Method and System for Rapid Scanning of Public Network Cells Based on Frame Timing Prediction

Abstract

The present invention discloses a method and system for fast scanning of public network cells based on frame timing prediction. Step 1, Initial scanning stage: Configure the field programmable gate array clock counter; Open the full-time domain search window and perform scanning in the large window mode. Step 2, Difference value calculation stage: After each round of scanning, select the cell with the strongest signal strength in this round as the reference cell. Step 3, Dynamic window adjustment stage: For the latched cells, according to the global timing compensation parameter and the time difference ΔT, as well as the preset frequency offset compensation coefficient. Step 4, Abnormal handling mechanism: Set the signal quality attenuation threshold. Step 5, Performance monitoring and strategy optimization. This method improves the scanning efficiency through initial scanning and dynamic window adjustment, applies a Kalman filter to enhance the timing accuracy, the abnormal handling mechanism keeps the system stable, and performance monitoring and strategy optimization ensure that the system can adapt to the changes of the network environment in the long term, showing the advantages of high efficiency, accuracy and stability as a whole.

Description

Technical Field

[0001] The present invention relates to the field of public network scanning, and in particular to a method and system for fast scanning of public network cells based on frame timing prediction. Background Art

[0002] In a mobile communication network, cell scanning is a key step for mobile devices or base stations to perform operations such as network access, handover, and positioning. Traditional cell scanning techniques usually adopt a large-range window search strategy with a fixed period.

[0003] Although this scanning method is simple and direct, it has many deficiencies. The large-range cell scanning method with a fixed period is simple but inefficient. Since the same search is performed regardless of the network environment, unnecessary operations increase in a stable environment. At the same time, the wide scanning range increases power consumption, affecting the battery life of the device, and the timing synchronization method does not utilize historical information, resulting in resource waste due to repeated calculations, which limits the scanning speed. Therefore, a method and system for fast scanning of public network cells based on frame timing prediction are proposed. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a method and system for fast scanning of public network cells based on frame timing prediction to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solution: A method for fast scanning of public network cells based on frame timing prediction, including the following steps:

[0006] Step 1, Initial scanning stage: Configure the clock counter of the field programmable gate array (FPGA), and set its period to the target network frame period; Open the full-time domain search window and perform scanning in the large window mode; When a valid cell is detected, latch the accurate count value corresponding to the frame header of the cell, and record the signal strength indicators of each cell, including but not limited to the reference signal received power (RSRP) and the signal-to-interference-plus-noise ratio (SINR).

[0007] Step 2, Difference value calculation stage: After each round of scanning, select the cell with the strongest signal strength in this round as the reference cell; Compare the frame header count values of the reference cell in the current round and the historical round, and calculate the time difference ΔT; Apply the Kalman filter to process ΔT to eliminate measurement noise, and generate a global timing compensation parameter based on the processed ΔT.

[0008] Step 3. Dynamic window adjustment phase: For the latched cells, calculate the new predicted time \(T_{new}=T_{old}+\Delta T\times\) frequency offset compensation coefficient according to the global timing compensation parameter, the time difference \(\Delta T\), and the preset frequency offset compensation coefficient; Generate a predicted time window \([T_{new}-\delta,T_{new}+\delta]\) centered on \(T_{new}\), where \(\delta\) is a preset small-range deviation value, which is less than the large window range; For the unlatched cells, continue to use the large window mode for scanning;

[0009] Step 4. Abnormal handling mechanism: Set the signal quality attenuation threshold as the RSRP drops by more than 3 dB; When it is detected that the signal quality of the cell is lower than the signal quality attenuation threshold, trigger a conditional full window rescan; At the same time, establish a timing error tolerance model to evaluate whether the timing error is within the acceptable range, and adjust the scanning strategy according to the evaluation result;

[0010] Step 5. Performance monitoring and strategy optimization: Continuously monitor the operating state of the system, evaluate the key performance indicators of timing accuracy, scanning efficiency, and signal quality, and optimize and adjust the system strategy according to the monitoring results to ensure the long-term stable operation of the system and adapt to the changes in the network environment;

