Method and apparatus for data analysis in a telecommunications network

Abstract

The present disclosure relates to a communication method and system for integrating a fifth-generation (5G) communication system that supports higher data rates than fourth-generation (4G) systems with Internet of Things (IoT) technology. The present disclosure can be applied to intelligent services based on 5G communication technology and IoT-related technologies, such as smart home, smart building, smart city, smart car, connected car, healthcare, digital education, smart retail, security, and safety services. A method for managing analytical data in a telecommunications network is provided. The method includes a consumer network function (NF) determining how to collect and analyze analytical data from multiple individual sources.

Description

Technical Field

[0001] This disclosure relates to the acquisition, processing, and use of data analytics in a telecommunications network. More specifically, this disclosure relates to fifth generation networks, although this is exemplary and other networks may similarly benefit. Background Art

[0002] To meet the increasing demand for wireless data traffic since the deployment of 4G communication systems, efforts have been made to develop improved 5G or pre-5G communication systems. Therefore, 5G or pre-5G communication systems are also referred to as "ultra 4G networks" or "post-LTE systems". The 5G communication system is considered to be implemented in a higher frequency (millimeter wave) band (e.g., 60 GHz band) in order to achieve higher data rates. To reduce the propagation loss of radio waves and increase the transmission distance, beamforming, massive multiple-input multiple-output (MIMO), full-dimensional MIMO (FD-MIMO), array antennas, analog beamforming, and massive antenna technologies are discussed in the 5G communication system. In addition, in the 5G communication system, system network improvements are being developed based on advanced small cells, cloud radio access network (RAN), ultra-dense networks, device-to-device (D2D) communication, wireless backhaul, mobile networks, cooperative communication, coordinated multi-point (CoMP), receiver interference cancellation, etc. In the 5G system, hybrid FSK and QAM modulation (FQAM) and sliding window superimposed coding (SWSC) as advanced coding modulation (ACM), and filter bank multi-carrier (FBMC), non-orthogonal multiple access (NOMA), and sparse code multiple access (SCMA) as advanced access technologies have been developed.

[0003] The Internet is a human-centered connected network in which humans generate and consume information, and is now evolving towards the Internet of Things (IoT), in which distributed entities (such as things) exchange and process information without human intervention. Through connection with cloud servers, the Internet of Everything (IoE) that combines IoT technology and big data processing technology has emerged. Since technical elements such as "sensing technology", "wired / wireless communication and network infrastructure", "service interface technology", and "security technology" are required for IoT implementation, recently, sensor networks, machine-to-machine (M2M) communication, machine type communication (MTC), etc. have been studied. Such an IoT environment can provide intelligent Internet technology services, which create new value for human life by collecting and analyzing data generated between interconnected things. Through the integration and combination of existing information technology (IT) and various industrial applications, IoT can be applied to various fields, including smart homes, smart buildings, smart cities, smart cars or connected cars, smart grids, healthcare, smart home appliances, and advanced medical services.

[0004] Accordingly, various attempts have been made to apply 5G communication systems to IoT networks. For example, technologies such as sensor networks, machine type communication (MTC), and machine-to-machine (M2M) communication can be implemented through beamforming, MIMO, and array antennas. Cloud radio access network (RAN), as an application of the above big data processing technology, can also be considered an example of the integration between 5G technology and IoT technology.

[0005] There is an increasing desire to improve the network automation of 5G telecommunication networks, namely enabling network automation (eNA). As part of this, the network data analytics function (NWDAF) is defined as part of the service-based architecture (SBA), which uses mechanisms and interfaces specified for 5G core and operation, administration, and maintenance (OAM).

[0006] In the service-based architecture, each network function (NF) includes a set of services that connect it (as the producer of these services) to other NFs (as the consumers of these services) through a common bus called the service-based interface (SBI).

[0007] Figure 1 A general schematic diagram showing various elements in a 5G network automation solution according to the related art is shown. For clarity, only those parts related to automation are shown. This shows the provision of active data and analysis from a first set of NFs 50 or application function (AF) 10 to the NWDAF 40. The NWDAF 40 also interfaces with the OAM 30 and the data repository 20. The NWDAF 40 analyzes the data from these sources and passes the analyzed data to a second set of NFs 50 or AF 10. The second set of NFs 50 may include some or all of the first set of NFs 50 or AF 10.

