A frequency offset estimation method, device, system, electronic equipment, and storage medium

Abstract

The application provides a frequency offset estimation method, device, system, electronic equipment and storage medium, wherein the frequency offset estimation method comprises the following steps: obtaining each data point in a target data symbol to construct an initial data set; removing abnormal data points in the initial data set to obtain a normal data set; extracting effective data points in the normal data set, and performing absolute value processing on each extracted effective data point to obtain an effective data set; calculating an actual phase of the target data symbol according to the effective data set; obtaining a theoretical phase of the target data symbol, and calculating a phase difference of the target data symbol according to the actual phase and the theoretical phase; and calculating a frequency offset estimation value of the target data symbol according to the phase difference. The method realizes frequency offset estimation with low complexity, has certain universality and good robustness, and further improves system performance. The method is suitable for the technical field of communication.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, in particular to a frequency offset estimation method, device, system, electronic equipment and storage medium. BACKGROUND

[0002] With the rapid development of the number of 5G terminals and network construction, various high-speed and large-bandwidth application scenarios are gradually increasing, and the performance requirements of communication network systems are further improved. Generally speaking, phase noise caused by unstable transceiver signal generators, Doppler shift caused by relative motion of transceivers, and other factors will cause a certain frequency deviation in the received signal, thereby causing a significant reduction in the reception decoding performance. To this end, it is usually necessary to estimate and compensate the frequency deviation of the received signal at the receiving end to eliminate the negative effects of the frequency deviation and improve the reception performance of the communication system.

[0003] In existing mobile communication technology, the phase deviation is usually calculated based on two-column pilot correlation algorithm, and then the frequency offset is further obtained. However, in actual communication scenarios, in order to improve the data transmission rate, the peak speed scenario may only configure a single column of pilots or even no pilots. At this time, due to the limitation of the number of pilots, the traditional two-column pilot correlation algorithm cannot be used, and a new frequency offset estimation algorithm is needed to handle the single-column pilot configuration with frequency offset and to compensate for the frequency offset to improve the system communication reliability.

[0004] In existing research work, the frequency offset estimation for single-column pilot scenarios is mostly based on clustering algorithms, including calculating the center of gravity, K-means, Gaussian mixture model clustering, etc. The complexity and performance of the clustering algorithm are obviously affected by the number of clusters and the number of iterations. Although the existing algorithm can solve the frequency offset estimation problem in some single-column pilot configuration scenarios, it has high computational complexity and has problems such as difficulty in fitting the model or difficulty in convergence when a small amount of resources are configured, thereby affecting the system performance. SUMMARY

[0005] To solve one of the above technical defects, the present application provides a frequency offset estimation method, device, system, electronic equipment and storage medium.

[0006] According to a first aspect of an embodiment of the present application, a frequency offset estimation method is provided for estimating the frequency offset of the equalized data symbol in a communication system, comprising:

[0007] Obtaining each data point in the target data symbol to construct an initial data set;

[0008] Removing abnormal data points in the initial data set to obtain a normal data set;

[0009] extracting valid data points in the normal data set, and performing absolute value processing on each of the extracted valid data points to obtain a valid data set;

[0010] calculating an actual phase of the target data symbol according to the valid data set;

[0011] obtaining a theoretical phase of the target data symbol, and calculating a phase difference of the target data symbol according to the actual phase and the theoretical phase;

[0012] calculating a frequency offset estimation value of the target data symbol according to the phase difference.

[0013] Preferably, the abnormal data points in the initial data set are removed to obtain a normal data set, and specifically includes:

[0014] comparing the real part and the imaginary part of each data point in the initial data set with a first reference value a1 respectively, and determining the data point with the real part or the imaginary part greater than a1 as an abnormal data point;

[0015] all abnormal data points are deleted, and a normal data set is formed according to all normal data points;

[0016] wherein a1 = a · a0, a is a constellation spread corresponding to the modulation mode of the target data symbol, and a0 is a maximum amplitude corresponding to the modulation mode of the target data symbol.

[0017] Preferably, the valid data points in the normal data set are extracted, and the absolute value processing is performed on each of the extracted valid data points to obtain a valid data set, and specifically includes:

[0018] according to the formula the P r value of the normal data point greater than or equal to is determined as a valid data point, and the valid data point is extracted;

[0019] the real part and the imaginary part of each of the extracted valid data points are subjected to absolute value processing, and a valid data set is formed according to each of the valid data points subjected to absolute value processing;

[0020] wherein is a normal data set, is a complex conjugate value of , and R0 is a selected radius corresponding to the modulation mode.

