Public network cell rapid scanning method and system based on frame timing prediction

Abstract

The invention discloses a public network cell rapid scanning method and system based on frame timing prediction. The method comprises the following steps: step 1, an initial scanning stage: configuring a field programmable gate array clock counter; opening a full-time-domain search window, and scanning in a large window mode; step 2, a difference value calculation stage: after each round of scanning is finished, screening a cell with the strongest signal intensity in the round as a reference cell; and step 3, a dynamic window adjustment stage: for the latched cell, according to the global timing compensation parameter, the time difference delta T and a preset frequency offset compensation coefficient; step 4, an exception handling mechanism: setting a signal quality attenuation threshold value; and step 5, performance monitoring and strategy optimization. According to the method, the scanning efficiency is improved through initial scanning and dynamic window adjustment, the timing accuracy is enhanced by applying a Kalman filter, the system stability is kept by an exception handling mechanism, the system is ensured to adapt to the network environment change for a long time through performance monitoring and strategy optimization, and the method integrally shows the advantages of high efficiency, accuracy and stability.

Description

Technical Field

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

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

[0003] Although this scanning method is simple and direct, it has many shortcomings. The fixed-period large-scale cell scanning method is simple but inefficient, because the same search is performed regardless of the network environment, which leads to an increase in unnecessary operations in a stable environment. At the same time, the wide scanning range increases energy consumption and affects the battery life of the device. The timing synchronization method does not utilize historical information, and repeated calculations lead to waste of resources and limit the scanning speed. Therefore, a public network cell fast scanning method and system based on frame timing prediction are proposed. Summary of the invention

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

[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, comprising the following steps: Step 1, initial scanning phase: configure the field programmable gate array (FPGA) clock counter, and set its period to the target network frame period; open the full time domain search window and scan in large window mode; when a valid cell is detected, latch the precise 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 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 value of the reference cell in the current round with that in the historical round, and calculate the time difference ΔT; apply the Kalman filter to process ΔT to eliminate measurement noise, and generate global timing compensation parameters based on the processed ΔT; Step 3, dynamic window adjustment stage: for locked cells, calculate the new predicted time T_new=T_old+ΔT×frequency offset compensation coefficient according to the global timing compensation parameter and time difference ΔT, as well as the preset frequency offset compensation coefficient; with T_new as the center, generate a predicted time window [T_new-δ,T_new+δ], where δ is the preset small range deviation value, which is smaller than the large window range; for unlocked cells, continue to scan in large window mode; Step 4: Exception handling mechanism: Set the signal quality attenuation threshold, when RSRP drops by more than 3dB; when the cell signal quality is detected to be lower than the signal quality attenuation threshold, trigger the conditional full-window rescan; at the same time, 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 results; Step 5: Performance monitoring and strategy optimization: Continuously monitor the system's operating status, evaluate the key performance indicators of timing accuracy, scanning efficiency, and signal quality, and optimize and adjust the system strategy based on the monitoring results to ensure the long-term stable operation of the system and adapt to changes in the network environment; Through the large window mode in the initial scanning stage, the effective cells in the network can be quickly captured, which improves the coverage and speed of the scanning; in the dynamic window adjustment stage, a smaller prediction time window is used to scan the locked cells according to historical data and prediction algorithms, which reduces unnecessary scanning time and further improves the scanning efficiency; in the difference value calculation stage, the frame header count value of the reference cell is compared to calculate the time difference, and the Kalman filter is applied to eliminate the measurement noise and generate the global timing compensation parameter, which significantly improves the timing accuracy; in the dynamic window adjustment stage, the new prediction time is calculated according to the global timing compensation parameter and time difference, as well as the frequency deviation compensation coefficient, which further refines the timing control; in the initial scanning stage, the signal strength index of each cell is recorded. The signal quality indicators (such as RSRP and SINR) are used to provide basic data for subsequent signal quality evaluation and processing; the exception handling mechanism sets the signal quality attenuation threshold. When the signal quality degradation is detected, the full window rescan is triggered to ensure that the system can respond to the changes in signal quality in a timely manner; the timing error tolerance model is used to evaluate whether the timing error is within an acceptable range, and the scanning strategy is adjusted according to the evaluation results, thereby enhancing the adaptability of the system; the performance monitoring and strategy optimization stage continuously monitors the operating status of the system, and optimizes and adjusts the system strategy according to the monitoring results, thereby ensuring the long-term stable operation of the system and adapting to changes in the network environment; through dynamic window adjustment and optimization of scanning strategies, unnecessary scanning and computing resource consumption are reduced, thereby reducing the overall energy consumption of the system.

