FFT-Based Adaptive Carrier Synchronization Method for Burst Communication

Abstract

The present application relates to a burst communication adaptive carrier synchronization method based on FFT. The method includes: performing a first-round non-linear transformation on the captured first sequence and then performing a Fourier transform in sequence, selecting the even-numbered spectra from multiple amplitude spectra, screening the position indices where the first peak and the second peak of each spectrum in the even-numbered spectra are located, and forming a position index set; taking the intersection of all the position index sets of the even-numbered spectra, selecting the position index element with the highest overlap degree, and determining a coarse frequency offset estimate value according to the position index element; obtaining the first peak, the second peak, and the third peak corresponding to the position index element from multiple amplitude spectra and then performing weighted averaging to obtain the final position index element; calculating a fine frequency offset estimate value according to the final position index element; obtaining an overall frequency offset estimate value by summing the coarse frequency offset estimate value and the fine frequency offset estimate value; and implementing carrier synchronization according to the overall frequency offset estimate value of the current round.

Description

Technical Field

[0001] The present application relates to the field of communication technologies, and particularly to an FFT-based adaptive carrier synchronization method for burst communication. Background Art

[0002] In view of the characteristics of channels such as 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 by reasonably setting the interval between pilots, ensure the use of rough estimation between the front and middle pilots, as well as fine estimation between the front and rear pilots to meet the carrier synchronization requirements of the actual scenario. 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, iteratively estimating and compensating repeatedly, and combined with 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 an FFT-based adaptive carrier synchronization method for burst communication that can reduce complexity, accurately estimate frequency offset, and achieve carrier synchronization.

[0004] An FFT-based adaptive carrier synchronization method for burst communication, the method comprising:

[0005] Set the burst frame structure to include a first unique word, a first message, a second unique word, and a second message;

[0006] 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;

[0007] Perform the first-round non-linear transformation on the captured first sequence to obtain the sequence after non-linear transformation. Then, perform Fourier transform on the sequence after non-linear transformation in turn to obtain multiple amplitude spectra. Select the spectra with even serial numbers from the multiple amplitude spectra, and screen the position indices corresponding to the first peak and the second peak of each spectrum in the spectra with even serial numbers to form a position index set. Take the intersection of all position index sets of the spectra with even serial numbers, 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 weighted averaging 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.

[0008] 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 set 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.

[0009] For the above-mentioned FFT-based burst communication adaptive carrier synchronization method, the present application first uses the first unique word in the burst frame structure to perform a correlation operation with the received signal to obtain the first sequence, and accurately captures the sequence by analyzing its capture performance and capture false alarm probability, laying a foundation for subsequent estimation. Perform multiple rounds of processing on the sequence. First, perform non-linear transformation and Fourier transform, then screen the position indices corresponding to the peaks of the spectra with even serial numbers and take the intersection to determine the coarse estimate value, and then obtain the fine estimate value by weighted averaging the relevant peaks. This operation of multiple rounds of 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 coarse estimation and fine estimation is adopted. The coarse estimation locates the general range, and the fine estimation makes fine adjustments. The two cooperate to broaden the estimation range. Finally, by selectively and screening the spectrum data and using the valid data to determine the estimate value, unnecessary calculations are reduced; a convergence determination mechanism is set, and when the difference between the frequency offset estimates of two adjacent rounds reaches the accuracy requirement, the calculation is stopped to avoid over-iteration, thereby reducing the overall calculation complexity. Description of the Drawings

[0010] Figure 1 It is a schematic flow chart of a method for FFT-based burst communication adaptive carrier synchronization in an embodiment;

[0011] Figure 2 It is a schematic diagram of the determination method of capture false alarm probability in an embodiment;

[0012] Figure 3 It is a schematic diagram of the simulation curve of the frequency offset iterative estimation performance in an embodiment. Detailed Embodiments

[0013] In order to make the objectives, technical solutions and advantages of this application clearer and more understandable, the following further details this application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely used to explain this application and are not used to limit this application.

