Signal code rate accurate calculation method

Abstract

The invention discloses a method for accurately calculating a signal code rate. The method comprises the following steps of: constructing a signal receiving model under a multi-path channel; generating a pseudo-random signal sequence by using a linear feedback shift register, fitting a signal receiving model corresponding to each channel path by using the pseudo-random signal sequence, optimizing an attenuation coefficient and delay of the signal receiving model by using a genetic variation algorithm, and outputting an optimal signal receiving model; fourier transform is utilized to obtain a frequency spectrum of the output signal, and the frequency spectrum is detected to obtain a candidate value of a code rate; and filtering a received signal output by the optimal signal receiving model by adopting an adaptive filter, and correcting a candidate value to obtain an accurate code rate. According to the method, the influence of multipath propagation on signals is considered, and calculation errors caused by neglecting the multipath effect are avoided. And in combination with adaptive filtering, the code rate can be corrected and accurately calculated, and the accuracy of code rate calculation is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of signal code rate calculation, and in particular to a method for accurately calculating signal code rate. Background Art

[0002] In digital communication systems, the signal code rate is a key parameter. Its accurate calculation is crucial for performance analysis, synchronization control, and signal demodulation. Currently, commonly used methods for calculating the signal code rate include zero-crossing detection, spectrum analysis, and autocorrelation. However, these methods have numerous limitations in practical applications.

[0003] For example, the zero-crossing detection method is susceptible to noise interference, resulting in reduced detection accuracy. The principle is to calculate the bit rate by detecting the time interval between the signal's zero crossings. However, when noise is present, the signal's zero crossings will shift, resulting in a large error in the calculated bit rate.

[0004] Spectrum analysis methods struggle to accurately extract bit rate information when the signal spectrum is complex or in the presence of multipath interference. This method relies on the presence of a peak in the signal spectrum that correlates with the bit rate. However, multipath interference can distort the signal spectrum, blurring the peak and making it difficult to accurately identify the bit rate. The autocorrelation method is computationally complex and sensitive to signal phase changes, resulting in poor adaptability. It calculates the signal's autocorrelation function and uses its peak value at the code rate period to determine the code rate. However, this complex calculation makes it difficult to apply to real-time systems. Furthermore, changes in the signal phase can affect the peak position of the autocorrelation function, reducing computational accuracy. Summary of the Invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for accurately calculating the signal code rate, which realizes accurate calculation of the signal code rate by optimizing the signal receiving model under the multipath channel.

[0006] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: A method for accurately calculating a signal code rate is provided, comprising: Step S1: construct a signal reception model under a multipath channel; Step S2: Generate a pseudo-random signal sequence using a linear feedback shift register, fit the signal reception model corresponding to each channel path using the pseudo-random signal sequence, optimize the attenuation coefficient and delay of the signal reception model using a genetic mutation algorithm, and output the optimal signal reception model; Step S3: After obtaining the optimal signal reception model corresponding to each channel path, the original signal from the transmitter is input into the optimal signal reception model, the received signal is output, the spectrum of the output signal is obtained by Fourier transform, and the spectrum is detected to obtain a candidate value of the code rate; Step S4: Adopting an adaptive filter to filter the received signal output by the optimal signal receiving model to obtain an output signal of the adaptive filter, and correcting the candidate value of the code rate based on Fourier transform to obtain an accurate code rate.

[0007] Furthermore, the signal reception model under the multipath channel is: ; in, i is the channel path number, I is the number of channel paths, For the i The attenuation coefficient of each channel path, For the i The delay of the channel path, For delay The original signal from the transmitter after is additive white Gaussian noise, is the received signal, and t is the signal time.

[0008] Furthermore, step S2 includes: Step S21: Generate a pseudo-random signal sequence using a linear feedback shift register, where the pseudo-random signal sequence is based on autocorrelation of a generator polynomial model; ; in, is the output random signal, x is the signal variable, n is the order of the shift register, For the n -Feedback coefficient of 1st order shift register; Step S22: According to the length of the pseudo-random signal sequence L Determine the order of the shift register n ,satisfy , set the shift register to a non-zero initial state, and use the sequence element of the shift register , generating length L Pseudo-random signal sequence; ; in, u is the order number of the shift register, For the u The feedback coefficient of the shift register is kis the element number of the shift register sequence, The first bit of the shift register k - u sequence elements; Step S23: Length L A pseudo-random signal sequence is used as a training signal sequence. Each original training signal in the training signal sequence is output from each channel path to obtain a received training signal. The signal reception model is fitted using the original training signals and the received training signals. The attenuation coefficient and delay are optimized based on the cross-genetic mutation algorithm, and the optimal signal reception model corresponding to each channel path is output.

