WO2023241802A1 - Appareils et procédés de mappage d'applications basées sur l'apprentissage automatique à des unités logiques d'intention - Google Patents

Appareils et procédés de mappage d'applications basées sur l'apprentissage automatique à des unités logiques d'intention Download PDF

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Publication number
WO2023241802A1
WO2023241802A1 PCT/EP2022/066455 EP2022066455W WO2023241802A1 WO 2023241802 A1 WO2023241802 A1 WO 2023241802A1 EP 2022066455 W EP2022066455 W EP 2022066455W WO 2023241802 A1 WO2023241802 A1 WO 2023241802A1
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intent
ilu
capability information
based application
mapped
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Borislava GAJIC
Stephen MWANJE
Tejas SUBRAMANYA
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Nokia Technologies Oy
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Nokia Technologies Oy
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Priority to CN202280097117.9A priority Critical patent/CN119366162A/zh
Priority to PCT/EP2022/066455 priority patent/WO2023241802A1/fr
Publication of WO2023241802A1 publication Critical patent/WO2023241802A1/fr
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence

Definitions

  • IBN Intent-Based Networking
  • the complexity of the specified intents may significantly vary.
  • the simple intents may be fulfilled with a single command to a single network object.
  • An intent may allow to solve specific network problem or achieve a specific network target.
  • the capability information received for a ML based application may include information representative of a type of function performed by the ML based application for at least one communication network, wherein the type is at least one of an optimization function, a control function, an analytics function and an ML orchestration function.
  • the capability information received for a ML based application may include information representative of one or more objects or one or more object types for which the function is performed.
  • a method comprising: sending a request for capability information, wherein the request is sent to at least one of a ML based application, a ML applications orchestrator and a ML capability producer; receiving capability information representative of capabilities of one or more machine learning, ML, based applications; generating one or more intent logic unit, ILUs, wherein an I LU is mapped with at least one corresponding ML based application, wherein the ILU is configured to launch an execution of the at least one corresponding ML based application mapped with this ILU; storing the one or more ILUs in an intent logic library, wherein an ILU is stored in association with capability information derived for the ILU from the capability information received for the at least one corresponding ML based application mapped with this ILU.
  • the apparatus according to the first aspect comprises means for performing one or more or all steps of the method according to the second aspect.
  • the means may include circuitry configured to perform one or more or all steps of the method.
  • the means may include at least one processor and at least one memory including computer program code, the at least one memory and the computer program code being configured to, with the at least one processor, cause the apparatus to perform one or more or all steps of the method.
  • an apparatus comprising means for performing a method, the method comprising: receiving a request for capability information from an intent logic execution platform; sending in response to the request capability information representative of capabilities of one or more machine learning, ML, based applications; executing the one or more machine learning, ML, based applications to fulfill an intent, wherein the executing is launched by at least one intent logic unit mapped with the one or more ML based application.
  • a method comprising: receiving a request for capability information from an intent logic execution platform; sending in response to the request capability information representative of capabilities of one or more machine learning, ML, based applications; executing the one or more machine learning, ML, based applications to fulfill an intent, wherein the executing is launched by at least one intent logic unit mapped with the one or more ML based application.
  • FIG. 2 is a schematic diagram illustrating an exemplary system for intent fulfilment according to an example.
  • FIG. 3 is a schematic diagram illustrating aspects of the relationships between a ML application, an Intent Logic Execution Platform and an Intent Logic Unit according to an example.
  • FIG. 5 is a flow diagram illustrating a method for intent fulfillment according to an example.
  • FIG. 6 is a flow diagram illustrating a method for intent fulfillment according to an example.
  • FIG. 7 is a flowchart illustrating a method for intent fulfillment according to an example.
  • FIG. 8 is a flowchart illustrating a method for intent fulfillment according to an example.
  • FIG. 9 is an exemplary hardware structure of an apparatus according to an example.
  • Exemplary embodiments provide apparatuses, methods, system and computer programs for mapping machine learning (ML) based applications to intent logic units.
  • Exemplary embodiments provide apparatuses, methods, system and computer programs for intent fulfillment.
  • ML functionality are used herein to designate a ML based application or a ML orchestrator.
  • ML based application or “ML application” are used herein to designate a software application that uses Al (Artificial Intelligence) I ML technology, e.g. by implementing one or more ML models.
  • ML orchestration function or “ML orchestrator” are used herein to designate a software application that is configured to orchestrate the execution of ML applications.
  • the orchestration of ML applications may include performing at least one of synchronizing, triggering, configuring, monitoring and controlling the execution of the orchestrated ML applications.
