NETWORK ANALYTICS-BASED ACTION

Apparatuses, methods, and systems are disclosed for determining an action for a network function based on network analytics. One apparatus includes a processor and a network interface that receives a first set of analytics from at least one network analytics function in a mobile communication network, the first set of analytics relating to a first KPI. The processor determines from the first set of analytics whether a first condition is met and determines a first set of actions when the first condition is not met. Via the network interface, the processor sends the first set of actions to at least one network function in the mobile communication network, each action in the first set of actions being sent to an associated network function.

The subject matter disclosed herein relates generally to wireless communications and more particularly relates to determining an action for a network function based on network analytics.

BACKGROUND

The following abbreviations and acronyms are herewith defined, at least some of which are referred to within the following description.

Third Generation Partnership Project (“3GPP”), Fifth-Generation Core (“5GC”), Application Function (“AF”), Access and Mobility Management Function (“AMF”), Access Network (“AN”), Access Point Name (“APN”), Application Server (“AS”), Application Programing Interface (“API”), Data Network Access Identifier (“DNAI”), Data Network Name (“DNN”), Downlink (“DL”), Edge Application Server (“EAS”), Edge Computing (“EC”), Enhanced Mobile Broadband (“eMBB”), Evolved Node-B (“eNB”), Evolved Packet Core (“EPC”), Evolved UMTS Terrestrial Radio Access Network (“E-UTRAN”), Home Subscriber Server (“HSS”), Identifier (“ID”), IP Multimedia Subsystem (“IMS,” aka “IP Multimedia Core Network Subsystem”), Internet Protocol (“IP”), Key Performance Indicator (“KPI”), Long Term Evolution (“LTE”), LTE Advanced (“LTE-A”), Medium Access Control (“MAC”), Machine Learning (“ML”), Mobile Network Operator (“MNO”), Mobility Management Entity (“MME”), Non-Access Stratum (“NAS”), Network Automation Function (“NAUF”), Narrowband (“NB”), Network Function (“NF”), Network Access Identifier (“NAI”), Next Generation (e.g., 5G) Node-B (“gNB”), Next Generation Radio Access Network (“NG-RAN”), New Radio (“NR”), Network Slice Selection Function (“NSSF”), Network Data Analytics Function (“NWDAF”), Operations, Administration and Maintenance (“OAM”), Policy Control Function (“PCF”), Packet Data Network (“PDN”), Packet Data Unit (“PDU”), PDN Gateway (“PGW”), Public Land Mobile Network (“PLMN”), Quality of Service (“QoS”), Radio Access Network (“RAN”), Radio Access Technology (“RAT”), RAT and/or Frequency Selection Policy (“RFSP”), Radio Resource Control (“RRC”), Receive (“Rx”), Single Network Slice Selection Assistance Information (“S-NSSAI”), Serving Gateway (“SGW”), Session Management Function (“SMF”), Tracking Area Identifier (“TAI”), Transmission Control Protocol (“TCP”), Transmit (“Tx”), Unified Data Management (“UDM”), User Entity/Equipment (Mobile Terminal) (“UE”), Uplink (“UL”), User Plane (“UP”), User Plane Function (“UPF”), Universal Mobile Telecommunications System (“UMTS”), User Datagram Protocol (“UDP”), Wireless Local Area Network (“WLAN”), and Worldwide Interoperability for Microwave Access (“WiMAX”).

In certain mobile communication networks, network data analytics may be performed by a Network Data Analytics Function (“NWDAF”). The NWDAF provides analytic output to one or more Analytics Consumer NFs based on Data Collected from one or more Data Producer NFs.

BRIEF SUMMARY

Methods for supporting prescriptive analytics are disclosed. In various embodiments, the disclosed methods are computer-implemented using a processor and machine-readable code. Apparatuses and systems also perform the functions of the methods.

One method of a network automation function for determining an action for a network function based on network analytics includes receiving a first set of analytics from at least one network analytics function in the mobile communication network, the first set of analytics relating to a first KPI. The method includes determining from the first set of analytics whether a first condition is met and determining a first set of actions when the first condition is not met. The method includes sending the first set of actions to at least one network function in the mobile communication network, each action in the first set of actions being sent to an associated network function.

DETAILED DESCRIPTION

The code may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other devices to produce a computer implemented process such that the code which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the schematic flowchart diagrams and/or schematic block diagram.

Methods, apparatuses, and systems are disclosed for supporting prescriptive analytics in a mobile communication network, specifically by defining centralized solutions for retrieving network analytics and determining actions to be taken to meet key performance indicators (“KPIs”). As discussed above, the mobile communication network may deploy a network data analytics function (“NWDAF”) that provides analytics info to the core network and other consuming entities. The Analytics that the NWDAF provides are based on training an ML model with data collected from the relevant Data Producer NFs. For each analytics output, identified by an Analytic ID there is a specific ML model used. The NWDAF uses the trained model to provide statistics or prediction to an NF's request. The requesting NF is referred to as “Consumer NF.” For example, a consumer NF—or Application Function (“AF”)—may request analytics information and prove an Analytic ID and parameters with the request. A Consumer NF may ask for analytics either in form of statistics or predictions.

The NWDAF derives the analytics by collecting relevant data from NFs. Candidate NFs include application functions, network exposure functions, control plane NFs, user plane functions, OAM, and the like. For example, the NWDAF derives statistics or predictions for UE location by collecting location changes events from the AMF. The NWDAF retrieves the related data from the NFs by using the Event Exposure Subscribe/Notify service operation. The NWDAF subscribes from the NF to retrieve specific data by including an Event ID (e.g., Location Changes). The NF/AFs then Notifies the NWDAF when the “Event” takes place. For example, an AMF NF notifies the NWDAF that the UE location changed. The NWDAF uses various data processing techniques to derive analytics information from the collected data and reports the analytics to the requesting NF.

Under a distributed approach, each NF may decide its own actions independently from other NFs and without having an overall picture of the whole network. For example, in a distributed approach both NSSF and PCF may be consumer for analytics for service experience. The NSSF may be a consumer of analytics for service experience for a slice and slice load and the PCF may be a consumer for service experience for one or more applications. Both NSSF and PCF may carry out actions independently based on the analytics received but both actions may counteract each other or both actions may not be required to improve the service experience. A similar problem exists for another use case when a UE's application is connected to an Edge Application Server via the 3GPP network. In such a case both SMF and AF may a consumer of analytics for service experience at a specific DNAI location. If both AF and SMF decide an action the action may counteract each other which may not be efficient in a network infrastructure.

This disclosure addresses this problem by defining a centralized solution in which a central network function, referred to as Network Automation Function (“NAUF”), retrieves network analytics from one or more NWDAFs and determines the actions that should be taken by the NFs to meet pre-configured system-wide KPIs. In contrast to the current distributed approach, in which every action is taken independently from and uncoordinated with other actions, the centralized solution exploits system-wide knowledge and can take coordinated actions that collectively optimize the entire network operation.

The central network function receives a first set of analytics from one or more network analytics functions in the mobile communication network. For example, the first set of analytics can be “Service Experience for a slice” according to the first use case, described in greater detail below. The central network function receives the first set of analytics after requesting the first set of analytics from the one or more network analytics functions.

