Signal data processing method, apparatus, device, storage medium and computer program product

Abstract

The present application relates to the technical field of digital signal processing, and discloses a signal data processing method, device, equipment, storage medium and computer program product, including: obtaining signal data in a target channel, and dividing the signal data according to a target quantization bit number to obtain a subset to be quantized; setting a corresponding uniform quantization mapping formula for each subset to be quantized, and performing uniform quantization on the subset to be quantized based on the uniform quantization mapping formula to obtain a set of quantized discrete values; performing inverse quantization on the set of discrete values to obtain processed signal data. In view of the distribution characteristics of the signal, the present application divides the signal into a target number of groups, and designs a uniform quantization mapping formula matching its characteristics for each subgroup. The entire data mapping is non-uniform quantization, which improves the flexibility and adaptability of quantization, effectively reduces the computational complexity and hardware resource requirements, and significantly improves the equalization performance and system transmission quality.

Description

Technical Field

[0001] This application relates to the field of digital signal processing technology, and particularly to a signal data processing method, apparatus, device, storage medium, and computer program product. Background Art

[0002] In view of the dynamics and complexity of signals in an IM / DD (Intensity modulation and direct detection) system, the automatic grouping non-uniform quantization method can group according to the signal distribution characteristics and assign quantization mapping functions to different groups. However, existing methods usually rely on manual setting or prior knowledge, with low automation and difficulty in meeting the requirements of real-time and efficient processing. Summary of the Invention

[0003] The main purpose of this application is to provide a signal data processing method, apparatus, device, storage medium, and computer program product, aiming to solve the technical problem that existing non-uniform quantization methods usually rely on manual setting or prior knowledge, with low automation and difficulty in meeting the requirements of real-time and efficient processing.

[0004] To achieve the above purpose, this application proposes a signal data processing method, which includes:

[0005] Obtain signal data in a target channel, and divide the signal data according to a target quantization bit number to obtain a subset to be quantized;

[0006] Set a corresponding uniform quantization mapping formula for each subset to be quantized, and perform uniform quantization on the subset to be quantized based on the uniform quantization mapping formula to obtain a set of quantized discrete values;

[0007] Perform inverse quantization on the set of discrete values to obtain processed signal data.

[0008] Optionally, the step of setting a corresponding uniform quantization mapping formula for each subset to be quantized, and performing uniform quantization on the subset to be quantized based on the uniform quantization mapping formula to obtain a set of quantized discrete values includes:

[0009] Generate a step size set and an offset set based on the subset to be quantized;

[0010] Determine the uniform quantization mapping formula corresponding to the subset to be quantized according to the step size set, the offset set, and the target quantization bit number;

[0011] Perform uniform quantization on the subset to be quantized according to the uniform quantization mapping formula to obtain a set of quantized discrete values.

[0012] Optionally, the step of generating a step size set and an offset set based on the subset to be quantified includes:

[0013] Statistically analyze the data range of the signal data in each subset to be quantified, where the data range includes the maximum value and the minimum value of the signal data;

[0014] Calculate the step size of the subset to be quantified based on the target quantization bit number and the data range to obtain a step size set;

[0015] Calculate the offset of the subset to be quantified according to the target quantization bit number and the minimum value of the signal data to obtain an offset set.

[0016] Optionally, the step of obtaining the signal data in the target channel and dividing the signal data according to the target quantization bit number to obtain subsets to be quantified includes:

[0017] Obtain the signal data in the target channel and determine the subset to be quantified data set and the target quantization bit number based on the signal data;

[0018] Sort the signal data in the subset to be quantified data set through a preset sorting algorithm to obtain a target data set;

[0019] Divide the target data set according to the target quantization bit number to determine each subset to be quantified after division.

[0020] Optionally, the step of determining the target quantization bit number and the subset to be quantified data set based on the received real-time signal includes:

[0021] Statistically analyze the received real-time signal in a preset time period to determine the subset to be quantified data set;

[0022] Generate the target quantization bit number according to the characteristics of the signal data in the subset to be quantified data set.

[0023] Optionally, after the step of inverse quantifying the discrete value set to obtain the processed signal data, it further includes:

[0024] Determine the quantization error index of the processed signal data;

[0025] If the quantization error index is greater than the preset error range threshold, then adjust the target quantization bit number, and return to the step of dividing the signal data according to the target quantization bit number to obtain subsets to be quantified until the quantization error index is less than the preset error range threshold.

