Public network base station signal scanning method in communication state

Abstract

The invention relates to the technical field of wireless communication, and discloses a public network base station signal scanning method in a communication state, and the method comprises the following steps: collecting a target wireless signal, obtaining a broadband power spectrum of the target signal through segmented FFT, and decomposing the power spectrum into a plurality of frequency bands to obtain the power information of each frequency band; envelope smoothing processing is carried out on the obtained power spectrum, narrow-band pits and burrs are removed, and the signal quality is improved; the system comprises a signal acquisition module used for acquiring a target wireless signal; the segmented FFT module is used for converting the collected signal into a broadband power spectrum and performing frequency band decomposition; and the envelope smoothing module is used for removing noise and interference in the power spectrum. According to the invention, by adopting the scheme of combining the segmented FFT technology and the intelligent spectrum resource allocation and prediction, the technical effect of efficiently identifying the effective base station frequency point is achieved through accurate spectrum analysis of the target signal and dynamic adjustment of the spectrum resource.

Description

Technical Field

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

[0002] At present, in the field of wireless communications, 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. The processing of spectrum information during signal acquisition is relatively simple and difficult to adapt to complex wireless environments. Existing scanning technologies usually use fixed frequency windows or standard processing methods to analyze signals, but this method is difficult to cope with the increasingly complex wireless communication needs, especially in multi-base station, multi-band and high-interference environments.

[0003] A significant technical shortcoming is that existing methods lack sufficient flexibility and real-time performance when processing spectrum. For example, the effectiveness of segmented FFT technology in practical applications has not been fully utilized. Traditional methods often use full-band scanning and FFT conversion through a fixed window. This method limits the spectrum resolution and makes it difficult to accurately analyze subtle changes in the spectrum, which in turn affects the accurate identification of base station signals. In addition, the mixture of base station signals and background noise makes it difficult to screen effective signals. Existing technologies often use simple noise suppression or static filtering to reduce noise interference, 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 to screen frequencies. This approach cannot automatically optimize the allocation of spectrum resources according to changes in the wireless environment. Especially in an environment where demand changes rapidly, the allocation strategy of spectrum resources is often too rigid, resulting in waste of resources or insufficient allocation. For example, when a frequency band is frequently interfered or there is a sudden demand, it is difficult for traditional methods to dynamically adjust spectrum resources, which may lead to reduced signal quality or low network efficiency.

[0005] There are also problems with the signal synchronization verification methods in the existing technology. Many traditional systems use a single synchronization algorithm and rely on preset thresholds to confirm the frequency point, which is difficult to effectively deal with multipath effects and signal attenuation. This makes it difficult for the system to accurately confirm the base station signal in complex environments, especially when the signal quality is poor or the frequency band overlap is serious, and misidentification and missed detection occur frequently.

[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 shortcomings 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 implemented through the following technical solutions: A method for scanning public network base station signals in a communication state, comprising the following steps: By collecting the target wireless signal, the broadband power spectrum of the target signal is obtained through segmented FFT, and the power spectrum is decomposed into multiple frequency bands to obtain the power information of each frequency band; Perform envelope smoothing on the acquired power spectrum to remove narrowband pits and burrs and improve signal quality; The power distribution is counted and the noise floor power is estimated based on the power spectrum, which is used as a reference value for subsequent frequency point screening to suppress interference; Based on the preset frequency band and bandwidth information, the in-band power in each frequency band is calculated, and the frequency band is used as a candidate frequency point through the signal-to-noise ratio threshold judgment; After removing the identified frequency points and bandwidth, the power spectrum is modified for edge detection to further estimate the existing candidate frequency points; Perform broadcast channel synchronization on the frequencies in the candidate frequency list for final confirmation of the base station; Through intelligent spectrum resource allocation and prediction technology, the spectrum resource allocation strategy is dynamically adjusted based on historical signal data and real-time signal changes, and future spectrum requirements are predicted according to environmental changes.

[0009] 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.

[0010] Preferably, the envelope smoothing process is implemented by a digital filter, and the filter dynamically adjusts its filtering parameters according to the characteristics of the real-time power spectrum.

[0011] Preferably, the background noise power of the power spectrum is obtained by performing histogram statistics on the power spectrum, and the background noise power is a benchmark value, which is used as a reference for subsequent frequency screening.

[0012] Preferably, the step of screening the candidate frequency points includes calculating a signal-to-noise ratio of in-band power to background noise power, and when the signal-to-noise ratio value exceeds a predetermined threshold, determining the frequency band as a valid candidate frequency point.

[0013] Preferably, the transition edge detection step comprises: After the identified frequency bins and bandwidths are removed, the corrected power spectrum is processed; Identify mutation points or boundary changes in the power spectrum and detect transition edges in the spectrum; When the change in the power spectrum exceeds a predetermined threshold, the frequency band is marked as a potential valid frequency point; The existing candidate frequency points are further estimated and the spectrum data is optimized through transition edge detection.

