WO2013044648A1 - Procédé et dispositif permettant une gestion d'optimisation du réseau - Google Patents

Procédé et dispositif permettant une gestion d'optimisation du réseau Download PDF

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Publication number
WO2013044648A1
WO2013044648A1 PCT/CN2012/077159 CN2012077159W WO2013044648A1 WO 2013044648 A1 WO2013044648 A1 WO 2013044648A1 CN 2012077159 W CN2012077159 W CN 2012077159W WO 2013044648 A1 WO2013044648 A1 WO 2013044648A1
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Prior art keywords
self
optimization
optimization function
function
priority
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PCT/CN2012/077159
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English (en)
Chinese (zh)
Inventor
刘生浩
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中兴通讯股份有限公司
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Publication of WO2013044648A1 publication Critical patent/WO2013044648A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition

Definitions

  • 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.
  • 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.
  • 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). ).
  • the self-optimization measures of MRO and MLB are specific to the same wireless configuration parameters, and the modification direction is reversed.
  • the cell individual offset cell individual offset, CIO for short
  • the optimization measure of the MRO is to reduce its CIO and reduce unnecessary handover
  • the optimization measure of the MLB is to increase its CIO, increasing the possibility of switching to this neighborhood.
  • MRO and MLB's self-optimization measures use different wireless configuration parameters, but the effect is reversed and the effects cancel each other out.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • a network optimization management method 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.
  • 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.
  • 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.
  • the method 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.
  • 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.
  • the method 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.
  • the method 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.
  • 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.
  • a network optimization management apparatus 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.
  • 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.
  • 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.
  • FIG. 1 is a flowchart of a network optimization management method according to an embodiment of the present invention. As shown in FIG. 1 ,
  • 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.
  • 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.
  • 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.
  • 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.
  • the self-optimization function of the conflict can be executed according to the applicable scenario, and at the same time Other self-optimizing goals.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • MRO_P MRO-based self-optimization function
  • 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.
  • MLB P MLB-based self-optimization function
  • 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 S220 it is judged whether there is improvement than before execution.
  • Step S222 the optimization measure is rolled back.
  • 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.
  • 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.
  • 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.
  • the system periodically compares the analysis criteria P according to the system indicator and the scenario.
  • 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 S320 determining whether the MLB_P optimization target is satisfied.
  • step S322 performing an MLB_P optimization measure.
  • step S324 it is judged whether there is improvement than before execution.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

La présente invention se rapporte à un procédé et à un dispositif permettant une gestion d'optimisation de réseau, le procédé comprenant les étapes suivantes : alors qu'une première fonction d'auto-optimisation est en conflit avec une deuxième fonction d'auto-optimisation, une troisième fonction d'auto-optimisation est configurée comme étant une combinaison d'une cible de fonction d'auto-optimisation ayant la priorité la plus élevée dans la première fonction d'auto-optimisation et la deuxième fonction d'auto-optimisation et la première fonction d'auto-optimisation et la deuxième fonction d'auto-optimisation comprennent l'une et l'autre l'une des fonctions suivantes : une fonction d'optimisation de la robustesse de mobilité et une fonction d'équilibre de charge de mobilité; la gestion d'optimisation est réalisée pour le réseau à l'aide de la fonction d'auto-optimisation configurée. La présente invention peut prêter attention à la cible d'optimisation de chaque fonction d'auto-optimisation et améliorer l'effet d'optimisation du réseau.
PCT/CN2012/077159 2011-09-27 2012-06-19 Procédé et dispositif permettant une gestion d'optimisation du réseau WO2013044648A1 (fr)

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CN104113864A (zh) * 2014-07-30 2014-10-22 中国联合网络通信集团有限公司 一种网络自优化的方法、装置
CN107371178B (zh) * 2017-08-28 2019-10-18 浪潮软件集团有限公司 高负荷小区优化方法和装置
WO2023212845A1 (fr) * 2022-05-05 2023-11-09 Nokia Shanghai Bell Co., Ltd. Procédé, appareil et programme informatique

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CN101626590A (zh) * 2009-08-04 2010-01-13 中国科学技术大学 一种避免移动负载均衡与移动鲁棒性优化功能冲突的方法
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WO2010105443A1 (fr) * 2009-03-20 2010-09-23 华为技术有限公司 Dispositif d'unité gérée, procédé et système d'auto-optimisation
CN102056336B (zh) * 2009-11-02 2013-01-09 华为技术有限公司 自组织操作的协调处理方法与装置、通信系统
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CN101626590A (zh) * 2009-08-04 2010-01-13 中国科学技术大学 一种避免移动负载均衡与移动鲁棒性优化功能冲突的方法
CN102036274A (zh) * 2009-09-29 2011-04-27 中国移动通信集团公司 一种自主冲突处理方法、系统、自主网元和自主管理中心

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