Method and system for estimating signal-to-noise ratio of digital modulation signal based on second-order cycle moment

Abstract

This invention discloses a method and system for estimating the signal-to-interference-plus-noise ratio (SIR) of digital modulated signals based on second-order cyclic moments, mainly addressing the problems of high computational complexity and low estimation accuracy in strong interference environments found in existing technologies. The scheme includes: calculating optimal time-shift parameters and pulse shaping response parameters based on the known digital modulated signal; segmenting and framing the received signal; calculating the autocorrelation function slice of the framed signal and performing signal presence detection based on the slice; calculating the total power of the framed signal and the power of the communication signal for the framed signal containing the communication signal; calculating the SIR of the framed signal based on the autocorrelation function; and calculating the SIR of the segmented signal using the SIR of the framed signal. This invention features fast computation speed, low resource consumption, and high SIR estimation accuracy, and can be used for long-distance digital modulated signal communication.

Description

Technical Field

[0001] This invention belongs to the field of signal processing technology, and specifically relates to a method for estimating the signal-to-interference-plus-noise ratio of digital modulated signals, which can be used to evaluate the transmission quality and reliability of communication transmission systems in the absence of prior information. Background Technology

[0002] The signal-to-interference-plus-noise ratio (SINR) of digital modulated signals is one of the key indicators for measuring signal quality. The realization of important functions in communication systems, such as power control, modulation identification, and adaptive modulation switching, all depend on the accurate estimation of the SINR parameter to achieve optimal performance.

[0003] Patent document CN202110907394.9 discloses a "blind estimation method for signal-to-noise ratio of digital signals in spectrum monitoring equipment." Its implementation steps are as follows: acquiring I and Q baseband data; establishing an autocorrelation matrix for the I and Q baseband data; performing eigenvalue decomposition on the autocorrelation; determining the dimensions of the signal subspace and noise subspace; and calculating and outputting the signal-to-noise ratio. This method estimates the signal-to-noise ratio of communication systems using eigenvalue decomposition of the autocorrelation matrix, without requiring prior knowledge of the modulation type of the digital signal. While it shows good estimation results for 2ASK, FSK, PSK, and QAM signals, at low signal-to-noise ratios, the eigenvalues ​​of the signal subspace and noise subspace are difficult to clearly separate. Furthermore, the eigenvalue operations on the matrix lead to decreased algorithm efficiency and high hardware implementation complexity.

[0004] Patent document CN201911371132.4 discloses "a signal-to-noise ratio estimation method based on deep learning using constellation diagrams". The implementation scheme is as follows: generate N digital signals with known signal-to-noise ratios in the range [m,n], preprocess them to obtain the corresponding N constellation diagrams, then perform data labeling and dataset division, input the data into a pre-configured deep neural network for training, and obtain a neural network model M; for the received k-th observation signal, perform preprocessing to obtain a constellation diagram of the same size, input it into the deep neural network model M, perform model prediction, and obtain the signal-to-noise ratio estimate of the received signal. This method fully utilizes the characteristic that constellation diagrams can completely and clearly reflect digitally modulated signals. By using constellation diagrams as the representation of signals, the traditional signal-to-noise ratio (SNR) estimation problem is transformed into an image recognition problem. Then, deep learning technology is used to estimate the SNR of the received signal relatively accurately through two stages: model training and online estimation. However, in order to achieve SNR estimation, the training set needs to cover as many scenarios as possible, such as different SNR, modulation methods, and interference types. This will lead to a decrease in the generalization ability of the model when the scene information increases, and it will be unable to effectively estimate the SNR for various scenarios. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the existing technology by proposing a method and system for estimating the signal-to-interference-plus-noise ratio (SINR) of digital modulated signals based on second-order cyclic moments. This method aims to reduce computational complexity and hardware resource consumption while improving robustness and estimation accuracy in complex electromagnetic environments.

[0006] To achieve the above objectives, the technical approach of this method is as follows: the communication signal and the interference signal are separated by the cyclostationary characteristics of the digital modulation signal; the power characteristic parameters of the communication signal are constructed by slicing the autocorrelation function of the received signal and the pulse shaping response of the digital signal; and the signal-to-interference-plus-noise ratio (SINR) is estimated by using the characteristic parameters and the power of the received signal, thereby improving the accuracy of the SINR estimation results and enhancing the robustness of the algorithm.

