Method and apparatus for setting timer values in a network

Abstract

The present disclosure provides a method for setting a value of an inactivity timer for transitioning between states of a data session in a network comprising a first entity and a second entity providing network analytics. The method comprises obtaining, by the second entity, input data comprising communication description information of at least one user equipment (UE), and providing, by the second entity, output analytics generated based on the input data to the first entity, the output analytics comprising UE communication analytics per data session, wherein the output analytics are used to determine whether to update the value of the inactivity timer of the data session.

Description

Technical Field

[0001] This disclosure relates to methods, apparatus, and systems for setting timer values ​​for transitions between states of a data session in a network. Background Technology

[0002] Given the successive generations of wireless communication development, these technologies have primarily been developed for human-oriented services such as voice calls, multimedia services, and data services. With the commercialization of fifth-generation (5G) communication systems, the number of connected devices is expected to grow exponentially. These will increasingly connect to communication networks. Examples of the Internet of Things (IoT) might include vehicles, robots, drones, home appliances, displays, smart sensors connected to various infrastructures, construction machinery, and factory equipment. Mobile devices are expected to evolve in various forms, such as augmented reality glasses, virtual reality headsets, and holographic devices. Efforts are underway to develop improved 6G communication systems to provide a wide range of services by connecting hundreds of billions of devices and things in the sixth-generation (6G) era. For these reasons, 6G communication systems are referred to as "super 5G" systems.

[0003] The 6G communication system, which is expected to be commercialized around 2030, will have peak data rates in the megabit (1,000 gigabit) bps range and radio latency of less than 100 μsec, making it 50 times faster than 5G communication systems and having 1 / 10 of their radio latency.

[0004] To achieve such high data rates and ultra-low latency, 6G communication systems have been considered for implementation in the terahertz band (e.g., the 95 GHz to 3 THz band). It is anticipated that technologies capable of ensuring signal transmission distance (i.e., coverage) will become even more critical, as path loss and atmospheric absorption in the terahertz band are more severe than those in the millimeter-wave (mmWave) band introduced in 5G. As a key technology for ensuring coverage, it is necessary to develop radio frequency (RF) components, antennas, and novel waveforms with better coverage than orthogonal frequency division multiplexing (OFDM), beamforming, massive MIMO, full-dimensional MIMO (FD-MIMO), array antennas, and multi-antenna transmission technologies such as massive MIMO. Furthermore, new technologies for improving terahertz band signal coverage have been discussed, such as metamaterial-based lenses and antennas, orbital angular momentum (OAM), and reconfigurable intelligent surfaces (RIS).

[0005] Furthermore, to improve spectrum efficiency and overall network performance, the following technologies have been developed for 6G communication systems: full-duplex technology enabling uplink and downlink transmissions to use the same frequency resources simultaneously; network technologies that comprehensively utilize satellites, high-altitude platform stations (HAPS), etc.; improved network architectures to support mobile base stations, enabling network operation optimization and automation; dynamic spectrum sharing technology based on spectrum usage prediction and conflict avoidance; the use of artificial intelligence (AI) in wireless communication to improve overall network operation by leveraging AI from the 6G development design phase and internalizing end-to-end AI support functions; and next-generation distributed computing technologies that overcome user equipment (UE) computing power limitations through ultra-high-performance communication and computing resources accessible on the network, such as mobile edge computing (MEC) and the cloud. In addition, efforts are continuing to enhance connectivity between devices, optimize networks, promote the software-defined networking of network entities, and increase the openness of wireless communication by designing new protocols to be used in 6G communication systems, developing mechanisms for achieving hardware-based secure environments and secure data use, and developing technologies to maintain privacy.

[0006] The research and development of 6G communication systems in hyper-connectivity, including person-to-machine (P2M) and machine-to-machine (M2M) communication, is expected to bring the next hyper-connected experience. Specifically, services such as truly immersive extended reality (XR), high-fidelity mobile holograms, and digital replicas are anticipated to be provided through 6G communication systems. Furthermore, services such as remote surgery for enhanced security and reliability, industrial automation, and emergency response will be offered via 6G communication systems, enabling the technology to be applied to various fields such as industry, healthcare, automotive, and home appliances.

[0007] This article references the following documents:

[0008] [1] 3GPP Technical Report (TR) 28.809: Study on Enhanced Management Data Analytics (MDA), Version 17 (06-2020).

[0009] [2] 3GPP TS 23.288: 5G System (5GS) Architecture Enhancement for Supporting Network Data Analytics Services, Version 16 (06-2020).

[0010] [3] 3GPP TR 23.700-91: Research on network automation enabling factors for 5G systems (5GS); Vol. 2, pp. 17 (06-2020).

[0011] [4] 3GPP TS 23.502: Program for 5G Systems (5GS), version 16 (06-2020).

[0012] The various acronyms, abbreviations, and definitions used in this disclosure are defined at the end of this specification.

[0013] Artificial intelligence (AI) has been identified as a key enabling factor for end-to-end network automation in 5G across all network domains, including those affected by standardized processes in the radio access network (RAN), core network (CN), and management systems (also known as operations, administration, and maintenance, OAM). Consequently, standardization and industry bodies are now developing specifications to support data analytics, enabling AI models to assist in the increasingly complex tasks of autonomously operating and managing networks.

[0014] In the RAN sector, leading operators established the groundbreaking O-RAN Alliance in 2018 with the vision of developing open specifications for open, efficient RAN, leveraging AI to automate different network functions (NFs) and reduce operating expense (OPEX).

[0015] In addition, 3GPP’s standardization support for data analytics is particularly advanced in version 16 on the CN side and control plane. A data analytics framework anchored in the new so-called Network Data Analytics Function (NWDAF) has been defined, which is located within 5GC as a network function that follows the service-based architecture principles of 5GC and aims to enhance the multiple control plane functions of the network. In addition, in terms of OAM, 3GPP has also specified the Management Data Analytics Service (MDAS) to help handle long-term management aspects of the network [1]. The joint operation of the RAN Analytics Entity, Network Data Analytics Function (NWDAF) and MDAS is still carried out within the relevant organizations. Summary of the Invention

[0016] Technical issues

[0017] The goal is to be able to activate and deactivate 5G Protocol Data Unit (PDU) sessions on the UE. Such functionality typically resides within the CN's control plane because it requires making decisions on a rapid timescale, often much faster than permitted by network management and coordination systems.

