Near field communication and distance measurement integrated method based on LOS MIMO

Abstract

The invention discloses a near field communication and distance measurement integrated method based on LOS MIMO, and belongs to the field of near field communication. According to the method, the characteristic that channel information is tightly coupled with azimuth information is combined, a plurality of parameters such as communication distance, azimuth angles of a transmitting antenna in the horizontal direction and the vertical direction and azimuth angles of a receiving antenna in the horizontal direction and the vertical direction are combined by constructing a channel estimation problem of a near-field LOS MIMO system under spherical waves, and the non-uniform characteristic of near-field communication is fully considered. Analyzing the partial derivative of the target function with respect to each parameter, performing step length design by using the partial derivative value of the target function, realizing non-uniform parameter traversal, and preliminarily positioning the range of the estimated value; starting from parameter values obtained through non-uniform traversal, each parameter is further optimized through a gradient descent method, and the error between an estimated value and an actual value is reduced. Before channel information is estimated, azimuth parameters related to a channel are estimated through a non-uniform traversal and accurate gradient optimization method, and integration of near-field millimeter wave communication and distance measurement can be achieved.

Description

Technical Field

[0001] The invention relates to an integrated method for distance measurement based on LOS MIMO near field communication, and belongs to the field of near field communication. Background Art

[0002] Based on the success of 5G / B5G (5th-generation and beyond), academia and industry are laying the foundation for the development of the next generation 6G (6th generation wireless systems) wireless networks and gradually studying high-frequency communication systems. In order to solve the limitation of large path loss in high-frequency communication systems, massive multiple-input multiple-output (MIMO) technology is often used to enhance signal reception, improve spatial multiplexing capabilities, and increase spectral efficiency. In millimeter-wave MIMO systems, due to the increase in frequency and antenna aperture, the near-field area 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 5-10dB. At the same time, as long as certain geometric conditions are met, pure line-of-sight (LOS) channels can also support spatial multiplexing. Therefore, if spatial multiplexing is achieved only through LOS channels in near-field communication, the spectral efficiency and energy efficiency of the millimeter-wave system can be significantly improved.

[0003] Many wireless systems rely heavily on channel state information (CSI) to design precoding to mitigate the effects of fading, noise, and interference during data transmission. Given the randomness of the channel, channel estimation is usually performed before transmission to obtain the necessary CSI. Traditional channel estimation methods, such as the least squares method and, usually generate a huge amount of computation as the number of system antenna arrays increases. General near-field communications are based on the spherical wave assumption, and their channel characteristics are closely related to the orientation information of the antenna array. Therefore, while channel estimation is in progress, the actual spatial distance between the receiving and receiving parties is also measured. Summary of the invention

[0004] In view of the problem that the traditional far-field plane wave cannot effectively describe the near-field electromagnetic model and the difficulty in obtaining channel information in the near-field LOS MIMO scenario under the spherical wave model, the present invention provides a communication ranging integration method based on the LOS MIMO near-field characteristics, combining the characteristics of the close coupling of channel information and azimuth information, constructs the channel estimation problem of the near-field LOS MIMO system under spherical waves, and estimates the azimuth information of the transmitting and receiving antennas while realizing channel estimation. The present invention can also reduce the error between the estimated value and the actual value, and realize multi-parameter, high-precision and fast response.

[0005] The purpose of the present invention is achieved through the following technical solutions.

[0006] The LOS MIMO-based near field communication ranging integrated method disclosed in the present invention comprises the following steps:

[0007] Step 1: 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 spacing of the transmitting and receiving antenna elements, and construct a channel matrix model; further construct the channel estimation problem based on the transmitted signal, the received signal and the established channel matrix;

[0008] Step 1.A: Set the estimation signal based on non-uniform spherical wave near-field LOS MIMO;

[0009] The receiving and transmitting ends of the LOS MIMO system are equipped with M r and N t antennas, each equipped with and RF links, the transmitted signal in the kth time slot is:

[0010]

[0011] Among them, ρ x is the average transmission power of each symbol, s is the signal containing information; the received signal corresponding to the kth time slot received by the receiving end is:

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

[0013] Where W represents the combined matrix at the receiving end, H represents the channel matrix, F represents the hybrid precoding matrix, and the receiving noise represents a circularly symmetric complex Gaussian distribution, Indicates dimension N RF The identity matrix of .

