WO2017108106A1 - Procédé et noeud réseau permettant d'identifier une zone spécifique d'un système de communication sans fil - Google Patents

Procédé et noeud réseau permettant d'identifier une zone spécifique d'un système de communication sans fil Download PDF

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Publication number
WO2017108106A1
WO2017108106A1 PCT/EP2015/081024 EP2015081024W WO2017108106A1 WO 2017108106 A1 WO2017108106 A1 WO 2017108106A1 EP 2015081024 W EP2015081024 W EP 2015081024W WO 2017108106 A1 WO2017108106 A1 WO 2017108106A1
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Prior art keywords
area
kpis
cause
network node
areas
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PCT/EP2015/081024
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English (en)
Inventor
M Josefa VERA NADALES
Alejandro AGUILAR
Raquel BARCO MORENO
Josko ZEC
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Telefonaktiebolaget Lm Ericsson (Publ)
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Priority to PCT/EP2015/081024 priority Critical patent/WO2017108106A1/fr
Publication of WO2017108106A1 publication Critical patent/WO2017108106A1/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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks

Definitions

  • Embodiments herein relate to wireless communication systems, such as cellular networks.
  • a method and a network node for identification of a specific area among a set of areas of a wireless communication system are disclosed.
  • KPIs Key Performance Indicators
  • DCR Drop Call Rate
  • CSFR Call Setup Failure Rate
  • SNR Signal-to-noise ratio
  • the operator When deploying a network, or an entity within an existing network, the operator typically optimizes the performance in view of one or more KPIs. This is a difficult and computational intensive task and involves analysis by engineers, typically highly skilled network engineers. As an outcome of the analysis, a particular design and/or optimization strategy may be proposed.
  • SON Self-Organizing Networks
  • the SON mechanisms help the engineers to save time and effort when planning, optimizing and troubleshooting the network.
  • the SON mechanisms shall ideally detect problems, diagnose the cause of the problems and change configuration parameters in order to compensate faults and improve network performance.
  • the operators define several KPIs to assess status of the network.
  • the KPIs usually measure metrics per cell or whole network.
  • operators build SON mechanisms to be triggered once a threshold is reached or a rule satisfied. With these mechanisms, operators improve network performance to ensure the end-user Quality of Experience (QoE).
  • QoE Quality of Experience
  • Radio Frequency (RF) signals for user equipments in a deployed network
  • drive testing is becoming less important since the tasks of drive testing can be performed using reports from regular user equipments.
  • integration of GPS receivers into user equipments provides an accurate measurement source for determining the user equipments location.
  • EP1334417 discloses how to combine several observable elements, such as KPIs, to detect anomalies of a telecommunication network. Each anomaly may be associated with a location or situation in the telecommunication network.
  • the method comprises assembling indicators indicating the behavior of the elements and arranging the assembled indicators such that each observable element's indicators are assigned to the same input data component.
  • the learning mechanism is taught so that the input data of the learning mechanism comprises the input data components which are based on the assembled indicators. Points which approximate the input data are placed in the input space.
  • a presentation of time is incorporated into at least one input data component wherein the presentation of time is periodic, continuous and unambiguous within the period of the at least one element with periodic time-dependent behavior.
  • the method of EP1334417 is very complex and advanced.
  • KPI Key Performance Indicator
  • a problem may be how to improve the method of detecting anomalies, aka problematic areas.
  • An object may thus be to improve detection of at least one specific area of a telecommunication system, in which specific area a degradation of performance is detected.
  • the object is achieved by a method performed by a network node.
  • the network node identifies at least a specific area among a set of areas of a wireless communication system. The identifying is based on relevance values for the set of areas and on a set of weighted Key Performance Indicators (KPIs) indicating performance of the wireless communication system.
  • KPIs Key Performance Indicators
  • the set of weighted KPIs comprises a respective set of weighted KPIs for each area of the set of areas. Each weighted KPI is associated with a respective weight value.
  • the object is achieved by a network node configured for identifying at least a specific area among a set of areas of a wireless communication system.
  • the network node is configured for performing the identifying based on relevance values for the set of areas and on a set of weighted KPIs indicating performance of the wireless communication system, wherein the set of weighted KPIs comprises a respective set of weighted KPIs for each area of the set of areas.
  • Each weighted KPI is associated with a respective weight value.
  • the object is achieved by a computer program and a carrier therefor corresponding to the aspects above. Thanks to that the network node identifies, or detects, the specific area based on the relevance values and on the set of weighted KPIs, improved identification of the specific area is achieved.
