A radio intelligent filtering method based on wireless base station

Abstract

The present invention relates to the field of radio signal transmission technology, and specifically discloses a radio intelligent filtering method based on a wireless base station. The method comprises the steps of signal acquisition, digital conversion, spectrum analysis, time domain and frequency domain filtering, transmission path adjustment, etc. By combining the technology of time domain filtering with frequency domain filtering and an adaptive digital signal conversion strategy and signal transmission path adjustment, the radio signal can maintain higher stability and anti-interference ability in a complex wireless environment, thereby effectively improving the quality of the communication signal and the overall stability of the system.

Description

Technical Field

[0001] The invention relates to the technical field of radio signal transmission, and in particular to a radio intelligent filtering method based on a wireless base station. Background Art

[0002] As wireless communication networks become increasingly complex, especially signal transmission issues in high-density environments, traditional fixed filters and signal transmission optimization technologies often have difficulty coping with changing radio environments. Radio signals are affected by a variety of factors during propagation, including multipath fading, time-varying interference, spectrum congestion, etc. These factors can lead to reduced signal quality, lower transmission rates, and even communication interruptions.

[0003] In view of this, there is an urgent need for a radio intelligent filtering method based on wireless base stations to ensure the stability of signals and the reliability of communication systems. Summary of the invention

[0004] The purpose of the present invention is to provide a radio intelligent filtering method based on a wireless base station to solve the following technical problems: It aims to effectively reduce the interference components in the signal by introducing a filtering technology that combines the time domain and the frequency domain, improve the stability of the signal through filtering, and thus achieve stable transmission of the signal in a complex environment.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A radio intelligent filtering method based on a wireless base station, the method comprising the following steps:

[0007] Acquiring radio signals through antenna arrays of wireless base stations;

[0008] Performing a signal conversion basic analysis based on the radio signal to determine a digital signal conversion preprocessing strategy;

[0009] Performing signal gain control on the radio signal based on the digital signal conversion preprocessing strategy to obtain a gain signal;

[0010] Performing digital conversion on the gain signal to obtain a digital signal;

[0011] Performing spectrum analysis on the digital signal, and determining a target filter for filtering the digital signal according to the result of the spectrum analysis; wherein the target filter includes a frequency domain filter and a time domain filter, and is expressed by the following formula:

[0012] ;

[0013] Among them, y(t) represents the filtering result, F -1represents an inverse Fourier transform, F{x(t)} represents the Fourier transform of the input digital signal, F{h(t)} represents the Fourier transform of the time domain filter, and h opt (t) represents the time domain optimization filter, F{h(t)·h opt (t)} is the frequency domain representation of the time domain filter and the time domain optimized filter;

[0014] Using the target filter to filter the digital signal to obtain a target signal;

[0015] Determining a signal transmission path according to a network state and a signal quality of the target signal;

[0016] The target signal is transmitted using the signal transmission path.

[0017] As a further solution of the present invention, performing spectrum analysis on the digital signal and determining a target filter for filtering the digital signal according to a result of the spectrum analysis comprises:

[0018] Performing spectrum density estimation and interference identification based on the digital signal to determine the result of the spectrum analysis; wherein the spectrum analysis result includes frequency components, noise, interference frequency bands, and effective signal frequency bands;

[0019] Based on the spectrum analysis result, determining effective parameters in the digital signal;

[0020] Based on the effective parameters, the target filter for filtering the digital signal is determined.

[0021] As a further solution of the present invention, determining the target filter for filtering the digital signal based on the effective parameters includes:

[0022] Determining a transformation formula F{x(t)} for frequency domain conversion; the transformation formula F{x(t)} is used to perform frequency domain conversion on the effective parameters to obtain frequency domain data;

[0023] Determine a frequency domain filter H(f) for performing frequency domain filtering on the frequency domain data; wherein the frequency domain filter H(f) is used to perform frequency domain filtering on the frequency domain data to obtain frequency domain filtered data;

[0024] Determine a time domain optimization filter h for optimizing the quality of the frequency domain filtering data opt (t);

[0025] Based on the transformation formula F{x(t)}, the frequency domain filter H(f) and the time domain optimization filter h opt(t), determine the target filter.

