WO2013044648A1

WO2013044648A1 - Method and device for network optimization management

Info

Publication number: WO2013044648A1
Application number: PCT/CN2012/077159
Authority: WO
Inventors: 刘生浩
Original assignee: 中兴通讯股份有限公司
Priority date: 2011-09-27
Filing date: 2012-06-19
Publication date: 2013-04-04
Also published as: CN103067932B; CN103067932A

Abstract

Disclosed in the present invention are a method and a device for network optimization management,wherein, the method comprises that while a first self-optimization function conflicting with a second self-optimization function, a third self-optimization function is configured as the combination of a self-optimization function target with the high priority in the first self-optimization function and the second self-optimization function, and the first self-optimization function and the second self-optimization function both include one of the following functions: a mobility robustness optimization function and a mobility load balancing function; the optimization management is done for the network by using the configured self-optimization function. The present invention can give attention to the optimization target of each self-optimization function, and improve the network optimization effect.

Description

TECHNICAL FIELD The present invention relates to the field of communications, and in particular to a network optimization management method and apparatus. BACKGROUND In the operation process of a mobile communication system, it is often necessary to optimize management of the network, improve network service quality and user experience, and improve resource utilization. In order to reduce the maintenance cost, the automation requirement in the network optimization process of the mobile communication system is to gradually increase the automatic processing technology in the system, adaptively adjust the parameters according to the operating conditions of the network equipment, optimize the network performance, and reduce the maintenance manpower. And time cost. At present, the self-optimization function is an important part of the Self-Organizing Network (SON) in the LTE phase. Mobility Robustness Optimization (MRO) and Mobility Load Balancing (MLB) are two self-optimizing functions that operators pay most attention to.

The main goal of MRO is to reduce the failure of handover-related radio links, dynamically improve the handover performance in the network, and improve the end user experience. The main optimization measures are to dynamically adjust the cell parameters according to the network performance indication feedback. Switch boundaries. The main goal of MLB is to transfer part of the traffic of a higher-load cell to a cell with lower load, so as to achieve uniform distribution of load between cells; the main optimization measure is to modify cell mobility parameters (such as cell reselection parameters/switching parameters). ). Consider the standard implementation process of self-optimizing SON function: Continuously monitor self-optimization indicators -> Start self-optimization measures when the indicators are not met -> Determine whether self-optimization indicators are better than before execution after the measures are executed -> End this optimization or rollback. It can be seen that the optimization goals of MRO and MLB are different, but the optimization measures are similar, all of which modify the cell reselection parameters or switch parameters, which brings the problem of conflicts from optimization operations. There are two situations in conflict:

A. The self-optimization measures of MRO and MLB are specific to the same wireless configuration parameters, and the modification direction is reversed. For example, the cell individual offset (cell individual offset, CIO for short), for the same neighboring cell of the cell, the optimization measure of the MRO is to reduce its CIO and reduce unnecessary handover, and the optimization measure of the MLB is to increase its CIO, increasing the possibility of switching to this neighborhood. B. MRO and MLB's self-optimization measures use different wireless configuration parameters, but the effect is reversed and the effects cancel each other out. For example, MLB uses the measures to increase the CIO to make cell switching easier, but the MRO adjusts other parameters such as the time To Trigger decision duration of the A3 measurement event or the Thresh decision threshold of the A4 measurement event, which objectively enables the cell handover. Difficulties, such conflicts are more subtle and the impact is hard to control. The existing SON function coordination processing method focuses on introducing a conflict coordination processing module in the back end of the self-optimization process, and implementing a conflict coordination processing module according to a user-defined strategy to avoid conflicting optimization parameters, thereby avoiding Conflict, this mechanism can partially solve the class A problem, but can not solve the class B problem. Because some A-type problems, the execution of a single self-optimization function may modify multiple parameters, and if one or two conflicting parameters are modified by the coordination module, the impact may not be consistent with not performing the self-optimization function. The coordination module in the B-type problem is difficult to find conflicts and will not intervene. In addition, the user's definition strategy can only be trade-off. Even if the conflict problem can be solved, there is no way to balance various optimization objectives, and in fact, the user's optimization effect on the network is still reduced. SUMMARY OF THE INVENTION The present invention has been made in view of the problem that the optimization goal of each self-optimization function cannot be balanced in the case of a self-optimization function conflict, thereby reducing the network optimization effect. Therefore, the main purpose of the embodiment of the present invention is to provide a network optimization management. Methods and apparatus to solve the above problems. In order to achieve the above object, according to an embodiment of the present invention, a network optimization management method is provided. The network optimization management method according to the present invention includes: setting a third self-optimization function to be high in the first self-optimization function and the second self-optimization function in a case where there is a conflict between the first self-optimization function and the second self-optimization function A combination of priority self-optimization targets, wherein the first self-optimization function and the second self-optimization function each include one of the following: a mobile robust optimization function, a mobile load balancing optimization function; using the set self-optimization function to perform network Optimize management. Preferably, before using the set self-optimization function to optimize management of the network, the method further includes: setting the fourth self-optimization function to the second self-optimization function and the high-priority self-optimization target in the first self-optimization function Combination; respectively set the applicable scenarios of the third self-optimization function and the fourth self-optimization function; using the set self-optimization function to optimize the management of the network includes: selecting the third self-optimization function or the fourth self according to the scenario of the network Optimize functions to optimize management of the network. Preferably, the applicable scenarios of setting the third self-optimization function and the fourth self-optimization function respectively include: setting a suitable scenario of the mobile robustness optimization function is a scenario of co-frequency optimization and insufficient coverage, and setting a suitable scenario of the mobile load balancing optimization function It is a scene with different frequency optimization and coverage. Preferably, before the fourth self-optimization function is set as the combination of the second self-optimization function and the high-priority self-optimization target in the first self-optimization function, the method further includes: performing, according to the optimization measure corresponding to the self-optimization target, the second The order of the self-optimization function in the first self-optimization function is prioritized from high to low. Preferably, according to the order of the optimization measures corresponding to the self-optimization target on the second self-optimization function, the order of priority of the self-optimization target in the first self-optimization function is from high to low, including: The bar-optimization function sets the priority of reducing the total number of handover failures to be higher than the priority of reducing the number of invalid handovers. The priority of reducing the number of invalid handovers is higher than the priority of reducing the premature handover events, and setting the priority of reducing handover premature events. The priority is higher than the priority of reducing the late handover event, and the priority of reducing the handover too late event is set to be higher than the priority of reducing the handover to the wrong cell event. Preferably, before the network is optimized and managed according to the scenario in which the network is located, before the network is optimized and managed, the method further includes: acquiring an indicator of the network according to a preset period; determining the network according to the indicator The scene in which it is