WO2013056530A1

WO2013056530A1 - Method, apparatus and system for determining call drop rate

Info

Publication number: WO2013056530A1
Application number: PCT/CN2012/072848
Authority: WO
Inventors: 王转莉
Original assignee: 中兴通讯股份有限公司
Priority date: 2011-10-20
Filing date: 2012-03-22
Publication date: 2013-04-25
Also published as: CN102387260A; CN102387260B

Abstract

Disclosed are a method, an apparatus and a system for determining a call drop rate. The method comprises: a foreground of a base station recording the number of abnormally released users and the number of successfully established users in a sampling period through summation accumulation, and calculating the number of current users being online in real time in the sampling period; after the sampling period expires, the foreground of the base station writing the number of abnormally released users and the number of successfully established users in the sampling period into an abnormally-released user counter and a successfully-established user counter respectively through the summation accumulation, and writing the number of current users in the sampling period into a current user counter; and after a reporting period expires, the foreground of the base station synchronizing data on the abnormally-released user counter, the successfully-established user counter and the current user counter to a network manager, so that the network manager determines the call drop rate in the reporting period according to the number of abnormally released users and the number of successfully established users in the reporting period and the number of current users in a previous reporting period. Therefore, accuracy of the call drop rate can be ensured.

Description

Call drop rate determination method, device and system

The present invention relates to the field of mobile communications, and in particular, to a call drop rate determination method, apparatus, and system. Background technique

In the prior art, dropped call rate is an important measure of system performance of mobile communication networks. As the fourth-generation technology of the mobile communication network evolution, the Long Term Evolution (LTE) system, the call drop rate indicator is more important.

Currently, the definition of the dropped call rate indicator includes two calculation methods: Method 1, dividing the number of users released by the abnormality by the total number of users released to determine the dropped call rate; Method 2, dividing the number of users released by the abnormality by the successfully established user The number determines the call drop rate. In the LTE system, the call drop rate indicator is determined mainly according to method two.

In the second method, the number of users who determine the dropped call rate indicator and the number of users that are successfully established by the denominator are not related to each other, that is, the number of abnormally released users and the number of successfully established users are not within one granularity. Interrelated. In the above case, the indicator that causes the dropped call rate will have an abnormality greater than 100%. In the case where the number of users is densely distributed and the statistical granularity is relatively large, such anomalies are not easily exposed, but in the case where the user distribution is sparse, the statistical granularity is relatively small, and the successful establishment and the abnormal release cross-granularity, the probability of such statistical anomalies is very high. High, which seriously affects the accuracy of the indicator. Summary of the invention

Embodiments of the present invention provide a method, device, and system for determining call drop rate to solve the prior art. An embodiment of the present invention provides a method for determining a dropped call rate, including:

The base station foreground records the number of abnormally released users and the number of successfully established users in the sampling period according to the number of added users reported by the control plane of the base station and the reduced number of users, and calculates the number of users successfully established in the sampling period. Current number of users;

After the sampling period expires, the base station foreground uses the summation and accumulation method to write the number of abnormally released users in the sampling period and the number of successfully established users to the abnormal release user counter and the successful user counter respectively, and adopts the coverage of the original data. The mode writes the current number of users in the sampling period to the current user counter;

After the reporting period expires, the base station foreground abnormally releases the user counter, establishes a successful user counter, and synchronizes data on the current user counter to the network management;

The network management system determines the call drop rate of the reporting period according to the number of abnormally released users in the reporting period, the number of successfully established users, and the current number of users in the previous reporting period.