[0014] Preferably, the broadcast channel synchronization synchronizes the candidate frequency points through an extensive synchronization algorithm, and the synchronized frequency points can be used as valid base station frequency points.

[0015] Preferably, the intelligent spectrum resource allocation and prediction technology dynamically adjusts the spectrum resource allocation strategy through deep neural network and LSTM network models, combined with historical signal samples and real-time data.

[0016] Preferably, the prediction technology uses environmental parameters to predict spectrum demand, and dynamically optimizes and allocates spectrum resources based on the prediction results.

[0017] The present invention also provides a public network base station signal scanning system in a communication state, comprising: A signal acquisition module, used to collect target wireless signals; Segmented FFT module, used to convert the collected signal into a broadband power spectrum and perform frequency band decomposition; Envelope smoothing module, used to remove noise and interference in the power spectrum; A noise floor estimation module is used to estimate the noise floor power in the power spectrum; Frequency point screening module, used to determine candidate frequency points based on signal-to-noise ratio; The transition edge detection module is used to correct the frequency points in the power spectrum and detect potential effective frequency points; Broadcast channel synchronization module, used to synchronize candidate frequencies and confirm valid base station signals; Intelligent spectrum resource allocation module, used to predict spectrum demand and dynamically adjust spectrum resources based on historical and real-time data.

[0018] The present invention provides a method for scanning public network base station signals in a communication state. It has the following beneficial effects: 1. The present invention achieves the technical effect of efficiently identifying effective base station frequencies by adopting a solution combining segmented FFT technology and intelligent spectrum resource allocation and prediction, through accurate spectrum analysis of target signals and dynamic adjustment of spectrum resources. Compared with the traditional single frequency band scanning method commonly used in the prior art, the present invention avoids frequency band information loss and resource waste through flexible segment processing and intelligent prediction, effectively improves spectrum utilization, and reduces network load and interference.

[0019] 2. The present invention adopts envelope smoothing and background noise estimation technology to effectively remove noise and interference in the signal, thereby improving signal quality and ensuring the accuracy of candidate frequency point screening. Compared with the method relying on simple filtering or static noise suppression in the prior art, the dynamic adjustment filtering and adaptive noise estimation of the present invention can better adapt to the spectrum changes in complex wireless environments, and significantly improve the stability and accuracy of frequency point identification.

[0020] 3. The present invention introduces deep learning and LSTM network models to perform intelligent spectrum resource allocation and prediction, so that the system can predict future spectrum requirements based on historical data and real-time signal changes, and automatically adjust resource allocation strategies. Different from the static spectrum resource allocation method in the prior art, the present invention can dynamically adjust spectrum usage, optimize resources for different regions and time periods, greatly reduce resource waste, and can adapt to complex and changing wireless environments.

[0021] 4. The present invention ensures the accuracy of candidate frequencies through broadcast channel synchronization verification technology, and realizes efficient use of spectrum resources by combining intelligent spectrum resource allocation technology. Compared with the prior art in which frequency synchronization relies on simple static threshold judgment, the multi-point synchronization and adaptive spectrum allocation method of the present invention improves the signal recognition accuracy, reduces misjudgment, ensures efficient recognition and rapid response of base station frequencies, and provides accurate data support for subsequent network optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of the method of the present invention; Figure 2 It is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the specification 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.

[0024] See also Figure 1 The embodiment of the present invention provides a method for scanning a public network base station signal in a communication state, comprising the following steps: S1. By collecting the target wireless signal, the broadband power spectrum of the target signal is obtained by segmented FFT, and the power spectrum is decomposed into multiple frequency bands to obtain the power information of each frequency band; S2, performing envelope smoothing processing on the acquired power spectrum to remove narrowband pits and burrs and improve signal quality; S3. Count the power distribution and estimate the background noise power according to the power spectrum, and use this as a reference value to perform interference suppression for subsequent frequency point screening; S4. Based on the preset frequency band and bandwidth information, calculate the in-band power in each frequency band, and determine the frequency band as a candidate frequency point through a signal-to-noise ratio threshold; S5, after removing the identified frequency points and bandwidth, modifying the power spectrum for edge detection, and further estimating the existing candidate frequency points; S6. Perform broadcast channel synchronization on the frequency points in the candidate frequency point list to finally confirm the base station; S7. Through intelligent spectrum resource allocation and prediction technology, the spectrum resource allocation strategy is dynamically adjusted based on historical signal data and real-time signal changes, and future spectrum requirements are predicted according to environmental changes.

[0025] In order to accurately identify and analyze the signals of public network base stations, S1 first needs to collect signals in the target area. This is the first key step in realizing the present invention. The collected signals may contain 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. In the process of signal collection, it is necessary to ensure that the equipment used has sufficient sensitivity to capture a wide spectrum including weak signals.

