Base station ping-pong effect optimization method and system

Abstract

The invention relates to a base station ping-pong effect optimization method, which comprises the following steps: generating mobile terminal measurement data based on measured base station signal strength and interference level; sending the data to a base station; the base station generates a base station data file in combination with the data, the load condition of the base station and a historical switching record; according to the file, summation is carried out through weight distribution and an optimization algorithm, and a comprehensive score of each base station is generated; dynamically adjusting a base station switching threshold or rule according to the score, and generating an optimization decision instruction; and an instruction is issued to the base station, and the base station executes switching operation according to the instruction. The scheme can solve the problems of poor adaptability, low efficiency and high cost in the prior art, and the effect is remarkable. Firstly, a switching decision is adjusted by weighting multiple dynamic factors, and the network stability is improved; secondly, the calculation is simple, the delay is reduced, and the switching efficiency is improved; thirdly, depending on the existing hardware optimization algorithm, the cost is reduced, and the resource utilization rate is improved.

Description

Technical Field

[0001] This application relates to the field of mobile communication networks, and in particular to a method and system for optimizing the ping-pong effect of base stations. Background Technology

[0002] In mobile communication networks, base stations (BS) are key nodes in wireless communication, responsible for transmitting, receiving, and processing wireless signals with mobile terminals (such as mobile phones and tablets). However, in practical applications, due to various factors such as signal strength, interference, and mobility management, mobile terminals may frequently switch between adjacent base stations; this phenomenon is known as the "ping-pong effect." The ping-pong effect not only leads to network instability, increasing handover failure rates and call drop rates, but also increases the complexity of network management and operational costs.

[0003] In existing technologies, optimization of the ping-pong effect at base stations mainly relies on adjusting handover parameters, optimizing handover algorithms, or introducing more complex mobility management strategies. However, these methods often depend on fixed thresholds or rules, lack adaptability to dynamic network changes, and are difficult to achieve optimal results in different scenarios.

[0004] Existing optimization methods for the ping-pong effect at base stations involve complex computational procedures and place a heavy burden on server computing. Specifically, this manifests as poor adaptability—fixed thresholds or rules struggle to adapt to dynamic changes in the network environment, leading to unstable optimization results; low efficiency—complex handover algorithms or mobility management strategies may increase processing latency, impacting user experience; and high cost—requiring additional hardware or software support, increasing network construction and operation costs. Summary of the Invention

[0005] In view of this, this application proposes a method for optimizing the ping-pong effect of base stations, including... Based on the measured base station signal strength and interference level, mobile terminal measurement data is generated; Send the mobile terminal's measurement data to the base station; The base station combines mobile terminal measurement data, its own load status, and historical handover records to generate a base station data file; Based on the base station data files, a comprehensive score for each base station is generated through weight allocation and optimization algorithms; The threshold or rules for base station handover are dynamically adjusted based on the comprehensive score of each base station to generate optimization decision instructions. The optimization decision instruction is sent to the base station, and the base station executes the handover operation according to the instruction.

[0006] In one possible implementation, the comprehensive score for each base station is generated based on the base station data file using a weighting and optimization algorithm, including: Obtain base station signal strength, interference level, moving speed, and historical handover records from the base station data file; Weights are assigned to base station signal strength, interference level, moving speed, and historical handover record data using a preset weight allocation method. Based on the aforementioned weighted data, a weighted sum is calculated using a weighting function to generate a comprehensive score for each base station.

[0007] In one possible implementation, the base station handover threshold or base station handover rules are dynamically adjusted based on the comprehensive score of each base station, generating optimization decision instructions including: The validity of the comprehensive score of the base station is verified, and a list of candidate base stations is generated in order of priority. Based on a priority-ranked candidate base station list, target base stations are set and handover thresholds are adjusted; Based on the real-time status of the target base station, assess the handover risk of the target base station and determine the base station handover target; Based on the final handover target or the decision to maintain the current base station, a handover decision instruction is generated.

[0008] In one possible implementation, setting the target base station and adjusting the handover threshold based on the priority-ranked candidate base station list includes: Determine the initial switching threshold by combining typical scenarios, historical experience, and industry standards; By comparing the comprehensive scores of candidate base stations with those of the currently serving base stations, candidate base stations that meet the initial threshold are marked as potential targets; The initial threshold is adjusted according to the base station load, terminal movement speed, and historical handover records.

