Switching parameter automatic optimization method and device and medium

Abstract

The invention provides a switching parameter automatic optimization method and device and a medium, and the method comprises the steps: obtaining user dimension current network data and multiple groups of event switching parameters of a to-be-optimized cell and an adjacent cell, and each group of event switching parameters corresponds to a switching event; and according to the user dimension current network data and the first group of values of each group of event switching parameters, performing simulation switching of users between the to-be-optimized cell and the adjacent cell to obtain a plurality of simulation evaluation indexes of the to-be-optimized cell, obtaining a simulation switching score according to the plurality of simulation evaluation indexes, and if some simulation evaluation indexes do not meet a preset optimization condition, switching the users between the to-be-optimized cell and the adjacent cell according to the simulation switching score. Optimizing to obtain a second group of values of the group of event switching parameters, iterating the first group of values by using the second group of values, and repeating the operation for a plurality of times to obtain a plurality of groups of values of the group of event switching parameters; and outputting an optimal group of values in the plurality of groups of values of each group of event switching parameters according to the simulation switching score. According to the method and the device, a plurality of switching events are simulated from the user dimension, and the switching parameters are automatically optimized by evaluating the simulation effect.

Description

Technical Field

[0001] The present disclosure at least relates 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. The optimization of handover parameters is an important part and means of handover optimization. Improper handover parameter settings will lead to problems such as premature / late handover, ping-pong handover, handover failure, reconstruction, and dropped calls that seriously affect user perception.

[0003] Currently, the discovery and solution of switching parameter problems mainly rely on manual data analysis, manual modification and adjustment of switching parameters. This process is highly subjective and labor-intensive, and it is impossible to consider the problem from a global perspective. The optimization effect cannot be estimated and evaluated, and the problem may recur in multiple rounds of optimization. The optimization cycle is long, time-consuming, and inefficient, which seriously affects the network's 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 provide a method, device and medium for automatic optimization of switching parameters in view of the above-mentioned shortcomings, so as to solve the problem of how to efficiently optimize the switching parameters and evaluate the optimization effect.

[0006] In a first aspect, the present disclosure provides a method for automatic optimization of switching parameters, the method comprising:

[0007] Obtain user-dimensional live network data of the cell to be optimized and the neighboring cells and multiple groups of event switching parameters, where each group of event switching parameters corresponds to a switching event;

[0008] According to the user dimension existing network data and the first set of values ​​of each set of event switching parameters, simulate switching users between the cell to be optimized and the neighboring cell to obtain multiple simulation evaluation indicators of the cell to be optimized, obtain the simulation switching score according to the multiple simulation evaluation indicators, and if some simulation evaluation indicators do not meet the preset optimization conditions, optimize and obtain the second set of values ​​of the group of event switching parameters, iterate the first set of values ​​with the second set of values ​​and repeat the above operation several times to obtain several sets of values ​​of the group of event switching parameters;

[0009] The best 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, the user dimension existing network data and multiple groups of event switching parameters of the cell to be optimized and the neighboring cells are obtained, 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 to be optimized in the list of cells to be optimized;

[0015] 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 switching parameters, the wireless trace data includes periodic measurement report MR data and switching signaling data, and the call record data includes user information;

[0016] 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 current network indicator parameters, and match the user information with the handover signaling data to obtain the user-dimensional current 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 same-frequency switching events, different-frequency switching events and different-system switching events, and multiple groups of event switching parameters of the same-frequency switching events, different-frequency switching events and different-system switching events are obtained.

[0018] Further, wherein:

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

[0020] The switching 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 heterogeneous frequency switching events, A3 event, A4 event and A5 event are homogeneous frequency switching events, and B1 event and B2 event are heterogeneous system switching events.

