Mirror surface path and dense multipath component joint estimation method and system

Abstract

The invention relates to a mirror surface path and dense multipath component joint estimation method and system. The method comprises the following steps: constructing a propagation channel initialization model based on a mirror surface path and a dense multipath component; calculating initial estimation values of dense multipath components and noise parameters in the propagation channel initialization model; according to the initial estimation values of the dense multipath components and the noise parameters, mirror surface path parameters and dense multipath component parameters in the propagation channel initialization model are optimized and updated through a maximum likelihood algorithm until the estimation values of the mirror surface path parameters and the dense multipath component parameters are converged; and the globally optimal solution of the mirror surface path parameters is obtained through optimization solution. The method disclosed by the invention is high in channel estimation precision, good in robustness and low in calculation complexity in a high-noise and multi-path environment.

Description

Technical Field

[0001] The present disclosure relates to the field of positioning and navigation, and particularly to a method and system for jointly estimating specular paths and dense multipath components. Background Art

[0002] In a wireless communication system, 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, the Internet of Things, and millimeter-wave communication, the accuracy and robustness of channel estimation are particularly important. Traditional channel estimation methods usually assume that the channel consists of a few main specular paths and ignore the influence of dense multipath components. However, in an actual wireless communication environment, especially in a complex urban environment or an indoor scenario, dense multipath components occupy a large part of the channel response. These multipath components are usually composed of a large number of weak signals and cannot be individually modeled as plane waves, resulting in a significant decline in the performance of traditional channel estimation methods in complex environments. Existing channel estimation methods have insufficient modeling of dense multipath components when dealing with them and usually treat dense multipath components as noise, resulting in a decrease in the accuracy of channel estimation. In an environment with strong noise and multipath interference, the robustness of traditional methods is poor and they are easily affected by interference.

[0003] Therefore, there is a need to propose an innovative method that can effectively separate and estimate specular paths and dense multipath components to improve the accuracy of channel estimation. Summary of the Invention

[0004] To solve the defect that traditional channel estimation methods treat dense multipath components as noise, resulting in a decrease in estimation accuracy and other problems. The present disclosure proposes a method for jointly estimating specular paths and dense multipath components to solve the above problems.

[0005] According to one aspect of the present disclosure, there is provided a method for jointly estimating specular paths and dense multipath components, including:

[0006] S10. Construct an initial propagation channel model based on specular paths and dense multipath components;

[0007] S20. Calculate the initial estimated values of the dense multipath components and noise parameters in the initial propagation channel model;

[0008] S30. According to the initial estimated values of the dense multipath components and noise parameters, optimize and update the specular path parameters and dense multipath component parameters in the initial propagation channel model through the maximum likelihood algorithm until the estimated values of the specular path parameters and dense multipath component parameters converge;

[0009] S40. Obtain the global optimal solution of the mirror path parameters through optimization and solution.

[0010] Preferably, based on the mirror path and the dense multipath components, construct an initial propagation channel model, and the initial propagation channel model is expressed as:

[0011]

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

[0013] Preferably, calculate the initial estimated values of the dense multipath component and the noise parameter in the initial propagation channel model, including: obtaining the initial estimated values of the dense multipath component and the noise parameter by calculating the estimated values of the initial solutions of the dense multipath component and the noise parameter;

[0014] The initial solutions of the dense multipath component and the noise parameter are expressed as:

[0015] θ dan =[α0,α1,β d ,τ d ,

[0016] In the formula, θ dan is the initial solution of the dense multipath component and the noise parameter, α0 is the variance of the circular normal distribution noise, α1 is the peak power of the dense multipath component, β d is the coherence bandwidth of the dense multipath component, and τ d is the delay spread of the dense multipath component.