[0014] In one embodiment, as Figure 1 shown, a burst communication adaptive carrier synchronization method based on FFT is provided, including the following steps:

[0015] 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 capture the first sequence based on the capture performance and the capture false alarm probability.

[0016] 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 first sequence can be captured more accurately. This accurate capture provides a good basis for subsequent high-precision frequency offset estimation because only by accurately obtaining the useful signal part can frequency offset estimation be better performed.

[0017] 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.

[0018] Step 104: Perform the first-round non-linear transformation on the captured first sequence to obtain the non-linearly transformed sequence. Then, perform Fourier transform on the non-linearly transformed sequence in turn to obtain multiple amplitude spectra. Select the spectra with even serial numbers from the multiple amplitude spectra, and screen the position indices of the spectral lines corresponding to the first peak and the second peak of each spectrum with an even serial number to form a position index set. Take the intersection of all the position index sets of the spectra with even serial numbers, select the position index element with the highest overlap degree, and determine the rough frequency offset estimate value according to the position index element. Calculate the weighted average of the first peak, the second peak, and the third peak corresponding to the position index element from the multiple amplitude spectra to obtain the final position index element. Calculate the fine frequency offset estimate value according to the final position index element. Sum the rough frequency offset estimate value and the fine frequency offset estimate value to obtain the overall frequency offset estimate value.

[0019] 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 serial numbers from the multiple amplitude spectra, and screen the position indices of the spectral lines corresponding to the first peak and the second peak of each spectrum with an even serial number to form a position index set. Determine the rough frequency offset estimate value by taking the intersection of all the position index sets of the spectra with even serial numbers and selecting the position index element with the highest overlap degree. This multi-round screening and intersection operation can effectively remove the abnormal values and the false peaks caused by noise, making the rough estimate value more accurate. Further, calculate the weighted average of the first peak, the second peak, and the third peak corresponding to the position index element from the multiple amplitude spectra 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 average method comprehensively considers the information of multiple peaks and can depict the frequency offset situation more precisely, thus improving the accuracy of the frequency offset estimate. The method of first determining the rough frequency offset estimate value, then obtaining the fine frequency offset estimate value, and finally summing them 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 relatively large frequency offset range, while the fine estimate makes fine adjustments based on the rough estimate. This combination of rough and fine estimates can cover a wider frequency offset range and overcome the problem of limited estimation range in some traditional methods.

[0020] During the processing, by selecting the even-numbered spectra and performing operations such as peak screening and intersection of position index sets, complex and undifferentiated processing of all spectral data is avoided. This selective processing method reduces unnecessary calculation steps, for example, reducing the processing of some spectral parts that may be severely affected by noise or do not contain valid 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 relatively less computational complexity compared to some complex iterative algorithms or global search algorithms that require a large amount of data. Because these operations are based on the selected valid data rather than exhaustive complex operations on the entire signal space.

[0021] 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 preset minimum accuracy of frequency estimation, 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.

[0022] By calculating the difference between the frequency offset estimates of two adjacent rounds, when the difference is not greater than the preset minimum accuracy of frequency estimation, output the overall frequency offset estimate value of the current round. This convergence determination mechanism enables the frequency offset estimation process to stop in a timely manner after meeting certain accuracy requirements, avoiding excessive calculation and iteration, thereby reducing the computational complexity.

[0023] 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, and accurately captures the 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 position indices corresponding to the peaks of the even-numbered spectra is screened to determine the 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 selecting and screening spectral data and using valid data to determine the estimate value, unnecessary calculations are reduced; a convergence determination mechanism is set, and when the difference between the frequency offset estimates of two adjacent rounds meets the accuracy requirements, the calculation stops, avoiding excessive iteration, thereby reducing the overall computational complexity.