The first set of analytics may be determined by the central network function based on a first KPI, which can be received by the processor or can be configured in the apparatus. For example, if the first KPI=“service experience for Slice-x”, then first set of analytics may be Observed Service Experience analytic information for Slice-x (optionally, for a target area serviced by Slice-x) to determine if the service experience is above or below SLA agreements. As another example, if the first KPI=“network performance for Slice-y”, then first set of analytics may be Network Performance analytics for Slice-y.

The central network function determines from the first set of analytics whether a first condition is met. For example, the condition could be “first KPI<high threshold” or “low threshold<first KPI<high threshold.” If the first condition is not met, then the central network function determines a first set of recommended actions.

Here, determining the first set of recommended actions when the first condition is not met, may include determining a second set of analytics based on the first KPI (the second set of analytics describe better why the condition is not met). The second set of analytics can be explanatory analytics, i.e., analytics explaining why the condition is not met. The central network function derives the first set of recommended action based on the second set of analytics.

Examples of recommended action are described in greater detail below. Each set of recommended actions can contain actions for AMF, for SMF, for PCF, etc. The central network function associates each recommended action in the first set of recommended actions with a network function in the mobile communication network and sends each recommended action in the first set of recommended actions to the associated network function.

After sending the recommended actions, the central network function keeps determining if the first condition is not met. If the first condition is not met after a first time period from sending the first set of recommended actions, then the apparatus can create a second set of recommended actions and send each recommended action in the second set of recommended actions to the associated network function. The second set of recommended actions can be based on a third set of analytics, wherein the third set of analytics is created after the first time period from sending the first set of recommended actions.

FIG.1depicts a wireless communication system100for determining an action for a network function based on network analytics, according to embodiments of the disclosure. In one embodiment, the wireless communication system100includes at least one remote unit105, at least one base unit121, at least one access network (“AN”)120, and a mobile core network130in a PLMN. The AN120may be composed of at least one base unit121. The remote unit105may communicate with the access network120using 3GPP communication links and/or non-3GPP communication links, according to a radio access technology deployed by the AN120. Even though a specific number of remote units105, base units121, ANs120, and mobile core networks130are depicted inFIG.1, one of skill in the art will recognize that any number of remote units105, base units121, ANs120, and mobile core networks130may be included in the wireless communication system100.

In one implementation, the wireless communication system100is compliant with the 5G system specified in the 3GPP specifications. More generally, however, the wireless communication system100may implement some other open or proprietary communication network, for example, LTE/EPC (referred as 4G) or WiMAX, among other networks. The present disclosure is not intended to be limited to the implementation of any particular wireless communication system architecture or protocol.

The remote units105may communicate directly with one or more of the base units121in the access network120via uplink (“UL”) and downlink (“DL”) communication signals. Furthermore, the UL and DL communication signals may be carried over the communication links123. Note, that the access network120is an intermediate network that provide the remote units105with access to the mobile core network130.

In some embodiments, the remote units105communicate with an application server and/or application function (“AS/AF”)151(or other communication peer) via a network connection with the mobile core network130. For example, an application in a remote unit105(e.g., web browser, media client, telephone/VoIP application) may trigger the remote unit105to establish a PDU session (or other data connection) with the mobile core network130using the access network120. The mobile core network130then relays traffic between the remote unit105and the AS/AF151(e.g., in the data network150) using the PDU session.

The PDU session represents a logical connection between the remote unit105and the UPF131. In order to establish the PDU session, the remote unit105must be registered with the mobile core network130. Note that the remote unit105may establish one or more PDU sessions (or other data connections) with the mobile core network130. As such, the remote unit105may have at least one PDU session for communicating with the data network150. The remote unit105may establish additional PDU sessions for communicating with other data networks and/or other communication peers.

The base units121may be distributed over a geographic region. In certain embodiments, a base unit121may also be referred to as an access terminal, an access point, a base, a base station, a Node-B, an eNB, a gNB, a Home Node-B, a relay node, a device, or by any other terminology used in the art. The base units121are generally part of a radio access network (“RAN”), such as the access network120, that may include one or more controllers communicably coupled to one or more corresponding base units121. These and other elements of radio access network are not illustrated but are well known generally by those having ordinary skill in the art. The base units121connect to the mobile core network130via the access network120.

The base units121may serve a number of remote units105within a serving area, for example, a cell or a cell sector, via a communication link123. The base units121may communicate directly with one or more of the remote units105via communication signals. Generally, the base units121transmit DL communication signals to serve the remote units105in the time, frequency, and/or spatial domain. Furthermore, the DL communication signals may be carried over the communication links123. The communication links123may be any suitable carrier in licensed or unlicensed radio spectrum. The communication links123facilitate communication between one or more of the remote units105and/or one or more of the base units121.

In one embodiment, the mobile core network130is a 5G core (“5GC”) or an evolved packet core (“EPC”), which may be coupled to a data network (e.g., the data network150, such as the Internet and private data networks, among other data networks). A remote unit105may have a subscription or other account with the mobile core network130. The present disclosure is not intended to be limited to the implementation of any particular wireless communication system architecture or protocol.

The mobile core network130includes several network functions (“NFs”). As depicted, the mobile core network130includes at least one user plane function (“UPF”)131that serves the access network120. The mobile core network130also includes multiple control plane functions including, but not limited to, an Access and Mobility Management Function (“AMF”)132, a Session Management Function (“SMF”)133, a Policy Control Function (“PCF”)134, a Network Repository Function (“NRF”)137(used by the various NFs to discover and communicate with each other over APIs), a Network Exposure Function (“NEF”)138, and a Unified Data Management function (“UDM”)139.

Currently, when an AF is an external entity to a PLMN the interaction between the AF and the network is via a Network Exposure Function (“NEF”)138. The NEF138supports external exposure of capabilities of network functions including for monitoring occurrences of events. In certain embodiments, the mobile core network130may also include, an Authentication Server Function (“AUSF”), or other NFs defined for the 5GC.

The mobile core network130also includes at least one NWDAF135and a Network Automation Function (“NAUF”)136. As described above, the NWDAF135provides analytics info to the core network functions and other consuming entities. As described in further detail below, the NAUF136receives a plurality of analytics (i.e., multiple analytic ID) from one or more NWDAFs and determines the actions that should be taken by one or more Network Functions, in order to meet Key Performance Indicators (“KPIs”). Note that the mobile core network130may be communicatively coupled to one or more OAM platforms140and a ML model provider145. The ML model provider145sends ML models to the NAUF136and/or NWDAF135.

In various embodiments, the mobile core network130support different types of mobile data connections and different types of network slices, wherein each mobile data connection utilizes a specific network slice. Each network slice includes a set of CP and UP network functions, wherein each network slice is optimized for a specific type of service or traffic class. The different network slices are not shown inFIG.1for ease of illustration, but their support is assumed. In one example, each network slice includes an SMF133and a UPF131, but the various network slices share the AMF132, the PCF134, and the UDM/UDR139. In another example, each network slice includes an AMF132, an SMF133and a UPF131. Although specific numbers and types of network functions are depicted inFIG.1, one of skill in the art will recognize that any number and type of network functions may be included in the mobile core network130.

FIG.2Adepicts a NWDAF architecture200for deriving analytics based on information collected by other NFs, according to embodiments of the disclosure. The NWDAF architecture200includes a NWDAF205(which may be one embodiment of the NWDAF135), a Consumer NF210, one or more data producer entities capable of providing data relating to various Event IDs, depicted as the AF/NF215, the NFs220, and the OAM entity225.

