A WIFI Parameter Estimation and Localization Method Assisted by Multiple RISs

Abstract

The present invention provides a WIFI parameter estimation and positioning method assisted by multiple RISs. Aiming at the indoor scenario with complex multipath effects, the present invention first constructs the received signal assisted by multiple RISs into a fourth-order parallel factor model based on tensors; secondly, utilizes subcarrier resources and adopts virtual antenna array technology to rearrange the model to meet the uniqueness of tensor decomposition; subsequently, uses the four-linear alternating least squares algorithm to decompose the rearranged tensor model, and extracts multipath channel parameters by using subspace methods; finally, adopts a search-free positioning method to achieve accurate positioning of users and multiple RIS nodes. Therefore, the indoor joint estimation and positioning based on the rearranged tensor assisted by multiple RISs proposed by the present invention has the advantage of high accuracy, and at the same time has stronger robustness compared with existing competing methods, thus better meeting the requirements of actual communication systems.

Description

Technical Field

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

[0002] Positioning systems based on mobile hotspots (WIFI) have been applied to positioning scenarios due to their advantages such as low cost, easy access, excellent scalability, and wide deployment range. Different from outdoor scenarios, in the actual indoor environment, it is often affected by complex multipaths, resulting in the positioning effect of WIFI being restricted by the physical environment. In addition, commercial WIFI devices are often limited by the configuration of a limited antenna array, resulting in limited ability to obtain spatial dimension information and making it difficult to achieve high-precision target positioning. Therefore, it is very necessary to develop a new solution for commercial WIFI indoor positioning systems.

[0003] With its low power consumption characteristics and hardware cost advantages, the reconfigurable intelligent 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 technologies have been widely applied to wireless communication scenarios of outdoor large-scale antennas. At the same time, considering the antenna limitation of commercial WIFI, the spatial degrees of freedom provided by RIS can be applied to the modulation of the signal distribution range, thereby improving the spatial resolution of antenna-limited 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.), the distributed RIS-assisted positioning technology is adopted, but the rich carrier frequency resources of the orthogonal frequency division multiplexing (OFDM) system are 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 Internet Things J., vol. 10, no. 23, pp. 20819-20833, Dec. 2023.), the receiver uses the reconstruction technology for the received channel state information (CSI) to make up for the problem of fewer device antennas, but the RIS technology is not used for assisted positioning. Summary of the Invention

[0005] Object of the Invention: Aiming at the deficiencies of the prior art, the present invention proposes a WIFI parameter estimation and positioning method assisted by multiple RISs.

[0006] Technical Solution: The WIFI parameter estimation and positioning method assisted by multiple RISs according to the present invention includes:

[0007] In an indoor scenario with complex multipaths, for the CSI to WIFI through the line-of-sight path or the non-line-of-sight path reflected by multiple indoor RISs, a fourth-order parallel factor model based on tensors is constructed;

[0008] Using the rich subcarrier resources, the virtual antenna array technology is adopted to rearrange the received CSI model to further meet the uniqueness of tensor decomposition;

[0009] The rearranged tensor model is decomposed by the four-linear alternating least squares algorithm, and the subspace method is further used to extract the multipath channel parameters;

[0010] Using the system geometric constraint relationship and the estimated parameter information, a positioning method without searching is adopted to achieve accurate positioning of the user and multiple RIS nodes;

[0011] Further, in an indoor scenario with complex multipath, for the CSI from the line-of-sight path or the non-line-of-sight path reflected by multiple indoor RISs 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 being , assuming that a WIFI with known location and a target user with unknown location are respectively equipped with and antenna elements. In addition, multiple passive RISs are distributed in the indoor space, and the number of elements loaded on each RIS is , where a total of RISs with unknown locations are used to reflect CSI, and the above nodes all adopt uniform linear arrays. Assume that there is 1 line-of-sight path and non-line-of-sight paths reflected by the corresponding RISs between the WIFI and the target user. The line-of-sight channel corresponding to the th subcarrier can be expressed as:

