Filtering method and device for channel estimation, electronic equipment and storage medium

Abstract

The invention provides a filtering method and device for channel estimation, electronic equipment and a storage medium. The method comprises the following steps: acquiring initial channel estimation of a reference signal, and determining optimal noise power in a current target channel scene based on the initial channel estimation; performing first-stage Wiener filtering on the initial channel estimation to obtain first filtering channel estimation; and performing second-stage Wiener filtering on the first filtering channel estimation based on the optimal noise power to obtain second filtering channel estimation. Therefore, according to the scheme, the optimal noise power in the target channel scene is acquired in advance, so that the second-stage Wiener filtering can be immediately carried out after the first-stage Wiener filtering is completed, the process of carrying out noise estimation on the reference signal after the first-stage filtering can be omitted, the processing waiting time is reduced, and the processing efficiency is improved. And the processing time delay is reduced, so that the two-stage filter circuit can be designed in a tight coupling manner to enhance the stability of the filter performance.

Description

Technical Field

[0001] This application relates to the field of communication technologies, and in particular, to a filtering method, apparatus, electronic device, and storage medium for channel estimation. Background Art

[0002] For an Orthogonal Frequency Division Multiplexing (OFDM) system, when performing channel estimation, two-dimensional joint estimation (such as two-stage Wiener filtering) can be used to sample the channel at different positions in the time-frequency space using reference signals, and then interpolation filtering is used to obtain the frequency response value of the entire channel to complete channel estimation. However, the prior art will bring a large processing waiting time in implementing two-stage Wiener filtering, resulting in a delay in the subsequent signal processing of reception. Summary of the Invention

[0003] This application aims to solve at least one of the technical problems in the related art to some extent.

[0004] To this end, the first object of this application is to propose a filtering method for channel estimation, so as to reduce the processing waiting time, reduce the processing delay, and enable the two-stage filtering circuit to be designed in a tightly coupled manner to enhance the stability of the filtering performance.

[0005] The second object of this application is to propose a filtering apparatus for channel estimation.

[0006] The third object of this application is to propose an electronic device.

[0007] The fourth object of this application is to propose a computer-readable storage medium.

[0008] The fifth object of this application is to propose a computer program product.

[0009] To achieve the above object, an embodiment of the first aspect of this application proposes a filtering method for channel estimation, including: obtaining an initial channel estimation of a reference signal, and determining an optimal noise power in a current target channel scenario based on the initial channel estimation; performing first-stage Wiener filtering on the initial channel estimation to obtain a first filtered channel estimation; performing second-stage Wiener filtering on the first filtered channel estimation based on the optimal noise power to obtain a second filtered channel estimation.

[0010] To achieve the above object, an embodiment of the second aspect of the present application provides a filtering device for channel estimation, including: a determination module, configured to obtain an initial channel estimation of a reference signal, and determine an optimal noise power in a current target channel scenario based on the initial channel estimation; a first filtering module, configured to perform a first-level Wiener filtering on the initial channel estimation to obtain a first-filtered channel estimation; and a second filtering module, configured to perform a second-level Wiener filtering on the first-filtered channel estimation based on the optimal noise power to obtain a second-filtered channel estimation.

[0011] To achieve the above object, an embodiment of the third aspect of the present application provides an electronic device, including: a processor; and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory, so that the processor can execute the channel estimation filtering method described in the first aspect embodiment above.

[0012] To achieve the above object, an embodiment of the fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer instructions are used to cause the computer to execute the channel estimation filtering method described in the above embodiment of one aspect.

[0013] To achieve the above object, an embodiment of the fifth aspect of the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the channel estimation filtering method described in the above embodiment of one aspect.

[0014] The channel estimation filtering method, device, electronic device, and storage medium provided by the present application obtain an initial channel estimation of a reference signal, perform a first-level Wiener filtering on the initial channel estimation to obtain a first-filtered channel estimation. At the same time, based on the initial channel estimation, the optimal noise power in the current target channel scenario is determined, and a second-level Wiener filtering is performed on the first-filtered channel estimation based on the optimal noise power to obtain a second-filtered channel estimation. In the embodiment of the present application, since the optimal noise power in the target channel scenario is obtained in advance, the second-level Wiener filtering can be immediately performed after the first-level Wiener filtering is completed, thereby omitting the process of noise estimation for the reference signal after the first filtering, reducing the processing waiting time, reducing the processing delay, and enabling the two-level filtering circuit to be closely coupled designed to enhance the stability of the filtering performance.

