Los mimo based near field communication ranging integration method

Abstract

The application discloses a near-field communication ranging integration method based on LOS MIMO, and belongs to the field of near-field communication.Combining the characteristics of close coupling of channel information and azimuth information, the application constructs a channel estimation problem of a near-field LOS MIMO system under a spherical wave, jointly considers multiple parameters such as a communication distance, azimuth angles of a horizontal direction and a vertical direction of a transmitting antenna, and azimuth angles of a horizontal direction and a vertical direction of a receiving antenna, and fully considers the non-uniform characteristics of near-field communication.The partial derivatives of a target function with respect to various parameters are analyzed, the step length is designed by using the size of the partial derivatives of the target function, non-uniform parameter traversal is realized, and a preliminary positioning estimation value range is obtained; starting from the parameter values obtained through the non-uniform traversal, each parameter is further optimized through a gradient descent method, and the error between an estimation value and an actual value is reduced.Before estimating channel information, the azimuth parameters related to the channel are estimated through the non-uniform traversal and the accurate gradient optimization method, and the integration of near-field millimeter wave communication and ranging can be realized.

Description

TECHNICAL FIELD

[0001] The application relates to a LOS MIMO near-field communication ranging integration method. BACKGROUND

[0002] On the basis of the success of 5G / B5G (5th-generation and beyond), the academic and industrial circles are laying the foundation for the development of the next generation 6G (6th generation wireless systems) wireless network, and gradually researching high-frequency communication systems. In order to solve the limitation of large path loss of high-frequency communication systems, large-scale multiple-input multiple-output (MIMO) technology is often used to enhance signal reception, improve spatial multiplexing capability, and increase spectral efficiency. In a millimeter wave MIMO system, because of the increase of frequency and the increase of antenna aperture, the near-field region of the system is expanded, and most of the transmission energy is concentrated in the LOS channel. Compared with the LOS path, the average power attenuation of the non-line-of-sight (NLOS) path is 5-10 dB. At the same time, as long as certain geometric conditions are met, the pure line-of-sight (LOS) channel can also support spatial multiplexing. Therefore, if spatial multiplexing is only realized through the LOS channel in near-field communication, the spectral efficiency and energy efficiency of the millimeter wave system can be significantly improved.

[0003] Many wireless systems are very dependent on channel state information (CSI) to design precoding to mitigate the influence of factors such as fading, noise and interference in the data transmission process. Due to the randomness of the channel, channel estimation is usually performed before transmission to obtain the necessary CSI. However, traditional channel estimation methods, such as least squares and maximum likelihood, usually produce huge computational load with the increase of the system antenna array. Generally, near-field communication is based on the assumption of spherical wave, and the channel characteristics and the azimuth information of the antenna array are closely related. Therefore, the actual spatial distance of the receiving parties is measured while the channel estimation is performed. SUMMARY

[0004] In view of the problems that the traditional far-field plane wave cannot effectively describe the near-field electromagnetic model, and that it is difficult to obtain channel information in the near-field LOS MIMO scene under the spherical wave model, and in combination with the characteristics that the channel information and the azimuth information are closely coupled, the application provides a communication ranging integration method based on the LOS MIMO near-field characteristics, constructs the channel estimation problem of the near-field LOS MIMO system under the spherical wave, estimates the azimuth information of the transmitting and receiving antennas while the channel estimation is performed. The application can also reduce the error between the estimated value and the actual value, realize multi-parameter, high-precision and fast response.

[0005] The purpose of the application is achieved by the following technical solutions.

[0006] The application discloses a near-field communication and ranging integrated method based on LOS MIMO, and comprises the following steps.

[0007] Step one, for the electromagnetic environment of a near-field LOS MIMO system, an estimated signal based on a non-uniform spherical wave near-field LOS MIMO is set; a coordinate system is established, the interval between the transmitting and receiving antenna elements is calculated, and a channel matrix model is constructed; according to the transmitting signal and the receiving signal and the established channel matrix, a channel estimation problem is further constructed;

[0008] Step 1.A: setting an estimated signal based on a non-uniform spherical wave near-field LOS MIMO;

[0009] The receiving end and the transmitting end of the LOS MIMO system are respectively equipped with M r and N t antennas, and are respectively provided with and RF links, so that the transmitting signal under the kth time slot is obtained as follows:

[0010]

[0011] Wherein, p x is the average transmission power of each symbol, and s is a signal containing information; the receiving signal of the receiving end corresponding to the kth time slot is as follows:

[0012] y k =WHFx k +n (2)

[0013] Wherein, W represents a combination matrix of the receiving end, H represents a channel matrix, F represents a hybrid precoding matrix, and n represents receiving noise The circularly symmetric complex Gaussian distribution is represented as The unit matrix with a dimension of N RF .