[0018] The 3GPP 5G standard has developed support for individual and dynamic activation and deactivation of each PDU session that the UE has established. However, the different transitions from PDU session deactivation to activation and the associated UE states result in significant control signaling overhead in the network.

[0019] Therefore, such transitions need to be carefully controlled so that the gains from deactivating the PDU session are not offset by the signaling overhead caused by the transition. Adaptive inactivity timers for individual UEs are a tool proposed to address this issue, but they do not consider the per-PDU session granularity required to optimize inactivity timer values ​​in 5G networks. Furthermore, setting appropriate values ​​at each time step is based on heuristic algorithms, thus leading to suboptimal performance.

[0020] To minimize UE battery power consumption and network resource usage, assigning an appropriate value to the inactivity timer is crucial. The inactivity timer is designed to control the timing of PDU sessions and ultimately UE state transitions. Shortening the inactivity timer can help the UE consume less battery power by keeping it in the CM-IDLE state for a longer period when the radio module is off; however, this leads to frequent transitions between the PDU session active state and the UE CM state, resulting in significant control signaling overhead in the network. Specifically, when changing the UE state from CM-IDLE to CM-CONNECTED, the required paging message is broadcast across several cells, consuming considerable radio resources. However, extending the inactivity timer too much can reduce the efficiency of radio resource utilization and lead to more battery power consumption in UEs experiencing a long tail, during which the UE remains in CM-CONNECTED before transitioning to CM-IDLE.

[0021] The desired technology is one that allows setting or adjusting the value of inactive timers to optimize overall performance.

[0022] The above information is presented as background information only to aid in understanding this disclosure. No determination or assertion is made regarding whether any of the above can be applied to this disclosure as prior art.

[0023] For example, certain examples of this disclosure may provide methods, apparatus, and systems for setting the value of an inactivity timer for transitioning between active and inactive states of a PDU session in a 3GPP 5G network based on NWDAF analysis.

[0024] Technical solution

[0025] The aspects of this disclosure at least address the aforementioned problems and / or disadvantages, and at least provide the following advantages. Therefore, one aspect of this disclosure is to provide a method and apparatus for setting the value of a timer used for transitions between states of a data session in a network.

[0026] Additional aspects will be set forth in part in the description which follows, and will be apparent in part from the description, or may be learned by practice of the presented embodiments.

[0027] According to one aspect of this disclosure, a method is provided for setting the value of an inactivity timer for transitioning between the states of a data session in a network including a first entity and a second entity providing network analysis. The method, performed by the second entity, includes obtaining input data including communication description information of at least one user equipment (UE), and providing the first entity with output analysis generated based on the input data, the output analysis including UE communication analysis for each data session, wherein the output analysis is used to determine whether to update the value of the inactivity timer for the data session.

[0028] According to another aspect of this disclosure, a telecommunications network operable to perform the method of the first aspect is provided.

[0029] According to another aspect of this disclosure, a method is provided for setting the value of an inactivity timer for transitioning between states of a data session in a network including a first entity and a second entity providing network analysis. The method, performed by the first entity, includes: sending input data, including communication description information of at least one user equipment (UE), to the second entity; receiving, by the first entity, output analysis generated based on the input data, the output analysis including UE communication analysis for each data session; and determining the transition between states of the data session by using the value of an inactivity timer for the data session updated based on the output analysis.

[0030] According to another aspect of this disclosure, an apparatus is provided for setting the value of an inactivity timer for transitioning between the states of a data session in a network including a first entity and a second entity providing network analysis. The apparatus for the second entity includes a transceiver and a processor coupled to the transceiver, the processor being configured to perform the following operations: obtaining input data including communication description information of at least one user equipment (UE); and providing the first entity with an output analysis generated based on the input data, the output analysis including UE communication analysis for each data session, wherein the output analysis is used to determine whether to update the value of the inactivity timer for the data session.

[0031] According to another aspect of this disclosure, an apparatus is provided for setting the value of an inactivity timer for transitioning between states of a data session in a network including a first entity and a second entity providing network analysis. The apparatus for the first entity includes a transceiver and a processor coupled to the transceiver, the processor being configured to perform the following operations: transmitting input data to the second entity including communication description information of at least one user equipment (UE); receiving from the second entity an output analysis generated based on the input data, the output analysis including UE communication analysis for each data session; and determining the transition between states of the data session by using the value of an inactivity timer for the data session updated based on the output analysis.

[0032] The purpose of certain examples of this disclosure is to at least partially address, resolve, and / or mitigate at least one problem and / or disadvantage associated with related technologies, such as at least one problem and / or disadvantage described herein. The purpose of certain examples of this disclosure is to provide at least one advantage relative to related technologies, such as at least one advantage described herein.

[0033] This disclosure is defined in the independent claims. Advantageous features are defined in the dependent claims.

[0034] Other aspects, advantages, and distinctive features of this disclosure will become apparent to those skilled in the art from the following detailed description of various embodiments disclosed in conjunction with the accompanying drawings. Attached Figure Description

[0035] The above and other aspects, features, and advantages of certain embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings, in which:

[0036] Figure 1 The operation of the Network Data Analysis Function (NWDAF) according to an embodiment of this disclosure is illustrated;

[0037] Figure 2 Examples of this disclosure based on NWDAF and multiple input data sources are shown according to embodiments of this disclosure;

[0038] Figure 3a and Figure 3b The process of supporting NWDAF-based user plane optimization according to various embodiments of this disclosure is illustrated; and

[0039] Figure 4 This is a block diagram of network entities that can be used in some examples according to embodiments of this disclosure.

[0040] In all the accompanying drawings, similar reference numerals will be understood to refer to similar parts, components and structures. Detailed Implementation

[0041] The following description, provided with reference to the accompanying drawings, is intended to aid in a full understanding of the various embodiments of this disclosure as defined by the claims and their equivalents. It includes various specific details to aid understanding, but these are to be considered exemplary only. 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 this disclosure. Furthermore, for clarity and brevity, descriptions of well-known functions and structures may be omitted.