[0006] Preferably, the step 1 specifically further includes: configuring the clock counter to set the counter precision according to the fluctuation range of the target network frame period to ensure accurate capture of the frame header; Set the counter accuracy according to the fluctuation range of the target network frame period to ensure accurate capture of the frame header and improve timing accuracy; adapt to frame period changes in different network environments and enhance system flexibility and adaptability.

[0007] Preferably, the step 1 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; Covering the entire time range, capturing all potential cell signals, and improving the comprehensiveness of the scan; ensuring that no possible cell signals are missed, providing more options for subsequent cell selection and synchronization.

[0008] Preferably, the step 1 further includes: when latching the precise count value corresponding to the frame header of the cell, it also includes recording characteristic information of the frame header, a frame synchronization sequence or a specific pilot signal to facilitate subsequent cell identification and synchronization; It facilitates subsequent cell identification and synchronization, improves the system's identification capability and synchronization efficiency; and enhances the system's robustness and reliability by recording frame synchronization sequences or specific pilot signals.

[0009] Preferably, the step 2 specifically further includes: in the difference value calculation stage, the application of the Kalman filter includes initializing filter parameters and updating filter states, and adjusting the filter estimation value based on the new observation value to achieve smoothing and denoising of ΔT; The smoothing and denoising of ΔT are achieved to improve the calculation accuracy of the time difference; the anti-interference ability and stability of the system are enhanced by initializing the filter parameters, updating the filter status and adjusting the filter estimation value.

[0010] Preferably, the step three specifically further includes: in the dynamic window adjustment stage, determining 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; Based on the historical scanning data and the changing trend of the current network environment, a more accurate frequency offset compensation coefficient is determined; the accuracy of the predicted time T_new is improved, thereby improving the adjustment efficiency and scanning efficiency of the dynamic window.

[0011] Preferably, the 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; Considering the impact of network dynamics and device mobility on timing prediction, adjust the size of δ to 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.

[0012] Preferably, the step 4 specifically further includes: in the exception handling mechanism, the triggering condition of the conditional full-window rescan also includes considering other signal quality indicators, such as the decrease amplitude of SINR or the signal fluctuation rate, and comprehensively evaluating the stability of the cell signal quality; when establishing the timing error tolerance model, considering the influence of network synchronization requirements, equipment clock accuracy and environmental factors on the timing error, and setting a reasonable error tolerance range based on these factors; 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 changes in the network environment.

[0013] Preferably, the step five also includes: real-time monitoring of 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 of each round of scanning, the calculation results of the time difference ΔT, and the application of the global timing compensation parameters; monitoring the hit rate of the predicted time window, that is, the proportion of the locked cells that are successfully detected within the predicted time window; regularly analyzing the changing trend of the signal strength index and the triggering 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; analyzing the causes and proposing optimization suggestions for situations where the timing error is large, the scanning efficiency is low, or the signal quality fluctuates greatly: adjusting the global timing compensation parameters, the frequency offset compensation coefficient, and the deviation value δ of the prediction time window to improve the timing accuracy and scanning efficiency: optimizing the exception handling mechanism, such as adjusting the signal quality attenuation threshold, improving the timing error tolerance model, and coping with changes in the network environment; recording key events and performance indicators during the system operation to form a system log; regularly generating performance reports, including statistical data and trend analysis on timing accuracy, scanning efficiency, and signal quality; providing the system log and performance report to relevant personnel for system maintenance, troubleshooting, and strategy optimization decisions; Monitor the stability of the FPGA clock counter in real time, track and record key data during the scanning process, and regularly analyze the signal strength indicators and the triggering of the exception handling mechanism; provide comprehensive performance monitoring data to provide strong support for system maintenance, troubleshooting, and strategy optimization decisions, ensuring the long-term stable operation of the system and adapting to changes in the network environment.

