Incoherent User Localization Method and System Based on UAV Integrated with Intelligent Reflecting Surface

Abstract

The present disclosure provides a non-coherent user positioning method and system based on an unmanned aerial vehicle integrated with an intelligent reflecting surface, relating to the technical field of integrated sensing and communication of intelligent reflecting surfaces, including: constructing a system model; determining known anchor points, turning off the intelligent reflecting surface, and estimating the angle information of the user to the base station; then turning on the intelligent reflecting surface to obtain an interference-free user-intelligent reflecting surface-base station reflection link signal, and estimating the direct component of the channel from the intelligent reflecting surface to the base station; calculating the orthogonal space of the direct component based on the direct component, and obtaining a received signal containing only the scattered component based on the orthogonal space; based on the received signal containing only the scattered component, solving by an exhaustive search non-coherent maximum likelihood detection method with alternating optimization to obtain the angle estimate of the user to the intelligent reflecting surface; based on the angle estimate of the user to the intelligent reflecting surface, and obtaining the positioning information of the target user according to the relationship between the position information and the angle.

Description

Technical Field

[0001] The present disclosure relates to the technical field of integrated intelligent reflecting surface sensing and communication, and particularly to a non-coherent user positioning method and system based on an unmanned aerial vehicle (UAV) integrated with an intelligent reflecting surface. Background Technique

[0002] The statements in this section merely provide background technical information related to the present disclosure and do not necessarily constitute prior art.

[0003] With the development of the fifth-generation wireless communication technology, the millimeter-wave band has become crucial for improving data transmission speed, reducing latency, and enhancing sensing capabilities due to its wide bandwidth. However, millimeter waves face severe path loss and are vulnerable to obstacles in practical applications, especially in urban environments with high-rise buildings, which leads to limited signal coverage and inconsistent user experiences. To address these issues, intelligent reflecting surface technology has been proposed as a new solution. An intelligent reflecting surface can actively adjust the characteristics of incident electromagnetic waves by integrating a large number of programmable reflecting units, thereby significantly improving the quality of the communication link without additional power consumption.

[0004] Although intelligent reflecting surface technology demonstrates its potential in enhancing signal coverage, providing a stable line-of-sight link in mobile scenarios still faces challenges, mainly because fixed-position intelligent reflecting surfaces are vulnerable to shadow effects or blockages. To overcome this limitation, UAV-assisted communication systems have been introduced as a flexible and efficient solution. UAVs have the characteristics of high mobility and easy deployment, can quickly reach the designated position according to needs, carry intelligent reflecting surface devices, and optimize the establishment and maintenance of the communication link in a dynamic manner, thereby ensuring high-quality millimeter-wave communication services in various environments.

[0005] However, most of the current UAV-assisted solutions focus on UAV-assisted communication, and relatively few studies have been conducted on the positioning of intelligent reflecting surfaces carried by UAVs. In a UAV swarm system, existing studies use the angle-of-arrival estimation method based on the atomic norm to obtain the direction of arrival of the target using multiple intelligent reflecting surfaces. Or through phase shifter optimization and deep reinforcement learning, UAV trajectory planning and intelligent reflecting surface phase shift configuration are carried out to balance positioning and communication performance, or the joint optimization problem of UAV position and UAV-intelligent reflecting surface passive beamforming is considered to improve positioning accuracy. However, there are still the following problems:

[0006] 1) The base station-intelligent reflecting surface channel estimation problem, which is crucial for user positioning and communication, is ignored; in fact, estimating the base station-intelligent reflecting surface channel has always been a challenging problem, even when the channel between the base station and the intelligent reflecting surface is a slow time-varying channel.

[0007] 2) Due to power and cost limitations, intelligent reflecting surfaces usually do not have complex signal processing capabilities, which makes traditional channel estimation methods infeasible.

[0008] 3) The flight altitude and speed of the unmanned aerial vehicle (UAV) can cause fast fading, making the channel estimation overhead non-negligible. This may also disrupt the payload data transmission, and this is a long-standing problem. SUMMARY OF THE INVENTION

[0009] To solve the above problems, the present disclosure proposes a non-coherent user positioning method and system based on an unmanned aerial vehicle integrated with an intelligent reflecting surface. Through an intelligent reflecting surface assisted millimeter-wave communication framework integrated with an unmanned aerial vehicle, effective channel estimation between the base station and the intelligent reflecting surface is achieved. The positioning information of multiple users is retrieved simultaneously using the second-order channel statistical information between the base station and the intelligent reflecting surface. A method for designing an alternating exhaustive search non-coherent maximum likelihood detector is proposed to simplify the complex multi-dimensional optimization problem into multiple more tractable one-dimensional optimization problems, maintaining the stability and reliability of the communication link even under fast fading conditions caused by relative motion.

