OFDM signal parameter estimation method, program product and equipment

Abstract

The invention discloses an OFDM (Orthogonal Frequency Division Multiplexing) signal parameter estimation method, a program product and equipment. The method comprises the following steps: receiving a signal to be tested; determining whether the to-be-measured signal is an orthogonal frequency division multiplexing (OFDM) signal based on the to-be-measured signal, and acquiring a target data length estimated for one OFDM signal under the condition that the to-be-measured signal is determined to be the OFDM signal; obtaining a cyclic prefix length range, and determining an initial cyclic prefix length based on the cyclic prefix length range and the target data length; and determining a search range of the cyclic prefix length based on the initial cyclic prefix length, and determining a target cyclic prefix length of the signal to be measured based on the search range.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to an OFDM signal parameter estimation method, a computer program product and an OFDM signal parameter estimation device. Background Art

[0002] In recent years, the technology of some signal source devices (such as drones) has developed rapidly, and the threats they bring have also penetrated into various fields: aviation safety threats, public safety threats, infrastructure safety threats, network security threats, etc. Therefore, it is urgent to respond to the challenges brought by drones. In order to accurately discover and identify signal source devices in complex electromagnetic environments, it is necessary to receive the signals of signal source devices and perform various analysis and processing to explore their unique signal characteristics.

[0003] Currently, some signal source devices (such as drones) mostly use orthogonal frequency division multiplexing (OFDM) signal transmission system. There are many methods for OFDM signal identification, such as time-frequency analysis, wavelet transform, carrier modulation method identification and envelope correlation spectrum, but these methods can only identify whether the signal is an OFDM signal, lack more detailed characteristic parameters, and cannot distinguish the image transmission signals of different types of signal source devices. Summary of the invention

[0004] In order to solve the existing technical problems, the present invention provides an OFDM signal parameter estimation method, a computer program product and an OFDM signal parameter estimation device, which can accurately estimate signal parameters, greatly reduce search time, reduce calculation amount and save system resources.

[0005] In a first aspect, a method for estimating OFDM signal parameters is provided, comprising: receiving a signal to be tested; determining whether the signal to be tested is an orthogonal frequency division multiplexing (OFDM) signal based on the signal to be tested, and obtaining a target data length estimated for the OFDM signal when it is determined that the signal to be tested is an OFDM signal; obtaining a cyclic prefix length range, and determining an initial cyclic prefix length based on the cyclic prefix length range and the target data length; determining a search range for a cyclic prefix length based on the initial cyclic prefix length, and determining a target cyclic prefix length for the signal to be tested based on the search range.

[0006] In a second aspect, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the OFDM signal parameter estimation method as described in any embodiment of the present application is implemented.

[0007] In a third aspect, an OFDM signal parameter estimation device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the OFDM signal parameter estimation method as described in any embodiment of the present application.

[0008] The present application receives a signal to be tested, first determines whether the signal to be tested is an orthogonal frequency division multiplexing (OFDM) signal, and when it is determined that the signal to be tested is an OFDM signal, obtains a target data length estimated for one of the OFDM signals, obtains a cyclic prefix length range, and within the cyclic prefix length range, based on the target data length, performs a coarse search on the cyclic prefix length to obtain an initial cyclic prefix length, and then performs an accurate estimate based on the initial cyclic prefix length to obtain a final cyclic prefix length, thereby obtaining signal parameters of the signal to be tested, thereby more accurately identifying signal parameters and more accurately identifying different categories of image transmission signals based on the signal parameters; since the cyclic prefix lengths are diverse and a priori unknown, the amount of traversal calculations is large, and to address the problem of large traversal calculations, a coarse search followed by a fine search is used, and this method of combining coarse and fine searches can greatly reduce search time, reduce the amount of calculations, and save system resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 1. is a diagram showing an application environment of an OFDM signal parameter estimation method in an embodiment;

[0010] Figure 2 is a flow chart of an OFDM signal parameter estimation method in one embodiment;

[0011] Figure 3 is a schematic diagram of two OFDM symbols in one embodiment;

[0012] Figure 4 is a schematic diagram of a first signal and a second signal in an embodiment;

[0013] Figure 5 is a schematic diagram of a mutual function value and a threshold value in one embodiment;

[0014] Figure 6 A flowchart of determining an initial cyclic prefix length in one embodiment;

[0015] Figure 7 is a cross-correlation function diagram corresponding to different candidate cyclic prefix lengths in an embodiment;

[0016] Figure 8 A schematic diagram of half-peak width comparison in one embodiment;

[0017] Fig. 9 is a schematic diagram of a fifth signal and a sixth signal in one embodiment;

[0018] Fig.10 is a schematic diagram of an OFDM signal parameter estimation device in one embodiment;

[0019] Fig.11 Schematic diagram of an OFDM signal parameter estimation device in an embodiment. DETAILED DESCRIPTION

[0020] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the technical field of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the scope of protection of the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0022] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it should be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0023] See also Figure 1 , is an application environment diagram of an OFDM signal parameter estimation method in an embodiment. The application environment diagram includes 10 and a signal source device 20, and the signal source device 20 can transmit a communication signal, for example, the signal source device 20 is a drone device. The OFDM signal parameter estimation device 10 is used to receive a signal transmitted by the signal source device 20 when working, and process the received signal to be tested, determine whether the received signal is an OFDM signal, and if the received signal is an OFDM signal, estimate the signal parameters of the received signal.

[0024] In recent years, drone technology has developed rapidly, and the threats it brings have also penetrated into various fields: aviation safety threats, public safety threats, infrastructure security threats, network security threats, etc. Therefore, it is urgent to respond to the challenges brought by drones. In order to accurately detect and identify drones in complex electromagnetic environments, it is necessary to receive drone signals and perform various analysis and processing to explore their unique signal characteristics.

