Timing estimation method and device, electronic equipment, storage medium and product
By constructing the target loss function and its gradient value, and using gradient descent iterative optimization, the problem of quadratic functions being difficult to match the actual changes in the impulse response function of raised cosine filters is solved, and higher-precision timing estimation is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- YUANCE INFORMATION TECHNOLOGY (CHENGDU) CO LTD
- Filing Date
- 2025-12-11
- Publication Date
- 2026-05-05
AI Technical Summary
Existing methods for estimating the impulse response function of raised cosine filters using quadratic functions are difficult to accurately match their actual changes, leading to deviations in timing estimation results and affecting estimation accuracy.
By obtaining the peak value, left-side correlation value, and right-side correlation value of the raised cosine filter, the target loss function and its gradient value are constructed, and gradient descent is used for iterative optimization until the target loss value meets the threshold to determine the timing estimation result.
It improves the accuracy of timing estimation, enabling more precise capture of the changes in the raised cosine filter at different sampling positions and reducing estimation bias.
Smart Images
Figure CN121984476A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of digital communication signal processing technology, and in particular to a timing estimation method, apparatus, electronic device, storage medium, and product. Background Technology
[0002] Currently, most existing estimation methods for raised cosine filters use quadratic functions (i.e., by constructing a quadratic function model and using its curve characteristics to approximate the raised cosine filter impulse response function) for calculation. However, there are fundamental differences in mathematical properties between quadratic functions and the raised cosine filter impulse response function. The curve shape of a quadratic function is relatively simple and fixed, while the raised cosine filter impulse response function has more complex and specific frequency and time domain characteristics. This makes it difficult to accurately match the actual changes of the raised cosine filter impulse response function when using quadratic functions for estimation, resulting in deviations in the timing estimation results and affecting the estimation accuracy. Summary of the Invention
[0003] This disclosure provides a timing estimation method, apparatus, electronic device, storage medium, and product, which to a certain extent solves the problem that existing estimation methods using quadratic functions are difficult to accurately match the actual changes in the impulse response function of raised cosine filters, resulting in deviations in timing estimation results and affecting estimation accuracy.
[0004] According to one aspect of this disclosure, a timing estimation method is provided, the method comprising: obtaining a raised cosine filter peak value, a correlation value to the left of the peak value, and a correlation value to the right of the peak value; determining a target loss function and its corresponding gradient value based on the peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value; iteratively descent to update the gradient value based on a preset adjustment step size, and determining the updated target loss value; and determining the timing estimation result when the updated target loss value meets a target threshold.
[0005] Furthermore, according to one aspect of the method of this disclosure, obtaining the peak value, the left-side correlation value, and the right-side correlation value of the raised cosine filter includes: determining the correlation sequence of the raised cosine filter, and determining the maximum value in the correlation sequence as the peak value; the correlation sequence is used to indicate the value of the raised cosine filter at different sampling positions; selecting the values of the sampling positions preceding and following the sampling position corresponding to the peak value to obtain the left-side correlation value and the right-side correlation value of the peak value.
[0006] Furthermore, according to one aspect of the method of this disclosure, determining the target loss function and its corresponding gradient value based on the peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value includes: determining the relative error based on the peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value; substituting the relative error into the impulse response function of the raised cosine filter and calculating the mean square error to obtain the target loss function; and performing derivative processing on the target loss function to obtain the gradient value; the derivative processing includes numerical gradient estimation.
[0007] Furthermore, according to one aspect of the method of this disclosure, the numerical gradient estimation method includes: performing finite difference estimation on the target loss function based on a preset perturbation value to obtain a gradient value.
[0008] Furthermore, according to one aspect of the method of this disclosure, based on a preset adjustment step size, the gradient value is iteratively updated by descent, and the updated target loss value is determined, including: obtaining a preset initial parameter for iteration; starting from the initial parameter, updating the gradient value sequentially with an adjustment step size according to the direction of gradient descent, and obtaining the target loss value after each update.