[0008] The above information is presented only as background information to aid in understanding the present disclosure. No determination has been made, and no assertion is made, as to whether any of the above can be applied as prior art to the present disclosure. Summary of the Invention

[0009] Technical Problem

[0010] There is an increasing desire to improve the network automation of 5G telecommunication networks, namely enabling network automation (eNA).

[0011] Solution

[0012] Aspects of the present disclosure at least address the above problems and / or disadvantages and at least provide the following advantages. Accordingly, one aspect of the present disclosure is to provide a method for managing analytical data in a telecommunication network, wherein a consumer network function (NF) determines how to collect analytical data from multiple individual sources and analyzes the analytical data in one of the following ways: a distributed manner from multiple network data analytics functions (NWDAFs), a centralized manner by aggregating analytical data from multiple NWDAFs before analyzing the analytical data at an aggregator NWDAF, or at least one of each of a distributed manner from multiple NWDAFs and a centralized manner by aggregating analytical data from multiple NWDAFs before analyzing the analytical data at an aggregator NWDAF.

[0013] Additional aspects will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the presented embodiments.

[0014] In one embodiment, in a case where multiple NWDAFs are provided in a telecommunication network, at least one has a dedicated function and at least one has a general function.

[0015] In one embodiment, the dedicated function is to aggregate analyses from multiple regions of interest or multiple target users, while the general function is to notify analyses for each region of interest or each group of target users.

[0016] In one embodiment, the capability information of any particular one of the multiple NWDAFs having a dedicated function is stored in a network repository function (NRF).

[0017] In one embodiment, the capability information relates to analytical aggregation capabilities.

[0018] In one embodiment, the consumer network function NF determines one or more NWDAFs from which to collect data based on NWDAF capability information that is part of its implemented internal selection criteria.

[0019] In one embodiment, the implemented selection criteria include one or more of capabilities newly registered in the NRF determined based on the load level of each NWDAF, the number of analytical identifiers (IDs) directly supported by each NWDAF, and other key performance indicators (KPIs) preconfigured by a network operator.

[0020] In one embodiment, an identifier such as an aggregation point identifier AP ID is defined as auxiliary information registered in the NRF for each aggregator NWDAF, where the identifier indicates which NWDAFs among the NWDAFs are capable of acting as an aggregation point.

[0021] In one embodiment, the consumer NF autonomously determines how multiple NWDAFs operate together.

[0022] In one embodiment, the determination is based on selection criteria, whereby the consumer NF considers all NWDAFs identified by the NRF and decides how to collect data from their combination based on the implemented selection criteria.

[0023] In one embodiment, each of the multiple NWDAFs pre - negotiates with one or more other aggregator NWDAFs how many analytics IDs it supports, and the aggregator NWDAFs announce an extended set of such supported analytics IDs within the NRF.

[0024] In one embodiment, further operations are provided where the network function service consumer sends a discovery request to the network repository function NRF, including all required (multiple) analytics IDs and area of interest, the NRF responds with one or more distributed NWDAF instance IDs, each instance ID covering a set of (multiple) analytics IDs and at least part of the supported area of interest, the network function service consumer sends a subscription request to each distributed NWDAF, each distributed NWDAF responds with analytics - specific parameters for each analytics ID, and for each analytics ID, the network function service consumer itself aggregates the targets of the analytics reports across the distributed NWDAFs corresponding to the area of interest.

[0025] In one embodiment, further operations are provided. The network function service consumer sends a discovery request to the Network Repository Function (NRF), including all required (multiple) analytics IDs and regions of interest. The NRF responds with one or more of the NWDAF instance IDs in the set of NWDAF instance IDs, where each NWDAF instance ID covers a set of (multiple) analytics IDs, at least a portion of the supported regions of interest, and an AP ID or other identifier indicating possible (multiple) aggregation points for each (multiple) aggregator NWDAF instance. The network function service consumer selects at least one NWDAF as the aggregator NWDAF based on its internal selection criteria, considering the registered NWDAF capabilities and the information from the NRF. The network function service consumer sends a subscription request to the aggregator NWDAF to specify it as an aggregation point. The subscription request includes the analytics IDs and regions of interest to be aggregated by each NWDAF. And either a) the aggregator NWDAF designates its specified identity as the aggregation point, or b) the aggregator NWDAF decides to map to a specific NWDAF for aggregating analytics based on configuration, implementation, or a query to the NRF. The aggregator NWDAF subscribes to all NWDAFs. The NWDAFs notify with analytics-specific parameters for each analytics ID in the set of analytics IDs. For each analytics ID, the aggregator NWDAF aggregates the targets of the analytics reports across different NWDAFs corresponding to the region of interest, and the aggregator NWDAF notifies the network function service consumer of the analytics-specific parameters for each analytics ID for all aggregated analytics IDs of each NWDAF.