[0011] Through the large window mode in the initial scanning phase, valid cells in the network can be quickly captured, improving the scanning coverage and speed; In the dynamic window adjustment phase, according to historical data and prediction algorithms, a smaller predicted time window is used for scanning the latched cells, reducing unnecessary scanning time and further improving the scanning efficiency; In the difference value calculation phase, by comparing the frame header count values of the reference cell, the time difference is calculated, and the Kalman filter is applied to eliminate measurement noise and generate the global timing compensation parameter, significantly improving the timing accuracy; In the dynamic window adjustment phase, according to the global timing compensation parameter, the time difference, and the frequency offset compensation coefficient, the new predicted time is calculated, further refining the timing control; In the initial scanning phase, the signal strength indicators (such as RSRP and SINR) of each cell are recorded, providing basic data for subsequent signal quality evaluation and processing; The abnormal handling mechanism sets the signal quality attenuation threshold, and when a signal quality drop is detected, a full window rescan is triggered to ensure that the system can respond to signal quality changes in a timely manner; The timing error tolerance model is used to evaluate whether the timing error is within the acceptable range, and the scanning strategy is adjusted according to the evaluation result, enhancing the adaptability of the system; In the performance monitoring and strategy optimization phase, the operating state of the system is continuously monitored, and the system strategy is optimized and adjusted according to the monitoring results, ensuring the long-term stable operation of the system and adapting to the changes in the network environment; Through dynamic window adjustment and optimized scanning strategy, unnecessary scanning and consumption of computing resources are reduced, and the overall energy consumption of the system is lowered.

[0012] Preferably, step one specifically further includes: configuring the clock counter to set the counter accuracy according to the fluctuation range of the target network frame period to ensure accurate capture of the frame header;

[0013] Set the counter accuracy according to the fluctuation range of the target network frame period to ensure accurate capture of the frame header, improve timing accuracy; adapt to the frame period changes in different network environments, and enhance the flexibility and adaptability of the system.

[0014] Preferably, step one specifically further includes: the large window mode of the full-time domain search window specifically covers the entire time range to capture all potential cell signals;

[0015] Cover the entire time range to capture all potential cell signals, improve the comprehensiveness of scanning; ensure that no possible cell signals are missed, and provide more options for subsequent cell selection and synchronization.

[0016] Preferably, step one further includes: when latching the accurate count value corresponding to the frame header of the cell, it also includes recording the characteristic information of the frame header, the frame synchronization sequence or specific pilot signal, which is convenient for subsequent cell identification and synchronization;

[0017] Convenient for subsequent cell identification and synchronization, improve the identification ability and synchronization efficiency of the system; by recording the frame synchronization sequence or specific pilot signal, enhance the robustness and reliability of the system.

[0018] Preferably, step two specifically further includes: in the difference value calculation stage, the application of the Kalman filter includes initializing the filter parameters and updating the filter state, and adjusting the filter estimation value based on the new observation value to achieve smoothing and denoising of ΔT;

[0019] Achieve smoothing and denoising of ΔT, improve the calculation accuracy of the time difference; by initializing the filter parameters, updating the filter state and adjusting the filter estimation value, enhance the anti-interference ability and stability of the system.

[0020] Preferably, step three specifically further includes: in the dynamic window adjustment stage, the determination of the frequency offset compensation coefficient is based on the frequency offset statistical information of each cell in the historical scan data and the change trend of the current network environment;

[0021] Based on the historical scan data and the change trend of the current network environment, determine a more accurate frequency offset compensation coefficient; improve the accuracy of the predicted time T_new, and then improve the adjustment efficiency of the dynamic window and the scanning efficiency.

[0022] Preferably, step three specifically further includes: the generation of the prediction time window [T_new - δ, T_new + δ] also includes considering the impact of network dynamics and device mobility on timing prediction, and adjusting the size of δ according to these factors;

[0023] Consider the impact of network dynamics and device mobility on timing prediction, adjust the size of δ, and improve the adaptability of the prediction window; ensure that the prediction time window can more accurately cover the appearance time of the actual cell signal, and improve the hit rate and efficiency of scanning.

[0024] Preferably, the specific steps of step four further include: in the exception handling mechanism, the triggering conditions for conditional full-window rescan also include considering other signal quality indicators, such as the decrease amplitude of SINR or signal volatility, to comprehensively evaluate the stability of cell signal quality; when establishing the timing error tolerance model, consider the impact of network synchronization requirements, device clock accuracy, and environmental factors on timing error, and set a reasonable error tolerance range according to these factors;

[0025] Comprehensively evaluate the stability of cell signal quality, set a reasonable error tolerance range, and improve the robustness of the system; by considering multiple signal quality indicators and network synchronization requirements, enhance the system's ability to cope with network environment changes.