The embodiment of the present invention further provides a call drop rate determination system, including a base station foreground and a network management, where the base station foreground includes:

The sampling module is configured to record the number of abnormally released users and the number of successfully established users in the sampling period according to the increased number of users reported by the control plane of the base station and the reduced number of users, and calculate the sampling period in the sampling period. The current number of users in real-time online;

The processing module is configured to write the number of users released by the abnormality of the sampling period and the number of successfully established users to the abnormal release user counter and establish a successful user counter respectively by using the summation accumulation method after the sampling period expires, and adopt the overlay The original data mode writes the current user count of the sampling period to the current user counter;

The reporting module is configured to synchronize the abnormality release user counter, the establishment of the successful user counter, and the data on the current user counter to the network management after the reporting period expires;

The network management system is configured to determine the call drop rate of the reporting period according to the number of abnormally released users in the reporting period, the number of successfully established users, and the current number of users in the previous reporting period. The embodiment of the present invention further provides a call drop rate determining apparatus, including:

a sampling module, configured to record, according to an increased number of users and a reduced number of users reported by the control plane of the base station, a number of users that are abnormally released during the sampling period and a number of successfully established users, and calculate the number of users The current number of users online in real time during the sampling period;

a processing module, configured to: after the sampling period expires, use the summation and accumulation manner to write the number of users released by the abnormality in the sampling period and the number of successfully established users to the abnormal release user counter and successfully establish a user counter, and the current user number of the sampling period is written into the current user counter by using the method of overwriting the original data;

The reporting module is configured to synchronize the data of the abnormality release user counter, the successful user counter, and the current user counter to the network management system after the reporting period expires, so that the network management system releases the abnormality according to the reporting period. The number of users, the number of successfully established users, and the current number of users in the previous reporting period determine the call drop rate of this reporting period.

The beneficial effects of the embodiments of the present invention are as follows:

By introducing the current number of online users at the end of the last granularity into the calculation of the call rate index of the next granularity, the problem of the dropped call rate accuracy caused by the dropped call rate greater than 100% in the special case in the prior art is solved. , can guarantee the accuracy of call drop rate under any circumstances. DRAWINGS

1 is a schematic flow chart of a call drop rate determining method according to an embodiment of the present invention;

2 is a schematic diagram of eNB processing according to an embodiment of the present invention;

3 is a schematic diagram of a process flow of an eNB performance sample MOD according to an embodiment of the present invention; FIG. 4 is a schematic flowchart of a process for summarizing the oldest value of OMC performance according to an embodiment of the present invention; FIG. Schematic. detailed description

In order to solve the call drop rate caused by the call drop rate greater than 100% in special cases in the prior art The present invention provides a method and system for determining call drop rate. Specifically, a new summary mode is introduced in the Operations & Maintenance Center ( OMC ) network management system, which is called the most The old value summary method. This approach supports the introduction of user data at the end of the last granularity into this granularity. In addition, in the foreground of the base station, a data processing mode is added, which is called a Modify (MOD) mode, and the current user number is sampled, and the original sample value is always replaced with the latest sample value that is not cleared. Through the combination of front and back, the call drop rate is finally accurately determined. The present invention will be further described in detail below in conjunction with the drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

Method embodiment

According to an embodiment of the present invention, a call drop rate determination method is provided. FIG. 1 is a schematic flowchart of a call drop rate determination method according to an embodiment of the present invention. Before performing the process shown in FIG. 1 , the base station foreground needs to be performed as follows. deal with:

For the number of users that are abnormally released, you need to set all the summary modes of the abnormally released user counters to the summation mode. All the summary methods include: Base station foreground calculation mode, network management time summary mode, and network management space summary mode.

For the number of successfully established users, you need to set all the summary methods for establishing a successful user counter to the summation mode. All the aggregation methods include: the base station foreground calculation mode, the network management time summary mode, and the network management space summary mode.

For the current number of users: The base station foreground calculation mode of the current user counter needs to be set to the sampling MOD mode, the network management time summary mode is set to the oldest value mode, and the network management space summary mode is set to the summation mode.