[0042] The terms and words used in the following description and claims are not limited to their documentary meaning, but are used solely by the inventors to enable a clear and consistent understanding of this disclosure. Therefore, it will be apparent to those skilled in the art that the following description of various embodiments of this disclosure is provided for illustrative purposes only and is not intended to limit the disclosure as defined by the appended claims and their equivalents.

[0043] It should be understood that the singular forms “a,” “an,” and “the” include plural indicators unless the context explicitly specifies otherwise. Thus, for example, referring to “component surface” includes referring to one or more such surfaces.

[0044] Identical or similar components may be indicated by the same or similar reference numerals, although they may be shown in different figures.

[0045] For the sake of clarity and brevity, and to avoid obscuring the subject matter of this disclosure, detailed descriptions of techniques, structures, constructions, functions, or processes known in the art may be omitted.

[0046] The terms and words used herein are not limited to their documentary or standard meanings, but are used solely to enable a clear and consistent understanding of this disclosure.

[0047] Throughout the description and claims of this specification, the words “constituting,” “comprising,” and “including,” as well as variations of these words such as “comprising” and “including,” mean “including but not limited to,” and are not intended to (and do not exclude) other features, elements, components, integrals, steps, processes, operations, functions, characteristics, properties, and / or combinations thereof.

[0048] For example, a reference to an "object" includes a reference to one or more such objects.

[0049] Throughout the description and claims of this specification, the language in the general form of “X for Y” (where Y is an action, process, operation, function, activity or step, and X is a means for performing that action, process, operation, function, activity or step) includes means X that is specifically, but not necessarily exclusively adapted, configured or arranged to perform Y.

[0050] Features, elements, components, integers, steps, processes, operations, functions, characteristics, properties, and / or combinations thereof described or disclosed in connection with a particular aspect, embodiment, example, or claim of this disclosure shall be construed as applicable to any other aspect, embodiment, example, or claim described herein, unless incompatible therewith.

[0051] Certain examples of this disclosure provide methods, apparatus, and systems for setting values ​​for timers used to transition between states of a data session in a network. The following examples are applicable to 3GPP 5G and use terminology associated with it. For instance, certain examples of this disclosure provide methods, apparatus, and systems for setting values ​​for an inactivity timer used to transition between active / inactive states of a PDU session in a 3GPP 5G network based on NWDAF analysis. However, those skilled in the art will understand that the techniques disclosed herein are not limited to these examples or 3GPP 5G and can be applied to any suitable system or standard, such as one or more existing and / or future generation wireless communication systems or standards.

[0052] For example, the functionality of the various network entities and other features disclosed herein can be applied to corresponding or equivalent entities or features in other communication systems or standards. Corresponding or equivalent entities or features can be considered as entities or features performing the same or similar roles, functions, operations, or purposes within the network.

[0053] For example, the functionality of NWDAF in the following examples can be applied to any other suitable type of entity that provides network analysis; the functionality of User Plane Function (UPF) in the following examples can be applied to any other suitable type of entity that provides user plane functions; the functionality of Access and Mobility Management Function (AMF) in the following examples can be applied to any other suitable type of entity that performs mobility management functions; the functionality of Session Management Function (SMF) in the following examples can be applied to any other suitable type of entity that performs session management functions; and the functionality of AF in the following examples can be applied to any other suitable type of entity that performs the corresponding application functions.

[0054] Those skilled in the art will understand that this disclosure is not limited to the specific examples disclosed herein. For example:

[0055] The technologies disclosed in this article are not limited to 3GPP 5G.

[0056] One or more entities in the examples disclosed herein can be replaced by one or more alternative entities that perform equivalent or corresponding functions, procedures, or operations.

[0057] One or more messages in the examples disclosed herein can be replaced by one or more alternative messages, signals, or other types of information carriers that convey equivalent or corresponding information.

[0058] One or more additional elements, entities, and / or messages may be added to the examples disclosed herein.

[0059] In some examples, one or more unnecessary elements, entities, and / or messages may be omitted.

[0060] In one example, the functionality, process, or operation of a particular entity can be divided among two or more separate entities in another example.

[0061] In one example, the functionality, process, or operation of two or more independent entities can be performed by a single entity in an alternative example.

[0062] In one example, the information carried by a particular message can be carried by two or more separate messages in another example.

[0063] In one example, information carried by two or more separate messages can be carried by a single message in another example.

[0064] In alternative examples, the order of operations can be modified if possible.

[0065] Information transmission between network entities is not limited to messages of a specific form, type, and / or order as described in conjunction with the examples disclosed herein.

[0066] Some examples of this disclosure may be provided in the form of an apparatus / device / network entity configured to perform one or more defined network functions and / or methods thereof. Some examples of this disclosure may be provided in the form of a system (e.g., a network) and / or methods thereof including one or more such apparatus / device / network entities.

[0067] The network may include one or more of the following: User Equipment (UE), Radio Access Network (RAN), Access and Mobility Management Function (AMF) entity, Session Management Function (SMF) entity, User Plane Function (UPF) entity, Network Data Analysis Function (NWDAF) entity, Application Function (AF) entity, and one or more other Network Function (NF) entities.

[0068] Specific network functions can be implemented as network elements on dedicated hardware, software instances running on dedicated hardware, or virtualized functions instantiated on an appropriate platform (e.g., on cloud infrastructure). NF services can be defined as functions that are presented by an NF through a service-based interface and consumed by other authorized NFs.

[0069] As mentioned above, a technique is needed to set or adjust the value of inactive timers to optimize overall performance.

[0070] Some examples of this disclosure are able to optimize the trade-off between UE battery consumption and network resource efficiency by leveraging a standardized data analytics framework. Therefore, compatible AI-based solutions using data analytics can be based on the NWDAF framework. A brief overview of the NWDAF framework, as defined for example in [2], will now be described.

[0071] In this disclosure, the following acronyms, abbreviations and definitions may be used.