[0015] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of embodiments in conjunction with the accompanying drawings, where:

[0017] Figure 1 It is a schematic flowchart of a filtering method for channel estimation provided by an embodiment of the present application;

[0018] Figure 2 It is a schematic flowchart of another filtering method for channel estimation provided by an embodiment of the present application;

[0019] Figure 3 It is a schematic flowchart of the process of obtaining a target channel scenario in a filtering method for channel estimation provided by an embodiment of the present application;

[0020] Figure 4 It is a schematic flowchart of another filtering method for channel estimation provided by an embodiment of the present application;

[0021] Figure 5 It is a schematic flowchart of the process of updating the second mapping relationship provided by an embodiment of the present application;

[0022] Figure 6 It is a schematic flowchart of another filtering method for channel estimation provided by an embodiment of the present application;

[0023] Figure 7 It is a schematic structural diagram of the implementation of Wiener filtering channel estimation provided by an embodiment of the present application;

[0024] Figure 8 It is a schematic structural diagram of a filtering device for channel estimation provided by an embodiment of the present application. Detailed implementation manners

[0025] The embodiments of the present application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application.

[0026] The filtering method and device for channel estimation according to embodiments of the present application are described below with reference to the accompanying drawings.

[0027] Figure 1 It is a flowchart of a filtering method for channel estimation shown according to an exemplary embodiment. As Figure 1 shown, the filtering method for channel estimation according to the embodiments of the present application includes, but is not limited to, the following steps:

[0028] S101. Obtain an initial channel estimate of a reference signal, and determine an optimal noise power in the current target channel scenario based on the initial channel estimate.

[0029] It should be noted that the execution subject of the channel estimation filtering method provided in the embodiments of the present application is an electronic device, and the electronic device may be a terminal device. Optionally, the terminal device may be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device may be a mobile phone, a tablet computer, a laptop computer, a handheld computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc., and the non-mobile electronic device may be a Network Attached Storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, etc. The embodiments of the present application do not make specific limitations.

[0030] In some implementations, the initial channel estimate is used to estimate the channel impact on the reference signal during transmission, and the initial channel estimate of the reference signal can be calculated based on a channel estimation algorithm (Least Squares Channel Estimation Algorithm, LS). Optionally, the reference signal may include, but is not limited to: an uplink reference signal, a downlink reference signal, a clock signal, a positioning signal, a pulse signal, etc.

[0031] Optionally, the initial channel estimate of the reference signal can be calculated based on given received data and a reference signal matrix. The formula for calculating the initial channel estimate is as follows:

[0032] h LS =X -1 y (1)

[0033] where h LS represents the initial channel estimate, X -1 represents the reference signal matrix, and y represents the received data.

[0034] Furthermore, noise power estimation and scenario recognition can be performed on the initial channel estimate to obtain a noise power measurement value and a target channel scenario, and noise power matching can be performed based on the noise power measurement value and the target channel scenario to obtain a noise power that matches the noise power measurement value and the target channel scenario as the optimal noise power in the target channel scenario.

[0035] S102. Perform the first - stage Wiener filtering on the initial channel estimate to obtain the first - filtered channel estimate.

[0036] It can be understood that the Wiener filtering algorithm realizes signal noise reduction by calculating the minimum mean - square error (the difference between the expected response and the actual output of the filter). It can effectively suppress noise and improve the signal quality, which is of great significance for scenarios where a clear signal needs to be extracted from noise interference.

[0037] In some implementations, a first - stage Wiener filter can be used to perform the first - stage Wiener filtering on the initial channel estimate. That is, the initial channel estimate is input into the first - stage Wiener filter, and the first - stage Wiener filter performs noise - elimination optimization processing on the initial channel estimate based on the minimum mean - square error to reduce noise and obtain the first - filtered channel estimate.

[0038] S103. Perform the second - stage Wiener filtering on the first - filtered channel estimate based on the optimal noise power to obtain the second - filtered channel estimate.

[0039] It can be understood that the noise and interference in the channel are usually unstable and random. If a fixed noise value is used for filtering, it may not be able to fully adapt to different channel noise characteristics, thus affecting the performance and effect of the filter. In the embodiments of the present application, the second - stage Wiener filtering can be performed on the first - filtered channel estimate based on the optimal noise power in the current target channel scenario, so as to be able to perform Wiener filtering on the first - filtered channel estimate again according to the actual noise characteristics and channel conditions, and further improve the adaptability of the Wiener filter.

[0040] In some implementations, the optimal noise power can be used as a parameter for the second - stage Wiener filtering to further process and recover the first - filtered channel estimate. A second - stage Wiener filter can be used to perform the second - stage Wiener filtering on the first - filtered channel estimate. That is, the first - filtered channel estimate is input into the second - stage Wiener filter, and the first - stage Wiener filter performs noise - elimination optimization processing on the first - filtered channel estimate based on the minimum mean - square error to obtain the second - filtered channel estimate, which can effectively improve the signal recovery and denoising ability and improve the reliability and quality of signal transmission.