[0017] Preferably, calculating the estimated values of the initial solutions of the dense multipath component and the noise parameter further includes:

[0018] Calculating the estimated value of the power delay profile; extracting the estimated values of the four parameters in the initial solutions of the dense multipath component and the noise parameter from the estimated value of the power delay profile;

[0019]

[0020] In the formula, is the estimated value of the variance of the circular normal distribution noise, is the estimated value of the peak power of the dense multipath component, is the estimated value of the coherence bandwidth of the dense multipath component, is the estimated value of the delay spread of the dense multipath component, is the estimated value of the power delay profile, is the average power of the propagation channel h.

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

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

[0023] Alternately optimizing the mirror path parameters and the dense multipath component parameters by maximizing the log-likelihood function;

[0024] Updating the mirror path parameters by the nonlinear least squares method.

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

[0026]

[0027] In the formula, is the initial estimate of the mirror path component parameter of the maximized log-likelihood function, is the initial estimate of the dense multipath component and noise parameter of the maximized log-likelihood function, R dan is the joint covariance matrix of the dense multipath component and the noise parameter, θ sp is the eigenvalue of the mirror path component S(θ sp ), and H represents the conjugate transpose.

[0028] Preferably, the mirror path parameters are updated by the nonlinear least squares method, which is expressed as:

[0029]

[0030] According to one aspect of the present disclosure, a joint estimation system for a mirror path and a dense multipath component is provided, including:

[0031] A propagation channel initialization model construction module, which constructs a propagation channel initialization model based on the mirror path and the dense multipath component;

[0032] An initial estimate acquisition module for parameters, which calculates the initial estimates of the dense multipath component and the noise parameter in the propagation channel initialization model;

[0033] A parameter optimization and update module, which optimizes and updates the mirror path parameters and the dense multipath component parameters in the propagation channel initialization model by the maximum likelihood algorithm according to the initial estimates of the dense multipath component and the noise parameter until the estimated values of the mirror path parameters and the dense multipath component parameters converge;

[0034] An optimal mirror path parameter acquisition module, which obtains the global optimal solution of the mirror path parameter through optimization and solution.

[0035] According to one aspect of the present disclosure, there is provided an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein, the processor is configured to: execute the above-mentioned joint estimation method for specular paths and dense multipath components.

[0036] According to one aspect of the present disclosure, there is provided a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the above-mentioned joint estimation method for specular paths and dense multipath components is implemented.

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

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

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

[0040] 3) The present disclosure jointly models specular paths and dense multipath components, and performs parameter estimation based on iterative optimization of the Rimax algorithm, effectively coping with noise and multipath interference.

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

[0042] According to the following detailed description of exemplary embodiments with reference to the accompanying drawings, other features and aspects of the present disclosure will become clear. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used to explain the technical solutions of the present disclosure.

[0044] Figure 1 Show a flowchart of a joint estimation method for specular paths and dense multipath components;

[0045] Figure 2 Show the power distribution diagrams of multipath parameter information estimated using the Rimax algorithm and a multipath channel simulated in a CDL-D model for LOS environment transmission under different TOA and AOA;

[0046] Figure 3Shows the comparison chart of TOA obtained with and without using the estimation method in the embodiments of the present disclosure at different signal-to-noise ratios in the CDL-D model;

[0047] Figure 4 Shows the comparison chart of the angle of arrival (AOA) obtained with and without using the estimation method in the embodiments of the present disclosure at different signal-to-noise ratios in the cluster delay line model (CDL-D);

[0048] Figure 5 Shows a block diagram of a joint estimation system for specular paths and dense multipath components. Detailed implementation manners

[0049] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

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

[0051] The term "and / or" herein merely describes an association relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set composed of A, B, and C.

[0052] In addition, for better illustration of the present disclosure, numerous specific details are given in the following detailed implementation manners. Those skilled in the art should understand that the present disclosure can be implemented without some specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art are not described in detail so as to highlight the gist of the present disclosure.

