WIFI parameter estimation and positioning method under assistance of multiple RISs

Abstract

The invention provides a WIFI parameter estimation and positioning method under the assistance of multiple RISs. For an indoor scene with a complex multipath effect, the method comprises the following steps: firstly, constructing a tensor-based fourth-order parallel factor model by using a received signal obtained through multi-RIS assistance; secondly, rearranging the model by using subcarrier resources and adopting a virtual antenna array technology so as to meet the uniqueness of tensor decomposition; then, a four-linear alternating least square algorithm is adopted to decompose the rearranged tensor model, and a subspace method is utilized to realize multipath channel parameter extraction; and finally, realizing accurate positioning of the user and the multiple RIS nodes by adopting a search-free positioning method. Therefore, the indoor joint estimation positioning based on the rearranged tensor under the assistance of multiple RISs has the advantage of high precision, and has stronger robustness compared with the existing competitive method, so that the requirements of an actual communication system are better met.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a WIFI parameter estimation and positioning method assisted by multiple RIS. Background Art

[0002] Positioning systems based on mobile hotspots (WIFI) have been applied to positioning scenarios due to their low cost, easy access, excellent scalability and wide deployment range. Unlike outdoor scenarios, the actual indoor environment is often affected by complex multipath, resulting in the WIFI positioning effect being constrained by the physical environment. In addition, commercial WIFI devices are often limited by limited antenna array configurations, which limits their ability to obtain spatial dimension information and makes it difficult to achieve high-precision target positioning. Therefore, it is very necessary to develop new solutions for commercial WIFI indoor positioning systems.

[0003] With its low power consumption and hardware cost advantages, reconfigurable smart surface (RIS) is considered to be one of the technologies with potential in the field of communication perception. At present, RIS-assisted channel estimation and target perception technology are widely used in wireless communication scenarios of outdoor large-scale antennas. At the same time, considering the antenna limitations of commercial WIFI, the spatial freedom provided by RIS can be used to modulate the signal distribution range, thereby improving the spatial resolution of antenna-constrained devices.

[0004] In the paper by T. Ma, Y. Xiao, X. Lei, W. Xiong, and M. Xiao (Distributed Reconfigurable Intelligent Surfaces Assisted Indoor Positioning[J]. IEEE Trans. Wireless Commun., vol. 22, no. 1, pp. 47-58, Jan. 2023.), distributed RIS assisted positioning technology was adopted, but the abundant carrier resources of the orthogonal frequency division multiplexing (OFDM) system were not fully utilized to increase the limited spatial resources. In the paper by B. Zhao, K. Hu, F. Wen, S. Cui and Y. Shen (TDLoc: Passive Localization for MIMO-OFDM System via Tensor Decomposition[J]. IEEE InternetThings J., vol. 10, no. 23, pp. 20819-20833, Dec. 2023.), the receiver uses reconstruction technology to compensate for the problem of fewer device antennas, but does not use RIS technology for auxiliary positioning. Summary of the invention

[0005] Purpose of the invention: In view of the shortcomings of the prior art, the present invention proposes a WIFI parameter estimation and positioning method assisted by multiple RIS.

[0006] Technical solution: The multi-RIS-assisted WIFI parameter estimation and positioning method described in the present invention includes:

[0007] In indoor scenarios with complex multipath, a fourth-order parallel factor model based on tensors is constructed for CSI from line-of-sight paths or non-line-of-sight paths reflected by multiple RIS indoors to WIFI.

[0008] By utilizing abundant subcarrier resources and adopting virtual antenna array technology, the received CSI model is rearranged to further meet the uniqueness of tensor decomposition;

[0009] The quad-linear alternating least squares algorithm is used to decompose the rearranged tensor model, and the subspace method is further used to extract the multipath channel parameters.

[0010] By using the geometric constraints of the system and the estimated parameter information, a search-free positioning method is adopted to achieve accurate positioning of users and multiple RIS nodes.

