Patent Description:
<NUM> of the standard 3GPP SA2, the NWDAF is considered as one logical entity which integrates the following functionalities according to TS <NUM>:.

The NWDAF usually executes data processing algorithms in order to analyze input data provided to the NWDAF. The data processing algorithms can be machine-learning (ML) algorithms implementing artificial intelligence (Al) models, also referred to as machine-learning models or models, being defined by model type and model parameters, which should be trained before deployment.

However, the cost of deploying AI models and training services, including hardware and software cost, as well as the development of numerous AI models and continuously updated R&D costs, are rather high.

There therefore is a need for a more efficient training of models which can be used by machine-learning, in particular Al, algorithms for processing data.

<CIT> discloses an Artificial Intelligence, Al, inference architecture with hardware acceleration. Various systems and methods of AI processing using hardware acceleration within edge computing settings are described herein. In an example, processing performed at an edge computing device includes: obtaining a request for an AI operation using an AI model; identifying, based on the request, an AI hardware platform for execution of an instance of the AI model; and causing execution of the AI model instance using the AI hardware platform. Further operations to analyze input data, perform an inference operation with the AI model, and coordinate selection and operation of the hardware platform for execution of the AI model, is also described.

It is the object of the invention to provide a more efficient training of models which can be used by machine-learning (ML), in particular Al, algorithms for processing data.

This object is achieved by the subject-maters of the independent claims. Further examples are disclosed in dependent claims, the description and the accompanying drawings.

The invention is based on the finding that the forgoing and other objects can be solved by separating a model training platform using e.g. machine-learning algorithms for training a model, e.g. a machine learning model, and an inference platform. The separation can be e.g. implemented in technologies such as federate machine learning which support joint training of data sets from different parties without disclosure of the detailed data sets between different parties or to the federate server or collaborator. This enables the sharing of the platform between different owners of the data set.

According to the disclosure, a method and apparatus for an NWDAF (Network Data Analytics Function) can be provided with limited data training capability, or fast analytics generation requirements to obtain an external model, e.g. a machine-learning model. Thereby, the following problems can efficiently be solved:.

According to the disclosure, provisioning of parameters of a model to be used by a NWDAF in 5GS and the model registration, discovery and provision services based on 5GS SBA (Service-based Architecture) can be provided.

This can enable e.g. a PLMN NWDAF to share the model training capability of another PLMN (Public Land Mobile Network) NF (Network Function) instance, and/or another PLMN or 3rd party without exposing the data of certain area or its own network. It allows for more flexible NWDAF functionality deployment with separated Model training platform. In consequence, operator CAPEX/OPEX (Capital expenditure/Operational expenditure) can be reduced. Moreover, the raw data does not need to be exposed in order to obtain the ML Model.

In addition, using an existing model can help the NWDAF to speed up the data analytics generation and increase the accuracy of data analytics generation. Moreover, the NWDAF does not need to train a model by itself or to train a model from the beginning on. The needed time for model training and related data collection can therefore be greatly reduced. Moreover, an existing model could be trained using the data with broader coverage and/or longer data collection period, e.g. comparing to the training data collected by the NWDAF after data analytics generation request has been received. This can increase the accuracy of the analytics generation of the NWDAF considering using a better trained model.

Accordingly, the disclosure relates to a method and an apparatus which provides a model for generating data analytics (e.g., at NWDAF) in 5GS. According to some embodiments, this may include:.

A model in this disclosure refers according to some examples to a machine-learning model, e.g. an AI model, or an equation that can be used to generate data analytics based on a set of input data.

In the following, identical reference signs refer to identical or at least functionally equivalent features.

In the following description, reference is made to the accompanying figures, which form part of the disclosure, and which show, by way of illustration, specific aspects of embodiments of the invention or specific aspects in which embodiments of the present invention may be used. It is understood that embodiments of the invention may be used in other aspects and comprise structural or logical changes not depicted in the figures. The following detailed description, therefore, is not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims.

