WO2003037018A1 - Procede et systeme d'optimisation de la performance d'un reseau - Google Patents

Procede et systeme d'optimisation de la performance d'un reseau Download PDF

Info

Publication number
WO2003037018A1
WO2003037018A1 PCT/EP2001/012374 EP0112374W WO03037018A1 WO 2003037018 A1 WO2003037018 A1 WO 2003037018A1 EP 0112374 W EP0112374 W EP 0112374W WO 03037018 A1 WO03037018 A1 WO 03037018A1
Authority
WO
WIPO (PCT)
Prior art keywords
network
parameters
cost function
values
performance
Prior art date
Application number
PCT/EP2001/012374
Other languages
English (en)
Inventor
Jaana Laiho
Albert HÖGLUND
Tomas Novosad
Original Assignee
Nokia Corporation
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nokia Corporation filed Critical Nokia Corporation
Priority to PCT/EP2001/012374 priority Critical patent/WO2003037018A1/fr
Priority to CNB028211200A priority patent/CN100348071C/zh
Priority to EP02733097A priority patent/EP1442623A1/fr
Priority to PCT/IB2002/001962 priority patent/WO2003037019A1/fr
Priority to US10/493,633 priority patent/US20040266442A1/en
Publication of WO2003037018A1 publication Critical patent/WO2003037018A1/fr

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5003—Managing SLA; Interaction between SLA and QoS
    • H04L41/5019—Ensuring fulfilment of SLA
    • H04L41/5025—Ensuring fulfilment of SLA by proactively reacting to service quality change, e.g. by reconfiguration after service quality degradation or upgrade
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q3/00—Selecting arrangements
    • H04Q3/0016—Arrangements providing connection between exchanges
    • H04Q3/0062—Provisions for network management

