Intelligent reflecting surface assisted TDOA positioning device

Abstract

The invention discloses a positioning (RIS NLOS TDOA Position, RNTP) algorithm for a non-line-of-sight (TDOA) of an intelligent reflecting surface, and belongs to the technical field of calculation, reckoning or counting. The intelligent reflecting surface (IRS) has flexible deployment and expansibility, is commonly used as a wireless relay, can change the propagation environment of wireless signals, and provides a new opportunity for the situation that the positioning service cannot be carried out in the environment with serious NLOS (Non Line Of Sight). The invention provides an intelligent reflector non-line-of-sight TDOA positioning algorithm. Firstly, a 5G positioning system based on an intelligent reflecting surface is constructed, and a plurality of IRSs deployed in the air are utilized to reflect signals, so that a base station can overcome the influence of NLOS to receive positioning signals of a to-be-positioned point. And secondly, establishing an optimization problem for minimizing the distance between the estimated coordinate and the actual coordinate, and solving the optimization problem through a PWLCM-grey wolf optimization algorithm, so that the positioning error is minimum. Simulation results show that compared with grey wolf optimization and particle swarm optimization algorithms, the positioning error obtained by the positioning algorithm for the non-line-of-sight TDOA of the intelligent reflecting surface provided by the invention is obviously improved, and it is proved that the algorithm has a good positioning effect.

Description

Technical Field

[0001] The present invention relates to communication technologies, and specifically discloses a TDOA positioning device assisted by a reconfigurable intelligent surface, belonging to the technical field of computing, reckoning or counting. Background Art

[0002] With the continuous development of wireless communication technologies, especially with the continuous development of 5G communication technologies, positioning has been integrated into people's lives in various aspects, such as in many fields like car navigation, driverless, logistics management, medical health, industrial Internet of Things, etc. To provide better location services, higher positioning accuracy is required, which brings various benefits such as navigation convenience, safety guarantee, optimization of resource management, personalized experience, etc., thus greatly improving people's work efficiency and quality of life. It can be seen that 5G-based positioning services have become an indispensable part of people's lives, and researching 5G positioning technologies has important practical significance.

[0003] Currently, certain research results have been achieved in 5G-based positioning technologies, including positioning methods for 5G cellular networks. The positioning method for 5G cellular networks determines the location of a mobile device by transmitting and receiving signals through a mobile communication base station. Its main methods include: Time of Arrival (TOA), Time Different of Arrival (TDOA), and Angle of Arrival (AoA).

[0004] In recent years, the reconfigurable intelligent surface (RIS) has played an important role in the development of next-generation wireless communication technologies as a new type of technology. The RIS consists of a large number of passive reflection elements, and each passive reflection element can independently control the amplitude and phase shift of the incident signal, thereby changing the signal propagation direction to facilitate reception by users located at different positions. In wireless communication, the RIS can be deployed on base stations, buildings, vehicles, or other objects as an intelligent reflector. When electromagnetic waves interact with the RIS, the RIS can adjust the reflected beam direction in real time, change the amplitude and phase of the signal, and even generate a wavefront shaping effect, thereby realizing dynamic adjustment and optimization of the signal. In this way, the RIS can improve the signal coverage range, enhance the reliability of communication, strengthen privacy and security, and significantly improve the energy efficiency of wireless communication systems.

[0005] The positioning method of RIS refers to using RIS as a positioning device to estimate the position of a mobile device by receiving the phase and amplitude information of signals. At the same time, RIS technology has the potential to address the occlusion problem. By deploying RIS on the signal propagation path, the signal propagation path can be adjusted to enable the signal to bypass the occluder or optimize the diffraction path, thereby reducing signal attenuation and blocking phenomena. Therefore, it can be used to solve the positioning problem in the NLOS scenario. Summary of the Invention

[0006] In view of the problem of incommunication between a base station and a user to be located in the NLOS environment, the present invention designs an intelligent reflecting surface-assisted TDOA positioning device. First, an intelligent reflecting surface-assisted TDOA positioning model is constructed to describe the positioning method of the user to be located in the NLOS scenario; secondly, a positioning error function is defined to establish an optimization problem of minimizing the distance between the estimated coordinates and the actual coordinates; finally, the intelligent reflecting surface non-line-of-sight TDOA positioning algorithm is used to solve the optimization problem. The simulation results show that the proposed algorithm has good positioning accuracy.