[0025] Most current drones use the OFDM signal transmission system. There are many methods for OFDM signal identification, such as time-frequency analysis, wavelet transform, carrier modulation method identification and envelope correlation spectrum, but these methods can only identify whether the signal is an OFDM signal. They lack more detailed feature parameters and cannot distinguish different types of drone image transmission signals. Therefore, the method based on feature extraction is particularly effective. It can not only identify unmanned image transmission signals, but also has the potential to distinguish different models. In 2018, the Information Engineering University of the Strategic Support Force of the Chinese People's Liberation Army proposed a patent for "OFDM Signal Identification Method and System Based on Signal Structural Characteristics", which detects and identifies the cyclic prefix of the OFDM signal, but it does not estimate the length of the signal cyclic prefix.

[0026] Orthogonal frequency division multiplexing (OFDM): Current NR terminal devices, or IoT terminal devices that support standard features such as NB-IoT, all use OFDM as the basic mechanism for signal transmission. OFDM is essentially a frequency division system that uses multiple carriers (called subcarriers) to transmit information streams. Multiple subcarriers are mutually orthogonal in the time domain and overlap in the frequency domain, dividing the channel into several orthogonal subchannels, converting high-speed data signals into parallel low-speed sub-data streams, and modulating them to each subchannel for transmission. Each subcarrier channel of OFDM can be considered as flat fading, which can improve the anti-multipath fading performance. In engineering, OFDM waveforms can be realized through FFT and its inverse process. At the same time, in order to resist the memory of the channel and eliminate inter-symbol interference and inter-code interference, OFDM usually introduces a cyclic prefix (CP) as a protection interval. In a general NR system, the transmitter maps the information bit stream into a phase shift keying (PSK) or quadrature amplitude modulation (QAM) symbol for modulation. The working principle of the cyclic prefix is ​​to copy the tail of the OFDM symbol and place it at the head of the OFDM symbol to form a cyclic structure. The reason for this is that in a multipath environment, the signal may propagate on different paths, resulting in different times for the signal to arrive at the receiving end. Without a cyclic prefix, the earlier arriving path may overlap with the subsequent OFDM symbol, causing interference. By adding a cyclic prefix, it can be ensured that the delay caused by the multipath effect will not exceed one OFDM symbol period during the entire duration of the OFDM symbol before the FFT (Fast Fourier Transform) processing is performed at the receiving end, thereby avoiding interference between symbols.

[0027] See also Figure 2, is a flow chart of an OFDM signal parameter estimation method provided in an embodiment of the present application. The OFDM signal parameter estimation method is applied to an OFDM signal parameter estimation device, and the OFDM signal parameter estimation method includes the following steps:

[0028] S11. Receive a signal to be tested.

[0029] In this embodiment, the signal to be tested is a signal received by the OFDM signal parameter estimation device 10. For example, the signal source device 20 is a drone. When the drone flies within the search range of the OFDM signal parameter estimation device 10, the OFDM signal parameter estimation device 10 can receive the signal of the drone.

[0030] S12: Based on the signal to be tested, determine whether the signal to be tested is an orthogonal frequency division multiplexing (OFDM) signal. If it is determined that the signal to be tested is an OFDM signal, obtain a target data length estimated for an OFDM signal.

[0031] In this embodiment, before determining whether the signal to be tested is an orthogonal frequency division multiplexing (OFDM) signal, a preprocessing operation may be performed on the signal to be tested to obtain a preprocessed signal s(n). The preprocessing operation includes but is not limited to: filtering, down-converting, and down-sampling the signal to be tested, wherein the filter bandwidth and the center frequency point used for down-conversion are output parameters of the spectrum sensing algorithm, and the sampling rate of down-sampling refers to the standard protocol. Then, based on the preprocessed signal, it is determined whether the signal to be tested is an OFDM signal. Figure 3 As shown, it is a schematic diagram of two OFDM symbols, one OFDM symbol is one OFDM signal, one OFDM symbol includes a cyclic prefix and a valid signal data part, wherein the valid signal data part is the data actually transmitted. The target data length indicates the estimated length of the valid signal data part.

[0032] S13. Obtain a cyclic prefix length range, and determine an initial cyclic prefix length based on the cyclic prefix length range and a target data length.

[0033] In this embodiment, the cyclic prefix length range is a range for roughly searching the initial cyclic prefix length, and the cyclic prefix length range may include a minimum cyclic prefix length value and a maximum cyclic prefix length value. In the cyclic prefix length range, multiple candidate cyclic prefix lengths are obtained with a configured step size, and the initial cyclic prefix length is determined based on the multiple candidate cyclic prefix lengths and the target data length, that is, a rough search is first performed in the cyclic prefix length range to obtain the initial cyclic prefix length. Since there is still a gap between two adjacent candidate cyclic prefix lengths during the search process, the obtained initial cyclic prefix length indicates an approximate value preliminarily estimated based on the cyclic prefix length range.

[0034] S14. Determine a search range of a cyclic prefix length based on the initial cyclic prefix length, and determine a target cyclic prefix length of the signal to be tested based on the search range.

[0035] In this embodiment, the search range is a floating length range determined based on the initial cyclic prefix length. Since there is still a gap between two adjacent candidate cyclic prefix lengths when determining the initial cyclic prefix length, it is necessary to continue to estimate a more accurate cyclic prefix length in a region of length near the initial cyclic prefix length, so as to obtain a more accurately estimated target cyclic prefix length. That is, after the cyclic prefix length is roughly searched to obtain the initial cyclic prefix length, an accurate estimation is performed based on the initial cyclic prefix length to obtain the final cyclic prefix length. In the blind estimation process, since there is no reference, a large range needs to be blindly searched, and the amount of calculation is huge, so it is divided into a rough search and a fine search step.