[0013]

[0014] where and are the steering vectors of the transmitter and receiver respectively, which can be expressed as:

[0015]

[0016]

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

[0018] For the non-line-of-sight path, the channels from the th RIS to the WIFI and from the target user to the th RIS are respectively and , where , . Since this scenario has the characteristics of mmWave high frequency, and are mainly line-of-sight propagations, where In addition, since the distances between the RIS and WIFI, and between the RIS and the target user are much larger than the antenna scales of the RIS, WIFI, and the target user, the signals from the target node to the RIS and from the RIS to WIFI can be approximated as uniform plane waves. And for the sub-carrier, the line-of-sight channels between the th RIS, WIFI, and the target user are respectively expressed as:

[0019]

[0020]

[0021] where and respectively represent the angle of arrival and the angle of departure of the th RIS-to-WIFI path, and respectively represent the angle of arrival and the angle of departure of the path from the target user to the th RIS. , , and respectively represent the free-space path loss, time of arrival, and transceiver steering vectors of the th RIS-to-WIFI path, , , and respectively represent the free-space path loss, time of arrival, and transceiver steering vectors of the path from the target user to the th RIS. Among them, , , and have the same structure as and .

[0022] Therefore, the channel between WIFI and the target user can be expressed under the th sub-carrier as:

[0023]

[0024] where is the phase control matrix corresponding to the th RIS, is the diagonalization operation, represents the beamforming vector, represents additive white Gaussian noise with zero mean.

[0025] Therefore, on the th RIS element, on the ​The CSI from the th transmitting antenna to the th receiving antenna on the

[0026]

[0027] subcarriers can be expressed as: represents a fourth-order tensor the th element, , , , . Among them, , and represent the cascaded gain and cascaded delay respectively. represents the combined received noise. represents the cascaded angle. For the line-of-sight path, , , .

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

[0029]

[0030] where represents the outer product, is the noise tensor at the receiving end, , , and are the steering vectors of the arrival time, arrival angle, departure angle, and cascaded angle of the rd path respectively, and can be expressed as:

[0031]

[0032] 1]

[0033]

[0034]

[0035] Furthermore, by utilizing the rich subcarrier resources and adopting the virtual antenna array technology to rearrange the received CSI model, the uniqueness of tensor decomposition is further satisfied, specifically including:

[0036] Assume that a total of subcarriers are extracted from , and are respectively denoted by , and Expand the scale of subcarriers for the transmitting antenna, receiving antenna, and RIS unit, and construct a virtual antenna array. The rearranged steering vector can be expressed as:

[0037]

[0038]

[0039]

[0040]

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

[0042]

[0043] where denotes the noise tensor after rearrangement.

[0044] Furthermore, use the fourth-order alternating least squares algorithm to decompose the rearranged tensor model, and further use the subspace method to extract multipath channel parameters, specifically including:

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

[0046]

[0047] where , , and denote the estimated values of the factor matrices , , and respectively, , , and denote the estimated values of , , and respectively, denotes the Frobenius norm operation.

[0048] Use the fourth-order alternating least squares algorithm to divide the original problem into four sub-problems for optimization, and update each factor matrix iteratively until convergence:

[0049]

[0050]

[0051]

[0052]

[0053] Among them is expressed as the Khatri-Rao product, represents the tensor of the mode-n expansion, which are respectively 、 、 and . Further, a spatial spectrum is constructed using the projection matrix, and the parameters are estimated by the method of spectral peak search based on the estimated factor matrix, that is:

[0054]

[0055] Among them , represents the estimated value of, represents the identity matrix.

