A method for adjusting base station signals based on user access

Abstract

The present invention relates to the field of signal processing technology, and solves the technical problems in the prior art of poor filtering effect on mixed signals and low signal correction accuracy. In particular, it relates to a method for adjusting base station signals based on user access, the method comprising the following steps: S1, obtaining a source signal received by a base station, and preprocessing the source signal to obtain a preprocessed signal x(t); S2, performing time transformation on the preprocessed signal x(t) to obtain a time-varying signal SP(t, f), and obtaining an instantaneous frequency pi(t) according to a peak value of the distribution of the time-varying signal SP(t, f); the present invention performs secondary processing on the mixed signal, first filtering out the clutter, and then performing detailed filtering on the 5G signal, thereby reducing the influence of noise on the signal time-frequency diagram, improving the signal-to-noise ratio of the signal to be measured, and improving the instantaneous frequency estimation accuracy, and greatly improving the signal-to-noise ratio of the reconstructed signal time-frequency distribution.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a method for adjusting a base station signal based on user access. Background Art

[0002] A base station generally refers to a radio coverage area within which information is transmitted between a mobile communication exchange center and mobile terminals. Although 5G network construction is accelerating globally, due to various limitations, such as technology, funding, and policies, 5G network coverage is not ubiquitous. In some areas, especially remote or rural areas, 5G network coverage may be relatively weak or non-existent. Therefore, in these areas, users may still need to rely on 4G signals to meet their communication needs. Therefore, in existing technologies, 4G and 5G signals are generally transmitted mixedly, and the signals need to be filtered after acquisition. For users in most cities who generally use 5G signals, certain noise is generated in the process of filtering out the 4G signal. Existing technologies are not effective in filtering out noise, and existing signal correction based on 5G signals has low accuracy, affecting signal transmission quality. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the present invention provides a method for adjusting base station signals based on user access, which solves the technical problems in the existing technology of poor filtering effect on mixed signals and low signal correction accuracy, thereby achieving the purpose of improving the filtering effect of mixed signals and optimizing the accuracy of signal correction.

[0004] To solve the above technical problems, the present invention provides the following technical solution: a method for adjusting a base station signal based on user access, the method comprising the following steps:

[0005] S1. Obtain the source signal received by the base station and preprocess the source signal to obtain a preprocessed signal x(t);

[0006] S2. Perform time transformation on the preprocessed signal x(t) to obtain a time-varying signal SP(t, f), and obtain the instantaneous frequency pi(t) based on the peak value of the distribution of the time-varying signal SP(t, f);

[0007] S3. Calculate the detected reconstructed signal cg2(t) based on the instantaneous frequency pi(t) and determine the iteration threshold D based on the historical data of the base station. n , and based on the iterative threshold D n Determine the reconstructed signal Cg(t);

[0008] If cg2(t)≥D n , then the iteration ends and the reconstructed signal Cg(t) is obtained;

[0009] If cg2(t) <D n , then return to step S2;

[0010] S4. Obtain a filtered signal Lb(t) through time transformation according to the reconstructed signal Cg(t), and obtain multiple discrete baseband signals of the filtered signal Lb(t) through two different channels;

[0011] S5. Calculate the offset value Py based on the discrete baseband signal s1 and Py s2 ;

[0012] S6, according to the offset value Py s1 and Py s2 Calculate the offset phase W, and calculate the delay time Jt according to the offset phase W;

[0013] S7. Transmit the delay time Jt to the processing module of the base station, and the processing module corrects the signal phase according to the delay time Jt.

[0014] Preferably, in step S1, the specific implementation steps are as follows:

[0015] S11. Separate the source signal into 5G signal and other signals by fractional Fourier transform method;

[0016] S12. Set the filtering frequency range u of the bandpass filter according to the frequency of the 5G signal, where u<2.6 GHz;

[0017] S13. Preprocess the source signal through a bandpass filter to obtain a preprocessed signal x(t).