[0014] The coherent time is set as K time slots, and the signals of the K time slots are collected to obtain:

[0015]

[0016] Wherein, the estimated signal X=[x1,x2,…,x k ,…,x K ] represents the sum of the signals of the K time slots, and the receiving noise in the coherent interval is represented as I K , which is a unit matrix with a dimension of K;

[0017] Step 1.B: establishing a coordinate system, calculating the interval between the transmitting and receiving antenna elements, and constructing a channel matrix model;

[0018] Based on the uniform linear array (ULA) antenna, a coordinate system is established, and the vector from the origin to the nth antenna element of the transmitting array is obtained as:

[0019]

[0020] wherein x n represents the x-axis coordinate of the nth antenna element of the transmitting array; y n represents the y-axis coordinate of the nth antenna element of the transmitting array; z n represents the z-axis coordinate of the nth antenna element of the transmitting array; the aperture of the transmitting-end antenna array is D t = (n-1) d t ; the spacing of the transmitting-end antenna array is d t ; the vertical angle of the transmitting array is θ t ; and the horizontal angle of the transmitting array is

[0021] The vector from the origin to the mth antenna element of the receiving array is:

[0022]

[0023] wherein x n represents the x-axis coordinate of the mth antenna element of the receiving array; y n represents the y-axis coordinate of the mth antenna element of the receiving array; z n represents the z-axis coordinate of the mth antenna element of the receiving array; the aperture of the receiving-end antenna array is D r = (m-1) d r ; the spacing of the receiving-end antenna array is d r ; the communication distance is R; the vertical angle of the receiving array is θ r ; and the horizontal angle of the receiving array is

[0024] The distance d m,n between the mth antenna element of the receiving array and the nth antenna element of the transmitting array is represented as:

[0025]

[0026] According to the NUSW, a channel matrix model is established:

[0027]

[0028] The element in the mth row and the nth column of the matrix H is h m,n ; the channel gain under the unit distance is ρ; and the wavelength of the signal is λ.

[0029] Step 1.C: Based on the transmit signal, receive signal and channel matrix model established in step 1.A, further construct the channel estimation problem;

[0030] Based on the receive signal and transmit signal obtained in step 1.A, the channel estimation problem of near-field LOS MIMO is written as:

[0031]

[0032] Further conversion (8) is obtained

[0033]

[0034] And because (9) in Fixed, formula (9) is simplified as:

[0035]

[0036] Step two, according to the channel matrix model established in step one, calculate the gain matrix of LOS MIMO channel, according to the gain matrix obtained by calculation, through the conversion of accumulation and integration, approximate analysis is carried out, the closed form of the degree of freedom is calculated, and the constraint problem of effective reusable distance is established, and the traversal upper and lower bounds of communication distance are obtained by solving the constraint;

[0037] Step 2.A: According to the channel matrix model established in step one, calculate the gain matrix of LOS MIMO channel:

[0038]

[0039] Wherein the elements of the gain matrix are calculated by the following formula:

[0040]

[0041] The element of the gain matrix G in the vth row and the sth column is g v,s .

[0042] Step 2.B: According to the gain matrix calculated in step 2.A, through the conversion of accumulation and integration, approximate analysis is carried out, and the closed form of the degree of freedom is calculated;

[0043] The denominator in formula (13) is approximated as:

[0044]

[0045]

[0046] The approximation of the fraction is realized by Taylor expansion, and the following formula is obtained:

[0047]

[0048] where the intermediate variable f1(s,v) is a function of s and v only; f2(s,v,m) is a function of s, v and m.

[0049] According to (14) (15), the elements of the gain matrix are split into the summation of multiple elements, denoted as:

[0050]

[0051] where the system fixed parameters is the i-th summed element, and its corresponding matrix is G i ;

[0052] Therefore, the gain matrix is denoted as (16):

[0053]

[0054] The degrees of freedom (DoF) of a LOS MIMO system is mathematically equivalent to the number of positive singular values or the rank of the channel gain matrix. The degrees of freedom constraint condition of a LOS MIMO is established as:

[0055]

[0056] where ω d represents the d-th largest singular value; represents the degrees of freedom of the gain matrix G. The closed-form of the degrees of freedom of a LOS MIMO equipped with N t transmit antennas and M r receive antennas is as follows:

[0057]

[0058] where the system parameters

[0059] Step 2.C: According to the formula (18) obtained in step 2.B, the constraint problem of the effective multiplexing distance is established, and the traversal upper and lower bounds of the communication distance are obtained by solving the constraint;

[0060] According to the closed-form of the degrees of freedom obtained in 2.B, the constraint problem about the effective multiplexing distance R EMD is as follows:

[0061]

[0062] The upper bound obtained by solving formula (19) is as follows:

[0063]

[0064] where Ψ takes its maximum value when The upper bound is calculated by equation (21) when the angle is unknown:

[0065]

[0066] The lower bound is calculated by solving equation (19):

[0067]

[0068] Step 3. Calculate the distance traversal grid Π r based on the upper and lower bounds of the distance obtained in step 2, and generate the angle traversal grid Π angle based on the range of the angle constraints in step 2.

[0069]

[0070] ΔR0is the grid spacing of the distance traversal grid Π r , and c is the traversal pointer.