[0072] 3GPP: Third Generation Partnership Project

[0073] 5G: Fifth Generation

[0074] 5GC: 5G Core Network

[0075] 5GS: 5G system

[0076] AF: Application Functions

[0077] AI: Artificial Intelligence

[0078] AMF: Access and Mobility Management Function

[0079] CM: Connection Management

[0080] CN: Core Network

[0081] CPU: Central Processing Unit

[0082] DL: Downlink

[0083] DNN: Data Network Name

[0084] gNB: 5G base station

[0085] GPSI: Public Subscription Identifier

[0086] ID: Identifier / Identifier

[0087] LTE: Long Term Evolution

[0088] MDA: Management Data Analysis

[0089] MDAS: Managed Data Analytics Service

[0090] N4: Interface between SMF and UPF

[0091] NF: Network Functions

[0092] NG: Next Generation

[0093] NRF: Network Repository Functionality

[0094] NWDAF: Network Data Analysis Function

[0095] OAM: Operation and Maintenance

[0096] OPEX: Operating Expenses

[0097] PDU: Protocol Data Unit

[0098] RAN: Radio Access Network

[0099] Rel: Version

[0100] RRC: Radio Resource Control

[0101] SLA: Service Level Agreement

[0102] SMF: Session Management Function

[0103] S-NSSAI: Auxiliary Information for Single Network Slice Selection

[0104] SUPI: Subscription Permanent Identifier

[0105] TA: Tracking Area

[0106] TAC: Type Assignment Code

[0107] TR: Technical Report

[0108] TS: Technical Specification

[0109] UE: User Equipment

[0110] UL: Uplink

[0111] UPF: User-Face Functionality

[0112] Figure 1 The operation of NWDAF according to an embodiment of this disclosure is illustrated. The recently frozen 3GPP release 16 has specified, as... Figure 1 The NWDAF frame shown. In Figure 1 In the basic operation of NWDAF shown, the consumer requests data analysis from NWDAF, and NWDAF collects data from different entities to perform training and inference before generating output analysis.

[0113] refer to Figure 1 Analysis consumer 102 can request specific types of data analysis from NWDAF 100, which can be provided by NWDAF 100 in the form of statistics and / or predictions. The analysis consumers (e.g., analysis consumer 102) defined in version 16 are 5GC NF, Application Function (AF), and OAM. NWDAF 100 then triggers input data collection through the public framework defined in [2], where the input data sources are (e.g., 5GC NF 104, AF 106, and / or OAM 108).

[0114] The collected data is then used by the NWDAF 100 to perform training and inference, and may be used by the AI ​​engine, but the model definition is outside the scope of standardization to provide sufficient flexibility to vendors. This also means that the AI ​​engine may reside outside of the NWDAF 100 itself, and the next version of the standard (version 17) has begun to explore any necessary interface standardization to achieve such NWDAF functional decomposition [3]. The same considerations apply to the input data collection module. In some examples of this disclosure, it is assumed that the AI ​​engine and the input data collection module reside within the NWDAF 100, although this disclosure is not limited to this case.

[0115] In any case, the inference results are then fed into the analytics production entity within the NWDAF 100, which delivers the statistics and / or forecasts requested by the service consumer.

[0116] 3GPP Release 16 also introduced many types of data analysis as described in [2], including analysis of network slice and application service experience, NF and network slice load, network performance, UE aspects (communication, mobility, expected and abnormal behavior), etc.

[0117] In addition to the basic operations mentioned above, the ongoing version 17 is expanding the version 16 NWDAF framework by addressing many new use cases and key issues, including the aforementioned NWDAF feature decomposition, the architecture and interaction of multiple NWDAF instances, an efficient data collection mechanism, and support for network slicing service level agreement (SLA) guarantees.

[0118] Based on the framework described above, certain examples of this disclosure directly embed autonomous capabilities within the 5G architecture to intelligently and dynamically set inactive timer values ​​to each UE's 5G PDU session. This approach has the advantage of being highly achievable in current and at least near-future networks, as the basic data collection capabilities of NWDAF used in certain examples of this disclosure are already defined in the specification.

[0119] The following section describes the instantiation of the framework for the above problem, as well as the description of the AI ​​problem constructed in the context of NWDAF data analysis.

[0120] However, those skilled in the art will understand that the techniques described herein are not limited to setting or adjusting timer values, but can be used to set or adjust any other suitable parameters in a network.

[0121] The following describes an AI-based technique utilizing NWDAF for setting / adjusting inactivity timers for activating and deactivating PDU sessions associated with multiple services consumed by the UE. Specifically, the overall NWDAF-based technique is described, highlighting its applicability to current standardized networks. A detailed process for instantiating a specific framework is also described.

[0122] Figure 2 An overall NWDAF-based design utilizing a version 16 data analytics framework (e.g., described in [1]) according to an embodiment of this disclosure is illustrated. This example is based on multiple input data sources (e.g., OAM 202, SMF 210, UPF / AF 204, and AMF 206, and optionally NG-RAN and UE), an AI-based training and inference module in NWDAF 200, and output analysis delivered to SMF 210 and forwarded to UPF 208. Those skilled in the art will understand that this disclosure is not limited to these examples.

[0123] refer to Figure 2 The internal NWDAF architecture in this example follows Figure 1 The general principle illustrated here is that general analytical models outside the scope of standardized work have been replaced by inference-based, trained models, which will be described further below. Furthermore, Figure 2 The optimal inactive timer value for agent learning is shown, and the input data source for the required analysis is provided.

[0124] To respect the framework already agreed upon and frozen in 3GPP, some examples of this disclosure may only require supported 5GC entities (i.e., SMF 210, UPF 208, AMF 206), AF 204, and OAM202 to provide input data. However, this disclosure is not limited to this. For example, Figure 2 The specification indicates that various other entities, namely NG-RAN and UE, can provide the required input data to NWDAF 200 that are not currently supported in the standard. In some examples, these additional entities are not required because they can be replaced by currently supported alternative entities. Future versions of the standard may support these additional entities as data sources.

[0125] Furthermore, in the following text, in the context of AI-based training and inference models, the specific input data that can be used is described. In some of the examples described below, all input data can be mapped to standardized NWDAF input data, such as UE communication data 222 (e.g., including start and end timestamps, uplink and downlink data rates, traffic volume, etc.), cell load information 220 measured by the number of active PDU sessions, and UE type [8]. In some examples, in contrast to UE communication data and cell load, UE type or UE information 224 may only need to be collected once, as it does not change during network operation.