[0021] Preferably, the actual phase of the target data symbol is calculated according to the valid data set, and specifically includes:

[0022] The real parts of the effective data points in the effective data set are accumulated, and an average of the accumulated real part values is calculated to obtain a real part of an actual cluster center of the target data symbol;

[0023] The imaginary parts of the effective data points in the effective data set are accumulated, and an average of the accumulated imaginary part values is calculated to obtain an imaginary part of the actual cluster center of the target data symbol;

[0024] According to the real part and the imaginary part of the actual cluster center, a real phase of the target data symbol is calculated.

[0025] Preferably, a theoretical phase of the target data symbol is obtained, and according to the real phase and the theoretical phase, a phase difference of the target data symbol is calculated, specifically including:

[0026] A maximum amplitude a0 corresponding to a modulation mode of the target data symbol is taken as the real part and the imaginary part of a theoretical cluster center of the target data symbol respectively;

[0027] According to the real part and the imaginary part of the theoretical cluster center, a theoretical phase of the target data symbol is calculated;

[0028] According to a formula a phase difference of the target data symbol is calculated

[0029] Wherein, is the theoretical phase of the target data symbol, is the real phase of the target data symbol.

[0030] Preferably, according to the phase difference, a frequency offset estimation value of the target data symbol is calculated, specifically including:

[0031] According to a formula a frequency offset estimation value Δf of the target data symbol is calculated;

[0032] Wherein, F c represents a sampling rate of a current system, Δl represents a symbol interval between the target data symbol and a demodulation reference signal (DMRS), and N s represents a symbol sample length.

[0033] According to a second aspect of the embodiments of the present application, a frequency offset estimation device is provided, including a module for implementing the frequency offset estimation method according to any one of the above.

[0034] According to a third aspect of the embodiments of the present application, a communication system is provided, including the frequency offset estimation device as described above.

[0035] According to a fourth aspect of the embodiments of the present application, an electronic device is provided, including:

[0036] a memory;

[0037] a processor; and

[0038] a computer program;

[0039] wherein the computer program is stored in the memory and configured to be executed by the processor to implement the frequency offset estimation method according to any one of the preceding embodiments.

[0040] According to a fifth aspect of the embodiments of the present application, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program; the computer program is executed by a processor to implement the frequency offset estimation method according to any one of the preceding embodiments.

[0041] The frequency offset estimation method provided by the present application estimates the frequency offset based on each data point in the target data symbol, without limiting the configuration of multiple columns of pilots, and can better solve the frequency offset estimation problem in the configuration of a single column of pilots or even no pilot, and has certain universality. Meanwhile, by eliminating the abnormal data points in the initial data set and performing absolute value processing on the effective data points, the algorithm complexity of the clustering process is reduced, and the problems such as insufficient clustering quantity in the small resource block (RB) resource configuration scenario and the easy occurrence of abnormal data points in the complex channel environment can be better solved, and the system performance is further improved. BRIEF DESCRIPTION OF DRAWINGS

[0042] The accompanying drawings, which are included to provide a further understanding of the present application, constitute a part of the present application and illustrate embodiments of the present application and the description thereof, which serve to explain the present application, and do not constitute improper limitations on the present application. In the drawings:

[0043] Figure 1 a flow chart of a frequency offset estimation method provided by the present application;

[0044] Figure 2 a functional structure schematic diagram of a frequency offset estimation device provided by the present application;

[0045] Figure 3 a functional structure schematic diagram of an initial data set construction module in the frequency offset estimation device; Figure 2

[0046] a functional structure schematic diagram of a normal data set acquisition module in the frequency offset estimation device; Figure 4 Figure 2 a functional structure schematic diagram of an effective data set acquisition module in the frequency offset estimation device;

[0047] Figure 5 Figure 2 a functional structure schematic diagram of an effective data set acquisition module in the frequency offset estimation device;

[0048] Figure 6 a functional structure schematic diagram of an effective data set acquisition module in the frequency offset estimation device; Figure 2 ​​A functional structure diagram of an actual phase acquisition module;

[0049] Figure 7 For Figure 2 A functional structure diagram of a theoretical phase acquisition module.