  • the improvement may be in terms of flexibility, since an operator, or rather an employee thereof, may choose the set of weighted KPIs, i.e. the values thereof, according to its own preferences. For example, the operator may wish to reduce number of dropped calls. Then, a weight of one or more corresponding KPI(s) can be tuned; for example, a weight value assigned to a certain -first- KPI can be set higher than another weight value assigned to another -second- KPI.
  • the relevance value may be set based on traffic volume in each area of the set of areas. Therefore, by taking the relevance values and the set of weighted KPIs into account, the network node identifies the specific area in a more flexible manner.
  • An advantage is that the network node may consistently and repeatedly perform the method above without, or with little, intervention by a human. That is to say, human intervention may only be required initially, e.g. in order to set the weighted KPIs and/or to set the relevance value or a basis on which the relevance value may be determined. As mentioned above, the basis may be traffic volume in each area.
  • Figure 1 is a schematic overview of an exemplifying wireless communication system in which embodiments herein may be implemented
  • Figure 2 is a flowchart illustrating embodiments of the method in the network node
  • FIG. 3 is another flowchart illustrating another embodiment of the method in more detail
  • Figure 4 is a further flowchart illustrating a further embodiment of the method in more detail
  • Figure 5 is yet another flowchart illustrating yet another embodiment of the method in more detail
  • Figures 6a-6d are diagrams illustrating maps in which performance indicated by
  • Figure 6e is a further diagram illustrating a map in which traffic volume is plotted
  • Figure 7 is a still further diagram illustrating a map in which a specific area has been identified
  • Figures 8a-8c are diagram illustrating maps in which causes is plotted
  • Figure 9 is a diagram illustrating influence per each cause in a specific area.
  • Figure 10 is a block diagram illustrating embodiments of the network node.
  • FIG. 1 depicts an exemplifying wireless communication system 100 in which embodiments herein may be implemented.
  • the wireless communication system 100 is a Long Term Evolution (LTE) system.
  • LTE Long Term Evolution
  • the wireless communication system 100 may be any cellular or wireless system, such as a Global System for Mobile communications (GSM), Universal Mobile Telecommunication System (UMTS), LTE and Worldwide
  • the wireless communication system 100 comprises a network node 1 10.
  • the network node 1 10 may be part of an Operation and Maintenance (OAM) system of the wireless communication system 100, or may be part of an Operations Support System (OSS) of said communication system 100.
  • the network node 110 may be hosted by a so called cloud service or the like.
  • the network node 1 10 may be external to a network domain of the wireless communication system 100.
  • the wireless communication system 100 comprises a set of areas 131 , 132, 133.
  • Each area 131 , 132, 133 may comprises one or more service areas, such as cells 140 (only one cell denoted with 140 for reasons of simplicity) or sectors of a cell.
  • the set of areas may thus be geographical areas, which may be predetermined or selected by an operator of the wireless communication system 100. In other cases, the areas may be selected independently of the service areas.
  • the areas may be overlapping (not shown). This means that a service area, of a portion thereof, may be associated with two areas that are overlapping. As an example, each area of the set of areas may enclose one or more service areas of the wireless communication system 100. Moreover, each area of the set of area may overlap with at least one other area. Overlap of the areas may ensure that a potential problematic area is not identified due to unfortunate splitting of the potential problematic area into two different neighboring areas. Such splitting of the potential problematic area would cause any degraded performance to been seen as a lesser degradation in the two different neighboring areas.
  • shapes of the set of areas 131 , 132, 133 may be the same or may be different for the set of areas. Examples of shapes are square, triangle, rectangle, pentagon, hexagon, polygon, titled versions thereof, and the like.
  • the radio network node 110 may be associated with one or more service areas, such as cells 140.
  • the term "radio network node” may refer to a Base Station System (BSS), a Radio Network Controller (RNC), a Radio Base Station (RBS), an evolved Node B (eNB), a control node controlling one or more Remote Radio Units (RRUs), an access point or the like.
  • the radio network node 150 may communicate with the network node 1 10. This communication may include user transmissions and/or control transmissions in respect to one or more wireless devices.
  • the user transmissions may include user data, payload data, content data etc.
  • the control transmissions may include control information relating to e.g. scheduling, authentication, mobility etc..
  • the communication may include uplink transmission and/or downlink transmission.
  • Figure 2 illustrates an exemplifying method according to embodiments herein when implemented in the wireless communication system 100 of Figure 1.
  • the network node 1 10 may initialize KPIs and/or weights and/or relevance values.
  • the operator may provide, e.g. via user interface, a respective weight value for each KPI.
  • the KPIs are thus associated with weight values, wherein each KPI is associated with the respective weight value for said each KPI.
  • At least two weight values are different from each other.