[0026] As a further solution of the present invention, the transformation formula F{x(t)}, the frequency domain filter H(f) and the time domain optimization filter h opt (t), determining the target filter, comprising:

[0027] Convert the time series filter h(t) to the frequency domain to obtain a frequency domain representation H(f) of the time series filter h(t);

[0028] The time domain optimization filter h opt (t) is converted to the frequency domain to obtain the time domain optimized filter h opt The frequency domain representation of (t) is H opt (f);

[0029] The frequency domain representation H(f) of the time series filter h(t) and the time domain optimized filter h opt The frequency domain representation of (t) is H opt (f) Combining the time domain filter and the time domain optimization filter to obtain the frequency domain representation F{h(t)·h opt (t)};

[0030] Based on the transformation formula F{x(t)}, the time domain filter and the frequency domain representation F{h(t)·h opt (t)} and the transformation formula F{x(t)}, determine the target filter.

[0031] As a further solution of the present invention, determining the target filter for filtering the digital signal based on the effective parameter includes:

[0032] Based on the effective parameters, determining whether there is spectrum leakage;

[0033] In response to the spectrum leakage, performing windowing processing on the digital signal;

[0034] Based on the result of the windowing process, the target filter is determined; wherein the target filter is:

[0035] ;

[0036] Where w(t) is the applied window function.

[0037] As a further solution of the present invention, the step of filtering the digital signal using the target filter to obtain the target signal includes:

[0038] Determining the signal size of the digital signal;

[0039] In response to the signal size exceeding a preset threshold, performing block processing on the digital signal;

[0040] Using the target filter to perform filtering processing on the digital signal blocks after the block processing respectively, to obtain multiple block filtering results;

[0041] The multiple block filtering results are combined to obtain the target signal.

[0042] As a further solution of the present invention, the signal conversion basic analysis based on the radio signal is performed to determine the digital signal conversion preprocessing strategy, including:

[0043] Performing a signal conversion basic analysis based on the radio signal to determine an optimization target for the radio signal;

[0044] Based on the optimization target, the digital signal conversion preprocessing strategy is determined.

[0045] A radio intelligent filtering system based on a wireless base station, the system comprising:

[0046] A signal acquisition module, used to acquire radio signals through an antenna array of a wireless base station;

[0047] A strategy determination module, configured to perform a signal conversion basic analysis based on the radio signal and determine a digital signal conversion preprocessing strategy;

[0048] A gain control module, used for performing signal gain control on the radio signal based on the digital signal conversion preprocessing strategy to obtain a gain signal;

[0049] A digital conversion module, used for performing digital conversion on the gain signal to obtain a digital signal;

[0050] A filter determination module is used to perform spectrum analysis on the digital signal, and determine a target filter for filtering the digital signal according to the result of the spectrum analysis; wherein the target filter includes a frequency domain filter and a time domain filter, and is expressed by the following formula:

[0051] ;

[0052] Among them, y(t) represents the filtering result, F -1 represents an inverse Fourier transform, F{x(t)} represents the Fourier transform of the input digital signal, F{h(t)} represents the Fourier transform of the time domain filter, and h opt (t) represents the time domain optimization filter, F{h(t)·h opt (t)} is the frequency domain representation of the time domain filter and the time domain optimized filter;

[0053] A filtering processing module, used to filter the digital signal using the target filter to obtain a target signal;

[0054] A transmission path determination module, used to determine a signal transmission path according to a network state and a signal quality of the target signal;

[0055] A transmission module is used to transmit the target signal using the signal transmission path.

[0056] Beneficial effects of the present invention: The present invention performs accurate signal conversion basic analysis on radio signals and designs intelligent digital signal preprocessing strategies, and uses a combination of time domain filtering and frequency domain filtering technology, an adaptive digital signal conversion strategy, and signal transmission path adjustment to enable radio signals to maintain higher stability and anti-interference capabilities in complex wireless environments, thereby effectively improving the signal quality, transmission efficiency, reliability, and adaptability of wireless communication systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The present invention will be further described below in conjunction with the accompanying drawings.

[0058] Figure 1 It is a flow chart of the radio intelligent filtering method based on wireless base station of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] See also Figure 1 As shown, the present invention is a radio intelligent filtering method based on a wireless base station. In some embodiments, Figure 1 The process 100 shown may be executed by a processor, and the process 100 may include the following operations.

[0061] Step 101: Acquire a radio signal through an antenna array of a wireless base station.