[0026] Preferably, step five further includes: real-time monitoring the stability of the FPGA clock counter to ensure that its cycle is consistent with the target network frame cycle; tracking and recording the changes in the reference cell for each round of scanning, the calculation results of the time difference ΔT, and the application of global timing compensation parameters; monitoring the hit rate of the prediction time window, that is, the proportion of latched cells successfully detected within the prediction time window; regularly analyzing the change trend of signal strength indicators and the triggering situation of the exception handling mechanism; evaluating the effectiveness of the current scanning strategy, timing compensation parameters, and prediction time window settings according to the performance monitoring data; for the situation of large timing errors, low scanning efficiency, or large signal quality fluctuations, analyze the reasons and put forward optimization suggestions: adjust global timing compensation parameters, frequency offset compensation coefficients, and the deviation value δ of the prediction time window to improve timing accuracy and scanning efficiency; optimize the exception handling mechanism, such as adjusting the signal quality attenuation threshold and improving the timing error tolerance model, to cope with network environment changes; record key events and performance indicators during the system operation to form a system log; regularly generate performance reports, including statistical data and trend analysis on timing accuracy, scanning efficiency, and signal quality; provide the system log and performance reports to relevant personnel for system maintenance, fault troubleshooting, and strategy optimization decisions;

[0027] Real-time monitor the stability of the FPGA clock counter, track and record key data during the scanning process, and regularly analyze the change trend of signal strength indicators and the triggering situation of the exception handling mechanism; provide comprehensive performance monitoring data to provide strong support for system maintenance, fault troubleshooting, and strategy optimization decisions, and ensure the long-term stable operation of the system and its adaptation to network environment changes.

[0028] A public network cell rapid scanning system based on frame timing prediction, comprising:

[0029] An FPGA clock counter configuration module for configuring the period of the FPGA clock counter to match the target network frame period;

[0030] By configuring the period of the FPGA clock counter to match the target network frame period, the time synchronization of the system can be ensured; this is the basis for accurate frame timing prediction, which helps the system accurately identify the start and end of network frames, thereby performing efficient signal processing; accurate clock counter configuration can reduce time errors, which is crucial for application scenarios that require high-precision timing; for example, in a wireless communication system, accurate timing can ensure the accurate transmission and reception of data; the programmability of the FPGA allows users to flexibly configure the period of the clock counter according to actual needs; this flexibility enables the system to adapt to different network environments and application scenarios, improving the versatility and adaptability of the system; since the FPGA clock counter configuration module can ensure the accuracy and stability of the clock signal, the overall reliability of the system is improved; this is particularly important for application scenarios that require long-term stable operation; the configuration of the clock counter can be conveniently implemented using the hardware description language of the FPGA (such as VHDL or Verilog); this hardware-based implementation method has higher real-time performance and determinacy than software implementation; although the hardware cost of the FPGA may be relatively high, by accurately configuring the clock counter, the performance and reliability of the system can be significantly improved, thereby reducing the long-term operation and maintenance costs and failure rates; in the long run, this cost-effectiveness is significant;

[0031] A full-time domain search window module for performing an initial scan in a large window mode and detecting valid cells;

[0032] Performing an initial scan in large window mode can ensure that the system can comprehensively detect all potential valid cells; this is crucial for quickly locating valid signals in a complex and ever-changing network environment; through the full-time-domain search window module, the system can cover a larger time range in one scan, thus reducing the number of scans and scan time; this is particularly important for application scenarios that require quick response; the full-time-domain search window module can dynamically adjust the size and position of the scan window according to actual needs; this flexibility enables the system to adapt to different network environments and application scenarios, improving the versatility and adaptability of the system; since the full-time-domain search window module can comprehensively detect all potential valid cells, the system can quickly locate valid signals, improving the response speed and efficiency of the system; in a wireless communication system, quickly locating valid cells means that users can establish connections faster, thus improving the user experience; by reducing the number of scans and scan time, the full-time-domain search window module can optimize the resource allocation of the system, reducing power consumption and costs; this is particularly important for application scenarios that require long-term stable operation.

[0033] Frame header latch and signal strength recording module, used to latch the accurate count value corresponding to the frame header and record the RSRP / SINR metrics of each cell.

[0034] This module can latch the accurate count value corresponding to the frame header, which is crucial for subsequent time difference calculation and timing compensation; accurately capturing the frame header can ensure that the system can accurately identify the start of the cell signal, enabling accurate timing analysis; the module records the RSRP (Reference Signal Received Power) and SINR (Signal-to-Interference-plus-Noise Ratio) metrics of each cell, and these metrics are important bases for evaluating the cell signal quality; comprehensively recording these metrics helps the system better understand the state of the cell signal, providing a basis for subsequent signal quality assessment and optimization; accurately capturing the frame header provides a reliable basis for the calculation of time difference, thus improving the timing accuracy; by recording the RSRP and SINR metrics, the system can more comprehensively evaluate the quality of the cell signal, providing strong support for optimizing network coverage and signal strength.

[0035] Difference value calculation and Kalman filter module, used to calculate the time difference ΔT and apply the Kalman filter to generate global timing compensation parameters.