  • Exemplary applications of ML comprise without limitation: voice recognition; image processing/computer vision; natural language processing; information retrieval; personalization and recommendation; robotics, data analytics including predictive and prescriptive analytics; use-cases for the design and/or planning and/or optimization and/or configuration and/or control and/or management of communication systems and I or networks.
  • Exemplary use-cases may be without limitation: use-cases related to the physical-layer of communication networks such as modulation, coding, decoding, signal detection, channel estimation, prediction, compression, interference mitigation; use-cases related to the medium access control layer of communication networks such as multiple access and resource allocation (e.g., power control, scheduling, spectrum management); channel modeling; network optimization; cell capacity estimation in cellular networks; routing; resource management; data traffic management; security and anomaly detection; root cause analysis; transport protocol design and optimization; user/network/application behavior analysis/prediction; transport-layer congestion control; user experience modeling and optimization; user mobility and positioning management; network slicing, network virtualization and software defined networking; non-linear impairments compensation in optical networks (e.g., visible-light communications, fiber-optics communications, and fiber-wireless converged networks), and quality-of-transmission estimation and optical performance monitoring in optical networks.
  • multiple access and resource allocation e.g., power control, scheduling, spectrum management
  • channel modeling e.g., power control, scheduling
  • Exemplary analytics and/or decision function comprise without limitation: coverage analysis, coverage problems analysis, handover problems analysis, faults detection, interference detection, coverage optimization, capacity optimization, handover optimization, interference reduction, energy saving optimization,
  • I BN Intent Based Network
  • Example intents may for example include, without limitation:
  • the ISP 120 may for example be configured to analyze (e.g. parse) the input data expressing the intent provided by the user in order to identify the data fields fitting to a predefined intent specification syntax.
  • Several data processing methods may be used for analyzing the specified intent and breaking down the intent into its constituent subparts to facilitate the identification of the tasks I sub-tasks to be performed for fulfillment of the intent.
  • Patent application published under number WO2021/164878 A1 which is hereby incorporated by reference discloses an example ISP that is configured to receive the request from a user through a user interface, wherein the request uses a specific intent specification syntax and may comprise a control object of the communication network and a verb which indicates what action has to be performed for the control object.
  • the executable task may be, without limitation:
  • the use of a single or multiple ML models may be needed or useful.
  • the approach to use the ML models to fulfill a given intent may depend on for example the complexity level of the intent, the availability of ML models and their suitability to fulfil the given intent.
  • ILU and ML based applications There may be one-to-one or one-to-many mapping between ILU and ML based applications; different combinations of ILUs and ML based applications may be possible for fulfilling a given intent; complex intent may be broken up into intent components, where each intent component may be fulfilled by using one or more ILUs with a mapping with one or more ML based applications (executed either in parallel or in sequence).
  • the specified intents may be mapped to other ILUs that are not themselves mapped to ML based applications.
  • the non-ML based solutions logic or commands
  • the non-ML based commands may still be applicable in addition for complete fulfilment of the given intent.
  • One or more intent logic units, ILUs may be generated by the ILEP with a mapping between the one or more ILUs and the of one or more ML based applications.
  • An ILU may be mapped with one or more corresponding ML based applications and the I LU is configured to launch an execution of the one or more corresponding ML based applications mapped with this ILU.
  • Each ILU that is mapped with one or more ML applications is configured to launch the execution of the one or more ML applications, either directly or through an ML orchestration function.
  • Launching the execution of an ML application may include sending, e.g. by the ILEP and I or the mapped ILU, configuration parameters to the launched ML application.
  • the configuration parameters may be sent before and I or when and I or after triggering the execution of the launched ML application.
  • the launching may be performed using a remote procedure call, a web interface or any appropriate software technology to trigger the execution of an ML application that may be executed on a remote device.
  • each of the mapped ML application may be executed by a host apparatus (e.g. a network entity), physically distinct and separate from the ILEP.
  • the ML application may receive input data from the ILU, from the host apparatus or from another network entity (e.g. from or network devices like sensors, data producers, etc).
  • the ML application may generate output data that may be used by the host apparatus or by another network entity.
  • This host apparatus may be located within the communication network such that the ML-based application has access to the required input data and may output data for concerned network entities.
  • Exemplary network entities comprise without limitation: radio access network entities such as base stations (e.g., cellular base stations like eNodeB in LTE and LTE-advanced networks and gNodeB used in 5G networks, and femtocells used at homes or at business centers); relay stations; control stations (e.g., radio network controllers, base station controllers, network switching sub-systems); access points in local area networks or ad-hoc networks; gateways and radio access network entities; network management entities (e.g., Operation, Administration and Management (OAM) entity); network automation systems; distributed analytics entities such as self-autonomous systems (D-SONs); network functions (e.g., network data analytics function or NWDAF defined in current 3GPP standards).
