CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a national phase entry of International Application No. PCT/IB2016/055288, filed Sep. 2, 2016, which is incorporated herein by reference in its entirety.

TECHNOLOGICAL FIELD

Embodiments described herein generally relate to the application of machine learning to network management. In particular, embodiments described herein relate to Cognitive Network Management (CNM) in radio access networks (for example, 5G or the like) and other, for example, existing or future generations of wireless/mobile networks and, specifically to a method, apparatus, and computer program product for the design of Cognitive Functions (CFs) and associated implementation options with respect to varying levels of centralization or distribution in Cognitive Network Management Systems.

BACKGROUND

Given the use of Self Organizing Networks (SON) and their associated shortcomings in terms of flexibility and adaptability to changing and complex environments, there is demand for more intelligent operations, administration and management (OAM) functions to be added to the networks.

In this regard, areas for improving known and existing conventional and/or existing systems have been identified. Through applied effort, ingenuity, and innovation, solutions to improve such systems have been realized and are described in connection with embodiments of the present invention.

BRIEF SUMMARY

Due to the demand for more intelligent OAM functions to be added to the networks, embodiments described herein, in particular regarding CNM, provide OAM functions enabled to 1) learn the environment in which the functions operate, 2) learn an optimal behavior fitting to the specific environment; 3) learn from experiences and that of other instances of the same or different OAM functions and 4) learn to achieve the higher level goals and objectives as defined by the network operator. This learning may be based on various types of data available in the network, comprising, for example, performance information, failures, configuration data, network planning data, or user, service, and application related data, as well as from the actions and the corresponding impact of the OAM function itself. The learning and the knowledge built from the learned information may then increase the autonomy of the OAM functions.

One aim of the improved system is to reduce the complexity of managing 5G networks or, for example, networks comprising multiple radio access technologies (RATs) with several logical layers within these RATs, operating within many different operational scenarios. Through CFs and CNM, these complex network environments may remain operable from a mobile network operator's (MNO) perspective, with increased efficiency of such network management and through considerably reduced necessity for manual OAM tasks.

Accordingly, embodiments described herein relate to the design of CFs and associated implementation options with respect to varying levels of centralization or distribution in Cognitive Network Management Systems. Embodiments described herein also provide corresponding interface descriptions.

Embodiments of the present invention may provide a cognitive management implementation allowing for the automated adding, deletion, and modification of network states, a direct interpretation of operator goals, the automated modification of management algorithms, and/or management decisions based on knowledge.

Embodiments of the present invention may provide for standard interfaces between the CFs, between sub functions of CNM, and between CFs and the centralized functions of CNM.

Embodiments of the present invention may provide for increased level of automation which is required for improving the operability of mobile networks, in particular 5G networks, and enable the efficient managing of hybrid networks, for example, comprised of virtualized and physical network functions.

In terms of network operability, embodiments described herein may facilitate the offloading of a significant amount of decision making from a human operator to the CNM system. CF behavior, for instance in the event of a legacy SON function, the decision matrices would not have to be adapted manually, rather, this would happen by means of an automated process.

Furthermore, embodiments described herein may provide a clear separation of entities that may be inherent to a CF and therefore enable a clear separation of the tasks that are to be handled by a CF. This separation may allow for a very flexible implementation of CF and therefore a CNM system, and enable the adaptation of a CNM system to various types of network management concepts, approaches and implementations. Despite the fact that CNM primarily targets 5G systems, embodiments described herein enable adaptation of CNM systems to changes in the network and network management architecture.

A method, apparatus and computer program product are therefore provided according to an example embodiment of the present invention for the application of machine learning to network management.

In some embodiments, a system for enabling cognitive network management (CNM) in radio access networks may be provided, the system comprising a network objective manager (NOM), an environment modelling and abstraction (EMA) engine, a configuration management engine (CME), and a decision and action engine (DAE), the NOM comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the processor, cause the network objective manager to interpret one or more operator goals for the CNM or for a specific cognitive function (CF) to ensure that the CNM or the specific CF adjusts its behavior in order to fulfil the operator goals, the EMA engine comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the processor, cause the environment modelling and abstraction engine to abstract an environment into states configured for use in subsequent decision making, wherein the abstracted environment represent are built from one or more of a combination of quantitative key performance indicators (KPIs), abstract state labels, and operational contexts, the CME comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the processor, cause the environment modelling and abstraction engine to define one or more legal candidate network configurations for different contexts of the CF based on the abstracted environments and operational contexts as derived by the EMA engine, the DAE comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the processor, cause the decision and action engine to match a current abstract state, abstracted environment, operational context, or combination thereof as derived by the EMA engine to an appropriate network configuration selected from the set of legal candidate network configurations.

