Method and apparatus for data analysis in telecommunications networks

Abstract

The present invention relates to a communication method and system for fusing a fifth generation (5G) communication system with Internet of Things (IoT) technology to support higher data rates than the fourth generation (4G) system. The present invention is applicable to intelligent services based on 5G communication technology and IoT related technology, such as smart homes, smart buildings, smart cities, smart cars, connected cars, healthcare, digital education, smart retail, security, and safety related services. A method for managing analytical data in a telecommunication network is provided. The method includes a consumer network function (NF) that determines how analytical data from multiple individual sources is collected and analyzed.

Description

[Technical Field]

[0001] The present invention relates to the acquisition, processing and use of data analytics in telecommunications networks, and more particularly to fifth generation networks. [Background technology]

[0002] Since the commercialization of 4G communication systems, efforts have been made to develop improved 5G (pre-5G) communication systems to meet the increasing demand for wireless data traffic. Therefore, 5G (pre-5G) communication systems are also called "Beyond 4G Networks" or "Post-LTE Systems." To achieve higher data rates, 5G communication systems are expected to be implemented in higher frequency (mmWave) bands, such as the 60 GHz band. To reduce propagation loss of wireless waveforms and extend transmission distances, beamforming, massive multiple-input multiple-output (massive MIMO), full-dimensional MIMO (FD-MIMO), array antennas, analog beamforming, and large-scale antenna technologies are being discussed for 5G communication systems. In addition, in the 5G communication system, development is underway to improve the system network based on advanced small cells, cloud Radio Access Networks (cloud RAN), ultra-dense networks, device-to-device (D2D) communications, wireless backhaul, moving networks, cooperative communications, CoMP (Coordinated Multi-Points), and receiver-side interference cancellation.In 5G systems, hybrid FSK and QAM modulation (FQAM) and sliding window superposition coding (SWSC) are being developed as advanced coding modulation (ACM), and filter bank multi-carrier (FBMC), non-orthogonal multiple access (NOMA), and sparse code multiple access (SCMA) are being developed as advanced access techniques.

[0003] The Internet is currently evolving from a human-centric connected network where humans generate and consume information to the Internet of Things (IoT), where distributed entities such as things exchange and process information without human intervention. The Internet of Everything (IoE) has emerged, combining IoT technology with big data processing technology connected to cloud servers. Realizing the IoT requires technological elements such as sensing technology, wired and wireless communication and network infrastructure, service interface technology, and security technology. Recently, research has focused on sensor networks, M2M (machine-to-machine) communication, and MTC (machine-type communication). This IoT environment collects and analyzes data generated by connected things, providing intelligent Internet technology services that create new value in human life. By combining existing information technology (IT) with various industrial applications, IoT is being applied to a variety of fields, including smart homes, smart buildings, smart cities, smart or connected cars, smart grids, healthcare, smart appliances, and advanced medical services.

[0004] Accordingly, various attempts are being made to apply 5G communication systems to IoT networks. For example, technologies such as sensor networks, machine-type communications (MTC), and man-to-man (M2M) communications are implemented using beamforming, MIMO, and array antennas. The application of cloud radio access networks (RANs) as the aforementioned big data processing technology is also considered an example of the fusion of 5G and IoT technologies.

[0005] As part of the growing desire to improve network automation in 5G telecommunications networks, known as enabling Network Automation (eNA), the Network Data Analytics Function (NWDAF) is defined as part of the Service Based Architecture (SBA) that uses mechanisms and interfaces specified for the 5G Core and Operations Administration and Maintenance (OAM).

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

[0007] FIG. 1 shows a general schematic diagram illustrating various elements in a related art 5G network automation scheme. For clarity, only the parts relevant to automation are shown. It shows that activity data and analytics are provided from a first group of NFs 50 or Application Functions (AFs) 10 to an NWDAF 40. The NWDAF 40 also interfaces with an OAM 30 and a data repository 20. The NWDAF 40 analyzes data from these sources and communicates analytics data to a second group of NFs 50 or AFs 10. The second group of NFs 50 includes some or all of the first group of NFs 50 or AFs 10.

