A method for scanning public network base station signals in a communication state

Abstract

This application relates to the field of wireless communication technologies, and discloses a method for scanning public network base station signals in a communication state. The method includes the following steps: collecting target wireless signals, obtaining the broadband power spectrum of the target signals through segmented FFT, and decomposing the power spectrum into multiple frequency bands to obtain the power information of each frequency band; performing envelope smoothing processing on the obtained power spectrum to remove narrow-band pits and burrs and improve the signal quality; The system includes: a signal acquisition module for collecting target wireless signals; a segmented FFT module for converting the collected signals into a broadband power spectrum and performing frequency band decomposition; an envelope smoothing module for removing noise and interference in the power spectrum. By adopting a solution that combines segmented FFT technology with intelligent spectrum resource allocation and prediction, and through precise spectrum analysis of target signals and dynamic adjustment of spectrum resources, the present invention achieves the technical effect of efficiently identifying valid base station frequency points.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and specifically to a method for scanning public network base station signals in a communication state. Background Art

[0002] Currently, in the field of wireless communication, base station signal scanning is a key technology to ensure network stability and optimization. Traditional base station signal scanning methods often rely on simple frequency band traversal, and the processing of spectrum information during signal acquisition is relatively single, making it difficult to adapt to complex wireless environments. Existing scanning technologies usually use fixed frequency windows or standard processing methods to analyze signals, but this approach is difficult to meet the increasingly complex wireless communication requirements, especially in multi-base station, multi-frequency band, and high-interference environments.

[0003] A significant technical deficiency is that existing methods lack sufficient flexibility and real-time performance in processing spectra. For example, the effectiveness of the segmented FFT technology has not been fully utilized in practical applications. Traditional methods often perform full-band scanning and FFT conversion through fixed windows, which results in limited spectrum resolution and makes it difficult to accurately analyze relatively subtle changes in the spectrum, thereby affecting the accurate identification of base station signals. In addition, the mixing of base station signals and background noise makes it difficult to screen effective signals. Existing technologies often reduce noise interference through simple noise suppression or static filtering, but these methods are difficult to adapt to dynamically changing signal environments, resulting in limited signal quality.

[0004] In addition, traditional methods rely on manual adjustment and preset spectrum thresholds for frequency point screening. This method cannot automatically optimize the allocation of spectrum resources according to changes in the wireless environment. Especially in an environment where demands change rapidly, the spectrum resource allocation strategy is often too rigid, resulting in resource waste or insufficient allocation. For example, when a certain frequency band is frequently interfered with or there are sudden demands, traditional methods are difficult to dynamically adjust spectrum resources, which may instead lead to a decline in signal quality or low network efficiency.

[0005] There are also problems with the signal synchronization verification methods in the prior art. Many traditional systems use a single synchronization algorithm and rely on preset thresholds for frequency point confirmation, making it difficult to effectively cope with multipath effects and signal attenuation problems. This results in the system often being unable to accurately confirm base station signals in the face of complex environments, especially in cases where the signal quality is poor or the frequency bands overlap severely, and misidentification and missed detection frequently occur.

[0006] Therefore, the present invention proposes a method for scanning public network base station signals in a communication state to solve the deficiencies of the prior art. Summary of the Invention

[0007] In view of the deficiencies of the prior art, the present invention provides a method for scanning public network base station signals in a communication state, which solves the problems of low spectrum analysis accuracy, inflexible resource allocation, and inaccurate signal synchronization verification in the existing base station signal scanning method in a complex wireless environment.

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for scanning public network base station signals in a communication state, comprising the following steps:

[0009] Collect the target radio signal, obtain the broadband power spectrum of the target signal through segmented FFT, and decompose the power spectrum into multiple frequency bands to obtain the power information of each frequency band;

[0010] Perform envelope smoothing processing on the obtained power spectrum to remove narrowband pits and burrs and improve the signal quality;

[0011] Statistically analyze the power distribution based on the power spectrum and estimate the noise floor power, and use this as a reference value to suppress interference in subsequent frequency point screening;

[0012] Based on the preset frequency band and bandwidth information, calculate the in-band power within each frequency band, and determine whether the frequency band is a candidate frequency point through the signal-to-noise ratio threshold judgment;

[0013] After removing the identified frequency points and bandwidths, correct the power spectrum for edge jump detection to further estimate the existing candidate frequency points;

[0014] Synchronize the broadcast channels for the frequency points in the candidate frequency point list for final base station confirmation;

[0015] Through intelligent spectrum resource allocation and prediction technology, based on historical signal data and real-time signal changes, dynamically adjust the spectrum resource allocation strategy and predict future spectrum requirements according to environmental changes.

[0016] Preferably, the size of each window of the segmented FFT is dynamically adjusted according to the bandwidth and frequency characteristics of the collected signal to adapt to signal changes in different wireless environments.

[0017] Preferably, the envelope smoothing processing is implemented through a digital filter, and the filter dynamically adjusts its filtering parameters according to the characteristics of the real-time power spectrum.

[0018] Preferably, the noise floor power of the power spectrum is obtained through histogram statistics of the power spectrum, and the noise floor power is used as a reference value for subsequent frequency point screening.

[0019] Preferably, the screening step of the candidate frequency points includes calculating the signal-to-noise ratio of the in-band power and the noise floor power, and when the signal-to-noise ratio exceeds a predetermined threshold, determining that the frequency band is a valid candidate frequency point.

[0020] Preferably, the edge transition detection step includes:

[0021] After the identified frequency points and bandwidth are removed, process the corrected power spectrum;

[0022] Identify the mutation points or boundary changes in the power spectrum and detect the edge transitions in the spectrum;

[0023] When the change in the power spectrum exceeds a predetermined threshold, mark the frequency band as a potential valid frequency point;

[0024] Further estimate the existing candidate frequency points and optimize the spectrum data through edge transition detection.