In some embodiments, the system may further comprise a coordination engine, the coordination engine comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the processor, cause the coordination engine to learn one or more effects of different CF decisions on other CFs, interpret the learned knowledge, and suggest modifications to the CME and DAE to minimize the one or more effects.

In some embodiments, the NOM further comprises computer program code configured to, with the processor, cause the apparatus to receive one or more key quality indicators (KQI) inputs, and provide weighted or prioritized KPI targets.

In some embodiments, the EMA engine is implemented as a classifier configured to cluster KPIs or combinations of KPIs into sets that are logically distinguishable from each other. In some embodiments, the CME is implemented as a supervised learning agent applying a Neural Network, a Support Vector Machine, or other learning algorithm that evaluates historical data indicative of a quality of different configurations in terms of achieving the defined KPI targets in different contexts to select the legal configurations.

In some embodiments, the DAE further comprises computer program code configured to, with the processor, cause the apparatus to learn a quality of different network configurations in different contexts as defined by the EMA, from an application of the legal network configurations and towards different operator objectives, and subsequently, select an optimal network configuration for the network context and state. In some embodiments, the DAE is implemented as a single objective RL agent, wherein the single objective is optimizing the CF requirements, the CF requirements comprising one or more targets for different technical objectives or KPIs.

In some embodiments, the system further comprises a first interface, the first interface configured provide the KPI targets expected to be achieved by the CFs and additional information indicative of how the targets are to be interpreted, the additional information comprising one or more of weights, priorities or utilities of the KPIs. In some embodiments, the system further comprises a second interface, the second interface configured to provide a connection between the EMA and one or more of the CE, the CME, and the DAE, and provide the abstract environment states or contexts as created by the EMA.

In some embodiments, the system further comprises a third interface, the third interface configured to provide a connection between the CE and the CME, and provide rules derived by the CE concerning the configuration of a specific CF. In some embodiments, the system further comprises a fourth interface, the fourth interface configured to provide a connection between the CE and the DAE, and provide the set of legal network configurations.

In some embodiments, the system further comprises a fifth interface, the fifth interface configured to provide a connection between the DAE and the EMA, and carry the current network configurations as selected by the DAE. In some embodiments, the system further comprises a fifth interface, the fifth interface configured to provide a connection between the DAE and one or more of the CME and the CE, and carry the report of current network configurations and related KPI measurements.

In some embodiments, a method for enabling cognitive network management (CNM) in radio access networks may be provided, the method comprising utilizing a network objective manager (NOM), an environment modelling and abstraction (EMA) engine, a configuration management engine (CME), and a decision and action engine (DAE), interpreting, by the NOM, one or more operator goals for the CNM or for a specific cognitive function (CF) to ensure that the CNM or the specific CF adjusts its behavior in order to fulfil the operator goals, abstracting, by the EMA engine, an environment into states configured for use in subsequent decision making, wherein the abstracted environment represent are built from one or more of a combination of quantitative key performance indicators (KPIs), abstract state labels, and operational contexts, defining, by the CME, one or more legal candidate network configurations for different contexts of the CF based on the abstracted environments and operational contexts as derived by the EMA engine, matching, by the DAE, a current abstract state, abstracted environment, operational context, or combination thereof as derived by the EMA engine to an appropriate network configuration selected from the set of legal candidate network configurations.

In some embodiments, the method may further comprise utilizing a coordination engine, learning, by the coordination engine, one or more effects of different CF decisions on other CFs, interpreting the learned knowledge, and suggesting modifications to the CME and DAE to minimize the one or more effects. In some embodiments, the method may further comprise receiving, at the NOM, one or more key quality indicators (KQI) inputs, and providing, by the NOM, weighted or prioritized KPI targets.

In some embodiments, the EMA engine is implemented as a classifier configured to cluster KPIs or combinations of KPIs into sets that are logically distinguishable from each other. In some embodiments, the CME is implemented as a supervised learning agent applying a Neural Network, a Support Vector Machine, or other learning algorithm that evaluates historical data indicative of a quality of different configurations in terms of achieving the defined KPI targets in different contexts to select the legal configurations.

In some embodiments, the method may further comprise learning, by the DAE, a quality of different network configurations in different contexts as defined by the EMA, from an application of the legal network configurations and towards different operator objectives, and subsequently, select an optimal network configuration for the network context and state. In some embodiments, the DAE is implemented as a single objective RL agent, wherein the single objective is optimizing the CF requirements, the CF requirements comprising one or more targets for different technical objectives or KPIs.