[0009] Furthermore, 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 a received training signal. The original training signal and the output received training signal are used as a set of training data groups to obtain L A set of training data sets is used to input any two sets of training data sets into the multipath channel model, and a set of attenuation coefficients and delays are output. , l Number the attenuation coefficient and delay data groups; Step S232: Get the corresponding attenuation coefficient and delay data sets , as the first initial population, the decay coefficient and delay data set as the population individuals, the decay coefficient and delay as the genes of the individuals, respectively, For the attenuation coefficient and delay data sets; Step S233: Return to step S231, recombine any two different training data sets from the training signal sequence, input them into the multipath channel model, output the attenuation coefficients and delays corresponding to the recombined two training data sets, and obtain a second initial population; Step S234: setting the heritability of the crossover transformation, performing a crossover transformation on the genes between the individuals in the first initial population and the second initial population according to the heritability, exchanging the attenuation coefficients between the first initial population and the second initial population, and exchanging 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 forming the first generation genetic population A and the first generation genetic population B, to obtain offspring individual a of the first generation genetic population A and offspring individual b of the first generation genetic population B; Step S235: constructing an objective function for evaluating genetic quality; ; in, For the l The original training signal, For the l The received signal corresponding to the original training signal is is the genetic quality coefficient; Step S236: Input the individuals in the first initial population and the second initial population into the signal receiving model respectively, and use L The training signal output for each individual L The target function is used to calculate the genetic quality coefficient of each individual in the first initial population and the second initial population. 、 , is the first initial population The genetic quality coefficient of each individual, is the first in the second initial population The genetic quality coefficient of each individual; Step S237: Screening genetic quality coefficient 、 The minimum value in , minimum The corresponding individual is regarded as the optimal individual of the initial population; Step S238: Repeat steps S236-S237, input the offspring individual a of the first generation genetic population A and the offspring individual b of the first generation genetic population B into the signal receiving model respectively, calculate the genetic quality coefficient corresponding to each offspring individual a and offspring individual b using the objective function, and screen out the minimum value of the genetic quality coefficient corresponding to the optimal offspring individual ; Step S238: Compare minimum values With minimum value The size between like , then output the first-generation genetic population A and the first-generation genetic population B, and execute step S239; like , then return to step S234, based on the heritability of the crossover transformation, reuse the genes between different individuals in the first initial population and the second initial population for crossover transformation, and execute steps S234-S238. If the genes between the individuals in the first initial population and the second initial population have all been crossover transformed and none of them meet , the first initial population and the second initial population are directly used as the first-generation genetic population A and the first-generation genetic population B, and 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: setting the attenuation coefficient and the delayed mutation rate to perform genetic mutation on the genes of the offspring individuals a and b in the first-generation genetic population A and the first-generation genetic population B; ; in, are the attenuation coefficient and delay corresponding to the genes of offspring individual a and offspring individual b, respectively. are the attenuation coefficient and delay corresponding to the genes genetically mutated by offspring individuals a and b, respectively. are the variation rates of attenuation coefficient and delay, respectively; Step S2310: Genetic mutation of offspring individuals a and b is performed to obtain mutant offspring individuals. , mutant offspring individuals , the mutant offspring individuals , mutant offspring individuals Input the signal into the signal receiving model respectively, execute steps S236-S237, and output the optimal sub-individual after mutation; Step S2311: Repeat steps S239-S2310 to continuously perform genetic mutation on the genes of offspring individuals a and b, obtaining a mutated optimal child individual each time a genetic mutation is performed, until a mutated optimal child individual has a genetic quality coefficient less than or equal to the target genetic quality coefficient; then output the mutated optimal child individual as the final optimal child individual; Step S2312: The attenuation coefficient corresponding to the gene of the final optimal sub-individual and delay In the input signal receiving model, the channel path is obtained i The corresponding optimal signal reception model; .