[0026] In one embodiment, further operations are provided. The network function service consumer sends a discovery request to the Network Repository Function (NRF). The discovery request includes all required (multiple) analytics IDs and regions of interest. The NRF responds with one or more of the following: at least one NWDAF instance ID, at least one NWDAF instance ID to be aggregated to at least one aggregator NWDAF instance ID registered in the NRF. The network function service consumer subscribes to all NWDAFs, including the aggregation point that acts as the central NWDAF and receives individual notifications. And for each analytics ID, the network function service consumer aggregates the analytics data from both distributed and (semi)-centralized NWDAF instances for the corresponding point of interest.

[0027] According to one aspect of the present disclosure, a telecommunication network operable to perform the method of the first aspect is provided.

[0028] A single instance or multiple instances of the NWDAF 40 can be deployed in a Public Land Mobile Network (PLMN). In the case of deploying multiple NWDAF 40 instances, embodiments of the present disclosure support deploying the NWDAF 40 as a central NF, a set of distributed NFs, or a combination of both (i.e., some centralized and some distributed).

[0029] When there are multiple NWDAFs, not all NWDAFs need to be able to provide the same type of analysis results. In other words, some of them can be dedicated to provide only certain types of analysis, while others can be more general in nature. Embodiments of the present disclosure define an Analysis ID information element, which is used to identify the types of supported analysis that a specific NWDAF can generate.

[0030] On the other hand, some of the NWDAFs in a network can provide the same type of analysis, so they can help each other, for example, for specific analysis of a specific target User Equipment (UE) or for specific analysis of a specific area of interest.

[0031] The capabilities of a specific NWDAF instance are described in the NWDAF profile stored in the Network Repository Function (NRF).

[0032] In the case of deploying multiple NWDAF instances, some instances are dedicated to providing a certain type of analysis, or in order to enable multiple NWDAFs to help each other to provide the same type of analysis, a coordination mechanism is defined across instances. More importantly, once a consumer network function (NF) discovers the corresponding NWDAF instance, it may require a flexible data collection mechanism and associated services to implement different deployment options (whether distributed, centralized, or a hybrid of both).

[0033] Embodiments of the present disclosure provide a data collection mechanism in an environment including multiple NWDAFs, thus flexibly supporting different deployment options.

[0034] According to another aspect of the present disclosure, there are provided an apparatus and a method as set forth in the appended claims. Other features of the present disclosure will become apparent from the dependent claims and the subsequent description.

[0035] Although several preferred embodiments of the present disclosure have been shown and described, those skilled in the art will understand that various changes and modifications can be made without departing from the scope of the present disclosure as defined by the appended claims.

[0036] Throughout the embodiments of the present disclosure, the terms "NWDAF" and "NWDAF instance" are used interchangeably.

[0037] In an embodiment of distributed data collection of the present disclosure, the consumer NF determines the set of NWDAFs (or NWDAF instances) from which to collect data based on its implemented selection criteria (e.g., the load level of each NWDAF, the number of analytics IDs directly supported by each NWDAF, or other key performance indicators (KPIs) pre-configured by the network operator).

[0038] In an embodiment of (semi-)centralized data collection of the present disclosure, a new aggregation point identifier (AP ID) is defined for each NWDAF, which indicates which other NWDAFs can be its potential aggregation points. Multiple factors can be considered to set the identifier, including the load level of each NWDAF (or NWDAF instance), the number of analytics IDs directly supported by each NWDAF, or other KPIs set by the network operator. The NWDAF information maintained in the NRF or any other specified data repository structure can save this identifier for each NWDAF. The consumer NF uses the AP ID as auxiliary information along with its other implemented selection criteria to determine how multiple NWDAF instances cooperate.

[0039] In an alternative embodiment of (semi-)centralized data collection of the present disclosure, the consumer NF intelligently determines how multiple NWDAF instances cooperate without the intervention of any other entity. The consumer NF considers all NWDAF instances (e.g., discovered via the NRF) and based on the implemented selection criteria, similar to distributed data collection, determines how to collect data from their combination. The NWDAF information maintained in the NRF or any other specified data repository structure can be agnostic to the aggregation information.

