Channel parameter estimation method, device, equipment, medium and product

Abstract

The invention relates to a channel parameter estimation method and device, equipment, a medium and a product, and the method comprises the steps: obtaining a frequency domain reference signal and a receiving signal of a to-be-estimated channel, and determining a first channel response of the to-be-estimated channel based on the frequency domain reference signal and the receiving signal; performing time domain sliding window search on the first channel response based on a minimum mean square error criterion and a maximum signal-to-noise ratio criterion to obtain a first parameter estimation result and symbol timing synchronization information of the to-be-estimated channel; performing statistical estimation processing on the first channel response based on the first parameter estimation result and the symbol timing synchronization information to obtain a second channel response; and performing frequency domain sliding window search based on a minimum mean square error criterion on the second channel response to obtain a target parameter estimation result after the first parameter estimation result is optimized. The accuracy of channel parameter estimation and the robustness of the system can be effectively improved on the premise of not depending on ideal synchronization.

Description

Technical Field

[0001] This disclosure relates to the field of wireless communication technology, and in particular to a method, apparatus, device, medium, and product for estimating channel parameters. Background Technology

[0002] In the field of wireless communication technology, channel estimation is a key link in ensuring communication quality. The performance of the channel directly depends on the accurate estimation of the channel parameters. Commonly used channel estimation methods include least square (LS) estimation, minimum mean square error (MMSE) estimation, and discrete fourier transform (DFT) estimation.

[0003] However, traditional channel parameter estimation methods are usually based on the assumption that the system has achieved ideal synchronization, which is fundamentally contradictory to the inherent characteristics of the synchronization state in the actual communication environment. This leads to a significant limitation on the estimation performance and overall robustness of traditional channel parameter estimation methods in practical applications. Summary of the Invention

[0004] This disclosure provides a method, apparatus, device, medium, and product for estimating channel parameters.

[0005] According to a first aspect of this disclosure, a method for estimating channel parameters is provided, the method comprising:

[0006] Acquire the frequency domain reference signal and the received signal of the channel to be estimated, and determine the first channel response of the channel to be estimated based on the frequency domain reference signal and the received signal; Based on the minimum mean square error criterion and the maximum signal-to-noise ratio criterion, a time-domain sliding window search is performed on the response of the first channel to obtain the first parameter estimation result and symbol timing synchronization information of the channel to be estimated. Based on the first parameter estimation result and the symbol timing synchronization information, the first channel response is statistically estimated to obtain the second channel response; A frequency domain sliding window search based on the minimum mean square error criterion is performed on the second channel response to obtain the target parameter estimation result after optimizing the first parameter estimation result.

[0007] Further, determining the first channel response of the channel to be estimated based on the frequency domain reference signal and the received signal includes: Least-squared channel estimation is performed on the frequency domain reference signal and the received signal to obtain the initial frequency domain channel estimate; Perform an inverse Fourier transform on the frequency domain initial channel estimate to obtain the time domain initial channel estimate; The initial channel estimate in the time domain is determined as the first channel response.

[0008] Further, the step of performing a time-domain sliding window search on the first channel response based on the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to obtain the first parameter estimation result and symbol timing synchronization information of the channel to be estimated includes: The first channel response is subjected to a time-domain sliding window search by jointly optimizing the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to determine the optimal time-domain sliding window. Based on the time-domain optimal sliding window, the first parameter estimation result and the symbol timing synchronization information are determined.

[0009] Furthermore, the method of jointly optimizing the first channel response using the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to perform a time-domain sliding window search and determine the optimal time-domain sliding window includes: The power delay distribution is determined based on the first channel response; Based on the minimum mean square error criterion, the starting point of the sliding window corresponding to the impulse response length of at least one candidate channel on the power delay distribution is searched to obtain the time-domain optimal candidate sliding window set. Based on the maximum signal-to-noise ratio criterion, the optimal sliding window in the time domain is determined from the set of optimal candidate sliding windows in the time domain.

[0010] Furthermore, the first parameter estimation result includes: the delay spread and noise variance of the channel to be estimated; The step of determining the first parameter estimation result and the symbol timing synchronization information based on the time-domain optimal sliding window includes: The time delay spread is determined based on the window length of the time-domain optimal sliding window; The noise variance is determined based on the power in the region outside the optimal sliding window in the time domain within the power delay distribution. The symbol timing synchronization information is determined based on the starting point of the time-domain optimal sliding window.

[0011] Further, the step of performing statistical estimation processing on the first channel response based on the first parameter estimation result and the symbol timing synchronization information to obtain the second channel response includes: Based on the first parameter estimation result and the symbol timing synchronization information, the first channel response is subjected to target processing to obtain the second channel response. The target processing includes minimum mean square error filtering and / or discrete Fourier transform smoothing.

[0012] According to a second aspect of this disclosure, a channel parameter estimation apparatus is provided, the apparatus comprising: The determination module is used to acquire the frequency domain reference signal and the received signal of the channel to be estimated, and to determine the first channel response of the channel to be estimated based on the frequency domain reference signal and the received signal; The first search module is used to perform a time-domain sliding window search on the response of the first channel based on the minimum mean square error criterion and the maximum signal-to-noise ratio criterion, so as to obtain the first parameter estimation result and symbol timing synchronization information of the channel to be estimated. The processing module is used to perform statistical estimation processing on the first channel response based on the first parameter estimation result and the symbol timing synchronization information to obtain the second channel response; The second search module is used to perform a frequency domain sliding window search on the second channel response based on the minimum mean square error criterion to obtain the target parameter estimation result after optimizing the first parameter estimation result.