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

[0054] Embodiment 1

[0055] Based on the above idea, the present invention proposes a joint estimation method for specular paths and dense multipath components. Figure 1 The flowchart of a joint estimation method for specular paths and dense multipath components is shown. The method includes:

[0056] S10. Construct an initial model of the propagation channel based on the specular path and the dense multipath components;

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

[0058] S30. According to the initial estimated values of the dense multipath components and the noise parameters, optimize and update the specular path parameters and the dense multipath component parameters in the initial model of the propagation channel through the maximum likelihood algorithm until the estimated values of the specular path parameters and the dense multipath component parameters converge;

[0059] S40. Obtain the global optimal solution of the specular path parameters through optimization and solution.

[0060] The embodiments of the present disclosure provide a joint estimation method for specular paths and dense multipath components, specifically the following steps:

[0061] S10. Construct an initial model of the propagation channel based on the specular path and the dense multipath components.

[0062] In this embodiment, the propagation channel (h) is initially modeled as the superposition of a specular path component S(θ sp ), a dense multipath component (DMC, D(θ dmc )) and a noise parameter (n). Therefore, the initial model of the propagation channel constructed based on the specular path and the dense multipath components is expressed as:

[0063]

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

[0065] For simplicity, the DMC and the noise are modeled together to form a zero-mean complex Gaussian process with a covariance matrix Rdan has a Toeplitz structure and includes additive white Gaussian noise (AWGN) from a measurement device:

[0066]

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

[0068]

[0069] where

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

[0071] In this embodiment, the initial estimates of the dense multipath components and the noise parameter θ dan initial solution, θ dan is composed of a set of four parameters α0, α1, β d and τ d The initial solution of the dense multipath components and the noise parameter is expressed as:

[0072] θ dan = [α0, α1, β d , τ d ,

[0073] where θ dan is the initial solution of the dense multipath components and the noise parameter, α0 is the variance of the circular normal distribution noise, α1 is the peak power of the dense multipath component, β d is the coherence bandwidth of the dense multipath component, and τ d is the delay spread of the dense multipath component.

[0074] The covariance matrix R dan in the frequency domain is expressed as:

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

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

[0077]

[0078] The nonparametric estimate of the covariance matrix is introduced as:

[0079]

[0080] To determine the initial solution From calculate the estimated value of the power delay profile (PDP), where F is the normalized discrete Fourier transform (DFT) matrix:

[0081]

[0082] Furthermore, extract the estimated values of four parameters from the initial solution of the dense multipath components and noise parameters. If the impulse response is observed over a sufficiently long time, then the estimated values of the four elements of

[0083]

[0084] In the formula, is the estimated value of the circular normal distribution noise variance, is the estimated value of the peak power of the dense multipath component, is the estimated value of the coherence bandwidth of the dense multipath component, is the estimated value of the delay spread of the dense multipath component, is the estimated value of the power delay profile, is the average power of the propagation channel h.

[0085] S30. According to the initial estimated values of the dense multipath components and noise parameters, optimize and update the specular path parameters and dense multipath component parameters in the propagation channel initialization model through the maximum likelihood algorithm until the estimated values of the specular path parameters and dense multipath component parameters converge.

[0086] In this embodiment, the initial estimated values of the specular path parameters and dense multipath component parameters are initialized; through iterative optimization by the maximum likelihood (Rimax) algorithm, gradually estimate and update the eigenvalue specular path parameters of the specular path component S(θ sp ) For the channel parameters in 5G New Radio, the complete set of spatio-temporal parameters that need to be noted is:

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

[0088] In the formula, L p is the total number of multipaths, h l ,τ l and v lrespectively represent the complex gains, TOAs, and Doppler frequency shifts of each arrival path. Whether in the 5G uplink or downlink, the parameter set is common.

