WO2010060483A1

WO2010060483A1 - Method for controlling self-optimization within a network

Info

Publication number: WO2010060483A1
Application number: PCT/EP2008/066340
Authority: WO
Inventors: Martin DÖTTLING
Original assignee: Nokia Siemens Networks Oy
Priority date: 2008-11-27
Filing date: 2008-11-27
Publication date: 2010-06-03

Abstract

It is described a method for controlling self-optimization within a network. For the network a plurality of use cases is defined. To each use case a specific priority is assigned, and each use case is characterized by parameters and metrics. To each parameter, a specific priority is assigned. The method comprises determining active use cases, determining if the parameters characterizing the use case are already set by a higher prioritized active use case, and, if the parameter is not already set, setting values of each of the parameters in sequence of the priorities of the parameters for all active use cases so that the metrics of the use cases are met. It is further described a control unit and a system comprising a control unit.

Description

Method for controlling self-optimization within a network

Field of invention

The present invention relates to the field of self- optimization within networks. In particular, the present invention relates to a method for controlling self- optimization within a network. Further, the present invention relates to a control unit for controlling self-optimization within a network and a system comprising a control unit. Moreover, the invention relates to a program element. Furthermore, the invention relates to a computer-readable medium.

Art Background

In networks, especially in mobile wireless communications such as 3GPP Long-Term Evolution, multiple processes have to be performed. Some of these processes may be self-optimizing.

For example configuration processes may be implemented as auto-configuration processes, which typically show a linear behavior. This means that configuration processes are initiated once and then either completed successfully or they need to be abandoned and reverted.

In the following the term "use case" will comprise the notion of goals and requirements for an optimization process, including the associated algorithm to implement these goals and requirements by adapting one or a plurality of parameters. The fulfillment of the goals and requirements may be evaluated by monitoring one or a plurality of metrics and defining threshold values.

Self-optimizing network use cases might have at least partly contradicting goals and try to tune the same parameter in a different fashion. Therefore a conflict can occur during the optimization of a parameter, but also with respect to the metric it tries to optimize. Furthermore, wireless communications networks are normally operated using products of multiple vendors, which might implement different self optimization use cases, or different algorithms for the same use cases.

For example in mobile communications, handover quality optimization might try to adjust the handover hysteresis such that immediate handover occurs when another cell is having better signal quality, that means small hysteresis, as this maximizes the handover quality. On the other hand, this will lead to excessive so called ping-pong handover. Therefore a ping-pong handover optimization algorithm might try to use larger hysteresis to prevent the latter problem. Obviously, this leads to degraded signal quality, as connections are maintained over the cell with worse signal quality for a longer time. In this case, there are different metrics, which are tried to be optimized by tuning the same parameter. Thus, a parameter conflict exists. Furthermore there is a metric conflict in that sense that optimizing the metric for the ping-pong handover use case, that means reducing the number of ping-pong handover, at the same time will worsen the metric for the handover quality use case, that means leading to less signal-to-interference ratio (SINR) of the connection during handover.

There may be a need for providing a reliable method for controlling self-optimization within network environments, which is based in a parameter and conflict resolution.

Summary of the Invention

This need may be met by the subject matter according to the independent claims. Advantageous embodiments of the present invention are described by the dependent claims. According to a first exemplary aspect of the invention there is provided a method for controlling self-optimization within a network, wherein for the network a plurality of use cases is defined, wherein to each use case a specific priority is assigned, and wherein each use case is characterized by parameters and metrics, wherein to each parameter a specific priority is assigned, the method comprising determining active use cases, determining if the parameters characterizing the use case are already set by a higher prioritized use case, and, if the parameter is not already set, setting values of each of the parameters in sequence of the priorities of the parameters for all active use cases so that the metrics of the use cases are met.