[0013] According to a third aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0014] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the methods described above.

[0015] According to a fifth aspect of this disclosure, a computer program product is provided. The computer program product includes a computer program that, when executed by a processor, implements the methods described above in this disclosure.

[0016] This disclosure provides a method, apparatus, device, medium, and product for estimating channel parameters. First, a frequency domain reference signal and a received signal of the channel to be estimated are acquired, and a first channel response of the channel to be estimated is determined based on the frequency domain reference signal and the received signal. Then, a time-domain sliding window search is performed on the first channel response based on the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to obtain the first parameter estimation result and symbol timing synchronization information of the channel to be estimated. Next, based on the first parameter estimation result and the symbol timing synchronization information, a statistical estimation process is performed on the first channel response to obtain a second channel response. Finally, a frequency domain sliding window search based on the minimum mean square error criterion is performed on the second channel response to obtain a target parameter estimation result optimized from the first parameter estimation result.

[0017] As described above, this embodiment employs a cascaded dual-domain sliding window estimation architecture. First, it determines the first channel response based on a frequency domain reference signal and the received signal. Then, it innovatively applies the minimum mean square error criterion and the maximum signal-to-noise ratio criterion jointly in the time domain to perform a sliding window search on the first channel response, solving for the first parameter estimation result and symbol timing synchronization information. This resolves the dilemma of channel parameter estimation and symbol synchronization in traditional methods. Subsequently, this embodiment uses the first parameter estimation result and symbol timing synchronization information to perform statistical estimation processing on the first channel response, obtaining a more accurate second channel response. Finally, this embodiment performs a sliding window search on the second channel response in the frequency domain based on the minimum mean square error criterion, thereby fine-tuning and optimizing the first parameter estimation result to obtain the target parameter estimation result. This embodiment can achieve joint high-precision estimation of channel parameters, noise variance, and timing information without requiring ideal symbol synchronization prior conditions, significantly improving the estimation performance and overall robustness of the channel parameter estimation method in practical applications. Attached Figure Description

[0018] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0019] Figure 1 A flowchart of a channel parameter estimation method provided for an exemplary embodiment of this disclosure; Figure 2 One of the flowcharts for a method of estimating channel parameters provided as another exemplary embodiment of this disclosure; Figure 3 A second flowchart of a method for estimating channel parameters provided as another exemplary embodiment of this disclosure; Figure 4 A third flowchart of a method for estimating channel parameters provided as another exemplary embodiment of this disclosure; Figure 5 A sliding window schematic diagram of channel impulse response provided as an exemplary embodiment of this disclosure; Figure 6 A flowchart of a method for estimating channel parameters provided as another exemplary embodiment of this disclosure; Figure 7 A schematic block diagram of the functional modules of a channel parameter estimation apparatus provided for an exemplary embodiment of the present disclosure; Figure 8 A structural block diagram of an electronic device provided as an exemplary embodiment of this disclosure; Figure 9 A structural block diagram of a computer system provided as an exemplary embodiment of this disclosure; Figure 10 A structural block diagram of a computer program product provided for an exemplary embodiment of this disclosure. Detailed Implementation

[0020] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0021] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0022] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc., used in this disclosure are only used to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0023] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0024] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0025] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0026] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0027] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understood that the above notification and user authorization process is merely illustrative and does not constitute a limitation on the implementation of this disclosure; other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0028] In one embodiment, such as Figure 1 As shown, a method for estimating channel parameters is provided, including the following steps: Step 101: Obtain the frequency domain reference signal and the received signal of the channel to be estimated, and determine the first channel response of the channel to be estimated based on the frequency domain reference signal and the received signal.

[0029] Here, the executing entity can acquire the frequency domain reference signal and the received signal of the channel to be estimated, and determine the first channel response of the channel to be estimated based on the frequency domain reference signal and the received signal. The channel to be estimated refers to the wireless transmission channel that needs to be parameter estimated. Its characteristics are usually described by the channel impulse response and include parameters that need to be estimated, such as delay spread and noise variance. The frequency domain reference signal refers to the pilot or training sequence signal known at the transmitting end and carried in the frequency domain. The received signal refers to the signal received at the receiving end and which contains the reference signal as well as noise and interference after being transmitted through the channel to be estimated. The first channel response refers to the preliminary estimation result of the channel impulse response obtained by the initial channel estimation method based on the frequency domain reference signal and the received signal.

[0030] In one possible embodiment, such as Figure 2 As shown, determining the first channel response of the channel to be estimated based on the frequency domain reference signal and the received signal includes the following steps: Step 1011: Perform least-squares channel estimation on the frequency domain reference signal and the received signal to obtain the initial frequency domain channel estimate.

[0031] Here, after obtaining the frequency domain reference signal and the received signal of the channel to be estimated, the executing entity can perform least-squares channel estimation on the frequency domain reference signal and the received signal to obtain the initial frequency domain channel estimate.