[0024] In one embodiment, sequence frequency offset compensation is performed according to the overall frequency offset estimate value of the current round. Coherent integration and averaging are performed on the modulation symbols after frequency offset compensation to determine the best sampling points for each symbol. According to the real and imaginary components of the amplitude a + bi of the best sampling point to form coordinates (a, b ), in combination with the modulation method of the symbol, perform a decision to demodulate the first unique word and the second unique word one by one and then compare them with the reference bits; if both the first unique word and the second unique word are correct, the minimum precision of the pre-set frequency estimation is reasonably set. If there is an error in the unique word, re-adjust the minimum precision of the pre-set frequency estimation, and continue to calculate the overall value of the frequency offset estimation until the unique words are all verified correctly.

[0025] In a specific embodiment, according to the amplitude of the optimal sampling point a + bi The coordinates ( a, b formed by the real and imaginary components of ) are located in the quadrant position, combined with the modulation method of the symbol (typically, BPSK or QPSK modulation), and a decision is made (for example, for BPSK constellation modulation, a > 0, the symbol is judged as bit 1, a < 0, the symbol is judged as bit 0).

[0026] In one of the embodiments, the capture performance of the first sequence is calculated as:

[0027] PAR = ;

[0028] Wherein, represents the first sequence, represents The maximum value in the amplitude spectrum after FFT, represents the average value in the amplitude spectrum after FFT.

[0029] In one of the embodiments, as Figure 2 shown, the capture false alarm probability is determined by conducting a semi-physical Monte Carlo simulation through the open-source software gnuradio combined with the USRP device, including:

[0030] The capture false alarm probability is determined by conducting a semi-physical Monte Carlo simulation through the open-source software gnuradio combined with the USRP device as

[0031] = ;

[0032] Wherein, represents the signal-to-noise ratio determined by conducting a semi-physical Monte Carlo simulation through the open-source software gnuradio combined with the USRP device, represents the threshold value determined by conducting a semi-physical Monte Carlo simulation through the open-source software gnuradio combined with the USRP device.

[0033] In one of the embodiments, the first sequence is captured according to the capture performance and the capture false alarm probability, including:

[0034] Determine the capture performance of the first sequence according to a preset performance threshold. If the capture performance is greater than the preset performance threshold, then judge the capture false alarm probability 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.

[0035] In one embodiment, perform a non-linear transformation on the captured first sequence to obtain a non-linearly transformed sequence, including:

[0036] Performing a non-linear transformation on the captured first sequence to obtain a non-linearly transformed sequence as

[0037] ;

[0038] ( );

[0039] 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.

[0040] In one embodiment, determine the coarse frequency offset estimate value according to the position index element, including:

[0041] Determine the coarse frequency offset estimate value according to the position index element as Wherein, represents the position index element, is the number of FFT points, T represents the sampling interval.

[0042] In one embodiment, calculate the fine frequency offset estimate value according to the final position index element, including:

[0043] Calculate the fine frequency offset estimate value according to the final position index element as Wherein, 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.

[0044] In a specific embodiment, after successfully completing the capture, save , The specific process of frequency offset estimation after the 0th non-linear transformation is recorded as:

[0045] Step 1: Set the minimum accuracy of frequency estimation ;

[0046] Step 2: Denote the th non-linear transformation, , ( ), where T is the sampling interval and N is the number of FFT points (take the power of 2 close to the actual number of operations). Then perform FFT transformation in sequence to obtain L amplitude spectra;

[0047] Step 3: Select the spectra with even sequence numbers among the L amplitude spectra , screen the first and second peaks of each spectrum, record the position index of the spectral line (the position index ranges from 1 to N), and form a position index set { };

[0048] 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 condition at the same time, choose either one), and map it to the actual frequency index K according to the FFT principle, thereby determining the rough frequency offset estimate ;

[0049] Step 5: Then, from the L amplitude spectra, obtain the first peak, and the second and third peaks corresponding to the index K, perform weighted averaging to obtain the index M, and determine the fine frequency offset estimate , and the overall frequency offset estimate is ;

[0050] Step 6: Repeat Steps 1-4, perform 2L non-linear transformations, and obtain the overall frequency offset estimate as ;

[0051] Step 7: Calculate the difference between the frequency estimates of the current round and the previous round , if , then continue the iteration and perform non-linear transformations. If , then stop the iteration;

[0052] Step 8: After compensating for the frequency offset, find the mean value through symbol coherent integration to determine the best sampling point for 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 , and continue Steps 1-7 until all unique words are verified correctly. The performance simulation curve of the frequency offset estimation of this application is as shown in Figure 3 .

