Target localization method for MIMO system assisted by reconfigurable intelligent surface

Abstract

The present invention belongs to the field of wireless positioning technology, and specifically relates to a MIMO system target positioning method assisted by a reconfigurable smart surface. The present invention integrates the MIMO system wireless positioning technology with the reconfigurable smart surface technology, and proposes a MIMO system target positioning method assisted by a reconfigurable smart surface. The method of the present invention mainly introduces a smart surface as a relay in the MIMO system, which not only can achieve low-power communication transmission, but also can establish a virtual line of sight path (Virtual Line of Sight, VLoS) in the non-line of sight (NLoS) path between the user and the base station to assist in the positioning of the user. Utilizing existing positioning technology, the positioning algorithm of users in the smart surface-assisted MIMO system is optimized to achieve high-precision positioning targets.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless positioning, and in particular relates to a reconfigurable intelligent surface-assisted MIMO system target positioning method. Background Art

[0002] Wireless positioning technology essentially processes wireless signals received by the receiving antenna array and obtains the positioning information it carries. Angle of Arrival (AOA)-based positioning algorithms are a typical range-based self-positioning algorithm for sensor network target nodes in the field of wireless positioning. The transmitting node antenna emits a direction-finding signal. Due to the different distances received in the array, phase differences are generated. The direction of arrival of the signal is obtained through sensing devices, and the relative direction angle between the receiving node and the reference node is calculated. The location information of the unknown node is then determined using triangulation or other methods. AOA-based positioning algorithms are common, requiring a small number of reference nodes, low communication overhead, no time synchronization requirements, and high positioning accuracy.

[0003] Reconfigurable Intelligent Surfaces (RIS) are a revolutionary product of communications research. Academia and industry agree that RIS will be a key technology for future intelligent communications. RIS can control the reflection of radio electromagnetic signals by real-time manipulating the physical parameters of reflective elements, creating a multipath, intelligently reconfigurable wireless transmission environment. Passive, low-cost, and low-power, RIS can achieve multiple goals in wireless systems, including increasing capacity, enhancing service quality, improving coverage gaps, and mitigating interference. Therefore, it has broad applications in high-frequency communications, secure communications, and indoor and outdoor sensing and positioning. Summary of the Invention

[0004] Based on the above-mentioned existing technologies, to further improve the performance of outdoor user positioning, the present invention integrates MIMO system wireless positioning technology with reconfigurable smart surface technology to propose a MIMO system target positioning method assisted by reconfigurable smart surfaces. The method of the present invention mainly introduces smart surfaces as relays in MIMO systems, which not only achieves low-power communication transmission but also establishes virtual line of sight (VLoS) paths in non-line of sight (NLoS) scenarios between users and base stations to assist in user positioning. Utilizing existing positioning technology, the positioning algorithm for users in smart surface-assisted MIMO systems is optimized to achieve high-precision positioning targets.

[0005] The technical solution of the present invention is:

[0006] A target positioning method for MIMO system assisted by reconfigurable intelligent surface. The MIMO system consists of a B A base station with 10 antenna elements, K reconfigurable smart surfaces (RIS) and a U Each RIS consists of M antenna elements arranged in a uniform planar array. Rx ×M Ry The position coordinates of the user, base station, and RIS are all set in a three-dimensional Cartesian coordinate system, where the position of the user is time-invariant, and the positions of the center of the base station array antenna, the kth RIS, and the center of the user array antenna in time slot τ are represented by the three-dimensional vector coordinates p B ,p R,k , Indicates that, and the specific locations of the base station and K RIS are known; the positioning method includes:

[0007] S1. The base station sends a signal to the user terminal, and the received signal at the user terminal is:

[0008]

[0009] in,

[0010]

[0011]

[0012]

[0013] n is Gaussian white noise, ζ represents the K factor of the Rice channel, and denote the line-of-sight components between base station and RIS and between RIS and user, respectively. and They represent the non-line-of-sight components of base station-RIS and RIS-user, respectively, and are both vectors that obey the zero-mean Gaussian distribution. And there is For the convenience of representation, the number of RIS antenna array elements is recorded as M R , that is, M R =M Rx ×M Ry , n represents the nth antenna element of RIS;

