Handover parameter automatic optimization method, device and medium

Abstract

The present disclosure provides a handover parameter automatic optimization method, device and medium, the method comprising: obtaining user dimension online data and multiple sets of event handover parameters of a to-be-optimized cell and a neighboring cell, each set of event handover parameters corresponding to a handover event; simulating handover users between the to-be-optimized cell and the neighboring cell according to the user dimension online data and a first set of values of each set of event handover parameters, to obtain multiple simulation evaluation indexes of the to-be-optimized cell, obtaining a simulation handover score according to the multiple simulation evaluation indexes, if some simulation evaluation indexes do not meet a preset optimization condition, optimizing a second set of values of the set of event handover parameters, repeating the above operation for several times with the second set of values iterating the first set of values to obtain several sets of values of the set of event handover parameters; and outputting an optimal set of values from the several sets of values of the set of event handover parameters according to the simulation handover score. The present disclosure simulates multiple handover events from the user dimension, and automatically optimizes the handover parameters by evaluating the simulation effect.

Description

Technical Field

[0001] The present disclosure relates at least to the field of network technology, and in particular to a method, device, and medium for automatic optimization of switching parameters. Background Art

[0002] In wireless networks, handover optimization is always accompanied by network optimization. Optimizing handover parameters is a key step and means of handover optimization. Improper handover parameter settings can lead to issues such as premature / late handovers, ping-pong handovers, handover failures, reestablishment, and dropped calls, which can seriously impact user experience.

[0003] Currently, the discovery and resolution of switching parameter issues primarily relies on manual data analysis and parameter modification and adjustment. This process is highly subjective and labor-intensive, lacks a holistic perspective, and optimization results are difficult to predict and evaluate. Problems can recur across multiple rounds of optimization, resulting in long, time-consuming, and inefficient optimization cycles, severely impacting network reputation and user satisfaction.

[0004] Therefore, there is an urgent need for a method that can efficiently optimize switching parameters and evaluate the optimization effect. Summary of the Invention

[0005] The technical problem to be solved by the present disclosure is to address the above-mentioned deficiencies and provide a method, device and medium for automatic optimization of switching parameters to solve the problem of how to efficiently optimize switching parameters and evaluate the optimization effect.

[0006] In a first aspect, the present disclosure provides a method for automatically optimizing handover parameters, the method comprising:

[0007] Obtain user-dimensional live network data and multiple sets of event handover parameters for the cell to be optimized and its neighboring cells. Each set of event handover parameters corresponds to a handover event.

[0008] Based on the user-dimensional live network data and the first set of values ​​for each set of event handover parameters, simulate user handovers between the cell to be optimized and the neighboring cell to obtain multiple simulation evaluation indicators for the cell to be optimized, obtain a simulation handover score based on the multiple simulation evaluation indicators, and if some simulation evaluation indicators do not meet the preset optimization conditions, optimize and obtain a second set of values ​​for the set of event handover parameters. Repeat the above operation several times with the second set of values ​​to iterate the first set of values ​​to obtain several sets of values ​​for the set of event handover parameters.

[0009] The optimal set of values ​​among several sets of values ​​of each set of event switching parameters is output according to the simulation switching score.

[0010] Further, wherein:

[0011] User-dimension live network data includes user-dimension live network indicator parameters and user-dimension live network switching delay;

[0012] Multiple simulation evaluation indicators include the distribution of simulation indicator parameters of the cell to be optimized, the distribution of the difference in simulation indicator parameters between the cell to be optimized and the neighboring cell, the simulated switching ratio, the simulated ping-pong switching ratio, the simulated switching delay distribution, and the simulated switching neighboring cell distribution.

[0013] Furthermore, user-dimensional live network data and multiple sets of event handover parameters for the cell to be optimized and its neighboring cells are obtained, specifically including:

[0014] Obtain a list of cells to be optimized, and determine the geographical area range that needs to be analyzed for optimizing the cells in the list;

[0015] Obtaining configuration parameters and wireless trace data of all existing base station cells within the geographic area, as well as call log data of the base station cells connected to the core network, wherein the configuration parameters include cell handover parameters, the wireless trace data includes periodic measurement report (MR) data and handover signaling data, and the call log data includes user information;

[0016] Determine the neighboring cells of the cell to be optimized based on periodic MR data, obtain user information based on call log data, match the user information with the periodic measurement report MR data to obtain user-dimensional live network indicator parameters, and match the user information with handover signaling data to obtain user-dimensional live network handover delay.

[0017] According to the configuration parameters, the switching events existing between the cell to be optimized and the neighboring cells are obtained. The switching events include intra-frequency switching events, inter-frequency switching events and inter-system switching events. Multiple sets of event switching parameters for intra-frequency switching events, inter-frequency switching events and inter-system switching events are obtained.

[0018] Further, wherein:

[0019] The user dimension network indicator parameters include the user-measured reference signal received power RSRP and / or user-measured reference signal received quality RSRQ of the cell to be optimized and the neighboring cell, as well as the user measurement result Ms of the cell to be optimized and the user measurement result Mn of the neighboring cell obtained based on the user-measured RSRP and / or user-measured RSRQ;

[0020] Handover events include A1 event, A2 event, A3 event, A4 event, A5 event, B1 event and B2 event. A1 event, A2 event, A3 event, A4 event and A5 event are inter-frequency handover events, A3 event, A4 event and A5 event are intra-frequency handover events, and B1 event and B2 event are inter-system handover events.

[0021] Furthermore, based on the user dimension live network data and the first set of values ​​of each set of event handover parameters, user handover is simulated between the cell to be optimized and the neighboring cell, specifically including:

[0022] If the current group event switching parameters are A1 event switching parameters, including the A1 event amplitude hysteresis parameter HysA1, the A1 event threshold parameter ThreshA1, and the A1 event time hysteresis TimeToTrigA1, determine whether each user in the cell to be optimized satisfies Ms-HysA1>ThreshA1 and lasts for TimeToTrigA1. If a user satisfies Ms-HysA1>ThreshA1, simulate an A1 switching event for the user in the cell to be optimized and the neighboring cell.

[0023] If the current group event switching parameters are A2 event switching parameters, including the A2 event amplitude hysteresis parameter HysA2, the A2 event threshold parameter ThreshA2, and the A2 event time hysteresis TimeToTrigA2, determine whether each user in the cell to be optimized satisfies Ms+HysA2>ThreshA2 and lasts for TimeToTrigA2. If a user satisfies Ms+HysA2>ThreshA2, simulate an A2 switching event for the user between the cell to be optimized and the neighboring cell;

[0024] If the current group event switching parameters are A3 event switching parameters, including the frequency offset Ofn of the neighboring cell, the cell-specific offset Ocn of the neighboring cell in the system, the A3 event amplitude hysteresis parameter HysA3, the frequency offset Ofs of the cell to be optimized, the specific offset Ocs of the cell to be optimized, the offset Off of the measurement result, and the time hysteresis TimeToTrigA3 of the A3 event, determine whether each user in the cell to be optimized satisfies Mn+Ofn+Ocn-HysA3>Ms+Ofs+Ocs+Off, and lasts for TimeToTrigA3. If a user does, simulate an A3 switching event for the user between the cell to be optimized and the neighboring cell;

[0025] If the current group event switching parameters are A4 event switching parameters, including the frequency offset Ofn of the neighboring cell, the cell-specific offset Ocn of the neighboring cell in the system, the A4 event amplitude hysteresis parameter HysA4, the A4 event threshold parameter ThreshA4, and the time hysteresis TimeToTrigA4 of the A4 event, determine whether each user in the cell to be optimized satisfies Mn+Ofn+Ocn-HysA4>ThreshA4 and lasts for TimeToTrigA4. If a user does, simulate an A4 switching event for the user between the cell to be optimized and the neighboring cell;

[0026] If the current group of event switching parameters is A5 event switching parameters, including the A5 event amplitude hysteresis parameter HysA5, the A5 event threshold 1 parameter ThreshA51, the frequency offset Ofn of the neighboring cell, the cell-specific offset Ocn of the intra-system neighboring cell, the A5 event threshold 2 parameter ThreshA52, and the time hysteresis TimeToTrigA5 of the A5 event, determine whether each user in the cell to be optimized satisfies Ms + HysA5 < ThreshA51 and Mn + Ofn + Ocn - HysA5 > ThreshA52, and lasts for the duration of TimeToTrigA5. If a certain user satisfies these conditions, simulate an A5 handover event for this user between the cell to be optimized and the neighboring cell;

[0027] If the current group of event switching parameters is B1 event switching parameters, including the frequency offset Ofn of the neighboring cell, the B1 event hysteresis parameter HysB1, the B1 event threshold parameter ThreshB1, and the time hysteresis TimeToTrigB1 of the B1 event, determine whether each user in the cell to be optimized satisfies Mn + Ofn - HysB1 > ThreshB1, and lasts for the duration of TimeToTrigB1. If a certain user satisfies these conditions, simulate a B1 handover event for this user between the cell to be optimized and the neighboring cell;

[0028] If the current group of event switching parameters is B2 event switching parameters, including the B2 event amplitude hysteresis parameter HysB2, the B2 event threshold 1 parameter ThreshB21, the frequency offset Ofn of the neighboring cell, the B2 event threshold 2 parameter ThreshB22, and the time hysteresis TimeToTrigB2 of the B2 event, determine whether each user in the cell to be optimized satisfies Ms + HysB2 < ThreshB21 and Mn + Ofn - HysB2 > ThreshB22, and lasts for the duration of TimeToTrigB2. If a certain user satisfies these conditions, simulate a B2 handover event for this user between the cell to be optimized and the neighboring cell.