located. Preferably, before the third self-optimization function is set as the combination of the first self-optimization function and the high-priority self-optimization target in the second self-optimization function, the method further includes: first, according to the optimization measure corresponding to the self-optimization target The order of the self-optimization function in the second self-optimization function is prioritized from high to low. Preferably, according to the order of the optimization measures corresponding to the self-optimization target on the first self-optimization function from small to large, the priority ranking of the self-optimization target in the second self-optimization function from high to low includes: The equalization optimization function sets the priority of the cell user equipment load balancing to be higher than the priority of reducing the wireless connection establishment abnormality caused by the load. The priority of reducing the wireless connection establishment abnormality caused by the load is higher than reducing the load cause. The wireless assignment establishes an abnormal priority, and the priority of reducing the wireless assignment establishment abnormality caused by the load is higher than the priority of the wireless connection abnormal release caused by the load, and the wireless connection abnormal release due to the load is reduced. The priority is higher than the priority of reducing the wireless assignment abnormal release caused by the load. In order to achieve the above object, according to another embodiment of the present invention, a network optimization management apparatus is also provided. The network optimization management apparatus according to the present invention includes: a first setting module, configured to set the third self-optimization function as the first self-optimization function and the first one in a case where there is a conflict between the first self-optimization function and the second self-optimization function a combination of high priority self-optimizing targets in the second self-optimizing function, wherein the first self-optimizing function and the second self-optimizing The functions include one of the following: mobile robust optimization function, mobile load balancing optimization function; network optimization management module, set to use the set self-optimization function to optimize management of the network. Preferably, the device further includes: a second setting module, configured to set the fourth self-optimization function as a combination of the second self-optimization function and the high-priority self-optimization target in the first self-optimization function; Set to set the applicable environment of the third self-optimization function and the fourth self-optimization function respectively; the network optimization management module includes: a network optimization management sub-module, which is set to select the third self-optimization function or the fourth according to the scenario of the network Self-optimizing function to optimize management of the network. Through the invention, the third self-optimization function is defined as a combination of the first self-optimization function and the high-priority self-optimization target in the second self-optimization function, so that the optimization goal of each self-optimization function can be taken into consideration, and the network optimization effect is improved. . BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are set to illustrate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 1 is a flowchart of a network optimization management method according to an embodiment of the present invention; FIG. 2 is a flowchart of a network optimization management method according to a preferred embodiment of the present invention; FIG. 3 is a preferred embodiment according to the present invention. 2 is a flow chart of a network optimization management apparatus according to an embodiment of the present invention; and FIG. 5 is a structural block diagram of a network optimization management apparatus according to a preferred embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS It should be noted that the embodiments in the present application and the features in the embodiments may be combined with each other without conflict. The invention will be described in detail below with reference to the drawings in conjunction with the embodiments. FIG. 1 is a flowchart of a network optimization management method according to an embodiment of the present invention. As shown in FIG. 1, the following steps S102 to S104 are included. Step S102, if there is a conflict between the first self-optimization function and the second self-optimization function, setting the third self-optimization function as the high-priority self-optimization target in the first self-optimization function and the second self-optimization function combination, The first self-optimization function and the second self-optimization function include one of the following: mobile robust optimization function, mobile load balancing optimization function. Step S104, using the set self-optimization function to optimize management of the network. In the related art, in the case where the self-optimization function conflicts, the optimization goal of each self-optimization function cannot be taken into consideration, thereby reducing the network optimization effect. In the embodiment of the present invention, the third self-optimization function is defined as a combination of the first self-optimization function and the high-priority self-optimization target in the second self-optimization function, so that the optimization goal of each self-optimization function can be taken into consideration, and the network is improved. Optimize the effect. Furthermore, in consideration of performing different self-optimization functions according to different optimization goals, the present invention may first set the fourth self-optimization function as the high-priority self-optimization target in the second self-optimization function and the first