  • base stations e.g., cellular base stations like eNodeB in LTE and LTE-advanced networks and gNodeB used in 5G networks, and femtocells used at homes or at business centers
  • relay stations
  • the one or more ILUs may be stored in an intent logic library (ILL).
  • An ILU is stored in association with ILU capability information derived for this ILU from the capability information received for the one or more corresponding ML based applications mapped with this I LU.
  • the ILL may be configured to perform a search of ILUs on the basis of search criteria related to capability information.
  • the I LU may be stored in the ILL in association with at least one of an identifier, a name and a description which is understandable by a user to allow identification of the ILU.
  • each ILU has a description intended for human users who may either want to revise, reuse or remove the ILU.
  • the proposed system provides a ML capability discovery process implemented between two functions: ML capability consumer and ML capability producer.
  • the ML capability consumer is a function that may be implemented by an Intent Logic Execution platform or an Intent Fulfillment System or any other entity configured to generate new ILUs or any other notion of execution logic.
  • the ML capability producer is a function that may be implemented by an ML based application itself or an ML orchestrator or a dedicated network entity (e.g. a network management function, an automation function, an analytics function, or a network function like a gNB, or cell) configured to provide an interface for ML capability exposure on behalf of one or more ML based applications or one or more ML orchestrator.
  • Authorizations may be necessary to allow one or more ML capability consumers to request capabilities such that only an authorized ML capability consumer may request the capabilities of existing ML based applications. Likewise ML based applications may report their capabilities only to an authorized ML capability consumer.
  • An authorized ML capability consumer may be configured to request from the ILL (Intent Logic Library) for a mapping of a specified ILU to the set of applicable ML based applications.
  • the ILL may be configured to report the mapping of a specified ILU to the set of applicable ML based applications.
  • the capability information may be exposed over an interface (e.g. an open interface) between a ML capability consumer and a ML capability producer.
  • This interface may be used for various operations including at least one of: sending a request for capability information from the ML capability consumer to the ML capability producer; receiving capability information by the ML capability consumer from the ML capability producer; sending configuration data of a ML application from the ML capability consumer to the ML capability producer.
  • Capability information representative of capabilities of one or more machine learning, ML, based applications are received by the ML capability consumer. The capability information may be received in response to a request sent from the ML capability consumer to one or more ML capability producers (e.g. to each new ML-based applications available).
  • the capability information may also be received on the basis of a subscription scheme in which the ML capability consumer sends at least one requests (e.g. to a network entity that implements a ML capability producer function on behalf of ML based applications) to receive capability information and receives the capability information matching its request when new ML applications are available without having to send a request to each of new ML-based application.
  • a subscription scheme in which the ML capability consumer sends at least one requests (e.g. to a network entity that implements a ML capability producer function on behalf of ML based applications) to receive capability information and receives the capability information matching its request when new ML applications are available without having to send a request to each of new ML-based application.
  • ML capability producer is a ML application 261 and the ML capability consumer is the ILEP 230.
  • the capability information may be exposed over an interface 265 between the ML capability consumer and the ML capability producer.
  • one ILU 251 in the ILL 235 is mapped to one ML-based application 261.
  • the ML-based application 261 may implement one or more ML models.
  • the ILEP 230 may load the ILU 251 from the database and execute the ILU 251 which itself launches the execution of the ML application 261.
  • each ML-based application may implement one or more ML models.
  • the capability information are exposed over interfaces 265-1 to 265-4 between the ML capability consumer (ILEP 230) and the ML capability producers (here the ML applications 271 to 273 and the ML orchestrator 280).
  • An interface 265-1 to 265-3 is implemented between the ILEP 230 and each of the ML application 271 to 273.
  • An interface 265-4 is implemented between the ILEP 230 and the ML orchestrator 280, without the need for an interface between the ILEP 230 and the ML application 274 to 276 orchestrated by the ML orchestrator 280.
  • one ILU is mapped to several ML-based applications. There are several options to map an ILU to several ML-based applications.
  • the ML capability consumer derives one ILU 252 from the ML based applications 271 to 273 without an overlooking ML based application (i.e., without ML orchestrator) such that the ILU 252 is mapped with the ML based applications 271 to 273.
  • the ILEP 230 may load the ILU 252 from the ILL 235 and execute the ILU 252.
  • the ILU 252 may launch the execution of one or more of the ML applications mapped with this ILU. For example the ILU 252 launches the execution of each of the ML applications 271 to 273.