[0026] In this embodiment, the collected wireless signal is decomposed into multiple frequency bands by using the segmented FFT technology to generate a broadband power spectrum of the target signal. The power information of each frequency band is obtained by calculation, thereby 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.

[0027] As an option, the size of each window can be dynamically adjusted based on the bandwidth of the acquired signal, the rate of signal change, 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 quickly, a smaller window can be selected to avoid signal loss or distortion. Dynamic adjustment of the window size ensures the flexibility of the segmented FFT processing process and can adapt to various complex wireless communication environments.

[0028] The calculation formula of segmented FFT is as follows: ; in, is the frequency The power spectrum below indicates the signal strength in this frequency band; It is the representation of the signal in the time city, that is, the target signal over time changes; is the frequency, which indicates the distribution of the signal in the frequency domain, and its unit is Hertz (Hz); It is the length of the signal's time window, which determines the resolution and accuracy of FFT analysis. Provides lower frequency resolution, while smaller Provides higher temporal resolution; is the imaginary unit, defined as , which is used to calculate the complex part of the Fourier transform.

[0029] In a possible implementation, in order to improve computational efficiency, the window length and sampling rate can be adaptively adjusted according to the actual changes in the signal. For example, in the high-frequency signal area, the window length can be appropriately reduced to improve the frequency resolution; while in the low-frequency signal area, the window length can be increased to enhance the stability and recognition ability of the signal.

[0030] Generally speaking, the purpose of segmented FFT processing is to convert time domain signals into frequency domain data, and then obtain a broadband power spectrum. The power information of each frequency band is extracted and used for subsequent interference suppression, candidate frequency point screening and other steps. By analyzing the power information of multiple frequency bands, strong signal bands can be extracted and weak interference bands can be eliminated, thereby improving the accuracy of subsequent steps.

[0031] Furthermore, in this embodiment, segmented FFT can not only provide spectrum information of the signal, but also help identify the signal strength of different frequency bands. The signal strength can be used as one of the base stations to determine whether the base station frequency is valid. By performing separate FFT processing on each frequency band, useful signal data can be efficiently separated from the complex spectrum.

[0032] In summary, in 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 changes in different wireless environments. This technical solution can accurately obtain the power information of each frequency band, provide accurate spectrum data for subsequent frequency point screening and base station confirmation, and provide basic data support for intelligent spectrum resource allocation and prediction technology.

[0033] For S2, in the aforementioned step S1, the signal is processed by segmented FFT to obtain a broadband power spectrum, and the power information is decomposed into multiple frequency bands for further analysis. Next, the envelope smoothing process in step S2 is performed to remove noise and irregular peaks in the power spectrum, thereby improving signal quality and providing more accurate data for subsequent frequency point screening and base station confirmation.

[0034] In this embodiment, when performing envelope smoothing, a digital filter is used to process the power spectrum. Specifically, the function of the digital filter is to eliminate the peaks and depressions caused by noise interference by smoothing the curve of the power spectrum. This noise 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 impact of errors, and enhances the availability of the signal.

[0035] 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 acquired signal. A low-pass filter removes high-frequency noise, while a band-pass filter retains the signal within a specific frequency band. The design of the filter is based on the characteristics of the real-time power spectrum and is dynamically adjusted to adapt to different signal environments. For example, in areas where the signal spectrum is clear, the filter can be appropriately loose to maintain the integrity of the signal; in areas where the signal is more chaotic, the filter can be more stringent to remove unnecessary noise.

[0036] Specifically, envelope smoothing is achieved through the following mathematical model: ; in, 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 peaks and irregular fluctuations of the original power spectrum will be smoothed out, generating a smoother spectrum curve.

[0037] In one possible implementation, envelope smoothing is not only processed using basic filters, but also combined with weighted smoothing techniques. Through weighted filtering, the weight of the filter is adjusted according to the strength of the signal or the characteristics of the frequency band. For example, for areas with high signal strength in the frequency band, a lower smoothing factor can be used to avoid excessive smoothing and loss of effective information; while for areas with weak signals, a higher smoothing factor is used to better remove noise.

[0038] Generally speaking, envelope smoothing helps 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, which is convenient for frequency point screening in subsequent steps.

[0039] As an option, after envelope smoothing, the fluctuation of the power spectrum will become more gentle, providing more stable spectrum data for subsequent frequency screening and candidate frequency judgment. For example, after noise and irregular fluctuations are filtered out, the true characteristics of the signal will be more apparent, effectively reducing the possibility of false detection or missed detection.

[0040] Specifically, envelope smoothing can not only improve signal quality in theory, but also significantly improve the accuracy of spectrum analysis in practical applications. Especially in complex wireless environments where interference and noise are prevalent, envelope smoothing can effectively enhance the accuracy of signal recognition, making the selection of candidate frequencies in subsequent steps more efficient.