self-optimization function. The combination of the third self-optimization function and the fourth self-optimization function are respectively set. Then, the network optimization management device selects a third self-optimization function or a fourth self-optimization function according to the scenario in which the network is located, and performs optimal management on the network. In this way, by setting the applicable scenario of the self-optimization function and selecting the appropriate self-optimization function to optimize the network according to the scenario of the network, the self-optimization function of the conflict can be executed according to the applicable scenario, and at the same time Other self-optimizing goals. Specifically, the foregoing setting application scenario may be that the applicable scenario in which the mobile robust optimization function is set is a scenario in which the same frequency optimization and coverage are insufficient, and the applicable scenario in which the mobile load balancing optimization function is set is a different-frequency different system optimization and a well-covered scenario. . Then, the network optimization management device acquires the indicators of the network according to a preset period, and then determines, according to the indicators, whether the network is in a scenario with the same frequency optimization and insufficient coverage, or a scenario with different frequency optimization and coverage. Finally, the network optimization management device selects a self-optimization function corresponding to the scenario to optimize management of the network. On the basis of the above, the present invention also provides a method for prioritizing self-optimizing targets in the self-optimizing function, that is, the order of the self-optimizing functions of the self-optimizing targets affects the self-optimizing function of the conflicts from small to large. Prioritize the self-optimizing targets in the self-optimization function from high to low. It should be noted that this sorting rule is only an example, and any other sorting rules in the practical application, for example, the minimum requirement of the high-priority optimization target satisfying the optimization purpose, and the low-priority optimization target satisfies the category. The expansion requirements for optimization purposes should be included in the scope of protection of the present invention. The above sorting rule can reduce the priority of the total number of handover failures, reduce the priority of switching premature events, reduce the priority of handover too late events, and reduce the priority of handover to the wrong cell event for the mobile robust optimization function. Sequencing the priority from high to low; For mobile load balancing optimization, you can prioritize the load balancing of the cell user equipment, reduce the priority of the wireless connection due to load, and reduce the wireless caused by the load. Assign an exception to establish an exception and reduce the wireless connection caused by the load The priority of the abnormal release, and the order of priority of the wireless assignment abnormal release due to the load cause are reduced in order of priority from high to low. Through the invention, the self-optimization function of the wireless network, which may be in conflict with the MRO and the MLB, can be coordinated from the optimization target, and the current coordination from the optimization measures is compensated, so that the conflicting self-optimization function has a key target according to the usage scenario. Execution, while taking into account other self-optimization goals. The implementation process of the embodiment of the present invention will be described in detail below with reference to examples. Preferred Embodiment 1 FIG. 2 is a flowchart of a network optimization management method according to a preferred embodiment of the present invention. As shown in FIG. 2, the following steps S202 to S222 are included. Steps S202 to S204, taking the MRO and the MLB as a self-optimizing function group that may conflict. The optimization goals for MRO and MLB are each prioritized. Specifically, the definition of the MRO is: reducing the total number of handover failures to the highest priority R0; reducing the number of invalid HOs to the priority R1; reducing the handover premature event to the priority R2; reducing the handover too late event to the priority R3; reducing the handover to The error cell event is priority R4. For the MLB definition: The balance of the UE UE load is the highest priority L0; the wireless connection establishment abnormality caused by the load is reduced to the priority L1; the wireless assignment establishment abnormality caused by the load is reduced to the priority L2; The wireless connection abnormality is released as the priority L3; the wireless assignment abnormality due to the load is reduced to the priority L4. Step S206 to step S210, redefining the MRO-based self-optimization function (MRO_P;), the optimization target includes all the optimization targets of the original MRO function (R0 R4) and the high-priority optimization target L0 of the original MLB function, considering these Optimize the goals and redefine the corresponding optimization measures. Redefine the MLB-based self-optimization function (MLB P), which includes all optimization objectives (L0 L4) of the original MLB function and the high-priority optimization target R0 of the original MRO function. Considering these optimization objectives, redefining the corresponding Optimization measures. Step S212, manually setting the system usage scenario according to information such as the project construction phase, and using the redefined self-optimization function MRO_P in the scenario of equal frequency optimization and insufficient coverage, in the case of different frequency and different system optimization and coverage perfection, use Redefining the self-optimizing function MLB_P. Steps S214 to S222 describe that the system performs the standard flow according to the 3GPP self-optimizing network function.