[0041] In the channel estimation filtering method provided by the embodiments of the present application, an initial channel estimation of a reference signal is obtained, and a first-stage Wiener filtering is performed on the initial channel estimation to obtain a first filtered channel estimation. At the same time, based on the initial channel estimation, the optimal noise power in the current target channel scenario is determined, and a second-stage Wiener filtering is performed on the first filtered channel estimation based on the optimal noise power to obtain a second filtered channel estimation. In the embodiments of the present application, since the optimal noise power in the target channel scenario is obtained in advance, the second-stage Wiener filtering can be immediately performed after the first-stage Wiener filtering is completed, thereby omitting the process of noise estimation for the reference signal after the first filtering, reducing the processing waiting time, reducing the processing delay, and enabling the two-stage filtering circuit to be tightly coupled designed to enhance the stability of the filtering performance.

[0042] Figure 2 is a flowchart of a channel estimation filtering method shown according to an exemplary embodiment, as Figure 2 shown, the channel estimation filtering method of the embodiments of the present application includes but is not limited to the following steps:

[0043] S201, obtain an initial channel estimation of the reference signal.

[0044] In the embodiments of the present application, the implementation manner of step S201 can be implemented by any one of the embodiments of the present application, and no limitation is made here and will not be elaborated.

[0045] S202, based on the initial channel estimation, obtain a noise power measurement value and a target channel scenario.

[0046] In some implementations, by performing noise power estimation and scenario recognition on the initial channel estimation, a noise power measurement value and a target channel scenario can be obtained. Optionally, based on the power spectral density of the initial channel estimation, the noise power measurement value is determined.

[0047] Optionally, the target channel scenario can be determined by calculating the channel small-scale fading parameter of the initial channel estimation and based on the first mapping relationship between the candidate channel small-scale fading parameter and the candidate channel scenario. Optionally, the first mapping relationship can be determined in advance, and after the channel small-scale fading parameter is obtained, based on the first mapping relationship, the target channel scenario can be determined from the candidate channel scenarios.

[0048] S203, based on the noise power measurement value and the target channel scenario, determine the optimal noise power in the target channel scenario.

[0049] In some implementations, the target signal-to-interference plus noise ratio (SNR) in the target channel scenario can be determined based on the noise power measurement value and the target channel scenario, and the optimal noise power can be determined based on the target SNR. Optionally, the target SNR can be determined based on the second mapping relationship among the candidate channel scenario - candidate noise power measurement value - candidate SNR.

[0050] Optionally, using the noise power measurement value and the target channel scenario as query conditions for the second mapping relationship among the candidate channel scenario - candidate noise power measurement value - candidate SNR, the SNR that has a mapping relationship with the noise power measurement value and the target channel scenario is obtained as the target SNR, and then based on the target SNR, the optimal noise power in the target channel scenario is determined.

[0051] Optionally, the optimal noise power can be obtained by calculating the reciprocal of the target SNR.

[0052] For exemplary illustration, a second mapping relationship table is established based on the second mapping relationship among the candidate channel scenario - candidate noise power measurement value - candidate SNR, and this table is shown in Table 1 below:

[0053] Table 1 Second Mapping Relationship Table

[0054]

[0055] If it is determined that the noise power measurement value is -6 and the target channel scenario is candidate channel scenario C, then based on Table 1 above, the target SNR can be determined to be 300, and the optimal noise power is 1 / 300.

[0056] S204, perform first-level Wiener filtering on the initial channel estimate to obtain a first-filtered channel estimate.

[0057] In the embodiments of the present application, the implementation manner of step S204 can be implemented in any one of the embodiments of the present application respectively, and no limitation is made here and it will not be elaborated further.

[0058] S205, perform second-level Wiener filtering on the first-filtered channel estimate based on the optimal noise power to obtain a second-filtered channel estimate.

[0059] In the embodiments of the present application, the implementation manner of step S205 can be implemented in any one of the embodiments of the present application respectively, and no limitation is made here and it will not be elaborated further.

[0060] In the channel estimation filtering method provided by the embodiments of the present application, an initial channel estimation of a reference signal is obtained, and a first-level Wiener filtering is performed on the initial channel estimation to obtain a first filtered channel estimation. At the same time, a noise power measurement value and a target channel scenario are obtained based on the initial channel estimation. According to the noise power measurement value and the target channel scenario, the optimal noise power in the current target channel scenario is determined, and a second-level Wiener filtering is performed on the first filtered channel estimation based on the optimal noise power to obtain a second filtered channel estimation. In the embodiments of the present application, since the optimal noise power in the target channel scenario is obtained in advance, the second-level Wiener filtering can be immediately performed after the first-level Wiener filtering is completed, so that the process of noise estimation for the reference signal after the first filtering can be omitted, reducing the processing waiting time and the processing delay, and enabling the two-level filtering circuit to be designed with a tight coupling to enhance the stability of the filtering performance.

[0061] Based on the above embodiments, the embodiments of the present application can explain the process of obtaining the target channel scenario, such as Figure 3 shown, the process of obtaining the target channel scenario includes but is not limited to the following steps:

[0062] S301, based on the initial channel estimation, obtain the channel small-scale fading parameters.

