Channel estimation method for enabling real-time channel knowledge graph

Abstract

The invention discloses a channel estimation method for enabling a real-time channel knowledge graph, and belongs to the technical field of information and communication. According to the invention, through low-cost and continuous communication receiving signals, channel statistical information related to a user position is extracted, the channel statistical information comprises PDP and PAP, local wireless propagation characteristics customized by a user are reflected, and corresponding frequency domain and airspace covariance matrixes have low-dimensional structured subspace characteristics; according to the invention, the blank that parameter extraction is neglected in the existing channel knowledge graph technology is filled; furthermore, two serial MMSE channel estimation schemes are designed by using a low-dimensional structured frequency-space domain covariance matrix, and compared with the existing non-customized statistical prior channel estimation method, the method has the advantage that the performance is greatly improved. Moreover, experiments show that the scheme provided by the invention still has performance advantages compared with an existing MMSE scheme based on historical sample covariance when the pilot frequency overhead is 1 / 4.

Description

Technical Field

[0001] The present invention relates to the field of information and communication technology, and in particular to a channel estimation method enabled by a real-time channel knowledge graph. Background Art

[0002] Wireless communication networks are evolving towards larger-scale antennas (such as ultra-large-scale multiple-input multiple-output, or XL MIMO), higher-frequency broadband transmission (such as millimeter-wave terahertz communication), and denser connections. While providing larger-scale access and higher transmission rates, this has also led to a significant increase in the dimension of wireless channels and an increase in signal interference, posing huge challenges to the performance and pilot overhead of wireless air interface algorithms such as channel estimation and beamforming. On the other hand, large-dimensional wireless channels and dense access nodes also bring new opportunities, carrying a large amount of wireless data that can be used. These data reflect the local characteristics of the wireless propagation environment, which has also led to the transition of wireless communications from non-environment-aware to environment-aware.

[0003] Existing environmental perception technologies include physical environment maps + ray tracing platforms. The design and applications of high-performance ray-tracing simulation platform for 5G and beyond wireless communications: A tutorial first describes the three-dimensional model of scatterers in the environment, the electromagnetic parameters of materials, and the propagation mode of electromagnetic waves. Based on this information, a ray tracing platform is established to simulate the deterministic propagation of electromagnetic waves in a specific environment. However, since ray tracing involves intensive calculations, physical environment maps are mainly used for off-grid system evaluation and optimization.

[0004] Recently, the concept of channel knowledge graph (CKM) has been proposed, which can be seen as a mapping from user location to channel statistics, where the input user location information can be obtained based on the synaesthesia integration technology, and the output channel statistics can be customized according to the needs of the wireless air interface algorithm, such as the existence of line-of-sight / non-line-of-sight paths to assist beamforming, and channel multipath angle delay information to assist channel estimation. And because CKM is regarded as a mapping function or a lookup table, it can not only be used for offline system optimization, but also assist in real-time signal processing. The existing CKM construction work "Channel knowledge map for environment-aware communications: EM algorithm for map construction" focuses on how to predict or interpolate the channel characteristics of the entire communication area based on the channel statistics obtained at some detection locations, ignoring the process of extracting channel characteristics from data. And the existing literature lacks research on how to extract channel features from real-time received signals and assist channel estimation. In view of this, it is necessary to propose a technical solution that focuses on the construction of real-time channel knowledge graphs and enables channel estimation. Summary of the invention

[0005] In view of the problem that the existing channel estimation technology lacks the ability to extract channel features from real-time received signals, the present invention proposes a channel estimation method enabled by a real-time channel knowledge graph to realize the construction of a real-time channel knowledge graph (CKM) under a MIMO-OFDM system and CKM-enabled high-performance channel estimation. Based on the matching principle, this method extracts the power delay profile (PDP) and power angle profile (PAP) of the channel from the uplink real-time received signal, and associates this information with the user position as user-customized channel prior information; further, two frequency-space serial MMSE (minimum mean square error) channel estimations are proposed using the customized prior information, which achieves a significant performance improvement compared with the existing non-customized statistical prior channel estimation method.