[0009] In one possible implementation, the target base station handover risk is assessed based on the real-time status of the target base station, and the base station handover target is determined by including: Based on the real-time status of the target base station, analyze the handover success rate and latency, and predict handover risks.

[0010] Candidate base stations that pass real-time verification and have a risk level below a preset value are selected as the final target; if none of them meet the requirements, the current serving base station is maintained.

[0011] In one possible implementation, the formula for the overall score of each base station is: in, , , , These are the weights of each factor, and ; Handover_Success_Rate represents the handover success rate, which is used to evaluate the probability of a user equipment successfully handing over from the current base station to the target base station; RSSI stands for Received Signal Strength Indicator, which indicates the strength of the wireless signal received by the mobile terminal. Interference Level refers to the degree of interference between other signals and the target signal in a wireless communication environment. Velocity^-1 is the reciprocal of the terminal's movement speed. The slower the terminal moves, the larger the Velocity^-1 value. S represents the overall score of the base station.

[0012] In one possible implementation, the formula for calculating the handover success rate is: Handover_Success_Rate represents the handover success rate.

[0013] In one possible implementation, the comprehensive score for each base station is generated based on the base station data file using a weighting and optimization algorithm, including: Obtain base station signal strength, interference level, moving speed, and historical handover records from the base station data file; Weights are assigned to base station signal strength, interference level, moving speed, and historical handover record data using a preset weight allocation method. Based on the aforementioned weighted data, a comprehensive score for each base station is generated using a machine learning algorithm.

[0014] In one possible implementation, the comprehensive score for each base station is generated based on the base station data file using a weighting and optimization algorithm, including: Obtain base station signal strength, interference level, moving speed, and historical handover records from the base station data file; Weights are assigned to base station signal strength, interference level, moving speed, and historical handover record data using a preset weight allocation method. Based on the aforementioned weight data, a comprehensive score for each base station is generated using a fuzzy logic algorithm.

[0015] The present invention also includes a base station ping-pong effect optimization system, characterized in that, for implementing the above method, the system includes a data acquisition module, a weight management module, a weighted calculation module, a decision optimization module, and an instruction issuance and execution module; The data acquisition module is used to acquire raw data from mobile terminals and perform validity verification on the acquired data to form a standardized dataset; The weight management module is responsible for the initialization, dynamic adjustment, and storage management of weights; The weighted calculation module calculates the comprehensive score of each base station based on the standardized dataset and the current weights using a linear weighting function; The decision optimization module uses the comprehensive score of each base station as the core basis to dynamically adjust the base station handover threshold and rules; The instruction issuance and execution module generates a handover decision instruction based on the decision optimization results and issues it to the current serving base station.

[0016] The beneficial effects of this invention are: This solution effectively addresses the problems of poor adaptability, low efficiency, and high cost associated with existing technologies, demonstrating significant technical benefits. Firstly, it enhances network stability by dynamically adjusting handover decisions based on multiple dynamic factors such as signal strength and interference levels through a weighted algorithm. This avoids the drawbacks of fixed thresholds, reduces handover failures and dropped calls caused by the ping-pong effect, and adapts to different network scenarios. Secondly, it improves handover efficiency by employing a simple linear weighted calculation, eliminating the need for complex strategies, reducing processing latency, and ensuring continuous communication for users on the move. Thirdly, it reduces costs by leveraging existing mobile terminals and base stations. Only server-side algorithm optimization is required, eliminating the need for additional hardware investment, thus reducing network construction and operation costs, minimizing invalid handovers, and improving network resource utilization.

[0017] Other features and aspects of this application will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0018] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this application together with the specification and serve to explain the principles of this application.

[0019] Figure 1 This paper presents an overall flowchart of the base station ping-pong effect optimization method according to an embodiment of this application; Figure 2 The flowchart of the base station comprehensive score generation based on the weighting function in this application embodiment is shown; Figure 3 A flowchart illustrating the generation of base station handover optimization decision instructions according to an embodiment of this application is shown; Figure 4 This application illustrates a flowchart of the target base station setting and dynamic adjustment of the handover threshold according to an embodiment of the present application. Figure 5 This application illustrates a flowchart of the target base station handover risk assessment and handover target determination process according to an embodiment of the present application. Figure 6 The diagram shows the structure of a base station ping-pong effect optimization system according to an embodiment of this application. Detailed Implementation

[0020] Various exemplary embodiments, features, and aspects of this application will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0021] It should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application or to simplify the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0022] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0023] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0024] Furthermore, to better illustrate this application, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that this application can be implemented without certain specific details. In some instances, methods, means, components, and circuits well-known to those skilled in the art have not been described in detail in order to highlight the main points of this application.

