A method for accurate calculation of signal code rate

Abstract

This invention discloses a method for accurately calculating signal code rate, comprising: constructing a signal receiving model under a multipath channel; generating a pseudo-random signal sequence using a linear feedback shift register; fitting the signal receiving model corresponding to each channel path using the pseudo-random signal sequence; optimizing the attenuation coefficient and delay of the signal receiving model using a genetic mutation algorithm; outputting the optimal signal receiving model; obtaining the spectrum of the output signal using Fourier transform and detecting the spectrum to obtain candidate values ​​for the code rate; filtering the received signal output by the optimal signal receiving model using an adaptive filter and correcting the candidate values ​​to obtain the accurate code rate. This invention considers the impact of multipath propagation on the signal, avoiding calculation errors caused by ignoring multipath effects. Combined with adaptive filtering, it can correct and accurately calculate the code rate, significantly improving the accuracy of code rate calculation.

Description

Technical Field

[0001] This invention relates to the field of signal code rate calculation, and more specifically to a method for accurate calculation of signal code rate. Background Technology

[0002] In digital communication systems, the signal code rate is a critical parameter, and its accurate calculation is of great significance for performance analysis, synchronization control, and signal demodulation of the communication system. Currently, commonly used methods for calculating the signal code rate include zero-crossing detection, spectral analysis, and autocorrelation. However, these methods have many limitations in practical applications.

[0003] For example, zero-crossing detection is susceptible to noise interference, leading to reduced detection accuracy. Its principle is to calculate the code rate by detecting the time interval between the zero-crossing points of the signal; however, when noise is present, the zero-crossing points of the signal will shift, resulting in a significant error in the calculated code rate.

[0004] Spectral analysis methods struggle to accurately extract code rate information when the signal spectrum is complex or multipath interference is present. This method relies on the existence of peaks in the signal spectrum that are related to the code rate; however, under multipath interference, the signal spectrum is distorted, and the peaks become blurred, making accurate code rate identification impossible.

[0005] The autocorrelation method has high computational complexity and is sensitive to signal phase changes, resulting in poor adaptability. It determines the code rate by calculating the autocorrelation function of the signal and using the peak value of the autocorrelation function at the code rate period. However, the complex calculation process makes it difficult to apply to real-time systems, and changes in signal phase can affect the peak position of the autocorrelation function, reducing computational accuracy. Summary of the Invention

[0006] To address the aforementioned shortcomings in the existing technology, this invention provides a method for accurately calculating the signal code rate. By optimizing the signal reception model under multipath channels, the method achieves accurate calculation of the signal code rate.

[0007] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0008] A method for accurately calculating the signal code rate is provided, comprising:

[0009] Step S1: Construct a signal reception model under multipath channels;

[0010] Step S2: Generate a pseudo-random signal sequence using a linear feedback shift register, fit the signal receiving model corresponding to each channel path using the pseudo-random signal sequence, optimize the attenuation coefficient and delay of the signal receiving model using a genetic mutation algorithm, and output the optimal signal receiving model.

[0011] Step S3: After obtaining the optimal signal receiving model corresponding to each channel path, input the original signal from the transmitter into the optimal signal receiving model, output the received signal, use Fourier transform to obtain the spectrum of the output signal, and detect the spectrum to obtain candidate values ​​for the code rate.

[0012] Step S4: Use an adaptive filter to filter the received signal output by the optimal signal receiving model to obtain the output signal of the adaptive filter, and correct the candidate value of the code rate based on the Fourier transform to obtain the accurate code rate.

[0013] Furthermore, the signal reception model under multipath channels is as follows:

[0014] ;

[0015] in, i This is the channel path number. I The number of channel paths, For the first i Attenuation coefficient of each channel path, For the first i The delay of each channel path, For delay The original signal from the transmitting end after that, It is additive white Gaussian noise. For receiving signals, t is the signal duration.

[0016] Further, step S2 includes:

[0017] Step S21: Generate a pseudo-random signal sequence using a linear feedback shift register. The pseudo-random signal sequence is based on autocorrelation of a generator polynomial model.