[0006] The technical solution adopted by the present invention is:

[0007] A channel estimation method enabled by a real-time channel knowledge graph performs the following steps in a communication system in which a receiver is configured as a uniform array antenna and performs uplink OFDM (orthogonal frequency division multiplexing) communication on multiple equally spaced subcarriers:

[0008] Step 1: construct a communication system model based on the channel knowledge graph CKM;

[0009] Divide the physical range of the receiver service into grids, and construct a signal model of the frequency-space domain received signal of the detection signal of each grid;

[0010] Step 2, based on multiple uniform samplings of the line spectrum corresponding to the frequency domain response of each channel path with respect to the delay τ, a frequency domain dictionary matrix for extracting the power delay spectrum (PDP) of the channel is constructed;

[0011] Step 3, iteratively update the frequency domain steering vector matrix and the delay based on the matching result of the column vector of the frequency domain dictionary matrix and each grid of the physical range served by the receiver about the frequency domain residual of the received signal, and at the same time, based on the current matching column index, set the column vector corresponding to the frequency domain dictionary matrix to the zero vector; then estimate the frequency domain channel complex coefficient of each iterative operation based on the frequency domain steering vector matrix updated after each iteration, and record the frequency domain channel complex coefficient obtained in each iterative operation process;

[0012] When the maximum number of iterations reaches the preset maximum number of frequency domain iterations, the iteration operation is stopped, and each iteration is regarded as a path. The frequency domain energy value of each path is obtained based on the complex coefficients of each frequency domain channel; based on the frequency domain energy values ​​of all paths and the frequency domain steering vector matrix obtained by the last update, the power delay spectrum characteristic information of the channel is obtained;

[0013] Step 4: Based on the line spectrum corresponding to the spatial response of each channel path, the horizontal angle component and the vertical angle component in the angle are uniformly sampled multiple times to obtain a line spectrum sequence about the horizontal angle component and a line spectrum sequence about the vertical angle component; then the two line spectrum sequences are subjected to Kronecker product operation to obtain a spatial dictionary matrix for extracting the power angle spectrum (PAP) of the channel;

[0014] Step 5, iteratively update the spatial domain steering vector matrix and angle based on the matching result of the column vector of the spatial domain dictionary matrix and each grid of the physical range served by the receiver about the spatial domain residual of the received signal, and at the same time, based on the current matching column index, set the column vector corresponding to the spatial domain dictionary matrix to a zero vector; then estimate the spatial domain channel complex coefficient of each iterative operation based on the spatial domain steering vector matrix updated after each iteration, and record the spatial domain channel complex coefficient obtained in each iterative operation process;

[0015] When the maximum number of iterations reaches the preset maximum number of spatial domain iterations, the iteration operation is stopped, and each iteration is regarded as a path. The spatial domain energy value of each path is obtained based on the complex coefficients of each spatial domain channel; based on the spatial domain energy values ​​of all paths and the spatial domain steering vector matrix obtained by the last update, the power angle spectrum characteristic information of the channel is obtained;

[0016] Step 6: In the channel estimation pilot transmission phase, K users transmit pilot signals at the same frequency resource point. According to the grid index where user k is located, the power delay spectrum feature information and power angle spectrum feature information of each user are obtained, thereby obtaining the frequency domain covariance and spatial domain covariance of each user;

[0017] Step 7: Based on the frequency domain covariance and spatial domain covariance of each user, a serial MMSE channel estimation method of first frequency domain and then spatial domain is adopted to obtain a channel estimation result.

[0018] Furthermore, step 7 may be replaced by: based on the frequency domain covariance and spatial domain covariance of each user, a serial MMSE channel estimation method of first spatial domain and then frequency domain is adopted to obtain a channel estimation result.

[0019] Furthermore, in step 1, the signal model is specifically:

[0020] For any single grid, the frequency-space domain received signal of the detection signal is expressed as:

[0021] Y=XG+ζ

[0022] Among them, Y represents the frequency-space domain received signal in the grid, represents the detection signal diagonal matrix. In the present invention, the receiver knows the signal matrix (ie, X is a known object to the receiver). represents the frequency-space domain channel matrix within the grid, Represents the additive white Gaussian noise (AWGN) matrix, whose elements are independent and follow a Gaussian distribution γ represents the noise variance, represents the complex field, M = M x ×M y Represents the number of array elements of the uniform array antenna, M x 、M y are the number of array elements in the horizontal and vertical directions of the array antenna, and N represents the number of subcarriers.