[0007] The intelligent reflecting surface-assisted TDOA positioning device of the present invention includes the following three steps:

[0008] 1) Construct an intelligent reflecting surface-assisted TDOA positioning model, which has a single-antenna base station BS, multiple intelligent reflecting surfaces RIS, and a single-antenna user UE to be located. Assume that the system contains I (I≥4) intelligent reflecting surfaces denoted as the set RIS = {RIS1, RIS2,..., RIS i ,..., RIS I}, where i = 1,..., I, and RIS i represents the i-th intelligent reflecting surface. Each RIS consists of N uniformly horizontally linearly arranged reflecting units, and the reflecting units of RIS i are denoted as the set where represents the n-th reflecting unit of RIS i . In the system model set in this paper, BS and UE cannot communicate directly. Therefore, when BS wants to communicate with UE, the I RIS first receive the signal transmitted by BS, and each reflecting unit on RIS adjusts the amplitude and phase shift of the received signal through circuit control and reflects it to UE. It is assumed that the positions of the base station BS and the intelligent reflecting surface RIS are fixed and known. Let the position of the base station be (x B , y B , z B ), the position of i in RIS is , and the position of the user UE to be located is unknown, denoted as (x, y, z).

[0009] Let the RIS i phase shift matrix be where diag(.) represents a diagonal matrix, represents the RIS i on U n phase shift, and represents the RIS i on U n amplitude reflection coefficient. In this paper, the change in signal amplitude is not considered. Therefore, for convenience of calculation, let The phase shift matrix of the RIS i can be simplified to

[0010] Let the channel from the BS to the nth reflection unit i of the RIS be:

[0011]

[0012] where represents the distance of the wireless communication channel from the BS to the RIS i of β is the path loss exponent, represents the delay phase shift. The wireless communication channels from the BS to the RIS are H1, H2,..., H i ..., H I can be expressed as:

[0013]

[0014] Let the channel from the nth reflection unit i of the RIS to the UE be:

[0015]

[0016] where represents the RIS i of to the UE wireless communication channel distance.

[0017] The wireless communication channels from the RIS to the UE are G1, G2,..., G i ..., G I can be expressed as:

[0018]

[0019] Let the signal transmitted from the BS pass through the nth reflection unit U i of the RIS n and reach the UE. The channel is:

[0020]

[0021] Then the RIS i The total channel for assisting the communication between the US and the BS can be expressed as:

[0022]

[0023] When Θ i is the identity matrix, taking the modulus of the left side of Equation (5) and rearranging, we get:

[0024]

[0025] From this, the distance from each unit to the user to be located can be obtained as:

[0026]

[0027] Since the total channel from the base station to the RIS to the UE, Equation (6), can be further expressed as:

[0028]

[0029] When the distance between the base station and the RIS is relatively large, the distance difference between each unit can be ignored. Therefore, it can be assumed that the distances from each unit in a RIS to the base station are equal. Then the RIS i to the UE of the wireless communication channel distance is the average of the distances from each unit in the RIS i to the user to be located, expressed as:

[0030]

[0031] When the BS sends a known signal s, let y be the sum of all I RIS reflected signals received by the UE, and its specific expression is as shown in (11)

[0032] y = (H1Θ1G1 + H2Θ2G2 + … H i Θ i G i … + H I Θ I G I )s + n (11)

[0033] where n is the Gaussian white noise of σ 2 .