[0025] Preferably, the broadcast channel synchronization synchronizes the candidate frequency points through a wide synchronization algorithm, and the synchronized frequency points can be used as valid base station frequency points.

[0026] Preferably, the intelligent spectrum resource allocation and prediction technology dynamically adjusts the spectrum resource allocation strategy through a deep neural network and an LSTM network model, combining historical signal samples and real-time data.

[0027] Preferably, the prediction technology uses environmental parameters to predict the spectrum demand and dynamically optimizes the allocation of spectrum resources according to the prediction results.

[0028] The present invention also provides a public network base station signal scanning system in a communication state, including:

[0029] A signal acquisition module for acquiring target wireless signals;

[0030] A segmented FFT module for converting the acquired signal into a broadband power spectrum and performing frequency band decomposition;

[0031] An envelope smoothing module for removing noise and interference in the power spectrum;

[0032] A background noise estimation module for estimating the background noise power in the power spectrum;

[0033] A frequency point screening module for judging candidate frequency points according to the signal-to-noise ratio;

[0034] An edge transition detection module for correcting the frequency points in the power spectrum and detecting potential valid frequency points;

[0035] A broadcast channel synchronization module for synchronizing the candidate frequency points and confirming valid base station signals;

[0036] An intelligent spectrum resource allocation module for predicting the spectrum demand according to historical and real-time data and dynamically adjusting the spectrum resources.

[0037] The present invention provides a public network base station signal scanning method in a communication state. It has the following beneficial effects:

[0038] 1. The present invention adopts a solution that combines segmented FFT technology with intelligent spectrum resource allocation and prediction. By precisely analyzing the spectrum of the target signal and dynamically adjusting the spectrum resources, the technical effect of efficiently identifying the frequency points of effective base stations is achieved. Compared with the traditional single-band scanning method commonly used in the prior art, through flexible segmented processing and intelligent prediction, the present invention avoids the loss of frequency band information and resource waste, effectively improves the spectrum utilization rate, and reduces network load and interference.

[0039] 2. The present invention adopts envelope smoothing processing and background noise estimation technology to effectively remove the noise and interference in the signal, thereby improving the signal quality and ensuring the accuracy of candidate frequency point screening. Compared with the methods in the prior art that rely on simple filtering or static noise suppression, the dynamic adjustment filtering and adaptive noise estimation of the present invention can better adapt to the spectrum changes in complex wireless environments, significantly improving the stability and accuracy of frequency point identification.

[0040] 3. The present invention introduces deep learning and LSTM network models for intelligent spectrum resource allocation and prediction, enabling the system to predict future spectrum requirements based on historical data and real-time signal changes, and automatically adjusting the resource allocation strategy. Different from the static spectrum resource allocation methods in the prior art, the present invention can dynamically adjust spectrum usage, optimize resources for different regions and time periods, greatly reducing resource waste, and can adapt to complex and changing wireless environments.

[0041] 4. The present invention ensures the accuracy of candidate frequency points through broadcast channel synchronization verification technology, and realizes the efficient utilization of spectrum resources in combination with intelligent spectrum resource allocation technology. Compared with the frequency point synchronization in the prior art that relies on simple static threshold judgment, the multi-point synchronization and adaptive spectrum allocation method of the present invention improves the signal identification accuracy, reduces misjudgment, ensures the efficient identification and rapid response of base station frequency points, and provides accurate data support for subsequent network optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is the flowchart of the method of the present invention;

[0043] Figure 2 is the system architecture diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] Please refer to Figure 1 , an embodiment of the present invention provides a method for scanning public network base station signals in a communication state, including the following steps:

[0046] S1. Collect target wireless signals, obtain the broadband power spectrum of the target signal through segmented FFT, and decompose the power spectrum into multiple frequency bands to obtain the power information of each frequency band;

[0047] S2. Perform envelope smoothing processing on the obtained power spectrum to remove narrow-band pits and burrs and improve the signal quality;

[0048] S3. Statistically analyze the power distribution according to the power spectrum and estimate the background noise power, and use this as a reference value to suppress interference in subsequent frequency point screening;

[0049] S4. Based on the preset frequency band and bandwidth information, calculate the in-band power within each frequency band, and determine whether the frequency band is a candidate frequency point through the signal-to-noise ratio threshold;

[0050] S5. After removing the identified frequency points and bandwidths, correct the power spectrum for edge detection to further estimate the existing candidate frequency points;

[0051] S6. Synchronize the broadcast channels for the frequency points in the candidate frequency point list for final base station confirmation;

[0052] S7. Through intelligent spectrum resource allocation and prediction technology, based on historical signal data and real-time signal changes, dynamically adjust the spectrum resource allocation strategy, and predict future spectrum requirements according to environmental changes.

[0053] For S1, in order to accurately identify and analyze the signals of public network base stations, signal collection needs to be carried out in the target area first. This is the key first step to implement the present invention. The collected signals may include multiple frequency band signals from multiple base stations. In order to analyze the spectrum information of these signals, segmented FFT (Fast Fourier Transform) must be used to perform frequency domain analysis on the signals, and then extract the frequency band information. During the signal collection process, it is necessary to ensure that the equipment used has sufficient sensitivity to capture a wide range of spectra including weak signals.

[0054] In this embodiment, by using the segmented FFT technology, the collected wireless signals are decomposed into multiple frequency bands to generate the broadband power spectrum of the target signal. The power information of each frequency band is calculated, thus providing effective spectrum data for subsequent frequency point screening and base station confirmation. The implementation method of segmented FFT is to divide the signal into multiple time periods and perform independent frequency domain conversion on each segment. This method can flexibly adjust the window size of each segment according to different spectrum characteristics.

