Frequency offset estimation method, device and system, electronic equipment and storage medium

Abstract

The invention provides a frequency offset estimation method, device and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining all data points in a target data symbol, and constructing 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; according to the effective data set, calculating to obtain an actual phase of the target data symbol; 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; according to the phase difference, calculating to obtain a frequency offset estimation value of the target data symbol; according to the method, frequency offset estimation is achieved with low complexity, certain universality and good robustness are achieved, and the system performance is further improved; the method is suitable for communication technology.

Description

Technical Field

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

[0002] With the rapid development of the number of 5G terminals and network construction, a variety of high-speed, large-bandwidth application scenarios are gradually increasing, and the performance requirements for communication network systems are also further improved. Generally speaking, factors such as phase noise caused by the instability of the signal generator at the transceiver end and Doppler frequency shift caused by the relative movement of the transceiver end will cause a certain frequency deviation in the received signal, resulting in a significant reduction in the receiving and decoding performance. In this regard, it is usually necessary to estimate and compensate the frequency deviation of the received signal at the receiving end to eliminate the negative impact of the frequency deviation and improve the receiving performance of the communication system.

[0003] In existing mobile communication technologies, phase deviation is usually calculated based on an algorithm related to two columns of pilots, and then frequency deviation is further obtained. However, in actual communication scenarios, in order to improve the data transmission rate, peak speed scenarios 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 algorithm based on the correlation of two columns of pilots cannot be applied. A new frequency deviation estimation algorithm is needed to handle the scenario where a single column of pilots has frequency deviation, and to perform frequency deviation compensation to improve the communication reliability of the system.

[0004] In existing research work, frequency offset estimation for single-column pilot scenarios is mostly directly based on clustering algorithms, including centroid calculation, K-means, Gaussian mixture model clustering, etc. The complexity and performance of clustering algorithms are obviously affected by the number of clusters and the number of iterations. Although the existing algorithms can solve the frequency offset estimation problem in some single-column pilot configuration scenarios, they have high computational complexity and are difficult to fit the model or difficult to converge when a small amount of resources are configured, thus affecting system performance. Summary of the invention

[0005] In order to solve one of the above technical defects, the present application provides a frequency offset estimation method, device, system, electronic device, 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 frequency offset of equalized data symbols in a communication system, comprising:

[0007] Obtain each data point in the target data symbol and construct an initial data set;

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

[0009] Extracting valid data points from the normal data set, and performing absolute value processing on each extracted valid data point to obtain a valid data set;

[0010] Calculating and obtaining the actual phase of the target data symbol according to the valid data set;

[0011] Acquire the theoretical phase of the target data symbol, and calculate the phase difference of the target data symbol according to the actual phase and the theoretical phase;

[0012] A frequency offset estimation value of the target data symbol is calculated based on the phase difference.

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

[0014] Compare the real part and the imaginary part of each data point in the initial data set with the first reference value a1, and determine the data point whose real part or imaginary part is greater than a1 as an abnormal data point;

[0015] Delete all abnormal data points and form a normal data set based on all normal data points;

[0016] Wherein, a1=α·a0, α is the divergence of the constellation diagram corresponding to the modulation mode of the target data symbol, and a0 is the maximum amplitude corresponding to the modulation mode of the target data symbol.

[0017] Preferably, extracting valid data points from the normal data set and performing absolute value processing on each extracted valid data point to obtain a valid data set specifically includes:

[0018] According to the formula Calculate each normal data point in the normal data set and convert P r Value greater than or equal to The normal data points are determined as valid data points, and the valid data points are extracted;

[0019] Performing absolute value processing on the real part and the imaginary part of each valid data point extracted, and forming a valid data set based on each valid data point after the absolute value processing;

[0020] in, For a normal data set, for The complex conjugate value of , R0 is the point selection radius corresponding to the modulation mode.

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

[0022] Accumulate the real part of each valid data point in the valid data set, and calculate the average value of the accumulated real part value to obtain the real part of the actual cluster center of the target data symbol;

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

[0024] The actual phase of the target data symbol is calculated based on the real part and the imaginary part of the actual cluster center.