This aspect is based on the idea to provide a control for self-optimization environment handling according to operator policy. Within a network operated by one operator or multiple operators, different configurations may be required for the same use case. Such a use case may be for example handover, network coverage enhancement or load balancing. A use case may therefore denote a description of an aim. For each use case, there may exist more than one metric. A metric may denote measurements or characteristics of the quality of a network. This may be for example key performance indicators, statements of the optimization, cell efficiency, lost links, capacity, spectral efficiencies, link failure rates or handover failure rates.

Use cases may be triggered by a value and may then be called "active". Active may denote current use cases. The active use cases may change according to the actual status of the network. As active use cases are determined, it may be possible to adapt changes within the network. This means that inactive use cases may not be considered, and that the method therefore may lead to a dynamic adaptation of the network and the control method. For meeting the requirements of the metrics, parameters may be adjusted for each use case. These parameters may be for example the transmitting power or other settings for the networks which may be adjusted in different elements of the network, in particular in base stations or may be transmitted to terminals.

Depending whether this method is applied in a centralized or decentralized manner, the coordination may take place in a central self-optimizing network (SON) entity, for example within an operations, administration and maintenance (OAM) node or in a base station (BS) . Accordingly, the configuration may need to be signaled to those nodes, for example using a centralized configuration server or by directly configuring corresponding configuration data in an enhanced Node B or base station. Additionally depending on the operator requirement for open OAM interfaces, standardization of these procedures might be required and the above procedures may provide a framework to operate in a simple work-flow based manner. They furthermore may provide a modular and scalable approach, as each vendor can support different sets of use cases and for each use case determine the supported optimization parameters and optimization goals, that means the optimization algorithm itself can remain fully vendor-specific.

Parameters may be adapted in sequence corresponding to the highest priority of all use cases adapting this parameter. For each parameter, the use case with the highest priority may be treated first. Therefore after a first step, for all parameters subject to optimization, an adaptation has taken place to meet the requirements or goals of the metrics concerning the highest prioritized use case using this parameter for adaptation. In all further steps, these parameters might either not be changed any more, or only be changed to a value, where the requirements of the metrics of all higher priority use cases are still fulfilled. In the following there will be described exemplary embodiments of the present invention.

According to an exemplary embodiment of the invention, priorities are assigned to each metric.

Each operator may require different metrics. The method for controlling self-optimization may therefore comprise priorities for each metric in each use case so that the most important metrics may be higher prioritized than less important metrics.

According to an exemplary embodiment of the invention, each metric comprises a trigger value, a minimum value and/or a target value.

With these three values, a metric may be described. The trigger value may trigger the optimization. The minimum value may denote a minimum acceptable value. The target value may denote a goal during the optimization process. With the minimum and target value, a range may be specified in which the goal of the metric may be fulfilled.

In the method, protocols may be exchanged over an interface between network operations and a SON control entity, in particular with a configuration data base. In these procedures, an exchanged list may contain entries in order of priority. For example, a MetricValue_List may contain the metric name, the trigger value, which corresponds to a threshold value associated to the start of the corresponding optimization use case, the target value, which characterizes the goal after optimization and the minimum value, which denotes a minimum acceptable value, determining the potential degradation allowed due to other optimization processes.

If the minimum value is set equal or close to the trigger value, there may be much opportunity for further optimization processes to act as they are allowed to degrade the corresponding metric, whereas if the minimum value is equal or close to the target value, further optimization processes may only be allowed as long as they have small impact on this metric .

According to a further exemplary embodiment of the invention, the method comprises changing priorities of use cases, parameters and metrics.

With this embodiment, it may be realized that the method is adapted according to the actual status of the network or to changes in operator policy. Thus, changes to the use cases caused by changes within the network or operator policy may be caught and implemented in the method.

According to a further exemplary embodiment of the invention, the values of the parameters are set so that the minimum values of the metrics of the use cases are met in sequence of the priorities of the metrics.

The metrics of different use cases may contradict each other. With this embodiment, it may be possible to meet at least the minimum values of multiple metrics of different use cases. The metrics are met in sequence of their priorities. As only the minimum values are tried to be met, it may be possible to meet the minimum values of multiple metrics.