[0009] Furthermore, according to one aspect of the method of this disclosure, when the updated target loss value meets the target threshold, determining the timing estimation result includes: when the absolute value of the updated target loss value is less than or equal to the target threshold, determining the timing estimation result; when the absolute value of the updated target loss value is greater than the target threshold, updating the target loss value again until the updated target loss value is less than or equal to the target threshold, and then determining the timing estimation result.
[0010] According to another aspect of this disclosure, a timing estimation apparatus is provided, comprising: an acquisition unit for acquiring a raised cosine filter peak value, a left-side correlation value, and a right-side correlation value; a determination unit for determining a target loss function and its corresponding gradient value based on the peak value, the left-side correlation value, and the right-side correlation value; an iteration unit for iteratively updating the gradient value by descending based on a preset adjustment step size, and determining the updated target loss value; and an estimation unit for determining a timing estimation result when the updated target loss value meets a target threshold.
[0011] According to another aspect of this disclosure, an electronic device is provided, comprising: a memory for storing computer-readable instructions; and a processor for executing the computer-readable instructions, causing the electronic device to perform the method as described in any embodiment of one aspect.
[0012] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided for storing computer-readable instructions that, when executed by a processor, cause the processor to perform the method as described in any embodiment of one aspect.
[0013] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method as described in any embodiment of one aspect.
[0014] This disclosure provides a timing estimation method, apparatus, electronic device, storage medium, and product. The disclosure obtains the peak value, left-side correlation value, and right-side correlation value of the raised cosine filter; based on the peak value, the left-side correlation value, and the right-side correlation value, it determines a target loss function and its corresponding gradient value; iteratively updates the gradient value using a preset adjustment step size; and determines the timing estimation result when the updated gradient value meets a target threshold. Thus, compared to existing estimation methods based on quadratic function approximation, this disclosure directly utilizes the peak value and correlation value information of the raised cosine filter itself to construct the target loss function and continuously optimizes it through gradient descent iteration, enabling estimation that more closely matches the actual characteristics of the raised cosine filter's impulse response function. In summary, the technical solution provided by this disclosure can more accurately capture the details of the raised cosine filter's changes at different sampling positions, effectively reducing estimation bias and improving the accuracy of timing estimation.
[0015] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description
[0016] 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.
[0017] Figure 1 A flowchart illustrating a timing estimation method provided in an embodiment of this disclosure; Figure 2 A schematic diagram of the simulation results of timing estimation error provided in the embodiments of this disclosure; Figure 3 A structural block diagram of a timing estimation device provided in an embodiment of this disclosure; Figure 4 A hardware block diagram of an electronic device provided in an embodiment of this disclosure; Figure 5 This is a schematic diagram of a computer-readable storage medium provided in an embodiment of this disclosure. Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of this disclosure more apparent, exemplary embodiments according to this disclosure will now be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments of this disclosure. It should be understood that this disclosure is not limited to the exemplary embodiments described herein.
[0019] Currently, most existing estimation methods for raised cosine filters use quadratic functions (i.e., by constructing a quadratic function model and using its curve characteristics to approximate the raised cosine filter impulse response function) for calculation. However, there are fundamental differences in mathematical properties between quadratic functions and the raised cosine filter impulse response function. The curve shape of a quadratic function is relatively simple and fixed, while the raised cosine filter impulse response function has more complex and specific frequency and time domain characteristics. This makes it difficult to accurately match the actual changes of the raised cosine filter impulse response function when using quadratic functions for estimation, resulting in deviations in the timing estimation results and affecting the estimation accuracy.
[0020] Therefore, to address the aforementioned problems, this disclosure provides a timing estimation method. Please refer to... Figure 1 , Figure 1 This is a flowchart illustrating a timing estimation method provided in an embodiment of this disclosure. Figure 1 As shown, the method includes: In step S101, the peak value of the raised cosine filter, the correlation value to the left of the peak value, and the correlation value to the right of the peak value are obtained; In step S102, the target loss function and its corresponding gradient value are determined based on the peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value. In step S103, the gradient value is iteratively updated by descending based on the preset adjustment step size, and the updated target loss value is determined. In step S104, when the updated target loss value meets the target threshold, the timing estimation result is determined.
