Apparatus and method for managing a resource through analyzing service demand information in wireless communication system

Abstract

The present disclosure relates to a 5G (5th generation) or pre-5G communication system for supporting higher data transmission rates than a 4G (4th generation) communication system such as LTE (long term evolution). According to the present disclosure, a method of operation of a network data analytics function (NWDAF) in a wireless communication system comprises: receiving information used for service analysis; receiving service analysis request information related to service usage for said service analysis; generating service analysis information including service prediction information based on said service analysis request information; and transmitting said service analysis information.

Description

Technology Field

[0001] The present disclosure generally relates to a wireless communication system, and more specifically to an apparatus and method for managing resources by analyzing demand information for a specific service in a wireless communication system and providing the analyzed information to a service provider. Background Technology

[0003] Efforts are being made to develop improved 5G (5th generation) communication systems or pre-5G communication systems to meet the increasing demand for wireless data traffic following the commercialization of 4G (4th generation) communication systems. For this reason, 5G communication systems or pre-5G communication systems are referred to as communication systems beyond 4G networks or post-LTE systems.

[0004] To achieve high data transmission rates, 5G communication systems are being considered for implementation in the mmWave band (e.g., the 60 GHz band). To mitigate path loss and increase the transmission distance of radio waves in the mmWave band, beamforming, massive MIMO, full-dimensional MIMO (FD-MIMO), array antenna, analog beamforming, and large-scale antenna technologies are being discussed for 5G communication systems.

[0005] In addition, to improve the network of the system, technologies such as advanced small cell, advanced small cell, cloud radio access network (cloud RAN), ultra-dense network, device-to-device communication (D2D), wireless backhaul, moving network, cooperative communication, CoMP (coordinated multi-points), and interference cancellation are being developed in 5G communication systems. Furthermore, advanced coding modulation (ACM) methods such as FQAM (hybrid frequency shift keying and quadrature amplitude modulation) and SWSC (sliding window superposition coding), as well as advanced access technologies such as FBMC (filter bank multi-carrier), NOMA (non-orthogonal multiple access), and SCMA (sparse code multiple access) are being developed in 5G systems.

[0006] The Internet is evolving from a human-centric network where humans generate and consume information into an IoT (Internet of Things) network where distributed components, such as objects, exchange and process information. Furthermore, Internet of Everything (IoE) technology is emerging, combining IoT with big data processing techniques via connections with cloud servers. The implementation of IoT requires technological elements such as sensing technology, wired and wireless communication and network infrastructure, service interface technology, and security technology. Additionally, technologies such as sensor networks, machine-to-machine (M2M) communication, and machine-type communication (MTC) are being researched to facilitate connectivity between objects. Intelligent IT services that create new value for human life can be provided by collecting and analyzing data generated from connected objects within an IoT environment. Through the convergence and integration of existing IT technologies with various industries, IoT can be applied to fields such as smart homes, smart buildings, smart cities, smart or connected cars, smart grids, healthcare, smart home appliances, and advanced medical services.

[0007] Accordingly, various attempts are being made to apply 5G communication systems to IoT networks. 5G communication, such as sensor networks, machine-to-machine (M2M) communication, and machine-type communication (MTC), is being implemented through techniques such as beamforming, MIMO, and array antennas. As an example of the convergence of 5G technology and IoT technology, a cloud radio access network (cloud RAN) can be applied as a big data processing technology.

[0008] In computing implementation structures utilizing wireless communication, mobile edge computing is being employed. This technology combines wireless communication with cloud computing or places servers close to users to process user data. Here, communication functions as a channel for information transmission and performs application-based computing.

[0009] Meanwhile, the Network Data Analytics Function (NWDAF) is one of the network functions of the 5G core network control plane that provides network data collection and analysis capabilities. Specifically, based on collected network data, the NWDAF analyzes the data using intelligent technologies such as machine learning and provides the analysis results to other 5G core network functions to help optimize and improve the performance of each network function. There are various potential problems that may arise when transmitting the network analysis information provided by the NWDAF, and research to resolve these issues is actively underway. The problem to be solved

[0011] Based on the discussion above, the present disclosure provides an apparatus and method for providing analysis information regarding service demand occurring in a mobile communication network in a wireless communication system.

