A mirror path and dense multipath component joint estimation method and system thereof

Abstract

The present disclosure relates to a mirror path and dense multipath component joint estimation method and system, the method comprising: constructing a propagation channel initialization model based on mirror path and dense multipath component; calculating the initial estimation value of the dense multipath component and noise parameter in the propagation channel initialization model; according to the initial estimation value of the dense multipath component and noise parameter, optimizing and updating the mirror path parameter and dense multipath component parameter in the propagation channel initialization model by maximum likelihood algorithm until the estimation value of the mirror path parameter and dense multipath component parameter converges; and obtaining the global optimal solution of the mirror path parameter by optimization solution. The method has high channel estimation accuracy, good robustness and low computational complexity in high noise and multipath environment.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of positioning and navigation, and in particular to a mirror path and dense multipath component joint estimation method and system. BACKGROUND

[0002] In wireless communication systems, channel estimation is one of the key technologies to ensure communication quality. Accurate channel estimation can help the system effectively eliminate multipath effects, noise interference and other channel distortions, thereby improving the transmission quality of signals and the overall performance of the system. In modern wireless communication systems such as 5G, Internet of Things and millimeter wave communication, the precision and robustness of channel estimation are particularly important. Traditional channel estimation methods usually assume that the channel is composed of a few main mirror paths, while ignoring the impact of dense multipath components. However, in actual wireless communication environments, especially in complex urban environments or indoor scenarios, dense multipath components account for a large part of the channel response. These multipath components are usually composed of a large number of weak signals and cannot be modeled as plane waves alone, resulting in a significant decline in the performance of traditional channel estimation methods in complex environments. Existing channel estimation methods lack sufficient modeling of dense multipath components when dealing with dense multipath components, often treating dense multipath components as noise, resulting in a decline in the accuracy of channel estimation. In environments with strong noise and multipath interference, the robustness of traditional methods is poor and is easily affected by interference.

[0003] Therefore, it is necessary to propose an innovative method that can effectively separate and estimate mirror paths and dense multipath components to improve the accuracy of channel estimation. SUMMARY

[0004] To solve the problem that traditional channel estimation methods treat dense multipath components as noise, resulting in a decline in estimation accuracy, the present disclosure proposes a mirror path and dense multipath component joint estimation method to solve the above problems.

[0005] According to an aspect of the present disclosure, a mirror path and dense multipath component joint estimation method is provided, comprising:

[0006] S10, constructing a propagation channel initialization model based on mirror paths and dense multipath components;

[0007] S20, calculating initial estimates of dense multipath components and noise parameters in the propagation channel initialization model;

[0008] S30, according to the initial estimates of dense multipath components and noise parameters, optimizing and updating the mirror path parameters and dense multipath component parameters in the propagation channel initialization model by a maximum likelihood algorithm until the estimates of the mirror path parameters and the dense multipath component parameters converge;

[0009] S40. The global optimal solution for the mirror path parameters is obtained through optimization.

[0010] Preferably, a propagation channel initialization model is constructed based on mirror paths and dense multipath components, and the propagation channel initialization model is expressed as:

[0011]

[0012] In the formula, h is the propagation channel, M and K are the number of antennas and subcarriers, respectively, and S(θ) sp ) represents the mirror path component, D(θ) dmc ) represents the dense multipath component, and n is the noise parameter.

[0013] Preferably, calculating the initial estimates of dense multipath components and noise parameters in the propagation channel initialization model includes: obtaining the initial estimates of dense multipath components and noise parameters by calculating the estimates of the initial solutions of dense multipath components and noise parameters;

[0014] The initial solution for the dense multipath components and noise parameters is expressed as:

[0015] θ dan =[α0,α1,β] d ,τ d ],

[0016] In the formula, θ dan Let α0 be the initial solution for the dense multipath components and noise parameters, α1 be the peak power of the dense multipath components, and β be the initial solution for the noise parameters. d For the coherence bandwidth of dense multipath components, τ d This is the time delay spread for dense multipath components.