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

[0022] If the current group event switching parameter is the A1 event switching parameter, 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 is, simulate an A1 switching event for a user between the cell to be optimized and the neighboring cell;

[0023] If the current group event switching parameter is the A2 event switching parameter, 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 is, simulate an A2 switching event for a 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 is, simulate an A3 switching event for a 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, it is determined whether each user in the cell to be optimized satisfies Mn+Ofn+Ocn-HysA4>ThreshA4 and lasts for TimeToTrigA4. If a user is, simulate an A4 switching event for a user between the cell to be optimized and the neighboring cell;

[0026] If the current group of event handover parameters is A5 event handover 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 > ThreshA52, and lasts for a duration of TimeToTrigA5. If a certain user satisfies the condition, simulate an A5 handover event of a certain user between the cell to be optimized and the neighbor cell;

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

[0028] If the current group of event handover parameters is B2 event handover parameters, including B2 event amplitude hysteresis parameter HysB2, B2 event threshold 1 parameter ThreshB21, neighbor cell frequency offset Ofn, B2 event threshold 2 parameter ThreshB22, and B2 event time hysteresis TimeToTrigB2, determine whether each user in 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 satisfies the condition, simulate a B2 handover event of a certain user between the cell to be optimized and the neighbor cell.

[0029] Furthermore, obtain multiple simulation evaluation indicators of 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 neighbor 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 neighbor 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 number of all users in the cell to be optimized, so as to obtain the distribution of the difference between the simulation index parameters of the cell to be optimized and the neighboring cell;

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

[0035] Calculate the number of third users whose number of handovers between neighboring cells of the cell to be optimized exceeds a second preset value in the simulated handover, and calculate the ratio of the number of third users to the number of all simulated handover users to obtain a simulated ping-pong handover ratio;

[0036] Estimate the simulated switching delay of each simulated switching user according to the existing network switching delay in the user dimension, calculate the distribution balance value of the simulated switching delay, and obtain the simulated switching delay distribution;

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

[0038] Furthermore, a simulation switching score is obtained according to a plurality of simulation evaluation indicators, including:

[0039] The simulation evaluation index is multiplied by the preset weight of each simulation evaluation index to obtain a 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 by optimization, specifically including:

[0042] Compare each simulation evaluation index 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 best set of values ​​among several sets of values ​​of each set of event switching parameters according to the simulation switching score, 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 switching parameter automatic optimization device, the device comprising:

[0046] An acquisition module is used to acquire user-dimensional existing network data of the cell to be optimized and the neighboring cells and multiple groups of event switching parameters, where each group of event switching parameters corresponds to a switching event;

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

[0049] In a third aspect, the present disclosure provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium. 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 switching parameters, which obtains user-dimensional existing network data, simulates multiple switching events from the user dimension, iteratively optimizes the values ​​of switching parameters through simulation evaluation indicators, and finally selects an optimal set of switching parameter values ​​through simulation switching scores, thereby achieving efficient optimization of switching 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 is a structural schematic 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 automatically optimizing switching parameters according to an embodiment of the present disclosure;

[0054] Figure 4 It 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 solution of the present disclosure, the embodiments of the present disclosure will be further described in 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 of the present disclosure and the various features in the embodiments can be combined with each other.

[0058] It can 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 or unit involved in the embodiments of the present disclosure may correspond to only one entity structure, or may be composed of multiple entity structures, or multiple modules or units may be integrated into one entity structure.

[0060] It will be understood that, in the absence of 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 may be implemented 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] Embodiment 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 existing network data of the cell to be optimized and the neighboring cells and multiple groups of event switching parameters, where each group of event switching parameters corresponds to a switching event;