[0014] S2. Simplify the received signal:

[0015]

[0016] in, It represents the sum of the interference signal caused by Gaussian white noise and NLoS components in the received signal. represents the equivalent complex gain of the cascaded channel, which can be written as:

[0017]

[0018]

[0019]

[0020] e U Represents the unit direction vector of the user-side antenna array element, a B 、a R and a U Represent the steering vectors of the signal at the base station, RIS and user side, ρ Bk and ρ Uk are the complex channel gains of the VLoS paths from base station to RIS and from RIS to user, respectively, B,k and are the cosine of the departure angle of the radio wave signal at the base station and the cosine of the arrival angle AOA of the signal at the user, respectively. and denote the cosine values ​​of AOA in the x and y directions on the kth RIS, υ R,k,x and υ R,k,y denote the cosine values ​​of the departure angles in the x and y directions on the k-th RIS, respectively;

[0021] S3. Obtain the estimated solution of AOA from the received signal using the MUSIC algorithm, and model the three-dimensional coordinates of the center of the base station array antenna, the kth RIS, and the center of the user array antenna as follows:

[0022] p B =(x B ,y B ,z B )

[0023] p R,k =(x k ,y k ,z k )

[0024] p U =(x U ,y U ,z U )

[0025] The function is established by the user position to be estimated and the known coordinates of RIS:

[0026]

[0027] Among them, θ irepresents the true value of the i-th AOA measurement, and s represents the number of experimental measurements. represents the ith measurement value of the azimuth angle AOA of the wireless signal reflected by the kth RIS reaching the user array antenna, e i represents the AOA i-th measurement error;

[0028] S4, perform position estimation, define the estimated value of the center position coordinate of the user array antenna as (x o ,y o ,z o ), and its relationship with the true value is written as:

[0029] x U =x o +ξ x

[0030] y U =y o +ξ y

[0031] z U =z o +ξ z

[0032] ξ=[ξ x ,ξ y ,ξ z ] T Represents the estimated error of the position coordinates, then the measured value is used to replace the user's true value, and the function can be expressed as f io =f i (x o ,y o ,z o ,x k ,y k ,z k ), and according to the Taylor series expansion method, retain the terms below the second order of the function, and obtain:

[0033]

[0034] in,

[0035]

[0036] Solve the position of the geometric model in the system that includes the AOA estimate and the user position coordinate estimate, and write it in the form of a matrix:

[0037]

[0038] in,

[0039]

[0040] The least squares error is:

[0041]

[0042] Where R = E[ee H ] represents the covariance matrix of the error vector and an iterative optimization of the position estimation of the user antenna center:

[0043]

[0044] S5. Estimation of the user's location The iterative solution of the least squares error is obtained by iteration until the error value approaches zero, and the position estimation solution of the center position of the user antenna in the three-dimensional coordinate system is obtained.

[0045] The beneficial effect of the present invention is that the present invention proposes a MIMO system target positioning method assisted by a reconfigurable smart surface. By controlling the smart surface to construct a VLoS path, the problem of blocked information transmission paths between base stations and users is solved. By adjusting the reflection coefficient of the reflection unit, the reconfigurable smart surface can enhance the information transmission between the base station and the user, and obtain the location information of the carried user by using the received signal without the need for additional frequency bands and energy usage. More importantly, the beamforming of the base station and the phase of the RIS are designed through the analysis of CRLB, which alleviates the problem of low positioning accuracy in traditional positioning scenarios. Through simulation and experimental verification, the user positioning method proposed in the present invention can achieve expanded signal coverage and high-precision positioning, and has important application value and development potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 : Schematic diagram of the system composition of the present invention;

[0047] Figure 2 : AOA estimation result of the time-invariant user received signal;

[0048] Figure 3 : User location distribution map of continuous time slots;

[0049] Figure 4 : Relationship between AOA estimation performance and reflection SNR under different snapshot numbers and RIS numbers;

[0050] Figure 5 : Relationship between AOA estimation reliability and reflection signal-to-noise ratio under different snapshot numbers and RIS numbers;

[0051] Figure 6 : Relationship between user coordinate estimation performance and reflection signal-to-noise ratio under different snapshot numbers and RIS numbers;

[0052] Figure 7: Relationship between user coordinate estimation performance and base station transmit power under different beamforming schemes. DETAILED DESCRIPTION

[0053] The present invention is described in detail below with reference to the accompanying drawings and simulation examples.