[0036] In the above embodiment, a signal to be tested is received, and it is first determined whether the signal to be tested is an orthogonal frequency division multiplexing (OFDM) signal. When it is determined that the signal to be tested is an OFDM signal, a target data length estimated for one of the OFDM signals is obtained, and a cyclic prefix length range is obtained. Within the cyclic prefix length range, based on the target data length, a coarse search is performed on the cyclic prefix length to obtain an initial cyclic prefix length, and then an accurate estimate is performed based on the initial cyclic prefix length to obtain a final cyclic prefix length, thereby obtaining signal parameters of the signal to be tested, thereby more accurately identifying signal parameters and more accurately identifying different types of image transmission signals based on the signal parameters. Since the cyclic prefix lengths are diverse and a priori unknown, the amount of traversal calculation is large. To address the problem of large traversal calculation, a coarse search followed by a fine search is used. This method of combining coarse and fine searches can greatly reduce search time, reduce the amount of calculation, and save system resources.

[0037] In some embodiments, determining whether the signal to be tested is an OFDM signal based on the signal to be tested includes:

[0038] Obtain a set of candidate data lengths, an initial window length, and a sliding length;

[0039] Acquire multiple candidate data lengths from the candidate data length set, for each of the candidate data lengths, use the candidate data length as a data interval, use the initial window length as a data segment length, and based on the sliding length and the signal to be measured, slide and select multiple groups of first signals and second signals, and calculate the cross-correlation value between each group of first signals and second signals, to obtain multiple cross-correlation values ​​corresponding to each candidate data length;

[0040] Calculating a peak threshold value corresponding to the signal to be measured;

[0041] Based on a plurality of cross-correlation values ​​corresponding to each candidate data length and the peak threshold value, it is determined whether the signal to be tested is an OFDM signal.

[0042] In this embodiment, this part belongs to the P parameter estimation part in OFDM signal parameter estimation, and the P parameter estimation belongs to the length estimation part of the effective signal data. Since the preprocessed signal has a limited number of possible data lengths, the data length that may appear in the current downsampling is L data (i), i = 1, 2, ..., I, the data length represents the length of the valid signal data part, wherein the candidate data length set represents a set of multiple possible data lengths, for example, there are I candidate data lengths, wherein i represents the i-th case, and the initial window length represents the length of the initialized data segment. For the first I candidate data lengths, under each candidate data length, based on the configured sliding length, multiple groups of first signals and second signals are sequentially selected from the preprocessed signal s(n) by sliding. Figure 4 As shown, Figure 4 FIG. 1 is a schematic diagram of a first signal and a second signal in an embodiment. The figure only illustrates a schematic diagram of signals under two time units. Under a candidate data length, the candidate data length is used as the data interval, and the initial window length is used as the data segment length to select the first signal and the second signal. Figure 4 A group of first signals and second signals are shown in FIG. After obtaining the two segments of the signal, the cross-correlation value of the two segments of the signal is calculated, wherein the length of the two segments of the signal is the initial window length. The sliding length is moved in the timing direction, and another group of first signals and second signals are taken, and the cross-correlation value of the two segments of the signal is continued to be calculated. The sliding is continued until the sliding is terminated, and multiple cross-correlation values ​​are obtained under one candidate data length. For example, if the sliding is performed 100 times, there will be 100 cross-correlation values ​​under one candidate data length. For example, if there are 20 candidate data lengths in total, 2000 cross-correlation values ​​will be obtained.

[0043] Based on the first cross-correlation function, the cross-correlation value between the two signals is calculated. The calculation formula of the first cross-correlation function is as follows:

[0044]

[0045] In the first cross-correlation function formula, L slide is the sliding length, L data is the candidate data length, x(n) is the first cross-correlation function, n is the sliding length index, i is the index of the signal point, s[i+(n-1)] is the first signal, s*[i+L data +(n-1)] represents the second signal, and Lwin represents the initial window length.

[0046] In this embodiment, the peak threshold value represents the maximum amplitude threshold of the signal to be tested, which can be calculated using a peak decision method, for example, using OS-CFAR (selected small unit average constant false alarm detection) to calculate the maximum amplitude threshold η.

[0047] Optionally, the determining whether the signal to be tested is an OFDM signal based on the multiple cross-correlation values ​​corresponding to each candidate data length and the peak threshold value includes:

[0048] Obtaining a maximum cross-correlation value from a plurality of cross-correlation values ​​corresponding to each candidate data length, and determining that the signal to be tested is an OFDM signal when the maximum cross-correlation value is greater than the peak threshold value;

[0049] The obtaining of a target data length estimated for one of the OFDM signals comprises:

[0050] The data length corresponding to the maximum cross-correlation value is used as the target data length.

[0051] In this embodiment, the maximum cross-correlation value MAX_Value of x(n) is then found, and MAX_Value is compared with η. If MAX_Value>η, the signal is judged as an OFDM signal, and the target data length is estimated to be the current L corresponding to the maximum cross-correlation value. data (i). like Figure 5 As shown, Figure 5 Schematic diagram of mutual function value and threshold value in an embodiment, different first mutual correlation functions and decision thresholds, the blue curve in the figure is the first mutual correlation function, the yellow is the peak threshold value, when a candidate data length matches the actual data length, the multiple mutual correlation values ​​corresponding to the candidate data length are taken as the ordinate, and the number of sliding points is taken as the abscissa, and the following is obtained: Figure 5 The figure shown on the left shows that the maximum cross-correlation value is greater than the peak threshold value. The peak threshold value can be determined to obtain an estimated value of the data length. After obtaining the accurate length of the valid signal data part, the data segment length can be traversed to estimate the cyclic prefix length.

[0052] In the above embodiment, multiple candidate data lengths are obtained from a set of candidate data lengths. For each candidate data length, the sliding length is moved multiple times in the timing direction to obtain multiple groups of first signals and second signals. The cross-correlation value between each group of first signals and second signals is calculated to obtain multiple cross-correlation values ​​corresponding to each candidate data length. The maximum cross-correlation value is found. When the maximum cross-correlation value is greater than the peak threshold value, it indicates that the candidate data length corresponding to the maximum cross-correlation value matches the actual data length, so that the length of the effective signal data portion in an OFDM symbol can be accurately estimated, thereby improving the basis for the subsequent accurate estimation of the cyclic prefix length.