[0029] Furthermore, obtain multiple simulation evaluation indicators for the cell to be optimized, specifically including:

[0030] Before simulating the handover user, obtain the first in-network index parameters of each user in the cell to be optimized and the second in-network index parameters in the neighboring cell;

[0031] After simulating the handover user, obtain the first simulation index parameters of each user in the cell to be optimized and the second simulation index parameters in the neighboring cell;

[0032] Calculate the distribution equilibrium value of the first simulation index parameters to obtain the distribution of the simulation index parameters of the cell to be optimized;

[0033] Calculate the number of first users in the cell to be optimized whose first simulation index parameter is less than the second simulation index parameter, and calculate the ratio of the number of first users to the total number of users in the cell to be optimized to obtain a distribution of simulation index parameter differences between the cell to be optimized and neighboring cells;

[0034] Calculate the number of second users for which the difference between the first live network index parameter and the second live network index parameter is less than a first preset value, and calculate the ratio of the total number of simulated switching users to the number of second users to obtain a simulated switching ratio;

[0035] Calculating the number of third users whose handover times between neighboring cells of the cell to be optimized exceed a second preset value during the simulated handover, and calculating the ratio of the number of third users to the total number of simulated handover users to obtain a simulated ping-pong handover ratio;

[0036] Estimate the simulated handover delay for each simulated handover user based on the existing network handover delay in the user dimension, calculate the distribution balance value of the simulated handover delay, and obtain the simulated handover delay distribution;

[0037] The number of neighboring cells for simulated handover and the number of users for simulated handover in each neighboring cell are obtained, and the distribution balance value of the number of users for simulated handover in each neighboring cell is calculated to obtain the distribution of neighboring cells for simulated handover.

[0038] Furthermore, a simulation switching score is obtained based on multiple simulation evaluation indicators, including:

[0039] The simulation evaluation indicators are multiplied by the preset weight of each simulation evaluation indicator to obtain the simulation switching score, and the sum of the preset weights of multiple simulation evaluation indicators is 1.

[0040] Further:

[0041] If some simulation evaluation indicators do not meet the preset optimization conditions, the second set of values ​​of the event switching parameters are obtained through optimization, specifically including:

[0042] Compare each simulation evaluation indicator with the corresponding judgment threshold. If the corresponding preset judgment condition is not met, optimize the unique parameters in the group of event switching parameters. If the optimization of the unique parameters cannot meet the preset optimization condition, optimize the non-unique parameters in the group of event switching parameters and bring the non-unique parameters into the simulation optimization of other groups of event switching parameters.

[0043] Output the optimal set of values ​​among several sets of values ​​of each set of event switching parameters based on the simulation switching score, specifically including:

[0044] Output a set of values ​​of each set of event switching parameters, which has the same non-unique parameter value as other sets of event switching parameters and has the highest sum of simulation switching scores.

[0045] In a second aspect, the present disclosure provides a device for automatic optimization of handover parameters, the device comprising:

[0046] An acquisition module is used to obtain user-dimensional live network data of the cell to be optimized and its neighboring cells and multiple sets of event handover parameters, where each set of event handover parameters corresponds to a handover event;

[0047] A simulation optimization module is connected to the acquisition module and is used to simulate user switching between the cell to be optimized and the neighboring cell based on the user dimension live network data and the first set of values ​​of each set of event switching parameters to obtain multiple simulation evaluation indicators of the cell to be optimized, and obtain a simulation switching score based on the multiple simulation evaluation indicators. If some simulation evaluation indicators do not meet the preset optimization conditions, a second set of values ​​of the set of event switching parameters is optimized and obtained, and the first set of values ​​are iterated with the second set of values. Repeat the above operation several times to obtain several sets of values ​​of the set of event switching parameters;

[0048] The output module is connected to the simulation optimization module and is used to output the best set of values ​​among the multiple sets of values ​​of each set of event switching parameters according to the simulation switching scores.

[0049] In a third aspect, the present disclosure provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the above-mentioned method for automatically optimizing switching parameters is implemented.

[0050] The present disclosure provides a method, device, and medium for automatic optimization of handover parameters. By acquiring user-dimensional live network data and simulating multiple handover events from the user dimension, the handover parameter values ​​are iteratively optimized using simulation evaluation indicators. Finally, the optimal set of handover parameter values ​​is selected based on the simulation handover score, thereby achieving efficient optimization of handover parameters with evaluable optimization effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flow chart of a method for automatic optimization of switching parameters according to an embodiment of the present disclosure;

[0052] Figure 2 It is a structural diagram of a switching parameter automatic optimization device according to an embodiment of the present disclosure;

[0053] Figure 3 is a flow chart of another method for automatic optimization of switching parameters according to an embodiment of the present disclosure;

[0054] Figure 4 This is a flow chart of a switching simulation method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the embodiments of the present disclosure will be described in further detail below with reference to the accompanying drawings.

[0056] It should be understood that the specific embodiments and drawings described herein are only used to explain the present disclosure rather than to limit the present disclosure.

[0057] It can be understood that, in the absence of conflict, the various embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0058] It will be understood that, for the convenience of description, the drawings of the present disclosure only show parts related to the present disclosure, while parts irrelevant to the present disclosure are not shown in the drawings.

[0059] It can be understood that each module and unit involved in the embodiments of the present disclosure may correspond to only one physical structure, or may be composed of multiple physical structures, or multiple modules and units may be integrated into one physical structure.

[0060] It will be understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of the present disclosure may occur in an order different from that marked in the drawings.

[0061] It is understood that the flowcharts and block diagrams of the present disclosure illustrate the possible architectures, functions, and operations of the systems, devices, equipment, and methods according to the various embodiments of the present disclosure. Each box in the flowchart or block diagram may represent a module, unit, program segment, or code, which contains executable instructions for implementing the specified functions. Moreover, each box or combination of boxes in the block diagram and flowchart may be implemented by a hardware-based device that implements the specified functions, or by a combination of hardware and computer instructions.

[0062] It can be understood that the modules and units involved in the embodiments of the present disclosure can be implemented by software or hardware, for example, the modules and units can be located in a processor.

[0063] Example 1:

[0064] like Figure 1 As shown, the present disclosure provides a method for automatic optimization of switching parameters, the method comprising:

[0065] S1. Obtain user-dimensional live network data of the cell to be optimized and its neighboring cells and multiple sets of event handover parameters, where each set of event handover parameters corresponds to a handover event.

[0066] S2. Based on the user-dimensional live network data and the first set of values ​​for each set of event handover parameters, simulate user handovers between the cell to be optimized and the neighboring cell to obtain multiple simulation evaluation indicators for the cell to be optimized, and obtain a simulation handover score based on the multiple simulation evaluation indicators. If some simulation evaluation indicators do not meet the preset optimization conditions, optimize and obtain a second set of values ​​for the set of event handover parameters. Repeat the above operation several times using the second set of values ​​to iterate the first set of values ​​to obtain several sets of values ​​for the set of event handover parameters.

[0067] S3. Outputting the best set of values ​​among several sets of values ​​of each set of event switching parameters according to the simulation switching score.

[0068] In this embodiment, by obtaining user-dimension live network data and simulating multiple handover events from the user dimension, the handover parameter values ​​are iteratively optimized through simulation evaluation indicators, and finally the optimal set of handover parameter values ​​is selected through simulation handover scoring, thus achieving efficient handover parameter optimization with evaluable optimization effect. Figure 1 The method shown is applied to Figure 2 The device shown.