[0041] In some embodiments, the envelope smoothing processing step can adaptively adjust the parameters of the filter, especially when the signal strength changes dramatically, the filter can be adjusted in real time according to the change to better meet the noise processing requirements under different signal strengths. This adaptive characteristic enables the present invention to respond flexibly in various environments, avoiding the problem of low efficiency of traditional methods when facing complex signals.

[0042] In summary, envelope smoothing, as an important step in signal scanning, plays a vital role in improving signal quality and reducing noise impact. 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 efficient operation of the system.

[0043] For S3, in the aforementioned step S2, after the signal is processed by envelope smoothing, 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, estimate the noise floor power in the power spectrum, and then perform interference suppression, providing more accurate data support for subsequent frequency point screening and base station confirmation.

[0044] In this embodiment, in this step, by performing 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 existing in the signal itself, and is the power of the non-signal part of the spectrum. In order to ensure that the selected frequency points are effective, we first need to remove this background noise from the entire spectrum, and then identify the actual effective base station signal.

[0045] Specifically, the process of estimating the background noise power can be expressed by the following formula: ; in, represents the estimated noise floor power, Indicates the power spectrum The power value of each frequency point, is the total number of frequency points. This formula shows that the noise floor power is obtained by calculating the average power of each frequency point in the power spectrum. In this way, those frequency bands that do not contain valid signals can be effectively eliminated.

[0046] As an option, in some embodiments, the noise floor power estimation can also be optimized by an adaptive algorithm. Specifically, the system can dynamically adjust the estimated 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 with large signal changes or severe interference, and further improve the accuracy of noise estimation.

[0047] Generally speaking, the result of the noise floor power estimation provides a benchmark for the frequency point screening in the subsequent steps. Based on this benchmark, we can determine whether the signal of each frequency band is valid. Those frequency bands with strong noise will be eliminated, 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 the noise frequency band as a valid base station signal.

[0048] In one possible implementation, in order to further suppress interference, other signal processing techniques (such as adaptive filtering or Kalman filtering) can be combined to optimize the noise suppression process. Adaptive filtering technology can adjust the filtering parameters according to the changes in real-time signals, thereby removing interference signals more accurately. Kalman filtering can estimate and predict signals in the presence of noise and uncertainty, further improving the quality of signals.

[0049] Specifically, through the noise floor power estimation and interference suppression steps, those frequency bands with strong interference can be effectively filtered out, leaving useful base station signal frequency points. The noise floor power provides a reference standard for subsequent frequency point screening, allowing the system to more accurately identify and confirm effective frequency points in complex wireless environments.

[0050] As another option, in complex environments, the technology of joint analysis of time domain and frequency domain can 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 complementary information for noise estimation, thereby improving the accuracy of noise estimation.

[0051] In summary, the noise floor 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 noise floor power and effectively suppressing interference, subsequent frequency point screening can be more accurate, thereby improving the accuracy of base station identification and the utilization efficiency of spectrum resources.

[0052] For S4, in the above step S3, we effectively removed the noise and unnecessary interference in the signal through noise floor power estimation and interference suppression processing, and obtained a more accurate signal power spectrum. The next step S4 aims to filter out the frequency bands that may contain valid base station signals from the power spectrum, and further identify candidate frequency points by calculating the signal-to-noise ratio (SNR) of each frequency band.

[0053] In this embodiment, step S4 mainly determines whether the frequency band contains a valid 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.

[0054] Specifically, the calculation of the signal-to-noise ratio can be expressed as: ; in, For the The in-band signal power of the frequency band, is the noise floor power in this frequency band.

[0055] As an option, the signal power It is obtained by integrating the power of the signal within the frequency band, and the noise floor power The signal-to-noise ratio calculation helps us determine whether each frequency band contains a strong enough signal to become a valid candidate frequency point.

[0056] Specifically, after calculating the signal-to-noise ratio, the system will compare it with the preset signal-to-noise ratio threshold. If the signal-to-noise ratio exceeds the preset threshold, the system will regard the frequency band as a valid candidate frequency; otherwise, the frequency band will be excluded. In this process, the selection of the signal-to-noise ratio threshold is crucial to the screening results. In general, 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 the valid frequency.

[0057] In a possible implementation, the SNR threshold value can be dynamic and adaptively adjusted according to the noise level and signal strength in the current wireless environment. For example, in areas with relatively large interference, the SNR threshold value may need to be increased to avoid misjudging the interference frequency band; while in areas with clearer signals, the threshold value may be appropriately lowered to capture more potential frequency points.