[0018] Preferably, in step S2, the specific implementation steps are as follows:

[0019] S21. The pre-processed signal x(t) is transformed by the deep transform method to obtain the analytical signal Jx(t), which is expressed as:

[0020]

[0021] Where Jx(t) represents the analytical signal, c represents the time delay variable of the preprocessed signal x(t), and t represents the time variable of the preprocessed signal x(t);

[0022] S22. Obtain the frequency f of the analytical signal Jx(t) by the average period method;

[0023] S23. Perform time transformation on the analytical signal Jx(t) to obtain the time-varying signal SP(t, f), which is expressed as:

[0024]

[0025] Wherein, SP(t, f) represents the time-varying signal, g(ut) represents the smoothing window function of the difference between the smoothing variable u and the time variable t, Represents the analysis signal The complex conjugate of , h(c) represents the smoothing window function in the time domain, j represents the imaginary unit, and e represents the Euler number;

[0026] S24. Extract the instantaneous frequency pi(t) from several peaks of the time-varying signal SP(t, f). The expression is:

[0027] pi(t)=arg{max[SP(t,f)]}

[0028] Here, max[SP(t, f)] represents the peak value of the time-varying signal SP(t, f).

[0029] Preferably, in step S3, the specific implementation steps are as follows:

[0030] S31, obtain the iterative threshold D by the maximum likelihood estimation method based on the historical data of the base station n ;

[0031] S32. Calculate the instantaneous phase according to the instantaneous frequency pi(t) The calculation formula is:

[0032]

[0033] in, represents the instantaneous phase of the instantaneous frequency pi(t);

[0034] S33, according to the instantaneous phase Calculate the initial reconstructed signal cg1(t) using the following formula:

[0035]

[0036] Where cg1(t) represents the initial reconstructed signal;

[0037] S34. Extract the time-varying frequency pb(t) from several peaks of the initial reconstructed signal cg1(t). The expression is:

[0038] pb(t)=arg{max[cg1(t,f)]}

[0039] Where pb(t) represents the time-varying frequency;

[0040] S35 , substitute the initial reconstructed signal cg1 (t) and the time-varying frequency pb (t) into steps S32 - S33 to obtain the detected reconstructed signal cg2 (t).

[0041] Preferably, in step S4, the specific implementation steps are as follows:

[0042] S41. Select two different channels from multiple channels of the base station, namely channel m and channel n;

[0043] S42. Through the amplifier and filter in channel m, the discrete baseband signal E of the filtered signal Lb(t) in channel m can be obtained. m (t) and F m (t), the expression is:

[0044]

[0045] Among them, kT l Indicates the kth sampling period, k=0,1,…,R-1, R represents the number of signal sampling points, Indicates the initial phase, A d represents the amplitude of the filtered signal Lb(t) in channel m, and f represents the frequency of the filtered signal Lb(t);

[0046] S43, the discrete baseband signal E of the filtered signal Lb(t) in channel n can be obtained through the amplifier and filter in channel n. n (t) and F n (t), the expression is:

[0047]

[0048] Among them, A o represents the amplitude of the filtered signal Lb(t) in channel n, kT p represents the kth sampling period, Indicates the preliminary measured phase change.

[0049] Preferably, in step S5, the specific implementation steps are as follows:

[0050] S51, according to the discrete baseband signal E m (t), E n (t) and F n (t) Get the first interconversion function Hb m (τ) and the second interconversion function Hb n (τ);

[0051] S52, let the first interconversion function Hb m (τ) and the second interconversion function Hb n (τ) with τ=0, and calculate the first interconversion function Hb respectively m (τ) and the second interconversion function Hb n (τ) offset value Py s1 and Py s2 .

[0052] Preferably, the first interconversion function Hb m (τ) and the second interconversion function Hb n The expressions of (τ) are:

[0053]

[0054]

[0055] Where R represents the number of signal sampling points, τ represents the first interconversion function Hb m (τ) and the second interconversion function Hb n (τ) is the independent variable.

[0056] Preferably, the offset value Py s1 and Py s2 The calculation formulas are:

[0057]

[0058] Among them, Py s1 and Py s2 Denote the first interconversion function Hb respectively m (τ) and the second interconversion function Hb n (τ) offset value.

[0059] Preferably, in step S6, the specific implementation steps are as follows:

[0060] S61, according to the offset value Py s1 and Py s2 Calculate the offset phase W using the following formula:

[0061]

[0062] Where W represents the offset phase;

[0063] S62. Calculate the delay time Jt according to the offset phase W. The calculation formula is:

[0064]

[0065] Wherein, Jt represents the delay time.