[0025] Preferably, the acquiring the theoretical phase of the target data symbol and calculating the phase difference of the target data symbol according to the actual phase and the theoretical phase specifically includes:

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

[0027] Calculating the theoretical phase of the target data symbol according to the real part and the imaginary part of the theoretical cluster center;

[0028] According to the formula Calculate the phase difference of the target data symbol

[0029] in, is the theoretical phase of the target data symbol, is the actual phase of the target data symbol.

[0030] Preferably, calculating the frequency offset estimation value of the target data symbol according to the phase difference specifically includes:

[0031] According to the formula Calculate and obtain a frequency offset estimation value Δf of the target data symbol;

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

[0033] According to a second aspect of an embodiment of the present application, a frequency offset estimation device is provided, comprising a module for implementing the frequency offset estimation method as described in any one of the above items.

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

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

[0036] Memory;

[0037] Processor; and

[0038] Computer programs;

[0039] The computer program is stored in the memory and is configured to be executed by the processor to implement the frequency offset estimation method as described in any one of the above items.

[0040] According to a fifth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored; the computer program is executed by a processor to implement the frequency offset estimation method as described in any one of the above items.

[0041] The frequency offset estimation method provided in the present application performs frequency offset estimation based on each data point in the target data symbol. There is no need to restrict the configuration of multiple pilot columns, and can better solve the frequency offset estimation problem of configuring a single pilot column or even a pilot-free scenario, and has a certain universality. At the same time, by eliminating abnormal data points in the initial data set and performing absolute value processing on valid data points, the algorithm complexity of the clustering process is reduced. At the same time, it can better deal with the problems of insufficient number of clusters in small resource block RB resource configuration scenarios and abnormal data points that are prone to appear in complex channel environments. It has good robustness and further improves system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0043] Figure 1 A flow chart of a frequency offset estimation method provided in this application;

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

[0045] Figure 3 for Figure 2 Schematic diagram of the functional structure of the initial dataset building module;

[0046] Figure 4 for Figure 2 Schematic diagram of the functional structure of the normal data set acquisition module;

[0047] Figure 5 for Figure 2 Schematic diagram of the functional structure of the effective data set acquisition module;

[0048] Figure 6 for Figure 2Schematic diagram of the functional structure of the actual phase acquisition module;

[0049] Figure 7 for Figure 2 Schematic diagram of the functional structure of the 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 a valid 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 a valid 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 more clearly understood, the exemplary embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than an exhaustive list of all the 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 the first aspect, a frequency offset estimation method is provided in an embodiment of the present application, which can be performed by a frequency offset estimation device or equipment, or by a component configured inside the frequency offset estimation device or equipment, such as a chip, a chip system, etc., or by a logic module or software having some or all of the functions of the frequency offset estimation device or equipment. This application does not limit this.

[0054] For example, Figure 1 As shown, the frequency offset estimation method is used to estimate the frequency offset of equalized data symbols in a communication system, including:

[0055] Obtain each data point in the target data symbol and construct an initial data set;

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

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

[0058] Calculating and obtaining the actual phase of the target data symbol according to the valid data set;

[0059] Acquire the theoretical phase of the target data symbol, and calculate the phase difference of the target data symbol according to the actual phase and the theoretical phase;

[0060] A frequency offset estimation value of the target data symbol is calculated based on the phase difference.

[0061] Based on the above scheme, the frequency offset estimation method provided in the present application performs frequency offset estimation based on each data point in the target data symbol, does not rely on the number of configured pilot columns for correlation, does not need to limit the configuration of multiple pilot columns, and can be used in extreme scenarios with a single pilot column or even no pilot. It can better solve the frequency offset estimation problem in scenarios with a single pilot column or even no pilot, and has a certain universality; at the same time, by eliminating abnormal data points in the initial data set and performing absolute value processing on valid data points, the algorithm complexity of the clustering process is reduced. At the same time, it can better deal with problems such as insufficient number of clusters in small resource block RB resource configuration scenarios and abnormal data points that are prone to appear in complex channel environments. It has good robustness and further improves system performance.

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

[0063] Get the target data symbol The corresponding real and imaginary parts of all the data in form the corresponding data points, that is, the complex signal (x i ,y i ),in, (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 ith data point, y i is the imaginary part of the ith data point; in specific implementation, let X = {real(r1), real(r2), ..., real(r N )}, Y={imag(r1),imag(r2),...,imag(r N )}; where 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] Based on all the data points, construct the initial data set {(x1,y1),(x2,y2)…,(x j ,y j )…,(x N ,y N )}.