[0058] In general, the signal-to-noise ratio calculation can not only effectively eliminate useless frequency bands, but also ensure the accurate identification of effective base station signals. By screening out frequency bands with signal-to-noise ratios exceeding a predetermined threshold, the system can effectively exclude those frequency bands with greater interference and weaker signals, providing more accurate data for subsequent frequency correction, transition edge detection, and broadcast channel synchronization.

[0059] As an option, in some embodiments, other algorithms can be combined to enhance the calculation result of the signal-to-noise ratio. For example, an adaptive signal processing algorithm is used to dynamically adjust the calculation method, and the calculation method of the signal-to-noise ratio is optimized according to different frequency band characteristics. This method can perform intelligent optimization according to the actual situation of the frequency band and improve the accuracy of frequency point screening.

[0060] Specifically, the calculation and screening results of the signal-to-noise ratio provide reliable data support for subsequent spectrum correction and candidate frequency synchronization. The construction and screening of the candidate frequency 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 effective frequency bands in various complex wireless environments, and improve the efficiency of wireless signal scanning and spectrum utilization.

[0061] In summary, the candidate frequency point screening and signal-to-noise ratio calculation process in step S4 provides effective technical support for one of the core objectives of the present invention, namely, efficiently and accurately identifying public network base station signals. Through accurate signal-to-noise ratio calculation and screening mechanism, the present invention can eliminate interference and ensure that the system can efficiently scan and identify base station signals in complex environments.

[0062] For S5, in the aforementioned step S4, the candidate frequencies have been screened according to the signal-to-noise ratio, and the qualified frequency bands have been included in the candidate frequency list. Next, step S5 will perform power spectrum correction and transition edge detection on these candidate frequencies to further improve the accuracy of frequency identification and ensure that the final selected frequency can accurately reflect the location of the effective base station signal.

[0063] In this embodiment, power spectrum correction and transition edge detection are key post-processing steps. In the signal spectrum, the 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. In this case, it is necessary to improve the accuracy of the frequency points by correcting the power spectrum.

[0064] Specifically, the process of correcting the power spectrum involves removing the identified frequencies and bandwidths, and then using these corrected data to perform edge detection. The purpose of edge detection is to identify frequencies where the spectrum boundaries change significantly, which usually indicates the presence or absence of a signal, and can help the system further confirm the presence of a base station.

[0065] As an option, the corrected power spectrum can be further optimized by smoothing algorithms such as Gaussian smoothing or mean smoothing. These algorithms can reduce 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.

[0066] In a possible implementation, the specific method of jump 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 the frequency point has jumped and is marked as a potential valid base station frequency point. This method can further eliminate invalid frequency bands that have not produced obvious changes, reducing unnecessary calculations and spectrum analysis.

[0067] Generally, the jump edge detection can be achieved by calculating the mutation point or change slope in the spectrum. For example, detecting the rate of change in the spectrum, that is, calculating the frequency Power change rate under , and compare it with a preset threshold. If the rate of change exceeds a certain threshold, it means that a significant change has occurred in the spectrum, and it may correspond to the appearance or disappearance of the base station signal. The formula can be expressed as: ; in, is the power value of the current frequency point, is the power value of its adjacent frequency point, is the frequency change step size. In this way, the system can identify the area with significant jumps in the spectrum and locate the base station signal.

[0068] Specifically, by correcting the spectrum and detecting the transition edge, the system can exclude those frequency bands that are interfered or unstable from the candidate frequency list, ensuring that the remaining frequency bands are closely related to the actual base station signal. This step not only improves the accuracy of frequency screening, but also enhances the system's adaptability to complex wireless environments.

[0069] As an option, in some embodiments, a machine learning algorithm can be combined to optimize the threshold setting of the transition edge detection. Through the training model, the system can learn the characteristics of the transition edge under different environments and conditions, thereby automatically adjusting the parameters of the detection algorithm, further improving the accuracy of frequency point detection and the system's adaptive ability.

[0070] In summary, the power spectrum correction and transition edge detection in step S5 can not only eliminate misjudgments caused by noise and interference, but also improve the accuracy of frequency point screening and ensure the accuracy of the final candidate frequency points. Through these processes, the system can effectively identify base station signals, reduce interference from invalid frequency bands, and provide more reliable data support for subsequent broadcast channel synchronization and spectrum resource allocation.

[0071] For S6, in the aforementioned step S5, after power spectrum correction and transition edge detection, the candidate frequency points have been accurately screened out from the power spectrum, and after further analysis and screening, they are ensured to have 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.

[0072] In this embodiment, the core purpose of broadcast channel synchronization is to determine whether these frequency points actually contain valid base station signals by performing synchronization verification on candidate frequency points. This process is achieved through an extensive synchronization algorithm, and its basic idea is to use the characteristics of the broadcast channel to exclude irrelevant signals through synchronization verification.