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

[0057] The user target position 、the th RIS position and the WIFI node position The spatial geometric relationship can be expressed as:

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064] Among them represents taking the modulus of a vector, represents the speed of light. The target positioning problem can be expressed as a maximum likelihood estimation problem:

[0065]

[0066] Among them and respectively represent and the estimated values. To solve the above problems, a search-free algorithm applicable to scenarios with both line-of-sight and non-line-of-sight is adopted. This algorithm can avoid the complex calculations of high-dimensional optimization problems. For scenarios with line-of-sight, the non-line-of-sight path is significantly greater than the line-of-sight path in terms of transmission delay, and different parameters corresponding to the two paths can be determined therefrom. For the th path , the user's target location can be expressed as:

[0067]

[0068] where represents the unknown weight, and respectively represent the received direction vector and the transmitted direction vector of the th path. When , the target user location can be expressed as , where . After rearranging the information for the th 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] where , represents the SNR-dependent weight value corresponding to the th path. Its least squares solution is obtained by minimizing

[0071]

[0072] where represents the inverse operation. After obtaining the user location estimate, the location of the reflection node can be represented by the intersection of the linear equations and , where . At this time, the least squares solution of is:

[0073]

[0074] Advantages: Compared with the prior art, its main advantages are as follows: The present invention can accurately estimate the positions of target users and multiple RIS nodes in an antenna-constrained indoor environment; by using the virtual antenna rearrangement algorithm based on the fourth-order tensor, accurate channel parameter estimation can be achieved; with the assistance of multiple RISs distributed in space for positioning, the multipath component information can be fully utilized to more accurately achieve the positioning goal. The advantages and methods of the present invention can be further understood through the following detailed description of the invention and the accompanying drawings. Description of the Drawings

[0075] Figure 1 It is a flowchart of a WIFI parameter estimation and positioning method assisted by multiple RISs according to the present invention;

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

[0077] Figure 3 For the present invention with different numbers of subcarriers and numbers of paths it is a performance graph of the relationship between the root mean square error (RMSE) of arrival angle estimation and the signal-to-noise ratio (SNR) compared with existing indoor positioning methods;

[0078] Figure 4 For the present invention with different numbers of subcarriers and numbers of paths it is a performance graph of the relationship between the RMSE of departure angle estimation and the SNR compared with existing indoor positioning methods;

[0079] Figure 5 For the present invention with different numbers of subcarriers and numbers of paths it is a performance graph of the relationship between the RMSE of time of arrival estimation and the SNR compared with existing indoor positioning methods;

[0080] Figure 6 For the present invention with different numbers of subcarriers and numbers of paths it is a performance graph of the relationship between the RMSE of cascaded angle estimation and the SNR compared with existing indoor positioning methods;

[0081] Figure 7 For the present invention with different numbers of subcarriers and numbers of paths it is a performance graph of the relationship between the RMSE of user target position estimation and the SNR compared with existing indoor positioning methods;

[0082] Figure 8 For the present invention with different numbers of subcarriers and numbers of paths Performance graph of the relationship between the RMSE of RIS position estimation for reflection and SNR in the existing indoor positioning method Detailed implementation mode

[0083] The following elaborates on the preferred embodiments of the present invention 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 the protection scope of the present invention more clearly defined.

[0084] Figure 2 Schematic diagram of the multi-RIS assisted WIFI positioning system of the present invention, as Figure 2 shown in the multi-RIS assisted indoor positioning uplink system, where the target user actively sends a signal, and the signal reaches the WIFI end through the line of sight or is reflected by a total of RIS devices composed of multiple passive electronic components closely arranged in space. The position of the WIFI is known, while the positions of the multi-RIS and the target user are unknown.

[0085] Embodiment 1

[0086] Please refer to Figure 3 , Figure 4 , Figure 5 and Figure 6 , these four figures are the performance graphs of the relationship between the RMSE of each channel parameter estimation and SNR of the present invention and the existing indoor positioning method under different subcarrier numbers and the number of paths . The system parameters are set as: , , , , the spatial range is . It can be observed that as the number of paths increases, the complexity of the indoor environment increases, and the RMSE of the corresponding parameter estimations of the proposed method and the existing method also increases. In addition, as the number of subcarriers increases, the RMSE of the channel parameter estimations of the proposed method and the existing method decreases accordingly. Moreover, as the SNR increases, the curves of each parameter of the proposed method are all below the curves of the existing method, which indicates that the proposed algorithm is superior to the existing algorithm in terms of parameter estimation performance.