[0014] Set the coherence time to K time slots, and collect the signals of these K time slots to obtain:

[0015]

[0016] Among them, the estimated signal X=[x1,x2,…,x k ,…,x K ] represents the sum of the signals of the K time slots transmitted, and the received noise in the coherent interval I K represents the identity matrix of dimension K;

[0017] Step 1.B: Establish a coordinate system, calculate the spacing between transmit and receive antenna elements, and construct a channel matrix model;

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

[0019]

[0020] Among them, 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 transmitting antenna array aperture D t =(n-1)d t ; The distance between the transmitting antenna array is d t ; The vertical angle of the transmitting array is θ t ; The horizontal angle of the transmitting array is

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

[0022]

[0023] Among them, 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 receiving end antenna array aperture D r =(m-1)d r ; The receiving antenna array spacing is d r ; The communication distance is R; The vertical angle of the receiving array is θ r ; The horizontal angle of the receiving array is

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

[0025]

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

[0027]

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

[0029] Step 1.C: Based on the transmitted signal and received signal of step 1.A and the channel matrix model established in step 1.B, further construct a channel estimation problem;

[0030] According to the received signal and transmitted signal obtained in step 1.A, the channel estimation problem of near-field LOS MIMO is written as:

[0031]

[0032] Further transforming (8), we get

[0033]

[0034] Also, because (9) Fixed, formula (9) is simplified to:

[0035]

[0036] Step 2: Calculate the LOS MIMO channel gain matrix according to the channel matrix model established in step 1. According to the calculated gain matrix, perform approximate analysis through accumulation and integration conversion, calculate the closed form of the degree of freedom, and use this to establish the constraint problem of the effective reusable distance. By solving the constraint, the ergodic upper and lower bounds of the communication distance are obtained;

[0037] Step 2.A: Calculate the LOS MIMO channel gain matrix based on the channel matrix model established in step 1:

[0038]

[0039] The elements of the gain matrix are calculated as follows:

[0040]

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

[0042] Step 2.B: Based on the gain matrix calculated in step 2.A, perform approximate analysis through the conversion of accumulation and integration to calculate the closed form of the degree of freedom;

[0043] The denominator of the cumulative part in formula (13) is approximated as:

[0044]

[0045]

[0046] The fraction is approximated by Taylor expansion, and we get:

[0047]

[0048] Among them, the intermediate variable f1(s,v) is a function that contains only s and v; f2(s,v,m) is a function that contains s, v and m.

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

[0050]

[0051] Among them, the system fixed parameters is the i-th element to be accumulated, and its corresponding matrix is ​​G i ;

[0052] Therefore, the gain matrix is ​​expressed as follows:

[0053]

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

[0055]

[0056] Among them, ω d The d-th largest singular value represented by ; Represents the degrees of freedom of the gain matrix G; get equipment N t transmit antennas and M r The closed form of the degrees of freedom for LOS MIMO with 4 receive antennas is as follows:

[0057]

[0058] Among them, the system parameters

[0059] Step 2.C: According to equation (18) obtained in step 2.B, establish the constraint problem of effective reusable distance, and obtain the traversal upper and lower bounds of the communication distance by solving the constraint;

[0060] According to the degree of freedom closed form obtained in 2.B, the effective reuse distance R is established. EMD The constraints of the problem are as follows:

[0061]

[0062] Solving equation (19) yields the following upper bound:

[0063]

[0064] Among them When , Ψ will reach the maximum value; when the upper boundary is unknown, it is calculated by formula (21):

[0065]

[0066] Solving equation (19) yields the lower bound:

[0067]

[0068] Step 3: Based on the upper and lower boundaries of the distance obtained in step 2, calculate and generate the distance traversal grid Π r ; Generate the angle traversal grid Π according to the angle constraint range in step 2 angle ;

[0069]

[0070] ΔR0 is the distance traversed through the grid Π r The grid spacing is , and c is the traversal pointer.