[0007] Based on the above ideas, the technical solution of the present invention includes the following:

[0008] 1. A method for estimating the signal-to-interference-plus-noise ratio (SINR) of a digital modulated signal based on second-order cyclic moments, characterized in that it includes:

[0009] (1) For the received signal The signal is segmented, and then the segmented signal is further divided into frames according to its frame length to obtain framed signals. ;

[0010] (2) Set the optimal time shift parameters according to the frame signal. Constructing preliminary autocorrelation function slices ;

[0011] (3) Using preliminary autocorrelation function slicing Eliminate noise interference and detect the presence of communication signals in the framed signals:

[0012] If no communication signal exists, output the estimated signal-to-interference-plus-noise ratio (SIR) of that frame. ;

[0013] If communication signals exist, calculate the total power of the framed signals;

[0014] (4) Estimate the communication signal power in the framed signal by slicing the autocorrelation function, and combine it with the framed signal obtained in (3).

[0015] The total power is used to calculate the signal-to-interference-plus-noise ratio of the framed signal. ;

[0016] (5) Repeat steps (3) to (4) to estimate the signal-to-interference-plus-noise ratio (SINR) of all frames of the received signal segment, and obtain the result.

[0017] The signal-to-interference-plus-noise ratio (SIR) estimation results for all framed signals are as follows: And count the number of frames in the received signal segment that were detected as non-existent. Calculate its relationship with the total number of frames. The ratio β;

[0018] (6) Preset segmented signal detection threshold ,Will Compare the ratio to it:

[0019] like Then the segment receives the signal. If no communication signal is present, the signal-to-interference-plus-noise ratio (SIR) estimation result of this segment of signal is directly output. ;

[0020] like Then the segment receives the signal. Assuming a communication signal is present, the estimation result is: :

[0021] , where mean represents the average value.

[0022] Preferably, the estimation of communication signal power in the framed signal by slicing the autocorrelation function in step (4) includes the following implementation:

[0023] (4a) Take a frequency interval of 1 and slice the autocorrelation function. In frequency region Maximum value of internal search , Indicates code rate; in frequency region Maximum value of internal search The power characteristic parameters of the communication signal are obtained as follows: ;

[0024] (4b) Based on communication signal power characteristic parameters With pulse shaping response parameters The estimated power of the communication signal is: .

[0025] 2. A signal-to-interference-plus-noise ratio (SIR / NNR) estimation system for digital modulated signals based on second-order cyclic moments, characterized in that it comprises:

[0026] The signal segmentation and framing module is used to segment and frame the received signal.

[0027] The autocorrelation slice calculation module is used to calculate the autocorrelation slice of the framed signal;

[0028] Optimal time shift parameters The configuration module is used to obtain the optimal time delay in the autocorrelation slice calculation.

[0029] The communication signal detection module is used to detect whether a communication signal exists in the framed signal;

[0030] A communication signal power module is used to estimate the communication signal power in an autocorrelation slice.

[0031] The signal-to-interference-plus-noise ratio (SINR) calculation module is used to estimate the SINR of communication signals.

[0032] Compared with the prior art, the present invention has the following advantages:

[0033] Firstly, this invention utilizes the non-cyclic nature of noise by slicing it using a preliminary autocorrelation function. Eliminating noise interference solves the problem of conventional signal-to-interference-plus-noise ratio (SNR) estimation methods failing under low SNR conditions, thus improving the estimation performance under low SNR conditions.

[0034] Secondly, this invention uses autocorrelation slices of digital modulated signals to estimate communication signal power, which solves the problem of excessive reliance on prior information in conventional signal-to-interference-plus-noise ratio estimation methods, and is limited to signals with specific modulation types, thus improving universality and reducing computational complexity.