[0008] The above information is presented solely as background information to aid in the understanding of the present invention. No determination has been made, and no assertion is being made, as to whether any of the above is applicable as prior art with respect to the present invention.

[0009] There is a growing desire to improve network automation in 5G communications networks, known as enabling network automation (eNA). Summary of the Invention [Problem to be solved by the invention]

[0010] The present invention has been made in consideration of the above-mentioned prior art, and an object of the present invention is to provide a method for managing analytical data in a telecommunications network including a consumer network function (NF), and a telecommunications network for managing analytical data. [Means for solving the problem]

[0011] In order to achieve the above object, one aspect of the present invention provides a method for managing analytical data in a telecommunications network including a consumer Network Function (NF), comprising determining how analytical data from a plurality of individual sources is to be collected and analyzed in one of the following ways: a distributed manner from a plurality of Network Data Analytics Functions (NWDAFs), a centralized manner by aggregating analytical data from the plurality of NWDAFs before analyzing the analytical data at an aggregator NWDAF, or at least one of a distributed manner from the plurality of NWDAFs and a centralized manner by aggregating analytical data from the plurality of NWDAFs before analyzing the analytical data at an aggregator NWDAF.

[0012] Additional aspects will be set forth in part in the detailed description that follows, and in part will be obvious from the detailed description, or may be learned by practice of the embodiments presented.

[0013] In one embodiment, when multiple NWDAFs are provided in the telecommunications network, at least one has a specialized function and at least one has a general function. In one embodiment, the specialized function is configured to aggregate analytics from multiple areas of interest or multiple target users, and the general function is configured to inform analytics per area of ​​interest or per set of target users. In one embodiment, capability information of a particular one of the plurality of NWDAFs having the special function is stored in a Network Repository Function (NRF). In one embodiment, the capability information relates to analytics aggregation capabilities. In one embodiment, the consumer NF determines one or more NWDAFs from which to collect data based on NWDAF capability information as part of the consumer NF's implemented internal selection criteria. In one embodiment, the implemented selection criteria include one or more of newly registered functions in the NRF determined based on the load level for each NWDAF, the number of analysis identifiers (IDs) directly supported for each NWDAF, and other Key Performance Indicators (KPIs) pre-configured by the network operator. In one embodiment, an identifier is defined for each aggregator NWDAF as support information registered in the NRF, and the identifier indicates an NWDAF that can function as an aggregation point among the NWDAFs, and the identifier is an aggregation point identifier (AP ID). In one embodiment, the consumer NF autonomously determines how the multiple NWDAFs will operate together. In one embodiment, the decision is based on selection criteria, and the consumer NF considers all NWDAFs identified by the NRF and determines how to collect data from their combination based on the embodied selection criteria. In one embodiment, each of the plurality of NWDAFs pre-negotiates with one or more other aggregator NWDAFs the number of analysis IDs that the NWDAF supports, and the aggregator NWDAF advertises such expanded set of analysis IDs supported within the NRF. In one embodiment, further operations are provided, including a network capability service consumer sending a discovery request to a Network Repository Function (NRF) including all required analysis IDs and areas of interest, the NRF responding with one or more distributed NWDAF instance IDs, each of the one or more distributed NWDAF instance IDs covering at least a portion of a set of analysis IDs and supported areas of interest, the network capability service consumer sending a subscription request to each distributed NWDAF, each distributed NWDAF responding with analysis-specific parameters for each analysis ID, and for each analysis ID, the network capability service consumer aggregating targets for analysis reports across the distributed NWDAFs for the corresponding areas of interest. In one embodiment, further operations are provided, wherein a network capability service consumer sends a discovery request including all required analysis IDs and areas of interest to a Network Repository Function (NRF), the NRF responding with a set of analysis IDs and a set of NWDAF instance IDs, each covering at least a portion of the supported areas of interest and AP IDs, or one or more other identifiers for each aggregator NWDAF instance indicating a possible aggregation point, the network capability service consumer selecting at least one NWDAF as an aggregator NWDAF based on the network capability service consumer's internal selection criteria and taking into account registered NWDAF capabilities and information from the NRF, the network capability service consumer sending a subscription request to the aggregator NWDAF including the analysis IDs and the areas of interest for each NWDAF to aggregate for designation as an aggregation point, the aggregator NWDAF identifying the designation as an aggregation point or The aggregator NWDAF either determines a mapping to a specific NWDAF for aggregating analytics based on configuration, implementation, or query to the NRF, the aggregator NWDAF subscribes to all NWDAFs, the NWDAF notifies with analysis-specific parameters for each analysis ID in the set of analysis IDs, and for each analysis ID, the aggregator NWDAF aggregates targets of analytics reports across different NWDAFs for the corresponding area of ​​interest, and the aggregator NWDAF notifies the network function service consumer with analysis-specific parameters for each analysis ID for all analysis IDs aggregated per NWDAF. In one embodiment, further operations are provided, including the network capability service consumer sending a discovery request to a Network Repository Function (NRF) including all required analysis IDs and areas of interest, the NRF responding with one or more of at least one NWDAF instance ID, at least one NWDAF instance ID aggregated within at least one aggregator NWDAF instance ID registered with the NRF, the network capability service consumer subscribing to all NWDAFs including an aggregation point acting as a central NWDAF to receive individual notifications, and for each analysis ID, the network capability service consumer aggregating analysis data from both decentralized and (semi-)centralized NWDAF instances for the corresponding point of interest.