[0062] A wireless base station is a facility that provides wireless communications and is responsible for receiving and sending radio signals. It is usually composed of multiple antennas and related hardware.

[0063] An antenna array refers to an array structure composed of multiple antennas that can receive signals at different angles, increasing the directionality and quality of the received signal.

[0064] Radio signals are signals transmitted in the form of electromagnetic waves and can carry information such as data, voice or video.

[0065] In some embodiments, the antenna array of the wireless base station receives radio signals from multiple directions. Each antenna unit receives the strength, phase and frequency information of the signal and converts it into an electrical signal. The antenna array can use beamforming technology to optimize the reception of signals and focus on receiving signals from a specific direction, thereby reducing interference and noise.

[0066] Step 102: Perform signal conversion basic analysis based on the radio signal to determine a digital signal conversion preprocessing strategy.

[0067] Signal conversion basic analysis refers to the preliminary analysis of the received radio signal, evaluating the characteristics of the signal (such as strength, frequency, noise, interference, etc.) and selecting an appropriate processing strategy for subsequent digital signal conversion.

[0068] Digital signal conversion preprocessing strategy refers to the preprocessing strategy for converting analog signals into digital signals based on the analysis results of the signal. The digital signal conversion preprocessing strategy can include signal gain control, anti-noise processing, sampling rate selection, etc.

[0069] In some embodiments, the frequency range, bandwidth, noise level and interference source of the signal can be determined by performing spectrum analysis on the radio signal. Based on the strength and quality of the signal, it can be determined whether gain control or filtering is required: if the signal is weak, a gain control strategy is selected to enhance the signal. If the signal contains noise or unwanted frequency components, a filtering strategy is selected. Based on the frequency characteristics of the signal, a suitable sampling rate can be selected (for example, according to the Nyquist theorem, a sampling rate greater than the signal bandwidth is selected). Select a suitable gain control method, such as digital gain or analog gain. And determine whether time domain or frequency domain filtering is required.

[0070] In some embodiments, performing a basic analysis of signal conversion based on the radio signal to determine a digital signal conversion preprocessing strategy includes: performing a basic analysis of signal conversion based on the radio signal to determine an optimization target for the radio signal; and determining the digital signal conversion preprocessing strategy based on the optimization target.

[0071] The optimization goal is the goal that we hope to achieve during the signal conversion process, which may include improving signal quality, reducing interference, enhancing the transmission of effective information, etc.

[0072] In some embodiments, a specific optimization target may be determined based on the result of the signal conversion basic analysis. For example, if the signal noise is too large, the optimization target may be to improve the signal-to-noise ratio; if the signal bandwidth is too wide, the optimization target may be to compress the bandwidth.

[0073] You can choose a suitable preprocessing strategy based on the optimization goal. For example, if the optimization goal is to improve the signal-to-noise ratio, you can choose signal denoising, such as mean filtering, wavelet transform denoising, etc.; if the optimization goal is to compress the bandwidth, you may need to choose bandpass filtering or sub-band decomposition methods, etc.

[0074] Step 103: Perform signal gain control on the radio signal based on the digital signal conversion preprocessing strategy to obtain a gain signal.

[0075] Signal gain control refers to adjusting the amplitude of a signal to ensure that it is suitable for further processing. Gain control can be performed by an amplifier or a digital signal processor (DSP), for example, and can be implemented by analog or digital means.

[0076] The gain signal refers to the signal after gain control, in which the signal amplitude is increased or adjusted for subsequent processing.

[0077] In some embodiments, the gain control target can be determined based on the aforementioned preprocessing strategy: if the signal amplitude is low, increase the gain; if the signal amplitude is too high, the gain may need to be attenuated to avoid overload. When applying gain control, if analog gain control is used, an amplifier is used to adjust the amplitude of the signal. If digital gain control is used, the amplitude of the signal is adjusted by a digital signal processor.

[0078] Step 104: digitally convert the gain signal to obtain a digital signal.

[0079] Digital conversion refers to the conversion of analog signals into digital signals through an analog-to-digital converter (ADC).

[0080] A digital signal is a sampled and quantized signal suitable for further analysis and processing by a computer or digital processing system. A digital signal is discrete and is usually represented by a series of discrete values.

