A channel estimation method, device, electronic device, storage medium, and program

Abstract

The application discloses a channel estimation method and device, electronic equipment, storage medium and program, and applies to the field of wireless communication, and the method comprises the steps of adding a window function on the basis of a pilot signal and a received signal of an obtained target channel, constructing a windowed channel estimation model, determining a posterior distribution probability density function of a channel estimation parameter in the windowed channel estimation model, obtaining an auxiliary manifold and a target manifold which are approximate to a nonlinear term in the posterior distribution probability density function, determining an auxiliary manifold parameter, generating a target manifold parameter based on the auxiliary manifold parameter, and generating a channel estimation parameter of the target channel based on the target manifold parameter. The embodiment of the application can reduce the calculation complexity of the channel estimation process, improve the channel estimation efficiency, reduce the signal transmission delay, and enhance the user experience.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a channel estimation method, apparatus, electronic device, storage medium, and program. Background Technology

[0002] Massive Multiple Input Multiple Output (MIMO) technology is one of the key technologies in fifth-generation (5G) cellular wireless communication, providing enormous spatial freedom by deploying a large number of antennas at base stations. MIMO technology can simultaneously serve multiple users on the same time-domain resources, thus significantly improving system spectrum and energy efficiency. Orthogonal Frequency Division Multiplexing (OFDM) is a multi-carrier modulation technique with excellent capabilities in providing high data rate transmission and robustness against frequency selectivity. Massive MIMO-OFDM plays a crucial role in 5G systems and is receiving increasing attention in future sixth-generation (6G) systems.

[0003] Obtaining accurate channel state information is crucial in large-scale MIMO-OFDM systems, and system performance is highly dependent on the channel estimation instructions. In practical systems, pilot-assisted channel estimation (PAE) is a common method, where the transmitter periodically sends pilot signals, and the receiver performs channel estimation based on the received signals. Channel estimation utilizes the received pilot signals to obtain posterior information about the channel parameters. However, due to the high dimensionality of the channel parameters, calculating the posterior mean and covariance is challenging. The optimal channel estimation method, namely the minimum mean square error (MMSE) method, requires large-dimensional matrix inversion operations, making its computational complexity unacceptable.

[0004] Recent information geometry channel estimation methods perform channel estimation iteratively, and their convergence performance can reach that of the MMSE algorithm, while offering the advantage of lower complexity. However, for practical applications, their complexity remains high. Therefore, a low-complexity channel estimation method is urgently needed. Summary of the Invention

[0005] This invention provides a channel estimation method, apparatus, electronic device, storage medium, and program to reduce the computational complexity of the channel estimation process, improve channel estimation efficiency, reduce signal transmission delay, and enhance user experience.

[0006] According to one aspect of the present invention, a channel estimation method is provided, wherein the method includes:

[0007] A windowed channel estimation model is constructed by adding a window function to the pilot signal and received signal of the target channel.

[0008] Determine the posterior probability density function of the channel estimation parameters within the windowed channel estimation model;

[0009] Obtain an auxiliary manifold and a target manifold that approximate the nonlinear term within the posterior probability density function, determine the parameters of the auxiliary manifold, and generate the parameters of the target manifold based on the parameters of the auxiliary manifold.

[0010] The channel estimation parameters for the target channel are generated based on the target manifold parameters.

[0011] According to another aspect of the present invention, a channel estimation apparatus is provided, wherein the apparatus comprises:

[0012] The model building module is used to add window functions to the acquired pilot signals and received signals of the target channel to build a windowed channel estimation model;

[0013] The probability density module is used to determine the posterior probability density function of the channel estimation parameters within the windowed channel estimation model.

[0014] The manifold construction module is used to obtain an auxiliary manifold and a target manifold that approximate the nonlinear term in the posterior probability density function, determine the parameters of the auxiliary manifold, and generate the parameters of the target manifold based on the parameters of the auxiliary manifold.

[0015] An estimation execution module is used to generate channel estimation parameters for the target channel based on the target manifold parameters.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the channel estimation method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the channel estimation method according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer program product is also provided, the computer program product comprising a computer program that, when executed by a processor, implements the channel estimation method according to any one of the embodiments of the present invention.

[0022] The technical solution of this invention obtains the pilot signal and received signal corresponding to the target channel, and constructs a windowed channel estimation model by adding a window function based on the pilot signal and received signal. It then determines the posterior probability density function of the channel estimation within the windowed channel estimation model, obtains the target manifold and auxiliary manifold that approximate the nonlinear term within the posterior probability density function, and determines the manifold parameters corresponding to the target manifold and auxiliary manifold. Based on the manifold parameters of the target manifold, it generates channel estimation parameters for the target channel. This reduces the computational complexity of channel estimation, simplifies the channel estimation calculation process, improves channel estimation efficiency, reduces signal transmission latency, and enhances the user experience.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of a channel estimation method provided according to an embodiment of the present invention;

[0026] Figure 2 This is a flowchart of another channel estimation method provided according to an embodiment of the present invention;

[0027] Figure 3 This is an amplitude diagram of a column element of a target channel's horizontal direction angle domain filtering matrix according to an embodiment of the present invention;

[0028] Figure 4 This is an amplitude diagram of a column element of a horizontal direction angle domain filtering matrix of another target channel provided by an embodiment of the present invention;

[0029] Figure 5 This is an amplitude diagram of a column element of a time-delay domain filtering matrix for a target channel, provided by an embodiment of the present invention.

[0030] Figure 6This is an amplitude diagram of a column element of a time-delay domain filtering matrix for another target channel provided by an embodiment of the present invention;

[0031] Figure 7 This is a schematic diagram of the structure of a channel estimation device according to an embodiment of the present invention;

[0032] Figure 8 This is a schematic diagram of the structure of an electronic device that implements the channel estimation method of this invention. Detailed Implementation

[0033] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0035] Recent proposed information geometry channel estimation methods perform channel estimation iteratively, and their convergence performance can reach that of the MMSE algorithm, while offering the advantage of lower complexity. However, for practical applications, their complexity remains high.

[0036] Accordingly, this invention proposes a low-complexity information geometry (WIG) channel estimation method, namely, a windowed information geometry (WIG) channel estimation method. This method boasts extremely low complexity; in typical application scenarios, its complexity is reduced by 10-50 times compared to the original information geometry method, and by approximately 1000 times compared to the MMSE algorithm. Furthermore, the performance of the proposed method can approach that of the MMSE algorithm, with a mean squared error performance difference of approximately 1 dB compared to the MMSE algorithm in typical scenarios. Therefore, the method of this invention is practical.