  • the ILU 252 may be configured to launch one of the ML applications mapped with this ILUs (for example the ML application 271) and one of the ML applications may itself be configured to launch at least one other ML applications (for example the ML applications 272 and I or 273) mapped with this ILU.
  • the ML applications 271 , 272, 273 may be executed in a sequential manner or at least partly in parallel or in parallel.
  • the ML capability consumer (ILEP 230) derives one ILU 253 from the ML based applications 274 to 276 along with an overlooking ML based application (i.e., ML orchestrator 280) which is responsible for orchestrating the execution of the ML based applications such that the ILU 253 is mapped with the ML based applications 274 to 276 and also with the ML orchestrator 280.
  • ML orchestrator 280 an overlooking ML based application which is responsible for orchestrating the execution of the ML based applications such that the ILU 253 is mapped with the ML based applications 274 to 276 and also with the ML orchestrator 280.
  • the ILU 253 launches the execution of one or more of the ML applications mapped with this ILU, the execution and performance of these ML applications being monitored by the ML orchestrator 280.
  • the ILU 252 launches the execution of the ML orchestrator 280 which itself launches the execution of the ML based applications 274 to 276 orchestrated by the ML orchestrator 280.
  • the ML applications 274, 275, 276 may be executed in a sequential manner or at least partly in parallel or in parallel.
  • the capability information that are exposed for the available ML functionalities may include various types of information.
  • the capability information derived for an ILU may include the same type of information.
  • the capability information of an ML based application may include a description describing the functionality or ML model implemented by the concerned ML based application.
  • the description may include at least one of a text (e.g. “predicting the QoS level within a QoS scope”), keywords, an identification a functionality within a list of predefined functionalities, etc.
  • the capability information of an ML based application may include information representative of entities for which the functionality implemented by ML based application is applied. These entities may be one or more objects or one or more object types (e.g. “UEs” to indicate that one object is a User Equipment). An object may be used as input or output of the ML based application. An object may correspond to any entity in the communication network: a UE, a network entity, a functional unit, a virtual function, a database, or other similar/related objects. The object or object type may be identified by a name or an identifier or using another type of identification method.
  • entities may be one or more objects or one or more object types (e.g. “UEs” to indicate that one object is a User Equipment).
  • An object may be used as input or output of the ML based application.
  • An object may correspond to any entity in the communication network: a UE, a network entity, a functional unit, a virtual function, a database, or other similar/related objects.
  • the capability information may include information representative of at least one configuration parameter for an object or object type for which the function is performed.
  • the configuration parameter for an object or object type may be any parameter that is usable to configure the concerned object.
  • a cell needs to have an antenna tilt to determine where the antenna should face to maximize coverage and minimize interference.
  • the antenna tilt in this example is a configuration parameter for the object cell.
  • a configuration parameter for an object or object type is an example of a configuration parameter of a ML based application.
  • the capability information of an ML based application may be coded and reported in various formats.
  • the capability information of an ML based application may be coded using a descriptive text and I or t-uples and I or tables, etc.
  • the capability information may represent a decision which can be reported.
  • the capability information may be in the form of a triple ⁇ x,y,z> indicating
  • the capability information may represent an analysis which can be reported. For example in the form of tuple ⁇ x,z> indicating:
  • FIG. 5 is a flow diagram illustrating an exemplary implementation of a method for intent fulfilment according to some embodiments.
  • the ML capability consumer 590 discovers the available ML based applications and their supported capabilities provided by the ML capability producer.
  • the ML capability consumer may send (step 501) a request for ML capability information and receive a response from the ML capability producer 590 including ML capability information (step 502).
  • the ML capability consumer 530 may receive the ML capability information from one or more ML capability producer 590 when one or more new ML based applications are available, e.g. on the basis of a subscription scheme and I or without the need to send a request.
  • This discovery phase (steps 501 and 502) may be repeated as needed.
  • Patent application published under number WO2021/213632 A1 which is hereby incorporated by reference discloses embodiments of a system for fulfilling user intents using Intent Logic Units (ILUs) that are stored in an Intent Logic Library (ILL).
  • ILUs Intent Logic Units
  • ILL Intent Logic Library
  • the ILEP 230 may search in the ILL 235 for an I LU 250 that matches a specified intent.
  • the search may be based on the capability information and I or description and I or mapping information of the ILUs.
  • the description of an ILU may include at least one of: a descriptive text, one or more functions of the ILU, one or more parameters of the ILU, etc.
  • the ILEP 230 may determine which ILUs can be combined to achieve the specified intent. If a combined ILU can be generated by combining several ILUs, the ILEP 230 may schedule the execution of the combined ILU. Otherwise the ILEP 230 may return an execution failure to the ISP 220.