[0014] The public network cell fast scanning system based on frame timing prediction includes: FPGA clock counter configuration module, used to configure the period of FPGA clock counter to match the period of target network frame; By configuring the FPGA clock counter cycle to match the target network frame cycle, the system can be synchronized in time. This is the basis for accurate frame timing prediction, which helps the system accurately identify the start and end of the network frame, 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 wireless communication systems, accurate timing can ensure accurate transmission and reception of data. The programmability of FPGA allows users to flexibly configure the clock counter cycle 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 especially important for application scenarios that require long-term stable operation. The clock counter configuration can be easily implemented using the FPGA hardware description language (such as VHDL or Verilog). This hardware-based implementation has higher real-time and deterministic performance than software implementation. Although the hardware cost of FPGA may be high, by accurately configuring the clock counter, the performance and reliability of the system can be significantly improved, thereby reducing long-term operation and maintenance costs and failure rates. In the long run, this cost-effectiveness is significant. Full time domain search window module, used to perform initial scanning and detect valid cells in large window mode; Performing an initial scan in large window mode ensures that the system can fully detect all potential valid cells; this is essential for quickly locating valid signals in complex and changing network environments; through the full-time search window module, the system can cover a larger time range in one scan, thereby reducing the number of scans and scanning time; this is particularly important for application scenarios that require rapid response; the full-time 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; because the full-time search window module can fully 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, thereby improving user experience; by reducing the number of scans and scanning time, the full-time search window module can optimize the system's resource allocation, reduce power consumption and cost; this is particularly important for application scenarios that require long-term stable operation; The frame header latching and signal strength recording module is used to latch the precise count value corresponding to the frame header and record the RSRP / SINR index of each cell; The module can latch the precise count value corresponding to the frame header, which is crucial for the subsequent time difference calculation and timing compensation; accurate capture of the frame header ensures that the system can accurately identify the start of the cell signal, thereby performing accurate timing analysis; the module records the RSRP (reference signal received power) and SINR (signal to interference plus noise ratio) indicators of each cell, which are important bases for evaluating the quality of cell signals; comprehensive recording of these indicators helps the system to more accurately understand the status of cell signals and provide a basis for subsequent signal quality evaluation and optimization; accurate capture of the frame header provides a reliable basis for the calculation of time difference, thereby improving the accuracy of timing; by recording RSRP and SINR indicators, the system can more comprehensively evaluate the quality of cell signals and provide strong support for optimizing network coverage and signal strength; A 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; 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 the 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 system performance and enhance the robustness and anti-interference ability of the system. A dynamic window adjustment module, used to generate a prediction time window according to a global timing compensation parameter and a time difference ΔT, and adjust a scanning window size; The module can generate a predicted time window based on 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 based on the predicted time window to improve scanning efficiency; by dynamically adjusting the size of the scanning window, the system can lock the effective cell more quickly and improve scanning efficiency; the dynamic window adjustment mechanism enables the system to respond to changes in the network environment more flexibly, improving the adaptability and reliability of the system; Exception handling module, used to set signal quality attenuation threshold, trigger conditional full-window rescan, and establish timing error tolerance model; The module can set the signal quality attenuation threshold to detect the degradation of cell signal quality; when it is detected that the 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 results; 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 respond to changes in timing errors more flexibly and improve the flexibility and adaptability of the system; at the same time, adjusting the scanning strategy according to the evaluation results also helps to optimize system performance.