[0054] The present invention is based on a MIMO system assisted by a reconfigurable intelligent surface, and its structure is as follows: Figure 1 As shown in the figure, it consists of a multi-antenna base station, K reconfigurable smart surfaces, and a multi-antenna user. The base stations and users in the system are equipped with M B and M U The K RISs are assumed to be made of the same material and size, and each RIS consists of M antenna elements arranged as a uniform planar array (UPA). Rx ×M Ry The system consists of reflection units, and the operating wavelength is millimeter wavelength.

[0055] The position coordinates of users, base stations, and RIS in the system are all set in a three-dimensional Cartesian coordinate system. The position of the user is time-invariant, so the positions of the center of the base station array antenna, the kth RIS, and the center of the user array antenna in time slot τ can be respectively represented by the three-dimensional vector coordinates p B ,p R,k , The base station and K RISs have known their specific locations when they are deployed at fixed locations, so in the present invention, it is assumed that the user has mastered the specific location coordinate information of the base station and RIS.

[0056] The basic operating principle of target positioning in a MIMO system assisted by a reconfigurable smart surface is as follows: First, in scenarios where the direct path between the base station and the user is blocked, the RIS acts as a passive relay, establishing a cascaded channel consisting of the base station, RIS, and user. Based on the time-invariant nature of the user's position, the AOA of the RIS reflected signal at the user's antenna is calculated using the MUSIC algorithm. The time-invariant, nonlinear geometric relationship between the base station and the RIS, given a known specific deployment location, is solved using the Taylor series expansion method. Then, based on the Cramér-Rao lower bound (CRLB) of the time-invariant user location estimate, the base station beamforming design and the RIS phase design are respectively implemented, thereby optimizing the positioning algorithm and improving positioning system performance.

[0057] In the reconfigurable intelligent surface-assisted MIMO system target positioning method proposed in this invention, the spectrum of the user's antenna array received signal is estimated based on the MUSIC algorithm. K RIS are K source signals. Therefore, considering a 4-RIS assisted positioning system, the AOA information obtained from the received signal is as follows: Figure 2 In addition, considering that the position estimation of user positioning is the result of solving nonlinear equations, after solving the positioning estimation of users in multiple time slots based on Taylor series expansion method, the result is as follows: Figure 3 shown.

[0058] Specifically:

[0059] The basic working principle of target positioning in MIMO system assisted by reconfigurable intelligent surface is: the channel from the base station to the kth RIS and from the kth RIS to the user is expressed as: and Assuming that the combined channel constructed on the base station-RIS-user side can be established as a Ricean channel model containing two components, LoS and NLoS, the channel modeling of the base station-RIS segment can be written as:

[0060]

[0061] Accordingly, the channel modeling of the RIS-user side can be written as:

[0062]

[0063] Where ζ represents the K factor of the Rice channel, and They represent the line-of-sight components between the base station and the RIS and between the base station and the RIS and the user, respectively. and They represent the non-line-of-sight components of the base station-RIS and RIS-user sides, respectively, and are generally vectors that obey a zero-mean Gaussian distribution. The phase shift vector contained in the kth RIS is expressed as in So the diagonal phase shift matrix of the kth RIS is written as:

[0064]

[0065] Where: And there is

[0066] In the composite channel, the LoS components can be written as:

[0067]

[0068]

[0069] Where, ρ Bk and ρ Uk are the complex channel gains of the VLoS paths from base station to RIS and from RIS to user side, υ B,k and The cosine of the departure angle of the radio wave signal at the base station and the cosine of the arrival angle AOA of the signal at the user side are respectively, and Represent the cosine values ​​of AOA in the x and y directions on the kth RIS. R,k,x and υ R,k,y Represent the cosine values ​​of the departure angles in the x and y directions on the kth RIS. B 、a R and a U denote the steering vectors of the signal at the base station, RIS, and user side, respectively. The steering vector of the AOA cosine value at the kth RIS can be written as:

[0070]

[0071]

[0072] Due to the UPA characteristics of the RIS antenna array design, its steering vector can be expressed as:

[0073]

[0074] Location information of base stations and RIS B ,p R As is known, when the base station sends a radio wave signal to the user end and is received, the received signal can be expressed as:

[0075]

[0076] The pilot signal x(t) sent is set to x(t)=1, where is the Gaussian white noise added to the received signal due to user-side signal processing. For user positioning research, the channel parameters of the received signal containing the VLoS component are of great importance. This is because the determination of the key parameter AOA of this component is a constraint on the system geometry and is related to the user's position. The geometric relationship of the kth RIS reflection signal on the user side can be expressed as:

[0077]

[0078] Where, e U It represents the unit direction vector of the user-side antenna array element and can be obtained in advance.

[0079] Considering that NLoS usually has very serious path loss, the interference plus noise term can be regarded as interference from an isotropic complex Gaussian distribution, and since the process of extracting AOA from the received signal is actually from the user's steering vector a U Get The received signal model can be further simplified to:

[0080]

[0081] in, It represents the sum of the interference signal caused by Gaussian white noise and NLoS components in the received signal. represents the equivalent complex gain of the cascaded channel, which can be written as:

[0082]

[0083] The equivalent complex path gain of the transmitted signal part transmitted through the reflected k-th RIS to the user's VLoS can be written as:

[0084]

[0085] The MUSIC algorithm can be used to obtain the estimated solution of AOA. Then, the actual position coordinates of the center of the base station array antenna, the kth RIS, and the center of the user array antenna can be modeled in three-dimensional coordinates as follows:

[0086] p B =(x B ,y B ,z B )

[0087] p R,k =(x k ,y k ,z k )

[0088] p U =(x U ,y U ,z U )

[0089] The function is established by the user position to be estimated and the known coordinates of RIS:

[0090]

[0091] Among them, θ i represents the true value of the i-th AOA measurement, and s represents the number of experimental measurements. represents the ith measurement value of the azimuth angle AOA of the wireless signal reflected by the kth RIS reaching the user array antenna, e irepresents the AOA i-th measurement error;

[0092] When estimating the position, the estimated value of the center position coordinate of the user array antenna is defined as (x o ,y o ,z o ), and its relationship with the true value is written as:

[0093] x U =x o +ξ x

[0094] y U =y o +ξ y

[0095] z U =z o +ξ z

[0096] ξ=[ξ x ,ξ y ,ξ z ] T Represents the estimated error of the position coordinates, then the measured value is used to replace the user's true value, and the function can be expressed as f io =f i (x o ,y o ,z o ,x k ,y k ,z k ), and according to the Taylor series expansion method, retain the terms below the second order of the function, and obtain:

[0097]

[0098] in,

[0099]

[0100] Solve the position of the geometric model in the system that includes the AOA estimate and the user position coordinate estimate, and write it in the form of a matrix:

[0101]

[0102] in,

[0103]

[0104] Then the least squares error can be written as:

[0105]

[0106] In the formula, R=E[eeH ], which means finding the covariance matrix of the error vector. So we get a definite solution for the coordinate estimation, and then we can perform an iterative optimization for the position estimation of the user antenna center:

[0107]

[0108] Then the estimated user location is Repeat the above steps to obtain an iterative solution of the least squares error until the error value approaches zero, and then obtain the position estimation solution of the center position of the user antenna in the three-dimensional coordinate system, that is, the specific coordinates of the user in the system.

[0109] The phase vector of K RIS can be written as ω=[ω1,ω2,...ω K ] T ,but Regarding the user's position coordinates p U The phase of RIS defines a vector function on adjacent time slots of the channel as shown in the formula: τ-1 =Φ τ-1 (h, ω), in the present invention, since the user is time-invariant, its vector at the kth RIS can be expressed as:

[0110]

[0111] Since the position of the RIS is fixed, the design of the base station's transmit beamforming can be written based on the CRLB analysis:

[0112]

[0113] Where, γ k represents the weight coefficient of the directional transmit beamforming when the base station sends a signal to the kth RIS. When each RIS receives a directional signal with clear directionality from the base station, the reflected signal of the RIS is also enhanced. Therefore, the problem of solving the directional signal weight coefficient is reduced to solving an optimization problem:

[0114]

[0115]

[0116] The maximum beam gain can be achieved when the reflected signal beam of the RIS is directed toward the target user. Therefore, the phase design (passive beamforming) and steering vector of the kth RIS for the reflected beam pointing toward the target user are written as:

[0117]

[0118] From the analysis of CRLB, we know that the phase of RIS affects CRLB, but it is difficult to obtain a closed-form solution for this lower bound from a mathematical relationship. Then, by observing the equivalent complex channel of the cascaded channel VLoS, it can be further rewritten as:

[0119]

[0120] The positions of RIS and base station are known, so the angle of departure (AOD) and angle of arrival (AOA) of the two can be determined, so we have where w R is a column vector of the RIS phase matrix. Observing the above complex channel, we can obtain:

[0121] |l0| 2 ≤M R 2

[0122] If l0 reaches its maximum value, the received signal y will obtain the maximum signal-to-noise ratio, which is beneficial to the positioning of the received signal. At this time, the phase matrix of RIS is:

[0123]

[0124] Then the reflected signal can be aligned with the user, but this is not the optimal phase design in the scenario of wireless positioning of users considered in this article, but may be the optimal phase for wireless communication. Therefore, to find the optimal phase shift of the RIS for positioning, it is also necessary to obtain the optimal phase shift from CRLB and as well as from the perspective of .

[0125] Depend on and From the derivation, we can see that both describe positioning performance. Both are derived from the Fisher information matrix, but their physical meanings are completely different. Therefore, simply superimposing them as the objective function for optimization is obviously not ideal. Therefore, one is fixed as the objective function and the other as a constraint.

[0126] When optimizing And will As a constraint:

[0127]

[0128]

[0129]

[0130] When optimizing And will As a constraint:

[0131]

[0132]

[0133]

[0134] Where ε0 and ε1 are both constants.

[0135] The practicability of the present invention is verified by simulation below.

[0136] The simulation parameters are set as follows: the operating frequency is 28 GHz, the number of RIS antennas is 32, the number of user antennas is 16, the number of base station antennas is 32, the noise power is -50 dBm, the initial true position of the user is set to (-10, 0, 0), and the number of RIS deployments is set to 4, 6, and 7. In order to illustrate the beneficial effect of the present invention on AOA estimation, the impact of the RIS reflection link signal-to-noise ratio (SNR) on the performance of AOA estimation from the received signal is studied from the number of RIS deployments and the snapshot number distribution of the MUSIC algorithm. Figure 4 As shown in Figure 2, when the signal-to-noise ratio is high, the performance of AOA estimation gradually improves with the increase of the number of RIS deployments and the number of snapshots. When the number of snapshots of the MUSIC algorithm is consistent, the more RIS deployments there are, the better the estimation performance. Figure 5 As shown in the figure, when an AOA estimation error within 1° is defined as reliable, the number of snapshots is 1000 and 5000, and the number of RISs (K) is 4 and 6, respectively. The estimation reliability varies under different signal-to-noise ratios (SNRs) of the RIS reflection signal link. As the SNR increases, the estimation reliability reaches 100% most quickly, achieving more reliable AOA estimation for users. Increasing the number of RISs and snapshots is effective in improving performance.