[0032] In one possible embodiment, the executing entity performs least square (LS) channel estimation on the frequency domain reference signal and the received signal to obtain an initial frequency domain channel estimate. Specifically, the least square channel estimate can be calculated using the following formula:

[0033] in, For frequency domain sample indices, and , For FFT points, For frequency domain reference signal, In order to receive signals, This is the initial channel estimation in the frequency domain.

[0034] Step 1012: Perform an inverse Fourier transform on the initial channel estimate in the frequency domain to obtain the initial channel estimate in the time domain.

[0035] Here, after obtaining the initial channel estimate in the frequency domain, the executing entity can perform an inverse fast fourier transform (IFFT) on the initial channel estimate in the frequency domain to obtain the initial channel estimate in the time domain.

[0036] In one possible embodiment, the executing entity performs an initial channel estimation in the frequency domain. Performing the inverse Fourier transform yields the initial channel estimate in the time domain. Specifically, the inverse Fourier transform can be calculated using the following formula:

[0037] in, For time-domain sample subscripts, and , For IFFT points, For the initial channel estimate in the time domain, it represents the preliminary time-domain sequence of the channel impulse response.

[0038] Step 1013: Determine the initial channel estimate in the time domain as the first channel response.

[0039] Here, the executing entity obtains the initial channel estimate in the time domain. Then, the initial channel estimation in the time domain is performed. The first channel response is determined to be the preliminary time-domain estimation result of the channel impulse response obtained by combining least squares estimation with inverse Fourier transform, which is used for subsequent time-domain sliding window search processing.

[0040] In this embodiment, firstly, the execution entity performs least-squares channel estimation on the frequency domain reference signal and the received signal to obtain the initial channel estimate in the frequency domain; then, the execution entity performs inverse Fourier transform on the initial channel estimate in the frequency domain to obtain the initial channel estimate in the time domain, and determines the initial channel estimate in the time domain as the first channel response.

[0041] As described above, in this embodiment, the executing entity obtains the initial channel estimate in the time domain and uses it as the first channel response by performing least squares estimation and inverse Fourier transform on the frequency domain reference signal and the received signal. This provides an accurate starting point for the subsequent sliding window search based on optimization criteria. Furthermore, this embodiment achieves a reliable conversion from the frequency domain received signal to the time domain channel response through explicit mathematical transformation, forming a complete initial channel estimation link and ensuring that the subsequent parameter estimation process has a reliable data foundation.

[0042] Step 102: Perform a time-domain sliding window search on the first channel response based on the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to obtain the first parameter estimation result and symbol timing synchronization information of the channel to be estimated.

[0043] Here, after determining the first channel response of the channel to be estimated based on the frequency domain reference signal and the received signal, the executing entity can perform a time-domain sliding window search on the first channel response based on the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to obtain the first parameter estimation result and symbol timing synchronization information of the channel to be estimated.

[0044] In one possible embodiment, such as Figure 3 As shown, a time-domain sliding window search is performed on the first channel response based on the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to obtain the first parameter estimation result and symbol timing synchronization information of the channel to be estimated, including the following steps: Step 301: Using a joint optimization method of minimum mean square error criterion and maximum signal-to-noise ratio criterion, a time-domain sliding window search is performed on the first channel response to determine the optimal time-domain sliding window.

[0045] Here, after determining the first channel response of the channel to be estimated based on the frequency domain reference signal and the received signal, the executing entity can use the method of joint optimization of the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to perform time-domain sliding window search on the first channel response and determine the optimal time-domain sliding window.

[0046] In one possible embodiment, such as Figure 4 As shown, a time-domain sliding window search is performed on the first channel response using a joint optimization method based on the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to determine the optimal time-domain sliding window. The steps include: Step 3011: Determine the power delay distribution based on the first channel response.

[0047] Here, after determining the first channel response of the channel to be estimated based on the frequency domain reference signal and the received signal, the executing entity can determine the power delay distribution based on the first channel response.

[0048] In one possible embodiment, the execution entity operates based on a frequency domain reference signal. and received signals The first channel response of the channel to be estimated is determined, i.e. Then, the power delay distribution can be determined based on the first channel response. Specifically, the power delay distribution PDP can be calculated from the square of the modulus of the first channel response, i.e., PDP = The Power Delay Distribution (PDP) characterizes the distribution of channel energy at different delays.

[0049] Step 3012: Based on the minimum mean square error criterion, search for the starting point of the sliding window corresponding to the impulse response length of at least one candidate channel on the power delay distribution to obtain the optimal candidate sliding window set in the time domain.

[0050] Here, after determining the power delay distribution based on the first channel response, the executing entity can search for the starting point of the sliding window corresponding to the impulse response length of at least one candidate channel on the power delay distribution based on the minimum mean square error criterion, that is, the time-domain Min-MSE criterion, to obtain the time-domain optimal candidate sliding window set.

[0051] In one possible implementation, the implementing entity combines sampling time. Extend latency Represented as:

[0052] in, Indicates the length of the Channel Impulse Response (CIR). It is an integer. It can be defined as:

[0053] in, and These are the first and last paths, respectively. The number of sample points between the first and last reaching paths can be used to indirectly represent... , and when for , for ,maximum for At that time, CIR length .