[0065] In some possible implementations of the first aspect, removing abnormal data points in the initial data set to obtain a normal data set specifically includes:

[0066] Compare the real part and the imaginary part of each data point in the initial data set with the first reference value a1, and determine the data point whose real part or imaginary part is greater than a1 as an abnormal data point;

[0067] Delete all abnormal data points and form a normal data set based on all normal data points;

[0068] Among them, a1=α·a0, a0 is the maximum amplitude corresponding to the modulation mode of the target data symbol, α is the divergence of the constellation diagram corresponding to the modulation mode of the target data symbol, and the size of α is mainly affected by factors such as noise level and channel environment. It is usually determined based on the empirical cumulative distribution results of the maximum amplitude corresponding to different modulation modes under different channel environments, which can correct the theoretical maximum amplitude. Specifically, the points in the constellation diagram corresponding to different modulation modes can indicate the possible states of the amplitude and phase of the target data symbol. The distance from the point to the origin is the amplitude after modulation, and the angle is the phase after modulation.

[0069] In a specific implementation, the corresponding maximum amplitude a0 is obtained according to a 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 formulas corresponding to each modulation mode are shown in the following table:

[0071]

[0072] It can be seen from the table that the maximum amplitude corresponding to QPSK is a0=1, and the maximum amplitude corresponding to 16QAM is The maximum amplitude corresponding to 64QAM is The maximum amplitude corresponding to 256QAM is

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

[0074] In some possible implementations of the first aspect, extracting valid data points from the normal data set and performing absolute value processing on each extracted valid data point to obtain a valid data set specifically includes:

[0075] According to the formula Calculate each normal data point in the normal data set and convert P r Value greater than or equal to The normal data points are judged as valid data points (x′ n ,y′ n ), and for valid data points (x′ n ,y′ n ) for extraction; wherein, For a normal data set, for The complex conjugate value of , R0 is the selected point radius corresponding to the modulation mode, x′ n is the real part of the nth valid data point, y n ′ is the imaginary part of the nth valid data point; specifically, The normal data points are the four cluster data in the outermost circle of the constellation diagram. Because the outermost adjacent constellation points are not prone to aliasing, the negative impact of the selection of valid data points on the clustering results can be minimized, and the robustness is better in low signal-to-noise ratio scenarios.

[0076] The real and imaginary parts of each valid data point extracted are processed by absolute value, and the valid data points after absolute value processing are Form a valid data set; among them, is the real part of the nth valid data point after absolute value processing, is the imaginary part of the nth valid data sample after absolute value processing; by performing absolute value processing on the real and imaginary parts of each valid data point, the valid data points of the target data symbol are concentrated in the first quadrant, thereby merging the four similar cluster results into one, and only one cluster center needs to be considered in the subsequent clustering process, which effectively reduces the clustering complexity and avoids the overall algorithm performance being affected when there is no data in a quadrant in the small resource block RB scenario.

[0077] In specific implementation, the value of R0 is the average of the maximum amplitude and the second maximum amplitude, and the second maximum amplitude is obtained according to the corresponding mapping formula. Taking 16QAM as an example, its maximum amplitude If the secondary amplitude is 1, then the selection point radius corresponding to 16QAM Similarly, the selection point radius corresponding to 64QAM The selection point radius corresponding to 256QAM However, for QPSK, there are only 4 ideal clusters, so this operation is not necessary.

[0078] Based on the above scheme, taking 16QAM as an example, the target data symbols are clustered, and in theory, 16 clusters can usually be obtained. Similarly, 64QAM can be clustered into 64 clusters, and 256QAM can be clustered into 256 clusters. Commonly used clustering algorithms such as the K-means algorithm and the Gaussian mixture algorithm usually require traversing all data and iterating multiple times to obtain a stable cluster structure. It can be seen that the complexity of this type of algorithm increases several times with the increase in the number of clusters and the number of iterations. In view of this, after removing abnormal data points, the present application also selects valid data points and merges valid data points of the same cluster, thereby reducing the number of theoretical clustering results, achieving the purpose of reducing the complexity of the algorithm and improving the performance of the method.