According to a further exemplary embodiment of the invention, the method comprises further changing the values of the parameters in the sequence of the priorities for all active use cases as long as the minimum values of the metrics are fulfilled.

With this embodiment, it may be realized that parameters may be set in any case as long as the minimum requirements of the metrics having a higher priority are fulfilled. This may be done also if the result of the determination, if a parameter is already set, is positive. It only may have to be ensured that the minimum values of the metrics with a higher priority are fulfilled.

According to a further exemplary embodiment of the invention, the values of the parameters are set so that the target values of the metrics having the highest priorities are met.

The metrics having the highest priorities of all use cases may be met first. Subsequently, the metrics with a lower priority may be met. In another embodiment, all metrics of the use case with the highest priority may be met and subsequently the metrics of the use cases with a lower priority may be met.

In one embodiment, the method may compare all metrics of all use cases and may try to meet as many metrics as possible.

According to a second aspect of the invention there is provided a control unit for controlling self-optimization within a network, wherein for the network a plurality of use cases is defined, wherein to each use case a specific priority is assigned, and wherein each use case is characterized by parameters and metrics, wherein to each parameter a specific priority is assigned , the control unit comprising a determination unit adapted to determining active use cases and further adapted to determining if the parameters characterizing the use case are already set by a higher prioritized active use case, and a setting unit adapted to, if the parameter is not already set, setting values of each of the parameters in sequence of the priorities of the parameters for all active use cases so that the metrics of the use cases are met.

According to an embodiment of the invention, the control unit is located in a central self-optimization entity or in a base station . Depending whether the self-optimization is applied in a centralized or decentralized manner, the coordination may take place in a central self-optimizing network (SON) entity, for example within an operations, administration and maintenance (OAM) node or in a base station (BS) .

According to a further aspect of the invention, there is provided a system for controlling self-optimization in a multi-vendor environment comprising a control unit.

The network may consist of products from different vendors. The system may provide a modular and scalable approach, as each vendor can support different sets of use cases and for each use case determine the supported optimization parameters and optimization goals, that means the optimization algorithm itself can remain fully vendor-specific.

According to a further embodiment of the invention, the system comprises an operating unit, and the control unit and the operating unit are adapted to exchange messages for performing a method for controlling self-optimization within a network.

To enable the mapping of operators' policies, different procedures may be necessary to configure the control unit. Thus, messages may be exchanged between the control unit and an operating unit of each vendor. The system may comprise more than one operating unit.

According to a further embodiment of the invention, the control unit and the operating unit are adapted to exchange messages for inquiring a status of a use case and the corresponding parameters and metrics and for changing the priorities of the parameters and metrics.

With this embodiment, a dynamic system may be provided. The priorities of the parameters and metrics may be changed according to the current status of the system. According to a further embodiment of the invention, the control unit and the operating unit are adapted to exchange messages for changing values of the metrics of the use cases and error handling.

With this embodiment, it may be possible to exchange information about occurring errors and to handle these errors. The error messages may indicate the cause of the problem, for example unknown use case, metric or parameter, inconsistent list caused for example by duplicates, value or range error of metrics.

According to a further aspect of the invention, a program element (for instance a software routine, in source code or in executable code) is provided, which, when being executed by a processor, is adapted to control or carry out a controlling method having the above mentioned features.

According to yet another aspect of the invention, a computer- readable medium (for instance a CD, a DVD, a USB stick, a floppy disk or a hard disk) is provided, in which a computer program is stored which, when being executed by a processor, is adapted to control or carry out a controlling method having the above mentioned features.

Controlling self-optimization within a network which may be performed according to aspects of the invention can be realized by a computer program, that is by software, or by using one or more special electronic optimization circuits, that is in hardware, or in hybrid form, that is by means of software components and hardware components.