[0010] According to some embodiments, the present disclosure adopts the following technical solutions:

[0011] A non-coherent user positioning method based on an unmanned aerial vehicle integrated with an intelligent reflecting surface, comprising:

[0012] Constructing an intelligent reflecting surface assisted system model and channel model based on the intelligent reflecting surface and the base station;

[0013] Determining a block fading model, determining time slot allocation, and dividing the time interval within the coherence time into three phases;

[0014] Determining known anchor points, turning off the intelligent reflecting surface, and estimating the angle information from the user to the base station according to the pilot signal sent by the anchor point to the base station;

[0015] Then turning on the intelligent reflecting surface to obtain an interference-free user-intelligent reflecting surface-base station reflection link signal, and estimating the direct component of the channel from the intelligent reflecting surface to the base station according to the MUSIC algorithm;

[0016] Calculating the orthogonal space of the direct component according to the direct component, and obtaining a received signal containing only scattered components based on the orthogonal space;

[0017] Based on the received signal containing only scattered components, solving by an alternating optimization exhaustive search non-coherent maximum likelihood detection method to obtain the angle estimation from the user to the intelligent reflecting surface;

[0018] Based on the angle information of the user to the base station and the angle estimation of the user to the intelligent reflecting surface, and according to the relationship between the position information and the angle, the positioning information of the target user is obtained.

[0019] According to some embodiments, the present disclosure adopts the following technical solutions:

[0020] An incoherent user positioning system based on an unmanned aerial vehicle integrated with an intelligent reflecting surface, comprising:

[0021] A model construction module, configured to construct an intelligent reflecting surface-assisted system model and a channel model based on the intelligent reflecting surface and the base station; determine a block fading model, determine time slot allocation, and divide the time interval within the coherence time into three phases;

[0022] A calculation and positioning module, configured to determine known anchor points, turn off the intelligent reflecting surface, estimate the angle information of the user to the base station according to the pilot signal sent by the anchor point to the base station; then turn on the intelligent reflecting surface to obtain an interference-free user-intelligent reflecting surface-base station reflection link signal, and estimate the direct component of the channel from the intelligent reflecting surface to the base station according to the MUSIC algorithm; calculate the orthogonal space of the direct component according to the direct component, and obtain a received signal containing only the scattered component based on the orthogonal space; based on the received signal containing only the scattered component, solve by an exhaustive search non-coherent maximum likelihood detection method with alternating optimization to obtain the angle estimation of the user to the intelligent reflecting surface; based on the angle information of the user to the base station and the angle estimation of the user to the intelligent reflecting surface, and according to the relationship between the position information and the angle, obtain the positioning information of the target user.

[0023] According to some embodiments, the present disclosure adopts the following technical solutions:

[0024] A computer program product, comprising a computer program, where when the computer program is executed by a processor, the incoherent user positioning method based on an unmanned aerial vehicle integrated with an intelligent reflecting surface is implemented.

[0025] According to some embodiments, the present disclosure adopts the following technical solutions:

[0026] A non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the incoherent user positioning method based on an unmanned aerial vehicle integrated with an intelligent reflecting surface is implemented.

[0027] According to some embodiments, the present disclosure adopts the following technical solutions:

[0028] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the incoherent user positioning method based on the integrated intelligent reflective surface of a drone.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The disclosed incoherent user positioning method based on drone-integrated intelligent reflective surface proposes an innovative drone-integrated intelligent reflective surface-assisted millimeter wave communication framework, which can perform effective channel estimation between the base station and the intelligent reflective surface, and can simultaneously retrieve the positioning information (such as azimuth) of multiple users by only using the second-order channel statistics between the base station and the intelligent reflective surface, and can maintain the stability and reliability of the communication link even under the rapid fading conditions caused by relative motion. By combining the flexibility of drones and the signal adjustment capabilities of intelligent reflective surfaces, the disclosed invention provides new ideas and technical means for achieving more efficient and reliable millimeter wave communications, which is suitable for mobile communication scenarios in urban environments.

[0031] The non-coherent user positioning method based on the UAV integrated intelligent reflective surface disclosed in the present invention is particularly suitable for the situation where the channel between the intelligent reflective surface and the base station is in a fast fading environment. The scheme aims to achieve accurate positioning of the target without increasing the pilot overhead of channel estimation. The second-order channel statistical information between the base station and the intelligent reflective surface is utilized, and a method of alternating exhaustive search of non-coherent maximum likelihood detectors is designed to simplify the complex multi-dimensional optimization problem into multiple one-dimensional optimization problems that are easier to handle. This method not only effectively reduces the computational complexity, but also realizes accurate positioning estimation of the user. Therefore, compared with traditional methods, the present invention significantly reduces the pilot overhead required for channel estimation between the intelligent reflective surface and the base station.

[0032] The disclosed incoherent user positioning method based on the drone integrated intelligent reflective surface uses the idea of ​​alternating optimization combined with an exhaustive search algorithm to solve the incoherent maximum likelihood function, thereby achieving the purpose of locating the target user. The positioning of the target by a single base station is achieved, which saves cost and overhead, avoids the problem of collaboration between base stations, improves the user positioning accuracy and stability, and can achieve reliable location information estimation for multiple users even in an environment where the channel changes dramatically. It effectively solves the problem of the sharp decline in performance of traditional positioning methods under adverse conditions such as occlusion and reflection. In addition, it also reduces the possibility of interference between systems and improves the utilization rate of spectrum resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings forming a part of this disclosure are used to provide a further understanding of the disclosure. The schematic embodiments and descriptions thereof of the disclosure are used to explain the disclosure and do not constitute an improper limitation of the disclosure.