[0071] Step 4: According to 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 ,calculate Judgment Satisfaction The largest R is obtained; according to the largest R, a new non-uniform traversal grid is regenerated; based on the calculated traversal matrix, the objective function is re-calculated to find a set of parameters that minimizes the objective function, that is, to obtain a preliminary channel estimation value;

[0072] Step 4.A: According to 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 operation of taking the real part; The elements of are calculated by formula (26):

[0075]

[0076] According to formula (6), It is expressed as:

[0077]

[0078] Step 4.B: Traverse Π r ,calculate Judgment Satisfaction The maximum R;

[0079] Take r∈Π r, given the gradient threshold τ partial ,judge To check whether the information is satisfied, do the following:

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

[0081] When the next satisfaction After the r appears, judge R midd <r is true, if true, assign the new distance to the middle distance: R midd = r, and repeat this cycle until the set is traversed and the final R is obtained. midd To satisfy The maximum R;

[0082] Step 4.C: R obtained by traversing step 4.B midd , regenerate a new non-uniform traversal grid;

[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 equation (30) in step 4.C, recalculate the objective function, find a set of parameters that minimize the objective function, and obtain a preliminary estimate.

[0088] Step 5: Update the channel parameters through an iterative method, calculate the step of the Armijo convergence condition of the communication distance R, update the channel parameters according to the step, make a threshold judgment on the obtained channel parameters, and output the final iterative result as the estimation result if it meets the threshold requirement; the iterative result corresponds to the estimated value of the communication distance

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

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

[0091]

[0092] Among them, α' R Indicates the step of the previous iteration. represents the convergence coefficient of the step; calculate the partial derivative according to formula (25), and judge whether formula (32) is established:

[0093]

[0094] Among them, β is a given constant; R i is the estimated value of the communication distance after the i-th iteration; if formula (32) is not satisfied, shorten the step according to formula (31) until the condition is satisfied; update the distance with the step that satisfies the condition and perform parameter optimization;

[0095]

[0096] Step 5.B: Perform threshold judgment on the channel distance parameter obtained in step 5.A. If it meets formula (34), output the final iteration result as the estimation result;

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

[0098]

[0099] If not, continue to repeat step 5.A until the above conditions are met; output the final iteration result as the estimated value of the communication distance

[0100] Step 6: Based on the iterative method of step 5, iteratively optimize the angle parameter θ t , θ r , Get the estimated value of the vertical angle of the transmitting array Estimated value of the horizontal angle of the transmitting array Estimated vertical angle of the receiving array Estimated value of the horizontal angle of the receiving array

[0101] Step 7: Estimated value of communication distance obtained based on the estimate Estimated vertical angle of the transmitting array Estimated value of the horizontal angle of the transmitting array Estimated vertical angle of the receiving array Estimated value of the horizontal angle of the receiving array Calculate the estimated matrix H es , realizing the estimation of LOS MIMO near-field channel and the ranging of the transmitting and receiving antenna positions, that is, realizing the integration of LOS MIMO near-field communication ranging.

[0102] Beneficial effects:

[0103] 1. The integrated method for near-field communication ranging based on LOS MIMO disclosed in the present invention aims at the problem that the traditional far-field plane wave cannot effectively describe the near-field electromagnetic model and the channel information is difficult to obtain in the near-field LOS MIMO scenario under the spherical wave model. By combining the characteristics of the close coupling of channel information and azimuth information, the channel estimation problem of the near-field LOS MIMO system under spherical waves is constructed, and multiple parameters such as the communication distance, the azimuth angles of the transmitting antenna in the horizontal and vertical directions, and the azimuth angles of the receiving antenna in the horizontal and vertical directions are combined. The non-uniform characteristics of near-field communication are fully considered, and the reliability of channel estimation can be guaranteed.

[0104] 2. The integrated method of near-field communication ranging based on LOS MIMO disclosed in the present invention aims at the problem that the traditional far-field plane wave cannot effectively describe the near-field electromagnetic model and the channel information is difficult to obtain in the near-field LOS MIMO scenario under the spherical wave model. By combining the characteristics of the close coupling of channel information and azimuth information, the estimation problem is transformed and the partial derivatives of the objective function with respect to each parameter are analyzed. The present invention optimizes the uniformly stepped traversal set into a non-uniform traversal set associated with the partial derivative value according to the size of the partial derivative value. The partial derivative value is large, which reflects that the objective function changes greatly with the parameter in this 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 small partial derivative values, by using a larger traversal step, the amount of calculation is reduced and the efficiency of near-field communication ranging is improved.

