance estimation and effectively improves the accuracy of distance estimation.

[0059] The wireless positioning method based on intelligent metasurface multipath suppression proposed in this embodiment of the invention utilizes the channel reshaping capability of intelligent metasurface and the controllability of reflection coefficient amplitude to achieve active and deep suppression of non-line-of-sight multipath interference at the physical level. The initial channel estimation deviation is corrected through an iterative feedback mechanism, thereby obtaining clean channel characteristics close to the line-of-sight propagation environment in complex scenarios with abundant multipaths. This effectively overcomes the influence of the initial channel estimation error and significantly improves positioning accuracy.

Claims

1. A wireless positioning method based on intelligent metasurface multipath suppression, characterized in that, Includes the following steps: Step S1: Use the base station to obtain initial channel state information, and separate the user-base station non-line-of-sight path channel component and the user-smart metasurface-base station cascaded channel component from the initial channel state information. Construct a discrete Fourier transform orthogonal basis matrix based on the number of base station antennas, and project the user-base station non-line-of-sight path channel component and the user-smart metasurface-base station cascaded channel component onto the orthogonal basis matrix to obtain the projection coefficients; Step S2: Based on the projection coefficient, the reflection coefficient of the smart metasurface is adjusted so that the user-smart metasurface-base station cascaded channel component and the user-base station non-line-of-sight path channel component achieve destructive interference on the orthogonal basis matrix, thereby suppressing the non-line-of-sight path component and purifying the physical propagation channel. Step S3: Use the channel after suppressing non-line-of-sight path components to perform user localization.

2. The method according to claim 1, characterized in that, Step S1 specifically includes: The base station receives the uplink pilot signal sent by the user equipment and estimates the initial channel state information between the base station and the user equipment based on the signal. A discrete Fourier transform orthogonal basis matrix based on the number of base station antennas is constructed, and the user-base station non-line-of-sight path channel component and the user-smart metasurface-base station cascaded channel component separated from the initial channel state information are projected onto the orthogonal basis matrix. The projection coefficients of the user-base station non-line-of-sight path channel component and the user-smart metasurface-base station cascaded channel component on the orthogonal basis matrix are calculated.

3. The method according to claim 2, characterized in that, Step S2 specifically includes: Based on the estimation accuracy of the acquired initial channel state information and the hardware constraints of the smart metasurface, a closed-loop solution or an optimized algorithm is selected to configure the smart metasurface: when the estimation error of the initial channel state information is lower than a preset threshold and the amplitude adjustment range of the smart metasurface reflection unit meets the complete suppression condition, the closed-loop solution is executed; otherwise, the optimized algorithm is executed.

4. The method according to claim 3, characterized in that, The closed-loop solution scheme specifically includes: Based on the projection coefficients, a closed-form solution for complete suppression of non-line-of-sight paths without hardware constraints is obtained. The closed-form condition is: for any orthogonal basis vector, the sum of the projection components of the user-smart metasurface-base station reflection path and the projection components of the user-base station non-line-of-sight path is zero. The optimal reflection coefficient configuration of the smart metasurface is obtained according to the closed-form solution.

5. The method according to claim 3, characterized in that, The optimization algorithm specifically includes: Considering the physical constraints of the intelligent metasurface reflective units, an optimization problem is constructed with the objective of minimizing the interference power residual. The objective function of the optimization problem is configured to minimize the sum of squared magnitudes of the projection coefficient residuals on all orthogonal bases. The projection coefficient residuals are the sum of the projection coefficients of the user-base station non-line-of-sight path channel components and the projection coefficients of the user-intelligent metasurface-base station cascaded channel components after weighting by the reflection coefficients. The physical constraints include that the reflection amplitude of each reflective unit does not exceed a preset maximum value and the reflection phase is adjustable within the range of 0 to 2π. The optimal reflection coefficient configuration of the intelligent metasurface is obtained by solving the optimization problem based on the projection gradient descent method.

6. The method according to claim 5, characterized in that, Step S2 further includes an iterative optimization process based on the channel estimation accuracy, specifically: Based on the optimal reflection coefficient configuration of the smart metasurface obtained by the solution, the wireless propagation channel is physically reshaped; the base station receives the uplink pilot signal sent by the user equipment again, performs channel estimation based on the reshaped channel, and calculates the difference measure between the current channel estimate and the previous channel estimate. If the difference metric is less than the threshold, proceed to step S3; if it is greater than or equal to the threshold, update the current new channel parameter estimate, return to the smart metasurface configuration step, and perform a new round of multipath suppression.

7. The method according to claim 6, characterized in that, Step S3 specifically includes: The base station calculates the distance estimate between the user and the base station based on the received signal strength, and estimates the angle of arrival of the signal based on beam scanning; the distance estimate and the angle of arrival are fused together to calculate the user's two-dimensional location coordinates through geometric relationships.

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