[0034] Figure 1 It is an example diagram of the system model assisted by an intelligent reflecting surface for the embodiments of this disclosure;

[0035] Figure 2 It is a schematic diagram of the fading time-varying channel model for the embodiments of this disclosure;

[0036] Figure 3 It is a schematic diagram of the flow of the non-coherent user positioning method based on an unmanned aerial vehicle integrated with an intelligent reflecting surface for the embodiments of this disclosure;

[0037] Figure 4 It is a schematic diagram of the comparison of performance indicators of different methods for the embodiments of this disclosure. Detailed implementation manners

[0038] The following further describes the present disclosure in conjunction with the accompanying drawings and embodiments.

[0039] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanations of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this disclosure belongs.

[0040] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0041] Embodiment 1

[0042] In one embodiment of the present disclosure, a non-coherent user positioning method based on an unmanned aerial vehicle integrated with an intelligent reflecting surface is provided, and the steps include:

[0043] Step 1: Construct a system model and a channel model assisted by an intelligent reflecting surface based on the intelligent reflecting surface and the base station;

[0044] Step 2: Determine the block fading model, determine the time slot allocation, and divide the time interval within the coherence time into three phases;

[0045] Step 3: Determine the known anchor points, turn off the intelligent reflecting surface, and estimate the angle information of the user to the base station according to the pilot signals sent by the anchor points to the base station;

[0046] Step 4: Then turn on the IRS to obtain the interference-free user-IRS-BS reflected link signal, and estimate the direct component of the channel from the IRS to the BS according to the MUSIC algorithm;

[0047] Step 5: Calculate the orthogonal space of the direct component according to the direct component, and obtain the received signal containing only the scattered component based on the orthogonal space;

[0048] Step 6: Based on the received signal containing only the scattered component, solve it by the alternating optimization exhaustive search non-coherent maximum likelihood detection method to obtain the angle estimate from the user to the IRS;

[0049] Step 7: Based on the angle information from the user to the BS and the angle estimate from the user to the IRS, and according to the relationship between the position information and the angle, obtain the positioning information of the target user.

[0050] As an embodiment, the non-coherent user positioning method based on UAV integrated IRS of the present disclosure proposes an IRS-assisted joint communication and positioning scheme, which can achieve single BS positioning and can simultaneously achieve positioning and communication. The specific implementation process is as follows:

[0051] Step 1: Based on the IRS and the BS, construct an IRS-assisted system model and channel model;

[0052] Specifically, the system model includes the BS and its number of antennas , the IRS and its number of elements , and a total of single-antenna users, as shown in the appendix Figure 1 . Among them, the IRS carried by the UAV creates an additional signal path between the user blocked by the building and the BS to assist in realizing the communication and positioning services between the user and the BS. In this system, the BS is a uniform linear array (ULA) with antennas, the IRS is also a uniform linear array equipped with reflection elements, the BS serves a total of users. In addition, it is assumed that a single-antenna anchor point is deployed near the surface of the IRS to assist in estimating the channel from the IRS to the BS. At the same time, it is considered that the distances between the BS, the user, and the IRS are far enough from each other, so they are in the far-field region of each other. In addition, it is assumed that the UAV can keep the two arrays parallel, and its direction is set as the X-axis. It is assumed that the coordinates of the BS and the IRS have been determined, while the coordinates of the user are randomly unknown.

[0053] All channels are modeled using the angle between the transmitter and the receiver. Since the IRS and the base station are basically fixed, the channel between the IRS and the base station remains almost unchanged for a long time. At this time, the received signal at the base station includes both the direct link from the user to the base station and the reflected link from the user to the IRS and then to the base station. The specific form of the signal is as follows:

[0054]

[0055] Among them, represents the direct link from the user to the base station, represents the reflection coefficient of the IRS, represents the channel from the UAV to the base station, represents the channel from the user to the IRS, represents the training symbol sent by the user, represents the noise.

[0056] Step 2: Determine the time slot allocation. Consider a block fading time-varying channel model, where the channel coefficients remain constant within each coherence time. The known anchor nodes for positioning send pilot sequences of length to the base station. The considered time interval is divided into three phases, namely the Figure 2 shown in . Assume that the time length of each pilot transmission phase is fixed. In phase A, pilot symbols are used for direct channel estimation between the user and the base station. When entering phase B, once the direct channel estimation between the user and the base station is completed, the previously "off" IRS will be turned on to perform channel estimation of the line-of-sight link in the IRS-base station channel. Finally, in phase C, the goal is to achieve azimuth estimation based on the user-IRS channel and ultimately achieve user positioning.