[0023] Furthermore, in step 2, the construction of the frequency domain dictionary matrix specifically includes:

[0024] Based on the property of OFDM frequency domain carriers being arranged at equal intervals, each path in the multipath channel exhibits equal phase shift characteristics in response to adjacent carriers, and the corresponding line spectrum can be expressed as:

[0025]

[0026] Where i is an imaginary symbol, τ∈[0,2π) is the normalized delay, the delay τ is uniformly sampled T (preset value) times, and the obtained line spectrum is arranged into a frequency domain dictionary matrix:

[0027]

[0028] Furthermore, in step 3, the column vector of the frequency domain dictionary matrix is ​​matched with the residual of each grid with respect to the received signal in the following manner:

[0029]

[0030] Among them, d f,n Denotes the frequency domain dictionary matrix D f The nth column, R f Represents the frequency domain residual, whose initial value is R f =Q=X -1 Y, where Q represents the frequency-space domain receiving signal corresponding to the grid after removing the pilot. If there are multiple frequency-space domain receiving signals in the grid, all the frequency-space domain receiving signals of the current grid are concatenated to obtain the corresponding frequency-space domain receiving signal.

[0031] Furthermore, in step 3, each iterative operation includes:

[0032] Based on the frequency domain dictionary matrix D on the current match f The column vector d f,n The iterative update frequency domain steering vector matrix is: A f =[A f ,d f,n ], that is, splicing A f ,d f,n Get the updated frequency domain steering vector matrix, where the frequency domain steering vector matrix A f The initial value of is an empty matrix, and n is the frequency domain dictionary matrix D f The column index of Wherein, the subscript t represents the number of iterations; and the frequency domain dictionary matrix D f The nth column of is set to zero vector;

[0033] Based on the current updated frequency domain steering vector matrix A f Estimate the complex channel coefficients: in, represents pseudo-inverse operation;

[0034] Based on the current updated frequency domain steering vector matrix A f and the current estimated channel complex coefficients (also called channel matrix) updates the frequency domain residual: Wherein Q represents the frequency-space domain received signal corresponding to the grid after removing the pilot.

[0035] Furthermore, in step 3, the frequency domain energy value of each path in, represents the estimated channel complex coefficient at the lth iteration The (l,m)th element of .

[0036] Furthermore, in step 4, the spatial domain dictionary matrix for extracting the power angle spectrum (PAP) of the channel is constructed as follows:

[0037] The corresponding line spectrum of the spatial response of each channel path is:

[0038]

[0039] Among them, θ and φ represent the horizontal and vertical angle components respectively. θ and φ are uniformly sampled M1 and M2 times respectively to obtain the spatial domain dictionary matrix:

[0040]

[0041] Wherein, represents the Kronecker product, and M1 and M2 are both preset values.

[0042] further In step 5, the column vector of the spatial dictionary matrix is ​​matched with the residual of each grid with respect to the received signal as follows:

[0043]

[0044] Among them, d s,m Denotes the frequency domain dictionary matrix D s The mth column, R s Represents the spatial residual, whose initial value is R s =Q T , where Q represents the frequency-space domain receiving signal corresponding to the grid after removing the pilot. If there are multiple frequency-space domain receiving signals in the grid, all the frequency-space domain receiving signals of the current grid are spliced ​​to obtain the corresponding frequency-space domain receiving signal.

[0045] Furthermore, in step 5, each iterative operation includes:

[0046] Based on the frequency domain dictionary matrix D on the current match s The column vector d s,m The iterative update of the spatial steering vector matrix is: A s =[A s ,d s,m ], and update the angle Among them, θ and φ represent the horizontal and vertical angle components respectively, the subscript t represents the number of iterations, and m is the frequency domain dictionary matrix D s The column index of the spatial dictionary matrix D s The mth column of is set to the zero vector;

[0047] Based on the currently updated spatial steering vector matrix A s and the current estimated channel complex coefficients (also called channel matrix) updates the spatial residual: Where Q represents the frequency-space domain received signal corresponding to the grid after removing the pilot signal, Represents a pseudo-inverse operation.

[0048] Furthermore, in step 5, the spatial energy value of each path in, represents the estimated channel complex coefficient at the lth iteration The (l,m)th element of .