[0050] In the figure: 10 is an initial data set construction module, 20 is a normal data set acquisition module, 30 is an effective data set acquisition module, 40 is an actual phase acquisition module, 50 is a theoretical phase acquisition module, 60 is a phase difference acquisition module, 70 is a frequency offset estimation value acquisition module, 101 is a data point acquisition unit, 102 is an initial data set acquisition unit, 201 is a first determination unit, 202 is a deletion unit, 203 is a normal data set forming unit, 301 is a first calculation unit, 302 is a second determination unit, 303 is an extraction unit, 304 is an absolute value processing unit, 305 is an effective data set forming unit, 401 is a second calculation unit, 402 is a third calculation unit, 403 is an actual phase acquisition unit, 501 is a theoretical cluster center acquisition unit, and 502 is a theoretical phase acquisition unit. DETAILED DESCRIPTION

[0051] In order to make the technical solutions and advantages in the embodiments of the present application clearer, the exemplary embodiments of the present application are further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and are not an exhaustive enumeration of all embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0052] In view of some problems existing in the prior art:

[0053] In a first aspect, the frequency offset estimation method provided in the embodiments of the present application can be executed by a frequency offset estimation device or equipment, or by a component such as a chip, a chip system, etc. configured inside the frequency offset estimation device or equipment, or by a logic module or software, etc. having part or all of the functions of the frequency offset estimation device or equipment. The present application does not make any limitation in this regard.

[0054] Exemplarily, as shown in the figure, the frequency offset estimation method is used for frequency offset estimation on the equalized data symbol in a communication system, which comprises: Figure 1

[0055] Acquiring each data point in the target data symbol to construct an initial data set;

[0056] Removing the abnormal data points in the initial data set to obtain a normal data set;

[0057] ​Extracting valid data points in the normal data set, and performing absolute value processing on each valid data point to obtain a valid data set;

[0058] According to the valid data set, the actual phase of the target data symbol is calculated;

[0059] The theoretical phase of the target data symbol is obtained, and the phase difference of the target data symbol is calculated according to the actual phase and the theoretical phase;

[0060] According to the phase difference, the frequency offset estimation value of the target data symbol is calculated.

[0061] Based on the above scheme, the frequency offset estimation method provided in the present application estimates the frequency offset based on each data point in the target data symbol, does not depend on configuring the number of pilot columns for correlation, does not need to limit the configuration of multiple pilot columns, and can be used in single-column pilot or even no pilot extreme scenarios. It can better solve the frequency offset estimation problem in the single-column pilot or even no pilot scenario, and has certain universality. At the same time, by removing the abnormal data points in the initial data set and performing absolute value processing on the valid data points, the algorithm complexity of the clustering process is reduced, and at the same time, it can better cope with the problems of insufficient clustering quantity in the small resource block (RB) resource configuration scenario and the easy occurrence of abnormal data points in the complex channel environment, has good robustness, and further improves the system performance.

[0062] In some possible implementation manners of the first aspect, the obtaining each data point in the target data symbol and constructing an initial data set specifically includes:

[0063] Obtaining all data corresponding to the real part and the imaginary part in the target data symbol , forming a corresponding data point, that is, a complex signal (x i ,y i ), wherein (x i ,y i ) is the data point of the i-th data in the target data symbol, r i is the i-th data in the target data symbol, N is the number of data in the target data symbol, x i is the real part of the i-th data point, and y i is the imaginary part of the i-th data point; in specific implementation, X={real(r1), real(r2),..., real(r N )} and Y={imag(r1), imag(r2),..., imag(r N )}; wherein, real(·) is the real part of the data point, imag(·) is the imaginary part of the data point, X is the real part of the target data symbol, and Y is the imaginary part of the target data symbol;

[0064] According to all data points, an initial data set {(x1, y1), (x2, y2), …, (x j j N N )} is constructed.

[0065] In some possible implementation modes of the first aspect, the removing of the abnormal data points in the initial data set to obtain the normal data set specifically comprises:

[0066] comparing the real part and the imaginary part of each data point in the initial data set with a first reference value a1 respectively, and determining the data point with the real part or the imaginary part greater than a1 as an abnormal data point;

[0067] all abnormal data points are deleted, and a normal data set is formed according to all normal data points;

[0068] wherein a1 = a a0, a0 is the maximum amplitude corresponding to the modulation mode of the target data symbol, and a is the constellation spread corresponding to the modulation mode of the target data symbol, the size of a is mainly affected by factors such as noise level and channel environment, and is usually determined based on the empirical cumulative distribution result of the maximum amplitude corresponding to different modulation modes in different channel environments, which can correct the theoretical maximum amplitude. Specifically, the point in the constellation corresponding to different modulation modes can indicate the possible state of the amplitude and phase of the target data symbol, and the distance from the point to the origin is the amplitude after modulation, and the angle is the phase after modulation.

[0069] In specific implementation, the corresponding maximum amplitude a0 is obtained according to the mapping formula corresponding to different modulation modes, and the modulation mode is quadrature phase shift keying (QPSK) or quadrature amplitude modulation (such as 16QAM, 64QAM, 256QAM, etc.).