[0015] In summary, compared with the prior art, the present invention provides a method and system for quickly scanning public network cells based on frame timing prediction, which has the following beneficial effects: This invention significantly improves scanning efficiency and reduces unnecessary scanning time through initial scanning and dynamic window adjustment, so that the system can lock valid cells more quickly. Secondly, the Kalman filter is used to process the time difference in the difference value calculation stage, which eliminates measurement noise and enhances timing accuracy, providing a reliable foundation for subsequent signal processing. In addition, the exception handling mechanism ensures that the system can respond in time when the signal quality degrades, and maintains the stability of the system through full-window rescanning and other methods. Finally, the performance monitoring and strategy optimization stage continuously monitors the operating status of the system, and optimizes and adjusts the system strategy according to the monitoring results, so that the system can adapt to changes in the network environment and operate stably for a long time. This method has shown significant advantages in improving scanning efficiency, enhancing timing accuracy, and maintaining system stability and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of a method for rapid scanning of public network cells based on frame timing prediction of the present invention. DETAILED DESCRIPTION

[0017] 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 , including the following steps: Step 1, initial scanning phase: configure the field programmable gate array (FPGA) clock counter, and set its period to the target network frame period; open the full time domain search window and scan in large window mode; when a valid cell is detected, latch the precise 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 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 value of the reference cell in the current round with that in the historical round, and calculate the time difference ΔT; apply the Kalman filter to process ΔT to eliminate measurement noise, and generate global timing compensation parameters based on the processed ΔT; Step 3, dynamic window adjustment stage: for locked cells, calculate the new predicted time T_new=T_old+ΔT×frequency offset compensation coefficient according to the global timing compensation parameters and time difference ΔT, as well as the preset frequency offset compensation coefficient; with T_new as the center, generate a predicted time window [T_new-δ,T_new+δ], where δ is the preset small range deviation value, which is much smaller than the large window range; for unlocked cells, continue to scan in large window mode; Step 4: Exception handling mechanism: Set the signal quality attenuation threshold, when RSRP drops by more than 3dB; when the cell signal quality is detected to be lower than the signal quality attenuation threshold, trigger the conditional full-window rescan; at the same time, 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 results; Step 5: Performance monitoring and strategy optimization: Continuously monitor the system's operating status, evaluate the key performance indicators of timing accuracy, scanning efficiency, and signal quality, and optimize and adjust the system strategy based on the monitoring results to ensure the long-term stable operation of the system and adapt to changes in the network environment; Through the large window mode in the initial scanning stage, the effective cells in the network can be quickly captured, which improves the coverage and speed of the scanning; in the dynamic window adjustment stage, a smaller prediction time window is used to scan the locked cells according to historical data and prediction algorithms, which reduces unnecessary scanning time and further improves the scanning efficiency; in the difference value calculation stage, the frame header count value of the reference cell is compared to calculate the time difference, and the Kalman filter is applied to eliminate the measurement noise and generate the global timing compensation parameter, which significantly improves the timing accuracy; in the dynamic window adjustment stage, the new prediction time is calculated according to the global timing compensation parameter and time difference, as well as the frequency deviation compensation coefficient, which further refines the timing control; in the initial scanning stage, the signal strength index of each cell is recorded. The signal quality indicators (such as RSRP and SINR) are used to provide basic data for subsequent signal quality evaluation and processing; the exception handling mechanism sets the signal quality attenuation threshold. When the signal quality degradation is detected, the full window rescan is triggered to ensure that the system can respond to the changes in signal quality in a timely manner; the timing error tolerance model is used to evaluate whether the timing error is within an acceptable range, and the scanning strategy is adjusted according to the evaluation results, thereby enhancing the adaptability of the system; the performance monitoring and strategy optimization stage continuously monitors the operating status of the system, and optimizes and adjusts the system strategy according to the monitoring results, thereby ensuring the long-term stable operation of the system and adapting to changes in the network environment; through dynamic window adjustment and optimization of scanning strategies, unnecessary scanning and computing resource consumption are reduced, thereby reducing the overall energy consumption of the system.