[0040] In an alternative embodiment of (semi-)centralized data collection of the present disclosure, each NWDAF instance pre-negotiates with other NWDAF instances (e.g., discovered via the NRF) the number of analytics IDs it can support (directly or indirectly), and it advertises within the NRF this extended set of supported analytics IDs (directly supported IDs plus indirectly supported IDs). The NWDAF information maintained in the NRF or any other specified data repository structure can clearly distinguish between directly supported analytics IDs and indirectly supported analytics IDs. In addition to its other implemented selection criteria, the consumer NF uses the directly and non-directly supported analytics IDs as auxiliary information to determine how multiple NWDAF instances cooperate.

[0041] In an embodiment of hybrid mode data collection of the present disclosure, the consumer NF adopts a combination of the embodiments of distributed and (semi-)centralized data collection.

[0042] From the following detailed description of various embodiments of the present disclosure in conjunction with the accompanying drawings, other aspects, advantages, and significant features of the present disclosure will become apparent to those skilled in the art.

[0043] Beneficial effects

[0044] The present disclosure will provide a method for effectively managing analysis data in a telecommunications network. Description of the drawings

[0045] From the following description in conjunction with the accompanying drawings, the above and other aspects, features, and advantages of certain embodiments of the present disclosure will become more apparent, where:

[0046] Figure 1 Shows a general representation of a 5G network automation framework according to the related art;

[0047] Figure 2 Shows a message exchange and method according to an embodiment of the present disclosure;

[0048] Figure 3 Shows a message exchange and method according to an embodiment of the present disclosure; and

[0049] Figure 4 Shows a message exchange and method according to an embodiment of the present disclosure.

[0050] Figure 5 Is a block diagram of a network entity according to an embodiment of the present disclosure.

[0051] In all the drawings, like reference numerals will be understood to refer to like components, elements, and structures. Detailed implementation manners

[0052] The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of various embodiments of the present disclosure defined by the claims and their equivalents. It includes various specific details to assist in the understanding, but these are only considered exemplary. Thus, those of ordinary skill in the art will recognize that various changes and modifications can be made to the various embodiments described herein without departing from the scope and spirit of the present disclosure. In addition, descriptions of well-known functions and structures may be omitted for clarity and conciseness.

[0053] The terms and words used in the following description and claims are not limited to the literal meanings, but are used only by the inventors to enable a clear and consistent understanding of the present disclosure. Thus, it will be apparent to those skilled in the art that the following description of the various embodiments of the present disclosure is provided for illustrative purposes only and is not intended to limit the present disclosure defined by the appended claims and their equivalents.

[0054] It should be understood that the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a component surface" includes reference to one or more such surfaces.

[0055] Figure 2 A scenario is shown where the consumer NF, NRF, NWDAF(k) form at least part of a system according to an embodiment of the present disclosure. This also shows the various processes and messaging between the corresponding elements.

[0056] Reference Figure 2 , it is assumed that the index (k) shows the NWDAF instance ID in a multi-instance deployment. Each instance NWDAF(k) 120 is dedicated to a set of data analysis types, identified by AnalyticsIDs(k); some overlap across different instances, and some are mutually exclusive. Instances with overlapping analysis IDs can assist each other, for example in order to cover different sets of UEs targeted by an analysis report, or in order to cover different sets of tracking areas within the area of interest. The tracking area indicator - TAI(k) refers to the area of interest that NWDAF(k) can cover.

[0057] Scenario A: Distributed data collection model

[0058] In a first embodiment of the present disclosure, the consumer NF 100 may decide to consume the services of different NWDAFs in a distributed manner based on selection criteria implemented by it, such as network configuration or the preferences of a pre-configured network operator.

[0059] Figure 2 Details of each of the operations shown are as follows:

[0060] 1. The NWDAF service consumer 100 sends an NF discovery request (1a) to the NRF 110, including all required (multiple) analysis IDs and the area of interest (e.g., in the form of a TAI). The request may also include additional information, such as network slice selection assistance information (i.e., single NSSAI or S-NSSAI). The NRF 110 response (1b) may include multiple NWDAF instance IDs, NWDAF(k), each covering a set of (multiple) analysis IDs, AnalyticsIDs(k), and the (part of the) area of interest supported by the instance (k), identified as TAI(k).