As depicted inFIG.2A, the Split-Architecture NWDAF205derives analytics based on an NF request, i.e., from the Consumer NF210(see messaging230). The NF request can either be a subscription to analytics or a one-time request. In various embodiments, the NF request includes the following: 1) an Analytic ID, identifying the type of analytics requested (e.g., UE mobility analytics); 2) Analytics Filter information, which provides additional information on the analytics information required (e.g., targeting a specific Application or a Specific area of interest); 3) the Target of Analytics Reporting, which identifies whether the information is for a specific UE a group of UEs or any UE; 4) Analytic Reporting Information, which provides information on when and how the NWDAF205should report analytics; 5) an Analytics Target Period, which provide information whether statistics or predictions are requested; 6) one or more Reporting Thresholds, which indicate conditions on the level of each requested analytics that when reached will be notified by the NWDAF205; 7) a preferred level of accuracy of the analytics (e.g., Low/High); and 8) a Time when the information should be provided.

The NWDAF205derives the analytics by collecting relevant data from NFs. The NWDAF205identifies the NFs215-220to collect the data based on the Analytic ID requested by the Consumer NF210. For example, if UE mobility analytics are requested, then the NWDAF205derives statistics or predictions for UE location by subscribing from the AMF132to retrieve location changes events of a specific UE. The NWDAF135retrieves the related data from the NFs by using the Event Exposure Subscribe/Notify service operation (see messaging235).

The NWDAF205perform data processing to derive analytic information (see action240). The NWDAF205also provides analytic information to the consumer NF210(see messaging245).

In some embodiments, a consumer NF210when requesting one-time analytics from the NWDAF205may provide an indication of the time when analytics information is needed. If the time is reached and the NWDAF205does not provide an answer (i.e., a response with Analytics information) the NWDAF205may indicate the reason for not providing a response. In addition, when a consumer NF210subscribes to receive periodic analytic information, the consumer NF210may request a preferred level of analytics of the accuracy.

In various embodiments, the NWDAF205may include in the response to the consumer NF the following information: 1) a Timestamp of analytics generation, which allows consumers to decide until when the received information shall be used; 2) a Validity period, which defines the time period for which the analytics information is valid; 3) a Probability assertion, i.e., confidence in the prediction, which may be based on preferred level of accuracy requested by the consumer NF. In some embodiments, a consumer NF210may deem a received notification from NWDAF205for a given feedback as invalid based on the Timestamp of analytics generation.

FIG.2Bdepicts a network deployment250for determining an action for a network function based on network analytics, according to embodiments of the disclosure. The network deployment250includes the multiple NWDAFs205(here depicted as a first NWDAF205A and a second NWDAF205B). In the depicted embodiment, the NFs include multiple Data Producer NFs (here depicted as the UE201, the AF215A, the 5G NFs220A, and OAM platform225A). The NWDAFs205A-205B collect data255from the Data Producer NFs. In some embodiments, the NWDAFs205A-205B share analytics260and/or perform federated learning, i.e., train a ML model across multiple servers (NWDAFs). The NWDAFs205A-205B then provide analytics to a set of Analytics Consumer NFs, here depicted as the AF215B, the 5G NFs220B (e.g., SMF, AMF, PCF) and OAM platform225B.

A more complete list of potential Analytics Consumer NF for each Analytics output the NWDAF provides is described in the Table 1 below.

The trigger for an Analytics Consumer NF to receive analytics from an NWDAF may be: A) based on the triggers (e.g., event reports or requests) from the other NF(s); or B) based on a local event, e.g., the detected UE behavior or network performance.

Based on the received analytics output, an Analytics Consumer NF may identify an issue, and, in response, it may take one or more actions to alleviate the issue. For example, if the analytics consumer is a PCF and determines that the Observed Service experience for an application is below expectations, the PCF may upgrade the QoS for these applications to ensure that the service experience is improved. Hence, each Analytics Consumer NF can request certain analytics and can apply the received analytics to improve some network parameters.

However, when the Analytics Consumer NFs act independently, their actions may counteract each other or may be redundant (e.g., all actions may not be required to improve the service experience), leading to network inefficiency.

FIG.3depicts a prescriptive analytics architecture300for enabling network automation, according to embodiments of the disclosure. In order to have a network with more informed decisions based on analytics provided by NWDAF, a new function is introduced, the Network Automation Function (“NAUF”)305, that receives a plurality of analytics (i.e., multiple analytic ID) from one or more NWDAFs205and determines the actions that should be taken by one or more 5G Network Functions220, in order to meet KPIs.

The analytics architecture300operates as follows: At Step 1, the NAUF305is configured by the network operator with one or more KPIs. The KPIs may be configured via an OAM platform310.

At Step 2, the NAUF305determines the analytics required to determine whether the KPIs are fulfilled, and requests from one or more NWDAFs205A-205B to receive these analytics.

At Step 3, each NWDAF205collects data from one or more 5G Network Functions220(e.g., AMF315, SMF320, PCF325, and/or AF330) and derives the analytics requested by the NAUF305. Note that while Core NFs are depicted, in other embodiments the NWDAFs205A-205B may collect data from RAN functions and/or UEs. The analytics derived by NWDAF205A-205B may include:a. Explanatory analytics, i.e., analytics that describe current network conditions. Examples of explanatory analytics includes: the current load of a network slice instance, the current NF load, the current network performance, etc.; orb. Descriptive analytics, i.e., analytics that describe the statistical behavior of certain network elements. Examples of descriptive analytics include: UE mobility analytics, abnormal behavior analytics, etc.; orc. Predictive analytics, i.e., analytics that describe a network condition in the future. For example, “how much will be load level of a network slice?” or “how much will be the user-plane congestion in a certain network area?”

At Step 4, the NWDAF(s)205A-205B provide the requested analytics to the NAUF305.

At Step 5, the NAUF305applies the received analytics from NWDAF(s)205A-205B to derive another type of analytics, namely, prescriptive analytics, that describe necessary actions for correcting identified issues. The NAUF305uses its prescriptive analytics engine (i.e., ML model) to identify network issues and to recommend actions for correcting these issues. Typically, the issues identified by NAUF305are deviations from desired KPI values. A new service-based procedure may be used when the NAUF305recommends actions to an NF220.

The actions set by the NAUF305will change the behavior of one or more NFs220which results in the NWDAF(s)205A-205B collecting updated behavior data from the 5G NFs220. Consequently, the NWDAF(s)205A-205B will derive updated analytics that will be received by the NAUF305. This forms a closed-loop system allowing the NAUF305to observe and evaluate if the action taken improves the performance of the system. The NAUF305evaluates continuously from the analytics received whether the actions have improved the performance of the system according to the target KPI configured by the network operator. If the target KPI has not improved the NAUF305uses the prescriptive engine ML model to identify if additional actions are required.

In one embodiment, the action derived by the NAUF305may be a recommendation, i.e., a policy recommendation to a PCF325. In an alternative embodiment, the NAUF305may collect input (real-time) data directly from 5G core NFs220to make real-time decisions. The NAUF305decides actions and observes if the action had the required results by inspecting real-time data collected by one or more NFs220or one or more analytics information collected by one or more NWDAF(s)205A-205B.