[0089] In this embodiment, the eigenvalue of the specular path component S(θ sp ) is the time delay (τ), AOA (θ AOA ), and complex gain (h l ), Figure 2 is the power distribution diagram of the multi-path parameter information estimated using the Rimax algorithm and the multi-path channel simulated in a cluster delay line model (CDL-D) for the LOS environment transmission at different time delays (TOA) and angles of arrival (AOA), Figure 2 showing the power distribution of the multi-path channel in the LOS environment and the effectiveness of the Rimax algorithm in multi-path parameter estimation. According to the multi-path parameter information estimated using the Rimax algorithm shown in the figure, including the TOA, AOA, and complex gain of each path, etc., through the iterative optimization of the Rimax algorithm, the parameters of the specular path and the dense multi-path components can be accurately estimated, significantly improving the accuracy of channel estimation.

[0090] Given the parameters of the channel models θ sp and θ dan , the probability density function of the propagation channel h is:

[0091]

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

[0093]

[0094] Since the parameter vectors θ sp and θ dan are independent parameter sets, the spatial alternating generalized expectation maximization algorithm (SAGE) is used to alternately maximize the above log-likelihood function. By maximizing the log-likelihood function, the specular path parameters and the dense multi-path component parameters are alternately optimized. The maximization of the log-likelihood function is expressed as:

[0095]

[0096] where is the initial estimate of the specular path component parameters for maximizing the log-likelihood function, is the initial estimate of the dense multi-path component and noise parameters for maximizing the log-likelihood function, R dan is the joint covariance matrix of the dense multi-path component and noise parameters, and θ sp is the specular path component S(θ spThe eigenvalues of [], where H represents the conjugate transpose.

[0097] Select a subset of parameters θ sp and θ dan , and alternately maximize the objective function for these subsets to solve the above joint maximization problem. Using the estimated values obtained above 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] where the matrix-valued function B(Θ l ) is the concatenated description of the channel information, and γ is the linear path weight.

[0102] B(Θ l ) consists of L p signal components b(Θ l ), expressed as:

[0103]

[0104] where p l are the l-th reference signal and base station index respectively, are vectors of the measurement time and frequency respectively, represents the Kronecker product operator.

[0105] Since the parameter γ is a linear parameter, given the parameter set , the maximization problem of the log-likelihood function can be directly solved. For any using the Best Linear Unbiased Estimate (BLUE), can be estimated to obtain

[0106]

[0107] Furthermore, the estimated specular component can be reconstructed as:

[0108]

[0109] If the random part of the MIMO channel observation is a zero-mean circular Gaussian independent and identically distributed process, then The solution is further simplified to a classical non - linear least - squares problem. Then, it becomes a problem of searching for the sp value that minimizes the error h - S(θ ). This can also be regarded as minimizing the Euclidean norm. The mirror - path parameters are updated by non - linear least - squares method as follows:

[0110]

[0111] S40. Obtain the global optimal solution of the mirror - path parameters through optimization.

[0112] In this embodiment, the global optimal solution is obtained through optimization and the best mirror - path parameter result is output

[0113] Figure 3 The figure shows the comparison graph of the time - of - arrival (TOA) obtained by using and not using the estimation method in the embodiment of the present disclosure in the cluster delay - line model (CDL - D) under different signal - to - noise ratios. Figure 4 The figure shows the comparison graph of the angle - of - arrival (AOA) obtained by using and not using the estimation method in the embodiment of the present disclosure in the cluster delay - line model (CDL - D) under different signal - to - noise ratios. Figure 3 、 Figure 4 In the above figure, the upper curve is the eigenvalue estimated without using the Rimax algorithm, and the lower curve is the eigenvalue estimated using the Rimax algorithm. The Cramer - Rao lower bound (CRLB) is the theoretical lower limit of the estimation variance. Through comparison, it can be seen that under different signal - to - noise ratio conditions, by using the channel estimation method based on the Rimax algorithm provided in the embodiment of the present disclosure, the estimation accuracy of TOA and AOA can be significantly improved. Especially in the low - signal - to - noise - ratio environment, the TOA and AOA estimation errors of the traditional method are large, while the method in the embodiment of the present disclosure can still maintain a high estimation accuracy. It proves the superiority of the method in the embodiment of the present disclosure in TOA and AOA estimation. Especially under low - signal - to - noise - ratio conditions, it can significantly improve the accuracy and robustness of channel estimation. By effectively jointly modeling the mirror path and dense multipath components, the accuracy of channel eigenvalues is significantly improved.