[0053] In some embodiments, Figure 6 As shown, Figure 6 This is a flow chart of determining an initial cyclic prefix length in an embodiment, wherein step S13 further includes:

[0054] S131. Acquire multiple candidate cyclic prefix lengths from the cyclic prefix length range, and for each of the candidate cyclic prefix lengths, use the target data length as the data interval and each of the candidate cyclic prefix lengths as the data segment length, and based on a preset sliding length and the signal to be tested, slide and select multiple groups of third signals and fourth signals, and calculate the cross-correlation value between each group of third signals and the fourth signal, to obtain multiple cross-correlation values ​​corresponding to each of the candidate cyclic prefix lengths.

[0055] In this embodiment, the D parameter estimation part in the step is used to estimate the cyclic prefix length, and the cyclic prefix length range can be a preset range value, including a minimum length and a maximum length. Since the length of the useful signal data part has been determined, that is, the target data length, it is necessary to estimate the cyclic prefix length at this time. Taking the minimum length as the starting point, traverse the cyclic prefix length range based on the preset step length, for example, the preset step length is 10 to 20 data points, and multiple candidate cyclic prefix lengths are obtained. For each candidate cyclic prefix length, the target data length is used as the data interval, and the candidate cyclic prefix length is used as the data segment length. Slide and select multiple groups of third signals and fourth signals under the candidate cyclic length. Under each candidate cyclic prefix length, the data segment length of the third signal and the fourth signal is each candidate cyclic prefix length. Then calculate the cross-correlation value between each group of the third signal and the fourth signal, and obtain multiple cross-correlation values ​​corresponding to each candidate cyclic prefix length. For example, if it slides 200 times, there will be 200 cross-correlation values ​​under one candidate cyclic prefix length. Then the candidate cyclic prefix length corresponds to 200 cross-correlation values.

[0056] Based on the second cross-correlation function, the cross-correlation value between the third signal and the fourth signal is calculated. The calculation formula of the second cross-correlation function is as follows:

[0057]

[0058] In the second cross-correlation function formula, L slide is the sliding length, L data1 is the target data length, x1(m) is the second cross-correlation function, m represents the sliding length index, j represents the index of the signal point, s[j+(m-1)] represents the third signal, s*[j+L data1 +(m-1)] represents the fourth signal, and Lwin represents the window length, that is, a candidate cyclic prefix length.

[0059] like Figure 7 As shown, Figure 7is a cross-correlation function diagram corresponding to different candidate cyclic prefix lengths in an embodiment. Figure 7 The window length in is the candidate cyclic prefix length. Under different candidate cyclic prefix lengths, the corresponding cross-correlation function is different. The corresponding cross-correlation functions are shown when the candidate cyclic prefix length is 72 units, 144 units, and 512 units. Under each cyclic prefix length, multiple cross-correlation values ​​are obtained. Figure 7 It can be seen that for any candidate cyclic prefix length, during the sliding process, the trend of the cross-correlation function value first increases and then decreases. This is related to the working principle of the cyclic prefix. The working principle of the cyclic prefix copies the tail of the OFDM symbol and places it at the head of the OFDM symbol to form a cyclic structure. Then, when the candidate cyclic prefix length is closest to the true cyclic prefix length, during the sliding process, the corresponding cross-correlation function value can present the sharpest peak, and the two signal segments with the maximum cross-correlation value can be found, namely the tail of the OFDM symbol and the head of the OFDM symbol.

[0060] S132. Calculate the evaluation parameter corresponding to each candidate cyclic prefix length based on the multiple cross-correlation values ​​corresponding to each candidate cyclic prefix length.

[0061] In this embodiment, the cross-correlation functions calculated based on different candidate cyclic prefix lengths have different shapes. Generally, the closer the candidate cyclic prefix length is to the actual cyclic prefix length, the sharper the peak of the cross-correlation function is. Figure 7 As shown in the figure, when the window length is 144, that is, the candidate cyclic prefix length is 144 units, the peak is the sharpest, and 144 units are closer to the actual cyclic prefix length. Therefore, the sharpest peak can be found from the cross-correlation functions corresponding to multiple candidate cyclic prefix lengths by judging the parameters, thereby finding the candidate cyclic prefix length corresponding to the sharpest peak.

[0062] Optionally, the calculating, based on the multiple cross-correlation values ​​corresponding to each candidate cyclic prefix length, the evaluation parameter corresponding to each candidate cyclic prefix length includes:

[0063] For any of the candidate cyclic prefix lengths, based on a plurality of cross-correlation values ​​corresponding to the candidate cyclic prefix length, obtaining a maximum cross-correlation peak value corresponding to the candidate cyclic prefix length;

[0064] Based on the maximum cross-correlation peak, calculating the half-peak width corresponding to the candidate cyclic prefix length;

[0065] The absolute value of the difference between the half-peak width and the candidate cyclic prefix length is calculated, and the sum of the absolute value of the difference and the half-peak width is used as the evaluation parameter.

[0066] In this embodiment, for a cross-correlation function, the half-peak width is the index difference corresponding to half of the maximum cross-correlation peak in the cross-correlation function. For example, in a cross-correlation function, the maximum cross-correlation peak is 100, and half of the maximum cross-correlation peak is 50. For 50 corresponding to two signal point positions, namely the position of the 100th signal point and the position of the 300th signal point, the index difference is 200. For a candidate cyclic prefix length, the corresponding evaluation parameter is the half-peak width corresponding to the candidate cyclic prefix length + abs (the half-peak width corresponding to the candidate cyclic prefix length - the candidate cyclic prefix length), where abs represents the absolute value. The smaller the evaluation parameter, the sharper the corresponding maximum cross-correlation peak. Figure 8 As shown, Figure 8 : is a schematic diagram of half-peak width comparison in an embodiment. The middle figure is when the window length is the value of the true cyclic prefix length, the peak of the calculated cross-correlation function is the sharpest. The left figure is an approximate trend diagram of the mutual function obtained when the window length is less than the true cyclic prefix length. The right figure is an approximate trend diagram of the mutual function obtained when the window length is greater than the true cyclic prefix length. Figure 8 From the comparison, we can see that when the window length is the actual cyclic prefix length, the peak of the calculated cross-correlation function is the sharpest.