[0036] This module can calculate the time difference ΔT between the frame header count values of the reference cell in the current round and historical rounds, which is crucial for subsequent global timing compensation; by applying a Kalman filter, the module can eliminate the influence of measurement noise on the time difference ΔT and improve the accuracy of timing compensation; accurately calculating the time difference ΔT and applying the Kalman filter for processing can significantly improve the accuracy of global timing compensation, thereby enhancing the overall performance of the system; the application of the Kalman filter can reduce the impact of measurement noise on the system performance and enhance the robustness and anti-interference ability of the system;

[0037] A dynamic window adjustment module, which is used to generate a predicted time window according to the global timing compensation parameter and the time difference ΔT, and adjust the size of the scanning window;

[0038] This module can generate a predicted time window according to the global timing compensation parameter and the time difference ΔT, providing a basis for subsequent adjustment of the scanning window; the module can dynamically adjust the size of the scanning window according to the predicted time window, improving the scanning efficiency; by dynamically adjusting the size of the scanning window, the system can lock the valid cell more quickly and improve the scanning efficiency; the dynamic window adjustment mechanism enables the system to more flexibly respond to changes in the network environment, improving the adaptability and reliability of the system;

[0039] An exception handling module, which is used to set the signal quality attenuation threshold, trigger a conditional full-window rescan, and establish a timing error tolerance model;

[0040] This module can set the signal quality attenuation threshold to detect the degradation of the cell signal quality; when the detected cell signal quality is lower than the set threshold, the module can trigger a conditional full-window rescan to ensure the stability and reliability of the system; the module can establish a timing error tolerance model to evaluate whether the timing error is within an acceptable range and adjust the scanning strategy according to the evaluation result; by setting the signal quality attenuation threshold and triggering a conditional full-window rescan, the system can take timely measures when the signal quality degrades to ensure the stability and reliability of the system; establishing a timing error tolerance model enables the system to more flexibly respond to changes in the timing error, improving the flexibility and adaptability of the system; at the same time, adjusting the scanning strategy according to the evaluation result also helps to optimize the system performance.

[0041] In summary, compared with the prior art, the present invention provides a method and system for fast scanning of public network cells based on frame timing prediction, having the following beneficial effects:

[0042] Through initial scanning and dynamic window adjustment, the present invention significantly improves the scanning efficiency, reduces unnecessary scanning time, enabling the system to lock onto valid cells more quickly. Secondly, during the difference value calculation phase, the Kalman filter is applied to process the time difference, eliminating measurement noise and enhancing the timing accuracy, providing a reliable basis for subsequent signal processing. In addition, the anomaly handling mechanism ensures that the system can respond promptly when the signal quality deteriorates, maintaining the system's stability through full-window rescan and other means. Finally, during the performance monitoring and policy optimization phase, the system's operating state is continuously monitored, and the system policy is optimized and adjusted based on the monitoring results, enabling the system to adapt to changes in the network environment and operate stably in the long term. This method exhibits significant advantages in improving scanning efficiency, enhancing timing accuracy, maintaining system stability, and adaptability. Description of the Drawings

[0043] Figure 1 It is a schematic diagram of the method for fast scanning of public network cells based on frame timing prediction of the present invention. Detailed Embodiments

[0044] The present invention provides a technical solution, a method for fast scanning of public network cells based on frame timing prediction. Please refer to Figure 1 , which includes the following steps:

[0045] Step 1. Initial scanning phase: Configure the field-programmable gate array (FPGA) clock counter with its period set to the target network frame period; turn on the full-time domain search window and perform scanning in the large window mode; when a valid cell is detected, latch the exact count value corresponding to the frame header of the cell and record the signal strength indicators of each cell, including but not limited to the reference signal received power (RSRP) and the signal-to-interference-plus-noise ratio (SINR).

[0046] Step 2. Difference value calculation phase: After each round of scanning, select the cell with the strongest signal strength in this round as the reference cell; compare the frame header count values of the reference cell in the current round and the historical round, and calculate the time difference ΔT; apply the Kalman filter to process ΔT, eliminate measurement noise, and generate global timing compensation parameters based on the processed ΔT.

[0047] Step 3. Dynamic window adjustment phase: For the latched cells, calculate the new predicted time T_new = T_old + ΔT × frequency offset compensation coefficient according to the global timing compensation parameters, the time difference ΔT, and the preset frequency offset compensation coefficient; generate a predicted time window [T_new - δ, T_new + δ] centered on T_new, where δ is a preset small range deviation value, much smaller than the large window range; for the unlatched cells, continue to perform scanning in the large window mode.