[0010] Furthermore, step S3 includes: Step S31: After obtaining the optimal signal reception model corresponding to each channel path, the original signal from the transmitter is input into the optimal signal reception model, the received signal is output, and Fourier transform is performed on the received signal; ; in, The original signal The spectrum, is the spectrum of the received signal, j is the imaginary unit, e is a natural constant, is the noise spectrum, f is the signal frequency; Step S32: Define the original signal The code rate is R , the original signal Spectrum exist There is a peak frequency at the receiving signal. The peak frequency in , and the candidate value of the code rate is determined according to the relationship between the peak frequency and the code rate .

[0011] Furthermore, step S4 includes: Step S41: Adopting an adaptive filter to process the received signal output by the optimal signal receiving model Filter and get the output signal of the adaptive filter ; ; ; in, is the weight coefficient of the adaptive filter, The adaptive filter is in the signal time t A weight coefficient of -1, is the error signal, To receive the signal The ideal signal signal, is the modified step size factor of the weight coefficient; Step S42: Filter the output signal Perform Fourier transform again to get the output signal spectrum and detect the output signal The peak frequency of the spectrum, the candidate value of the code rate Make corrections to get the exact code rate .

[0012] The beneficial effects of the present invention are as follows: the present invention generates a pseudo-random signal sequence based on a shift register, optimizes and fits the signal reception model under a multipath channel using a random signal sequence, outputs the signal reception model under each channel path, thereby reducing the error of the output received signal, optimizing the delay and attenuation of the signal transmission process under the multipath channel, and using a genetic mutation algorithm to optimize the delay and attenuation, so that the signal transmission under different channel paths can be infinitely close to the actual signal transmission error, providing a guarantee for the accuracy of the subsequent calculation of the signal code rate. The present invention takes into account the impact of multipath propagation on the signal and avoids the calculation error caused by ignoring the multipath effect. Combined with adaptive filtering, the code rate can be corrected and accurately calculated, significantly improving the accuracy of the code rate calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The figure is a flow chart of a method for accurately calculating the signal code rate. DETAILED DESCRIPTION

[0014] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0015] like Figure 1 As shown, a method for accurately calculating a signal code rate includes: Step S1: construct a signal reception model under a multipath channel; ; in, i is the channel path number, I is the number of channel paths, For the i The attenuation coefficient of each channel path, For the i The delay of the channel path, For delay The original signal from the transmitter after is additive white Gaussian noise, To receive the signal, t is the signal time; Step S2: Generate a pseudo-random signal sequence using a linear feedback shift register, fit the signal reception model corresponding to each channel path using the pseudo-random signal sequence, optimize the attenuation coefficient and delay of the signal reception model using a genetic mutation algorithm, and output the optimal signal reception model.

[0016] Step S2 specifically includes the following steps: Step S21: Generate a pseudo-random signal sequence using a linear feedback shift register, where the pseudo-random signal sequence is based on autocorrelation of a generator polynomial model; ; in, is the output random signal, x is the signal variable, n is the order of the shift register, For the n -Feedback coefficient of 1st order shift register; Step S22: According to the length of the pseudo-random signal sequence L Determine the order of the shift register n ,satisfy , set the shift register to a non-zero initial state, and use the sequence element of the shift register , generating length L Pseudo-random signal sequence; ; in, u is the order number of the shift register, For the u The feedback coefficient of the shift register is k is the element number of the shift register sequence, The first bit of the shift register k - u sequence elements; Step S23: Length L A pseudo-random signal sequence is used as a training signal sequence. Each original training signal in the training signal sequence is output from each channel path to obtain a received training signal. The signal reception model is fitted using the original training signals and the received training signals. The attenuation coefficient and delay are optimized based on the cross-genetic mutation algorithm, and the optimal signal reception model corresponding to each channel path is output.