[0105] 3. The integrated method of near-field communication and ranging based on LOS MIMO disclosed in the present invention addresses the problem that traditional far-field plane waves cannot effectively describe the near-field electromagnetic model and the channel information is difficult to obtain in the near-field LOS MIMO scenario under the spherical wave model. By combining the characteristics of the close coupling of channel information and azimuth information, the channel-related azimuth parameters are estimated through non-uniform traversal and precise gradient optimization methods before estimating the channel information, thereby realizing the integration of near-field millimeter wave communication and ranging. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] Figure 1 It is an overall flow chart of the LOS MIMO near-field communication ranging integrated method and embodiment 1 of the present invention;

[0107] Figure 2 It is a flowchart of establishing a non-uniform traversal grid based on the LOS MIMO near field communication ranging integrated method of the present invention and embodiment 1;

[0108] Figure 3 It is a flowchart of the present invention based on the LOS MIMO near field communication ranging integrated method and the optimization of various parameters according to gradient descent in Example 1;

[0109] Figure 4 This is a millimeter wave LOS MIMO system model diagram in which both the transmitting and receiving ends are linear antenna arrays according to the LOS MIMO near-field communication ranging integrated method of the present invention and Example 1. DETAILED DESCRIPTION

[0110] The LOS MIMO near-field communication ranging integrated method according to the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0111] Example 1

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

[0113] This case considers that the channel information in the near-field millimeter wave LOS MIMO system is unclear. The party sending the signal needs to correctly estimate the channel information before communication. At the same time, it estimates the azimuth information related to the channel information to achieve ranging and positioning of the receiver. However, since the traditional plane wave model cannot correctly reflect the near-field electromagnetic environment, its estimation error is greatly increased, which in turn limits the near-field communication performance. To this end, an integrated method of near-field communication ranging based on LOS MIMO is adopted to improve the reliability of channel estimation and achieve positioning and perception at the receiving end.

[0114] Figure 1 This is the overall flow chart of the LOS MIMO near-field communication ranging integrated method and embodiment 1 of the present invention, Figure 2 It is a flowchart of establishing a non-uniform traversal grid based on the LOS MIMO near field communication ranging integrated method and embodiment 1 of the present invention, Figure 3 It is a flowchart of the present invention based on the LOS MIMO near-field communication ranging integrated method and the optimization of various parameters according to gradient descent in Example 1.

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

[0116] like Figure 1As shown, the LOS MIMO near-field communication ranging integrated method disclosed in this embodiment is specifically implemented in the following steps:

[0117] Step 1: 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 spacing of the transmitting and receiving antenna elements, and construct a channel matrix model; further construct the channel estimation problem based on the transmitted signal, the received signal and the established channel matrix;

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

[0119] The receiving and transmitting ends of the LOS MIMO system are equipped with M r and N t antennas, each equipped with and RF links, the transmitted signal in the kth time slot is:

[0120]

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

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

[0123] Where W represents the combined matrix at the receiving end, H represents the channel matrix, F represents the hybrid precoding matrix, and the receiving noise The noise variance is σ 2 , represents a circularly symmetric complex Gaussian distribution, Indicates dimension N RF The identity matrix of .

[0124] Set the coherence time to K time slots, and collect the signals of these K time slots to obtain:

[0125]

[0126] Among them, the estimated signal X=[x1,x2,…,x k ,…,x K ] represents the sum of the signals of the K time slots transmitted, and the received noise in the coherent interval I K represents the identity matrix of dimension K;

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

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

[0129]

[0130] Among them, 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 transmitting antenna array aperture D t =(n-1)d t ; The distance between the transmitting antenna array is d t ; The vertical angle of the transmitting array is θ t ; The horizontal angle of the transmitting array is

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

[0132]

[0133] Among them, 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 receiving end antenna array aperture D r =(m-1)d r ; The receiving antenna array spacing is d r ; The communication distance is R; The vertical angle of the receiving array is θ r ; The horizontal angle of the receiving array is

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

[0135]

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

[0137]

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

[0139] Step 1.C: Based on the transmitted signal and received signal of step 1.A and the channel matrix model established in step 1.B, further construct a channel estimation problem;

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

[0141]

[0142] Further transforming (8), we get

[0143]

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

[0145]

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

[0147] Consider a LOS MIMO system with M receivers and M transmitters. r and N t antennas, each equipped with and RF links, here for convenience, the RF links at both ends of the receiving end are set equal, that is, Under such conditions, we get the transmitted signal in the kth time slot as:

[0148]

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

[0150]

[0151] Among them, 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 these K time slots to obtain:

[0153]

[0154] in, represents the signal of K time slots transmitted,

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

[0156] Step 1.B: Based on the non-uniform spherical wave hypothesis, establish a coordinate system, calculate the spacing between the transmitting and receiving antenna elements, and construct a channel matrix model.