[0025] The invention of this application is a base station ping-pong effect optimization method and system, which is applied in the field of mobile communication networks. It can reduce the frequent handover of mobile terminals between adjacent base stations, improve network connection stability, improve handover efficiency, and reduce network construction and operation costs.

[0026] method specifically refer to Figure 1This is a flowchart of a base station ping-pong effect optimization method according to an embodiment of this application. As shown in the figure, this embodiment includes step S100, generating mobile terminal measurement data based on measured base station signal strength and interference level. Step S200, sending the mobile terminal measurement data to the base station. Step S300, the base station combines the mobile terminal measurement data, its own load status, and historical handover records to generate a base station data file. Step S400, generating a comprehensive score for each base station based on the base station data file through weight allocation and optimization algorithms. Step S500, dynamically adjusting the base station handover threshold or base station handover rules based on the comprehensive score of each base station, generating an optimization decision instruction. And step S600, issuing the optimization decision instruction to the base station, and the base station executes the handover operation according to the instruction. This method dynamically collects multi-dimensional data and generates a comprehensive base station score through a weighted algorithm, dynamically adjusting the handover threshold and rules. It can reduce unnecessary handovers and improve network connection stability; it allows handover decisions to adapt to the real-time network environment and improve handover efficiency; it reduces network construction and operation costs without additional hardware, relying solely on software optimization.

[0027] Specifically, the mobile terminal measures the base station signal strength and interference level, generates measurement data, and sends it to the base station. The base station combines the terminal's measurement data, its own load status, and historical handover records to generate a base station data file. Based on this data file, a comprehensive score for each base station is obtained through weighted allocation and optimization algorithms. Then, the base station's handover threshold or rules are dynamically adjusted based on the comprehensive score to generate optimization decision instructions. Finally, the instructions are sent to the base station, which executes the handover operation according to the instructions. The entire process dynamically collects multi-dimensional data and optimizes handover decisions through weighted algorithms, which can reduce invalid handovers, improve efficiency, and eliminate the need for additional hardware.

[0028] As one possible implementation method, such as Figure 2 As shown, the process of generating a comprehensive score for each base station based on base station data files, using weight allocation and optimization algorithms, includes the following steps: Step S301, obtaining base station signal strength, interference level, mobile speed, and historical handover record data from the base station data files. Step S302, assigning corresponding weights to the base station signal strength, interference level, mobile speed, and historical handover record data using a preset weight allocation method. Step S303, generating a comprehensive score for each base station by performing a weighted summation based on the aforementioned weighted data using a weighting function. This method first accurately extracts multi-dimensional base station data, then allocates weights reasonably according to a preset method, and finally obtains the comprehensive score by summing the data using a weighting function. This allows the score to closely reflect the actual network conditions, providing a scientific basis for dynamically adjusting handover thresholds / rules, effectively reducing the ping-pong effect, improving network connection stability and handover efficiency, and eliminating the need for additional hardware, thus reducing costs.

[0029] Specifically, signal strength is measured in dBm, directly reflecting the strength of the received signal. Interference level is quantified by measuring the signal strength or signal-to-noise ratio of neighboring cells. Movement speed is estimated using GPS positioning or Doppler frequency shift. Historical handover records are used to record the number of handovers and the handover success rate of the mobile terminal over a past period.

[0030] In one specific embodiment, weight allocation is based on the degree of influence of each factor on the handover decision. For example, signal strength and interference level may have a larger weight because they directly affect communication quality; while mobility speed and historical handover records are used to assist in the decision-making process and have a relatively smaller weight. The weights can be set through expert experience or optimized and adjusted based on historical data using machine learning algorithms.

[0031] In one specific embodiment, the weighting function employs a linear weighted summation method.

[0032] In one specific embodiment, the weighting function employs a non-linear weighting method, such as exponential weighting or logarithmic weighting, to better reflect the non-linear effects of each factor.