  • the respective weight value for a first KPI is different from the respective weight value for a second KPI.
  • the weight values may be predefined.
  • the operator may set the weight values according to its own policies.
  • KPI(s) relating to DCR may be defined as twice as important as KPI(s) relating to CSFR.
  • the operator may provide, e.g. via the user interface, the relevance values for the set of areas 131 , 132, 133.
  • a first relevance value for a first area among the set of areas 131 , 132, 133 may be equal to a second relevance value for a second area among the set of areas 131 , 132, 133.
  • the relevance values may be predefined.
  • the relevance values may be calculated from a metric, such as a data volume in said each area per time unit, a number of wireless
  • the network node 1 10 When the network node 1 10 initializes the KPIs, it may be that the network node 1 10 receives, reads, or measures, values of KPIs for one or more of the set of areas 131 , 132, 133.
  • one or more of weights, a selection of KPIs, a basis for relevance value and target KPIs may be configured by the operator.
  • the network node 1 10 identifies at least a specific area among the set of areas of the wireless communication system 100.
  • the identifying is based on relevance values for the set of areas and on a set of weighted Key Performance Indicators (KPIs) indicating performance of the wireless communication system 100.
  • KPIs Key Performance Indicators
  • the respective relevance value may be indicated by one or more of:
  • a basis, on which the respective relevance value may be generated from may be any one or more of these above listed items.
  • the data volume may be referred to as traffic volume herein.
  • the number of wireless communication devices may refer to in-active and/or active wireless device in said each area, wherein active may refer to attached and/or connected mode with respect to the wireless communication network 100.
  • the set of weighted KPIs may comprise one or more KPIs relating to a service area and/or one or more KPIs relating geo-located events reported by a user equipment.
  • the set of weighted KPIs may comprise one or more of:
  • the set of weighted KPIs comprises a respective set of weighted KPIs for each area of the set of areas.
  • each area of the set of areas may have its own weighting of the KPIs.
  • KPI(s) relating to Handover failures are assigned a weight, or weight value aka value of weight, higher than the one assigned to other KPIs.
  • each weighted KPI is associated with a respective weight value.
  • the weighted KPIs may be associated with weight values.
  • at least two weight values may be different from each other.
  • a first one of the weighted KPIs may be different from a second one of the weighted KPIs.
  • the relevance values may comprise a respective relevance value for each area of the set of areas, wherein a set of penalty values comprises a respective penalty value for each area of the set of areas.
  • a higher penalty value indicates a more problematic area.
  • the network node 1 10 identifies the specific area by calculating, for each area of the set of areas, the respective penalty value representing weighted and normalized KPIs multiplied by the respective relevance value for said each area.
  • the respective penalty value for the specific area exceeds a first threshold value for identification of at least the specific area.
  • the first threshold value sets out limits for the set of penalty values.
  • the expression "exceeds" means that the respective penalty value is above the first threshold and/or that the respective penalty value is below the first threshold value. The latter case may be applicable when lower penalty values indicates a more problematic area.
  • the first threshold value may be set as an absolute value or as a relative value.
  • the first threshold is set as an absolute value, it is ensured that the specific area is problematic in an absolute sense, not only that the specific area is problematic relatively other areas. However, a risk is that no area is identified as the specific area.
  • the first threshold is set as a relative value, it is instead ensured that the specific area is problematic in view of the other areas.
  • the first threshold may be set as a percentage value, which may have an effect of identifying one or more specific areas.
  • the network node 1 10 performs a method for selecting a Self-Organizing-Network "SON" function to be applied to the specific area among the set of areas of the wireless communication system 100.
  • action 230 and 240 below may be performed.
  • the SON function may comprise one or more of a tool for adjusting network parameters to mitigate at least one cause, an activity adjusting network parameters to mitigate at least one cause, a system for adjusting network parameters to mitigate at least one cause, an automatic execution from any network entity adjusting network parameters to mitigate at least one cause, and the like.
  • This action will help operators, such as employees, engineers of the operator, to concentrate optimization efforts to one or more areas with a highest penalty. This will reduce time and required resources, which means that cost for operational maintenance will be less. Additionally, the identified area may be an input to action 230.
  • Reduction of resources may be that instead of applying a number of optimization techniques to an area, only the SON functions relating to the cause(s) of problems in the area are applied since the SON function will obtain a larger impact than more or less arbitrarily applying the number of optimization techniques.
  • the SON function is applied only to one or more areas identified as problematic. It may also be that there is no need for engineers to analyze multiple KPIs in order to find out which SON function to apply.