The sampling MOD mode is specifically as follows: sampling refers to the base station foreground (platform performance module) periodically reporting the number of users according to the control plane (service module) of the base station, performing addition and subtraction calculations, and obtaining the value of the sampling period. MOD means that this period is calculated after each sampling timer expires. The value reached is written to the counter. As the value of this counter, the original value is always overwritten with the latest value. The method of finding the oldest value is: The summary of OMC is in the unit of granularity, and has 15 minutes of granularity, 30 minutes of granularity, 1 hour of granularity, 1 day of granularity, 1 week of granularity, 1 month of granularity, and the like. When the oldest value is defined as the granularity calculation indicator, the counter whose summary mode is the oldest value is the value saved by the previous granularity. For example: Calculate the call drop rate from 2011-09-7 11:15 to 2011-09-07 11:30, the current number of users is taken from 2011-09-07 11:00 to 2011- 09-07 11:15 The value of the counter reported during this time.

Based on the above description and processing, as shown in FIG. 1, the call drop rate determining method according to an embodiment of the present invention includes the following processing:

Step 101: The base station foreground records the number of abnormally released users and the number of successfully established users in the sampling period according to the increased number of users reported by the control plane of the base station and the reduced number of users, and calculates the number of users in the sampling period. The current number of users in real-time online;

That is, in step 101, it is necessary to accumulate the total number of users that are abnormally released in the current sampling period and the total number of successfully established users. For example, UE1 is abnormally released after the connection is successful, and then UE1 is connected again after the connection is successful. Release, at this time, the number of users released abnormally is 2, and the number of successfully established users is also 2; for the current number of users, it is necessary to calculate the current number of users online in the sampling period, for example, in one sampling period, there is UE1 The UEs, UE2, UE3, UE4, and UE5 are connected to each other successfully, and the current number of users is 5. If two users UE2 and UE3 are abnormally released during the sampling period, one user UE6 is successfully connected. In the online state, the current number of users is 5-2+1=4. In the next sampling period, if one user UE5 is abnormally released, and two users UE7 and UE8 are successfully connected, the current number of users is 4. -1+2=5.

Step 102: After the sampling period expires, the base station foreground uses the summation and accumulation manner to write the number of abnormally released users in the sampling period and the number of successfully established users to the abnormal release user counter and the successful user counter respectively, and adopt the coverage. The way the original data will be current in this sampling period The number of users is written to the current user counter;

For example, before the base station foreground writes the number of abnormally released users of the sampling period and the number of successfully established users to the abnormal release user counter and the successful user counter respectively, the abnormal release user counter and the establishment successful user counter are respectively 4 and 5, the number of abnormally released users in the sampling period and the number of successfully established users are 2 and 1, respectively, and the base station foreground uses the summation and accumulation method to write the number of abnormally released users of the sampling period and the number of successfully established users respectively. After the exception is released to the user counter and the successful user counter is established, the value of the abnormally released user counter is 4+2=6, and the value of the successful user counter is 5+1=6.

For the current user counter, before the current user number of the sampling period is written, the current user counter value is 5, and the current user number of the sampling period is 8, then the current user number of the sampling period is written to the current user. When the counter is used, replace 8 with 8 . After the current user count of this sampling period is written to the current user counter, the current user counter has a value of 8.

Step 103: After the reporting period expires, the base station foreground synchronizes the abnormally releasing the user counter, establishing the successful user counter, and the data on the current user counter to the network management, such as the OMC. In step 103, the base station foreground can be measured by the measurement object. , the abnormal release user counter, the establishment of a successful user counter, and the data on the current user counter are synchronized to the network management. The measurement unit includes: a cell, a network element, and the like.

Step 104: The network management system determines the call drop rate of the reporting period according to the number of abnormally released users in the reporting period, the number of successfully established users, and the current number of users in the previous reporting period.

Specifically, in step 104, the dropped call rate of the reporting period is the number of users that are abnormally released in the reporting period / (the current number of users in the last reporting period + the number of successfully established users in the reporting period).

That is, if the number of users that are abnormally released in the reporting period reported by the base station is 3, the number of successfully established users is 5, the current number of users is 6, and the current number of users in the previous reporting period is 4, the reporting period is The call drop rate = 3 / (4 + 5). When calculating the call drop rate of the next reporting period, the current number of users 6 of the reporting period needs to be called. Preferably, in the embodiment of the present invention, the base station is an evolved Node B (eNB) in the LTE system.