It has to be noted that embodiments of the invention have been described with reference to different subject matters. In particular, some embodiments have been described with reference to method type claims whereas other embodiments have been described with reference to apparatus type claims. However, a person skilled in the art will gather from the above and the following description that, unless other notified, in addition to any combination of features belonging to one type of subject matter also any combination between features relating to different subject matters, in particular between features of the apparatus type claims and features of the method type claims is considered as to be disclosed with this application.

The aspects defined above and further aspects of the present invention are apparent from the examples of embodiment to be described hereinafter and are explained with reference to the examples of embodiment. The invention will be described in more detail hereinafter with reference to examples of embodiment but to which the invention is not limited.

Brief Description of the Drawings

Figure 1 shows a diagram of prioritization being used in an embodiment of the invention.

Figure 2 shows an overview of a self-optimization network control and coordination according to an embodiment of the invention.

Figure 3 shows a transaction flow diagram of changing priority according to an embodiment of the invention.

Figure 4 shows a transaction flow diagram of changing metric values and error handling according to an embodiment of the invention .

Figure 5 shows a diagram illustrating a first embodiment of the described method.

Figure 6 shows a diagram illustrating a second embodiment of the described method. Figure 7 shows a diagram illustrating a third embodiment of the described method.

Detailed Description

The illustration in the drawing is schematically. It is noted that in different figures, similar or identical elements are provided with reference signs, which are different from the corresponding reference signs only within the first digit.

Figure 1 shows a diagram of prioritization being used in an embodiment of the invention. The self-optimization control is based on orders of priority of use cases UC, of parameters P, and metrics M within each use case UC. Furthermore for each metric M₁,-,, three values are given, a trigger value, which triggers the optimization, a minimum value, which is a minimum acceptable value, and a target value, which is the goal during optimization process. The parameters may be set to meet the goal of the metrics M within each use case UC.

To enable the mapping of operators' policies to this control method, a series of procedures is required to configure for example the self-optimizing network (SON) control and coordination entity in a way that is suitable for multi- vendor operation. In particular, this may be a procedure to inquire supported use cases UC, supported optimization parameters P and metrics M per use case, and current priorities and settings across and within each use case UC, procedures to change priorities and settings of use cases, parameters and metrics, procedures to set regular optimization intervals per use case, and error and fault handling procedures.

Figure 2 shows an overview of a self-optimization network control and coordination according to an embodiment of the invention. The overview shows the entities involved in the control and coordination of a SON. It works based on measurement, counters, and alarms. The configuration data base contains default settings, which also indicate which use cases UC or optimization algorithms are supported, and which optimization parameters and metrics are supported for each use case.

Based on external, for example operator, input these settings can be changed. In each time step the list of active use cases is determined based on the trigger settings and regular optimization intervals configured. The coordination includes mechanisms for resolution of parameter and metric conflicts. It controls the individual algorithms and workflows of the optimization processes in a sequential and iterative manner based on priorities.

Figure 3 shows a transaction flow diagram of changing priority according to an embodiment of the invention. The protocols are exchanged over the interface between the network operations, called operator in the following, and the SON control entity, in particular with the configuration data base. In this procedures, the lists contain entries in order of priority. The MetricValue List contains a metric name, a trigger value to start the corresponding optimization use case, a target value, which is the goal after optimization and a minimum value, which is the minimum acceptable value, determining the potential degradation allowed due to other optimization processes.

In a first step, the operator sends a message requesting the supported use cases. The SON control sends a list with the supported use cases. In a second step, the operator sends a message requesting the use case configuration. The SON control sends the use case configuration comprising parameters and metrics.

In a further step, the operator sends a list comprising the priorities of the use cases, which is confirmed by the SON control with an acknowledgement message. In a further step, the operator sends a list comprising the parameters and their priorities for a specific use case, which is also confirmed by the SON control. In a further step, the operator sends a list comprising the metrics and their priorities for a specific use case, which is also confirmed by the SON control .

The steps described above can be performed in arbitrary order.