[0049] Furthermore, in step 6, the frequency domain covariance and spatial domain covariance of each user are specifically:

[0050]

[0051]

[0052] Among them, A f,k , A s,k are the frequency domain steering vector matrix in the power delay spectrum feature information of user k and the spatial domain steering vector matrix in the power angle spectrum feature information, respectively, f,l,k , s,l,k are the frequency domain energy value and spatial domain energy value of the l-th path of user k respectively. The corresponding A can be obtained according to the physical grid where each user is located. f,k , A s,k , f,l,k and ρ s,l,k .

[0053] Further, in step 7, obtaining the channel estimation result by using a frequency domain first and then spatial domain serial MMSE channel estimation method specifically includes:

[0054] Calculate five auxiliary quantities:

[0055]

[0056] Compute the channel estimate for each user:

[0057]

[0058] Among them, the frequency-space domain pilot receiving signal The pilot signal diagonal matrix X = diag(X1,...,X K ), frequency-space domain multi-user channel matrix X k represents the pilot diagonal matrix of user k, C k represents the frequency-space channel matrix of user k, ζ ′ represents thermal noise and interference, and its covariance matrix estimate is defined as R I , energy is defined as (i.e. ′ variance), I represents the identity matrix, P k represents the pilot energy of user k, and the frequency domain covariance C f=(C f,1 ,...,C f,K ), represents an operator that takes the diagonal elements of a matrix to form a vector, Represents the channel prior variance, the intermediate quantity ||·||1 represents the vector 1-norm.

[0059] Further, in step 7, obtaining the channel estimation result by using a serial MMSE channel estimation method of first the spatial domain and then the frequency domain specifically includes:

[0060] Calculate four auxiliary quantities:

[0061]

[0062] Compute the channel estimation matrix:

[0063]

[0064] Among them, the pilot signal received in the frequency-space domain S = XG + ζ′, and the pilot signal diagonal matrix X = diag (X1, ..., X K ), channel matrix ζ′ represents thermal noise and interference, and its covariance matrix estimate is defined as R I , energy is defined as I represents the identity matrix, P k represents the pilot energy of user k, Z post represent The estimated value of represent The prior covariance of . The frequency domain covariance C f =(C f,1 ,...,C f,K ), represents an operator that takes the diagonal elements of a matrix to form a vector,

[0065] The technical solution provided by the present invention brings at least the following beneficial effects:

[0066] The present invention extracts channel statistics related to user locations, including PDP and PAP, through low-cost and continuous communication reception signals, which reflect the user-customized local wireless propagation characteristics, and the corresponding frequency domain and space domain covariance matrices have low-dimensional structured subspace characteristics; the present invention fills the gap of the existing channel knowledge graph technology ignoring parameter extraction; further, the present invention uses the low-dimensional structured frequency and space domain covariance matrix to design two serial MMSE channel estimation schemes, which achieve a significant performance improvement compared with the existing non-customized statistical prior channel estimation method. In addition, experiments show that the proposed scheme of the present invention still has performance advantages over the existing MMSE scheme based on historical sample covariance when the pilot overhead is 1 / 4. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0068] Figure 1 Schematic diagram of regional grid division and channel knowledge graph;

[0069] Figure 2 The MSE and SINR variation curves of channel estimation for different schemes;

[0070] Figure 3 This is a curve showing the change of channel estimation MSE with SINR under different grid sizes in the solution proposed in the embodiment of the present invention. DETAILED DESCRIPTION

[0071] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in detail and completely in conjunction with the drawings in the implementation of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings can be arranged and designed using different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present invention.

[0072] The embodiment of the present invention provides a channel estimation method enabled by a real-time channel knowledge graph to realize the construction of a real-time channel knowledge graph under a MIMO-OFDM system and enable high-performance channel estimation. In the embodiment of the present invention, the channel data is obtained by field measurement, including 192 OFDM subcarriers, a carrier spacing of 240KHz, a carrier frequency of 4.9G, sending two signals, 32 antennas at the receiving end (4×8 uniform array), and an antenna spacing of 1 / 2 wavelength. 3000 channel samples are collected along a straight line at a pedestrian speed and an interval of 5ms, which is about 10 meters of sampling data. A total of two straight line route data are collected, the first section is used as a dual-user channel, and the second section is used as a dual-interference channel. The 1m graph grid granularity corresponds to 300 channel samples. The pilot transmission adopts a pseudo-random sequence, and the dual users use a random sequence under the same random seed number. The user 2 pilot adds a cyclic shift of 1 / 4 OFDM period. The pilot generation method of dual interference is similar, and the number of seeds is different from that of the user. The pilot occupies 1 / 2 of the subcarrier, that is, the odd bits of the subcarrier.