[0079] In some possible implementations of the first aspect, calculating, according to the valid data set, an actual phase of a target data symbol specifically includes:

[0080] Accumulate the real part of each valid data point in the valid data set, and calculate the average value of the accumulated real part value to obtain the real part of the actual cluster center of the target data symbol;

[0081] The imaginary parts of the valid data points in the valid data set are accumulated, and the average value of the accumulated imaginary part values ​​is calculated to obtain the imaginary part of the actual cluster center of the target data symbol;

[0082] The actual phase of the target data symbol is calculated based on the real part and imaginary part of the actual cluster center.

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

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

[0085] In some possible implementations of the first aspect, the acquiring a theoretical phase of a target data symbol and calculating a phase difference of the target data symbol according to the actual phase and the theoretical phase specifically includes:

[0086] The maximum amplitude a0 corresponding to the modulation mode of the target data symbol is used as the real part and imaginary part of the theoretical cluster center of the target data symbol respectively;

[0087] The theoretical phase of the target data symbol is calculated based on the real part and imaginary part of the theoretical cluster center. Specifically,

[0088] According to the formula Calculate the phase difference of the target data symbol

[0089] in, is the theoretical phase of the target data symbol, is the actual phase of the target data symbol.

[0090] In some possible implementations of the first aspect, calculating, according to the phase difference, a frequency offset estimation value of a target data symbol specifically includes:

[0091] According to the formula Calculate and obtain a frequency offset estimation value Δf of the target data symbol;

[0092] Among them, F c represents the current system sampling rate, Δl represents the symbol interval between the target data symbol and the DMRS, N s represents the symbol sample length, and N s It is only related to the number of system Fourier transform (FFT) points and cyclic prefix (CP) length.

[0093] The frequency offset estimation method provided in the embodiment of the present application can be applicable to a variety of systems, especially 5G systems. For example, the applicable system 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, a LTE frequency division duplex (FDD) system, a LTE time division duplex (TDD) system, an advanced long term evolution (LTE-A) system, a universal mobile telecommunication system (UMTS), a world-wide interoperability for microwave access (WiMAX) system, a 5G new air interface (NR) system, etc. The various systems include terminal equipment and network equipment. The system may also include a core network part, such as an evolved packet system (EPS), a 5G system (5GS), etc.

[0094] In an optional embodiment of the present application, after the above step calculates the frequency offset estimation value of the target data symbol according to the phase difference, the above method further includes: estimating and compensating the frequency offset of the received signal based on the frequency offset estimation value to obtain the 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] Acquire a target received signal, and process the target received signal in sections to obtain a plurality of sub-signals to be processed;

[0097] For each target sub-signal in the multiple sub-signals to be processed, a conjugate product operation is performed 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, wherein the second sampling value represents a difference between sampling values ​​of two adjacent sampling points of the current sampling point;

[0098] 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;

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

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

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

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

[0103] If it is determined based on the total data length that the data length of the last initial sub-signal to be processed is less than the preset data length, data padding is performed on the last initial sub-signal to be processed so that the data length of the last initial sub-signal to be processed after data padding is equal to the preset data length, thereby obtaining the multiple sub-signals to be processed; or, if it is determined based on the total data length that the data length of the last initial sub-signal to be processed is equal to the preset data length, the multiple initial sub-signals to be processed are determined as the multiple sub-signals to be processed.

[0104] In an optional embodiment of the present application, the target received signal is a signal of a superframe structure, and the superframe structure is composed of a head protection symbol, a tail protection 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 correction of the clock error value includes:

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

[0107] Normalizing the clock error value of the target sub-signal;

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

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

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

[0111] According to the fitting factors of the respective fitting orders, a polynomial fitting function of the preset fitting order is constructed for the corrected clock error value to obtain a sampling clock deviation of the target sub-signal;

[0112] The interpolation filtering operation is performed on the target sub-signal based on the sampling clock deviation to obtain the target signal after the symbol synchronization processing is performed on each of the sub-signals to be processed, including:

[0113] Performing data padding before the start sampling point and after the end sampling point in the target sub-signal to obtain the target sub-signal after data padding;

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

[0115] An interpolation filtering operation is performed 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 is performed on each of the sub-signals to be processed.