[0021] In this disclosure, the raised cosine filter peak value can be understood as the maximum value in the correlation sequence obtained after processing the signal with raised cosine filtering. It reflects the strongest response of the filtering result at a specific sampling position and is an important manifestation of the raised cosine filter characteristics at that position. The correlation value to the left of the peak value can be understood as the value at the sampling position preceding the peak value in the raised cosine filter correlation sequence. This value is related to the peak value and the filtering characteristics and can be used to assist in analyzing the filtering changes near the peak value. The correlation value to the right of the peak value can be understood as the value at the sampling position following the peak value in the raised cosine filter correlation sequence, and can also be used to analyze the variation characteristics of the raised cosine filter in the peak region.
[0022] In this disclosure, the target loss function can be understood as a function used to measure the degree of error in timing estimation based on the peak value and its left and right correlation values. This function is constructed by substituting the peak value, the left correlation value of the peak value, and the right correlation value of the peak value into a specific calculation method; the magnitude of the function value reflects the deviation of the current estimate from the ideal situation.
[0023] In this disclosure, the gradient value can be understood as the rate of change of the target loss function at the current parameter point. It can indicate the trend and magnitude of the change of the target loss function in each parameter direction at that point, providing directional guidance for subsequent optimization and reduction of the target loss function value through iterative methods.
[0024] In this disclosure, adjusting the step size can be understood as adjusting the parameter magnitude along the gradient direction each time the parameter is iteratively updated using the gradient descent method. It can affect the speed and accuracy of parameter updates.
[0025] In this disclosure, the target threshold can be understood as a preset standard for judging whether the gradient value is small enough and whether the parameters are close to the optimal solution. When the updated gradient value meets the target threshold, it means that the parameter adjustment has reached a relatively stable state, and at this time, a relatively satisfactory timing estimation result can be considered to have been obtained.
[0026] Specifically, the timing estimation process can be performed as follows: First, the input signal is subjected to raised cosine filtering to obtain a correlation sequence. The peak value of the raised cosine filter is accurately identified, and the corresponding correlation values to the left and right of the peak are obtained. Next, using these obtained peak values and correlation values, a target loss function is constructed according to preset rules, and the gradient value corresponding to this target loss function is obtained through numerical calculations. Then, an appropriate adjustment step size is set, and the gradient value is iterated continuously using gradient descent, with the target loss value recalculated after each update. During the iteration process, the updated target loss value can be continuously monitored. When the target loss value meets a preset target threshold, the iteration stops, and the timing estimation result is determined based on the parameter values at this point. This result can accurately reflect the timing information of the signal.
[0027] The following will explain in detail how to obtain the peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value, including: Determine the correlation sequence of the raised cosine filter, and identify the maximum value in the correlation sequence as the peak value; the correlation sequence is used to indicate the value of the raised cosine filter at different sampling positions; By selecting the values of the sampling positions preceding and following the peak value, we obtain the correlation values to the left and right of the peak value.
[0028] In this disclosure, the correlation sequence of the raised cosine filter can be understood as a set of filter response values obtained at different sampling times or sampling positions after the input signal has been processed by the raised cosine filter.
[0029] Specifically, to obtain the peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value, the following steps can be performed: First, the input signal to be processed is input into a pre-designed raised cosine filter, and the input signal is filtered according to the impulse response characteristics of the raised cosine filter. During the operation, the filtered signal is sampled according to the set sampling rate, resulting in a series of discrete sampled values. These values, arranged in the sampling order, constitute the correlation sequence of the raised cosine filter. Then, the correlation sequence is traversed, and the maximum value is found by comparing the values at each sampling position. This maximum value is the peak value of the raised cosine filter, and the sampling position index corresponding to the peak value is recorded. Finally, based on the recorded peak sampling position index, the value of the previous sampling position corresponding to that index is selected from the correlation sequence as the correlation value to the left of the peak value, and the value of the next sampling position is selected as the correlation value to the right of the peak value.