[0011] Furthermore, in indoor scenarios with complex multipath, for CSI from line-of-sight paths or non-line-of-sight paths reflected by multiple RIS indoors to WIFI, a tensor-based fourth-order parallel factor model is constructed, specifically including:

[0012] Consider an OFDM indoor system with the number of subcarriers , assuming that the WIFI with known location and the target user with unknown location are equipped with and In addition, there are multiple passive RIS distributed in the indoor space, and the number of elements loaded in each RIS is , of which The unknown position RIS is used to reflect CSI, and the above nodes are all uniform linear arrays. Assume that there is a line-of-sight path between WIFI and the target user. A non-line-of-sight path via the corresponding RIS reflection, The line-of-sight channel corresponding to the subcarrier can be expressed as:

[0013]

[0014] in and are the steering vectors of the transmitting and receiving ends respectively, which can be expressed as:

[0015]

[0016]

[0017] in and denote conjugate transpose and transpose operations respectively. , , and They are the arrival angle, departure angle, arrival time and free space path loss in the path from the target user to WIFI, and the subcarrier spacing is In addition, the distance between adjacent antenna elements is half a wavelength. , Indicates wavelength.

[0018] For non-line-of-sight paths, RIS to WIFI and target user to The channels of each RIS are and ,in , Due to the high frequency characteristics of mmWave, and They are mainly based on line-of-sight propagation, among which In addition, since the distances between RIS and WIFI, and between RIS and the target user are much larger than the antenna scales of RIS, WIFI, and the target user, the signals from the target node to RIS and from RIS to WIFI can be approximated as uniform plane waves. Subcarrier, The line-of-sight channels between RIS and WIFI and the target user are expressed as:

[0019]

[0020]

[0021] in and Respectively represent The arrival angle and departure angle of each RIS to WIFI path, and Respectively represent the target users to The angles of arrival and departure of each RIS path. , , and Respectively represent Free space path loss, arrival time, and transceiver steering vectors for each RIS to WIFI path. , , and Respectively represent the target users to The free space path loss, arrival time, and transceiver steering vector of each RIS path. , , and With and Same structure.

[0022] Therefore, the channel between WIFI and the target user is subcarriers can be expressed as:

[0023]

[0024] in It is The phase control matrix corresponding to each RIS is: is the diagonalization operation, represents the beamforming vector, represents additive white Gaussian noise with zero mean.

[0025] Therefore, the RIS element The transmitting antenna to the The receiving antenna is The CSI on the subcarrier can be expressed as:

[0026]

[0027] in Represents a fourth-order tensor No. elements, , , , .in, , and Represent the cascade gain and cascade delay respectively. Represents the combined receive noise. represents the cascade angle. For the line-of-sight path, , , .

[0028] Furthermore, the tensor form of the receiving end CSI can be expressed as:

[0029]

[0030] in represents the outer product, is the noise tensor at the receiving end, , , and They are The arrival time, arrival angle, departure angle and turning vector of the cascade angle of each path can be expressed as:

[0031]

[0032]

[0033]

[0034]

[0035] Furthermore, by utilizing abundant subcarrier resources, virtual antenna array technology is used to rearrange the received CSI model to further meet the uniqueness of tensor decomposition, including:

[0036] Assuming that from the total CCP Extraction subcarriers, respectively. , and The subcarriers are used to expand the scale of the transmitting antenna, receiving antenna and RIS unit, and a virtual antenna array is constructed. The rearranged steering vector can be expressed as:

[0037]

[0038]

[0039]

[0040]

[0041] in represents the Kronecker product, and the reconstructed tensor model can be expressed as:

[0042]

[0043] in Represents the noise tensor after shuffling.

[0044] Furthermore, the quad-linear alternating least squares algorithm is used to decompose the rearranged tensor model, and the subspace method is further used to extract the multipath channel parameters, including:

[0045] The channel parameter estimation problem can be further formulated as a fourth-order low-rank tensor model decomposition problem:

[0046]

[0047] in , , and Represents the factor matrix , , and The estimated value of , , and Respectively , , and The estimated value of Represents the Frobenius norm operation.

[0048] The quadlinear alternating least squares algorithm is used to divide the original problem into four sub-problems for optimization, and each factor matrix is ​​updated sequentially through iteration until convergence:

[0049]

[0050]

[0051]

[0052]

[0053] in Expressed as Khatri-Rao product, Representing a tensor The mode-n expansions are , , and . Further, the spatial spectrum is constructed using the projection matrix , the parameters are estimated based on the estimated factor matrix using the spectrum peak search method, namely:

[0054]

[0055] in , express The estimated value of Represents the identity matrix.