[0018] ;

[0019] in, For the output random signal, x For signal variables, n The order of the shift register. For the first n Feedback coefficients of a -1 order shift register;

[0020] Step S22: Based on the length of the pseudo-random signal sequence L Determine the order of the shift register n ,satisfy The shift register is set to a non-all-zero initial state, and the sequence elements of the shift register are used. , Generate length L A pseudo-random signal sequence;

[0021] ;

[0022] in, u The order number of the shift register. For the first u The feedback coefficient of the shift register. k Number the elements of the shift register sequence. For the shift register's first k - u 1 sequence element;

[0023] Step S23: Length L The pseudo-random signal sequence is used as the training signal sequence. Each original training signal in the training signal sequence is output from each channel path to obtain the received training signal. The original training signal and the received training signal are used to fit the signal receiving model. The attenuation coefficient and delay are optimized based on the cross-genetic mutation algorithm to output the optimal signal receiving model corresponding to each channel path.

[0024] Further, step S23 includes:

[0025] Step S231: Length L The pseudo-random signal sequence is used as the training signal sequence. Each original training signal in the training signal sequence is output from each channel path to obtain the received training signal. The original training signal and the output received training signal are used as a set of training data to obtain... L Given two sets of training data, input any two sets of training data into the model in the multipath channel, and output a set of attenuation coefficients and delays. , l Number the attenuation coefficient and the delay data group;

[0026] Step S232: Obtain the corresponding channel path A set of attenuation coefficient and delay data As the first initial population, the decay coefficient and delay data sets represent the individuals in the population, and the decay coefficient and delay represent the genes of each individual. For the first One set of attenuation coefficients and delay data;

[0027] Step S233: Return to step S231, recombine any two different training data sets from the training signal sequence, input them into the model in the multipath channel, output the attenuation coefficients and delays corresponding to the two recombined training data sets, and obtain the second initial population;

[0028] Step S234: Set the heritability of the crossover transformation. Perform crossover transformation on the genes between individuals in the first initial population and the second initial population according to the heritability. Exchange the attenuation coefficients between the first initial population and the second initial population, and exchange the delays between the first initial population and the second initial population to obtain offspring individuals after crossover inheritance. The offspring individuals after crossover inheritance form the first generation genetic population A and the first generation genetic population B, and obtain offspring individual a of the first generation genetic population A and offspring individual b of the first generation genetic population B.

[0029] Step S235: Construct an objective function to assess genetic quality;

[0030] ;

[0031] in, For the first l One original training signal, For the first l The received signal corresponding to each original training signal Genetic quality coefficient;

[0032] Step S236: Input individuals from the first initial population and the second initial population into the signal receiving model respectively, and utilize... L Each training signal outputs the corresponding training signal for each individual. L The system receives signals and uses an objective function to calculate the genetic quality coefficient for each individual in the first and second initial populations. , , The first initial population The genetic quality coefficient of an individual. The second initial population The genetic quality coefficient of each individual;

[0033] Step S237: Screening for genetic quality coefficients , minimum value minimum value The corresponding individual is taken as the optimal individual in the initial population;

[0034] Step S238: Repeat steps S236-S237, inputting offspring individual a from the first generation genetic population A and offspring individual b from the first generation genetic population B into the signal receiving model respectively. Calculate the genetic quality coefficient corresponding to each offspring individual a and b using the objective function, and select the minimum value of the genetic quality coefficient corresponding to the optimal offspring individual. ;

[0035] Step S238: Compare the minimum values and minimum value The size between;

[0036] like If so, output the first generation genetic population A and the first generation genetic population B, and execute step S239;

[0037] like If the genetic rate of the crossover is set, then return to step S234. Based on the set genetic rate of the crossover, reuse the genes between different individuals in the first initial population and the second initial population for crossover, and execute steps S234-S238. If all genes between individuals in the first initial population and the second initial population have been crossovered, and none of them satisfy the condition... If the first initial population and the second initial population are used directly as the first generation genetic population A and the first generation genetic population B, then step S239 is executed; otherwise, the first generation genetic population A and the first generation genetic population B are directly output, and step S239 is executed.