[0114] The embodiments of the present disclosure propose a method for jointly estimating the specular path and dense multipath components of a wireless channel based on the Rimax algorithm. By jointly modeling the specular path and dense multipath components and iteratively optimizing based on the Rimax algorithm for parameter estimation, it effectively copes with noise and multipath interference, and solves the defect that traditional channel estimation methods treat dense multipath components as noise, resulting in a decrease in estimation accuracy. The embodiments of the present disclosure propose an initial solution estimation method based on the power delay profile (PDP), which can quickly estimate the initial parameters of dense multipath components and noise, providing a good starting point for subsequent iterative optimization. The embodiments of the present disclosure are applicable to multiple-input multiple-output (MIMO) systems and orthogonal frequency division multiplexing (OFDM) systems, and can effectively handle channel estimation problems in multi-antenna and multi-carrier environments.

[0115] Embodiment 2

[0116] As another aspect of the embodiments of the present disclosure, there is also provided a joint estimation system 100 for the specular path and dense multipath components, as Figure 5 shown, including:

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

[0118] An initial estimated value acquisition module 2 for parameters, which calculates the initial estimated values of the dense multipath component and noise parameters in the propagation channel initialization model;

[0119] A parameter optimization and update module 3, which optimizes and updates the specular path parameters and dense multipath component parameters in the propagation channel initialization model according to the initial estimated values of the dense multipath component and noise parameters until the estimated values of the specular path parameters and dense multipath component parameters converge;

[0120] An optimal specular path parameter acquisition module 4, which obtains the global optimal solution of the specular path parameters through optimization and solution.

[0121] Without contradiction, the above modules in the system of the embodiments of the present disclosure can implement any of the above implementation manners of the method.

[0122] Based on the description of the above embodiments, the embodiments of the present disclosure can achieve the following technical effects:

[0123] 1) The method for jointly estimating the specular path and dense multipath components of a wireless channel proposed by the present disclosure can make full use of the information of the specular path and dense multipath components, and has many advantages such as high channel estimation accuracy, good algorithm robustness, low computational complexity, and wide applicability.

[0124] 2) The present disclosure proposes an initial solution estimation method based on the power delay profile (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) The present disclosure jointly models the specular path and dense multipath components, and performs parameter estimation based on the iterative optimization of the Rimax algorithm, effectively coping with noise and multipath interference.

[0126] An embodiment of the present disclosure also proposes an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein, the processor is configured to perform the above-mentioned joint estimation method for the specular path and dense multipath components. Among them, the electronic device can be provided as a terminal, a server or other forms of devices.

[0127] An embodiment of the present disclosure also proposes a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the above-mentioned joint estimation method for the specular path and dense multipath components is implemented. The computer-readable storage medium can be a non-volatile computer-readable storage medium.

[0128] Those skilled in the art can understand that in the above-mentioned joint estimation method and system for the specular path and dense multipath components in the specific implementation manner, the writing order of each step does not mean a strict execution order and does not constitute any limitation to the implementation process. The specific execution order of each step should be determined according to its function and possible internal logic.