[0073] Specifically, broadcast channel synchronization verifies whether the candidate frequency actually corresponds to a base station signal by receiving the signal sent from the candidate frequency and comparing it with the known base station synchronization signal. If the synchronization process is successful, it means that the frequency is a valid base station frequency; if the synchronization fails, it means that the frequency does not contain a valid signal and the system excludes it.

[0074] Alternatively, the synchronization process may include multiple steps, such as: Preliminary synchronization detection: First, perform preliminary synchronization, use known synchronization signal characteristics to perform coarse synchronization on candidate frequency points, and screen out possible effective frequency bands.

[0075] Fine synchronization check: After the initial synchronization, the selected frequency bands are finely checked. At this time, the synchronization accuracy can be improved by using a refined algorithm, such as using a phase synchronization algorithm or a delay estimation algorithm to further improve the synchronization accuracy.

[0076] In a possible implementation, in order to further improve the accuracy of synchronization, broadcast channel synchronization may also be combined with other technical means, such as: Adaptive synchronization algorithm: Dynamically adjusts synchronization parameters based on signal strength, noise level and other wireless environment factors to optimize the synchronization process.

[0077] Signal phase estimation: By estimating the phase of the signal, it ensures that the candidate frequency point can be successfully synchronized with the base station even in a high interference environment.

[0078] Generally speaking, broadcast channel synchronization can ensure accurate signal recognition 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, further improving the efficiency of base station signal discovery.

[0079] Specifically, broadcast channel synchronization is not only a key step in signal verification, but also the last step in the entire frequency selection process. Through this step, the candidate frequencies can undergo the final verification to ensure that the remaining frequencies are truly valid base station signal frequencies.

[0080] As another option, in some embodiments, the reliability and accuracy of synchronization can be improved by introducing multi-point synchronization technology. For example, collecting signals at multiple locations and performing synchronous comparison can further reduce the impact caused by single-point errors and ensure more accurate base station identification.

[0081] In summary, 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 validity of the final candidate frequency points. Through this process, it is possible to further confirm which frequency points represent valid base station signals, thereby providing more accurate and reliable data support for subsequent spectrum resource allocation and network optimization.

[0082] For S7, in step S6, the candidate frequency point has been verified through broadcast channel synchronization to ensure its validity. Next, the intelligent spectrum resource allocation and prediction technology introduced in step S7 will further dynamically adjust the spectrum resource allocation strategy based on historical signal data and real-time signal changes, and predict future spectrum requirements based on environmental changes. The purpose of this technology is to improve the efficiency of spectrum resource utilization, reduce interference, and ensure that spectrum resources are optimally configured in a dynamically changing wireless environment.

[0083] In this embodiment, the intelligent spectrum resource allocation and prediction technology combines deep learning models with machine learning algorithms, uses historical signal data and real-time signal change analysis to predict future spectrum demand, and adjusts the spectrum resource allocation strategy based on 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.

[0084] Specifically, in intelligent spectrum resource allocation, the system first collects a large amount of historical signal data, including past spectrum usage, signal strength changes in different time periods, environmental noise fluctuations, etc. Through in-depth analysis of these 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.

[0085] The model predicts the signal strength changes and spectrum demand of each frequency band in the future based on historical data and real-time monitoring data. Through such predictions, the system can identify in advance which frequency bands may face a surge in spectrum demand and which frequency bands may have idle spectrum, and then reasonably adjust the spectrum allocation.

[0086] 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, 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 the frequency band decreases due to environmental factors, the system can automatically increase the resource allocation of the frequency band; on the contrary, when the demand for a certain frequency band decreases, the system will recycle unnecessary spectrum resources, thereby reducing resource waste.

[0087] In one possible implementation, the prediction algorithm combines environmental parameters (such as user density, geographic location, climate change, etc.) for intelligent scheduling. By analyzing the user distribution, the system can predict the spectrum demand in certain areas in the future and adjust resource allocation. For example, in areas with high user density, the system will prioritize allocating more resources to frequency bands with high spectrum demand to ensure user experience; while in low-density areas, the system can recycle unnecessary spectrum resources and reallocate them to areas with higher demand.

[0088] In general, the workflow of intelligent spectrum resource allocation and prediction technology can be summarized into the following key steps: Historical data analysis: Collect and analyze signal data over a certain period of time in the past, including spectrum usage, signal quality, and interference.

[0089] Prediction model training: Use deep learning (such as LSTM) to train historical data to generate a spectrum demand prediction model.

[0090] Real-time data monitoring: Real-time monitoring of the usage of each frequency band in the network to obtain the current spectrum status.

[0091] Dynamic adjustment: Combining prediction results with real-time data, the spectrum resource allocation strategy is adjusted through an adaptive scheduling algorithm.