[0066] S2. According to the user dimension existing network data and the first set of values ​​of each set of event switching parameters, simulate switching users between the cell to be optimized and the neighboring cell to obtain multiple simulation evaluation indicators of the cell to be optimized, obtain the simulation switching score according to the multiple simulation evaluation indicators, and if some simulation evaluation indicators do not meet the preset optimization conditions, optimize and obtain the second set of values ​​of the group of event switching parameters, iterate the first set of values ​​with the second set of values ​​and repeat the above operation several times to obtain several sets of values ​​of the group of event switching 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 the user dimension live network data, simulating multiple switching events from the user dimension, optimizing the switching parameter values ​​through simulation evaluation indicators, and finally selecting the best set of switching parameter values ​​through simulation switching scores, efficient optimization of switching parameters with evaluable optimization effects is achieved. Figure 1 The method shown is applicable 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 finding problems during network testing. The optimization personnel analyze the data to determine the problem, and then make manual modifications and adjustments. The existing methods have 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 be repeated in multiple rounds of optimization, and the cost of problem verification is high. In view of this, the present embodiment provides a method for automatic optimization of switching parameters based on existing network data simulation, which collects data from the existing network base station side, simulates the user-dimensional switching process processing of multiple groups of switching parameters, counts the evaluation indicators related to each group of switching parameter simulation and calculates the evaluation score, and then compares the evaluation scores of each group of parameters 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. 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 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 user use and user switching are obtained. In terms of user use, the indicator parameters of the user in the cell can be used for measurement, such as RSRP (Reference Signal Receiving Power), RSRQ (Reference Signal Receiving Quality), etc., and 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 implementation, obtaining user dimension existing network data and multiple groups of event switching parameters of the cell to be optimized and the neighboring cells 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 to be optimized in the list of cells to be optimized;

[0076] 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 switching parameters, the wireless trace data includes periodic measurement report MR data and switching signaling data, and the call record data includes user information;

[0077] 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 current network indicator parameters, and match the user information with the handover signaling data to obtain the user-dimensional current 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 same-frequency switching events, different-frequency switching events and different-system switching events, and multiple groups of event switching parameters of the same-frequency switching events, different-frequency switching events and different-system switching events are obtained.

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

[0080] 1. Select the cell list that needs to be optimized for handover parameters: 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 the existing network within 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, the 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) port, 4G (4th Generation Mobile Communication Technology) S1 / X2 interface, 5G (5th Generation Mobile Communication Technology) Ng / Xn interface.

[0083] 2.2. User information: user triplet / quadruple information, etc. 4G triplet / 5G quadruple is used to match and associate 4G / 5G wireless network TRACE signaling data with core network call record data (including user identification IMSI number) at the same time, and fill in the user IMSI (International Mobile Subscriber Identity) number to solve the problem of wireless network TRACE signaling data without user identification. Among them, 4G triplet refers to: MME GroupID, MME Code, MME UE S1AP ID; 5G quadruple refers to: AMF Region ID, AMF Set ID, AMF Pointer, AMFUE NGAP ID.

[0084] 2.3. Configuration information of the serving cell (cell to be optimized) and the neighboring cell (neighboring cell of the cell to be optimized): CI, PCI (physical cell identifier), frequency (frequency is mainly used to determine whether the serving cell and the neighboring cell are co-frequency cells, hetero-frequency cells or hetero-system cells, and to distinguish co-frequency switching, hetero-frequency switching and hetero-system switching), etc. The serving cell configuration parameters include cell name, base station ID, cell ID, CI, PCI, frequency, etc. The 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 existing base stations (from the CM file in the operator's OMC (Operation and Maintenance Center)).

[0086] In one embodiment, wherein:

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

[0088] The switching 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 heterogeneous frequency switching events, A3 event, A4 event and A5 event are homogeneous frequency switching events, and B1 event and B2 event are heterogeneous system switching events.

[0089] In this embodiment, the cell switching 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 information contains Ms and Mn, which correspond to the RSRP and other indicator parameters of the serving cell and neighboring cell, respectively. The switching parameters such as TimeToTrig, Ofn, Ocn, Hys, Ofs, Ocs, and Off come from the CM configuration file of the operator's OMC.

[0090] 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 open the inter-frequency measurement and activate the Gap. A3 event: The quality of the neighboring cell of the same priority is higher than the serving cell by a relative threshold, 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 (adjusting soft switching parameters), etc.

