A time bias estimation method and apparatus

Abstract

This application provides a time deviation estimation method and apparatus. The method includes: determining a first data matrix corresponding to the original channel frequency domain response matrix based on a first channel estimation period using a sliding window; the first data matrix is ​​an L×J matrix, where L is the length of the sliding window and J is the number of times the sliding window slides within the original channel frequency domain response matrix, and both L and J are positive integers; performing a sliding window discrete Fourier transform on the first data matrix to determine a first subspace matrix; and estimating the time deviation based on the first subspace matrix. This application reduces the computational complexity of multiple discrete Fourier transforms by determining the subspace matrix through a sliding window Fourier transform of the channel frequency domain response matrix. Furthermore, it uses a subspace approximation method instead of the eigenvalue decomposition method in modern spectral estimation methods, further reducing computational complexity.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a time deviation estimation method and apparatus. Background Technology

[0002] In 5G multi-user multiple-input multiple-output (MU-MIMO) communication scenarios, the timing synchronization between the base station and the terminal is subject to time-varying deviations due to frequency drift caused by changes in the clock oscillators of the base station and the terminal over time and temperature. This time deviation leads to inaccurate frequency domain channel estimation in orthogonal frequency division multiplexing (OFDM) systems.

[0003] There are two existing methods for time offset calibration. One is based on the Inverse Discrete Fourier Transform (IDFT) to convert the channel frequency domain estimation into a time domain channel estimation, then searches for the location of the peak and calculates its corresponding time offset. The other is based on modern spectral estimation methods, such as the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and Multiple Signal Classification (MUSIC). These methods mainly distinguish between the signal subspace and the noise subspace through subspace decomposition, and then extract phase information from the frequency domain signal space to obtain the time offset. The original channel estimation result is then calibrated using the time offset.

[0004] However, in multipath scenarios, IDFT-based time offset calibration suffers from interference between different subpaths, causing the IDFT peak position to fail to reflect the true time offset value, resulting in low accuracy of the final channel prediction. Modern spectral estimation methods offer higher multipath resolution, but their computational complexity is too high, limiting their practicality. Summary of the Invention

[0005] This application provides a time deviation estimation method, base station, device, and storage medium to address the shortcomings of existing technologies where computational complexity and accuracy cannot be guaranteed simultaneously, thereby ensuring accuracy while reducing computational complexity.

[0006] In a first aspect, embodiments of this application provide a time deviation estimation method, including:

[0007] The first data matrix corresponding to the original channel frequency domain response matrix based on the first channel estimation period is determined according to the sliding window. The first data matrix is ​​an L×J matrix, where L is the length of the sliding window and J is the number of times the sliding window slides in the original channel frequency domain response matrix. Both L and J are positive integers.

[0008] Perform a sliding window discrete Fourier transform on the first data matrix to determine the first subspace matrix;

[0009] Time bias estimation is performed based on the first subspace matrix.

[0010] Optionally, according to a time deviation estimation method of one embodiment of this application, performing a sliding window discrete Fourier transform on the first data matrix to determine the first subspace matrix includes:

[0011] Perform a sliding window discrete Fourier transform on the row vectors corresponding to each row in the first data matrix to determine the second data matrix;

[0012] Determine the modulus of the column vector corresponding to each column in the second data matrix;

[0013] The first subspace matrix is ​​determined based on the column vectors whose modulus exceeds a first preset threshold.

[0014] Optionally, according to a time deviation estimation method of one embodiment of this application, a sliding window discrete Fourier transform is performed on the row vectors corresponding to each row in the first data matrix to determine the second data matrix, including:

[0015] According to Formula 1, a discrete Fourier transform with N points is performed on the row vector corresponding to the first row of the first data matrix. The expression of Formula 1 is:

[0016]

[0017] Among them, X k The result is the Discrete Fourier Transform (DFT), where l is the row number of the first data matrix, k is the frequency point number, x(i) is the sampled signal, and N is the number of points in the DFT. N and J have the same value; l, k, and N are all positive integers.

[0018] When l is greater than 1, the discrete Fourier transform result of the row vector corresponding to the l-th row of the first data matrix is ​​determined according to Formula 2. The expression of Formula 2 is:

[0019] X k (l)=e j2πk / N [X k (l-1)+x(l+N-1)-x(l-1)]

[0020] The second data matrix is ​​determined based on Formula 1 and Formula 2. The expression for the second data matrix is ​​as follows:

[0021]

[0022] Where V is the second data matrix.

[0023] Optionally, according to one embodiment of the time deviation estimation method of this application, time deviation estimation is performed based on a first subspace matrix, including:

[0024] The first matrix is ​​determined by the first M-1 row vectors (excluding the last row) and the last M-1 row vectors (excluding the first row) of the first subspace matrix; where M is the number of rows in the first subspace matrix.

[0025] The spectrum matrix is ​​determined based on the row vector corresponding to the Mth row of the first subspace matrix and the first matrix.

[0026] The time deviation estimation result of the channel estimation based on the first channel estimation period is determined according to the spectrum matrix.

[0027] Optionally, according to one embodiment of the time deviation estimation method of this application, the formula for calculating the spectrum matrix based on the row vector corresponding to the Mth row of the first subspace matrix and the first matrix is ​​as follows:

[0028]

[0029] Where Φ is the spectrum matrix, Ψ is the first matrix, and v is the vector obtained by conjugate transpose of the row vector corresponding to the Mth row of the first subspace matrix.

[0030] Optionally, according to one embodiment of the time deviation estimation method of this application, time deviation estimation is performed based on a first subspace matrix, including:

[0031] The spectral peak search interval of the first channel estimation period is determined based on the time deviation estimation result of the second channel estimation period; wherein, the second channel estimation period is the channel estimation period preceding the first channel estimation period;

[0032] If the length of the spectral peak search interval in the first channel estimation period is greater than the second preset threshold, the time deviation estimation result of the first channel estimation period is determined by performing a golden section search on the spatial spectral function.

[0033] Optionally, according to one embodiment of the time offset estimation method of this application, after determining the time offset estimation result of the channel estimation based on the first channel estimation period according to the spectrum matrix, it further includes:

[0034] The spectral peak search interval for the third channel estimation period is determined based on the time deviation estimation result of the first channel estimation period; wherein, the third channel estimation period is the channel estimation period following the first channel estimation period, and the first channel estimation period is the first channel estimation period after the start of channel estimation.

[0035] If the length of the spectral peak search interval of the third channel estimation period is greater than the second preset threshold, the time deviation estimation result of the third channel estimation period is determined by performing a golden section search on the spatial spectral function.

[0036] Optionally, according to a time offset estimation method of one embodiment of this application, the time offset estimation result of the first channel estimation period is determined by performing a golden section search on the spatial spectral function, including:

[0037] Using the second preset threshold value as the convergence accuracy, the minimum value of the spatial spectral function within the spectral peak search interval of the first channel estimation period is determined according to the golden section search.

[0038] The target search interval is determined based on the minimum value, and the time deviation estimation result of the first channel estimation period is the median of the target search interval.

[0039] Secondly, embodiments of this application also provide a network device, including: a memory, a transceiver, and a processor.

[0040] Memory is used to store computer programs; transceiver is used to send and receive data under the control of the processor; processor is used to read the computer programs from memory and perform the following operations:

[0041] The first data matrix corresponding to the original channel frequency domain response matrix based on the first channel estimation period is determined according to the sliding window. The first data matrix is ​​an L×J matrix, where L is the length of the sliding window and J is the number of times the sliding window slides in the original channel frequency domain response matrix. Both L and J are positive integers.

[0042] Perform a sliding window discrete Fourier transform on the first data matrix to determine the first subspace matrix;

[0043] Time bias estimation is performed based on the first subspace matrix.

[0044] Optionally, according to one embodiment of the network device of this application, performing a sliding window discrete Fourier transform on the first data matrix to determine the first subspace matrix includes:

[0045] Perform a sliding window discrete Fourier transform on the row vectors corresponding to each row in the first data matrix to determine the second data matrix;

[0046] Determine the modulus of the column vector corresponding to each column in the second data matrix;

[0047] The first subspace matrix is ​​determined based on the column vectors whose modulus exceeds a first preset threshold.

[0048] Optionally, according to one embodiment of the network device of this application, performing a sliding window discrete Fourier transform on the row vectors corresponding to each row of the first data matrix to determine the second data matrix includes:

[0049] According to Formula 1, a discrete Fourier transform with N points is performed on the row vector corresponding to the first row of the first data matrix. The expression of Formula 1 is:

[0050]

[0051] Among them, X k The result is the Discrete Fourier Transform (DFT), where l is the row number of the first data matrix, k is the frequency point number, x(i) is the sampled signal, and N is the number of points in the DFT. N and J have the same value; l, k, and N are all positive integers.