[0017] Preferably, calculating the estimated values ​​of the initial solutions for the dense multipath components and noise parameters further includes:

[0018] Calculate the estimated value of the power delay curve; extract the estimated values ​​of four parameters from the initial solution of the dense multipath component and noise parameter from the estimated value of the power delay curve;

[0019]

[0020] In the formula, This is an estimate of the variance of the circularly normally distributed noise. This is an estimate of the peak power of the dense multipath components. This is an estimate of the coherence bandwidth of the dense multipath components. This is an estimate of the time delay spread of dense multipath components. This is an estimate of the power delay curve. Let h be the average power of the propagation channel.

[0021] Preferably, the mirror path parameters and dense multipath component parameters are optimized and updated using the maximum likelihood algorithm, including:

[0022] Initialize the initial estimates of the mirror path parameters and dense multipath component parameters;

[0023] The mirror path parameters and dense multipath component parameters are alternately optimized by maximizing the log-likelihood function.

[0024] The mirror path parameters are updated using a nonlinear least squares method.

[0025] Preferably, the mirror path parameters and dense multipath component parameters are alternately optimized by maximizing the log-likelihood function, wherein the maximized log-likelihood function is expressed as:

[0026]

[0027] In the formula, To maximize the initial estimate of the mirror path component parameters of the log-likelihood function, To maximize the initial estimates of the dense multipath components and noise parameters of the log-likelihood function, R dan θ is the joint covariance matrix of dense multipath components and noise parameters. sp For the mirror path component S(θ) sp The eigenvalues ​​of ), where H represents the conjugate transpose.

[0028] Preferably, the mirror path parameters are updated using a nonlinear least squares method, as follows:

[0029]

[0030] According to one aspect of this disclosure, a joint estimation system for mirror path and dense multipath components is provided, comprising:

[0031] The propagation channel initialization model construction module constructs a propagation channel initialization model based on mirror paths and dense multipath components.

[0032] The initial parameter estimation module calculates the initial estimates of dense multipath components and noise parameters in the propagation channel initialization model.

[0033] The parameter optimization and update module optimizes and updates the mirror path parameters and dense multipath component parameters in the propagation channel initialization model based on the initial estimates of the dense multipath components and noise parameters using the maximum likelihood algorithm, until the estimates of the mirror path parameters and dense multipath component parameters converge.

[0034] The optimal mirror path parameter acquisition module obtains the globally optimal solution for the mirror path parameters through optimization.

[0035] According to one aspect of this disclosure, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: perform the above-described joint estimation method of mirror path and dense multipath components.

[0036] According to one aspect of this disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the above-described joint estimation method for mirror paths and dense multipath components.

[0037] Compared to the prior art, the beneficial effects of this disclosure are as follows:

[0038] 1) The proposed method for joint estimation of wireless channel mirror path and dense multipath component based on the Rimax algorithm can make full use of mirror path and dense multipath component information, and has many advantages such as high channel estimation accuracy, good algorithm robustness, low computational complexity and wide applicability.

[0039] 2) This disclosure proposes an initial solution estimation method based on power delay curve (PDP), which can quickly estimate the initial parameters of dense multipath components and noise, providing a good starting point for subsequent iterative optimization.

[0040] 3) This disclosure effectively addresses noise and multipath interference by jointly modeling mirror paths and dense multipath components and performing parameter estimation based on the Rimax algorithm through iterative optimization.

[0041] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0042] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0044] Figure 1 A flowchart of a joint estimation method for mirror path and dense multipath components is shown;

[0045] Figure 2 The diagram shows the multipath parameter information estimated using the Rimax algorithm and the power distribution of the multipath channel simulating LOS transmission in a CDL-D model under different TOA and AOA.

[0046] Figure 3The graph shows a comparison of TOA obtained using the estimation method in the embodiments of this disclosure with different signal-to-noise ratios in the CDL-D model;

[0047] Figure 4 A comparison graph showing the angle of arrival (AOA) obtained by using and not using the estimation method in the embodiments of this disclosure at different signal-to-noise ratios in the cluster delay line model (CDL-D);

[0048] Figure 5 A block diagram of a joint estimation system for mirror path and dense multipath components is shown. Detailed Implementation

[0049] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0050] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0051] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0052] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Example 1

[0055] Based on the above ideas, this invention proposes a joint estimation method for mirror path and dense multipath components. Figure 1 A flowchart of a joint estimation method for mirror path and dense multipath components is shown. The method includes:

[0056] S10. Construct a propagation channel initialization model based on mirror path and dense multipath components;

[0057] S20. Calculate the initial estimates of dense multipath components and noise parameters in the propagation channel initialization model;

[0058] S30. Based on the initial estimates of the dense multipath components and noise parameters, optimize and update the mirror path parameters and dense multipath component parameters in the propagation channel initialization model using the maximum likelihood algorithm until the estimates of the mirror path parameters and dense multipath component parameters converge.