2 is a schematic diagram of eNB processing according to an embodiment of the present invention. As shown in FIG. 2, it relates to a control plane of an eNB, a platform performance module of an eNB, and an OMC.

The above technical solution of the embodiment of the present invention will be described below by taking an eNB as an example. 3 is a schematic diagram of a process flow of an eNB performance sampling MOD according to an embodiment of the present invention, as shown in FIG. 3: Step 1: When the number of users increases, the control plane of the eNB reports a performance module that adds 1 to the foreground of the eNB. When there is a decrease in the number of users, the platform performance module that reduces 1 to the eNB is reported.

Step 2: The platform performance module of the eNB is responsible for periodic sampling of the number of users. When the control surface is reported, the data of the sampling period is incremented by one, and when the report is subtracted, the data of the sampling period is decremented by one. When the sampling timer expires, the value of this sampling period is written into the current user count counter.

Step 3: When the reporting time comes, the foreground data of the eNB reports the performance collection data to the OMC in units of measurement objects. If the reporting time does not arrive, step 2 is performed.

FIG. 4 is a schematic diagram of a process flow of summarizing the oldest values of OMC performance according to an embodiment of the present invention. As shown in FIG. 4, after the performance acquisition data is reported to the OMC in units of measurement objects in the foreground of the eNB, the OMC stores the performance collection data. And according to the granularity, the performance collection data in the library is summarized. If the summary mode is the oldest value mode, the value of the last reported granularity of the granularity is taken as the final summary value of the counter.

Example 1

The following technical solution of the present invention is exemplified by taking the implementation process of the Radio Resource Control (RRC) call drop rate under the cell object as an example. Assume that the sampling period is 30S. The reporting period between the OMC and the foreground is 15 minutes. The specific implementation steps are as follows: Step 1 : There are 5 users UE1 to UE5 connected successfully at 11:11 to 11:15 on a certain day.

Among them, UE1 succeeds between 11:11 and 11:30, and the control plane gives the platform performance. The module reports the number of users plus 1 event. The platform performance module has a sample value of 1 at this sample size. The sampling timer expires, and the sample value 1 is written into the current user count counter. The counter value is 1.

If there is no user access between 11:30 and 11:10, the sampling is always 1 and the counter value is 1.

From 11:10 to 11:10:30, UE2 to UE4 Attach succeeded. The control plane reported 4 times the number of users plus 1 event to the platform performance module. The platform performance module is added 4 times in this sample size, so the sample value is 5. The sampling timer expires and the sampled value is written to the current user count counter with a counter value of 5.

Before 11:15, no user access, the counter value of the current number of users is always 5. Step 2, 11:15, the report time arrives, and the foreground synchronizes the data to OMC. The current RRC user count counter value of the granularity is 5, and the granularity of the granularity abnormality release is 0. The granularity of the granularity is 5.

According to the dropped call rate index formula = the number of users released abnormally / (the current number of users + the number of successfully established users); then the call drop rate indicator of this granularity is 0 / (0 + 5) = 0 (the current number of RRC users is 0, because the granularity calculation value takes the value of the previous granularity).

Step 3, 11:15 to 11:30: UE1 and UE2 are abnormally dropped due to RLC ERROR IND.

Among them, UE1 is abnormally dropped due to RLC ERROR IND between 11:15 and 11:15:30, and the control plane reports the number of users minus 1 event to the platform performance module. The platform performance module is decremented by 1 in this sample size, so the sample value is 4. The sampling timer expires and the sampled value is written to the current user count counter with a counter value of 4.

There is no user change between 11:15, 30, and 11:20. The sampling is always 4 and the counter value is 4.

Between 11:20 and 11:20:30, UE2 is abnormally dropped due to RLC ERROR IND. The control plane reports the number of users minus 1 event to the platform performance module. Platform performance module The sample size is reduced by 1, so the sample value is 3. The sampling timer expires and the sampled value is written to the current user count counter. The counter value is 3.