[0087] Embodiment 2

[0088] Please refer to Figure 7 and Figure 8 , these two figures are the performance graphs of the relationship between the RMSE of user target position estimation and RIS position estimation for reflection and SNR of the present invention and the existing indoor positioning method under different subcarrier numbers and the number of paths . The parameters are set as: , , , , the spatial range is . The simulation results show that as the number of non-line-of-sight paths increases, due to the increased interference of indoor multipath components, the estimation accuracy of the user target and the RIS position of the proposed method and the existing methods decreases. In addition, as the number of subcarriers increases, the RMSE of the estimation accuracy of the target user position and the RIS position participating in reflection corresponding to the proposed method and the existing methods decreases, which indicates that the increase in spectral efficiency helps to accurately locate the target, further proving the effectiveness of distributed RIS assistance. As the SNR increases, the RMSE of the proposed method is always lower than that of the existing method, that is, it shows 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 RISs. The target user actively sends a signal, and the signal directly reaches or is reflected by multiple RISs distributed in space to the WIFI side. The WIFI side performs a rearranged tensor modeling on the received signal and uses a four-linear alternating least squares algorithm to estimate the parameters, so that the user target and the participating RIS nodes can be accurately located.

[0090] The description of the above embodiments is only to help understand the method and its main idea of the present invention. The content of this specification cannot be used to limit the scope of the rights of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. A WIFI parameter estimation and positioning method assisted by multiple RISs, characterized in that The method includes: In an indoor scenario with complex multipath, for the CSI from the line-of-sight path or the non-line-of-sight path reflected by multiple indoor RISs to WIFI, a tensor-based fourth-order parallel factor model is constructed, specifically including: Considering an OFDM indoor system with K subcarriers, assuming that the WIFI with known location and the target user with unknown location are respectively equipped with W and U antennas, multiple passive RISs are distributed in the indoor space, and each RIS is loaded with M elements. Among them, a total of L RISs with unknown locations are used to reflect the CSI, and the above nodes all adopt uniform linear arrays. There is 1 line-of-sight path and L non-line-of-sight paths reflected by the corresponding RISs between the WIFI and the target user. The line-of-sight channel corresponding to the k-th subcarrier can be expressed as wherein and are the steering vectors of the transceiver and can be expressed as where (·) H and [·] T denote the conjugate transpose and transpose operations respectively, φ R,0 , θ T,0 , τ0 and ρ0 are the angle of arrival, angle of departure, time of arrival, and free-space path loss in the path from the target user to WIFI respectively. The subcarrier spacing is Δf, the distance between adjacent antenna elements is half wavelength d = λ / 2, where λ represents the wavelength. For the non-line-of-sight path, the channels from the l-th RIS to WIFI and from the target user to the l-th RIS are respectively and where k = 1, 2,..., K, l = 1, 2,..., L. For the k-th subcarrier, the line-of-sight channels between the l-th RIS and WIFI, and the target user are respectively expressed as where φ R,l and θ I,l represent the angle of arrival and angle of departure of the l-th RIS-to-WIFI path, respectively, φ I,l and θ T,l represent the angle of arrival and angle of departure of the target user to the l-th RIS path, respectively, ρ WR τ WR,l and and represent the free-space path loss, time of arrival, and transceiver steering vectors of the l-th RIS-to-WIFI path, respectively, ρ RU τ RU,l and and represent the free-space path loss, time of arrival, and transceiver steering vectors of the target user to the l-th RIS path, respectively, where a(φ R,l ), a(θ I,l ), a(φ I,l ) and a(θ T,l ) have the same structure as a(φ R,0 ) and a(θ T,0 ), and the channel between WIFI and the target user at the k-th subcarrier can be expressed as where is the phase control matrix corresponding to the l-th RIS, and diag{·} is the diagonalization