[0057] Step 2: Determine the known anchor points. Turn off all IRS elements. At this time, the IRS will be in the "off" state. At this time, estimate the angle information from the user to the base station according to the pilot signal sent by the anchor point to the base station;

[0058] Specifically, determine the known anchor points, set the reflection coefficient of the IRS to zero, and then the anchor point sends a pilot signal to the base station. At this time, the specific form of the pilot signal received by the base station in phase A is:

[0059]

[0060] Among them, Y A is the pilot signal received by the base station in phase A, represents the direct link from the user to the base station, is the training symbol sent by the user in phase A, is the noise in phase A. The specific form of

[0061]

[0062] is as follows: refers to the steering vector of the K th user, represents the azimuth angle of the K th user to the base station.

[0063] Then, according to the received direct link signal from the user to the base station only, the channel matrix is estimated by using the classical least squares method, that is:

[0064]

[0065] where , refers to the pseudo-inverse matrix of the signal sent in phase A, is 's conjugate transpose matrix. Assuming that the power of each UE is independently constrained, such that:

[0066]

[0067] where is the power of the k th user. Subsequently, each user is allowed to transmit a series of known pilot signals with a duration of symbols. Using orthogonal training sequences enables each user to transmit signals simultaneously at maximum power without interfering with other users. Therefore, a training pilot signal matrix with orthogonal rows is adopted, satisfying the following conditions:

[0068]

[0069] where represents a diagonal matrix whose diagonal elements are the transmission powers of each user, is a "short" matrix with uniform orthogonal row vectors such that holds, is the identity matrix, is 's conjugate transpose matrix. Since there are infinitely many options for the training matrix, for example, if each element of the training matrix has the same magnitude, for practical applications, a normalized discrete Fourier transform (DFT) sub-matrix can be used to implement it, which can be written as follows:

[0070]

[0071] Among them, the matrix is a discrete Fourier transform sub-matrix, denotes the imaginary part, is the number of time slots, and K is the number of users. Then, the channel estimation from the user to the base station and the corresponding azimuth estimation can be obtained from That is: That is:

[0072]

[0073] Among them, is the channel between the k-th user and the base station, is the channel estimation value between the k-th user and the base station, denotes the estimated azimuth angle from the -th user to the base station.

[0074] Step 4: Then, turn on the intelligent reflecting surface to obtain the interference-free user-intelligent reflecting surface-base station reflection link signal, and estimate the direct component of the channel from the intelligent reflecting surface to the base station according to the MUSIC algorithm;

[0075] Specifically, after estimating the channel from the user to the base station in Step 3 , turn on the intelligent reflecting surface, configure the reflecting elements according to the environmental information so that it provides services for the reflecting users. At this time, send signals to the base station and the intelligent reflecting surface through the anchor point. At this time, the initial received signal at the base station contains both the link angle from the user to the base station and the link angle from the user to the intelligent reflecting surface. By regarding the signal of the direct link known after channel estimation in Step 3 as a constant and subtracting it from the initial received signal obtained in Step 3, the received signal containing only the reflection link is obtained. The specific form of the signal is as follows:

[0076]

[0077] Among them, are the received signal matrix, the channel matrix from the anchor node to the RIS, the training signal matrix, the RIS reflection coefficient matrix, and the noise matrix at this stage, respectively. The received signal matrix, the channel matrix from the anchor node to the RIS, the training signal matrix, the RIS reflection coefficient matrix, and the noise matrix at this stage are defined as follows:

[0078]

[0079]

[0080]

[0081]

[0082]

[0083] Among them, , , , , are the received signal vector under the first training symbol in this stage, the first element of the channel matrix from the anchor node to the RIS, the first training signal, the first element of the RIS reflection coefficient matrix, and the noise vector under the first training symbol. The channel from the intelligent reflecting surface to the base station is expressed as:

[0084]

[0085] In the above formula, and represent the normalized line-of-sight link component and the normalized non-line-of-sight link component. Among them, the Rice factor is determined by the relative strength of the line-of-sight link and the non-line-of-sight link. represents the large-scale fading coefficient of the channel from the intelligent reflecting surface to the base station. It is assumed that the non-line-of-sight link component follows a Rayleigh distribution, and its elements are independently drawn from a circularly symmetric complex Gaussian distribution (CSCG) with zero mean and unit variance. The line-of-sight link component in

[0086]

[0087] can be written as: and are the angle of arrival and the angle of departure in the intelligent reflecting surface-base station channel, respectively. More specifically, and are the array steering vectors related to the base station and the intelligent reflecting surface, respectively. Then for a typical linear uniform array, the array steering vectors of the base station and the intelligent reflecting surface can be written as:

[0088]

[0089]

[0090] Among them, M is the number of base station antennas, is the transpose operation, and represent the array element spacings of the base station and the intelligent reflecting surface, respectively, while represents the carrier wavelength.