[0048] Step 4. Abnormal handling mechanism: Set the signal quality attenuation threshold as the RSRP drops by more than 3 dB; when it is detected that the cell signal quality is lower than the signal quality attenuation threshold, trigger a conditional full-window rescan; meanwhile, establish a timing error tolerance model to evaluate whether the timing error is within an acceptable range, and adjust the scanning strategy according to the evaluation result;

[0049] Step 5. Performance monitoring and strategy optimization: Continuously monitor the operating state of the system, evaluate the key performance indicators of timing accuracy, scanning efficiency, and signal quality, and optimize and adjust the system strategy according to the monitoring results to ensure the long-term stable operation of the system and its adaptation to changes in the network environment;

[0050] Through the large-window mode in the initial scanning stage, valid cells in the network can be quickly captured, improving the scanning coverage and speed; in the dynamic window adjustment stage, based on historical data and prediction algorithms, a smaller predicted time window is used to scan the latched cells, reducing unnecessary scanning time and further improving the scanning efficiency; in the difference value calculation stage, by comparing the frame header count values of the reference cell, the time difference is calculated, and the Kalman filter is applied to eliminate measurement noise, generating global timing compensation parameters, significantly improving the timing accuracy; in the dynamic window adjustment stage, according to the global timing compensation parameters, time difference, and frequency offset compensation coefficient, a new predicted time is calculated to further refine the timing control; in the initial scanning stage, the signal strength indicators (such as RSRP and SINR) of each cell are recorded, providing basic data for subsequent signal quality evaluation and processing; the abnormal handling mechanism sets the signal quality attenuation threshold, and when a signal quality drop is detected, a full-window rescan is triggered to ensure that the system can promptly respond to changes in signal quality; the timing error tolerance model is used to evaluate whether the timing error is within an acceptable range and adjust the scanning strategy according to the evaluation result, enhancing the adaptability of the system; in the performance monitoring and strategy optimization stage, the operating state of the system is continuously monitored, and the system strategy is optimized and adjusted according to the monitoring results, ensuring the long-term stable operation of the system and its adaptation to changes in the network environment; through dynamic window adjustment and optimized scanning strategy, unnecessary scanning and consumption of computing resources are reduced, and the overall energy consumption of the system is lowered.

[0051] Please refer to Figure 1 For the above, the specific content of Step 1 further includes: The configuration of the clock counter sets the counter accuracy according to the fluctuation range of the target network frame period to ensure accurate capture of the frame header;

[0052] Set the counter accuracy according to the fluctuation range of the target network frame period to ensure accurate capture of the frame header, improving the timing accuracy; adapt to the frame period changes in different network environments, enhancing the flexibility and adaptability of the system.

[0053] Please refer to Figure 1, the specific steps of step one further include: The large window mode of the full-time domain search window specifically covers the entire time range to capture all potential cell signals;

[0054] Covering the entire time range to capture all potential cell signals improves the comprehensiveness of the scan; ensuring that no possible cell signals are missed provides more options for subsequent cell selection and synchronization.

[0055] Please refer to Figure 1 , step one also includes: When latching the exact count value corresponding to the frame header of the cell, it also includes recording the characteristic information of the frame header, frame synchronization sequence or specific pilot signal, which is convenient for subsequent cell identification and synchronization;

[0056] It is convenient for subsequent cell identification and synchronization, improving the system's identification ability and synchronization efficiency; by recording the frame synchronization sequence or specific pilot signal, the robustness and reliability of the system are enhanced.

[0057] Please refer to Figure 1 , the specific steps of step two further include: In the difference value calculation stage, the application of the Kalman filter includes initializing the filter parameters and updating the filter state, as well as adjusting the filter estimate based on the new observation value to achieve smoothing and denoising of ΔT;

[0058] Achieving smoothing and denoising of ΔT improves the calculation accuracy of the time difference; by initializing the filter parameters, updating the filter state, and adjusting the filter estimate, the anti-interference ability and stability of the system are enhanced.

[0059] Please refer to Figure 1 , the specific steps of step three further include: In the dynamic window adjustment stage, the determination of the frequency offset compensation coefficient is based on the frequency offset statistical information of each cell in the historical scan data and the change trend of the current network environment;

[0060] Based on the historical scan data and the change trend of the current network environment, a more accurate frequency offset compensation coefficient is determined; improving the accuracy of the predicted time T_new, thereby improving the adjustment efficiency of the dynamic window and the scan efficiency.

[0061] Please refer to Figure 1 , the specific steps of step three further include: The generation of the prediction time window [T_new - δ, T_new + δ] also includes considering the impact of network dynamics and device mobility on timing prediction, and adjusting the size of δ according to these factors;

[0062] Considering the impact of network dynamics and device mobility on timing prediction and adjusting the size of δ improves the adaptability of the prediction window; ensuring that the prediction time window can more accurately cover the appearance time of the actual cell signal, improving the hit rate and efficiency of the scan.