In some embodiments, the method may further comprise providing a first interface, the first interface configured provide the KPI targets expected to be achieved by the CFs and additional information indicative of how the targets are to be interpreted, the additional information comprising one or more of weights, priorities or utilities of the KPIs. In some embodiments, the method may further comprise utilizing a second interface, the second interface configured to provide a connection between the EMA and one or more of the CE, the CME, and the DAE, and provide the abstract environment states or contexts as created by the EMA.

In some embodiments, the method may further comprise utilizing a third interface, the third interface configured to provide a connection between the CE and the CME, and provide rules derived by the CE concerning the configuration of a specific CF. In some embodiments, the method may further comprise utilizing a fourth interface, the fourth interface configured to provide a connection between the CE and the DAE, and provide the set of legal network configurations.

In some embodiments, the method may further comprise utilizing a fifth interface, the fifth interface configured to provide a connection between the DAE and the EMA, and carry the current network configurations as selected by the DAE. In some embodiments, the method may further comprise utilizing a fifth interface, the fifth interface configured to provide a connection between the DAE and one or more of the CME and the CE, and carry the report of current network configurations and related KPI measurements.

In some embodiments, computer program product for enabling cognitive network management (CNM) in radio access networks may be provided, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions for utilizing a network objective manager (NOM), an environment modelling and abstraction (EMA) engine, a configuration management engine (CME), and a decision and action engine (DAE), interpreting, by the NOM, one or more operator goals for the CNM or for a specific cognitive function (CF) to ensure that the CNM or the specific CF adjusts its behavior in order to fulfil the operator goals, abstracting, by the EMA engine, an environment into states configured for use in subsequent decision making, wherein the abstracted environment represent are built from one or more of a combination of quantitative key performance indicators (KPIs), abstract state labels, and operational contexts, defining, by the CME, one or more legal candidate network configurations for different contexts of the CF based on the abstracted environments and operational contexts as derived by the EMA engine, and matching, by the DAE, a current abstract state, abstracted environment, operational context, or combination thereof as derived by the EMA engine to an appropriate network configuration selected from the set of legal candidate network configurations.

In some embodiments, the computer-executable program code instructions further comprise program code instructions for utilizing a coordination engine, learning, by the coordination engine, one or more effects of different CF decisions on other CFs, interpreting the learned knowledge, and suggesting modifications to the CME and DAE to minimize the one or more effects. In some embodiments, the computer-executable program code instructions further comprise program code instructions for receiving, at the NOM, one or more key quality indicators (KQI) inputs, and providing, by the NOM, weighted or prioritized KPI targets.

In some embodiments, the EMA engine is implemented as a classifier configured to cluster KPIs or combinations of KPIs into sets that are logically distinguishable from each other. In some embodiments, the CME is implemented as a supervised learning agent applying a Neural Network, a Support Vector Machine, or other learning algorithm that evaluates historical data indicative of a quality of different configurations in terms of achieving the defined KPI targets in different contexts to select the legal configurations.

In some embodiments, the computer-executable program code instructions further comprise program code instructions for learning, by the DAE, a quality of different network configurations in different contexts as defined by the EMA, from an application of the legal network configurations and towards different operator objectives, and subsequently, select an optimal network configuration for the network context and state. In some embodiments, the DAE is implemented as a single objective RL agent, wherein the single objective is optimizing the CF requirements, the CF requirements comprising one or more targets for different technical objectives or KPIs.

In some embodiments, the computer-executable program code instructions further comprise program code instructions for utilizing a first interface, the first interface configured provide the KPI targets expected to be achieved by the CFs and additional information indicative of how the targets are to be interpreted, the additional information comprising one or more of weights, priorities or utilities of the KPIs. In some embodiments, the computer-executable program code instructions further comprise program code instructions for utilizing a second interface, the second interface configured to provide a connection between the EMA and one or more of the CE, the CME, and the DAE, and provide the abstract environment states or contexts as created by the EMA.

In some embodiments, the computer-executable program code instructions further comprise program code instructions for utilizing a third interface, the third interface configured to provide a connection between the CE and the CME, and provide rules derived by the CE concerning the configuration of a specific CF. In some embodiments, the computer-executable program code instructions further comprise program code instructions for utilizing a fourth interface, the fourth interface configured to provide a connection between the CE and the DAE, and provide the set of legal network configurations. In some embodiments, the computer-executable program code instructions further comprise program code instructions for utilizing a fifth interface, the fifth interface configured to provide a connection between the DAE and the EMA, and carry the current network configurations as selected by the DAE. In some embodiments, the computer-executable program code instructions further comprise program code instructions for utilizing a fifth interface, the fifth interface configured to provide a connection between the DAE and one or more of the CME and the CE, and carry the report of current network configurations and related KPI measurements.