[0071] Step 4: Based on the channel estimation problem in step 1, calculate the partial derivative of the objective function with respect to the communication distance R; traverse Π r , and calculate to find the maximum R that satisfies ; based on the maximum R, regenerate a new non-uniform traversal grid; based on the calculated traversal matrix, re-traverse the calculation of the objective function to find a set of parameters that minimize the objective function, i.e., obtain the preliminary channel estimation value;

[0072] Step 4.A: Based on the channel estimation problem in step 1.C, calculate the partial derivative of the objective function with respect to the communication distance R:

[0073]

[0074] where Re(·) represents the real part operation; The elements of Π r are calculated by equation (26):

[0075]

[0076] where, according to formula (6), is represented as:

[0077]

[0078] Step 4.B: Traverse Π r , calculate to find the maximum R that satisfies ;

[0079] Take r ∈ Π r, given gradient threshold τ partial , determine if the threshold requirement is met, the following operations are performed:

[0080] The communication distance that meets the threshold requirement is defined as the intermediate distance, i.e. R midd = r;

[0081] When the next r that meets appears, determine if R midd < r is true, if true, assign the new distance to the intermediate distance: R midd = r, and so on until the set is traversed, and the final R midd is obtained, which is the maximum R that meets ;

[0082] Step 4.C: According to the R midd traversed in step 4.B, a new non-uniform traversal grid is regenerated;

[0083] Set two traversal steps, ΔR1 and ΔR2; calculate the following two traversal sets:

[0084]

[0085] The new traversal grid is:

[0086] Π r1 ∪Π r2 (30)

[0087] Step 4.D: Based on formula (30) in step 4.C, re-traverse the target function to find a set of parameters that minimizes the target function, and obtain the preliminary estimate value

[0088] Step five: Update the channel parameters by iteration method, calculate the step of Armijo convergence condition of communication distance R, update the channel parameters according to the step, and perform threshold judgment on the obtained channel parameters. If the threshold requirement is met, output the final iteration result as the estimated result; the iteration result corresponds to the estimated value of the communication distance

[0089] Step 5.A: Calculate the step of Armijo convergence condition of communication distance R, and update the channel parameters according to the step;

[0090] Calculate the convergence step α R according to (31):

[0091]

[0092] Where, α' R represents the step of the last iteration. convergence coefficient representing step size; partial derivative is calculated according to formula (25), and it is determined whether formula (32) is established:

[0093]

[0094] where β is a given constant; R i is the communication distance estimation value obtained after the i-th iteration; if formula (32) is not satisfied, the step size is shortened according to formula (31) until the condition is satisfied; the distance is updated with the step size satisfying the condition, and the parameter is optimized;

[0095]

[0096] Step 5.B: threshold judgment is performed on the channel distance parameter obtained in step 5.A, and if formula (34) is satisfied, the final iteration result is output as the estimation result;

[0097] For the iteration result R i+1 and R i , the following judgment is performed:

[0098]

[0099] If not, continue to repeat step 5.A until the above condition is established; the final iteration result is output as the estimation value of the communication distance

[0100] Step six: based on the iteration method of step five, the angle parameter θ t is iteratively optimized θ r is iteratively optimized to obtain the estimation value of the vertical included angle of the transmitting array to obtain the estimation value of the horizontal included angle of the transmitting array to obtain the estimation value of the vertical included angle of the receiving array to obtain the estimation value of the horizontal included angle of the receiving array

[0101] Step seven: according to the estimation value of the communication distance the estimation value of the vertical included angle of the transmitting array the estimation value of the horizontal included angle of the transmitting array the estimation value of the vertical included angle of the receiving array the estimation value of the horizontal included angle of the receiving array the estimation matrix H es is calculated to realize the estimation of the LOS MIMO near-field channel and the ranging of the transmitting and receiving antenna directions, that is, to realize the integration of the near-field communication ranging of the LOS MIMO.

[0102] Advantages:

[0103] 1. The LOS MIMO near-field communication ranging integration method disclosed in the present application, in view of the problems that the traditional far-field plane wave cannot effectively describe the near-field electromagnetic model and it is difficult to obtain channel information in the near-field LOS MIMO scene under the spherical wave model, combined with the characteristics of the close coupling of channel information and azimuth information, by constructing the channel estimation problem of the near-field LOS MIMO system under the spherical wave, jointly considering multiple parameters such as communication distance, horizontal direction and vertical direction of the transmitting antenna azimuth, horizontal direction and vertical direction of the receiving antenna azimuth, and fully considering the non-uniform characteristics of near-field communication, the reliability of channel estimation can be ensured.

[0104] 2. The LOS MIMO near-field communication ranging integration method disclosed in the present application, in view of the problems that the traditional far-field plane wave cannot effectively describe the near-field electromagnetic model and it is difficult to obtain channel information in the near-field LOS MIMO scene under the spherical wave model, combined with the characteristics of the close coupling of channel information and azimuth information, by converting the estimation problem, analyzing the partial derivative of the objective function with respect to each parameter, and according to the size of the partial derivative value, the uniformly stepped traversal set is optimized into a non-uniform traversal set associated with the partial derivative value. The larger the partial derivative value is, the greater the change of the objective function with respect to the parameter in the interval, and the corresponding traversal step is also small, so that the change of the objective function can be obtained more accurately and effectively. For the interval with a small partial derivative value, a larger traversal step is used to reduce the calculation amount and improve the efficiency of near-field communication ranging.