[0067] S133: Determine the initial cyclic prefix length based on the evaluation parameter corresponding to each candidate cyclic prefix length.

[0068] Optionally, determining the initial cyclic prefix length based on the evaluation parameter corresponding to each of the candidate cyclic prefix lengths includes:

[0069] Determine a minimum evaluation parameter based on the evaluation parameter corresponding to each of the candidate cyclic prefix lengths;

[0070] The candidate cyclic prefix length corresponding to the minimum evaluation parameter is used as the initial cyclic prefix length.

[0071] In this embodiment, the smaller the evaluation parameter is, the sharper the corresponding maximum cross-correlation peak is. From the evaluation parameters corresponding to the candidate cyclic prefix lengths, the minimum evaluation parameter is screened out, and the candidate cyclic prefix length corresponding to the minimum evaluation parameter is closest to the actual cyclic prefix length.

[0072] In the above embodiment, multiple candidate cyclic prefix lengths are obtained from the cyclic prefix length range. For each candidate cyclic prefix length, the target data length is used as the data interval and the candidate cyclic prefix length is used as the data segment length. The third signal and the fourth signal are selected from the preprocessed signal to obtain multiple cross-correlation values ​​corresponding to each candidate cyclic prefix length, thereby obtaining a cross-correlation function corresponding to each candidate cyclic prefix length. Based on the cross-correlation function corresponding to each candidate cyclic prefix length, an evaluation parameter corresponding to each candidate cyclic prefix length is obtained. The smaller the evaluation parameter, the sharper the corresponding maximum cross-correlation peak value, and the sharper the maximum cross-correlation peak value is, the closer it is to the actual cyclic prefix length. Therefore, a rough search can be performed to estimate the approximate cyclic prefix length first, thereby greatly saving computing time and computing resources and ensuring the real-time processing capability of the system.

[0073] In some embodiments, determining a search range of a cyclic prefix length based on the initial cyclic prefix length, and determining a target cyclic prefix length of the signal to be tested based on the search range comprises:

[0074] Based on the initial cyclic prefix length, the search range is extended in a direction greater than the initial cyclic prefix length and in a direction less than the initial cyclic prefix length, and a plurality of estimated cyclic prefix lengths are determined;

[0075] For each of the estimated cyclic prefix lengths, taking the target data length as a data interval and each of the estimated cyclic prefix lengths as a data segment length, based on the signal to be measured, slidingly selecting a fifth signal and a sixth signal corresponding to each of the estimated cyclic prefix lengths, and calculating a timing function value corresponding to each of the estimated cyclic prefix lengths based on the fifth signal and the sixth signal corresponding to each of the estimated cyclic prefix lengths;

[0076] The target cyclic prefix length is determined based on the timing function value corresponding to each of the estimated cyclic prefix lengths.

[0077] In this embodiment, the initial cyclic prefix length is the approximate cyclic prefix length estimated by the coarse search. The initial cyclic prefix length is already relatively close to the actual cyclic prefix length. However, since a preset step size is used for traversal during the coarse search, there are still some length values ​​near the initial cyclic prefix length that have not been evaluated. Therefore, it is necessary to accurately estimate the cyclic prefix length again. The search range can be a length floating range centered on the initial cyclic prefix length, and then the target cyclic prefix length is further accurately estimated based on the search range. Fig. 9 As shown, Fig. 9 FIG. 4 is a schematic diagram of a fifth signal and a sixth signal according to an embodiment, and is a schematic diagram of a group of a fifth signal and a sixth signal under an estimated cyclic prefix length.

[0078] Optionally, the determining the target cyclic prefix length based on the timing function value corresponding to each of the estimated cyclic prefix lengths includes:

[0079] Based on the timing function value corresponding to each of the estimated cyclic prefix lengths, a maximum timing function value is obtained, and the estimated cyclic prefix length corresponding to the maximum timing function value is used as the target cyclic prefix length.

[0080] In this embodiment, the initial cyclic prefix length is L cp1 , with L cp1 The corresponding peak index is taken as the starting point, and then L cp1 The left and right k lengths are used as the estimated cyclic prefix lengths for the new round, that is, There are a total of 2k estimated cyclic prefix lengths, and the value of k is generally around 5. The target data length is used as the interval, and the estimated cyclic prefix length is used as the data segment length. Two segments of data x1(n) and x2(n), namely the fifth signal and the sixth signal, are obtained. Based on the fifth signal and the sixth signal corresponding to each estimated cyclic prefix length, the timing function value corresponding to each estimated cyclic prefix length is calculated using the timing function.

[0081] The timing function is The superscript * indicates the conjugation operation, where L CP Represents the estimated cyclic prefix length, and n represents the nth signal point. The timing function value corresponding to each estimated cyclic prefix length can be calculated through the timing function, and the estimated cyclic prefix length L corresponding to the largest ε1 is selected. CP The final estimated cyclic prefix length is the target cyclic prefix length.

[0082] In the above embodiment, a search range is obtained based on an initial cyclic prefix length obtained by a coarse search, and a plurality of estimated cyclic prefix lengths are obtained based on the search range. For each estimated cyclic prefix length, a target data length is a data interval, and each estimated cyclic prefix length is a data segment length. Based on the signal to be measured, a fifth signal and a sixth signal corresponding to each estimated cyclic prefix length are slidably selected to calculate a timing function value corresponding to each estimated cyclic prefix length. Based on the timing function value corresponding to each estimated cyclic prefix length, the target cyclic prefix length is further accurately estimated. Since the cyclic prefix lengths are diverse and a priori unknown, the amount of traversal calculation is large. To address the problem of large amount of traversal calculation, these methods combining coarse and fine searches can greatly reduce the search time, reduce the amount of calculation, and save system resources.