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

[0093] If the current group event switching parameter is the A1 event switching parameter, 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 is, simulate an A1 switching event for a user between the cell to be optimized and the neighboring cell;

[0094] If the current set of event handover parameters is A2 event handover 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 does, simulate an A2 handover event occurring between the cell to be optimized and its neighboring cell for that user;

[0095] If the current set of event handover parameters is A3 event handover 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 does, simulate an A3 handover event occurring between the cell to be optimized and its neighboring cell for that user;

[0096] If the current set of event handover parameters is A4 event handover 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 does, simulate an A4 handover event occurring between the cell to be optimized and its neighboring cell for that user;

[0097] If the current set of event handover parameters is A5 event handover 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 does, simulate an A5 handover event occurring between the cell to be optimized and its neighboring cell for that user;

[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 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 does, simulate a B1 handover event of a certain user between the cell to be optimized and the neighboring cell;

[0099] If the current set of event handover parameters is the 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 does, simulate a B2 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 handover processing flow simulation at the user dimension: 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 at 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 only be triggered when certain conditions are met respectively. Specifically:

[0101] (1) Configuration of handover parameters for A1 event:

[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 of the A2 event trigger condition being met, i.e., 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 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 of the A3 event trigger condition being 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 neighboring cell;

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

[0126] TimeToTrig: the duration of the A4 event trigger condition being 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 meeting the A5 event triggering condition, i.e., time hysteresis;

[0135] (6) Handover 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 meeting the B1 event triggering condition, i.e., time hysteresis;

[0141] (7) Handover 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 meeting the B2 event triggering condition, i.e., time hysteresis;

[0148] Periodic MR contains serving cell and neighboring cell information, as well as metric parameters, and is used for handover decision and handover process simulation.

[0149] In one implementation, 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 simulating 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 a first simulation index parameter to obtain a distribution of simulation index parameters of a 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 number of all users in the cell to be optimized, so as to obtain the distribution of the difference between the simulation index parameters of the cell to be optimized and the neighboring cell;

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

[0155] Calculate the number of third users whose number of handovers between neighboring cells of the cell to be optimized exceeds a second preset value in the simulated handover, and calculate the ratio of the number of third users to the number of all simulated handover users to obtain a simulated ping-pong handover ratio;

[0156] Estimate the simulated switching delay of each simulated switching user according to the existing network switching delay in the user dimension, calculate the distribution balance value of the simulated switching delay, and obtain the simulated switching delay distribution;

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

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

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

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

[0161] S23: Handover decision: According to the input handover parameters, if the handover conditions are met and the CM neighbor cell configuration contains the target cell information, the handover is judged 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; MR contains the RSRP, RSRQ and other indicator parameters of the serving cell and the neighbor cell, namely Ms and Mn. According to the input handover parameter group, if a certain neighbor cell preferentially meets the handover conditions of A1, A2, A3, A4, A5, B1, and B2, and at the same time, the CM neighbor cell configuration contains this neighbor cell information, then this neighbor cell is the target cell for handover.

[0162] The handover delay is estimated based on the neighbor cell type and the RRC / X2 / S1 interface signaling interaction delay.

[0163] Taking the A3 switching parameters as an example, the conditions for switching are: 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: bias of the measurement result; Hys: amplitude hysteresis of the measurement result; Ofs: frequency bias of the serving cell; Ofn: frequency bias of the neighboring cell; Ocs: serving cell specific bias CIO; Ocn: cell specific bias 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 switching parameter evaluation indicators in S13 include: for each set of switching parameters, the switching parameter evaluation indicators are counted by cell dimension. The evaluation indicators include but are not limited to: the RSPR distribution of the serving cell (cell to be optimized), the RSRP difference distribution of the serving cell and the neighboring cell, the switching ratio (number of switching times * 100% / (number of MR samples of the serving cell-neighboring cell RSRP <3db), the proportion of ping-pong switching, the distribution of switching delays, and the distribution of switching neighboring cells (the number and proportion of switching between the serving cell and the neighboring cell, and the number of switching neighboring cells). The calculation of the specific value of each evaluation indicator can be designed based on experience. For example, the distribution indicators can calculate the root mean square to evaluate whether their distribution is balanced.