[0066] By means of the above technical solution, the present invention provides a method for adjusting base station signals based on user access, which has at least the following beneficial effects:

[0067] 1. The present invention performs secondary processing on the mixed signal, first filtering out the clutter to obtain the 5G signal, and then performing detailed filtering on the 5G signal. When the signal is interfered with by noise, especially when the signal-to-noise ratio is low, the time-frequency diagram is blurred, and the energy value corresponding to the signal frequency point at certain moments may not be the maximum value. The error of estimating the instantaneous frequency of the signal by only detecting the peak of the time-frequency diagram once is large. In order to reduce the impact of noise on the signal time-frequency diagram and improve the signal-to-noise ratio of the signal to be measured, the method of iteratively detecting the time-frequency peak is used to improve the instantaneous frequency estimation accuracy, and the signal-to-noise ratio of the reconstructed signal time-frequency distribution is greatly improved.

[0068] 2. The present invention can improve the accuracy of signal correction by performing correction operations on different signal channels. It can not only improve the quality of signal correction, but also have a certain anti-interference ability against excess noise, thereby improving the signal-to-noise ratio of the signal while also improving the stability of transmission. The collaborative processing of the dual channels also improves the signal processing speed.

[0069] 3. The present invention obtains multiple discrete signals through dual channels, which can quickly calculate and accurately estimate the phase offset, making the signal correction result more accurate and improving the accuracy of signal correction. The high-accuracy correction can also improve system performance and response speed, effectively enhancing the robustness of the system during operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0071] Figure 1 The present invention is a flow chart of a method for adjusting a base station signal based on user access. DETAILED DESCRIPTION

[0072] To make the above-mentioned objectives, features, and advantages of the present invention more clearly understood, the present invention is further described below in detail with reference to the accompanying drawings and specific embodiments. This will enable a full understanding of how this application uses technical means to solve technical problems and achieve technical effects, and to implement the invention accordingly.

[0073] Since the technical problems of poor filtering effect of mixed signals and low signal correction accuracy in the existing technology are solved, please refer to Figure 1 This embodiment provides a method for adjusting a base station signal based on user access, which can improve the filtering effect of mixed signals and optimize the accuracy of signal correction. The method includes the following steps:

[0074] S1. Obtain the source signal received by the base station and preprocess the source signal to obtain a preprocessed signal x(t). The source signal is a mixed signal containing multiple signals. Since existing communication needs require the simultaneous presence of multiple signals, signals are often transmitted with multiple frequency signals mixed together. Filtering is required for the signal. In step S1, the specific implementation steps are as follows:

[0075] S11. Separate the source signal into 5G signals and other signals through the fractional Fourier transform method. The fractional Fourier transform method refers to a time-frequency transformation method. As the order continuously increases from 0 to 1, it shows all the changing characteristics of the signal from the time domain to the frequency domain, and then distinguishes the signal through the changing characteristics. The fractional Fourier transform method can directly separate the 5G signal and other signals without the involvement of other steps. The fractional Fourier transform method is a common method for distinguishing signals and will not be described in detail here.

[0076] S12. Set the filtering frequency range u of the bandpass filter according to the frequency of the 5G signal, where u<2.6GHz. A bandpass filter is a device that allows waves in a specific frequency band to pass through while shielding other frequency bands. The pass frequency of the bandpass filter needs to be set before use. Since the clutter includes 4G signals and other signals, the average frequency is 1.8GHz, which is much smaller than the 2.6GHz of the 5G signal. Therefore, the frequency allowed to pass is set to less than 2.6GHz.

[0077] S13. Preprocess the source signal through a bandpass filter to obtain a preprocessed signal x(t). In this step, the bandpass filter only needs to be used to obtain the desired effect without additional steps. The bandpass filter is a common filtering method and will not be elaborated here. Through the processing of the bandpass filter, the signal can be preliminarily filtered to filter out the clutter in the signal and obtain a preliminary 5G signal. Although there is still some clutter in the 5G signal and further processing is required, the preliminary preprocessing can help the subsequent steps to quickly limit the frequency range of the signal, reduce the processing volume of the subsequent filtering steps, and improve the overall filtering work efficiency.