[0073] According to the above parameter settings, the specific steps of this implementation example are as follows:

[0074] S1. Communication system model when CKM is constructed: The receiver is equipped with M = 4 × 8 uniform array antennas and performs uplink OFDM communication on N = 192 equally spaced subcarriers. The receiver service physical range is divided into grids, such as Figure 1 As shown, for a single grid, considering the uploading detection signal within the grid, the frequency-space domain receiving signal model is

[0075] Y=XG+N

[0076] in, Denote the diagonal matrix of the detection signal of the grid, assuming that the receiver knows the signal matrix. represents the frequency-space domain channel matrix, Represents the additive white Gaussian noise (AWGN) matrix, whose elements are independent and follow a Gaussian distribution Multiply the received signal Y by the inverse matrix of the pilot matrix, and the equivalent receiving model is:

[0077] Q=X -1 Y=G+X -1 N

[0078] That is, Q is the frequency-space domain received signal corresponding to a single grid after removing the pilot.

[0079] S2. Frequency domain dictionary matrix construction for extracting PDP:

[0080] Based on the property of OFDM frequency domain carriers being arranged at equal intervals, each path in the multipath channel exhibits equal phase shift characteristics in response to adjacent carriers, corresponding to the line spectrum Where i is an imaginary symbol, τ∈[0,2π) is the normalized delay, the delay τ is uniformly sampled T=2N times, and the obtained line spectrum is arranged into a frequency domain dictionary matrix:

[0081]

[0082] S3, initialize the number of iterations t = 0, the frequency domain steering vector matrix is ​​an empty matrix A f = []. When there are multiple received signals in the same grid, the multiple received signals are spliced ​​and processed in the same way as S1 to obtain the spliced ​​form of Q, the residual R f =Q.

[0083] S4. Select the line spectrum with the highest matching degree with the residual in the frequency domain dictionary matrix, and obtain its index as follows:

[0084]

[0085] where d f,n Represents the frequency domain dictionary matrix D f The nth column of , ∥·∥2 represents the vector 2-norm.

[0086] S5. Update frequency domain steering vector matrix and delay:

[0087] A f ←[A f ,d f,n ],

[0088]

[0089] The frequency domain dictionary matrix D f The nth column of is set to zero vector.

[0090] S6. Obtain channel complex coefficient estimation:

[0091]

[0092] in Stands for pseudo-inverse operation.

[0093] S7. Update the frequency domain residual:

[0094]

[0095] And let the number of iterations t increase by 1, that is, t = t + 1;

[0096] S8, when the number of iterations t reaches the maximum number of frequency domain iterations L f =50, terminate the iteration and output A f As a frequency-domain steered vector matrix, output As the energy of the lth path, Representative Matrix The (l,m)th element of A. f and ρ f,l If the iteration is not terminated, then based on the updated number of iterations t, continue to execute steps S4 to S7.

[0097] S9. Construction of spatial dictionary matrix for extracting PAP:

[0098] The spatial response of each channel path corresponds to the line spectrum θ and φ represent the horizontal and vertical angle components respectively, and θ and φ are uniformly sampled M1=2M x and M2=2M y Second, we get the spatial domain dictionary matrix:

[0099]

[0100] in, Stands for Kronecker product.

[0101] S10, initialize the number of iterations t = 1, the spatial domain steering vector matrix is ​​an empty matrix A s = []. When there are multiple received signals in the same grid, the multiple received signals are spliced ​​and processed in the same way as S1 to obtain the spliced ​​form of Q, the residual R s =Q r .

[0102] S11. Select the line spectrum with the highest degree of matching with the residual in the spatial domain dictionary matrix, and obtain its index as follows

[0103]

[0104] Among them, d s,m Represents the spatial dictionary matrix D s The mth column of .