[0081] In some embodiments, an analog-to-digital converter (ADC) can be used to sample and quantize the gain signal. When sampling, first select an appropriate sampling rate: based on the bandwidth of the signal, select a sampling rate that is at least twice the signal bandwidth (satisfying the Nyquist sampling theorem). After that, the sampled signal is quantized to map the amplitude of the signal into a digital range. The quantization accuracy can be controlled by the bit depth of the ADC.

[0082] Step 105: Perform spectrum analysis on the digital signal, and determine a target filter for filtering the digital signal according to the result of the spectrum analysis.

[0083] Spectral analysis refers to the Fourier transform of digital signals to obtain the signal's performance in the frequency domain and analyze the signal's frequency components and noise characteristics.

[0084] The target filter is a filter used to filter the digital signal, which may be a frequency domain filter and / or a time domain filter. According to the spectrum analysis result, the most suitable filter is selected to remove the unnecessary frequency components or noise.

[0085] In some embodiments, a digital signal may be subjected to a Fourier transform (e.g., a fast Fourier transform FFT) to obtain its spectrum X(f). Afterwards, the main frequency components in the signal are determined, and the frequency range of the noise or interference is identified; based on the spectrum analysis results, a suitable filter is selected, for example, if the noise is in the high frequency range, a low-pass filter may be selected; if interference in a certain frequency band needs to be removed, a band-pass or band-stop filter may be selected.

[0086] In some embodiments, the target filter determined for filtering the digital signal includes a frequency domain filter and a time domain filter, and is expressed by the following formula (1):

[0087] (1)

[0088] Among them, y(t) represents the filtering result, F -1 represents an inverse Fourier transform, F{x(t)} represents the Fourier transform of the input digital signal, F{h(t)} represents the Fourier transform of the time domain filter, and h opt (t) represents the time domain optimization filter, F{h(t)·h opt (t)} is the frequency domain representation of the time domain filter and the time domain optimized filter.

[0089] In some embodiments, performing spectrum analysis on the digital signal and determining a target filter for filtering the digital signal according to a result of the spectrum analysis includes:

[0090] S10, performing spectrum density estimation and interference identification based on the digital signal to determine a result of the spectrum analysis.

[0091] Spectral density estimation refers to estimating the power distribution of a signal at different frequencies by performing frequency domain analysis on a digital signal.

[0092] In some embodiments, the spectrum of the digital signal may be analyzed by a method such as Fast Fourier Transform (FFT) to obtain its spectrum diagram. An adaptive estimation method (such as the Welch method or the Periodogram method) is used to estimate the spectrum density.

[0093] Interference identification refers to the process of distinguishing valid signals from interference signals in the spectrum.

[0094] In some embodiments, frequency components that do not belong to valid signal frequency bands can be identified in the spectrum diagram and marked as interference frequency bands. Interference frequency bands can be accurately identified by comparing the signal spectrum density estimate with a known interference model (such as interference source characteristics in a communication system).

[0095] The spectrum analysis results include information such as frequency components, noise, interference frequency bands and effective signal frequency bands.

[0096] In some embodiments, each frequency component can be extracted from the spectrum diagram, and the effective signal frequency band, noise frequency band and interference frequency band can be divided according to the energy intensity, so as to finally obtain a complete spectrum analysis result.

[0097] S11, determining effective parameters in the digital signal based on the spectrum analysis result.

[0098] Effective parameters refer to key parameters that have a significant impact on signal quality and processing effects. Specifically, they may include:

[0099] Frequency component represents the frequency characteristics of the signal, usually including the center frequency and bandwidth of the effective signal frequency band.

[0100] Noise, random fluctuations or irregularities in a signal, usually located in invalid frequencies.

[0101] Interference frequency band refers to the frequency components in the signal that are not valid signals, usually caused by other communication systems or environmental noise.

[0102] Effective signal frequency band, the frequency range in which the signal contains useful information.

[0103] In some embodiments, effective parameters can be extracted from the spectrum analysis results. For example, based on the spectrum analysis results, the frequency range in the signal related to effective communication (i.e., the effective signal frequency band) is identified. The bandwidth, center frequency, etc. of the effective signal frequency band are quantitatively analyzed, and combined with the distribution of noise and interference, specific effective parameters are obtained.

[0104] S12: Determine the target filter for filtering the digital signal based on the effective parameters.

[0105] In some embodiments, determining the target filter may include the following operations.