For instance, it is to be understood that a disclosure in connection with a described method may also hold true for a corresponding device or system configured to perform the method and vice versa. For example, if one or a plurality of specific method steps are described, a corresponding device may include one or a plurality of units, e.g., functional units, to perform the described one or plurality of method steps (e.g., one unit performing the one or plurality of steps, or a plurality of units each performing one or more of the plurality of steps), even if such one or more units are not explicitly described or illustrated in the figures. On the other hand, for example, if a specific apparatus is described based on one or a plurality of units, e.g., functional units, a corresponding method may include one step to perform the functionality of the one or plurality of units (e.g., one step performing the functionality of the one or plurality of units, or a plurality of steps each performing the functionality of one or more of the plurality of units), even if such one or plurality of steps are not explicitly described or illustrated in the figures.

<FIG> shows schematically a communication scenario in an example with a network entity <NUM>, a registering entity <NUM> and a requesting entity <NUM> being arranged to communicate over a communication network <NUM>, e.g. a <NUM> communication network.

The network entity <NUM> is configured for determining at least one model parameter of a model with model parameters which can be used by a machine-learning data analysis algorithm digitally analyze input data depending on the at least one model parameter of the model. The model can be a machine-learning (ML), in particular an Al, model.

The network entity <NUM> is configured to receive a model request from the requesting entity <NUM> over the communication network <NUM>. The model request requests at least one model parameter of the model. In this way, the requesting entity <NUM> can omit training its machine-learning model or only train a partial machine-learning model in order to obtain model parameters suitable for analyzing input data to obtain e.g. a network analytic information.

The network entity <NUM> is configured to obtain the requested at least one model parameter by at least one of the following: executing a machine-learning model training algorithm, the machine-learning model training algorithm being configured to train the model with input data in order to determine at least one of the requested model parameter, searching a local data base such as a storage for an existing ML model, or requesting the at least one model parameter from a further network entity, and to send the requested at least one model parameter towards the requesting network entity over the communication network. The network entity may generate or determine a model based on the requested at least one parameter and send to the requesting entity the determined model instead of the model parameters.

The further network entity can have the features of the network entity <NUM>. The further network entity can be arranged in a further communication network that may be another <NUM> communication network of a different network operator. The further network entity can be arranged in the communication network <NUM> as well.

The registering entity <NUM> is for providing information on available models for e.g. machine-learning algorithms in the communication network <NUM>.

The registering entity <NUM> is configured to receive a registration signal from the network entity <NUM>, the registration signal indicating a model that can be provided by the network entity <NUM>, and to store information on the model and/or a communication network identifier, e.g. identifying a <NUM> communication network <NUM> or a subnetwork thereof, such as a <NUM> slice, and/or network address of the network entity <NUM>.

In an example, the registering entity <NUM> can be configured to receive a discovery request from the requesting entity <NUM>, the discovery request indicating a model and/or a model parameter to be trained, to determine the network entity <NUM> for proving and/or training the model, and to send a network identifier and/or address of the determined network entity <NUM> to the requesting network entity <NUM>.

The requesting entity <NUM> is configured to implement a machine-learning data analyzing algorithm for analyzing input data upon the basis of the model to be trained. The requesting network entity <NUM> is configured to send a discovery request, comprising indication of a model such as model type, model input/output towards the registering entity <NUM> over the communication network, receive the network identifier and/or address of a network entity from the registering entity, send a request for the model, e.g. together with a model parameter towards the network entity, receive the requested model from the network entity, and execute the machine-learning data analysis algorithm with the received model for analyzing the input data.

<FIG> shows a schematic diagram of a method for determining at least one model parameter of a machine-learning data analysis algorithm in a communication network, the machine-learning data analysis algorithm being configured to digitally analyze input data depending on the at least one model parameter of a machine-learning model.

The method can be executed by the network entity <NUM> according to the scenario as shown in <FIG>. The method comprises receiving <NUM> a model request e.g., from the requesting entity <NUM> over the communication network <NUM>, the model request requesting the at least one model and/or model parameter, obtaining <NUM> the requested at least one model and/or model parameter by at least one of the following: executing <NUM> a machine-learning model training algorithm, the machine-learning model training algorithm being configured to train the model with input data in order to determine at least one of the requested model parameter, or searching <NUM> a local data base of the network entity <NUM> or of the communication network <NUM>, e.g. a data storage, for an existing model, or requesting <NUM> at least one model parameter and/or model from a further network entity. The network entity <NUM> may generate or determine <NUM> a model based on the requested at least one model parameter. The requested model parameter and/or model is sent <NUM> towards the requesting entity <NUM> over the communication network.