Definitions

  • the invention relates to a method and system for optimising the performance of a network.
  • Telecommunications Management Network (TMN) model provides a widely accepted view about how the business of a service provider is to be managed.
  • the TMN model consists of four layers, usually arranged in a triangle or pyramid, with business management at the top, service man- agement the second layer, network management the third layer, and element management at the bottom. Management decisions at each layer are different but related to each other. Working from the top down, each layer imposes requirements on the layer below. Working from the bottom up, each layer provides important source of data to the layer above.
  • TMF TeleManagement Forum's
  • the 3GPP has adopted the same model. The scope of TMF is to find standardised way to define service quality, set requirements for networks in terms of quality of service (QoS) measurements, and make it possible to have QoS reports between providers and systems that implement the service.
  • QoS quality of service
  • the TMN model According to the TMN model the information from the upper level systems flows down, guaranteeing seamless operation and optimisation possibili- ties for the network.
  • the TMN model is depicted in Fig. 3.
  • the information flow from the business management layers all the way down to the service management and network management layers is essential since the business aspects have to be considered carefully in the optimisation and network development process.
  • the TMN model demonstrates the change of the abstraction level in the operator's daily work.
  • the business plan efficiency can be measured with capital and operational expenditure (CAPEX, OPEX) and revenue.
  • the wanted business scenario is then translated to offered sen/ices, service priorities and service QoS requirements.
  • On the lowest (network element) level of the TMN model the business related issues are converted into configuration parameter settings.
  • TMN's Business Management Systems Functions supported by TMN's Business Management Systems are, for example, to create an investment plan, to define the main QoS criteria for the proposed network and its services, to create a technical development path (expansion plan) to ensure that the anticipated growth in subscriber numbers is provided for.
  • Sen/ice Management Systems Functions supported by Sen/ice Management Systems are for example to take care of subscriber data, the provision of services and subscribers, to collect and rate bill offered services, to create, promote and monitor services.
  • NMS Network Management Systems
  • Element management systems can be considered as part of Network Element functionality with the responsibility to monitor the functioning of the equipment, to collect raw data (performance indicators), provide local graphical user interface (GUI) for site engineers, and to mediate towards the NMS system.
  • GUI graphical user interface
  • Telecom Operations Map In addition to TMN, the TMF also defines a Telecom Operations Map (TOM). Telecom and data sen/ice providers must apply a customer ori- ented service management approach using business process management methodologies to cost effectively manage their businesses and deliver the service and quality customers require.
  • TOM identifies a number of operations management processes covering Customer Care, Sen/ice Management and Network Management.
  • the Telecom Operations Map uses the layers of the TMN model as core business processes, but divides the service management layer into 2 parts: Customer Care and Service Development and Operations. Customer Interface Management is separately delineated, because Customer Interface Management may be man- aged within the individual Customer Care sub-process or, in combination across one or more of the Customer Care sub-processes.
  • Fig. 4 shows the high-level structure of Network Management processes and the supporting Function Set Groups. According to the framework pro- vided by TOM it is possible to map each of the high level processes to a series of component functions (arranged in function set groups). Provided that:
  • NMS network management system
  • Fig. 4 the TOM and its components are presented.
  • the functionalities of the layers are the same as in Fig. 3 to indicate the corresponding management layers.
  • a network optimising process serves to improve the overall network quality as experienced by the mobile subscriber and to ensure an efficient use of the network resources.
  • the optimising process includes the analysis of the network and improvements in the network configuration and performance.
  • Statistics of key performance indicators (KPI) for the operational network are fed to a tool for analysing the network status and the radio resource management (RRM) parameters can be manually tuned for the better performance.
  • the key performance indicators (KPI) are defined in an initial phase of the optimisation process. They consist for example of measurements in the network management system (NMS) and of field measurement data or any other information, which can be used to determine the quality of service (QoS) of the network.
  • NMS network management system
  • QoS quality of service
  • quality of service QoS has consisted for example of dropped call statistics, dropped call cause analysis, handover statistics and measurements of successful call attempts, while for third generation systems with a greater variety of sen ices new definitions of quality of service QoS for quality analysis must be generated.
  • This object is solved by a method for optimising the performance of a network according to claim 1, a corresponding system according to claim 12.