[0038] Step S239: Set the decay coefficient and the delayed mutation rate to perform genetic mutation on the genes of offspring individuals a and b in the first generation genetic population A and the first generation genetic population B;

[0039] ;

[0040] in, The decay coefficients and delays for the genes of offspring individuals a and b are respectively. These represent the decay coefficient and delay corresponding to the genes resulting from genetic mutations in offspring individuals a and b, respectively. These are the attenuation coefficient and the rate of change of delay, respectively.

[0041] Step S2310: Genetic mutation is performed on the genes of offspring individuals a and b to obtain mutated offspring individuals. Mutant offspring individuals , mutated offspring individuals Mutant offspring individuals Input the data into the signal receiving model respectively, execute steps S236-S237, and output the optimal child individual after mutation;

[0042] Step S2311: Repeat steps S239-S2310 to continuously perform genetic mutations on the genes of offspring individuals a and b. Each genetic mutation will result in an optimal offspring individual after mutation, until the genetic quality coefficient of the optimal offspring individual after mutation is less than or equal to the target genetic quality coefficient. Then, output the optimal offspring individual after mutation as the final optimal offspring individual.

[0043] Step S2312: Calculate the decay coefficient corresponding to the genes of the final optimal sub-individual. and delay In the input signal reception model, the channel path is obtained. i The corresponding optimal signal receiving model;

[0044] .

[0045] Further, step S3 includes:

[0046] Step S31: After obtaining the optimal signal receiving model corresponding to each channel path, input the original signal from the transmitter into the optimal signal receiving model, output the received signal, and perform a Fourier transform on the received signal.

[0047] ;

[0048] in, Original signal The spectrum, The spectrum of the received signal. j The imaginary unit, e It is a natural constant. The noise spectrum, f The signal frequency;

[0049] Step S32: Define the original signal The code rate is R Original signal Spectrum exist There is a peak frequency at this point; the spectrum of the received signal is detected. The peak frequency is used to determine candidate values ​​for the code rate based on the relationship between the peak frequency and the code rate. .

[0050] Further, step S4 includes:

[0051] Step S41: Apply an adaptive filter to the received signal output by the optimal signal receiving model. Filtering is performed to obtain the output signal of the adaptive filter. ;

[0052] ;

[0053] ;

[0054] in, These are the weighting coefficients of the adaptive filter. For adaptive filters in signal time t A weighting coefficient of -1 For error signals, To receive signals Ideal signal This is the correction step size factor for the weighting coefficients;

[0055] Step S42: Filter the output signal Perform another Fourier transform to obtain the output signal. The spectrum of the signal and the output signal are detected. The peak frequency of the spectrum, for candidate values ​​of the code rate. Make corrections to obtain the accurate code rate. .

[0056] The beneficial effects of this invention are as follows: This invention generates a pseudo-random signal sequence based on a shift register, and uses this random signal sequence to optimize and fit the signal reception model under multipath channels. It outputs the signal reception model for each channel path, thereby reducing the error of the output received signal, optimizing the delay and attenuation of the signal transmission process under multipath channels, and using a genetic mutation algorithm to optimize the delay and attenuation. This allows the signal transmission under different channel paths to be infinitely close to the actual signal transmission error, ensuring the accuracy of subsequent code rate calculations. This invention considers the impact of multipath propagation on the signal, avoiding calculation errors caused by ignoring multipath effects. Combined with adaptive filtering, it can correct and accurately calculate the code rate, significantly improving the accuracy of code rate calculation. Attached Figure Description

[0057] Figure 1 This is a flowchart of a method for accurately calculating the signal code rate. Detailed Implementation

[0058] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0059] like Figure 1 As shown, a method for accurately calculating signal code rate includes:

[0060] Step S1: Construct a signal reception model under multipath channels;

[0061] ;

[0062] in, i This is the channel path number. I The number of channel paths, For the first i Attenuation coefficient of each channel path, For the first i The delay of each channel path, For delay The original signal from the transmitting end after that, It is additive white Gaussian noise. In order to receive signals, t For signal time;

[0063] Step S2: Generate a pseudo-random signal sequence using a linear feedback shift register, fit the signal receiving model corresponding to each channel path using the pseudo-random signal sequence, optimize the attenuation coefficient and delay of the signal receiving model using a genetic mutation algorithm, and output the optimal signal receiving model.