[0034] Assuming that the channel coefficients remain unchanged, by setting the phase shift matrices of the I intelligent reflecting surfaces M (M = I) times, the distance from the RIS i to the UE can be obtained. When m = 1, let the phase shift matrices be Θ1, Θ2, … Θ i , …, ΘI =E, where E is the identity matrix, we can get:

[0035]

[0036] When m=2, let the phase shift matrix Θ1, Θ2, ..., Θ i ,…Θ I-1 , -Θ I =E, where the phase shift matrix of the first I-1 smart reflectors is a unit vector and the phase shift matrix of the last smart reflector is a negative unit vector, we can get:

[0037]

[0038] Similarly, when m = (3, ..., M), let the phase shift matrix We can get:

[0039]

[0040] Since there may be noise interference in signal propagation, through equations (7)-(9), we can estimate that the product of the channel from the base station to the RIS and the channel from the RIS to the user UE to be located in this case is:

[0041]

[0042] Substituting equation (15) into equation (10), we can obtain the wireless communication channel distance from RIS to the user UE to be located when there is noise:

[0043]

[0044] The distance difference including noise is Can be expanded to:

[0045]

[0046] Select 3 different By combining them, we can get the estimated coordinates (x, y, z), for example:

[0047]

[0048] In this paper, RIS1 is used as the reference point, and the distance from RIS1 to UE is used as the reference distance. RIS2, ..., RIS i ,…,RIS I The distance difference between RIS1 and UE is R 2，1 , ..., R i，1 , ..., R I，1 As the estimated distance difference, As the actual distance difference, the coordinates of the estimated point can be solved through the principle of TDOA. However, there may be some outliers among these estimated points whose coordinates far exceed the positioning range, and these outliers can be removed through preprocessing by the quartile method.

[0049] In (2), the positioning error function is defined, and an optimization problem of minimizing the distance between the estimated coordinates and the actual coordinates is established. By calculating the difference between each estimated point and the actual position respectively, and making the sum of them minimum as the principle, the function F(x, y, z) is designed to find the individual with the best consistency between the actual value and the estimated value. The function is shown as follows:

[0050]

[0051] 3) The intelligent reflecting surface non-line-of-sight TDOA positioning algorithm is used to solve this optimization problem. The intelligent reflecting surface non-line-of-sight TDOA positioning algorithm specifically includes the following steps:

[0052] (1) Obtain the distance between the RIS and the user equipment (UE) to be located;

[0053] (2) Solve the estimated point coordinates through the TDOA principle, and use the quartile method to remove the outliers;

[0054] (3) Generate the initial position of the individual according to the PWLCM chaotic mapping;

[0055] (4) Calculate the fitness of all individuals and find the three individuals with the minimum fitness;

[0056] (5) Obtain the optimal solution according to the grey wolf optimization principle. Description of the Drawings

[0057] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the drawings, wherein:

[0058] Figure 1 is the TDOA positioning model assisted by the intelligent reflecting surface;

[0059] Figure 2 is the three-dimensional positioning schematic diagram of the proposed algorithm;

[0060] Figure 3 is the schematic diagram of the relationship between the positioning error of the proposed algorithm, signal-to-noise ratio and the number of RISs. Detailed Embodiment

[0061] The following further describes the detailed embodiment of the present invention in conjunction with the drawings.

[0062] Figure 1TDOA positioning model assisted by intelligent reflecting surface. The system model of RIS-based positioning (RBP) is as follows Figure 1 shown. There is a single-antenna base station BS, multiple intelligent reflecting surfaces RIS, and a single-antenna user equipment UE to be located in this system. Assume that there are I (I≥4) intelligent reflecting surfaces in the system, denoted as the set RIS = {RIS1, RIS2, …, RIS i , …, RIS I}, where i = 1, …, I, and RIS i represents the i-th intelligent reflecting surface. Each RIS consists of N reflection units arranged linearly and uniformly in the horizontal direction. The reflection units of RIS i are denoted as the set where represents the n-th reflection unit of RIS i . In the system model set in this paper, BS and UE cannot communicate directly. Therefore, when BS wants to communicate with UE, the I RIS first receive the signal transmitted by BS, and each reflection unit on the RIS adjusts the amplitude and phase shift of the received signal through circuit control, and reflects it to UE. And assume that the positions of the base station BS and the intelligent reflecting surface RIS are fixed and known. Let the position of the base station be (x B , y B , z B ), and the position of i in RIS is . The position of the user equipment UE to be located is unknown, denoted as (x, y, z).