  • Step 601 discovery phase.
  • the Intent Logic Execution Platform discovers the available ML based applications, here for example the ML orchestrator 680 and the ML application 660.
  • Steps 602 and 603 requests for capability information.
  • the Intent Logic Execution Platform 630 requests from one or more ML capability producers 690 (ML based application 660 and / or ML orchestrator 680) the details on their supported capabilities.
  • ML capability producers 690 ML based application 660 and / or ML orchestrator 680
  • Each request for capability information may be sent to an ML-based application 660 (step 603) or to an ML orchestrator 680 (step 602).
  • Steps 604 and 605 Publication of capabilities.
  • the one or more ML capability producers 690 (ML based application 660 and / or ML orchestrator 680) may report their capabilities.
  • an ML orchestrator 680 may send capability information related to the ML applications orchestrated by this ML orchestrator 680.
  • an ML application 660 may send its own capability information.
  • the capability information received for an ML application 660 may be associated with an identifier (e.g. ModellDI) of an ML model implemented by the ML based application 660 or an identifier of the ML based application itself.
  • the capability information received for an ML orchestrator 680 may be associated with an identifier (e.g. MLOrchestratorlDI) of the ML orchestrator.
  • the capability information of an ML based application includes: ModellDI, predicting the QoS level within a QoS scope, [Ues, QoS ID (QCI/5QI), QoS objective] wherein
  • the capability information of an ML based application includes:
  • reducing the handover metrics by percentage P is the capability information related to the functionality performed by the ML application, and wherein the handover metrics include: ping pongs, Radio Link Failures due to too early handovers, Radio Link Failures due to too late handovers.
  • the capability information of an ML based application includes: ModellD3, “move Ues from cell/frequency layer X to cell/frequency layer Y by reducing the cell or layer load below threshold T”.
  • X, Y, T are configuration parameter of the ML based application
  • ModellD4 “predict the location, speed, trajectory of Ues at time instance X or time period Y to Z” wherein X, Y, Z are configuration parameter of the ML based application
  • the capability information of an ML orchestrator includes:
  • MLOrchestratorlDI “ensure orchestration of models with following IDs: ModellD3, ModellD4, supporting performance monitoring of ML QoS metrics: KPI1 , KPI2”
  • ILU1 is the identifier of the ILU
  • MAPPING (ModellDI , ILU1) is the mapping information defining a mapping between ILU identified by ILU1 and the ML model identified by ModellDI .
  • ILU3 “Move Ues from technology/cell/frequency layer X to technology/cell/frequency layer Y”, MAPPING (ModellD3, ILU3)
  • Step 607 the Intent Logic Execution Platform may create ILUs mapped to several ML applications so as to combine their capabilities. There are several ways to combine the capabilities of the ML applications.
  • the Intent Logic Execution Platform derives an ILU which is mapped to an ML orchestrator such that the newly derived ILU combines the capacities of the ML applications orchestrated by the ML orchestrator.
  • ILU6 “Use technology/cell/frequency layer X for Ues which QoS meets certain condition", MAPPING (ModellDI, Model I D3, I LU 6)
  • Use technology/cell/frequency layer X for Ues which QoS meets certain condition is the capability information that defines the functionality of the ILU;
  • the capability information of the newly derived ILU is derived from the capability information of the corresponding ML based application(s) mapped with this ILU.
  • the capability information of the newly derived ILU may be the same as the capability information of this ML based application.
  • the capability information of the newly derived ILU correspond to the capability information of all the mapped ML based applications.
  • the capability information of the newly derived ILU may be generated automatically and I or be checked and I or approved by a user (operator).
  • Step 609 the Intent Logic Execution Platform receives a request to fulfil new intents.
  • a user may request the fulfilment of intents of different kinds. For example:
  • Step 610 the Intent Logic Execution Platform searches and selects in the ILL the ILUs matching the received intents.
  • the search in the ILL may include a semantic analysis and I or keywords analysis of the capability information of the ILUs stored in the ILL on the basis of the specified intent. The selection may be checked and I or approved by a user (operator). If several matching ILUs are found in the ILL, the selection of the ILUs to be used may be finally performed and I or approved by a user (operator).
  • the ILU identified by ILU6 mapped to (ModellDI , ModellD3) is selected;
  • the ILU identified by ILU7 mapped to (ModellD3, ModellD4) and the ILU identified by ILU8 mapped to (MLOrchestratorlDI) are selected;
  • Step 611 the Intent Logic Execution Platform launches the execution of ML based applications using one or more ILUs selected in step 610, where each selected I LU launches the one or more ML based applications mapped with the concerned I LU.