[0030] For example, this disclosure also provides specific embodiments for obtaining the peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value, including: For the correlation peak obtained based on sliding correlation that needs to be estimated at a given time, find its peak value and record it as... Record the amplitude value at the point before the peak as... Record the amplitude value at the point after the peak as The above records are set as three equidistant time points, with coordinates as follows: , and ,in , , d represents the sampling factor corresponding to the relevant peak data.
[0031] The following will explain in detail how to determine the target loss function and its corresponding gradient value, including the following methods: The relative error is determined based on the peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value; Substitute the relative error into the impulse response function of the raised cosine filter and calculate the mean square error to obtain the target loss function; The gradient value is obtained by performing derivative processing on the target loss function; derivative processing includes numerical gradient estimation.
[0032] In this disclosure, relative error can be understood as a normalized measure of the difference between the peak and the correlation values on the left and right sides of the peak. It can reflect the degree of local fluctuation of the filter response near the peak position and is used to quantify the bias tendency of timing estimation.
[0033] In this disclosure, the raised cosine filter impulse response function can be understood as a mathematical function describing the standard response characteristics of the raised cosine filter to a unit pulse input under an ideal noiseless environment. It can be expressed as the theoretical output value of the filter at the precise sampling time and serves as a benchmark for evaluating the deviation between the actual filtering result and the ideal state.
[0034] In this disclosure, the mean square error can be understood as the average of the squared differences between the actual observed peak and correlation values and the predicted values of the raised cosine filter impulse response function under the assumed timing offset. Used to quantify the overall error level of the timing estimation model.
[0035] In this disclosure, derivative processing can be understood as the process of differentiating the target loss function to obtain its gradient information. The gradient indicates the steepest descent direction of the loss function in the parameter space. Numerical gradient estimation can be understood as a method that approximates the analytical derivative using finite differences, suitable for scenarios where the loss function has a complex form or where the analytical derivative is difficult to solve.
[0036] Specifically, the following steps can be performed to determine the target loss function and its corresponding gradient value: Step 1: Calculate the relative error: Use a normalization formula to quantify the local deviation of the response near the peak. This value reflects the degree of deviation between the actual filtering result and the ideal symmetry. Step 2: Map the relative error to the raised cosine filter impulse response function, and generate a predicted value sequence to simulate the theoretical response under different offsets through interpolation or fitting. Step 3: Calculate the mean squared error: Compare the actual observed value sequence (obtained by extending the peak value and left and right correlation values) with the predicted value sequence, and calculate the target loss function value using the mean squared error formula. Step 4: Perform derivative processing on the target loss function to obtain the gradient value. The specific method for obtaining the gradient value can be found below.
[0037] The numerical gradient estimation method will be explained in detail below, including: Based on the preset perturbation value, the target loss function is estimated by finite difference to obtain the gradient value.
[0038] In this disclosure, the perturbation value can be understood as a tiny offset in the parameter space. Its magnitude needs to balance numerical stability and computational accuracy. That is, if it is too small, it is easily affected by floating-point errors, and if it is too large, it will introduce truncation errors.
[0039] In this disclosure, finite difference processing can be understood as a method of approximating the derivative by function value difference, such as using the central difference formula to calculate the gradient. This method has second-order accuracy and can effectively suppress numerical noise.
[0040] Specifically, when obtaining gradient values, the following steps can be performed: initialize the timing offset and perturbation value; calculate the loss value after positive and negative perturbation for each parameter dimension; apply the central difference formula to calculate the gradient components of each dimension; combine the gradient vectors for updating in the gradient descent iteration; during the iteration process, the perturbation value can be dynamically adjusted (e.g., adaptive scaling according to the gradient norm) to optimize the balance between convergence speed and accuracy.
[0041] For example, this disclosure provides specific embodiments for determining the target loss function and its gradient, including: Step 1: Set the estimated target (the deviation of the x-coordinate relative to the relevant peak) as e, and define the loss function as mean squared error (i.e., the target loss function of this disclosure):
[0042] in, The mean squared error function (i.e., the target loss function of this disclosure) is used. It is the expression for the impulse response function of the raised cosine filter. The following conditions can be met:
[0043] Where T is the symbol period.