[0037] Taking a Multiple-input Multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system as an example, it is understood that the MIMO system is merely an example to facilitate understanding of the channel estimation method provided in this embodiment of the invention. The application scenarios of the channel estimation method provided in this embodiment of the invention are not limited to MIMO scenarios. Specifically, the base station is equipped with N... r =N r,v ×N r,h A uniform array of antennas, with N antennas in the vertical and horizontal directions respectively. r,v and N r,h The base station has K users, and each user can be equipped with a single antenna. For OFDM modulation, the number of subcarriers is N. c The system sampling interval is T s The cyclic prefix is ​​N g The subcarriers used for uplink channel estimation are Where N1 and N Np These are the start and end subcarrier index numbers, respectively. N p This represents the total number of subcarriers used for channel estimation.

[0038] Taking uplink channel signal transmission as an example, at the nth subcarrier, the frequency domain received signal is:

[0039]

[0040] in, To receive the signal, [Y] j,i Let be the received signal of the i-th subcarrier on the j-th antenna. It is a diagonal array, specifically the transmitted pilot signal, where [X k ] i,i Let be the pilot signal for user k on the i-th subcarrier. Let [G] be the spatial frequency domain signal matrix of user k.k ] j,i Let i be the channel between user k and the j-th antenna on subcarrier i. It is Gaussian white noise with a noise variance of .

[0041] Substituting the frequency domain received signal into the beam domain channel model, the spatial frequency domain channel coefficient matrix can be written as:

[0042] G k =VH k F T (2)

[0043] in, This is for the statistical beam domain channel matrix. For the time-delay domain sampling matrix, F τ This is the oversampling factor in the time delay domain; For angle domain measurement matrix, Indicates the Kronecker product; This is the angle domain measurement matrix in the vertical direction. N v =F v N r,v F v The oversampling factor in the vertical direction; This is the angle domain measurement matrix in the horizontal direction. N h =F h N r,h F h This is the oversampling factor in the horizontal direction.

[0044] Substituting equation (2) into the received signal model yields the following result:

[0045] Y = VH a M+Z (3)

[0046] in, H a This represents a matrix composed of K user beam domain channels;

[0047] M represents the time delay domain measurement matrix;

[0048] Define the beam domain energy matrix as follows:

[0049]

[0050] Due to channel sparsity, many elements in the beam domain energy matrix have values ​​close to zero. During channel estimation, these low-energy elements can be set to zero.

[0051] Vectorize formula (3).

[0052]

[0053] in, y = vec(Y), z = vec(Z). Define υ = vec{Ω}, and define M = ||υ||0 as the number of non-zero elements in v, with its index set being P. υ ={p1,p2,...,p M Define the extraction matrix as follows:

[0054]

[0055] Where s i for The i-th column of the identity matrix. Remove formula (5). Elements with zero energy and From the corresponding column, we can obtain:

[0056] y = Ah + z (7)

[0057] in, The measurement matrix is ​​given by M, where M is the number of non-zero coefficients in the beam domain, and N = N. r N p The prior distribution of h follows a complex Gaussian distribution, i.e. Where D = Diag(E) T υ), Diag(E) T υ) represents based on E T υ Creates a diagonal matrix.

[0058] Figure 1 This is a flowchart of a channel estimation method according to an embodiment of the present invention. This embodiment is applicable to channel estimation situations. The method can be executed by a channel estimation device, which can be implemented in hardware and / or software, and can be configured in a base station or user terminal. Figure 1 As shown, the method includes:

[0059] Step 110: Based on the acquired pilot signal and received signal of the target channel, add a window function to construct a windowed channel estimation model.

[0060] The target channel can be a channel used for transmitting signals, and can include uplink or downlink channels. The pilot signal, also known as the reference signal, is a known signal provided by the transmitter to the receiver for channel estimation or channel sounding. The received signal can be a signal transmitted through the target channel, and can be a feedback signal from the receiver to the reference signal, including measurement results related to the reference signal. The windowed channel estimation model can be a model used to estimate the channel state. This model can be determined by the physical model of the target channel, which includes at least parameters such as the reference signal, the received signal, and noise information of the target channel. A windowed function can also be added to the windowed channel estimation model. This windowed function can balance inter-carrier interference caused by the Doppler effect, facilitating the smoothing of noise within the windowed channel estimation model and reducing the computational complexity of the channel estimation process.

[0061] In this embodiment of the invention, the pilot signal of the target channel can be determined and the received signal of the corresponding pilot signal can be collected. The physical parameters of the target channel, such as the pilot signal and the received signal, can be substituted into the physical model corresponding to the target channel, such as the physical model provided by formula (7) in this application embodiment. A window function is added to the constructed physical model, and the obtained physical model can be used as a windowed channel estimation model.

[0062] For example, for formula (7), a window function can be introduced to approximate formula (7) as follows:

[0063]

[0064] in, W can be a diagonal matrix, and w is a window function. It is Gaussian white noise with a noise variance of . ω n =([W]) n,n ) * [W] n,n The physical model that incorporates a window function can be used as a windowed channel estimation model.

[0065] Step 120: Determine the posterior probability density function of the channel estimation parameters within the windowed channel estimation model.

[0066] Among them, the channel estimation parameters can be the channel state information determined by channel estimation of the target channel. The channel estimation parameters can include parameters such as the impulse response, order, Doppler frequency shift, and multipath delay of the target channel. The posterior probability density function can be the probability density of the distribution of the channel estimation parameters after the received signal is acquired. The posterior probability density function can represent the probability of the distribution of different values ​​of the channel estimation parameters. The posterior probability density function can be used to determine the specific values ​​of the channel estimation parameters.

[0067] In this embodiment of the invention, the posterior probability density function of the channel estimation parameters can be calculated using a windowed channel estimation model, and the channel estimation parameters of the target channel can be determined using the posterior probability density function. For example, the posterior probability density function of the target channel can have the following form:

[0068]

[0069] Where p(h|y) represents the posterior probability density function of the channel estimate h under the received signal y, and C, C' and y represents the normalization factor. n p is the nth element of y s (h s Let w be the probability density function of the s-th element in h. n Let be the nth element of w, and D represent the diagonal matrix formed by the vectorized beam domain capability matrices of the target channel. d h With t h The inner product of complex variables, d h =f(0,-D) -1 ), t h =f(h,I⊙(hh) H )), in p n (y n |h) represents y under channel estimation h. n The probability of occurrence, w n s represents the nth element of the applied window function, s∈1,2,3,...,M, where M represents the number of non-zero coefficients in the beam domain, and n∈1,2,3,...,N, where N represents the channel dimension in the spatial frequency domain.