[0035] Third, this invention segments and frames the signal, and estimates the signal-to-interference-plus-noise ratio (SINR) of each segment separately. This solves the problem that existing methods have a single method for selecting signal power estimates and ignore the influence of various factors in the actual environment on the signal power estimates when estimating SINR, thus improving the stability and robustness of the estimated SINR. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating the overall implementation of the digital modulation signal-to-interference-plus-noise ratio estimation method of the present invention.

[0037] Figure 2 This is a sub-block diagram of the communication signal power estimation module in the system of the present invention;

[0038] Figure 3 The figure shows the simulation results of signal-to-interference-plus-noise ratio estimation under different interference modulation types using this invention;

[0039] Figure 4 The figure shows the estimation results of the simulation experiment of signal-to-interference-plus-noise ratio estimation under different interference bandwidths using the present invention;

[0040] Figure 5 The figure shows the estimation results of the simulation experiment of signal-to-interference-plus-noise ratio estimation under different interference-to-noise ratios using the present invention;

[0041] Figure 6 The figure shows the estimation results of the simulation experiment of signal-to-interference-plus-noise ratio estimation under different source signals using the present invention. Detailed Implementation

[0042] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0043] It should be noted that the step numbers in the specification and claims of this invention are only for the purpose of clearly describing the embodiments of this invention and facilitating understanding, and their order is not limited.

[0044] Example 1: A method for estimating the signal-to-interference-plus-noise ratio (SINR) of digital modulated signals based on second-order cyclic moments.

[0045] Reference Figure 1 The implementation steps for this example are as follows:

[0046] Step 1, receive the signal Perform segmentation and frame division.

[0047] The received signal It is expressed as follows:

[0048] ;

[0049] in The signal amplitude after symbol normalization. It is the symbol amplitude. , , The duration of a single symbol. For pulse shaping function, , For carrier frequency.

[0050] (1.1) Set signal length , will receive signal According to the length of the time domain Segmentation is performed to obtain segmented signals:

[0051] ;

[0052] in, For the first Segmented signals of a segment, , This represents the total number of signal segments after segmentation.

[0053] (1.2) Signal after segmentation Frame segmentation is performed to obtain the framed signal:

[0054] ;

[0055] in, For the first Frame segmentation signal, , Divide each signal segment into frames. For frame length, For frame shift.

[0056] Step 2: Select the optimal time shift parameter based on the framed signal. .

[0057] Considering the construction of autocorrelation slices Time, time shift The selection of the optimal time delay significantly affects the cyclic characteristics of the signal, thus impacting the signal-to-interference-plus-noise ratio (SINR) estimation results of this invention. Therefore, an optimal time delay needs to be selected before estimating the SINR. To obtain the most obvious signal cyclic features, the specific implementation includes the following:

[0058] (2.1) Calculate the frame signal Total signal power :

[0059] ;

[0060] (2.2) Constructing time shift Correlation-based framed communication signal :

[0061] ;

[0062] in , , It is the sampling frequency. It is the code rate;

[0063] (2.3) Based on the framing signal With the corresponding autocorrelation frame received signal Calculate the preliminary autocorrelation function :

[0064] ,

[0065] in This represents taking the conjugate of the signal. Represents the exponential signal function. ;

[0066] (2.3) Repeat steps (2.1) to (2.2) until all frame received signals are received. In time shift Autocorrelation function under After the calculation is complete, the corresponding time shift is obtained. error coefficient :

[0067] ,

[0068] in This represents taking the average value. For bitrate, The frequency point corresponding to the cycle frequency of the framed signal. This refers to the frequency point corresponding to the negative cycle frequency of the signal code rate. , This represents rounding down. It represents rounding up;

[0069] (2.4) Calculation All Corresponding error coefficient The choice makes Get the minimum value As the optimal time shift parameter .

[0070] Step 3: Perform a communication signal presence detection.