[0061] 2. The NWDAF service consumer 100 sends a subscription request (2a) to each NWDAF(k) 120, including AnalyticsIDs(k) and TAI(k) (e.g., as an analytics filter). The request can be a set of tuples of (AnalyticsIDs(k), Analytics Filter = TAI(k)) as shown in operation 2a to distinguish the area of interest for each analytics ID. As shown in operation 2b, the NWDAF(k) 120 notifies with analytics-specific parameters for each analytics ID.

[0062] 3. The service consumer NF 100 can aggregate the targets of the analytics reports across the NWDAF(k) of the AnalyticsIDs(k) corresponding to the area of interest TAI(k).

[0063] Scenario B: (Semi-)centralized data collection model with an AP ID

[0064] Figure 3 A scenario is shown where the consumer NF, NRF, NWDAF(j), and NWDAF(i) form at least part of a system according to an embodiment of the present disclosure.

[0065] Reference Figure 3 , in addition to the selection criteria for its implementation, the consumer NF 200 decides to consume the services of different NWDAFs in a (semi)-centralized manner based on the AP ID as auxiliary information, designating one (set of) NWDAF(s) as the (set of) aggregation point(s).

[0066] In this case, when registering an aggregation point similar to NWDAF(j) 220 in the NRF 210, in addition to the set of analytics IDs supported by the NWDAF(j) 220 and the area of interest covered by the NWDAF(j) 220, the AP ID is also configured to be equivalent to the NWDAF(j) 220 ID. This also identifies the NWDAF(j) 220 as the aggregation point.

[0067] When registering a distributed NWDAF similar to NWDAF(i) 230 within the NRF 210, in addition to the set of analytics IDs supported by the NWDAF(i) 230 and the area of interest covered by the NWDAF(i) 230, the AP ID is also configured to be equivalent to one of the NWDAF(j) 220s that have been registered as aggregation points.

[0068] The mapping between NWDAF(j)220 and NWDAF(i)230 in the AP ID can consider multiple factors, including the load level of each NWDAF, the analytics IDs supported by each NWDAF, the regions of interest supported by each NWDAF, any predefined mapping hierarchy, or other KPIs set by the network operator. The NWDAF information maintained in the NRF 210 or any other specified data repository structure can save such a mapping between NWDAFs based on the AP ID. In case B, both the NRF 210 and the NWDAF service consumer 200 know the mapping between the central and distributed NWDAFs based on the AP ID.

[0069] Figure 3 A scenario is shown in which the consumer NF, the NRF, NWDAF(j), and NWDAF(i) form at least part of a system according to an embodiment of the present disclosure. Figure 3 Details of each of the operations shown are as follows:

[0070] 1. Similar to Figure 2 Operation 1 (distributed deployment) of case A shown in, except that the NRF 210 response can include both the distributed NWDAF(i) and the central NWDAF(j). The NRF 210 response also includes the AP ID of each NWDAF(i)230 instance, which indicates the possible (multiple) aggregation point NWDAF(j)220 with different (j) values, that is, the mapping between the central and distributed NWDAFs. The NWDAF service consumer 200 determines the central aggregation point as the AP ID based on the mapping received from the NRF 210.

[0071] 2. The NWDAF service consumer 200 sends a subscription request to NWDAF(j)220 (designated as the aggregation point), and the subscription request includes AnalyticsIDs(i) for NWDAF(i)230 and TAI(i) (as an analysis filter). NWDAF(j)220 designates its identity as the aggregation point that is the recipient of the service consumer's request. Alternatively, another explicit flag or parameter can be set by the NWDAF service consumer 200 as an input parameter to explicitly specify the aggregation point NWDAF(j)220.

[0072] 3. NWDAF(j)220 subscribes to all NWDAF(i)230 in a process similar to case A (single-instance subscription process). All NWDAF(i) are notified with the analysis-specific parameters for each analysis ID in the set of AnalyticsIDs(i).

[0073] 4. The NWDAF(j) 220 can aggregate the targets of the analysis reports across different NWDAF(i) 230 of the AnalyticsIDs(i) corresponding to the area of interest TAI(i).

[0074] 5. The NWDAF(j) 220 notifies all the aggregated analysis IDs for each NWDAF(i) 230 with the analysis-specific parameters of each analysis ID.

[0075] Scenario C: (Semi-)centralized data collection model without an AP ID

[0076] In an alternative embodiment of the present disclosure, similar to Case B, Operation 1 is exactly similar to Case A (distributed deployment), where the data stored in the NRF 210 or any other data repository structure remains agnostic to the deployment information (i.e., the aggregation point identifier). As a result, the NWDAF service consumer 200 determines the (multiple) aggregation points without any additional auxiliary information.