[0014] According to one aspect of the present invention there is provided a telecommunications network operable to perform the method of the first aspect.

[0015] A single instance or multiple instances of the NWDAF 40 may be deployed in a public land mobile network (PLMN). When multiple NWDAF 40 instances are deployed, embodiments of the present invention support deploying the NWDAF 40 as a central NF, as a collection of distributed NFs, or a combination of both (i.e., some centralized and some distributed).

[0016] When multiple NWDAFs exist, they need not all provide the same type of analysis results. In other words, some of them may be specialized to provide only certain types of analysis, while some may be more general in nature. Embodiments of the present invention define an Analysis ID information element that is used to identify the type of supported analysis for which a particular NWDAF is generated.

[0017] On the other hand, several NWDAFs of one network may provide the same type of analysis, thereby assisting each other, for example, for a specific analysis of a specific target user equipment (UE) or a specific analysis of a specific area of ​​interest.

[0018] The capabilities of a particular NWDAF instance are described in an NWDAF profile stored in the Network Repository Function (NRF).

[0019] When multiple instances of NWDAFs are deployed, some may specialize in providing specific types of analysis, or multiple NWDAFs may collaborate to provide the same type of analysis. More importantly, once a consumer Network Function (NF) discovers a corresponding NWDAF instance, it needs a flexible data collection mechanism and associated services to enable various deployment options (distributed, centralized, or a mix of both).

[0020] Embodiments of the present invention provide a data collection mechanism in an environment containing multiple NWDAFs, thereby flexibly supporting different deployment options.

[0021] According to other aspects of the invention there are provided apparatus and methods as set out in the accompanying claims. Further features of the invention will become apparent from the dependent claims and the following detailed description.

[0022] While several preferred embodiments of the present invention have been shown and described, it would be understood by those skilled in the art that various changes and modifications can be made therein without departing from the scope of the invention, as defined in the claims.

[0023] In an embodiment of the present invention, the terms "NWDAF" and "NWDAF instance" are used interchangeably.

[0024] In one embodiment of the invention related to distributed data collection, a consumer NF determines the set of NWDAFs (or NWDAF instances) from which to collect data based on its implemented selection criteria (e.g., load level per NWDAF, number of analysis IDs directly supported per NWDAF, or other Key Performance Indicators (KPIs) pre-configured by the network operator).

[0025] In one embodiment of the present invention for (semi-)centralized data collection, a new aggregation point identifier (AP ID) is defined for each NWDAF, indicating which other NWDAFs are potential aggregation points. The identifier is set taking into account multiple factors, including the load level per NWDAF (or NWDAF instance), the number of analysis IDs directly supported per NWDAF, or other KPIs set by the network operator. The NWDAF information maintained in the NRF or any other designated data repository structure maintains this identifier for each NWDAF. The consumer NF uses the AP ID as assistance information, in addition to other implemented selection criteria, to determine how multiple NWDAF instances should cooperate.