[0126] Regarding the output analysis provided by NWDAF 200, certain examples of this disclosure can conform to the current 3GPP framework by generating data analyses (e.g., including UE communication analysis 226 and / or network performance analysis) in the form of "best predicted values" for session inactivity timers, which can be directly fed into SMF 210. NWDAF 200 can also provide past statistics on timer values.

[0127] Therefore, the data analysis delivered by NWDAF 200 can then be used by SMF 210 to (i) activate or deactivate the PDU session when needed, and (ii) use NWDAF predictions to update timer values ​​(e.g., timer value 228 for the PDU session) and notify UPF 208 of such updates. These two actions can be performed by SMF 210, for example, by following the standardized procedures for PDU session activation and deactivation and user plane management as defined in [4].

[0128] Data Analysis

[0129] In some examples, to achieve user plane connection optimization based on PDU session timers, the following existing NWDAF analyses defined in [2] can be used. However, in their current form, they may not be able to support the various examples of this disclosure. Therefore, some examples of this disclosure extend the current definition as described below. UE communication analysis 226 and network performance analysis are described below. However, those skilled in the art will understand that this disclosure is not limited to these specific examples.

[0130] UE Communication Analysis

[0131] The NWDAF, which supports UE communication analysis, can collect communication descriptions for each application from the AF. In some examples, if the consumer NF provides an application ID, the NWDAF can consider only the data corresponding to that application ID from the AF, SMF, and UPF.

[0132] Consumers of these analyses may specify one or more of the following non-restricted examples in their requests:

[0133] The target of the analysis report can be a single UE or a group of UEs.

[0134] The analysis filter information may optionally include one or more of the following non-limiting examples:

[0135] o S-NSSAI;

[0136] o DNN;

[0137] oApplication ID;

[0138] o Region of interest.

[0139] -Analyze the target period, indicating the time period for requesting statistics and / or forecasts.

[0140] - Preferred analysis accuracy level (e.g., low / high).

[0141] - Maximum number of objects;

[0142] - The subscription can include the notification-related ID and the notification target address.

[0143] a) Input Data: Table 1 shows the current input data specifications used for UE communication analysis in [2]. Some examples of this disclosure may use one or more such pieces of information. Those skilled in the art will understand that the exact form of the input data and / or the source of such information is not necessarily limited to the specific examples shown in Table 1. Table 1 shows service data related to UE communication from 5GC.

[0144] Table 1

[0145]

[0146]

[0147] In some examples of this disclosure, for instance, one or more input data shown in Table 2 may be used in addition to one or more input data according to Table 1. In some examples, some or all of the input data according to Table 2 may be collected as part of the UE communication service data or as a separate entry for each PDU session. Those skilled in the art will understand that the exact form of the input data or the source of such information is not necessarily limited to the specific examples shown in Table 2. Table 2 shows examples of additional service data related to UE communication from the 5GC.

[0148] Table 2

[0149]

[0150] b) Output Analysis: Table 3 shows the current output analysis specifications for UE communication analysis as described in [2]. Statistics may not require an input of "confidence level," while predictions may. Some examples of this disclosure can generate one or more output analyses based on Table 3. Those skilled in the art will understand that the exact form of the output analysis is not limited to the specific examples shown in Table 3. Table 3 shows UE communication output analyses.

[0151] Table 3

[0152]

[0153]

[0154] In some examples of this disclosure, for instance, one or more output analyses as shown in Table 4 may be generated in addition to those based on one or more output analyses in Table 3. Those skilled in the art will understand that the exact form of the output analysis is not limited to the specific examples shown in Table 4. Table 4 illustrates examples of additional output analysis data for UE communication.

[0155] Table 4

[0156] Additional Information describe (>) PDU Session ID (1...maximum) PDU session identifier N4 Session ID N4 session identifier Inactivity detection time Session inactivity timer values ​​(average, variance)

[0157] Network performance analysis

[0158] In some examples of this disclosure, network performance analysis by NWDAF (in addition to or as a substitute for UE communication and / or other analyses) can be used to optimize user plane performance. For example, in addition to UE communication analysis, SMF can also use network performance analysis to derive timer values ​​that optimize not only the performance of individual UEs but also the performance of the entire network, especially the RAN.

[0159] a) Input Data: Table 5 shows the current input data specifications for UE communication analysis as described in [2]. Some examples of this disclosure may use one or more such pieces of information. Those skilled in the art will understand that the exact form of the input data and / or the source of such information is not necessarily limited to the specific examples shown in Table 5. Table 5 shows the input data for network performance analysis.

[0160] Table 5

[0161]

[0162]

[0163] b) Output Analysis: Table 6 shows the current output analysis specifications for network performance analysis as described in [2]. Statistics may not require input of "confidence level," while predictions may. Some examples of this disclosure can generate one or more output analyses based on Table 6. Those skilled in the art will understand that the exact form of the output analysis is not limited to the specific examples shown in Table 6. Table 6 shows network performance output analyses.

[0164] Table 6

[0165]

[0166] In some examples of this disclosure, for instance, in addition to one or more output analyses as shown in Table 6, one or more output analyses as shown in Table 7 may also be generated. Those skilled in the art will understand that the exact form of the output analysis is not limited to the specific examples shown in Table 7. For example, some examples might generate output analyses displayed in bold + italics. Table 7 shows additional examples of network performance output analyses.

[0167] Table 7

[0168]

[0169] Figure 3a and Figure 3b The process of supporting NWDAF-based user plane optimization according to various embodiments of the present disclosure is illustrated.

[0170] Figure 3a and Figure 3b The process supporting NWDAF-based user plane optimization is illustrated. Various operations in the procedure are described below. In various examples, certain operations may be omitted (e.g., those indicated by dashed arrows / boxes). For simplicity, Figure 3a and Figure 3b Two sets of alternative operations (Alternative 1 and Alternative 2) are illustrated. In various examples, one or the other of these alternatives may be used. Those skilled in the art will understand that this disclosure is not limited to... Figure 3a and Figure 3b A specific example.