[0070] Represents the nonlinear term, γ n =[A H ] :,n .

[0071] Step 130: Obtain the auxiliary manifold and the target manifold that approximate the nonlinear term in the posterior probability density function, determine the auxiliary manifold parameters, and generate the target manifold parameters based on the auxiliary manifold parameters.

[0072] The target manifold can be a manifold used to estimate channel estimation parameters, the target manifold parameters can be channel estimation parameters, and the auxiliary manifold can be information used to assist in the calculation of the target manifold and reduce the complexity of determining the manifold parameters of the target manifold.

[0073] In this embodiment of the invention, a target manifold and an auxiliary manifold can be constructed for nonlinear terms in the posterior probability density function that are inconvenient to calculate. The auxiliary manifold is then m-projected onto the target manifold, and the auxiliary manifold parameters are updated. This process is iterated multiple times, and the number of iterations can be preset. The target manifold parameters are then updated based on the last updated auxiliary manifold parameters.

[0074] For example, based on formula (9), since the density function of the posterior probability includes nonlinear terms, directly calculating the density function of the posterior probability is computationally intensive. The computational complexity can be reduced by approximating the density function of the posterior probability using a manifold. Specifically, a target manifold can be defined for the nonlinear terms:

[0075]

[0076] Where ψ0=f(θ0,Θ0), Let Θ0 be the first-order natural parameter of the target manifold, Θ0 be the M×M real diagonal matrix, and Θ2 be the second-order natural parameter of the target manifold. The target manifold can be constructed using formula (10). The nonlinear term within the density function that approximates the posterior probability distribution

[0077] However, directly using the target manifold to approximate the posterior probability density function still has high computational complexity. An auxiliary manifold can be defined to simplify the calculation process. This auxiliary manifold can be represented as follows:

[0078]

[0079] Where, ψ n =f(θ) n ,Θ n ) is a natural parameter. Θ n Let M be an M×M real diagonal matrix. Comparing formulas (11) and (9), it can be seen that the auxiliary manifold uses... The density function that approximates the posterior probability distribution These n-1 nonlinear terms retain c within the auxiliary manifold. n The nonlinear term (h) and the aforementioned auxiliary manifold parameter can be denoted as ψ. n Define the first-order common natural parameters and second-order common natural parameters of the auxiliary manifold as the mean of all auxiliary manifold parameters:

[0080]

[0081] That is to say It is evident that the common natural parameters of the auxiliary manifolds can be determined by the mean of each auxiliary manifold parameter.

[0082] In this embodiment of the invention, the manifold parameters of the auxiliary manifold can be iteratively updated by m-projecting the auxiliary manifold onto the target manifold. The target manifold parameters are determined by the auxiliary manifold parameters after the last iteration update. The above process is iterated multiple times, and the number of iterations can be determined based on a pre-configured number of iterations.

[0083] Step 140: Generate channel estimation parameters for the target channel based on the target manifold parameters.

[0084] In this embodiment of the invention, after determining the target manifold parameters, the channel estimation parameters can be calculated using the target manifold parameters. The target manifold parameters can be converted into a mean, and the mean can be used as the channel estimation parameters.

[0085] For example, in the target manifold defined by the formula, the manifold parameters include... It approximates the nonlinear term within the posterior probability density function. Where ψ0=f(θ0,Θ0), Θ0 is an M×M real diagonal matrix. and The natural parameters of the m-projection can be represented. When the target manifold parameters are obtained, the mean value corresponding to the target manifold parameters can be determined. The calculation process is as follows:

[0086]

[0087] Where, μ T It can be the mean of each nonlinear term in the density function of the corresponding channel estimation parameters, and ψ0 can represent the target manifold parameters, ψ0=f(θ0,Θ0). D represents a diagonal matrix composed of the vectorized beam domain capability matrices of the target channel.

[0088] Specifically, the mean μ is obtained. T This refers to the estimation value of the beam domain channel h by the algorithm (windowed information geometry algorithm) proposed in this application, and the calculation of μ. T After that, the spatial frequency domain channel can be recovered using formula (2), thus completing the entire channel estimation process.

[0089] This invention, through obtaining pilot and received signals corresponding to a target channel, constructs a windowed channel estimation model by adding a window function based on the pilot and received signals, determines the posterior probability density function of the channel estimation within the windowed channel estimation model, obtains a target manifold and an auxiliary manifold that approximate the nonlinear term within the posterior probability density function, determines the auxiliary manifold parameters, generates target manifold parameters based on the auxiliary manifold parameters, and generates channel estimation parameters for the target channel based on the target manifold parameters. This reduces the computational complexity of channel estimation, simplifies the channel estimation calculation process, improves channel estimation efficiency, reduces signal transmission latency, and enhances the user experience.

[0090] Furthermore, based on the above embodiments of the invention, a window function is added to the acquired pilot signal and received signal of the target channel to construct a windowed channel estimation model, including:

[0091] A windowed channel estimation model is obtained by adding a preset window function to the channel estimation model consisting of the measurement matrix, the received signal vector, the pilot signal, and the noise vector; wherein, the received signal vector is determined by the received signal.

[0092] The measurement matrix can be a data matrix composed of acquired pilot signal data. Each element in the measurement matrix corresponds to the signal information of the pilot signal transmitted by the target channel on different subcarriers and antennas. Similarly, each element in the received signal vector corresponds to the signal information of the received signal received by the target channel on different subcarriers and antennas. Gaussian white noise can be an ideal physical model for analyzing the noise of the target channel. Gaussian white noise refers to the signal noise distribution of the target channel conforming to Gaussian white noise. Gaussian white noise can mean that the instantaneous value of the target channel signal follows a Gaussian distribution and that the power spectrum follows a Gaussian distribution. The noise vector can be a vector constructed based on Gaussian white noise for the target channel. The window function can be a function used to apply a windowing operation to the channel estimation model. Preset window functions can include rectangular windows, Hanning windows, Hamming windows, Blackman windows, etc.

