Network Data Analytics Function NWDAF Entities and Methods Performed Thereby

Abstract

A communication method and system are provided for converging a fifth-generation (5G) communication system for supporting higher data rates beyond a fourth-generation (4G) system with technologies for the Internet of Things (IoT). The present disclosure is 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 performed by a first entity executing a network data analytics function (NWDAF) is provided, the method comprising: receiving, from a second entity executing a network function (NF), a first message for requesting an analysis of an observed service experience, the first message including single-network slice selection assistance information (S-NSSAI) indicating a network slice; sending, to a third entity executing an application function (AF) associated with the S-NSSAI, a second message for requesting service data associated with the analysis of the observed service experience, the second message including information about at least one application; receiving, from the third entity, service data including at least one service experience for at least one application; identifying the analysis of the observed service experience based on the service data; and sending the analysis of the observed service experience to the second entity.

Description

Technical Field

[0001] The present disclosure relates to improved means for providing data analysis in a communication network. More specifically, the present disclosure relates to providing data analysis for each network slice. Background Art

[0002] To meet the demand for increased wireless data traffic since the deployment of the 4th generation (4G) communication systems, improved 5th generation (5G) or pre-5G communication systems are being actively developed. Therefore, 5G or pre-5G communication systems are also referred to as "beyond 4G networks" or "post-LTE systems". 5G communication systems are considered to be implemented in higher frequency bands (millimeter waves), such as the 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 (massive MIMO), full-dimensional multiple-input multiple-output (FD-MIMO), array antenna, analog beam-forming, large-scale antenna technologies are being discussed in 5G communication systems. In addition, in 5G communication systems, developments for system network improvement are being carried out based on advanced small cells, cloud radio access networks (cloud RANs), ultra-dense networks, device-to-device (D2D) communication, wireless backhaul, mobile networks, cooperative communication, coordinated multi-point (CoMP), receiver-side interference cancellation, etc. In 5G systems, 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, a human - centric connected network where humans generate and consume information, is now evolving towards the Internet of Things (IoT) where distributed entities such as things exchange and process information without human intervention. The Internet of Everything (IoE), in which IoT technologies and big - data processing technologies are combined by connecting to cloud servers, has emerged. As technical elements such as "sensing technology", "wired / wireless communication and network infrastructure", "service interface technology", and "security technology" are required for IoT implementation, sensor networks, machine - to - machine (M2M) communication, machine - type communication (MTC), etc. have been recently studied. Such an IoT environment can provide intelligent Internet technology services that create new value for human life by collecting and analyzing data generated among the connected things. IoT can be applied to various fields including smart homes, smart buildings, smart cities, smart cars or connected vehicles, smart grids, healthcare, smart appliances, and advanced medical services through the integration and combination of existing information technology (IT) and various industrial applications.

[0004] In line with this, 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 by beamforming, MIMO, and array antennas. The application of cloud radio access network (RAN) as the above - mentioned big - data processing technology can also be considered as an example of the integration between 5G technology and IoT technology. Summary of the Invention

[0005] Technical Problem

[0006] In addition to the above - mentioned background art, there is an increasing expectation to improve the performance of communication networks so as to enhance the user experience without the network operator making unnecessary investments in too many devices. In other words, network operators are eager to optimize the performance of their installed infrastructure clusters.

[0007] In the past, network optimization was mainly a manually managed process where technical operators adjusted network parameters as needed. Over time, more automation has been introduced. Recently, artificial intelligence (AI) and machine learning (ML) technologies have been adopted.

[0008] In the 5th - generation (5G) network, there are different network structures and protocols that have been adopted to enhance the user experience.

[0009] Therefore, there is a need for methods to make full use of these new structures and protocols to improve network performance and / or user experience.

[0010] Technical Solution

[0011] Aspects of the present disclosure will at least address the above problems and / or disadvantages and at least provide the following advantages. Accordingly, one aspect of the present disclosure will provide a unit for use in a communication network, the unit being operable to receive inputs from at least one data source associated with a network slice, process the received input data, and output analysis of observed service experiences associated with the network slice.