[0012] In addition, the present disclosure provides an apparatus and method for a network data analytics function (NWDAF) in a wireless communication system to provide service usage prediction information to mobile edge computing (MEC) on an application-by-application basis.

[0013] In addition, the present disclosure provides an apparatus and method for NWDAF to provide service usage prediction information to a service provider in a float communication system.

[0014] In addition, the present disclosure provides an apparatus and method for managing service resources through service demand forecasting in a wireless communication system.

[0015] In addition, the present disclosure provides an apparatus and method for supporting service data migration through service resource demand forecasting in a wireless communication system. means of solving the problem

[0017] According to various embodiments of the present disclosure, a method for operating a network data analytics function (NWDAF) in a wireless communication system is provided, comprising the steps of: receiving information used for service analysis; receiving service analysis request information related to service usage for the service analysis; generating service analysis information including service prediction information based on the service analysis request information; and transmitting the service analysis information.

[0018] According to various embodiments of the present disclosure, an NWDAF in a wireless communication system comprises at least one transceiver and at least one processor functionally coupled to the at least one transceiver, wherein the at least one processor receives information used for service analysis, receives service analysis request information related to service usage for service analysis, generates service analysis information including service prediction information based on the service analysis request information, and controls the transmission of the service analysis information.

[0019] According to various embodiments of the present disclosure, a method of operation of a service provider in a wireless communication system is provided, comprising the steps of: transmitting information used for service analysis; transmitting service analysis request information related to service usage for said service analysis; and receiving service analysis information generated according to said service analysis request information.

[0020] According to various embodiments of the present disclosure, a service provider in a wireless communication system comprises at least one transceiver and at least one processor functionally coupled to the at least one transceiver, wherein the at least one processor transmits information used for service analysis, transmits service analysis request information related to service usage for service analysis, and controls the service provider to receive service analysis information generated according to the service analysis request information. Effects of the invention

[0022] The apparatus and method according to various embodiments of the present disclosure provide a technology for optimized resource management by analyzing and predicting service demand.

[0023] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs from the description below. Brief explanation of the drawing

[0025] FIG. 1 illustrates a wireless communication system according to various embodiments of the present disclosure. FIG. 2 illustrates objects of a mobile communication system and a service provider according to various embodiments of the present disclosure. FIG. 3 illustrates the configuration of a core network object in a wireless communication system according to various embodiments of the present disclosure. FIG. 4 illustrates a process of managing service resources according to the movement of a terminal in a wireless communication system according to various embodiments of the present disclosure. FIG. 5 illustrates an example of data collection for the use of service analysis information in a wireless communication system according to various embodiments of the present disclosure. FIG. 6 illustrates a flowchart of the operation method of a network data analytics function (NWDAF) in a wireless communication system according to various embodiments of the present disclosure. FIG. 7 illustrates the process of an NWDAF providing service analysis information to a network function (NF) located within a core network in a wireless communication system according to various embodiments of the present disclosure. FIG. 8 illustrates the process in which an NWDAF provides service analysis information to an AF (application function) through an NEF in a wireless communication system according to various embodiments of the present disclosure. FIG. 9 illustrates the process of an NWDAF providing service analysis information to a trusted AF in a wireless communication system according to various embodiments of the present disclosure. FIG. 10 illustrates a process in which a service provider provides service-related analysis information through an intermediate entity in a wireless communication system according to various embodiments of the present disclosure. FIG. 11 illustrates a flowchart of a method of operation of a service provider in a wireless communication system according to various embodiments of the present disclosure. Specific details for implementing the invention

[0026] The terms used in this disclosure are used merely to describe specific embodiments and are not intended to limit the scope of other embodiments. A singular expression may include a plural expression unless the context clearly indicates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as generally understood by those skilled in the art described in this disclosure. Terms used in this disclosure that are defined in a general dictionary may be interpreted as having the same or similar meaning as they have in the context of the relevant technology, and are not to be interpreted in an ideal or overly formal sense unless explicitly defined in this disclosure. In some cases, even terms defined in this disclosure are not to be interpreted to exclude the embodiments of this disclosure.