[0078] S2. Perform time transformation on the preprocessed signal x(t) to obtain a time-varying signal SP(t, f), and obtain the instantaneous frequency pi(t) based on the peak value of the distribution of the time-varying signal SP(t, f). Conventional signal processing methods have disadvantages such as complex calculations and being greatly affected by noise. Therefore, conventional processing methods have limitations in practical applications. In step S2, the specific implementation steps are as follows:

[0079] S21. The pre-processed signal x(t) is transformed by the deep transform method to obtain the analytical signal Jx(t), which is expressed as:

[0080]

[0081] Among them, Jx(t) represents the analytical signal, c represents the time delay variable of the preprocessed signal x(t), and t represents the time variable of the preprocessed signal x(t); the deep transformation method can generate a functional linear operator with the same domain of definition for the function. Through this method, the analytical expression of the signal can be derived, that is, the analytical signal Jx(t), and then the subsequent steps can be processed through the obtained analytical signal Jx(t).

[0082] S22. Obtain the frequency f of the analytical signal Jx(t) through the average period method; the average period method is based on the periodic characteristics of the signal, and obtains the average frequency of the signal by obtaining the average value of multiple periods. It has high accuracy and high computational efficiency. In this step, the frequency f can be obtained only through the average period method, and no additional steps are required. The average period method is a very common frequency acquisition method and will not be described in detail here.

[0083] S23. Perform time transformation on the analytical signal Jx(t) to obtain the time-varying signal SP(t, f), which is expressed as:

[0084]

[0085] Wherein, SP(t, f) represents the time-varying signal, g(ut) represents the smoothing window function of the difference between the smoothing variable u and the time variable t, Represents the analysis signal The complex conjugate of h(c) represents the smoothing window function in the time domain, j represents the imaginary unit, and e represents the Euler number. In the case of low signal-to-noise ratio, the interference caused by noise will lead to deviations in the instantaneous frequency information detected by the time-frequency peak. The ideal estimated value of the instantaneous frequency cannot be obtained through a single estimation. Since the instantaneous frequency estimate obtained for the first time contains all the time-varying frequency information of the signal, the instantaneous frequency obtained by the initial estimation can be used to construct a reconstructed signal containing the previous instantaneous frequency estimation information, so as to facilitate the analysis and operation of the subsequent reconstructed signal.

[0086] S24. Extract the instantaneous frequency pi(t) from several peaks of the time-varying signal SP(t, f). The expression is:

[0087] pi(t)=arg{max[SP(t,f)]}

[0088] Among them, max[SP(t, f)] represents the peak value of the time-varying signal SP(t, f). The time-frequency distribution of the reconstructed signal is recalculated, and the time-frequency peak detection is performed on the recalculated signal so that the instantaneous frequency of the signal can be estimated again in the subsequent steps to increase the accuracy. This step increases the accuracy of the signal by performing multiple transformations on the preprocessed signal x(t), and can make the signal have a certain anti-interference ability, which is convenient for subsequent further analysis and iteration, thereby obtaining a more accurate signal, and indirectly improving the accuracy of signal analysis.

[0089] S3. Calculate the detected reconstructed signal cg2(t) based on the instantaneous frequency pi(t) and determine the iteration threshold D based on the historical data of the base station. n , and based on the iterative threshold D n Determine the reconstructed signal Cg(t);

[0090] If cg2(t)≥D n , then the iteration ends and the reconstructed signal Cg(t) is obtained;

[0091] If cg2(t) <D n , then return to step S2; iterate through the obtained reconstructed signal to gradually improve the accuracy of the signal video distribution. In step S3, the specific implementation steps are as follows:

[0092] S31, obtain the iterative threshold D by the maximum likelihood estimation method based on the historical data of the base station n ; Maximum likelihood estimation is a statistical method used to estimate the threshold parameters of a probability model by obtaining the iterative threshold D n It can play a role in comparison and ending iteration in subsequent iterative steps, thereby obtaining a reconstructed signal with better accuracy.

[0093] S32. Calculate the instantaneous phase according to the instantaneous frequency pi(t) The calculation formula is:

[0094]

[0095] in, Indicates the instantaneous phase of the instantaneous frequency pi(t); the instantaneous phase indicates the phase size displayed in a short period of time.