[0069] Specifically, the current problem discovery method for switching parameter optimization is passive, usually through user complaints, manual data analysis, or problems discovered during network testing. The optimization personnel analyze the data to determine the problem, and then make manual modifications and adjustments. The existing method has the following defects: 1. Manual data analysis is time-consuming and labor-intensive, with poor timeliness and a long problem-solving cycle, and it is impossible to complete large-scale problem processing in a short period of time; 2. Manual analysis to generate optimization solutions is highly subjective, and it is easy to make misjudgments, resulting in problems that cannot be solved or not solved thoroughly, and even causing other new problems; 3. The effect of the optimization solution cannot be predicted and evaluated, and the problem may recur in multiple rounds of optimization, and the problem verification cost is high. In view of this, this embodiment provides a method for automatic optimization of switching parameters based on the simulation of existing network data. By collecting data from the base station side of the existing network, multiple groups of switching parameters are simulated in the user dimension switching process, and the evaluation indicators related to the simulation of each group of switching parameters are counted and the evaluation scores are calculated. Then, the evaluation scores of each group of parameters are compared to automatically select the optimal switching parameters. This method uses real data from the existing network as the basis for switching simulation, close to the complex wireless environment on site, evaluates the optimization effect of the switching parameters, and selects the optimal parameters based on the evaluation of the optimization effect. As Figure 3 As shown, the method includes: S11: data collection and preprocessing; S12: switching process simulation; S13: switching parameter evaluation index statistics and scoring; S14: judging whether there are new switching parameter groups that need to be processed; S15: switching parameter optimization and outputting optimization results.

[0070] In one embodiment, wherein:

[0071] User-dimension live network data includes user-dimension live network indicator parameters and user-dimension live network switching delay;

[0072] Multiple simulation evaluation indicators include the distribution of simulation indicator parameters of the cell to be optimized, the distribution of the difference in simulation indicator parameters between the cell to be optimized and the neighboring cell, the simulated switching ratio, the simulated ping-pong switching ratio, the simulated switching delay distribution, and the simulated switching neighboring cell distribution.

[0073] In this embodiment, simulation evaluation indicators of both user usage and user switching are obtained. In terms of user usage, the user's indicator parameters in the cell can be used for measurement, such as RSRP (Reference Signal Receiving Power) and RSRQ (Reference Signal Receiving Quality). In terms of switching, the switching delay can be used for measurement. The evaluation indicators may include but are not limited to: RSPR distribution of the serving cell (i.e., the cell to be optimized), RSRP difference distribution between the serving cell and the neighboring cell, switching ratio, ping-pong switching ratio, switching delay distribution, and switching neighboring cell distribution. The simulation evaluation indicators are the above data obtained based on the simulation statistics of the existing network data during the simulation process, and the simulation program runs on the hardware server.

[0074] In one embodiment, obtaining user-dimensional live network data of the cell to be optimized and the neighboring cells and multiple sets of event handover parameters in S1 specifically includes:

[0075] Obtain a list of cells to be optimized, and determine the geographical area range that needs to be analyzed for optimizing the cells in the list;

[0076] Obtaining configuration parameters and wireless trace data of all existing base station cells within the geographic area, as well as call log data of the base station cells connected to the core network, wherein the configuration parameters include cell handover parameters, the wireless trace data includes periodic measurement report (MR) data and handover signaling data, and the call log data includes user information;

[0077] Determine the neighboring cells of the cell to be optimized based on periodic MR data, obtain user information based on call log data, match the user information with the periodic measurement report MR data to obtain user-dimensional live network indicator parameters, and match the user information with handover signaling data to obtain user-dimensional live network handover delay.

[0078] According to the configuration parameters, the switching events existing between the cell to be optimized and the neighboring cells are obtained. The switching events include intra-frequency switching events, inter-frequency switching events and inter-system switching events. Multiple sets of event switching parameters for intra-frequency switching events, inter-frequency switching events and inter-system switching events are obtained.

[0079] In this embodiment, if Figure 3 As shown in Figure 1, during S11 data collection and preprocessing, the live network data to be collected and the information to be preprocessed and analyzed include:

[0080] 1. Select the cell list that needs handover parameter optimization: cell (cell to be optimized) information: name, CI (cell identity), etc.

[0081] 2. Trace data on the existing base station side (all base station cells in a certain geographical area) (TRACE data refers to the wireless signaling data generated by the base station):

[0082] 2.1. Signaling information: periodic MR (Measurement Report), handover signaling (RRC (Radio Resource Control) / S1 / X2 / Ng / Xn interface signaling), etc. More specifically, handover signaling includes: RRC Connection Reconfigration, RRC Connection Reconfigration Complete, Handover Request, Handover Request ACK, etc., involving the RRC layer of the Uu (User Unit) interface, 4G (4th Generation Mobile Communication Technology) S1 / X2 interface, and 5G (5th Generation Mobile Communication Technology) Ng / Xn interface.

[0083] 2.2 User Information: User triplet / quadruple information, etc. The 4G triplet / 5G quadruple is used to match and correlate simultaneous 4G / 5G wireless network TRACE signaling data with core network call log data (including the user's IMSI number), backfilling the user's IMSI (International Mobile Subscriber Identity) number to resolve the issue of wireless network TRACE signaling data lacking user identification. The 4G triplet is: MME Group ID, MME Code, MME UE S1AP ID; the 5G quadruple is: AMF Region ID, AMF Set ID, AMF Pointer, AMFUE NGAP ID.

[0084] 2.3. Serving cell (cell to be optimized) and neighboring cell (neighboring cell to be optimized) configuration information: CI, PCI (Physical Cell Identifier), frequency (frequency is mainly used to determine whether the serving cell and the neighboring cell are co-frequency cells, inter-frequency cells, or inter-system cells, and to distinguish co-frequency handover, inter-frequency handover, and inter-system handover), etc. Serving cell configuration parameters include cell name, base station ID, cell ID, CI, PCI, frequency, etc. Neighboring cell configuration parameters include neighboring cell name, neighboring cell base station ID, neighboring cell ID, neighboring cell CI, neighboring cell frequency, etc. Serving cell and neighboring cell indicator parameters: RSRP, RSRQ, etc. Periodic MR mainly includes: serving cell base station ID, serving cell ID, serving cell frequency, serving cell PCI, serving cell RSRP, serving cell RSRQ, neighboring cell base station ID, neighboring cell ID, neighboring cell frequency, neighboring cell PCI, neighboring cell RSRP, neighboring cell RSRQ, etc.

[0085] 3. CM (Configuration Management) configuration data of the existing base station (from the CM file in the operator's OMC (Operation and Maintenance Center)).

[0086] In one embodiment, wherein:

[0087] The user dimension network indicator parameters include the user-measured reference signal received power RSRP and / or user-measured reference signal received quality RSRQ of the cell to be optimized and the neighboring cell, as well as the user measurement result Ms of the cell to be optimized and the user measurement result Mn of the neighboring cell obtained based on the user-measured RSRP and / or user-measured RSRQ;

[0088] Handover events include A1 event, A2 event, A3 event, A4 event, A5 event, B1 event and B2 event. A1 event, A2 event, A3 event, A4 event and A5 event are inter-frequency handover events, A3 event, A4 event and A5 event are intra-frequency handover events, and B1 event and B2 event are inter-system handover events.

[0089] In this embodiment, the cell handover parameters include absolute thresholds, relative thresholds, hysteresis thresholds, time hysteresis, offsets, etc. corresponding to events such as A1, A2, A3, A4, A5, B1, and B2. Each MR message contains Ms and Mn, which correspond to indicator parameters such as RSRP of the serving cell and neighboring cell, respectively. Handover parameters such as TimeToTrig, Ofn, Ocn, Hys, Ofs, Ocs, and Off are derived from the operator's OMC's CM configuration file.

[0090] The events include: A1 event: The quality of the serving cell is higher than an absolute threshold, which is used to close the ongoing inter-frequency measurement and deactivate the Gap. A2 event: The quality of the serving cell is lower than an absolute threshold, which is used to turn on the inter-frequency measurement and activate the Gap. A3 event: The quality of the neighboring cell of the same priority is higher than a relative threshold than the serving cell, which is used for intra-frequency / inter-frequency coverage-based switching. A4 event: The quality of the neighboring cell is higher than an absolute threshold, which is mainly used for load-based switching. A5 event: The quality of the serving cell is lower than an absolute threshold 1, and the quality of the neighboring cell is higher than an absolute threshold 2, which is used for intra-frequency / inter-frequency coverage-based switching. B1 event: The quality of the neighboring cell of the different system is higher than an absolute threshold, which is used for load-based switching. B2 event: The quality of the serving cell is lower than an absolute threshold 1, and the quality of the neighboring cell of the different system is higher than an absolute threshold 2, which is used for coverage-based switching.