[0070] Specifically, according to TS 36.211, the mapping formula corresponding to each modulation mode is shown in the following table:

[0071]

[0072] As shown in the table, the maximum amplitude corresponding to QPSK is a0 = 1, the maximum amplitude corresponding to 16QAM is the maximum amplitude corresponding to 64QAM is and the maximum amplitude corresponding to 256QAM is

[0073] Based on the above scheme, by removing the abnormal data points in the initial data set, the influence of the abnormal data points on the subsequent clustering process is effectively reduced, and the robustness of the method is effectively improved.

[0074] ​​​In some possible implementation manners of the first aspect, the valid data points in the normal data set are extracted, and the real part and the imaginary part of each extracted valid data point are subjected to absolute value processing to obtain a valid data set, specifically including:

[0075] According to the formula Pn is calculated for each normal data point in the normal data set, and the normal data point with Pn r greater than or equal to is determined as a valid data point (x′ n , y′ n ), and the valid data point (x′ n , y′ n ) is extracted; wherein, the normal data set is the complex conjugate value of , R0 is a radius corresponding to a modulation mode, x′ n is the real part of the nth valid data point, and y′ n is the imaginary part of the nth valid data point; specifically, the normal data point satisfying is the four cluster data in the outermost circle of the constellation diagram, because the adjacent constellation points in the outermost layer are not easy to be mixed, the valid data point selection can maximally reduce the negative influence on the clustering result, and the robustness is better in a low signal-to-noise ratio scenario;

[0076] The real part and the imaginary part of each extracted valid data point are subjected to absolute value processing, and the valid data set is formed according to each valid data point subjected to the absolute value processing ; wherein, x′ is the real part of the nth valid data point subjected to the absolute value processing, and y′ is the imaginary part of the nth valid data point subjected to the absolute value processing; by subjecting the real part and the imaginary part of each valid data point to the absolute value processing, the valid data points of the target data symbol are concentrated in the first quadrant, so that the four similar cluster results are merged into one, and only one clustering center needs to be considered in the subsequent clustering process, the clustering complexity is effectively reduced, and the overall algorithm performance can also be avoided from being affected when there is no data in a certain quadrant in a small resource block (RB) scenario.

[0077] In specific implementation, the value of R0 is the average of the maximum amplitude and the second largest amplitude, and the second largest amplitude is obtained according to a corresponding mapping formula. Taking 16QAM as an example, the maximum amplitude is the second largest amplitude is 1, the radius corresponding to 16QAM is the radius corresponding to 64QAM is and the radius corresponding to 256QAM is And for QPSK, it has only 4 clusters, so it does not need this operation.

[0078] Based on the above scheme, taking 16QAM as an example, the target data symbol is clustered, and theoretically, 16 clusters can be obtained. Similarly, 64QAM can be clustered into 64 clusters, and 256QAM can be clustered into 256 clusters. Common clustering algorithms such as K-means algorithm and Gaussian mixture algorithm usually need to traverse all data and iterate multiple times to obtain a stable cluster structure. Therefore, the complexity of this kind of algorithm is multiplied by the increase of the number of clusters and the number of iterations. Therefore, after the elimination of abnormal data points, the effective data points are selected and the same cluster effective data points are merged, thereby reducing the number of theoretical clustering results, achieving the purpose of reducing the algorithm complexity, and improving the performance of the method.

[0079] In some possible implementation manners of the first aspect, the actual phase of the target data symbol is calculated according to the effective data set, and specifically includes the following steps.

[0080] The real parts of the effective data points in the effective data set are accumulated, and the average value of the accumulated real part is calculated to obtain the real part of the actual clustering center of the target data symbol.

[0081] The imaginary parts of the effective data points in the effective data set are accumulated, and the average value of the accumulated imaginary part is calculated to obtain the imaginary part of the actual clustering center of the target data symbol.

[0082] The actual phase of the target data symbol is calculated according to the real part and the imaginary part of the actual clustering center.

[0083] Specifically, the calculation formula of the actual phase is as follows:

[0084] In the formula, n is the number of effective data points after absolute value processing, and arctan(·) is the inverse tangent function.

[0085] In some possible implementation manners of the first aspect, the theoretical phase of the target data symbol is obtained, and the phase difference of the target data symbol is calculated according to the actual phase and the theoretical phase, and specifically includes the following steps.

[0086] The maximum amplitude a0 corresponding to the modulation mode of the target data symbol is taken as the real part and the imaginary part of the theoretical clustering center of the target data symbol, respectively.