  • the network node 1 10 may determine cause scores for at least the specific area based on the set of weighted KPIs, wherein the cause scores comprises a respective cause score for each of a plurality of causes, wherein a respective subset of weighted KPIs is related to the respective cause score, wherein the respective subset of weighted KPIs is selected from the set of weighted KPIs.
  • this action is performed for any one of the areas of the set of area.
  • the network node 1 10 may determine respective cause scores for said each area.
  • the cause scores may be a set of cause scores.
  • the set of cause scores comprises sub-sets of cause scores. Each sub-set is a respective sub-set of cause scores for said each area.
  • the network node 110 may identify at least the specific area and determine the cause scores and/or the respective cause scores at least partially concurrently. Thus, allowing for efficient scalability of at least some embodiments herein.
  • the network node 1 10 selects, based on the cause scores, a Self-Organizing-Network (SON) function to be applied to the specific area.
  • the SON function is selected, by the network node 1 10, from among a set of SON functions.
  • Each SON function of the set of SON functions is adapted to mitigate performance degradation due to at least one cause of the plurality of causes.
  • each cause score indicates a respective cause, such as Radio Frequency (RF) problems, missing neighbours, or the like.
  • the network node 1 10 may select the SON function for which the respective cause score exceeds a second threshold value for selection of SON function.
  • a second threshold value for selection of SON function.
  • one or more SON functions may be selected due to that the respective cause score exceeds the second threshold value.
  • the second threshold value sets out limits for the cause scores.
  • the expression “exceeds” means that the respective cause score is above the second threshold and/or that the respective cause score is below the second threshold value.
  • the second threshold value may be set as an absolute value or as a relative value.
  • An aim of this action is to propose an appropriate optimization strategy, as an example of a SON function, to be deployed in a polygon area in order to efficiently focus efforts in resolving faults.
  • a SON function is selected.
  • the SON function may be an optimization activity, design activity and/or the most suitable software tool from an optimization tool portfolio that operators should deploy to improve performance of their cellular network.
  • the selection of the SON function is at least based on chosen KPIs combined with flexible multiplicative weights.
  • the proposed method automatically identifies problematic areas, i.e. the specific areas, where the network performance is degraded. Identified areas are prioritized for optimization and design actions. Such problematic area detection and optimization approach selection should significantly help operators and engineers in complex troubleshooting and optimization tasks.
  • the embodiments herein may be realized at a low computational cost.
  • a requirement on input from the operator is low. This means that the embodiments herein are simple to apply for the operator (e.g. telecommunications network operator owning at least part of the wireless communication system 100), and also minimizes human user intervention.
  • the operator e.g. telecommunications network operator owning at least part of the wireless communication system 100
  • the operator may need to identify for which causes the cause scores may be determined.
  • the operator may be able to tune the wireless communication system 100 by providing a small amount of input, e.g. at a high level, such as in terms of the target KPI values, weight values for weighting KPIs, metric for indicating relevance and/or the like.
  • the operator may allow tuning, by means of the method herein, to be performed as often as deemed necessary, e.g. twice a day, one a month, etc.
  • a problematic area may be identified by use of known methods, such as described in aforementioned EP1334417. Thereafter, action 230 and 240 may be applied. Thus, in this further example, action 220 is not performed.
  • Figure 3 illustrates exemplifying sub-actions of action 210 in "STAGE 1 ".
  • One or more of the following actions may be performed in any suitable order.
  • the network node 1 10 may read KPIs, which may be computed from geolocated trace events and/or cell level counters that can e.g. be received in the network node 1 10.
  • This action may be separated from the initialization in STAGE 1.
  • the reading, or gathering, of the KPIs i.e. the values of the KPIs for each area, may preferably be performed before the values of the KPIs are used in action 220 and/or action 230.
  • action 310 is a sub-action of action 220 and/or action 230.
  • the network node 1 10 may select KPIs and a relevance indicator.
  • an operator can control the network node 1 10, e.g. via a user interface, to select the KPIs and the relevance indicator. This means that the operator may select a sub-set of KPIs to be monitored and used in order to identify the specific area, e.g. as in action 220.
  • the relevance indicator may be used to create a respective relevance value for each of the areas of the wireless communication system 100. As it may turn out, due to the relevance indicator, one or more areas may have the same relevance value. For example, if traffic volume is used as basis for the respective relevance value, any two areas will have the same relevance value when the traffic volume in these two areas is equal. Action 330
  • the network node 1 10 may define weights and target KPIs ( ⁇ _ ⁇ ,). Similarly, to the preceding action, the operator may control the network node 110, e.g. via the user interface, to perform this action. As an example, the operator may set dynamically weights for one or more target KPIs, such as as 0.5% for DCR related KPI(s) and 0.1 % for CSFR related KPI(s).