[0054] In this embodiment, the execution entity adopts Indicates a CIR sliding window, where Indicates the starting point of the CIR sliding window. Indicates the window length of a CIR sliding window, such as Figure 5 As shown, Figure 5 An exemplary sliding window diagram of the channel impulse response is shown. and The location and size of the sliding window can be uniquely determined, such as Figure 5 As shown, the solid box represents the CIR sliding window, and the dashed box represents the noise portion outside the CIR window. The executing entity changes the starting point of the CIR sliding window. Window length of CIR sliding window The energy portion of the channel in the PDP can be selected. Based on the above calculations, the time-domain candidate CIR length, i.e., the candidate channel impulse response length, is... Based on the time-domain Min-MSE criterion, the executing entity can determine the optimal set of candidate CIR sliding windows in the time domain. The specific formula is as follows:

[0055] in, Indicates the window length is The optimal starting point for sliding windows , This represents the power delay distribution (PDP).

[0056] In one possible implementation, the executing entity selects the impulse response length of the alternative channel. and change the starting point By implementing a CIR sliding window, one can find the window inside. The starting point of maximum power Thus determine The optimal set of candidate CIR sliding windows The specific steps are as follows: Execute the main body to define LS channel estimation. and the original time-domain channel The mean square error is MSE, and the formula in the above embodiment is... The cost function is derived based on the Min-MSE criterion. The main derivation process for the time-domain MSE (TD-MSE) is as follows:

[0057] Among them, equation (a) applies to and , For the original time-domain noise, it is applied in equation (b) Application in equation (c) , This represents the noise spectral density. It should be noted that, if the goal is to determine the optimal CIR sliding window using the minimum MSE... Preset is required And it will produce errors, because and All are positive values, and the minimum MSE is equivalent to the maximum MSE in this embodiment. In other words, by combining the execution entity with CIR sliding window search, the optimal candidate sliding window set in the time domain can be determined. .

[0058] Step 3013: Based on the maximum signal-to-noise ratio criterion, determine the optimal sliding window in the time domain from the set of optimal candidate sliding windows in the time domain.

[0059] Here, after obtaining the set of time-domain optimal candidate sliding windows, the executing entity can determine the time-domain optimal sliding window from the set of time-domain optimal candidate sliding windows based on the maximum signal-to-noise ratio criterion.

[0060] In one possible embodiment, the executing agent obtains the time-domain optimal candidate sliding window set. Then, based on the maximum signal-to-noise ratio criterion, i.e., the Max-SNR criterion in the time domain, the optimal candidate sliding window set in the time domain can be selected. Determining the optimal sliding window in the time domain Time-domain optimal sliding window The calculation formula is as follows:

[0061] in, Represents the optimal starting point of the CIR sliding window in the time domain. and window length The executing entity selects the optimal alternative CIR sliding window set. By considering different alternatives and the cost function defined by the signal-to-noise ratio (SNR) in the above formula, the optimal CIR sliding window can be determined. In other words, the optimal sliding window in the instantaneous domain.

[0062] In this embodiment, firstly, the execution entity determines the power delay distribution based on the first channel response; then, based on the minimum mean square error criterion, the execution entity searches for the starting point of the sliding window corresponding to the impulse response length of at least one candidate channel on the power delay distribution to obtain the time-domain optimal candidate sliding window set; finally, based on the maximum signal-to-noise ratio criterion, the execution entity determines the time-domain optimal sliding window from the time-domain optimal candidate sliding window set.

[0063] As described above, the execution entity in this embodiment uses a two-step joint optimization time-domain sliding window search process. First, it selects the optimal starting point for each candidate length based on the minimum mean square error criterion to form a candidate sliding window set. Then, it determines the unique optimal sliding window from among them based on the maximum signal-to-noise ratio criterion. This embodiment can directly and synchronously estimate the effective window of the channel impulse response without relying on any external symbol timing synchronization information. This solves the traditional interlocking problem that channel parameter estimation requires timing synchronization, which in turn depends on channel parameters. It provides accurate and self-synchronizing time-domain parameters for subsequent channel estimation refinement and frequency domain fine-tuning.

[0064] Step 302: Based on the optimal sliding window in the time domain, determine the first parameter estimation result and symbol timing synchronization information.

[0065] Here, after determining the optimal sliding window in the time domain, the executing entity can determine the first parameter estimation result and symbol timing synchronization information based on the optimal sliding window in the time domain.

[0066] In one possible embodiment, the first parameter estimation result includes: the delay spread and noise variance of the channel to be estimated, such as... Figure 6 As shown, based on the optimal sliding window in the time domain, the determination of the first parameter estimation result and symbol timing synchronization information includes the following steps: Step 3021: Determine the time delay spread based on the window length of the optimal sliding window in the time domain.

[0067] Here, the executing entity can determine the delay spread based on the window length of the optimal sliding window in the time domain. The delay spread reflects the time dispersion characteristics of multipath propagation in the channel and is a key parameter for evaluating the degree of frequency selective fading in the channel.

[0068] In one possible embodiment, the executing agent uses a time-optimal sliding window, i.e. The optimal window length for a CIR sliding window This allows for the determination of optimal channel parameter estimation information. Simultaneously, the executing entity can also... The optimal CIR sliding window starting point By determining the symbol timing synchronization information, the delay spread estimation when symbols are out of sync is solved, and the delay spread is obtained.

[0069] Step 3022: Determine the noise variance based on the power in the region outside the optimal sliding window in the time domain of the power delay distribution.