[0026] In addition, to achieve the above object, the present application also proposes a signal data processing device, and the signal data processing device includes:

[0027] A data division module, configured to obtain signal data in a target channel, and divide the signal data according to a target quantization bit number to obtain a subset to be quantized;

[0028] A data quantization module, configured to set a corresponding uniform quantization mapping formula for each subset to be quantized, and perform uniform quantization on the subset to be quantized based on the uniform quantization mapping formula to obtain a set of quantized discrete values;

[0029] A data dequantization module, configured to dequantize the set of discrete values to obtain processed signal data.

[0030] In addition, to achieve the above object, the present application further provides a signal data processing device, where the device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the signal data processing method as described above.

[0031] In addition, to achieve the above object, the present application further provides a storage medium, where the storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the signal data processing method as described above are implemented.

[0032] In addition, to achieve the above object, the present application further provides a computer program product, where the computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the signal data processing method as described above are implemented.

[0033] In the present application, signal data in a target channel is obtained, the signal data is divided according to a target quantization bit number to obtain a subset to be quantized; a corresponding uniform quantization mapping formula is set for each subset to be quantized, and the subset to be quantized is uniformly quantized based on the uniform quantization mapping formula to obtain a set of quantized discrete values; the set of discrete values is dequantized to obtain processed signal data. According to the distribution characteristics of the signal, the signal is divided into a target number of groups, and a uniform quantization mapping formula matching its characteristics is designed for each subgroup, and the entire data mapping is non-uniform quantization, which improves the flexibility and adaptability of quantization, effectively reduces the computational complexity and hardware resource requirements, and significantly improves the equalization performance and system transmission quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0035] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the attached drawings required in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other attached drawings can also be obtained based on these attached drawings.

[0036] Figure 1 It is a schematic flowchart of the first embodiment of the signal data processing method of the present application;

[0037] Figure 2 It is a test environment diagram of the signal data processing method of the present application;

[0038] Figure 3 It is a schematic flowchart of the second embodiment of the signal data processing method of the present application;

[0039] Figure 4 It is a non-uniform quantization flowchart of automatic grouping of the signal data processing method of the present application;

[0040] Figure 5 It is a schematic flowchart of the third embodiment of the signal data processing method of the present application;

[0041] Figure 6 It is a schematic module structure diagram of the signal data processing device in the embodiment of the present application;

[0042] Figure 7 It is a schematic device structure diagram of the hardware operating environment involved in the signal data processing method in the embodiment of the present application.

[0043] The realization of the purpose, functional features and advantages of the present application will be further described with reference to the attached drawings in combination with the embodiments. Specific embodiments

[0044] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0045] To better understand the technical solutions of the present application, the following will be described in detail in combination with the attached drawings of the specification and specific embodiments.

[0046] The main solution of the embodiment of the present application is: obtaining signal data in a target channel, and dividing the signal data according to a target quantization bit number to obtain a subset to be quantized; setting a corresponding uniform quantization mapping formula for each subset to be quantized, and performing uniform quantization on the subset to be quantized based on the uniform quantization mapping formula to obtain a set of quantized discrete values; performing inverse quantization on the set of discrete values to obtain processed signal data.

[0047] Short-distance optical interconnection is an indispensable key technology in modern data centers, high-performance computing, and high-speed access networks. With the rapid development of fields such as cloud computing, big data, and artificial intelligence, data traffic has increased explosively, and the communication requirements for high bandwidth, low latency, and low power consumption have been continuously improved. Short-distance optical interconnection realizes high-speed data transmission by using optical communication technology. Compared with electronic interconnection, it has higher bandwidth density, lower transmission loss, and power consumption, and can significantly improve system performance. Especially in the internal interconnection of data centers, optical communication provides an ideal solution for large-scale parallel data exchange between switches, servers, and storage devices. With the rapid development of information technology, optical communication has become the core technology of modern communication systems. IM / DD systems have become the mainstream solution for short-distance optical communication due to their advantages of simple structure, low cost, and low power consumption. However, when high-bandwidth signals are transmitted in IM / DD systems, they often face problems such as channel nonlinear distortion, group delay, bandwidth limitation, and noise, which pose great challenges to system performance. To address these problems, digital signal processing technology has been widely used, especially the equalization algorithm is outstanding in suppressing nonlinear distortion. Among them, the Volterra nonlinear equalizer, as a classic method, can effectively improve system performance. However, its computational complexity increases exponentially with the increase of the nonlinear order and signal bandwidth, making hardware implementation face severe challenges.

[0048] To reduce the complexity of digital signal processing algorithms, quantization technology has become a key means. By reducing the data bit width, the computational amount and hardware power consumption can be significantly reduced. However, traditional uniform quantization methods are difficult to perform quantization according to the characteristics of data distribution. Aiming at the dynamics and complexity of signals in IM / DD systems, the automatic grouping non-uniform quantization method can group according to the signal distribution characteristics and assign quantization mapping functions to different groups, showing great potential in balancing quantization accuracy and complexity. However, existing methods usually rely on manual setting or prior knowledge, with low automation and difficulty in meeting the requirements of real-time and efficient processing.