[0093] In this embodiment of the invention, pilot signals transmitted in the target channel can be obtained for different antennas and different subcarriers, and received signals received in the target channel can be extracted. Based on the physical model of the target channel, a channel estimation model is constructed by measuring matrix, received signal vector, pilot signal and noise vector. A window function can be added to the channel estimation model to obtain a windowed channel estimation model.

[0094] For example, the physical model provided by formula (7) in the embodiments of this application, and by adding a window function to the constructed physical model, can be used as a windowed channel estimation model. The windowed channel estimation model can include the following forms:

[0095]

[0096] in, W can be a diagonal matrix, and w is a window function. It is Gaussian white noise with a noise variance of . ω n =([W]) n,n ) * [W] n,n

[0097] Figure 2 This is a flowchart of another channel estimation method provided by an embodiment of the present invention. The embodiment of the present invention is a concretization based on the above embodiment, describing the process of determining the manifold parameters of the target manifold and the auxiliary manifold. See [link to documentation]. Figure 2 The method provided in this embodiment of the invention specifically includes the following steps:

[0098] Step 210: Based on the obtained pilot signal and received signal of the target channel, add a window function to construct a windowed channel estimation model.

[0099] In some embodiments of the invention, the window function added to the windowed channel estimation model includes at least the Kronecker product of the time delay domain window function matrix and the angle domain window function matrix, wherein the angle domain window function matrix includes the vertical angle domain window function matrix and the horizontal angle domain window function matrix.

[0100] The vertical angle domain window function matrix includes:

[0101] The horizontal angle domain window function matrix includes:

[0102] The time-delay domain window function matrix includes:

[0103] in, This represents the window function matrix in the vertical angular domain. This represents the horizontal angle domain window function matrix. Represents the time-delay domain window function matrix, [] n,n N represents the nth row and nth column of the matrix. p N represents the number of training subcarriers. r,v N represents the number of antennas in the vertical direction corresponding to the target channel. r,h This indicates the number of antennas in the horizontal direction corresponding to the target channel.

[0104] In this embodiment of the invention, the window function added to the windowed channel estimation model can be constructed by the Kronecker product of a time-delay domain window function matrix and an angle-domain window function matrix. Each element in the time-delay domain window function matrix and the angle-domain window function matrix can respectively include a window function for windowing the pilot signal in the time-delay domain or a window function for windowing in the angle domain. The angle-domain window function can include a vertical angle-domain window function matrix and a horizontal angle-domain window function matrix. Specifically, the vertical angle-domain window function matrix includes:

[0105] The horizontal angle domain window function matrix includes:

[0106] The time-delay domain window function matrix includes:

[0107] in, This represents the window function matrix in the vertical angular domain. This represents the horizontal angle domain window function matrix. Represents the time-delay domain window function matrix, [] n,n N represents the nth row and nth column of the matrix. p Number of training subcarriers, N r,v N represents the number of antennas in the vertical direction corresponding to the target channel. r,h This indicates the number of antennas in the horizontal direction corresponding to the target channel.

[0108] Step 220: Determine the posterior probability density function of the channel estimation parameters within the windowed channel estimation model.

[0109] Step 230: Initialize the auxiliary manifold parameters for the nonlinear terms within the posterior probability density function.

[0110] The auxiliary manifold parameters can be the manifold parameters of the corresponding auxiliary manifold.

[0111] In this embodiment of the invention, for determining the nonlinear term within the posterior probability density function, the nonlinear term can be a parameter term arising from the nonlinear interaction between variables within the posterior probability density function. The nonlinear term increases the complexity of calculating the posterior probability density function. A target manifold and an auxiliary manifold can be constructed for the nonlinear term, and the manifold parameters of the auxiliary manifold can be initialized, that is, the auxiliary manifold parameters can be initialized. In some embodiments of the invention, the auxiliary manifold parameters can be initialized based on a pre-configured damping coefficient.

[0112] The posterior probability density function includes:

[0113]

[0114] Where p(h|y) represents the posterior probability density function of the channel estimate h under the received signal y, and C, C' and y represents the normalization factor. n w is the nth element of y n Let be the nth element of w, and D represent the diagonal matrix formed by the vectorized beam domain capability matrices of the target channel. d h With t h The inner product of complex variables, d h =f(0,-D) -1 ), t h =f(h,I⊙(hh) H )), in p s (h s ) represents channel estimation h s The probability of occurrence, p n (y n |h) represents y under channel estimation h. n The probability of occurrence, w n The nth element represents the window function added to the windowed channel estimation model, i∈1,2,3,...,M, where M represents the number of non-zero coefficients in the beam domain and n∈1,2,3,...,N, where N represents the channel dimension in the space-frequency domain.

[0115] Represents the nonlinear term, γ n =[A H ] :,n .

[0116] Step 240: Perform m-projection of the auxiliary manifold to the target manifold and update the auxiliary manifold parameters.

[0117] Here, m-projection can be used to find a distribution that minimizes the KL divergence from the distribution on the auxiliary manifold to the target manifold. KL divergence measures the similarity or difference between the probability distributions of the target manifold and the auxiliary manifold.

[0118] In this embodiment of the invention, an m-projection can be performed on the auxiliary manifold to the target manifold to obtain the manifold parameters of the auxiliary manifold with the minimum KL divergence.

[0119] Step 250: Repeat the m-projection and auxiliary manifold parameter update a preset number of times, and obtain the target manifold parameters of the target manifold based on the auxiliary manifold parameters updated in the last iteration.

[0120] In this embodiment of the invention, the auxiliary manifold parameters of the auxiliary manifold can be iteratively updated by m-projecting the auxiliary manifold onto the target manifold during the iteration process. The update process of the auxiliary manifold can be obtained by using the update formula for the auxiliary manifold parameters, and the auxiliary manifold parameters of the next iteration can be obtained from the previous iteration of the auxiliary manifold parameters.

[0121] Specifically, after iterating and updating the auxiliary manifold parameters of the auxiliary manifold a preset number of times, the auxiliary manifold parameters of the last iteration can be obtained, and the manifold parameters of the target manifold can be determined according to the last auxiliary manifold parameters. The manifold parameters of the target manifold can be denoted as the target manifold parameters.