[0059] S40. The global optimal solution for the mirror path parameters is obtained through optimization.

[0060] This disclosure provides a joint estimation method for mirror path and dense multipath components, which includes the following steps:

[0061] S10. Construct a propagation channel initialization model based on mirror path and dense multipath components.

[0062] In this embodiment, the propagation channel (h) is initialized and modeled as a mirror path component S(θ). sp Dense multipath components (DMC, D(θ)) dmc The propagation channel initialization model, based on the mirror path and dense multipath components, is thus represented by the superposition of the mirror path and noise parameter (n).

[0063]

[0064] In the formula, M and K represent the number of antennas and subcarriers, respectively. DMC describes the random portion of the propagation channel, assumed to consist of a large number of individual weak signal components that cannot be individually estimated as plane waves, for example, due to underlying physical processes (scattering, wavefront curvature, etc.). Therefore, due to the central limit theorem, D(θ) dmc It is modeled as a zero-mean complex circular symmetric Gaussian random vector with a covariance matrix. Right now Assume the square of the measured noise is a random vector of Gaussian white noise. The variance is σ N 2 .

[0065] For simplicity, the DMC and noise are modeled together, forming a zero-mean complex Gaussian process with a covariance matrix. Rdan It features a Toplitz structure and includes additive white Gaussian noise (AWGN) from the measuring device:

[0066]

[0067] Therefore, the propagation channel model can be written in a more compact form, as follows:

[0068]

[0069] in,

[0070] S20. Calculate the initial estimates of dense multipath components and noise parameters in the propagation channel initialization model.

[0071] In this embodiment, the dense multipath components and noise parameter θ are calculated. dan The estimated value of the initial solution, θ dan Given a set of four parameters α0, α1, β d and τ d The initial solution for the dense multipath components and noise parameters is expressed as follows:

[0072] θ dan =[α0, α1, β] d , τ d ],

[0073] In the formula, θ dan Let α0 be the initial solution for the dense multipath components and noise parameters, α1 be the peak power of the dense multipath components, and β be the initial solution for the noise parameters. d For the coherence bandwidth of dense multipath components, τ d This is the time delay spread for dense multipath components.

[0074] Covariance matrix R in the frequency domain dan Represented as:

[0075] R dan =toep(κ(θ) dan ),κ(θ dan ) H ),

[0076] In the formula, toep is the Toeplitz operator, and κ(θ) dan () represents the sampled version of the power spectral density, expressed as:

[0077]

[0078] Introducing the nonparametric estimation of the covariance matrix:

[0079]

[0080] To determine the initial solution from The estimated value of the power delay curve (PDP) is calculated, where F is the normalized discrete Fourier transform (DFT) matrix:

[0081]

[0082] Furthermore, estimates of four parameters are extracted from the initial solution of the dense multipath component and noise parameters from the power delay curve estimates. If the impulse response is observed over a sufficiently long time, then... The estimated values ​​of the four elements are:

[0083]

[0084] In the formula, This is an estimate of the variance of the circularly normally distributed noise. This is an estimate of the peak power of the dense multipath components. This is an estimate of the coherence bandwidth of the dense multipath components. This is an estimate of the time delay spread of dense multipath components. This is an estimate of the power delay curve. Let h be the average power of the propagation channel.

[0085] S30. Based on the initial estimates of the dense multipath components and noise parameters, optimize and update the mirror path parameters and dense multipath component parameters in the propagation channel initialization model using the maximum likelihood algorithm until the estimates of the mirror path parameters and dense multipath component parameters converge.