  • Configuration parameters extracted from the intent to be fulfilled may be used by the ILEP or by the I LU selected in step 610 for the configuration of the one or more ML based applications.
  • the configuration parameters extracted from the intent A includes the level of QoS targets decrease (X%) and the indication on frequency layers (e.g. from any frequency layer to 5G frequency layer); these configuration parameters are used by the ILEP for the configuration of the ML application ModellDI and ModellD3; the ILEP executes the selected I LU identified by ILU6 mapped to (ModellDI , ModellD3); the ILU ILU6 triggers the execution of the ML based applications implementing the ML applications ModellDI , ModellD3; the ML application ModellDI is configured to detect Ues which QoS objectives experiences X% decrease; the ML application ModellD3 is configured to move Ues detected by the ML application ModellDI from any frequency layer to 5G frequency layer.
  • Steps 612, 613 and 614 the Intent Logic Execution Platform launches the execution of ML based applications 660 (step 613) and ML orchestrator 680 (step 612) using one or more ILUs selected in step 610, where each selected ILU launches the one or more ML based applications mapped with the concerned ILU.
  • the ML orchestrator 680 monitors (step 614) the performance of the launched ML based applications 660.
  • the ILEP For fulfilling the above intent B, the ILEP extracts configuration parameters from intent B including: the location (e.g. urban area coordinates) of the concerned Ues, required time (e.g. “any time instance”), the indication on frequency layers (e.g. from 5G frequency layer to any frequency layer) and accuracy (e.g. 90%). These configuration parameters shall be used by the ILEP for configuring the ML applications ModellD4, ModellD3 and ML orchestrator MLOrch estrato rlD1. Then: the ILEP executes the selected ILUs, i.e.
  • the ILU7 triggers the execution of the ML applications (ModellD3, ModellD4) (step
  • the ML orchestrator MLOrchestratorlDI monitors the execution of the ModellD3, ModellD4 (step 614); the ILEP performs the configuration of the ML application ModellD4 to cause it to identify Ues within urban area (e.g. based on urban area coordinates) for any time instance; the ILEP performs the configuration of the ML application Modelld3 to cause it to move Ues identified by ModellD4 from 5G frequency layer to any frequency layer; the ILEP performs the configuration of the ML orchestrator MLOrchestratorlDI to cause it to monitor and ensure the accuracy of monitored ML models, i.e., ModellD3 and ModellD4, does not fall below 90%.
  • FIG. 7 shows a flowchart of a method for intent fulfillment. The method involves a ML capability consumer as disclosed herein and ML capability producers as disclosed herein.
  • the ML capability consumer may send a request for capability information to at least one ML capability producer.
  • the request may be sent to at least one of: a ML based application, a ML applications orchestrator and another ML capability producer.
  • the request may be a request to receive in response capability information for currently available capabilities or a subscription request to receive (e.g. on a regular basis) capability information for currently available capabilities and capabilities that may be available in the future.
  • the ML capability consumer receives (e.g. from one or more at least one ML capability producers) capability information representative of capabilities of one or more machine learning, ML, based applications.
  • the ML capability consumer generates one or more intent logic unit, ILUs.
  • ILUs intent logic unit
  • a newly created I LU is mapped with at least one corresponding ML based application and is configured to launch an execution of the at least one corresponding ML based application mapped with this I LU.
  • the ML capability consumer stores the one or more ILUs in an intent logic library.
  • Each I LU is stored in association with capability information derived for the I LU from the capability information received for the at least one corresponding ML based application mapped with this I LU .
  • the ML capability consumer may store in the intent logic library, in association with the stored I LU, mapping information for the mapping performed between this I LU and the at least one corresponding ML based application.
  • step 750 the ML capability consumer receives a request including intent information representative of an intent to be achieved for at least one communication network.
  • the ML capability consumer identifies, based on ILU capability information stored in the intent logic library, one or more ILUs configured to launch an execution of one or more corresponding ML based applications adapted to fulfill the intent for the at least one communication network.
  • the ML capability consumer executes the identified one or more ILUs.
  • the execution of the identified one or more ILUs causes the launching of the execution of the one or more ML based applications mapped with the identified one or more ILUs.
  • the launching may include configuring the mapped ML based application(s) with one or more configuration parameters.
  • the one or more configuration parameters of an ML based application may be received from the ILU mapped with this ML based application or from the ML capability consumer (e.g. from the ILEP).
  • FIG. 8 shows a flowchart of a method for intent fulfillment. The method involves a ML capability consumer as disclosed herein and a ML capability producer as disclosed herein.