In one embodiment, the NAUF305is configured by the network operator with a KPI goal ensuring that the service experience provided by a slice used by a service provider is according to SLA agreements made between a network operator and the service provider. The KPI goal configuration may target a specific area of interest. To support such KPI goal, the NAUF305collects a first set of analytics from one or more NWDAF(s)205A-205B. The NAUF305may subscribe to the first set of analytics from the NWDAF(s)205A-205B. A first set of analytics may be the following: Service Experience for a slice (optionally, in a target area) to determine if the service experience is above or below SLA agreements.

If the analytics indicate that the service experience does not meet the KPI goals, the NAUF305uses a prescriptive ML model to identify issues and determine actions. The NAUF305obtains a second set of analytics to determine issues with NF serving the slice. The NAUF305may subscribe to the second set of analytics from the NWDAF(s)205A-205B. The second set of analytics may include the following:a. Slice Load for a slice to determine the load of the sliceb. NF load to determine the load status of AMF315and/or SMF320serving the slicec. The NAUF305may also identify risks by collecting real time data from one or more NFs (i.e., current load of NF from the NRF, current number of UEs served in a slice from OAM).

The NAUF305uses the analytics for slice load and NF load to determine if the slice is overloaded. If the NAUF305identifies issues, e.g., slice load is high, the NAUF305determines an action and identify the NFs that will carry out such action. Examples of potential actions are shown below in Table 2.

TABLE 2Potential recommended actions by NAUFbased on second set of analyticsIssue IdentifiedRecommended ActionTarget NFSlice load is highDo not accept new UENSSFregistration at a specific sliceAMFs are overloadedDo not select overloadedNSSF, AMFAMF at new UE registrationsAMFs are overloadedSuggest backoff time forAMFoverloaded AMF

After the NAUF305has performed an action, the NAUF305evaluates if the action had the required results by continuously evaluating the first set of analytics provided by one or more NWDAF(s)205A-205B. If the NAUF305determines that after a certain period of time (configurable at the NAUF305by the network operator) the actions did not achieve the required results, then the NAUF305may determine additional actions. The NAUF305may also update the ML model that the previously performed action did not achieve the required results. This will allow the NAUF305to determine which action provides the best result in meeting the KPIs configured at the NAUF305.

In some embodiments, the NAUF305requests a third set of analytics to determine new actions if none of the actions performed based on the second set of analytics had the required result. The NAUF305may subscribe to the third set of analytics from the NWDAF(s)205A-205B.

A third set of analytics may include the following analytics: A) User Data Congestion Analytics for a slice, optionally in a target area to determine if UEs are experiencing congestion when transferring data. In addition, the NAUF305may also request from the NWDAF(s)205A-205B to provide the top applications that cause peak throughput in the network (i.e., UPF131); B) Traffic Dispersion analytics to determine if UEs are in an area that use most of their data volume; C) Abnormal UE behavior analytics (optionally in a target area) to determine if there are any misbehaving UEs that impact the service experience for the slice; and/or D) Service Experience per RFSP (optionally in a target area) to determine the RFSP that offers the best service experience for UEs accessing an application via a slice.

The NAUF305uses the third set of analytics to identify the following risks: A) Potential areas where there is high load, according to user data congestion analytics and traffic dispersion analytics; B) UEs that are experiencing congestion in that area; C) UEs that contribute to the congestion (e.g., cause high peak throughput); and/or D) Misbehaving UEs.

If issues are identified the NAUF305can determine and suggest actions as described in Table 3.

TABLE 3Potential recommended actions by NAUFbased on third set of analyticsIssue IdentifiedRecommended ActionTarget NFList of UEs that cause peakNew RFSP policies for thesePCFthroughput.UEs (NOTE 1) or new(identified via user dataAM/SM policies blockingcongestion and trafficthese UEs to access thedispersion analytics)networkList of UEs experiencingNew RFSP policies for thesePCFcongestionUEs (NOTE 1)Misbehaving UEsnew AM/SM policiesPCFblocking these UEs to accessthe networkNOTE 1:The NAUF uses Service Experience per RFSP analytics to determine the optimal RFSP for UEs

Similar to the behavior of the NAUF305after performing an action based on the second set of analytics, after the NAUF305has performed an action based on a third set of analytics the NAUF305evaluates if the action had the required results by continuously evaluating the first set of analytics provided by one or more NWDAF(s)205A-205B. The NAUF305may also update the ML model that the previously performed action did not achieve the required results. This will allow the NAUF305to determine which action provides the best result in meeting the KPIs configured at the NAUF305.

In an alternative embodiment, the NAUF305may trigger the NWDAF(s)205A-205B to collect the second and third set analytics at the same time.

FIGS.4A-4Bdepicts the overall procedure400for the NAUF305to identify issues, determine actions and evaluate if action had the required result. Starting atFIG.4A, the procedure400starts and the NAUF305monitors analytics for service experience for a slice (see block405). The NAUF305determines whether the service experience meets the KPIs (see decision point410). If yes, then the NAUF305continues to monitor analytics for service experience (see block405). Otherwise, if no, then the NAUF305collects a first set of analytics and identifies risk (see block415). The NAUF305determines whether analytics for slice load show an issue (see decision point420). If yes, then the NAUF305suggests to stop accepting new UE(s) at the slice or suggests new AMF selection rules (see block425) and monitors analytics for service experience for the slice (see block430). If no, the NAUF305monitors analytics for service experience for the slice (see block430). The NAUF305determines whether service experience for a slice has improved (see decision point435). If yes, then the NAUF305continues to monitor analytics for service experience (see block405).

Continuing onFIG.4B, if the NAUF305determines that service experience for a slice has not improved (from decision point435), then the NAUF305collects a second set of analytics and identifies risk (see block440). The NAUF305determines whether there are any misbehaving UEs (see decision point445). If yes, then the NAUF305suggests blocking access for misbehaving UE(s) in a specific area (see block450) and monitors analytics for the service experience for a slice (see block465). If no, the NAUF305determines whether there is an area with user data congestion (see block455). If yes, then the NAUF305suggests new RFSP policies for UEs (see block460) and monitors analytics for the service experience for a slice (see block465). If no, the NAUF305monitors analytics for the service experience for a slice (see block465). The NAUF305determines whether service experience for a slice has improved (see decision point470). If yes, then the NAUF305continues to monitor analytics for service experience (see block405). If no, the NAUF305continues to collect a second set of analytics and identifies risk (see block440).

FIGS.5A-5Bdepict a procedure500for optimizing service experience for a slice, according to embodiments of the disclosure. The procedure500involves a NAUF305, at least one NWDAF205, and one or more 5G NFs, depicted here as the NSSF501, the AMF315and a PCF325. The procedure300details signaling flow for a the NAUF305205to provide prescriptive analytics that describe necessary actions of the 5G NFs for correcting identified issues.

The actions by the NAUF may be provided to each NF via a new service-based API. The service-based API can include information about the Risk by including a Risk Type attribute and the recommended action in the Action attribute.The Risk Type attribute may include the following:Slice Congestion indicationNF congestion indicationMisbehaving UEsUEs experiencing congestionUser Plane CongestionEAS server performance

The Action Attribute may include the following actions:Stop accepting new UEs in a sliceSuggested backoff time for an AMFSuggest new AM policies for misbehaving UE(s)Suggest new RFSP policies for UE(s) experiencing congestionSuggest new DNAI/UPF for a user plane connectionSuggest an optimal EAS server

The procedure300begins at Step 0 as the NAUF305is configured to identify risks and resolve issues for service experience for a slice (see block507).