[0063] Let the phase shift matrix of RIS i be where diag(.) represents the diagonal matrix, represents the phase shift of i on RIS n , and represents the amplitude reflection coefficient of i on RIS n . In this paper, the change of signal amplitude is not considered. Therefore, for the convenience of calculation, let The phase shift matrix of RIS i can be simplified to

[0064] Figure 2 is the three-dimensional positioning schematic diagram of the proposed algorithm. This schematic diagram shows the three-dimensional positioning result of the RNTP positioning algorithm proposed in this paper when the signal-to-noise ratio is 18 db and there are 50 known nodes distributed in the space. In the figure The green "*" in the figure represents the true location of the user to be located, and the green "*" represents the user location estimated by the algorithm in this paper. It can be seen from the figure that the location estimated by the RNTP algorithm has a high degree of coincidence with the true location of the user to be located. Thus, it can be seen that the algorithm in this paper has high positioning accuracy.

[0065] Figure 3 It is a schematic diagram of the relationship between the positioning error of the proposed algorithm, the signal-to-noise ratio, and the number of RISs. This schematic diagram compares the positioning errors at different signal-to-noise ratios with different numbers of RISs. The x-axis in the figure represents the signal-to-noise ratio, the y-axis represents the number of RISs, and the z-axis represents the positioning error. It can be seen from the figure that as the SNR and the number of RISs increase, the positioning error of the RNTP algorithm gradually decreases. This is because the increase in the number of RISs enables the system to apply the principle of TDOA to solve for more estimated coordinates, thereby obtaining better positioning accuracy through the RNTP algorithm in this paper. When SNR = 35, when the number of RISs is 4, the positioning error is 1.12836 m, and when the number of RISs is greater than 4, the positioning error is less than 1 m.

[0066] The above specific embodiments further elaborate on the invention purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above specific embodiments are only used as exemplary illustrations and do not limit the protection scope of the present invention. Any modification, equivalent replacement, or transformation made within the spirit and principle of the present invention should be included within the protection scope of the present invention.

Claims

1. Establish an intelligent reflecting surface-assisted TDOA positioning device, characterized in that, It includes the following steps: 1) An intelligent reflecting surface-assisted TDOA positioning model is constructed to describe the positioning method of the user to be located in the NLOS scenario; 2) The positioning error function is defined, and an optimization problem of minimizing the distance between the estimated coordinates and the actual coordinates is established; 3) The intelligent reflecting surface non-LOS TDOA positioning algorithm is used to solve the optimization problem, and the simulation results show that the proposed algorithm has good positioning accuracy.