[0055] As an option, the size of each window can be dynamically adjusted according to the bandwidth of the acquired signal, the signal change rate, and the frequency characteristics. If the signal spectrum is wide and the frequency changes slowly, a larger FFT window can be selected to improve the resolution of spectrum analysis. If the signal changes rapidly, a smaller window can be selected to avoid signal loss or distortion. Dynamically adjusting the window size ensures the flexibility of the segmented FFT processing process and can adapt to various complex wireless communication environments.

[0056] The calculation formula of the segmented FFT is as follows:

[0057] ;

[0058] Where, is the power spectrum at frequency and represents the signal strength in this frequency band; is the representation of the signal in the time domain, that is, how the target signal changes with time ; is the frequency, representing the distribution of the signal in the frequency domain, with the unit of Hertz (Hz); is the time window length of the signal, which determines the resolution and accuracy of the FFT analysis. A larger provides lower frequency resolution, while a smaller provides higher time resolution; is the imaginary unit, defined as and is used to calculate the complex part in the Fourier transform.

[0059] In a possible implementation, to improve the calculation efficiency, the window length and the sampling rate can be adaptively adjusted according to the actual change of the signal. For example, in the high-frequency signal region, the window length can be appropriately reduced to improve the frequency resolution; while in the low-frequency signal region, the window length can be increased to enhance the signal stability and recognition ability.

[0060] Generally, the purpose of the segmented FFT processing is to convert the time-domain signal into frequency-domain data, and then obtain the broadband power spectrum. The power information of each frequency band is extracted and used in subsequent steps such as interference suppression and candidate frequency point screening. By analyzing the power information of multiple frequency bands, the strong signal frequency bands can be extracted and the weaker interference frequency bands can be removed, thereby improving the accuracy of the subsequent steps.

[0061] Furthermore, in this embodiment, the segmented FFT can not only provide the spectrum information of the signal, but also help to identify the signal strength of different frequency bands. The signal strength can be used as one of the bases for determining whether the base station frequency point is valid. By performing separate FFT processing on each frequency band, useful signal data can be efficiently separated from the complex spectrum.

[0062] In summary, during the process of signal acquisition and segmented FFT processing, by reasonably selecting and dynamically adjusting the window size, it is possible to flexibly adapt to signal variations in different wireless environments. This technical solution can accurately obtain the power information of each frequency band, providing accurate spectrum data for subsequent frequency point screening and base station confirmation, and providing basic data support for intelligent spectrum resource allocation and prediction technologies.

[0063] For S2 in the foregoing step S1, the signal undergoes segmented FFT processing to obtain a broadband power spectrum, and the power information is decomposed into multiple frequency bands for further analysis. Next, for the envelope smoothing processing in step S2, the purpose is to remove the noise and irregular spikes in the power spectrum, thereby improving the signal quality and providing more accurate data for subsequent frequency point screening and base station confirmation.

[0064] In this embodiment, when performing envelope smoothing, a digital filter is used to process the power spectrum. Specifically, the role of the digital filter is to smooth the curve of the power spectrum and eliminate the spikes and depressions caused by noise interference, which usually comes from multipath interference, environmental noise, etc. during the transmission of wireless signals. Envelope smoothing makes the power spectrum of the signal smoother, reduces the influence of errors, and enhances the usability of the signal.

[0065] As an option, the digital filter used can be a low-pass filter or a band-pass filter, and the specific selection can be adjusted according to the characteristics of the collected signal. The low-pass filter will remove high-frequency noise, while the band-pass filter will retain the signals within a specific frequency band. The design of the filter is dynamically adjusted according to the characteristics of the real-time power spectrum to adapt to different signal environments. For example, in areas where the signal spectrum is relatively clear, the filter can be appropriately relaxed to maintain the integrity of the signal; in areas where the signal is relatively chaotic, the filter can more strictly remove unnecessary noise.

[0066] Specifically, the envelope smoothing processing is achieved through the following mathematical model:

[0067] ;

[0068] Where, is the original power spectrum, is the response function of the filter, is the power spectrum after envelope smoothing. This formula shows that through the convolution of the filter and the power spectrum, the spikes and irregular fluctuations of the original power spectrum will be smoothed out, generating a smoother spectrum curve.

[0069] In a possible implementation, envelope smoothing not only uses a basic filter for processing, but can also be combined with weighted smoothing techniques. Through weighted filtering, the weights of the filter are adjusted according to the signal strength or the characteristics of the frequency band. For example, for regions with higher signal strength within a frequency band, a lower smoothing factor can be adopted to avoid losing effective information due to excessive smoothing; while for regions with weaker signals, a higher smoothing factor is adopted to better remove noise.

[0070] Generally, envelope smoothing processing helps to improve the quality of the spectrum, making the spectrum of the signal smoother and more continuous. This process not only reduces unnecessary noise, but also makes the contrast of each frequency band of the power spectrum more obvious, facilitating the frequency point screening in subsequent steps.

[0071] As an option, after envelope smoothing, the fluctuations of the power spectrum will become more gentle, providing more stable spectrum data for subsequent frequency point screening and the judgment of candidate frequency points. For example, after filtering out noise and irregular fluctuations, the true characteristics of the signal will be more obvious, effectively reducing the possibility of false detection or missed detection.

[0072] Specifically, envelope smoothing processing can not only improve the signal quality in theory, but also significantly improve the accuracy of spectrum analysis in practical applications. Especially in a complex wireless environment where interference and noise are widespread, envelope smoothing can effectively enhance the accuracy of signal recognition, making the subsequent candidate frequency point screening more efficient.

[0073] In some embodiments, the processing steps of envelope smoothing can adaptively adjust the parameters of the filter. Especially when the signal strength changes drastically, the filter can be adjusted in real time according to the change situation to better meet the noise processing requirements under different signal strengths. This adaptive characteristic enables the present invention to flexibly respond in various environments, avoiding the problem of low efficiency of traditional methods when facing complex signals.