[0087] The theoretical phase of the target data symbol is calculated according to the real part and the imaginary part of the theoretical clustering center. Specifically, ​

[0088] According to the formula The phase difference of the target data symbol is calculated

[0089] Wherein, The theoretical phase of the target data symbol, The actual phase of the target data symbol.

[0090] In some possible implementation manners of the first aspect, the frequency offset estimation value of the target data symbol is calculated according to the phase difference, specifically comprising:

[0091] According to the formula The frequency offset estimation value Δf of the target data symbol is calculated;

[0092] Wherein, F c Indicates the current system sampling rate, Δl indicates the symbol interval between the target data symbol and the DMRS, N s Indicates the symbol sample length, and N s Only related to the system Fourier transform (FFT) point number and the cyclic prefix (CP) length.

[0093] The frequency offset estimation method provided by the embodiments of the present application can be applied to various systems, especially 5G systems. For example, the applicable systems can be a global system of mobile communication (GSM) system, a code division multiple access (CDMA) system, a wideband code division multiple access (WCDMA) general packet radio service (GPRS) system, a long term evolution (LTE) system, an LTE frequency division duplex (FDD) system, an LTE time division duplex (TDD) system, a long term evolution advanced (LTE-A) system, a universal mobile telecommunication system (UMTS), a worldwide interoperability for microwave access (WiMAX) system, a 5G new radio (NR) system, and the like. The various systems all include terminal devices and network devices. The system can also include a core network part, for example, an evolved packet system (EPS), a 5G system (5GS), and the like.

[0094] In an optional embodiment of the present application, after the above step of calculating the frequency offset estimation value of the target data symbol according to the phase difference, the method further includes performing frequency offset estimation and compensation on the received signal based on the frequency offset estimation value to obtain a target received signal.

[0095] In an optional embodiment of the present application, the method further includes performing symbol synchronization on the target received signal, and the synchronization process includes:

[0096] obtaining a target received signal and performing segmentation processing on the target received signal to obtain a plurality of to-be-processed sub-signals;

[0097] For each target sub-signal in the plurality of to-be-processed sub-signals, performing conjugate multiplication operation on a first sampling value and a second sampling value corresponding to a current sampling point in the target sub-signal to obtain a clock error value of the target sub-signal; the second sampling value represents a difference between sampling values of two adjacent sampling points of the current sampling point;

[0098] error correcting the clock error value, and determining the sampling clock deviation of the target sub-signal based on the corrected clock error value;

[0099] performing an interpolation filtering operation on the target sub-signal based on the sampling clock deviation to obtain a target signal after symbol synchronization processing of each of the to-be-processed sub-signals.

[0100] In an optional embodiment of the present application, the segmenting the target received signal to obtain a plurality of to-be-processed sub-signals comprises:

[0101] obtaining a total data length of the target received signal;

[0102] segmenting the target received signal according to a preset data length to obtain a plurality of initial to-be-processed sub-signals with equal data length;

[0103] determining, based on the total data length, that a data length of a last initial to-be-processed sub-signal is less than the preset data length, and performing data padding on the last initial to-be-processed sub-signal so that a data length of the data-padded last initial to-be-processed sub-signal is equal to the preset data length to obtain the plurality of to-be-processed sub-signals; or determining, based on the total data length, that a data length of a last initial to-be-processed sub-signal is equal to the preset data length, and determining the plurality of initial to-be-processed sub-signals as the plurality of to-be-processed sub-signals.

[0104] In an optional embodiment of the present application, the target received signal is a signal in superframe structure, and the superframe structure is composed of a head guard symbol, a tail guard symbol, a superframe start (SOSF), a superframe format indicator (SFFI), a superframe header (SFH), a physical layer frame (PLF), and a pilot.

[0105] In an optional embodiment of the present application, the error correcting the clock error value comprises:

[0106] obtaining a roll-off factor of a raised cosine roll-off (RRC) filter and a signal-to-noise ratio, and determining a compensation factor for the clock error value based on the roll-off factor and the signal-to-noise ratio;

[0107] performing normalization processing on the clock error value of the target sub-signal;

[0108] error correcting the normalized clock error value based on the compensation factor to obtain a corrected clock error value of the target sub-signal.