[0092] Specifically, in the process of adjusting 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: ; in, It's in time Under this, the allocation strategy for spectrum resources; It is a forecast of future spectrum demand based on historical data and real-time monitoring data; Is the current time point The actual signal strength of Is the current time point The actual interference level; Environmental factors, such as user density, geographical location, and climatic conditions, influence spectrum demand.

[0093] As another option, the system can further optimize the spectrum resource allocation strategy through intelligent scheduling algorithms (such as genetic algorithms and particle swarm optimization algorithms). These algorithms can provide global optimal solutions in complex wireless environments and find the best spectrum resource allocation solution by simulating multiple allocation strategies.

[0094] 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 spectrum, but also optimize network performance and ensure the balance of base station load.

[0095] In summary, through intelligent spectrum resource allocation and prediction technology, the system can predict future spectrum demand based on historical data and real-time changes, and dynamically adjust resource allocation strategies. Through this adaptive adjustment, the system can not only effectively optimize spectrum usage and reduce spectrum waste, but also improve the overall performance of the network and enhance the flexibility and efficiency of spectrum resources.

[0096] See also Figure 2 The present invention also provides a public network base station signal scanning system in a communication state, comprising: A signal acquisition module, used to collect target wireless signals; The signal acquisition module is responsible for collecting wireless signals in the target area. The module receives and collects wireless signals in the target area through highly sensitive radio frequency equipment or antenna arrays, and captures signals from different base stations and different frequency bands. The signal acquisition process has a wide frequency band coverage range and can receive low to high intensity signals to ensure signal integrity. In some embodiments, the module may have a multi-channel parallel acquisition function, allowing signals from multiple frequency bands to be received simultaneously, reducing information loss caused by frequency interference or frequency band overlap. The collected signals will be transmitted to the subsequent segmented FFT module for spectrum analysis and processing.

[0097] Segmented FFT module, used to convert the collected signal into a broadband power spectrum and perform frequency band decomposition; The segmented FFT module converts the collected time domain signal into a frequency domain signal through the fast Fourier transform (FFT) technology to generate a broadband power spectrum. The signal power information of each frequency band will be extracted for subsequent analysis. In order to adapt to the frequency characteristics of different signals, the module adopts dynamic window adjustment technology to adjust the FFT window size according to the bandwidth and frequency characteristics of the signal. This adjustment can ensure that the spectrum resolution is optimized, thereby improving the accuracy of signal analysis. In addition, the module also supports sliding window technology to improve computing efficiency and reduce processing delays.

[0098] Envelope smoothing module, used to remove noise and interference in the power spectrum; The function of the envelope smoothing module is to remove noise and interference in the power spectrum and improve signal quality. This module uses digital filters (such as low-pass filters and band-pass filters) to smooth the power spectrum and remove spikes and irregular fluctuations in the spectrum. 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 actual power spectrum characteristics collected to adapt to the changes in different signals, ensuring that the key features of the signal are retained while reducing errors.

[0099] A noise floor estimation module is used to estimate the noise floor power in the power spectrum; The noise floor estimation module is responsible for estimating the noise floor power in the signal through statistical analysis of the power spectrum. The noise floor power represents the invalid noise part of the signal, while the valid signal is obtained through subsequent screening. In this module, a stable noise floor level is estimated by performing histogram analysis or other statistical methods on the power spectrum, which is used as a reference standard for subsequent frequency screening. The accuracy of the noise floor power estimation directly affects the effect of subsequent frequency screening. The output results of this module will be used as a benchmark for other modules (such as the frequency screening module) to determine the effectiveness of the signal frequency band.

[0100] Frequency point screening module, used to determine candidate frequency points based on signal-to-noise ratio; The frequency screening module determines whether the candidate frequency is valid by calculating the signal-to-noise ratio (SNR) of each frequency band. The module calculates the signal-to-noise ratio by comparing the in-band signal power with the background noise power, and compares it with the preset signal-to-noise ratio threshold. When the signal-to-noise ratio value exceeds the threshold, the frequency band is considered to be a valid candidate frequency and enters the subsequent processing flow. The accuracy of frequency screening is crucial to the final identification of base station signals. It can effectively eliminate false signals caused by background noise or interference, ensuring that the screened frequency band is a truly valid base station signal.

[0101] The transition edge detection module is used to correct the frequency points in the power spectrum and detect potential effective frequency points; The function of the transition edge detection module is to further correct the frequency points in the power spectrum and precisely detect the base station signals that may exist in the spectrum. By analyzing the mutation points or frequency changes in the power spectrum, the module can identify areas where significant changes have occurred in the spectrum, which often correspond to the appearance or disappearance of base station signals. Through a detailed analysis of the spectrum, the transition edge detection can accurately locate the boundaries of valid signals in the spectrum, eliminate those frequency bands that have not produced significant changes, and reduce the possibility of misidentification. This step greatly improves the accuracy of the candidate frequency points and provides accurate data for subsequent signal verification and spectrum synchronization.