[0063] Please refer to Figure 1 In the fourth step, it specifically further includes: In the exception handling mechanism, the triggering conditions for conditional full-window rescan also include considering other signal quality indicators, such as the drop amplitude of SINR or signal volatility, to comprehensively evaluate the stability of cell signal quality; when establishing the timing error tolerance model, consider the network synchronization requirements, device clock accuracy, and the impact of environmental factors on the timing error, and set a reasonable error tolerance range based on these factors;

[0064] Comprehensively evaluate the stability of cell signal quality, set a reasonable error tolerance range, and improve the robustness of the system; by considering multiple signal quality indicators and network synchronization requirements, enhance the system's ability to respond to network environment changes.

[0065] Please refer to Figure 1 In the fifth step, it further includes: Real-time monitor the stability of the FPGA clock counter to ensure that its period is consistent with the target network frame period; track and record the changes in the reference cell in each round of scanning, the calculation results of the time difference ΔT, and the application of the global timing compensation parameters; monitor the hit rate of the prediction time window, that is, the proportion of the latched cells that are successfully detected within the prediction time window; regularly analyze the change trend of the signal strength indicator and the triggering situation of the exception handling mechanism; evaluate the effectiveness of the current scanning strategy, timing compensation parameters, and prediction time window settings based on the performance monitoring data; for the situations of large timing errors, low scanning efficiency, or large signal quality fluctuations, analyze the reasons and propose optimization suggestions: adjust the global timing compensation parameters, frequency offset compensation coefficient, and the deviation value δ of the prediction time window to improve the timing accuracy and scanning efficiency; optimize the exception handling mechanism, such as adjusting the signal quality attenuation threshold and improving the timing error tolerance model, to cope with the changes in the network environment; record the key events and performance indicators during the system operation to form a system log; regularly generate performance reports, including the statistical data and trend analysis on timing accuracy, scanning efficiency, and signal quality; provide the system log and performance reports to relevant personnel for system maintenance, fault troubleshooting, and strategy optimization decisions;

[0066] Real-time monitor the stability of the FPGA clock counter, track and record the key data during the scanning process, regularly analyze the signal strength indicator and the triggering situation of the exception handling mechanism; provide comprehensive performance monitoring data to provide strong support for system maintenance, fault troubleshooting, and strategy optimization decisions, and ensure the long-term stable operation of the system and its adaptation to the changes in the network environment.

[0067] A public network cell fast scanning system based on frame timing prediction, including:

[0068] An FPGA clock counter configuration module for configuring the period of the FPGA clock counter to match the target network frame period;

[0069] By configuring the period of the FPGA clock counter to match the target network frame period, the synchronization of the system in time can be ensured; this is the basis for accurate frame timing prediction, which helps the system accurately identify the start and end of network frames, thus enabling efficient signal processing; precise clock counter configuration can reduce time errors, which is crucial for application scenarios that require high-precision timing; for example, in wireless communication systems, precise timing can ensure accurate data transmission and reception; the programmability of FPGA allows users to flexibly configure the period of the clock counter according to actual needs; this flexibility enables the system to adapt to different network environments and application scenarios, improving the versatility and adaptability of the system; since the FPGA clock counter configuration module can ensure the accuracy and stability of the clock signal, the overall reliability of the system is improved; this is particularly important for application scenarios that require long-term stable operation; using the hardware description language of FPGA (such as VHDL or Verilog) can easily implement the configuration of the clock counter; this hardware-based implementation method has higher real-time performance and determinacy than software implementation; although the hardware cost of FPGA may be relatively high, by precisely configuring the clock counter, the performance and reliability of the system can be significantly improved, thus reducing the long-term operation and maintenance costs and failure rates; in the long run, this cost-effectiveness is significant;

[0070] Full-time domain search window module, used for initial scanning in large window mode and detecting valid cells;

[0071] Initial scanning in large window mode can ensure that the system can comprehensively detect all potential valid cells; this is crucial for quickly locating valid signals in a complex and changing network environment; through the full-time domain search window module, the system can cover a larger time range in one scan, thus reducing the number of scans and scan time; this is particularly important for application scenarios that require quick response; the full-time domain search window module can dynamically adjust the size and position of the scanning window according to actual needs; this flexibility enables the system to adapt to different network environments and application scenarios, improving the versatility and adaptability of the system; since the full-time domain search window module can comprehensively detect all potential valid cells, the system can quickly locate valid signals, improving the response speed and efficiency of the system; in wireless communication systems, quickly locating valid cells means that users can establish connections faster, thus improving the user experience; by reducing the number of scans and scan time, the full-time domain search window module can optimize the resource configuration of the system, reducing power consumption and costs; this is particularly important for application scenarios that require long-term stable operation;

[0072] Frame header latch and signal strength recording module, used for latching the accurate count value corresponding to the frame header and recording the RSRP / SINR indicators of each cell;