[0109] In an optional embodiment of the present application, the determining the sampling clock deviation of the target sub-signal based on the corrected clock error value comprises:

[0110] determining a preset fitting order and determining a fitting factor of each fitting order in the preset fitting order;

[0111] constructing a polynomial fitting function for the modified clock error value in the preset fitting order according to the fitting factor of each fitting order, to obtain a sampling clock deviation of the target sub-signal;

[0112] The interpolation filtering operation on the sampling clock deviation of the target sub-signal to obtain a target signal after symbol synchronization processing of each to-be-processed sub-signal includes:

[0113] performing data padding on the target sub-signal before a starting sampling point and after a terminal sampling point to obtain a data-padded target sub-signal;

[0114] determining a second interpolation coefficient according to the data-padded target sub-signal and a pre-configured first interpolation coefficient; the first interpolation coefficient is a matrix vector of the preset fitting order;

[0115] performing interpolation filtering operation on the sampling clock deviation of the target sub-signal based on the second interpolation coefficient to obtain a target signal after symbol synchronization processing of each to-be-processed sub-signal.

[0116] In an optional embodiment of the present application, the above method further includes:

[0117] determining at least two pilot data sequences of the target sub-signal based on a current sampling point at a pilot position in the target sub-signal and at least one adjacent sampling point of the current sampling point;

[0118] performing correlation calculation on the at least two pilot data sequences and a local pilot data sequence respectively to obtain a cross-correlation value between the at least two pilot data sequences and the local pilot data sequence of the target sub-signal;

[0119] determining a correlation power value of the target sub-signal according to the cross-correlation value between the at least two pilot data sequences and the local pilot data sequence.

[0120] In an optional embodiment of the present application, the target received signal is a 2-times symbol rate digital signal.

[0121] In a second aspect, the present application provides a frequency offset estimation device, which includes modules for implementing the frequency offset estimation method as described above.

[0122] Exemplarily, as shown in Figure 2 The frequency offset estimation device includes:

[0123] An initial data set construction module 10 is configured to construct an initial data set for each data point in a target data symbol;

[0124] A normal data set acquisition module 20 is configured to eliminate abnormal data points in the initial data set to obtain a normal data set;

[0125] An effective data set acquisition module 30 is configured to extract effective data points in the normal data set and perform absolute value processing on each extracted effective data point to obtain an effective data set;

[0126] An actual phase acquisition module 40 is configured to calculate an actual phase of the target data symbol according to the effective data set;

[0127] A theoretical phase acquisition module 50 is configured to acquire a theoretical phase of the target data symbol;

[0128] A phase difference acquisition module 60 is configured to calculate a phase difference of the target data symbol according to the actual phase and the theoretical phase;

[0129] A frequency offset estimation value acquisition module 70 is configured to calculate a frequency offset estimation value of the target data symbol according to the phase difference.

[0130] Optionally, as shown in Figure 3 , the initial data set construction module 10 comprises:

[0131] A data point acquisition unit 101 is configured to acquire real parts and imaginary parts corresponding to all data points in a target data symbol , to form corresponding data points, i.e., complex signals (x i ,y i ), wherein (x i ,y i ) is a data point of an i-th data in the target data symbol, x i is a real part of the i-th data point, and y i is an imaginary part of the i-th data point;

[0132] An initial data set acquisition unit 102 is configured to construct an initial data set {(x1,y1),(x2,y2)…,(x j ,y j )…,(x N ,y N )} according to all the data points.

[0133] Optionally, as shown in Figure 4 , the normal data set acquisition module 20 comprises:

[0134] The first determining unit 201 is configured to compare the real part and the imaginary part of each data point in the initial data set with a first reference value a1 respectively, and determine the data point as an abnormal data point if both the real part and the imaginary part are greater than a1; wherein a1=a0*α, α is a constellation spread corresponding to the modulation mode of the target data symbol, and a0 is a maximum amplitude corresponding to the modulation mode of the target data symbol.

[0135] The deleting unit 202 is configured to delete all the abnormal data points.

[0136] The normal data set forming unit 203 is configured to form a normal data set according to all the normal data points.

[0137] Optionally, as shown in the figure, Figure 5 the effective data set obtaining module 30 comprises:

[0138] The first calculating unit 301 is configured to calculate each normal data point in the normal data set according to the formula ; wherein is the normal data set, is the complex conjugate value of

[0139] The second determining unit 302 is configured to determine the normal data point as an effective data point if the value of P r is greater than or equal to ; wherein R0 is a selected radius corresponding to the modulation mode.

[0140] The extracting unit 303 is configured to extract the effective data point.

[0141] The absolute value processing unit 304 is configured to perform absolute value processing on the real part and the imaginary part of each extracted effective data point.

[0142] The effective data set forming unit 305 is configured to form an effective data set according to each effective data point after the absolute value processing.

[0143] Optionally, as shown in the figure, Figure 6 the actual phase obtaining module 40 comprises:

[0144] The second calculating unit 401 is configured to add the real part of each effective data point in the effective data set, and calculate the average value of the added real part value to obtain the real part of the actual clustering center of the target data symbol.