[0018] See also Figure 1 , the step 1 specifically further includes: configuring the clock counter to set the counter precision according to the fluctuation range of the target network frame period to ensure accurate capture of the frame header; Set the counter accuracy according to the fluctuation range of the target network frame period to ensure accurate capture of the frame header and improve timing accuracy; adapt to frame period changes in different network environments and enhance system flexibility and adaptability.

[0019] See also Figure 1 , the step 1 specifically also includes: the large window mode of the full time domain search window specifically covers the entire time range and captures all potential cell signals; Covering the entire time range, capturing all potential cell signals, and improving the comprehensiveness of the scan; ensuring that no possible cell signals are missed, providing more options for subsequent cell selection and synchronization.

[0020] See also Figure 1 , the step 1 further includes: when latching the precise 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 the specific pilot signal, so as to facilitate the subsequent cell identification and synchronization; It facilitates subsequent cell identification and synchronization, improves the system's identification capability and synchronization efficiency; and enhances the system's robustness and reliability by recording frame synchronization sequences or specific pilot signals.

[0021] See also Figure 1 , the step 2 specifically includes: in the difference value calculation stage, the application of the Kalman filter includes initializing filter parameters and updating filter states, and adjusting the filter estimation value based on the new observation value to achieve smoothing and denoising of ΔT; The smoothing and denoising of ΔT are achieved to improve the calculation accuracy of the time difference; the anti-interference ability and stability of the system are enhanced by initializing the filter parameters, updating the filter status and adjusting the filter estimation value.

[0022] See also Figure 1 , the step three specifically also includes: in the dynamic window adjustment stage, the frequency offset compensation coefficient is determined based on the frequency offset statistical information of each cell in the historical scanning data, and the change trend of the current network environment; Based on the historical scanning data and the changing trend of the current network environment, a more accurate frequency offset compensation coefficient is determined; the accuracy of the predicted time T_new is improved, thereby improving the adjustment efficiency and scanning efficiency of the dynamic window.

[0023] See also Figure 1 , the step three specifically 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; Considering the impact of network dynamics and device mobility on timing prediction, adjust the size of δ to 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] See also Figure 1 , the step 4 specifically includes: in the exception handling mechanism, the triggering condition of the conditional full-window rescan also includes considering other signal quality indicators, such as the decline of SINR or the signal fluctuation rate, to comprehensively evaluate the stability of the cell signal quality; when establishing the timing error tolerance model, the influence of network synchronization requirements, equipment clock accuracy and environmental factors on the timing error is considered, and a reasonable error tolerance range is set according to these factors; 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 changes in the network environment.

[0025] See also Figure 1, the step five also includes: real-time monitoring of 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 benchmark cells in each round of scanning, the calculation results of the time difference ΔT, and the application of the global timing compensation parameters; monitoring the hit rate of the predicted time window, that is, the proportion of the locked cells that are successfully detected within the predicted time window; regularly analyzing the change trend of the signal strength index and the triggering of the exception handling mechanism; evaluating the effectiveness of the current scanning strategy, timing compensation parameters, and prediction time window settings based on performance monitoring data; analyzing the causes and proposing optimization suggestions for situations where the timing error is large, the scanning efficiency is low, or the signal quality fluctuates greatly: adjusting the global timing compensation parameters, the frequency offset compensation coefficient, and the deviation value δ of the prediction time window to improve the timing accuracy and scanning efficiency: optimizing the exception handling mechanism, such as adjusting the signal quality attenuation threshold and improving the timing error tolerance model to cope with changes in the network environment; recording key events and performance indicators during the system operation to form a system log; regularly generating performance reports, including statistical data and trend analysis on timing accuracy, scanning efficiency, and signal quality; providing system logs and performance reports to relevant personnel for system maintenance, troubleshooting, and strategy optimization decisions; Monitor the stability of the FPGA clock counter in real time, track and record key data during the scanning process, and regularly analyze the signal strength indicators and the triggering of the exception handling mechanism; provide comprehensive performance monitoring data to provide strong support for system maintenance, troubleshooting, and strategy optimization decisions, ensuring the long-term stable operation of the system and adapting to changes in the network environment.