  • a ML capability producer receives (e.g. from a ML capability consumer like an ILEP) a request for capability information.
  • the ML capability producer sends, in response to the request, capability information representative of capabilities of one or more ML based applications. If the ML capability producer is a ML orchestrator, the ML capability producer sends the capability information representative of capabilities of one or more ML based applications monitored by the ML orchestrator. If the ML capability producer is a ML based application, the ML capability producer sends the capability information representative of capabilities this ML based application. If the ML capability producer is a network entity that implements a ML capability producer function on behalf of one or more ML based applications, the ML capability producer may send capability information representative of capabilities of these one or more or all ML based applications.
  • a host apparatus executes one or more ML based applications.
  • the execution is launched by the one or more ILUs mapped with these one or more ML based applications.
  • These one or more ILUs are selected and executed to fulfill a given intent.
  • the launching may include configuring, by each concerned ILU, the one or more mapped ML based applications with one or more configuration parameters.
  • the configuration parameters may be stored in the ILU such that when selecting a given ILU the configuration parameters stored by the ILU are selected).
  • the launching may be performed through an ML orchestrator.
  • the ML orchestrator may receive (e.g. from a ML consumer or from the ILU mapped with the ML orchestrator) one or more configuration parameters for the one or more ML based applications monitored by the ML orchestrator.
  • the ML orchestrator may itself configure the one or more ML based applications monitored by the ML orchestrator with the received one or more configuration parameters before and I or when and I or after triggering the execution of the monitored one or more ML based applications.
  • An intent driven management service may correspond to a management service that allows a user (e.g. a consumer of the intent driven management service) to express an intent.
  • An intent driven management service allows the user to express desired intent for managing the network and services.
  • the Intent driven management service producer paraphrases the expressed intent to transform the intent into executable tasks.
  • the proposed solution is also relevant for the management of ML functionalities, in particular the configuration of the ML functionalities.
  • the proposed solution is also relevant for creating new relations between management function of ML functionalities and other entities (e.g. gNBs) within the communication network.
  • the ML applications may be configured to implement a given analytics and/or decision function that may be used in the context of a use-cases in a wide variety of applications and in different systems and various industries.
  • a process may be terminated when its operations are completed but may also have additional steps not disclosed in the figure or description.
  • a process may correspond to a method, function, procedure, subroutine, subprogram, etc. When a process corresponds to a function, its termination may correspond to a return of the function to the calling function or the main function.
  • the host apparatus or system may be a general-purpose computer and I or computing system, a special purpose computer and I or computing system, a programmable processing apparatus and I or system, a machine, etc.
  • the host apparatus or system may be or include or be part of: a user equipment, client device, mobile phone, laptop, computer, network element, data server, network resource controller, network apparatus, router, gateway, network node, computer, cloud-based server, web server, application server, proxy server, etc.
  • the apparatus 1000 may include at least one processor 1010 and at least one memory 1010.
  • the apparatus 1000 may include one or more communication interfaces 1040 (e.g. network interfaces for access to a wired / 1 wireless network, including Ethernet interface, WIFI interface, USB interfaces etc) connected to the processor and configured to communicate via wired I non wired communication link(s).
  • the apparatus 1000 may include other associated hardware such as user interfaces 1030 (e.g. keyboard, mouse, display screen, etc) connected with the processor.
  • the apparatus 1000 may further include one or more media drives 1050 for reading a computer-readable storage medium (e.g. digital storage disc 1060 (CD-ROM, DVD, Blue Ray, etc), USB key 1080, etc).
  • the processor 1010 is connected to each of the other components 1030, 1040, 1050 in order to control operation thereof.
  • the processor 1010 may be configured to store, read, load, execute and/or otherwise process instructions 1070 stored in a computer-readable storage medium 1060, 1080 and I or in the memory 1020 such that, when the instructions are executed by the processor, causes the apparatus 1000 to perform one or more or all steps of a method described herein for the concerned apparatus 1000.
  • a processor or likewise a processing circuit may correspond to a digital signal processor (DSP), a network processor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a System-on-Chips (SoC), a Central Processing Unit (CPU), a Processing Unit (CPU), an arithmetic logic unit (ALU), a programmable logic unit (PLU), a processing core, a programmable logic, a microprocessor, a controller, a microcontroller, a microcomputer, any device capable of responding to and/or executing instructions in a defined manner and/or according to a defined logic. Other hardware, conventional or custom, may also be included.
  • a processor or processing circuit may be configured to execute instructions adapted for causing the host apparatus or system to perform one or more functions disclosed herein for the concerned host apparatus or system.
  • a computer readable medium or computer readable storage medium may be any tangible storage medium suitable for storing instructions readable by a computer or a processor.