[0157] Take the uniform linear array (ULA) antenna as an example. Figure 4 Establish a coordinate system and obtain the coordinates of the nth antenna element of the transmitting array:

[0158]

[0159] Among them, 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:

[0161]

[0162] Among them, D r =(m-1)d r Represents the aperture of the receiving antenna array.

[0163] Then the distance between the mth antenna element of the receiving array and the nth antenna element of the transmitting array is expressed as:

[0164]

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

[0166]

[0167] Step 1.C: Based on the transmitted signal and received signal of step 1.A and the channel matrix model established in step 1.B, further construct a channel estimation problem;

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

[0169]

[0170] Further transforming (8), we get

[0171]

[0172] Because (9) Fixed, formula (9) is simplified to:

[0173]

[0174] Step 2: Calculate the LOS MIMO channel gain matrix according to the channel matrix model established in step 1. According to the calculated gain matrix, perform approximate analysis through accumulation and integration conversion, calculate the closed form of the degree of freedom, and use this to establish the constraint problem of the effective reusable distance. By solving the constraint, the ergodic upper and lower bounds of the communication distance are obtained;

[0175] Step 2.A: Calculate the LOS MIMO channel gain matrix based on the channel matrix model established in step 1:

[0176]

[0177] The elements of the gain matrix are calculated as follows:

[0178]

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

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

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

[0182]

[0183]

[0184] The fraction is approximated by Taylor expansion, and we get:

[0185]

[0186] Among them, the intermediate variable f1(s,v) is a function that contains only s and v; f2(s,v,m) is a function that contains s, v and m.

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

[0188]

[0189] Among them, the system fixed parameters is the i-th element to be accumulated, and its corresponding matrix is ​​G i ;

[0190] Therefore, the gain matrix is ​​expressed as follows:

[0191]

[0192] The degree of freedom (DoF) of the LOS MIMO system is mathematically equivalent to the number of positive singular values ​​or the rank of the channel gain matrix. The degree of freedom constraint of LOS MIMO is established as follows:

[0193]

[0194] Among them, ω d The d-th largest singular value represented by ; Represents the degrees of freedom of the gain matrix G; get equipment N t transmit antennas and M r The closed form of the degrees of freedom for LOS MIMO with 4 receive antennas is as follows:

[0195]

[0196] Among them, the system parameters

[0197] Step 2.C: According to equation (18) obtained in step 2.B, establish the constraint problem of effective reusable distance, and obtain the traversal upper and lower bounds of the communication distance by solving the constraint;

[0198] According to the degree of freedom closed form obtained in 2.B, the effective reuse distance R is established. EMD The constraints of the problem are as follows:

[0199]

[0200] Solving equation (19) yields the following upper bound:

[0201]

[0202] Among them When , Ψ will reach the maximum value; when the upper boundary is unknown, it is calculated by formula (21):

[0203]

[0204] Solving equation (19) yields the lower bound:

[0205]

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

[0207]

[0208] Step 3: Based on the upper and lower boundaries of the distance obtained in step 2, calculate and generate the distance traversal grid Π r; Generate the angle traversal grid Π according to the angle constraint range in step 2 angle ;

[0209]

[0210] ΔR0 is the distance traversed through the grid Π r The grid spacing is , and c is the traversal pointer.

[0211] Specifically to this embodiment, ΔR0=5.