[0017] Step S23 specifically includes the following steps: 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 a received training signal. The original training signal and the output received training signal are used as a set of training data groups to obtain L A set of training data sets is used to input any two sets of training data sets into the multipath channel model, and a set of attenuation coefficients and delays are output. , l Number the attenuation coefficient and delay data groups; Step S232: Get the corresponding attenuation coefficient and delay data sets , as the first initial population, the decay coefficient and delay data set as the population individuals, the decay coefficient and delay as the genes of the individuals, respectively, For the attenuation coefficient and delay data sets; Step S233: Return to step S231, recombine any two different training data sets from the training signal sequence, input them into the multipath channel model, output the attenuation coefficients and delays corresponding to the recombined two training data sets, and obtain a second initial population; Step S234: setting the heritability of the crossover transformation, performing a crossover transformation on the genes between the individuals in the first initial population and the second initial population according to the heritability, exchanging the attenuation coefficients between the first initial population and the second initial population, and exchanging 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 forming the first generation genetic population A and the first generation genetic population B, to obtain offspring individual a of the first generation genetic population A and offspring individual b of the first generation genetic population B; This embodiment first uses crossover genetics to optimize the individuals in the second initial population and the first initial population. The inheritance rate of crossover transformation can limit the number of individual gene crossover exchanges in the population. The individuals selected for gene crossover transformation in the second initial population and the first initial population are random. During gene exchange, genes of the same type are correspondingly transformed. One crossover transformation can form two different offspring individuals. By reasonably setting the inheritance rate 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 after the first crossover genetics, the next step can be executed to perform variation genetics.

[0018] Step S235: constructing an objective function for evaluating genetic quality; ; in, For the l The original training signal, For the l The received signal corresponding to the original training signal is is the genetic quality coefficient; Step S236: Input the individuals in the first initial population and the second initial population into the signal receiving model respectively, and use L The training signal output for each individual L The target function is used to calculate the genetic quality coefficient of each individual in the first initial population and the second initial population. 、 , is the first initial population The genetic quality coefficient of each individual, is the first in the second initial population The genetic quality coefficient of each individual; Step S237: Screening genetic quality coefficient 、 The minimum value in , minimum The corresponding individual is regarded as the optimal individual of the initial population; Step S238: Repeat steps S236-S237, input the offspring individual a of the first generation genetic population A and the offspring individual b of the first generation genetic population B into the signal receiving model respectively, calculate the genetic quality coefficient corresponding to each offspring individual a and offspring individual b using the objective function, and screen out the minimum value of the genetic quality coefficient corresponding to the optimal offspring individual ; Step S238: Compare minimum values With minimum value The size between like , then output the first-generation genetic population A and the first-generation genetic population B, and execute step S239; like , then return to step S234, based on the heritability of the crossover transformation, reuse the genes between different individuals in the first initial population and the second initial population for crossover transformation, and execute steps S234-S238. If the genes between the individuals in the first initial population and the second initial population have all been crossover transformed and none of them meet , the first initial population and the second initial population are directly used as the first-generation genetic population A and the first-generation genetic population B, and 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: setting the attenuation coefficient and the delayed mutation rate to perform genetic mutation on the genes of the offspring individuals a and b in the first-generation genetic population A and the first-generation genetic population B; ; in, are the attenuation coefficient and delay corresponding to the genes of offspring individual a and offspring individual b, respectively. are the attenuation coefficient and delay corresponding to the genes genetically mutated by offspring individuals a and b, respectively. are the variation rates of attenuation coefficient and delay, respectively; Based on the cross-inheritance of superior offspring individuals, this embodiment continues to perform mutation inheritance on 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, so that the signal reception model corresponding to each channel path is optimized.

[0019] Step S2310: Genetic mutation of offspring individuals a and b is performed to obtain mutant offspring individuals. , mutant offspring individuals , the mutant offspring individuals , mutant offspring individuals Input the signal into the signal receiving model respectively, execute steps S236-S237, and output the optimal sub-individual after mutation; Step S2311: Repeat steps S239-S2310 to continuously perform genetic mutation on the genes of offspring individuals a and b, obtaining a mutated optimal child individual each time a genetic mutation is performed, until a mutated optimal child individual has a genetic quality coefficient less than or equal to the target genetic quality coefficient; then output the mutated optimal child individual as the final optimal child individual; Step S2312: The attenuation coefficient corresponding to the gene of the final optimal sub-individual and delay In the input signal receiving model, the channel path is obtained i The corresponding optimal signal reception model; .