  • the network node 1 10 may define the set of areas of the wireless communication system 100. Again, it may be that the operator controls the network node, via the user interface, to perform this action.
  • Figure 4 illustrates exemplifying sub-actions of action 220 in "STAGE 2".
  • Action 410, 420, 430 and 440 may be performed for each area of a set of areas of the wireless communication system 100.
  • One or more of the following actions may be performed in any suitable order.
  • the network node 1 10 may select an area for which a respective penalty value is to be calculated.
  • the calculation of the respective penalty value may be based on values of KPIs, e.g. computed from geolocated events and/or cell level counters, for the selected areas.
  • the values of KPIs may be retrieved from a memory, gathered or otherwise obtained by the network node 110.
  • this selected area is not yet necessarily compared to other areas.
  • the network node 1 10 has no knowledge about whether or not this selected area is, or will be considered to be, the specific area, e.g. a more problematic area compared to other areas.
  • action 420 may be performed.
  • action 430 is performed after action 410.
  • actions 420 and 430 are performed at least partially concurrently after action 410, i.e. any one of actions 420 and 430 may begin slightly before or after the other.
  • actions 420 and 430 are performed at least partially concurrently, use of parallel processing is possible. This means that at least some embodiments herein are scalable while not suffering serious performance degradation.
  • the network node 1 10 may determine a normalized KPI factor based on weight values, values of KPIs and target KPIs.
  • the target KPI may, similarly to the weight values, be predetermined or selected by the operator.
  • the target KPI may define a maximum acceptable KPI value, while assuming a low KPI means "good" conditions.
  • the network node 1 10 may calculate the respective relevance value for the selected area.
  • the respective relevance value may be determined based on a metric, such as one or more of a data volume in said each area per time unit, a number of wireless communication devices in said each area, a number of connections in said each area, and the like.
  • the network node 1 10 calculates a penalty value, e.g. according to a penalty function (or more generally ction):
  • the factor may have been calculated
  • actions 420, 430 and 440 may be seen as one action, in which the penalty value is calculated.
  • the network node 1 10 may check whether there are further areas for which a respective penalty value is to be calculated according to action 440.
  • the method proceeds with action 410. Otherwise, the method proceeds to action 460 below.
  • the network node Based on the penalty values determined for each of the areas, the network node
  • FIG. 1 10 may build, e.g. construct, generate or similar, information from which the specific area, i.e. one or the most problematic areas, may be extracted.
  • the information may be organized a map illustrating the areas, where a pattern, or color of the area indicates the penalty value. Maps for different KPIs are illustrated in Figures 6a to 6d below and a map for the relevance values is illustrated in Figure 6e.
  • Figure 7 illustrates a further map in which one or more specific areas are marked, i.e. one or more areas that are considered to be more problematic than others in view of the penalty values determined in the preceding actions.
  • the most problematic area may be given by argmax( ) a ).
  • Figure 5 illustrates exemplifying sub-actions of action 230 in "STAGE 2".
  • Action 510, 520, 530, 531 ...539, 540, 541 ...549, and 550 may be performed for each area of a set of areas of the wireless communication system 100.
  • these sub-actions causes that are main contributors to a selected area's KPIs are identified.
  • One or more of the following actions may be performed in any suitable order.
  • Action 510, 520, 530, 531 ...539, 540, 541 ...549, and 550 may be performed for each area of a set of areas of the wireless communication system 100.
  • One or more of the following actions may be performed in any suitable order.
  • Action 510, 520, 530, 531 ...539, 540, 541 ...549, and 550 may be performed for each area of a set of areas of the wireless communication system 100.
  • One or more of the following actions may be performed in any suitable order.
  • the network node 110 may select an area for which a respective contribution value C l ause j S to ca
  • n some examples, the respective contribution value (-cause j g on
  • the network node 1 10 may find out, e.g. by input from the operator as explained for the weight values, which KPIs are related to which cause or causes.
  • the network node 110 may select a sub-set, or even all, KPIs of the KPIs used when calculating the respective penalty values in order to obtain a respective influence value i ⁇ ause as determined in action 540, 541 ... 549 below.
  • the selection of sub-sets may be performed for each area as the method of Figure 5 proceeds to the next area in action 550, or this action 520 may be performed before action 510 in order to selected sub-sets in advance for each area. That is to say, this action may not necessarily be repeated on a per each area basis.
  • the network node 1 10 may calculate, or determine, which KPIs are relevant for each cause to be analyzed.
  • the network node 1 10 may calculate the respective influence value i ⁇ ause for each cause.