[0091] Event switching parameters include: Absolute threshold Threshold: The measured value reaches an absolute value, such as the absolute threshold of A1 and A2 events. Relative threshold Reporting Range: The one-way difference between two measured values, such as the relative threshold of A3 event. Hysteresis threshold Hysteresis: refers to the two-way difference when comparing two measured values, which is used to reduce the number of cell switching events triggered due to wireless signal fluctuations, reduce ping-pong switching and misjudgment. Time hysteresis Time to trigger: The time to reach the above threshold and must be maintained to prevent ping-pong switching. Offset Offset: Cell individual offset Oc, frequency offset Of, used to control the difficulty of triggering switching between the serving cell and the neighboring cell. Neighboring cell switching parameters: CIO (adjustment of soft switching parameters), etc.

[0092] In one embodiment, S2 simulates user handover between the cell to be optimized and the neighboring cell based on the user dimension live network data and the first set of values ​​of each set of event handover parameters, specifically including:

[0093] If the current group event switching parameters are A1 event switching parameters, including the A1 event amplitude hysteresis parameter HysA1, the A1 event threshold parameter ThreshA1, and the A1 event time hysteresis TimeToTrigA1, determine whether each user in the cell to be optimized satisfies Ms-HysA1>ThreshA1 and lasts for TimeToTrigA1. If a user satisfies Ms-HysA1>ThreshA1, simulate an A1 switching event for the user in the cell to be optimized and the neighboring cell.

[0094] If the current group of event switching parameters is A2 event switching parameters, including A2 event amplitude hysteresis parameter HysA2, A2 event threshold parameter ThreshA2, and A2 event time hysteresis TimeToTrigA2, determine whether each user in the cell to be optimized satisfies Ms + HysA2 > ThreshA2 and lasts for the duration of TimeToTrigA2. If a certain user meets the condition, simulate an A2 handover event for the user between the cell to be optimized and its neighbor cell;

[0095] If the current group of event switching parameters is A3 event switching parameters, including neighbor cell frequency offset Ofn, cell-specific offset Ocn of the intra-system neighbor cell, A3 event amplitude hysteresis parameter HysA3, frequency offset Ofs of the cell to be optimized, cell-specific offset Ocs of the cell to be optimized, offset Off of the measurement result, and A3 event time hysteresis TimeToTrigA3, determine whether each user in the cell to be optimized satisfies Mn + Ofn + Ocn - HysA3 > Ms + Ofs + Ocs + Off and lasts for the duration of TimeToTrigA3. If a certain user meets the condition, simulate an A3 handover event for the user between the cell to be optimized and its neighbor cell;

[0096] If the current group of event switching parameters is A4 event switching parameters, including neighbor cell frequency offset Ofm, cell-specific offset Ocn of the intra-system neighbor cell, A4 event amplitude hysteresis parameter HysA4, A4 event threshold parameter ThreshA4, and A4 event time hysteresis TimeToTrigA4, determine whether each user in the cell to be optimized satisfies Mn + Ofn + Ocn - HysA4 > ThreshA4 and lasts for the duration of TimeToTrigA4. If a certain user meets the condition, simulate an A4 handover event for the user between the cell to be optimized and its neighbor cell;

[0097] If the current group of event switching parameters is A5 event switching parameters, including A5 event amplitude hysteresis parameter HysA5, A5 event threshold 1 parameter ThreshA51, neighbor cell frequency offset Ofn, cell-specific offset Ocn of the intra-system neighbor cell, A5 event threshold 2 parameter ThreshA52, and A5 event time hysteresis TimeToTrigA5, determine whether each user in the cell to be optimized satisfies Ms + HysA5 < ThreshA51 and Mn + Ofn + Ocn - HysA5 > ThreshA$2 and lasts for the duration of TimeToTrigA5. If a certain user meets the condition, simulate an A5 handover event for the user between the cell to be optimized and its neighbor cell;

[0098] If the current set of event handover parameters is the B1 event handover parameters, including the frequency offset Ofn of the neighboring cell, the B1 event hysteresis parameter HysB1, the B1 event threshold parameter ThreshB1, and the time hysteresis TimeToTrigB of the B1 event, determine whether each user of the cell to be optimized satisfies Mn + Ofn - HysB1 > ThreshB1 and lasts for the duration of TimeToTrigB1. If a certain user does, simulate a B1 handover event of a certain user between the cell to be optimized and the neighboring cell; <O000OZZ8> [[Id=2]]

[0099] If the current set of event handover parameters is the BZ event handover parameters, including the BZ event amplitude hysteresis parameter HysBZ, the BZ event threshold parameter ThreshBZ1, the frequency offset Ofn of the neighboring cell, the BZ event threshold parameter ThreshBZ2, and the time hysteresis TimeToTrigBZ of the BZ event determine whether each user of the cell to be optimized satisfies Ms + HysBZ < ThreshBZ1 and Mn + Ofn - HysBZ > ThreshBZ2 and lasts for the duration of TimeToTrigBZ. If a certain user does, simulate a BZ handover event of a certain user between the cell to be optimized and the neighboring cell.

[0100] In this embodiment, as Figure 3 shown, the S12 handover process simulation is specifically the user - dimension handover process simulation: For each set of input handover parameters, associate them with the user information, cell information, signaling information, and periodic MR information in the existing network Trace data respectively, and complete the handover decision, handover process, and simulation of the entire handover trajectory according to the user dimension. The handover parameters come from the CM configuration file of the operator's OMC, including cell handover parameters and neighboring cell handover parameters. Ms and Mn are the measurement results of the serving cell and the neighboring cell respectively. The handover parameters are grouped according to handover events, namely seven events: A1, A2, A3, A4, A5, B1, and B2, corresponding to seven sets of event handover parameters, and can be triggered only when certain conditions are met respectively. Specifically:

[0101] (1) Configuration of handover parameters for A1 event:<00002Z7>

[0102] A1 event trigger condition: Ms - Hys > Thresh

[0103] Hys: A1 event amplitude hysteresis parameter;

[0104] Thresh: A1 event threshold parameter;

[0105] TimeToTrig: Duration of continuously satisfying the A1 event trigger condition, that is, time hysteresis;

[0106] (2) Configuration of handover parameters for A2 event:

[0107] A2 event trigger condition: Ms+Hys <Thresh

[0108] Hys: A2 event amplitude hysteresis parameter;

[0109] Thresh: A2 event threshold parameter;

[0110] TimeToTrig: The duration during which the triggering conditions of event A2 are continuously met, i.e., the time delay;

[0111] (3) Switching parameter configuration of A3 event:

[0112] A3 event triggering condition: Mn+Ofn+Ocn-Hys>Ms+Ofs+Ocs+Off

[0113] Hys: A3 event amplitude hysteresis parameter;

[0114] Ofs: frequency offset of the serving cell;

[0115] Ofn: frequency offset of the neighboring cell;

[0116] Ocs: serving cell specific offset CIO;

[0117] Ocn: cell-specific offset CIO of neighboring cells within the system;

[0118] Off: bias the measurement result;

[0119] TimeToTrig: The duration during which the triggering conditions of the A3 event are continuously met, i.e., the time delay;

[0120] (4) Switching parameter configuration of A4 event:

[0121] A4 event triggering condition: Mn+Ofn+Ocn-Hys>Thresh

[0122] Thersh: A4 event threshold parameter;

[0123] Hys: A4 event amplitude hysteresis parameter;

[0124] Ofn: frequency offset of the neighboring cell;

[0125] Ocn: cell-specific offset CIO of neighboring cells within the system;

[0126] TimeToTrig: The duration during which the triggering conditions of the A4 event are continuously met, i.e., the time delay;

[0127] (5) Switching parameter configuration of A5 event:

[0128] A5 event triggering condition: Ms + Hys < Thresh1 and Mn + Ofn + Ocn - Hys > Thresh2

[0129] Thresh1: A5 event threshold 1 parameter;

[0130] Thresh2: A5 event threshold 2 parameter;

[0131] Hys: A5 event amplitude hysteresis parameter;

[0132] Ofn: Frequency offset of the neighboring cell;

[0133] Ocn: Cell-specific offset CIO of the in-system neighboring cell;

[0134] TimeToTrig: Duration of continuously satisfying the A5 event triggering condition, i.e., time hysteresis;

[0135] (6) Switching parameter configuration for B1 event:

[0136] B1 event triggering condition: Mn + Ofn - Hys > Thresh

[0137] Thresh: B1 event threshold parameter;

[0138] Hys: B1 event hysteresis parameter;

[0139] Ofn: Frequency offset of the neighboring cell;

[0140] TimeToTrig: Duration of continuously satisfying the B1 event triggering condition, i.e., time hysteresis;

[0141] (7) Switching parameter configuration for B2 event:

[0142] B2 event triggering condition: Ms + Hys < Thresh1 and Mn + Ofn - Hys > Thresh2

[0143] Thresh1: B2 event threshold 1 parameter;

[0144] Thresh2: B2 event threshold 2 parameter;

[0145] Hys: B2 event amplitude hysteresis parameter;

[0146] Ofn: Frequency offset of the neighboring cell;

[0147] TimeToTrig: Duration of continuously satisfying the B2 event triggering condition, i.e., time hysteresis;

[0148] Periodic MR includes information and metric parameters of the serving cell and neighboring cells, and is used for handover decision and handover process simulation.