Before 11:30, no user access, the counter value of the current number of users is always 3. Step 4, 11:30, the report time arrives, and the foreground synchronizes the data to OMC. The current RRC user number counter statistic value of the granularity is 3, and the statistic value of the granularity abnormal release times is

2. The granularity of this granularity is successfully established.

According to the dropped call rate index formula = the number of users released by the abnormality / (the current number of users + the number of successfully established users); then the call drop rate indicator of this granularity is 2 / (5 + 0) = 40% (the current number of RRC users) It is 5 because the calculation value of this granularity takes the value of the previous granularity).

Example 2

The technical solution of the embodiment of the present invention is taken as an example of the implementation of the Evolved Universal Terrestrial Radio Access Network (EBB) drop rate of the Evolved Universal Terrestrial Radio Access Network (ERAB). For details, assume that the sampling period is 30S, and the reporting period between the OMC and the foreground is 15 minutes. The specific implementation steps are as follows: Step 1. There are 5 users UE1 to UE5 at 11:11 to 11:15 on a certain day. Users create a default load.

The UE1 successfully connects between 11:00 and 11:30, and the control plane reports the ERAB number plus 1 event to the platform performance module. The platform performance module has a sample value of 1 at this sample size. The sampling timer expires, and the sample value 1 is written into the current ERAB counter. The counter value is 1.

Between 11:10 and 11:10:30, UE2 to UE4 Attach succeeded. The control plane reported 4 times of ERAB plus 1 event to the platform performance module. The platform performance module is added 4 times in this sampling granularity, so the sampling value is 5. The sampling timer expires and the sampled value is written to the current ERAB counter with a counter value of 5. Before 11:15, no user access, the counter value of the current ERAB number is always step 2, 11:15, the reporting time arrives, and the foreground synchronization data is sent to the OMC network management. The statistic value of the current ERAB number counter of the current granularity is 5, and the statistic value of the ERAB number of the abnormal granularity is 0. The granularity of the ERAB number is 5.

According to the call drop rate index formula = the number of ERABs released abnormally / (current ERAB number + number of ERABs successfully established); then the ERAB call drop rate indicator of this granularity is 0/(0+5)=0 (current ERAB users) The number is 0 because the granularity calculation value takes the value of the previous granularity).

Among them, UE1 is abnormally dropped due to RLC ERROR IND between 11:15 and 11:15:30, and the control plane reports the ERAB number minus 1 event to the platform performance module. The platform performance module is decremented by 1 in this sample size, so the sample value is 4. The sampling timer expires and the sampled value is written to the current ERAB counter. The counter value is 4.

There is no user change between 11:15, 30, and 11:20, and the sampling is always 4. The counter value is 4.

Between 11:20 and 11:20:30, UE2 is abnormally dropped due to RLC ERROR IND. The control plane reports the ERAB number minus 1 event to the platform performance module. The platform performance module is decremented by 1 in this sample size, so the sample value is 3. The sampling timer expires and the sampled value is written to the current ERAB counter with a counter value of 3.

Before 11:30, if there is no ERAB access, the current ERAB counter value is always 3.

Step 4, 11:30, the reporting time arrives, and the foreground synchronizes the data to the OMC. The current ERAB number counter of the current granularity has a statistical value of 3, and the granularity of the granularity abnormal release number is 2, and the granularity of the ERAB number is successfully established. According to the call drop rate index formula = the number of ERABs released abnormally / (the current number of ERABs + the number of ERABs successfully established); then the call drop rate indicator of this granularity is 2/(5+0)=40% (the current ERAB number is 5, because the calculation of the granularity takes the value of the previous granularity).

In summary, by means of the technical solution of the embodiment of the present invention, the current number of users online at the end of the last granularity is introduced into the calculation of the call rate index of the next granularity, which solves the problem in the prior art in the special case. The accuracy of the call rate.