[0044] Step 2: Calculate the gradient of the loss function with respect to the estimated parameters (i.e., the gradient value of this disclosure). First, the gradient of the loss function with respect to the parameter e is defined as:
[0045] in It is the derivative of the impulse response function of the raised cosine filter.
[0046] Next, the gradient of the parameter is approximated by the numerical gradient: Considering the complexity of the impulse response function of the raised cosine filter, to avoid complex analytical expressions... The operation, using the numerical gradient function right Replace it. The expression is as follows:
[0047] in, It is a numerical gradient approximation function. In this embodiment of the disclosure, the perturbation step size (a tiny increment) is... .
[0048] The following will explain in detail how to perform iterative descent, including the following methods: Obtain the starting parameters for the preset iteration; Starting with the initial parameters, the gradient values are updated sequentially with adjusting step sizes according to the direction of gradient descent, and the target loss value is obtained after each update.
[0049] In this disclosure, the initial parameter of the preset iteration can be understood as the initial offset estimated at a given time. And the initial adjustment step size η. Where, the initial offset It is the initial timing deviation estimate based on the peak position of the raised cosine filter correlation sequence (unit: sampling interval, usually 0 or ±0.5 sampling interval, corresponding to the initial phase assumption within the symbol period); the initial adjustment step size η is a coefficient used to control the offset update amplitude in the gradient descent iteration (it needs to be preset according to the system sampling rate and noise level, for example, the value range is 0.01~0.1, to avoid iterative oscillation or slow convergence).
[0050] Specifically, during iterative descent, the following steps can be performed: Step 1: Initialize the iteration counter k=0, and set the preset initial offset. As the offset estimate for the k-th iteration First, read the preset adjustment step size η; second, based on the current offset estimate... The third step involves combining the obtained raised cosine filter peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value with the input to the target loss function to calculate the target loss value for the k-th iteration. The fourth step uses numerical gradient estimation methods (such as the finite difference method) based on a preset perturbation value. Calculate the gradient value of the target loss function at δ, where δ is a small increment that does not affect the calculation accuracy; Step 4: Update the offset estimate according to the gradient descent direction to adjust the timing bias estimate along the direction of decreasing loss function; Step 5: Set the iteration counter k=k+1 and repeat steps 2 to 4 until the target loss value meets the convergence condition or reaches the preset maximum number of iterations.
[0051] The following details the convergence determination method for the target loss value, including: when the absolute value of the updated target loss value is less than or equal to the target threshold, the timing estimation result is determined; when the absolute value of the updated target loss value is greater than the target threshold, the target loss value is updated again until the updated target loss value is less than or equal to the target threshold, at which point the timing estimation result is determined.
[0052] Specifically, the convergence determination can be performed using the following steps: Step 1: Preset a target threshold (this threshold is the minimum error threshold for the convergence of the quantization loss function, and can be set according to the system's timing accuracy requirements); Step 2: After each iteration updates and obtains a new offset estimate, calculate the corresponding target loss value and obtain its absolute value; Step 3: Compare it with the target threshold. If the loss function has converged, the current estimate is the final timing estimate (corresponding to the precise timing deviation within the symbol period, which can be converted into the actual sampling time offset); if If the convergence fails, the gradient value is iterated and updated again to obtain the updated target loss value, until... The timing estimation results are obtained.
[0053] For example, this disclosure also provides specific embodiments of gradient descent iterations, including: Iteration step 1: Initialize offset value Define the learning step size s as 1 and the convergence condition as 0. for .
[0054] Iteration step 2: Using the formula Calculate the current predicted value , The expression is as follows:
[0055] Iteration step 3: Calculate the current gradient The expression is as follows:
[0056] Iteration step 4: Update the parameter estimates, as shown in the following expression:
[0057] Iteration step 5: Perform convergence judgment: when When this happens, steps 2, 3, and 4 are executed sequentially again until step 5, and the convergence check is performed again; when At this time The value is the final result of the timed estimation.