[0083] In some embodiments, the method further comprises:

[0084] Outputting an estimation result of the signal to be measured;

[0085] The outputting of the estimation result of the signal to be tested includes at least one of the following:

[0086] Outputting whether the signal to be tested is the OFDM signal;

[0087] In a case where the signal to be tested is the OFDM signal, the target data length and the target cyclic prefix length corresponding to the signal to be tested are output.

[0088] In the above embodiment, the estimation result of the signal to be measured may be output through the user interface so that the user can intuitively understand the estimation result, thereby improving the user experience.

[0089] In some embodiments, when there are several cyclic prefix lengths of the signal to be tested, these cyclic prefix lengths form a preset cyclic prefix length set, that is, several preset cyclic prefix lengths are pre-stored in the local library, wherein the estimation based on the local library is that the cyclic prefix may be known, and the matching operation can be performed according to the preset cyclic prefix length in the local library. Specifically including:

[0090] Acquire multiple preset cyclic prefix lengths from the preset cyclic prefix length set, for each of the preset cyclic prefix lengths, take the target data length as the data interval, take each of the preset cyclic prefix lengths as the data segment length, and based on the preset sliding length and the signal to be tested, slide and select multiple groups of seventh signals and eighth signals, and calculate the cross-correlation value between each group of seventh signals and the eighth signal, to obtain multiple cross-correlation values ​​corresponding to each of the preset cyclic prefix lengths;

[0091] Calculating the evaluation parameter corresponding to each of the preset cyclic prefix lengths based on a plurality of cross-correlation values ​​corresponding to each of the preset cyclic prefix lengths;

[0092] A target cyclic prefix length is determined based on the evaluation parameter corresponding to each of the preset cyclic prefix lengths.

[0093] Optionally, determining the target cyclic prefix length based on the evaluation parameter corresponding to each of the preset cyclic prefix lengths includes:

[0094] Determine a minimum evaluation parameter value based on the evaluation parameter corresponding to each of the preset cyclic prefix lengths;

[0095] The preset cyclic prefix length corresponding to the minimum evaluation parameter value is used as the target cyclic prefix length.

[0096] In this embodiment, the evaluation parameter corresponding to each preset cyclic prefix length is the same as the calculation method described in the above embodiment, which will not be repeated here. In some embodiments, the cyclic prefix length can be estimated in two ways, that is, matching transportation based on the preset cyclic prefix length set in the local library, and the other way is the OFDM signal parameter estimation of the rough search followed by the precise search described in the embodiment of the present application.

[0097] In the above embodiment, the preset cyclic prefix length in the local library may also be used to perform the matching operation, so that the signal parameters can be calculated in a diversified manner.

[0098] On the other hand, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the OFDM signal parameter estimation method described in any embodiment of the present application.

[0099] Among them, in the computer program product, an optional implementation form of the program module architecture of the computer program that implements each step of the OFDM signal parameter estimation method can be an OFDM signal parameter estimation device.

[0100] See also Fig.10 An embodiment of the present application provides an OFDM signal parameter estimation device, comprising: a receiving module 60, used to receive a signal to be tested; a determining module 61, used to determine whether the signal to be tested is an orthogonal frequency division multiplexing OFDM signal based on the signal to be tested, and when it is determined that the signal to be tested is an OFDM signal, obtain a target data length estimated for the OFDM signal; the determining module 61 is also used to obtain a cyclic prefix length range, and determine an initial cyclic prefix length based on the cyclic prefix length range and the target data length; the determining module 61 is also used to determine a search range for a cyclic prefix length based on the initial cyclic prefix length, and determine a target cyclic prefix length of the signal to be tested based on the search range.

[0101] Optionally, the determination module 61 is further used for:

[0102] Obtain a set of candidate data lengths, an initial window length, and a sliding length;

[0103] Acquire multiple candidate data lengths from the candidate data length set, for each of the candidate data lengths, use the candidate data length as a data interval, use the initial window length as a data segment length, and based on the sliding length and the signal to be measured, slide and select multiple groups of first signals and second signals, and calculate the cross-correlation value between each group of first signals and second signals, to obtain multiple cross-correlation values ​​corresponding to each candidate data length;

[0104] Calculating a peak threshold value corresponding to the signal to be measured;

[0105] Based on a plurality of cross-correlation values ​​corresponding to each candidate data length and the peak threshold value, it is determined whether the signal to be tested is an OFDM signal.

[0106] Optionally, the determination module 61 is further used for:

[0107] Obtaining a maximum cross-correlation value from a plurality of cross-correlation values ​​corresponding to each candidate data length, and determining that the signal to be tested is an OFDM signal when the maximum cross-correlation value is greater than the peak threshold value;

[0108] The obtaining of a target data length estimated for one of the OFDM signals comprises:

[0109] The data length corresponding to the maximum cross-correlation value is used as the target data length.

[0110] Optionally, the determination module 61 is further used for:

[0111] Acquire multiple candidate cyclic prefix lengths from the cyclic prefix length range, for each of the candidate cyclic prefix lengths, take the target data length as the data interval, take each of the candidate cyclic prefix lengths as the data segment length, and based on a preset sliding length and the signal to be tested, slide and select multiple groups of third signals and fourth signals, and calculate the cross-correlation value between each group of third signals and the fourth signal, to obtain multiple cross-correlation values ​​corresponding to each of the candidate cyclic prefix lengths;

[0112] Calculate the evaluation parameter corresponding to each candidate cyclic prefix length based on a plurality of cross-correlation values ​​corresponding to each candidate cyclic prefix length;

[0113] The initial cyclic prefix length is determined based on the evaluation parameter corresponding to each of the candidate cyclic prefix lengths.