[0129] The flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of an instruction, and the module, program segment, or part of an instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the blocks may also occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0130] The embodiments of the present disclosure have been described above. The above description is exemplary and not exhaustive, and is also not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the technology in the market, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. A method for jointly estimating a mirror path and dense multipath components, characterized in that: The steps include: S10, constructing a propagation channel initialization model based on the mirror path and dense multipath components; S20, calculating initial estimates of dense multipath components and noise parameters in the propagation channel initialization model; S30, optimizing and updating the mirror path parameters and the dense multipath component parameters in the propagation channel initialization model by a maximum likelihood algorithm according to the initial estimated values ​​of the dense multipath component and the noise parameter, until the estimated values ​​of the mirror path parameters and the dense multipath component parameters converge; S40, obtaining the global optimal solution of the mirror path parameters through optimization.

2. The method according to claim 1, characterized in that A propagation channel initialization model is constructed based on the mirror path and the dense multipath component. The propagation channel initialization model is expressed as: Where h is the propagation channel, M and K are the number of antennas and subcarriers respectively, S(θ sp ) is the mirror path component, D(θ dmc ) is the dense multipath component, and n is the noise parameter.

3. The method according to claim 2, characterized in that Calculating initial estimated values ​​of dense multipath components and noise parameters in the propagation channel initialization model, including: obtaining initial estimated values ​​of dense multipath components and noise parameters by calculating estimated values ​​of initial solutions of dense multipath components and noise parameters; The initial solution of the dense multipath components and noise parameters is expressed as: i dan =[α0, α1, β d ,t d ], In the formula, θ dan is the initial solution of the dense multipath component and noise parameters, α0 is the variance of the circular normal distribution noise, α1 is the peak power of the dense multipath component, β d is the coherence bandwidth of dense multipath components, τ d is the delay spread of dense multipath components.

4. The method according to claim 3, characterized in that Calculates estimates of the dense multipath components and initial solutions for noise parameters, including: Calculating an estimated value of the power delay profile; extracting estimated values ​​of four parameters in an initial solution of dense multipath components and noise parameters from the estimated value of the power delay profile; In the formula, is an estimate of the variance of the circular normal distribution noise, is the estimated value of the peak power of the dense multipath component, is the estimated value of the coherence bandwidth of dense multipath components, is the estimated value of the delay spread of dense multipath components, is the estimated value of the power delay curve, is the average power of the propagation channel h.

5. The method according to claim 1, characterized in that Optimizing and updating the mirror path parameters and dense multipath component parameters by a maximum likelihood algorithm includes: Initialize the initial estimates of the specular path parameters and the dense multipath component parameters; The specular path parameters and the dense multipath component parameters are optimized alternately by maximizing the log-likelihood function; The mirror path parameters are updated via a nonlinear least squares method.

6. The method according to claim 5, characterized in that The specular path parameters and the dense multipath component parameters are alternately optimized by maximizing the log-likelihood function, which is expressed as: In the formula, is the initial estimate of the specular path component parameters that maximizes the log-likelihood function, To maximize the initial estimates of the dense multipath components and noise parameters of the log-likelihood function, R dan is the joint covariance matrix of dense multipath components and noise parameters, θ sp is the mirror path component S(θ sp ), and H represents the conjugate transpose.

7. The method according to claim 6, characterized in that The mirror path parameters are updated by nonlinear least squares method, which is expressed as:

8. A system for jointly estimating a mirror path and dense multipath components, characterized in that: include: A propagation channel initialization model building module, which builds a propagation channel initialization model based on the mirror path and dense multipath components; An initial estimated value acquisition module for parameters, which calculates initial estimated values ​​of dense multipath components and noise parameters in the propagation channel initialization model; A parameter optimization and updating module, which optimizes and updates the mirror path parameters and the dense multipath component parameters in the propagation channel initialization model by a maximum likelihood algorithm according to the initial estimated values ​​of the dense multipath component and the noise parameter, until the estimated values ​​of the mirror path parameters and the dense multipath component parameters converge; The best mirror path parameter acquisition module obtains the global optimal solution of the mirror path parameters through optimization.

9. 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, the method for jointly estimating the mirror path and dense multipath components according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for jointly estimating the mirror path and dense multipath components described in any one of claims 1 to 7 is implemented.

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