[0105] 3. The LOS MIMO near-field communication ranging integration method disclosed in the present application, in view of the problems that the traditional far-field plane wave cannot effectively describe the near-field electromagnetic model and it is difficult to obtain channel information in the near-field LOS MIMO scene under the spherical wave model, combined with the characteristics of the close coupling of channel information and azimuth information, before estimating the channel information, the azimuth parameters related to the channel are estimated by the method of non-uniform traversal and accurate gradient optimization, so that the integration of near-field millimeter wave communication and ranging can be realized. BRIEF DESCRIPTION OF DRAWINGS

[0106] Figure 1 is the overall flowchart of the LOS MIMO near-field communication ranging integration method and embodiment 1 in the present application;

[0107] Figure 2 is the flowchart of establishing a non-uniform traversal grid in the LOS MIMO near-field communication ranging integration method and embodiment 1 in the present application;

[0108] Figure 3 is the flowchart of optimizing each parameter according to gradient descent in the LOS MIMO near-field communication ranging integration method and embodiment 1 in the present application;

[0109] Figure 4 This is a model diagram of the millimeter-wave LOS MIMO system based on the LOS MIMO near-field communication ranging integrated method of the present invention and in Example 1, where both the transmitting and receiving ends are linear antenna arrays. Detailed Implementation

[0110] The LOS MIMO-based near-field communication ranging integration method of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0111] Example 1

[0112] This embodiment details the specific steps of implementing the LOS MIMO near-field communication ranging integration method of the present invention.

[0113] This case study considers the unclear channel information in near-field millimeter-wave LOS MIMO systems. The transmitting party needs to accurately estimate the channel information before communication, and simultaneously estimate its associated azimuth information to achieve ranging and positioning of the receiver. However, traditional plane wave models cannot accurately reflect the near-field electromagnetic environment, significantly increasing estimation errors and thus limiting near-field communication performance. Therefore, a LOS MIMO-based integrated near-field communication ranging method is adopted to improve the reliability of channel estimation while simultaneously enabling receiver positioning and sensing.

[0114] Figure 1 This is an overall flowchart of the LOS MIMO near-field communication ranging integrated method and embodiment 1 of the present invention. Figure 2 This is a flowchart illustrating the LOS MIMO near-field communication ranging integration method and the establishment of a non-uniform traversal grid in Example 1 of this invention. Figure 3 This is a flowchart of the LOS MIMO near-field communication ranging integration method and embodiment 1 of the present invention, which optimizes various parameters based on gradient descent.

[0115] In this example, the total number of transmitting array antennas is N. t =256, antenna spacing is d t =0.0054m, the total number of receiving array antennas is M r =256, antenna spacing is d r =0.0054m, noise variance σ 2 =1,ρ x =1, the number of coherent cumulative time slots is K=64. The communication distance is R=110m, and the included angle of the vertical direction of the transmitting array is... The horizontal angle of the transmitting array is

[0116] like Figure 1As shown, the embodiment disclosed herein is based on the LOS MIMO near-field communication ranging integration method, and the specific implementation steps are as follows:

[0117] Step one, for the electromagnetic environment of the near-field LOS MIMO system, set the estimation signal based on the non-uniform spherical wave near-field LOS MIMO; establish a coordinate system, calculate the interval between the transmitting and receiving antenna elements, and construct a channel matrix model; according to the transmitting signal and the receiving signal and the established channel matrix, further construct the channel estimation problem;

[0118] Step 1.A: Set the estimation signal based on the non-uniform spherical wave near-field LOS MIMO;

[0119] The receiving end and the transmitting end of the LOS MIMO system are respectively equipped with M r and N t antennas, and are respectively equipped with and RF links, so as to obtain the transmitting signal under the kth time slot:

[0120]

[0121] Wherein, ρ x is the average transmission power of each symbol, and s is the signal containing information; then the receiving signal corresponding to the kth time slot received by the receiving end is:

[0122] y k = WHFx k + n (36)

[0123] Wherein, W represents the combination matrix of the receiving end, H represents the channel matrix, F represents the hybrid precoding matrix, and n represents the receiving noise The noise variance is σ 2 , represents the circularly symmetric complex Gaussian distribution, represents the unit matrix with a dimension of N RF .

[0124] The coherence time is set to K time slots, and by collecting the signals of the K time slots, the following is obtained:

[0125]

[0126] Wherein, the estimation signal X = [x1, x2, …, x k , …, x K ] represents the sum of the signals of the K time slots transmitted, and the receiving noise I K represents the unit matrix with a dimension of K;

[0127] Step 1.B: Establish a coordinate system, calculate the spacing between the transceiving antenna elements, and construct a channel matrix model;

[0128] Based on the ULA antenna, a coordinate system is established, and the vector from the nth antenna element of the transmitting array to the origin is obtained as:

[0129]

[0130] wherein x n represents the x-axis coordinate of the nth antenna element of the transmitting array; y n represents the y-axis coordinate of the nth antenna element of the transmitting array; z n represents the z-axis coordinate of the nth antenna element of the transmitting array; the aperture of the transmitting-end antenna array is D t = (n-1)d t ; the spacing of the transmitting-end antenna array is d t ; the vertical direction angle of the transmitting array is θ t ; the horizontal direction angle of the transmitting array is