[0171] refer to Figure 3a and Figure 3b In Operation 300, a PDU session can be established through the UE, RAN, AMF, SMF, and UPF. A corresponding user plane connection needs to be activated for data transmission. During this process, if an inactive timer expires, the user plane connection can be deactivated, and if new data services become available, it can be activated.

[0172] In operation 301, the SMF (e.g., SMF 210) subscribes to UE communication analysis from the NWDAF (e.g., NWDAF 200).

[0173] In Operation 302, [optionally] SMF can subscribe to network performance analysis from NWDAF.

[0174] Input data collection: There are two alternatives for data collection related to N4 sessions.

[0175] Alternative 1 uses SMF and its corresponding service exposure framework to retrieve the desired input data described in this disclosure, while Alternative 2 relies on an implementation-specific mechanism for UPF input data retrieval.

[0176] Alternative Option 1 [All messages are optional]: SMF-based N4 session data collection

[0177] In Operation 303a, the NWDAF can request N4 session-related input data from the SMF, as defined in Table 2. For example, as specified in TS 23.288[2] and Table 2, it can also request other UE communication data with the SMF as the source NF.

[0178] In Operation 303b, the SMF can request an N4 session level report from the UPF (e.g., UPF 208).

[0179] In Operation 303c, for example, according to Clause 4.4.2.2 of TS 23.502[4], the UPF may provide the SMF with the requested N4 session level report.

[0180] In operation 303d, SMF can provide NWDAF with the requested N4 session-related input data.

[0181] Alternative Option 2: UPF-based N4 Session Data Collection

[0182] In operation 304, [optional] NWDAF can collect N4 session-related input data directly from UPF via a specific mechanism.

[0183] In operation 305, NWDAF can, for example, collect the remaining input data required to generate the requested analysis, according to TS 23.288[2].

[0184] In Operation 306, NWDAF can provide UE communication analysis to SMF, for example, as defined in TS 23.288[2] and Table 4.

[0185] In operation 307, [optionally] if operation 302 is performed, the NWDAF can provide network performance analysis to the SMF, for example, as specified in TS 23.288[2]. It can also add output analysis data, as shown in Table 7.

[0186] In operation 308, while the SMF continues its tasks of activating and deactivating PDU sessions, the SMF can also process the received analytics provided by the NWDAF.

[0187] In operation 309, based on its analysis of the NWDAF, the SMF can decide to update the user plane inactivity timer for certain PDU sessions associated with the corresponding N4 session.

[0188] In operation 310, for example according to clause 4.4.1.3 in TS 23.502[4], the SMF may trigger the N4 session modification procedure to notify the UPF of the update of the inactive timer.

[0189] Certain examples of this disclosure provide a method for setting the value of an inactivity timer for transitioning between the states of a data session in a network including a first entity and a second entity providing network analysis. The method, performed by the second entity, may include obtaining input data from the second entity including communication description information of at least one user equipment (UE), and providing the first entity with output analysis generated based on the input data, the output analysis including UE communication analysis for each data session, wherein the output analysis is used to determine whether to update the value of the inactivity timer for the data session.

[0190] Certain examples of this disclosure provide a method for setting the value of an inactivity timer for transitioning between states of a data session in a network including a first entity and a second entity providing network analysis. The method, performed by the first entity, may include: sending input data, including communication description information of at least one user equipment (UE), to the second entity; receiving output analysis generated from the second entity based on the input data, the output analysis including UE communication analysis for each data session; and determining the transition between states of the data session by using the value of an inactivity timer for the data session updated based on the output analysis.

[0191] Certain examples of this disclosure provide an apparatus for setting the value of an inactivity timer for transitioning between the state of a data session in a network including a first entity and a second entity providing network analysis. The apparatus of the second entity may include a transceiver and a processor coupled to the transceiver, the processor being configured to perform the following operations: obtaining input data including communication description information of at least one user equipment (UE), and providing the first entity with an output analysis generated based on the input data, the output analysis including UE communication analysis for each data session, wherein the output analysis is used to determine whether to update the value of the inactivity timer for the data session.

[0192] Certain examples of this disclosure provide an apparatus for setting the value of an inactive timer for transitioning between states of data sessions in a network including a first entity and a second entity providing network analysis. The apparatus of the first entity may include a transceiver and a processor coupled to the transceiver, the processor being configured to perform the following operations: sending input data to the second entity including communication description information of at least one user equipment (UE); receiving output analysis generated from the second entity based on the input data, the output analysis including UE communication analysis for each data session; and determining transitions between states of the data sessions by using the value of an inactive timer for the data sessions updated based on the output analysis.

[0193] Certain examples of this disclosure provide a method for a second entity (e.g., NWDAF) to provide network analysis in a network including a first entity (e.g., SMF) and the second entity. The method includes: obtaining input data including communication description information; and determining an output analysis based on the input data, including communication analysis for each data session user equipment (UE), and providing the output analysis to the first entity. Based on the output analysis, the first entity can determine whether to update a timer value for the data session, the timer (e.g., an inactivity timer) used to transition between states (e.g., active / inactive states) of the data session (e.g., a PDU session).

[0194] Certain examples of this disclosure provide a second entity (e.g., NWDAF) that provides network analysis in a network including a first entity (e.g., SMF) and a second entity. This second entity is configured to: obtain input data including communication description information; and determine an output analysis based on the input data, including communication analysis for each data session user equipment (UE), and provide the output analysis to the first entity. Based on the output analysis, the first entity can determine whether to update a timer value for the data session (e.g., an inactivity timer) used to transition between states (e.g., active / inactive states) of the data session (e.g., a PDU session).

[0195] Certain examples of this disclosure provide a method for setting the value of a timer (e.g., an inactivity timer) for transitioning between the state (e.g., an active / inactive state) of a data session (e.g., a PDU session) in a network including a first entity (e.g., an SMF) and a second entity (e.g., an NWDAF) providing network analysis, the method comprising: obtaining input data including communication description information by the second entity; determining, by the second entity, an output analysis including communication analysis of each data session user equipment (UE) based on the input data, and providing the output analysis to the first entity; and determining, by the first entity, whether to update the timer value of the data session based on the output analysis.

[0196] In some examples, the method may further include a second entity receiving a request for output analysis from the first entity (e.g., a subscription).

[0197] In some examples, the request may include one or more of the following: a request for analysis related to a specific UE or a group of UEs; and analysis filters.