[0064] Step S2 specifically includes the following steps:

[0065] Step S21: Generate a pseudo-random signal sequence using a linear feedback shift register. The pseudo-random signal sequence is based on autocorrelation of a generator polynomial model.

[0066] ;

[0067] in, For the output random signal, x For signal variables, n The order of the shift register. For the first n Feedback coefficients of a -1 order shift register;

[0068] Step S22: Based on the length of the pseudo-random signal sequence L Determine the order of the shift register n ,satisfy The shift register is set to a non-all-zero initial state, and the sequence elements of the shift register are used. , Generate length L A pseudo-random signal sequence;

[0069] ;

[0070] in, u The order number of the shift register. For the first u The feedback coefficient of the shift register. k Number the elements of the shift register sequence. For the shift register's first k - u 1 sequence element;

[0071] Step S23: Length LThe pseudo-random signal sequence is used as the training signal sequence. Each original training signal in the training signal sequence is output from each channel path to obtain the received training signal. The original training signal and the received training signal are used to fit the signal receiving model. The attenuation coefficient and delay are optimized based on the cross-genetic mutation algorithm to output the optimal signal receiving model corresponding to each channel path.

[0072] Step S23 specifically includes the following steps:

[0073] Step S231: Length L The pseudo-random signal sequence is used as the training signal sequence. Each original training signal in the training signal sequence is output from each channel path to obtain the received training signal. The original training signal and the output received training signal are used as a set of training data to obtain... L Given two sets of training data, input any two sets of training data into the model in the multipath channel, and output a set of attenuation coefficients and delays. , l Number the attenuation coefficient and the delay data group;

[0074] Step S232: Obtain the corresponding channel path A set of attenuation coefficient and delay data As the first initial population, the decay coefficient and delay data sets represent the individuals in the population, and the decay coefficient and delay represent the genes of each individual. For the first One set of attenuation coefficients and delay data;

[0075] Step S233: Return to step S231, recombine any two different training data sets from the training signal sequence, input them into the model in the multipath channel, output the attenuation coefficients and delays corresponding to the two recombined training data sets, and obtain the second initial population;

[0076] Step S234: Set the heritability of the crossover transformation. Perform crossover transformation on the genes between individuals in the first initial population and the second initial population according to the heritability. Exchange the attenuation coefficients between the first initial population and the second initial population, and exchange the delays between the first initial population and the second initial population to obtain offspring individuals after crossover inheritance. The offspring individuals after crossover inheritance form the first generation genetic population A and the first generation genetic population B, and obtain offspring individual a of the first generation genetic population A and offspring individual b of the first generation genetic population B.

[0077] In this embodiment, crossover inheritance is first used to optimize individuals in the second and first initial populations. The heritability of crossover transformation can limit the number of individuals that can undergo gene crossover within the population. The individuals selected for gene crossover transformation in the second and first initial populations are randomized. During gene exchange, genes of the same type are transformed accordingly. One crossover transformation can form two different offspring individuals. By reasonably setting the heritability of crossover transformation, the complexity of inheritance can be reduced and the amount of data processing can be reduced. When a better offspring individual can be obtained from the first crossover inheritance, the next step can be performed to carry out mutation inheritance.

[0078] Step S235: Construct an objective function to assess genetic quality;

[0079] ;

[0080] in, For the first l One original training signal, For the first l The received signal corresponding to each original training signal Genetic quality coefficient;

[0081] Step S236: Input individuals from the first initial population and the second initial population into the signal receiving model respectively, and utilize... L Each training signal outputs the corresponding training signal for each individual. L The system receives signals and uses an objective function to calculate the genetic quality coefficient for each individual in the first and second initial populations. , , The first initial population The genetic quality coefficient of an individual. The second initial population The genetic quality coefficient of each individual;

[0082] Step S237: Screening for genetic quality coefficients , minimum value minimum value The corresponding individual is taken as the optimal individual in the initial population;

[0083] Step S238: Repeat steps S236-S237, inputting offspring individual a from the first generation genetic population A and offspring individual b from the first generation genetic population B into the signal receiving model respectively. Calculate the genetic quality coefficient corresponding to each offspring individual a and b using the objective function, and select the minimum value of the genetic quality coefficient corresponding to the optimal offspring individual. ;