[0052] When l is greater than 1, the discrete Fourier transform result of the row vector corresponding to the l-th row of the first data matrix is ​​determined according to Formula 2. The expression of Formula 2 is:

[0053] X k (l)=e j2πk / N [X k (l-1)+x(l+N-1)-x(l-1)]

[0054] The second data matrix is ​​determined based on Formula 1 and Formula 2. The expression for the second data matrix is ​​as follows:

[0055]

[0056] Where V is the second data matrix.

[0057] Optionally, a network device according to one embodiment of this application performs time offset estimation based on a first subspace matrix, including:

[0058] The first matrix is ​​determined by the first M-1 row vectors (excluding the last row) and the last M-1 row vectors (excluding the first row) of the first subspace matrix; where M is the number of rows in the first subspace matrix.

[0059] The spectrum matrix is ​​determined based on the row vector corresponding to the Mth row of the first subspace matrix and the first matrix.

[0060] The time deviation estimation result of the channel estimation based on the first channel estimation period is determined according to the spectrum matrix.

[0061] Optionally, according to one embodiment of the network device of this application, the formula for calculating the spectrum matrix based on the row vector corresponding to the Mth row of the first subspace matrix and the first matrix is ​​as follows:

[0062]

[0063] Where Φ is the spectrum matrix, Ψ is the first matrix, and v is the vector obtained by conjugate transpose of the row vector corresponding to the Mth row of the first subspace matrix.

[0064] Optionally, a network device according to one embodiment of this application performs time offset estimation based on a first subspace matrix, including:

[0065] The spectral peak search interval of the first channel estimation period is determined based on the time deviation estimation result of the second channel estimation period; wherein, the second channel estimation period is the channel estimation period preceding the first channel estimation period;

[0066] If the length of the spectral peak search interval in the first channel estimation period is greater than the second preset threshold, the time deviation estimation result of the first channel estimation period is determined by performing a golden section search on the spatial spectral function.

[0067] Optionally, the network device according to one embodiment of this application, after determining the time offset estimation result of the channel estimation based on the first channel estimation period according to the spectrum matrix, further includes:

[0068] The spectral peak search interval for the third channel estimation period is determined based on the time deviation estimation result of the first channel estimation period; wherein, the third channel estimation period is the channel estimation period following the first channel estimation period, and the first channel estimation period is the first channel estimation period after the start of channel estimation.

[0069] If the length of the spectral peak search interval of the third channel estimation period is greater than the second preset threshold, the time deviation estimation result of the third channel estimation period is determined by performing a golden section search on the spatial spectral function.

[0070] Optionally, according to one embodiment of the network device of this application, determining the time offset estimation result of the first channel estimation period by performing a golden section search on the spatial spectral function includes:

[0071] Using the second preset threshold value as the convergence accuracy, the minimum value of the spatial spectral function within the spectral peak search interval of the first channel estimation period is determined according to the golden section search.

[0072] The target search interval is determined based on the minimum value, and the time deviation estimation result of the first channel estimation period is the median of the target search interval.

[0073] Thirdly, embodiments of this application also provide a time deviation estimation device, comprising:

[0074] The first determining unit is used to determine the first data matrix corresponding to the original channel frequency domain response matrix based on the first channel estimation period according to the sliding window. The first data matrix is ​​an L×J matrix, where L is the length of the sliding window and J is the number of times the sliding window slides in the original channel frequency domain response matrix. Both L and J are positive integers.

[0075] The second determining unit is used to perform a sliding window discrete Fourier transform on the first data matrix to determine the first subspace matrix;

[0076] The estimation unit is used to estimate the time bias based on the first subspace matrix.

[0077] Optionally, in a time deviation estimation apparatus according to an embodiment of the present application, the second determining unit includes a first determining module, a second determining module, and a third determining module;

[0078] The first determining module is used to perform a sliding window discrete Fourier transform on the row vector corresponding to each row in the first data matrix to determine the second data matrix;

[0079] The second determining module is used to determine the modulus of the column vector corresponding to each column in the second data matrix;

[0080] The third determining module is used to determine the first subspace matrix based on column vectors whose modulus exceeds the first preset threshold value.

[0081] Optionally, in a time deviation estimation apparatus according to an embodiment of the present application, the first determining module includes a first determining submodule, a second determining submodule, and a third determining submodule;

[0082] The first determining submodule is used to perform a discrete Fourier transform of N points on the row vector corresponding to the first row of the first data matrix according to the following formula: The expression of formula one is:

[0083]

[0084] Among them, X k The result is the Discrete Fourier Transform (DFT), where l is the row number of the first data matrix, k is the frequency point number, x(i) is the sampled signal, and N is the number of points in the DFT. N and J have the same value; l, k, and N are all positive integers.

[0085] The second determining submodule is used to determine the discrete Fourier transform result of the row vector corresponding to the l-th row of the first data matrix according to Formula 2 when l is greater than 1. The expression of Formula 2 is:

[0086] X k(l)=e j2πk / N [X k (l-1)+x(l+N-1)-x(l-1)]

[0087] The third determining submodule is used to determine the second data matrix based on Formula 1 and Formula 2. The expression for the second data matrix is:

[0088]

[0089] Where V is the second data matrix.

[0090] Optionally, according to one embodiment of the time deviation estimation apparatus of this application, the estimation unit includes a fourth determining module, a fifth determining module, and a sixth determining module;

[0091] The fourth determining module is used to determine the first matrix based on the first M-1 row vectors (excluding the last row) and the last M-1 row vectors (excluding the first row) of the first subspace matrix; where M is the number of rows in the first subspace matrix.

[0092] The fifth determining module is used to determine the spectrum matrix based on the row vector corresponding to the Mth row of the first subspace matrix and the first matrix;

[0093] The sixth determining module is used to determine the time deviation estimation result of the channel estimation based on the first channel estimation period according to the spectrum matrix.

[0094] Optionally, according to one embodiment of the time deviation estimation apparatus of this application, the formula for calculating the spectrum matrix based on the row vector corresponding to the Mth row of the first subspace matrix and the first matrix is ​​as follows:

[0095]

[0096] Where Φ is the spectrum matrix, Ψ is the first matrix, and v is the vector obtained by conjugate transpose of the row vector corresponding to the Mth row of the first subspace matrix.

[0097] Optionally, in a time deviation estimation apparatus according to an embodiment of the present application, the estimation module includes a seventh determining module and an eighth determining module;

[0098] The seventh determining module is used to determine the spectral peak search interval of the first channel estimation period based on the time deviation estimation result of the second channel estimation period; wherein, the second channel estimation period is the channel estimation period preceding the first channel estimation period;

[0099] The eighth determining module is used to perform a golden section search on the spatial spectral function to determine the time deviation estimation result of the first channel estimation period when the length of the spectral peak search interval of the first channel estimation period is greater than the second preset threshold value.

[0100] Optionally, in a time deviation estimation apparatus according to an embodiment of the present application, the estimation unit further includes a ninth determining module and a tenth determining module;

[0101] The ninth determining module is used to determine the spectral peak search interval of the third channel estimation period based on the time deviation estimation result of the first channel estimation period; wherein, the third channel estimation period is the channel estimation period following the first channel estimation period, and the first channel estimation period is the first channel estimation period after the start of channel estimation.

[0102] The tenth determination module is used to perform a golden section search on the spatial spectral function to determine the time deviation estimation result of the third channel estimation period when the length of the spectral peak search interval of the third channel estimation period is greater than the second preset threshold value.

[0103] Optionally, in a time deviation estimation apparatus according to an embodiment of the present application, the eighth determining module includes a fourth determining submodule and a fifth determining submodule;

[0104] The fourth determination submodule is used to determine the minimum value of the spatial spectral function within the spectral peak search interval of the first channel estimation period based on the golden section search, with the second preset threshold value as the convergence accuracy.

[0105] The fifth determination submodule is used to determine the target search interval based on the minimum value, and the time deviation estimation result of the first channel estimation period is the median of the target search interval.

[0106] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program for causing a processor to execute the steps of the time deviation estimation method described in the first aspect.

[0107] Fifthly, embodiments of this application also provide a time deviation estimation apparatus, which includes a processor and an interface circuit. The interface circuit is used to receive computer execution instructions and transmit them to the processor. The processor executes the computer execution instructions to implement the steps in the time deviation estimation method described in the first aspect.

[0108] Optionally, the time deviation estimation apparatus further includes a memory coupled to a processor, the processor being used to implement the steps in the time deviation estimation method described in the first aspect.

[0109] Optionally, the memory is used to store program instructions and data. The memory is coupled to the processor, which can call and execute the program instructions stored in the memory to implement the steps in the time deviation estimation method described in the first aspect.

[0110] Optionally, the time deviation estimation device further includes a communication interface for communicating with other devices.

[0111] Optionally, when the device for estimating the time deviation is a chip or chip system, the communication interface includes input / output interfaces, interface circuits, output circuits, input circuits, pins, or related circuits on the chip or chip system. The processor is embodied as processing circuitry or logic circuitry.