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

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

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

[0119] The correlation power value of the target sub-signal is determined according to the mutual 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 digital signal with a 2 times symbol rate.

[0121] In a second aspect, an embodiment of the present application provides a frequency offset estimation device, comprising a module for implementing the frequency offset estimation method as described above.

[0122] For example, Figure 2 As shown, the frequency offset estimation device includes:

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

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

[0125] The valid data set acquisition module 30 is used to extract valid data points from the normal data set and perform absolute value processing on each extracted valid data point to obtain a valid data set;

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

[0127] Theoretical phase acquisition module 50, used to acquire the theoretical phase of the target data symbol;

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

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

[0130] Alternatively, if Figure 3 As shown, the initial data set construction module 10 includes:

[0131] Data point acquisition unit 101, used to acquire target data symbols The corresponding real and imaginary parts of all the data in form the corresponding data points, that is, the complex signal (x i ,y i ),in, (x i ,y i ) is the data point of the i-th data in the target data symbol, x i is the real part of the ith data point, y i is the imaginary part of the i-th data point;

[0132] The initial data set acquisition unit 102 is used to construct an initial data set {(x1, y1), (x2, y2)…, (x j ,y j )…,(x N ,y N )}.

[0133] Alternatively, if Figure 4 As shown, the normal data set acquisition module 20 includes:

[0134] The first determination unit 201 is used to compare the real part and the imaginary part of each data point in the initial data set with the first reference value a1, and determine the data point whose real part and imaginary part are both greater than a1 as an abnormal data point; wherein a1=α·a0, α is the constellation diagram divergence corresponding to the modulation mode of the target data symbol, and a0 is the maximum amplitude corresponding to the modulation mode of the target data symbol;

[0135] A deletion unit 202 is used to delete all abnormal data points;

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

[0137] Alternatively, if Figure 5 As shown, the effective data set acquisition module 30 includes:

[0138] The first calculation unit 301 is used to calculate the Calculate each normal data point in the normal data set; wherein, For a normal data set, for The complex conjugate value of ;

[0139] The second determination unit 302 is used to determine P r Value greater than or equal to The normal data points are determined as valid data points; where R0 is the point selection radius corresponding to the modulation mode;

[0140] An extraction unit 303, used to extract valid data points;

[0141] An absolute value processing unit 304 is used to perform absolute value processing on both the real part and the imaginary part of each extracted valid data point;

[0142] The valid data set forming unit 305 is used to form a valid data set according to each valid data point after absolute value processing.

[0143] Alternatively, if Figure 6 As shown, the actual phase acquisition module 40 includes:

[0144] The second calculation unit 401 is used to accumulate the real part of each valid data point in the valid data set, and calculate the average value of the accumulated real part value to obtain the real part of the actual cluster center of the target data symbol;

[0145] The third calculation unit 402 is used to accumulate the imaginary parts of the valid data points in the valid data set, and calculate the average value of the accumulated imaginary part values ​​to obtain the imaginary part of the actual cluster center of the target data symbol;

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

[0147] Alternatively, if Figure 7 As shown, the theoretical phase acquisition module 50 includes:

[0148] Theoretical cluster center acquisition unit 501, used to use the maximum amplitude a0 corresponding to the modulation mode of the target data symbol as the real part and imaginary part of the theoretical cluster center of the target data symbol respectively;

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

[0150] Optionally, the phase difference acquisition module 60 is specifically configured to obtain the phase difference according to the formula Calculate the phase difference of the target data symbol in, 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 obtain the frequency offset estimation value according to the formula The frequency offset estimation value Δf of the target data symbol is calculated; where Δf represents the frequency offset estimation value of the data symbol after equalization, Represents the phase deviation of the valid data sample point, F c represents the current system sampling rate, Δl represents the symbol interval between the equalized data symbol and the demodulation reference signal, N s Indicates the symbol sample length.

[0152] In each embodiment of the present application, the method and the device are based on the same application concept. Since the method and the device solve the problem in a similar manner, the implementation of the device and the method can refer to each other, and the repeated parts will not be repeated.

[0153] In a third aspect, an embodiment of the present application provides a communication system, comprising the frequency offset estimation device as described above.

[0154] In a fourth aspect, an electronic device is provided in an embodiment of the present application. The electronic device can be any device that can implement the frequency deviation estimation function. The device can be various terminal devices, such as: desktop computers, laptops, tablet computers, handheld devices, etc., which can be implemented specifically through software and / or hardware.