Figure 4 shows a transaction flow diagram of changing metric values and error handling according to an embodiment of the invention. The operator sends a message comprising a list with the metrics and their values for a specific use case to the SON control. The SON control confirms this message by an acknowledgement message. In a further step, the operator sends a message comprising a desired optimization interval for a specific use case to the SON control. This message is also confirmed by an acknowledgement.

If an error occurs during any step of the procedures, the SON control sends an error messages comprising the cause of the error. This may be for example an unknown use case, metric or parameter or an inconsistent list.

The steps described above can be performed in arbitrary order .

Figure 5 shows a diagram illustrating a first embodiment of the described method. At each instance there is a subset of use cases UCi to UCu active according to settings of optimization intervals and triggers, which means that a subset of parameters Pi to P_P and metrics are addressed. This may be operator specific. In this embodiment, a simple parameter conflict resolution by sequential consideration according to priority is used, based on the following principles . For each optimization parameter P, the use cases UC with highest priority are treated first. In further iterations, also use cases UC with lower priority might be considered to adapt the same parameter P, but minimum values for all metrics M of uses cases UC with higher priority need to be maintained.

This approach is shown in Figure 5 along with an example. An operator has ranked use case UCi, handover quality optimization, higher than use case UC2, load balancing.

Parameter P₂, which is the handover parameter, will therefore be tuned to fulfill goals and target metrics of UCi first.

Parameter P₂, the handover parameter, will be tuned in a second iteration to also fulfill goals and target metrics of

UC₂ provided minimum values for metrics of UCi for example the radio link failure (RLF) rate is still fulfilled.

Figure 6 shows a diagram illustrating a second embodiment of the described method. It is shown that minimum metric values of higher priority use cases need not only be obeyed when the same parameter is changed, but also for all further optimization processes which might act on different parameters. This is required since these different optimization processes might have side effects on other metrics .

As an example, in Figure 6, Parameter P₂, which is the handover parameter, will first be tuned to fulfill goals and target metrics of use case UCi, which means in this case handover quality optimization. If now afterwards, parameter Pi, which is a parameter for downlink (DL) Tx power, is tuned to fulfil goals and target metrics of use case UC₂, which is the use case load balancing, it needs to be ensured that minimum values for metrics of UCi, for example the RLF rate, are still fulfilled. In case different minimum values requirements are collected in this process, the most stringent one is taken. The most stringent metrics are normally higher prioritized. The benefit of this approach is that only the metric value requirements of higher priority use cases need to be considered, so that maximum optimization opportunity is maintained according to the policies.

Figure 7 shows a diagram illustrating a third embodiment of the described method. This embodiment is an alternative approach of Figure 6, which is simpler but more stringent. It is based on an overall metric conflict resolution. This approach is more stringent and maybe a bit simpler, but might yield worse optimization results, since the metric requirements are more stringent. This is due to the fact that for the minimum and target metric values the most stringent settings across all active use cases are searched first. The trigger values remain as set per use case, since they only affect the activation of the optimization processes.

The trigger, minimum and target values of metric M₂ will be tuned to the most stringent requirements from use case UCi, handover quality optimization, and UC₂, load balancing. Afterwards, the sequential parameter tuning starts as outlined for Figure 6. Although being more stringent this approach is simpler, since in this case the metric conflict resolution can be applied before the parameter conflict resolution and an iterative refinement of the metric requirements is avoided.

The embodiments according to this invention provide a multi- vendor capable solution with a simple sequential approach suitable for implementation and aligned with basic product portfolio structure. The embodiments are modular and scalable and can be implemented for few use cases, parameters and metrics first and updated time after time. Also specific use cases and the associated algorithms can be replace with use cases having broader scope, such as algorithms optimizing multiple parameters to meet multiple goals concurrently. Therefore this approach allows also gradual upgrade from the simple sequential approach to more advanced multi-dimensional optimizations .