[0073] This module can latch the exact count value corresponding to the frame header, which is crucial for subsequent time difference calculation and timing compensation; accurately capturing the frame header can ensure that the system can accurately identify the start of the cell signal, thus enabling accurate timing analysis; the module records the RSRP (Reference Signal Received Power) and SINR (Signal-to-Interference-plus-Noise Ratio) indicators of each cell, and these indicators are important bases for evaluating the cell signal quality; comprehensively recording these indicators helps the system to more accurately understand the state of the cell signal and provides a basis for subsequent signal quality evaluation and optimization; accurately capturing the frame header provides a reliable basis for the calculation of time difference, thereby improving the timing accuracy; by recording the RSRP and SINR indicators, the system can more comprehensively evaluate the quality of the cell signal and provide strong support for optimizing network coverage and signal strength;

[0074] The difference value calculation and Kalman filter module is used to calculate the time difference ΔT and apply the Kalman filter to generate global timing compensation parameters;

[0075] This module can calculate the time difference ΔT between the frame header count values of the reference cell in the current round and the historical round, which is crucial for subsequent global timing compensation; by applying the Kalman filter, the module can eliminate the influence of measurement noise on the time difference ΔT and improve the accuracy of timing compensation; accurately calculating the time difference ΔT and applying the Kalman filter for processing can significantly improve the accuracy of global timing compensation, thereby improving the overall performance of the system; the application of the Kalman filter can reduce the influence of measurement noise on the system performance and enhance the robustness and anti-interference ability of the system;

[0076] The dynamic window adjustment module is used to generate a predicted time window according to the global timing compensation parameters and the time difference ΔT, and adjust the size of the scanning window;

[0077] This module can generate a predicted time window according to the global timing compensation parameters and the time difference ΔT, providing a basis for subsequent scanning window adjustment; the module can dynamically adjust the size of the scanning window according to the predicted time window, improving the scanning efficiency; by dynamically adjusting the size of the scanning window, the system can lock the effective cells more quickly and improve the scanning efficiency; the dynamic window adjustment mechanism enables the system to more flexibly respond to changes in the network environment and improves the adaptability and reliability of the system;

[0078] The exception handling module is used to set the signal quality attenuation threshold, trigger conditional full-window rescan, and establish a timing error tolerance model;

[0079] This module can set a signal quality attenuation threshold to detect the degradation of cell signal quality. When the detected cell signal quality is lower than the set threshold, the module can trigger a conditional full-window rescan to ensure the stability and reliability of the system. The module can establish a timing error tolerance model to evaluate whether the timing error is within an acceptable range and adjust the scanning strategy according to the evaluation result. By setting the signal quality attenuation threshold and triggering a conditional full-window rescan, the system can take timely measures when the signal quality degrades to ensure the stability and reliability of the system. Establishing a timing error tolerance model enables the system to more flexibly respond to changes in timing error, improving the flexibility and adaptability of the system. At the same time, adjusting the scanning strategy according to the evaluation result also helps to optimize the system performance.

[0080] It should be noted that in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0081] 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, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for rapid scanning of public network cells based on frame timing prediction, characterized in that It includes the following steps: Step 1, Initial Scanning Phase: Configure the Field-Programmable Gate Array (FPGA) clock counter with its period set to the target network frame period; Enable the full-time domain search window and perform scanning in the large window mode; When a valid cell is detected, latch the exact count value corresponding to the cell's frame header and record the signal strength indicators of each cell, including but not limited to the Reference Signal Received Power (RSRP) and Signal-to-Interference plus Noise Ratio (SINR). Step 2, Difference Value Calculation Phase: After each round of scanning, select the cell with the strongest signal strength in this round as the reference cell; Compare the frame header count values of the reference cell in the current round and the historical rounds, and calculate the time difference ΔT; Apply the Kalman filter to process ΔT to eliminate measurement noise and generate the global timing compensation parameter based on the processed ΔT. Step 3, Dynamic Window Adjustment Phase: For the latched cells, calculate the new predicted time T_new = T_old + ΔT × frequency offset compensation coefficient according to the global timing compensation parameter, the time difference ΔT, and the preset frequency offset compensation coefficient; Generate a predicted time window [T_new - δ, T_new + δ] centered on T_new, where δ is a preset small range deviation value, smaller than the large window range; For the unlatched cells, continue to perform scanning in the large window mode. Step 4, Abnormal Handling Mechanism: Set the signal quality attenuation threshold as the signal received power drops by more than 3 dB; When the cell signal quality is detected to be lower than the signal quality attenuation threshold, trigger a conditional full window rescan; At the same time, establish a timing error tolerance model to evaluate whether the timing error is within the acceptable range and adjust the scanning strategy according to the evaluation result. Step 5, Performance Monitoring and Strategy Optimization: Continuously monitor the operating status of the system, evaluate the key performance indicators, including timing accuracy, scanning efficiency, and signal quality, and optimize and adjust the system strategy according to the monitoring results to ensure the long-term stable operation of the system and its adaptation to the changes in the network environment.