[0102] Broadcast channel synchronization module, used to synchronize candidate frequencies and confirm valid base station signals; The broadcast channel synchronization module is responsible for synchronization verification of the selected candidate frequencies. Through the extensive synchronization algorithm, the module matches and verifies the candidate frequencies to confirm whether they actually correspond to valid base station signals. The synchronization algorithm compares the signal of the candidate frequency with the characteristics of the base station broadcast signal. If the synchronization verification is successful, it means that the frequency band is a valid base station signal frequency; if the synchronization fails, it means that the frequency 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 final accuracy of the candidate frequency.

[0103] Intelligent spectrum resource allocation module, used to predict spectrum demand and dynamically adjust spectrum resources based on historical and real-time data; The intelligent spectrum resource allocation module is responsible for dynamically adjusting the spectrum resource allocation strategy based on historical signal data and real-time signal changes. This module uses deep learning and machine learning algorithms, combined with historical data analysis and real-time environmental monitoring, to predict future spectrum demand. Through this process, the system can adjust spectrum resources in real time to ensure efficient use of spectrum and reduce resource waste. The intelligent spectrum resource allocation module can dynamically optimize the allocation of spectrum resources based on factors such as user demand, interference conditions, and network load. For example, in areas with high demand, the system may increase resource allocation; while in areas with low demand, the system will reduce the use of spectrum resources to improve the overall system performance and spectrum utilization.

[0104] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that 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: The following steps are involved: By collecting the target wireless signal, the broadband power spectrum of the target signal is obtained through segmented FFT, and the power spectrum is decomposed into multiple frequency bands to obtain the power information of each frequency band; Perform envelope smoothing on the acquired power spectrum to remove narrowband pits and burrs and improve signal quality; The power distribution is counted and the noise floor power is estimated based on the power spectrum, which is used as a reference value for subsequent frequency point screening to suppress interference; Based on the preset frequency band and bandwidth information, the in-band power in each frequency band is calculated, and the frequency band is used as a candidate frequency point through the signal-to-noise ratio threshold judgment; After removing the identified frequency points and bandwidth, the power spectrum is modified for edge detection to further estimate the existing candidate frequency points; Perform broadcast channel synchronization on the frequencies in the candidate frequency list for final confirmation of the base station; Through intelligent spectrum resource allocation and prediction technology, the spectrum resource allocation strategy is dynamically adjusted based on historical signal data and real-time signal changes, and future spectrum requirements are predicted according to environmental changes.

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

4. The method for scanning public network base station signals in a communication state according to claim 1, characterized in that: The noise floor power of the power spectrum is obtained by performing histogram statistics on the power spectrum, and the noise floor power is a benchmark value, which is used as a reference for subsequent frequency screening.

5. The method for scanning public network base station signals in a communication state according to claim 1, characterized in that: The step of screening 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 value exceeds a predetermined threshold, determining that the frequency band is a valid candidate frequency point.

6. The method for scanning public network base station signals in a communication state according to claim 1, characterized in that: The transition edge detection step comprises: After the identified frequency bins and bandwidths are removed, the corrected power spectrum is processed; Identify mutation points or boundary changes in the power spectrum and detect transition edges in the spectrum; When the change in the power spectrum exceeds a predetermined threshold, the frequency band is marked as a potential valid frequency point; The existing candidate frequency points are further estimated and the spectrum data is optimized through transition edge detection.

7. The 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 an extensive synchronization algorithm, and the synchronized frequency points can be used as effective base station frequency points.

8. The method for scanning public network base station signals in a communication state according to claim 1, characterized in that: The intelligent spectrum resource allocation and prediction technology dynamically adjusts the spectrum resource allocation strategy through deep neural network and LSTM network models, combined with historical signal samples and real-time data.

9. The method for scanning public network base station signals in a communication state according to claim 1, characterized in that: The prediction technology uses environmental parameters to predict spectrum demand and dynamically optimizes the allocation of spectrum resources based on the prediction results.

10. A system for scanning signals of a public network base station in a communication state, applied to a method for scanning signals of a public network base station in a communication state as claimed in any one of claims 1 to 9, characterized in that: include: A signal acquisition module, used to collect target wireless signals; Segmented FFT module, used to convert the collected signal into a broadband power spectrum and perform frequency band decomposition; Envelope smoothing module, used to remove noise and interference in the power spectrum; A noise floor estimation module is used to estimate the noise floor power in the power spectrum; Frequency point screening module, used to determine candidate frequency points based on signal-to-noise ratio; The transition edge detection module is used to correct the frequency points in the power spectrum and detect potential effective frequency points; Broadcast channel synchronization module, used to synchronize candidate frequencies and confirm valid base station signals; Intelligent spectrum resource allocation module, used to predict spectrum demand and dynamically adjust spectrum resources based on historical and real-time data.

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