[0212] Step 4: According to 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 ,calculate Judgment Satisfaction The largest R is obtained; according to the largest R, a new non-uniform traversal grid is regenerated; based on the calculated traversal matrix, the objective function is recalculated to find a set of parameters that minimizes the objective function, that is, to obtain a preliminary estimate;

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

[0214]

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

[0216]

[0217] According to formula (6), It is expressed as:

[0218]

[0219] Step 4.B: Traverse Π r ,calculate Judgment Satisfaction The maximum R;

[0220] Take r∈Π r , given the gradient threshold τ partial ,judge To check whether the information is satisfied, do the following:

[0221] The communication distance that meets the threshold requirement is defined as the middle distance, i.e., R midd = r;

[0222] When the next satisfaction After the r appears, judge R midd<r is true, if true, assign the new distance to the middle distance: R midd = r, and repeat this cycle until the set is traversed and the final R is obtained. midd To satisfy The maximum R;

[0223] Specifically in this embodiment, τ partial =10.

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

[0225] Set two traversal steps, ΔR1 and ΔR2; calculate the following two traversal sets:

[0226]

[0227] The new traversal grid is:

[0228] Π r1 ∪Π r2 (74)

[0229] Step 4.D: Based on equation (30) in step 4.C, recalculate the objective function, find a set of parameters that minimize the objective function, and obtain a preliminary estimate.

[0230] In this case, the initial estimate is R co =100.535141m,

[0231] Step 5: Calculate the step of the Armijo convergence condition of the communication distance R, update the channel parameters according to the step, make a threshold judgment on the obtained channel parameters, and output the final iteration result as the estimation result if it meets the threshold requirement;

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

[0233] Calculate the convergence step α according to (31) R :

[0234]

[0235] Among them, α' R Indicates the step of the previous iteration. represents the convergence coefficient of the step; calculate the partial derivative according to formula (25), and judge whether formula (32) is established:

[0236]

[0237] Among them, β is a given constant; R i is the estimated value of the communication distance after the i-th iteration; if formula (32) is not satisfied, shorten the step according to formula (31) until the condition is satisfied; update the distance with the step that satisfies the condition and perform parameter optimization;

[0238]

[0239] Step 5.B: Perform threshold judgment on the channel distance parameter obtained in step 5.A. If it meets formula (34), output the final iteration result as the estimation result;

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

[0241]

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

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

[0244] Step 6: Based on the iterative method of step 5, iteratively optimize the angle parameter θ t , θ r , Get the estimated value of the vertical angle of the transmitting array Estimated value of the horizontal angle of the transmitting array Estimated vertical angle of the receiving array Estimated value of the horizontal angle of the receiving array

[0245] With θ t Take the following example to calculate the convergence step:

[0246]

[0247] in, Indicates the step of the previous iteration. represents the convergence coefficient of the step; according to the calculated partial derivative, it is judged whether the formula (80) is established:

[0248]

[0249] Among them, β is a given constant; θ t i is the estimated value of the vertical angle of the transmitting end after the i-th iteration; if formula (32) is not satisfied, shorten the step according to formula (31) until the condition is satisfied; update the distance with the step that satisfies the condition and perform parameter optimization;

[0250]

[0251] Step 5.B: Perform threshold judgment on the channel distance parameter obtained in step 5.A. If it meets formula (34), output the final iteration result as the estimation result;

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

[0253]

[0254] If not, continue to repeat step 5.A until the above conditions are met; output the final iteration result as the estimated value of the communication distance

[0255] Step 7: Estimated value of communication distance obtained based on the estimate Estimated vertical angle of the transmitting array Estimated value of the horizontal angle of the transmitting array Estimated vertical angle of the receiving array Estimated value of the horizontal angle of the receiving array Calculate the estimated matrix H es , the estimation of the LOS MIMO near-field channel and the ranging of the transmitting and receiving antenna positions are realized, realizing the integration of LOS MIMO near-field communication and ranging.