[0063] In some implementations, the purpose of the initial channel estimation is to obtain the initial state and characteristics of the channel. Since the signal is affected by factors such as multipath effect and Doppler effect during transmission, the channel small-scale fading parameters can be determined based on the initial channel estimation, where the channel small-scale fading parameters include: channel impulse response (CIR), channel maximum delay, and channel maximum Doppler frequency offset.

[0064] Optionally, an inverse discrete Fourier transform can be performed on the initial channel estimation to obtain the channel impulse response. Then, based on the channel impulse response, the channel maximum delay can be determined. For example, let the initial channel estimation vector be [x(0,i), x(1,i), x(2,i)…x(N,i)], where i represents the i-th reference symbol and N represents the number of reference signals included in the symbol. Performing an inverse discrete Fourier transform on the initial channel estimation vector can obtain the channel impulse response.

[0065] Optionally, the maximum Doppler frequency shift of the channel can be determined based on the initial channel estimate. The difference product can be performed on the initial channel estimate to obtain the maximum Doppler frequency shift of the channel. For example, let the maximum Doppler frequency shift of the channel be fd, and the difference product calculation formula is fd(0) = arg(HLS(0,i), conj(HLS(0,i+1))), where arg represents the principal value interval operation of finding the phase of a complex number. Then the maximum Doppler frequency shift of the channel is fd = mean(fd(0), fd(1), fd(2)…fd(N)).

[0066] S302. Determine the target channel scenario based on the small-scale fading parameters of the channel.

[0067] In some implementations, a mapping table can be established in advance based on the first mapping relationship between the candidate channel scenarios and the candidate small-scale fading parameters of the channel. After obtaining the small-scale fading parameters of the channel, the mapping table can be queried to determine the target channel scenario from the candidate channel scenarios.

[0068] Optionally, using the small-scale fading parameters of the channel as the query condition, query the first mapping relationship between the candidate channel scenarios and the candidate small-scale fading parameters of the channel to obtain the channel scenario that has a mapping relationship with the small-scale fading parameters of the channel as the target channel scenario.

[0069] Exemplarily, taking the maximum channel delay and the maximum Doppler frequency shift of the channel as examples of the candidate small-scale fading parameters of the channel, and based on the first mapping relationship between the candidate channel scenarios and the candidate small-scale fading parameters of the channel, the established mapping table is shown in Table 2 below:

[0070] Table 2 First mapping relationship between candidate channel scenarios and candidate small-scale fading parameters of the channel

[0071]

[0072] If it is determined based on the small-scale fading parameters of the channel that the maximum channel delay is 15 and the maximum Doppler frequency shift of the channel is 6, then based on Table 2 above, it can be determined that candidate channel scenario B is the target channel scenario.

[0073] In the channel estimation filtering method provided by the embodiments of the present application, an initial channel estimation of a reference signal is obtained, and a first-level Wiener filtering is performed on the initial channel estimation to obtain a first-filtered channel estimation. At the same time, an optimal noise power in the current target channel scenario is determined based on the initial channel estimation, and a second-level Wiener filtering is performed on the first-filtered channel estimation based on the optimal noise power to obtain a second-filtered channel estimation. In the embodiments of the present application, since the optimal noise power in the target channel scenario is obtained in advance, the second-level Wiener filtering can be immediately performed after the first-level Wiener filtering is completed, so that the process of noise estimation for the reference signal after the first filtering can be omitted, reducing the processing waiting time, reducing the processing delay, and enabling the two-level filtering circuit to be closely coupled designed to enhance the stability of the filtering performance. By determining the channel small-scale fading parameter of the initial channel estimation and determining the target channel scenario based on the first mapping relationship, it helps to determine the optimal noise power of different target channel scenarios.

[0074] Figure 4 is a flowchart of a channel estimation filtering method shown according to an exemplary embodiment, as Figure 4 shown, the channel estimation filtering method of the embodiments of the present application includes but is not limited to the following steps:

[0075] S401, obtain an initial channel estimation of a reference signal, and determine an optimal noise power in the current target channel scenario based on the initial channel estimation.

[0076] In the embodiments of the present application, the implementation manner of step S401 can be implemented by any one of the embodiments of the present application respectively, and no limitation is made here and will not be elaborated.

[0077] S402, perform a first-level Wiener filtering on the initial channel estimation to obtain a first-filtered channel estimation.

[0078] In the embodiments of the present application, the implementation manner of step S402 can be implemented by any one of the embodiments of the present application respectively, and no limitation is made here and will not be elaborated.

[0079] S403, perform a second-level Wiener filtering on the first-filtered channel estimation based on the optimal noise power to obtain a second-filtered channel estimation.

[0080] In the embodiments of the present application, the implementation manner of step S403 can be implemented by any one of the embodiments of the present application respectively, and no limitation is made here and will not be elaborated.