[0012] Additional aspects will be set forth in part in the following description, and will in part become apparent from the description, or may be learned by practice of the presented embodiments.

[0013] In one embodiment of the present disclosure, the unit is a Network Data Analytics Function (NWDAF).

[0014] In one embodiment of the present disclosure, the processing of data received from at least one input source includes aggregating analysis of observed service experiences of multiple applications running on a network slice and multiple user equipments (UEs) associated with the network slice.

[0015] In one embodiment of the present disclosure, data received from at least one input data source is aggregated.

[0016] In one embodiment of the present disclosure, the aggregation is performed by an averaging process.

[0017] In one embodiment of the present disclosure, the unit is further operable to perform a mapping operation, whereby data indicating observed service experiences received from at least one data source is mapped to the network slice.

[0018] According to one aspect of the present disclosure, a method for providing slice-specific data analysis in a communication network is provided. The method includes a service consumer subscribing to an analysis service provided by the NWDAF, which includes providing a network slice identifier, the NWDAF requesting data from at least one network function (NF), in response, at least one NF providing the requested data to the NWDAF, the NWDAF subscribing to a service data subscription service related to at least one application function (AF), in response, at least one AF providing service data to the NWDAF, and the NWDAF processing data received from at least one NF and at least one AF to provide data analysis related to the network slice to the service consumer.

[0019] According to another aspect of the present disclosure, an operation of the NWDAF for requesting data from at least one network function (NF) is provided. The operation includes requesting an application identifier, and wherein, in response, the operation of the NF providing the requested data to the NWDAF includes providing the requested application identifier.

[0020] In one embodiment of the present disclosure, data received from at least one application function (AF) is aggregated.

[0021] In one embodiment of the present disclosure, NWDAF maps data received from at least one network function (NF) and at least one application function (AF) such that the data analysis output is related to a network slice.

[0022] In one embodiment of the present disclosure, the mapping is performed based on one or more of a single-network slice selection assistance information (S-NSSAI), a subscription permanent identifier (SUPI), and an application active on the network slice.

[0023] According to one aspect of the present disclosure, there is provided a method performed by a first device executing a network data analytics function (NWDAF). The method includes: receiving, from a second entity executing a network function (NF), a first message for requesting an analysis of an observed service experience, the first message including a single-network slice selection assistance information (S-NSSAI) indicating a network slice; sending, to a third entity executing an application function (AF) associated with the S-NSSAI, a second message for requesting service data associated with the analysis of the observed service experience, the second message including information about at least one application; receiving, from the third entity, service data including at least one service experience for at least one application; identifying the analysis of the observed service experience based on the service data; and sending the analysis of the observed service experience to the second entity.

[0024] In one embodiment of the present disclosure, identifying the analysis of the observed service experience for a network slice includes: sending, to a fourth entity executing a network function (NF), a third message for requesting network data associated with the information about at least one application; receiving, from the fourth entity, network data including information about a quality of service (QoS) flow; and identifying the analysis of the observed service experience based on the service data and the network data.

[0025] In one embodiment of the present disclosure, the analysis of the observed service experience includes a service experience for the network slice and at least one service experience for at least one application, wherein the service experience for the network slice includes at least one of a service experience for a UE, a service experience for a UE group, and a service experience for any UE in the network slice, wherein each of the at least one service experience for at least one application includes at least one of a service experience for a UE, a service experience for a UE group, and a service experience for any UE, and wherein the analysis of the observed service experience includes statistics on the performance of the network slice and predictions of the performance of the network slice.

[0026] In one embodiment of the present disclosure, the service experience for a network slice is determined as the average of at least one service experience for at least one application.

[0027] According to one aspect of the present disclosure, there is provided a method performed by a second entity that executes a network function (NF). The method includes: sending a first message for requesting an analysis of an observed service experience to a first entity that executes a network data analytics function (NWDAF), the first message including a single-network slice selection assistance information (S-NSSAI) indicating a network slice; and receiving the analysis of the observed service experience from the first entity, and wherein the analysis of the observed service experience is identified based on service data including at least one service experience for at least one application.