[0106] Determine the filter type and select the appropriate filter type (such as low-pass, high-pass, band-pass or band-stop filter, time domain filter, frequency domain filter, etc.) based on the effective parameters. If the effective signal is concentrated in a specific frequency band, design a band-pass filter; if the interference is concentrated in certain frequency bands, design a band-stop filter.

[0107] Design the filter and design the frequency response of the filter based on the bandwidth and center frequency of the effective signal frequency band. For example, use an ideal filter, Butterworth filter, or Chebyshev filter for design. Ensure that the filter can retain the effective signal to the greatest extent possible while removing noise and interference.

[0108] Optimize filter parameters. According to actual application scenarios and system requirements, optimize filter parameters (such as filter order, bandwidth, etc.) so that the target filter has higher filtering performance and lower computational complexity.

[0109] In some embodiments, the target filter may also be determined in other ways. For example, determining the target filter for filtering the digital signal based on the effective parameters may include the following operations.

[0110] S20, determining a transformation formula F{x(t)} for frequency domain conversion; the transformation formula F{x(t)} is used to perform frequency domain conversion on the effective parameters to obtain frequency domain data.

[0111] The transformation formula F{x(t)} is a mathematical operation used to convert the time domain signal x(t) into a frequency domain signal. Commonly used transformations include Fourier Transform and Laplace Transform, which can represent the time domain signal as a combination of different frequency components.

[0112] In some embodiments, an appropriate frequency domain transformation formula may be selected according to the characteristics of the digital signal and the processing requirements. For example, if the characteristics of the signal are mainly reflected in the frequency, Fourier transform is usually used; if the time-varying characteristics of the signal need to be considered, short-time Fourier transform (STFT) or wavelet transform may need to be used.

[0113] Frequency domain data is the signal data obtained after frequency domain conversion, showing the energy distribution of the signal at each frequency. Frequency domain data helps identify useful frequency components in the signal and unnecessary noise or interference.

[0114] In some embodiments, effective parameters (eg, signal frequency, amplitude, noise, etc.) are converted from the time domain to the frequency domain by applying a transformation formula F{x(t)}.

[0115] S21 , determining a frequency domain filter H(f) for performing frequency domain filtering on the frequency domain data.

[0116] The frequency domain filter H(f) is a tool for operating a signal in the frequency domain. The frequency domain filter H(f) is used to perform frequency domain filtering on the frequency domain data to obtain frequency domain filtered data. It can filter out noise or interference components in the signal and retain the valid signal.

[0117] In some embodiments, a suitable frequency domain filter can be designed based on the effective signal frequency band in the frequency domain data. For example, if the effective signal is concentrated in a certain frequency range, a bandpass filter can be used; if the interference is located in a specific frequency band, a bandstop filter can be designed. After that, the cutoff frequency and other parameters of the filter can be selected based on the bandwidth and frequency characteristics of the effective signal.

[0118] S22, determining a time domain optimization filter h for optimizing the quality of the frequency domain filtering data opt (t).

[0119] Time domain optimized filter h opt (t) is a filter that optimizes the data that has been filtered in the frequency domain in the time domain. By processing the time domain signal, it can further remove the possible residual noise or interference and optimize the signal quality.

[0120] In some embodiments, a time domain optimization filter can be designed based on the characteristics of the frequency domain filtering data. For example, if the frequency domain filter removes some frequency components, but the time domain signal still has residual noise, it can be optimized by designing an appropriate time domain filter (such as a low-pass filter, a mean filter, etc.).

[0121] S23, based on the transformation formula F{x(t)}, the frequency domain filter H(f) and the time domain optimization filter h opt (t), determine the target filter.

[0122] In some embodiments, the transformation formula F{x(t)}, the frequency domain filter H(f) and the time domain optimization filter h opt (t), the final target filter can be obtained. For example, the characteristics of the transformation formula, the frequency domain filter and the time domain optimization filter are combined to obtain the target filter.

[0123] In some embodiments, the transform formula F{x(t)}, the frequency domain filter H(f) and the time domain optimization filter h opt(t), determining the target filter may include the following operations.

[0124] S30, converting the time series filter h(t) into the frequency domain to obtain a frequency domain representation H(f) of the time series filter h(t).