[0071] When estimating the power of communication signals, if the framed signals are directly analyzed... This estimate would cause a large error, therefore it is necessary to analyze the framed signal. To improve the signal-to-interference-plus-noise ratio (SINR) estimation results, the detection of the presence of communication signals is performed. The specific implementation includes the following:

[0072] (3.1) Calculate the optimal time shift parameters The frame signal below Autocorrelation framing signal :

[0073] ;

[0074] (3.2) According to and Calculate the frame signal Time shift Corresponding preliminary autocorrelation function slice :

[0075] ;

[0076] in, This is the autocorrelation communication framing signal corresponding to the optimal delay. Represents the frequency variable, conj represents taking the conjugate, and exp represents taking the numerical exponential function;

[0077] (3.3) Calculate the slice of autocorrelation function Cyclic frequency amplitude and :

[0078] (3.3.1) Calculate the left-circular frequency of the interval and interval right cyclic frequency :

[0079] ;

[0080] ;

[0081] in This represents the maximum signal bandwidth between the received signal and the interfering signal. Represents the cyclic frequency point;

[0082] (3.3.2) In Summing the amplitudes within the range yields the sum of amplitudes within the cyclic frequency of the i-th frame. :

[0083] ,

[0084] in This represents summing the signals;

[0085] (3.4) Slicing the autocorrelation function of the i-th frame of communication signal Summing yields the total autocorrelation slice magnitudes of the frame. :

[0086] ;

[0087] (3.5) Set the frame signal detection threshold Calculate the proportional relationship and compare it with the threshold Comparison:

[0088] like If the signal is positive, the frame is considered to be a communication signal and step 4 is executed.

[0089] like If there is no communication signal in that frame, then the signal-to-interference-plus-noise ratio (SIR) estimate for that frame is: .

[0090] Step 4: Adjust the communication signal power Estimate and calculate the signal-to-interference-plus-noise ratio (SIR) of the signal in that frame.

[0091] When detecting framing signals The result indicates the presence of a communication signal, which requires estimation of the communication signal power characteristic parameters. and pulse shaping response parameters To estimate communication signal power The signal-to-interference-plus-noise ratio (SIR) of the frame signal is calculated, and the specific implementation includes:

[0092] (4.1) In the autocorrelation function slice Frequency region The maximum value of the internal search amplitude is used to obtain the positive cycle frequency power parameter of the communication signal. Then in the frequency region The maximum value of the internal search amplitude is obtained to obtain the negative cycle frequency power parameter of the communication signal. The power characteristic parameters of the communication signal are obtained as follows: ;

[0093] (4.2) Based on pulse shaping response parameters Estimation parameters for pulse shaping response :

[0094] (4.2.1) Select the following pulse shaping response parameters :

[0095] ;

[0096] in For pulse shaping function, For symbol period, For symbolic rate, conj represents taking the conjugate;

[0097] (4.2.2) In this embodiment, square root raised cosine pulse shaping is used, that is, based on the variable Different values ​​generate different pulse shaping functions :

[0098] ,

[0099] in The roll-off factor, The symbol period;

[0100] (4.2.3) Shaping function of square root raised cosine impulse Substitute the pulse shaping response parameters The pulse shaping estimation parameters are obtained by simplifying the expression. :

[0101] ;

[0102] (4.3) Based on the power characteristic parameters of the communication signal and pulse shaping response estimation parameters The estimated communication signal power is calculated. :

[0103] ;

[0104] (4.4) According to and Estimate the signal-to-interference-plus-noise ratio (SIR) of this frame:

[0105] .

[0106] Step 5: Detect segmented signals Does a communication signal exist, and output the signal-to-interference-plus-noise ratio (SIR) of that signal segment? .

[0107] (5.1) For segmented signals All frame signals After performing interference-to-noise ratio (INR) estimation, the INR results for all framed signals are as follows: And calculate the segmented signal for each frame. The number of frames in which communication signals do not exist Calculate the received signal of this segment. The ratio of frames without signals to the total number of frames: ;

[0108] (5.2) Set the frame signal detection threshold and with Comparison:

[0109] like Then the segment receives the signal. Treat it as if no signal exists, and output directly. ;

[0110] like Then the segment receives the signal. Assuming a signal is present, calculate the signal-to-interference-plus-noise ratio (SIR) of the segmented received signal. :

[0111] .