<FIG> show schematically communication scenarios according to this disclosure in an example, with a model <NUM>, e.g. a data model, a consumer <NUM> of the data model <NUM> such as the requesting entity <NUM>, and optional further data model(s) <NUM>, data storage <NUM> forming an example of a local or external data base, and a local or external model training platform <NUM>, which may be an AI training platform, in particular a machine-learning model training algorithm. The model <NUM> also referred to as "combined data model" may be generated by the network entity <NUM> by combining the parameters of the further model(s) <NUM> (also indicated in the figure as "original data model").

The procedure in an embodiment may have the following phases.

In the model provision preparation phase <NUM>, a network entity <NUM>, forming an embodiment of the network entity <NUM>, implements as provider the local model training platform <NUM> or a local data storage <NUM>, and checks its model provision capability directly from itself, or with the assistance of other network entities, implements as provider the external model training platform <NUM> or an external data storage <NUM>, e.g., via subscription to other network entities services, or by a combination thereof.

In the model registration phase <NUM>, the network entity <NUM> registers the model(s) to a PLMN NRF <NUM>, forming an embodiment of the registering entity <NUM>, either directly or via a NEF. Alternatively, an OAM <NUM> can directly write Network Entity registration information in the NRF <NUM>.

In the model discovery phase <NUM>, a consumer NE <NUM> such as NWDAF or model inference platform, both forming examples of the requesting entity <NUM>, discovers the NE <NUM> which is able to provide the model e.g. via NRF discovery service with a discovery request providing a communication address of the NE <NUM>.

In the model consumption phase <NUM>, in step 4a, the consumer NE <NUM> requests the model and/or model parameters from the provider NE <NUM> directly or via a NEF.

In step 4b, the provider NE <NUM> may obtain the requested model parameters, e.g. from itself, from an OAM configuration, from other network entities, from the data storage <NUM>, or from any combination of the described distribution approaches, e.g. via processing. In step 4c, the provider NE <NUM> responses with the requested model parameters or a "combined data model".

As shown in <FIG>, providing, i.e. provisioning, the model training platform <NUM> can be arranged in a PLMN <NUM>, forming an embodiment of a communication network, in which further NFs such NF1 <NUM>, NF2 <NUM> connected to the NWDAF <NUM> via a communication link <NUM> are present. The NWDAF <NUM> can implement the requesting entity <NUM>, e.g. as inference platform <NUM>.

According to the example shown in <FIG>, the NF1 <NUM> or the NF2 <NUM>, both forming examples of the network entity <NUM>, can implement the model training platform and provide the model provision service. These entities are arranged in the PLMN <NUM> forming an embodiment of the communication network, implementing the 5GC (<NUM> core).

According to the example shown in <FIG>, the model provision service can be provided by an external NE <NUM> such as AF, NWDAF or UDR, forming embodiments of the network entity <NUM> that implements the model training platform <NUM> outside of the PLMN <NUM>. The external NE <NUM> can communicate with the PLMN <NUM> via e.g. a NEF <NUM>.

In an example, the model provision service can be defined for the provider NE forming an example of the network entity <NUM>, implementing the model training platform <NUM>.

As to the model provision service, the provider NE <NUM> implementing the model training platform <NUM> can be federate server, a data storage in a PLMN or a trusted AF or untrusted AF, forming examples of the network entity <NUM>.

The consumer network entity can be formed by a model inference platform, such as NWDAF, which may form an example of the requesting entity <NUM>.

For requesting a model and/or training a model, the following input information can be provided: model type, feature sets, event IDs, analytics ID. Optionally, tracking Area ID (TAI) - which indicate area of interest or NE serving area, UE types, application ID, Network Slice Selection Assistance Information (NSSAI), additional model information, e.g., accuracy level of the model, model time, model ID, model version, etc. Optionally, requested model parameters, e.g. unknown weights, can be indicated.

As output of the model training or response to the model request, one or more model parameters, e.g. weights, or a combined model can be provided.

The model type can be formed by one or more of the following: linear regression; Logistic regression, linear discriminant analysis, decision trees, naive Bayes, K-Nearest Neighbors, learning vector quantization, support vector machines, bagging and random forest, deep neural networks, etc..

In the following example, a model in case of linear regression is described as follows: <MAT>.

The features can be derived from the data indicated by event ID or OAM measurement types.

An Event ID identifies the type of event being subscribed to. This is specified in 3GPP TS23.