[0056] Furthermore, by using the system geometric constraints and the estimated parameter information, a search-free positioning method is adopted to achieve accurate positioning of the user and multiple RIS nodes, including:

[0057] User target location , No. RIS locations Wi-Fi node location The spatial geometric relationship can be expressed as:

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064] in It means to find the modulus length of a vector. Represents the speed of light. The target positioning problem can be expressed as a maximum likelihood estimation problem:

[0065]

[0066] in and Respectively and To solve the above problem, a search-free algorithm suitable for scenarios with both line-of-sight and non-line-of-sight is used. This algorithm can avoid the complex calculation of high-dimensional optimization problems. For scenarios with line-of-sight, the transmission delay of the non-line-of-sight path is significantly greater than that of the line-of-sight path, and the different parameters corresponding to the two paths can be determined accordingly. Path , the user target location can be expressed as:

[0067]

[0068] in represents unknown weight, and Respectively represent The receiving direction vector and the transmitting direction vector of the path. When , the target user location can be expressed as ,in . After rearranging the information under each path, we get , Therefore, the target user positioning problem can be described as the intersection problem of linear equations, and the cost function can be defined as:

[0069]

[0070] in , express The weight values ​​corresponding to each path depend on the SNR. The least squares solution is obtained by minimizing get:

[0071]

[0072] in Represents the inverse operation. After obtaining the estimated value of the user's position, the position of the reflected node The linear equation and The intersection of .at this time, The least squares solution of is:

[0073]

[0074] Beneficial effects: Compared with the prior art, the main advantages of the present invention are: the present invention can accurately estimate the position of the target user and multiple RIS nodes in an indoor environment with limited antennas; the virtual antenna rearrangement algorithm based on the fourth-order tensor can achieve accurate channel parameter estimation; with the aid of multiple RISs distributed in space to assist in positioning, the multipath component information can be fully utilized to achieve more accurate positioning targets. The advantages and methods of the present invention can be further understood through the following detailed description of the invention and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a flow chart of a WIFI parameter estimation and positioning method assisted by multiple RIS of the present invention;

[0076] Figure 2 It is a structural schematic diagram of the multi-RIS assisted WIFI positioning system of the present invention;

[0077] Figure 3 The present invention has different subcarrier numbers. and number of paths Below is the performance diagram of the relationship between the root mean square error (RMSE) and signal-to-noise ratio (SNR) of the arrival angle estimation compared with the existing indoor positioning methods;

[0078] Figure 4 The present invention has different subcarrier numbers. and number of paths Below is the performance diagram of the relationship between RMSE and SNR of departure angle estimation compared with existing indoor positioning methods;

[0079] Figure 5 The present invention has different subcarrier numbers. and number of paths Below is the performance diagram of the relationship between RMSE and SNR of arrival time estimation and existing indoor positioning methods;

[0080] Figure 6 The present invention has different subcarrier numbers. and number of paths Below is the performance diagram of the relationship between RMSE and SNR of the cascade angle estimation with the existing indoor positioning method;

[0081] Figure 7 The present invention has different subcarrier numbers. and the number of paths Below is the performance diagram of the relationship between RMSE and SNR of user target position estimation and existing indoor positioning methods;

[0082] Figure 8 The present invention has different subcarrier numbers. and number of paths Below is a performance diagram of the relationship between RMSE and SNR of RIS position estimation for reflection and existing indoor positioning methods. DETAILED DESCRIPTION

[0083] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more definite definition of the protection scope of the present invention.

[0084] Figure 2 FIG. 1 is a schematic diagram of the structure of the multi-RIS assisted WIFI positioning system of the present invention, as shown in FIG. Figure 2 The multi-RIS assisted indoor positioning uplink system shown in the figure, in which the target user actively sends a signal, and the signal is transmitted through line of sight or through a common network distributed in space. The signal is reflected by a RIS device composed of multiple passive electronic components arranged closely together to reach the WIFI end, where the location of the WIFI is known, but the locations of the multiple RIS and the target user are unknown.