[0049] Therefore, this application provides a signal data processing method of automatic grouping non-uniform quantization, which realizes effective compensation for nonlinear distortion by combining signal characteristic analysis and dynamic optimization design. By designing an automatic grouping method, analyze the size distribution characteristics of the input signal, and divide the signal into groups with the target number of bits according to the size of the data to be quantized. Subsequently, design a uniform quantization mapping formula that matches the characteristics of each subgroup, and the entire data mapping is non-uniform quantization. After completing signal grouping and quantization, design a lightweight nonlinear equalizer and quantize the tap coefficients in the equalizer to a lower bit. The implementation of this equalizer combines the automatic grouping strategy, and further reduces the computational complexity and hardware power consumption by reducing the total number of bits occupied by the tap coefficients.

[0050] It should be noted that the execution entity of this embodiment can be a computing service device with data processing, network communication, and program running functions, such as a computer, or an electronic device capable of implementing the above functions. Hereinafter, taking a non-linear equalizer as an example, this embodiment and the following embodiments will be described.

[0051] Based on this, an embodiment of the present application provides a signal data processing method. Refer to Figure 1 , Figure 1 which is a schematic flowchart of the first embodiment of the signal data processing method of the present application.

[0052] In this embodiment, the signal data processing method includes:

[0053] Step S10, obtaining signal data in a target channel, and dividing the signal data according to a target quantization bit number to obtain a subset to be quantized.

[0054] It should be noted that the target channel refers to a specific channel that needs to perform signal processing in a specific communication or data transmission scenario. It can be a certain frequency band channel in wireless communication or a transmission link in wired communication (such as optical fiber, cable). Signal data is the digital data corresponding to electrical signals, optical signals, or other forms of signals containing specific information transmitted in the target channel. These data can represent various types of information such as sound, image, and text, and will be affected by factors such as noise and attenuation during transmission. Therefore, quantization and other processing are required to optimize data storage and transmission.

[0055] It can be understood that the target quantization bit number determines the number of discrete values that the quantized signal can represent. For example, 8-bit quantization can represent 2 8 = 256 different discrete values. The higher the bit number, the higher the quantization accuracy, but the greater the computational amount and storage space requirements; the lower the bit number, the lower the quantization accuracy, but the corresponding reduction in computing and storage costs. The subset to be quantized is a plurality of sub-datasets obtained by dividing the signal data in the target channel according to certain rules (such as numerical size, data distribution, etc.) based on the target quantization bit number. The data within each subset has similar characteristics or distribution rules, and subsequent quantization processing will be performed on these subsets respectively to better adapt to the characteristics of the data and improve the quantization effect and efficiency.

[0056] Specifically, the division rule can be determined according to the target quantization bit number and the characteristics of the signal data (such as data range, distribution rule, etc.). If the target quantization bit number is n, the value range of the signal data can be divided into 2 nIntervals; if the data distribution is uneven, methods such as cluster analysis can be used to divide according to the density and distribution characteristics of the data, so that the data within each interval has similar statistical characteristics. Then, according to the determined division rules, the signal data is assigned to different subsets. If divided by data range, the signal data needs to be sorted from small to large, and the data is sequentially placed into the corresponding intervals to form the subsets to be quantized.

[0057] Step S20, set a corresponding uniform quantization mapping formula for each of the subsets to be quantized, and perform uniform quantization on the subsets to be quantized based on the uniform quantization mapping formula to obtain a set of quantized discrete values.

[0058] It should be noted that the uniform quantization mapping formula is used to map the signal data in the subsets to be quantized to a finite number of discrete quantization values, and through specific quantization parameters, ensure that the signal data is quantized according to a unified rule within the subsets.

[0059] It can be understood that the parameters in the uniform quantization mapping formulas corresponding to different subsets to be quantized are different. For each subset to be quantized, the key parameters in the uniform quantization mapping formula need to be determined, such as the quantization step size and offset, etc. According to the determined quantization step size and offset, a specific uniform quantization mapping formula is constructed.

[0060] It should be understood that after uniform quantization, the originally continuous signal data in the subsets to be quantized is converted into a set of discrete numerical values, and the set of these discrete numerical values is the set of quantized discrete values. The number of elements in the set is determined by the number of quantization bits, and each element is the result of the quantization mapping formula acting on the original data.