[0081] S404, obtain an error measurement value between the initial channel estimation and the second-filtered channel estimation.

[0082] In some implementations, after obtaining the second filtered channel estimate, the filtering effect can be evaluated based on the initial channel estimate and the second filtered channel estimate to determine the filtering performance, and the second mapping relationship can also be corrected based on the error.

[0083] Optionally, an error measurement value can be calculated based on the difference between the initial channel estimate and the second filtered channel estimate, the conjugate complex number of the difference, the second filtered channel estimate, and the conjugate complex number of the second filtered channel estimate. The formula for calculating the error measurement value is as follows:

[0084] Error measurement value = ((Initial channel estimate - Second filtered channel estimate) * conj(Initial channel estimate - Second filtered channel estimate)) / ((Second filtered channel estimate) * conj(Second filtered channel estimate)) (2)

[0085] Where conj represents calculating the conjugate complex number.

[0086] S405. Obtain an error evaluation value based on the error measurement value and the noise addition correction factor.

[0087] In some implementations, the error evaluation value can be determined based on the average value of the error measurement values of multiple signals and the noise addition correction factor. The product of the error measurement mean and the noise addition correction factor can be calculated to obtain the error evaluation value. The formula for calculating the error evaluation value is as follows:

[0088] Error evaluation value = Error measurement mean * Noise addition correction factor (3)

[0089] In some implementations, based on the error evaluation value, it can be determined whether the current frame where the reference signal is located satisfies the update condition of the second mapping relationship. When the update condition is satisfied, the second mapping relationship can be updated based on the cumulative error.

[0090] Optionally, in response to satisfying the update condition, obtain the error evaluation values of multiple historical frames in the target channel scenario. By fitting the error evaluation value of the current frame and the error evaluation values of multiple historical frames, a first error curve is generated, and the first error curve is compared with the standard second error curve to obtain the cumulative error. The second error curve can be an empirical curve.

[0091] Furthermore, based on the cumulative error, the second mapping relationship is updated. Optionally, the second mapping relationship can be updated based on the magnitude of the cumulative error. The signal-to-noise ratio in the second mapping relationship can be adjusted to update the second mapping relationship.

[0092] Optionally, in response to the absolute value of the cumulative error being less than the first threshold, maintain the second mapping relationship; in response to the absolute value of the cumulative error being greater than the second threshold and greater than the first threshold, and the cumulative error being positive, increase the signal-to-noise ratio of the target channel scenario in the second mapping relationship; in response to the absolute value of the cumulative error being greater than the second threshold and greater than the first threshold, and the cumulative error being negative, decrease the signal-to-noise ratio of the target channel scenario in the second mapping relationship.

[0093] Exemplarily, based on Table 1 above, if the absolute value of the cumulative error is greater than the second threshold and greater than the first threshold, and the cumulative error is negative, and the target channel scenario is the candidate channel scenario C, then decrease the signal-to-noise ratio corresponding to the candidate channel scenario C, and the decrease can be of magnitude X.

[0094] In the channel estimation filtering method provided by the embodiments of the present application, an initial channel estimation of a reference signal is obtained, and a first-level Wiener filter is performed on the initial channel estimation to obtain a first filtered channel estimation. At the same time, based on the initial channel estimation, the optimal noise power in the current target channel scenario is determined, and a second-level Wiener filter is performed on the first filtered channel estimation based on the optimal noise power to obtain a second filtered channel estimation. In the embodiments of the present application, since the optimal noise power in the target channel scenario is obtained in advance, the second-level Wiener filter can be immediately performed after the first-level Wiener filter is completed, thereby omitting the process of performing noise estimation on the reference signal after the first filter, reducing the processing waiting time, reducing the processing delay, enabling the two-level filter circuit to be designed with a tight coupling to enhance the stability of the filtering performance. Further, by evaluating the filtering effect, determining the filtering performance, and correcting the second mapping relationship based on the error, the determination of the optimal noise power can be optimized, and the filtering performance can be better improved.

[0095] As Figure 5 The flowchart of updating the second mapping relationship as shown. Calculate the error measurement value based on the above formula (2), and calculate the error evaluation value based on formula (3) to determine whether the current frame where the reference signal is located satisfies the update condition of the second mapping relationship. If the update condition is satisfied, further determine the first error curve fitted by the error evaluation value, and based on the first error curve and the standard second error curve, obtain the cumulative error to determine whether the cumulative error has deteriorated. It can be determined whether the cumulative error has deteriorated based on the error threshold. If the absolute value of the cumulative error is less than the first threshold, it is determined that the cumulative error has not deteriorated, and the second mapping relationship is maintained; if the absolute value of the cumulative error is greater than the second threshold and greater than the first threshold, and the cumulative error is positive, it is determined that the cumulative error has deteriorated, and the signal-to-noise ratio of the target channel scenario in the second mapping relationship is increased; if the absolute value of the cumulative error is greater than the second threshold and greater than the first threshold, and the cumulative error is negative, it is determined that the cumulative error has deteriorated, and the signal-to-noise ratio of the target channel scenario in the second mapping relationship is decreased.