[0114] Optionally, the determination module 61 is further used for:

[0115] For any of the candidate cyclic prefix lengths, based on a plurality of cross-correlation values ​​corresponding to the candidate cyclic prefix length, obtaining a maximum cross-correlation peak value corresponding to the candidate cyclic prefix length;

[0116] Based on the maximum cross-correlation peak, calculating the half-peak width corresponding to the candidate cyclic prefix length;

[0117] The absolute value of the difference between the half-peak width and the candidate cyclic prefix length is calculated, and the sum of the absolute value of the difference and the half-peak width is used as the evaluation parameter.

[0118] Optionally, the determination module 61 is further used for:

[0119] Determine a minimum evaluation parameter based on the evaluation parameter corresponding to each of the candidate cyclic prefix lengths;

[0120] The candidate cyclic prefix length corresponding to the minimum evaluation parameter is used as the initial cyclic prefix length.

[0121] Optionally, the determination module 61 is further used for:

[0122] Based on the initial cyclic prefix length, the search range is extended in a direction greater than the initial cyclic prefix length and in a direction less than the initial cyclic prefix length, and a plurality of estimated cyclic prefix lengths are determined;

[0123] For each of the estimated cyclic prefix lengths, taking the target data length as a data interval and each of the estimated cyclic prefix lengths as a data segment length, based on the signal to be measured, slidingly selecting a fifth signal and a sixth signal corresponding to each of the estimated cyclic prefix lengths, and calculating a timing function value corresponding to each of the estimated cyclic prefix lengths based on the fifth signal and the sixth signal corresponding to each of the estimated cyclic prefix lengths;

[0124] The target cyclic prefix length is determined based on the timing function value corresponding to each of the estimated cyclic prefix lengths.

[0125] Optionally, the determination module 61 is further used for:

[0126] Acquire multiple preset cyclic prefix lengths from the preset cyclic prefix length set, for each of the preset cyclic prefix lengths, take the target data length as the data interval, take each of the preset cyclic prefix lengths as the data segment length, and based on the preset sliding length and the signal to be tested, slide and select multiple groups of seventh signals and eighth signals, and calculate the cross-correlation value between each group of seventh signals and the eighth signal, to obtain multiple cross-correlation values ​​corresponding to each of the preset cyclic prefix lengths;

[0127] Calculating the evaluation parameter corresponding to each of the preset cyclic prefix lengths based on a plurality of cross-correlation values ​​corresponding to each of the preset cyclic prefix lengths;

[0128] A target cyclic prefix length is determined based on the evaluation parameter corresponding to each of the preset cyclic prefix lengths.

[0129] Optionally, the determination module 61 is further used for:

[0130] Based on the timing function value corresponding to each of the estimated cyclic prefix lengths, a maximum timing function value is obtained, and the estimated cyclic prefix length corresponding to the maximum timing function value is used as the target cyclic prefix length.

[0131] Optionally, the determination module 61 is further used for:

[0132] Outputting an estimation result of the signal to be measured;

[0133] The outputting of the estimation result of the signal to be tested includes at least one of the following:

[0134] Outputting whether the signal to be tested is the OFDM signal;

[0135] In a case where the signal to be tested is the OFDM signal, the target data length and the target cyclic prefix length corresponding to the signal to be tested are output.

[0136] It can be understood by those skilled in the art that Fig.10 The structure of the OFDM signal parameter estimation device does not constitute a limitation on the OFDM signal parameter estimation device, and the various modules can be implemented in whole or in part by software, hardware, and a combination thereof. The above modules can be embedded in or independent of the processor in the OFDM signal parameter estimation device in the form of hardware, or can be stored in the memory in the OFDM signal parameter estimation device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. In other embodiments, the OFDM signal parameter estimation device may include more or fewer modules than those shown in the figure.

[0137] See also Fig.11 On the other hand, an embodiment of the present application further provides an OFDM signal parameter estimation device 10, including a processor 13 and a memory 14, wherein the memory 14 stores a computer program, and when the computer program is executed by the processor, the processor 13 executes the steps of the OFDM signal parameter estimation method provided in any of the above embodiments of the present application.

[0138] The processor 13 is the control center, which uses various interfaces and lines to connect various parts of the entire OFDM signal parameter estimation device, and executes various functions and processes data of the OFDM signal parameter estimation device by running or executing software programs and / or modules stored in the memory 14, and calling data stored in the memory 14. Optionally, the processor 13 may include one or more processing cores; preferably, the processor 13 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user pages and applications, etc., and the modem processor mainly processes wireless communications. It can be understood that the above-mentioned modem processor may not be integrated into the processor 13.

[0139] The memory 14 can be used to store software programs and modules. The processor 13 executes various functional applications and data processing by running the software programs and modules stored in the memory 14. The memory 14 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the OFDM signal parameter estimation device, etc. In addition, the memory 14 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 14 can also include a memory processor to provide the processor 13 with access to the memory 14.

[0140] On the other hand, an embodiment of the present application further provides a storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the OFDM signal parameter estimation method provided in any of the above embodiments of the present application.

[0141] In another aspect of an embodiment of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the OFDM signal parameter estimation method as described in any embodiment of the present application is implemented.

[0142] Those skilled in the art can understand that all or part of the processes in the methods provided in the above embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0143] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. The protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for estimating OFDM signal parameters, characterized in that: include: receiving a signal to be tested; Based on the signal to be tested, determining whether the signal to be tested is an orthogonal frequency division multiplexing (OFDM) signal, and if it is determined that the signal to be tested is an OFDM signal, obtaining a target data length estimated for the OFDM signal; Acquire a cyclic prefix length range, and determine an initial cyclic prefix length based on the cyclic prefix length range and the target data length; Based on the initial cyclic prefix length, a search range of a cyclic prefix length is determined, and based on the search range, a target cyclic prefix length of the signal to be tested is determined.

