Burst communication adaptive carrier synchronization method based on FFT

Abstract

The invention relates to a burst communication adaptive carrier synchronization method based on FFT. The method comprises the following steps: carrying out a first round of nonlinear transformation on a captured first sequence, then sequentially carrying out Fourier transformation, selecting even number spectrums from a plurality of amplitude spectrums, screening position indexes of spectral lines corresponding to a first peak value and a second peak value of each spectrum in the even number spectrums, and forming a position index set; solving an intersection of all position index sets of the even number spectrum, selecting a position index element with the highest overlapping degree, and determining a frequency offset coarse estimation value according to the position index element; obtaining a first peak value, a second peak value and a third peak value corresponding to the position index element from the plurality of amplitude spectrums, and then carrying out weighted averaging to obtain a final position index element; according to the final position index element, calculating to obtain a frequency offset precise estimation value; summing the coarse frequency offset estimation value and the fine frequency offset estimation value to obtain a frequency offset estimation overall value; and realizing carrier synchronization according to the frequency offset estimation overall value of the current round.

Description

Technical Field

[0001] The present application relates to the field of communication technologies, and in particular, to a burst communication adaptive carrier synchronization method based on FFT. Background Art

[0002] In view of the channel characteristics of large Doppler frequency offset and low signal-to-noise ratio of low-earth orbit satellites, during the process of carrier synchronization, a contradiction between the frequency offset estimation range and the frequency offset estimation accuracy will be encountered. Existing methods either design a flexible frame structure, disperse and insert pilots, and ensure that the coarse estimation between the front and middle pilots and the fine estimation between the front and back pilots are used to meet the carrier synchronization requirements of the actual scenario by reasonably setting the interval between pilots. However, the pilot overhead is large and the information utilization rate is not high. Or, the M&M forward frequency offset estimation algorithm is adopted, which iteratively estimates and compensates repeatedly, and combines the classical L&R algorithm in a small range to improve the accuracy of frequency estimation. The M&M estimation range is [-0.4, 0.4] under normalized frequency offset, the estimation range is limited, and the computational complexity is relatively high. Summary of the Invention

[0003] Based on this, in view of the above technical problems, it is necessary to provide a burst communication adaptive carrier synchronization method based on FFT that can reduce complexity, accurately estimate the frequency offset, and achieve carrier synchronization.

[0004] A burst communication adaptive carrier synchronization method based on FFT, the method includes: Set the burst frame structure to include a first unique word, a first message, a second unique word, and a second message; Interpolate the first unique word to the symbol rate, perform a correlation operation with the received signal to obtain a first sequence; calculate the capture performance of the first sequence; determine the capture false alarm probability through semi-physical Monte Carlo simulation using open-source software gnuradio combined with a USRP device; capture the first sequence according to the capture performance and the capture false alarm probability; Perform a first-round non-linear transformation on the captured first sequence to obtain a non-linearly transformed sequence, perform a Fourier transform on the non-linearly transformed sequence in sequence to obtain a plurality of amplitude spectra; select the even-numbered spectra from the plurality of amplitude spectra, screen the position indexes of the first peak and the second peak corresponding to each spectrum in the even-numbered spectra to form a position index set; find the intersection of all position index sets of the even-numbered spectra, select the position index element with the highest overlap degree, and determine the coarse frequency offset estimation value according to the position index element; obtain the first peak, the second peak, and the third peak corresponding to the position index element from the plurality of amplitude spectra and then perform weighted averaging to obtain the final position index element; calculate the fine frequency offset estimation value according to the final position index element; use the sum of the coarse frequency offset estimation value and the fine frequency offset estimation value to obtain the overall frequency offset estimation value; Calculate the difference between the frequency offset estimates of adjacent rounds. If the difference between the frequency offset estimates is not greater than the pre-set minimum accuracy of frequency estimation, output the overall value of the frequency offset estimate for the current round; achieve carrier synchronization based on the overall value of the frequency offset estimate for the current round.