[0026] In an alternative embodiment of the present invention for (semi-)centralized data collection, the consumer NF intelligently determines how multiple NWDAF instances work together without the intervention of other entities. The consumer NF considers all NWDAF instances (e.g., those discovered via the NRF) and determines how to aggregate data from their combinations, similar to distributed data collection, based on embodied selection criteria. The NWDAF information held in the NRF or any other designated data repository structure is agnostic to the aggregation information.

[0027] In an alternative embodiment of the present invention for (semi-)centralized data collection, each NWDAF instance pre-negotiates (directly or indirectly) the number of analytics IDs it supports with other NWDAF instances (e.g., via NRF discovery) and advertises an expanded set of such supporting analytics IDs (direct and indirect analytics IDs) within the NRF. The NWDAF information maintained within the NRF or any other designated data repository structure explicitly distinguishes directly supported analytics IDs from indirect analytics IDs. The consumer NF uses the direct versus indirect supporting analytics IDs as supporting information, in addition to other embodied selection criteria, to determine how multiple NWDAF instances should cooperate.

[0028] In one embodiment of the present invention for mixed mode data collection, a combination of embodiments for distributed and (semi-) centralized data collection is employed by the consumer NF.

[0029] Other aspects, advantages and important features of the present invention will become apparent to those skilled in the art from the following detailed description, which, taken in conjunction with the drawings, discloses various embodiments of the invention. [Effects of the Invention]

[0030] According to the present invention, it is possible to provide a method for efficiently managing analytical data in a telecommunications network.

[0031] These and other aspects, features, and advantages of particular embodiments of the present invention will become more apparent from the following description taken in conjunction with the drawings. [Brief explanation of the drawings]

[0032] [Figure 1] 1 shows a general representation of a 5G network automation framework according to related technologies. [Figure 2] 1 illustrates a message exchange and method according to one embodiment of the present invention. [Figure 3]1 illustrates a message exchange and method according to one embodiment of the present invention. [Figure 4] 1 illustrates a message exchange and method according to one embodiment of the present invention. [Figure 5] FIG. 2 is a block diagram of a network entity according to one embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0033] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Specific examples of the present invention will now be described in detail with reference to the drawings, wherein like reference numerals are understood to refer to like parts, components, and structures throughout.

[0034] The following description, taken in conjunction with the drawings, is provided to facilitate a comprehensive understanding of various embodiments of the present invention, as defined by the claims and their equivalents. To facilitate this understanding, various specific details are included, but these details should be considered merely as examples. Therefore, those skilled 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 invention. Furthermore, for the sake of clarity and conciseness, descriptions of well-known functions and configurations are omitted.

[0035] The terms and phrases used in the following description and claims are not limited to their bibliographical meanings, but are merely used by the inventor to enable a clear and consistent understanding of the present invention. Therefore, it will be apparent to those skilled in the art that the following description of various embodiments of the present invention is provided for illustrative purposes only, and not for the purpose of limiting the present invention, which is defined by the claims and their equivalents.

[0036] The singular forms "a," "an," and "the" should be understood to include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to a "component surface" includes a reference to one or more such surfaces.

[0037] 2 illustrates a scenario in which there are consumer NFs, NRFs, and NWDAFs(k) that form at least part of a system according to an embodiment of the present invention. It also illustrates the various processes and messaging between each element.

[0038] Referring to FIG. 2, it is assumed that index (k) indicates an NWDAF instance ID in a multi-instance deployment. Each instance NWDAF(k) 120 is specialized for a set of data analysis types identified by analysis ID(k), some of which overlap between different instances and some of which are mutually exclusive. Instances with overlapping analysis IDs may mutually assist each other, for example, to cover different sets of UEs as targets for analysis reports or to cover different sets of tracking areas within a region of interest. A tracking area indicator TAI(k) refers to the region of interest covered by NWDAF(k).

[0039] <Case A: Distributed data collection model>

[0040] In a first embodiment of the present invention, consumer NFs 100 decide to consume various NWDAF services in a distributed manner based on their implemented selection criteria, e.g., network configuration or pre-configured network operator preferences.