Device embodiment

According to an embodiment of the present invention, a dropped call rate determining system is provided. FIG. 5 is a schematic structural diagram of a dropped call rate determining system according to an embodiment of the present invention. As shown in FIG. 5, the dropped call rate is determined according to an embodiment of the present invention. The system includes a base station foreground 50 and a network management unit 52. The base station foreground 50 includes: a sampling module 502, a processing module 504, and a reporting module 506. The modules of the embodiments of the present invention are described in detail below.

First, the base station foreground 50 needs to perform the following processing:

For the number of users that are abnormally released, you need to set all the summary modes of the abnormally released user counters to the summation mode. All the aggregation methods include: Base station foreground 50 calculation mode, network management 52 time summary mode, and network management 52 space summary mode;

For the number of successfully established users, you need to set all the summary methods for establishing a successful user counter to the summation mode. All the aggregation methods include: base station foreground 50 calculation mode, network management 52 time summary mode, network management 52 space summary mode;

For the current number of users: The base station foreground 50 calculation mode of the current user counter needs to be set to the sampling MOD mode, and the network management 52 time summary mode is set to the oldest value mode, and the network management 52 space summary mode is set to the summation mode.

The sampling MOD mode is specifically: sampling refers to the base station foreground 50 (platform performance module) periodically reporting the number of users according to the control plane (service module) of the base station, performing the addition and subtraction Calculate, get the value of the sampling period. MOD means that after each sampling timer expires, the value calculated in this cycle is written into the counter. As the value of this counter, the original value is always overwritten with the latest value.

The method for finding the oldest value is: The summary of OMC is in the unit of granularity, and has a particle size of 15 minutes, a particle size of 30 minutes, a particle size of 1 hour, a particle size of 1 day, a particle size of 1 week, a particle size of 1 month, and the like. When the oldest value is defined as the granularity calculation indicator, the counter whose summary mode is the oldest value is the value saved by the previous granularity. For example: Calculate the call drop rate from 2011-09-7 11:15 to 2011-09-07 11:30, the current number of users is taken from 2011-09-07 11:00 to 2011- 09-07 11:15 The value of the counter reported during this time.

The sampling module 502 is configured to record the number of abnormally released users and the number of successfully established users in the sampling period according to the increased number of users reported by the control plane of the base station and the reduced number of users, and calculate the number of users that are successfully established. The current number of users online in real time during the cycle;

That is, the sampling module 502 needs to accumulate the total number of users that are abnormally released in the current sampling period and the total number of successfully established users. For example, UE1 is abnormally released after the connection is successful, and then UE1 is connected again and then abnormally released. At this time, the number of users that are abnormally released is 2, and the number of successfully established users is also 2. For the current number of users, the current number of users online in the sampling period needs to be calculated. For example, in one sampling period, there are UE1 and UE2. If the five users of UE3, UE4, and UE5 are connected to the online state, the current number of users is 5. If two users UE2 and UE3 are abnormally released during the sampling period, one user UE6 is successfully connected. The current number of users is 5-2+1=4. In the next sampling period, if one user UE5 is abnormally released, and two users UE7 and UE8 are successfully connected, the current number of users is 4-1. +2=5.

The processing module 504 is configured to: after the sampling period expires, use the summation and accumulation manner to write the number of abnormally released users of the sampling period and the number of successfully established users to the abnormal release user counter and establish a successful user counter, respectively, and adopt The way to cover the original data will be the current of this sampling period The number of users is written to the current user counter;

For example, before the processing module 504 writes the number of abnormally released users of the sampling period and the number of successfully established users to the abnormal release user counter and the successful user counter respectively, the counts of the abnormal release user counter and the establishment successful user counter are respectively 4 And 5, the number of abnormally released users and the number of successfully established users in the sampling period are 2 and 1, respectively, and the base station foreground 50 uses the summation and accumulation method to release the number of abnormal users in the sampling period and the number of successfully established users. After the abnormal release user counter and the successful user counter are respectively written, the value of the abnormal release user counter is 4+2=6, and the value of the successful user counter is 5+1=6.