The embodiments according to this invention encompass multi- vendor capable procedures to configure SON use case handling according to operator policy. These procedures allow to change the default configuration including procedures to inquire supported use cases and their current settings, procedures to change the priorities between use cases, procedures to change the priorities of optimization parameters and metric per use case, procedure to change the trigger, minimum, and target value of a particular metric, procedure for scheduling of use cases and error handling procedures. Further, a parameter conflict resolution mechanism is provided using sequential adjustment of different parameters, where for each parameter the optimization algorithm with highest priority is performed first. Finally, a metric conflict resolution mechanism is provided based on sequential processing of individual optimization algorithms according to priority and inheriting the goals, that means minimum metric values, of all use cases with higher priority, or alternatively, on selecting the most stringent settings for trigger, minimum and target values from all currently active use cases.

It should be noted that the term "comprising" does not exclude other elements or steps and "a" or "an" does not exclude a plurality. Also elements described in association with different embodiments may be combined. It should also be noted that reference signs in the claims should not be construed as limiting the scope of the claims. List of reference signs:

UC use case

M metric P parameter

SON self-optimizing networks

BS base station

Claims

CLAIMS :

1. A method for controlling self-optimization within a network, wherein for the network a plurality of use cases is defined, wherein to each use case a specific priority is assigned, and wherein each use case is characterized by parameters and metrics, wherein to each parameter a specific priority is assigned, the method comprising determining active use cases, determining if the parameters characterizing the use case are already set by a higher prioritized active use case, and if the parameter is not already set, setting values of each of the parameters in sequence of the priorities of the parameters for all active use cases so that the metrics of the use cases are met.

2. The method as set forth in claim 1, wherein priorities are assigned to each metric.

3. The method as set forth in any one of the preceding claims, wherein each metric comprises a trigger value, a minimum value and/or a target value.

4. The method as set forth in any one of the preceding claims, further comprising changing priorities of use cases, parameters and/or metrics.

5. The method as set forth in claim 3, wherein the values of the parameters are set so that the minimum value of the metrics of the use cases are met in sequence of the priorities of the metrics.

6. The method as set forth in claim 3 or 5, further comprising changing the values of the parameters in the sequence of the priorities for all active use cases as long as the minimum values of the metrics are fulfilled.

7. The method as set forth in any one of the preceding claims, wherein the values of the parameters are set so that the target values of the metrics having the highest priorities are met.

8. A control unit for controlling self-optimization within a network, wherein for the network a plurality of use cases is defined, wherein to each use case a specific priority is assigned, and wherein each use case is characterized by parameters and metrics, wherein to each parameter a specific priority is assigned, the control unit comprising a determination unit adapted to determining active use cases and further adapted to determining if the parameters characterizing the use case are already set by a higher prioritized active use case, and a setting unit adapted to, if the parameter is not already set, setting values of each of the parameters in sequence of the priorities of the parameters for all active use cases so that the metrics of the use cases are met.

9. The control unit as set forth in claim 8, wherein the control unit is located in a central self-optimizing entity or in a base station.

10. A system for controlling self-optimization in a multi- vendor environment comprising a control unit as set forth in any one of the claims 8 or 9.

11. The system as set forth in claim 10, wherein the control unit and a operating unit of each network are adapted to exchange messages for performing a method for controlling self-optimization within a network.

12. The system as set forth in claim 11, wherein the control unit and the operating unit are adapted to exchange messages for inquiring a status of a use case and the corresponding parameters and metrics and for changing the priorities of the parameters and metrics.

13. The system as set forth in any one of the claims 11 or 12, wherein the control unit and the operating unit are adapted to exchange messages for changing values of the metrics of the use cases and error handling.

14. A computer-readable medium, in which a computer program of controlling self-optimization within a network is stored, which computer program, when being executed by a processor, is adapted to carry out or control a method according to claims 1 to 7.

15. A program element of controlling self-optimization within a network, which program element, when being executed by a processor, is adapted to carry out or control a method according to claim 1 to 7.