[0061] In an example, in the uniform quantization of the Volterra equalizer, when uniformly quantizing the tap coefficients, first the floating-point tap coefficient R is passed into the uniform quantization mapping formula to obtain the quantized low-bit integer, where represents the quantized low-bit integer, with a range of represents the number of bits to be converted. The expression of the uniform quantization mapping formula is as follows:

[0062]

[0063] where S is the step size and Z is the offset, and the calculation formulas are as follows:

[0064]

[0065]

[0066] where represents the maximum value in the previously calculated quantized low-bit integers, The function represents the operation of taking the closest integer, which is a key step in converting a floating-point number to an integer. and represent the maximum and minimum values of the floating-point tap coefficients The initial value of the setting is 0. The meaning of the clamp function is as follows:

[0067]

[0068] Among them, w, a, and c are parameters for demonstration.

[0069] Finally, the quantization operation can be performed as follows:

[0070]

[0071] Among them, the dequantized tap coefficient Due to the rounding operation, it does not exactly match

[0072] In step S30, the discrete value set is dequantized to obtain the processed signal data.

[0073] It can be understood that, contrary to the quantization process, dequantization is a process of restoring the quantized discrete values to values closer to the original signal data. Through specific calculation methods, the discrete values are re-converted into numerical values with a certain precision to restore the original characteristics of the signal or approach the original state as much as possible. The processed signal data is the data obtained through the dequantization operation. These data retain the information of the original signal to a certain extent, but due to the inevitable errors in the quantization and dequantization processes, there may be a certain difference between the processed signal data and the original signal data.

[0074] It should be understood that the dequantization operation needs to be based on the parameters used during the previous quantization, such as the quantization step size and offset. These parameters determine how the original data is mapped to discrete values during quantization and are used to restore the discrete values during dequantization.

[0075] In one example, when using non-uniform quantization, different quantization intervals have different step sizes and offsets. For example, and are the step size and offset of the i-th subset to be quantized respectively, and need to be calculated in different sub-data. When performing non-uniform quantization, the formula for the dequantization operation is:

[0076]

[0077] Among them, is the final quantization value of each data point in the i-th subset to be quantized. ​Are the data points after inverse quantization. Each subset adopts an independent quantization rule, thus forming a non-uniform quantization characteristic, making the overall data distribution more in line with the signal characteristics. By allocating different step sizes and offsets, regional optimization of quantization accuracy is achieved, while significantly reducing the computational complexity of data processing.

[0078] Furthermore, in order to continuously optimize the quantization result in practical applications, ensure the accuracy and reliability of the final quantized data, and reduce the problem of excessive errors caused by improper setting of initial quantization parameters. After the step S30, the following steps are also included:

[0079] Determine the quantization error index of the processed signal data;

[0080] If the quantization error index is greater than the preset error range threshold, then adjust the target quantization bit number, and return to the step of dividing the signal data according to the target quantization bit number to obtain the subsets to be quantized, until the quantization error index is less than the preset error range threshold.

[0081] It should be noted that the quantization error index is a parameter used to quantitatively describe the quantization error size between the processed signal data and the original signal data, such as mean square error, peak signal-to-noise ratio, etc. The preset error range threshold is a quantization error boundary value preset according to specific application scenarios and requirements. When the quantization error index is less than this threshold, it is considered that the signal data after quantization processing is within an acceptable error range and can meet the requirements of practical applications; on the contrary, when the quantization error index is greater than this threshold, it indicates that the quantization effect is not ideal and the quantization process needs to be adjusted.

[0082] Specifically, compare the processed signal data with the original signal data to calculate the quantization error index value of the current processed signal data. When the quantization error index is greater than the preset error range threshold, the target quantization bit number needs to be adjusted. If the error is too large, the target quantization bit number will be appropriately increased to improve the quantization accuracy and reduce the quantization error; if the quantization error index is close to the threshold of the preset error range threshold, the target quantization bit number can also be finely adjusted to observe the change of the quantization error.

[0083] In an example, refer to Figure 2 , Figure 2This is a test environment diagram for the signal data processing method of this application. The diagram includes the digital signal processing (DSP) processes and related components of the transmitter (Tx) and receiver (Rx). The physical devices in the test environment include: AWG (Arbitrary Waveform Generator): An arbitrary waveform generator with a sampling rate of 224 GSa / s, used to generate signals with specific waveforms and produce the original signal at the transmitter; DML (Directly Modulated Laser): A directly modulated laser that converts electrical signals into optical signals for transmission; 10-km SSMF (10-kilometer standard single-mode fiber): Transmits optical signals but is affected by factors such as dispersion; VOA (Variable Optical Attenuator): A variable optical attenuator used to adjust the intensity of optical signals; 60-GHz PD (60GHz photodetector): Converts optical signals back into electrical signals; RTO (Real-Time Oscilloscope): A real-time oscilloscope with a sampling rate of 256 GSa / s, used for the observation and analysis of receiver signals.