[0122] Furthermore, based on the above embodiments of the invention, the formula used to update the auxiliary manifold parameters includes:

[0123]

[0124] Where α represents the damping coefficient. and The natural parameters representing the m-projection. and Let represent the common first-order natural parameters and common second-order natural parameters of the auxiliary manifold at iteration number t. and Let t represent the common first-order natural parameters and common second-order natural parameters of the auxiliary manifold with iteration number t, and N represent the spatial frequency domain channel dimension.

[0125] Based on the above embodiments of the invention, the auxiliary manifold is m-projected onto the target manifold, and the auxiliary manifold parameters are updated, including:

[0126] Step 2501: Determine the target replacement item in the m-projection process, wherein the computational complexity of the target replacement item is greater than a threshold.

[0127] The target to be replaced can be a parameter with high computational complexity within the m-projection. The target to be replaced can include at least one computation symbol. The computational complexity of the target to be replaced can be determined by dimensions such as computation time and space. For example, the computation time of the target to be replaced exceeds the threshold time or the storage space required for the computation process of the target to be replaced exceeds the threshold space.

[0128] In this embodiment of the invention, a target replacement item with a computational complexity greater than a threshold can be determined within the m-projection. This computational complexity can be determined statistically by the time or spatial dimensions of the target replacement item.

[0129] Step 2502: Obtain an approximate value of the target item to be replaced, wherein the approximate value is determined by the time delay domain filtering matrix, the vertical direction angle domain filtering matrix, and the horizontal direction angle domain filtering matrix of the target channel.

[0130] In this embodiment of the invention, the approximation value can be a numerical value that is approximated by the target to be replaced. The approximation value can be determined by the pilot signal of the target signal, and can be determined by the filtering matrix of the transmitted pilot signal in the horizontal direction domain, vertical direction domain, or time delay domain. The approximation value can be defined as obtained by processing the target to be replaced using an extraction matrix.

[0131] Step 2503: Determine the value of the target item to be replaced based on the approximate value.

[0132] Specifically, the determined approximate value can be replaced with the target replacement item. This replacement can include directly replacing the approximate value or calculating the target replacement item from the approximate value. For example, approximation This can be defined as extracting the product of matrix E and the target replacement term r. After obtaining an approximate value, it can be... Identify the target to be replaced.

[0133] Step 260: Generate channel estimation parameters for the target channel based on the target manifold parameters.

[0134] In this embodiment of the invention, a windowed channel estimation model is obtained by adding a window function to the pilot signal and received signal acquired for the target channel. The posterior probability density function corresponding to the windowed channel estimation model is determined. A target manifold and an auxiliary manifold with nonlinear terms within the posterior probability density function are constructed, and the manifold parameters of the target and auxiliary manifolds are determined. The auxiliary manifold is then m-projected onto the target manifold, and its manifold parameters are updated. Based on the above process, the manifold parameters of the auxiliary manifold are iteratively updated multiple times. The target manifold parameters of the target manifold are obtained based on the auxiliary manifold parameters updated in the last iteration. During the m-projection process, a target replacement term is determined, and an approximate value of the target replacement term is obtained. In some embodiments of the invention, the target replacement term r = A H W H WA, where A represents the measurement matrix of the target channel, W represents the window function added to the windowed channel estimation model, and H represents the conjugate transpose;

[0135] Correspondingly, approximate value in, This represents the vertical angle-domain filtering matrix of the target channel. V v This is the measurement matrix for the vertical angle domain. Let be the window function matrix in the vertical angular domain within the window function. This represents the horizontal angle-domain filtering matrix of the target channel. V h Represents the horizontal angle domain measurement matrix. The horizontal angle domain window function matrix within the window function is determined, A M The time-delay domain filtering matrix represents the target channel. M represents the time delay domain measurement matrix of the target channel. This represents the time-delay domain window function matrix within the window function. U represents the matrix to be filtered.

[0136] Specifically, the target to be replaced is r = A H W H WA represents the part with the highest computational complexity. A denotes the measurement matrix, W denotes the window function added to the windowed channel estimation model, and H denotes the conjugate transpose. This target replacement term can be calculated using approximations, which may include... in, The vertical angle domain filtering matrix is ​​determined by the vertical angle domain measurement matrix and the vertical angle window function matrix. The horizontal angle domain filtering matrix is ​​determined by the horizontal angle domain measurement matrix and the horizontal angle domain window function matrix. A M The time-delay domain filtering matrix is ​​determined by the time-delay domain measurement matrix and the time-delay domain window function matrix. U represents the matrix to be filtered.

[0137] In some embodiments of the invention, determining the value of the target replacement item according to an approximation further includes:

[0138] Identify the elements whose filter matrix amplitude is less than a threshold within the approximate value and set the elements to zero. The filter matrix amplitude includes at least one of the following: the amplitude of the time delay domain filter matrix, the amplitude of the vertical angle domain filter matrix, and the amplitude of the horizontal angle domain filter matrix.

[0139] In this embodiment of the invention, during the approximation calculation of the posterior probability density function based on the target manifold parameters, since the approximation calculation process may result in a large number of elements with very small magnitudes, these elements can be zeroed out. During matrix multiplication, since element-wise multiplication is only required when the corresponding elements of the two matrices are not zero, the computational overhead can be reduced. Specifically, during the approximation calculation based on the window function, before calculation, the filter matrix of the window function can be checked for elements within the filter matrix whose magnitude is less than a threshold. If there is an element within the filter matrix with a magnitude less than the threshold, the value of the element corresponding to that magnitude is set to zero, thereby reducing computational complexity.

[0140] In an exemplary implementation, taking the uplink channel transmission of a MIMO system as an example, a windowed channel estimation model is constructed based on the physical model shown in equation (7):

[0141]

[0142] in, W can be a diagonal matrix, and w is a window function. It is Gaussian white noise with a noise variance of . ω n =([W]) n,n ) * [W] n,n The physical model that incorporates a window function can be used as a windowed channel estimation model.

[0143] The purpose of channel estimation is to calculate the probability density of the posterior distribution of h, whose probability density function can be written as:

[0144]

[0145] Where p(h|y) represents the posterior probability density function of the channel estimate h under the received signal y, and C, C' and y represents the normalization factor. n w is the nth element of y n Let be the nth element of w, and D represent the diagonal matrix formed by the vectorized beam domain capability matrices of the target channel. d h With t h The inner product of complex variables, d h =f(0,-D) -1 ), t h =f(h,I⊙(hh) H )), in p s (h s ) represents channel estimation h s The probability of occurrence, p n (y n |h) represents y under channel estimation h. n The probability of occurrence, w n The window function added to the windowed channel estimation model corresponding to the nth element is s∈1,2,3,...,M, where M represents the number of non-zero coefficients in the beam domain and n∈1,2,3,...,N, where N represents the channel dimension in the space-frequency domain.