[0112] Sixthly, embodiments of this application also provide a computer program product containing instructions, which, when executed by a computer, cause the computer to perform the steps of the time deviation estimation method described in the first aspect. The time deviation estimation method and apparatus provided in this application significantly reduce the computational complexity of multiple discrete Fourier transforms by determining the subspace matrix through a sliding window Fourier transform of the channel frequency domain response matrix. Simultaneously, it uses a subspace approximation method instead of the eigenvalue decomposition method in modern spectral estimation methods, ensuring computational accuracy while further reducing computational complexity. Attached Figure Description

[0113] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0114] Figure 1 It is a schematic diagram of the time offset changing over time provided by existing technology;

[0115] Figure 2 This is one of the flowcharts illustrating the time deviation estimation method provided in the embodiments of this application;

[0116] Figure 3 This is a second schematic flowchart of the time deviation estimation method provided in the embodiments of this application;

[0117] Figure 4 This is a schematic diagram of the base station structure provided in an embodiment of this application;

[0118] Figure 5 This is a schematic diagram of the time deviation estimation device provided in the embodiments of this application. Detailed Implementation

[0119] For any wireless transceiver system, whether 5G or 4G, there will be a physical discrepancy between the crystal oscillator frequencies used by the receiver and transmitter. The receiver needs to estimate this discrepancy and compensate for it in the data.

[0120] In 5G multi-user multiple-input multiple-output communication scenarios, the clock crystal oscillators between the base station and the terminal experience frequency drift due to changes in time and temperature, resulting in time deviations. This leads to inaccurate channel domain estimation in the orthogonal frequency division multiplexing system and a decrease in channel prediction capability.

[0121] When users move at high speed, the wireless channel between the base station and the terminal changes rapidly, and the beamforming weights of the base station and the wireless channel become mismatched. This requires the use of multiple sets of historical channel estimation results to predict the channel at future moments, thereby obtaining more accurate beamforming weights and improving the transmission rate.

[0122] However, due to the existence of time deviation, the accuracy of channel estimation at historical moments is insufficient, which will increase the error of channel prediction.

[0123] Figure 1 This is a schematic diagram of the time offset changing over time provided by existing technology, such as... Figure 1 As shown, due to frequency drift caused by clock crystal oscillators changing with time and temperature, there is a time-varying deviation in the timing synchronization of the base station and the terminal, known as time offset. The horizontal axis represents time variation (Snap Index), with each sample value representing a snapshot of a channel estimation result. The vertical axis represents the time offset (Delay) in the current channel estimation result.

[0124] from Figure 1 As can be seen, the time offset value exhibits slight jitter over time, with the jitter range in the nanometer range, which is comparable to the clock period selected by the terminal. The base station uses an uplink timing synchronization algorithm to measure the uplink timing deviation during the uplink timing tracking phase, and it itself has a certain time offset calibration capability.

[0125] In existing technologies, uplink timing synchronization algorithms mainly determine the start and end points of OFDM symbols for adjustment purposes. The accuracy requirements for time offset calibration are not high, only needing to be on the order of integer clock cycles.

[0126] However, the accuracy requirements for historical channel time offset calibration during channel prediction are very high, needing to be on the order of fractional clock cycles, in order to meet the high accuracy requirements of channel prediction.

[0127] In multipath scenarios, IDFT-based time offset calibration suffers from interference between channel subpaths, causing the IDFT peak position to fail to reflect the true time offset value, resulting in low accuracy of the final channel prediction. While modern spectral estimation methods offer high multipath resolution, their computational complexity is too high, limiting their practicality.

[0128] To address the aforementioned technical problems, embodiments of this application provide a time deviation estimation method and apparatus that, while ensuring the accuracy of time deviation estimation during channel prediction, reduces computational complexity.

[0129] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0130] Figure 2 This is one of the flowcharts illustrating the time deviation estimation method provided in the embodiments of this application, such as... Figure 2 As shown in the embodiment of this application, a time deviation estimation method is provided, the execution subject of which can be a base station, and the method includes:

[0131] Step 201: Determine the first data matrix corresponding to the original channel frequency domain response matrix based on the first channel estimation period according to the sliding window. The first data matrix is ​​an L×J matrix, where L is the length of the sliding window and J is the number of times the sliding window slides in the original channel frequency domain response matrix. Both L and J are positive integers.

[0132] Specifically, after obtaining the original channel frequency domain response matrix carrying the time offset, the first data matrix is ​​constructed according to the sliding window.

[0133] The original channel frequency domain response matrix of a certain channel in a certain antenna array is selected as follows:

[0134] H=[x(0),x(1),x(2),…,x(N f -1)] T

[0135] Where x(k) represents the channel coefficient at the k-th frequency point, i.e., the channel coefficient at the k-th sampling point, N f Represents the total number of frequency points, k = 0, 1, 2, ..., N f -1.

[0136] The sliding window is: x k =[x(k),x(k+1),…,x(k+L-1)] T ,

[0137] Where L is the length of the sliding window, and the value of L is generally no more than 0.5 (N). f The largest integer greater than or equal to -1. k The observation vectors are called observation vectors. The sliding window slides across the entire bandwidth as much as possible to obtain the observation vectors x0, x1, ..., x2. J-1 The multiple observation vectors are arranged into a matrix S of dimension L×J, which is the first data matrix, and its expression is:

[0138]

[0139] Where J = N - L + 1 represents the number of observation vectors.

[0140] Step 202: Perform a sliding window discrete Fourier transform on the first data matrix to determine the first subspace matrix.

[0141] Specifically, the first subspace matrix is ​​determined by performing a sliding window discrete Fourier transform on each row of the first data matrix.

[0142] First, perform a Discrete Fourier Transform (DFT) with N points on the first row of matrix S to obtain the expression:

[0143]

[0144] Among them, X k The result is the Discrete Fourier Transform (DFT). l is the row number of the first data matrix, l = 7, 2, ..., L. The superscript k is the frequency point number, x(i) is the sampled signal, N is the number of points in the DFT, N and J have the same value, and l, k and N are all positive integers.

[0145] The iterative formula is:

[0146] X k (l)=e j2πk / N [X k (l-1)+x(l+N-1)-x(l-1)]

[0147] The discrete Fourier transform result of the l-th (1>1) row of matrix S is quickly determined using the iterative formula and stored row by row in matrix V. The expression for V is:

[0148]

[0149] For matrix V, calculate the modulus or square of the column vector corresponding to each column, and select the submatrix corresponding to the M column vector with the largest modulus or square as the approximate subspace matrix U'.

[0150] The value of M can be determined by setting an energy percentage threshold and selecting the minimum number of columns whose sum of the squares of the modulus exceeds the threshold value.

[0151] The value of M can also be determined by setting a threshold value, and selecting the number of vectors whose modulus or the square of the modulus is greater than the threshold value as the value of M.

[0152] The first subspace matrix U is obtained by orthogonalizing the approximate subspace matrix U'. The orthogonalization method can be the Schmitt orthogonalization method, and the embodiments of this application do not limit this.

[0153] Step 203: Estimate the time bias based on the first subspace matrix.

[0154] Specifically, after obtaining the orthogonalized first subspace matrix U, time bias estimation is performed based on this subspace matrix.

[0155] The signal subspace and noise subspace are separated according to the above method, and then the time offset is estimated using modern spectral estimation methods. The modern spectral estimation methods involved in the embodiments of this application include the MUSIC algorithm and the ESPRIT algorithm, and improvements have been made to both algorithms in this application.

[0156] After obtaining the orthogonalized first subspace matrix, the time bias can be estimated using the improved MUSIC algorithm or the improved ESPRIT algorithm proposed in this application, or by combining the two improved algorithms.

[0157] The time deviation estimation method provided in this application significantly reduces the computational complexity of multiple discrete Fourier transforms by determining the subspace matrix through a sliding window Fourier transform of the channel frequency domain response matrix. At the same time, it uses a subspace approximation method instead of the eigenvalue decomposition method in modern spectral estimation methods, further reducing the computational complexity of the time deviation estimation process.

[0158] Optionally, a sliding window discrete Fourier transform is performed on the first data matrix to determine the first subspace matrix, including:

[0159] Perform a sliding window discrete Fourier transform on the row vectors corresponding to each row in the first data matrix to determine the second data matrix;

[0160] Determine the modulus of the column vector corresponding to each column in the second data matrix;

[0161] The first subspace matrix is ​​determined based on the column vectors whose modulus exceeds a first preset threshold.

[0162] Specifically, a Discrete Fourier Transform (DFT) is performed on the row vectors corresponding to the first row of the first data matrix. For the row vectors outside the first row, the DFT result is determined using the recursive formula for the sliding window DFT, thus determining the second data matrix. The approximate space matrix is ​​formed by using the magnitude or square of the magnitude of the column vectors corresponding to each column of the second data matrix, identifying column vectors whose magnitudes exceed a preset threshold. This approximate space matrix is ​​then orthogonalized to determine the first subspace matrix.