[0155] Exemplarily, the electronic device includes:

[0156] Memory;

[0157] Processor; and

[0158] Computer programs;

[0159] The computer program is stored in the memory and is 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 an embodiment of the present application. The computer-readable storage medium may be: ROM, RAM, a disk or an optical disk, etc.

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

[0162] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt 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.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, C language, VHDL language, Verilog language, object-oriented programming language Java and literal scripting language JavaScript, etc.

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

[0164] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0166] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0167] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0168] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A frequency offset estimation method, characterized in that: For performing frequency offset estimation on equalized data symbols in a communication system, the method comprising: Obtain each data point in the target data symbol and construct an initial data set; Eliminate abnormal data points in the initial data set to obtain a normal data set; Extracting valid data points from the normal data set, and performing absolute value processing on each extracted valid data point to obtain a valid data set; Calculating and obtaining the actual phase of the target data symbol according to the valid data set; Acquire the theoretical phase of the target data symbol, and calculate the phase difference of the target data symbol according to the actual phase and the theoretical phase; A frequency offset estimation value of the target data symbol is calculated based on the phase difference.

2. The frequency offset estimation method according to claim 1, wherein: The removing of abnormal data points from the initial data set to obtain a normal data set specifically includes: Compare the real part and the imaginary part of each data point in the initial data set with the first reference value a1, and determine the data point whose real part or imaginary part is greater than a1 as an abnormal data point; Delete all abnormal data points and form a normal data set based on all normal data points; Wherein, a1=α·a0, α is the divergence of the constellation diagram corresponding to the modulation mode of the target data symbol, and a0 is the maximum amplitude corresponding to the modulation mode of the target data symbol.

3. The frequency offset estimation method according to claim 2, wherein: The extracting of valid data points from the normal data set and performing absolute value processing on each extracted valid data point to obtain a valid data set specifically includes: According to the formula Calculate each normal data point in the normal data set and convert P r Value greater than or equal to The normal data points are determined as valid data points, and the valid data points are extracted; Performing absolute value processing on the real part and the imaginary part of each valid data point extracted, and forming a valid data set based on each valid data point after the absolute value processing; in, For a normal data set, for The complex conjugate value of , R0 is the point selection radius corresponding to the modulation mode.

4. The frequency offset estimation method according to claim 3, wherein: The calculating, according to the valid data set, an actual phase of the target data symbol specifically includes: Accumulate the real part of each valid data point in the valid data set, and calculate the average value of the accumulated real part value to obtain the real part of the actual cluster center of the target data symbol; The imaginary parts of the valid data points in the valid data set are accumulated, and the average value of the accumulated imaginary part values ​​is calculated to obtain the imaginary part of the actual cluster center of the target data symbol; The actual phase of the target data symbol is calculated based on the real part and the imaginary part of the actual cluster center.

5. The frequency offset estimation method according to claim 4, characterized in that: The acquiring the theoretical phase of the target data symbol and calculating the phase difference of the target data symbol according to the actual phase and the theoretical phase specifically includes: The maximum amplitude a0 corresponding to the modulation mode of the target data symbol is used as the real part and imaginary part of the theoretical cluster 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 cluster center; According to the formula Calculate the phase difference of the target data symbol in, is the theoretical phase of the target data symbol, is the actual phase of the target data symbol.

6. The frequency offset estimation method according to claim 5, characterized in that: The calculating, according to the phase difference, a frequency offset estimation value of a target data symbol specifically includes: According to the formula Calculate and obtain a frequency offset estimation value Δf of the target data symbol; Among them, F c represents the sampling rate of the current system, Δl represents the symbol interval between the target data symbol and the demodulation reference signal, N s Indicates the symbol sample length.

7. A frequency offset estimation device, characterized in that: The method comprises a module for implementing the frequency offset estimation method according to any one of claims 1 to 6.

8. A communication system, characterized in that: It includes the frequency offset estimation device described in claim 7.

9. An electronic device, characterized in that: include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and is configured to be executed by the processor to implement the frequency offset estimation method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon; the computer program is executed by a processor to implement the frequency offset estimation method according to any one of claims 1 to 6.

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