At Step 1, the NAUF305determines the Analytics required by one or more NWDAFs according to the configuration received in step 0 (see block509).

At Step 2, the NAUF305sends a request to one or more NWDAFs205with the analytics required (see messaging511).

At Step 3, the NWDAF(s)205provide the analytics requested (see messaging513).

At Step 4, the NAUF305uses that analytics provided by the NWDAF(s)205as input data to its prescriptive engine and determines risks/issues with the network (see block515).

Continuing onFIG.5B, at Step 5, if the NAUF305identifies that the network slice is congested the NAUF determines action to resolve the issue (see block517).

At Step 6, the NAUF305sends a recommendation action to the NSSF501to resolve the slice congestion (see messaging519). Example actions include: reject new UE(s) registering to the slice (NSSAI=x), and indication of an AMF503being congested.

At Step 7, the NSSF501performs the recommendation action, e.g., reject UE registration at NSSAI=x (see block521).

At Step 8, if the NAUF305identifies that an NF of a slice is congested, the NAUF305determines the NF to send the action (see block523).

At Step 9, the NAUF305sends a recommended action to the AMF503to resolve the NF congestion (see messaging525). Example actions include: configuring a backoff timer.

At Step 10, the AMF503performs the recommended action (see block527).

At Step 11, if the NAUF305identifies misbehaving UEs, then the NAUF305determines the PCF505that serves these UEs (see block529).

At Step 12, the NAUF305sends a recommended action to the PCF505including a list of UE(s) that are misbehaving (see messaging531). The NAUF305may recommend new AM/SM and/or RFSP policies.

At Step 13, the PCF505sends updated policies to the misbehaving UEs (see block533).

At Step 14, the NAUF305evaluates if the actions have resolved the issue by analyzing new analytics information from the NWDAF(s)205(see block535).

In a second solution, the NAUF305may be configured by the network operator with a KPI goal ensuring that the service experience for one (or more) applications meets the KPI goal when a UE(s) application(s) traffic is routed to an edge network (i.e., the application is accessing services in an edge network via the 3GPP network).

FIG.6depicts a network architecture including an RAN610that serves multiple UEs605, a 5G core network615and an Edge provider620. Application traffic of a UE605may be routed via different “DNAI paths” to an Edge Application Server (“EAS”), where each DNAI identifies a specific location in 3GPP network and is associated to a specific UPF.

In addition, there may be multiple Application servers at a DNAI location. As depicted, the first Edge Application Server625is associated to both DNAI1and DNAI2locations. Using analytics from the NWDAF, the NAUF can select the best DNAI path or the best Application Server at a UE or DNAI location.

To support the KPI goal, the NAUF305collects a first set of analytics from one or more NWDAFs205. The NAUF305may subscribe to the first set of analytics from the NWDAFs205. A first set of analytics may be the following: Service Experience analytics for an Application over one or more DNAIs to determine if the service experience meets the KPI goal(s). In one embodiment, the analytics may be as described in solution 31 of 3GPP TR 23.700-91. The target of analytics may be Any UE or a Group of UEs.

If the analytics information provided from the first set of analytics requested indicate that the service experience at a DNAI location does not meet the KPI goals the NAUF uses a prescriptive ML model to identify risks and determine actions. The NAUF may subscribe to the following second set of analytics from the NWDAF: A) NF load of SMF and UPF to determine whether an NF is overloaded; B) Real-time UPF, SMF load data from NRF; C) UE mobility analytics to determine UE mobility at DNAI location; and/or D) Analytics for misbehaving UEs to determines if there are any UEs introducing congestion at DNAI location.

If a risk is identified the determines an action and identify the NFs that will carry out such action, as described in Table 4:

TABLE 4Potential recommended actions by NAUFbased on second set of analyticsIssue IdentifiedRecommended ActionTarget NFUPF load is high at a firstSuggest an optimal DNAISMF, AFDNAI locationpath for UEs (NOTE 1)(NOTE 2)UPF load is high at a firstReduce QoS of UE(s) (NOTEPCFDNAI location2)Misbehaving UEsnew AM/SM policiesPCFblocking these UEs to accessthe networkNOTE 1:The NAUF determines optimal DNAI path based on collecting service experience analytics from multiple DNAI locations. How the NAUF knows which DNAI allows UE traffic to connect to the same Edge Application Server is not covered by this disclosure.NOTE 2:The NAUF is aware of UEs at DNAI location based on UE mobility analytics

The NAUF305evaluates if the action had the required results by continuously evaluating the first set of analytics provided by one or more NWDAF205. If the NAUF305determines that after a certain period of time (configurable at the NAUF by the network operator), the actions did not achieve the required results the NAUF may determine additional actions. The NAUF305may also update the ML model that the previously performed action did not achieve the required results. This will allow the NAUF305to determine which action provides the best result in meeting the KPIs configured at the NAUF305.

The NAUF350may request a third set of analytics to determine new actions if none of the actions performed based on the second set of analytics had the required result. The NAUF305may subscribe to the third set of analytics from the NWDAF205. A third set of analytics may include the following analytics: A) Analytics for Edge Application Server performance (e.g., as described in Solution 49 of 3GPP TR 23.700-91); and/or B) Real-time Edge Application Server performance data (e.g., as described in Table 6.49.2.2-1 of Solution 49 of 3GPP TR 23.700-91).

If the Analytics indicate that an EAS server performance is low (i.e., does not meet the KPIs set by the network operator) at the UE location the NAUF305can determine the following actions as described in Table 5.

TABLE 5Potential recommended actions by NAUFbased on third set of analyticsIssue IdentifiedRecommended ActionTarget NFEAS server performanceSuggest an optimal EASAFserver for UEs (NOTE 1)NOTE 1:The NAUF is aware of UEs at DNAI location based on UE mobility analytics

In an alternative embodiment the NAUF may decide action based on real-time edge application server performance.

Similar to the behavior of the NAUF305after performing an action based on the second set of analytics, after the NAUF305has performed an action based on a third set of analytics, the NAUF305evaluates if the action had the required results by continuously evaluating the first set of analytics provided by one or more NWDAFs205. The NAUF305may also update the ML model that the previously performed action did not achieve the required results. This will allow the NAUF305to determine which action provides the best result in meeting the KPIs configured at the NAUF305.

In an alternative embodiment, the NAUF305may trigger the NWDAF(s)205to collect the second and third set analytics at the same time.