[0084] The transmitter (Tx DSP) process includes: 1. Bit Generation: Generates the original binary data; 2. PAM-4 / 6 Modulation: Pulse amplitude modulation, selects PAM-4 (4 levels) or PAM-6 (6 levels) modulation methods, and maps the bit data to different amplitude levels; 3. Up-sampling: Increases the sampling rate of the signal to prepare for subsequent processing; 4. RRC Filter: Root raised cosine filter, used to filter the signal, reduce inter-symbol interference, and make the signal spectrum meet the transmission requirements; 5. Resampling to 224 GSa / s: Resamples to 224 GSa / s to match the sampling rate of the AWG.

[0085] The receiving end (Rx DSP) process includes: 1. Resampling&Matched filtering: Resample the received signal and enhance the useful signal through a matched filter to suppress noise and interference; 2. Frame synchronization: Determine the start position of the signal frame for correct data reception and decoding; 3. Quantized Volterra Equalization: Equalize the signal to compensate for channel distortion; 4. Down-sampling and GD-NW: Reduce the sampling rate and perform group delay equalization to further optimize the signal; 5. PAM-4 / 6 DeMod and BER count: Demodulate PAM-4 / 6 and perform bit error rate (BER) statistics after demodulation to demodulate the received signal back to the original bit data and evaluate the performance of the communication system.

[0086] In the experimental tests, the present application successfully carried out 50-70 GBd 5 / 10 km pulse amplitude modulation (PAM)-4 and 50-60 GBd 10 km PAM-6 C-band IM / DD transmissions, and compared the present application with the uniform quantization method. The experimental results show that, with little loss in communication quality, the present application reduces the tap coefficients of the equalizer to a lower bit amount to reduce the total storage space. In the case of PAM4@5km transmission, the effective number of quantization bits can be as low as 7 bits, saving up to 75.6% of the storage space and improving the best storage of traditional uniform quantization by 22.8%. In the case of PAM6@10km transmission, compared with traditional uniform quantization, the storage efficiency of the present application is increased by 24.0%.

[0087] In this embodiment, signal data in the target channel is obtained, and the signal data is divided according to the target quantization bits to obtain subsets to be quantized; a corresponding uniform quantization mapping formula is set for each subset to be quantized, and the subsets to be quantized are uniformly quantized based on the uniform quantization mapping formula to obtain a set of quantized discrete values; the set of discrete values is dequantized to obtain the processed signal data. According to the distribution characteristics of the signal, the signal is divided into a target number of groups, and a uniform quantization mapping formula matching its characteristics is designed for each subgroup. The entire data mapping is non-uniform quantization, which improves the flexibility and adaptability of quantization, effectively reduces the computational complexity and hardware resource requirements, and significantly improves the equalization performance and system transmission quality.

[0088] Refer to Figure 3 , Figure 3This is a schematic flowchart of the second embodiment of the signal data processing method of this application. Based on the above first embodiment, the second embodiment of the signal data processing method of this application is proposed.

[0089] In the second embodiment, step S10 includes:

[0090] Step S101, obtain signal data in the target channel, and determine a data set to be quantized and a target quantization bit number based on the signal data.

[0091] It should be noted that the data set to be quantized is a subset of the acquired signal data, which can be selected from the original signal data according to specific requirements or conditions, or signal data within a specific frequency band can be selected according to the frequency range of the signal as the data set to be quantized for quantization and subsequent processing of the signal characteristics of this frequency band.

[0092] Furthermore, in order to divide the data orderly according to the target quantization bit number and ensure a more reasonable division of each subset to be quantized. Step S101 may include:

[0093] Statistically analyze the received real-time signal in a preset time period as a cycle to determine the data set to be quantized; generate the target quantization bit number according to the characteristics of the signal data in the data set to be quantized.

[0094] It should be noted that the preset time period refers to a fixed time interval set in advance for periodic processing and analysis of real-time signals, and the preset time period can be determined according to specific application scenarios and requirements.

[0095] It can be understood that when statistically analyzing the received real-time signal in a preset time period as a cycle, first obtain the real-time signal through corresponding receiving devices (such as sensors, antennas, etc.). These signals may be analog signals and need to go through preprocessing steps such as analog-to-digital conversion (ADC) to convert them into digital signals. Taking the set preset time period as a cycle, statistically analyze the received real-time digital signal. The statistical content can include various characteristic parameters such as the mean, variance, maximum value, minimum value, and frequency distribution of the signal. For example, within each preset time period, calculate statistical quantities such as the average value and standard deviation of the signal, and these statistical quantities can reflect the overall characteristics of the signal during this time period.