[0070] Here, the executing entity can determine the noise variance based on the power in the region outside the optimal sliding window in the time domain of the power delay distribution.

[0071] In one possible embodiment, the executing agent determines the noise variance estimate based on the power in the power delay distribution (PDP) located outside the optimal sliding window in the time domain, i.e., the power in the noise region. Specifically, it can be calculated using the following formula:

[0072] The linear ratio of the statistical average power of the CIR region defined by the executing entity to the statistical average power of the noise floor component in the PDP is called SNR. It should be noted that in this embodiment, the time-domain optimal sliding window... The optimization cost function in the calculation formula is obtained based on the Max-SNR criterion. The main derivation process of time-domain SNR (TD-SNR) is as follows:

[0073] In the above expression, the numerator of equation (a) represents the total average power of the signal and noise within the CIR sliding window minus the average power of the noise region outside the CIR sliding window, thus obtaining an estimate of the signal power. The denominator is the average power of the noise region outside the CIR sliding window. Therefore, the physical meaning of equation (a) is the linear ratio of signal power to noise power in the power delay distribution (PDP), i.e., the signal-to-noise ratio (SNR). By maximizing this SNR through sliding window search, the execution entity can avoid directly presetting the noise spectral density. The resulting estimation error.

[0074] Step 3023: Determine symbol timing synchronization information based on the starting point of the optimal sliding window in the time domain.

[0075] Here, the executing entity can determine symbol timing synchronization information based on the starting point of the optimal sliding window in the time domain.

[0076] In one possible embodiment, the executing entity uses the optimal CIR sliding window. The starting point of the time-domain optimal sliding window Determine the symbol timing synchronization information, which refers to the time reference point used to adjust the receiver symbol timing and achieve time-domain alignment between the received and transmitted signals, and the starting point of the optimal time-domain sliding window. It identifies the starting position of the channel impulse response, i.e. the estimated position of the first path in the time domain, which can be directly used as a reference for symbol timing to correct the start time of the receiver sampling window, thereby eliminating inter-symbol interference caused by timing deviation and achieving stable and accurate symbol synchronization.

[0077] In this embodiment, the execution entity determines the delay spread based on the window length of the time-domain optimal sliding window, determines the noise variance based on the power in the region outside the time-domain optimal sliding window in the power delay distribution, and determines the symbol timing synchronization information based on the starting point of the time-domain optimal sliding window.

[0078] As can be seen from the above description, this embodiment solves the problem of the interdependence between channel parameter estimation and symbol timing within a unified framework. Furthermore, this embodiment does not require any prior symbol synchronization information or manually set thresholds, significantly improving the integrity and systematic nature of the channel parameter estimation method under non-ideal synchronization conditions.

[0079] Step 103: Based on the first parameter estimation result and symbol timing synchronization information, perform statistical estimation processing on the first channel response to obtain the second channel response.

[0080] Here, after the execution entity performs a time-domain sliding window search on the first channel response based on the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to obtain the first parameter estimation result and symbol timing synchronization information of the channel to be estimated, it can perform statistical estimation processing on the first channel response based on the first parameter estimation result and symbol timing synchronization information to obtain the second channel response. Here, statistical estimation refers to the algorithm process of optimizing the initial channel estimate by utilizing the statistical characteristics of the channel, such as delay spread and noise variance. The second channel response refers to the more accurate channel response estimation result obtained after statistical estimation processing.

[0081] In one possible embodiment, based on the first parameter estimation result and symbol timing synchronization information, a statistical estimation process is performed on the first channel response to obtain the second channel response, including the following steps: Based on the first parameter estimation result and symbol timing synchronization information, the first channel response is processed to obtain the second channel response.

[0082] Specifically, after obtaining the first parameter estimation result and symbol timing synchronization information of the channel to be estimated, the executing entity can perform target processing on the first channel response based on the first parameter estimation result and symbol timing synchronization information to obtain the second channel response. The target processing includes minimum mean square error filtering and / or discrete Fourier transform smoothing.

[0083] In one possible embodiment, based on the first parameter estimation result and symbol timing synchronization information, a statistical estimation process is performed on the first channel response to obtain the second channel response, including the following steps: Based on the first parameter estimation result and symbol timing synchronization information, the first channel response is processed to obtain the second channel response.

[0084] Specifically, after obtaining the first parameter estimation result and symbol timing synchronization information of the channel to be estimated, the executing entity can perform target processing on the first channel response based on the first parameter estimation result and symbol timing synchronization information to obtain the second channel response. The target processing includes minimum mean square error filtering and / or discrete Fourier transform smoothing.

[0085] In one possible embodiment, the target processing is either Minimum Mean Square Error (MMSE) filtering or Discrete Fourier Transform (DFT) smoothing. The executing entity constructs MMSE filter coefficients based on the delay spread or channel impulse response length, noise variance estimation, and symbol timing synchronization information from the first parameter estimation results. This filters the first channel response; that is, the executing entity performs frequency domain fine-channel estimation. Specifically, the calculation formula for frequency domain fine-channel estimation is as follows:

[0086] in, These can be MMSE filter coefficients, and It can also be a windowing factor for the DFT. For the second channel response, The specific value can be obtained through the time-domain optimal CIR sliding window obtained in the aforementioned embodiments. Provided and and noise variance estimation information To determine, therefore, statistical estimation It can be uniformly represented as This is because and Decide , Depend on and It can be determined, and the initial channel estimation in the time domain... Initial Channel Estimation in Frequency Domain This is a deterministic estimate.