[0074] All in all, envelope smoothing, as an important step in signal scanning, plays a crucial role in improving signal quality and reducing the impact of noise. Through reasonable filter design and adaptive adjustment, noise and interference in the signal can be effectively removed, providing more accurate and stable signal data for subsequent frequency point screening, base station confirmation, and intelligent spectrum resource allocation, ensuring the efficient operation of the system.

[0075] Regarding S3, in the foregoing step S2, after the signal undergoes envelope smoothing processing, the power spectrum of the signal has become smoother and most of the noise has been removed. Next, step S3 will further process the signal by estimating the background noise power in the power spectrum, and then performing interference suppression, providing more accurate data support for subsequent frequency point screening and base station confirmation.

[0076] In this embodiment, in this step, by performing a histogram statistics on the processed power spectrum, we can estimate the background noise power in the signal. The background noise power represents the noise level inherently present in the signal and is the power of the non-signal part in the spectrum. To ensure the effectiveness of the selected frequency points, we first need to remove this background noise from the entire spectrum and then identify the actually effective base station signals.

[0077] Specifically, the process of estimating the background noise power can be expressed by the following formula:

[0078] ;

[0079] where, represents the estimated background noise power, denotes the power value of the th frequency point in the power spectrum, is the total number of frequency points. This formula indicates that the background noise power is obtained by calculating the average power of each frequency point in the power spectrum. By this method, the frequency bands that do not contain effective signals can be effectively filtered out.

[0080] As an option, in some embodiments, the estimation of the background noise power can also be optimized by an adaptive algorithm. Specifically, the system can dynamically adjust the estimation window size or select different statistical methods according to the actual characteristics of the signal, such as signal strength and frequency band distribution. This dynamic adjustment can effectively cope with environments where the signal changes greatly or the interference is severe, and further improve the accuracy of noise estimation.

[0081] Generally, the result of the background noise power estimation provides a benchmark for the subsequent frequency point screening. Based on this benchmark, we can determine whether the signals in each frequency band are effective. Those frequency bands with strong noise will be excluded, thereby reducing the interference in the spectrum. In this way, the accuracy of frequency point screening can be significantly improved, ensuring that the candidate frequency points have sufficient signal strength and avoiding misjudging noise frequency bands as effective base station signals.

[0082] In a possible implementation, to further suppress interference, other signal processing techniques (such as adaptive filtering or Kalman filtering) can be combined to optimize the noise suppression process. The adaptive filtering technique can adjust the filtering parameters according to the changes in the real-time signal, thereby more accurately removing the interference signals. And Kalman filtering can estimate and predict the signal in the presence of noise and uncertainty, further improving the signal quality.

[0083] Specifically, through the background noise power estimation and interference suppression steps, it is possible to effectively filter out those frequency bands with strong interference, leaving the useful base station signal frequency points. The background noise power provides a reference standard for subsequent frequency point screening, enabling the system to more accurately identify and confirm the effective frequency points in a complex wireless environment.

[0084] As an alternative, in a complex environment, the technology of joint time-domain and frequency-domain analysis can also be introduced when estimating the background noise power. For example, the time-domain characteristics of the signal (such as the duration and change rate of the signal) can provide supplementary information for noise estimation, thereby improving the accuracy of noise estimation.

[0085] In summary, the background noise power estimation and interference suppression in step S3 are not only necessary steps for signal cleaning but also key technologies to ensure signal accuracy and frequency point screening efficiency. By reasonably estimating the background noise power and effectively suppressing interference, it is possible to make the subsequent frequency point screening more accurate, thereby improving the accuracy of base station identification and the utilization efficiency of spectrum resources.

[0086] Regarding S4, in the aforementioned step S3, we effectively removed the noise and unnecessary interference in the signal through background noise power estimation and interference suppression processing, obtaining a relatively accurate signal power spectrum. The next step S4 aims to screen out the frequency bands that may contain effective base station signals from the power spectrum and further identify the candidate frequency points by calculating the signal-to-noise ratio (SNR) of each frequency band.

[0087] In this embodiment, step S4 mainly determines whether a frequency band contains an effective base station signal by calculating the ratio between the in-band power and the background noise power of each frequency band, that is, the signal-to-noise ratio. The signal-to-noise ratio is an important indicator to measure the relationship between signal strength and noise strength. A high signal-to-noise ratio means better signal quality and less interference.

[0088] Specifically, the calculation of the signal-to-noise ratio can be expressed as:

[0089] ;

[0090] where is the in-band signal power of the frequency band, is the background noise power of this frequency band.

[0091] As an option, the signal power is obtained by integrating the power of the signal within the frequency band, while the background noise power is obtained through the background noise power estimation in the aforementioned step S3. The calculation of the signal-to-noise ratio helps us determine whether each frequency band contains a strong enough signal to be an effective candidate frequency point.

[0092] Specifically, after calculating the signal-to-noise ratio, the system will compare it with a preset signal-to-noise ratio threshold. If the signal-to-noise ratio exceeds the preset threshold, the system will regard this frequency band as a valid candidate frequency point; otherwise, this frequency band will be excluded. In this process, the selection of the signal-to-noise ratio threshold is crucial for the screening result. Generally, the signal-to-noise ratio threshold should be adjusted according to the complexity of the actual wireless environment, signal strength, and noise level to ensure the accuracy of valid frequency points.

[0093] In a possible implementation, the signal-to-noise ratio threshold can be dynamic and adaptively adjusted according to the noise level and signal strength in the current wireless environment. For example, in an area with relatively large interference, it may be necessary to increase the signal-to-noise ratio threshold to avoid misjudging interference frequency bands; while in an area with relatively clear signals, the threshold can be appropriately reduced to capture more potential frequency points.

[0094] Generally, signal-to-noise ratio calculation can not only effectively eliminate useless frequency bands but also ensure the accurate identification of valid base station signals. By screening out frequency bands with a signal-to-noise ratio exceeding a predetermined threshold, the system can effectively exclude those frequency bands with large interference and weak signals, providing more accurate data for subsequent frequency point correction, edge detection of jumps, and synchronization of broadcast channels.