[0096] It should be understood that when screening the dataset to be quantified based on statistical results, if the signal fluctuation range is small, all the signal data within this time period can be used as the dataset to be quantified; or when there are outliers or special change patterns in the signal during certain time periods, some data can be selectively selected as the dataset to be quantified according to requirements and characteristics. When generating the target quantization bits, it is necessary to conduct a detailed analysis of the signal data in the dataset to be quantified, including the dynamic range of the data (the difference between the maximum value and the minimum value), the data distribution (such as whether it is uniformly distributed, normally distributed, etc.), and the frequency components of the data.

[0097] Step S102: Sort the signal data in the dataset to be quantified through a preset sorting algorithm to obtain a target dataset.

[0098] It should be noted that the preset sorting algorithm refers to a specific algorithm determined in advance for sorting data, such as quicksort, mergesort, and selection sort, etc. When sorting, the desired sorting algorithm can also be selected according to the characteristics of the dataset. The target dataset is the dataset obtained after sorting the dataset to be quantified through the preset sorting algorithm.

[0099] Step S103: Divide the target dataset according to the target quantization bits to determine each subset to be quantified after division.

[0100] It should be understood that the target quantization bits correspond to the number of subsets to be quantified after division. When grouping, the last subset may contain fewer elements than the previous subsets due to uneven data distribution.

[0101] It can be understood that to determine each subset to be quantified after division, a deep learning model (such as an autoencoder or variational autoencoder) can also be used to automatically learn the signal characteristics to optimize the grouping rules and use an adaptive quantization algorithm to adjust the quantization level allocation in real time.

[0102] In one example, refer to Figure 4 , Figure 4 is the non-uniform quantization flowchart for automatic grouping of the signal data processing method of this application. In the figure, { x} represents the original data sequence, which contains a series of data points, such as etc. First, sort the data value x to be quantified according to the need, arrange the data in ascending order to ensure that the data can be more effectively allocated to different subsets during subsequent grouping. Then, evenly divide the sorted data into multiple subsets, and the number of subsets corresponds to the target quantization bits b to obtain each subset to be quantified after division:

[0103]

[0104] Among them, is the subset to be quantized. Each subset is subjected to uniform quantization in groups, and mapped to a set of discrete values within the quantization range:

[0105] ,

[0106] Finally, the de - quantization operation is performed one by one:

[0107]

[0108] wherein, is the final quantization value of each data point in the i - th subset to be quantized. is the data point after de - quantization. and are the step size and offset of the i - th subset to be quantized, respectively.

[0109] In this embodiment, signal data in the target channel is acquired, and a dataset to be quantized and a target quantization bit number are determined based on the signal data; the signal data in the dataset to be quantized is sorted through a preset sorting algorithm to obtain a target dataset; the target dataset is divided according to the target quantization bit number to determine each subset to be quantized after division. According to the different characteristics of signal data in different time periods, a target quantization bit number that can adapt to the dynamic changes of the signal is generated, avoiding performance degradation caused by a fixed quantization bit number, and improving the quantization accuracy and processing efficiency of signal data.

[0110] Referring to Figure 5 , Figure 5 is a schematic flowchart of the third embodiment of the signal data processing method of the present application. Based on the above - mentioned second embodiment, the third embodiment of the signal data processing method of the present application is proposed.

[0111] In the third embodiment, step S20 includes:

[0112] Step S201, generating a step size set and an offset set based on the subset to be quantized.

[0113] It can be understood that by using the step size set and the offset set to perform quantization mapping on each subset, non - uniform quantization mapping based on the statistical characteristics of the signal can be realized. When each subset is uniformly quantized into a fixed number of quantization points, a step size set and an offset of a specific number of bits can be generated.

[0114] Furthermore, in order to optimize according to their own characteristics in subsets with different data ranges, avoiding quantization deviation caused by unified quantization, which helps to improve the quantization effect and resource utilization efficiency. Step S201 may include:

[0115] Statistically calculate the data range of the signal data in each subset to be quantized, where the data range includes the maximum value and the minimum value of the signal data; calculate the step size of the subset to be quantized based on the target quantization bit number and the data range to obtain a set of step sizes; calculate the offset of the subset to be quantized based on the target quantization bit number and the minimum value of the signal data to obtain a set of offsets.

[0116] It can be understood that for each subset to be quantized, the step size can be calculated according to its data range and the target quantization bit number, and the offset can be calculated based on the target quantization bit number and the minimum value of the signal data in this subset.

[0117] In one example, for each data point, a set of step sizes with a specific number of bits is generated and offsets The calculation formulas are as follows:

[0118]

[0119]

[0120] where represents the subset to be quantized, and b is the target quantization bit number.