[0131] The vector from the mth antenna element of the receiving array to the origin is:

[0132]

[0133] wherein x n represents the x-axis coordinate of the mth antenna element of the receiving array; y n represents the y-axis coordinate of the mth antenna element of the receiving array; z n represents the z-axis coordinate of the mth antenna element of the receiving array; the aperture of the receiving-end antenna array is D r = (m-1)d r ; the spacing of the receiving-end antenna array is d r ; the communication distance is R; the vertical direction angle of the receiving array is θ r ; the horizontal direction angle of the receiving array is

[0134] The distance d m,n between the mth antenna element of the receiving array and the nth antenna element of the transmitting array is represented as:

[0135]

[0136] According to the NUSW, a channel matrix model is established:

[0137]

[0138] The element in the mth row and the nth column of the matrix H is h m,n ; the channel gain under unit distance is ρ; and the wavelength of the signal is λ.

[0139] Step 1.C: Based on the transmit signal, receive signal and channel matrix model established in step 1.A, further construct the channel estimation problem;

[0140] According to the receive signal and transmit signal obtained in step 1.A, the channel estimation problem of near-field LOS MIMO is written as:

[0141]

[0142] Further conversion (8) is obtained

[0143]

[0144] Because (9) in Fixed, formula (9) is simplified as:

[0145]

[0146] Step 1.A: Set the estimation signal of near-field LOS MIMO based on non-uniform spherical wave assumption.

[0147] Consider a LOS MIMO system, the receiving end and the transmitting end are respectively equipped with M r and N t antennas, and are respectively equipped with and RF links, here we set the RF links of the receiving end to be equal, that is Under such conditions, we get the transmit signal in the kth time slot:

[0148]

[0149] Where, ρ x is the average transmission power of each symbol, and s is the signal containing information. Then the receive signal corresponding to the kth time slot received by the receiving end is:

[0150]

[0151] Where, W represents the combination matrix of the receiving end, H represents the channel matrix, and F represents the hybrid precoding matrix.

[0152] Set the coherence time to K time slots, and collect the signals of the K time slots to get:

[0153]

[0154] Where, represents the transmitted K time slot signal,

[0155] represents the received noise within the coherence interval.

[0156] Step 1.B: Establish a coordinate system, calculate the interval of the transmitting and receiving antenna elements, and construct a channel matrix model according to the non-uniform spherical wave (NUSW) assumption.

[0157] Taking a uniform linear array (ULA) antenna as an example, the coordinate system is established, and the coordinates of the nth antenna element of the transmitting array are obtained as follows: Figure 4

[0158]

[0159] where D t = (n-1) d represents the aperture of the transmitting antenna array.

[0160] The coordinates of the mth antenna element of the receiving array are as follows:

[0161]

[0162] where D r = (m-1) d r represents the aperture of the receiving antenna array.

[0163] The distance between the mth antenna element of the receiving array and the nth antenna element of the transmitting array is represented as follows:

[0164]

[0165] According to the NUSW assumption, the channel matrix is established as follows

[0166]

[0167] Step 1.C: According to the transmitting signal of step 1.A, the receiving signal, and the channel matrix model established in step 1.B, further construct the channel estimation problem;

[0168] According to the receiving signal and the transmitting signal obtained in step 1.A, the channel estimation problem of near-field LOS MIMO is written as follows:

[0169]

[0170] Further conversion (8) is obtained

[0171]

[0172] Because is fixed in (9), formula (9) is simplified as:

[0173]

[0174] ​Step two, calculate the LOS MIMO channel gain matrix according to the channel matrix model established in step one, and through the conversion of accumulation and integration, approximate analysis is carried out to calculate the closed form of the degree of freedom, and the constraint problem of the effective reusable distance is established, and the upper and lower bounds of the communication distance are obtained by solving the constraint;

[0175] Step 2.A: Calculate the LOS MIMO channel gain matrix according to the channel matrix model established in step one:

[0176]

[0177] Wherein the elements of the gain matrix are calculated by the following formula:

[0178]

[0179] The element of the gain matrix G in the vth row and the sth column is g v,s .

[0180] Step 2.B: According to the gain matrix calculated in step 2.A, through the conversion of accumulation and integration, approximate analysis is carried out to calculate the closed form of the degree of freedom;

[0181] The denominator of the accumulation part in formula (13) is approximated as:

[0182]

[0183]

[0184] The approximation of the fraction is realized by Taylor expansion, and the following formula is obtained:

[0185]

[0186] Wherein, the intermediate variable f1(s,v) is a function containing only s and v; f2(s,v,m) is a function containing s, v and m.

[0187] According to (14) (15), the elements of the gain matrix are split into the accumulation of multiple elements, which are expressed as:

[0188]

[0189] Wherein, the system fixed parameters The ith accumulated element is G i .