[0041] The details of each operation shown in Figure 2 are as follows:

[0042] 1. The NWDAF service consumer 100 sends an NF discovery request (1a) containing all required analysis IDs and areas of interest (e.g., in the format of TAIs) to the NRF 110. The discovery request includes additional information, such as Network Slice Selection Assistance Information (i.e., a single NSSAI or S-NSSAI). The NRF 110 response (1b) includes multiple NWDAF instance IDs, NWDAF(k), each covering a set of analysis IDs, analysis ID(k), and a set of (parts of) areas of interest supported by instance(k), identified as TAI(k).

[0043] 2. The NWDAF service consumer 100 sends a subscription request (2a) to each NWDAF(k) 120 containing the analysis ID(k) and TAI(k) (e.g., as an analytics filter). The subscription request identifies the area of ​​interest for each analysis ID as a set of (analysis ID(k), analysis filter = TAI(k)) tuples as shown in operation 2a. The NWDAF(k) 120 notifies with analytics specific parameters for each analysis ID as shown in operation 2b.

[0044] 3. The service consumer NF100 aggregates the analysis report targets across NWDAF(k) for analysis ID(k) of the corresponding area of ​​interest TAI(k).

[0045] <Case B: (Semi-) centralized data collection model using AP ID>

[0046] FIG. 3 illustrates a scenario in which there are consumers NF, NRF, NWDAF(j), and NWDAF(i) that form at least part of a system according to an embodiment of the present invention.

[0047] Referring to FIG. 3, the consumer NF 200 designates one (set) of NWDAFs as an aggregation point based on the AP ID as support information in addition to its implemented selection criteria, and decides to consume different NWDAF services in a (semi-)centralized manner.

[0048] In this case, when an aggregation point such as NWDAF(j) 220 registers with the NRF 210, in addition to the set of analysis IDs supported by the NWDAF(j) 220 and the area of ​​interest covered by the NWDAF(j) 220, the AP ID is also set equal to the NWDAF(j) 220 ID, which also identifies the NWDAF(j) 220 as an aggregation point.

[0049] When a distributed NWDAF such as NWDAF(i) 230 is registered within the NRF 210, in addition to the set of analysis IDs supported by the NWDAF(i) 230 and the area of ​​interest covered by the NWDAF(i) 230, the AP ID is also configured equivalent to one of the NWDAF(j) 220 already registered as an aggregation point.

[0050] The mapping between NWDAF(j) 220 and NWDAF(i) 230 in AP IDs takes into account multiple factors, including the load level per NWDAF, the analysis IDs supported per NWDAF, the areas of interest supported per NWDAF, a predefined hierarchy for the mapping, or other KPIs set by the network operator. The NWDAF information maintained in the NRF 210 or any other designated data repository structure maintains this mapping between NWDAFs based on AP IDs. In Case B, both the NRF 210 and the NWDAF service consumer 200 become aware of the mapping between the central NWDAF and the distributed NWDAFs based on AP IDs.

[0051] 3 illustrates a scenario in which there are consumer NFs, NRFs, NWDAFs(j), and NWDAFs(i) that form at least part of a system according to an embodiment of the present invention. The details of each operation illustrated in FIG. 3 are as follows:

[0052] 1. Similar to operation 1 in case A shown in Figure 2 (distributed deployment), except that the NRF 210 response includes both distributed NWDAF(i) and central NWDAF(j). The NRF 210 response also includes the AP ID for each NWDAF(i) 230 instance indicating the possible aggregation point(s) NWDAF(j) 220 for the different values ​​of (j), i.e., the mapping between the central and distributed NWDAFs. The NWDAF service consumer 200 determines the central aggregation point based on the mapping received as AP ID from the NRF 210.

[0053] 2. The NWDAF service consumer 200 sends a subscription request including the analysis ID(i), TAI(i) (as an analysis filter) of NWDAF(i) 230 to NWDAF(j) 220 (to designate it as an aggregation point). NWDAF(j) 220 identifies the designation as an aggregation point that is the destination of the service consumer request. Alternatively, another explicit flag or parameter is set as an input parameter by the NWDAF service consumer 200 to explicitly designate the aggregation point NWDAF(j) 220.