[0149] In one embodiment, obtaining multiple simulation evaluation indicators of the cell to be optimized in S2 specifically includes:

[0150] Obtaining a first existing network index parameter of each user in the cell to be optimized and a second existing network index parameter in the neighboring cell before the simulated handover of the user;

[0151] After obtaining the simulated handover user, the first simulation index parameter of each user in the cell to be optimized and the second simulation index parameter in the neighboring cell;

[0152] Calculating a distribution equilibrium value of the first simulation index parameter to obtain a distribution of simulation index parameters of the cell to be optimized;

[0153] Calculate the number of first users in the cell to be optimized whose first simulation index parameter is less than the second simulation index parameter, and calculate the ratio of the number of first users to the total number of users in the cell to be optimized to obtain a distribution of simulation index parameter differences between the cell to be optimized and neighboring cells;

[0154] Calculate the number of second users for which the difference between the first live network index parameter and the second live network index parameter is less than a first preset value, and calculate the ratio of the total number of simulated switching users to the number of second users to obtain a simulated switching ratio;

[0155] Calculating the number of third users whose handover times between neighboring cells of the cell to be optimized exceed a second preset value during the simulated handover, and calculating the ratio of the number of third users to the total number of simulated handover users to obtain a simulated ping-pong handover ratio;

[0156] Estimate the simulated handover delay for each simulated handover user based on the existing network handover delay in the user dimension, calculate the distribution balance value of the simulated handover delay, and obtain the simulated handover delay distribution;

[0157] The number of neighboring cells for simulated handover and the number of users for simulated handover in each neighboring cell are obtained, and the distribution balance value of the number of users for simulated handover in each neighboring cell is calculated to obtain the distribution of neighboring cells for simulated handover.

[0158] In this embodiment, Figure 3 The detailed process of S12 switching process simulation can be as follows Figure 4 Shown, including:

[0159] S21: Data preprocessing: Associate all user signaling by user dimension through user triplet / quadruple information, and process in chronological order according to the user dimension: parse the serving cell and neighbor cell information in the periodic MR, as well as indicator information such as RSRP.

[0160] S22: Determine whether there is any MR to be processed.

[0161] S23: Handover decision: Based on the input handover parameters, if the handover conditions are met and the CM neighbor cell configuration contains the target cell information, the handover is determined to be successful. The OMC's CM configuration file contains the neighbor cell information of the serving cell, and the target cell is located in the neighbor cell. The MR contains indicator parameters such as RSRP and RSRQ of the serving cell and neighbor cell, namely Ms and Mn. Based on the input handover parameter group, if a neighbor cell preferentially meets the handover conditions A1, A2, A3, A4, A5, B1, and B2, and the CM neighbor cell configuration contains this neighbor cell information, then this neighbor cell is the target cell for the handover.

[0162] Estimate the handover delay based on the neighboring cell type and RRC / X2 / S1 interface signaling interaction delay.

[0163] Taking the A3 switching parameters as an example, the switching conditions are met: Mn+Ofn+Ocn-Hys>Ms+Ofs+Ocs+Off, and last for TimeToTrig time, where: Ms: measurement result of the serving cell; Mn: measurement result of the neighboring cell; TimeToTrig: duration for which the event entry condition is continuously met, that is, time hysteresis; Off: offset of the measurement result; Hys: amplitude hysteresis of the measurement result; Ofs: frequency offset of the serving cell; Ofn: frequency offset of the neighboring cell; Ocs: serving cell specific offset CIO; Ocn: cell specific offset CIO of the neighboring cell within the system.

[0164] S24: Update the serving cell information after the handover is completed.

[0165] S25: Statistics of handover results: Update statistics of handover results, handover delay, RSRP and other evaluation indicator information, and go to S22 to process the next MR information of the user.

[0166] like Figure 3 As shown, the statistics and scoring of the handover parameter evaluation indicators in S13 include: for each set of handover parameters, the handover parameter evaluation indicators are counted by cell dimension. The evaluation indicators include but are not limited to: RSPR distribution of the serving cell (cell to be optimized), RSRP difference distribution of the serving cell and the neighboring cell, handover ratio (number of handovers * 100% / (number of MR samples with RSRP < 3db for the serving cell - neighboring cell), ping-pong handover ratio, handover delay distribution, handover neighboring cell distribution (number and ratio of handovers between serving cell and neighboring cell pairs, number of handover neighboring cells). The calculation of the specific value of each evaluation indicator can be designed based on experience. For example, the root mean square of distribution indicators can be calculated to evaluate whether the distribution is balanced.

[0167] Based on indicators such as RSRP / RSRQ of the serving cell and neighboring cells in periodic MR, as well as handover signaling, the rationality of cell and neighboring cell handover parameters corresponding to events A1, A2, A3, A4, A5, B1, and B2 is evaluated. During the simulation, no actual serving cell switching occurs; instead, a neighboring cell is marked as the new serving cell for a user, and handover statistics are updated for the original serving cell (the cell to be optimized). The handover results and evaluation indicators such as RSRP are simulated data.

[0168] In one embodiment, obtaining a simulation switching score according to multiple simulation evaluation indicators in S2 specifically includes:

[0169] The simulation evaluation indicators are multiplied by the preset weight of each simulation evaluation indicator to obtain the simulation switching score, and the sum of the preset weights of multiple simulation evaluation indicators is 1.

[0170] In this embodiment, if Figure 3 As shown, S13 Handover Parameter Evaluation Indicator Statistics and Scoring includes calculating the handover parameter score: multiple evaluation indicators corresponding to each set of handover parameters for the cell are weighted and normalized to generate a handover parameter score. Example weightings for each indicator include: RSRP difference distribution between serving cell and neighboring cell: 0.2; Handover ratio: 0.2; Ping-Pong handover ratio: 0.2; Serving cell RSRP distribution: 0.15; Handover latency distribution: 0.15; Handover neighboring cell distribution: 0.1.

[0171] In one embodiment:

[0172] If some simulation evaluation indicators do not meet the preset optimization conditions in S2, the second set of values ​​of the event switching parameters is obtained through optimization, specifically including:

[0173] Compare each simulation evaluation indicator with the corresponding judgment threshold. If the corresponding preset judgment condition is not met, optimize the unique parameters in the group of event switching parameters. If the optimization of the unique parameters cannot meet the preset optimization condition, optimize the non-unique parameters in the group of event switching parameters and bring the non-unique parameters into the simulation optimization of other groups of event switching parameters.

[0174] S3 outputs the best set of values ​​among several sets of values ​​of each set of event switching parameters according to the simulation switching score, specifically including:

[0175] Output a set of values ​​of each set of event switching parameters, which has the same non-unique parameter value as other sets of event switching parameters and has the highest sum of simulation switching scores.