[0121] Step S202, determine the uniform quantization mapping formula corresponding to the subset to be quantized according to the set of step sizes, the set of offsets, and the target quantization bit number.

[0122] It can be understood that by dynamically allocating different quantization mapping formulas for each subgroup according to the grouping result, the effect of lower bit quantization can be achieved.

[0123] In one example, for each data point, its final quantization value mapping formula is:

[0124]

[0125] where s i and z i are calculated in different sub - data respectively, and each subset adopts independent quantization rules, thereby forming a non - uniform quantization characteristic, making the overall data distribution more in line with the signal characteristics.

[0126] Step S203, perform uniform quantization on the subset to be quantized according to the uniform quantization mapping formula to obtain a set of quantized discrete values.

[0127] In this embodiment, a step set and an offset set are generated based on the subset to be quantized; a uniform quantization mapping formula corresponding to the subset to be quantized is determined according to the step set, the offset set, and the target quantization bit number; and the subset to be quantized is uniformly quantized according to the uniform quantization mapping formula to obtain a quantized discrete value set. By setting independent quantization rules for each subset, a non-uniform quantization characteristic is formed, so that the overall data distribution is more in line with the signal characteristic.

[0128] It should be noted that the above examples are only for understanding the present application and do not constitute a limitation on the signal data processing method of the present application. Based on this technical concept, more forms of simple transformations are within the protection scope of the present application.

[0129] The present application also provides a signal data processing device. Please refer to Figure 6 , the signal data processing device includes:

[0130] A data partitioning module 10, configured to obtain signal data in a target channel and partition the signal data according to a target quantization bit number to obtain a subset to be quantized;

[0131] A data quantization module 20, configured to set a corresponding uniform quantization mapping formula for each of the subsets to be quantized, and uniformly quantize the subsets to be quantized based on the uniform quantization mapping formula to obtain a quantized discrete value set;

[0132] A data dequantization module 30, configured to dequantize the discrete value set to obtain processed signal data.

[0133] The signal data processing device provided by the present application adopts the signal data processing method in the above embodiment, and can solve the technical problem that existing non-uniform quantization methods usually rely on manual setting or prior knowledge, have a low degree of automation, and are difficult to meet the requirements of real-time and efficient processing. Compared with the prior art, the beneficial effects of the signal data processing device provided by the present application are the same as those of the signal data processing method provided by the above embodiment, and other technical features in the signal data processing device are the same as those disclosed in the method of the above embodiment, and will not be elaborated here.

[0134] The present application provides a signal data processing device. The signal data processing device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the signal data processing method in the first embodiment above.

[0135] Next, refer to Figure 7, which shows a schematic structural diagram of a signal data processing device suitable for implementing the embodiments of the present application. The signal data processing device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description: tablet computers), PMPs (Portable Media Player: portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The shown signal data processing device is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0136] As Figure 7 shown, the signal data processing device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the signal data processing device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the signal data processing device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a signal data processing device with various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems may be alternatively implemented or had.

[0137] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by a processing device 1001, the above functions defined in the methods of the embodiments disclosed in the present application are executed.

[0138] The signal data processing device provided by the present application adopts the signal data processing method in the above embodiments, and can solve the technical problem that existing non-uniform quantization methods usually rely on manual setting or prior knowledge, have a low degree of automation, and are difficult to meet the requirements of real-time and efficient processing. Compared with the prior art, the beneficial effects of the signal data processing device provided by the present application are the same as those of the signal data processing method provided by the above embodiments, and other technical features in the signal data processing device are the same as those disclosed in the method of the previous embodiment, and will not be elaborated here.

[0139] It should be understood that the various parts disclosed in the present application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0140] As mentioned above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0141] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the signal data processing method in the above embodiments.

[0142] The computer-readable storage medium provided by the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0143] The above computer-readable storage medium may be included in a signal data processing device; or may exist separately without being assembled into the signal data processing device.

[0144] The above computer-readable storage medium carries one or more programs, and when the one or more programs are executed by a signal data processing device, the signal data processing device is caused to execute the signal data processing method described above.

[0145] Computer program code for performing the operations of the present application may be written in one or more programming languages or combinations thereof. The above programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0146] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0147] The modules described in the embodiments of the present application can be implemented in software or in hardware. In some cases, the name of the module does not constitute a limitation on the unit itself.

[0148] The readable storage medium provided by the present application is a computer-readable storage medium, and the computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for executing the above signal data processing method, which can solve the technical problem that existing non-uniform quantization methods usually rely on manual setting or prior knowledge, have a low degree of automation, and are difficult to meet the requirements of real-time and efficient processing. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as those of the signal data processing method provided by the above embodiments, and will not be elaborated here.