[0146] Represents the nonlinear term, γ n =[A H ] :,n.

[0147] The posterior probability density function of the target channel has high computational complexity due to the presence of nonlinear terms. This complexity can be reduced by approximating these nonlinear terms. The target manifold is defined to address the nonlinear terms as follows:

[0148]

[0149] Where ψ0=f(θ0,Θ0), Θ0 is an M×M real diagonal matrix, which can be constructed by using the target manifold. The nonlinear term within the density function that approximates the posterior probability density

[0150] However, directly using the target manifold to approximate the posterior probability density function still has high computational complexity. An auxiliary manifold can be defined to simplify the calculation process. This auxiliary manifold can be represented as follows:

[0151]

[0152] Where, ψ n =f(θ) n ,Θ n ) is a natural parameter. Θ n Let ψ be an M×M real diagonal matrix. The auxiliary manifold parameter of the above auxiliary manifold can be denoted as ψ. n Embodiments of the present invention can utilize auxiliary manifolds. The density function that approximates the posterior probability density These n-1 nonlinear terms retain c within the auxiliary manifold. n The nonlinear term (h) can be replaced by an auxiliary manifold that assists the target manifold.

[0153] The common natural parameter is defined as the mean of all auxiliary manifold parameters, i.e.:

[0154]

[0155] In other words, the common natural parameters of the auxiliary manifolds can be determined by the mean of each auxiliary manifold parameter.

[0156] In the t-th iteration, M n An m-projection is performed onto the target manifold M0. This m-projection minimizes the KL divergence between the probability distributions of the auxiliary manifold and the target manifold. The resulting natural parameters are denoted as... That is, the target manifold parameters. These can be obtained through calculation:

[0157]

[0158] Based on the above embodiments of the invention, the process of determining the target manifold parameters may include:

[0159] 1. Initialization: t = 0, select an appropriate damping coefficient α, and initialize the target manifold and auxiliary manifold parameters.

[0160] 2. Calculate the m-projection according to equations (13) and (14).

[0161] 3. Update the auxiliary manifold parameters according to the following formula.

[0162]

[0163] 4. t = t + 1, repeat 2 to 3 times until the number of iterations T.

[0164] 5. Update the target manifold parameters according to the following formula.

[0165]

[0166] 6. Convert the target manifold parameters to mean values.

[0167]

[0168] μ in equation (17) T This is the estimate of the beam domain channel h by the windowed channel estimation model, and μ is calculated. T After that, the spatial frequency domain channel can be recovered using formula (2), thus completing the entire channel estimation process.

[0169] Furthermore, the part with the highest computational complexity is step r = A in formula (14). H W H WA. To achieve low-complexity computation, this invention does not directly calculate r, but instead calculates... Calculate Then, r is calculated using the following formula:

[0170]

[0171] Assume the window function can be written as:

[0172]

[0173] It can be calculated as:

[0174]

[0175] in, and Defined as a time-delay domain window function matrix. Defined as the vertical angular domain window function matrix. Defined as the horizontal angular domain window function matrix. This is the vertical angle domain filtering matrix. This is the horizontal angle domain filtering matrix. This is the time-delay domain filtering matrix.

[0176] This invention employs a cosine matrix window function matrix, namely:

[0177]

[0178] After using the above cosine window function matrix and A M There will be a large number of elements with very small moduli in the matrix. During calculation, these elements can be set to zero. In addition, there are many zero elements in U in equation (20). When performing matrix multiplication, element-wise multiplication is only required when the corresponding elements of the two matrices are not zero. The specific method is defined below:

[0179]

[0180] and These are the sparse filtering matrices in the vertical angle domain, the horizontal angle domain, and the time delay domain, respectively. and δ M These are the threshold values ​​for the vertical angle domain, the horizontal angle domain, and the time delay domain, respectively.

[0181] Finally, r can be calculated as

[0182]

[0183] In calculating matrix multiplication, multiplication is performed only when all corresponding elements are non-zero. The method provided in this invention reduces the computational complexity of the channel estimation process. U represents the matrix to be filtered.

[0184] See Figure 3 The amplitude diagram of a column element of a horizontal angle-domain filtering matrix for a target channel is given, where N in the horizontal angle-domain filtering matrix... r,h =32, F h =2, the horizontal angular domain filtering matrix of the target channel can use a rectangular window, that is, For N r,h An identity matrix of 1 / 2 dimensionality. See also: Figure 4 ,and Figure 3For the same target channel, a column of the horizontal angle domain filtering matrix can be represented by a cosine window function matrix:

[0185]

[0186] from Figure 4 The horizontal angular domain filtering matrix is ​​known. With more elements close to zero, it is easier to reduce the computational load of the channel estimation process and improve the efficiency of channel estimation.

[0187] Similarly, see Figure 5 The amplitude diagram of a column element of the time-delay domain filtering matrix of the target channel, where N is the amplitude value. p =552, N f =39, F τ =2, K=14, this amplitude map can be obtained by processing the time-delay domain filtering matrix through a rectangular window, that is, See Figure 6 ,and Figure 5 For the same target channel, a column of the time-delay domain filtering matrix can be represented by a cosine window function matrix:

[0188]

[0189] from Figure 6 It can be known that the time-delay domain filtering matrix A M With more elements close to zero, it is easier to reduce the computational load of the channel estimation process and improve the efficiency of channel estimation.

[0190] Figure 7 This is a schematic diagram of a channel estimation device according to an embodiment of the present invention. Figure 7 As shown, the device includes: a model building module 301, a probability density module 302, a manifold building module 303, and an estimation execution module 304.

[0191] The model building module 301 is used to add a window function to the acquired pilot signal and received signal of the target channel to build a windowed channel estimation model.

[0192] The probability density module 302 is used to determine the posterior probability density function of the channel estimation parameters within the windowed channel estimation model.

[0193] The manifold construction module 303 is used to obtain an auxiliary manifold and a target manifold that approximate the nonlinear term in the posterior probability density function, determine the auxiliary manifold parameters, generate the target manifold parameters and the auxiliary manifold based on the auxiliary manifold parameters, and determine the manifold parameters corresponding to the target manifold and the auxiliary manifold.