[0033] Specifically, when using exponential weighting, the impact of key factors is amplified in base station ping-pong effect optimization. For example, when signal strength is good, exponential processing significantly enhances its support for decisions that do not trigger handover; when interference levels rise, it also more prominently promotes handover. It can quickly respond to subtle changes in factors, adapt to scenarios with rapidly changing signals, and reduce invalid handovers.

[0034] Specifically, when logarithmic weighting is used, it can mitigate the impact of fluctuations in factors on handover decisions. For example, if a user's movement speed suddenly increases or the handover success rate slightly decreases, logarithmic processing can weaken the impact of such fluctuations, avoid frequent handovers, ensure decision stability, and adapt to complex network scenarios such as transportation hubs.

[0035] In one possible implementation, such as Figure 3As shown, the method dynamically adjusts the base station handover threshold or handover rules based on the comprehensive score of each base station to generate optimization decision instructions. This includes steps S401: validating the comprehensive score of the base station and forming a priority-ranked candidate base station list; S402: setting the target base station and adjusting the handover threshold based on the priority-ranked candidate base station list; S403: assessing the handover risk of the target base station based on its real-time status and determining the base station transition target; and S404: generating a handover decision instruction based on the final handover target or the decision to maintain the current base station. This method first verifies the validity of the score and ranks the candidate base stations, then sets the target base station and handover threshold according to the list, and also assesses the handover risk to determine the final target. It ensures the accuracy of handover decisions, reduces invalid handovers, and improves network connection stability; it adapts to the real-time status of base stations, reduces the handover failure rate, optimizes user experience, and requires no additional hardware, thus controlling costs.

[0036] Specifically, the comprehensive score of base stations is validated for validity, and base stations that meet the requirements are selected and prioritized to form a candidate base station list. Based on this list, target base stations suitable for the current network requirements are selected, and the handover threshold is adjusted simultaneously to prepare for subsequent handovers. The status of target base stations is monitored in real time, and their load, signal stability, etc., are assessed to determine handover risks and further clarify the base station conversion target. Finally, based on the determined final handover target or the decision to maintain the current base station, a handover decision instruction is generated. The entire process ensures accurate decision-making, reduces invalid handovers, and adapts to the real-time status of base stations.

[0037] As one possible implementation method, such as Figure 4 As shown, the process of setting target base stations and adjusting handover thresholds based on a priority-ranked candidate base station list includes steps S4021, which determines the initial handover threshold by combining common scenarios, historical experience, and industry standards; step S4022, which compares the comprehensive scores of candidate base stations with the currently serving base station and marks candidate base stations that meet the initial threshold as potential targets; and step S4023, which adapts and adjusts the initial threshold based on base station load, terminal mobility speed, and historical handover records. This method first determines the initial handover threshold based on common scenarios, then selects potential target base stations that meet the threshold, and finally adapts and adjusts the threshold based on base station load, etc. This allows the handover threshold to be more realistic, reduces the ping-pong effect, and improves network stability and handover efficiency.

[0038] By combining typical scenarios, historical experience, and industry standards, an initial handover threshold is determined. Then, by comparing the comprehensive scores of candidate base stations with the currently serving base station, candidate base stations that meet the initial threshold are marked as potential targets. Finally, the initial threshold is adjusted adaptively based on base station load, terminal mobility speed, and historical handover records. This ensures the handover threshold closely reflects reality, reduces the ping-pong effect, and improves network stability and handover efficiency.

[0039] In one specific embodiment, a typical scenario refers to a weekday evening in a densely populated urban residential area. At this time, mobile terminals in the area are mostly stationary or moving at low speeds, users have high demands for network stability, and the number of terminals within the base station's coverage area is relatively stable, resulting in low interference levels. Referring to the signal propagation characteristics in this scenario, an initial handover threshold is set: when the comprehensive score of a candidate base station is 8% higher than that of the current serving base station, and the candidate base station's signal strength is not lower than -85dBm and its interference level is not lower than 25dB, it is marked as a potential target base station, ensuring that users reduce unnecessary handovers in stable usage scenarios.