[0087] In another possible embodiment, the target processing employs a combination of minimum mean square error filtering (MMSE) and discrete Fourier transform smoothing (DFT). The execution entity first bases its processing on the optimal CIR sliding window. Construct the corresponding minimum mean square error filter coefficients and filter the first channel response to complete the initial optimization.

[0088] Subsequently, based on the initial optimization, the execution entity further performs discrete Fourier transform smoothing processing. Specifically, the execution entity uses the obtained symbol timing synchronization start point and channel impulse response length to define a corresponding time window in the time domain, transforms the filtered channel response to the time domain and performs windowing truncation, retaining only the part within the window to eliminate noise outside the window. Finally, the final optimized frequency domain channel response is obtained through transformation again, which is the second channel response.

[0089] As described above, in this embodiment, the execution entity can adopt a combination of minimum mean square error filtering (MMSE) and discrete Fourier transform smoothing (DFT), which fully integrates the theoretical advantages of minimum mean square error filtering in statistical optimal estimation and the effectiveness of discrete Fourier transform smoothing in time-domain focusing and noise suppression. This can significantly improve the estimation accuracy and overall robustness of the final channel response under complex actual channel conditions.

[0090] Step 104: Perform a frequency domain sliding window search based on the minimum mean square error criterion on the second channel response to obtain the target parameter estimation result after optimizing the first parameter estimation result.

[0091] Here, the execution entity performs statistical estimation processing on the first channel response based on the first parameter estimation result and symbol timing synchronization information to obtain the second channel response. Then, it can perform a frequency domain sliding window search based on the minimum mean square error criterion on the second channel response to obtain the target parameter estimation result after optimizing the first parameter estimation result.

[0092] In one possible embodiment, the executing entity performs statistical estimation processing on the first channel response based on the first parameter estimation result and symbol timing synchronization information to obtain the second channel response. That is, after frequency domain fine channel estimation, a frequency domain sliding window search based on the minimum mean square error criterion can be performed on the second channel response to obtain the final target parameter estimation result after fine-tuning and optimization of the first parameter estimation result. The specific process of the frequency domain sliding window search based on the minimum mean square error criterion is as follows: The executing entity determines the length of the frequency domain candidate CIR as follows: The frequency domain candidate FAP position is , and Given a positive integer, the execution entity determines the optimal CIR sliding window in the frequency domain based on the frequency domain Min-MSE criterion. Optimal CIR sliding window in the frequency domain The specific expression is:

[0093] The executing entity fine-tunes the starting point of the sliding window through the frequency domain CIR sliding window. and window length The optimal CIR sliding window in the frequency domain can be determined. It should be noted that the cost function in the above formula is obtained based on the Min-MSE criterion. The main derivation process of the frequency domain MSE (FD-MSE) is as follows:

[0094] Among them, equation (a) applies to As can be seen through fine-tuning and This can minimize MSE, and the frequency domain CIR sliding window can make channel parameter estimation more accurate, which can improve robustness in scenarios such as discontinuous reception (DRX).

[0095] The executing entity has determined the optimal CIR sliding window in the frequency domain. Then, it is transformed into the final parameter estimate: the target delay spread is determined by the window length. Based on the system sampling time calculation, the precise timing information is derived from the window start point. Provided as the final fine-tuning amount for symbol timing synchronization, the final noise variance is re-estimated precisely from the second channel response based on the signal and noise regions divided by the optimal sliding window. Thus, the execution entity systematically completes high-precision and high-robust channel parameter estimation through a two-level architecture of coarse search in the time domain and fine-tuning in the frequency domain without the need for ideal synchronization priors.

[0096] This disclosure provides a method, apparatus, device, medium, and product for estimating channel parameters. First, a frequency domain reference signal and a received signal of the channel to be estimated are acquired, and a first channel response of the channel to be estimated is determined based on the frequency domain reference signal and the received signal. Then, a time-domain sliding window search is performed on the first channel response based on the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to obtain the first parameter estimation result and symbol timing synchronization information of the channel to be estimated. Next, based on the first parameter estimation result and the symbol timing synchronization information, a statistical estimation process is performed on the first channel response to obtain a second channel response. Finally, a frequency domain sliding window search based on the minimum mean square error criterion is performed on the second channel response to obtain a target parameter estimation result optimized from the first parameter estimation result.

[0097] As described above, this embodiment employs a cascaded dual-domain sliding window estimation architecture. First, it determines the first channel response based on a frequency domain reference signal and the received signal. Then, it innovatively applies the minimum mean square error criterion and the maximum signal-to-noise ratio criterion jointly in the time domain to perform a sliding window search on the first channel response, solving for the first parameter estimation result and symbol timing synchronization information. This resolves the dilemma of channel parameter estimation and symbol synchronization in traditional methods. Subsequently, this embodiment uses the first parameter estimation result and symbol timing synchronization information to perform statistical estimation processing on the first channel response, obtaining a more accurate second channel response. Finally, this embodiment performs a sliding window search on the second channel response in the frequency domain based on the minimum mean square error criterion, thereby fine-tuning and optimizing the first parameter estimation result to obtain the target parameter estimation result. This embodiment can achieve joint high-precision estimation of channel parameters, noise variance, and timing information without requiring ideal symbol synchronization prior conditions, significantly improving the estimation performance and overall robustness of the channel parameter estimation method in practical applications.