[0190] Therefore, the gain matrix is expressed by formula (16):

[0191]

[0192] The degrees of freedom (DoF) of a LOS MIMO system is mathematically equivalent to the number of positive singular values or the rank of the channel gain matrix, and the degrees of freedom constraint condition of a LOS MIMO is established as follows:

[0193]

[0194] where ω d represents the dth largest singular value; represents the degrees of freedom of the gain matrix G; and the closed form of the degrees of freedom of a LOS MIMO equipped with N t transmit antennas and M r receive antennas is as follows:

[0195]

[0196] where the system parameters

[0197] Step 2.C: According to formula (18) obtained in step 2.B, the constraint problem of the effective multiplexing distance is established, and the upper and lower bounds of the communication distance are obtained by solving the constraint;

[0198] According to the closed form of the degrees of freedom obtained in 2.B, the constraint problem about the effective multiplexing distance R EMD is as follows:

[0199]

[0200] The upper bound obtained by solving formula (19) is as follows:

[0201]

[0202] When , Ψ takes the maximum value; and the upper bound is calculated by formula (21) when the angle is unknown:

[0203]

[0204] The lower bound obtained by solving formula (19) is as follows:

[0205]

[0206] In this embodiment, λ = 0.0107 m, ∈ = 0.0190,

[0207]

[0208] Step three, according to the distance upper bound and lower bound obtained in step two, the distance traversal grid Π r; according to the constraint range of the angle in step two, an angle traversal grid Π is generated angle ;

[0209]

[0210] ΔR0is the grid spacing of the distance traversal grid Π r , and c is the traversal pointer.

[0211] Specific to the embodiment, ΔR0= 5.

[0212] Step four: according to the channel estimation problem in step one, the partial derivative of the objective function with respect to the communication distance R is calculated; traverse Π r , and calculate the maximum R that satisfies ; according to the maximum R, a new non-uniform traversal grid is regenerated; based on the calculated traversal matrix, the objective function is recalculated, and a set of parameters that minimize the objective function is found, that is, the preliminary estimated value is obtained;

[0213] Step 4.A: according to the channel estimation problem in step 1.C, the partial derivative of the objective function with respect to the communication distance R is calculated:

[0214]

[0215] where Re(·) represents the real part operation; The elements of are calculated by formula (26):

[0216]

[0217] where, according to formula (6), is expressed as:

[0218]

[0219] Step 4.B: traverse Π r , and calculate the maximum R that satisfies ;

[0220] Take r ∈ Π r , and given the gradient threshold τ partial , judge whether satisfies, and do the following operations:

[0221] Define the communication distance that satisfies the threshold requirement as the intermediate distance, that is, R midd = r.

[0222] When the next r that satisfies appears, judge R midd< whether it is true, if true, the new distance is assigned to the intermediate distance: R midd = r, so the cycle is traversed until the set is traversed, and the final R is obtained midd That is, the maximum R that satisfies ;

[0223] Specific to this embodiment, τ partial = 10.

[0224] Step 4.C: According to the R obtained by traversing step 4.B, a new non-uniform traversal grid is regenerated; midd

[0225] Two traversal steps, ΔR1 and ΔR2, are set; The following two traversal sets are calculated:

[0226]

[0227] The new traversal grid is:

[0228] Π r1 ∪Π r2 (74)

[0229] Step 4.D: Based on formula (30) of step 4.C, the target function is recalculated by traversal to find a set of parameters that minimizes the target function, and a preliminary estimate value is obtained

[0230] Specific to this example, the preliminary estimate value is R co = 100.535141m,

[0231] Step five: Calculate the step of the Armijo convergence condition of the communication distance R, update the channel parameters according to the step, and perform threshold judgment on the obtained channel parameters. If the threshold requirement is met, output the final iteration result as the estimated result;

[0232] Step 5.A: Calculate the step of the Armijo convergence condition of the communication distance R, and update the channel parameters according to the step;

[0233] According to (31), the convergence step α R is calculated:

[0234]

[0235] Where, α' R represents the step of the last iteration. represents the convergence coefficient of the step; According to formula (25), the partial derivative is calculated, and it is judged whether formula (32) is true:

[0236] ​

[0237] where β is a given constant; R i is the communication distance estimation value after the i-th iteration; if the condition is not satisfied, the step is shortened according to formula (31) until the condition is satisfied; the distance is updated with the step satisfying the condition, and the parameter is optimized;

[0238]

[0239] Step 5.B: Threshold judgment is performed on the channel distance parameters obtained in step 5.A. If formula (34) is met, the final iteration result is output as the estimation result;

[0240] For the iteration result R i+1 and R i , the following judgment is performed:

[0241]

[0242] If not, continue to repeat step 5.A until the above condition is met; the final iteration result is output as the estimation value of the communication distance

[0243] Specifically, τ R = 10 -20 , τ angle = 10 -9 .