[0086] In this embodiment, initial estimates of the mirror path parameters and dense multipath component parameters are initialized; the mirror path component S(θ) is gradually estimated and updated through iterative optimization using the maximum likelihood (Rimax) algorithm. sp ) eigenvalue mirror path parameters For channel parameters in 5G New Radio, the complete set of spatiotemporal parameters that needs to be noted is as follows:

[0087] Θ l =[h l ,τ l ,v l ],l=1,…, p ,

[0088] In the formula, L p h represents the total number of paths. l τ l and v lThese represent the complex gain, TOA, and Doppler shift for each arrival path, respectively. The parameter set is universal in both 5G uplink and downlink.

[0089] In this embodiment, the mirror path component S(θ) sp eigenvalues For time delay (τ), AOA (θ) AOA ) and complex gain (h l ), Figure 2 To compare the multipath parameter information estimated using the Rimax algorithm with the power distribution of multipath channels in a simulated line-of-sight (LOS) environment using a cluster delay line model (CDL-D) under different time delays (TOA) and angles of arrival (AOA), we present the multipath parameter information and the power distribution of multipath channels in a simulated line-of-sight (LOS) environment using a cluster delay line model (CDL-D). Figure 2 This paper demonstrates the power distribution of multipath channels in a LOS environment and the effectiveness of the Rimax algorithm in multipath parameter estimation. Based on the multipath parameter information estimated using the Rimax algorithm shown in the figure, including the TOA, AOA, and complex gain of each path, iterative optimization of the Rimax algorithm can accurately estimate the parameters of mirror paths and dense multipath components, significantly improving the accuracy of channel estimation.

[0090] Given the channel model θ sp and θ dan The parameters and probability density function of the propagation channel h are:

[0091]

[0092] After removing the constant term from the probability density function above, the log-likelihood function is:

[0093]

[0094] Due to the parameter vector θ sp and θ dan It is an independent set of parameters. The spatial alternation generalized expectation-maximization algorithm (SAGE) is used to alternately maximize the above log-likelihood function. By maximizing the log-likelihood function, the mirror path parameters and dense multipath component parameters are optimized alternately. The maximized log-likelihood function is expressed as:

[0095]

[0096] In the formula, To maximize the initial estimate of the mirror path component parameters of the log-likelihood function, To maximize the initial estimates of the dense multipath components and noise parameters of the log-likelihood function, R dan θ is the joint covariance matrix of dense multipath components and noise parameters. sp For the mirror path component S(θ) spThe eigenvalues ​​of ), where H represents the conjugate transpose.

[0097] Select parameter subset θ sp and θ dan The objective function is then alternately maximized on these subsets to solve the aforementioned joint maximization problem. The estimates obtained above are then used... That is, the parameter θ is already known. dan Therefore, the maximization problem can be simplified to:

[0098]

[0099] The general structure S(θ) of the model for the contribution of the concentrated propagation path to the channel is:

[0100] S(θ)=B(Θ l )γ,

[0101] In the formula, the matrix-valued function B(Θ) l ) represents the concatenated description of channel information, and γ represents the linear path weight.

[0102] B(Θ l ) by L p Each signal component b(Θ) l Composed of, and represented as:

[0103]

[0104] In the formula, p l These are the l-th reference signal and the base station index, respectively. These are vectors representing the measurement time and frequency, respectively. This represents the Kronecker product operator.

[0105] Since the parameter γ is a linear parameter, in a given parameter set In this case, The problem of maximizing the log-likelihood function can be solved directly. For any The best linear unbiased estimator (BLUE) can be used to estimate the value.

[0106]

[0107] Furthermore, the estimated mirror component It can be rebuilt as:

[0108]

[0109] If the random component of the MIMO channel observations is a zero-mean circular Gaussian independent and identically distributed process, then we can... The solution further simplifies to a classic nonlinear least squares problem. Then, it becomes searching for the error hS(θ). sp Minimize The problem is about the value. This can also be viewed as minimizing the Euclidean norm, and updating the mirror path parameters using a nonlinear least squares method can be expressed as:

[0110]

[0111] S40. The global optimal solution for the mirror path parameters is obtained through optimization.

[0112] In this embodiment, the global optimal solution is obtained through optimization. It also outputs the optimal mirror path parameters.