[0167] Based on the RSRP / RSRQ and other indicator parameters of the service cell and neighboring cell in the periodic MR, as well as the switching signaling, the rationality of the cell switching parameters and neighboring cell switching parameters corresponding to the A1, A2, A3, A4, A5, B1 and B2 events are evaluated. During the simulation, the service cell is not actually switched, but a neighboring cell is marked as a new service cell for a user, and the switching statistics are updated for the original service cell (cell to be optimized). The switching results and evaluation indicator information such as RSRP are simulated data.

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

[0169] The simulation evaluation index is multiplied by the preset weight of each simulation evaluation index to obtain a 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 index statistics and scoring includes calculating the handover parameter score: multiple evaluation indicators corresponding to each group of handover parameters of the cell are weighted and normalized to generate a handover parameter score. Examples of weights of each indicator: 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 delay 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 by optimization, specifically including:

[0173] Compare each simulation evaluation index 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 there are new handover parameter groups that need to be processed: according to the evaluation indicators and scoring results of the cell, if the optimization effect is achieved or all candidate parameter groups have been traversed, go to S15, otherwise, generate the parameter group to be evaluated according to the evaluation indicators and optimization goals and go to S12. For example, in the current parameter evaluation indicators, if the ping-pong handover ratio is high, then increase the A3 relative threshold, hysteresis threshold or hysteresis time, etc.; if the serving cell is smaller than the neighboring cell RSRP ratio, then lower the A3 relative threshold, hysteresis threshold, hysteresis time or lower CIO, etc. The handover parameters are grouped according to the handover events, that is, the seven events A1, A2, A3, A4, A5, B1, and B2 correspond to seven groups of handover parameters, which come from the CM configuration file of the operator's OMC. Only one set of handover parameter simulations for one event can be performed each time, or multiple sets of handover parameter simulations corresponding to multiple events can be performed simultaneously. The evaluation indicators are all statistically obtained during the execution of S12. If one or more of the seven groups of switching parameters A1, A2, A3, A4, A5, B1, and B2 do not achieve the optimization effect, it is necessary to adjust the size of the switching parameter group that does not meet the standard, and re-simulate for optimization evaluation. For the parameters that appear in multiple switching events (non-unique parameters), you can first optimize and determine, and then optimize the remaining parameters (unique parameters of a switching event). You can also optimize the remaining parameters first. If the optimization effect is not achieved, optimize the parameters shared by multiple switching events (non-unique parameters, which should have the same value). For example, if the ping-pong switching ratio is high, then for the A3 event, according to the switching trigger condition Mn+Ofn+Ocn-Hys>Ms+Ofs+Ocs+Off, it is necessary to increase Hys, Off or hysteresis time TimeToTrig, etc., so as to reduce the frequent triggering of switching conditions caused by the amplitude fluctuations of the measurement results Ms and Mn, and improve 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-have-been-switched-but-not-switched" problem, and the switching parameters need to be adjusted to trigger the switching as soon as possible.

[0177] S15 Handover parameter optimization output results: Compare the score values ​​of all handover parameter groups by cell dimension, select the best set of handover parameters, and output the handover parameter optimization results of each cell. For example, the seven events A1, A2, A3, A4, A5, B1, and B2 correspond to handover parameter groups configured in the existing network, which are 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 optimization effect, a new set of handover parameters will be generated for re-evaluation. Therefore, there may be multiple sets of handover parameters for the seven events A1, A2, A3, A4, A5, B1, and B2.

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

[0179] Embodiment 2:

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

[0181] Acquisition module 1, used to acquire user dimension existing network data of the cell to be optimized and the neighboring cells and multiple groups of event switching parameters, each group of event switching parameters corresponds to a switching event;

[0182] The simulation optimization module 2 is connected to the acquisition module 1 and is used to simulate switching users between the cell to be optimized and the neighboring cell according to the user dimension existing 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 the simulation switching score according to the multiple simulation evaluation indicators. If some simulation evaluation indicators do not meet the preset optimization conditions, the second set of values ​​of the group 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 group 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 score.