[0028] According to one aspect of the present disclosure, there is provided a first apparatus that executes a network data analytics function (NWDAF). The first apparatus includes: a transceiver; a controller coupled to the transceiver and configured to: control the transceiver to receive, from a second entity that executes a network function (NF), a first message for requesting an analysis of an observed service experience, the first message including a single-network slice selection assistance information (S-NSSAI) indicating a network slice, control the transceiver to send a second message for requesting service data associated with the analysis of the observed service experience to a third entity that executes an application function (AF) associated with the S-NSSAI, the second message including information about at least one application, control the transceiver to receive service data including at least one service experience for at least one application from the third entity, identify the analysis of the observed service experience based on the service data, and control the transceiver to send the analysis of the observed service experience to the second entity.

[0029] According to one aspect of the present disclosure, there is provided a second apparatus that executes a network function (NF). The second apparatus includes: a transceiver; a controller coupled to the transceiver and configured to: control the transceiver to send a first message for requesting an analysis of an observed service experience to a first entity that executes a network data analytics function (NWDAF), the first message including a single-network slice selection assistance information (S-NSSAI) indicating a network slice, and control the transceiver to receive the analysis of the observed service experience from the first entity, wherein the analysis of the observed service experience is identified based on service data including at least one service experience for at least one application.

[0030] Other aspects, advantages, and salient features of the present disclosure will become apparent to those skilled in the art from the following detailed description of various embodiments of the present disclosure in conjunction with the accompanying drawings.

[0031] Beneficial effects

[0032] Embodiments of the present disclosure provide methods and apparatuses for providing network slice data analysis, thereby enabling improvement in network performance and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In the following description with reference to the accompanying drawings, the above and other aspects, features, and advantages of certain embodiments of the present disclosure will become more apparent, where:

[0034] Figure 1 Shows a slice structure in a fifth-generation (5G) network according to an embodiment of the present disclosure;

[0035] Figure 2 Shows a representation of a network data analytics function (NWDAF) adopted in a 5G network according to an embodiment of the present disclosure;

[0036] Figure 3 Shows a representation of the functions of NWDAF according to the related art;

[0037] Figure 4 Shows a representation of the functions of NWDAF according to an embodiment of the present disclosure;

[0038] Figure 5 Shows a message flow according to the related art;

[0039] Figure 6 Shows a message flow according to an embodiment of the present disclosure;

[0040] Figure 7 Shows a block diagram of an entity that executes a network data analytics function (NWDAF) according to an embodiment of the present disclosure; and

[0041] Figure 8 Shows a block diagram of an entity that executes a network function (NF) according to an embodiment of the present disclosure.

[0042] Like reference numerals throughout the drawings will be understood to refer to like parts, components, and structures. DETAILED DESCRIPTION

[0043] The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of various embodiments of the present disclosure as 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 known functions and configurations may be omitted for clarity and conciseness.

[0044] The terms and words used in the following description and claims are not limited to the literal meanings, but are merely used 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 for illustrative purposes only and is not for the purpose of limiting the present disclosure as defined by the appended claims and their equivalents.

[0045] It should be understood that the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, a reference to "a component surface" includes a reference to one or more such surfaces.

[0046] The following discussion Figures 1 to 6 and the various embodiments for describing the principles of the present disclosure in this patent document are for illustration only and should not be construed in any way to limit the scope of the present disclosure. Those skilled in the art will understand that the principles of the present disclosure can be implemented in any suitably arranged system or device.

[0047] Figure 1 Fig. shows a slice structure in a 5G network according to an embodiment of the present disclosure. A network slice is a type of virtual networking architecture that allows for better network flexibility by partitioning the network architecture into virtual elements. In essence, network slicing allows multiple virtual networks to be created on top of a shared physical infrastructure.

[0048] Referring to Figure 1 , although three such slices are shown, more or fewer slices may be provided. Different slices can be configured and optimized for different functions / user profiles. In the example shown, different slices are configured for smart phones, automotive devices, and large-scale IoT devices. In practice, different slices can be configured in a very flexible manner, and importantly, the physical resources allocated to different slices can be adjusted as needed.