[0163] By performing a simple transformation on the fundamental formula of the Discrete Fourier Transform, we can obtain:

[0164]

[0165] Among them, X k The result is the Discrete Fourier Transform (DFT). l is the row number of the first data matrix, l = 1, 2, ..., L. The superscript k is the frequency point number, x(i) is the sampled signal, and N is the number of points in the DFT. N and J have the same value.

[0166] For any time window l⁻¹ (1 > 1), the above formula can be rewritten as:

[0167]

[0168] At time l, the formula is rewritten as:

[0169]

[0170] Let p = i + 1, then:

[0171]

[0172] After simplification, the recurrence formula is obtained:

[0173] X k (l)=e j2πk / N [X k (l-1)+x(l+N-1)-x(l-1)]

[0174] The discrete Fourier transform of the row vectors in each row of matrix S (excluding the first row) is determined using the recursive formula, resulting in the second data matrix. The expression for the second data matrix is ​​as follows:

[0175]

[0176] For matrix V, calculate the modulus or square of each column vector, and select the submatrix corresponding to the M column vector with the largest modulus or square as the approximate subspace matrix U'.

[0177] The value of M can be determined by setting an energy percentage threshold and selecting the minimum number of columns whose sum of the squares of the modulus exceeds the threshold value.

[0178] The value of M can also be determined by setting a threshold value, selecting the number of column vectors whose modulus or the square of the modulus is greater than the threshold value as the value of M.

[0179] The first subspace matrix U is obtained by orthogonalizing the approximate subspace matrix U'. The orthogonalization method can be the Schmitt orthogonalization method, and the embodiments of this application do not limit this.

[0180] The time deviation estimation method provided in this application reduces the computational complexity of multiple discrete Fourier transforms by determining the subspace matrix through a sliding window Fourier transform of the channel frequency domain response matrix, thereby reducing the computational complexity of time deviation estimation.

[0181] Optionally, time bias estimation is performed based on the first subspace matrix, including:

[0182] The first matrix is ​​determined by the first M-1 row vectors (excluding the last row) and the last M-1 row vectors (excluding the first row) of the first subspace matrix; where M is the number of rows in the first subspace matrix.

[0183] The spectrum matrix is ​​determined based on the row vector corresponding to the Mth row of the first subspace matrix and the first matrix.

[0184] The time deviation estimation result of the channel estimation based on the first channel estimation period is determined according to the spectrum matrix.

[0185] Specifically, the first matrix is ​​determined based on the first M-1 rows and the last M-1 rows of the first subspace matrix; the spectrum matrix is ​​determined based on the row vector corresponding to the Mth row of the first subspace matrix and the first matrix; and time bias estimation is performed based on the spectrum matrix.

[0186] The ESPRIT algorithm estimates signal parameters by utilizing the rotation invariance of the signal subspace of the data covariance matrix. This requires the array's geometry to be invariant or to obtain two or more identical subarrays through transformation. Only the rotation invariance relationship Φ between two subarrays needs to be obtained to estimate the signal parameters; in this application, the signal parameters refer to time offset values.

[0187] Let v be the row vector corresponding to the last row of the first subspace matrix U. H Let U be the matrix corresponding to the first M-1 rows of the first subspace matrix U. ↓ Let U be the matrix corresponding to the last M-1 rows of the first subspace matrix U. ↑ , where M is the number of rows in the first subspace matrix.

[0188] The first matrix Ψ is determined by the first M-1 rows and the last M-1 rows of the first subspace matrix. The expression for the first matrix Ψ is:

[0189]

[0190] Calculate the vector based on the first matrix. Its expression is:

[0191]

[0192] According to the vector The spectrum matrix Φ is determined by the first matrix Ψ, and its expression is:

[0193]

[0194] The existing ESPRIT algorithm requires inversion operations to determine the rotation-invariant relation Φ, resulting in excessive computational complexity. However, this embodiment avoids the original inversion operation by multiplying vectors and matrices, thus reducing computational complexity.

[0195] After determining the spectrum matrix Φ, its eigenvalues ​​are decomposed to obtain the complex eigenvalue vector λ. The time delay of the M paths is then calculated, and its expression is as follows:

[0196]

[0197] Where angle(λ) represents the phase or radians of the variable within parentheses, and Δf represents the adjacent subcarrier spacing of the channel frequency response matrix H. The time delays of the M paths are determined based on the above expression, and the smallest τ, i.e., the first path delay, is selected as the time deviation estimation result for the first channel estimation period. The first path refers to the fastest arriving sub-path in a multipath environment.

[0198] The time deviation estimation method provided in this application replaces the eigenvalue decomposition method used in the existing ESPRIT algorithm with a subspace approximation method, which reduces the computational complexity. At the same time, it improves the original matrix inversion process by transforming the originally complex matrix inversion problem into a relatively simple matrix and vector multiplication by avoiding inversion based on orthogonal matrices, which further reduces the computational complexity.

[0199] Optionally, the formula for calculating the spectrum matrix is ​​as follows: (The formula is missing from the provided text.)

[0200]

[0201] Where Φ is the spectrum matrix, Ψ is the first matrix, and v is the vector obtained by conjugate transpose of the row vector corresponding to the Mth row of the first subspace matrix.

[0202] Specifically, based on the detailed description in the previous embodiment, the formula for calculating the spectrum matrix can be obtained as follows:

[0203]

[0204] Where Φ is the spectrum matrix, Ψ is the first matrix, and v is the vector obtained by conjugate transpose of the row vector corresponding to the Mth row of the first subspace matrix.

[0205] The time deviation estimation method provided in this application improves the matrix inversion process in the original ESPRIT algorithm. By avoiding the complex matrix inversion problem based on orthogonal matrix inversion, it transforms the original complex matrix inversion problem into a relatively simple matrix and vector multiplication, further reducing the computational complexity.

[0206] Optionally, time bias estimation is performed based on the first subspace matrix, including:

[0207] The spectral peak search interval of the first channel estimation period is determined based on the time deviation estimation result of the second channel estimation period; wherein, the second channel estimation period is the channel estimation period preceding the first channel estimation period;

[0208] If the length of the spectral peak search interval in the first channel estimation period is greater than the second preset threshold, the time deviation estimation result of the first channel estimation period is determined by performing a golden section search on the spatial spectral function.

[0209] Specifically, after obtaining the orthogonalized first subspace matrix through SDFT, the spectral peak search interval for the current channel estimation period is determined based on the time offset estimation result of the previous channel estimation period. If the length of the spectral peak search interval is greater than the second preset threshold, the time offset estimation result for the current channel estimation period is determined by performing a golden section search on the spatial spectral function.

[0210] The MUSIC algorithm is a subspace decomposition algorithm that utilizes the orthogonality of the signal subspace and the noise subspace to construct a spatial spectrum function. It then estimates the signal parameters by searching for spectral peaks. In this application, the signal parameters refer to the time offset values.

[0211] Assume the current time is the nth (n>1) channel estimation after the start of channel prediction, and the time bias estimation result in the previous channel estimation was τ. n-1 Then the spectral peak search interval in this channel estimation is [τ]. n-1 -T, τ n-1 +T|, where the value of T can be determined by the actual situation.

[0212] Compared to the existing technology that uses traversal search to estimate signal parameters, this application uses the golden section algorithm to estimate the first path delay and determine the time offset value.

[0213] The golden section algorithm changes the way intermediate nodes are determined in the original search process. Instead of being obtained from intermediate values ​​or interpolation, intermediate nodes are located near the golden section points. The golden section algorithm can quickly determine the extreme points of the spatial spectral function.

[0214] Let a = τ n-1 -T, b=τ n-1+T, the spatial spectral function expression determined by the MUSIC algorithm is:

[0215]

[0216] Where, vector u i Let e ​​be the column vector corresponding to the i-th column of the signal subspace matrix U. τ The frequency steering vector is a row vector, and its expression is:

[0217]

[0218] in, This represents the frequency corresponding to the k-th frequency point.

[0219] Before performing the golden ratio search, let That is, the golden ratio, with the initialization flag gold-flag set to 0.

[0220] If the iteration condition |ab|>Δτ is met, where Δτ is a preset threshold value, the value of which can be determined according to the required convergence accuracy, the iterative search begins. In each iteration, the following steps are performed based on the gold-flag flag:

[0221] Step 301: If gold-flag = 0, execute: τ1 = a + (1 - ρ)(ba), τ2 = a + ρ(ba), calculate the pseudo-spectrum P1 = P(τ1), P2 = P(τ2) based on the spatial spectrum function; jump to step 304;

[0222] Step 302: If gold-flag = 1, execute: P1 = P2, τ2 = a + ρ(ba), update the pseudo-spectrum P2 = P(τ2) according to the spatial spectrum function; jump to step 304;

[0223] Step 303: If gold-flag = 2, execute: P2 = P1, τ1 = a + (1 - ρ)(ba), update the pseudo-spectrum P1 = P(τ1) according to the spatial spectrum function; jump to step 304;

[0224] Step 304: If P1 > P2, then a = τ1, τ1 = τ2, and set gold-flag = 1; if P1 ≤ P2, then b = τ2, τ2 = τ1, and set gold-flag = 2; proceed to the next iteration;

[0225] Step 305: When |ab|≤Δτ, the iteration converges, and the iteration result τ is output. est = (a+b) / 2.