[0026] The public network cell fast scanning system based on frame timing prediction includes: FPGA clock counter configuration module, used to configure the period of FPGA clock counter to match the period of target network frame; By configuring the FPGA clock counter cycle to match the target network frame cycle, the system can be synchronized in time. This is the basis for accurate frame timing prediction, which helps the system accurately identify the start and end of the network frame, 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 wireless communication systems, accurate timing can ensure accurate transmission and reception of data. The programmability of FPGA allows users to flexibly configure the clock counter cycle 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 especially important for application scenarios that require long-term stable operation. The clock counter configuration can be easily implemented using the FPGA hardware description language (such as VHDL or Verilog). This hardware-based implementation has higher real-time and deterministic performance than software implementation. Although the hardware cost of FPGA may be high, by accurately configuring the clock counter, the performance and reliability of the system can be significantly improved, thereby reducing long-term operation and maintenance costs and failure rates. In the long run, this cost-effectiveness is significant. Full time domain search window module, used to perform initial scanning and detect valid cells in large window mode; Performing an initial scan in large window mode ensures that the system can fully detect all potential valid cells; this is essential for quickly locating valid signals in complex and changing network environments; through the full-time search window module, the system can cover a larger time range in one scan, thereby reducing the number of scans and scanning time; this is particularly important for application scenarios that require rapid response; the full-time 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; because the full-time search window module can fully 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, thereby improving user experience; by reducing the number of scans and scanning time, the full-time search window module can optimize the system's resource allocation, reduce power consumption and cost; this is particularly important for application scenarios that require long-term stable operation; The frame header latching and signal strength recording module is used to latch the precise count value corresponding to the frame header and record the RSRP / SINR index of each cell; The module can latch the precise count value corresponding to the frame header, which is crucial for the subsequent time difference calculation and timing compensation; accurate capture of the frame header ensures that the system can accurately identify the start of the cell signal, thereby performing accurate timing analysis; the module records the RSRP (reference signal received power) and SINR (signal to interference plus noise ratio) indicators of each cell, which are important bases for evaluating the quality of cell signals; comprehensive recording of these indicators helps the system to more accurately understand the status of cell signals and provide a basis for subsequent signal quality evaluation and optimization; accurate capture of the frame header provides a reliable basis for the calculation of time difference, thereby improving the accuracy of timing; by recording RSRP and SINR indicators, the system can more comprehensively evaluate the quality of cell signals and provide strong support for optimizing network coverage and signal strength; A 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; 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 the 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 system performance and enhance the robustness and anti-interference ability of the system. A dynamic window adjustment module, used to generate a prediction time window according to a global timing compensation parameter and a time difference ΔT, and adjust a scanning window size; The module can generate a predicted time window based on 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 based on the predicted time window to improve scanning efficiency; by dynamically adjusting the size of the scanning window, the system can lock the effective cell more quickly and improve scanning efficiency; the dynamic window adjustment mechanism enables the system to respond to changes in the network environment more flexibly, improving the adaptability and reliability of the system; Exception handling module, used to set signal quality attenuation threshold, trigger conditional full-window rescan, and establish timing error tolerance model; The module can set the signal quality attenuation threshold to detect the degradation of cell signal quality; when it is detected that the 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 results; 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 respond to changes in timing errors more flexibly and improve the flexibility and adaptability of the system; at the same time, adjusting the scanning strategy according to the evaluation results also helps to optimize system performance.