[0053] It should be understood that although Figure 1The steps in the flowchart are shown in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified 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, Figure 1 At least some of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in rotation with at least some of the sub-steps or stages of other steps or other steps.

[0054] 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.

[0055] The above-described embodiments merely represent several implementation manners of the present 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 the present application, several modifications and improvements can be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present 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: Setting a burst frame structure including a first unique word, a first message, a second unique word, and a second message; Interpolating the first unique word to the symbol rate, performing a correlation operation with the received signal to obtain a first sequence; calculating the capture performance of the first sequence; determining the capture false alarm probability through a semi-physical Monte Carlo simulation using the open-source software gnuradio combined with a USRP device; capturing the first sequence according to the capture performance and the capture false alarm probability; Performing a first-round non-linear transformation on the captured first sequence to obtain a non-linearly transformed sequence, sequentially performing a Fourier transform on the non-linearly transformed sequence to obtain a plurality of amplitude spectra; selecting the even-numbered spectra from the plurality of amplitude spectra, screening the position indices of the spectral lines corresponding to the first peak and the second peak of each spectrum in the even-numbered spectra to form a position index set; taking the intersection of all position index sets of the even-numbered spectra, selecting the position index element with the highest overlap degree, and determining a coarse frequency offset estimate value according to the position index element; obtaining the first peak, the second peak, and the third peak corresponding to the position index element from the plurality of amplitude spectra and then performing a weighted average to obtain a final position index element; calculating a fine frequency offset estimate value according to the final position index element; and summing the coarse frequency offset estimate value and the fine frequency offset estimate value to obtain an overall frequency offset estimate value; Calculating the frequency offset estimate difference between two adjacent rounds. If the frequency offset estimate difference is not greater than a pre-set minimum frequency estimation accuracy, output the overall frequency offset estimate value of the current round; and achieving carrier synchronization according to the overall frequency offset estimate value of the current round.

2. The method according to claim 1, wherein 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 find the mean value, determine the optimal sampling points for each symbol, and based on the amplitude of the optimal sampling points a + bi The coordinates formed by the real and imaginary components of a, b ), determine the quadrant position where they are located, and perform demodulation one by one by combining the modulation method of the symbol to demodulate the first unique word and the second unique word and then compare them with the reference bits; if both the first unique word and the second unique word are correct, the preset minimum precision of frequency estimation is set reasonably. If there is an error in the unique word, readjust the preset minimum precision 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, Calculating 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, wherein Determining the capture false alarm probability through a semi-physical Monte Carlo simulation using the open-source software gnuradio combined with a USRP device, including: Determining the capture false alarm probability through a semi-physical Monte Carlo simulation using the open-source software gnuradio combined with a USRP device as = Among them, represents the signal-to-noise ratio determined by carrying out semi-physical Monte Carlo simulation through the open-source software gnuradio combined with USRP devices, represents the threshold value determined by carrying out semi-physical Monte Carlo simulation through the open-source software gnuradio combined with USRP devices.

5. The method according to any one of claims 1 to 4, characterized in that Capturing the first sequence according to the capture performance and the capture false alarm probability, including: Judging the capture performance of the first sequence according to a pre-set performance threshold. If the capture performance is greater than the pre-set performance threshold, then judge the capture false alarm probability using a pre-set capture false alarm probability threshold. 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.

6. The method according to claim 1, wherein Performing a non-linear transformation on the captured first sequence to obtain a non-linearly transformed sequence, including: Performing a non-linear transformation on the captured first sequence to obtain a non-linearly transformed sequence as （ ） 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, Determining a coarse frequency offset estimate value according to the position index element, including: 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, wherein Calculating a 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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