[0149] The present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the steps of the signal data processing method as described above.

[0150] The computer program product provided by the present application can solve the technical problem that existing non-uniform quantization methods usually rely on manual setting or prior knowledge, have a low degree of automation, and are difficult to meet the requirements of real-time and efficient processing. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the signal data processing method provided by the above embodiments, and will not be elaborated here.

[0151] The above are only some embodiments of the present application, and do not limit the patent scope of the present application. All equivalent structural transformations made under the technical concept of the present application by using the content of the specification and drawings of the present application, or directly / indirectly applied in other related technical fields, are included in the patent protection scope of the present application.

Claims

1. A signal data processing method, characterized in that: The signal data processing method comprises: Acquire signal data in a target channel, and divide the signal data according to a target quantization bit number to obtain a subset to be quantized, wherein the signal data is non-uniformly distributed signal data after frame synchronization; Setting a corresponding uniform quantization mapping formula for each of the subsets to be quantized, and uniformly quantizing the subsets to be quantized based on the uniform quantization mapping formula to obtain a quantized discrete value set for optimizing data storage; Dequantizing the discrete value set to obtain processed signal data; The step of acquiring signal data in the target channel and dividing the signal data according to the target quantization bit number to obtain a subset to be quantized includes: Acquire signal data in a target channel, and determine a data set to be quantized and a target number of quantization bits based on the signal data; Sorting the signal data in the data set to be quantified by a preset sorting algorithm to obtain a target data set; Dividing the target data set according to the target quantization bit number, and determining each subset to be quantized after the division; The step of setting a corresponding uniform quantization mapping formula for each of the subsets to be quantized, and uniformly quantizing the subsets to be quantized based on the uniform quantization mapping formula to obtain a quantized discrete value set includes: Generate a step size set and an offset set based on the subset to be quantized; Determine a uniform quantization mapping formula corresponding to the subset to be quantized according to the step size set, the offset set and the target number of quantization bits; The subset to be quantized is uniformly quantized according to the uniform quantization mapping formula to obtain a quantized discrete value set.

2. The signal data processing method according to claim 1, characterized in that: The step of generating a step size set and an offset set based on the subset to be quantized comprises: Counting the data range of the signal data in each of the subsets to be quantized, wherein the data range includes the maximum value and the minimum value of the signal data; Calculate the step size of the subset to be quantized based on the target number of quantization bits and the data range to obtain a step size set; The offset of the subset to be quantized is calculated according to the target number of quantization bits and the minimum value of the signal data to obtain an offset set.

3. The signal data processing method according to claim 1, characterized in that: The step of acquiring signal data in the target channel and determining the data set to be quantized and the target number of quantization bits based on the signal data includes: The signal data received in the target channel is counted at a preset time period to determine the data set to be quantified; A target number of quantization bits is generated according to the characteristics of the signal data in the data set to be quantized.

4. The signal data processing method according to claim 1, characterized in that: After the step of dequantizing the discrete value set to obtain processed signal data, the method further includes: Determining a quantization error index of the processed signal data; If the quantization error index is greater than the preset error range threshold, the target quantization bit number is adjusted, and the step of dividing the signal data according to the target quantization bit number to obtain the subset to be quantized is returned until the quantization error index is less than the preset error range threshold.

5. A signal data processing device, characterized in that: The device comprises: A data partitioning module, used to obtain signal data in a target channel, and to partition the signal data according to a target quantization bit number to obtain a subset to be quantized, wherein the signal data is non-uniformly distributed signal data after frame synchronization; A data quantization module, used to set a corresponding uniform quantization mapping formula for each of the subsets to be quantized, and to uniformly quantize the subsets to be quantized based on the uniform quantization mapping formula to obtain a quantized discrete value set for optimizing data storage; A data dequantization module, used for dequantizing the discrete value set to obtain processed signal data; The data partitioning module is further used to obtain signal data in the target channel, and determine the data set to be quantized and the target quantization bit number based on the signal data; sort the signal data in the data set to be quantized by a preset sorting algorithm to obtain the target data set; divide the target data set according to the target quantization bit number, and determine each subset to be quantized after the division; The data quantization module is also used to generate a step set and an offset set based on the subset to be quantized; determine the uniform quantization mapping formula corresponding to the subset to be quantized according to the step set, the offset set and the target number of quantization bits; and uniformly quantize the subset to be quantized according to the uniform quantization mapping formula to obtain a quantized discrete value set.

6. A signal data processing device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the signal data processing method according to any one of claims 1 to 4.

7. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the signal data processing method according to any one of claims 1 to 4 are implemented.

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