[0058] For example, this disclosure provides specific experimental results obtained using the timing estimation method of the embodiments of this disclosure, including: Figure 2 Figure 2 is a schematic diagram illustrating the simulation results of the timing estimation error provided in the embodiments of this disclosure. As can be seen from Figure 2, under the experimental conditions of 4x sampling speed and a signal-to-noise ratio of 40dB, the timing estimation error of the embodiments of this disclosure has decreased to [missing information]. Within the range, unbiased timing offset estimation is achieved, effectively eliminating the inherent error caused by quadratic function numerical estimation, and maintaining extremely high estimation accuracy throughout the entire timing offset interval.
[0059] This disclosure also provides a timing estimation device. Figure 3 A structural block diagram of a timing estimation device provided in an embodiment of this disclosure is shown below. Figure 3As shown, the timing estimation device 300 includes: Acquisition unit 301 is used to acquire the peak value of the raised cosine filter, the correlation value to the left of the peak value, and the correlation value to the right of the peak value; The determination unit 302 is used to determine the target loss function and its corresponding gradient value based on the peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value; The iteration unit 303 is used to iteratively descent and update the gradient value based on a preset adjustment step size, and determine the updated target loss value. The estimation unit 304 is used to determine the timing estimation result when the updated target loss value meets the target threshold.
[0060] In one exemplary embodiment, the acquisition unit 301 is specifically used to: determine the correlation sequence of the raised cosine filter, and determine the maximum value in the correlation sequence as the peak value; the correlation sequence is used to indicate the value of the raised cosine filter at different sampling positions; select the values of the sampling positions before and after the peak value to obtain the correlation value to the left of the peak value and the correlation value to the right of the peak value.
[0061] In one exemplary embodiment, the determining unit 302 is specifically used to: determine the relative error based on the peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value; substitute the relative error into the impulse response function of the raised cosine filter and calculate the mean square error to obtain the target loss function; perform derivative processing on the target loss function to obtain the gradient value; the derivative processing includes the numerical gradient estimation method.
[0062] In one exemplary embodiment, the determining unit 302 is specifically used to: perform finite difference estimation on the target loss function based on a preset perturbation value to obtain a gradient value.
[0063] In one exemplary embodiment, the iteration unit 303 is specifically used to: obtain the preset iteration start parameters; starting from the start parameters, update the gradient values sequentially with an adjustment step size according to the direction of gradient descent, and obtain the target loss value after each update.
[0064] In one exemplary embodiment, the estimation unit 304 is specifically used to: determine a timing estimation result when the absolute value of the updated target loss value is less than or equal to the target threshold; and update the target loss value again when the absolute value of the updated target loss value is greater than the target threshold, until the updated target loss value is less than or equal to the target threshold, and then determine the timing estimation result.
[0065] Figure 4This is a hardware block diagram of an electronic device provided according to an embodiment of the present disclosure. The electronic device 400 according to an embodiment of the present disclosure includes at least a processor; and a memory for storing computer-readable instructions. When the computer-readable instructions are loaded and executed by the processor, the processor performs the timing estimation method described in any of the preceding embodiments of the present disclosure.
[0066] Figure 4 The illustrated electronic device 400 specifically includes a central processing unit (CPU) 401, a graphics processing unit (GPU) 402, and a memory 403. These units are interconnected via a bus 404. The CPU 401 and / or GPU 402 can function as the aforementioned processor, and the memory 403 can function as the aforementioned memory storing computer-readable instructions. Furthermore, the electronic device 400 may also include a communication unit 405, a storage unit 406, an output unit 407, an input unit 408, and an external device 409, all of which are also connected to the bus 404.
[0067] Figure 5 This is a schematic diagram of a computer-readable storage medium provided in an embodiment of this disclosure. (As shown...) Figure 5 As shown, a computer-readable storage medium 500 according to an embodiment of the present disclosure stores computer-readable instructions 501 thereon. When the computer-readable instructions 501 are executed by a processor, the timing estimation method described with reference to the above figures according to any of the embodiments described in the foregoing description of the present disclosure is performed. The computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.
[0068] This disclosure further provides a computer program product, including a computer program that, when executed by a processor, implements the timing estimation method described in any of the preceding embodiments of this disclosure.