[0137] In order to further verify the beneficial effects of the present invention, the estimation performance of the user's position coordinates is compared when the signal-to-noise ratio of the reflection link is -10dBm to 20dBm, as shown in FIG. Figure 6 As shown. Since the estimation of position coordinates depends on the estimation performance of AOA, the number of snapshots and RIS are still variables in the experimental simulation. It can be seen that when the signal-to-noise ratio is above -10dBm and the number of snapshots is between 1000 and 5000, the estimation performance of the position coordinates is almost the same, and when the number of RIS is 4 and 6, the RMSE of the position coordinate estimation shows that the more RIS, the better the performance. Therefore, under a higher signal-to-noise ratio, increasing the number of distributed deployments of RIS can achieve more accurate user position coordinate estimation. An actual system is built to verify the method proposed in the present invention. As Figure 7The figure shows the performance of estimating user location coordinates after implementing base station beamforming and RIS phase design in the wireless positioning system described in the present invention. In the simulation experiment, the number of RIS phase optimization iterations was set to 10,000, the number of snapshots was set to 1,000, the RIS reflection link signal-to-noise ratio was set to 10 dBm, the number of RISs was set to K = 4, and the angular spacing of the RIS reflection signals was set to greater than 10°. The RMSE curve for RIS-assisted user location coordinate estimation shows that when only base station beamforming is implemented and the RIS phase is randomly designed, the estimation performance is poor when the transmit power is below 40 dBm, and high-precision location estimation cannot be achieved. However, when the base station performs beamforming design while optimizing the RIS phase matrix, the location coordinate estimation performance gradually improves when the base station transmit power is below 40 dBm, and gradually improves with increasing base station transmit power. When the base station transmit power is greater than 40 dBm, the algorithm's location coordinate estimation performance curve approaches CRLB. This means that the location coordinate estimation algorithm that combines base station beamforming and RIS phase design in a wireless positioning system performs extremely well.

Claims

1. Target positioning method of MIMO system assisted by reconfigurable intelligent surface. The MIMO system includes a A base station with antenna elements, A reconfigurable intelligent surface RIS and a antenna elements, each RIS consists of The position coordinates of the user, base station and RIS are all set in a three-dimensional Cartesian coordinate system, where the position of the user is time-invariant, the center of the base station array antenna, the The center of each RIS and user array antenna is in the time slot The positions are represented by the three-dimensional vector coordinates Indicates that the base station and The specific location of each RIS is known; it is characterized by The positioning method includes: S1. The base station sends a signal to the user terminal, and the received signal at the user terminal is: ， in, ， ， ， From the base station to the RIS channels, It is RIS to user channels, is Gaussian white noise, represents the K factor of the Ricean channel, and denote the line-of-sight components between base station and RIS and between RIS and user, respectively. and They represent the non-line-of-sight components of the base station-RIS and RIS-user, respectively, and are both vectors that obey the zero-mean Gaussian distribution. , and there are , the number of RIS antenna array elements is recorded as ,Right now ; Indicates RIS antenna array elements; S2. Simplify the received signal: ， in, It represents the sum of the interference signal caused by Gaussian white noise and NLoS components in the received signal. represents the equivalent complex gain of the cascaded channel, specifically: ， ， ， represents the unit direction vector of the user-side antenna array element, 、 as well as Represent the signal steering vectors at the base station, RIS and user side respectively, and are the complex channel gains of the VLoS paths from base station to RIS and from RIS to user, respectively, and are the cosine of the departure angle of the radio wave signal at the base station and the cosine of the arrival angle AOA of the signal at the user, respectively. and Respectively represent About RIS and The cosine of the AOA of the direction, and Respectively represent About RIS and The cosine of the departure angle of the direction; S3, obtain the estimated solution of AOA from the received signal through the MUSIC algorithm, and calculate the center of the base station array antenna, the The three-dimensional coordinates of the center of the RIS and the user array antenna are modeled as follows: ， The function is established by the coordinates of the user position to be estimated and the RIS: ， in, Indicates the The true value of the AOA measurement, represents the number of experimental measurements, Indicates the The wireless signal reflected by the RIS reaches the user array antenna at the direction angle AOA of The measured value, Indicates AOA Secondary measurement error; S4, perform position estimation, define the estimated value of the center position coordinate of the user array antenna as , and its relationship with the true value is written as: ， Represents the estimated error of the position coordinates, using the measured value instead of the user's true value. The function is expressed as , and according to the Taylor series expansion method, retain the terms below the second order of the function, and obtain: ， in, ， Solve the position of the geometric model in the system that includes the AOA estimate and the user position coordinate estimate, and write it in the form of a matrix: ， in, ， The least squares error is: ， in, It represents finding the covariance matrix of the error vector and performing an iterative optimization on the position estimation of the user antenna center: ； S5. Estimation of the user's location The iterative solution of the least squares error is obtained by iteration until the error value approaches zero, and the position estimation solution of the center position of the user antenna in the three-dimensional coordinate system is obtained.

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