[0125] The time filter h(t) is a filter that acts on a signal in the time domain and is often used to remove noise or interference from a signal. It can be in the form of a function in the time domain, such as a low-pass filter, a band-pass filter, etc.

[0126] H(f) is the frequency domain representation of the time domain filter h(t), which is converted into a frequency domain filter by a transformation formula (such as Fourier transform). It describes how the filter modulates and filters the signal at different frequencies.

[0127] S31, the time domain optimization filter h opt (t) is converted to the frequency domain to obtain the time domain optimized filter h opt The frequency domain representation of (t) is H opt (f).

[0128] Time domain optimized filter h opt (t) is a filter that optimizes the signal in the time domain. It is used to further optimize the signal after frequency domain filtering and remove any residual time domain noise.

[0129] H opt (f) is the time domain optimized filter h opt (t) The representation in the frequency domain can be obtained through Fourier transform. It describes how the filter processes the signal in the frequency domain.

[0130] S32, frequency domain representation H(f) of the time series filter h(t) and the time domain optimization filter h opt The frequency domain representation of (t) is H opt (f) Combining the time domain filter and the time domain optimization filter to obtain the frequency domain representation F{h(t)·h opt (t)}.

[0131] The definition of a combined frequency domain representation is to convolve or multiply the frequency domain representations of two frequency domain filters to obtain a combined frequency domain filter. Specifically, if the time domain representations of the two filters are h(t) and h opt (t), their frequency domain representations are H(f) and H opt (f), the frequency domain representation of the modified combination can be obtained by a multiplication operation.

[0132] S33, based on the transformation formula F{x(t)}, the time domain filter and the frequency domain representation F{h(t)·h opt (t)} and the transformation formula F{x(t)}, determine the target filter.

[0133] According to the existing transformation formula F{x(t)}, combined with the combined frequency domain filter F{h(t)·h opt (t)}, the target filter can be inferred.

[0134] In this embodiment, by effectively converting and combining between the time domain and the frequency domain, a comprehensive optimized target filter is designed to ensure that the final signal processing can achieve the best effect and remove unnecessary noise and interference.

[0135] In some other embodiments, determining the target filter for filtering the digital signal based on the effective parameters may also include:

[0136] S40: Determine whether there is spectrum leakage based on the effective parameters.

[0137] The determination of spectrum leakage refers to the process of whether there is spectrum leakage, which usually depends on the matching degree between the frequency components of the signal and the window function. If the frequency of the signal is not aligned with the boundary of the window, spectrum leakage will occur. The specific method of determining whether there is spectrum leakage can be achieved through spectrum analysis. The common methods are as follows: calculate the spectrum of the signal, perform Fourier transform on the digital signal, and obtain its spectrum; analyze the energy distribution of the spectrum, check the distribution of the signal in the spectrum, and check whether there is a significant energy extension to other frequency bands to determine whether there is spectrum leakage.

[0138] Spectrum leakage can also be determined by comparing with an ideal spectrum, for example, by comparing with an ideal spectrum shape. For example, the spectrum of an ideal low-pass filter should be a rectangle. If the actual spectrum shows a relatively smooth or extended shape, it indicates that spectrum leakage may exist.

[0139] S41: In response to the spectrum leakage, performing windowing processing on the digital signal.

[0140] Windowing refers to the process of weighting digital signals using window functions to address spectrum leakage issues. The purpose of windowing is to reduce spectrum leakage, make the energy distribution of the signal in the frequency domain more concentrated, and reduce the side lobes of the spectrum.

[0141] The window function w(t) is a smooth mathematical function, which usually has good frequency domain characteristics. It can limit the signal within a limited time range and reduce the spectrum leakage of the signal. In some embodiments, the window function applied by the windowing process can include a rectangular window, a Hamming window, a Blackman window, etc.

[0142] S42: Determine the target filter based on the result of the windowing process.

[0143] The target filter obtained by windowing can be shown in the following formula (2):

[0144] (2)

[0145] Where w(t) is the applied window function.

[0146] Step 106: Filter the digital signal using the target filter to obtain a target signal.

[0147] The target filter is designed to remove unwanted frequency components (such as noise or interference) in the signal. The target filter includes time domain filters (such as convolution filters) and frequency domain filters.

[0148] The target signal is the signal that has been filtered and the unnecessary frequency components have been removed.

[0149] In some embodiments, the selected target filter may be applied to the digital signal to obtain a target signal.