[0077] In another case of centralized aggregation (referred to herein as Case C), the NRF 210 does not indicate the mapping between the central and distributed NWDAF. In this case, no AP ID is configured for the NWDAF, and only the aggregation points are distinguished when implicitly (refer to Case D below) or explicitly (e.g., via a configuration identifier) registered in the NRF 210. In Case C, the NRF 210 becomes agnostic to the mapping between the central and distributed NWDAF.

[0078] Details of each operation are as follows:

[0079] 1. Similar to Figure 2 Operation 1 of Case A as shown, except that the NRF 210 response may include both the distributed NWDAF(i) 230 and the NWDAF(j) 220 identified as the aggregation points. The NWDAF service consumer 200 determines the (multiple) central aggregation points based on the selection criteria configured or implemented by it.

[0080] 2 - 5. As described above for Case B

[0081] Scenario D: Pre-negotiated (semi-)centralized data collection model

[0082] In an alternative embodiment of the present disclosure similar to Case B, Operation 1 is similar to Case A (distributed deployment), except that with respect to the data stored within the NRF 210 or any other data repository structure, the AnalyticsIDs(j) advertised by the NWDAF(j) 220 are an extended set of the analytics IDs from different NWDAF(i) 230, which can be pre-negotiated for instance j, e.g., when each NWDAF registers within the NRF 210, based on some configuration or predefined hierarchy. As a result, unlike Case B, no explicit identifier is defined for each NWDAF within the NRF 210, and there is no indication within the NRF 210 of the mapping between the central and distributed NWDAFs.

[0083] The extended set of supported analytics IDs can be distinguished from the analytics IDs directly supported by each NWDAF. Some central NWDAFs may only aggregate analytics, and in such cases, the extended list may not have directly supported analytics. Thus, in addition to the selection criteria of its implementation, the NWDAF service consumer 200 can also utilize this information to decide how multiple NWDAFs cooperate (e.g., it may be preferable to extend the list of (a) NWDAF(s) that directly support more analytics IDs to avoid additional signaling overhead or network latency).

[0084] Details of each operation are as follows:

[0085] 1. Similar to Figure 2 Operation 1 of Case A as shown, except that the NRF 210 response can include both the distributed NWDAF(i) 230 and NWDAF(j) 220 identified as aggregation points. The NWDAF service consumer 200 determines the central aggregation point based on the selection criteria of its implementation.

[0086] 2. The NWDAF service consumer sends a subscription request to NWDAF(j) 220 (designated as the aggregation point), including all the required analytics IDs, TAI, without indicating any mapping of each NWDAF(i) 230.

[0087] 3. The NWDAF(j) 220 decides on the mapping to specific distributed NWDAFs based on the extended set of supported analytics IDs and configuration, implementation, or query to the NRF210 to aggregate analytics from them and subscribe to them.

[0088] 4. The NWDAF(j) 220 aggregates the targets of the analytics reports across different NWDAF(i) 230 for the corresponding analytics ID(i) within the area of interest.

[0089] 5. NWDAF(j)220 notifies for all aggregated analysis IDs with analysis-specific parameters for each analysis ID without indicating any mapping of each NWDAF(i)230.

[0090] Scenario D1: (Semi-)centralized data collection model with central NWDAF mapping

[0091] In another case of centralized aggregation (referred to here as case D1, as a sub-case of case D), similar to case D, there is no indication at the NRF 210 of the mapping between the central and distributed NWDAFs. Also in this option, when registering implicitly (again similar to case D) or explicitly (e.g., via a configuration identifier) in the NRF 210, no AP ID is configured and only the aggregation points are distinguished. Additionally, apart from the NRF 210, the NWDAF service consumer 200 also becomes unaware of the mapping between the central and distributed NWDAFs. Instead, each central NWDAF 220 decides the mapping to a specific distributed NWDAF based on configuration, implementation, or a query to the NRF 210 or a predefined hierarchy (when registering with the NRF).

[0092] Details of each operation are as follows:

[0093] 1. Similar to Figure 2 Operation 1 of case A as shown, except that the NRF 210 response may include both the distributed NWDAF(i)230 and NWDAF(j)220 identified as aggregation points. The NWDAF service consumer 200 selects the central aggregation point.