[0145] The third calculating unit 402 is configured to add the imaginary part of each effective data point in the effective data set, and calculate the average value of the added imaginary part value to obtain the imaginary part of the actual clustering center of the target data symbol.

[0146] The actual phase acquisition unit 403 is configured to calculate an actual phase of the target data symbol according to the real part and the imaginary part of the actual clustering center.

[0147] Optionally, as shown in the figure, Figure 7 The theoretical phase acquisition module 50 includes:

[0148] The theoretical clustering center acquisition unit 501 is configured to take the maximum amplitude a0 corresponding to the modulation mode of the target data symbol as the real part and the imaginary part of the theoretical clustering center of the target data symbol respectively.

[0149] The theoretical phase acquisition unit 502 is configured to calculate a theoretical phase of the target data symbol according to the real part and the imaginary part of the theoretical clustering center.

[0150] Optionally, the phase difference acquisition module 60 is specifically configured to calculate the phase difference of the target data symbol according to the formula Wherein, is the theoretical phase of the target data symbol, is the actual phase of the target data symbol.

[0151] Optionally, the frequency offset estimation value acquisition module 70 is specifically configured to calculate the frequency offset estimation value Δf of the target data symbol according to the formula Wherein, Δf represents the frequency offset estimation value of the equalized data symbol, c F represents the phase offset of the valid data sample, and F s represents the current system sampling rate, ΔI represents the symbol interval between the equalized data symbol and the demodulation reference signal, and N s represents the symbol sample length.

[0152] In the embodiments of the present application, the method and the device are based on the same application concept. Since the principles of the method and the device for solving problems are similar, the implementation of the device and the method can be mutually referred to, and the repeated parts will not be described again.

[0153] In a third aspect, the embodiments of the present application provide a communication system, which includes the frequency offset estimation device as described above.

[0154] In a fourth aspect, the embodiments of the present application provide an electronic device, which can be any device capable of realizing the frequency offset estimation function. The device can be various terminal devices, for example: desktop computers, laptop computers, tablet computers, handheld devices, etc. The device can be realized by software and / or hardware.

[0155] Exemplarily, the electronic device includes:

[0156] a memory;

[0157] a processor; and

[0158] a computer program;

[0159] The computer program is stored in the memory and configured to be executed by the processor to implement the frequency offset estimation method as described above.

[0160] In a fifth aspect, a computer readable storage medium is provided in the embodiments of the present application. The computer readable storage medium can be a ROM, a RAM, a magnetic disk or an optical disk, etc.

[0161] Exemplarily, the computer readable storage medium stores a computer program. The computer program is executed by the processor to implement the frequency offset estimation method as described above.

[0162] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, such as C language, VHDL language, Verilog language, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0163] The present application is described with reference to the flowcharts and / or block diagrams according to the methods, devices (systems) and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing apparatus to produce a machine, so that the instructions executed by the computer or other programmable data processing apparatus produce a device implemented in the flowcharts and / or block diagrams. Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or blocks. Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or blocks.

[0164] These computer program instructions can also be stored in a computer readable storage medium that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a manufactured product including instruction devices that implement the flowcharts and / or block diagrams. Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or blocks. Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or blocks.

[0165] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable devices provide a process for implementing the functions specified in the flowchart Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0166] In addition, the terms "first", "second", "third", etc. are used only to describe the purpose of distinguishing between different features, and are not intended to indicate or imply relative importance or a number of the indicated technical features. Thus, a feature with a "first" and / or "second" designation can include one or more of the features and vice versa. In the description of the application, the meaning of "a plurality" is at least two, for example, two, three, etc., unless otherwise specifically defined.

[0167] Although the preferred embodiments of the application have been described, those skilled in the art will be able to make additional changes and modifications to these embodiments once they have the benefit of the foregoing description. Therefore, it is intended to include all such changes and modifications in the scope of the application as set forth in the appended claims.

[0168] Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the spirit and scope of the present application. Thus, it is intended that the present application encompass such modifications and changes as fall within the scope of the appended claims and their equivalents.