[0084] Step S238: Compare the minimum values and minimum value The size between;

[0085] like If so, output the first generation genetic population A and the first generation genetic population B, and execute step S239;

[0086] like If the genetic rate of the crossover is set, then return to step S234. Based on the set genetic rate of the crossover, reuse the genes between different individuals in the first initial population and the second initial population for crossover, and execute steps S234-S238. If all genes between individuals in the first initial population and the second initial population have been crossovered, and none of them satisfy the condition... If the first initial population and the second initial population are used directly as the first generation genetic population A and the first generation genetic population B, then step S239 is executed; otherwise, the first generation genetic population A and the first generation genetic population B are directly output, and step S239 is executed.

[0087] Step S239: Set the decay coefficient and the delayed mutation rate to perform genetic mutation on the genes of offspring individuals a and b in the first generation genetic population A and the first generation genetic population B;

[0088] ;

[0089] in, The decay coefficients and delays for the genes of offspring individuals a and b are respectively. These represent the decay coefficient and delay corresponding to the genes resulting from genetic mutations in offspring individuals a and b, respectively. These are the attenuation coefficient and the rate of change of delay, respectively.

[0090] This embodiment, based on the superior offspring individuals produced by cross-genesis, continues to mutate and genetically modify the offspring individuals. By reasonably setting the gene mutation rate, the attenuation coefficient and delay are adjusted within a small range, gradually approaching the optimal attenuation coefficient and delay, thus optimizing the signal reception model corresponding to each channel path.

[0091] Step S2310: Genetic mutation is performed on the genes of offspring individuals a and b to obtain mutated offspring individuals. Mutant offspring individuals , mutated offspring individuals Mutant offspring individuals Input the data into the signal receiving model respectively, execute steps S236-S237, and output the optimal child individual after mutation;

[0092] Step S2311: Repeat steps S239-S2310 to continuously perform genetic mutations on the genes of offspring individuals a and b. Each genetic mutation will result in an optimal offspring individual after mutation, until the genetic quality coefficient of the optimal offspring individual after mutation is less than or equal to the target genetic quality coefficient. Then, output the optimal offspring individual after mutation as the final optimal offspring individual.

[0093] Step S2312: Calculate the decay coefficient corresponding to the genes of the final optimal sub-individual. and delay In the input signal reception model, the channel path is obtained. i The corresponding optimal signal receiving model;

[0094] .

[0095] Step S3: After obtaining the optimal signal receiving model corresponding to each channel path, input the original signal from the transmitter into the optimal signal receiving model, output the received signal, use Fourier transform to obtain the spectrum of the output signal, and detect the spectrum to obtain candidate values ​​for the code rate.

[0096] Step S3 specifically includes the following steps:

[0097] Step S31: After obtaining the optimal signal receiving model corresponding to each channel path, input the original signal from the transmitter into the optimal signal receiving model, output the received signal, and perform a Fourier transform on the received signal.

[0098] ;

[0099] in, Original signal The spectrum, The spectrum of the received signal. j The imaginary unit, e It is a natural constant. The noise spectrum, f The signal frequency;

[0100] Step S32: Define the original signal The code rate is R Original signal Spectrum exist There is a peak frequency at this point; the spectrum of the received signal is detected. The peak frequency is used to determine candidate values ​​for the code rate based on the relationship between the peak frequency and the code rate. .

[0101] Step S4: Use an adaptive filter to filter the received signal output by the optimal signal receiving model to obtain the output signal of the adaptive filter, and correct the candidate value of the code rate based on the Fourier transform to obtain the accurate code rate.

[0102] Step S4 specifically includes the following steps:

[0103] Step S41: Apply an adaptive filter to the received signal output by the optimal signal receiving model. Filtering is performed to obtain the output signal of the adaptive filter. ;

[0104] ;

[0105] ;

[0106] in, These are the weighting coefficients of the adaptive filter. For adaptive filters in signal time t A weighting coefficient of -1 For error signals, To receive signals Ideal signal This is the correction step size factor for the weighting coefficients;

[0107] Step S42: Filter the output signal Perform another Fourier transform to obtain the output signal. The spectrum of the signal and the output signal are detected. The peak frequency of the spectrum, for candidate values ​​of the code rate. Make corrections to obtain the accurate code rate. .