[0095] As an option, in some embodiments, other algorithms can also be combined to enhance the calculation result of the signal-to-noise ratio. For example, an adaptive signal processing algorithm can be used to dynamically adjust the calculation method and optimize the calculation method of the signal-to-noise ratio according to the characteristics of different frequency bands. This method can perform intelligent optimization according to the actual situation of the frequency band and improve the accuracy of frequency point screening.

[0096] Specifically, the calculation and screening result of the signal-to-noise ratio provide reliable data support for subsequent spectrum correction and synchronization of candidate frequency points. The construction and screening of the candidate frequency point list will have an important impact on base station identification, interference management, and intelligent spectrum resource allocation. In this way, the present invention can quickly and accurately identify valid frequency bands in various complex wireless environments and improve the efficiency of wireless signal scanning and spectrum utilization.

[0097] In summary, the process of screening candidate frequency points and calculating the signal-to-noise ratio in step S4 is one of the core objectives of the present invention, providing effective technical support for efficiently and accurately identifying public network base station signals. Through an accurate signal-to-noise ratio calculation and screening mechanism, the present invention can eliminate interference and ensure that the system can efficiently perform base station signal scanning and identification in complex environments.

[0098] For S5 in the foregoing step S4, the candidate frequency points have been screened according to the signal-to-noise ratio, and the eligible frequency bands have been included in the candidate frequency point list. Next, step S5 will perform power spectrum correction and edge detection on these candidate frequency points to further improve the accuracy of frequency point identification and ensure that the finally selected frequency points can accurately reflect the position of the effective base station signal.

[0099] In this embodiment, power spectrum correction and edge detection are key post-processing steps. In the signal spectrum, candidate frequency points may be affected by multiple factors, such as multipath effects, signal attenuation, external interference, etc., resulting in unclear signal boundaries, and even local fluctuations or irregular changes may occur. In this case, it is necessary to correct the power spectrum to improve the accuracy of the frequency points.

[0100] Specifically, the process of correcting the power spectrum includes removing the identified frequency points and bandwidths, and then using these corrected data for edge detection. The purpose of edge detection is to identify those frequency points where significant changes occur in the spectrum boundary. These changes usually indicate the presence or disappearance of the signal and can help the system further confirm the presence of the base station.

[0101] As an option, the corrected power spectrum can be further optimized through smoothing algorithms (such as Gaussian smoothing or mean smoothing). These algorithms can reduce the spikes and irregular fluctuations in the spectrum caused by multipath interference or noise, thereby improving the accuracy of the spectrum. After smoothing, the signal boundaries in the spectrum will become clearer, providing more reliable data support for edge detection.

[0102] In a possible implementation, the specific method of edge detection can adopt the threshold method. By setting a predetermined threshold, when the change in the power spectrum exceeds this threshold, it is considered that a jump occurs at this frequency point and is marked as a potential effective base station frequency point. This method can further eliminate those invalid frequency bands that do not produce obvious changes, reducing unnecessary calculations and spectrum analysis.

[0103] Generally, edge detection can be achieved by calculating the mutation points or change slopes in the spectrum. For example, detecting the change rate in the spectrum, that is, calculating the power change rate at the frequency , and comparing it with a preset threshold. If the change rate exceeds a certain threshold, it indicates that a significant change has occurred in the spectrum and may correspond to the appearance or disappearance of the base station signal. The formula can be expressed as:

[0104] ;

[0105] where is the power value of the current frequency point, is the power value of its adjacent frequency point, is the change step of the frequency. In this way, the system can identify the regions in the spectrum where the jumps are significant, thus locating the base station signal.

[0106] Specifically, through the correction of the spectrum and the detection of the jump edges, the system can exclude those interfered or unstable frequency bands from the candidate frequency point list, ensuring that the remaining frequency points are closely related to the actual base station signal. Through this step, not only the accuracy of frequency point screening is improved, but also the adaptability of the system to complex wireless environments is enhanced.

[0107] As an option, in some embodiments, machine learning algorithms can also be combined to optimize the threshold setting for jump edge detection. By training the model, the system can learn the characteristics of jump edges under different environments and conditions, thereby automatically adjusting the parameters of the detection algorithm and further improving the accuracy of frequency point detection and the adaptability of the system.

[0108] In summary, the power spectrum correction and jump edge detection in step S5 can not only eliminate misjudgments caused by noise and interference, but also improve the accuracy of frequency point screening, ensuring the accuracy of the final candidate frequency points. Through these processes, the system can effectively identify the base station signal, reduce the interference of invalid frequency bands, and provide more reliable data support for subsequent broadcast channel synchronization and spectrum resource allocation.

[0109] Regarding S6, in the foregoing step S5, through power spectrum correction and jump edge detection, the candidate frequency points have been accurately screened out from the power spectrum and further analyzed and screened to ensure their strong effectiveness in complex wireless environments. Next, step S6 will perform broadcast channel synchronization to confirm whether these candidate frequency points represent valid base station signals.

[0110] In this embodiment, the core purpose of broadcast channel synchronization is to verify through synchronization whether these candidate frequency points actually contain valid base station signals. This process is achieved through a wide synchronization algorithm, and its basic idea is to utilize the characteristics of the broadcast channel and exclude irrelevant signals through synchronization verification.

[0111] Specifically, broadcast channel synchronization verifies whether a candidate frequency point truly corresponds to a base station signal by receiving the signal sent from the candidate frequency point and comparing it with the known base station synchronization signal. If the synchronization process is successful, it means that the frequency point is a valid base station frequency point; if the synchronization fails, it indicates that the frequency point does not contain a valid signal, and the system excludes it.

[0112] As an option, the synchronization process can include multiple steps, such as:

[0113] Initial synchronization detection: First, perform initial synchronization. Use the known synchronization signal characteristics to perform rough synchronization on the candidate frequency points and screen out the potentially effective frequency bands.