Step S214, monitoring system performance data and performing index analysis. Step S216, determining whether the MRO_P optimization target is satisfied. Step S218, at the beginning of the current project construction, the MRO_P optimization measure is executed. In step S220, it is judged whether there is improvement than before execution. Step S222, the optimization measure is rolled back. Preferred Embodiment 2 FIG. 3 is a flowchart of a network optimization management method according to a preferred embodiment 2 of the present invention. As shown in FIG. 3, the following steps S302 to S326 are included. Steps S302 to S304, the MRO and the MLB are regarded as self-optimizing function groups of possible conflicts, and the optimization goals of the MRO and the MLB are respectively subdivided into priorities. Specifically, the definition of the MRO is: reducing the total number of handover failures to the highest priority R0; reducing the number of invalid HOs to the priority R1; reducing the handover premature event to the priority R2; reducing the handover too late event to the priority R3; reducing the handover to The error cell event is priority R4. For the MLB definition: The balance of the UE UE load is the highest priority L0; the wireless connection establishment abnormality caused by the load is reduced to the priority L1; the wireless assignment establishment abnormality caused by the load is reduced to the priority L2; The wireless connection abnormality is released as the priority L3; the wireless assignment abnormality due to the load is reduced to the priority L4. Step S306 to step S310, redefining the MRO-based self-optimization function MRO_P, the optimization target includes all optimization targets (R0 R4) of the original MRO function and the high-priority optimization target L0 of the original MLB function, and redefines the corresponding optimization measures. Redefine the MLB-based self-optimization function MLB_P, whose optimization goal includes all optimization targets (L0 L4 ) of the original MLB function and the high-priority optimization target R0 of the original MRO function, and redefine the corresponding optimization measures. Step S312, defining a self-optimization scenario self-analysis standard P for the self-optimizing conflict group of MRO and MLB. In step S314, the system periodically compares the analysis criteria P according to the system indicator and the scenario. In step S316, the system determines that the current scene conforms to the MLB-based usage scenario, and the system automatically enables the MLB P function, and turns off the MRO_P function and the original MRO\original MLB function. Steps S318 to S326 describe that the system performs MLB_P according to the standard procedure of the 3GPP self-optimizing network function. Step S318, monitoring system performance data and performing index analysis. Step S320, determining whether the MLB_P optimization target is satisfied. Step S322, performing an MLB_P optimization measure. In step S324, it is judged whether there is improvement than before execution. Step S326, the optimization measure is rolled back. It should be noted that the steps shown in the flowchart of the accompanying drawings may be performed in a computer system such as a set of computer executable instructions, and, although the logical order is shown in the flowchart, in some cases, The steps shown or described may be performed in an order different than that herein. The embodiment of the invention provides a network optimization management device, which can be used to implement the network optimization management method described above. FIG. 4 is a structural block diagram of a network optimization management apparatus according to an embodiment of the present invention. As shown in FIG. 4, the first setting module 42 and the network optimization management module 44 are included. The structure is described in detail below. The first setting module 42 is configured to set the third self-optimization function as a high priority in the first self-optimization function and the second self-optimization function in a case where there is a conflict between the first self-optimization function and the second self-optimization function The combination of the self-optimization target, wherein the first self-optimization function and the second self-optimization function each comprise one of the following: a mobile robust optimization function, a mobile load balancing optimization function; a network optimization management module 44, connected to the first setting module 42, set to use the set self-optimization function to optimize the management of the network. 5 is a structural block diagram of a network optimization management apparatus according to a preferred embodiment of the present invention. As shown in FIG. 5, the network optimization management apparatus further includes a second setting module 46 and a third setting module 48. The network optimization management module 44 includes network optimization. The sub-module 442 is managed, and its structure will be described in detail below. The second setting module 46 is configured to set the fourth self-optimizing function as a combination of the second self-optimizing function and the high-priority self-optimizing target in the first self-optimizing function; the third setting module 48 is connected to the first setting The module 42 and the second setting module 46 are configured to respectively set a third self-optimization function set by the first setting module 42 and a fourth self-optimization function set by the second setting module 46. The network optimization management module 44 includes: a network The optimization management sub-module 442 is connected to the first setting module 42, the second setting module 46, and the third setting module 48, and is configured to match the applicable scenario set by the third setting module 48 according to the scenario in which the network is located, and select the first setting module. The third self-optimizing function set by 42 and the fourth self-optimizing function set by the second setting module 46 optimize the management of the network. It should be noted that the network optimization management device described in the device embodiment corresponds to the foregoing method embodiment, and the specific implementation process has been described in detail in the method embodiment, and details are not described herein again. In summary, according to the above embodiments of the present invention, a network optimization management method and apparatus are provided. Through the invention, the third self-optimization function is defined as a combination of the first self-optimization function and the high-priority self-optimization target in the second self-optimization function, so that the optimization goal of each self-optimization function can be taken into consideration, and the network optimization effect is improved. . Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general-purpose computing device, which can be concentrated on a single computing device or distributed over a network composed of multiple computing devices. Alternatively, they may be implemented by program code executable by the computing device, such that they may be stored in the storage device by the computing device, or they may be separately fabricated into individual integrated circuit modules, or they may be Multiple modules or steps are made into a single integrated circuit module. Thus, the invention is not limited to any specific combination of hardware and software. The above is only the preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes can be made to the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and scope of the present invention are intended to be included within the scope of the present invention.