[0108] This invention considers the impact of multipath propagation on the signal, avoiding calculation errors caused by neglecting multipath effects. Combined with adaptive filtering, it enables correction and accurate calculation of the code rate, significantly improving the accuracy of code rate calculation.

Claims

1. A method for accurately calculating signal code rate, characterized in that, include: Step S1: Construct a signal reception model under multipath channels; Step S2: Generate a pseudo-random signal sequence using a linear feedback shift register, fit the signal receiving model corresponding to each channel path using the pseudo-random signal sequence, optimize the attenuation coefficient and delay of the signal receiving model using a genetic mutation algorithm, and output the optimal signal receiving model. Step S3: After obtaining the optimal signal receiving model corresponding to each channel path, input the original signal from the transmitter into the optimal signal receiving model, output the received signal, use Fourier transform to obtain the spectrum of the output signal, and detect the spectrum to obtain candidate values ​​for the code rate. Step S4: Use an adaptive filter to filter the received signal output by the optimal signal receiving model to obtain the output signal of the adaptive filter, and correct the candidate value of the code rate based on the Fourier transform to obtain the accurate code rate.

2. The method for accurately calculating the signal code rate according to claim 1, characterized in that, The signal reception model under the multipath channel is as follows: ； in, i This is the channel path number. I The number of channel paths, For the first i Attenuation coefficient of each channel path, For the first i The delay of each channel path, For delay The original signal from the transmitting end after that, It is additive white Gaussian noise. For receiving signals, t is the signal duration.

3. The method for accurately calculating the signal code rate according to claim 2, characterized in that, Step S2 includes: Step S21: Generate a pseudo-random signal sequence using a linear feedback shift register. The pseudo-random signal sequence is based on autocorrelation of a generator polynomial model. ； in, For the output random signal, x For signal variables, n The order of the shift register. For the first n Feedback coefficients of a -1 order shift register; Step S22: Based on the length of the pseudo-random signal sequence L Determine the order of the shift register n ,satisfy The shift register is set to a non-all-zero initial state, and the sequence elements of the shift register are used. , Generate length L A pseudo-random signal sequence; ； in, u The order number of the shift register. For the first u The feedback coefficient of the shift register. k Number the elements of the shift register sequence. For the shift register k - u 1 sequence element; Step S23: Length L The pseudo-random signal sequence is used as the training signal sequence. Each original training signal in the training signal sequence is output from each channel path to obtain the received training signal. The original training signal and the received training signal are used to fit the signal receiving model. The attenuation coefficient and delay are optimized based on the cross-genetic mutation algorithm to output the optimal signal receiving model corresponding to each channel path.