The reporting module 506 is configured to synchronize the abnormally released user counter, establish a successful user counter, and data on the current user counter to the network management 52 after the reporting period expires;

The reporting module 506 can synchronize the abnormally released user counter, the established successful user counter, and the data on the current user counter to the network management unit 52 in units of measurement objects. The measurement unit includes: a cell, a network element, and the like.

The network management system 52 is configured to determine the call drop rate of the reporting period according to the number of abnormally released users in the reporting period, the number of successfully established users, and the current number of users in the previous reporting period.

Specifically, the dropped call rate of the reporting period is the number of users who are abnormally released in the reporting period / (the current number of users in the previous reporting period + the number of successfully established users in the reporting period).

That is, if the number of abnormally released users in the current reporting period reported by the base station 50 is 3, the number of successfully established users is 5, the current number of users is 6, and the current number of users in the previous reporting period is 4, the report is reported. The call drop rate of the cycle = 3 / (4 + 5). When calculating the call drop rate of the next reporting period, the current number of users 6 of the reporting period needs to be called. Preferably, in the embodiment of the present invention, the base station is an eNB in the LET system.

2 is a schematic diagram of eNB processing according to an embodiment of the present invention. As shown in FIG. 2, the control plane of the eNB, the platform performance module of the eNB, and the OMC are involved. The platform performance module of the eNB is the base station foreground 50 shown in FIG. 5, and the OMC is the network management 52.

Step 2: The platform performance module of the eNB is responsible for periodic sampling of the number of users. When the control surface is reported, the data of this sampling period is incremented by one, and when the report is subtracted, the data of the sampling period is decremented by one. When the sampling timer expires, the value of this sampling period is written into the current user count counter.

Example 1

The following describes the above technical solution of the present invention by taking the implementation process of the RRC call drop rate under the cell object as an example. Assume that the sampling period is 30S. The reporting period between the OMC, that is, the network management system 52 and the base station front office 50 is 15 minutes. The specific implementation steps are as follows:

Step 1 : There are 5 users UE1 to UE5 connected successfully at 11:11 to 11:15 on a certain day.

Before 11:15, no user access, the counter value of the current number of users is always 5. Step 2, 11:15, the reporting time arrives, and the base station foreground 50 synchronizes the data to the network management 52. The current RRC user number counter of the current granularity is 5, and the granularity abnormality release count is 0. The granularity is successfully established.

Before 11:30, no user access, the counter value of the current number of users is always 3. Step 4: 11:30, the reporting time arrives, and the base station foreground 50 synchronizes the data to the network management 52. The current RRC user number counter statistic value of the granularity is 3, and the granularity abnormal release number is 2, and the granularity is successfully established.

Example 2

The following describes the technical solution of the ERAB drop rate in the cell object as an example. The technical solution of the embodiment of the present invention is described in detail. The sampling period is 30S, and the reporting period between the OMC, that is, the network management system 52 and the base station foreground 50 is 15 minutes. The specific implementation steps are as follows:

Step 1 : There are 5 users. UE1 to UE5 are successfully connected from 11:00 to 11:15 on a certain day, and each user establishes a default load.

From 11:10 to 11:10:30, UE2 to UE4 Attach succeeded. The control plane reported 4 times of ERAB plus 1 event to the platform performance module. The platform performance module adds 4 times to the sample size, so the sample value is 5. The sampling timer expires and the sampled value is written to the current ERAB counter with a counter value of 5.

Before 11:15, no user access, the counter value of the current ERAB number is always Step 2, 11:15, the reporting time arrives, and the base station foreground 50 synchronizes the data to the network management unit 52. The statistic value of the current ERAB number counter of the current granularity is 5, and the statistic value of the ERAB number of the abnormal granularity is 0. The granularity of the ERAB number is 5.