[0085] Implementation Example 1

[0086] See also Figure 3 , Figure 4 , Figure 5 and Figure 6 These four figures show the present invention in different subcarrier numbers. and number of paths Below is the performance diagram of the relationship between RMSE and SNR of each channel parameter estimation and the existing indoor positioning method. The system parameters are set as: , , , , the spatial range is It can be observed that as the number of paths increases As the number of subcarriers increases, the complexity of the indoor environment increases, and the RMSE of the corresponding parameter estimation of the proposed method and the existing methods increases accordingly. As the SNR increases, the RMSE of each channel parameter estimation of the proposed method and the existing method decreases accordingly. Moreover, as the SNR increases, the parameter curves of the proposed method are all below the curves of the existing method, which shows that the proposed algorithm is better than the existing algorithm in parameter estimation performance.

[0087] Implementation Example 2

[0088] See also Figure 7 and Figure 8 These two figures show the present invention in different subcarrier numbers. and number of paths Below is the performance diagram of the relationship between RMSE and SNR of the user target position estimation and the RIS position estimation for reflection with the existing indoor positioning method. The parameters are set as: , , , , the spatial range is The simulation results show that as the number of non-line-of-sight paths increases As the number of subcarriers increases, the estimation accuracy of the user target and RIS position of the proposed method and the existing method decreases due to the increase of indoor multipath interference. The RMSE of the proposed method and the existing method for estimating the position of the target user and the RIS participating in the reflection is reduced, which indicates that the increase in spectrum utilization is conducive to the accurate positioning of the target, further proving the effectiveness of distributed RIS assistance. As the SNR increases, the RMSE of the proposed method is always lower than the RMSE of the existing method, which means that the proposed method has better robustness in target estimation.

[0089] In summary, the present invention considers a WIFI parameter estimation and positioning method assisted by multiple RIS, in which the target user actively sends a signal, and the signal reaches the WIFI end directly or through multiple RIS distributed in space. The WIFI end performs a rearranged tensor modeling on the received signal and estimates the parameters using a four-linear alternating least squares algorithm, so that the user target and the participating RIS nodes can be accurately located.

[0090] The above embodiments are only for helping to understand the method and main idea of ​​the present invention. The contents of this specification cannot be used to limit the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A multi-RIS-assisted WIFI parameter estimation and positioning method, characterized in that The method includes: In indoor scenarios with complex multipath, a fourth-order parallel factor model based on tensors is constructed for CSI from line-of-sight paths or non-line-of-sight paths reflected by multiple RIS indoors to WIFI. By utilizing abundant subcarrier resources and adopting virtual antenna array technology, the received CSI model is rearranged to further meet the uniqueness of tensor decomposition; The quad-linear alternating least squares algorithm is used to decompose the rearranged tensor model, and the subspace method is further used to extract the multipath channel parameters. By utilizing the geometric constraints of the system and the estimated parameter information, a search-free positioning method is adopted to achieve accurate positioning of users and multiple RIS nodes.