[0096] S33, according to the instantaneous phase Calculate the initial reconstructed signal cg1(t) using the following formula:

[0097]

[0098] Where cg1(t) represents the initial reconstructed signal; the initial reconstructed signal cg1(t) is at the instantaneous phase This is an initial signal that will be gradually optimized through iterations in the later stages.

[0099] S34. Extract the time-varying frequency pb(t) from several peaks of the initial reconstructed signal cg1(t). The expression is:

[0100] pb(t)=arg{max[cg1(t,f)]}

[0101] Here, pb(t) represents the time-varying frequency. This step is similar to step S24, and the time-varying frequency is extracted again from the reconstructed signal to facilitate the calculation in subsequent steps.

[0102] S35. Substitute the initial reconstructed signal cg1(t) and the time-varying frequency pb(t) into steps S32-S33 to obtain the detected reconstructed signal cg2(t). By performing secondary processing on the mixed signal, first filter out the clutter to obtain the 5G signal, and then perform detailed filtering on the 5G signal. When the signal is interfered with by noise, especially when the signal-to-noise ratio is low, the time-frequency diagram is blurred. The energy value corresponding to the signal frequency point at certain moments may not be the maximum value. The error of estimating the instantaneous frequency of the signal by only detecting the peak of the time-frequency diagram once is large. In order to reduce the impact of noise on the signal time-frequency diagram and improve the signal-to-noise ratio of the signal to be measured, the instantaneous frequency estimation accuracy is improved by iteratively detecting the time-frequency peak, and the signal-to-noise ratio of the reconstructed signal time-frequency distribution is greatly improved.

[0103] S4. The filtered signal Lb(t) is obtained by time transformation based on the reconstructed signal Cg(t), and multiple discrete baseband signals of the filtered signal Lb(t) are obtained through two different channels. Since the 5G signal is a discrete digital signal, it needs to be converted into a discrete baseband signal for further analysis. In step S4, the specific implementation steps are as follows:

[0104] S41. Select two different channels from multiple channels of the base station, namely channel m and channel n;

[0105] S42. Through the amplifier and filter in channel m, the discrete baseband signal E of the filtered signal Lb(t) in channel m can be obtained. m (t) and F m (t), the expression is:

[0106]

[0107] Among them, kT l Indicates the kth sampling period T l , k=0,1,…,R-1, R represents the number of signal sampling points, Indicates the initial phase, A drepresents the amplitude of the filtered signal Lb(t) in channel m, and f represents the frequency of the filtered signal Lb(t); this step requires sampling period T within a certain period of time. l Perform multiple sampling and obtain the number of sampling points, the amplitude A of the filtered signal Lb(t) d The sum frequency f can be obtained through the amplifier and filter in channel m. In this step, for the sake of accuracy, the filtered signal Lb(t) is filtered again to ensure the accuracy of the signal. Channel m and channel n are two different channels. Through this dual-channel analysis method, the accuracy of signal correction can be improved.

[0108] S43, the discrete baseband signal E of the filtered signal Lb(t) in channel n can be obtained through the amplifier and filter in channel n. n (t) and F n (t), the expression is:

[0109]

[0110] Among them, A o represents the amplitude of the filtered signal Lb(t) in channel n, kT p represents the kth sampling period, Indicates the preliminary measured phase change. Although it can be obtained, it is a preliminary measurement and its accuracy needs to be corrected in subsequent steps.

[0111] S5. Calculate the offset value Py based on the discrete baseband signal s1 and Py s2 After obtaining multiple discrete baseband signals, it is necessary to further calculate the offset value to facilitate the correction of subsequent steps. In step S5, the specific implementation steps are as follows:

[0112] S51, according to the discrete baseband signal E m (t), E n (t) and F n (t) Get the first interconversion function H m (τ) and the second interconversion function Hb n (τ), the first interconversion function Hb m (τ) and the second interconversion function Hb n The expressions of (τ) are:

[0113]

[0114] Where R represents the number of signal sampling points, τ represents the first interconversion function Hb m (τ) and the second interconversion function Hb nThe independent variable of (τ), the number of sampling points, has the same meaning as the number of sampling points in step S42. The signals of the two channels are associated together through the mutual transformation function to facilitate phase correction.