[0098] By dividing each functional module according to its corresponding function, this disclosure provides a channel parameter estimation device, which can be a server or a chip applied to a server. Figure 7 A schematic block diagram of the functional modules of a channel parameter estimation apparatus provided for an exemplary embodiment of this disclosure. Figure 7 As shown, the channel parameter estimation device includes: The determining module 701 is used to acquire the frequency domain reference signal and the received signal of the channel to be estimated, and to determine the first channel response of the channel to be estimated based on the frequency domain reference signal and the received signal; The first search module 702 is used to perform a time-domain sliding window search on the response of the first channel based on the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to obtain the first parameter estimation result and symbol timing synchronization information of the channel to be estimated. Processing module 703 is used to perform statistical estimation processing on the first channel response based on the first parameter estimation result and the symbol timing synchronization information to obtain the second channel response; The second search module 704 is used to perform a frequency domain sliding window search on the second channel response based on the minimum mean square error criterion to obtain the target parameter estimation result after optimizing the first parameter estimation result.

[0099] In one embodiment, the determining module 701 includes: The first processing unit is used to perform least-squares channel estimation on the frequency domain reference signal and the received signal to obtain an initial channel estimate in the frequency domain. The second processing unit is used to perform an inverse Fourier transform on the frequency domain initial channel estimate to obtain the time domain initial channel estimate. The first determining unit is used to determine the initial channel estimate in the time domain as the first channel response.

[0100] In one embodiment, the first search module 702 includes: The first search unit is used to perform a time-domain sliding window search on the first channel response by using the joint optimization method of the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to determine the optimal time-domain sliding window. The second determining unit is used to determine the first parameter estimation result and the symbol timing synchronization information based on the time-domain optimal sliding window.

[0101] In one embodiment, the first search module 702 includes: The third determining unit is used to determine the power delay distribution based on the first channel response; The second search unit is used to search for the starting point of the sliding window corresponding to the impulse response length of at least one candidate channel on the power delay distribution based on the minimum mean square error criterion, so as to obtain the time-domain optimal candidate sliding window set. The fourth determining unit is used to determine the time-domain optimal sliding window from the set of time-domain optimal candidate sliding windows based on the maximum signal-to-noise ratio criterion.

[0102] In one embodiment, the first search module 702 includes: The fifth determining unit, used to determine the first parameter estimation result and the symbol timing synchronization information based on the time-domain optimal sliding window, includes: The sixth determining unit is used to determine the time delay spread based on the window length of the time-domain optimal sliding window; The seventh determining unit is used to determine the noise variance based on the power in the region outside the optimal sliding window in the time domain of the power delay distribution; The eighth determining unit is used to determine the symbol timing synchronization information based on the starting point of the time-domain optimal sliding window.

[0103] In one embodiment, the processing module 703 includes: The third processing unit is used to perform target processing on the first channel response based on the first parameter estimation result and the symbol timing synchronization information to obtain the second channel response, wherein the target processing includes minimum mean square error filtering and / or discrete Fourier transform smoothing.

[0104] Figure 8 This is a schematic diagram of the structure of an electronic device provided as an exemplary embodiment of this disclosure. For example... Figure 8As shown, the electronic device 800 includes at least one processor 801 and a memory 802 coupled to the processor 801. The processor 801 can perform the corresponding steps in the methods disclosed in the embodiments of this disclosure.

[0105] The processor 801 described above can also be called a central processing unit (CPU), which can be an integrated circuit chip with signal processing capabilities. Each step in the method disclosed in this embodiment can be implemented by the integrated logic circuitry in the processor 801 or by software instructions. The processor 801 can be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this embodiment can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can be located in the memory 802, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor 801 reads information from the memory 802 and, in conjunction with its hardware, completes the steps of the method described above.

[0106] Furthermore, various operations / processes according to this disclosure, implemented via software and / or firmware, can be transmitted from a storage medium or network to a computer system with a dedicated hardware architecture, such as... Figure 9 The computer system 900 shown is equipped with the programs that constitute the software. When various programs are installed, the computer system is able to perform various functions, including those described above. Figure 9 A block diagram of a computer system provided for an exemplary embodiment of this disclosure.

[0107] Computer system 900 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, 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 present disclosure described and / or claimed herein.

[0108] like Figure 9 As shown, the computer system 900 includes a computing unit 901, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 902 or a computer program loaded into a random access memory (RAM) 903 from a storage unit 908. The RAM 903 may also store various programs and data required for the operation of the computer system 900. The computing unit 901, ROM 902, and RAM 903 are interconnected via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0109] Multiple components in the computer system 900 are connected to the I / O interface 905, including: an input unit 906, an output unit 907, a storage unit 908, and a communication unit 909. The input unit 906 can be any type of device capable of inputting information into the computer system 900. The input unit 906 can receive input numerical or character information and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 907 can be any type of device capable of presenting information and may include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. The storage unit 908 may include, but is not limited to, a hard disk and an optical disk. The communication unit 909 allows the computer system 900 to exchange information / data with other devices via a network such as the Internet, and may include, but is not limited to, a modem, network card, infrared communication device, wireless communication transceiver, and / or chipset, such as Bluetooth™ device, WiFi device, WiMax device, cellular communication device, and / or the like.