[0113] Figure 3 The diagram shows a comparison of time delay (TOA) obtained using the estimation method described in the embodiments of this disclosure with different signal-to-noise ratios in the cluster delay line model (CDL-D). Figure 4 The diagram shows a comparison of the angle of arrival (AOA) obtained using the estimation method described in this disclosure with and without the method, at different signal-to-noise ratios in the cluster delay line model (CDL-D). Figure 3 , Figure 4 The upper curve represents the eigenvalues ​​estimated without using the Rimax algorithm, while the lower curve represents the eigenvalues ​​estimated using the Rimax algorithm. The Cramer-Rao lower bound (CRLB) is the theoretical lower limit of the estimated variance. The comparison shows that, under different signal-to-noise ratio (SNR) conditions, the channel estimation method based on the Rimax algorithm provided in this disclosure can significantly improve the estimation accuracy of TOA and AOA. Especially in low SNR environments, traditional methods have larger TOA and AOA estimation errors, while the method in this disclosure still maintains high estimation accuracy. This demonstrates the superiority of the method in this disclosure in TOA and AOA estimation, especially under low SNR conditions, significantly improving the accuracy and robustness of channel estimation. By effectively jointly modeling mirror paths and dense multipath components, the accuracy of channel eigenvalues ​​is significantly improved.

[0114] This disclosure proposes a joint estimation method for mirror paths and dense multipath components in wireless channels based on the Rimax algorithm. By jointly modeling mirror paths and dense multipath components, parameter estimation is performed iteratively based on the Rimax algorithm, effectively addressing noise and multipath interference. This overcomes the shortcomings of traditional channel estimation methods that treat dense multipath components as noise, leading to decreased estimation accuracy. This disclosure also proposes an initial solution estimation method based on power delay curves (PDPs), which can quickly estimate the initial parameters of dense multipath components and noise, providing a good starting point for subsequent iterative optimization. This disclosure is applicable to multiple-input multiple-output (MIMO) systems and orthogonal frequency division multiplexing (OFDM) systems, effectively handling channel estimation problems in multi-antenna and multi-carrier environments.

[0115] Example 2

[0116] As another aspect of the embodiments of this disclosure, a joint estimation system 100 for mirror path and dense multipath components is also provided, such as... Figure 5 As shown, it includes:

[0117] Propagation channel initialization model construction module 1, which constructs a propagation channel initialization model based on mirror path and dense multipath components;

[0118] Module 2, which obtains the initial estimates of parameters, calculates the initial estimates of dense multipath components and noise parameters in the propagation channel initialization model.

[0119] The parameter optimization and update module 3 optimizes and updates the mirror path parameters and dense multipath component parameters in the propagation channel initialization model using the maximum likelihood algorithm based on the initial estimates of the dense multipath components and noise parameters, until the estimates of the mirror path parameters and dense multipath component parameters converge.

[0120] The optimal mirror path parameter acquisition module 4 obtains the globally optimal solution for the mirror path parameters through optimization.

[0121] Without causing contradictions, the above-described modules in the system of the present disclosure embodiments can implement any of the above-described methods.

[0122] Based on the description of the above embodiments, it can be seen that the embodiments of this disclosure can achieve the following technical effects:

[0123] 1) The proposed method for joint estimation of wireless channel mirror path and dense multipath component based on the Rimax algorithm can make full use of mirror path and dense multipath component information, and has many advantages such as high channel estimation accuracy, good algorithm robustness, low computational complexity and wide applicability.

[0124] 2) This disclosure proposes an initial solution estimation method based on power delay curve (PDP), which can quickly estimate the initial parameters of dense multipath components and noise, providing a good starting point for subsequent iterative optimization.

[0125] 3) This disclosure effectively addresses noise and multipath interference by jointly modeling mirror paths and dense multipath components and performing parameter estimation based on the Rimax algorithm through iterative optimization.

[0126] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to use the aforementioned joint estimation method for mirror paths and dense multipath components. The electronic device can be provided as a terminal, a server, or other type of device.