[0226] The final τ est This is the first path delay, which is the final time deviation estimate.

[0227] The time deviation estimation method provided in this application determines the spectral peak search interval of the current channel estimation period by using the time deviation estimation result of the previous channel estimation period, thus narrowing the search range. At the same time, it replaces the traversal search in the original MUSIC algorithm with the golden section search, reducing the search range and the number of searches. Under the same accuracy, it significantly reduces the computational complexity.

[0228] Optionally, after determining the time offset estimation result of the channel estimation based on the first channel estimation period according to the spectrum matrix, the method further includes:

[0229] The spectral peak search interval for the third channel estimation period is determined based on the time deviation estimation result of the first channel estimation period; wherein, the third channel estimation period is the channel estimation period following the first channel estimation period, and the first channel estimation period is the first channel estimation period after the start of channel estimation.

[0230] If the length of the spectral peak search interval of the third channel estimation period is greater than the second preset threshold, the time deviation estimation result of the third channel estimation period is determined by performing a golden section search on the spatial spectral function.

[0231] Specifically, the time offset value output by the improved ESPRIT algorithm is used as the initial value of the improved MUSIC algorithm, and the two algorithms are combined to determine the time offset value.

[0232] For the first channel estimation after the start of channel prediction, the row vector corresponding to the last row of the first subspace matrix U is denoted as v. H Let U be the matrix corresponding to the first M-1 rows of the first subspace matrix U. ↓ Let U be the matrix corresponding to the last M-1 rows of the first subspace matrix U. ↑ , where M is the number of rows in the first subspace matrix.

[0233] The first matrix Ψ is determined by the first M-1 rows and the last M-1 rows of the first subspace matrix. The expression for the first matrix Ψ is:

[0234]

[0235] Calculate the vector based on the first matrix. Its expression is:

[0236]

[0237] According to the vector The spectrum matrix Φ is determined by the first matrix Ψ, and its expression is:

[0238]

[0239] After determining the spectrum matrix Φ, its eigenvalues ​​are decomposed to obtain the complex eigenvalue vector λ. The time delay of the M paths is then calculated, and its expression is as follows:

[0240]

[0241] Where angle(λ) represents the phase or radians of the variable within parentheses, and Δf represents the adjacent subcarrier spacing of the channel frequency response matrix H. Based on the above expression, the time delays of the M paths are determined, and the smallest τ, i.e., the first path delay, is selected as the time deviation estimation result for the first channel estimation, denoted as τ1.

[0242] For the second channel estimation after the start of channel prediction, the spectral peak search interval is [τ1-T, τ1+T], where the value of T can be determined by the actual situation.

[0243] Let a = τ1 - T, b = τ1 + T, the expression for the spatial spectral function determined by the MUSIC algorithm is:

[0244]

[0245] Where, vector u i Let e ​​be the column vector corresponding to the i-th column of the signal subspace matrix U. τ The frequency steering vector is a row vector, and its expression is:

[0246]

[0247] in, This represents the frequency corresponding to the k-th frequency point.

[0248] Before performing the golden ratio search, let That is, the golden ratio, with the initialization flag gold-flag set to 0.

[0249] If the iteration condition |ab|>Δτ is met, where Δτ is a preset threshold value, the value of which can be determined according to the required convergence accuracy, then proceed with the iterative search. In each iteration, the following steps are executed based on the gold-flag flag:

[0250] Step 401: If gold-flag = 0, execute: τ a =a+(1-ρ)(ba),τ b =a+ρ(ba), calculate the pseudospectrum P based on the spatial spectral function. a =P(τ) a ), P b =P(τ) b Proceed to step 404;

[0251] Step 402: If gold-fag = 1, execute: P a =P b , τ b =a+ρ(ba), update the pseudospectral P according to the spatial spectral function. b =P(τ) b Proceed to step 404;

[0252] Step 403: If gold-flag = 2, execute: P b =P a , τ a =a+(1-ρ)(ba), update the pseudospectral P according to the spatial spectral function. a =P(τ) a Proceed to step 404;

[0253] Step 404, if P a >P b Then a = τ a , τ a =τ b Set gold-flag = 1; if P a ≤P b Then b = τ b , τ b =τ a Set gold-flag = 2; proceed to the next iteration;

[0254] Step 405: When |ab|≤Δτ, the iteration converges, and the output iteration result τ2=(a+b) / 2 is output.

[0255] The final τ2 is the first path delay, which is the time deviation estimation result in the second channel estimation.

[0256] For each subsequent channel estimation, the spectral peak search interval is determined based on the time offset value of the previous channel estimation. Then, the above operation is repeated to obtain the time offset estimation result in the nth (n>2)th channel estimation after the start of channel prediction.

[0257] The time deviation estimation method provided in this application uses the time deviation estimation result of the improved ESPRT algorithm as the initial value and combines it with the improved MUSIC algorithm to balance search accuracy and search count. Under the condition of the same accuracy, the number of searches is further reduced.

[0258] Optionally, a golden section search is performed on the spatial spectral function to determine the time offset estimation result of the first channel estimation period, including:

[0259] Using the second preset threshold value as the convergence accuracy, the minimum value of the spatial spectral function within the spectral peak search interval of the first channel estimation period is determined according to the golden section search.

[0260] The target search interval is determined based on the minimum value, and the time deviation estimation result of the first channel estimation period is the median of the target search interval.

[0261] Specifically, the second preset threshold value is used as the convergence accuracy. The minimum value of the spatial spectral function in the spectral peak search interval is determined according to the golden section search. Based on this minimum value, the spectral peak search interval can be narrowed to the convergence accuracy, thereby determining the time offset value.

[0262] The golden section method, a trial-and-error approach in one-dimensional search algorithms, ensures that adjacent search intervals have the same shortening rate. The spectral peak search interval for the current channel estimation period is determined by the time offset of the previous channel estimation period, thus narrowing the search range.

[0263] The minimum value of the spatial spectral function within the spectral peak search interval is determined by the golden section method. For the spatial spectral function P(τ), the spectral peak search interval is [a, b], and the second preset threshold value and convergence accuracy are Δτ, where Δτ is the preset threshold value, and its value can be determined according to the required convergence accuracy.

[0264] Before performing the golden ratio search, let That is, the golden ratio, with the initialization flag gold-flag set to 0.

[0265] If the iteration condition |ab|>Δτ is met, proceed with the iterative search. In each iteration, perform the following steps based on the gold-flag flag:

[0266] Step 501: If gold-flag = 0, execute: τ1 = a + (1 - ρ)(ba), τ2 = a + ρ(ba), calculate the pseudo-spectrum P1 = P(τ1), P2 = P(τ2) based on the spatial spectrum function; jump to step 504;

[0267] Step 502: If gold-flag = 1, execute: P1 = P2, τ2 = a + ρ(ba), update the pseudo-spectrum P2 = P(τ2) according to the spatial spectrum function; jump to step 504;

[0268] Step 503: If gold-flag = 2, execute: P2 = P1, τ1 = a + (1 - ρ)(ba), update the pseudo-spectrum P1 = P(τ1) according to the spatial spectrum function; jump to step 504;

[0269] Step 504: If P1 > P2, then a = τ1, τ1 = τ2, set gold-flag = 1; if P1 ≤ P2, then b = τ2, τ2 = τ1, set gold-flag = 2; proceed to the next iteration;

[0270] Step 505: When |ab|≤Δτ, the iteration converges, and the iteration result τ is output. est = (a+b) / 2.

[0271] The final τ est This is the first path delay, which is the final time deviation estimate.

[0272] The time deviation estimation method provided in this application replaces the traversal search in the original MUSIC algorithm with a golden section search, which significantly reduces the number of searches and reduces the computational complexity of the time deviation estimation process while maintaining the same accuracy.

[0273] The time deviation estimation method provided in this application will be described below with reference to a specific embodiment. Figure 3 This is a second schematic flowchart of the time deviation estimation method provided in the embodiments of this application, as shown below. Figure 3 As shown, after obtaining the original channel frequency domain response matrix H carrying the time offset, the signal subspace and noise subspace are distinguished by the subspace approximation method provided in this application.

[0274] A first data matrix is ​​constructed using the original channel frequency domain response matrix H. An SDFT is then performed on the first data matrix to obtain a second data matrix. Column vectors in the second data matrix whose magnitudes exceed a preset threshold are used to form an approximate subspace matrix. This approximate subspace matrix is ​​then orthogonalized to obtain the final subspace matrix.