[0176] In this embodiment, if Figure 3As shown, S14 determines whether new handover parameter sets require processing. Based on the cell's evaluation metrics and scoring results, if optimization is achieved or all candidate parameter sets have been traversed, the process proceeds to S15. Otherwise, the process generates parameter sets to be evaluated based on the evaluation metrics and optimization objectives, and proceeds to S12. For example, if the ping-pong handover ratio is high, the A3 relative threshold, hysteresis threshold, or hysteresis time are increased. If the serving cell is smaller than the neighboring cell and the RSRP ratio is high, the A3 relative threshold, hysteresis threshold, hysteresis time, or CIO are lowered. Handover parameters are grouped according to handover events: seven sets of handover parameters for each of the seven events, A1, A2, A3, A4, A5, B1, and B2, are derived from the operator's OMC configuration file. Simulation can be performed on a single set of handover parameters for each event, or multiple sets of handover parameters corresponding to multiple events can be simulated simultaneously. Evaluation metrics are statistically derived during S12. If one or more of the seven handover parameter groups (A1, A2, A3, A4, A5, B1, and B2) do not achieve the optimal effect, the size of the non-optimized handover parameter group needs to be adjusted and the simulation needs to be repeated for optimization and evaluation. Parameters that appear in multiple handover events (non-unique parameters) can be optimized first, followed by the remaining parameters (parameters unique to a single handover event). Alternatively, the remaining parameters can be optimized first. If the optimal effect is not achieved, the parameters shared by multiple handover events (non-unique parameters, which should have the same value) can be optimized. For example, if the ping-pong handover ratio is high, then for event A3, based on the handover trigger condition Mn+Ofn+Ocn-Hys>Ms+Ofs+Ocs+Off, it is necessary to increase Hys, Off, or the hysteresis time TimeToTrig, etc., to reduce the frequent triggering of handover conditions caused by fluctuations in the measurement results Ms and Mn, thereby improving user perception. For example, the percentage of serving cells with RSRP lower than that of neighboring cells = the number of MRs with RSRP lower than that of neighboring cells / the total number of MRs in serving cells. A high percentage of serving cells with RSRP lower than that of neighboring cells indicates a "should-be-handed-over" problem, and the switching parameters need to be adjusted to trigger the switching as soon as possible.

[0177] S15 outputs the results of handover parameter optimization: Compare the scores of all handover parameter groups by cell dimension, select the optimal set of handover parameters, and output the handover parameter optimization results for each cell. For example, the seven events A1, A2, A3, A4, A5, B1, and B2 each correspond to a handover parameter group configured in the existing network, obtained from the CM file of the operator's OMC. According to S14, for the seven events A1, A2, A3, A4, A5, B1, and B2, if the handover parameter group configured in the existing network does not achieve the optimized effect, a new set of handover parameters will be generated for re-evaluation. Therefore, there may be multiple sets of handover parameters for each of the seven events A1, A2, A3, A4, A5, B1, and B2.

[0178] This embodiment proposes a method for automatically optimizing handover parameters based on simulation of existing network data, proposes a simulation method for user-dimensional handover process processing based on existing network data, implements automatic optimization of handover parameters based on simulation results, and proposes a system and evaluation method for handover effect evaluation. Parameters are optimized based on the evaluation results, and effect evaluation indicators, rules, and algorithms can be statistically analyzed and flexibly adjusted. Because they are closer to the user's actual handover path and scenario, the statistical indicators and evaluation effects of the handover simulation are more realistic and reliable, providing a feasible solution for the automatic optimization of handover parameters. It is suitable for handover parameter optimization scenarios in 4 / 5G wireless network optimization.

[0179] Example 2:

[0180] like Figure 2 As shown, the present disclosure provides a device for automatic optimization of switching parameters, the device comprising:

[0181] Acquisition module 1 is used to obtain user-dimensional live network data of the cell to be optimized and the neighboring cells and multiple sets of event handover parameters, where each set of event handover parameters corresponds to a handover event;

[0182] The simulation optimization module 2 is connected to the acquisition module 1 and is used to simulate user switching between the cell to be optimized and the neighboring cell based on the user dimension live network data and the first set of values ​​of each set of event switching parameters to obtain multiple simulation evaluation indicators of the cell to be optimized, and obtain a simulation switching score based on the multiple simulation evaluation indicators. If some simulation evaluation indicators do not meet the preset optimization conditions, the second set of values ​​of the set of event switching parameters is optimized and obtained, and the first set of values ​​are iterated with the second set of values. Repeat the above operation several times to obtain several sets of values ​​of the set of event switching parameters;

[0183] The output module 3 is connected to the simulation optimization module 2 and is used to output the best set of values ​​among the multiple sets of values ​​of each set of event switching parameters according to the simulation switching scores.

[0184] In one embodiment, wherein:

[0185] User-dimension live network data includes user-dimension live network indicator parameters and user-dimension live network switching delay;

[0186] Multiple simulation evaluation indicators include the distribution of simulation indicator parameters of the cell to be optimized, the distribution of the difference in simulation indicator parameters between the cell to be optimized and the neighboring cell, the simulated switching ratio, the simulated ping-pong switching ratio, the simulated switching delay distribution, and the simulated switching neighboring cell distribution.

[0187] In one embodiment, the acquisition module 1 specifically includes:

[0188] A list unit is used to obtain a list of cells to be optimized and determine a geographical area range that needs to be analyzed for optimizing the cells in the list of cells to be optimized;

[0189] an existing network data unit, connected to the list unit, and configured to obtain configuration parameters and wireless trace data of all existing base station cells within the geographical area, as well as call record data of the base station cells connected to the core network, wherein the configuration parameters include cell handover parameters, the wireless trace data includes periodic measurement report (MR) data and handover signaling data, and the call record data includes user information;

[0190] The data preprocessing unit is connected to the existing network data unit and is used to determine the neighboring cells of the cell to be optimized based on the periodic MR data, obtain user information based on the call record data, match the user information with the periodic measurement report MR data to obtain the user-dimensional existing network indicator parameters, and match the user information with the handover signaling data to obtain the user-dimensional existing network handover delay;

[0191] The switching parameter unit is connected to the data preprocessing unit and is used to obtain the switching events existing between the cell to be optimized and the neighboring cell according to the configuration parameters. The switching events include same-frequency switching events, different-frequency switching events and different-system switching events, and obtain multiple sets of event switching parameters for same-frequency switching events, different-frequency switching events and different-system switching events.

[0192] In one embodiment, wherein:

[0193] The user dimension network indicator parameters include the user-measured reference signal received power RSRP and / or user-measured reference signal received quality RSRQ of the cell to be optimized and the neighboring cell, as well as the user measurement result Ms of the cell to be optimized and the user measurement result Mn of the neighboring cell obtained based on the user-measured RSRP and / or user-measured RSRQ;

[0194] Handover events include A1 event, A2 event, A3 event, A4 event, A5 event, B1 event and B2 event. A1 event, A2 event, A3 event, A4 event and A5 event are inter-frequency handover events, A3 event, A4 event and A5 event are intra-frequency handover events, and B1 event and B2 event are inter-system handover events.

[0195] In one embodiment, the simulation optimization module 2 specifically includes a simulation unit for:

[0196] If the current group event switching parameters are A1 event switching parameters, including the A1 event amplitude hysteresis parameter HysA1, the A1 event threshold parameter ThreshA1, and the A1 event time hysteresis TimeToTrigA1, determine whether each user in the cell to be optimized satisfies Ms-HysA1>ThreshA1 and lasts for TimeToTrigA1. If a user satisfies Ms-HysA1>ThreshA1, simulate an A1 switching event for the user in the cell to be optimized and the neighboring cell.

[0197] If the current group of event switching parameters is A2 event switching parameters, including the A2 event amplitude hysteresis parameter HysA2, the A2 event threshold parameter ThreshA2, and the A2 event time hysteresis TimeToTrigA2, determine whether each user in the cell to be optimized satisfies Ms + HysA2 > ThreshA2 and lasts for a duration of TimeToTrigA2. If a certain user does, simulate an A2 handover event for the user between the cell to be optimized and the neighboring cell;

[0198] If the current group of event switching parameters is A3 event switching parameters, including the neighboring cell frequency offset Ofn, the cell-specific offset Ocn of the intra-system neighboring cell, the A3 event amplitude hysteresis parameter HysA3, the frequency offset Ofs of the cell to be optimized, the cell-specific offset Ocs of the cell to be optimized, the offset Off of the measurement result, and the A3 event time hysteresis TimeToTrigA3, determine whether each user in the cell to be optimized satisfies Mn + Ofn + Ocn - HysA3 > Ms + Ofs + Ocs + Off and lasts for a duration of TimeToTrigA3. If a certain user does, simulate an A3 handover event for the user between the cell to be optimized and the neighboring cell;

[0199] If the current group of event switching parameters is A4 event switching parameters, including the neighboring cell frequency offset Ofn, the cell-specific offset Ocn of the intra-system neighboring cell, the A4 event amplitude hysteresis parameter HysA4, the A4 event threshold parameter ThreshA4, and the A4 event time hysteresis TimeToTrigA4, determine whether each user in the cell to be optimized satisfies Mn + Ofn + Ocn - HysA4 > ThreshA4 and lasts for a duration of TimeToTrigA4. If a certain user does, simulate an A4 handover event for the user between the cell to be optimized and the neighboring cell;