[0005] For the above-mentioned FFT-based burst communication adaptive carrier synchronization method, the present application first performs a correlation operation on the first unique word in the burst frame structure and the received signal to obtain a first sequence, and accurately captures this sequence by analyzing its capture performance and capture false alarm probability, laying a foundation for subsequent estimation. The sequence is processed in multiple rounds. First, after non-linear transformation and Fourier transformation, the intersection of the peak position indexes corresponding to the even-numbered spectra is determined to obtain a rough estimate value, and then the refined estimate value is obtained by weighted averaging of the relevant peaks. This operation of multiple-round screening and weighted averaging effectively removes interference and synthesizes multi-peak information, improving the estimation accuracy. The frequency estimation range is expanded through FFT processing. On the other hand, a combination of rough estimation and refined estimation is adopted. The rough estimation locates the approximate range, and the refined estimation makes fine adjustments. The two cooperate to broaden the estimation range. Finally, by selectively and screening spectrum data and using valid data to determine the estimated value, unnecessary calculations are reduced; a convergence determination mechanism is set, and when the difference between the frequency offset estimates of adjacent rounds reaches the accuracy requirement, the calculation is stopped to avoid excessive iteration, thereby reducing the overall calculation complexity. Brief Description of the Drawings

[0006] Figure 1 It is a schematic flowchart of a method for FFT-based burst communication adaptive carrier synchronization in an embodiment; Figure 2 It is a schematic diagram of a method for determining the capture false alarm probability in an embodiment; Figure 3 It is a schematic diagram of a simulation curve of the frequency offset iterative estimation performance in an embodiment. Detailed Embodiment

[0007] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.

[0008] In one embodiment, as Figure 1 shown, a method for FFT-based burst communication adaptive carrier synchronization is provided, including the following steps: Step 102: Set the burst frame structure to include a first unique word, a first message, a second unique word, and a second message; interpolate the first unique word to the symbol rate, perform a correlation operation with the received signal to obtain a first sequence; calculate the capture performance of the first sequence; determine the capture false alarm probability through a semi-physical Monte Carlo simulation using the open-source software gnuradio in combination with a USRP device; and perform capture on the first sequence based on the capture performance and the capture false alarm probability.

[0009] Set the burst frame structure, where the first unique word is interpolated to the symbol rate and then a correlation operation is performed with the received signal to obtain a first sequence. This correlation operation can effectively extract information related to the unique word from the received signal and reduce the influence of interference factors such as noise. By calculating the capture performance of the first sequence and determining the capture false alarm probability through a semi-physical Monte Carlo simulation using gnuradio and a USRP device, the capture of the first sequence can be performed more accurately. This accurate capture provides a good foundation for subsequent high-precision frequency offset estimation because only by accurately obtaining the useful signal part can better frequency offset estimation be carried out.

[0010] The setting of elements such as the first unique word and the subsequent second unique word in the burst frame structure provides a richer signal structure for frequency offset estimation. Through the processing and correlation operation of the first unique word, it can adapt to different degrees of frequency offset to a certain extent. Compared with traditional methods, this method based on unique words and a specific frame structure can better handle larger frequency offset situations because the distribution and characteristics of unique words in the signal can help with effective signal capture and processing within a wider frequency offset range.

[0011] Step 104: Perform a first-round non-linear transformation on the captured first sequence to obtain a non-linearly transformed sequence, perform a Fourier transform on the non-linearly transformed sequence in sequence to obtain multiple amplitude spectra; select the even-numbered spectra from the multiple amplitude spectra, screen the position indices corresponding to the first peak and the second peak of each spectrum in the even-numbered spectra to form a position index set; find the intersection of all position index sets of the even-numbered spectra, select the position index element with the highest overlap degree, and determine the rough frequency offset estimate value according to the position index element; obtain the first peak, the second peak, and the third peak corresponding to the position index element from the multiple amplitude spectra and then perform weighted averaging to obtain the final position index element; calculate the fine frequency offset estimate value according to the final position index element; and obtain the overall frequency offset estimate value by summing the rough frequency offset estimate value and the fine frequency offset estimate value.