[0040] In one specific embodiment, historical experience refers to the operator's summary of base station handover data for a certain urban main road over the past 12 months. Data shows that mobile terminals on this road segment mostly move at speeds of 30-60 km / h, with handover demand concentrated during morning and evening peak hours. Furthermore, when a fixed threshold was previously used, the ping-pong effect occurred at a rate of 12% because speed factors were not considered. Based on this historical experience, an initial handover threshold is set: candidate base stations with a comprehensive score 6% higher than the current serving base station, whose mobile speed adaptation coefficient multiplied by their comprehensive score meets the threshold requirement, and whose historical handover success rate is higher than 90%, are marked as potential targets to reduce the ping-pong effect during peak hours.

[0041] In one specific embodiment, the industry standard refers to the specifications for LTE system base station handover in the TS36.304 protocol developed by 3GPP. This standard clarifies that in ordinary urban scenarios covered by macro base stations, handover triggering must meet basic indicators such as signal strength difference and interference level. Based on this standard, an initial handover threshold is set: the signal strength difference between the candidate base station and the current serving base station is ≥6dB, the interference level of the candidate base station is ≤-100dBm / 180kHz, and the comprehensive score is 5% higher than that of the current serving base station. Candidate base stations meeting the standard requirements are marked as potential targets, ensuring that handover decisions comply with unified industry technical specifications and guaranteeing network compatibility and reliability.

[0042] It should be noted that although the above embodiments are used as examples to illustrate common scenarios, historical experience, and industry standards, those skilled in the art will understand that this application should not be limited thereto. In fact, users can flexibly set the settings for common scenarios, historical experience, and industry standards according to their personal preferences and / or actual application scenarios, as long as they meet their own business needs.

[0043] In one specific embodiment, such as Figure 5As shown, the method assesses the handover risk of the target base station based on its real-time status and determines the target base station for transition. This includes step S4031, analyzing the handover success rate and latency based on the target base station's real-time status to predict handover risks; and step S4032, selecting candidate base stations that pass real-time verification and have risks below a preset value as the final target. If neither condition is met, the current serving base station is maintained. This method combines the analysis of the target base station's real-time status to predict handover success rate and latency risks, selecting low-risk candidate base stations as targets, and maintaining the current base station if none are met. This can reduce handover failure rates, decrease dropped calls, improve network connection stability, and ensure user experience.

[0044] This process analyzes key indicators such as handover success rate and latency based on the real-time status of the target base station, and predicts potential risks during the handover process. From the candidate base stations, those that pass real-time verification and have risks below a preset value are selected as the final handover target; if none of the candidate base stations meet the criteria, the current serving base station is maintained. This method reduces handover failure rate, minimizes dropped calls, improves network connection stability, and ensures a better user experience.

[0045] Specifically, the formula for the comprehensive score of each base station is as follows: in, , , , These are the weights of each factor, and ; Handover_Success_Rate represents the handover success rate, which is used to evaluate the probability of a user equipment successfully handing over from the current base station to the target base station; RSSI stands for Received Signal Strength Indicator, which indicates the strength of the wireless signal received by the mobile terminal. Interference Level refers to the degree of interference between other signals and the target signal in a wireless communication environment. Velocity^-1 is the reciprocal of the terminal's movement speed. The slower the terminal moves, the larger the Velocity^-1 value. S represents the overall score of the base station.

[0046] Specifically, the formula for calculating the success rate of the switchover is as follows: Handover_Success_Rate represents the handover success rate.

[0047] In one possible implementation, generating a comprehensive score for each base station based on base station data files through weight allocation and optimization algorithms includes step S311, obtaining base station signal strength, interference level, mobile speed, and historical handover record data from the base station data files; step S312, assigning corresponding weights to the base station signal strength, interference level, mobile speed, and historical handover record data using a preset weight allocation method; and step S313, generating a comprehensive score for each base station based on the aforementioned weight data using a machine learning algorithm. This scheme uses machine learning to generate comprehensive scores, automatically learning network characteristics from historical data, dynamically adapting to environmental changes, and resulting in more accurate scoring. It can reduce the ping-pong effect and improve the scientific nature of handover decisions and network stability.