  • the invention is based on the idea to optimise network resources by means of one centralised cost function rather than optimising the network resources separately.
  • radio resource management algorithms are parameterised separately: handover control, admission control, power control etc. parameter values are set independently and one can identify cases where for example hand over problems are due to wrong power control (CPICH) setting. Change in the admission control setting can result in a change in the quality of the packet data.
  • CPICH wrong power control
  • the relevant key performance indicators for a specific entity within the network as well as first parameters, which influence the key performance indica- tors, are determined.
  • a number of entities similar to said specific entity is selected, wherein relevant key performance indicators are associated to every entity.
  • the key performance indicators as well as the selected number of entities are used as elements in a first cost function, i.e. said first cost function is calculated on the basis of the KPI and the number of entities. Said first cost function is calculated in order to evaluate the network performance. Accordingly, since said first parameters directly relate to the key performance indicators, the network performance will be depend on first values of said first parameters.
  • the values of said first parameters are adjusted, so that a sec- ond set of values of said first parameters are obtained.
  • the key performance indicators are determined again but this time on the basis of the second values of said first parameters and said first cost function is recalculated on the basis of these key performance indicators.
  • the result of said first cost function calculated on the basis of said first values of said first parameters is compared to the result of said first cost function recalculated on the basis of said second values of said first parameters. This comparison is carried out to determine whether the network performance has improved.
  • said second values of said first parame- ters are adopted as permanent parameters.
  • the respective key performance indicators are weighted with different weight coefficients within said first cost function. Using different weight coefficients allows to allocate more influence of one or more key performance indicators on the first cost function.
  • reference values for the key performance indicators are set and the key performance indicators in the first cost function are replaced by the difference between the current key performance indicators and the respective reference values (to define the "cost” see equation (1 )).
  • the first cost function is now calcu- lated on the basis of the difference between the current key performance indicators and the respective reference of values. This allows to set quality of service targets based on the cost of the KPI(s) on the system.
  • said first cost func- tion is composed of a second and a third cost function, wherein said second cost function represents the quality requirements within the network and said third cost function represents the capacity requirements within the network.
  • Said second cost function is weighted with a second weight coefficient while said third cost function is weighted with a third weight co- efficient.
  • the second and third cost function are composed of the selected entities, wherein the determined key performance indicators are associated to each entity. This allows to incorporate a broad distribution of key performance indicators from across the network.
  • said entity can be represented by the cell or the user group within the network. Accordingly, the cost function can be calculated for example on the basis of a cell or a cluster of cells.
  • the steps for optimising the network performance are iterated, so that the optimising process can be automated.
  • the values of the KPI ' s together with the respective first parameters and the cor- responding result of the first cost function are stored to create a history database.
  • the current result of said first cost function is compared with previous results thereof stored in the history database in order to determine whether the network performance has improved within a predetermined time interval.
  • a respective notification is being issued. Issuing the notification when no improvements are detected for a predetermined time interval, can avoid the occurrence of deadlock during the automated process and point out to possible problems.
  • Fig. 1 shows a flow chart of an automated process for optimising the network performance
  • Fig. 2 shows an example of a KPI cost function
  • Fig. 3 shows a diagram of the telecommunications management network (TMN) model
  • Fig. 4 shows a diagram of the Telecom operation map (TOM)
  • Fig. 5 shows an illustration of the combination of monitoring and optimising functions to combine different management layers.
  • Fig 1 a flow chart of an automated process for optimising the network performance according to the first embodiment is shown.
  • step S1 those key performance indicators, which describe the performance of the part of interest of the network, are selected.
  • step S2 those configuration parameters, upon which the KPI ' s depend on, are determined.
  • step S3 the number of cells, which are to be included into the optimising process, are selected, i.e. selecting a cluster of cells.
  • the current values of the KPI ' s are determined based on the respective configuration parameters in step S4.
  • the cost function is cal- culated on the basis of the current values of the KPI's and the number of cells.