[0275] The improved ESPRIT algorithm is used to determine the time offset value in the first channel estimation process during channel prediction. Based on this time offset value, the spectral peak search interval in the second channel estimation process is determined. The improved MUSIC algorithm is used to perform spectral peak search to determine the time offset value in the second channel estimation process.

[0276] Each subsequent time offset estimation can be performed using the improved MUSIC algorithm. The time offset value of the previous channel estimation is used to determine the spectral peak search interval in the current channel estimation. Based on the golden section search, the spatial spectral function determined by the MUSIC algorithm is used to determine the minimum value in the corresponding spectral peak search interval, thereby continuously narrowing the spectral peak search interval to the convergence accuracy. The median of the narrowed search interval is used as the time offset value of the current channel estimation.

[0277] The original channel frequency domain response matrix is ​​calibrated based on the time offset value in each channel estimation process.

[0278] Figure 4 This is a schematic diagram of the network device provided in the embodiments of this application, such as... Figure 4 As shown, this application embodiment provides a network device, including a memory 420, a transceiver 400, and a processor 410:

[0279] Memory 420 is used to store computer programs; transceiver 400 is used to send and receive data under the control of the processor; processor 410 is used to read the computer program from the memory and perform the following operations:

[0280] The first data matrix corresponding to the original channel frequency domain response matrix based on the first channel estimation period is determined according to the sliding window. The first data matrix is ​​an L×J matrix, where L is the length of the sliding window and J is the number of times the sliding window slides in the original channel frequency domain response matrix. Both L and J are positive integers.

[0281] Perform a sliding window discrete Fourier transform on the first data matrix to determine the first subspace matrix;

[0282] Time bias estimation is performed based on the first subspace matrix.

[0283] Specifically, transceiver 400 is used to receive and send data under the control of processor 410.

[0284] Among them, Figure 4 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 410) and memory (memory 420). The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 400 can be multiple elements, including transmitters and receivers, providing units for communicating with various other devices over transmission media, including wireless channels, wired channels, optical fibers, etc. The processor 410 is responsible for managing the bus architecture and general processing, and the memory 420 can store data used by the processor 410 during operation.

[0285] The processor 410 can be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a complex programmable logic device (CPLD). The processor can also adopt a multi-core architecture.

[0286] Optionally, a sliding window discrete Fourier transform is performed on the first data matrix to determine the first subspace matrix, including:

[0287] Perform a sliding window discrete Fourier transform on the row vectors corresponding to each row in the first data matrix to determine the second data matrix;

[0288] Determine the modulus of the column vector corresponding to each column in the second data matrix;

[0289] The first subspace matrix is ​​determined based on the column vectors whose modulus exceeds a first preset threshold.

[0290] Optionally, a sliding window discrete Fourier transform is performed on the row vectors corresponding to each row of the first data matrix to determine the second data matrix, including:

[0291] According to Formula 1, a discrete Fourier transform with N points is performed on the row vector corresponding to the first row of the first data matrix. The expression of Formula 1 is:

[0292]

[0293] Among them, X k The result is the Discrete Fourier Transform (DFT), where l is the row number of the first data matrix, k is the frequency point number, x(i) is the sampled signal, and N is the number of points in the DFT. N and J have the same value; l, k, and N are all positive integers.

[0294] When l is greater than 1, the discrete Fourier transform result of the row vector corresponding to the l-th row of the first data matrix is ​​determined according to Formula 2. The expression of Formula 2 is:

[0295] X k (l)=e j2πk / N [X k (l-1)+x(l+N-1)-x(l-1)]

[0296] The second data matrix is ​​determined based on Formula 1 and Formula 2. The expression for the second data matrix is ​​as follows:

[0297]

[0298] Where V is the second data matrix.

[0299] Optionally, time bias estimation is performed based on the first subspace matrix, including:

[0300] The first matrix is ​​determined by the first M-1 row vectors (excluding the last row) and the last M-1 row vectors (excluding the first row) of the first subspace matrix; where M is the number of rows in the first subspace matrix.

[0301] The spectrum matrix is ​​determined based on the row vector corresponding to the Mth row of the first subspace matrix and the first matrix.

[0302] The time deviation estimation result of the channel estimation based on the first channel estimation period is determined according to the spectrum matrix.

[0303] Optionally, the formula for calculating the spectrum matrix is ​​as follows: (The formula is missing from the provided text.)

[0304]

[0305] Where Φ is the spectrum matrix, Ψ is the first matrix, and v is the vector obtained by conjugate transpose of the row vector corresponding to the Mth row of the first subspace matrix.

[0306] Optionally, time bias estimation is performed based on the first subspace matrix, including:

[0307] The spectral peak search interval of the first channel estimation period is determined based on the time deviation estimation result of the second channel estimation period; wherein, the second channel estimation period is the channel estimation period preceding the first channel estimation period;

[0308] If the length of the spectral peak search interval in the first channel estimation period is greater than the second preset threshold, the time deviation estimation result of the first channel estimation period is determined by performing a golden section search on the spatial spectral function.

[0309] Optionally, after determining the time offset estimation result of the channel estimation based on the first channel estimation period according to the spectrum matrix, the method further includes:

[0310] The spectral peak search interval for the third channel estimation period is determined based on the time deviation estimation result of the first channel estimation period; wherein, the third channel estimation period is the channel estimation period following the first channel estimation period, and the first channel estimation period is the first channel estimation period after the start of channel estimation.

[0311] If the length of the spectral peak search interval of the third channel estimation period is greater than the second preset threshold, the time deviation estimation result of the third channel estimation period is determined by performing a golden section search on the spatial spectral function.

[0312] Optionally, a golden section search is performed on the spatial spectral function to determine the time offset estimation result of the first channel estimation period, including:

[0313] Using the second preset threshold value as the convergence accuracy, the minimum value of the spatial spectral function within the spectral peak search interval of the first channel estimation period is determined according to the golden section search.

[0314] The target search interval is determined based on the minimum value, and the time deviation estimation result of the first channel estimation period is the median of the target search interval.

[0315] It should be noted that the network device provided in this embodiment of the invention can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0316] Figure 5 This is a schematic diagram of the time deviation estimation device provided in the embodiments of this application, as shown below. Figure 5 As shown in the figure, this application provides a time deviation estimation device, including:

[0317] The first determining unit 501 is used to determine the first data matrix corresponding to the original channel frequency domain response matrix of the channel estimation based on the first channel estimation period according to the sliding window. The first data matrix is ​​an L×J matrix, where L is the length of the sliding window and J is the number of times the sliding window slides in the original channel frequency domain response matrix. Both L and J are positive integers.

[0318] The second determining unit 502 is used to perform a sliding window discrete Fourier transform on the first data matrix to determine the first subspace matrix.

[0319] Estimation unit 503 is used to estimate time bias based on the first subspace matrix.

[0320] Optionally, the second determining unit includes a first determining module, a second determining module, and a third determining module;

[0321] The first determining module is used to perform a sliding window discrete Fourier transform on the row vector corresponding to each row in the first data matrix to determine the second data matrix;

[0322] The second determining module is used to determine the modulus of the column vector corresponding to each column in the second data matrix;

[0323] The third determining module is used to determine the first subspace matrix based on column vectors whose modulus exceeds the first preset threshold value.

[0324] Optionally, the first determining module includes a first determining submodule, a second determining submodule, and a third determining submodule;

[0325] The first determining submodule is used to perform a discrete Fourier transform of N points on the row vector corresponding to the first row of the first data matrix according to Formula 1. The expression of Formula 1 is:

[0326]

[0327] Among them, X kThe result is the Discrete Fourier Transform (DFT), where l is the row number of the first data matrix, k is the frequency point number, x(i) is the sampled signal, and N is the number of points in the DFT. N and J have the same value; l, k, and N are all positive integers.

[0328] The second determining submodule is used to determine the discrete Fourier transform result of the row vector corresponding to the l-th row of the first data matrix according to Formula 2 when l is greater than 1. The expression of Formula 2 is:

[0329] X k (l)=e j2πk / N [X k (l-1)+x(l+N-1)-x(l-1)]

[0330] The third determining submodule is used to determine the second data matrix based on Formula 1 and Formula 2. The expression for the second data matrix is:

[0331]

[0332] Where V is the second data matrix.

[0333] Optionally, the estimation unit includes a fourth determination module, a fifth determination module, and a sixth determination module;

[0334] The fourth determining module is used to determine the first matrix based on the first M-1 row vectors (excluding the last row) and the last M-1 row vectors (excluding the first row) of the first subspace matrix; where M is the number of rows in the first subspace matrix.

[0335] The fifth determining module is used to determine the spectrum matrix based on the row vector corresponding to the Mth row of the first subspace matrix and the first matrix;

[0336] The sixth determining module is used to determine the time deviation estimation result of the channel estimation based on the first channel estimation period according to the spectrum matrix.