[0112] Example 2: Digital Modulated Signal-to-Interference-Ratio (SIR) Estimation System Based on Second-Order Cyclic Moments

[0113] Reference Figure 2 This example includes: signal segmentation and framing module 1, autocorrelation slice calculation module 2, and optimal time shift parameters. The system comprises a setup module 3, a communication signal detection module 4, a communication signal power module 5, and a signal-to-interference-plus-noise ratio (SINR) calculation module 6. The communication signal power module 5 includes a communication signal characteristic parameter estimation submodule 51, a signal pulse shaping parameter estimation submodule 52, and a framed signal total power calculation submodule 53.

[0114] The working principle of the entire system is as follows:

[0115] The signal segmentation and framing module 1 is used to segment and frame the received signal. The segmentation and framing processes are performed, and the processed frames are transmitted to the autocorrelation slice calculation module 2.

[0116] The autocorrelation slice calculation module 2 calculates the autocorrelation slice of the framed signal based on the framed signal, and transmits the calculated autocorrelation slice of the framed signal to the optimal delay parameter. Set up module 3;

[0117] The optimal time shift parameters Module 3 is configured based on the minimum error coefficient. The corresponding delay is taken as the optimal delay. The autocorrelation slice of the framed signal under the optimal time delay is calculated, and then the obtained autocorrelation slice is transmitted to the communication signal detection module 4.

[0118] The communication signal detection module 4, based on the amplitude within the autocorrelation slice cyclic frequency and... The sum of amplitudes within the cyclic frequency of the i-th frame Calculate their ratio And compare this ratio with the set threshold. The comparison is performed to determine whether a communication signal exists in the frame signal, and the result indicating that no communication signal exists is directly output. The result of determining the presence of a communication signal is transmitted to the communication signal power module 5;

[0119] The communication signal power module 5 is used to estimate the communication signal power in the autocorrelation slice, wherein: the communication signal feature parameter estimation submodule 51 calculates the communication signal feature parameter estimation parameters using the framed signal. The signal pulse shaping parameter estimation submodule 52 calculates the signal pulse shaping parameter estimation parameters using the framed signal. This is used to estimate parameters by combining them with the characteristic parameters of the communication signal. Multiplying yields the estimated power of the communication signal. The submodule 53 calculates the total power of the framed signal based on the framed signal. And compare it with the estimated power of the communication signal. Transmitted to the signal-to-interference-plus-noise ratio (SINR) calculation module 6;

[0120] The signal-to-interference-plus-noise ratio (SINR) calculation module 6 calculates the SINR based on the power of the obtained communication signal. Total power of framed signals Estimate the signal-to-interference-plus-noise ratio (SIR) of the framed signal: .

[0121] The effects of this invention can be further illustrated by the following simulation results:

[0122] I. Simulation Parameters

[0123] The interference type, interference bandwidth, input signal-to-interference-plus-noise ratio range, communication signal type, and number of experiments are set as shown in Table 1.

[0124] Table 1 Simulation Parameters

[0125]

[0126] II. Simulation Content

[0127] Simulation 1: Using the present invention, with a communication signal modulation scheme of 16QAM, a signal bandwidth of 4K, an interference bandwidth of 25K, and an interference-to-noise ratio (INR) of 0dB, INR estimation simulations were performed for the nine interference types listed in Table 1. The results are as follows: Figure 3 .

[0128] Depend on Figure 3 It can be seen that when the input signal-to-interference-plus-noise ratio (SNR) is -10dB to -6dB, this technique will produce errors under BPSK modulated interference signals; under the conditions of modulation types CW, AM, FM, ASK, QPSK, MSK, 2FSK, and NoiFM, the SNR estimation is very effective; when the input SNR is -6dB to 6dB, the maximum estimation error does not exceed 0.5dB.

[0129] Simulation 2: Using the present invention, signal-to-interference-plus-noise ratio (SIR) estimation simulations were performed under the following conditions: 16QAM modulation mode, 4K signal bandwidth, 5dB interference-to-noise ratio (INR), BPSK interference modulation type, and interference bandwidths of 1K, 4K, 15K, 25K, 30K, 60K, and 100K. The results are as follows: Figure 4 .