[0150] In some embodiments, the filtering the digital signal using the target filter to obtain the target signal includes:

[0151] S50, determining the signal size of the digital signal.

[0152] Signal size refers to the amplitude or power of the signal. Common measurement methods include the maximum amplitude of the signal, the root mean square value (RMS value), or the power of the signal.

[0153] S51, in response to the signal size exceeding a preset threshold, performing block processing on the digital signal.

[0154] The preset threshold is a value set during design. When the signal exceeds the threshold, subsequent processing operations are triggered. The threshold can be set according to the characteristics of the signal or the processing capacity of the system.

[0155] S52, using the target filter to perform filtering processing on the digital signal blocks after the block processing, to obtain multiple block filtering results.

[0156] Block filtering refers to using the target filter designed above to filter each block signal to obtain a filtering result for each block.

[0157] S53, merging the multiple block filtering results to obtain the target signal.

[0158] Merging is the process of recombining multiple filtered signal blocks into a complete signal to form the final target signal.

[0159] The final target signal is obtained by splicing the filtered block signals.

[0160] Step 107: determine a signal transmission path according to the network status and the signal quality of the target signal.

[0161] Network status refers to the operation status of the network, including signal strength, bandwidth, delay, interference, etc.

[0162] Signal quality is usually measured by indicators such as signal-to-noise ratio (SNR). The higher the signal quality, the better the transmission effect.

[0163] The signal transmission path refers to the communication path from the sending end to the receiving end.

[0164] In some embodiments, the network status can be evaluated to collect information about the current network environment, such as signal strength, bandwidth, delay, congestion, etc. The signal transmission path is selected based on the quality of the target signal (such as SNR): if the signal quality is good, a shorter path or direct transmission is selected; if the signal quality is poor, a low-interference path is selected or signal enhancement measures are taken.

[0165] Step 108: Transmit the target signal using the signal transmission path.

[0166] Transmission refers to the process of a signal moving from a sender to a receiver through a physical or wireless communication network. In wireless communication, transmission can be done over an air interface, while in wired networks, it can be done over media such as optical fibers and cables.

[0167] The signal transmission path refers to the path from the signal source to the receiving end. The path selection will be optimized based on factors such as network conditions, signal quality, interference sources, etc. It can be a direct path, a path through multiple hops, or a path through a relay.

[0168] Working principle of the invention: The invention performs accurate signal conversion basic analysis on radio signals and designs intelligent digital signal preprocessing strategies, and uses the technology combining time domain filtering and frequency domain filtering, adaptive digital signal conversion strategies and signal transmission path adjustment to enable radio signals to maintain higher stability and anti-interference capabilities in complex wireless environments, thereby effectively improving the signal quality, transmission efficiency, reliability and adaptability of wireless communication systems.

[0169] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A radio intelligent filtering method based on a wireless base station, characterized in that: The method comprises the following steps: Acquiring radio signals through antenna arrays of wireless base stations; Performing a signal conversion basic analysis based on the radio signal to determine a digital signal conversion preprocessing strategy; Performing signal gain control on the radio signal based on the digital signal conversion preprocessing strategy to obtain a gain signal; Performing digital conversion on the gain signal to obtain a digital signal; Performing spectrum analysis on the digital signal, and determining a target filter for filtering the digital signal according to the result of the spectrum analysis; wherein the target filter includes a frequency domain filter and a time domain filter, and is expressed by the following formula: ； Among them, y(t) represents the filtering result, F -1 represents an inverse Fourier transform, F{x(t)} represents the Fourier transform of the input digital signal, F{h(t)} represents the Fourier transform of the time domain filter, and h opt (t) represents the time domain optimization filter, F{h(t)·h opt (t)} is the frequency domain representation of the time domain filter and the time domain optimized filter; Using the target filter to filter the digital signal to obtain a target signal; Determining a signal transmission path according to a network state and a signal quality of the target signal; The target signal is transmitted using the signal transmission path.

2. The radio intelligent filtering method based on a wireless base station according to claim 1, characterized in that: The performing spectrum analysis on the digital signal and determining a target filter for filtering the digital signal according to the result of the spectrum analysis comprises: Performing spectrum density estimation and interference identification based on the digital signal to determine the result of the spectrum analysis; wherein the spectrum analysis result includes frequency components, noise, interference frequency bands, and effective signal frequency bands; Based on the spectrum analysis result, determining effective parameters in the digital signal; Based on the effective parameters, the target filter for filtering the digital signal is determined.