  • the result of the cost function, the values of the KPI ' s and the configuration parameters are stored in a history database in step S6.
  • At least one value of the respective configuration parameters is adjusted in step S7, resulting in a new set of configuration parameters.
  • new KPI values are determined in step S4 and the cost function is re-calculated in step S5 on the basis of the new KPI values and the (unchanged) number of cells as selected in step S3.
  • the new result of the cost function, the new KPI and configura- tion parameter values are also stored in the history database in step S6.
  • the new result of the cost function - based on the new/adjusted set of configuration parameters - is compared to previous results of the cost function stored in the history database in step S8 in order to determine whether the network performance of interest has improved after adjusting the configuration parameters.
  • step S9 If the network performance has improved after adjusting the configuration parameters, the adjusted set of configuration parameters are adopted as permanent parameters in step S9. While, if it has been determined in step S8 that the network performance has not improved after adjusting the con- figuration parameters, the first set of configuration parameters, as stored in the history database in step S6, are adopted as permanent parameters in step S9.
  • step S10 is checked whether the network performance has improved within a predetermined time interval.
  • the network operator is notified in step S12 that a problem has occurred with the automated process for optimising the network performance. Since it is clear that many of the parameter values will not be auto-tuned, and that auto- tuning cannot always optimise the network, the operator can then check whether this problem is due to hardware problems or whether - under the current network conditions - it is not possible to automatically optimise the network performance. In such a case of the network operator will have to resume to manually optimise the network performance.
  • step S7 the configuration parameters are adjusted again in order to further optimise the network performance.
  • the flow will then continue as described above.
  • a second embodiment not only the relevant KPI ' s are selected in step S1 but also a set of QoS targets is determined, which is expressed in a set of reference KPI.
  • the automated process for optimising the network performance according to the second embodiment corresponds substantially to the optimising process according to the first embodiment. The only difference is that the difference between the KPI and the reference KPI is used instead of the KPI value when the calculating the cost function in step S5.
  • the operator sets capacity requirements for certain capacity KPIs denoted KPI_C with "ref" in the sub-index.
  • the operator sets quality requirements for certain KPI_Qs.
  • the quality and capacity costs can then be calculated as in equation (1).
  • weight coefficients ⁇ and ⁇ can be combined or summed with weight coefficients ⁇ and ⁇ .
  • weight coefficients ⁇ and ⁇ By controlling or changing weight coefficients ⁇ and ⁇ a certain type of cost can be emphasised and the overall some.
  • the mathematical formulation of the task of optimising the network performance can be seen as to find a combination of air interface configuration parameters based on which the KPIs are as close to the desired area as possible.
  • Fig. 2 shows an example of a KPI cost function f.
  • the cost for KPI values higher than KPI_ref is increasing linearly.
  • the cost functions can also take other shapes.
  • the total cost function to be optimised, i.e. minimized, is presented in equation (3).
  • a trade-off between capacity and quality requirements can be accomplished using the parameter W.
  • the minimization is performed by adjusting the configuration parameters (2).
  • the KPI values also depend on the service distribution, e.g. different costs and parameter settings will be achieved depending on the service distribution.
  • KPI _C / /(Configuration parameters, Service Distribution)
  • KPI Q j /(Configuration parameters, Service Distribution) ( v 2) '
  • Factors that may affect the optimisation process are for example the traffic profile (service mix), traffic density, pricing of each service etc.
  • the ulti- mate goals when minimizing the total cost include to optimise the operators revenue, to minimise CAPEX and OPEX, as well as to maintain good reputation of the operator.
  • the optimisation challenge is to combine seamlessly all the different TOM management layers, wherein the fact, that the measurements (quality and cost indicators) from different layers use different language, should be taken into account.
  • Radio Access Network parameter settings
  • certain configured functional entity is monitored by certain set of measurements.
  • the performance of the entity is derived with a cost function utilising the defined measurements.
  • the following means translation of measurements of larger en- tity (i.e. cell, traffic class, etc.) to user level entity be able to statistically conclude the quality of individual users. Also this step is performed with a with weighted cost function(s). Furthermore, it is possible to combine these individual translations with cost function to achieve wanted end user quality indication.
  • a technical translation mapping from Radio Access Network measurements (network performance) to end user flow level grade of service (experienced quality).
  • Fig. 5 shows an illustration of the combination of network monitoring and optimising functions which are used to combine different management layers within the network by mapping.