[0012] Perform the first-round non-linear transformation on the captured first sequence, and then obtain multiple amplitude spectra through Fourier transform. Select the spectra with even sequence numbers from the multiple amplitude spectra, and screen the position indices corresponding to the first peak and the second peak of each spectrum with an even sequence number to form a set of position indices. By taking the intersection of all sets of position indices of the spectra with even sequence numbers, select the position index element with the highest overlap degree to determine the rough frequency offset estimate value. Such multi-round screening and intersection operations can effectively remove the false peaks caused by outliers and noise, making the rough estimate value more accurate. Further, after obtaining the first peak, the second peak, and the third peak corresponding to the position index element from the multiple amplitude spectra, perform weighted averaging to obtain the final position index element, and then calculate the fine frequency offset estimate value according to the final position index element. The weighted averaging method comprehensively considers the information of multiple peaks and can depict the frequency offset situation more precisely, thereby improving the accuracy of frequency offset estimation. The method of first determining the rough frequency offset estimate value, then obtaining the fine frequency offset estimate value, and finally summing to obtain the overall frequency offset estimate value expands the estimation range. The rough estimate can quickly locate the approximate range of the frequency offset within a large frequency offset range, while the fine estimate makes fine adjustments based on the rough estimate. This combination of rough and fine methods can cover a wider frequency offset range and overcome the problem of limited estimation ranges in some traditional methods.

[0013] During the processing, by selecting the spectra with even sequence numbers and performing operations such as peak screening and taking the intersection of the sets of position indices, it is possible to avoid complex and undifferentiated processing of all spectral data. This selective processing method reduces unnecessary calculation steps. For example, it reduces the processing of some spectral parts that may be severely affected by noise or do not contain effective frequency offset information. The method of determining the rough frequency offset estimate value using the position index element with the highest overlap degree and calculating the final position index element through weighted averaging has a relatively small computational complexity compared to some complex iterative algorithms or global search algorithms that require a large amount of data to participate. Because these operations are based on the selected valid data rather than exhaustive complex operations on the entire signal space.

[0014] Step 106, calculate the difference between the frequency offset estimates of two adjacent rounds. If the difference between the frequency offset estimates is not greater than the minimum frequency estimation accuracy preset in advance, output the overall frequency offset estimate value of the current round; achieve carrier synchronization according to the overall frequency offset estimate value of the current round.

[0015] By calculating the difference between the frequency offset estimates of two adjacent rounds and outputting the overall frequency offset estimate value of the current round when the difference is not greater than the minimum frequency estimation accuracy preset in advance. This convergence determination mechanism enables the frequency offset estimation process to stop in a timely manner after reaching a certain accuracy requirement, avoiding excessive calculations and iterations, thereby reducing the computational complexity.

[0016] For the above-mentioned FFT-based adaptive carrier synchronization method for burst communication, the present application first performs a correlation operation on the first unique word in the burst frame structure and the received signal to obtain a first sequence, accurately captures the sequence by analyzing its capture performance and capture false alarm probability, and lays a foundation for subsequent estimation. The sequence is processed in multiple rounds. First, after non-linear transformation and Fourier transformation, the intersection of the peak position indices corresponding to the even-numbered spectra is selected to determine the rough estimation value, and then the refined estimation value is obtained by weighted averaging of the relevant peaks. This operation of multiple-round screening and weighted averaging effectively removes interference and synthesizes multi-peak information, improving the estimation accuracy. The frequency estimation range is expanded through FFT processing; on the other hand, a combination of rough estimation and refined estimation is adopted. The rough estimation locates the approximate range, and the refined estimation makes fine adjustments. The two cooperate to broaden the estimation range. Finally, by selectively screening spectral data and using valid data to determine the estimation value, unnecessary calculations are reduced; a convergence determination mechanism is set. When the difference between the frequency offset estimations of two adjacent rounds reaches the accuracy requirement, the calculation stops to avoid over-iteration, thereby reducing the overall computational complexity.

[0017] In one embodiment, sequence frequency offset compensation is performed according to the overall value of the frequency offset estimation in the current round, the coherent integration of the frequency offset-compensated modulation symbols is averaged to determine the optimal sampling points of each symbol, and according to the real and imaginary components of the amplitude a + bi of the optimal sampling point to form coordinates ( a, b ), combined with the modulation mode of the symbol, the decision is carried out to demodulate the first unique word and the second unique word one by one and compare with the reference bits; if both the first unique word and the second unique word are correct, the preset minimum frequency estimation accuracy is set reasonably. If the unique word is incorrect, the preset minimum frequency estimation accuracy is readjusted, and the overall value of the frequency offset estimation is recalculated until the unique word is verified correctly.

[0018] In a specific embodiment, according to the real and imaginary components of the amplitude a + bi of the optimal sampling point to form coordinates ( a, b ), combined with the modulation mode of the symbol (typically, BPSK or QPSK modulation), the decision is carried out (for example, in BPSK constellation modulation, when a > 0, the symbol is judged as bit 1, and when a < 0, the symbol is judged as bit 0).