  • a computer readable medium may be more generally any storage medium capable of storing and/or containing and/or carrying instructions and/or data.
  • a computer- readable medium may be a portable or fixed storage medium.
  • a computer readable medium may include one or more storage device like a permanent mass storage device, magnetic storage medium, optical storage medium, digital storage disc (CD-ROM, DVD, Blue Ray, etc), USB key or dongle or peripheral, a memory suitable for storing instructions readable by a computer or a processor.
  • a memory suitable for storing instructions readable by a computer or a processor may be for example: read only memory (ROM), a permanent mass storage device such as a disk drive, a hard disk drive (HDD), a solid state drive (SSD), a memory card, a core memory, a flash memory, or any combination thereof.
  • ROM read only memory
  • HDD hard disk drive
  • SSD solid state drive
  • memory card a memory card
  • core memory a flash memory, or any combination thereof.
  • the wording "means configured to perform one or more functions” or “means for performing one or more functions” may correspond to one or more functional blocks comprising circuitry that is adapted for performing or configured to perform the concerned function(s).
  • the block may perform itself this function or may cooperate and I or communicate with other one or more blocks to perform this function.
  • the "means” may correspond to or be implemented as "one or more modules", “one or more devices", “one or more units”, etc.
  • the means may include at least one processor and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause an apparatus or system to perform the concerned function(s).
  • combinations of hardware circuits and software such as (as applicable) : (i) a combination of analog and/or digital hardware circuit(s) with software/fi rmware and (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions); and
  • circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and/or firmware.
  • circuitry also covers, for example and if applicable to the particular claim element, an integrated circuit for a network element or network node or any other computing device or network device.
  • circuitry may cover digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), etc.
  • the circuitry may be or include, for example, hardware, programmable logic, a programmable processor that executes software or firmware, and/or any combination thereof (e.g. a processor, control unit/entity, controller) to execute instructions or software and control transmission and receptions of signals, and a memory to store data and/or instructions.
  • the circuitry may also make decisions or determinations, generate frames, packets or messages for transmission, decode received frames or messages for further processing, and other tasks or functions described herein.
  • the circuitry may control transmission of signals or messages over a radio network, and may control the reception of signals or messages, etc., via one or more communication networks.
  • first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of this disclosure. As used herein, the term "and/or,” includes any and all combinations of one or more of the associated listed items.

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  • Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Databases & Information Systems (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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Abstract

Procédé consistant à : envoyer une demande d'informations de capacité, la demande étant envoyée à au moins l'un d'une application basée sur ML, d'un orchestrateur d'applications ML et d'un producteur de capacité ML ; recevoir des informations de capacité représentatives de capacités d'une ou de plusieurs applications basées sur l'apprentissage machine, ML ; générer une ou plusieurs unités logiques d'intention, ILU, une ILU étant mappée avec au moins une application ML correspondante, l'ILU étant configurée pour lancer une exécution de la ou des applications basées sur ML correspondantes mappées avec ladite ILU ; stocker le ou les ILU dans une bibliothèque logique d'intentions, une ILU étant stockée en association avec des informations de capacité dérivées pour l'ILU à partir des informations de capacité reçues pour la ou les applications basées sur ML correspondantes mappées avec ladite ILU.
PCT/EP2022/066455 2022-06-16 2022-06-16 Appareils et procédés de mappage d'applications basées sur l'apprentissage automatique à des unités logiques d'intention Ceased WO2023241802A1 (fr)

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CN202280097117.9A CN119366162A (zh) 2022-06-16 2022-06-16 将基于机器学习的应用映射到意图逻辑单元的装置和方法
PCT/EP2022/066455 WO2023241802A1 (fr) 2022-06-16 2022-06-16 Appareils et procédés de mappage d'applications basées sur l'apprentissage automatique à des unités logiques d'intention

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Citations (2)

* Cited by examiner, † Cited by third party
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WO2021164878A1 (fr) 2020-02-20 2021-08-26 Nokia Solutions And Networks Oy Spécification d'intention permettant une commande automatisée de réseaux de communication
WO2021213632A1 (fr) 2020-04-21 2021-10-28 Nokia Solutions And Networks Oy Exécution d'intention pour commande automatisée de réseaux de communication

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WO2021164878A1 (fr) 2020-02-20 2021-08-26 Nokia Solutions And Networks Oy Spécification d'intention permettant une commande automatisée de réseaux de communication
WO2021213632A1 (fr) 2020-04-21 2021-10-28 Nokia Solutions And Networks Oy Exécution d'intention pour commande automatisée de réseaux de communication

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