[0049] The problem with existing slice arrangements is that it is not always possible to obtain performance data with sufficient granularity to allow for efficient optimization and resource allocation. Alternatively, such available data is required to be processed in an Operation and Administration Maintenance (OAM) function that is remote from the Core Network (CN).

[0050] The part that permits the control of enhanced performance 5G networks involves the use of data analytics, which is employed to assist in managing and optimizing resources. The key component of this functionality is the Network Data Analytics Function (NWDAF) that is used for data collection and data analytics. The NWDAF can be used for the analysis of one or more network slices. The NWDAF can serve use cases belonging to one or several domains, such as quality of service, traffic steering, dimensioning, security.

[0051] The input data of the NWDAF can come from multiple sources, and the resulting actions undertaken by the consuming network function (NF) or application function (AF) can involve several domains (e.g., mobility management, session management, service quality management, application layer, security management, NF lifecycle management).

[0052] Figure 2 A representation of the NWDAF adopted in a 5G network according to an embodiment of the present disclosure is shown.

[0053] Referring to Figure 2 , the NWDAF implementation according to an embodiment of the present disclosure, whereby the NWDAF 10 receives inputs from one or more NFs 20, one or more AFs 30, a unified data repository (UDR) 40, and an OAM 50. The data received from these sources is processed according to one or more analysis models, and outputs are created and, in this case, fed back to one or more NFs 60, one or more AFs 70, and an OAM 80. Note that the NFs 20, 60 can be the same NF or at least have some commonalities. The AFs 30, 70 and the OAMs 50, 80 are similar.

[0054] The problem with existing NWDAF implementations in the prior art is that they are not able to provide service experience data analytics on a per-slice basis, or if they can, such analysis requires the involvement of the OAMs 50, 80, which is not desirable and takes this functionality away from the core network (especially the control plane, CP). This is useful in terms of optimizing network performance and / or configuration on a per-slice basis. This becomes even more beneficial as more and more network features are organized on a per-slice basis.

[0055] Referring to Figure 2 , embodiments of the present disclosure permit the creation and use of application service experience analysis at the slice level within the control plane, i.e., without the direct involvement of the OAM.

[0056] The operation of the embodiments is best illustrated by comparison with the operation of the prior art.

[0057] Figure 3 Shows a representation of the functions of the NWDAF according to the related art.

[0058] Referring Figure 3 , the NWDAF 100 is communicatively coupled to at least one consumer NF 110, at least one AF 120, and at least one NF (Network Data Provider) 130. Messages are passed between each of these and the NWDAF 100 as follows:

[0059] 3.1 The consumer NF 110 makes an observed service experience analysis subscription request (e.g., AppID, UE group) to the NWDAF 100. In the context of this application, "observed service experience" (OSE) and "quality of experience" (QoE) are used interchangeably.

[0060] 3.2 The NWDAF 100 may request an application service data subscription (mean opinion score) from the AF 120.

[0061] 3.3 The AF 120 reports an application service data notification (mean opinion score) to the NWDAF 100.

[0062] 3.4 The NWDAF 100 may submit an NF data request (QoS flow identifier (QFI), location, etc.) to the NF 130.

[0063] 3.5 The NF 130 provides an NF data notification (QFI, location, etc.) to the NWDAF 100.

[0064] 3.6 The NWDAF 100 implements a training algorithm to learn from the provided data.

[0065] 3.7 The NWDAF 100 provides data analysis to the consumer NF 110, including statistics on past performance or predictions of future performance.

[0066] The reference numerals (3.1, 3.2, etc.) only indicate the corresponding messages and do not imply any order of transmission. In addition, messages may or may not be sent.

[0067] Referring Figure 3 , this operation represents the prior art and completely disregards network slicing.

[0068] Figure 4 Shows a representation of the functions of the NWDAF according to an embodiment of the present disclosure.

[0069] Referring Figure 4 , the consumer NF 210, AF_1 - AF_n 220, and NF 230 are associated with Figure 3Components with similar numbers are similar and have substantially the same functions. According to an embodiment of the present disclosure, the NWDAF 200 is operable to process different messages and / or data, and is thus operable to provide analysis to the consumer NF 210 on a per-slice basis.