[0115] S52, let the first interconversion function Hb m (τ) and the second interconversion function Hb n (τ) with τ=0, and calculate the first interconversion function Hb respectively m (τ) and the second interconversion function Hb n (τ) offset value Py s1 and Py s2 , offset value Py s1 and Py s2 The calculation formulas are:

[0116]

[0117] Among them, Py s1 and Py s2 Denote the first interconversion function Hb respectively m (τ) and the second interconversion function Hb n The offset value of (τ) can be used to calculate the offset phase, which is convenient for correction. By obtaining multiple discrete signals through dual channels, the phase offset can be quickly calculated and accurately estimated, making the signal correction result more accurate and improving the accuracy of signal correction. High-accuracy correction can also improve system performance and response speed, and effectively enhance the robustness of the system during operation.

[0118] S6, according to the offset value Py s1 and Py s2 Calculate the offset phase W, and calculate the delay time Jt based on the offset phase W; the delay time can be further calculated by the offset phase. In step S6, the specific implementation steps are as follows:

[0119] S61, according to the offset value Py s1 and Py s2 Calculate the offset phase W using the following formula:

[0120]

[0121] Wherein, W represents the offset phase; the offset phase can be understood as the offset angle of the phase.

[0122] S62. Calculate the delay time Jt according to the offset phase W. The calculation formula is:

[0123]

[0124] Among them, Jt represents the delay time. The specific delay time of the phase can be calculated through the phase offset angle, which is convenient for correction. By correcting the delay time and offset angle, the signal can be flexibly and accurately corrected. By performing correction operations on different signal channels, the accuracy of signal correction can be improved, which not only improves the quality of signal correction, but also has a certain anti-interference ability against excess noise, thereby improving the signal-to-noise ratio of the signal while improving the stability of transmission. The collaborative processing of the dual channels also improves the signal processing speed.

[0125] S7. The delay time Jt is transmitted to the processing module of the base station. The processing module completes the correction of the signal phase according to the delay time Jt. After obtaining the delay time Jt and the offset phase W, the signal processing module of the base station can complete the phase correction and other corrections of the signal according to the delay time Jt.

[0126] Those skilled in the art will appreciate that all or part of the steps in the above-described embodiment methods can be accomplished by instructing the relevant hardware through a program. Therefore, the present application may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] The above embodiments provide a detailed introduction to the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A method for adjusting a base station signal based on user access, characterized in that: The method comprises the following steps: S1. Obtain the source signal received by the base station and preprocess the source signal to obtain a preprocessed signal x(t); S2. Perform time transformation on the preprocessed signal x(t) to obtain a time-varying signal SP(t, f), where f is the frequency, and obtain the instantaneous frequency pi(t) based on the peak value of the distribution of the time-varying signal SP(t, f); S3. Calculate the detected reconstructed signal cg2(t) based on the instantaneous frequency pi(t) and determine the iteration threshold D based on the historical data of the base station. n , and based on the iterative threshold D n Determine the reconstructed signal Cg(t); If cg2(t)≥D n , then the iteration ends and the reconstructed signal Cg(t) is obtained; If cg2(t) <D n , then return to step S2; S4. Obtain a filtered signal Lb(t) through time transformation according to the reconstructed signal Cg(t), and obtain a discrete baseband signal of the filtered signal Lb(t) through two different channels; S5. Calculate the offset value Py based on the discrete baseband signal s1 and Py s2 ; S6, according to the offset value Py s1 and Py s2 Calculate the offset phase W, and calculate the delay time Jt according to the offset phase W; S7. Transmit the delay time Jt to the processing module of the base station, and the processing module corrects the signal phase according to the delay time Jt.

2. The method for adjusting base station signals based on user access according to claim 1, characterized in that: In step S1, the specific implementation steps are as follows: S11. Separate the source signal into 5G signal and other signals by fractional Fourier transform method; S12. Set the filtering frequency range u of the bandpass filter according to the frequency of the 5G signal, where u is less than 2.6 GHz. S13. Preprocess the source signal through a bandpass filter to obtain a preprocessed signal x(t).