[0105] S12. Update the spatial steering vector matrix and angle:

[0106] A s ←[A s ,d s,m ]

[0107]

[0108] The spatial dictionary matrix D s The mth column of is set to zero vector.

[0109] S13. Obtain channel complex coefficient estimation:

[0110]

[0111] S14. Update spatial residual:

[0112]

[0113] And let the number of iterations t increase by 1, that is, t = t + 1

[0114] S15, when the number of iterations t reaches the maximum number of spatial domain iterations L s =20, the iteration is terminated. Output A s As a frequency-domain steered vector matrix, output As the energy of the lth path, Representative Matrix The (l,n)th element of A. s and ρ s,l If the iteration is not terminated, then based on the updated number of iterations t, steps S11 to S14 are continued.

[0115] S16, considering the channel estimation pilot transmission stage, different from step S1, at this time K=2 users transmit pilot signals at the same frequency resource point, and the pilot reception signal in the frequency-space domain is expressed as:

[0116]

[0117] Among them, the pilot diagonal matrix X = diag (X1, ..., X K ), X k represents the pilot diagonal matrix of user k, and the frequency-space domain multi-user channel matrix diag(·) represents the diagonalization operation, and the pilot energy of user k is P k ;ζ ′ represents thermal noise + interference, and its covariance matrix is ​​estimated as R I , the energy is

[0118] S17, according to the grid index where user k is located, call S1-S15 to extract the PDP and PAP information based on the historical detection signal in the same grid index. To distinguish users, the PDP information of user k is represented as A f,k and ρ f,l,k , the PAP information of user k is represented as A s,k and ρ s,l,k The frequency domain covariance and spatial domain covariance of user k are expressed as:

[0119]

[0120] S18, using serial MMSE channel estimation (first frequency domain and then spatial domain):

[0121]

[0122]

[0123] Among them, C f =(C f,1 ,...,C f,K ), represents an operator that takes the diagonal elements of a matrix to form a vector, represents the channel prior variance, ||·||1 represents the vector 1-norm.

[0124] S19, output matrix k=1,...,K is used as serial MMSE (frequency domain first and then spatial domain) channel estimation.

[0125] In addition, the embodiment of the present invention may also adopt a serial MMSE channel estimation in the spatial domain first and then in the frequency domain, that is, replacing step S18 and step S19 with the following two steps:

[0126] (1) Using serial MMSE channel estimation (first in spatial domain and then in frequency domain):

[0127]

[0128] in,

[0129] (2) Output matrix As a serial-sequence MMSE (first spatial domain then frequency domain) channel estimation.

[0130] Figure 2 The curve of channel estimation MSE changing with SINR is shown. The channel estimation method enabled by a real-time channel knowledge graph proposed in the embodiment of the present invention adopts a 1-meter granularity graph grid. The traditional MMSE scheme uses the truncation zeroing method in time domain noise reduction. The covariance matrix used by the spatial domain MMSE is obtained based on the channel estimation value of the previous adjacent sampling position. Figure 2 It can be seen that the channel estimation performance of the solution proposed in the embodiment of the present invention is still better than that of the traditional MMSE solution when the pilot overhead is 1 / 4.