[0094] 2. The NWDAF service consumer sends a subscription request to NWDAF(j)220 (designated as the aggregation point), which includes all the required analysis IDs, TAI, without indicating any mapping of the analysis ID or TAI of each NWDAF(i)230.

[0095] 3. NWDAF(j)220 decides the mapping to a specific distributed NWDAF230 based on configuration, implementation, or a query to the NRF 210 to aggregate the analysis from them and subscribe to them accordingly.

[0096] 4. NWDAF(j)220 may aggregate the targets of the analysis reports across different NWDAF(i)230 for the analysis ID(i) corresponding to the area of interest.

[0097] 5. NWDAF(j)220 notifies for all aggregated analysis IDs with analysis-specific parameters for each analysis ID without indicating any mapping of the analysis ID or TAI of each NWDAF(i)230.

[0098] Scenario E: Hybrid mode data collection model

[0099] In a third embodiment of the present disclosure, a hybrid of distributed and (semi)-centralized deployment modes can be used.

[0100] Figure 4 A scenario is shown in which a consumer NF, NRF, NWDAF(k), NWDAF(j), and NWDAF(i) form at least part of a system according to an embodiment of the present disclosure.

[0101] Referring Figure 4 , the details of each operation are as follows:

[0102] 1. The NWDAF service consumer 300 sends an NF discovery request (1a) to the NRF 310, which includes all required (multiple) analysis IDs and the area of interest (e.g., in the form of TAI). The request may also include additional information, such as network slice selection assistance information (i.e., single NSSAI or S-NSSAI). The NRF response may include (1b) a set of (multiple) NWDAF instance IDs deployed in a distributed manner, i.e., NWDAF(k) 320. The NRF 310 response may also include (1c) a set of (multiple) NWDAF instance IDs (i.e., NWDAF(i) 340) that will be aggregated in the set of NWDAF instance IDs (i.e., NWDAF(j) 330).

[0103] 2. Similar to the distributed deployment process in case A, the NWDAF service consumer 300 subscribes to all NWDAF(k) 320 and receives individual notifications.

[0104] 3. The NWDAF service consumer 300 also subscribes to NWDAF(j) 330. Similar to the (semi)-centralized deployment process in case B or C or D or D1, NWDAF(j) 330 subscribes to all relevant NWDAF(i) 340 to be aggregated and provides an aggregated notification to the NWDAF service consumer 300.

[0105] 4. The NWDAF service consumer 300 aggregates the analysis data from both the distributed and (semi)-centralized NWDAF instances.

[0106] Figure 5 is a block diagram of a network entity according to an embodiment of the present disclosure. The network entity may correspond to Figures 1 - 4 each of the network entities shown. For example, the network entity may refer to Figures 1 - 4 each of the network functions shown (e.g., NRF, NWDAF).

[0107] Referring Figure 5, the network entity may include a transceiver 510, a controller 520, and a storage device 530. In the present disclosure, the controller 520 may include circuitry, an ASIC, or at least one processor.

[0108] The transceiver 510 may send signals to and receive signals from a terminal or another network entity.

[0109] According to an embodiment, the controller 520 may control the overall operation of the network entity. For example, the controller 520 may control the signal flow to perform the operations described above Figures 1 - 4 in. For example, the control unit 520 may determine how to collect and analyze the analysis data from multiple individual sources.

[0110] The memory 530 may store at least one of the information exchanged through the transceiver 510 and the information generated by the controller 530.

[0111] At least some of the example embodiments described herein may be constructed in whole or in part using special hardware. Terms such as "component", "module", or "unit" used herein may include, but are not limited to, hardware devices such as circuitry in the form of discrete or integrated components, a field programmable gate array (FPGA), or an application specific integrated circuit (ASIC), which perform specific tasks or provide related functions. In some embodiments, the elements described may be configured to reside on a tangible, persistent, addressable storage medium and may be configured to execute on one or more processors. In some embodiments, these functional elements may include, for example, components such as software components, object-oriented software components, class components, and task components, processes, functions, attributes, procedures, subroutines, program code segments, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. Although example embodiments have been described with reference to the components, modules, and units discussed herein, these functional elements may be combined into fewer elements or separated into additional elements. Various combinations of optional features have been described herein, and it should be understood that the described features may be combined in any suitable combination. In particular, the features of any one example embodiment may be appropriately combined with the features of any other embodiment, unless such combination is mutually exclusive. Throughout the specification, the term "comprising" means including the specified (s) component(s), but not excluding the presence of other components.