[0200] If the current group of event switching parameters is A5 event switching parameters, including the A5 event amplitude hysteresis parameter HysA5, the A5 event threshold 1 parameter ThreshA51, the neighboring cell frequency offset Ofn, the cell-specific offset Ocn of the intra-system neighboring cell, the A5 event threshold 2 parameter ThreshA52, and the A5 event time hysteresis TimeToTrigA5, determine whether each user in the cell to be optimized satisfies Ms + HysA5 < ThreshA51 and Mn + Ofn + Ocn - HysA5 > ThreshA52 and lasts for a duration of TimeToTrigA5. If a certain user does, simulate an A5 handover event for the user between the cell to be optimized and the neighboring cell;

[0201] If the current group of event handover parameters is B1 event handover parameters, including the frequency offset Ofn of the neighboring cell, the B1 event hysteresis parameter HysB1, the B1 event threshold parameter ThreshB1, and the time hysteresis TimeToTrigB1 of the B1 event, determine whether each user of the cell to be optimized satisfies Mn + Ofn - HysB1 > ThreshB1 and lasts for a duration of TimeToTrigB1. If a certain user meets the condition, simulate a B1 handover event of a certain user between the cell to be optimized and the neighboring cell.

[0202] If the current group of event handover parameters is B2 event handover parameters, including the B2 event amplitude hysteresis parameter HysB2, the B2 event threshold 1 parameter ThreshB21, the frequency offset Ofn of the neighboring cell, the B2 event threshold 2 parameter ThreshB22, and the time hysteresis TimeToTrigB2 of the B2 event, determine whether each user of the cell to be optimized satisfies Ms + HysB2 < ThreshB21 and Mn + Ofn - HysB2 > ThreshB22 and lasts for a duration of TimeToTrigB2. If a certain user meets the condition, simulate a B2 handover event of a certain user between the cell to be optimized and the neighboring cell.

[0203] In an embodiment, the simulation optimization module 2 specifically includes a simulation evaluation index unit, which is connected to the simulation unit and is used for:

[0204] Before simulating the handover user, obtain the first in-network index parameters of each user in the cell to be optimized and the second in-network index parameters in the neighboring cell;

[0205] After simulating the handover user, obtain the first simulation index parameters of each user in the cell to be optimized and the second simulation index parameters in the neighboring cell;

[0206] Calculate the distribution equilibrium value of the first simulation index parameters to obtain the distribution of the simulation index parameters of the cell to be optimized;

[0207] Calculate the number of the first users in the cell to be optimized whose first simulation index parameters are less than the second simulation index parameters, and calculate the ratio of the number of the first users to the total number of users in the cell to be optimized to obtain the difference distribution of the simulation index parameters between the cell to be optimized and the neighboring cell;

[0208] Calculate the number of the second users whose difference between the first in-network index parameters and the second in-network index parameters is less than the first preset value, and calculate the ratio of the total number of simulated handover users to the number of the second users to obtain the simulation handover ratio;

[0209] Calculate the number of the third users whose number of handovers between neighboring cells in the cell to be optimized during the simulation handover exceeds the second preset value, and calculate the ratio of the number of the third users to the total number of simulated handover users to obtain the proportion of simulation ping-pong handovers;

[0210] Estimate the simulated handover delay for each simulated handover user based on the existing network handover delay in the user dimension, calculate the distribution balance value of the simulated handover delay, and obtain the simulated handover delay distribution;

[0211] The number of neighboring cells for simulated handover and the number of users for simulated handover in each neighboring cell are obtained, and the distribution balance value of the number of users for simulated handover in each neighboring cell is calculated to obtain the distribution of neighboring cells for simulated handover.

[0212] In one embodiment, the simulation optimization module 2 specifically includes a simulation switching scoring unit, which is configured to:

[0213] The simulation evaluation indicators are multiplied by the preset weight of each simulation evaluation indicator to obtain the simulation switching score, and the sum of the preset weights of multiple simulation evaluation indicators is 1.

[0214] In one embodiment:

[0215] The simulation optimization module 2 specifically includes an optimization unit connected to the simulation evaluation index unit and is used to:

[0216] Compare each simulation evaluation indicator with the corresponding judgment threshold. If the corresponding preset judgment condition is not met, optimize the unique parameters in the group of event switching parameters. If the optimization of the unique parameters cannot meet the preset optimization condition, optimize the non-unique parameters in the group of event switching parameters and bring the non-unique parameters into the simulation optimization of other groups of event switching parameters.

[0217] Output module 3 is specifically used for:

[0218] Output a set of values ​​of each set of event switching parameters, which has the same non-unique parameter value as other sets of event switching parameters and has the highest sum of simulation switching scores.

[0219] Example 3:

[0220] Embodiment 3 of the present disclosure provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the automatic switching parameter optimization method as described in embodiment 1 or the automatic switching parameter optimization device as described in embodiment 2 is implemented.

[0221] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program elements or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable read only memory), flash memory or other memory technology, CD-ROM (Compact Disc Read-Only Memory), digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer.

[0222] In addition, the present disclosure may also provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the handover parameter automatic optimization method as described in Example 1. The computer device may be the handover parameter automatic optimization device as described in Example 2.

[0223] The memory is connected to the processor, the memory may be a flash memory, a read-only memory or other memory, and the processor may be a central processing unit or a single-chip microcomputer.

[0224] Embodiments 1-3 of the present disclosure provide a method, device, and medium for automatic optimization of switching parameters. By obtaining user-dimensional live network data and simulating multiple switching events from the user dimension, the values ​​of the switching parameters are iteratively optimized through simulation evaluation indicators. Finally, the optimal set of switching parameter values ​​is selected through simulation switching scores, thereby achieving efficient optimization of switching parameters with evaluable optimization effects.

[0225] It is understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present disclosure, and the present disclosure is not limited thereto. Those skilled in the art may make various modifications and improvements without departing from the spirit and substance of the present disclosure, and such modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A method for automatic optimization of switching parameters, characterized in that: The method comprises: Obtain user-dimensional live network data and multiple sets of event handover parameters for the cell to be optimized and its neighboring cells. Each set of event handover parameters corresponds to a handover event. Based on the user-dimensional live network data and the first set of values ​​for each set of event handover parameters, simulate user handovers between the cell to be optimized and the neighboring cell to obtain multiple simulation evaluation indicators for the cell to be optimized, obtain a simulation handover score based on the multiple simulation evaluation indicators, and if some simulation evaluation indicators do not meet the preset optimization conditions, optimize and obtain a second set of values ​​for the set of event handover parameters. Repeat the above operation several times with the second set of values ​​to iterate the first set of values ​​to obtain several sets of values ​​for the set of event handover parameters. The optimal set of values ​​among several sets of values ​​of each set of event switching parameters is output according to the simulation switching score.

2. The method according to claim 1, characterized in that in: User-dimension live network data includes user-dimension live network indicator parameters and user-dimension live network switching delay; Multiple simulation evaluation indicators include the distribution of simulation indicator parameters of the cell to be optimized, the distribution of the difference in simulation indicator parameters between the cell to be optimized and the neighboring cell, the simulated switching ratio, the simulated ping-pong switching ratio, the simulated switching delay distribution, and the simulated switching neighboring cell distribution.

3. The method according to claim 2, characterized in that Obtain user-dimensional live network data and multiple sets of event handover parameters for the cell to be optimized and its neighboring cells, including: Obtain a list of cells to be optimized, and determine the geographical area range that needs to be analyzed for optimizing the cells in the list; Obtaining configuration parameters and wireless trace data of all existing base station cells within the geographic area, as well as call log data of the base station cells connected to the core network, wherein the configuration parameters include cell handover parameters, the wireless trace data includes periodic measurement report (MR) data and handover signaling data, and the call log data includes user information; Determine the neighboring cells of the cell to be optimized based on periodic MR data, obtain user information based on call log data, match the user information with the periodic measurement report MR data to obtain user-dimensional live network indicator parameters, and match the user information with handover signaling data to obtain user-dimensional live network handover delay. According to the configuration parameters, the switching events existing between the cell to be optimized and the neighboring cells are obtained. The switching events include intra-frequency switching events, inter-frequency switching events and inter-system switching events. Multiple sets of event switching parameters for intra-frequency switching events, inter-frequency switching events and inter-system switching events are obtained.