[0096] Figure 6 is a flowchart of a filtering method for channel estimation shown according to an exemplary embodiment. As Figure 6 shown, the filtering method for channel estimation in the embodiments of the present application includes but is not limited to the following steps:

[0097] S601, obtain an initial channel estimate of a reference signal.

[0098] S602, based on the initial channel estimate, obtain a noise power measurement value and a target channel scenario.

[0099] S603, based on the noise power measurement value and the target channel scenario, determine the optimal noise power in the target channel scenario.

[0100] S604, perform a first-stage Wiener filter on the initial channel estimate to obtain a first filtered channel estimate.

[0101] S605, based on the optimal noise power, perform a second-stage Wiener filter on the first filtered channel estimate to obtain a second filtered channel estimate.

[0102] S606, obtain an error measurement value between the initial channel estimate and the second filtered channel estimate.

[0103] S607, based on the error measurement value and a noise addition correction factor, obtain an error evaluation value.

[0104] S608, determine whether the current frame where the reference signal is located satisfies the update condition of the second mapping relationship.

[0105] S609, in response to satisfying the update condition, obtain the error evaluation values of multiple historical frames in the target channel scenario.

[0106] S610, fit the error evaluation value of the current frame and the error evaluation values of multiple historical frames to generate a first error curve.

[0107] S611, compare the first error curve with a standard second error curve to obtain a cumulative error.

[0108] S612, based on the cumulative error, update the second mapping relationship.

[0109] In the channel estimation filtering method provided by the embodiments of the present application, the initial channel estimation of the reference signal is obtained, and the first-level Wiener filtering is performed on the initial channel estimation to obtain the first filtered channel estimation. At the same time, the optimal noise power in the current target channel scenario is determined based on the initial channel estimation, and the second-level Wiener filtering is performed on the first filtered channel estimation based on the optimal noise power to obtain the second filtered channel estimation. In the embodiments of the present application, since the optimal noise power in the target channel scenario is obtained in advance, the second-level Wiener filtering can be immediately performed after the first-level Wiener filtering is completed, thereby omitting the process of noise estimation of the reference signal after the first filtering, reducing the processing waiting time, reducing the processing delay, and enabling the two-level filtering circuit to be closely coupled and designed to enhance the stability of the filtering performance.

[0110] As Figure 7 shown in the structural diagram of the Wiener filtering channel estimation implementation, the structural diagram includes an initial channel estimation module 1, a noise power measurement module 1A, a scenario recognition module 1B, a noise power matching module 1C, a first-level Wiener filtering module 2, a second-level Wiener filtering module 3, and a scenario training module 4.

[0111] The initial channel estimation module 1 can calculate the initial channel estimation of the reference signal and input the initial channel estimation into the noise power measurement module 1A to obtain the noise power measurement value, and input the initial channel estimation into the scenario recognition module 1B to determine the target channel scenario. Then, the noise power measurement value and the target channel scenario are input into the noise power matching module 1C to determine the optimal noise power in the target channel scenario. At the same time, the initial channel estimation is input into the first-level Wiener filtering module 2 to perform the first-level Wiener filtering to obtain the first filtered channel estimation, and the first filtered channel estimation and the optimal noise power are input into the second-level Wiener filtering module 3 to perform the second-level Wiener filtering to obtain the second filtered channel estimation.

[0112] Further, the initial channel estimation and the second filtered channel estimation are input into the scenario training module 4 to evaluate the filtering effect. By calculating the error measurement value between the initial channel estimation and the second filtered channel estimation, and performing error evaluation based on the error measurement value. The measurement error curve is obtained by fitting the evaluation value and compared with the empirical curve data to determine the cumulative error K. By comparing the absolute value of the cumulative error K with the set value, the second mapping relationship in the noise power matching module 1C can be updated. If the absolute value of K is less than the first threshold, the second mapping relationship is maintained; if the absolute value of K is greater than the second threshold and greater than the first threshold, and K is positive, the signal-to-noise ratio value in the second mapping relationship is adjusted by +X; if the absolute value of K is greater than the second threshold and greater than the first threshold, and K is negative, the signal-to-noise ratio value in the second mapping relationship is adjusted by -X. Here, X refers to the adjustment value.

[0113] To implement the above embodiments, the present application also proposes a filtering device for channel estimation.

[0114] Figure 8 FIG. is a schematic structural diagram of a filtering device for channel estimation provided by an embodiment of the present application.

[0115] As Figure 8 shown, the filtering device 800 for channel estimation includes:

[0116] A determination module 801, configured to obtain an initial channel estimation of a reference signal, and determine an optimal noise power in a current target channel scenario based on the initial channel estimation.