[0256] The specific description above further illustrates the purpose, technical solutions and beneficial effects of the invention in detail. It should be understood that the above is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A near field communication ranging integrated method based on LOS MIMO, characterized in that: The following steps are included: Step 1: 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 spacing of the transmitting and receiving antenna elements, and construct a channel matrix model; further construct the channel estimation problem based on the transmitted signal, the received signal and the established channel matrix; Step 2: Calculate the LOS MIMO channel gain matrix according to the channel matrix model established in step 1. According to the calculated gain matrix, perform approximate analysis through accumulation and integration conversion, calculate the closed form of the degree of freedom, and use this to establish the constraint problem of the effective reusable distance. By solving the constraint, the ergodic upper and lower bounds of the communication distance are obtained; Step 3: Calculate and generate the distance traversal grid Π according to the upper and lower bounds of the distance traversal obtained in step 2 r ; Generate the angle traversal grid Π according to the angle constraint range in step 2 angle ; Step 4: According to 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 ,calculate Judgment Satisfaction The largest R is obtained; according to the largest R, a new non-uniform traversal grid is regenerated; based on the calculated traversal matrix, the objective function is re-calculated to find a set of parameters that minimizes the objective function, that is, to obtain a preliminary channel estimation value; Step 5: Update the channel parameters through the iteration method, calculate the step of the Armijo convergence condition of the communication distance R, update the channel parameters according to the step, and make a threshold judgment on the obtained channel parameters. If the threshold requirement is met, the final iteration result is output as the estimation result. The iteration result corresponds to the estimated value of the communication distance. Step 6: Based on the iterative method of step 5, iteratively optimize the angle parameter θ t , θ r , Get the estimated value of the vertical angle of the transmitting array Estimated value of the horizontal angle of the transmitting array Estimated vertical angle of the receiving array Estimated value of the horizontal angle of the receiving array Step 7: Estimated value of communication distance obtained based on the estimate Estimated vertical angle of the transmitting array Estimated value of the horizontal angle of the transmitting array Estimated vertical angle of the receiving array Estimated value of the horizontal angle of the receiving array Calculate the estimated matrix H es , realizing the estimation of LOS MIMO near-field channel and the ranging of the transmitting and receiving antenna positions, that is, realizing the integration of LOS MIMO near-field communication ranging.

2. The LOS MIMO-based near field communication ranging integrated method according to claim 1, characterized in that: The implementation method of step one is: Step 1.A: Set the estimation signal based on non-uniform spherical wave near-field LOS MIMO; The receiving and transmitting ends of the LOS MIMO system are equipped with M r and N t antennas, each equipped with and RF links, the transmitted signal in the kth time slot is: Among them, ρ x is the average transmission power of each symbol, s is the signal containing information; the received signal corresponding to the kth time slot received by the receiving end is: y k =WHFx k +n (2) Where W represents the combined matrix at the receiving end, H represents the channel matrix, F represents the hybrid precoding matrix, and the receiving noise The noise variance is σ 2 , represents a circularly symmetric complex Gaussian distribution, Indicates that the dimension is N RF The identity matrix of Set the coherence time to K time slots, and collect the signals of these K time slots to obtain: Among them, the estimated signal X=[x1,x2,…,x k ,…,x K ] represents the sum of the signals of the K time slots transmitted, and the received noise in the coherent interval I K represents the identity matrix of dimension K; Step 1.B: Establish a coordinate system, calculate the spacing between the transmitting and receiving antenna elements, and construct a channel matrix model; Based on the uniform linear array ULA antenna, a coordinate system is established to obtain the vector from the nth antenna element of the transmitting array to the origin: Among them, 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 transmitting antenna array aperture D t =(n-1)d t ; The distance between the transmitting antenna array is d t ; The vertical angle of the transmitting array is θ t ; The horizontal angle of the transmitting array is The vector from the mth antenna element of the receiving array to the origin is: Among them, 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 receiving end antenna array aperture D r =(m-1)d r ; The receiving antenna array spacing is d r ; The communication distance is R; The vertical angle of the receiving array is θ r ; The horizontal angle of the receiving array is Then the distance d between the mth antenna element of the receiving array and the nth antenna element of the transmitting array is mn It is expressed as: According to NUSW, the channel matrix model is established: The element in the mth row and nth column of matrix H is h m,n ; The channel gain per unit distance is ρ; The wavelength of the signal is λ; Step 1.C: Based on the transmitted signal and received signal of step 1.A and the channel matrix model established in step 1.B, further construct a channel estimation problem; According to the received signal and transmitted signal obtained in step 1.A, the channel estimation problem of near-field LOS MIMO is written as: Further transforming (8), we get Also, because (9) Fixed, formula (9) is simplified to:

3. The LOS MIMO-based near field communication ranging integrated method as claimed in claim 2, characterized in that: The implementation method of step 2 is: Step 2.A: Calculate the LOS MIMO channel gain matrix based on the channel matrix model established in step 1: The elements of the gain matrix are calculated as follows: The element in the vth row and sth column of the gain matrix G is g v,s ; Step 2.B: Based on the gain matrix calculated in step 2.A, perform approximate analysis through the conversion of accumulation and integration to calculate the closed form of the degree of freedom; The denominator of the cumulative part in formula (13) is approximated as: The fraction is approximated by Taylor expansion, and we get: Among them, the intermediate variable f1(s,v) is a function that contains only s and v; f2(s,v,m) is a function that contains s, v, and m; According to (14)-(15), the elements of the gain matrix are split into the cumulative sum of multiple elements, expressed as: Among them, the system fixed parameters is the i-th element to be accumulated, and its corresponding matrix is ​​G i ; Therefore, the gain matrix is ​​expressed as follows: The degree of freedom (DoF) of the LOS MIMO system is mathematically equivalent to the number of positive singular values ​​or the rank of the channel gain matrix. The degree of freedom constraint of LOS MIMO is established as follows: Among them, ω d represents the dth largest singular value; D(G) represents the degree of freedom of the gain matrix G; we get the equipment N t transmit antennas and M r The closed form of the degrees of freedom for LOS MIMO with 4 receive antennas is as follows: Among them, the system parameters Step 2.C: According to equation (18) obtained in step 2.B, establish the constraint problem of effective reusable distance, and obtain the traversal upper and lower bounds of the communication distance by solving the constraint; According to the degree of freedom closed form obtained in 2.B, the effective reuse distance R is established. EMD The constraints of the problem are as follows: Solving equation (19) yields the following upper bound: Among them When , Ψ will reach the maximum value; when the upper boundary is unknown, it is calculated by formula (21): Solving equation (19) yields the lower bound:

4. The LOS MIMO-based near field communication ranging integrated method as claimed in claim 3, characterized in that: In step 3, the angle traverses the grid Π angle as follows, ΔR0 is the distance traversed through the grid Π r The grid spacing is , and c is the traversal pointer.

5. The LOS MIMO-based near field communication ranging integrated method as claimed in claim 4, characterized in that: The implementation method of step 4 is: Step 4.A: According to the channel estimation problem in step 1.C, calculate the partial derivative of the objective function with respect to the communication distance R: Where Re(·) represents the operation of taking the real part; The elements of are calculated by formula (26): According to formula (6), It is expressed as: Step 4.B: Traverse Π r ,calculate Judgment Satisfaction The maximum R; Take r∈Π r , given the gradient threshold τ partial ,judge To check whether the information is satisfied, do the following: The communication distance that meets the threshold requirement is defined as the middle distance, i.e., R midd = r; When the next satisfaction After the r appears, judge R midd <r is true, if true, assign the new distance to the middle distance: R midd = r, and repeat this cycle until the set is traversed and the final R is obtained. midd To satisfy The maximum R; Step 4.C: R obtained by traversing step 4.B midd , regenerate a new non-uniform traversal grid; Set two traversal steps, ΔR1 and ΔR2; calculate the following two traversal sets: The new traversal grid is: Π r1 ∪Π r2 (30) Step 4.D: Based on equation (30) in step 4.C, recalculate the objective function and find a set of parameters that minimizes the objective function to obtain a preliminary estimate.

6. The LOS MIMO-based near field communication ranging integrated method as claimed in claim 5, characterized in that: The implementation method of step five is: Step 5.A: Calculate the step of the Armijo convergence condition for the communication distance R, and update the channel parameters according to the step; Calculate the convergence step α according to (31) R : a R =ρ αR a' R (31) Among them, α′ R Indicates the step of the previous iteration; represents the convergence coefficient of the step; calculate the partial derivative according to formula (25), and judge whether formula (32) is established: Among them, β is a given constant; R i is the estimated value of the communication distance after the i-th iteration; if formula (32) is not satisfied, shorten the step according to formula (31) until the condition is satisfied; update the distance with the step that satisfies the condition and perform parameter optimization; Step 5.B: Perform threshold judgment on the channel distance parameter obtained in step 5.A. If it meets formula (34), output the final iteration result as the estimation result; For the iteration result R i+1 and R i , make the following judgment: If not, continue to repeat step 5.A until the above conditions are met; output the final iteration result as the estimated value of the communication distance

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