  • mapping is carried out from one layer to the next one by combining the network measurements, the performance indicators PI and/or the KPI with a cost function
  • GOS C(Service Availability) + C(Delay and Jittering) + C(Quality) + C(Dropping) + C(Service Accessibility) + C(Equivalent Bitrate or User throughput)
  • the delay is composed of Service Access Delay and Queuing Transmission Delay.
  • Non real-time quality is influenced by packet loss, Radio Link Control RLC, Packet Data Convergence Protocol PDCP, i.e. by the bit error rate BER and the block error rate BLER.
  • the quality is bad if uplink UL block error rate BLER is significantly higher than the target BLER.
  • the real-time quality is influenced by the downlink DL connection power outage.
  • the input of the above cost function comprises capacity requests and traffic distribution.
  • the measurements of the total throughput is carried out in kbps/cell/MHz.
  • the spectral efficiency of the cost function equals to the throughput in kbps/cell/MHz when 98 % of the users are satisfied. This means that the service accessibility and the blocking probability is 2%.
  • the equivalent bit- rate is greater than 10 % of the bearer service data rate and 98% of a us- ers are not dropped. The motivation behind this approach is to metrically assess the benefits of the optimisation in terms of GOS.
  • This mapping has to be done for all services which are provided, i.e. services which are controlled with different parameter settings or other attrib- utes.
  • mapping is statistically correct. Due to the fact that the operation is carried out in statistical level the best location for the mapping functions is NMS. Fur- thermore, NMS implementation is also able to handle the Radio Network Controller RNC-RNC (or other network element) border areas.
  • RNC-RNC Radio Network Controller
  • the proposed cost function method is applied. In some of the cases the service QoS targets can cause conflict in the parameter settings, therefore a cost function is needed to solve the conflict. This can be carried out by providing different weight coefficients for the different elements in the cost function. This idea will gain importance when different customer classes (silver, bronze, gold, etc) are introduced into the network system.
  • the next major step when changing to the last management layer of TOM model is to perform the evaluation of the network optimisation, service prioritising as well as customer differentiation operation in terms of €, $ or £.
  • the billing and charging information from the Invoicing/Collecting subsystem in the customer care layer of the TOM is needed.
  • optimise the business case of the operator it is possible - on the basis of a cost function - to optimise the business case of the operator to the direction that is the most beneficial. It is worth noting that changing the customer priorities and offered QoS for business reasons will cause change in the customer behaviour and the business management level optimisation is thus iterative.
  • the operator to have flexible means to set the QoS target based on the system KPIs (key performance indicator) and/or a cost function derived from those.
  • the QoS targets may either be set for a cell cluster or per cell basis.
  • the QoS can be evaluated in terms of blocked calls due to hardware resources, "soft" blocked calls (in interference limited networks), dropped calls, bad quality calls, number of retransmissions and delay in case of packed data, diversity handover probability, hard handover success rate, loading situation (uplink UL or downlink DL), ratio of packed data to circuit switched services etc.
  • GSM-WCDMA Global System for Mobile Communications - Wideband Code Division Multiple access
  • KPI KPI
  • the quality manager QM i.e. the optimising process, provides a central monitoring function and monitors the status of the parameter values and identify automatically the problem situation by comparing the history information of the parameter values as stored in the history database.
  • E.g. GERAN and UMTS Terrestrial Radio Access Network UTRAN can be split into auto-tuning subsystems as small and independent as possible. Inter- dependencies between subsystems are taken into account in upper layers of quality manager, by providing weight coefficients for the KPI's of their respective subsystems.
  • the optimising process is carried out on the basis of user groups (like business users, free time uses etc.).
  • the initial parameter setting could be made less important.
  • the admission control and handover control could work with very "loose" limits admitting all the users to the network, based on the current QoS situation (KPIs at the operating service system OSS) and the set QoS targets the relevant parameters can be auto-tuned.
  • the new situation i.e.
  • the new KPI values is compared to the KPI history data and the "test" parameters are accepted if the change in the QoS performance (or the cost function of the QoS requirements) is improved.
  • the length of the history data depends on the amount of the traffic in the network (total number of samples should be high enough). It is important that the QoS cost function contains items from the whole RRM and multi-radio area.
  • the key parameters (in terms of optimum capacity and quality) are currently initially set to a "default" value, which in most cases guarantees operation of the network but not the optimum performance.
  • the optimising process according to the invention automatically changes the settings for the essential parameters to the optimum operating point in terms of overall QoS.
  • the adjustments of the configuration parameters can be constant increments or decrements. Alternatively, the increments or decrements can be made variable.