[0019] In one embodiment, the capture performance of the first sequence is calculated as: PAR = ; where represents the first sequence, represents the maximum value in the amplitude spectrum after FFT of represents the average value in the amplitude spectrum after FFT.

[0020] In one embodiment, as Figure 2 shown, the capture false alarm probability is determined by carrying out a hardware-in-the-loop Monte Carlo simulation through the open-source software gnuradio in combination with a USRP device, including: The capture false alarm probability determined by carrying out a hardware-in-the-loop Monte Carlo simulation through the open-source software gnuradio in combination with a USRP device is = ; wherein, represents the signal-to-noise ratio determined by carrying out a hardware-in-the-loop Monte Carlo simulation through the open-source software gnuradio in combination with a USRP device, represents the threshold value determined by carrying out a hardware-in-the-loop Monte Carlo simulation through the open-source software gnuradio in combination with a USRP device.

[0021] In one embodiment, the first sequence is captured according to the capture performance and the capture false alarm probability, including: The capture performance of the first sequence is determined according to a preset performance threshold. If the capture performance is greater than the preset performance threshold, the capture false alarm probability is judged by using a preset capture false alarm probability threshold. If the capture false alarm probability is greater than the preset capture false alarm probability threshold, it is determined that the capture of the first sequence is successful.

[0022] In one embodiment, a non-linear transformation is carried out on the captured first sequence to obtain a non-linearly transformed sequence, including: Carrying out a non-linear transformation on the captured first sequence, and the non-linearly transformed sequence obtained is ; ( ); wherein, represents the first sequence, represents the serial number of the non-linear transformation, represents the number of non-linear transformation times, is the number of FFT points.

[0023] In one embodiment, a coarse frequency offset estimate value is determined according to the position index element, including: The coarse frequency offset estimate value determined according to the position index element is , wherein, represents the position index element, is the number of FFT points, T represents the sampling interval.

[0024] In one embodiment, a fine frequency offset estimate value is calculated according to the final position index element, including: The fine frequency offset estimation value is calculated according to the element of the final position index as , where represents the element of the final position index, represents the number of non-linear transformation times, is the number of FFT points, T represents the sampling interval.

[0025] In a specific embodiment, after successful acquisition, save , The specific process of frequency offset estimation after the 0th non-linear transformation is as follows: Step 1: Set the minimum accuracy of frequency estimation ; Step 2: Denote the th non-linear transformation, , ( ), T is the sampling interval, and N is the number of FFT points (take the power of 2 close to the actual operation points). Then perform FFT transformation in sequence to obtain L amplitude spectra; Step 3: Select the even-numbered spectra among the L amplitude spectra , screen the first and second peaks of each spectrum, record the position index where the spectrum line is located (the position index ranges from 1 to N), and form a position index set { }; Step 4: Take the intersection of the L / 2 position index sets, select the position index element k with the highest overlap degree (if two elements meet the conditions at the same time, choose either one), and map it to the actual frequency index K according to the FFT principle, so as to determine the coarse frequency offset estimation value ; Step 5: Then, from the L amplitude spectra, obtain the first peak, the second and third peaks corresponding to the index K, perform weighted averaging to obtain the index M, and determine the fine frequency offset estimation value , and the overall frequency offset estimation is ; Step 6: Repeat steps 1-4, perform 2L non-linear transformations, and obtain the overall frequency offset estimation as ; Step 7: Calculate the difference between the frequency estimations of the current round and the previous round , if , then continue the iteration and perform non-linear transformations, if , then stop the iteration; Step 8: After compensating the frequency offset, find the mean value through symbol coherent integration to determine the best sampling point of each symbol, perform constellation decision, demodulate the unique word symbols one by one, and compare them with the reference bits; if all the unique words are correct, then is set reasonably, if there is an error in the unique word, then readjust , continue with steps 1-7 until the unique words are all verified correctly. The simulation curve of the frequency offset estimation performance of this application is as Figure 3 shown.

[0026] It should be understood that although Figure 1 the steps in the flowchart of Figure 1 are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover,

[0027] the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0028] The above-described embodiments only represent several implementation manners of this application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of this application should be subject to the appended claims.