[0112] Note all papers and documents that are submitted simultaneously with or prior to this specification and are related to this application. These papers and documents are publicly available together with this specification, and the content of all these papers and documents is incorporated herein by reference.

[0113] All features disclosed in this specification (including any appended claims, abstract and drawings), and / or all operations of any method or process so disclosed, may be combined in any combination, except combinations in which at least some of such features and / or operations are mutually exclusive.

[0114] Each feature disclosed in this specification (including any appended claims, abstract and drawings) may be replaced by an alternative feature serving the same, equivalent or similar purpose, unless expressly stated otherwise. Thus, each feature disclosed is only an example of a series of equivalent or similar features, unless expressly stated otherwise.

[0115] Although the disclosure has been shown and described with reference to various embodiments thereof, those skilled in the art will understand that various changes in form and detail may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents.

Claims

1. A method performed by a consumer network function NF entity in a telecommunications network, the method comprising: Sending a discovery request to a network repository function NRF entity; Receiving, from the NRF entity, a discovery response based on the discovery request, the discovery response including NWDAF instance IDs of at least one network data analytics function NWDAF entity; Selecting, based on NWDAF capabilities and information from the NRF entity, an aggregator NWDAF entity from the at least one NWDAF entity; Sending a subscription request to the selected aggregator NWDAF entity, the subscription request including an analysis identifier ID and information on an area of interest; And Receiving, from the selected aggregator NWDAF entity, a notification including an aggregated analysis for the analysis ID.

2. The method according to claim 1, wherein the aggregator NWDAF entity is configured to aggregate analyses for the analysis ID received from other NWDAF entities associated with the area of interest.

3. The method according to claim 1, wherein analysis aggregation capability information of the at least one NWDAF entity is stored in the NRF entity.

4. The method according to claim 3, wherein the aggregator NWDAF entity is configured to: Determine other NWDAF entities for which analyses are to be aggregated, based on a configuration of or a query to the NRF entity; Send a subscription request to the determined other NWDAF entities; and Receive, from the other NWDAF entities, notifications including analyses for the analysis ID.

5. A consumer network function NF entity, the consumer NF entity comprising: A transceiver; And A controller configured to: Send a discovery request to a network repository function NRF entity, Receive, from the NRF entity, a discovery response based on the discovery request, the discovery response including NWDAF instance IDs of at least one network data analytics function NWDAF entity, Select, based on NWDAF capabilities and information from the NRF entity, an aggregator NWDAF entity from the at least one NWDAF entity, Send a subscription request to the selected aggregator NWDAF entity, the subscription request including an analysis identifier ID and information on an area of interest, and Receive, from the selected aggregator NWDAF entity, a notification including an aggregated analysis for the analysis ID.

6. The consumer NF entity according to claim 5, wherein the controller is further configured to aggregate analyses from other NWDAF entities for the requested analysis ID for the area of interest.

7. The consumer NF entity according to claim 5, wherein analysis aggregation capability information of the at least one NWDAF entity is stored in the NRF entity.

8. A method performed by an aggregator network data analytics function NWDAF entity in a telecommunications network, the method comprising: Receiving a first subscription request from a consumer network function NF entity, the first subscription request including an analysis identifier ID and information on an area of interest; Determine one or more other NWDAF entities for aggregation analysis based on the configuration or query of the network repository function NRF entity; Send a second subscription request for the analysis ID to the determined one or more other NWDAF entities; Receive a second notification from the determined one or more other NWDAF entities, the second notification including the analysis for the analysis ID; Aggregate the analysis for the analysis ID; and Send a first notification including the aggregated analysis for the analysis ID to the consumer NF entity.

9. An aggregator network data analytics function NWDAF entity for managing the aggregation of analytics data in a telecommunications network, the aggregator NWDAF entity comprising: A transceiver; and A controller configured to: Receive a first subscription request including information of an analysis identifier ID and an area of interest from a consumer network function NF entity, Determine one or more other NWDAF entities for aggregation analysis based on the configuration or query of the network repository function NRF entity, Send a second subscription request for the analysis ID to the determined one or more other NWDAF entities, Receive a second notification from the determined one or more other NWDAF entities, the second notification including the analysis for the analysis ID, Aggregate the analysis for the analysis ID, and Send a first notification to the consumer NF entity, the first notification including the aggregated analysis for the analysis ID.

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