[0114] Fine synchronization verification: After initial synchronization, perform fine verification on the screened frequency bands. At this time, refined algorithms can be used to improve the synchronization accuracy. For example, use phase synchronization algorithms or time delay estimation algorithms to further improve the synchronization accuracy.

[0115] In a possible implementation, to further improve the synchronization accuracy, broadcast channel synchronization can also combine other technical means, such as:

[0116] Adaptive synchronization algorithm: Dynamically adjust the synchronization parameters according to signal strength, noise level, and other wireless environment factors to optimize the synchronization process.

[0117] Signal phase estimation: Estimate the phase of the signal to ensure that even in a high-interference environment, the candidate frequency points can successfully synchronize with the base station.

[0118] Generally, broadcast channel synchronization can ensure the accurate identification of signals and avoid misjudging some false signals caused by environmental interference or noise as valid base station signals. The synchronized frequency points can be used as basic data for subsequent network optimization and spectrum resource management to further improve the discovery efficiency of base station signals.

[0119] Specifically, broadcast channel synchronization is not only a key step in signal verification but also the last process in the entire frequency point screening process. Through this step, the candidate frequency points can undergo final verification to ensure that the remaining frequency points are all truly valid base station signal frequencies.

[0120] As another option, in some embodiments, multi-point synchronization technology can also be introduced to improve the reliability and accuracy of synchronization. For example, collecting signals at multiple locations and performing synchronization comparison can further reduce the impact caused by single-point errors and ensure more accurate base station identification.

[0121] All in all, the broadcast channel synchronization in step S6 is an important step in the entire signal scanning method and plays an important role in ensuring the effectiveness of the final candidate frequency points. Through this process, it can be further confirmed which frequency points represent valid base station signals, thereby providing more accurate and reliable data support for subsequent spectrum resource allocation and network optimization.

[0122] For S7 in step S6, the candidate frequency points have passed the synchronization verification through the broadcast channel, ensuring their effectiveness. Next, the intelligent spectrum resource allocation and prediction technology introduced in the step will further dynamically adjust the spectrum resource allocation strategy based on historical signal data and real-time signal changes, and predict future spectrum requirements according to environmental changes. The purpose of this technology is to improve the utilization efficiency of spectrum resources, reduce interference, and ensure that spectrum resources are optimally allocated in a dynamically changing wireless environment.

[0123] In this embodiment, the intelligent spectrum resource allocation and prediction technology combines a deep learning model and a machine learning algorithm, uses historical signal data and real-time signal change analysis to predict future spectrum requirements, and adjusts the spectrum resource allocation strategy according to the prediction results. Through this process, the system can automatically adapt to changes in network load, user demand, interference environment, etc., thereby effectively optimizing the allocation of spectrum resources.

[0124] Specifically, in intelligent spectrum resource allocation, the system first collects a large amount of historical signal data, which includes information such as past spectrum usage, signal strength changes in different time periods, and fluctuations in environmental noise. Through in-depth analysis of this historical data, the system uses a deep neural network (DNN) or long short-term memory network (LSTM) model to train a spectrum demand prediction model.

[0125] Based on the historical data and real-time monitoring data, this model predicts the signal strength changes and spectrum requirements of each frequency band in the future for a period of time. Through such prediction, the system can identify in advance which frequency bands may face a surge in spectrum demand and which frequency bands may become idle, and then reasonably adjust the spectrum allocation.

[0126] As an option, for real-time signal changes, the system will monitor the signal status in the current network environment in real time, feedback data such as signal strength and interference situation, and dynamically adjust the spectrum resource allocation strategy based on these data. For example, when the number of user requests for a certain frequency band increases, or the signal quality of this frequency band decreases due to environmental factors, the system can automatically increase the resource allocation for this frequency band; on the contrary, when the demand for a certain frequency band decreases, the system will reclaim unnecessary spectrum resources, thereby reducing resource waste.

[0127] In a possible implementation, the prediction algorithm combines environmental parameters (such as user density, geographical location, climate change, etc.) for intelligent scheduling. By analyzing the user distribution, the system can predict the spectrum requirements in certain areas in the future, and then adjust the resource allocation. For example, in areas with high user density, the system will preferentially allocate more resources to frequency bands with high spectrum demand to ensure the user experience; while in low-density areas, the system can reclaim unnecessary spectrum resources and reallocate them to areas with higher demand.

[0128] Generally, the workflow of intelligent spectrum resource allocation and prediction technology can be briefly described as the following key steps:

[0129] Historical data analysis: Collect and analyze signal data over a certain period of the past, including spectrum usage, signal quality, and interference conditions, etc.

[0130] Prediction model training: Use deep learning (such as LSTM) to train the historical data to generate a spectrum demand prediction model.

[0131] Real-time data monitoring: Monitor the usage of each frequency band in the network in real time to obtain the current spectrum status.

[0132] Dynamic adjustment: Combine the prediction results and real-time data, and adjust the spectrum resource allocation strategy through an adaptive scheduling algorithm.

[0133] Specifically, during the adjustment process of the spectrum resource allocation strategy, the system adjusts the resource allocation according to the predicted spectrum demand and real-time signal changes. The formula can be expressed as:

[0134] ;

[0135] where, is the allocation strategy for spectrum resources at time ; is the future spectrum demand prediction based on historical data and real-time monitoring data; is the actual signal strength at the current time point ; is the actual interference level at the current time point ; is the impact of environmental factors, such as user density, geographical location, climate conditions, etc. on the spectrum demand.

[0136] As another option, the system can also further optimize the spectrum resource allocation strategy through intelligent scheduling algorithms (such as genetic algorithms, particle swarm optimization algorithms). These algorithms can provide a global optimal solution in a complex wireless environment. By simulating multiple allocation strategies, the best spectrum resource allocation scheme can be found.