[0130] Depend on Figure 4 It can be seen that when the input signal-to-interference-plus-noise ratio (SNR) is -10dB to -4dB, errors occur when the interference bandwidth is 4K, 15K, 30K, 60K, and 100K. Among these, the estimation error is the largest when the interference bandwidth is 4K. Under the conditions of interference bandwidths of 1K and 25K, the SNR under the corresponding conditions can be estimated relatively accurately. When the input SNR is -4dB to 6dB, the maximum estimation error does not exceed 0.3dB.

[0131] Simulation 3: Using this invention, signal-to-interference-plus-noise ratio (SIR) estimation simulations were performed under the following conditions: communication signal is 16QAM, signal bandwidth is 4kHz, interference bandwidth is 25kHz, interference signal modulation type is BPSK modulation, and interference-to-noise ratio (IRR) is -10dB, -5dB, 0dB, 5dB, and 10dB. The results are as follows: Figure 5.

[0132] Depend on Figure 5 It is known that when the input signal-to-interference-plus-noise ratio (SNR) is -10dB to -4dB, errors will occur under the conditions of SNR of -10dB, 0dB, 5dB, and 10dB, with the largest error when the JNR is 10dB. Under the condition of SNR of -5dB, the SNR under the corresponding condition can be estimated relatively accurately. When the input SNR is -6dB to 6dB, the maximum estimation error does not exceed 0.5dB.

[0133] Simulation 4: Using the present invention, signal-to-interference-plus-noise ratio (SIR) estimation simulations were performed under the following conditions: signal bandwidth of 4K, interference-to-noise ratio (INR) of 5dB, interference signal modulation type of BPSK, and signal modulation types of BPSK, QPSK, 8PSK, 16QAM, 32QAM, and 64QAM. The results are as follows: Figure 6 .

[0134] Depend on Figure 6 It is known that the signal-to-interference-plus-noise ratio (SIR) estimation effect of this technique is best when the source signal is 8PSK or QPSK modulated; when the source signal is BPSK, 16QAM, 32QAM, or 64QAM modulated, the maximum mean square error of the SIR estimation of this technique does not exceed 0.70, and the maximum estimation error does not exceed 0.6dB when the input SIR is -10dB to 6dB.

[0135] The simulation results above show that the present invention has excellent signal-to-interference-plus-noise ratio (SIR) estimation performance under different interference bandwidth conditions. It is less affected by the interference bandwidth. Moreover, the SIR estimation result of the present invention has an error of no more than 1 dB relative to the SIR of the real mixed signal when the input SIR is -4 dB to 6 dB. Therefore, it can provide accurate SIR estimation results for subsequent signal quality assessment.

Claims

1. A method for estimating the signal-to-interference-plus-noise ratio (SINR) of a digitally modulated signal based on second-order cyclic moments, characterized in that, include: (1) For the received signal The signal is segmented, and then the segmented signal is further divided into frames according to its frame length to obtain framed signals. ; (2) Set the optimal time shift parameters according to the frame signal. Constructing preliminary autocorrelation function slices ; (3) Using preliminary autocorrelation function slicing Eliminate noise interference and detect the presence of communication signals in the framed signal: If no communication signal exists, output the estimated signal-to-interference-plus-noise ratio (SIR) of that frame. ; If communication signals exist, calculate the total power of the framed signals; (4) Estimate the communication signal power in the framed signal by slicing the autocorrelation function, and combine it with the framed signal obtained in (3). The signal-to-interference-plus-noise ratio (SIR) of the framed signal is calculated based on the total power of the signal. ; (5) Repeat steps (3) to (4) to estimate the signal-to-interference-plus-noise ratio (SIR) of all frames of the received signal segment. The signal-to-interference-plus-noise ratio (SIR) estimation results for all framed signals are as follows: And count the number of frames in the received signal segment that were detected as non-existent. Calculate its relationship with the total number of frames. ratio ; (6) Preset segmented signal detection threshold and will Compare the value with: like Then the segment receives the signal. If no communication signal is present, the signal-to-interference-plus-noise ratio (SIR) estimation result of this segment of signal is directly output. ; like Then the segment receives the signal. Assuming a communication signal is present, the estimation result is: : ， Where mean represents the average value.