2. The multi-RIS-assisted WIFI parameter estimation and positioning method according to claim 1, characterized in that: In indoor scenarios with complex multipath, for CSI from line-of-sight paths or non-line-of-sight paths reflected by multiple RIS indoors to WIFI, a tensor-based fourth-order parallel factor model is constructed, including: Considering an OFDM indoor system with the number of subcarriers , assuming that the WIFI with known location and the target user with unknown location are equipped with and antennas, multiple passive RIS are distributed in the indoor space, and the number of elements loaded in each RIS is , of which The unknown position RIS is used to reflect CSI, and the above nodes are all uniform linear arrays. There is a line-of-sight path between WIFI and the target user. A non-line-of-sight path via the corresponding RIS reflection, The line-of-sight channel corresponding to the subcarrier can be expressed as ， in and are the steering vectors at the transmitting and receiving ends, respectively, and can be expressed as ， ， in and denote conjugate transpose and transpose operations respectively, , , and They are the arrival angle, departure angle, arrival time and free space path loss in the path from the target user to WIFI, and the subcarrier spacing is , the distance between adjacent antenna elements is half a wavelength , represents the wavelength. For non-line-of-sight paths, RIS to WIFI and target user to The channels of each RIS are and ,in , , for the Subcarrier, The line-of-sight channels between RIS and WIFI and the target user are expressed as ， ， in and Respectively represent The arrival angle and departure angle of each RIS to WIFI path, and Respectively represent the target users to The arrival and departure angles of the RIS paths, , , and Respectively represent Free space path loss, arrival time, and transceiver steering vectors for each RIS to WIFI path. , , and Respectively represent the target users to The free space path loss, arrival time, and transceiver steering vector of each RIS path are: , , and With and With the same structure, the channel between WIFI and the target user is subcarriers can be expressed as ， in It is The phase control matrix corresponding to each RIS is: is the diagonalization operation, represents the beamforming vector, represents additive Gaussian white noise with zero mean, RIS element The transmitting antenna to the The receiving antenna is The CSI on the subcarriers can be expressed as ， in Represents a fourth-order tensor No. elements, , , , , , and Respectively represent the cascade gain and cascade delay, represents the combined received noise, represents the cascade angle. For the line-of-sight path, , , , the tensor form of the receiving end CSI can be expressed as ， in represents the outer product, is the noise tensor at the receiving end, , , and They are The arrival time, arrival angle, departure angle and turning vector of the cascade angle of each path can be expressed as ， ， ， 。 3. The multi-RIS-assisted WIFI parameter estimation and positioning method according to claim 1, characterized in that: By utilizing abundant subcarrier resources, the received CSI model is rearranged using virtual antenna array technology to further satisfy the uniqueness of tensor decomposition. Specifically, CCP Extraction subcarriers, respectively. , and The subcarriers are used to expand the size of the transmitting antenna, receiving antenna and RIS unit, and a virtual antenna array is constructed. The rearranged steering vector can be expressed as ， ， ， ， in represents the Kronecker product, and the reconstructed tensor model can be expressed as ， in Represents the noise tensor after shuffling.

4. The multi-RIS-assisted WIFI parameter estimation and positioning method according to claim 1, characterized in that: The quad-linear alternating least squares algorithm is used to decompose the rearranged tensor model, and the subspace method is further used to extract the multipath channel parameters. Specifically, the channel parameter estimation problem can be further expressed as a fourth-order low-rank tensor model decomposition problem. ， in , , and Represents the factor matrix , , and The estimated value of , , and Respectively , , and The estimated value of It represents the Frobenius norm operation. The quadrilinear alternating least squares algorithm is used to divide the original problem into four sub-problems for optimization, and each factor matrix is ​​updated sequentially through iteration until convergence. ， ， ， ， in Expressed as Khatri-Rao product, Representing a tensor The mode-n expansions are , , and , using the projection matrix to construct the spatial spectrum , the parameters are estimated using the spectral peak search method based on the estimated factor matrix ， in , express The estimated value of Represents the identity matrix.

5. The multi-RIS-assisted WIFI parameter estimation and positioning method according to claim 1, characterized in that: Using the system geometric constraints and the estimated parameter information, a search-free positioning method is used to achieve accurate positioning of users and multiple RIS nodes, including: user target location , No. RIS locations Wi-Fi node location The spatial geometric relationship can be expressed as ， ， ， ， ， ， in It means to find the modulus length of a vector. Represents the speed of light, the target positioning problem can be expressed as a maximum likelihood estimation problem ， in and Respectively and In order to solve the above problem, a search-free algorithm suitable for scenes with both line-of-sight and non-line-of-sight is adopted. This algorithm can avoid the complex calculation of high-dimensional optimization problems. For scenes with line-of-sight, the transmission delay of the non-line-of-sight path is significantly greater than that of the line-of-sight path. Therefore, different parameters corresponding to the two paths can be determined. Path , the user target location can be expressed as ， in represents unknown weight, and Respectively represent The receiving direction vector and the transmitting direction vector of the path, when When , the target user location can be expressed as ,in , for After rearranging the information under each path, we get , , the target user positioning problem can be described as the intersection problem of linear equations, and the cost function can be defined as ， in , express The weight values ​​corresponding to each path depend on the SNR, and the least squares solution is obtained by minimizing get ， in Represents the inverse operation, after obtaining the estimated value of the user's position, the position of the reflected node The linear equation and The intersection of , The least squares solution of 。

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