Claims

1. A frequency offset estimation method, characterized by: A method for frequency offset estimation of equalized data symbols in a communication system, the method comprising: obtaining each data point in a target data symbol to construct an initial data set; eliminating abnormal data points in the initial data set to obtain a normal data set; The valid data points in the normal data set are extracted, and the real part and the imaginary part of each extracted valid data point are processed by absolute value to obtain a valid data set, specifically including: according to the formula The valid data points in the normal data set are extracted, and the real part and the imaginary part of each extracted valid data point are processed by absolute value to obtain a valid data set, specifically including: according to the formula r The valid data points in the normal data set are extracted, and the real part and the imaginary part of each extracted valid data point are processed by absolute value to obtain a valid data set, specifically including: according to the formula The valid data points in the normal data set are extracted, and the real part and the imaginary part of each extracted valid data point are processed by absolute value to obtain a valid data set, specifically including: according to the formula The valid data points in the normal data set are extracted, and the real part and the imaginary part of each extracted valid data point are processed by absolute value to obtain a valid data set, specifically including: according to the formula The valid data points in the normal data set are extracted, and the real part and the imaginary part of each extracted valid data point are processed by absolute value to obtain a valid data set, specifically including: according to the formula The valid data points in the normal data set are extracted, and the real part and the imaginary part of each extracted valid data point are processed by absolute value to obtain a valid data set, specifically including: according to the formula calculating an actual phase of the target data symbol according to the valid data set; obtaining a theoretical phase of the target data symbol, and calculating a phase difference of the target data symbol according to the actual phase and the theoretical phase; calculating a frequency offset estimation value of the target data symbol according to the phase difference; performing frequency offset estimation and compensation on a received signal based on the frequency offset estimation value to obtain a target received signal; performing symbol synchronization on the target received signal, the synchronization process comprising: obtaining the target received signal and performing segmentation processing on the target received signal to obtain a plurality of to-be-processed sub-signals; for each target sub-signal in the plurality of to-be-processed sub-signals, performing conjugate multiplication operation on a first sampling value corresponding to a current sampling point and a second sampling value in the target sub-signal to obtain a clock error value of the target sub-signal; the second sampling value represents a difference between sampling values of two adjacent sampling points of the current sampling point; performing error correction on the clock error value, and determining a sampling clock deviation of the target sub-signal based on the corrected clock error value; performing interpolation filtering operation on the target sub-signal based on the sampling clock deviation to obtain a target signal after symbol synchronization processing of each to-be-processed sub-signal.

2. The frequency offset estimation method of claim 1, wherein: The elimination of abnormal data points in the initial data set to obtain a normal data set specifically comprises: comparing the real part and the imaginary part of each data point in the initial data set with a first reference value a1 respectively, and determining the data points with a real part or an imaginary part greater than a1 as abnormal data points; deleting all abnormal data points, and forming a normal data set according to all normal data points; wherein a1 = a0 * a, a is a constellation spread corresponding to the modulation mode of the target data symbol, and a0 is a maximum amplitude corresponding to the modulation mode of the target data symbol.

3. The frequency offset estimation method of claim 2, wherein: The calculation of the actual phase of the target data symbol according to the valid data set specifically comprises: accumulating the real parts of each valid data point in the valid data set, and calculating the average value of the accumulated real part value to obtain the real part of the actual clustering center of the target data symbol; accumulating the imaginary parts of each valid data point in the valid data set, and calculating the average value of the accumulated imaginary part value to obtain the imaginary part of the actual clustering center of the target data symbol; calculating the actual phase of the target data symbol according to the real part and the imaginary part of the actual clustering center.

4. The frequency offset estimation method of claim 3, wherein: The calculation of the phase difference of the target data symbol according to the actual phase and the theoretical phase specifically comprises: taking the maximum amplitude a0 corresponding to the modulation mode of the target data symbol as the real part and the imaginary part of the theoretical clustering center of the target data symbol respectively; calculating the theoretical phase of the target data symbol according to the real part and the imaginary part of the theoretical clustering center; According to the formula The phase difference of the target data symbol is calculated wherein is the theoretical phase of the target data symbol, is the actual phase of the target data symbol.

5. The frequency offset estimation method of claim 4, wherein: The calculation of the frequency offset estimation value of the target data symbol according to the phase difference specifically comprises: According to the formula The frequency offset estimation value Δf of the target data symbol is calculated. where F c represents the sampling rate of the current system, Δ1 represents the symbol interval between the target data symbol and the demodulation reference signal, N s represents the symbol sample length.

6. A frequency offset estimation apparatus characterized by comprising: A module for implementing the frequency offset estimation method according to any one of claims 1-5.

7. A communication system, characterized by: A frequency offset estimation device according to claim 6.

8. An electronic device, comprising: comprising: a memory; a processor; and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the frequency offset estimation method according to any one of claims 1-5.

9. A computer-readable storage medium, characterized in that: A computer program product, comprising a computer program stored thereon, wherein the computer program is executed by a processor to implement the frequency offset estimation method according to any one of claims 1-5.

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