In an example, the event IDs as specified in TS23. <NUM> can be PDU Session release, UE mobility out of an Area of Interest, etc..

In an example, the analytics IDs as specified in TS23. <NUM> can be: Service Experience (Clause <NUM>), NF load information (Clause <NUM>), Network performance (Clause <NUM>), UE mobility (Clause <NUM>. <NUM>), UE communication etc..

For the registration of the model provision service, the following parameters can be provided:.

In some examples, the model is provided by the model training platform <NUM>, a model storage, or a federate server.

The model training platform <NUM> can be implemented and/or distributable as a software code that implements a machine-learning model training algorithm.

In an example, a model can be directly provided by a model training platform located at different 5GC NFs, e.g., a NWDAF, a trusted/untrusted AF. An example of service enhancement of each NF/NEs for different cases is described in the following.

In some examples, the model can be provided by the NWDAF as summarized in the following table:.

The NF services can be provided by NWDAF AS DESCRIBED IN TS23.

In some examples, a model can be provided by AF and/or 3rd party AF, as summarized in the following table:.

The NF services provided by the AF can be implemented as described in the TS23.

In case the model is provided by a <NUM>rd party AF, a NEF exposure capability can be enhanced to bridge the Data Model Provision service of <NUM>rd party AF to 5GC NF, as summarized in the following table:.

The NF Services can be provided by the NEF as described in the TS23.

As depicted in <FIG>, a model can be provided by an external/<NUM>rd party application/NF/NE considering one PLMN domain via AF (Application Function). An external/<NUM>rd party/ untrusted AF <NUM> is used in case the provider NF/NE is untrusted for the PLMN. An internal/trusted AF <NUM> is used in case the provider NF/NE is trusted for the PLMN. In case the external AF <NUM> provides the model, forming another example of network entity <NUM>, reverse NEF exposure capability of the PLMN NEF <NUM> can be used to obtain the model from the external AF <NUM>. Then the procedure as depicted in <FIG> may be applied, according to which the NWDAF discovers and obtains the model provided by external AF <NUM>. Optionally, an internal AF <NUM> may be present.

With reference to <FIG>, in step 1a, the external AF <NUM> sends model registration request to the NEF <NUM> forming an embodiment of the registration entity <NUM>, e.g., using previous IPR on AF service registration. The registration request includes the Naf_DataModelProvision service profile in the AF Profile, which further includes the service parameter list with e.g. model type, analytics ID, feature sets/event IDs, etc..

In step 1b, the NEF <NUM> generates a data model exposure service to expose the supported model info including service parameters list with e.g. model type, analytics ID, feature sets/event IDs, etc..

In step 1c, the NEF <NUM> responses to the registration request with a model ID for each item in the list.

In step <NUM>, the NEF <NUM> registers its data model exposure service at the NRF with the list with e.g. model type, analytics ID, feature sets/event IDs, etc..

In step <NUM>, the NWDAF <NUM> discovers the NEF <NUM> which provides the required model via NRF.

In step <NUM>, the NWDAF <NUM> subscribes to the data model exposure service provided by the NEF <NUM> including the parameters of model type, analytics ID, feature sets/event IDs, requested model parameters, etc..

In step <NUM>, the NEF <NUM> subscribes to the data model provision service provided by external AF including the parameters of model type, analytics ID, feature sets/event IDs, requested Model parameters, etc..

In step <NUM>, external AF <NUM> provides the requested model and/or model parameters to NEF <NUM> via response or notification message.

In step <NUM>, NEF <NUM> provides the requested model and/or model parameters to NWDAF <NUM> via response or notification message.

With reference to <FIG>, a model can also be provided by application/NF/NE, e.g., a federate server or model training platform implemented as AF <NUM>, in other PLMN (indicated as PLMN <NUM><NUM> ). In this case, the NEF <NUM> and an iNEF <NUM> in roaming architecture can be used for the signaling exchange between the AF <NUM> and 5GC NFs of a different PLMN <NUM>, e.g. PLMN <NUM>. The model can be provided by another operator's CP NF, e.g. the AF <NUM>.

With reference to <FIG>, the procedure shown in <FIG> can be applied, where the PLMN2 <NUM> NWDAF <NUM> can discover and obtain the model provided by the PLMN1 <NUM>.