[0020] Step S3: After obtaining the optimal signal reception model corresponding to each channel path, the original signal from the transmitter is input into the optimal signal reception model, the received signal is output, the spectrum of the output signal is obtained using Fourier transform, and the spectrum is detected to obtain a candidate value of the code rate.

[0021] Step S3 specifically includes the following steps: Step S31: After obtaining the optimal signal reception model corresponding to each channel path, the original signal from the transmitter is input into the optimal signal reception model, the received signal is output, and Fourier transform is performed on the received signal; ; in, The original signal The spectrum, is the spectrum of the received signal, j is the imaginary unit, e is a natural constant, is the noise spectrum, f is the signal frequency; Step S32: Define the original signal The code rate is R , the original signal Spectrum exist There is a peak frequency at the receiving signal. The peak frequency in , and the candidate value of the code rate is determined according to the relationship between the peak frequency and the code rate .

[0022] Step S4: Adopting an adaptive filter to filter the received signal output by the optimal signal receiving model to obtain an output signal of the adaptive filter, and correcting the candidate value of the code rate based on Fourier transform to obtain an accurate code rate.

[0023] Step S4 specifically includes the following steps: Step S41: Adopting an adaptive filter to process the received signal output by the optimal signal receiving model Filter and get the output signal of the adaptive filter ; ; ; in, is the weight coefficient of the adaptive filter, The adaptive filter is in the signal time t A weight coefficient of -1, is the error signal, To receive the signal The ideal signal signal, is the modified step size factor of the weight coefficient; Step S42: Filter the output signal Perform Fourier transform again to get the output signal spectrum and detect the output signal The peak frequency of the spectrum, the candidate value of the code rate Make corrections to get the exact code rate .

[0024] The present invention takes into account the impact of multipath propagation on the signal, avoiding calculation errors caused by ignoring the multipath effect. Combined with adaptive filtering, it can correct and accurately calculate the code rate, significantly improving the accuracy of code rate calculation.

Claims

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

2. The method for accurately calculating the signal code rate according to claim 1, wherein: The signal receiving model under the multipath channel is: ； in, i is the channel path number, I is the number of channel paths, For the i The attenuation coefficient of each channel path, For the i The delay of the channel path, For delay The original signal from the transmitter after is additive white Gaussian noise, is the received signal, and t is the signal time.

3. The method for accurately calculating the signal code rate according to claim 2, wherein: The step S2 comprises: Step S21: Generate a pseudo-random signal sequence using a linear feedback shift register, where the pseudo-random signal sequence is based on autocorrelation of a generator polynomial model; ； in, is the output random signal, x is the signal variable, n is the order of the shift register, For the n -Feedback coefficient of 1st order shift register; Step S22: According to the length of the pseudo-random signal sequence L Determine the order of the shift register n ,satisfy , set the shift register to a non-zero initial state, and use the sequence element of the shift register , generating length L Pseudo-random signal sequence; ； in, u is the order number of the shift register, For the u The feedback coefficient of the shift register is k is the element number of the shift register sequence, The first bit of the shift register k - u sequence elements; Step S23: Length L A pseudo-random signal sequence is used as a training signal sequence. Each original training signal in the training signal sequence is output from each channel path to obtain a received training signal. The signal reception model is fitted using the original training signals and the received training signals. The attenuation coefficient and delay are optimized based on the cross-genetic mutation algorithm, and the optimal signal reception model corresponding to each channel path is output.