  • the respective influence value i a cause may be given by:
  • KPI i l where a identifies the studied polygon area, N indicates the number of analyzed KPIs, Wj is the weight related to KPI i, KPl (cause) is the KPI i in the area a calculated with those events labelled with the studied cause cause and T_KPIi is the "target KPI" of KPI i. This action (and at least the preceding action) may be repeated for every area.
  • I ⁇ ause P a when studied cause cause is related to all analyzed KPIs.
  • the network node 1 10 may check whether there are further areas for which a respective influence value i ⁇ ause is to be calculated according to action 530 and 540.
  • the method proceeds with action 510. Otherwise, the method proceeds to action 560 below.
  • the respective influence values for only the specific area may be determined, if the specific area is known from e.g. action 220. Action 560
  • r cause l where cause identify the studied cause and a identify the selected (studied) area and R indicates a number of analyzed causes.
  • Each cause of the number of analyzed causes R is related to at least one of the KPIs under study, i.e. at least one of the N KPIs.
  • a most contributing cause may be given by ( cause ⁇
  • FIG. 6a through 6d illustrates maps for a number of KPIs.
  • the operator is interested in improving the network performance by analyzing the following KPIs: CSFR (call setup failure rate), DCR (drop call rate), IRAT HO rate (Inter radio access technology handover) and bad coverage percentage.
  • KPIs are computed based on geolocated traced events in an LTE network with a 75% overlap between test polygons. The test polygons are thus examples of the set of areas.
  • Figure 6e illustrates relevance of each test polygon in terms of traffic volume within each test polygon.
  • Figure 7 provides ratios of each KPI where test polygons are drawn depending if KPI value is high, medium or low.
  • the distributions shown by these figures are inputs to the cost function, together with the KPI weights and the maximum acceptable KPI value.
  • the defined thresholds and weights given to each KPI are shown in the table below:
  • Example values of T_KPIi has been provided. The contribution of each KPI the cost function is the same. Therefore, all the weights are set to 1. In other examples, at least two weight values may differ from each other. This allows the operator to fine tune influence of one or more KPIs.
  • Figures 8a through 8c illustrates contribution of each cause per test polygon for the general case. It is however enough to calculate the contribution of each cause for the most problematic area identified in the preceding stages. In this case, three kinds of root causes have been analyzed: missing neighbors, RF issues and unknown issues.
  • the appropriate SON function i.e. one or more optimization methods and/or software tools, is identified based on the contribution of each root cause.
  • the contribution of each root cause in the most problematic area is depicted in Figure 9, which means that the operator should focus its resources at a SON function that optimizes and troubleshoots RF issues as the penalty of RF issues presents the 90% of the contribution.
  • the SON function that optimizes and troubleshoots RF issues may be that there are several SON functions to choose between.
  • one of the several SON functions is selected based on e.g. network topology and/or performance, where the performance again may be given by one or more of the KPIs.
  • any commercial optimization tool available may be loaded to execute design and optimization activities in the most problematic area.
  • improved network performance is achieved in the most problematic area, e.g. even without human intervention.
  • These embodiments may be applied to multiple problematic area with low requirements in investments and resources compared to requirements on operators and engineers to manually achieve a similar result.
  • Figure 10 a schematic block diagram of embodiments of the network node 1 10 of Figure 1 is shown.
  • the network node 1 10 may comprise a processing module 1001 , such as a means for performing the methods described herein.
  • the means may be embodied in the form of one or more hardware modules and/or one or more software modules
  • the network node 1 10 may further comprise a memory 1002.
  • the memory may comprise, such as contain or store, a computer program 1003.
  • the processing module 1001 comprises, e.g. 'is embodied in the form of or 'realized by', a processing circuit 1004 as an exemplifying hardware module.
  • the memory 1002 may comprise the computer program 1003, comprising computer readable code units executable by the processing circuit 1004, whereby the network node 1 10 is operative to perform the method of Figure 2.
  • the computer readable code units may cause the network node 1 10 to perform the method according to Figure 2 when the computer readable code units are executed by the network node 1 10.
  • Figure 10 further illustrates a carrier 1005, or program carrier, which comprises the computer program 1003 as described directly above.
  • the processing module 1001 comprises an Input/Output module 1006, which may be exemplified by a receiving module and/or a sending module as described below when applicable.
  • the processing module 1001 may comprise one or more of an initiating module 1010, an identifying module 1020, a determining module 1030 and a selecting module 1040 as exemplifying hardware modules.
  • the processing module 1001 may comprise one or more of an initiating module 1010, an identifying module 1020, a determining module 1030 and a selecting module 1040 as exemplifying hardware modules.