[0027] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0028] 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 fast scanning of public network cells based on frame timing prediction, characterized in that: The steps include: Step 1, initial scanning phase: configure the field programmable gate array clock counter, and set its period to the target network frame period; open the full time domain search window and scan in large window mode; when a valid cell is detected, latch the precise 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 and signal to interference plus noise ratio; 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 value of the reference cell in the current round with that in the historical round, and calculate the time difference ΔT; apply the Kalman filter to process ΔT to eliminate measurement noise, and generate global timing compensation parameters based on the processed ΔT; Step 3, dynamic window adjustment stage: for locked cells, calculate the new predicted time T_new=T_old+ΔT×frequency offset compensation coefficient according to the global timing compensation parameter and time difference ΔT, as well as the preset frequency offset compensation coefficient; with T_new as the center, generate a predicted time window [T_new-δ,T_new+δ], where δ is the preset small range deviation value, which is smaller than the large window range; for unlocked cells, continue to scan in large window mode; Step 4: Exception handling mechanism: Set the signal quality attenuation threshold, when the signal receiving power drops by more than 3dB; when the cell signal quality is detected to be lower than the signal quality attenuation threshold, trigger the conditional full-window rescan; at the same time, 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 results; Step 5: Performance monitoring and strategy optimization: Continuously monitor the system's operating status, evaluate key performance indicators, including timing accuracy, scanning efficiency, and signal quality, and optimize and adjust system strategies based on the monitoring results to ensure long-term stable operation of the system and adapt to 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: The step 1 specifically also includes: configuring the clock counter to set the counter precision 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, characterized in that: The step 1 specifically also includes: the large window mode of the full time domain search window specifically covers the entire time range and captures all potential cell signals.

4. The method for fast scanning of public network cells based on frame timing prediction according to claim 3, characterized in that: The step 1 also includes: when latching the precise 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 the specific pilot signal to facilitate the 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: The step 2 specifically also includes: in the difference value calculation stage, the application of the Kalman filter includes initializing filter parameters and updating filter states, and adjusting the filter estimation value based on the new observation value 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: The step three specifically also includes: in the dynamic window adjustment stage, the frequency offset compensation coefficient is determined 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: The step three specifically 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, characterized in that: The step four specifically also includes: in the exception handling mechanism, the triggering conditions of the conditional full-window rescan also include considering other signal quality indicators, the decrease in the interference plus noise ratio or the signal fluctuation rate, and comprehensively evaluating the stability of the cell signal quality; when establishing the timing error tolerance model, considering the network synchronization requirements, equipment clock accuracy and environmental factors on the timing error, and setting a reasonable error tolerance range based on these factors.

9. The method for fast scanning of public network cells based on frame timing prediction according to claim 8, characterized in that: The step five also includes: Monitor the stability of the clock counter in real time to ensure that its period is consistent with the target network frame period; Track and record the changes in the reference cells 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 locked cells that are successfully detected within the prediction time window; Regularly analyze the changing trends of signal strength indicators and the triggering of abnormal handling mechanisms; Based on the performance monitoring data, evaluate the effectiveness of the current scanning strategy, timing compensation parameters, and prediction time window settings; In the case of large timing error, low scanning efficiency or large fluctuation in signal quality, analyze the causes and put forward optimization suggestions, adjust the global timing compensation parameters, frequency offset compensation coefficient and the deviation value δ of the predicted time window, improve timing accuracy and scanning efficiency, optimize the exception handling mechanism, and cope with changes in the network environment; Record key events and performance indicators during system operation to form system logs; Generate regular performance reports including statistics and trend analysis on timing accuracy, scanning efficiency, signal quality; Provide system logs and performance reports to relevant personnel for system maintenance, troubleshooting, and strategy optimization decisions.

10. A system for quickly scanning public network cells based on frame timing prediction, using a method for quickly scanning public network cells based on frame timing prediction as claimed in any one of claims 1 to 9, characterized in that: include: A programmable gate array clock counter configuration module, used to configure the cycle of the programmable gate array clock counter to match the target network frame cycle; Full time domain search window module, used to perform initial scanning and detect valid cells in large window mode; The frame header latching and signal strength recording module is used to latch the precise count value corresponding to the frame header and record the signal receiving power / interference plus noise ratio index of each cell; A 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; A dynamic window adjustment module, used to generate a prediction time window according to a global timing compensation parameter and a time difference ΔT, and adjust a scanning window size; 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.

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