[0117] A first filtering module 802, configured to perform a first-level Wiener filtering on the initial channel estimation to obtain a first-filtered channel estimation.

[0118] A second filtering module 803, configured to perform a second-level Wiener filtering on the first-filtered channel estimation based on the optimal noise power to obtain a second-filtered channel estimation.

[0119] In a possible implementation manner of the embodiment of the present application, the determination module 801 is further configured to: obtain a noise power measurement value and the target channel scenario based on the initial channel estimation; and determine the optimal noise power in the target channel scenario based on the noise power measurement value and the target channel scenario.

[0120] In a possible implementation manner of the embodiment of the present application, the determination module 801 is further configured to: obtain a channel small-scale fading parameter based on the initial channel estimation; and determine the target channel scenario based on the channel small-scale fading parameter.

[0121] In a possible implementation manner of the embodiment of the present application, the determination module 801 is further configured to: perform an inverse discrete Fourier transform on the initial channel estimation to obtain a channel impulse response; determine a channel maximum delay based on the channel impulse response; and determine a channel maximum Doppler frequency offset based on the initial channel estimation.

[0122] In a possible implementation manner of the embodiment of the present application, the determination module 801 is further configured to: query a first mapping relationship between candidate channel scenarios and candidate channel small-scale fading parameters with the channel small-scale fading parameter as a query condition, and obtain a channel scenario having a mapping relationship with the channel small-scale fading parameter as the target channel scenario.

[0123] In a possible implementation manner of the embodiment of the present application, the determining module 801 is further configured to: use the noise power measurement value and the target channel scenario as query conditions to query the second mapping relationship between the candidate channel scenario - candidate noise power measurement value - candidate signal-to-noise ratio, and obtain the signal-to-noise ratio that has a mapping relationship with the noise power measurement value and the target channel scenario as the target signal-to-noise ratio; based on the target signal-to-noise ratio, determine the optimal noise power in the target channel scenario.

[0124] In a possible implementation manner of the embodiment of the present application, the second filtering module 803 is further configured to: obtain an error measurement value between the initial channel estimate and the second filtered channel estimate; based on the error measurement value and the noise addition correction factor, obtain an error evaluation value.

[0125] In a possible implementation manner of the embodiment of the present application, the second filtering module 803 is further configured to: determine whether the current frame where the reference signal is located satisfies the update condition of the second mapping relationship; in response to satisfying the update condition, obtain the error evaluation values of multiple historical frames in the target channel scenario; fit the error evaluation value of the current frame and the error evaluation values of the multiple historical frames to generate a first error curve; compare the first error curve with a standard second error curve to obtain a cumulative error; based on the cumulative error, update the second mapping relationship.

[0126] In a possible implementation manner of the embodiment of the present application, the second filtering module 803 is further configured to: in response to the absolute value of the cumulative error being less than a first threshold, maintain the second mapping relationship; or, in response to the absolute value of the cumulative error being greater than the second threshold and greater than the first threshold, and the cumulative error being positive, increase the signal-to-noise ratio of the target channel scenario in the second mapping relationship; or, in response to the absolute value of the cumulative error being greater than the second threshold and greater than the first threshold, and the cumulative error being negative, decrease the signal-to-noise ratio of the target channel scenario in the second mapping relationship.

[0127] In the channel estimation filtering device provided by the embodiments of the present application, an initial channel estimation of a reference signal is obtained, and a first-stage Wiener filtering is performed on the initial channel estimation to obtain a first filtered channel estimation. At the same time, an optimal noise power in the current target channel scenario is determined based on the initial channel estimation, and a second-stage Wiener filtering is performed on the first filtered channel estimation based on the optimal noise power to obtain a second filtered channel estimation. In the embodiments of the present application, since the optimal noise power in the target channel scenario is obtained in advance, the second-stage Wiener filtering can be immediately performed after the first-stage Wiener filtering is completed, thereby omitting the process of noise estimation for the reference signal after the first filtering, reducing the processing waiting time, reducing the processing delay, enabling the two-stage filtering circuit to be designed with tight coupling, and enhancing the stability of the filtering performance.

[0128] It should be noted that the foregoing explanation of the embodiments of the channel estimation filtering method also applies to the channel estimation filtering device of this embodiment, and will not be elaborated here.

[0129] To implement the above embodiments, the present application also proposes an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the foregoing embodiments.

[0130] To implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method provided by the foregoing embodiments.

[0131] To implement the above embodiments, the present application also proposes a computer program product, including a computer program, which when executed by a processor, implements the method provided by the foregoing embodiments.