  • one or more of the aforementioned exemplifying hardware modules may be implemented as one or more software modules.
  • the network node 1 10 and/or the processing module 1001 and/or the identifying module 1020 is configured for identifying at least a specific area among a set of areas of a wireless communication system 100, wherein the network node 110 is configured for performing the identifying based on relevance values for the set of areas and on a set of weighted KPIs indicating performance of the wireless communication system 100, wherein the set of weighted KPIs comprises a respective set of weighted KPIs for each area of the set of areas. Each weighted KPI is associated with a respective weight value.
  • the set of weighted KPIs may comprise one or more KPIs relating to a service area and/or one or more KPIs relating geo-located events reported by a user equipment.
  • the set of weighted KPIs may comprise one or more of:
  • determining module 1030 may be configured for determining cause scores for at least the specific area based on the set of weighted KPIs, wherein the cause scores comprises a respective cause score for each of a plurality of causes, wherein a respective subset of weighted KPIs is related to the respective cause score, wherein the respective subset of weighted KPIs is selected from the set of weighted KPIs.
  • the network node 1 10 and/or the processing module 1001 and/or the selecting module 1040 may be configured for selecting, based on the cause scores, a SON function to be applied to the specific area, wherein the SON function is selected from among a set of SON functions, wherein each SON function of the set of SON functions is adapted to mitigate performance degradation due to at least one cause of the plurality of causes.
  • the SON function may comprise one or more of:
  • a tool for adjusting network parameters to mitigate at least one cause an activity adjusting network parameters to mitigate at least one cause, a system for adjusting network parameters to mitigate at least one cause, an automatic execution from any network entity adjusting network parameters to mitigate at least one cause, and the like.
  • the relevance values may comprise a respective relevance value for each area of the set of areas, wherein a set of penalty values comprises a respective penalty value for each area of the set of areas.
  • the respective relevance value may be indicated by one or more of:
  • the network node 1 10 and/or the processing module 1001 and/or the identifying module 1020, or another identifying module (not shown), may be configured for identifying the specific area by calculating, for each area of the set of areas, the respective penalty value representing weighted and normalized KPIs multiplied by the respective relevance value for said each area, wherein the respective penalty value for the specific area exceeds a first threshold value for identification of at least the specific area.
  • the network node 1 10 and/or the processing module 1001 and/or the selecting module 1040 , or another selecting module not shown, may be configured for selecting of the SON function by selecting the SON function for which the respective cause score exceeds a second threshold value for selection of SON function.
  • determining module 1030 may be configured for determining cause scores by determining respective cause scores for said each area.
  • Said each area may enclose one or more service areas of the wireless communication system 100.
  • Each area of the set of area may overlap with at least one other area.
  • the network node 1 10 and/or the processing module 1001 and/or the identifying module 1020 and the determining module 1030 may be configured for performing the identifying of at least the specific area and the determining of the cause scores and/or the respective cause scores at least partially concurrent.
  • the term "node”, or “network node” may refer to one or more physical entities, such as devices, apparatuses, computers, servers or the like. This may mean that embodiments herein may be implemented in one physical entity. Alternatively, the embodiments herein may be implemented in a plurality of physical entities, such as an arrangement comprising said one or more physical entities, i.e. the embodiments may be implemented in a distributed manner, such as on a set of server machines of a cloud system.
  • module may refer to one or more functional modules, each of which may be implemented as one or more hardware modules and/or one or more software modules and/or a combined software/hardware module in a node.
  • the module may represent a functional unit realized as software and/or hardware of the node.
  • wireless communication device may refer to a user equipment, a machine-to-machine (M2M) device, a mobile phone, a cellular phone, a Personal Digital Assistant (PDA) equipped with radio communication capabilities, a smartphone, a laptop or personal computer (PC) equipped with an internal or external mobile broadband modem, a tablet PC with radio communication capabilities, a portable electronic radio communication device, a sensor device equipped with radio
  • M2M machine-to-machine
  • PDA Personal Digital Assistant
  • smartphone a laptop or personal computer (PC) equipped with an internal or external mobile broadband modem
  • tablet PC with radio communication capabilities
  • portable electronic radio communication device a sensor device equipped with radio
  • the senor may be any kind of weather sensor, such as wind, temperature, air pressure, humidity etc.
  • the sensor may be a light sensor, an electronic or electric switch, a microphone, a loudspeaker, a camera sensor etc.
  • the term "user" may indirectly refer to the wireless device.
  • the term "user” may be used to refer to the user equipment or the like as above. It shall be understood that the user may not necessarily involve a human user.
  • the term “user” may also refer to a machine, a software component or the like using certain functions, methods and similar.