4. The method according to claim 3, characterized in that in: The user dimension network indicator parameters include the user-measured reference signal received power RSRP and / or user-measured reference signal received quality RSRQ of the cell to be optimized and the neighboring cell, as well as the user measurement result Ms of the cell to be optimized and the user measurement result Mn of the neighboring cell obtained based on the user-measured RSRP and / or user-measured RSRQ; Handover events include A1 event, A2 event, A3 event, A4 event, A5 event, B1 event and B2 event. A1 event, A2 event, A3 event, A4 event and A5 event are inter-frequency handover events, A3 event, A4 event and A5 event are intra-frequency handover events, and B1 event and B2 event are inter-system handover events.

5. The method according to claim 4, characterized in that Based on the user-dimensional live network data and the first set of values ​​for each set of event handover parameters, simulate user handovers between the cell to be optimized and the neighboring cell, specifically including: If the current set of event switching parameters is the A1 event switching parameters, including the A1 event amplitude hysteresis parameter HysA1, the A1 event threshold parameter ThreshA1, and the A1 event time hysteresis TimeToTrigA1, determine whether each user in the cell to be optimized satisfies Ms - HysA1 > ThreshA1 and lasts for the duration of TimeToTrigA1. If a certain user meets the condition, simulate an A1 handover event for that user between the cell to be optimized and the neighboring cell; If the current set of event switching parameters is the A2 event switching parameters, including the A2 event amplitude hysteresis parameter HysA2, the A2 event threshold parameter ThreshA2, and the A2 event time hysteresis TimeToTrigA2, determine whether each user in the cell to be optimized satisfies Ms + HysA2 > ThreshA2 and lasts for the duration of TimeToTrigA2. If a certain user meets the condition, simulate an A2 handover event for that user between the cell to be optimized and the neighboring cell; If the current set of event switching parameters is the A3 event switching parameters, including the neighboring cell frequency offset Ofn, the cell-specific offset Ocn of the intra-system neighboring cell, the A3 event amplitude hysteresis parameter HysA3, the frequency offset Ofs of the cell to be optimized, the cell-specific offset Ocs of the cell to be optimized, the offset Off of the measurement result, and the A3 event time hysteresis TimeToTrigA3, determine whether each user in the cell to be optimized satisfies Mn + Ofn + Ocn - HysA3 > Ms + Ofs + Ocs + Off and lasts for the duration of TimeToTrigA3. If a certain user meets the condition, simulate an A3 handover event for that user between the cell to be optimized and the neighboring cell; If the current set of event switching parameters is the A4 event switching parameters, including the neighboring cell frequency offset Ofn, the cell-specific offset Ocn of the intra-system neighboring cell, the A4 event amplitude hysteresis parameter HysA4, the A4 event threshold parameter ThreshA4, and the A4 event time hysteresis TimeToTrigA4, determine whether each user in the cell to be optimized satisfies Mn + Ofn + Ocn - HysA4 > ThreshA4 and lasts for the duration of TimeToTrigA4. If a certain user meets the condition, simulate an A4 handover event for that user between the cell to be optimized and the neighboring cell; If the current set of event switching parameters is the A5 event switching parameters, including the A5 event amplitude hysteresis parameter HysA5, the A5 event threshold 1 parameter ThreshA51, the neighboring cell frequency offset Ofn, the cell-specific offset Ocn of the intra-system neighboring cell, the A5 event threshold 2 parameter ThreshA52, and the A5 event time hysteresis TimeToTrigA5, determine whether each user in the cell to be optimized satisfies Ms + HysA5 < ThreshA51 and Mn + Ofn + Ocn - HysA5 > ThreshA52 and lasts for the duration of TimeToTrigA5. If a certain user meets the condition, simulate an A5 handover event for that user between the cell to be optimized and the neighboring cell; If the current group of event handover parameters is B1 event handover parameters, including the frequency offset Ofn of the neighboring cell, the B1 event hysteresis parameter HysB1, the B1 event threshold parameter ThreshB1, and the time hysteresis TimeToTrigB1 of the B1 event, determine whether each user of the cell to be optimized satisfies Mn + Ofn - HysB1 > ThreshB1 and lasts for the duration of TimeToTrigB1. If a certain user meets the condition, simulate a B1 handover event of a certain user between the cell to be optimized and the neighboring cell. If the current group of event handover parameters is B2 event handover parameters, including the B2 event amplitude hysteresis parameter HysB2, the B2 event threshold 1 parameter ThreshB21, the frequency offset Ofn of the neighboring cell, the B2 event threshold 2 parameter ThreshB22, and the time hysteresis TimeToTrigB2 of the B2 event, determine whether each user of the cell to be optimized satisfies Ms + HysB2 < ThreshB21 and Mn + Ofn - HysB2 > ThreshB22 and lasts for the duration of TimeToTrigB2. If a certain user meets the condition, simulate a B2 handover event of a certain user between the cell to be optimized and the neighboring cell.

6. The method according to any one of claims 2 to 5, characterized in that: Obtain multiple simulation evaluation metrics of the cell to be optimized, specifically including: Before simulating handover users, obtain the first in-network metric parameters of each user in the cell to be optimized and the second in-network metric parameters in the neighboring cell. After simulating handover users, obtain the first simulation metric parameters of each user in the cell to be optimized and the second simulation metric parameters in the neighboring cell. Calculate the distribution equilibrium value of the first simulation metric parameters to obtain the distribution of the simulation metric parameters of the cell to be optimized. Calculate the number of the first users in the cell to be optimized whose first simulation metric parameters are less than the second simulation metric parameters, and calculate the ratio of the number of the first users to the total number of users in the cell to be optimized to obtain the difference distribution of the simulation metric parameters between the cell to be optimized and the neighboring cell. Calculate the number of the second users whose difference between the first in-network metric parameters and the second in-network metric parameters is less than the first preset value, and calculate the ratio of the total number of simulated handover users to the number of the second users to obtain the simulation handover ratio. Calculate the number of the third users whose number of handovers between neighboring cells in the cell to be optimized during simulation handover exceeds the second preset value, and calculate the ratio of the number of the third users to the total number of simulated handover users to obtain the proportion of simulation ping-pong handovers. Estimate the simulation handover delay of each simulated handover user according to the in-network handover delay of the user dimension, and calculate the distribution equilibrium value of the simulation handover delay to obtain the simulation handover delay distribution. Obtain the number of neighboring cells of the simulation handover and the number of users of each neighboring cell in the simulation handover, and calculate the distribution equilibrium value of the number of users of each neighboring cell in the simulation handover to obtain the simulation handover neighboring cell distribution.

7. The method according to claim 6, characterized in that Obtain the simulation handover score according to multiple simulation evaluation metrics, specifically including: Multiply the simulation evaluation metrics by the preset weights of each simulation evaluation metric respectively to obtain the simulation handover score, and the sum of the preset weights of multiple simulation evaluation metrics is 1.

8. The method according to claim 7, wherein: If some simulation evaluation metrics do not meet the preset optimization conditions, optimize and obtain the second set of values of the group of event handover parameters, specifically including: Compare each simulation evaluation indicator with the corresponding judgment threshold. If the corresponding preset judgment condition is not met, optimize the unique parameters in the group of event switching parameters. If the optimization of the unique parameters cannot meet the preset optimization condition, optimize the non-unique parameters in the group of event switching parameters and bring the non-unique parameters into the simulation optimization of other groups of event switching parameters. Output the optimal set of values ​​among several sets of values ​​of each set of event switching parameters based on the simulation switching score, specifically including: Output a set of values ​​of each set of event switching parameters, which has the same non-unique parameter value as other sets of event switching parameters and has the highest sum of simulation switching scores.

9. A switching parameter automatic optimization device, characterized in that: The device comprises: An acquisition module is used to obtain user-dimensional live network data of the cell to be optimized and its neighboring cells and multiple sets of event handover parameters, where each set of event handover parameters corresponds to a handover event; A simulation optimization module is connected to the acquisition module and is used to simulate user switching between the cell to be optimized and the neighboring cell based on the user dimension live network data and the first set of values ​​of each set of event switching parameters to obtain multiple simulation evaluation indicators of the cell to be optimized, and obtain a simulation switching score based on the multiple simulation evaluation indicators. If some simulation evaluation indicators do not meet the preset optimization conditions, a second set of values ​​of the set of event switching parameters is optimized and obtained, and the first set of values ​​are iterated with the second set of values. Repeat the above operation several times to obtain several sets of values ​​of the set of event switching parameters; The output module is connected to the simulation optimization module and is used to output the best set of values ​​among the multiple sets of values ​​of each set of event switching parameters according to the simulation switching scores.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for automatic optimization of handover parameters according to any one of claims 1 to 8 is implemented.

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