2. The method for fast scanning of public network cells based on frame timing prediction according to claim 1, characterized in that, Specifically, Step 1 further includes: The configuration of the clock counter sets the counter accuracy according to the fluctuation range of the target network frame period to ensure accurate capture of the frame header.

3. The method for fast scanning of public network cells based on frame timing prediction according to claim 2, wherein Specifically, Step 1 further includes: The large window mode of the full-time domain search window specifically covers the entire time range to capture all potential cell signals.

4. The method for fast scanning of public network cells based on frame timing prediction according to claim 3, wherein Step 1 also includes: When latching the exact count value corresponding to the cell's frame header, it also includes recording the characteristic information of the frame header, such as the frame synchronization sequence or specific pilot signal, for subsequent cell identification and synchronization.

5. The method for fast scanning of public network cells based on frame timing prediction according to claim 4, characterized in that, Specifically, Step 2 further includes: In the difference value calculation phase, the application of the Kalman filter includes initializing the filter parameters and updating the filter state, as well as adjusting the filter estimate based on the new observation values to achieve smoothing and denoising of ΔT.

6. The method for fast scanning of public network cells based on frame timing prediction according to claim 5, characterized in that, Specifically, Step 3 further includes: In the dynamic window adjustment phase, the determination of the frequency offset compensation coefficient is based on the frequency offset statistical information of each cell in the historical scanning data and the change trend of the current network environment.

7. The method for fast scanning of public network cells based on frame timing prediction according to claim 6, characterized in that Step 3 specifically further includes: The generation of the prediction time window [T_new - δ, T_new + δ] also includes considering the impact of network dynamics and device mobility on timing prediction, and adjusting the size of δ according to these factors.

8. The method for fast scanning of public network cells based on frame timing prediction according to claim 7, wherein Step 4 specifically further includes: In the exception handling mechanism, the triggering conditions for conditional full-window rescan also include considering other signal quality metrics, the decrease amplitude of the signal-to-interference-plus-noise ratio or signal volatility, to comprehensively evaluate the stability of cell signal quality; when establishing the timing error tolerance model, consider the impact of network synchronization requirements, device clock accuracy, and environmental factors on timing error, and set a reasonable error tolerance range according to these factors.

9. The method for fast scanning of public network cells based on frame timing prediction according to claim 8, wherein Step 5 further includes: Real-time monitor the stability of the clock counter to ensure that its period is consistent with the target network frame period; Track and record the changes of the reference cell in each round of scan, the calculation results of the time difference ΔT, and the application status of the global timing compensation parameter; Monitor the hit rate of the prediction time window, that is, the proportion of successfully detected latched cells within the prediction time window; Regularly analyze the change trend of the signal strength indicator and the triggering situation of the exception handling mechanism; According to the performance monitoring data, evaluate the effectiveness of the current scan strategy, timing compensation parameter, and prediction time window setting; For the situation of large timing error, low scan efficiency, or large signal quality fluctuation, analyze the reasons and put forward optimization suggestions, adjust the global timing compensation parameter, frequency offset compensation coefficient, and the deviation value δ of the prediction time window to improve timing accuracy and scan efficiency, optimize the exception handling mechanism, and cope with the changes in the network environment; Record the key events and performance indicators during the system operation process to form a system log; Regularly generate performance reports, including statistical data and trend analysis on timing accuracy, scan efficiency, and signal quality; Provide the system log and performance reports to relevant personnel for system maintenance, fault troubleshooting, and policy optimization decision-making.

10. A public network cell rapid scanning system based on frame timing prediction, which adopts the method for rapid scanning of public network cells based on frame timing prediction according to any one of claims 1-9, characterized in that, It includes: A programmable gate array clock counter configuration module for configuring the period of the programmable gate array clock counter to match the target network frame period; A full-time domain search window module for initial scanning in large window mode and detecting valid cells; A frame header latch and signal strength recording module for latching the accurate count value corresponding to the frame header and recording the signal reception power / interference-plus-noise ratio indicators of each cell; A difference value calculation and Kalman filter module for calculating the time difference ΔT and applying the Kalman filter to generate the global timing compensation parameter; A dynamic window adjustment module for generating a prediction time window according to the global timing compensation parameter and the time difference ΔT and adjusting the size of the scan window; An exception handling module for setting the signal quality attenuation threshold, triggering conditional full-window rescan, and establishing a timing error tolerance model.

Images