[0127] This disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the aforementioned joint estimation method for mirror paths and dense multipath components. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0128] Those skilled in the art will understand that, in the above-described method and system for joint estimation of mirror path and dense multipath components in specific embodiments, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0129] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0130] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A joint estimation method for mirror path and dense multipath components, characterized in that, Includes the following steps: S10. Construct a propagation channel initialization model based on mirror path and dense multipath components; S20. Calculate the initial estimates of dense multipath components and noise parameters in the propagation channel initialization model; S30. Based on the initial estimates of the dense multipath components and noise parameters, optimize and update the mirror path parameters and dense multipath component parameters in the propagation channel initialization model using the maximum likelihood algorithm until the estimates of the mirror path parameters and dense multipath component parameters converge. The mirror path parameters and dense multipath component parameters are optimized and updated using the maximum likelihood algorithm, including: Initialize the initial estimates of the mirror path parameters and dense multipath component parameters; The mirror path parameters and dense multipath component parameters are alternately optimized by maximizing the log-likelihood function. Update the mirror path parameters using a nonlinear least squares method; S40. The global optimal solution for the mirror path parameters is obtained through optimization.

2. The method according to claim 1, characterized in that, A propagation channel initialization model is constructed based on mirror paths and dense multipath components. The propagation channel initialization model is expressed as follows: ， In the formula, For propagation channels, M , K These are the number of antennas and the number of subcarriers, respectively. For mirror path components, For dense multipath components, This is a noise parameter.

3. The method according to claim 2, characterized in that, Calculate the initial estimates of dense multipath components and noise parameters in the propagation channel initialization model, including: obtaining the initial estimates of dense multipath components and noise parameters by calculating the estimates of the initial solutions of dense multipath components and noise parameters; The initial solution for the dense multipath components and noise parameters is expressed as: ， In the formula, This is the initial solution for dense multipath components and noise parameters. Let Variance be the variance of the circularly normally distributed noise. For the peak power of dense multipath components, For the coherent bandwidth of dense multipath components, This is the time delay spread for dense multipath components.

4. The method according to claim 3, characterized in that, Calculating estimates of the initial solutions for dense multipath components and noise parameters also includes: Calculate the estimated value of the power delay curve; extract the estimated values ​​of four parameters from the initial solution of the dense multipath component and noise parameter from the estimated value of the power delay curve; ， ， ， ， In the formula, This is an estimate of the variance of the circularly normally distributed noise. This is an estimate of the peak power of the dense multipath components. This is an estimate of the coherence bandwidth of the dense multipath components. This is an estimate of the time delay spread of dense multipath components. This is an estimate of the power delay curve. For propagation channel The average power.

5. The method according to claim 3, characterized in that, By alternately optimizing the mirror path parameters and dense multipath component parameters by maximizing the log-likelihood function, the maximized log-likelihood function is expressed as: ， In the formula, To maximize the initial estimate of the mirror path component parameters of the log-likelihood function, To maximize the initial estimates of the dense multipath components and noise parameters of the log-likelihood function, The joint covariance matrix of dense multipath components and noise parameters. For mirror path components eigenvalues, H This indicates the conjugate transpose.

6. The method according to claim 5, characterized in that, The mirror path parameters are updated using the nonlinear least squares method, expressed as: 。 7. A joint estimation system for mirror path and dense multipath components, characterized in that, include: The propagation channel initialization model construction module constructs a propagation channel initialization model based on mirror paths and dense multipath components. The initial parameter estimation module calculates the initial estimates of dense multipath components and noise parameters in the propagation channel initialization model. The parameter optimization and update module optimizes and updates the mirror path parameters and dense multipath component parameters in the propagation channel initialization model based on the initial estimates of the dense multipath components and noise parameters using the maximum likelihood algorithm, until the estimates of the mirror path parameters and dense multipath component parameters converge. The mirror path parameters and dense multipath component parameters are optimized and updated using the maximum likelihood algorithm, including: Initialize the initial estimates of the mirror path parameters and dense multipath component parameters; The mirror path parameters and dense multipath component parameters are alternately optimized by maximizing the log-likelihood function. Update the mirror path parameters using a nonlinear least squares method; The optimal mirror path parameter acquisition module obtains the globally optimal solution for the mirror path parameters through optimization.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the joint estimation method of mirror path and dense multipath components as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the joint estimation method of mirror path and dense multipath components as described in any one of claims 1 to 6.

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