Claims

1. An adaptive carrier synchronization method for burst communication based on FFT, characterized in that, The method includes: Set the burst frame structure to include a first unique word, a first message, a second unique word, and a second message; Interpolate the first unique word to the symbol rate, perform a correlation operation with the received signal to obtain a first sequence; calculate the capture performance of the first sequence; determine the capture false alarm probability through semi-physical Monte Carlo simulation using the open-source software gnuradio combined with a USRP device; capture the first sequence according to the capture performance and the capture false alarm probability; Perform a first-round non-linear transformation on the captured first sequence to obtain a non-linearly transformed sequence, perform a Fourier transform on the non-linearly transformed sequence in turn to obtain multiple amplitude spectra; select the even-numbered spectra from the multiple amplitude spectra, screen the position indices corresponding to the first peak and the second peak of each spectrum in the even-numbered spectra to form a position index set; take the intersection of all position index sets of the even-numbered spectra, select the position index element with the highest overlap degree, and determine the coarse frequency offset estimate value according to the position index element; obtain the first peak, the second peak, and the third peak corresponding to the position index element from the multiple amplitude spectra and then perform a weighted average to obtain the final position index element; calculate the fine frequency offset estimate value according to the final position index element; use the sum of the coarse frequency offset estimate value and the fine frequency offset estimate value to obtain the overall frequency offset estimate value; Calculate the frequency offset estimate difference between two adjacent rounds. If the frequency offset estimate difference is not greater than the pre-set minimum frequency estimation accuracy, output the overall frequency offset estimate value of the current round; achieve carrier synchronization according to the overall frequency offset estimate value of the current round.

2. The method according to claim 1, characterized in that, The method further includes: Perform sequence frequency offset compensation based on the overall value of the frequency offset estimation in the current round, perform coherent integration on the frequency offset compensated modulation symbols to obtain the mean value, determine the optimal sampling points for each symbol, and based on the real and imaginary components of the amplitude a + bi of the coordinate formed by ( a, b ), in the quadrant position where it is located, combine the modulation method of the symbol to expand the decision, demodulate the first unique word and the second unique word one by one and compare them with the reference bits; if both the first unique word and the second unique word are correct, the preset minimum accuracy of frequency estimation is set reasonably, if there is an error in the unique word, readjust the preset minimum accuracy of frequency estimation, continue to calculate the overall value of frequency offset estimation until the unique words are all verified correctly.

3. The method according to claim 1, characterized in that, Calculate the capture performance of the first sequence as: PAR= Among them, represents the first sequence, represents the maximum value in the amplitude spectrum after FFT, represents the average value in the amplitude spectrum after FFT.

4. The method according to claim 1, characterized in that, Determine the capture false alarm probability through semi-physical Monte Carlo simulation using the open-source software gnuradio combined with a USRP device, including: Determine that the capture false alarm probability is = Among them, represents the signal-to-noise ratio determined by the hardware-in-the-loop Monte Carlo simulation using the open-source software gnuradio in combination with the USRP device, represents the threshold value determined by the hardware-in-the-loop Monte Carlo simulation using the open-source software gnuradio in combination with the USRP device.

5. The method according to any one of claims 1 to 4, characterized in that, through semi-physical Monte Carlo simulation using the open-source software gnuradio combined with a USRP device Capture the first sequence according to the capture performance and the capture false alarm probability, including:

6. The method according to claim 1, characterized in that, Judge the capture performance of the first sequence according to the pre-set performance threshold. If the capture performance is greater than the pre-set performance threshold, then use the pre-set capture false alarm probability threshold to judge the capture false alarm probability. If the capture false alarm probability is greater than the pre-set capture false alarm probability threshold, it is determined that the first sequence capture is successful. Perform a non-linear transformation on the captured first sequence to obtain a non-linearly transformed sequence, including: （ ） Among them, represents the first sequence, represents the serial number of the non-linear transformation, represents the number of non-linear transformation times, is the number of FFT points.

7. The method according to claim 1, characterized in that, Perform a non-linear transformation on the captured first sequence to obtain a non-linearly transformed sequence as The coarse frequency offset estimate value is determined according to the position index element as , where represents the position index element, is the number of FFT points, T represents the sampling interval.

8. The method according to claim 1, characterized in that, Determine the coarse frequency offset estimate value according to the position index element, including: Calculate the fine frequency offset estimate value according to the final position index element, including: The fine frequency offset estimation value calculated according to the final position index element is , where represents the final position index element, represents the number of non-linear transformation times, is the number of FFT points, T represents the sampling interval.

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