[0132] The collection, storage, use, processing, transmission, provision, and application of the user's personal information involved in the present application all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0133] It should be noted that personal information from users should be collected for legal and reasonable purposes and not shared or sold outside of these legal uses. In addition, such collection / sharing should be carried out after obtaining the informed consent of the user, including but not limited to notifying the user to read the user agreement / user notice and signing an agreement / authorization including authorizing relevant user information before the user uses the function. In addition, any necessary steps need to be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0134] This application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, this application is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting the data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of users.

[0135] In the description of the foregoing embodiments, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0136] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0137] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a customized logic function or process, and the scope of the preferred implementation of this application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application belong.

[0138] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0139] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0140] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0141] In addition, in each embodiment of the present application, each functional unit may be integrated into one processing module, may exist separately as individual physical units, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0142] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A filtering method for channel estimation, characterized in that, The method includes: Obtaining an initial channel estimate of a reference signal, and determining an optimal noise power in a current target channel scenario based on the initial channel estimate; Performing first-level Wiener filtering on the initial channel estimate to obtain a first filtered channel estimate; Performing second-level Wiener filtering on the first filtered channel estimate based on the optimal noise power to obtain a second filtered channel estimate.

2. The method according to claim 1, wherein The determining of the optimal noise power in the current target channel scenario based on the initial channel estimate includes: Obtaining a noise power measurement value and the target channel scenario based on the initial channel estimate; Determining the optimal noise power in the target channel scenario based on the noise power measurement value and the target channel scenario.

3. The method according to claim 2, wherein The obtaining process of the target channel scenario includes: Obtaining channel small-scale fading parameters based on the initial channel estimate; Determining the target channel scenario based on the channel small-scale fading parameters.

4. The method according to claim 3, characterized in that The obtaining of the channel small-scale fading parameters based on the initial channel estimate includes: Performing an inverse discrete Fourier transform on the initial channel estimate to obtain a channel impulse response; Determining a maximum channel delay based on the channel impulse response; Determining a maximum Doppler frequency offset of the channel based on the initial channel estimate.

5. The method according to claim 4, characterized in that The determining of the target channel scenario based on the channel small-scale fading parameters includes: Querying a first mapping relationship between candidate channel scenarios and candidate channel small-scale fading parameters with the channel small-scale fading parameters as query conditions, and obtaining a channel scenario having a mapping relationship with the channel small-scale fading parameters as the target channel scenario.

6. The method according to any one of claims 1-5, characterized in that, The determining of the optimal noise power in the target channel scenario based on the noise power measurement value and the target channel scenario includes: Querying a second mapping relationship among candidate channel scenarios - candidate noise power measurement values - candidate signal-to-noise ratios with the noise power measurement value and the target channel scenario as query conditions, and obtaining a signal-to-noise ratio having a mapping relationship with the noise power measurement value and the target channel scenario as a target signal-to-noise ratio; Determining the optimal noise power in the target channel scenario based on the target signal-to-noise ratio.

7. The method according to claim 6, wherein After performing the second-level Wiener filtering on the first filtered channel estimate based on the optimal noise power to obtain the second filtered channel estimate, it further includes: Obtaining an error measurement value between the initial channel estimate and the second filtered channel estimate; Obtaining an error evaluation value based on the error measurement value and a noise addition correction factor.

8. The method according to claim 7, characterized in that, After obtaining the error evaluation value based on the error measurement value and the noise addition correction factor, it further includes: Determining whether the current frame where the reference signal is located satisfies an update condition of the second mapping relationship; In response to satisfying the update condition, obtaining error evaluation values of multiple historical frames in the target channel scenario; Fitting the error evaluation value of the current frame and the error evaluation values of the multiple historical frames to generate a first error curve; Comparing the first error curve with a standard second error curve to obtain a cumulative error; Updating the second mapping relationship based on the cumulative error.

9. The method according to claim 8, wherein Updating the second mapping relationship based on the cumulative error includes: Maintaining the second mapping relationship in response to the absolute value of the cumulative error being less than a first threshold; or, In response to the absolute value of the cumulative error being greater than the second threshold and greater than the first threshold, and the cumulative error being positive, increasing the signal-to-noise ratio of the target channel scenario in the second mapping relationship; or, In response to the absolute value of the cumulative error being greater than the second threshold and greater than the first threshold, and the cumulative error being negative, decreasing the signal-to-noise ratio of the target channel scenario in the second mapping relationship.

10. A filtering device for channel estimation, characterized in that The apparatus includes: A determination module, configured to obtain an initial channel estimate of a reference signal, and determine an optimal noise power in a current target channel scenario based on the initial channel estimate; A first filtering module, configured to perform a first-level Wiener filtering on the initial channel estimate to obtain a first-filtered channel estimate; A second filtering module, configured to perform a second-level Wiener filtering on the first-filtered channel estimate based on the optimal noise power to obtain a second-filtered channel estimate.

11. An electronic device, characterized in that, including: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, Computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of claims 1-9.

13. A computer program product, characterized in that, including a computer program, which when executed by a processor, implements the method according to any one of claims 1-9.

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