[0070] The messages passed between various entities are

[0071] 4.1 The consumer NF 210 makes an Observed Service Experience Analysis Subscription Request (S-NSSAI, any UE) to the NWDAF 200. Note that the S-NSSAI (Single-Network Slice Selection Assistance Information) is an identifier for a specific slice, and "any UE" refers to the presence of any UE on that slice. By setting the target of the analysis report to "any UE", the consumer NF 210 obtains analysis for any UE on that network slice. At the same time, it is possible to request analysis for one or several UEs or UE groups as well as any UE on that network slice. In this embodiment, it is assumed that analysis for any UE on that network slice is requested. As Figure 3 described, the App ID related to the identifier of the specific application for which analysis is requested can be included in the Observed Service Experience Analysis Subscription Request. In this case, the consumer NF 210 can obtain analysis for the application corresponding to the App ID operating on that network slice. Alternatively, the App ID may not be included in the request for analysis, in which case the consumer NF 210 obtains analysis for all applications operating on that network slice. In this embodiment, it is assumed that the App ID is included in Message 4.1.

[0072] 4.2 The NWDAF 200 can make an NF Data Request (QFI, location, App ID, etc.) to the NF 230.

[0073] 4.3 The NF 230 provides an NF Data Notification (QFI, location, App ID, etc.) to the NWDAF 200.

[0074] 4.4a The NWDAF 200 can request an Application_1 Service Data Subscription (Mean Opinion Score) from the AF_1 220_1. Application_1 corresponds to one of the App IDs included in Message 4.1. Message 4.4a may include the App ID corresponding to Application_1.

[0075] 4.5a The AF_1 220_1 reports an Application_1 Service Data Notification (Mean Opinion Score) to the NWDAF 200.

[0076] 4.4b and 4.5b repeat 4.4a and 4.5a n times for each application AF.

[0077] 4.6 The NWDAF 200 implements a training algorithm to learn from the provided data for slice service experience.

[0078] 4.7 The NWDAF 200 provides data analysis to the consumer NF 210, including statistics on past performance or predictions of future performance on a per-slice basis. The provided data analysis is typically statistics related to the past (e.g., QoS at a previous time) and / or predictions of future performance. The prediction can be based on past performance and / or can utilize additional information available for future events.

[0079] The reference numerals (4.1, 4.2, etc.) only indicate the corresponding messages and do not represent any sending order. Additionally, a message may or may not be sent.

[0080] The consumer NF 210 can specify whether it wants to receive the data analysis in a specific form (e.g., statistics on past performance or predictions of future performance).

[0081] As an example, the consumer NF 210 is a Network Slice Selection Function (NSSF). Examples of the operation of the NSSF are as follows:

[0082] · The NSSF monitors the network slice service experience by subscribing to the NWDAF for slice service experience analysis for the S-NSSAIs (e.g., S-NSSAI_1, S-NSSAI_2, S-NSSAI_3) in operation.

[0083] · If / When the service experience of S-NSSAI_1 is predicted to decline or past statistics indicate a continuous decline, the NSSF can detect the decline in service experience.

[0084] · Then, the NSSF can perform slice-level load distribution using new UE registrations and / or PDU session establishments and assign them to different S-NSSAIs (e.g., S-NSSAI_2).

[0085] · Thus, the NSSF assists in guaranteeing the Service Level Agreement (SLA) of network slices in the 5GC.

[0086] In addition, in slice-level load distribution, the NSSF can efficiently perform slice selection based on service experience analysis on network slices. More specifically, when a UE needs to register with the network to obtain authorization to receive services, the UE initiates the registration process by sending a registration request to the Access and Mobility Management Function (AMF). The AMF subscribes to network slice information from the NSSF by invoking the Nnssf_NSSelectionGet service operation. As described above, the NSSF collects network slice service experience by subscribing to NWDAF slice service experience analysis for S-NSSAIs (e.g., S-NSSAI_1, S-NSSAI-2, S-NSSAI3, etc.). The NSSF efficiently selects a network slice for serving the UE based on the service experience corresponding to the S-NSSAI and notifies the AMF of the information about the selected network slice.