4. The method for accurately calculating the signal code rate according to claim 3, characterized in that, Step S23 includes: Step S231: Length L The pseudo-random signal sequence is used as the training signal sequence. Each original training signal in the training signal sequence is output from each channel path to obtain the received training signal. The original training signal and the output received training signal are used as a set of training data to obtain... L Given two sets of training data, input any two sets of training data into the model in the multipath channel, and output a set of attenuation coefficients and delays. , l Number the attenuation coefficient and the delay data group; Step S232: Obtain the corresponding channel path A set of attenuation coefficient and delay data As the first initial population, the decay coefficient and delay data sets represent the individuals in the population, and the decay coefficient and delay represent the genes of each individual. For the first One set of attenuation coefficients and delay data; Step S233: Return to step S231, recombine any two different training data sets from the training signal sequence, input them into the model in the multipath channel, output the attenuation coefficients and delays corresponding to the two recombined training data sets, and obtain the second initial population; Step S234: Set the heritability of the crossover transformation. Perform crossover transformation on the genes between individuals in the first initial population and the second initial population according to the heritability. Exchange the attenuation coefficients between the first initial population and the second initial population, and exchange the delays between the first initial population and the second initial population to obtain offspring individuals after crossover inheritance. The offspring individuals after crossover inheritance form the first generation genetic population A and the first generation genetic population B, and obtain offspring individual a of the first generation genetic population A and offspring individual b of the first generation genetic population B. Step S235: Construct an objective function to assess genetic quality; ； in, For the first l One original training signal, For the first l The received signal corresponding to each original training signal Genetic quality coefficient; Step S236: Input individuals from the first initial population and the second initial population into the signal receiving model respectively, and utilize... L Each training signal outputs the corresponding training signal for each individual. L The system receives signals and uses an objective function to calculate the genetic quality coefficient for each individual in the first and second initial populations. , , The first initial population The genetic quality coefficient of an individual. The second initial population The genetic quality coefficient of each individual; Step S237: Screening for genetic quality coefficients , minimum value minimum value The corresponding individual is taken as the optimal individual in the initial population; Step S238: Repeat steps S236-S237, inputting offspring individual a from the first generation genetic population A and offspring individual b from the first generation genetic population B into the signal receiving model respectively. Calculate the genetic quality coefficient corresponding to each offspring individual a and b using the objective function, and select the minimum value of the genetic quality coefficient corresponding to the optimal offspring individual. ; Step S238: Compare the minimum values and minimum value The size between; like If so, output the first generation genetic population A and the first generation genetic population B, and execute step S239; like If the genetic rate of the crossover is set, then return to step S234. Based on the set genetic rate of the crossover, reuse the genes between different individuals in the first initial population and the second initial population for crossover, and execute steps S234-S238. If all genes between individuals in the first initial population and the second initial population have been crossovered, and none of them satisfy the condition... If the first initial population and the second initial population are used directly as the first generation genetic population A and the first generation genetic population B, then step S239 is executed; otherwise, the first generation genetic population A and the first generation genetic population B are directly output, and step S239 is executed. Step S239: Set the decay coefficient and the delayed mutation rate to perform genetic mutation on the genes of offspring individuals a and b in the first generation genetic population A and the first generation genetic population B; ； in, The decay coefficients and delays for the genes of offspring individuals a and b are respectively. These represent the decay coefficient and delay corresponding to the genes resulting from genetic mutations in offspring individuals a and b, respectively. These are the attenuation coefficient and the rate of change of delay, respectively. Step S2310: Genetic mutation is performed on the genes of offspring individuals a and b to obtain mutated offspring individuals. Mutant offspring individuals , mutated offspring individuals Mutant offspring individuals Input the data into the signal receiving model respectively, execute steps S236-S237, and output the optimal child individual after mutation; Step S2311: Repeat steps S239-S2310 to continuously perform genetic mutations on the genes of offspring individuals a and b. Each genetic mutation will result in an optimal offspring individual after mutation, until the genetic quality coefficient of the optimal offspring individual after mutation is less than or equal to the target genetic quality coefficient. Then, output the optimal offspring individual after mutation as the final optimal offspring individual. Step S2312: Calculate the decay coefficient corresponding to the genes of the final optimal sub-individual. and delay In the input signal reception model, the channel path is obtained. i The corresponding optimal signal receiving model; 。 5. The method for accurately calculating the signal code rate according to claim 4, characterized in that, Step S3 includes: Step S31: After obtaining the optimal signal receiving model corresponding to each channel path, input the original signal from the transmitter into the optimal signal receiving model, output the received signal, and perform a Fourier transform on the received signal. ； in, Original signal The spectrum, The spectrum of the received signal. j The imaginary unit, e It is a natural constant. The noise spectrum, f The signal frequency; Step S32: Define the original signal The code rate is R Original signal Spectrum exist There is a peak frequency at this point; the spectrum of the received signal is detected. The peak frequency is used to determine candidate values ​​for the code rate based on the relationship between the peak frequency and the code rate. .

6. The method for accurately calculating the signal code rate according to claim 5, characterized in that, Step S4 includes: Step S41: Apply an adaptive filter to the received signal output by the optimal signal receiving model. Filtering is performed to obtain the output signal of the adaptive filter. ; ； ； in, These are the weighting coefficients of the adaptive filter. For adaptive filters in signal time t A weighting coefficient of -1 For error signals, To receive signals Ideal signal This is the correction step size factor for the weighting coefficients; Step S42: Filter the output signal Perform another Fourier transform to obtain the output signal. The spectrum of the signal and the output signal are detected. The peak frequency of the spectrum, for candidate values ​​of the code rate. Make corrections to obtain the accurate code rate. .

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