Landscapes

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

Abstract

Selon l'invention, lors de l'optimisation de la performance d'un réseau, des indicateurs de rendement clés (KPI) pertinents sont, en premier lieu, déterminés pour une entité spécifique située dans le réseau, ainsi que des premiers paramètres qui influencent les KPI. Un certain nombre d'entités identiques à ladite entité spécifique est sélectionné, les KPI pertinents étant associés à chaque entité. Les KPI ainsi que le nombre sélectionné d'entités sont utilisés en tant qu'éléments dans une première fonction de coût, c'est-à-dire que ladite fonction de coût est calculée en fonction des KPI et du nombre d'entités. Ladite première fonction de coût est calculée afin d'évaluer la performance du réseau. Ainsi, comme lesdits premiers paramètres sont directement associés aux KPI, la performance du réseau dépendra des valeurs desdits premiers paramètres. Ensuite, les valeurs desdits premiers paramètres sont ajustées, de sorte qu'un deuxième ensemble de valeurs desdits premiers paramètres soit obtenu. Les KPI sont de nouveau déterminés, mais cette fois-ci en fonction des deuxièmes valeurs desdits premiers paramètres, et ladite première fonction de coût est recalculée en fonction de ces KPI. Le résultat de ladite première fonction de coût calculée en fonction desdites premières valeurs desdits premiers paramètres, est comparé au résultat de ladite première fonction de coût recalculée en fonction desdites deuxièmes valeurs desdits premiers paramètres. Cette comparaison est effectuée afin de déterminer si la performance du réseau a été améliorée. Lorsque la performance du réseau a été améliorée en raison de l'ajustement desdits premiers paramètres, lesdites deuxièmes valeurs desdits premiers paramètres sont adoptées en tant que paramètres permanents.
PCT/EP2001/012374 2001-10-25 2001-10-25 Procede et systeme d'optimisation de la performance d'un reseau WO2003037018A1 (fr)

Priority Applications (5)

Application Number Priority Date Filing Date Title
PCT/EP2001/012374 WO2003037018A1 (fr) 2001-10-25 2001-10-25 Procede et systeme d'optimisation de la performance d'un reseau
CNB028211200A CN100348071C (zh) 2001-10-25 2002-05-31 用于优化网络性能的方法和系统
EP02733097A EP1442623A1 (fr) 2001-10-25 2002-05-31 Procede et systeme permettant d'optimiser les performances d'un reseau
PCT/IB2002/001962 WO2003037019A1 (fr) 2001-10-25 2002-05-31 Procede et systeme permettant d'optimiser les performances d'un reseau
US10/493,633 US20040266442A1 (en) 2001-10-25 2002-05-31 Method and system for optimising the performance of a network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/EP2001/012374 WO2003037018A1 (fr) 2001-10-25 2001-10-25 Procede et systeme d'optimisation de la performance d'un reseau

Publications (1)

Publication Number Publication Date
WO2003037018A1 true WO2003037018A1 (fr) 2003-05-01

Family

ID=8164652

Family Applications (2)

Application Number Title Priority Date Filing Date
PCT/EP2001/012374 WO2003037018A1 (fr) 2001-10-25 2001-10-25 Procede et systeme d'optimisation de la performance d'un reseau
PCT/IB2002/001962 WO2003037019A1 (fr) 2001-10-25 2002-05-31 Procede et systeme permettant d'optimiser les performances d'un reseau

Family Applications After (1)

Application Number Title Priority Date Filing Date
PCT/IB2002/001962 WO2003037019A1 (fr) 2001-10-25 2002-05-31 Procede et systeme permettant d'optimiser les performances d'un reseau

Country Status (2)

Country Link
CN (1) CN100348071C (fr)
WO (2) WO2003037018A1 (fr)