[0087] The above example illustrates how the consumer NF 210 can benefit from improved data analysis at the slice level, which can not only improve the user experience but also improve the overall network performance by ensuring better load distribution and allocation of limited resources.

[0088] Examples of NF (Network Data Provider) 130, 230 entities include Access and Mobility Management Function (AMF), Session Management Function (SMF), and User Plane Function (UPF).

[0089] By comparing Figure 3 and Figure 4 , the different parameters required to implement the embodiments of the present disclosure are clear. In particular, the use of S-NSSAIs associated with any UE utilizes defined slice identifiers that were not previously used in the analysis provided.

[0090] The advantage of performing the analysis provided away from the OAM in this way is that the OAM is typically likely to be a bottleneck in the network, and by performing these operations in the control plane (CP), the overall network performance can be enhanced. In addition, the OAM may be inflexible, and the embodiments of the present disclosure can be more easily adjusted to meet the needs of network operators.

[0091] By providing the analysis according to the embodiments of the present invention, the NF is able to utilize statistics and predictions to replace or supplement the functions previously provided by the OAM.

[0092] In addition, the embodiments of the present disclosure are capable of distinguishing the observed service experience for application performance from the observed service experience for UE (or UE group) performance.

[0093] Each slice metric is derived from the service experience values observed for each UE.

[0094] By means ofFigure 4 In the illustrated embodiment, it is determined that slice-level analysis is possible by leveraging the observed service experience for the application and the observed service experience analysis for a single UE or UE group. This is due to Figure 4 the new parameters introduced and described.

[0095] Furthermore, embodiments of the present disclosure permit multi-application aggregation for slice QoE analysis by means of a mechanism that aggregates the observed service experience analysis for the application to obtain a slice-wide analysis in the control plane CP.

[0096] Furthermore, the mapping and aggregation of multi-UE multi-application slice QoE analysis are provided to a single slice by means of the mapping of a set of UEs and a set of applications. In addition, a mechanism is provided that aggregates the observed service experience analysis for a UE or UE group to obtain an analysis of the slice scope in the control plane CP.

[0097] Embodiments of the present disclosure utilize the observed service experience to derive slice-level analysis at the NWDAF 200. This is achieved by the NWDAF 200 requesting the necessary data related to the application and / or UE.

[0098] In one embodiment of the present disclosure, QoE analysis can be used to guarantee the service level agreement (SLA) for each slice. This can be done by aggregating the application service experience on the same slice and / or by using the set of UEs using several applications to map and aggregate the service experience on the same slice.

[0099] In another embodiment of the present disclosure, the observed service experience analysis is provided to the application, and the slice QoE analysis is derived by averaging. In this alternative embodiment, the analysis of the slice scope is derived by adopting the observed service experience analysis for the set of applications. Aggregation mechanisms (e.g., averaging) are also used to derive the slice QoE metric as data analysis.

[0100] A specific problem solved and overcome by embodiments of the present disclosure is that the prior art observed service experience output analysis is not suitable for each slice QoE measurement. This is because deriving a suitable slice QoE metric in a multi-UE multi-application scenario on a single slice requires application-UE mapping, yet such mapping is not provided as an analysis output in the prior art. In the prior art, attempts have been made to address this shortcoming by providing a list of subscription permanent identifiers (SUPI) requesting analysis and a list of applications on the slice, but no mapping from the application to the UE is provided. Accordingly, the slice QoE metric derived using the prior art analysis derived from such output analysis will be incorrect because the request may be for only a subset of UEs.

[0101] In yet another embodiment of the present disclosure, a mapping of UEs obtained from observed user experiences to optimized slice objectives applied to slice QoE analysis is included. The mapping can be performed by providing a structure of the following output parameters:

[0102] · The S-NSSAI is given as an output for the observed service experience

[0103] · A list of applications is provided to the slice, each application including a list of SUPIs that utilize such a slice

[0104] · The observed service experience is provided as an output parameter of the list

[0105] As an alternative embodiment, the IDs of the registered subscribers of the slice can also be provided.