[0131] Figure 3 The performance changes of the solution proposed in the embodiment of the present invention under different grid granularities are demonstrated. When the grid granularity is in the range of 0-3 meters, the performance difference of the solution proposed in the embodiment of the present invention is about 1 dB.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0133] The above are only some embodiments of the present invention. For those skilled in the art, several modifications and improvements can be made without departing from the creative concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A channel estimation method enabled by a real-time channel knowledge graph, characterized in that: In a communication system in which a receiver is configured as a uniform array antenna and performs uplink orthogonal frequency division multiplexing communication on a plurality of equally spaced subcarriers, the following steps are performed: Step 1: construct a communication system model based on the channel knowledge graph; Divide the physical range of the receiver service into grids, and construct a signal model of the frequency-space domain received signal of the detection signal of each grid; Step 2, based on multiple uniform samplings of the line spectrum corresponding to the frequency domain response of each channel path with respect to the delay τ, a frequency domain dictionary matrix for extracting the power delay spectrum of the channel is constructed; Step 3, iteratively update the frequency domain steering vector matrix and the delay based on the matching result of the column vector of the frequency domain dictionary matrix and each grid of the physical range served by the receiver about the frequency domain residual of the received signal, and at the same time, based on the current matching column index, set the column vector corresponding to the frequency domain dictionary matrix to the zero vector; then estimate the frequency domain channel complex coefficient of each iterative operation based on the frequency domain steering vector matrix updated after each iteration, and record the frequency domain channel complex coefficient obtained in each iterative operation process; When the maximum number of iterations reaches the preset maximum number of frequency domain iterations, the iteration operation is stopped, and each iteration is regarded as a path. The frequency domain energy value of each path is obtained based on the complex coefficients of each frequency domain channel; based on the frequency domain energy values ​​of all paths and the frequency domain steering vector matrix obtained by the last update, the power delay spectrum characteristic information of the channel is obtained; Step 4: Based on the line spectrum corresponding to the spatial response of each channel path, the horizontal angle component and the vertical angle component in the angle are uniformly sampled multiple times to obtain a line spectrum sequence about the horizontal angle component and a line spectrum sequence about the vertical angle component; then the two line spectrum sequences are subjected to Kronecker product operation to obtain a spatial dictionary matrix for extracting the power angle spectrum of the channel; Step 5, iteratively update the spatial domain steering vector matrix and angle based on the matching result of the column vector of the spatial domain dictionary matrix and each grid of the physical range served by the receiver about the spatial domain residual of the received signal, and at the same time, based on the current matching column index, set the column vector corresponding to the spatial domain dictionary matrix to a zero vector; then estimate the spatial domain channel complex coefficient of each iterative operation based on the spatial domain steering vector matrix updated after each iteration, and record the spatial domain channel complex coefficient obtained in each iterative operation process; When the maximum number of iterations reaches the preset maximum number of spatial domain iterations, the iteration operation is stopped, and each iteration is regarded as a path, and the spatial domain energy value of each path is obtained based on the complex coefficients of each spatial domain channel; Based on the spatial energy values ​​of all paths and the spatial steering vector matrix obtained by the last update, the power angle spectrum characteristic information of the channel is obtained; Step 6: In the channel estimation pilot transmission phase, multiple users transmit pilot signals at the same frequency resource point, and obtain the power delay spectrum characteristic information and power angle spectrum characteristic information of each user according to the grid index where the user is located, so as to obtain the frequency domain covariance and spatial domain covariance of each user; Step 7: Based on the frequency domain covariance and spatial domain covariance of each user, a channel estimation result is obtained by using a serial minimum mean square error channel estimation method of frequency domain first and then spatial domain; or a serial minimum mean square error channel estimation method of spatial domain first and then frequency domain is used to obtain the channel estimation result.

2. The method according to claim 1, characterized in that In step 1, the signal model is specifically: For any single grid, the frequency-space domain received signal of the detection signal is expressed as: Y=XG+ζ Among them, Y represents the frequency-space domain received signal in the grid, represents the detection signal diagonal matrix, represents the frequency-space domain channel matrix within the grid, Represents an additive Gaussian white noise matrix whose elements are independent and follow a Gaussian distribution γ represents the noise variance, represents the complex domain, M represents the number of array elements of the uniform array antenna, and N represents the number of subcarriers.

3. The method according to claim 1, characterized in that In step 3, the column vector of the frequency domain dictionary matrix is ​​matched with the residual of each grid with respect to the received signal as follows: Among them, d f，n Denotes the frequency domain dictionary matrix D f The nth column, R f Represents the frequency domain residual, whose initial value is R f =Q=X -1 Y, where Q represents the frequency-space domain receiving signal corresponding to the grid after removing the pilot. If there are multiple frequency-space domain receiving signals in the grid, all the frequency-space domain receiving signals of the current grid are spliced ​​to obtain the corresponding frequency-space domain receiving signal.