3. The radio intelligent filtering method based on a wireless base station according to claim 2 is characterized in that: The step of determining the target filter for filtering the digital signal based on the effective parameters comprises: Determining a transformation formula F{x(t)} for Fourier transform; the transformation formula F{x(t)} is used to perform frequency domain conversion on the effective parameters to obtain frequency domain data; Determine a frequency domain filter H(f) for performing frequency domain filtering on the frequency domain data; wherein the frequency domain filter H(f) is used to perform frequency domain filtering on the frequency domain data to obtain frequency domain filtered data; Determine a time domain optimization filter h for optimizing the quality of the frequency domain filtering data opt (t); Based on the transformation formula F{x(t)}, the frequency domain filter H(f) and the time domain optimization filter h opt (t), determining the target filter; The transformation formula F{x(t)}, the frequency domain filter H(f) and the time domain optimization filter h opt (t), determining the target filter, comprising: Convert the time series filter h(t) to the frequency domain to obtain a frequency domain representation H(f) of the time series filter h(t); The time domain optimization filter h opt (t) is converted to the frequency domain to obtain the time domain optimized filter h opt The frequency domain representation of (t) is H opt (f); The frequency domain representation H(f) of the time series filter h(t) and the time domain optimized filter h opt The frequency domain representation of (t) is H opt (f) Combining the time domain filter and the time domain optimization filter to obtain the frequency domain representation F{h(t)·h opt (t)}; Based on the transformation formula F{x(t)}, the time domain filter and the frequency domain representation F{h(t)·h opt (t)} and the transformation formula F{x(t)}, determine the target filter.

4. The radio intelligent filtering method based on a wireless base station according to claim 2 is characterized in that: The step of determining the target filter for filtering the digital signal based on the effective parameters comprises: Based on the effective parameters, determining whether there is spectrum leakage; In response to the spectrum leakage, performing windowing processing on the digital signal; Based on the result of the windowing process, the target filter is determined; wherein the target filter is: ； Where w(t) is the applied window function.

5. The radio intelligent filtering method based on a wireless base station according to claim 1, characterized in that: The step of filtering the digital signal using the target filter to obtain a target signal includes: Determining the signal size of the digital signal; In response to the signal size exceeding a preset threshold, performing block processing on the digital signal; Using the target filter to perform filtering processing on the digital signal blocks after the block processing respectively, to obtain multiple block filtering results; The multiple block filtering results are combined to obtain the target signal.

6. The radio intelligent filtering method based on a wireless base station according to claim 1, characterized in that: The signal conversion basic analysis based on the radio signal is performed to determine the digital signal conversion preprocessing strategy, including: Performing a signal conversion basic analysis based on the radio signal to determine an optimization target for the radio signal; Based on the optimization target, the digital signal conversion preprocessing strategy is determined.

7. A radio intelligent filtering system based on a wireless base station, characterized in that: The system comprises: A signal acquisition module, used to acquire radio signals through an antenna array of a wireless base station; A strategy determination module, configured to perform a signal conversion basic analysis based on the radio signal and determine a digital signal conversion preprocessing strategy; A gain control module, used for performing signal gain control on the radio signal based on the digital signal conversion preprocessing strategy to obtain a gain signal; A digital conversion module, used for performing digital conversion on the gain signal to obtain a digital signal; A filter determination module is used to perform spectrum analysis on the digital signal, and determine a target filter for filtering the digital signal according to the result of the spectrum analysis; wherein the target filter includes a frequency domain filter and a time domain filter, and is expressed by the following formula: ； Among them, y(t) represents the filtering result, F -1 represents an inverse Fourier transform, F{x(t)} represents the Fourier transform of the input digital signal, F{h(t)} represents the Fourier transform of the time domain filter, and h opt (t) represents the time domain optimization filter, F{h(t)·h opt (t)} is the frequency domain representation of the time domain filter and the time domain optimized filter; A filtering processing module, used to filter the digital signal using the target filter to obtain a target signal; A transmission path determination module, used to determine a signal transmission path according to a network state and a signal quality of the target signal; A transmission module is used to transmit the target signal using the signal transmission path.

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