4. The method for accurately calculating the signal code rate according to claim 3, wherein: The 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 a received training signal. The original training signal and the output received training signal are used as a set of training data groups to obtain L A set of training data sets is used to input any two sets of training data sets into the multipath channel model, and a set of attenuation coefficients and delays are output. , l Number the attenuation coefficient and delay data groups; Step S232: Get the corresponding attenuation coefficient and delay data sets , as the first initial population, the decay coefficient and delay data set as the population individuals, the decay coefficient and delay as the genes of the individuals, respectively, For the attenuation coefficient and delay data sets; Step S233: Return to step S231, recombine any two different training data sets from the training signal sequence, input them into the multipath channel model, output the attenuation coefficients and delays corresponding to the recombined two training data sets, and obtain a second initial population; Step S234: setting the heritability of the crossover transformation, performing a crossover transformation on the genes between the individuals in the first initial population and the second initial population according to the heritability, exchanging the attenuation coefficients between the first initial population and the second initial population, and exchanging 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 forming the first generation genetic population A and the first generation genetic population B, to obtain offspring individual a of the first generation genetic population A and offspring individual b of the first generation genetic population B; Step S235: constructing an objective function for evaluating genetic quality; ； in, For the l The original training signal, For the l The received signal corresponding to the original training signal is is the genetic quality coefficient; Step S236: Input the individuals in the first initial population and the second initial population into the signal receiving model respectively, and use L The training signal output for each individual L The target function is used to calculate the genetic quality coefficient of each individual in the first initial population and the second initial population. 、 , is the first initial population The genetic quality coefficient of each individual, is the first in the second initial population The genetic quality coefficient of each individual; Step S237: Screening genetic quality coefficient 、 The minimum value in , minimum The corresponding individual is regarded as the optimal individual of the initial population; Step S238: Repeat steps S236-S237, input the offspring individual a of the first generation genetic population A and the offspring individual b of the first generation genetic population B into the signal receiving model respectively, calculate the genetic quality coefficient corresponding to each offspring individual a and offspring individual b using the objective function, and screen out the minimum value of the genetic quality coefficient corresponding to the optimal offspring individual ; Step S238: Compare minimum values With minimum value The size between like , then output the first-generation genetic population A and the first-generation genetic population B, and execute step S239; like , then return to step S234, based on the heritability of the crossover transformation, reuse the genes between different individuals in the first initial population and the second initial population for crossover transformation, and execute steps S234-S238. If the genes between the individuals in the first initial population and the second initial population have all been crossover transformed and none of them meet , the first initial population and the second initial population are directly used as the first-generation genetic population A and the first-generation genetic population B, and 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: setting the attenuation coefficient and the delayed mutation rate to perform genetic mutation on the genes of the offspring individuals a and b in the first-generation genetic population A and the first-generation genetic population B; ； in, are the attenuation coefficient and delay corresponding to the genes of offspring individual a and offspring individual b, respectively. are the attenuation coefficient and delay corresponding to the genes genetically mutated by offspring individuals a and b, respectively. are the variation rates of attenuation coefficient and delay, respectively; Step S2310: Genetic mutation of offspring individuals a and b is performed to obtain mutant offspring individuals. , mutant offspring individuals , the mutant offspring individuals , mutant offspring individuals Input the signal into the signal receiving model respectively, execute steps S236-S237, and output the optimal sub-individual after mutation; Step S2311: Repeat steps S239-S2310 to continuously perform genetic mutation on the genes of offspring individuals a and b, obtaining a mutated optimal child individual each time a genetic mutation is performed, until a mutated optimal child individual has a genetic quality coefficient less than or equal to the target genetic quality coefficient; then output the mutated optimal child individual as the final optimal child individual; Step S2312: The attenuation coefficient corresponding to the gene of the final optimal sub-individual and delay In the input signal receiving model, the channel path is obtained i The corresponding optimal signal reception model; 。 5. The method for accurately calculating the signal code rate according to claim 4, wherein: The step S3 comprises: Step S31: After obtaining the optimal signal reception model corresponding to each channel path, the original signal from the transmitter is input into the optimal signal reception model, the received signal is output, and Fourier transform is performed on the received signal; ； in, The original signal The spectrum, is the spectrum of the received signal, j is the imaginary unit, e is a natural constant, is the noise spectrum, f is the signal frequency; Step S32: Define the original signal The code rate is R , the original signal Spectrum exist There is a peak frequency at the receiving signal. The peak frequency in , and the candidate value of the code rate is determined according to 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: The step S4 comprises: Step S41: Adopting an adaptive filter to process the received signal output by the optimal signal receiving model Filter and get the output signal of the adaptive filter ; ； ； in, is the weight coefficient of the adaptive filter, The adaptive filter is in the signal time t A weight coefficient of -1, is the error signal, To receive the signal The ideal signal signal, is the modified step size factor of the weight coefficient; Step S42: Filter the output signal Perform Fourier transform again to get the output signal spectrum and detect the output signal The peak frequency of the spectrum, the candidate value of the code rate Make corrections to get the exact code rate .

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