4. The method according to claim 1, characterized in that In step 3, each iteration operation includes: Based on the frequency domain dictionary matrix D on the current match f The column vector d f，n The iterative update frequency domain steering vector matrix is: A f =[A f , d f，n ], where the frequency domain steering vector matrix A f The initial value of is an empty matrix, and n is the frequency domain dictionary matrix D f Column index of Update delay Wherein, the subscript t represents the number of iterations; The frequency domain dictionary matrix D f The nth column of is set to zero vector; Based on the current updated frequency domain steering vector matrix A f Estimate the complex channel coefficients: in, represents pseudo-inverse operation; Based on the current updated frequency domain steering vector matrix A f and the current estimated channel complex coefficients Update the frequency domain residual: Where Q represents the frequency-space domain received signal corresponding to the grid.

5. The method according to claim 4, characterized in that In step 3, the frequency domain energy value of each path in, represents the estimated channel complex coefficient at the lth iteration The (l, m)th element of .

6. The method according to claim 1, characterized in that In step 4, the spatial domain dictionary matrix for extracting the power angle spectrum of the channel is constructed as follows: The corresponding line spectrum of the spatial response of each channel path is: Among them, θ and φ represent the horizontal and vertical angle components respectively, M x 、M y are the number of array elements in the horizontal and vertical directions of the planar array antenna, respectively, and the superscript i is an imaginary unit; Uniformly sample θ and φ M1 and M2 times respectively, and get the spatial domain dictionary matrix: in, Represents Kronecker product, where the sampling numbers M1 and M2 are preset values.

7. The method according to claim 1, characterized in that In step 5, the column vector of the spatial dictionary matrix is ​​matched with the residual of each grid with respect to the received signal as follows: Among them, d s，m Denotes the frequency domain dictionary matrix D s The mth column, R s Represents the spatial residual, whose initial value is Rs=Q T , where Q represents the frequency-space domain receiving signal corresponding to the grid after removing the pilot. If there are multiple frequency-space domain receiving signals in the grid, all the frequency-space domain receiving signals of the current grid are spliced ​​to obtain the corresponding frequency-space domain receiving signal.

8. The method according to claim 1, characterized in that In step 5, each iteration operation includes: Based on the frequency domain dictionary matrix D on the current match s The column vector d s，m The iterative update of the spatial steering vector matrix is: A s =[A s , d s，m ], and update the angle Among them, θ and φ represent the horizontal and vertical angle components respectively, the subscript t represents the number of iterations, and m is the frequency domain dictionary matrix D s , M1 and M2 are the uniform sampling numbers about θ and φ respectively; The spatial dictionary matrix D s The mth column of is set to the zero vector; Based on the currently updated spatial steering vector matrix A s and the current estimated channel complex coefficients Update the spatial residual: Where Q represents the frequency-space domain received signal corresponding to the grid after removing the pilot signal, Represents a pseudo-inverse operation.

9. The method according to claim 1, characterized in that In step 6, the frequency domain covariance and spatial domain covariance of each user are specifically: Among them, A f，k , A s，k are the frequency domain steering vector matrix in the power delay spectrum feature information of user k and the spatial domain steering vector matrix in the power angle spectrum feature information, respectively, f，l,k , s，l,k are the frequency domain energy value and spatial domain energy value of the l-th path of user k respectively.

10. The method according to claim 9, characterized in that Step 7 is as follows: (1) The channel estimation result is obtained by using a serial minimum mean square error channel estimation method in the frequency domain first and then in the spatial domain, which specifically includes: Calculate five auxiliary quantities: Compute the channel estimate for each user: Among them, the pilot reception signal in the frequency and space domain is The signal diagonal matrix X = diag(X1, ..., X K ), frequency-space domain multi-user channel matrix X k represents the pilot diagonal matrix of user k, G k represents the frequency-space domain received signal of user k, ζ′ represents the thermal noise and interference term, and its covariance matrix estimation is defined as R I , energy is defined as I represents the identity matrix, P k represents the pilot energy of user k, and the frequency domain covariance C f =diag(C f，1 , ..., C f，K ), K represents the number of users, represents an operator that takes the diagonal elements of a matrix to form a vector, represents the channel prior variance, the intermediate quantity ||·||1 represents the vector 1 norm; (2) The channel estimation result is obtained by using a sequential minimum mean square error channel estimation method in the spatial domain first and then in the frequency domain, including: Calculate four auxiliary quantities: Compute the channel estimation matrix: in, represents the channel estimation of user k, k=1, 2, ..., K, K represents the number of users.

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