[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, 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 to be optimized in the list of cells to be optimized;

[0189] An existing network data unit, connected to the list unit, for obtaining configuration parameters and wireless trace data of all existing network base station cells within the geographical area, and call record data of the base station cells connected to the core network, wherein the configuration parameters include cell switching parameters, the wireless trace data includes periodic measurement report MR data and switching 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 area of ​​the cell to be optimized according to the periodic MR data, obtain the user information according to the call record data, match the user information with the periodic measurement report MR data to obtain the user dimension existing network indicator parameters, and match the user information with the switching signaling data to obtain the user dimension existing network switching 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 groups of event switching parameters for the same-frequency switching events, different-frequency switching events and different-system switching events.

[0192] In one embodiment, wherein:

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

[0194] The switching 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 heterogeneous frequency switching events, A3 event, A4 event and A5 event are homogeneous frequency switching events, and B1 event and B2 event are heterogeneous system switching events.

[0195] In one embodiment, the simulation optimization module 2 specifically includes a simulation unit, which is used to:

[0196] If the current group event switching parameter is the A1 event switching parameter, 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 is, simulate an A1 switching event for a user between the cell to be optimized and the neighboring cell;

[0197] If the current set of event handover parameters is A2 event handover 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 occurring between the cell to be optimized and its neighbor cell;

[0198] If the current set of event handover parameters is A3 event handover parameters, including the frequency offset Ofn of the neighbor cell, the cell-specific offset Ocn of the intra-system neighbor 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 occurring between the cell to be optimized and its neighbor cell;

[0199] If the current set of event handover parameters is A4 event handover parameters, including the frequency offset Ofn of the neighbor cell, the cell-specific offset Ocn of the intra-system neighbor 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 occurring between the cell to be optimized and its neighbor cell;

[0200] If the current set of event handover parameters is A5 event handover parameters, including the A5 event amplitude hysteresis parameter HysA5, the A5 event threshold 1 parameter ThreshA51, the frequency offset Ofn of the neighbor cell, the cell-specific offset Ocn of the intra-system neighbor 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 occurring between the cell to be optimized and its neighbor cell;

[0201] If the current group 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 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 does, 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 the 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 does, simulate a B2 handover event of a certain user between the cell to be optimized and the neighboring cell.

[0203] In one embodiment, the simulation optimization module 2 specifically includes a simulation evaluation index unit, connected to the simulation unit, 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 switching delay of each simulated switching user according to the existing network switching delay in the user dimension, calculate the distribution balance value of the simulated switching delay, and obtain the simulated switching delay distribution;

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

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

[0213] The simulation evaluation index is multiplied by the preset weight of each simulation evaluation index to obtain a 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 used for:

[0216] Compare each simulation evaluation index 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, 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] Embodiment 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 disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, 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 a computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor executes the automatic switching parameter optimization method as described in Example 1. The computer device may be the automatic switching parameter optimization device as described in Example 2.

[0223] The memory is connected to the processor, the memory may be a flash memory or 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, which obtains existing network data in the user dimension, simulates multiple switching events from the user dimension respectively, iteratively optimizes the values ​​of switching parameters through simulation evaluation indicators, and finally selects the optimal set of switching parameter values ​​through simulation switching scores, thereby realizing efficient optimization of switching parameters with evaluable optimization effects.

[0225] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present disclosure, but the present disclosure is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and substance of the present disclosure, and these 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 of the cell to be optimized and the neighboring cells and multiple groups of event switching parameters, where each group of event switching parameters corresponds to a switching event; According to the user dimension existing network data and the first set of values ​​of each set of event switching parameters, simulate switching users between the cell to be optimized and the neighboring cell to obtain multiple simulation evaluation indicators of the cell to be optimized, obtain the simulation switching score according to the multiple simulation evaluation indicators, and if some simulation evaluation indicators do not meet the preset optimization conditions, optimize and obtain the second set of values ​​of the group of event switching parameters, iterate the first set of values ​​with the second set of values ​​and repeat the above operation several times to obtain several sets of values ​​of the group of event switching parameters; The best 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 switching parameters for the cell to be optimized and the 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 to be optimized in the list of cells to be optimized; 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 switching parameters, the wireless trace data includes periodic measurement report MR data and switching signaling data, and the call record data includes user information; 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 current network indicator parameters, and match the user information with the handover signaling data to obtain the user-dimensional current 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 same-frequency switching events, different-frequency switching events and different-system switching events, and multiple groups of event switching parameters of the same-frequency switching events, different-frequency switching events and different-system switching events are obtained.