[0337] Optionally, the formula for calculating the spectrum matrix is ​​as follows: (The formula is missing from the provided text.)

[0338]

[0339] Where Φ is the spectrum matrix, Ψ is the first matrix, and v is the vector obtained by conjugate transpose of the row vector corresponding to the Mth row of the first subspace matrix.

[0340] Optionally, the estimation module includes a seventh determination module and an eighth determination module;

[0341] The seventh determining module is used to determine the spectral peak search interval of the first channel estimation period based on the time deviation estimation result of the second channel estimation period; wherein, the second channel estimation period is the channel estimation period preceding the first channel estimation period;

[0342] The eighth determining module is used to perform a golden section search on the spatial spectral function to determine the time deviation estimation result of the first channel estimation period when the length of the spectral peak search interval of the first channel estimation period is greater than the second preset threshold value.

[0343] Optionally, the estimation unit may further include a ninth determining module and a tenth determining module;

[0344] The ninth determining module is used to determine the spectral peak search interval of the third channel estimation period based on the time deviation estimation result of the first channel estimation period; wherein, the third channel estimation period is the channel estimation period following the first channel estimation period, and the first channel estimation period is the first channel estimation period after the start of channel estimation.

[0345] The tenth determination module is used to perform a golden section search on the spatial spectral function to determine the time deviation estimation result of the third channel estimation period when the length of the spectral peak search interval of the third channel estimation period is greater than the second preset threshold value.

[0346] Optionally, the eighth determining module includes a fourth determining submodule and a fifth determining submodule;

[0347] The fourth determination submodule is used to determine the minimum value of the spatial spectral function within the spectral peak search interval of the first channel estimation period based on the golden section search, with the second preset threshold value as the convergence accuracy.

[0348] The fifth determination submodule is used to determine the target search interval based on the minimum value, and the time deviation estimation result of the first channel estimation period is the median of the target search interval.

[0349] It should be noted that the time deviation estimation device provided in this embodiment of the invention can implement all the method steps implemented in the above embodiment of the time deviation estimation method with the base station as the execution subject, and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0350] It should be noted that the division of units / modules in the embodiments of this application is illustrative and only represents one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional units / modules in the various embodiments of this application can be integrated into one processing unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated into one unit / module. The integrated units / modules described above can be implemented in hardware or as software functional units.

[0351] If the integrated unit / module is implemented as a software functional unit / module and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0352] On the other hand, embodiments of this application also provide a computer-readable storage medium storing a computer program for causing a processor to execute the time deviation estimation methods provided in the above embodiments, including:

[0353] The first data matrix corresponding to the original channel frequency domain response matrix based on the first channel estimation period is determined according to the sliding window. The first data matrix is ​​an L×J matrix, where L is the length of the sliding window and J is the number of times the sliding window slides in the original channel frequency domain response matrix. Both L and J are positive integers. The first data matrix is ​​subjected to a sliding window discrete Fourier transform to determine the first subspace matrix. The time deviation is estimated based on the first subspace matrix.

[0354] Computer-readable storage media can be any available medium or data storage device that can be accessed by a processor, including but not limited to magnetic storage (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0355] It should be noted that computer-readable storage media can be any available medium or data storage device that can be accessed by a processor, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROMs, EPROMs, EEPROMs, non-volatile memory (NAND FLASH), solid-state drives (SSDs)).

[0356] On the other hand, embodiments of this application also provide a computer program product containing instructions, which, when run on a computer, causes the computer to execute the time deviation estimation methods provided in the above embodiments.

[0357] On the other hand, embodiments of this application also provide a chip, including a processor. The processor is used to read and run a computer program stored in a memory to execute corresponding operations and / or processes performed by a terminal device in the time deviation estimation method provided in embodiments of this application. Optionally, the chip also includes a memory, which is connected to the processor via a circuit or wire. The processor is used to read and execute the computer program in the memory. Further, the chip also includes a communication interface, to which the processor is connected. The communication interface is used to receive data and / or information that needs to be processed. The processor obtains the data and / or information from the communication interface and processes the data and / or information. The communication interface may be an input / output interface.

[0358] Additionally, it should be noted that the term "and / or" in the embodiments of this application describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the related objects before and after it have an "or" relationship.

[0359] The terms "first," "second," "target," etc., used in the specification and claims of this application are used to distinguish similar objects and are not used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," "target," etc., are generally of the same class and are not limited in number; for example, a first object can be one or more.

[0360] In the embodiments of this application, the term "multiple" refers to two or more, and other quantifiers are similar.

[0361] The technical solutions provided in this application can be applied to various systems, especially 5G systems. For example, applicable systems include Global System for Mobile Communication (GSM), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA) General Packet Radio Service (GPRS), Long Term Evolution (LTE), LTE Frequency Division Duplex (FDD), LTE Time Division Duplex (TDD), Long Term Evolution Advanced (LTE-A), Universal Mobile Telecommunication System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), and 5G New Radio (NR). All of these systems include terminal equipment and network equipment. The systems may also include a core network component, such as Evolved Packet System (EPS) and 5G systems (5GS).

[0362] The terminal devices involved in the embodiments of this application can be devices that provide voice and / or data connectivity to users, handheld devices with wireless connectivity, or other processing devices connected to a wireless modem. The names of the terminal devices may differ in different systems; for example, in a 5G system, a terminal device can be called User Equipment (UE). Wireless terminal devices can communicate with one or more core networks (CNs) via a Radio Access Network (RAN). Wireless terminal devices can be mobile terminal devices, such as mobile phones (or "cellular" phones) and computers with mobile terminal devices, for example, portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile devices that exchange voice and / or data with the RAN. Examples include Personal Communication Service (PCS) phones, cordless phones, Session Initiated Protocol (SIP) phones, Wireless Local Loop (WLL) stations, and Personal Digital Assistants (PDAs). Wireless terminal equipment can also be referred to as a system, subscriber unit, subscriber station, mobile station, mobile station, remote station, access point, remote terminal, access terminal, user terminal, user agent, or user device, but this application does not limit the terminology.

[0363] The network device involved in this application embodiment can be a base station, which may include multiple cells providing services to terminals. Depending on the specific application, a base station may also be called an access point, or a device in an access network that communicates with a wireless terminal device through one or more sectors on the air interface, or other names. The network device can be used to exchange received air frames with Internet Protocol (IP) packets, acting as a router between the wireless terminal device and the rest of the access network, where the rest of the access network may include an Internet Protocol (IP) communication network. The network device can also coordinate the attribute management of the air interface. For example, the network equipment involved in the embodiments of this application can be a base transceiver station (BTS) in a Global System for Mobile communications (GSM) or Code Division Multiple Access (CDMA), a NodeB in a Wide-band Code Division Multiple Access (WCDMA) system, an evolved Node B (eNB or e-NodeB) in a long term evolution (LTE) system, a 5G base station (gNB) in a next-generation 5G network architecture, a Homeevolved Node B (HeNB), a relay node, a femto, a pico, etc., and is not limited in the embodiments of this application. In some network structures, the network equipment may include centralized unit (CU) nodes and distributed unit (DU) nodes, and the centralized unit and distributed unit may also be geographically separated.

[0364] Network devices and terminal devices can each use one or more antennas for Multiple Input Multiple Output (MIMO) transmission. MIMO transmission can be Single User MIMO (SU-MIMO) or Multiple User MIMO (MU-MIMO). Depending on the configuration and number of antenna combinations, MIMO transmission can be 2D-MIMO, 3D-MIMO, FD-MIMO, or massive-MIMO, and can also be diversity transmission, precoding transmission, or beamforming transmission, etc.

[0365] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this 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 and optical storage) containing computer-usable program code.

[0366] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0367] These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0368] These processors can execute instructions that can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0369] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method of time bias estimation, the method comprising: The method comprises: determining a first data matrix corresponding to a raw channel frequency domain response matrix of channel estimation based on a first channel estimation period according to a sliding window, the first data matrix being an L×J matrix, L being a length of the sliding window, J being a number of times of sliding of the sliding window in the raw channel frequency domain response matrix, L and J being positive integers; performing sliding window discrete Fourier transform on the first data matrix to determine a first subspace matrix; performing time bias estimation according to the first subspace matrix; wherein the performing sliding window discrete Fourier transform on the first data matrix to determine a first subspace matrix comprises: performing the sliding window discrete Fourier transform on a row vector corresponding to each row in the first data matrix to determine a second data matrix; determining a modulus value of a column vector corresponding to each column in the second data matrix; determining the first subspace matrix according to a column vector whose modulus value exceeds a first preset threshold value; wherein the performing time bias estimation according to the first subspace matrix comprises: determining a first matrix according to M-1 row vectors in front of the last row and M-1 row vectors behind the first row in the first subspace matrix, M being a number of rows in the first subspace matrix; determining a spectrum matrix according to a row vector corresponding to the Mth row in the first subspace matrix and the first matrix; determining a time bias estimation result of channel estimation based on the first channel estimation period according to the spectrum matrix; or determining a spectrum peak search interval of the first channel estimation period according to a time bias estimation result of a second channel estimation period, the second channel estimation period being a channel estimation period preceding the first channel estimation period; in a case where a length of the spectrum peak search interval of the first channel estimation period is greater than a second preset threshold value, performing golden section search on a spatial spectrum function to determine a time bias estimation result of the first channel estimation period.