[0091] Next, the goal is to preliminarily estimate the line-of-sight (LoS) link component of the intelligent reflecting surface (IRS)-base station channel. Therefore, the multiple signal classification (MUSIC) algorithm is used to estimate the angle of departure (AoD) from the IRS , as well as the angle of arrival (AoA) at the base station . For the estimation of , based on the received signals, the covariance matrix of the signals can be obtained:

[0092]

[0093] where is the expectation operator, is the received signal at this stage, represents the conjugate transpose operation, is the noise variance, is the identity matrix, refers to the LoS link component of the IRS-base station channel. is the transmit power of the 0th user (anchor node). The Rice factor is determined by the relative strength of the LoS and non-LoS links. represents the large-scale fading coefficient of the IRS-base station channel. In the above equation, the non-LoS link component is independent of the LoS link component, and all information about the 0th user is known, including the steering vector matrix . To minimize the influence of irrelevant variables on the estimation, the matrix is constructed such that , where is a discrete Fourier transform (DFT) matrix of dimension , and its form can be written as:

[0094]

[0095] Therefore, represents the imaginary part, is the number of transmitted time slots, and the above equation can be rewritten as:

[0096]

[0097] where is the transmit power of the 0th user (anchor node). The Rice factor is determined by the relative strength of the LoS and non-LoS links. represents the large-scale fading coefficient of the IRS-base station channel. refers to the LoS link component of the IRS-base station channel. represents the conjugate transpose operation, is the noise variance, is the identity matrix, and are the angle of arrival and the angle of departure in the intelligent reflecting surface-base station channel, respectively. Next, the multiple signal classification algorithm will be applied to estimate the line-of-sight link component. More specifically, the signal subspace and the noise subspace can be obtained by performing eigenvalue decomposition on such that:

[0098]

[0099] where is the eigenvector matrix, is a diagonal matrix with the diagonal elements being the eigenvalues, arranged in descending order, represents the conjugate transpose operation. In practice, there are usually multiple reflectors between the base station and the intelligent reflecting surface, and the subspace of the reflected signals can be obtained by eigenvalue decomposition. To simplify the discussion, it is assumed that the channel between the base station and the intelligent reflecting surface follows the Rice distribution and contains only one direct path. With the help of eigenvalue decomposition, the eigenvector corresponding to the largest eigenvalue can be determined as part of the signal subspace. The remaining eigenvectors are then used to construct the noise subspace matrix:

[0100]

[0101] where is the noise subspace matrix, is the eigenvector corresponding to the second eigenvalue is the eigenvector corresponding to the Mth eigenvalue. Then, the spectrum function of multiple signal classification is constructed:

[0102]

[0103] where is the noise subspace matrix, represents the conjugate transpose operation. is the test vector and can be written as:

[0104]

[0105] where , and represents the angle resolution. M represents the number of antennas of the base station, and represents the array element spacing of the base station, while represents the carrier wavelength, is the transpose operation.

[0106] By performing a spectral peak search on the above spectrum function of multiple signal classification, the angle corresponding to the peak obtained It is the azimuth angle from the intelligent reflecting surface to the base station to be estimated.

[0107] After obtaining the estimate, it is also necessary to obtain the estimate of the steering vector . Therefore, by means of the received signal , there is:

[0108]

[0109] where is the transmit power of the 0th user (anchor node). The Rice factor is determined by the relative strength of the line-of-sight link and the non-line-of-sight link, represents the large-scale fading coefficient of the intelligent reflecting surface to the base station channel, refers to the line-of-sight link component of the RIS-base station channel, refers to the non-line-of-sight link component of the RIS-base station channel, represents the conjugate transpose operation, is the noise variance, is the identity matrix, and are the angle of arrival and the angle of departure in the intelligent reflecting surface-base station channel respectively.

[0110] By observing the structure of the above formula, let vary in each time slot, and in each time slot can be set to . Therefore, the above formula can be simplified to:

[0111]

[0112] where is the transmit power of the 0th user (anchor node). The Rice factor is determined by the relative strength of the line-of-sight link and the non-line-of-sight link. represents the large-scale fading coefficient of the intelligent reflecting surface to the base station channel. M is the number of base station antennas, is the discrete Fourier transform matrix. refers to the line-of-sight link component of the RIS-base station channel, represents the conjugate transpose operation, is the noise variance, is the identity matrix, is the angle of departure in the intelligent reflecting surface-base station channel. Similarly, can estimate by the multiple signal classification algorithm according to the above steps.

[0113] Step 5: Calculate the orthogonal space of the direct component according to the direct component, and project the signal received by the base station onto this orthogonal space to obtain a received signal containing only the scattered component;

[0114] Specifically, in the final stage C, based on the received signal containing only the reflection link obtained in step 3, the corresponding received signal matrix of users can be obtained, that is:

[0115]

[0116] where are the received signal matrix, the channel matrix between the RIS and the base station, the RIS phase matrix, the channel matrix from the user to the intelligent reflecting surface, the training signal matrix, and the noise matrix at this stage, respectively. The received signal matrix, the channel matrix from the anchor node to the intelligent reflecting surface, the training signal matrix, and the noise matrix at this stage are defined as follows:

[0117]

[0118]

[0119]

[0120]