[0106] To aggregate per-UE per-application measurements, averaging can be used. The averaging can be a simple arithmetic mean, using the median value, root mean square average, or any form of averaging suitable for the environment.

[0107] Figure 5 and Figure 6 are respectively similar to Figure 1 and Figure 2 and show a flow in a manner typically used in applicable standard specifications.

[0108] Figure 5 Shows a message flow according to the related art.

[0109] Referring to Figure 5 , the prior art call flow involves Figure 3 the prior art system shown.

[0110] Figure 6 Shows a message flow according to an embodiment of the present disclosure.

[0111] Referring to Figure 6 , in operation 1, the consumer 210 requests the analysis ID "service experience" for all UEs or a group of UEs on a specific network slice identified by the S-NSSAI.

[0112] In operation 2a, the NWDAF 200 can subscribe to network data from the NF 230 by invoking the Nnf_EventExposure_Subscribe service operation using an event ID.

[0113] In addition, it may be necessary to collect service experience data from multiple applications.

[0114] In operations 2b and 2c, if each application is hosted in a separate AF, the NWDAF 200 may subscribe to the service data in Table 1 from different AFs (220_1, 220_2, etc.) by invoking the Naf_EventExposure_Subscribe or Naf_EventExposure_Subscribe service for each application (event ID = service data, event filter information = (application ID, area of interest), target of event reporting = any UE), as defined in TS 23.502. Figure 6 An example process with two AFs 220_1, 220_2 is shown. If one AF provides service experience data for multiple applications, the NWDAF 200 uses the Naf_EventExposure_Subscribe service operation to provide the AF with a set of application IDs, as defined in TS 23.502.

[0115] Table 1

[0116]

[0117] In operation 3, the NWDAF 200 provides data analysis to the consumer 210 using data received from at least one of the AFs 220_1, 220_2 (directly or via the NEF 240), and possibly from at least one NF 230, including statistics on past performance or predictions of future performance on a per-slice basis.

[0118] Statistics on past performance are provided as Table 2 below.

[0119] Table 2

[0120]

[0121]

[0122] Predictions of future performance are provided as Table 3 below.

[0123] Table 3

[0124]

[0125]

[0126] Figure 7 A block diagram of an entity that performs a network data analytics function (NWDAF) according to an embodiment of the present disclosure is shown.

[0127] Refer to Figure 7, the entity implementing the NWDAF 700 of the embodiment includes a transceiver 702, a memory 704, and a controller 706.

[0128] The transceiver 702 is capable of sending signaling to other entities and receiving signaling from other entities.

[0129] The memory 704 is capable of storing at least one of the following: information related to the entity implementing the NWDAF 700 and information sent / received via the transceiver 702.

[0130] The controller 706 is capable of controlling the operation of the entity implementing the NWDAF 700. The controller 706 is capable of controlling the entity implementing the NWDAF to perform operations related to the entity implementing the NWDAF as described in the embodiment.

[0131] Figure 8 A block diagram of an entity implementing a network function (NF) according to an embodiment of the present disclosure is shown.

[0132] Referring to Figure 8 , the entity implementing the NF 800 of the embodiment includes a transceiver 802, a memory 804, and a controller 806.

[0133] The transceiver 802 is capable of sending signaling to other entities and receiving signaling from other entities.

[0134] The memory 804 is capable of storing at least one of the following: information related to the entity implementing the NF 800 and information sent / received via the transceiver 802.

[0135] The controller 806 is capable of controlling the operation of the entity implementing the NF 800. The controller 806 is capable of controlling the entity implementing the NF to perform operations related to the entity implementing the NF as described in the embodiment.