[0137] In some embodiments, the system can also dynamically adjust other network parameters, such as power control, transmission rate, etc. according to the usage of spectrum resources. This dynamic adjustment can not only further improve the utilization efficiency of the spectrum, but also optimize the network performance and ensure the balance of the base station load.

[0138] In summary, through intelligent spectrum resource allocation and prediction technology, the system can predict future spectrum demands based on historical data and real-time changes, and dynamically adjust the resource allocation strategy. Through this adaptive adjustment, the system can not only effectively optimize the use of the spectrum, reduce spectrum waste, but also improve the overall performance of the network, enhancing the flexibility and efficiency of spectrum resources.

[0139] Please refer to Figure 2 , the present invention also provides a public network base station signal scanning system in a communication state, including:

[0140] A signal acquisition module, configured to acquire target wireless signals;

[0141] The signal acquisition module is responsible for acquiring wireless signals within a target area. This module receives and collects wireless signals within the target area through highly sensitive radio frequency devices or antenna arrays, capturing signals from different base stations and different frequency bands. The signal acquisition process has a wide frequency band coverage range, capable of receiving signals with low to high intensities to ensure signal integrity. In some embodiments, this module may have a multi-channel parallel acquisition function, allowing simultaneous reception of signals from multiple frequency bands, reducing information loss caused by frequency interference or frequency band overlap. The acquired signals will be transmitted to the subsequent segmented FFT module for spectrum analysis processing.

[0142] A segmented FFT module, configured to convert the acquired signals into broadband power spectra and perform frequency band decomposition;

[0143] The segmented FFT module converts the acquired time-domain signals into frequency-domain signals through fast Fourier transform (FFT) technology to generate broadband power spectra. The signal power information of each frequency band will be extracted for subsequent analysis. To adapt to the frequency characteristics of different signals, this module adopts a dynamic window adjustment technology to adjust the window size of the FFT according to the signal bandwidth and frequency characteristics. Such adjustment can ensure the optimization of the spectrum resolution, thereby improving the accuracy of signal analysis. In addition, the module also supports the sliding window technology to improve the calculation efficiency and reduce the processing delay.

[0144] An envelope smoothing module, configured to remove noise and interference in the power spectra;

[0145] The role of the envelope smoothing module is to remove noise and interference in the power spectra and improve the signal quality. This module uses digital filters (such as low-pass filters, band-pass filters) to smooth the power spectra, removing spikes and irregular fluctuations in the spectra. Through envelope smoothing, the signal curve becomes smoother and more continuous, thereby reducing misjudgments caused by noise in subsequent steps. The filter parameters of the smoothing process can be dynamically adjusted according to the characteristics of the actually acquired power spectra to adapt to different signal changes, ensuring that the key features of the signal are retained while reducing errors.

[0146] The background noise estimation module is used to estimate the background noise power in the power spectrum;

[0147] The background noise estimation module is responsible for estimating the background noise power in the signal through statistical analysis of the power spectrum. The background noise power represents the ineffective noise part in the signal, and the effective signal is obtained through subsequent screening. In this module, a stable background noise level is estimated through histogram analysis or other statistical methods of the power spectrum, and this level is used as a reference standard for subsequent frequency point screening. The accuracy of the background noise power estimation directly affects the effect of subsequent frequency point screening. The output result of this module will be used as a benchmark for other modules (such as the frequency point screening module) to judge the effectiveness of the signal frequency band.

[0148] The frequency point screening module is used to judge candidate frequency points according to the signal-to-noise ratio;

[0149] The frequency point screening module judges whether a candidate frequency point is valid by calculating the signal-to-noise ratio (SNR) of each frequency band. This module calculates the signal-to-noise ratio by comparing the in-band signal power with the background noise power and compares it with a preset signal-to-noise ratio threshold. When the signal-to-noise ratio value exceeds the threshold, this frequency band is considered a valid candidate frequency point and enters the subsequent processing flow. The accuracy of frequency point screening is crucial for the final identification of base station signals, and it can effectively eliminate false signals caused by background noise or interference, ensuring that the screened frequency bands are truly valid base station signals.

[0150] The edge detection module is used to correct the frequency points in the power spectrum and detect potential valid frequency points;

[0151] The role of the edge detection module is to further correct the frequency points in the power spectrum and finely detect possible base station signals in the spectrum. By analyzing the mutation points or frequency changes in the power spectrum, the module can identify the regions in the spectrum where significant changes occur, and these regions often correspond to the appearance or disappearance of base station signals. Edge detection can accurately locate the boundaries of valid signals in the spectrum through detailed analysis of the spectrum, eliminate those frequency bands that do not produce obvious changes, and reduce the possibility of misidentification. This step greatly improves the accuracy of candidate frequency points and provides accurate data for subsequent signal verification and spectrum synchronization.

[0152] The broadcast channel synchronization module is used to synchronize the candidate frequency points and confirm valid base station signals;

[0153] The broadcast channel synchronization module is responsible for performing synchronization verification on the selected candidate frequency points. Through a wide synchronization algorithm, the module performs matching verification on the candidate frequency points to confirm whether they actually correspond to valid base station signals. The synchronization algorithm compares the signals of the candidate frequency points with the characteristics of the base station broadcast signals. If the synchronization verification is successful, it indicates that the frequency band is a valid base station signal frequency point; if the synchronization fails, it means that the frequency point is not a valid base station signal and needs to be removed from the candidate list. This module is a crucial step in the entire signal scanning system, ensuring the ultimate accuracy of the candidate frequency points.