[0054] 3. NWDAF(j) 220 subscribes to all NWDAF(i) 230 using a procedure similar to Case A (single instance subscription procedure). All NWDAF(i) notify with analysis-specific parameters for each analysis ID in the set of analysis ID(i).

[0055] 4. NWDAF(j) 220 aggregates the targets of the analysis report across different NWDAF(i) 230 for the analysis ID(i) of the corresponding area of ​​interest TAI(i).

[0056] 5. NWDAF(j) 220 notifies all analysis IDs aggregated for each NWDAF(i) 230 with analysis-specific parameters for each analysis ID.

[0057] <Case C: (Semi) centralized data collection model without AP ID>

[0058] In an alternative embodiment of the present invention, as in Case B, operation 1 is exactly the same as in Case A (distributed deployment), and the data held in the NRF 210 or any other data repository structure remains agnostic to deployment information (i.e., aggregation point identifiers). As a result, the NWDAF service consumer 200 determines the aggregation point without other assisting information.

[0059] In another case of centralized aggregation (referred to here as Case C), no mapping between the central NWDAF and the distributed NWDAFs is indicated at the NRF 210. In this case, no AP ID is configured in the NWDAF, and only the aggregation point is distinguished when it registers with the NRF 210 either implicitly (see Case D below) or explicitly, for example, by configuring an identifier. In Case C, the NRF 210 no longer relies on a mapping between the central NWDAF and the distributed NWDAFs.

[0060] The details of each operation are as follows:

[0061] 1. The NRF 210 response is similar to operation 1 in case A shown in Figure 2, except that it includes 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 its configuration or implemented selection criteria.

[0062] 2 to 5 are the same as in Case B above.

[0063] Case D: Pre-negotiated (semi-)centralized data collection model

[0064] In an alternative embodiment of the invention similar to Case B, operation 1 is similar to Case A (distributed deployment), except that with respect to data held within the NRF 210 or any other data repository structure, for example, when each NWDAF is registered within the NRF 210, the analysis ID(j) advertised by NWDAF(j) 220 is an expanded set of analysis IDs from various NWDAF(i) 230 that are pre-negotiated based on some configuration or pre-defined hierarchy. As a result, unlike Case B, no explicit identifier is defined for each NWDAF within the NRF 210, and no mapping between the central NWDAF and the distributed NWDAFs is indicated in the NRF 210.

[0065] The expanded set of supported analysis IDs is distinct from the analysis IDs directly supported per NWDAF. Some central NWDAFs only aggregate analysis, and therefore in such situations the expanded list does not support direct analysis. As a result, the NWDAF service consumer 200 uses this information in addition to its implemented selection criteria to determine how multiple NWDAFs cooperate (e.g., NWDAFs that directly support more analysis IDs preferably expand their list to avoid extra signaling overhead or network latency).

[0066] The details of each operation are as follows:

[0067] 1. The NRF 210 response is similar to operation 1 in case A shown in Figure 2, except that it includes 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 its implemented selection criteria.

[0068] 2. The NWDAF service consumer sends a subscription request to the NWDAF(j) 220 (to designate it as an aggregation point) including all required analysis IDs, TAIs, without indicating the mapping per NWDAF(i) 230.

[0069] 3. NWDAF(j) 220 determines the extended set of supporting analysis IDs and their mapping to specific distributed NWDAFs based on the configuration, instantiation, or query of NRF 210, from which it aggregates analyses and subscribes to them.

[0070] 4. NWDAF(j) 220 aggregates the analysis report targets across different NWDAF(i) 230 for the corresponding area of ​​interest analysis ID(i).

[0071] 5. NWDAF(j) 220 notifies all aggregated analysis IDs with analysis specific parameters per analysis ID without indicating mapping per NWDAF(i) 230.