[0121] where , , , is the received signal vector under the first training symbol at this stage, the channel vector from the first user to the RIS, the training symbol vector sent by users in the first time slot, and the noise vector under the training symbol vector in the first time slot. In stage C, set the intelligent reflecting surface phase matrix such that its elements correspond to a certain column of the discrete Fourier transform matrix , for example , where is the th column in the discrete Fourier transform matrix. For simplicity of processing, assume , that is, the number of pilot symbols is equal to the number of users, and all users have the same transmit power, set to 1, that is

[0122] According to the line-of-sight link component estimated in step 3, by projecting the received signal onto the orthogonal subspace of to remove the influence of has a rank of 1 and can be represented using singular value decomposition , that is, it can be written as:

[0123]

[0124] where and are unitary matrices, is the singular value matrix, and is the non - zero singular value. is the zero matrix, , and , The expressions of

[0125]

[0126] where , and , are the singular vectors corresponding to the non - zero singular values respectively, the matrix composed of the remaining singular vectors, M is the number of base station antennas, and L is the number of intelligent reflecting surface elements. In fact, according to the Rician channel model, the above formula can be regarded as:

[0127]

[0128]

[0129]

[0130] where , represents the singular value vector corresponding to the non - zero singular value, is the non - zero singular value, and are the angle of arrival and the angle of departure in the intelligent reflecting surface - base station channel respectively, refers to the norm operation.

[0131] Based on the property of the orthogonal matrix , that is . Therefore, can be written as:

[0132]

[0133] where , and , are the singular vectors corresponding to the non - zero singular values respectively, the matrix composed of the remaining singular vectors, M is the number of base station antennas, and L is the number of intelligent reflecting surface elements, , denotes the singular value vectors corresponding to non - zero singular values, is a non - zero singular value, The orthogonal projection matrix of can be written as , that is, there is . The expression of the whitened projection matrix is given as follows:

[0134]

[0135] where, is the matrix composed of the remaining singular vectors The conjugate transpose matrix of. Substituting the above formula into the received signal containing only the reflection link, the received signal containing only the non - line - of - sight link component is obtained, expressed as:

[0136]

[0137] where, is The orthogonal projection matrix of, and the Rice factor is determined by the relative strength of the line - of - sight link and the non - line - of - sight link. represents the large - scale fading coefficient of the intelligent reflecting surface to the base station channel. denotes the conjugate transpose operation, are the received signal matrix, RIS phase matrix, RIS - base station channel matrix, training signal matrix and noise matrix at this stage, respectively. is the matrix composed of the remaining singular vectors The conjugate transpose matrix of. refers to the non - line - of - sight link component of the RIS - base station channel.

[0138] By vectorizing the above formula and then calculating the covariance matrix of the received signal containing only the non - line - of - sight link component, the specific form is:

[0139]

[0140] where, is the expectation operator, and the Rice factor is determined by the relative strength of the line - of - sight link and the non - line - of - sight link. represents the large - scale fading coefficient of the intelligent reflecting surface to the base station channel. refers to the line - of - sight link component of the RIS - base station channel, denotes the conjugate transpose operation, is the noise variance, and are identity matrices, They are the RIS phase matrix, the RIS-base station channel matrix, and the training signal matrix at this stage, respectively. is obtained by column vectorization. represents the Kronecker product. represents the identity matrix.

[0141] After further simplification, we can get:

[0142]

[0143] where , the Rice factor is determined by the relative strength of the line-of-sight link and the non-line-of-sight link. represents the large-scale fading coefficient of the channel from the intelligent reflecting surface to the base station. is the identity matrix. represents the Kronecker product. is the RIS-base station channel matrix. represents the conjugate transpose operation.

[0144] Step 6: Based on the received signal containing only the scattered components, solve it through the non-coherent maximum likelihood detection method with alternating optimization and exhaustive search to obtain the angle estimation from the user to the intelligent reflecting surface.

[0145] Specifically, according to the covariance matrix obtained in Step 5, the conditional distribution of the received signal containing only the scattered components at the base station for the channel from any user to the intelligent reflecting surface can be written as:

[0146]

[0147] where is the received signal matrix containing only the non-line-of-sight link components obtained by column vectorization. , M is the number of base station antennas, K is the number of users. represents the determinant operation. is the RIS-base station channel matrix. refers to the exponential e.