[0048] In one possible implementation, generating a comprehensive score for each base station based on the base station data file using a weighted allocation and optimization algorithm includes step S321, obtaining base station signal strength, interference level, moving speed, and historical handover record data from the base station data file. Step S322, assigning corresponding weights to the base station signal strength, interference level, moving speed, and historical handover record data using a preset weighted allocation method. Step S323, generating a comprehensive score for each base station based on the aforementioned weight data using a fuzzy logic algorithm. The fuzzy logic algorithm can handle fuzzy information such as signal fluctuations and interference uncertainties in the network, resulting in a score that better reflects complex dynamic scenarios. This improves the accuracy of the base station comprehensive score, optimizes handover decisions, and reduces the ping-pong effect.

[0049] system Based on the same concept, this application also provides a base station ping-pong effect optimization system for implementing the methods of the above embodiments. As a specific embodiment of the base station ping-pong effect optimization system 600, such as... Figure 6 The system 600 shown includes a data acquisition module 601, a weight management module 602, a weighted calculation module 603, a decision optimization module 604, and an instruction issuance and execution module 605; The data acquisition module 601 is used to acquire raw data from the mobile terminal and perform validity verification on the acquired data to form a standardized dataset. Specifically, the data acquisition module 601 is responsible for acquiring raw data such as signal strength and moving speed from the mobile terminal, and acquiring interference level, base station load, and terminal historical handover records from the base station; it performs validity verification, timestamp synchronization, and format unification on the acquired data to form a standardized dataset, providing high-quality input for subsequent weighted calculations.

[0050] The weight management module 602 is responsible for the initialization, dynamic adjustment, and storage management of weights. Specifically, the weight management module 602 is responsible for the initialization, dynamic adjustment, and storage management of weights; the initial weights can be configured based on expert experience, and machine learning optimization based on historical data is also supported. Emergency weight adjustments can be triggered according to network scenarios.

[0051] The weighted calculation module 603 calculates the comprehensive score of each base station based on the standardized dataset and the current weights using a linear weighting function. Specifically, the weighted calculation module 603 calculates the comprehensive score of the base station based on the standardized dataset and the current weights using a linear weighting function, and supports non-linear weighting function extensions, allowing the selection of the optimal weighting method based on network test results.

[0052] The decision optimization module 604 dynamically adjusts the base station handover threshold and rules. Specifically, based on a comprehensive score, the module dynamically adjusts the base station handover threshold and rules, calculating a comprehensive score for each terminal's current serving base station and neighboring base stations. When the difference between the comprehensive score of a neighboring base station and the comprehensive score of the current serving base station exceeds a preset handover threshold, a handover decision is triggered. This module supports scenario-based configuration of the handover threshold. Secondary decisions can be made in conjunction with base station load information.

[0053] The instruction issuance and execution module 605 generates a handover decision instruction based on the decision optimization results and issues it to the current serving base station. Specifically, the instruction issuance and execution module 605 generates a handover decision instruction based on the decision optimization results and issues it to the current serving base station through the core network. After receiving the instruction, the base station sends a handover request to the mobile terminal and simultaneously establishes a pre-connection with the target base station. After the mobile terminal completes signal synchronization and authentication with the target base station, it disconnects from the current serving base station, completing the handover.

[0054] In one specific embodiment, the system can be implemented by a combination of a base station, an optimization server, and a mobile terminal. Specifically, the data acquisition module 601 is deployed at the base station and the optimization server, the weight management module 602 is integrated into the optimization server, the weighted calculation module 603 is deployed at the optimization server, the decision optimization module 604 is integrated into the optimization server, and the instruction issuance and execution module 605 consists of the instruction generation unit of the optimization server and the instruction execution unit of the base station.

[0055] Specifically, the various components of the system achieve data transmission and command coordination through the following interaction mechanisms to ensure the efficient operation of the optimization process. In the mobile terminal-base station interaction, the mobile terminal reports measurement data, and the base station issues handover commands. Event-triggered reporting of measurement reports is also supported, reducing data transmission latency. The base station-optimization server interaction uses a segmented transmission protocol to transmit historical handover data; critical commands are marked with priority. Internal modules of the optimization server achieve data interaction through shared memory or message queues. Distributed deployment is also supported; when the optimization server is overloaded, weighted calculation tasks can be offloaded to edge nodes to ensure system response speed.