4. The method according to claim 3, characterized in that in: The user dimension existing network indicator parameters include the user measurement reference signal received power RSRP and / or user measurement reference signal received quality RSRQ of the cell to be optimized and the neighboring cell, and the user measurement result Ms of the cell to be optimized and the neighboring cell user measurement result Mn obtained according to the user measurement RSRP and / or user measurement RSRQ; The switching 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 heterogeneous frequency switching events, A3 event, A4 event and A5 event are homogeneous frequency switching events, and B1 event and B2 event are heterogeneous system switching events.

5. The method according to claim 4, characterized in that According to the user dimension live network data and the first set of values ​​of each set of event switching parameters, simulate user switching between the cell to be optimized and the neighboring cell, including: If the current group of event handover parameters is A1 event handover 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 a duration of TimeToTrigA1. If a certain user does, simulate an A1 handover event for a certain user between the cell to be optimized and its neighbor cell; If the current group of event handover parameters is A2 event handover 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 a certain user between the cell to be optimized and its neighbor cell; If the current group of event handover parameters is A3 event handover parameters, including the neighbor cell frequency offset Ofn, the cell-specific offset Ocn of the intra-system neighbor 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 a certain user between the cell to be optimized and its neighbor cell; If the current group of event handover parameters is A4 event handover parameters, including the neighbor cell frequency offset Ofn, the cell-specific offset Ocn of the intra-system neighbor 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 a certain user between the cell to be optimized and its neighbor cell; If the current group of event handover parameters is A5 event handover parameters, including the A5 event amplitude hysteresis parameter HysA5, the A5 event threshold 1 parameter ThreshA51, the neighbor cell frequency offset Ofn, the cell-specific offset Ocn of the intra-system neighbor 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 a certain user between the cell to be optimized and its neighbor cell; 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 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 does, simulate a B1 handover event of a certain user between the cell to be optimized and the neighboring cell; If the current set of event handover parameters is the 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 does, 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 the handover user, 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 the handover user, 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 distribution of the difference between the simulation metric parameters of 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 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; 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 distribution of the simulation handover delay; Obtain the number of neighboring cells of the simulation handover and the number of users of each neighboring cell for the simulation handover, and calculate the distribution equilibrium value of the number of users of each neighboring cell for the simulation handover to obtain the distribution of the neighboring cells of the simulation handover.

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 the multiple simulation evaluation metrics is 1.

8. The method according to claim 7, wherein: If some of the simulation evaluation metrics do not meet the preset optimization conditions, optimize and obtain the second set of values of the set of event handover parameters, specifically including: Compare each simulation evaluation index 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 best set of values ​​among several sets of values ​​of each set of event switching parameters according to the simulation switching score, 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 acquire user-dimensional existing network data of the cell to be optimized and the neighboring cells and multiple groups of event switching parameters, where each group of event switching parameters corresponds to a switching event; A simulation optimization module is connected to the acquisition module and is used to simulate switching users between the cell to be optimized and the neighboring cell according to the user dimension existing network data and the first set of values ​​of each set of event switching parameters, so as to obtain multiple simulation evaluation indicators of the cell to be optimized, and obtain a simulation switching score according to the multiple simulation evaluation indicators. If some simulation evaluation indicators do not meet the preset optimization conditions, a second set of values ​​of the group of event switching parameters is optimized and obtained, and the first set of values ​​are iterated with the second set of values ​​and the above operation is repeated several times to obtain several sets of values ​​of the group 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 several sets of values ​​of each set of event switching parameters according to the simulation switching score.

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 switching parameters according to any one of claims 1 to 8 is implemented.

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