3. The method for adjusting base station signals based on user access according to claim 1, characterized in that: In step S2, the specific implementation steps are as follows: S21. The pre-processed signal x(t) is transformed by the deep transform method to obtain the analytical signal Jx(t), which is expressed as: Where Jx(t) represents the analytical signal, c represents the time delay variable of the preprocessed signal x(t), and t represents the time variable of the preprocessed signal x(t); S22. Obtain the frequency f of the analytical signal Jx(t) by the average period method; S23. Perform time transformation on the analytical signal Jx(t) to obtain the time-varying signal SP(t, f), which is expressed as: Wherein, SP(t, f) represents the time-varying signal, g(ut) represents the smoothing window function of the difference between the smoothing variable u and the time variable t, Represents the analysis signal The complex conjugate of , h(c) represents the smoothing window function in the time domain, j represents the imaginary unit, and e represents the Euler number; S24. Extract the instantaneous frequency pi(t) from several peaks of the time-varying signal SP(t, f). The expression is: pi(t)=arg{max[SP(t,f)]} Here, max[SP(t, f)] represents the peak value of the time-varying signal SP(t, f).

4. The method for adjusting base station signals based on user access according to claim 1, characterized in that: In step S3, the specific implementation steps are as follows: S31, obtain the iterative threshold D by the maximum likelihood estimation method based on the historical data of the base station n ; S32. Calculate the instantaneous phase according to the instantaneous frequency pi(t) The calculation formula is: in, represents the instantaneous phase of the instantaneous frequency pi(t); S33, according to the instantaneous phase Calculate the initial reconstructed signal cg1(t) using the following formula: Where cg1(t) represents the initial reconstructed signal; S34. Extract the time-varying frequency pb(t) from several peaks of the initial reconstructed signal cg1(t). The expression is: pb(t)=arg{max[cg1(t,f)]} Where pb(t) represents the time-varying frequency; S35 , substitute the initial reconstructed signal cg1 (t) and the time-varying frequency pb (t) into steps S32 - S33 to obtain the detected reconstructed signal cg2 (t).

5. The method for adjusting base station signals based on user access according to claim 1, characterized in that: In step S4, the specific implementation steps are as follows: S41. Select two different channels from multiple channels of the base station, namely channel m and channel n; S42. Through the amplifier and filter in channel m, the discrete baseband signal E of the filtered signal Lb(t) in channel m can be obtained. m (t) and F m (t), the expression is: Among them, kT l Indicates the kth sampling period, k=0, 1,…, R-1, R represents the number of signal sampling points, Indicates the initial phase, A d represents the amplitude of the filtered signal Lb(t) in channel m, and f represents the frequency of the filtered signal Lb(t); S43, the discrete baseband signal F of the filtered signal Lb(t) in channel n can be obtained through the amplifier and filter in channel n. n (t) and F n (t), the expression is: Among them, A o represents the amplitude of the filtered signal Lb(t) in channel n, kT p represents the kth sampling period, Indicates the preliminary measured phase change.

6. The method for adjusting base station signals based on user access according to claim 5, characterized in that: In step S5, the specific implementation steps are as follows: S51, according to the discrete baseband signal E m (t), E n (t) and F n (t) Get the first interconversion function Hb m (τ) and the second interconversion function Hb n (τ); S52, let the first interconversion function Hb m (τ) and the second interconversion function Hb n (τ) with τ=0, and calculate the first interconversion function Hb respectively m (τ) and the second interconversion function Hb n (τ) offset value Py s1 and Py s2 .

7. The method for adjusting base station signals based on user access according to claim 6, characterized in that: The first interconversion function Hb m (τ) and the second interconversion function Hb n The expressions of (τ) are: Where R represents the number of signal sampling points, τ represents the first interconversion function Hb m (τ) and the second interconversion function Hb n (τ) is the independent variable.

8. The method for adjusting base station signals based on user access according to claim 6, characterized in that: The offset value Py s1 and Py s2 The calculation formulas are: Among them, Py s1 and Py s2 Denote the first interconversion function Hb respectively m (τ) and the second interconversion function Hb n The offset value of (τ), Indicates the preliminary measured phase change.

9. The method for adjusting base station signals based on user access according to claim 1, characterized in that: In step S6, the specific implementation steps are as follows: S61, according to the offset value Py s1 and Py s2 Calculate the offset phase W using the following formula: Where W represents the offset phase; S62. Calculate the delay time Jt according to the offset phase W. The calculation formula is: Wherein, Jt represents the delay time.

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