[0136] At least some of the example embodiments described herein can be constructed, in part or in whole, using dedicated special-purpose hardware. Terms used herein, such as "component", "module", or "unit" can include, but are not limited to, hardware devices that perform certain tasks or provide associated functionality, such as circuits in the form of discrete or integrated components, field-programmable gate arrays (FPGAs), or application-specific integrated circuits (ASICs). In some embodiments of the present disclosure, the described elements can be configured to reside on a tangible, permanent, addressable storage medium and can be configured to execute on one or more processors. In some embodiments, these functional elements can include, by way of 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, circuits, data, databases, data structures, tables, arrays, and variables. Although example embodiments have been described with reference to the components, modules, and units discussed herein, such functional elements can be combined into fewer elements or separated into additional elements. Various combinations of alternative features have been described herein, and it should be understood that the described features can be combined in any suitable combination. In particular, the features of any one example embodiment can be combined, as appropriate, with the features of any other embodiment of the present disclosure, unless such a combination is mutually exclusive. The term "comprising" or "including" throughout this specification means including the specified components, but not excluding the presence of other components.

[0137] Take note of all papers and documents related to this application that are filed simultaneously with or before this specification, as well as all papers and documents that are publicly available for inspection in conjunction with this specification, and the contents of all such papers and documents are incorporated herein by reference.

[0138] All features (including any appended claims, abstract, and drawings) disclosed in this specification and / or all operations of any method or process so disclosed can be combined in any combination, unless at least some of such features and / or operations are mutually exclusive.

[0139] Unless otherwise expressly stated, each feature disclosed in this specification (including any appended claims, abstract, and drawings) can be replaced by an alternative feature serving the same, equivalent, or similar purpose. Thus, unless otherwise expressly stated, each feature disclosed is only one example of a general series of equivalent or similar features.

[0140] The present disclosure is not limited to the details of the foregoing embodiments. The present disclosure extends to any novel one or any novel combination of the features disclosed in this specification (including any appended claims, abstract and drawings), or to any novel one or any novel combination of the operations of any method or process so disclosed.

[0141] Although the present disclosure has been shown and described with reference to various embodiments thereof, those skilled in the art will understand that various changes may be made therein in form and detail without departing from the spirit and scope of the present disclosure as defined by the appended claims and their equivalents.

Claims

1. A method performed by a Network Data Analytics Function (NWDAF) entity in a communication system, the method comprises: Receiving a first message from a Network Function (NF) entity, the first message being for requesting an analysis with an identifier (ID) set to service experience for all User Equipments (UEs) or a group of UEs on a network slice, wherein the network slice is identified by a Single Network Slice Selection Assistance Information (S-NSSAI); Subscribing to service data of multiple applications related to the observed service experience information of the network slice; Based on the service data, determining the observed service experience information of the network slice by averaging the service experiences on the multiple applications on the network slice; and Sending a second message to the NF entity for providing the observed service experience information of the network slice.

2. The method according to claim 1, wherein, The observed service experience information is provided by service experience statistical information or service experience prediction information.

3. The method according to claim 1, wherein, The observed service experience information of the network slice includes the service experience information of all UEs or a group of UEs in the network slice.

4. The method according to claim 1, wherein, The observed service experience information of the network slice includes a list of Subscription Permanent Identifiers (SUPI) to which the service experience applies.

5. A Network Data Analytics Function (NWDAF) entity in a communication system, the NWDAF entity comprises: A transceiver; and A controller, coupled to the transceiver and configured to: Receive a first message from a Network Function (NF) entity, the first message being for requesting an analysis with an identifier (ID) set to service experience for all User Equipments (UEs) or a group of UEs on a network slice, wherein the network slice is identified by a Single Network Slice Selection Assistance Information (S-NSSAI); Subscribe to service data of multiple applications related to the observed service experience information of the network slice; Based on the service data, determine the observed service experience information of the network slice by averaging the service experiences on the multiple applications on the network slice; and Send a second message to the NF entity for providing the observed service experience information of the network slice.

6. The NWDAF entity according to claim 5, wherein, The observed service experience information is provided by service experience statistical information or service experience prediction information.

7. The NWDAF entity according to claim 5, wherein, The observed service experience information of the network slice includes the service experience information of all UEs or a group of UEs in the network slice.

8. The NWDAF entity according to claim 5, wherein, The observed service experience information of the network slice includes a list of Subscription Permanent Identifiers (SUPI) to which the service experience applies.

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