2. The TDOA positioning device assisted by the intelligent reflecting surface according to claim 1, wherein In step 1), an intelligent reflecting surface-assisted TDOA positioning model is constructed. In this model, there is a single-antenna base station BS, multiple intelligent reflecting surfaces RIS, and a single-antenna user equipment UE to be located; it is assumed that the system contains I (I≥4) intelligent reflecting surfaces, denoted as the set RIS = {RIS1, RIS2, …, RIS i , …, RIS I}, where i = 1, …, I, and RIS i represents the i-th intelligent reflecting surface; each RIS consists of N uniformly horizontally linearly arranged reflecting elements, and the reflecting elements of RIS i are denoted as the set where represents the n-th reflecting element of RIS i ; in the system model set in this paper, BS and UE cannot communicate directly; therefore, when BS wants to communicate with UE, the I RISs first receive the signal transmitted by BS, and through circuit control, each reflecting element on the RIS adjusts the amplitude and phase shift of the received signal and reflects it to UE; and it is assumed that the positions of the base station BS and the intelligent reflecting surface RIS are fixed and known. Let the position of the base station be (x B , y B , z B ), and the position of the i in RIS is The position of the user equipment UE to be located is unknown and is set as (x, y, z); Let the RIS i phase shift matrix be where diag(.) represents a diagonal matrix, represents the phase shift of the RIS i on U n and represents the amplitude reflection coefficient of the RIS i on U n In this paper, the change in signal amplitude is not considered. Therefore, for convenience of calculation, let The phase shift matrix of the RIS i can be simplified to Let the nth reflection unit from the BS to the RIS i be the channel: Among them represents the wireless communication channel distance from the BS to the RIS i of where β is the path loss exponent represents the delay phase shift; the wireless communication channels from the BS to the RIS are H1, H2,..., H i ...,H I can be expressed as: Let the channel from the i n-th reflecting element of the RIS to the UE be: Among them represents the distance of the radio communication channel from the RIS i to the UE; The wireless communication channels from the RIS to the UE are G1, G2, ..., G i …, G I which can be expressed as: Assume that the signal transmitted from the BS passes through the RIS i The nth reflection unit U n The channel reaching the UE is: Then RIS i The total channel assisting the communication between the US and the BS can be expressed as: When Θ i is an identity matrix, taking the modulus of the left side of Equation (5) and rearranging gives: Thus, the distance from each unit to the user to be located can be obtained as follows: Since the total channel from the base station to the RIS to the UE in Equation (6) can be further expressed as: When the distance between the base station and the RIS is relatively far, the distance differences between the individual units can be ignored. Therefore, it can be assumed that the distances from each unit in an RIS to the base station are equal; then the i radio communication channel distance between the RIS i and the UE is the average of the distances from each unit in the RIS to the user to be located, expressed as: When the BS sends a known signal s, let y be the sum of all I RIS reflected signals received by the UE, and its specific expression is shown in (11). y = (H1Θ1G1 + H2Θ2G2 + … H i Θ i G i … + H I Θ I G I )s + n (11) where n is Gaussian white noise of σ 2 ; Assuming that the channel coefficients remain unchanged, by setting the phase shift matrix of I intelligent reflecting surfaces M (M = I) times, the distance from the RIS i to the UE can be obtained. When m = 1, let the phase shift matrices Θ1, Θ2, … Θ i , …, Θ I = E, where E is the identity matrix, and we can get: When \(m = 2\), let the phase shift matrices \(\Theta_1,\Theta_2,\cdots,\Theta\) i ,\cdots\Theta I-1 ,-\Theta I = E. When the phase shift matrices of the first \(I - 1\) intelligent reflecting surfaces are unit vectors and the phase shift matrix of the last intelligent reflecting surface is the negative unit vector, we can obtain: Similarly, when m = (3,...., M), let the phase shift matrix It can be obtained that: Due to the presence of noise interference in signal propagation, through Equations (7)-(9), we can estimate the product of the channel from the base station to the RIS and the channel from the RIS to the user to be located UE in this case as: Substituting Equation (15) into Equation (10), the wireless communication channel distance value from the RIS to the user to be located UE with noise can be obtained as The range difference with noise is It can be expanded as: Select 3 different By combining them, we can get the estimated coordinates (x, y, z), for example: This article takes RIS1 as the reference point and the distance from RIS1 to the UE as the reference distance, and calculates the distances from RIS2, …, RIS i , …, RIS I to the UE and the distance differences R 2，1 ,..., R i，1 ,..., R I，1 as the estimated distance differences. Taking them as the actual distance differences, the coordinates of the estimated points can be solved through the principle of TDOA. However, there may be some outliers among these estimated points whose coordinates far exceed the positioning range, and these outliers can be preprocessed and removed by the quartile method.

3. The TDOA positioning device assisted by the intelligent reflecting surface according to claim 1, wherein In step 2), the positioning error function is defined, and an optimization problem of minimizing the distance between the estimated coordinates and the actual coordinates is established. By calculating the difference between each estimated point and the actual position respectively and making their sum minimum as the principle, the function F(x, y, z) is designed to find the individual with the best consistency between the actual value and the estimated value. The function is shown as follows:

4. The TDOA positioning device assisted by the intelligent reflecting surface according to claim 1, wherein In step 3), the intelligent reflecting surface non-LOS TDOA positioning algorithm is used to solve the optimization problem. The intelligent reflecting surface non-LOS TDOA positioning algorithm specifically includes the following steps: (1) Obtain the distance between the RIS and the user to be located UE; (2) Solve the estimated point coordinates through the TDOA principle and use the quartile method to remove outliers; (3) Generate the initial position of the individual according to the PWLCM chaotic mapping; (4) Calculate the fitness of all individuals and find the three individuals with the smallest fitness; (5) Obtain the optimal solution according to the grey wolf optimization principle.

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