[0198] In some examples, the analysis filter may specify one or more of the following as filtering criteria: specifying one or more S-NSSAI information; specifying one or more DNN information; one or more application IDs; information indicating one or more regions of interest; information specifying the analysis target period, which indicates the time period for requesting statistics and / or predictions; information indicating the preferred level of analysis accuracy (e.g., low / high); information specifying the maximum number of objects; and, in the subscription, notifying the relevant ID and notification target address.

[0199] In some examples, obtaining input data may include: a second entity sending a request for session parameters (e.g., N4 session parameters) to a first entity; a first entity sending a request for a session report (e.g., N4 session report) to a third entity (e.g., UPF); a first entity receiving session parameters from a third entity; and a first entity sending session parameters to a second entity.

[0200] In some examples, obtaining input data may include performing a process with a third entity (e.g., UPF) to obtain session parameters (e.g., N4 session parameters) directly from the third entity.

[0201] In some examples, the input data may also include additional input data obtained from one or more network entities (e.g., AMF, SMF, UPF, OAM, one or more AFs, NG-RAN, and / or UEs).

[0202] In some examples, obtaining input data can be performed sequentially.

[0203] In some examples, the method may also include initiating a procedure (e.g., an N4 session modification procedure) to update the timer value if it is determined that the timer value needs to be updated.

[0204] In some examples, the method may also include transitions between states of the data connection based on corresponding timer values ​​(and optional services).

[0205] In some examples, the input data may include one or more pieces of information specified in Table 1.

[0206] In some examples, the input data may include one or more of the following: the identifier of one or more PDU sessions (e.g., obtained from the SMF); the identifier of the N4 session (e.g., obtained from the SMF and / or UPF); the value of the session inactivity timer (e.g., obtained from the SMF and / or UPF); information indicating the status (e.g., active or deactivated) of one or more PDU sessions (e.g., obtained from the SMF); and one or more UE states throughout the target analysis period (e.g., obtained from the AMF).

[0207] In some examples, UE communication analysis may include one or more pieces of information specified in Table 3.

[0208] In some examples, UE communication analysis may include one or more of the following: the identifier of one or more PDU sessions; the identifier of an N4 session; and the value of a session inactivity timer (e.g., average or variance).

[0209] In some examples, the output analysis may further include network performance analysis.

[0210] In some examples, the input data may include one or more pieces of information specified in Table 5.

[0211] In some examples, network performance analysis may include one or more pieces of information specified in Table 6.

[0212] In some examples, network performance analysis may include one or more of the following: average usage of allocated resources (e.g., spectrum, CPU, memory, and / or disk); and average network outages in a subset of regions during the target analysis period.

[0213] In some examples, the input data may include communication description information associated with one or more of the following: application function (AF); data session; UE; network slice; and data network.

[0214] Some examples of this disclosure provide a network including a first entity (e.g., SMF) and a second entity (e.g., NWDAF), which is configured to operate according to any of the methods disclosed herein.

[0215] Some examples of this disclosure provide a first entity (e.g., SMF) or a second entity (e.g., NWDAF) configured to operate in a network according to the foregoing examples.

[0216] Some examples of this disclosure provide computer programs that include instructions that, when executed by a computer or processor, cause the computer or processor to perform any of the methods disclosed herein.

[0217] Some examples of this disclosure provide a computer or processor-readable data carrier having a computer program according to the foregoing examples stored thereon.

[0218] In some examples of this disclosure, the value of an inactivity timer can be set for the activation and deactivation of data sessions associated with multiple services in the network.

[0219] In some examples of this disclosure, the input data may include UE communication data, cell load as a measure of the number of active data sessions, and UE type.

[0220] In some examples of this disclosure, UE communication data may include at least one of start and end timestamps, uplink and downlink data rates, and traffic volume.

[0221] Figure 4 This is a block diagram of network entities that can be used in examples according to embodiments of this disclosure. For example, it can be... Figure 4 The network entities shown are provided in the form of UE, AMF, SMF, UPF, NWDAF, AF, and / or other NFs. Those skilled in the art will understand that... Figure 4 The network entities shown can be implemented as, for example, network elements on dedicated hardware, software instances running on dedicated hardware, or virtualization functions instantiated on a suitable platform (e.g., on cloud infrastructure).

[0222] refer to Figure 4Entity 400 may include at least one of a processor (or controller) 401, a transmitter 403, and a receiver 405. Receiver 405 may be configured to receive one or more messages or signals wirelessly or wiredly from one or more other network entities. Transmitter 403 may be configured to transmit one or more messages or signals wirelessly or wiredly to one or more other network entities. Processor 401 may be configured to perform one or more operations and / or functions as described above. For example, processor 401 may be configured to perform operations of a UE, AMF, SMF, UPF, NWDAF, AF, and / or other NFs.

[0223] The techniques described herein can be implemented using any suitably configured apparatus and / or system. Such apparatus and / or system can be configured to perform methods according to any aspect, embodiment, example, or claim disclosed herein. Such an apparatus may include one or more elements, such as receivers, transmitters, transceivers, processors, controllers, modules, units, etc., each element configured to perform one or more corresponding process, operation, and / or method steps to implement the techniques described herein. For example, operation / function of X can be performed by a module (or X module) configured to perform X. One or more elements can be implemented in hardware, software, or any combination of hardware and software.

[0224] It should be understood that the examples of this disclosure can be implemented in hardware, software, or any combination of hardware and software. Any such software can be stored in volatile or non-volatile memory, such as a storage device similar to read-only memory (ROM), whether erasable or rewritable, or in the form of memory, such as random access memory (RAM), memory chips, devices, or integrated circuits, or stored on optical or magnetically readable media, such as optical discs (CDs), digital versatile discs (DVDs), magnetic disks, or magnetic tapes.

[0225] It should be understood that storage devices and storage media are embodiments of machine-readable storage media adapted to store one or more programs comprising instructions that, when executed, implement certain examples of this disclosure. Thus, certain examples provide programs comprising code for implementing methods, apparatus, or systems according to any example, embodiment, aspect, and / or claim disclosed herein, and / or machine-readable storage media for storing such programs. Furthermore, such programs can be transmitted electronically via any medium, such as communication signals transmitted via wired or wireless connections.