[0148] Apply non-coherent maximum likelihood detection to solve the channel from the user to the intelligent reflecting surface , and its specific form is:

[0149]

[0150] where is the RIS-base station channel matrix. is the estimated RIS-base station channel matrix, and the Rice factor Determined by the relative strength of the line-of-sight link and the non-line-of-sight link Represents the large-scale fading coefficient of the intelligent reflecting surface to the base station channel , Is the received signal matrix containing only the non-line-of-sight link component Denotes the conjugate transpose operation , Is the noise variance Is the identity matrix Denotes taking the trace of the matrix Denotes taking the determinant of the matrix

[0151] By using the exhaustive search method, traverse all possible angles, calculate the value of the objective function, and further use the idea of alternation for exhaustive search to avoid the high algorithm complexity in the case of a large number of users. First, assume there is only one user, and obtain the angle estimate of the first user through exhaustive search of the above function ; Then fix the first user and solve the angle estimate of the second user. The specific form is:

[0152]

[0153] Where Is the angle of the second user Is the angle estimate value of the second user Is the channel matrix of the RIS-base station, and the Rice factor Is determined by the relative strength of the line-of-sight link and the non-line-of-sight link Represents the large-scale fading coefficient of the intelligent reflecting surface to the base station channel , Is the received signal matrix containing only the non-line-of-sight link component Denotes the conjugate transpose operation Is the inverse operation , Is the noise variance Is the identity matrix Denotes taking the trace of the matrix Denotes taking the determinant of the matrix

[0154] And so on until obtaining The angle estimates of users

[0155] Step 7: Based on the angle information of the user to the base station and the angle estimate of the user to the intelligent reflecting surface, and according to the relationship between the position information and the angle, obtain the positioning information of the target user

[0156] Specifically, based on the angle estimations in Steps 3 and 6, and according to the relationship between the position information and the angles, the relationship between the azimuth angles is represented by a mathematical equation. For convenience of representation, assume that the spatial coordinates of the initial antenna of the base station and the initial element of the intelligent reflecting surface are represented as and . To implement the positioning function of the user, it is necessary to associate the coordinates of the base station and the intelligent reflecting surface with the obtained azimuth angles:

[0157]

[0158]

[0159] where, are respectively the ordinate of the base station, the azimuth angle from the user to the base station, the abscissa of the user and the base station, are respectively the ordinate of the RIS and the user, the azimuth angle from the user to the RIS, the abscissa of the user and the RIS. According to the obtained azimuth angle estimations of the user-base station and the user-intelligent reflecting surface, there are:

[0160]

[0161]

[0162] where, is the estimated abscissa of the user, are respectively the ordinate of the base station, the estimated azimuth angle from the user to the base station, the abscissa of the base station, is the estimated ordinate of the user, is the abscissa of the RIS, is the ordinate of the RIS. is the estimated azimuth angle from the user to the RIS. According to the above steps, the target user can be located to obtain the position information of the user.

[0163] To verify the effectiveness of the present disclosure in the user positioning accuracy, the performance differences of different methods are compared. The minimum mean squared error of the difference between the estimated value and the true value is used to evaluate the positioning performance, and the results shown in Figure 4 are obtained. The red curve represents the method of the present disclosure. After Figure 4 is shown, it can be obtained that compared with the existing scheme, the user positioning performance of the method of the present disclosure is better.

[0164] Embodiment 2

[0165] In an embodiment of the present disclosure, a non-coherent user positioning system based on an unmanned aerial vehicle integrated with an intelligent reflecting surface is provided, including:

[0166] A model construction module, configured to construct an intelligent reflecting surface-assisted system model and a channel model based on an intelligent reflecting surface and a base station;

[0167] A calculation and positioning module, configured to determine known anchor points, turn off the intelligent reflecting surface, estimate the angle information of the user to the base station according to the pilot signal sent by the anchor point to the base station; then turn on the intelligent reflecting surface, based on the angle information of the user to the base station, obtain an interference-free user-intelligent reflecting surface-base station reflection link signal, and estimate the direct component of the channel from the intelligent reflecting surface to the base station according to the MUSIC algorithm; calculate the orthogonal space of the direct component according to the direct component, and obtain a received signal containing only scattered components based on the orthogonal space; based on the received signal containing only scattered components, solve by using an exhaustive search non-coherent maximum likelihood detection method with alternating optimization to obtain the angle estimate of the user to the intelligent reflecting surface; based on the angle estimate of the user to the intelligent reflecting surface, and according to the relationship between the position information and the angle, obtain the positioning information of the target user.

[0168] Embodiment 3

[0169] In an embodiment of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, it implements the non-coherent user positioning method based on an unmanned aerial vehicle integrated with an intelligent reflecting surface.

[0170] Embodiment 4

[0171] In an embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, and the non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, it implements the non-coherent user positioning method based on an unmanned aerial vehicle integrated with an intelligent reflecting surface.

[0172] Embodiment 5

[0173] In an embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory, so that the electronic device executes and implements the non-coherent user positioning method based on an unmanned aerial vehicle integrated with an intelligent reflecting surface.

[0174] The present disclosure is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one or more flows and / or one or more blocks in the flow Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more blocks.

[0175] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows and / or one or more blocks in the flow Figure 1 one or more flows and / or blocks Figure 1 or steps for implementing the functions specified in one or more blocks.

[0176] Although the specific embodiments of the present disclosure have been described above in conjunction with the accompanying drawings, they are not intended to limit the protection scope of the present disclosure. Those skilled in the art should understand that, based on the technical solutions of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative efforts are still within the protection scope of the present disclosure.