operation, represents the beamforming vector, N[k] represents additive white Gaussian noise with zero mean, and the CSI from the u-th transmit antenna to the w-th receive antenna on the m-th RIS element at the k-th subcarrier can be expressed as Among them represents a fourth-order tensor The (k, u, w, m)-th element ρ l = ρ WR × ρ RU and τ l = τ WR,l + τ RU,l represent the cascaded gain and the cascaded delay respectively represents the combined received noise represents the cascaded angle. For the line-of-sight path The tensor form of the received CSI at the receiver can be expressed as Among them represents the outer product is the noise tensor at the receiving end, a(τ l ), a(φ l ), a(θ l ) and are the steering vectors of the arrival time, arrival angle, departure angle, and cascaded angle of the l-th path, respectively, and can be expressed as Using subcarrier resources, the received CSI model is rearranged by adopting the virtual antenna array technology to further meet the uniqueness of tensor decomposition, specifically including: assuming that a total of n u +n w +n m subcarriers are extracted from a total of K, and the scale of the transmit antenna, receive antenna, and RIS unit is expanded using n u , n w , and n m subcarriers respectively, and a virtual antenna array is constructed. The steering vector after rearrangement can be expressed as where denotes the Kronecker product, and the reconstructed tensor model can be expressed as Among them represents the noise tensor after rearrangement; The rearranged tensor model is decomposed using the fourth-order alternating least squares algorithm, and the subspace method is further used to extract the multipath channel parameters, specifically including: The channel parameter estimation problem is further formulated as a fourth-order low-rank tensor model decomposition problem Among them and respectively represent the estimated values of the factor matrices and ; and respectively represent the estimated values of τ l , φ l , θ l and ; ||·|| F represents the operation of calculating the Frobenius norm. The original problem is divided into four sub-problems for optimization by using the four-linear alternating least squares algorithm, and each factor matrix is updated iteratively until convergence where ⊙ denotes the Khatri-Rao product, and X (n) denotes the mode-n unfolding of the tensor χ, respectively and and denote the estimated values of the factor matrices and at the (i + 1)-th iteration, respectively and denote the estimated values of the factor matrices and at the i-th iteration, respectively. The spatial spectrum is constructed using the projection matrix According to the estimated factor matrices, the parameters are estimated by means of spectral peak search wherein represents the estimated value of z l , and I represents the identity matrix; Using the system geometric constraint relationship and the estimated parameter information, a search-free positioning method is adopted to achieve precise positioning of the user and multiple RIS nodes, specifically including: the target position of the user The position of the l-th RIS And the position of the WIFI node The spatial geometric relationship of can be expressed as τ0 = ||p U -p W ||2 / c, θ T,0 = π + φ R,0 , τ l =(||p U -p R,l ||² + ||p W -p R,l ||²) / c, l ≥ 1, where ||·||2 represents the norm of a vector, c represents the speed of light, and the target localization problem can be represented by the maximum likelihood estimation problem Among them and respectively represent the estimated values of p U and p R,l . To solve the above problems, a geometry-based search-free algorithm is adopted to estimate p U and p R,l . For the l-th path, l = 1, …, L, the user's target location can be expressed as where η l ∈ [0, 1] represents an unknown weight, and respectively represent the receiving direction vector and the transmitting direction vector of the l-th path. When l = 0, the target user location can be expressed as where r W,U = r W,0 = -r U,0 After rearranging the information under the l-th 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 where γ l ≥0 represents the SNR-dependent weight values corresponding to l paths, and its least-squares solution is obtained by minimizing C(p U ) where (·) -1 represents the inverse operation. After obtaining the estimated user position, the position of the reflection node can be represented by the intersection of the linear equations p U +β U r U,l and p W +β W r W,l . Among them, the least squares solution of is

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