2. The time bias estimation method of claim 1, wherein, The performing the sliding window discrete Fourier transform on a row vector corresponding to each row in the first data matrix to determine a second data matrix comprises: performing discrete Fourier transform on a row vector corresponding to a first row in the first data matrix by N points according to formula one, an expression of the formula one being: wherein X k is a discrete Fourier transform result, l is a row number of the first data matrix, k is a frequency point number, x(i) is a sampling signal, N is a point number of the discrete Fourier transform, N and J have the same value; l, k and N are positive integers; in a case where the l is greater than 1, determining a discrete Fourier transform result of a row vector corresponding to the lth row in the first data matrix according to formula two, an expression of the formula two being: X k (l) = e j2πk / N [X k (l-1) + x(l+N-1) - x(l-1)] determining the second data matrix according to the formula one and the formula two, an expression of the second data matrix being: wherein V is the second data matrix.

3. The time bias estimation method of claim 1, wherein, a calculation formula for determining the spectrum matrix according to a row vector corresponding to the Mth row in the first subspace matrix and the first matrix is: wherein Φ is the spectrum matrix, Ψ is the first matrix, and v is a vector obtained by performing conjugate transposition on the row vector corresponding to the Mth row in the first subspace matrix.

4. The time bias estimation method of claim 1, wherein, after the determining the time bias estimation result of channel estimation based on the first channel estimation period according to the spectrum matrix, the method further comprises: determine a spectrum peak search interval of a third channel estimation period according to the time deviation estimation result of the first channel estimation period, wherein the third channel estimation period is a next channel estimation period of the first channel estimation period, and the first channel estimation period is a first channel estimation period after channel estimation starts; in a case where a length of the spectrum peak search interval of the third channel estimation period is greater than a second preset threshold value, perform golden section search on the spatial spectrum function to determine a time deviation estimation result of the third channel estimation period.

5. The time bias estimation method of claim 1, wherein, The golden section search on the spatial spectrum function to determine the time deviation estimation result of the first channel estimation period comprises: taking the second preset threshold value as a convergence precision, and determining a minimum value of the spatial spectrum function in the spectrum peak search interval of the first channel estimation period according to the golden section search; determining a target search interval according to the minimum value, and the time deviation estimation result of the first channel estimation period being a middle value of the target search interval.

6. A network device, comprising: The network device comprises a memory, a transceiver and a processor: the memory is configured to store a computer program; the transceiver is configured to transceive data under control of the processor; and the processor is configured to read the computer program in the memory and perform the following operations: determine a first data matrix corresponding to a raw channel frequency domain response matrix of channel estimation based on a first channel estimation period according to a sliding window, the first data matrix being an L×J matrix, L being a length of the sliding window, J being a number of times of sliding of the sliding window in the raw channel frequency domain response matrix, and L and J both being positive integers; perform sliding window discrete Fourier transform on the first data matrix to determine a first subspace matrix; perform time deviation estimation according to the first subspace matrix; wherein the sliding window discrete Fourier transform on the first data matrix to determine the first subspace matrix comprises: perform the sliding window discrete Fourier transform on a row vector corresponding to each row in the first data matrix to determine a second data matrix; determine a modulus value of a column vector corresponding to each column in the second data matrix; determine the first subspace matrix according to column vectors whose modulus values exceed a first preset threshold value; wherein the time deviation estimation according to the first subspace matrix comprises: determine a first matrix according to M-1 row vectors other than a last row and M-1 row vectors other than a first row in the first subspace matrix, wherein M is a number of rows in the first subspace matrix; determine a spectrum matrix according to a row vector corresponding to an Mth row in the first subspace matrix and the first matrix; determine a time deviation estimation result of channel estimation based on the first channel estimation period according to the spectrum matrix; or determine a spectrum peak search interval of the first channel estimation period according to a time deviation estimation result of a second channel estimation period, wherein the second channel estimation period is a previous channel estimation period of the first channel estimation period. ​ In a case where a length of the spectrum peak search interval of the first channel estimation period is greater than a second preset threshold value, the spatial spectrum function is searched by golden section to determine a time offset estimation result of the first channel estimation period.

7. The network device of claim 6, wherein, The method further includes: performing a point number N discrete Fourier transform on a row vector corresponding to a first row of the first data matrix according to Formula One, where an expression of the Formula One is: wherein X k is a discrete Fourier transform result, l is a row number of the first data matrix, k is a frequency point number, x(i) is a sampling signal, N is a point number of the discrete Fourier transform, N and J have the same value; l, k and N are positive integers; In a case where the l is greater than 1, a discrete Fourier transform result of a row vector corresponding to an lth row of the first data matrix is determined according to Formula Two, where an expression of the Formula Two is: X k (l) = e j2πk / N [X k (l-1)+x(l+N-1)-x(l-1)] The second data matrix is determined according to the Formula One and the Formula Two, where an expression of the second data matrix is: wherein the V is the second data matrix.

8. The network device of claim 6, wherein, A calculation formula for determining a spectrum matrix according to a row vector corresponding to an Mth row of the first subspace matrix and the first matrix is: wherein the Φ is the spectrum matrix, the Ψ is the first matrix, and the v is a vector after a conjugate transpose of the row vector corresponding to the Mth row of the first subspace matrix.

9. The network device of claim 6, wherein, After the time offset estimation result of the channel estimation based on the first channel estimation period is determined according to the spectrum matrix, the method further includes: determining a spectrum peak search interval of a third channel estimation period according to the time offset estimation result of the first channel estimation period, wherein the third channel estimation period is a next channel estimation period of the first channel estimation period, and the first channel estimation period is a first channel estimation period after a start of the channel estimation; In a case where a length of the spectrum peak search interval of the third channel estimation period is greater than the second preset threshold value, the spatial spectrum function is searched by golden section to determine a time offset estimation result of the third channel estimation period.

10. The network device of claim 6, wherein, The method further includes: taking the second preset threshold value as a convergence precision, and determining a minimum value of the spatial spectrum function in the spectrum peak search interval of the first channel estimation period according to the golden section search; determining a target search interval according to the minimum value, and taking a median value of the target search interval as the time offset estimation result of the first channel estimation period.

11. A time bias estimation apparatus characterized by comprising: The method further includes: a first determining unit configured to determine a first data matrix corresponding to an original channel frequency domain response matrix of a channel estimation based on a first channel estimation period according to a sliding window, the first data matrix being an L×J matrix, L being a length of the sliding window, J being a number of times of sliding of the sliding window in the original channel frequency domain response matrix, and L and J being positive integers; a second determining unit configured to perform a sliding window discrete Fourier transform on the first data matrix to determine a first subspace matrix; an estimating unit configured to perform time offset estimation according to the first subspace matrix; wherein the second determining unit includes: The first determining module is configured to perform the sliding window discrete Fourier transform on a row vector corresponding to each row in the first data matrix to determine a second data matrix. The second determining module is configured to determine a modulus value of a column vector corresponding to each column in the second data matrix. The third determining module is configured to determine the first subspace matrix according to the column vector whose modulus value exceeds a first preset threshold. The estimation unit includes: The fourth determining module is configured to determine a first matrix according to M-1 row vectors in the first subspace matrix except the last row and M-1 row vectors except the first row; the M is a number of rows of the first subspace matrix. The fifth determining module is configured to determine a spectrum matrix according to a row vector corresponding to the Mth row of the first subspace matrix and the first matrix. The sixth determining module is configured to determine a time deviation estimation result of channel estimation based on the first channel estimation period according to the spectrum matrix. Alternatively, The seventh determining module is configured to determine a spectrum peak search interval of the first channel estimation period according to a time deviation estimation result of a second channel estimation period; the second channel estimation period is a previous channel estimation period of the first channel estimation period. The eighth determining module is configured to perform a golden section search on a spatial spectrum function to determine the time deviation estimation result of the first channel estimation period when a length of the spectrum peak search interval of the first channel estimation period is greater than a second preset threshold.

12. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is used to make the computer execute the method in any one of claims 1 to 5.

13. An apparatus for time bias estimation, the apparatus comprising: The computer readable storage medium stores a computer program, and the computer program is used to make the computer execute the method in any one of claims 1 to 5.

14. A computer program product, characterised in that, The computer readable storage medium stores a computer program, and the computer program is used to make the computer execute the method in any one of claims 1 to 5. The instructions make the computer execute the method in any one of claims 1 to 5 when the computer program product runs on the computer.

Images