[0194] The estimation execution module 304 is used to generate channel estimation parameters for the target channel based on the target manifold parameters.

[0195] In this embodiment of the invention, a model building module acquires the pilot signal and received signal corresponding to the target channel, and constructs a windowed channel estimation model by adding a window function based on the pilot signal and received signal. A probability density module determines the posterior probability density function of the channel estimation within the windowed channel estimation model, obtains an auxiliary manifold and a target manifold that approximate the nonlinear term within the posterior probability density function, determines the auxiliary manifold parameters, generates the target manifold parameters based on the auxiliary manifold parameters, and an estimation execution module generates the channel estimation parameters of the target channel based on the target manifold parameters. This reduces the computational complexity of channel estimation, simplifies the channel estimation calculation process, improves channel estimation efficiency, reduces signal transmission latency, and enhances the user experience.

[0196] Furthermore, based on the above embodiments of the invention, the model building module 301 includes:

[0197] The model building unit is used to add a preset window function to the channel estimation model composed of the measurement matrix, the received signal vector, the pilot signal and the noise vector to obtain the windowed channel estimation model.

[0198] The received signal vector is determined by the received signal.

[0199] Furthermore, based on the above embodiments of the invention, the manifold construction module 303 includes:

[0200] An initialization unit is used to initialize the auxiliary manifold parameters of the auxiliary manifold for the nonlinear term in the posterior probability density function.

[0201] The projection processing unit is used to perform m-projection of the auxiliary manifold to the target manifold and update the parameters of the auxiliary manifold.

[0202] An iterative processing unit is used to repeatedly perform the m-projection and auxiliary manifold parameter update a preset number of times, and obtain the target manifold parameter based on the auxiliary manifold parameter updated in the last iteration.

[0203] In some embodiments of the invention, the iterative processing unit is further configured to: determine the target item to be replaced during the processing of the m-projection, wherein the computational complexity of the target item to be replaced is greater than a threshold;

[0204] Obtain an approximate value of the target item to be replaced, wherein the approximate value is determined by the time delay domain filtering matrix, the vertical direction angle domain filtering matrix, and the horizontal direction angle domain filtering matrix of the target channel;

[0205] The value of the target item to be replaced is determined according to the approximation.

[0206] Based on the above embodiments of the invention, the target to be replaced item r = A H W H WA, where A represents the measurement matrix of the target channel, W represents the window function added to the windowed channel estimation model, and H represents the conjugate transpose;

[0207] Accordingly, the approximate value Among them, A Vv This represents the vertical angle domain filtering matrix of the target channel. V v This is the measurement matrix for the vertical angle domain. The window function matrix is ​​the vertical angle domain within the window function. This represents the horizontal angle domain filtering matrix of the target channel. V h Represents the horizontal angle domain measurement matrix. This indicates that the horizontal angle domain window function matrix within the window function is determined, A M The time-delay domain filtering matrix represents the target channel. M represents the time delay domain measurement matrix of the target channel. This represents the time-delay domain window function matrix within the window function. U represents the matrix to be filtered.

[0208] Furthermore, based on the above embodiments of the invention, the window function added to the windowed channel estimation model includes at least the Kronecker product of the time delay domain window function matrix and the angle domain window function matrix, wherein the angle domain window function matrix includes the vertical direction angle domain window function matrix and the horizontal direction angle domain window function matrix.

[0209] The vertical angle domain window function matrix includes:

[0210] The horizontal angle domain window function matrix includes:

[0211] The time-delay domain window function matrix includes:

[0212] in, This represents the window function matrix in the vertical angular domain. This represents the horizontal angle domain window function matrix. Represents the time-delay domain window function matrix, [] n,n N represents the nth row and nth column of the matrix. pNumber of training subcarriers, N r,v N represents the number of antennas in the vertical direction corresponding to the target channel. r,h This indicates the number of antennas in the horizontal direction corresponding to the target channel.

[0213] Furthermore, based on the above embodiments of the invention, the probability density module 302 further includes: a zeroing processing unit, used to determine the elements whose filter matrix amplitude is less than a threshold within the approximate value, and set the elements to zero, wherein the filter matrix amplitude includes at least one of the time delay domain filter matrix amplitude, the vertical direction angle domain filter matrix amplitude, and the horizontal direction angle domain filter matrix amplitude.

[0214] Furthermore, based on the above embodiments of the invention, the channel estimation unit within the device is specifically configured to include: obtaining a correspondence formula between the approximate value and the target replacement item; substituting the approximate value into the correspondence formula to determine the target replacement item; wherein, the correspondence formula includes:

[0215]

[0216] r represents the target item to be replaced. Let E represent the approximate value, and let E represent the extraction matrix.

[0217] Furthermore, based on the above embodiments of the invention, the formula used in the iterative processing unit to update the auxiliary manifold parameters includes:

[0218]

[0219] Where α represents the damping coefficient. and The natural parameters representing the m-projection. and Let represent the common first-order natural parameters and common second-order natural parameters of the auxiliary manifold at iteration number t. and Let t represent the common first-order natural parameters and common second-order natural parameters of the auxiliary manifold with iteration number t, and N represent the spatial frequency domain channel dimension.

[0220] The channel estimation device provided in the embodiments of the present invention can execute the channel estimation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0221] Figure 8A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0222] like Figure 8 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0223] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0224] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as channel estimation methods.

[0225] In some embodiments, the channel estimation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded into and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the channel estimation method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the channel estimation method by any other suitable means (e.g., by means of firmware).