Claims

1. A network optimization management method, comprising:

Setting a third self-optimization function as a high-priority self-optimization target in the first self-optimization function and the second self-optimization function in a case where there is a conflict between the first self-optimization function and the second self-optimization function The combination of the first self-optimization function and the second self-optimization function includes one of the following: a mobile robust optimization function, a mobile load balancing optimization function;

Use the set self-optimization feature to optimize management of the network.

2. The method according to claim 1, wherein

Before using the set self-optimization function to optimize management of the network, the method further includes: setting a fourth self-optimization function to the second self-optimization function and the high-priority self-optimization target in the first self-optimization function The combination;

Setting the applicable scenarios of the third self-optimization function and the fourth self-optimization function respectively; using the set self-optimization function to optimize the network management includes: selecting the third self according to the scenario in which the network is located The optimization function or the fourth self-optimization function optimizes the management of the network.

The method according to claim 2, wherein the applicable scenarios of setting the third self-optimization function and the fourth self-optimization function respectively comprise: setting a suitable scenario of the mobile robustness optimization function is co-frequency optimization and In the case of insufficient coverage, the applicable scenario for setting the mobile load balancing optimization function is the scenario where the different frequency system is optimized and the coverage is perfect.

4. The method according to claim 2, wherein before the fourth self-optimization function is set as a combination of the second self-optimization function and the high-priority self-optimization target in the first self-optimization function, The method further includes: prioritizing the self-optimizing targets in the first self-optimizing function from high to low according to an order in which the optimization measures corresponding to the self-optimization target affect the second self-optimizing function from small to large.

The method according to claim 4, wherein, according to the order of the optimization measures corresponding to the self-optimization target, the second self-optimization function has a small-to-large order, and the self-optimization target in the first self-optimization function The order of priority from high to low includes: For the mobile robust optimization function, setting the priority of reducing the total number of handover failures is higher than the priority of reducing the number of invalid handovers, and setting the priority of reducing the number of invalid handovers is higher than reducing the handover The priority of premature events, setting the priority to reduce switching premature events is higher than The priority of switching too late events is reduced, and the priority of reducing the switching too late event is set higher than the priority of reducing the handover to the wrong cell event. The method according to claim 2, wherein, before selecting the third self-optimization function or the fourth self-optimization function according to the scenario in which the network is located, before optimizing management of the network, the method further includes: The set period acquires indicators of the network;

Determining, according to the indicator, a scenario in which the network is located. The method according to claim 1, wherein before the third self-optimization function is set as a combination of the first self-optimization function and the high-priority self-optimization target in the second self-optimization function, The priority of the self-optimization target in the second self-optimization function is ranked from high to low according to the order in which the optimization measures corresponding to the self-optimization target affect the first self-optimization function from small to large. The method according to claim 7, wherein the self-optimization target in the second self-optimization function is prioritized according to an order in which the optimization measures corresponding to the self-optimization target affect the first self-optimization function from small to large The order of the highest to the lowest level includes: For the mobile load balancing optimization function, setting the priority of the cell user equipment load balancing is higher than the priority of reducing the wireless connection establishment abnormality caused by the load, and setting the wireless connection caused by the load reason The priority of establishing an abnormality is higher than the priority of reducing the abnormality of the wireless assignment establishment caused by the load, and setting the priority of reducing the wireless assignment establishment abnormality caused by the load reason is higher than the priority of reducing the wireless connection abnormal release due to the load cause , setting the priority of reducing the wireless connection abnormal release caused by the load reason is higher than reducing the priority of the wireless assignment abnormal release caused by the load cause. A network optimization management device, comprising:

a first setting module, configured to set a third self-optimization function to be the first self-optimization function and the second self-optimization function in a case where there is a conflict between the first self-optimization function and the second self-optimization function a combination of high-priority self-optimization targets, wherein the first self-optimization function and the second self-optimization function each include one of the following: a mobile robust optimization function, a mobile load balancing optimization function;

The network optimization management module is set to use the set self-optimization function to optimize the management of the network. The device according to claim 9, wherein the device further comprises:

a second setting module, configured to set a fourth self-optimization function as a combination of the second self-optimization function and a high-priority self-optimization target in the first self-optimization function; a third setting module, configured to separately set an applicable scenario of the third self-optimizing function and the fourth self-optimizing function;

The network optimization management module includes: a network optimization management sub-module, configured to select the third self-optimization function or the fourth self-optimization function according to a scenario in which the network is located, to perform optimal management on the network.