FIGS.7A-7Bdepicts the overall procedure700for the NAUF305to identify issues, determine actions and evaluate if action had the required result. Starting atFIG.7A, the procedure700starts and the NAUF305monitors analytics for service experience for at least one DNAI (see block705). The NAUF305determines whether the service experience meets the KPIs (see decision point710). If yes, then the NAUF305continues to monitor analytics for service experience (see block705). Otherwise, if no, then the NAUF305collects a first set of analytics and identifies risk (see block715). The NAUF305determines whether analytics for UPF load show an issue (see decision point720). If yes, then the NAUF305suggests to the SMF an optimal DNAI/UPF for UE(s), taking into account UE mobility analytics (see block725) and monitors the first set analytics (see block740). If no, the NAUF305determines whether there are any misbehaving UEs (see block730). If yes, then the NAUF305suggests Access Management and/or Session Management policies to the PCF (see block735) and monitors the first set analytics (see block740). If no, the NAUF305monitors the first set analytics (see block740). The NAUF305determines whether service experience for a DNAI has improved (see decision point745). If yes, then the NAUF305continues to monitor analytics for service experience (see block705).

Continuing onFIG.4B, if the NAUF305determines that service experience for a DNAI has not improved (from decision point745), then the NAUF305collects a second set of analytics and identifies risk (see block750). The NAUF305determines whether EAS server performance is low (see block755). If yes, then the NAUF305suggests relocation to an optimal EAS server to an AF (see block460) and monitors analytics for the service experience for a DNAI (see block765). If no, the NAUF305monitors analytics for the service experience for a DNAI (see block765). The NAUF305determines whether service experience for a DNAI has improved (see decision point770). If yes, then the NAUF305continues to monitor analytics for service experience (see block705). If no, the NAUF305continues to collect a second set of analytics and identifies risk (see block750).

The actions by the NAUF may be provided to each NF via a new service-based API. The service-based API can include information about the Risk by including a Risk Type attribute and the recommended action in the Action attribute.

The Risk Type attribute may include the following:Slice Congestion indicationNF congestion indicationMisbehaving UEsUEs experiencing congestionUser Plane Congestion

The Action Attribute may include the following actions:Stop accepting new UEs in a sliceSuggested backoff time for an AMFSuggest new AM policies for misbehaving UE(s)Suggest new RFSP policies for UE(s) experiencing congestionSuggest new DNAI/UPF for a user plane connectionSuggest an optimal EAS server

An example procedure800for the NAUF305to provide actions to one or more NF(s) to optimize service experience for a user plane connection when the UE is accessing services to an Edge Network is described below:

At Step 0, the NAUF305is configured to identify risks and resolve issues for service experience for UEs accessing services to an Edge network (see block805).

At Step 1, determines the Analytics required by one or more NWDAFs205according to the configuration received in step 0 (see block807).

At Step 2, sends a request to one or more NWDAFs205with the analytics required (see messaging809).

At Step 3, the NWDAF(s)205provide the analytics requested (see messaging811).

At Step 4, the NAUF305uses that analytics provided by the NWDAF(s)205as input data to its prescriptive engine and determines risks/issues with the network (see block813).

At Step 5, if the NAUF identifies that a UPF that provides connectivity to an edge network according to a DNAI is congested the NAUF determines the optimal UPF/DNAI path as described above (see block815).

Note that either steps 6a and 7a or steps 6b and 7b are carried out:

At Step 6a, the NAUF305sends a recommended action to the SMF801for optimal UPF/DNAI path (see messaging817). Optionally a list of UEs is included if the NAUF305considers UE mobility analytics within the DNAI area.

Alternatively, at Step 6b, the NAUF305sends a recommended action to the AF803for optimal DNAI selection (see messaging819). Optionally a list of UEs is included if the NAUF305considers UE mobility analytics within the DNAI area.

At Step 7a, the SMF801performs the recommended action (see block821).

Alternatively, at Step 7b, the AF803performs the recommended action (see block823)

At Step 8, if the NAUF305identifies misbehaving UEs, as described above, then the NAUF305determines the PCF505that serves these UEs (see block825).

At Step 9, the NAUF305sends a recommended action to the PCF505including a list of UE(s) that are misbehaving (see messaging827). The NAUF305may recommend new AM/SM and/or RFSP policies.

At Step 10, the PCF505sends updated policies to the misbehaving UEs (see block829).

At Step 11, if the NAUF305identifies that the EAS server performance is low, then the NAUF305determines an optimal EAS server based on analytics received in step 1 (see block831).

At Step 12, the NAUF305sends a recommended action to the AF803including an optimal EAS server (see messaging833).

At Step 13, the AF803relocates the EAS server and provides updated traffic influence request to the SMF801(see block835). Note that how the AF803relocates the EAS server not covered in this disclosure.

At Step 14, the NAUF305evaluates if the actions have resolved the issue by analyzing new analytics information from the NWDAFs205(see block837).

FIG.9depicts one embodiment of a network equipment apparatus900that may be used for determining an action for a network function based on network analytics, according to embodiments of the disclosure. In some embodiments, the network equipment apparatus900may be one embodiment of a central data analytics function or a network automation function, such as the NAUF136and/or the NAUF305. Furthermore, network equipment apparatus900may include a processor905, a memory910, an input device915, an output device920, a transceiver925.

In some embodiments, the input device915and the output device920are combined into a single device, such as a touch screen. In certain embodiments, the network equipment apparatus900does not include any input device915and/or output device920. In various embodiments, the network equipment apparatus900may include one or more of: the processor905, the memory910, and the transceiver925, and does not include the input device915and/or the output device920.

As depicted, the transceiver925includes at least one transmitter930and at least one receiver935. Here, the transceiver925communicates with one or more network functions in the mobile communication network (e.g., PLMN). Additionally, the transceiver925may support at least one network interface940and/or application interface945. The application interface(s)945may support one or more APIs. The network interface(s)940may support 3GPP reference points, such as the N23 reference point. Other network interfaces940may be supported, as understood by one of ordinary skill in the art. In some embodiments, the transceiver925supports different interfaces940for communicating with the various network functions in a mobile core network (e.g., a 5GC).

The processor905, in one embodiment, may include any known controller capable of executing computer-readable instructions and/or capable of performing logical operations. For example, the processor905may be a microcontroller, a microprocessor, a central processing unit (“CPU”), a graphics processing unit (“GPU”), an auxiliary processing unit, a field programmable gate array (“FPGA”), or similar programmable controller. In some embodiments, the processor905executes instructions stored in the memory910to perform the methods and routines described herein. The processor905is communicatively coupled to the memory910, the input device915, the output device920, and the transceiver925.

In various embodiments, the processor905controls the network equipment apparatus900to implement the above described NAUF behaviors. For example, via the network interface940, the processor905that receives a first set of analytics from at least one network analytics function in the mobile communication network, the first set of analytics relating to a first KPI. The processor905determines from the first set of analytics whether a first condition is met and determines a first set of actions when the first condition is not met. Via the network interface940, the processor905sends each action in the first set of actions to an associated network function in the mobile communication network.

In some embodiments, the first key performance indicator relates to fulfilling a service experience KPI for a slice or fulfilling a service experience KPI when user plane traffic of applications of one or more UEs is routed to an Edge Application Server in an edge network via the mobile communication network. In some embodiments, the first set of analytics corresponds to one or more of: a service experience of a network slice and a service experience for an application over one or more DNAIs.

In some embodiments, receiving the first set of analytics occurs in response to requesting analytics from the at least one network analytics function, said analytics being requested based on the first KPI. The first set of analytics may be determined by the network function based on the first KPI. In some embodiments, the first condition is met when a value for the first KPI is within a predetermined range, said value determined from the first set of analytics. For example, the condition may be “first KPI<high threshold.” As another example, the condition may be “low threshold<first KPI<high threshold.”