  • program carrier may refer to one of an electronic signal, an optical signal, a radio signal, and a computer readable medium.
  • the program carrier may exclude transitory, propagating signals, such as the electronic, optical and/or radio signal.
  • the carrier may be a non-transitory carrier, such as a non-transitory computer readable medium.
  • processing module may include one or more hardware modules, one or more software modules or a combination thereof. Any such module, be it a hardware, software or a combined hardware-software module, may be a determining means, estimating means, capturing means, associating means, comparing means, identification means, selecting means, receiving means, sending means or the like as disclosed herein.
  • the expression “means” may be a module
  • software module may refer to a software application, a Dynamic Link Library (DLL), a software component, a software object, an object according to Component Object Model (COM), a software component, a software function, a software engine, an executable binary software file or the like.
  • DLL Dynamic Link Library
  • COM Component Object Model
  • processing circuit may refer to a processing unit, a processor, an Application Specific integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or the like.
  • the processing circuit or the like may comprise one or more processor kernels.
  • the expression “configured to/for” may mean that a processing circuit is configured to, such as adapted to or operative to, by means of software configuration and/or hardware configuration, perform one or more of the actions described herein.
  • action may refer to an action, a step, an operation, a response, a reaction, an activity or the like. It shall be noted that an action herein may be split into two or more sub-actions as applicable. Moreover, also as applicable, it shall be noted that two or more of the actions described herein may be merged into a single action.
  • memory may refer to a hard disk, a magnetic storage medium, a portable computer diskette or disc, flash memory, random access memory (RAM) or the like. Furthermore, the term “memory” may refer to an internal register memory of a processor or the like.
  • the term "computer readable medium” may be a Universal Serial Bus (USB) memory, a DVD-disc, a Blu-ray disc, a software module that is received as a stream of data, a Flash memory, a hard drive, a memory card, such as a MemoryStick, a Multimedia Card (MMC), Secure Digital (SD) card, etc.
  • USB Universal Serial Bus
  • MMC Multimedia Card
  • SD Secure Digital
  • aforementioned examples of computer readable medium may be provided as one or more computer program products.
  • computer readable code units may be text of a computer program, parts of or an entire binary file representing a computer program in a compiled format or anything there between.
  • the expression “transmit” and “send” are considered to be interchangeable. These expressions include transmission by broadcasting, uni-casting, group-casting and the like. In this context, a transmission by broadcasting may be received and decoded by any authorized device within range. In case of uni-casting, one specifically addressed device may receive and decode the transmission. In case of group-casting, a group of specifically addressed devices may receive and decode the transmission.
  • number and/or value may be any kind of digit, such as binary, real, imaginary or rational number or the like. Moreover, “number” and/or “value” may be one or more characters, such as a letter or a string of letters. “Number” and/or “value” may also be represented by a string of bits, i.e. zeros and/or ones.
  • a set of may refer to one or more of something.
  • a set of devices may refer to one or more devices
  • a set of parameters may refer to one or more parameters or the like according to the embodiments herein.
  • the common abbreviation "e.g.” which derives from the Latin phrase “exempli gratia,” may be used to introduce or specify a general example or examples of a previously mentioned item, and is not intended to be limiting of such item. If used herein, the common abbreviation “i.e.”, which derives from the Latin phrase “id est,” may be used to specify a particular item from a more general recitation.
  • the common abbreviation “etc.”, which derives from the Latin expression “et cetera” meaning “and other things” or “and so on” may have been used herein to indicate that further features, similar to the ones that have just been enumerated, exist.

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

Abstract

L'invention concerne un procédé et un noeud réseau (110). Le noeud réseau (110) identifie (220) au moins une zone spécifique parmi un ensemble de zones d'un système de communication sans fil (100). L'identification (220) est basée sur des valeurs de pertinence pour l'ensemble de zones et sur un ensemble d'indicateurs clés de performance pondérés "KPI" indiquant les performances du système de communication sans fil (100), l'ensemble d'indicateurs clés de performance pondérés comprenant un ensemble respectif d'indicateurs clés de performance pondérés pour chaque zone de l'ensemble de zones. Chaque indicateur clé de performance pondéré est associé à une valeur de pondération respective. L'invention concerne également un programme informatique correspondant et un support pour celui-ci.
PCT/EP2015/081024 2015-12-22 2015-12-22 Procédé et noeud réseau permettant d'identifier une zone spécifique d'un système de communication sans fil WO2017108106A1 (fr)

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JP2022028633A (ja) * 2020-07-30 2022-02-16 ジオ プラットフォームズ リミティド 重要業績評価指標の階層的計算のためのシステム及び方法
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