[0069] In summary, this disclosure provides a timing estimation method, apparatus, electronic device, storage medium, and product. This disclosure obtains the peak value, left-side correlation value, and right-side correlation value of the raised cosine filter; based on the peak value, the left-side correlation value, and the right-side correlation value, it determines the target loss function and its corresponding gradient value; iteratively updates the gradient value using a preset adjustment step size; and determines the timing estimation result when the updated gradient value meets a target threshold. Thus, compared to existing estimation methods based on quadratic function approximation, this disclosure directly utilizes the peak value and correlation value information of the raised cosine filter itself to construct the target loss function, and continuously optimizes it through gradient descent iteration, enabling estimation that more closely matches the actual characteristics of the raised cosine filter's impulse response function. In summary, the technical solution provided by this disclosure can more accurately capture the details of the raised cosine filter's changes at different sampling positions, effectively reducing estimation bias and improving the accuracy of timing estimation.
[0070] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.
[0071] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.
[0072] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.
[0073] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.
[0074] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.
[0075] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.
[0076] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.
[0077] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.
Claims
1. A timing estimation method, characterized in that, The method includes: Obtain the peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value of the raised cosine filter; Based on the peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value, the target loss function and its corresponding gradient value are determined; Based on a preset adjustment step size, the gradient value is iteratively updated by descent, and the updated target loss value is determined. When the updated target loss value meets the target threshold, the timing estimation result is determined.
2. The method according to claim 1, characterized in that, The acquisition of the raised cosine filter peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value includes: The correlation sequence of the raised cosine filter is determined, and the maximum value in the correlation sequence is determined as the peak value; the correlation sequence is used to indicate the value of the raised cosine filter at different sampling positions; By selecting the values of the sampling positions preceding and following the peak value, the correlation value to the left of the peak and the correlation value to the right of the peak are obtained.
3. The method according to claim 1, characterized in that, The determination of the target loss function and its corresponding gradient value based on the peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value includes: The relative error is determined based on the peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value; Substitute the relative error into the raised cosine filter impulse response function and calculate the mean square error to obtain the target loss function; The gradient value is obtained by performing derivative processing on the target loss function; the derivative processing includes numerical gradient estimation.
4. The method according to claim 3, characterized in that, The numerical gradient estimation method includes: Based on the preset perturbation value, the target loss function is estimated by finite difference to obtain the gradient value.
5. The method according to claim 1, characterized in that, The step of iteratively descent to update the gradient value based on a preset adjustment step size, and determining the updated target loss value, includes: Obtain the starting parameters for the preset iteration; Starting from the initial parameters, the gradient values are updated sequentially with the adjustment step size according to the direction of gradient descent, and the target loss value is obtained after each update.
6. The method according to claim 1, characterized in that, When the updated target loss value meets the target threshold, determining the timing estimation result includes: When the absolute value of the updated target loss is less than or equal to the target threshold, the timing estimation result is determined; When the absolute value of the updated target loss value is greater than the target threshold, the target loss value is updated again until the updated target loss value is less than or equal to the target threshold, and then the timing estimation result is determined.
7. A timing estimation device, characterized in that, The device includes: The acquisition unit is used to acquire the peak value of the raised cosine filter, the correlation value to the left of the peak value, and the correlation value to the right of the peak value; The determining unit is used to determine the target loss function and its corresponding gradient value based on the peak value, the correlation value to the left of the peak value, and the correlation value to the right of the peak value; An iterative unit is used to iteratively descent and update the gradient value based on a preset adjustment step size, and determine the updated target loss value. An estimation unit is used to determine a timing estimation result when the updated target loss value meets a target threshold.
8. An electronic device, characterized in that, include: Memory, used to store computer-readable instructions; as well as A processor for executing the computer-readable instructions, causing the electronic device to perform the method as described in any one of claims 1-6.
9. A non-transitory computer-readable storage medium for storing computer-readable instructions, characterized in that, When the computer-readable instructions are executed by a processor, the processor performs the method as described in any one of claims 1-6.
10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1-6.