[0056] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for base station ping-pong effect optimization, the method comprising: The method comprises the following steps: Generating mobile terminal measurement data based on measured base station signal strength and interference level; Sending the mobile terminal measurement data to the base station; Generating a base station data file by the base station in combination with the mobile terminal measurement data, its own load condition and historical handover records; Generating a comprehensive score of each base station through a weight distribution and optimization algorithm according to the base station data file; Adjusting the threshold value or the base station handover rule of the base station handover according to the comprehensive score of each base station to generate an optimized decision instruction; Issuing the optimized decision instruction to the base station, and the base station performing a handover operation according to the instruction.

2. The method of claim 1, wherein, The method of generating a comprehensive score of each base station through a weight distribution and optimization algorithm according to the base station data file comprises the following steps: Obtaining base station signal strength, interference level, mobile speed and historical handover record data from the base station data file; Distributing corresponding weights to the base station signal strength, interference level, mobile speed and historical handover record data through a preset weight distribution method; Generating a comprehensive score of each base station through weighted summation by a weighting function according to the weight data.

3. The method of claim 1, wherein, The method of adjusting the threshold value or the base station handover rule of the base station handover according to the comprehensive score of each base station to generate an optimized decision instruction comprises the following steps: Performing validity check on the base station comprehensive score to form a priority-ordered candidate base station list; Setting a target base station and adjusting a handover threshold value based on the priority-ordered candidate base station list; Evaluating the target base station handover risk based on the real-time state of the target base station to determine a base station conversion target; Generating a handover decision instruction according to the decision of the final handover target or maintaining the current base station.

4. The method of claim 3, wherein, The method of setting a target base station and adjusting a handover threshold value based on the priority-ordered candidate base station list comprises the following steps: Determining an initial handover threshold value in combination with a conventional scene, historical experience and industry standards; Marking a candidate base station satisfying the initial threshold value as a potential target by comparing the comprehensive scores of the candidate base station and the current service base station; Adapting and adjusting the initial threshold value according to the base station load, terminal mobile speed and historical handover records.

5. The method of claim 3, wherein, The method of evaluating the target base station handover risk based on the real-time state of the target base station to determine a base station conversion target comprises the following steps: Analyzing the handover success rate and delay based on the real-time state of the target base station to predict the handover risk; Selecting a candidate base station with a risk lower than a preset value through real-time check to determine as the final target; if none of them satisfies the condition, maintaining the current service base station.

6. The method of claim 2, wherein, The formula of the comprehensive score of each base station is as follows: wherein, , , , are the weights of each factor, respectively, and ; Handover_Success_Rate represents the handover success rate, which is used to evaluate the success probability of the user equipment switching from the current base station to the target base station; RSSI represents the received signal strength indication, which represents the strength of the wireless signal received by the mobile terminal; InterferenceLevel is the interference level, which refers to the interference degree of other signals to the target signal in the wireless communication environment; Velocity^-1 is the reciprocal of the terminal mobile speed, and the slower the terminal moves, the larger the value of Velocity^-1 is; S represents the comprehensive score of the base station.

7. The method of claim 6, wherein, The formula of the handover success rate is as follows: wherein Handover_Success_Rate represents the handover success rate.

8. The method of claim 1, wherein, The method comprises the following steps: Obtaining base station signal strength, interference level, mobile speed and historical handover record data from the base station data file; Assigning corresponding weights to the base station signal strength, interference level, mobile speed and historical handover record data through a preset weight assignment method; Generating a comprehensive score of each base station through a machine learning algorithm according to the weight data.

9. The method of claim 1, wherein, The method comprises the following steps: Obtaining base station signal strength, interference level, mobile speed and historical handover record data from the base station data file; Assigning corresponding weights to the base station signal strength, interference level, mobile speed and historical handover record data through a preset weight assignment method; Generating a comprehensive score of each base station through a fuzzy logic algorithm according to the weight data.

10. A base station ping-pong effect optimization system, comprising: The system comprises a data acquisition module, a weight management module, a weighted calculation module, a decision optimization module and an instruction issuing and executing module. The data acquisition module is used to obtain original data from a mobile terminal, and to perform validity check on the collected data to form a standardized data set. The weight management module is responsible for weight initialization, dynamic adjustment and storage management. The weighted calculation module calculates a comprehensive score of each base station through a linear weighting function based on the standardized data set and the current weight. The decision optimization module dynamically adjusts the base station handover threshold and rules based on the comprehensive score of each base station. The instruction issuing and executing module generates a handover decision instruction according to the decision optimization result and issues the instruction to the current service base station.

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