[0072] <Case D1: (Semi-)Centralized Data Collection Model Mapped to a Central NWDAF>

[0073] In yet another case of centralized aggregation (herein referred to as Case D1, a sub-case of Case D), similar to Case D, no mapping between the central NWDAF and distributed NWDAFs is indicated in the NRF 210. Again, in this option, no AP IDs are configured, and only aggregation points are distinguished, for example, by configuring an identifier, either implicitly (again, similar to Case D) or explicitly in the NRF 210. Furthermore, the NRF 210 as well as the NWDAF service consumer 200 become agnostic to the mapping between the central NWDAF and distributed NWDAFs. Instead, the NRF 210 or each central NWDAF 220 based on configuration, instantiation, or query of a predefined hierarchy (when registering with the NRF) determines the mapping to a particular distributed NWDAF.

[0074] The details of each operation are as follows:

[0075] 1. The NRF 210 response is similar to operation 1 in case A shown in Figure 2, except that it includes both the distributed NWDAF(i) 230 and NWDAF(j) 220 identified as aggregation points. The NWDAF service consumer 200 selects a central aggregation point.

[0076] 2. The NWDAF service consumer sends a subscription request to the NWDAF(j) 220 (to designate it as an aggregation point) containing all the required analysis IDs, TAIs, without indicating the mapping of analysis IDs or TAIs for each NWDAF(i) 230.

[0077] 3. The NWDAF(j) 220 based on the configuration, realization, or query to the NRF 210 determines the mapping to specific distributed NWDAFs 230 from which to aggregate analytics and subscribe to them accordingly.

[0078] 4. NWDAF(j) 220 aggregates the analysis report targets across different NWDAF(i) 230 for the corresponding area of ​​interest analysis ID(i).

[0079] 5. NWDAF(j) 220 notifies all aggregated analysis IDs with analysis specific parameters per analysis ID without indicating mapping of analysis ID or TAI per NWDAF(i) 230.

[0080] Case E: Mixed-mode data collection model

[0081] A third embodiment of the invention uses a mixture of distributed and (semi-) concentrated modes of configuration.

[0082] FIG. 4 illustrates a scenario in which there are consumers NF, NRF, NWDAF(k), NWDAF(j), and NWDAF(i) that form at least part of a system according to an embodiment of the present invention.

[0083] Referring to FIG. 4, the details of each operation are as follows.

[0084] 1. The NWDAF service consumer 300 sends an NF discovery request (1a) to the NRF 310, including all required analysis IDs and areas of interest (e.g., in the form of TAIs). The NF discovery request includes additional information, such as network slice selection assistance information (i.e., a single NSSAI or S-NSSAI). The NRF response includes (1b) a (set of) NWDAF instance IDs, i.e., NWDAF(k) 320, arranged in a distributed manner. The NRF 310 response also includes (1c) a (set of) NWDAF instance IDs (i.e., NWDAF(i) 340) that are aggregated to (a) NWDAF instance ID (i.e., NWDAF(j) 330).

[0085] 2. The NWDAF service consumer 300 subscribes to all NWDAF(k) 320 to receive individual notifications, similar to the distributed deployment procedure in Case A.

[0086] 3. The NWDAF service consumer 300 also subscribes to the NWDAF(j) 330. The NWDAF(j) 330 subscribes to all associated NWDAF(i) 340, which are aggregated similarly to the (semi-)centralized configuration procedure in Case B, Case C, or Case D or Case D1, and provides aggregate notifications to the NWDAF service consumer 300.

[0087] 4. The NWDAF service consumer 300 aggregates analytical data from both decentralized and (semi-) centralized NWDAF instances.

[0088] Figure 5 is a block diagram of a network entity according to one embodiment of the present invention. The network entity corresponds to each of the network entities shown in Figures 1 to 4. For example, the network entity refers to each of the network functions (e.g., NRF, NWDAF) shown in Figures 1 to 4.

[0089] 5, the network entity includes a transceiver 510, a controller 520, and storage 530. In this embodiment, the controller 520 includes a circuit, an ASIC, or at least one processor.

[0090] The transceiver 510 transmits and receives signals to and from terminals or other network entities.

[0091] Controller 520 controls the overall operation of the network entity according to one embodiment. For example, controller 520 controls the signal flow that implements the operations of Figures 1-4 above. For example, controller 520 determines how analytical data from multiple individual sources is collected and analyzed.

[0092] The storage 530 stores at least one of information exchanged via the transceiver 510 and information generated by the controller 530 .