In some embodiments, determining the first set of actions comprises obtaining a second set of analytics based on the first KPI not met and selecting a first set of actions based on the second set of analytics. The second set of analytics may be explanatory analytics, i.e., analytics explaining why the condition is not met. In various embodiments, the first set of actions comprises one or more of: forbidding new UE registrations at a network slice, forbidding selection of an overloaded AMF, configuring a backoff timer for the overloaded AMF, a policy recommendation, and a data path recommendation.

In certain embodiments, a policy recommendation may include sending new RFSP rules for one or more UE based on identifying UEs experiencing congestion or based on identifying UEs contributing to an increased load in the network identified via the second set of analytics. In certain embodiments, a policy recommendation may include sending new Access and Mobility policies for one or more UEs based on identifying misbehaving UEs or based on identifying UEs contributing to an increased load in a network identified via the second set of analytics. In certain embodiments, a data path recommendation may include suggesting new DNAI/UPF for a user plane connection of one or more UEs to an SMF/AF or suggesting a new optimal application server in an edge network to AMF based on the second set of analytics.

In some embodiments, the at least one network function comprises one or more of: an access management function (e.g., AMF), a mobility management function (e.g., AMF), a SMF, a PCF, a NSSF, and an AF. In some embodiments, the processor905continues to monitor whether the first condition is met according to the first KPI by monitoring the first set of analytics corresponding to the first KPI after sending the first set of actions. In such embodiments, the processor905may also create a second set of actions in response to the first condition not being met after a first time period from the sending of the first set of actions and sending each action in the second set of actions to an associated network function in the mobile communication network.

In some embodiments, creating the second set of actions comprises requesting a third set of analytics based on the first KPI and selecting the second set of actions using the third set of analytics. In such embodiments, the second set of actions comprises one or more of: forbidding new UE registrations at a network slice, forbidding selection of an overloaded AMF, configuring a backoff timer for the overloaded AMF, a policy recommendation, and a data path recommendation.

In certain embodiments, a policy recommendation may include sending new RFSP rules for one or more UE based on identifying UEs experiencing congestion or based on identifying UEs contributing to an increased load in the network identified via the third set of analytics. In certain embodiments, a policy recommendation may include sending new Access and Mobility policies for one or more UEs based on identifying misbehaving UEs or based on identifying UEs contributing to an increased load in a network identified via the third set of analytics. In certain embodiments, a data path recommendation may include suggesting new DNAI and/or UPF for a user plane connection of one or more UEs to an SMF and/or AF or suggesting a new optimal application server in an edge network to AMF based on the third set of analytics.

The memory910, in one embodiment, is a computer readable storage medium. In some embodiments, the memory910includes volatile computer storage media. For example, the memory910may include a RAM, including dynamic RAM (“DRAM”), synchronous dynamic RAM (“SDRAM”), and/or static RAM (“SRAM”). In some embodiments, the memory910includes non-volatile computer storage media. For example, the memory910may include a hard disk drive, a flash memory, or any other suitable non-volatile computer storage device. In some embodiments, the memory910includes both volatile and non-volatile computer storage media. In some embodiments, the memory910stores data relating to determining an action for a network function based on network analytics, for example storing ML models, training data, Analytics IDs, lists of Events to monitor, occurrence of Events, Event filters, monitoring/reporting subscriptions, subscription targets, subscription IDs, NF profiles, and the like. In certain embodiments, the memory910also stores program code and related data, such as an operating system (“OS”) or other controller algorithms operating on the network equipment apparatus900and one or more software applications.

The input device915, in one embodiment, may include any known computer input device including a touch panel, a button, a keyboard, a stylus, a microphone, or the like. In some embodiments, the input device915may be integrated with the output device920, for example, as a touchscreen or similar touch-sensitive display. In some embodiments, the input device915includes a touchscreen such that text may be input using a virtual keyboard displayed on the touchscreen and/or by handwriting on the touchscreen. In some embodiments, the input device915includes two or more different devices, such as a keyboard and a touch panel.

The output device920, in one embodiment, may include any known electronically controllable display or display device. The output device920may be designed to output visual, audible, and/or haptic signals. In some embodiments, the output device920includes an electronic display capable of outputting visual data to a user. For example, the output device920may include, but is not limited to, an LCD display, an LED display, an OLED display, a projector, or similar display device capable of outputting images, text, or the like to a user. As another, non-limiting, example, the output device920may include a wearable display such as a smart watch, smart glasses, a heads-up display, or the like. Further, the output device920may be a component of a smart phone, a personal digital assistant, a television, a table computer, a notebook (laptop) computer, a personal computer, a vehicle dashboard, or the like.

In certain embodiments, the output device920includes one or more speakers for producing sound. For example, the output device920may produce an audible alert or notification (e.g., a beep or chime). In some embodiments, the output device920includes one or more haptic devices for producing vibrations, motion, or other haptic feedback. In some embodiments, all or portions of the output device920may be integrated with the input device915. For example, the input device915and output device920may form a touchscreen or similar touch-sensitive display. In other embodiments, all or portions of the output device920may be located near the input device915.

As discussed above, the transceiver925may communicate with one or more remote units and/or with one or more interworking functions that provide access to one or more PLMNs. The transceiver925may also communicate with one or more network functions (e.g., in the mobile core network130). The transceiver925operates under the control of the processor905to transmit messages, data, and other signals and also to receive messages, data, and other signals. For example, the processor905may selectively activate the transceiver (or portions thereof) at particular times in order to send and receive messages.

The transceiver925may include one or more transmitters930and one or more receivers935. In certain embodiments, the one or more transmitters930and/or the one or more receivers935may share transceiver hardware and/or circuitry. For example, the one or more transmitters930and/or the one or more receivers935may share antenna(s), antenna tuner(s), amplifier(s), filter(s), oscillator(s), mixer(s), modulator/demodulator(s), power supply, and the like. In one embodiment, the transceiver925implements multiple logical transceivers using different communication protocols or protocol stacks, while using common physical hardware.

FIG.10depicts one embodiment of a method1000for determining an action for a network function based on network analytics, according to embodiments of the disclosure. In various embodiments, the method1000is performed by a network automation function, such as the NAUF136, the NAUF305, and/or the network equipment apparatus900, described above. In some embodiments, the method1000is performed by a processor, such as a microcontroller, a microprocessor, a CPU, a GPU, an auxiliary processing unit, a FPGA, or the like.

The method1000begins and receives1005a first set of analytics from at least one network analytics function in the mobile communication network, the first set of analytics relating to a first KPI. The method1000includes determining1010from the first set of analytics whether a first condition is met. The method1000includes determining1015a first set of actions when the first condition is not met. The method1000includes sending1020the first set of actions to at least one network function in the mobile communication network, each action in the first set of actions being sent to an associated network function. The method1000ends.

Disclosed herein is a first apparatus for determining an action for a network function based on network analytics, according to embodiments of the disclosure. The first apparatus may be implemented by a network automation function, such as the NAUF136, the NAUF305, and/or the network apparatus600. The first apparatus includes a network interface that communicates with a mobile communication network and a processor that receives a first set of analytics from at least one network analytics function in the mobile communication network, the first set of analytics relating to a first KPI. The processor determines from the first set of analytics whether a first condition is met and determines a first set of actions when the first condition is not met. Via the network interface, the processor sends each action in the first set of actions to an associated network function in the mobile communication network.