2. The OFDM signal parameter estimation method according to claim 1, wherein: The determining, based on the signal to be tested, whether the signal to be tested is an OFDM signal comprises: Obtain a set of candidate data lengths, an initial window length, and a sliding length; Acquire multiple candidate data lengths from the candidate data length set, for each of the candidate data lengths, use the candidate data length as a data interval, use the initial window length as a data segment length, and based on the sliding length and the signal to be measured, slide and select multiple groups of first signals and second signals, and calculate the cross-correlation value between each group of first signals and second signals, to obtain multiple cross-correlation values ​​corresponding to each candidate data length; Calculating a peak threshold value corresponding to the signal to be measured; Based on a plurality of cross-correlation values ​​corresponding to each candidate data length and the peak threshold value, it is determined whether the signal to be tested is an OFDM signal.

3. The OFDM signal parameter estimation method according to claim 2, characterized in that: The determining whether the signal to be tested is an OFDM signal based on the multiple cross-correlation values ​​corresponding to each candidate data length and the peak threshold value comprises: Obtaining a maximum cross-correlation value from a plurality of cross-correlation values ​​corresponding to each candidate data length, and determining that the signal to be tested is an OFDM signal when the maximum cross-correlation value is greater than the peak threshold value; The obtaining of a target data length estimated for one of the OFDM signals comprises: The data length corresponding to the maximum cross-correlation value is used as the target data length.

4. The OFDM signal parameter estimation method according to claim 1, wherein: Acquiring a cyclic prefix length range, and determining an initial cyclic prefix length based on the cyclic prefix length range and the target data length includes: Acquire multiple candidate cyclic prefix lengths from the cyclic prefix length range, for each of the candidate cyclic prefix lengths, take the target data length as the data interval, take each of the candidate cyclic prefix lengths as the data segment length, and based on a preset sliding length and the signal to be tested, slide and select multiple groups of third signals and fourth signals, and calculate the cross-correlation value between each group of third signals and the fourth signal, to obtain multiple cross-correlation values ​​corresponding to each of the candidate cyclic prefix lengths; Calculate the evaluation parameter corresponding to each candidate cyclic prefix length based on a plurality of cross-correlation values ​​corresponding to each candidate cyclic prefix length; The initial cyclic prefix length is determined based on the evaluation parameter corresponding to each of the candidate cyclic prefix lengths.

5. The OFDM signal parameter estimation method according to claim 4, characterized in that: The calculating, based on the multiple cross-correlation values ​​corresponding to each candidate cyclic prefix length, the evaluation parameter corresponding to each candidate cyclic prefix length comprises: For any of the candidate cyclic prefix lengths, based on a plurality of cross-correlation values ​​corresponding to the candidate cyclic prefix length, obtaining a maximum cross-correlation peak value corresponding to the candidate cyclic prefix length; Based on the maximum cross-correlation peak, calculating the half-peak width corresponding to the candidate cyclic prefix length; The absolute value of the difference between the half-peak width and the candidate cyclic prefix length is calculated, and the sum of the absolute value of the difference and the half-peak width is used as the evaluation parameter.

6. The OFDM signal parameter estimation method according to claim 5, characterized in that: Determining the initial cyclic prefix length based on the evaluation parameter corresponding to each of the candidate cyclic prefix lengths includes: Determine a minimum evaluation parameter based on the evaluation parameter corresponding to each of the candidate cyclic prefix lengths; The candidate cyclic prefix length corresponding to the minimum evaluation parameter is used as the initial cyclic prefix length.

7. The OFDM signal parameter estimation method according to claim 1, wherein: The step of determining a search range of a cyclic prefix length based on the initial cyclic prefix length, and determining a target cyclic prefix length of the signal to be tested based on the search range comprises: Based on the initial cyclic prefix length, the search range is extended in a direction greater than the initial cyclic prefix length and in a direction less than the initial cyclic prefix length, and a plurality of estimated cyclic prefix lengths are determined; For each of the estimated cyclic prefix lengths, taking the target data length as a data interval and each of the estimated cyclic prefix lengths as a data segment length, based on the signal to be measured, slidingly selecting a fifth signal and a sixth signal corresponding to each of the estimated cyclic prefix lengths, and calculating a timing function value corresponding to each of the estimated cyclic prefix lengths based on the fifth signal and the sixth signal corresponding to each of the estimated cyclic prefix lengths; The target cyclic prefix length is determined based on the timing function value corresponding to each of the estimated cyclic prefix lengths.

8. The OFDM signal parameter estimation method according to claim 7, characterized in that: The determining the target cyclic prefix length based on the timing function value corresponding to each of the estimated cyclic prefix lengths comprises: Based on the timing function value corresponding to each of the estimated cyclic prefix lengths, a maximum timing function value is obtained, and the estimated cyclic prefix length corresponding to the maximum timing function value is used as the target cyclic prefix length.

9. The OFDM signal parameter estimation method according to claim 1, wherein: The method further comprises: Acquire multiple preset cyclic prefix lengths from the preset cyclic prefix length set, for each of the preset cyclic prefix lengths, take the target data length as the data interval, take each of the preset cyclic prefix lengths as the data segment length, and based on the preset sliding length and the signal to be tested, slide and select multiple groups of seventh signals and eighth signals, and calculate the cross-correlation value between each group of seventh signals and the eighth signal, to obtain multiple cross-correlation values ​​corresponding to each of the preset cyclic prefix lengths; Calculating the evaluation parameter corresponding to each of the preset cyclic prefix lengths based on a plurality of cross-correlation values ​​corresponding to each of the preset cyclic prefix lengths; A target cyclic prefix length is determined based on the evaluation parameter corresponding to each of the preset cyclic prefix lengths.

10. The OFDM signal parameter estimation method according to claim 1, wherein: The method further comprises: Outputting an estimation result of the signal to be measured; The outputting of the estimation result of the signal to be tested includes at least one of the following: Outputting whether the signal to be tested is the OFDM signal; In a case where the signal to be tested is the OFDM signal, the target data length and the target cyclic prefix length corresponding to the signal to be tested are output.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the OFDM signal parameter estimation method according to any one of claims 1 to 10 is implemented.

12. An OFDM signal parameter estimation device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the OFDM signal parameter estimation method according to any one of claims 1 to 10.

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