[0226] Although this 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 without departing from the spirit and scope of this disclosure as defined by any of the appended claims and their equivalents.

Claims

1. A method for setting the value of an inactivity timer related to a Protocol Data Unit (PDU) session in a network including a Session Management Function (SMF) and a Network Data Analysis Function (NWDAF) providing network data analysis, the method being performed by the NWDAF comprising: The first input data is received from the SMF, which includes: a PDU session ID for identifying a Protocol Data Unit (PDU) session associated with the User Equipment (UE), an N4 session ID for identifying an N4 session between the SMF and the User Plane Function (UPF), a value of a session inactivity timer, and a PDU session state indicating whether the PDU session state is active or deactivated. The UE receives second input data from the Access and Mobility Management (AMF) function, which includes the UE's Connection Management (CM) status; and The output analysis provided by the NWDAF based on at least one of the first input data and the second input data is sent to the SMF. The output analysis includes UE communication analysis, which includes the N4 session ID and the value of the session inactivity timer. The UE communication analysis is used to determine the value of the session inactivity timer of the PDU session.

2. The method according to claim 1, further comprising: Receive the N4 session ID and the value of the session inactivity timer from the UPF.

3. The method according to claim 1, in, Output analysis also includes network performance analysis, and Network performance analysis includes one or more of the following: The average use of allocated resources, and The average number of network outages in a subset of regions during the target period of analysis.

4. The method according to claim 1, further comprising: Receive a request for output analysis from SMF. The request includes an analysis filter, and The analysis filter specifies one or more of the following as filtering criteria: information specifying one or more individual network slice selection auxiliary information; information indicating one or more regions of interest; information specifying the analysis target period, which indicates the time period for requesting statistics and / or predictions; information indicating the preferred level of analysis accuracy; information specifying the maximum number of objects; and, in the subscription, notification related IDs and notification target addresses.

5. A method for setting the value of an inactivity timer related to a Protocol Data Unit (PDU) session in a network including a Session Management Function (SMF) and a Network Data Analysis Function (NWDAF) providing network data analysis, the method being performed by the SMF comprising: Send first input data to NWDAF. The first input data includes: PDU session ID for identifying Protocol Data Unit (PDU) sessions associated with User Equipment (UE), N4 session ID for identifying N4 sessions between SMF and User Plane Function (UPF), value of session inactivity timer, and PDU session state indicating whether the PDU session state is active or deactivated. Receive output analysis from NWDAF based on at least one of first input data and second input data, the second input data including the UE's connection management CM state and provided to NWDAF by the access and mobility function AMF, the output analysis including UE communication analysis, the UE communication analysis including the N4 session ID and the value of the session inactivity timer; The value of the session inactivity timer for the PDU session is determined based on output analysis; and The updated value is notified to the UPF so that the transition between PDU session states can be determined by using the updated session inactivity timer value.

6. The method according to claim 5, wherein, At least one of the N4 session ID and the value of the session inactivity timer is received from the UPF.

7. The method according to claim 5, further comprising: Send a request for output analysis to NWDAF. The request includes an analysis filter, and The analysis filter specifies one or more of the following as filtering criteria: information specifying one or more individual network slice selection auxiliary information; information indicating one or more regions of interest; information specifying the analysis target period, which indicates the time period for requesting statistics and / or predictions; information indicating the preferred level of analysis accuracy; information specifying the maximum number of objects; and, in the subscription, notification related IDs and notification target addresses.

8. The method according to claim 5, wherein, Output analysis also includes network performance analysis, and Network performance analysis includes one or more of the following: The average use of allocated resources, and The average number of network outages in a subset of regions during the target period of analysis.

9. The method according to claim 5, further comprising: Receive requests from NWDAF for session parameters related to the session between SMF and UPF; Send a request for a session report to the UPF; Receive session parameters from UPF; and Send session parameters to NWDAF.

10. An apparatus for setting the value of an inactivity timer related to a Protocol Data Unit (PDU) session in a network including a Session Management Function (SMF) and a Network Data Analysis Function (NWDAF) providing network data analysis, the NWDAF apparatus comprising: transceiver; and The processor, coupled to the transceiver, is configured to perform the following operations: The system receives first input data from the SMF. The first input data includes: a PDU session ID for identifying a Protocol Data Unit (PDU) session associated with the User Equipment (UE); an N4 session ID for identifying an N4 session between the SMF and the User Plane Function (UPF); a value of a session inactivity timer; and a PDU session state indicating whether the PDU session is active or deactivated. The UE receives second input data from the Access and Mobility Management (AMF), which includes the UE's Connection Management (CM) status, and... The output analysis provided by the NWDAF based on at least one of the first input data and the second input data is sent to the SMF. The output analysis includes UE communication analysis, which includes the N4 session ID and the value of the session inactivity timer. The UE communication analysis is used to determine the value of the session inactivity timer of the PDU session.

11. The apparatus according to claim 10, wherein, The processor is configured to perform the method according to any one of claims 2 to 4.

12. An apparatus for setting the value of an inactivity timer related to a Protocol Data Unit (PDU) session in a network including a Session Management Function (SMF) and a Network Data Analysis Function (NWDAF) providing network data analysis, the SMF comprising: transceiver; and The processor, coupled to the transceiver, is configured to execute: Send first input data to NWDAF. The first input data includes: PDU session ID for identifying Protocol Data Unit (PDU) sessions associated with User Equipment (UE), N4 session ID for identifying N4 sessions between SMF and User Plane Function (UPF), value of session inactivity timer, and PDU session state indicating whether the PDU session state is active or deactivated. Receive output analysis from NWDAF based on at least one of first input data and second input data, the second input data including the UE's connection management CM state and provided to NWDAF by the access and mobility function AMF, the output analysis including UE communication analysis, the UE communication analysis including the N4 session ID and the value of the session inactivity timer; The value of the session inactivity timer for the PDU session is determined based on output analysis; and The updated value is notified to the UPF so that the transition between PDU session states can be determined by using the updated session inactivity timer value.

13. The apparatus according to claim 12, wherein, The processor is configured to perform the method according to any one of claims 5 to 9.

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