[0244] Step six: Based on the iteration method of step five, the angle parameter θ t is iteratively optimized θ r is obtained to obtain the estimation value of the vertical angle of the transmitting array the estimation value of the horizontal angle of the transmitting array the estimation value of the vertical angle of the receiving array the estimation value of the horizontal angle of the receiving array

[0245] Take θ t as an example, the convergence step is calculated

[0246]

[0247] wherein denotes the step of the last iteration. denotes the convergence coefficient of the step; according to the calculation of the partial derivative, it is judged whether formula (80) is established:

[0248]

[0249] where β is a given constant; θ t i is the estimation of the vertical angle of the transmitting array after the i-th iteration; if the condition (32) is not satisfied, the step is shortened according to the formula (31) until the condition is satisfied; the distance is updated with the step that satisfies the condition, and the parameter is optimized;

[0250]

[0251] Step 5.B: threshold judgment is performed on the channel distance parameter obtained in step 5.A, and if formula (34) is met, the final iteration result is output as the estimation result;

[0252] For the iteration result R i+1 and R i , the following judgment is performed:

[0253]

[0254] If not, step 5.A is continued to be repeated until the above condition is met; the final iteration result is output as the estimation value of the communication distance

[0255] Step seven: according to the estimation value of the communication distance estimated by the estimation, the estimation value of the vertical angle of the transmitting array is obtained the estimation value of the horizontal angle of the transmitting array is obtained the estimation value of the vertical angle of the receiving array is obtained the estimation value of the horizontal angle of the receiving array is obtained the estimation matrix H es is calculated, the estimation of the LOS MIMO near-field channel and the ranging of the transmitting and receiving antenna azimuth are realized, and the integration of the near-field communication ranging of the LOS MIMO is realized.

[0256] The above specific description further details the purpose, technical scheme and beneficial effects of the application, and it should be understood that the above description is only a specific embodiment of the application and is not used to limit the protection scope of the application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application should be included in the protection scope of the application.

Claims

1. A near-field communication ranging integration method based on LOS MIMO, characterized in that: Includes the following steps, Step 1: For the electromagnetic environment of the near-field LOS MIMO system, set the estimated signal based on non-uniform spherical wave near-field LOS MIMO; establish a coordinate system, calculate the spacing between the transmitting and receiving antenna elements, and construct a channel matrix model; based on the transmitted and received signals and the established channel matrix, further construct the channel estimation problem. The implementation method for step one is as follows: Step 1.A: Set the estimated signal based on non-uniform spherical wave near-field LOS MIMO; The LOS MIMO system is equipped with separate receiver and transmitter. and Each antenna is equipped with and The transmitted signal in the k-th time slot is obtained from the RF link as follows: （1） in, It is the average transmission power per symbol. It is a signal containing information; the received signal corresponding to the k-th time slot at the receiving end is: （2） in, The combination matrix representing the receiver, Represents the channel matrix. Represents the hybrid precoding matrix, received noise The noise variance is , This represents a circularly symmetric complex Gaussian distribution. The dimension is The identity matrix; The coherence time is set to K time slots. By collecting the signals from these K time slots, we obtain: （3） Among them, the estimated signal The sum of the transmitted signals across K time slots, and the received noise within the coherent interval. ; The dimension is The identity matrix; Step 1.B: Establish a coordinate system, calculate the spacing between transmit and receive antenna elements, and construct a channel matrix model; Based on a uniform linear array ULA antenna, a coordinate system is established, and the vector from the nth antenna element of the transmitting array to the origin is obtained as follows: （4） in, This represents the nth antenna element of the transmitting array. Axis coordinates; This represents the nth antenna element of the transmitting array. Axis coordinates; This represents the nth antenna element of the transmitting array. Axial coordinates; transmitter antenna array aperture The spacing between the transmitting antenna arrays is... The included angle of the vertical direction of the transmitting array is The horizontal angle of the transmitting array is ; The vector from the m-th antenna element of the receiving array to the origin is: （5） in, This represents the m-th antenna element of the receiving array. Axis coordinates; This represents the m-th antenna element of the receiving array. Axis coordinates; This represents the m-th antenna element of the receiving array. Axial coordinates; receiver antenna array aperture The spacing between the receiving antenna arrays is... Communication distance is The angle between the vertical direction of the receiving array and the vertical direction is... The horizontal angle of the receiving array is... ; The distance between the m-th antenna element of the receiving array and the n-th antenna element of the transmitting array Represented as: （6） Establish a channel matrix model based on NUSW: （7） matrix The element in the m-th row and n-th column is The channel gain per unit distance is The wavelength of the signal is ; Step 1.C: Based on the transmitted signal and received signal from Step 1.A and the channel matrix model established in Step 1.B, further construct the channel estimation problem; Based on the received and transmitted signals obtained in step 1.A, the channel estimation problem for near-field LOS MIMO can be written as: （8） Further transformation (8) yields （9） And because of (9) With the equation fixed, equation (9) simplifies to: （10） Step 2: Calculate the LOS MIMO channel gain matrix based on the channel matrix model established in Step 1. Based on the calculated gain matrix, perform approximate analysis through the conversion of accumulation and integration to calculate the closed-form of the degrees of freedom. Then, establish the constraint problem of the effective reusable distance and obtain the upper and lower bounds of the communication distance by solving the constraints. The second step is implemented as follows: Step 2.A: Calculate the LOS MIMO channel gain matrix based on the channel matrix model established in Step 1: （11） The elements of the gain matrix are calculated using the following formula: （12） Gain matrix The Line number Column elements are ; Step 2.B: Based on the gain matrix calculated in Step 2.A, perform an approximate analysis by converting between accumulation and integration to calculate the closed-form expression of the degrees of freedom; The denominator of the summation part in equation (13) can be approximated as: （13） By approximating the fraction using Taylor expansion, we obtain: （14） Among them, intermediate variables ; For only containing and The function; For inclusion , as well as The function; According to (13)-(14), the elements of the gain matrix are split into a sum of multiple elements, expressed as: （15） Among them, system fixed parameters ; For the first The sum of the sums ... ; Therefore, the gain matrix is ​​expressed by equation (16): （16） The degrees of freedom (DoF) of a LOS MIMO system are mathematically equivalent to the number of positive singular values ​​or the rank of the channel gain matrix. The following constraints are established for the degrees of freedom of LOS MIMO: （17） in, The first One maximum singular value; Represents the gain matrix Degrees of freedom; obtaining equipment One transmitting antenna and The closed-form expression for the degrees of freedom of a LOS MIMO with one receiving antenna is as follows: （18） Among them, system parameters ; Step 2.C: Based on equation (18) obtained in step 2.B, establish a constraint problem for the effective reusable distance, and obtain the upper and lower bounds of the communication distance by solving the constraints; Based on the closed-form equation of degrees of freedom obtained in 2.B, establish the relationship regarding the effective reuse distance. The constraint problem is as follows: （19） Solving equation (19) yields the following upper boundary: （20） Among them when hour, It will reach its maximum value; the upper boundary is calculated by equation (21) when the angle is unknown: （21） Solving equation (19) yields the lower boundary: （22） Step 3: Based on the upper and lower bounds of the distance traversal obtained in Step 2, calculate and generate the distance traversal mesh. Based on the angle constraints in step two, generate an angle traversal mesh. ; In step three, the angle is traversed through the mesh. as follows, （23） （24） For distance traversal of the grid Grid spacing, For traversal pointers; Step 4: Based on the channel estimation problem in Step 1, calculate the objective function with respect to the communication distance. Partial derivatives; traversal ,calculate Determine if the condition is satisfied. The largest , For the gradient threshold; based on the largest A new non-uniform distance traversal grid is regenerated; based on the calculated traversal matrix, the objective function is recalculated and the set of parameters that minimizes the objective function is found, thus obtaining the preliminary channel estimate. Step 5: Update the channel parameters using an iterative method and calculate the communication distance. The Armijo convergence condition is used to advance the channel parameters. The channel parameters are then updated based on this advance step. A threshold check is performed on the obtained channel parameters. If the threshold requirement is met, the final iteration result is output as the estimation result, and this iteration result corresponds to the estimated communication distance. ; Step Six: Based on the iterative method in Step Five, iteratively optimize the angle parameters. , , , ; Obtain the estimated value of the vertical angle of the transmission array. The estimated horizontal angle of the transmission array The estimated value of the vertical angle of the receiving array The estimated horizontal angle of the receiving array ; Step 7: Based on the estimated communication distance... The estimated vertical angle of the transmission array The estimated horizontal angle of the transmission array The estimated value of the vertical angle of the receiving array The estimated horizontal angle of the receiving array Calculate the estimation matrix This enables the estimation of the near-field channel of LOS MIMO and the ranging of the transmit and receive antennas, thus realizing the integration of near-field communication ranging in LOS MIMO.