Step <NUM> comprises PLMN1 model registration at NRF <NUM>. In Step <NUM>, the PLMN1 NEF <NUM> discovers the model. In step <NUM>, the PLMN1 NEF <NUM> registers model at PLMN2 NRF <NUM>. In Step <NUM>, the PLMN2 NWDAF <NUM> discovers model via PLMN2 NRF <NUM>. In steps <NUM> to <NUM>, the PLMN2 NWDAF <NUM> consumes the model provided by PLMN1 <NUM> via NEF <NUM>.

With reference to <FIG>, the model is provided by PLMN <NUM>5GC NFs/NEs, e.g. NWDAF or UDR forming an example of a data storage. For example, the NWDAF <NUM> or data storage <NUM> in PLMN <NUM> can provide the model to PLMN2 consumer NWDAF <NUM>. If the service discover is already agreed between the operators, e.g., via NRF exchange, then the roaming architecture via SEPP <NUM> can be used for secure signaling exchange between 5GC NFs of different PLMN.

A model can be configured directly in the data storage of 5GC (i.e., UDR) forming an example of a data storage configured by the OAM. In this case, UDR <NUM> can be enhanced to provide the information of available model to the consumer NFs in 5GC as following. Data keys are used to search the data base of UDR to find the required storage data. To search for an ML Model, the data key would be the analytics ID, e.g. as specified in TS23. <NUM>, and the Data Sub keys would be Model type, Features sets, event IDs, area of interests, UE types, application ID, NSSAI, model information such as model ID, model time, model version etc..

The model time indicates, in an example, the time when the model is trained and/or when the model expires. The model time can be implemented in the form of a timestamp.

A model can be provided by a data storage as summarized in the following table. The Exposure data can be stored in an UDR, as e.g. specified in TS23.

In some embodiments, a model can be provided by an NE by combining the results from other NEs.

In an example, the model can also be provided by a Network Entity which obtains a model or model parameters from other network entities (e.g., federate server instead of a model training platform) to NWDAF. In this case, one more step can be useful, where the model provider may obtain the ML Model/model parameters from another NE such as the federate client, data storage or OAM configuration. It may further process the obtained model and model parameters, then provide the combined model to the consumer network functions.

The registration and discovery of model provided by other NEs may follow exactly what is described above, or may use an existing communication interfaces, e.g., the federate server/client communication mechanism as specified by SoA. The model provider may have some local information on the combined model (e.g., via OAM configuration or historical model processing).

Thereby, the model provider is informed what types of the combined model could be provided to the consumer NFs in 5GC. It registers the combined model to 5GC as described above. Then the 5GC NFs may discover and consume the model provided by the model provider.

In an example, the approach described herein is based on a signaling between NWDAF and other NFs on the model inquiry and model retrieval. In an example, there is no raw data sent in order to obtain the model.

The person skilled in the art will understand that the "blocks" ("units") of the various figures (method and apparatus) represent or describe functionalities of embodiments of the invention (rather than necessarily individual "units" in hardware or software) and thus describe equally functions or features of apparatus embodiments as well as method embodiments (unit = step).

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus, and method may be implemented in other manners. For example, the described apparatus embodiment is merely exemplary.

Claim 1:
A network entity (<NUM>) for a communication network, the network being a <NUM> communication network, for determining at least one model parameter of a model for digitally analyzing input data depending on the at least one model parameter of a model, the network entity (<NUM>) being configured to:
receive a model request from a requesting entity (<NUM>) over the communication network (<NUM>), the model request requesting the at least one model parameter of the model;
obtain the requested at least one model parameter by at least one of the following:
executing a machine-learning model training algorithm, the machine-learning model training algorithm being configured to train the model with input data in order to determine at least one of the requested model parameter;
searching a local data base for an existing model; or
requesting the at least one model parameter from a further network entity; and
send the at least one requested model parameter or the model towards the requesting entity (<NUM>) over the communication network, the model being determined by the network entity based on the at least one model parameter;
wherein the communication network (<NUM>) comprises a registering entity (<NUM>), the registering entity (<NUM>) being configured to register models, wherein the network entity (<NUM>) is configured to send a registration signal to the registering entity (<NUM>) over the communication network (<NUM>), the registration signal comprising information on the model in order to register the model with the registering entity (<NUM>);
wherein the registering entity (<NUM>) is a network function repository function, NRF, in the communication network.