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004102434A1 (fr) * 2003-05-15 2004-11-25 Sap Aktiengesellschaft Appel de taches analytiques
WO2005032186A1 (fr) * 2003-09-30 2005-04-07 Telefonaktiebolaget Lm Ericsson (Publ) Gestion de performance pour reseaux de donnees par paquets cellulaires
EP1530389A1 (fr) * 2003-11-05 2005-05-11 Mitsubishi Electric Information Technology Centre Europe B.V. Procédé d'optimisation d'un paramètre ou jeu de paramètres d'un réseau de télécommunications
FR2880506A1 (fr) * 2005-01-06 2006-07-07 Evolium Sas Soc Par Actions Si Procede et systeme pour l'exploitation d'un reseau cellulaire de communication mobiles
EP1703754A1 (fr) * 2005-03-17 2006-09-20 Evolium Sas Station de travail pour l'analyse et l'optimisation d'un réseau de télécommunication mobile cellulaire
CN1301624C (zh) * 2004-01-15 2007-02-21 中兴通讯股份有限公司 蜂窝移动通讯网络性能数据处理方法
GB2430330A (en) * 2005-09-19 2007-03-21 Agilent Technologies Inc Allocation of a performance indicator among cells in a cellular communication system
EP1786223A1 (fr) * 2005-11-14 2007-05-16 Societé Française du Radiotéléphone Simulation et gestion des ressources d'un réseau de téléphonie mobile
US7360215B2 (en) 2003-05-15 2008-04-15 Sap Ag Application interface for analytical tasks
US7370316B2 (en) 2003-06-03 2008-05-06 Sap Ag Mining model versioning
US7373633B2 (en) 2003-06-03 2008-05-13 Sap Ag Analytical application framework
DE102009054883A1 (de) * 2009-12-17 2011-06-22 Endress + Hauser Process Solutions Ag Verfahren zur Optimierung der Verbindungsparameter eines Übertragungsweges zwischen einzelnen Vorort-Feldzugriffseinheiten
US8600384B1 (en) 2007-08-29 2013-12-03 Optimi Corporation Optimization of interlayer handovers in multilayer wireless communication networks

Families Citing this family (26)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7818745B2 (en) * 2003-09-29 2010-10-19 International Business Machines Corporation Dynamic transaction control within a host transaction processing system
CN100440796C (zh) * 2004-12-04 2008-12-03 华为技术有限公司 一种获取网络关键性能指标的方法及关键性能指标组件
US7769850B2 (en) 2004-12-23 2010-08-03 International Business Machines Corporation System and method for analysis of communications networks
FR2880505B1 (fr) * 2004-12-31 2007-05-11 Evolium Sas Soc Par Actions Si Procede et systeme pour l'exploitation d'un reseau de communications mobiles
US7840896B2 (en) 2006-03-30 2010-11-23 Microsoft Corporation Definition and instantiation of metric based business logic reports
US7716592B2 (en) 2006-03-30 2010-05-11 Microsoft Corporation Automated generation of dashboards for scorecard metrics and subordinate reporting
US8261181B2 (en) 2006-03-30 2012-09-04 Microsoft Corporation Multidimensional metrics-based annotation
US8190992B2 (en) 2006-04-21 2012-05-29 Microsoft Corporation Grouping and display of logically defined reports
US7716571B2 (en) 2006-04-27 2010-05-11 Microsoft Corporation Multidimensional scorecard header definition
US9058307B2 (en) 2007-01-26 2015-06-16 Microsoft Technology Licensing, Llc Presentation generation using scorecard elements
US8321805B2 (en) 2007-01-30 2012-11-27 Microsoft Corporation Service architecture based metric views
US8495663B2 (en) 2007-02-02 2013-07-23 Microsoft Corporation Real time collaboration using embedded data visualizations
US8488460B2 (en) * 2007-03-29 2013-07-16 Telefonaktiebolaget Lm Ericsson (Publ) Method and apparatus for evaluating services in communication networks
US20100145755A1 (en) * 2007-06-19 2010-06-10 Aito Technologies Oy Arrangement and a related method for providing business assurance in communication networks
GB2467236B (en) * 2007-07-11 2011-08-17 Ericsson Telefon Ab L M Method and apparatus for determining service performance
CN101420714B (zh) * 2007-10-26 2012-05-30 摩托罗拉移动公司 用于对从通信网络中的元件收集关键性能指示器进行调度的方法
JP5077835B2 (ja) * 2010-01-06 2012-11-21 横河電機株式会社 プラント解析システム
CN103190173B (zh) * 2010-11-11 2017-03-22 诺基亚通信公司 网络管理
EP2832150B1 (fr) 2012-03-25 2017-11-22 Intucell Ltd. Appareil de communication et procédé d'optimisation de performances d'un réseau de communication
CN102801556B (zh) * 2012-07-23 2015-02-18 中国联合网络通信集团有限公司 网络性能优化方法及装置
CN102801586B (zh) * 2012-08-28 2014-11-05 盛科网络（苏州）有限公司 QoS粒度与精度的自动化测试方法及装置
EP2763349A1 (fr) * 2013-02-05 2014-08-06 Telefonaktiebolaget L M Ericsson AB (Publ) Procédés et appareil permettant de déterminer des indicateurs clé de performance de réseau mobile améliorés
WO2014202676A1 (fr) * 2013-06-21 2014-12-24 Abb Technology Ag Système et procédé de mise en service
US10462688B2 (en) * 2015-06-29 2019-10-29 Cisco Technology, Inc. Association rule analysis and data visualization for mobile networks
CN107465526B (zh) * 2016-06-03 2020-05-15 德科仕通信(上海)有限公司 互联网视频cdn服务器质量监测系统及方法
CN108876078B (zh) * 2017-05-10 2023-06-20 株式会社日立制作所 计算耗能系统性能的改善策略的方法和耗能系统监控装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5598532A (en) * 1993-10-21 1997-01-28 Optimal Networks Method and apparatus for optimizing computer networks
US5809282A (en) * 1995-06-07 1998-09-15 Grc International, Inc. Automated network simulation and optimization system
EP0889656A2 (fr) * 1997-06-12 1999-01-07 Nortel Networks Corporation Architecture de commande en temps réel pour la commande de l'admission dans un réseau de communications
EP1098546A2 (fr) * 1999-11-04 2001-05-09 Lucent Technologies Inc. Procédés et appareil pour optimisation en fonction de dérivatives de la performance de réseaux sans fils
EP1098544A2 (fr) * 1999-11-04 2001-05-09 Lucent Technologies Inc. Evaluation et interpolation routière de paramètres d'un réseau sans fil