[0110] The computing unit 901 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 901 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 computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 901 performs the various methods and processes described above. For example, in some embodiments, the methods disclosed in this disclosure can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 800 via ROM 902 and / or communication unit 909. In some embodiments, the computing unit 901 can be configured to perform the methods disclosed in this disclosure by any other suitable means (e.g., by means of firmware).

[0111] This disclosure also provides a computer-readable storage medium, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is able to perform the methods disclosed in this disclosure.

[0112] The computer-readable storage medium in this disclosure can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. The aforementioned 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 of the foregoing. More specifically, the aforementioned computer-readable storage medium may include electrical connections based on one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0113] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0114] Figure 10 A computer program product 1000 is provided as an exemplary embodiment of the present disclosure. The computer program product 1000 includes a computer program 1001, wherein the computer program 1001, when executed by a processor, implements the methods disclosed in the embodiments of the present disclosure.

[0115] In embodiments of this disclosure, computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or it can be connected to an external computer.

[0116] 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 this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated 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, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0117] The modules, components, or units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the modules, components, or units do not necessarily constitute a limitation on the module, component, or unit itself.

[0118] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary hardware logic components that can be used include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0119] The above description is merely an embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0120] While specific embodiments of this disclosure have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of this disclosure. The scope of this disclosure is defined by the appended claims.

Claims

1. A method for estimating channel parameters, characterized in that, The method includes: Acquire the frequency domain reference signal and the received signal of the channel to be estimated, and determine the first channel response of the channel to be estimated based on the frequency domain reference signal and the received signal; Based on the minimum mean square error criterion and the maximum signal-to-noise ratio criterion, a time-domain sliding window search is performed on the response of the first channel to obtain the first parameter estimation result and symbol timing synchronization information of the channel to be estimated. Based on the first parameter estimation result and the symbol timing synchronization information, the first channel response is statistically estimated to obtain the second channel response; A frequency domain sliding window search based on the minimum mean square error criterion is performed on the second channel response to obtain the target parameter estimation result after optimizing the first parameter estimation result.

2. The method according to claim 1, characterized in that, The step of determining the first channel response of the channel to be estimated based on the frequency domain reference signal and the received signal includes: Least-squared channel estimation is performed on the frequency domain reference signal and the received signal to obtain the initial frequency domain channel estimate; Perform an inverse Fourier transform on the frequency domain initial channel estimate to obtain the time domain initial channel estimate; The initial channel estimate in the time domain is determined as the first channel response.

3. The method according to claim 1, characterized in that, The time-domain sliding window search of the first channel response based on the minimum mean square error criterion and the maximum signal-to-noise ratio criterion yields the first parameter estimation result and symbol timing synchronization information of the channel to be estimated, including: The first channel response is subjected to a time-domain sliding window search by jointly optimizing the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to determine the optimal time-domain sliding window. Based on the time-domain optimal sliding window, the first parameter estimation result and the symbol timing synchronization information are determined.

4. The method according to claim 3, characterized in that, The method of jointly optimizing the first channel response using the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to perform a time-domain sliding window search and determine the optimal time-domain sliding window includes: The power delay distribution is determined based on the first channel response; Based on the minimum mean square error criterion, the starting point of the sliding window corresponding to the impulse response length of at least one candidate channel on the power delay distribution is searched to obtain the time-domain optimal candidate sliding window set. Based on the maximum signal-to-noise ratio criterion, the optimal sliding window in the time domain is determined from the set of optimal candidate sliding windows in the time domain.

5. The method according to claim 4, characterized in that, The first parameter estimation result includes: the delay spread and noise variance of the channel to be estimated; The step of determining the first parameter estimation result and the symbol timing synchronization information based on the time-domain optimal sliding window includes: The time delay spread is determined based on the window length of the time-domain optimal sliding window; The noise variance is determined based on the power in the region outside the optimal sliding window in the time domain within the power delay distribution. The symbol timing synchronization information is determined based on the starting point of the time-domain optimal sliding window.

6. The method according to claim 1, characterized in that, The step of performing statistical estimation processing on the first channel response based on the first parameter estimation result and the symbol timing synchronization information to obtain the second channel response includes: Based on the first parameter estimation result and the symbol timing synchronization information, the first channel response is subjected to target processing to obtain the second channel response. The target processing includes minimum mean square error filtering and / or discrete Fourier transform smoothing.

7. A channel parameter estimation device, characterized in that, include: The determination module is used to acquire the frequency domain reference signal and the received signal of the channel to be estimated, and to determine the first channel response of the channel to be estimated based on the frequency domain reference signal and the received signal; The first search module is used to perform a time-domain sliding window search on the response of the first channel based on the minimum mean square error criterion and the maximum signal-to-noise ratio criterion to obtain the first parameter estimation result and symbol timing synchronization information of the channel to be estimated. The processing module is used to perform statistical estimation processing on the first channel response based on the first parameter estimation result and the symbol timing synchronization information to obtain the second channel response; The second search module is used to perform a frequency domain sliding window search on the second channel response based on the minimum mean square error criterion to obtain the target parameter estimation result after optimizing the first parameter estimation result.

8. An electronic device, characterized in that, include: At least one processor; Memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

Images