Claims

1. A non-coherent user positioning method based on an intelligent reflective surface integrated with an unmanned aerial vehicle, characterized in that: include: Based on the intelligent reflector and base station, a system model and a channel model assisted by the intelligent reflector are constructed; a block fading model is determined, time slot allocation is determined, and the time interval within the coherence time is divided into three stages; Determine a known anchor point, turn off the smart reflective surface, and estimate the angle from the user to the base station based on the pilot signal sent from the anchor point to the base station; Then, the smart reflective surface is turned on to obtain the interference-free user-smart reflective surface-base station reflection link signal, and the direct component of the smart reflective surface to base station channel is estimated according to the MUSIC algorithm; Calculating the orthogonal space of the direct component according to the direct component, and obtaining a received signal containing only the scattered component based on the orthogonal space; Based on the received signal containing only scattered components, the angle estimation from the user to the intelligent reflector is obtained by exhaustive search of non-coherent maximum likelihood detection method with alternating optimization. Based on the angle information from the user to the base station and the angle estimation from the user to the intelligent reflective surface, the positioning information of the target user is obtained according to the relationship between the position information and the angle.

2. The incoherent user positioning method based on the drone integrated intelligent reflective surface as claimed in claim 1, characterized in that: Based on the smart reflecting surface and the base station, a system model and a channel model assisted by the smart reflecting surface are constructed, including: the system model includes the base station and its antenna, the smart reflecting surface and its components, and the single-antenna user, all channels are modeled with the angle between the transmitting end and the receiving end, the smart reflecting surface and the base station are fixed, and the channel between the smart reflecting surface and the base station is unchanged. At this time, the received signal at the base station includes a direct link from the user to the base station, and also includes a reflection link from the user to the smart reflecting surface to the base station.

3. The incoherent user positioning method based on the drone integrated intelligent reflective surface as claimed in claim 1, characterized in that: Determine the known anchor point, turn off the smart reflector, and then send a pilot signal to the base station. The specific form of the pilot signal received by the base station is: in, represents the direct link from the user to the base station, N represents noise, is the training symbol sent by the user in phase A, is the noise of phase A.

4. The incoherent user positioning method based on an unmanned aerial vehicle integrated intelligent reflective surface as claimed in claim 1, characterized in that: The smart reflector is turned on. At this time, the signal received by the base station contains both the link angle from the user to the base station and the link angle from the user to the smart reflector. Based on the received pilot signal, a received signal containing only the reflection link is obtained. The channel from the smart reflector to the base station is expressed as: in, and represents the normalized line-of-sight link component and the normalized non-line-of-sight link component; Rice factor Determined by the relative strength of the line-of-sight link and the non-line-of-sight link, Represents the large-scale fading coefficient of the channel from the smart reflector to the base station.

5. The incoherent user positioning method based on an unmanned aerial vehicle integrated intelligent reflective surface as claimed in claim 1, characterized in that: According to the direct component estimation, the orthogonal space of the direct component is calculated, and the received signal is projected into the orthogonal space. The projected signal is the received signal of only the scattered component. The received signal of only the scattered component is vectorized, and then the covariance matrix of the received signal of only the scattered component is calculated.

6. The incoherent user positioning method based on an unmanned aerial vehicle integrated intelligent reflective surface as claimed in claim 5, characterized in that: According to the covariance matrix, the conditional distribution of the channel from any user to the smart reflector at the base station with only the scattered component is obtained. The non-coherent maximum likelihood detection is used to solve the channel from the user to the smart reflector. First, assume that there is only one user and obtain the angle estimate of the first user by exhaustively searching the function. Then fix the first user and solve the angle estimate of the second user, and so on, until you get K Angle estimation for each user.

7. An incoherent user positioning system based on an unmanned aerial vehicle integrated with an intelligent reflective surface, characterized in that: include: A model building module is used to build a system model and a channel model assisted by an intelligent reflector based on an intelligent reflector and a base station; determine a block fading model, determine time slot allocation, and divide the time interval within the coherence time into three stages; The calculation and positioning module is used to determine a known anchor point, turn off the intelligent reflective surface, and estimate the angle information from the user to the base station based on the pilot signal sent by the anchor point to the base station; Then, the smart reflective surface is turned on to obtain the interference-free user-smart reflective surface-base station reflection link signal, and the direct component of the smart reflective surface to base station channel is estimated according to the MUSIC algorithm; Calculating the orthogonal space of the direct component according to the direct component, and obtaining a received signal containing only the scattered component based on the orthogonal space; Based on the received signal containing only scattered components, the angle estimation from the user to the intelligent reflecting surface is obtained through the exhaustive search of alternating optimization and non-coherent maximum likelihood detection method. Based on the angle information from the user to the base station and the angle estimation from the user to the intelligent reflecting surface, the positioning information of the target user is obtained according to the relationship between the position information and the angle.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the incoherent user positioning method based on the drone integrated intelligent reflective surface as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the incoherent user positioning method based on the drone integrated intelligent reflective surface as described in any one of claims 1-6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the incoherent user positioning method based on the drone integrated intelligent reflective surface as described in any one of claims 1-6.

Images