[0154] The intelligent spectrum resource allocation module is used to predict spectrum requirements based on historical and real-time data and dynamically adjust spectrum resources;

[0155] The intelligent spectrum resource allocation module is responsible for dynamically adjusting the spectrum resource allocation strategy according to historical signal data and real-time signal changes. The module predicts future spectrum requirements through deep learning and machine learning algorithms, combined with historical data analysis and real-time environmental monitoring. Through this process, the system can adjust spectrum resources in real time to ensure efficient use of the spectrum and reduce resource waste. The intelligent spectrum resource allocation module can dynamically optimize the allocation of spectrum resources according to factors such as user requirements, interference conditions, and network load. For example, in high-demand areas, the system may increase the allocation of resources; while in low-demand areas, the system will reduce the use of spectrum resources to improve the overall performance of the system and the utilization rate of the spectrum.

[0156] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for scanning public network base station signals in a communication state, characterized in that, It includes the following steps: By collecting the target wireless signal, obtaining the broadband power spectrum of the target signal through segmented FFT, and decomposing the power spectrum into multiple frequency bands to obtain the power information of each frequency band; The calculation formula of the segmented FFT is as follows: where P(f) is the power spectrum at frequency f; x(t) is the representation of the signal in the time domain, i.e., the variation of the target signal with time t; f is the frequency; T is the time window length of the signal; j is the imaginary unit, defined as used to calculate the complex part in the Fourier transform; Perform envelope smoothing processing on the obtained power spectrum to remove the narrow-band pits and burrs and improve the signal quality; The envelope smoothing processing is implemented through the following mathematical model: P smooth (f) = P(f) * H(f); Among them, P(f) is the original power spectrum, H(f) is the response function of the filter, and P smooth (f) is the power spectrum after envelope smoothing; Statistically analyze the power distribution based on the power spectrum and estimate the background noise power, and use this as a reference value to suppress interference in subsequent frequency point screening; The process of estimating the background noise power is represented by the following formula: Among them, P noise represents the estimated noise floor power, and P i represents the power value of the i-th frequency point in the power spectrum, and N is the total number of frequency points; Based on the preset frequency band and bandwidth information, calculate the in-band power within each frequency band, and determine this frequency band as a candidate frequency point through the signal-to-noise ratio threshold judgment; The calculation of the signal-to-noise ratio is expressed as: Among them, P signal (f) is the in-band signal power of the f-th frequency band, and P noise (f) is the noise floor power of this frequency band; After removing the identified frequency points and bandwidths, correct the power spectrum for edge jump detection to further estimate the existing candidate frequency points; The edge jump detection is achieved by calculating the mutation points or change slopes in the spectrum, detecting the change rate in the spectrum, that is, calculating the power change rate ΔP(f) at the frequency f and comparing it with a preset threshold, and the formula is expressed as: ΔP(f) = |P(f) - P(f - Δf)|; Wherein, P(f) is the power value of the current frequency point, P(f - Δf) is the power value of its adjacent frequency point, and Δf is the change step of the frequency; Synchronize the frequency points in the candidate frequency point list for broadcast channels to finally confirm the base station; Through intelligent spectrum resource allocation and prediction technology, based on historical signal data and real-time signal changes, dynamically adjust the allocation strategy of spectrum resources, and predict future spectrum requirements according to environmental changes; The intelligent spectrum resource allocation and prediction technology dynamically adjusts the spectrum resource allocation strategy through a deep neural network and an LSTM network model, combining historical signal samples and real-time data.

2. The method for scanning the public network base station signal in a communication state according to claim 1, wherein The size of each window of the segmented FFT is dynamically adjusted according to the bandwidth and frequency characteristics of the collected signal to adapt to signal changes in different wireless environments.

3. A method for scanning the public network base station signal in a communication state according to claim 1, characterized in that, The envelope smoothing processing is implemented through a digital filter, and the filter dynamically adjusts its filtering parameters according to the characteristics of the real-time power spectrum.

4. A method for scanning public network base station signals in a communication state according to claim 1, characterized in that, The background noise power of the power spectrum is obtained through histogram statistics of the power spectrum, and the background noise power is used as a reference value for subsequent frequency point screening.

5. A method for scanning the public network base station signal in a communication state according to claim 1, characterized in that, The screening step of the candidate frequency points includes calculating the signal-to-noise ratio of the in-band power and the background noise power, and when the signal-to-noise ratio exceeds a predetermined threshold, determining this frequency band as a valid candidate frequency point.

6. A method for scanning the signal of a public network base station in a communication state according to claim 1, wherein The edge jump detection step includes: After removing the identified frequency points and bandwidths, process the corrected power spectrum; Identify the mutation points or boundary changes in the power spectrum and detect the edge jumps in the spectrum; When the change in the power spectrum exceeds a predetermined threshold, mark this frequency band as a potential valid frequency point; Further estimate the existing candidate frequency points and optimize the spectrum data through edge jump detection.

7. A method for scanning public network base station signals in a communication state according to claim 1, characterized in that, The broadcast channel synchronization synchronizes the candidate frequency points through a wide synchronization algorithm, and the synchronized frequency points can be used as valid base station frequency points.

8. A method for scanning the signal of a public network base station in a communication state according to claim 1, characterized in that, The prediction technology uses environmental parameters to predict spectrum requirements and dynamically optimizes the allocation of spectrum resources according to the prediction results.

9. A public network base station signal scanning system in a communication state, which is used to implement a public network base station signal scanning method according to any one of claims 1-8, characterized in that, It includes: The signal acquisition module is used to acquire the target wireless signal; The segmented FFT module is used to convert the acquired signal into a broadband power spectrum and perform frequency band decomposition; The envelope smoothing module is used to remove noise and interference in the power spectrum; The background noise estimation module is used to estimate the background noise power in the power spectrum; The frequency point screening module is used to judge candidate frequency points according to the signal-to-noise ratio; The edge detection module is used to correct the frequency points in the power spectrum and detect potential valid frequency points; The broadcast channel synchronization module is used to synchronize the candidate frequency points and confirm the valid base station signal; The intelligent spectrum resource allocation module is used to predict the spectrum demand based on historical and real-time data and dynamically adjust the spectrum resources.

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