In some embodiments, the first key performance indicator relates to fulfilling a service experience KPI for a slice or fulfilling a service experience KPI when user plane traffic of applications of one or more UEs is routed to an Edge Application Server in an edge network via the mobile communication network. In some embodiments, the first set of analytics corresponds to one or more of: a service experience of a network slice and a service experience for an application over one or more DNAIs.

In some embodiments, receiving the first set of analytics occurs in response to requesting analytics from the at least one network analytics function, said analytics being requested based on the first KPI. The first set of analytics may be determined by the network function based on the first KPI. In some embodiments, the first condition is met when a value for the first KPI is within a predetermined range, said value determined from the first set of analytics. For example, the condition may be “first KPI<high threshold.” As another example, the condition may be “low threshold<first KPI<high threshold.”

In some embodiments, determining the first set of actions comprises obtaining a second set of analytics based on the first KPI not met and selecting a first set of actions based on the second set of analytics. The second set of analytics may be explanatory analytics, i.e., analytics explaining why the condition is not met. In various embodiments, the first set of actions comprises one or more of: forbidding new UE registrations at a network slice, forbidding selection of an overloaded AMF, configuring a backoff timer for the overloaded AMF, a policy recommendation, and a data path recommendation.

In certain embodiments, a policy recommendation may include sending new RFSP rules for one or more UE based on identifying UEs experiencing congestion or based on identifying UEs contributing to an increased load in the network identified via the second set of analytics. In certain embodiments, a policy recommendation may include sending new Access and Mobility policies for one or more UEs based on identifying misbehaving UEs or based on identifying UEs contributing to an increased load in a network identified via the second set of analytics. In certain embodiments, a data path recommendation may include suggesting new DNAI/UPF for a user plane connection of one or more UEs to an SMF/AF or suggesting a new optimal application server in an edge network to AMF based on the second set of analytics.

In some embodiments, the at least one network function comprises one or more of: an access management function (e.g., AMF), a mobility management function (e.g., AMF), a SMF, a PCF, a NSSF, and an AF. In some embodiments, the processor continues to monitor whether the first condition is met according to the first KPI by monitoring the first set of analytics corresponding to the first KPI after sending the first set of actions. In such embodiments, the processor may also create a second set of actions in response to the first condition not being met after a first time period from the sending of the first set of actions and sending each action in the second set of actions to an associated network function in the mobile communication network.

In some embodiments, creating the second set of actions comprises requesting a third set of analytics based on the first KPI and selecting the second set of actions using the third set of analytics. In such embodiments, the second set of actions comprises one or more of: forbidding new UE registrations at a network slice, forbidding selection of an overloaded AMF, configuring a backoff timer for the overloaded AMF, a policy recommendation, and a data path recommendation.

In certain embodiments, a policy recommendation may include sending new RFSP rules for one or more UE based on identifying UEs experiencing congestion or based on identifying UEs contributing to an increased load in the network identified via the third set of analytics. In certain embodiments, a policy recommendation may include sending new Access and Mobility policies for one or more UEs based on identifying misbehaving UEs or based on identifying UEs contributing to an increased load in a network identified via the third set of analytics. In certain embodiments, a data path recommendation may include suggesting new DNAI/UPF for a user plane connection of one or more UEs to an SMF/AF or suggesting a new optimal application server in an edge network to AMF based on the third set of analytics.

Disclosed herein is a first method for determining an action for a network function based on network analytics, according to embodiments of the disclosure. The first method may be performed by a network automation function in a mobile communication network, such as the NAUF136, the NAUF305, and/or the network apparatus600. The first method includes receiving a first set of analytics from at least one network analytics function in the mobile communication network, the first set of analytics relating to a first KPI. The first method includes determining from the first set of analytics whether a first condition is met and determining a first set of actions when the first condition is not met. The first method includes sending the first set of actions to at least one network function in the mobile communication network, each action in the first set of actions being sent to an associated network function.

In some embodiments, the first key performance indicator relates to fulfilling a service experience KPI for a slice or fulfilling a service experience KPI when user plane traffic of applications of one or more UEs is routed to an Edge Application Server in an edge network via the mobile communication network. In some embodiments, the first set of analytics corresponds to one or more of: a service experience of a network slice and a service experience for an application over one or more DNAIs.

In some embodiments, receiving the first set of analytics occurs in response to requesting analytics from the at least one network analytics function, said analytics being requested based on the first KPI. The first set of analytics may be determined by the network function based on the first KPI. In some embodiments, the first condition is met when a value for the first KPI is within a predetermined range, said value determined from the first set of analytics. For example, the condition may be “first KPI<high threshold.” As another example, the condition may be “low threshold<first KPI<high threshold.”

In some embodiments, determining the first set of actions comprises obtaining a second set of analytics based on the first KPI not met and selecting a first set of actions based on the second set of analytics. The second set of analytics may be explanatory analytics, i.e., analytics explaining why the condition is not met. In various embodiments, the first set of actions comprises one or more of: forbidding new UE registrations at a network slice, forbidding selection of an overloaded AMF, configuring a backoff timer for the overloaded AMF, a policy recommendation, and a data path recommendation.

In certain embodiments, a policy recommendation may include sending new RFSP rules for one or more UE based on identifying UEs experiencing congestion or based on identifying UEs contributing to an increased load in the network identified via the second set of analytics. In certain embodiments, a policy recommendation may include sending new Access and Mobility policies for one or more UEs based on identifying misbehaving UEs or based on identifying UEs contributing to an increased load in a network identified via the second set of analytics. In certain embodiments, a data path recommendation may include suggesting new DNAI/UPF for a user plane connection of one or more UEs to an SMF/AF or suggesting a new optimal application server in an edge network to AMF based on the second set of analytics.

In some embodiments, the at least one network function comprises one or more of: an access management function (e.g., AMF), a mobility management function (e.g., AMF), a SMF, a PCF, a NSSF, and an AF. In some embodiments, the first method includes continuing to monitor whether the first condition is met according to the first KPI by monitoring the first set of analytics corresponding to the first KPI after sending the first set of actions. In such embodiments, the first method may also include creating a second set of actions in response to the first condition not being met after a first time period from the sending of the first set of actions and sending each action in the second set of actions to an associated network function in the mobile communication network.

In some embodiments, creating the second set of actions comprises requesting a third set of analytics based on the first KPI and selecting the second set of actions using the third set of analytics. In such embodiments, the second set of actions comprises one or more of: forbidding new UE registrations at a network slice, forbidding selection of an overloaded AMF, configuring a backoff timer for the overloaded AMF, a policy recommendation, and a data path recommendation.

In certain embodiments, a policy recommendation may include sending new RFSP rules for one or more UE based on identifying UEs experiencing congestion or based on identifying UEs contributing to an increased load in the network identified via the third set of analytics. In certain embodiments, a policy recommendation may include sending new Access and Mobility policies for one or more UEs based on identifying misbehaving UEs or based on identifying UEs contributing to an increased load in a network identified via the third set of analytics. In certain embodiments, a data path recommendation may include suggesting new DNAI/UPF for a user plane connection of one or more UEs to an SMF/AF or suggesting a new optimal application server in an edge network to AMF based on the third set of analytics.