2. The near-field communication ranging integration method based on LOS MIMO as described in claim 1, characterized in that: Step four is implemented as follows: Step 4.A: Based on the channel estimation problem in Step 1.C, calculate the objective function with respect to the communication distance. Partial derivative: （25） in, This indicates the operation of taking the real part; The elements are calculated using equation (26): （26） According to formula (6), Represented as: （27） Step 4.B: Traversal ,calculate Determine if the condition is satisfied. The largest ; Pick Given a gradient threshold ,judge To determine if the conditions are met, perform the following steps: The communication distance that meets the threshold requirement is defined as the intermediate distance, i.e. ; When the next satisfaction of After it appears, determine If true, assign the new distance to the intermediate distance: This process is repeated until the entire set has been traversed, yielding the final result. That is to satisfy The largest ; Step 4.C: Based on the traversal obtained in Step 4.B Regenerate a new non-uniform traversal mesh; Set two traversal steps, and The following two sets were calculated: （28） （29） The new traversal grid is obtained as follows: （30） Step 4.D: Based on equation (30) from Step 4.C, recalculate the objective function to find the set of parameters that minimizes the objective function, and obtain a preliminary estimate. .

3. The near-field communication ranging integration method based on LOS MIMO as described in claim 2, characterized in that: Step five is implemented as follows: Step 5.A: Calculate the communication distance The Armijo convergence condition is stepped, and the channel parameters are updated according to the step. The convergence step is calculated according to (31). : （31） in, Indicates the step size of the previous iteration; The step represents the convergence coefficient; calculate the partial derivative according to formula (25) and determine whether formula (32) holds true: （32） in, It is a given constant; For the first After several iterations, the estimated communication distance is obtained; if equation (32) is not satisfied, the step size is shortened according to equation (31) until the condition is met; the distance is updated with the step size that meets the condition, and the parameters are optimized. （33） Step 5.B: Perform threshold judgment on the channel distance parameters obtained in Step 5.A. If they meet the equation (34), output the final iteration result as the estimation result. Regarding the iteration results and Make the following judgment: （34） If the condition is not met, repeat step 5.A until the above condition is met; output the final iteration result as an estimate of the communication distance. .

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