Step 4: 11:30, the reporting time arrives, and the base station foreground 50 synchronizes the data to the network management 52. The statistic value of the current ERAB counter of the current granularity is 3, and the statistics of the number of abnormal releases of the granularity is 2, and the statistic value of the ERAB is 0.

According to the call drop rate index formula = the number of ERABs released abnormally / (current ERAB number + successful construction The number of ERABs for the ERAB is 2/(5+0)=40% (the current ERAB number is

5, because the calculation of the granularity takes the value of the previous granularity).

While the preferred embodiments of the present invention have been disclosed for purposes of illustration, those skilled in the art will recognize that various modifications, additions and substitutions are possible, and the scope of the invention should not be limited to the embodiments described above.

Claims

Claim

A method for determining a dropped call rate, characterized in that it comprises:

The base station foreground records the number of abnormally released users and the number of successfully established users in the sampling period according to the increased number of users reported by the control plane of the base station and the reduced number of users, and calculates the number of users successfully established in the sampling period. The current number of users online in real time during the cycle;

After the sampling period expires, the foreground uses the summation and accumulation manner to write the number of the abnormally released users of the sampling period and the successfully established number of users to the abnormal release user counter and the successful user counter respectively. And writing the current user number of the sampling period to the current user counter by covering the original data;

After the reporting period expires, the foreground synchronizes the abnormal release user counter, the establishment successful user counter, and the data on the current user counter to the network management system, so that the network management user is released according to the abnormality of the reporting period. The number, the number of successfully established users, and the current number of users in the previous reporting period determine the dropped call rate for this reporting period.

The method according to claim 1, wherein the network management determines the number of users in the present reporting period according to the number of abnormally released users in the reporting period, the number of successfully established users, and the current number of users in the previous reporting period. The call drop rate is specifically as follows:

Call drop rate of this report cycle = number of users released abnormally in this report cycle / (current user number of last report cycle + number of users successfully established in this report cycle).

The method according to claim 1, wherein the foreground, the synchronizing the user counter, the establishing a successful user counter, and the data on the current user counter to the network management specifically includes:

The foreground synchronizes the abnormal release user counter, the established successful user counter, and the data on the current user counter to the network management unit in units of measurement objects.

The method according to claim 3, wherein the unit of measurement comprises: a cell and a network element.

The method according to any one of claims 1 to 4, wherein the base station is an evolved base station in a long term evolution system.

A call drop rate determination system, comprising: a base station foreground and a network management, wherein the base station foreground includes:

a sampling module, configured to record, according to the increased number of users and the reduced number of users reported by the control plane of the base station, the number of users that are abnormally released during the sampling period and the number of successfully established users, and calculate The current number of users online in real time during the sampling period;

The reporting module is configured to synchronize the abnormal release user counter, the establishment successful user counter, and the data on the current user counter to the network management system after the reporting period expires; the network management system is configured to use the reporting period according to the reporting period The number of abnormally released users, the number of successfully established users, and the current number of users in the previous reporting period determine the dropped call rate of this reporting period.

The system according to claim 6, wherein the network management is specifically configured to: determine, according to the following formula, the dropped call rate of the reporting period:

The system according to claim 6, wherein the reporting module is specifically configured to: in the unit of the measurement object, release the abnormality user counter, the establishment successful user counter, and the current user counter The data is synchronized to the network management.

The system according to claim 8, wherein the unit of measurement comprises: a cell and a network element.

10. The system according to any one of claims 6 to 9, wherein the base station is long Evolved base station in the evolved system.

11. A call drop rate determining apparatus, comprising:

The device according to claim 11, wherein the network management is specifically configured to: determine, according to the following formula, the dropped call rate of the reporting period:

The apparatus according to claim 11, wherein the reporting module is specifically configured to: in the unit of the measurement object, release the abnormality user counter, the establishment successful user counter, and the current user counter The data is synchronized to the network management.

14. The apparatus according to claim 13, wherein the unit of measurement comprises: a cell, a network element.

The apparatus according to any one of claims 11 to 14, wherein the base station is an evolved base station in a long term evolution system.