2. The method according to claim 1, characterized in that: In step (2), the optimal time shift parameter is set according to the frame signal. Its implementation includes: (2a) Framing is performed to obtain framed communication signals: ； in, , Divide each signal segment into frames. For frame length, For frame shift; (2b) Based on the framed communication signal Construct a given time shift The corresponding autocorrelation framed communication signal: ； in , , It is the sampling frequency. It is the code rate; (2c) Based on the framed communication signal Its corresponding autocorrelation framing communication signal Calculate the preliminary autocorrelation function : ； Where conj represents taking the conjugate, and exp represents taking the exponent of the number e; (2d) Repeat (2b) to (2c) until all framed communication signals are received. In time shift After the autocorrelation function is calculated, the framed communication signal is calculated. Total signal power Then calculate the corresponding time shift. Error coefficient: ； (2e) Calculation All Corresponding error coefficient ,choose Get the minimum value As the optimal time shift parameter .

3. The method according to claim 1, characterized in that: In (2), a preliminary autocorrelation function slice is constructed. The formula is as follows: ； in, This is the autocorrelation communication framing signal corresponding to the optimal delay. Represents frame length, The variable represents frequency, conj represents taking the conjugate, and exp represents taking the exponent of the number e.

4. The method according to claim 1, characterized in that: The step (3) involves slicing using the preliminary autocorrelation function. The detection of whether a communication signal exists in the framed signal includes the following steps: (3a) Slicing the autocorrelation function exist Summing the range yields the summation result. , in, For detecting the negative cyclic frequency point of the signal, Fs represents the sampling frequency. Represents the cyclic frequency point. Represents rounding down To detect the positive cycle frequency point of the signal, The function represents rounding up; (3b) Slicing the autocorrelation function Summation yields the summation result. ; (3c) Set the frame signal detection threshold Calculate the proportional relationship Compare these two: like If so, the frame signal is considered to contain a communication signal; like If the signal in that frame is not a communication signal, then the frame does not contain any communication signal.

5. The method according to claim 1, characterized in that: The calculation of the frame signal in (3) Total power The formula is as follows: ， in Represents frame length.

6. The method according to claim 1, characterized in that: In (4), the estimation is performed by slicing the autocorrelation function. The communication signal power in the scoring frame signal is implemented as follows: (4a) Take a frequency interval of 1 and slice the autocorrelation function. In frequency region Maximum value of internal search , This represents the code rate; similarly, in the frequency region... Maximum value of internal search The power characteristic parameters of the communication signal are obtained as follows: ； (4b) Based on communication signal power characteristic parameters With pulse shaping response parameters The estimated power of the communication signal is: , in: abs indicates taking the absolute value of the signal amplitude, and conj indicates taking the conjugate. Indicates the symbol period, The pulse shaping function is expressed as follows: 。 7. The method according to claim 1, characterized in that: The signal-to-interference-plus-noise ratio of the framed signal is calculated in (4). The formula is as follows: ， in, To estimate the communication signal power, This represents the total power of the framed signal.

8. A signal-to-interference-plus-noise ratio (SIR / NNR) estimation system for digital modulated signals based on second-order cyclic moments, characterized in that, include: The signal segmentation and framing module is used to segment and frame the received signal. The autocorrelation slice calculation module is used to calculate the autocorrelation slice of the framed signal; Optimal time shift parameters The configuration module is used to obtain the optimal time delay in the autocorrelation slice calculation. The communication signal detection module is used to detect whether a communication signal exists in the framed signal; A communication signal power module is used to estimate the communication signal power in an autocorrelation slice. The signal-to-interference-plus-noise ratio (SINR) calculation module is used to estimate the SINR of communication signals.

9. The system according to claim 8, characterized in that, The communication signal power module includes: The communication signal characteristic parameter estimation submodule is used to estimate the communication signal characteristic parameters through autocorrelation slice cyclic frequency points; The signal pulse shaping parameter estimation submodule is used to estimate the signal pulse shaping parameters based on the principle of digital modulation signals; the frame signal total power calculation submodule is used to calculate the total power of the signal frame power.

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