[0226] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0227] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0228] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0229] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0230] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0231] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0232] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0233] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A channel estimation method, characterized in that, The method includes: A windowed channel estimation model is constructed by adding a window function to the pilot signal and received signal of the target channel. Determine the posterior probability density function of the channel estimation parameters within the windowed channel estimation model; The auxiliary manifold parameters are initialized for the nonlinear term within the posterior probability density function; the auxiliary manifold is then approximated to the target manifold. m - Project, update the auxiliary manifold parameters; repeat the approximation a preset number of times. m - Projection and auxiliary manifold parameter update, obtaining the target manifold parameter based on the auxiliary manifold parameter updated in the last iteration; Wherein, the step of approximating the auxiliary manifold to the target manifold m - Projection, including: determining the approximation m - In the projection process, a target to be replaced is obtained, wherein the computational complexity of the target to be replaced is greater than a threshold; an approximate value of the target to be replaced is obtained, wherein the approximate value is determined by the time delay domain filtering matrix, the vertical direction angle domain filtering matrix, and the horizontal direction angle domain filtering matrix of the target channel; the value of the target to be replaced is determined according to the approximate value; The target to be replaced item is the part with the highest computational complexity within the calculation process parameters of the target manifold parameter. The target to be replaced item is: ,in, The measurement matrix representing the target channel. H represents the window function added to the windowed channel estimation model; Accordingly, the approximate value is: ,in, This represents the vertical angle domain filtering matrix of the target channel. , This is the measurement matrix for the vertical angle domain. The window function matrix is ​​the vertical angle domain within the window function. This represents the horizontal angle domain filtering matrix of the target channel. , Represents the horizontal angle domain measurement matrix. This represents the horizontal angle domain window function matrix within the window function. This represents the time-delay domain filtering matrix of the target channel. , This represents the time-delay domain measurement matrix of the target channel. This represents the time-delay domain window function matrix within the window function. U represents the matrix to be filtered; The windowed channel estimation model adds at least the Kronecker product of the time delay domain window function matrix and the angle domain window function matrix, wherein the angle domain window function matrix includes the vertical angle domain window function matrix and the horizontal angle domain window function matrix. The vertical direction angle domain window function matrix includes: ; The horizontal direction angle domain window function matrix includes: ; The time-delay domain window function matrix includes: ; in, This represents the window function matrix in the vertical angular domain. This represents the horizontal angle domain window function matrix. Represents the time-delay domain window function matrix. This represents the nth row and nth column of the matrix. Indicates the number of training subcarriers. This indicates the number of antennas corresponding to the target channel in the vertical direction. This indicates the number of antennas corresponding to the target channel in the horizontal direction; The channel estimation parameters for the target channel are generated based on the target manifold parameters.

2. The method according to claim 1, characterized in that, The step of adding a window function to the acquired pilot signal and received signal of the target channel to construct a windowed channel estimation model includes: A preset window function is added to the channel estimation model consisting of the measurement matrix, the received signal vector, the pilot signal, and the noise vector to obtain the windowed channel estimation model. The received signal vector is determined by the received signal.

3. The method according to claim 1, characterized in that, The step of determining the value of the target replacement item according to the approximation also includes: The elements whose amplitude of the filter matrix within the approximate value is less than a threshold are identified, and the elements are set to zero. The amplitude of the filter matrix includes at least one of the following: the amplitude of the time-delay domain filter matrix, the amplitude of the vertical angle domain filter matrix, and the amplitude of the horizontal angle domain filter matrix.

4. The method according to claim 1, characterized in that, Determining the value of the target replacement item according to the approximation includes: Obtain the formula for the correspondence between the approximate value and the target item to be replaced; Substitute the approximate value into the correspondence formula to determine the value of the target item to be replaced; The correspondence formula includes: This indicates the target item to be replaced. This represents the approximate value. This represents the extraction matrix.

5. The method according to claim 1, characterized in that, The formulas used to update the auxiliary manifold parameters include: ； ； in, Indicates the damping coefficient. and express m -Natural parameters of the projection and Indicates the number of iterations. t The common first-order natural parameters and common second-order natural parameters of the auxiliary manifold, and Indicates the number of iterations. t The common first-order natural parameters and common second-order natural parameters of the auxiliary manifold, +1 N This represents the spatial frequency domain channel dimension.

6. A channel estimation device, characterized in that, The device includes: The model building module is used to add window functions to the acquired pilot signals and received signals of the target channel to build a windowed channel estimation model; The probability density module is used to determine the posterior probability density function of the channel estimation parameters within the windowed channel estimation model. The manifold construction module is used to initialize the auxiliary manifold parameters of the auxiliary manifold for the nonlinear term in the posterior probability density function; and to convert the auxiliary manifold into the target manifold. m - Project, update the auxiliary manifold parameters; repeat the process a preset number of times. m - Projection and auxiliary manifold parameter update, obtaining the target manifold parameter based on the auxiliary manifold parameter updated in the last iteration; Wherein, the step of directing the auxiliary manifold toward the target manifold m - Projection, including: determining the m - In the projection process, a target to be replaced is obtained, wherein the computational complexity of the target to be replaced is greater than a threshold; an approximate value of the target to be replaced is obtained, wherein the approximate value is determined by the time delay domain filtering matrix, the vertical direction angle domain filtering matrix, and the horizontal direction angle domain filtering matrix of the target channel; the value of the target to be replaced is determined according to the approximate value; The target to be replaced is the part of the target manifold parameter calculation process with the highest computational complexity. The target to be replaced is: ,in, The measurement matrix representing the target channel. H represents the window function added to the windowed channel estimation model; Accordingly, the approximate value is: ,in, This represents the vertical angle domain filtering matrix of the target channel. , This is the measurement matrix for the vertical angle domain. The window function matrix is ​​the vertical angle domain within the window function. This represents the horizontal angle domain filtering matrix of the target channel. , Represents the horizontal angle domain measurement matrix. This represents the horizontal angle domain window function matrix within the window function. This represents the time-delay domain filtering matrix of the target channel. , This represents the time-delay domain measurement matrix of the target channel. This represents the time-delay domain window function matrix within the window function. U represents the matrix to be filtered; The windowed channel estimation model adds at least the Kronecker product of the time delay domain window function matrix and the angle domain window function matrix, wherein the angle domain window function matrix includes the vertical angle domain window function matrix and the horizontal angle domain window function matrix. The vertical direction angle domain window function matrix includes: ; The horizontal direction angle domain window function matrix includes: ; The time-delay domain window function matrix includes: ; in, This represents the window function matrix in the vertical angular domain. This represents the horizontal angle domain window function matrix. Represents the time-delay domain window function matrix. This represents the nth row and nth column of the matrix. Indicates the number of training subcarriers. This indicates the number of antennas corresponding to the target channel in the vertical direction. This indicates the number of antennas corresponding to the target channel in the horizontal direction; An estimation execution module is used to generate channel estimation parameters for the target channel based on the target manifold parameters.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the channel estimation method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the channel estimation method according to any one of claims 1-5.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the channel estimation method according to any one of claims 1-5.

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