[0093] At least some of the exemplary embodiments described herein are constructed, partially or entirely, using dedicated, special-purpose hardware. As used herein, terms such as "component," "module," or "unit" include, but are not limited to, circuitry in the form of discrete or integrated components, hardware devices such as field programmable gate arrays (FPGAs) or application specific integrated circuits (ASICs) that perform particular tasks or provide related functionality. In some embodiments, the elements described above are configured to reside on tangible, persistent, addressable storage media and to execute on one or more processors. These functional elements, in some embodiments, include 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, circuits, data, databases, data structures, tables, arrays, variables, and the like. While the exemplary embodiments are described with reference to components, modules, and units described herein, such functional elements may be combined into fewer elements or separated into additional elements. It should be understood that various combinations of optional features are described herein, and that the described features may be combined in any suitable combination. In particular, features of any exemplary embodiment may be combined with features of any other embodiment as appropriate, except where such combinations are mutually exclusive. Throughout this specification, the terms "comprising" or "comprises" mean including the specified components but do not exclude the presence of other components.

[0094] Attention is directed to all papers and documents related to this invention that have been filed contemporaneously with or prior to this application and that are open to public inspection herewith, and the contents of all such papers and documents are hereby incorporated by reference.

[0095] All features disclosed in this specification (including the claims, abstract, and drawings), and / or all operations of any method or process so disclosed, may be combined in any combination, except where at least some of such features and / or operations are mutually exclusive.

[0096] Every feature disclosed in this specification (including the claims, abstract, and drawings), unless expressly stated otherwise, may be replaced by other features serving the same, equivalent, or similar purpose. Thus, unless expressly stated otherwise, each disclosed feature is only an example of a generic series of equivalent or similar features.

[0097] While the present invention has been shown and described with reference to various embodiments thereof, it will be understood by those skilled in the art that various changes in form and detail can be made therein without departing from the spirit and scope of the invention as defined by the claims and their equivalents. [Explanation of symbols]

[0098] 10 Application Functions (AF) 20 Data Repositories 30 Operations, Administration and Maintenance (OAM) 40 Network Data Analysis Facility (NWDAF) 50 Network Function (NF) 100, 200 NWDAF Service Consumer (Consumer NF) 110, 210 Network Repository Facility (NRF) 120, 320 instances NWDAF(k) 220, 330 instances NWDAF(j) 230, 340 instances NWDAF(i) 510 Transceiver 520 Controller 530 Storage

Claims

1. 1. A method performed by a consumer Network Function (NF) entity in a telecommunications network, comprising: sending a discovery request to a network repository function (NRF) entity; receiving a discovery response from the NRF entity based on the discovery request, the discovery response including information regarding at least one network data analytics function (NWDAF) entity; selecting an aggregator NWDAF entity from the at least one NWDAF entity based on information from the NRF entity; sending a subscription request including an analysis identifier (ID) to the selected aggregator NWDAF entity; receiving a notification from the selected aggregator NWDAF entity containing aggregated analyses for the analysis ID.

2. The method of claim 1 , wherein the discovery request includes the analysis ID.

3. 3. The method of claim 2, wherein the discovery request further includes information regarding an area of ​​interest.

4. The method of claim 1 , wherein the subscription request further includes information regarding an area of ​​interest.

5. A consumer Network Function (NF) entity, comprising: A transceiver; a controller; The controller Sending a discovery request to a network repository function (NRF) entity; receiving a discovery response from the NRF entity based on the discovery request, the response including information regarding at least one network data analytics function (NWDAF) entity; selecting an aggregator NWDAF entity from the at least one NWDAF entity based on information from the NRF entity; sending a subscription request including an analysis identifier (ID) to the selected aggregator NWDAF entity; A consumer NF entity configured to receive a notification from the selected aggregator NWDAF entity containing aggregated analytics for the analytics ID.

6. The consumer NF entity of claim 5 , wherein the discovery request includes the analysis ID.

7. 7. The consumer NF entity of claim 6, wherein the discovery request further includes information regarding an area of ​​interest.

8. The consumer NF entity of claim 5 , wherein the subscription request further includes information regarding an area of ​​interest.

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