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5598532A (en) * 1993-10-21 1997-01-28 Optimal Networks Method and apparatus for optimizing computer networks
US5809282A (en) * 1995-06-07 1998-09-15 Grc International, Inc. Automated network simulation and optimization system
EP0889656A2 (fr) * 1997-06-12 1999-01-07 Nortel Networks Corporation Architecture de commande en temps réel pour la commande de l'admission dans un réseau de communications
EP1098546A2 (fr) * 1999-11-04 2001-05-09 Lucent Technologies Inc. Procédés et appareil pour optimisation en fonction de dérivatives de la performance de réseaux sans fils
EP1098544A2 (fr) * 1999-11-04 2001-05-09 Lucent Technologies Inc. Evaluation et interpolation routière de paramètres d'un réseau sans fil

Cited By (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7360215B2 (en) 2003-05-15 2008-04-15 Sap Ag Application interface for analytical tasks
WO2004102434A1 (fr) * 2003-05-15 2004-11-25 Sap Aktiengesellschaft Appel de taches analytiques
US7694307B2 (en) 2003-05-15 2010-04-06 Sap Ag Analytical task invocation
US7373633B2 (en) 2003-06-03 2008-05-13 Sap Ag Analytical application framework
US7370316B2 (en) 2003-06-03 2008-05-06 Sap Ag Mining model versioning
US7929512B2 (en) 2003-09-30 2011-04-19 Telefonaktiebolaget Lm Ericsson (Publ) Performance management of cellular mobile packet data networks
WO2005032186A1 (fr) * 2003-09-30 2005-04-07 Telefonaktiebolaget Lm Ericsson (Publ) Gestion de performance pour reseaux de donnees par paquets cellulaires
EP1530389A1 (fr) * 2003-11-05 2005-05-11 Mitsubishi Electric Information Technology Centre Europe B.V. Procédé d'optimisation d'un paramètre ou jeu de paramètres d'un réseau de télécommunications
CN1301624C (zh) * 2004-01-15 2007-02-21 中兴通讯股份有限公司 蜂窝移动通讯网络性能数据处理方法
FR2880506A1 (fr) * 2005-01-06 2006-07-07 Evolium Sas Soc Par Actions Si Procede et systeme pour l'exploitation d'un reseau cellulaire de communication mobiles
WO2006072731A1 (fr) * 2005-01-06 2006-07-13 Evolium S.A.S. Procede et systeme pour l'exploitation d'un reseau cellulaire de communications mobiles
EP1679920A1 (fr) * 2005-01-06 2006-07-12 Evolium Sas Procédé et système pour l'exploitation d'un réseau cellulaire de communications mobiles
FR2883446A1 (fr) * 2005-03-17 2006-09-22 Evolium Sas Soc Par Actions Si Station de travail pour l'analyse et l'optimisation d'un reseau de telecommunication mobile cellulaire
EP1703754A1 (fr) * 2005-03-17 2006-09-20 Evolium Sas Station de travail pour l'analyse et l'optimisation d'un réseau de télécommunication mobile cellulaire
GB2430330B (en) * 2005-09-19 2010-03-10 Agilent Technologies Inc Allocation of a performance indicator among cells in a cellular communication system
GB2430330A (en) * 2005-09-19 2007-03-21 Agilent Technologies Inc Allocation of a performance indicator among cells in a cellular communication system
FR2893476A1 (fr) * 2005-11-14 2007-05-18 Radiotelephone Sfr Procede et systeme de simulation et de gestion des ressources d'un reseau de telephonie mobile.
EP1786223A1 (fr) * 2005-11-14 2007-05-16 Societé Française du Radiotéléphone Simulation et gestion des ressources d'un réseau de téléphonie mobile
US8600384B1 (en) 2007-08-29 2013-12-03 Optimi Corporation Optimization of interlayer handovers in multilayer wireless communication networks
US9179386B1 (en) 2007-08-29 2015-11-03 Ericsson Inc. Optimization of interlayer handovers in multilayer wireless communication networks
DE102009054883A1 (de) * 2009-12-17 2011-06-22 Endress + Hauser Process Solutions Ag Verfahren zur Optimierung der Verbindungsparameter eines Übertragungsweges zwischen einzelnen Vorort-Feldzugriffseinheiten

Also Published As

Publication number Publication date
CN100348071C (zh) 2007-11-07
CN1575614A (zh) 2005-02-02
WO2003037019A1 (fr) 2003-05-01

Similar Documents

Publication Publication Date Title
WO2003037018A1 (fr) Procede et systeme d'optimisation de la performance d'un reseau
US20040266442A1 (en) Method and system for optimising the performance of a network
US7542779B2 (en) Communication system
JP5749349B2 (ja) ネットワーク管理
US6085335A (en) Self engineering system for use with a communication system and method of operation therefore
EP1833266B1 (fr) Gestion d'un réseau de télécommunications distribué et système de contrôle
US6832085B1 (en) Method and apparatus for radio network management
US8897795B2 (en) Method and apparatus for planning mobile switching centers in a wireless network
US9392467B2 (en) Method and apparatus for planning base station controllers in a wireless network
US7908209B2 (en) Means and a method relating to optimization of network operation and planning
US6584189B1 (en) Call routing data management
Gustas et al. Real-time performance monitoring and optimization of cellular systems
Lindemann et al. Adaptive performance management for universal mobile telecommunications system networks
EP1442623A1 (fr) Procede et systeme permettant d'optimiser les performances d'un reseau
WO2002104054A1 (fr) Systeme de communication
WO2003047291A1 (fr) Procede et dispositif permettant de determiner un plan de reutilisation de frequences dans un systeme de communication cellulaire
Emeruwa Analysis of standalone dedicated control channel congestion for 4g networks in Yenagoa–Southern Nigeria
Piqueras et al. Dynamic pricing for decentralised RAT selection in heterogeneous scenarios
EP1543694B1 (fr) Gestion de capacites dans un environnement de communications
Soldani et al. QoE and QoS Monitoring
Aguiar et al. A framework for the evaluation of converged mobile and wireless communication systems
Laiho et al. Radio network optimisation process
Calin et al. An approach for just-in-time radio access network capacity planning in CDMA networks
Lawabni et al. Congestion relief in wireless LANs
MXPA00009701A (en) Method and apparatus for radio network management

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A1

Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BY BZ CA CH CN CO CR CU CZ DE DK DM DZ EC EE ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NO NZ PH PL PT RO RU SD SE SG SI SK SL TJ TM TR TT TZ UA UG US UZ VN YU ZA ZW

AL Designated countries for regional patents

Kind code of ref document: A1

Designated state(s): GH GM KE LS MW MZ SD SL SZ TZ UG ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE TR BF BJ CF CG CI CM GA GN GQ GW ML MR NE SN TD TG

121 Ep: the epo has been informed by wipo that ep was designated in this application
122 Ep: pct application non-entry in european phase
NENP Non-entry into the national phase

Ref country code: JP