[0027] The various embodiments of the present disclosure described below illustrate a hardware-based approach. However, since the various embodiments of the present disclosure include techniques using both hardware and software, the various embodiments of the present disclosure do not exclude a software-based approach.

[0028] Each block of the process flowcharts and combinations of the flowcharts described below may be executed by computer program instructions. Since computer program instructions may be loaded into the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, the instructions executed through the processor of the computer or other programmable data processing equipment may create means for performing the functions described in the flowchart blocks. Since computer program instructions may be stored in computer-available or computer-readable memory that can be directed toward the computer or other programmable data processing equipment to implement functions in a specific way, the instructions stored in such computer-available or computer-readable memory may also produce manufactured items containing instruction means for performing the functions described in the flowchart blocks. Since computer program instructions may also be loaded onto a computer or other programmable data processing equipment, instructions that perform a series of operation steps on the computer or other programmable data processing equipment to create a computer-executable process may provide steps for executing the functions described in the flowchart blocks.

[0029] Additionally, each block may represent a module, segment, or part of code containing one or more executable instructions for executing a specific logical function. Furthermore, in some alternative execution examples, the functions mentioned in the blocks may occur out of order. For instance, two blocks described in succession may actually be executed substantially simultaneously, or the blocks may be executed in reverse order according to their corresponding functions.

[0030] In this embodiment, the term "part" refers to a software or hardware component such as an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit), and the "part" performs certain roles. However, the meaning of "part" is not limited to software or hardware. The "part" may be configured to reside in an addressable storage medium or configured to run one or more processors. Thus, as an example, the "part" includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and "parts" may be combined into a smaller number of components and "parts" or further separated into additional components and "parts." In addition, the components and 'parts' may be implemented to utilize one or more CPUs within the device or secure multimedia card. Also, in the embodiments, 'parts' may include one or more processors.

[0031] In describing the embodiments of the present disclosure in detail, the focus is primarily on the new RAN (NR) wireless access network and the packet core (5G system, or 5G core network, or NG Core: next generation core) of the 5G mobile communication standard specified by the 3GPP (3rd generation partnership project long term evolution), a mobile communication standardization organization. However, the main point of the present disclosure is that it can be applied to other communication systems having a similar technical background with slight modifications without significantly departing from the scope of the present disclosure, and this will be possible at the judgment of a person with skilled technical knowledge in the technical field of the present disclosure.

[0032] For convenience of explanation below, some terms and names defined in 3GPP standards (specifications for 5G, NR, LTE, or similar systems) may be used. However, the present disclosure is not limited by the above terms and names and may be equally applied to systems conforming to other standards.

[0033] Furthermore, terms used in the following description to identify connected nodes, terms referring to network entities, terms referring to messages, terms referring to interfaces between network entities, terms referring to various identification information, etc., are provided as examples for the convenience of explanation. Accordingly, the present disclosure is not limited to the terms used herein, and other terms referring to objects having equivalent technical meanings may be used.

[0034] The present disclosure may be applied to intelligent services (e.g., smart homes, smart buildings, smart cities, smart cars or connected cars, healthcare, digital education, retail, security and safety-related services, etc.) based on 5G communication technology and IoT-related technology.

[0035] The present disclosure discloses a method for collecting, analyzing, predicting, and transmitting network information related to the utilization of a specific service provided outside the network in a mobile communication system. By utilizing such analysis information, the service provider can increase the efficiency of resource utilization by pre-allocating optimal resources necessary for the smooth provision of services. Furthermore, the system according to the present disclosure can reduce service latency by utilizing service analysis information that considers the mobility of the terminal, thereby pre-allocating resources or transferring or replicating information necessary for service provision to a server at a specific location, and thus improving the overall quality of the service.

[0036] The present disclosure relates to the provision of service-related analysis information in a network data analytic function (NWDAF). Specifically, the present disclosure describes a technology for extending the function of a network data analytic function (NWDAF) in a wireless communication system to analyze and predict the mobility of a terminal and the demand or traffic for a specific service, and to transmit this information to an externally located edge computing or an entity managing edge computing resources.

[0037] At this time, the external entity is assumed to be edge computing in the following description, but the same method can be provided not only for edge computing but also for operating servers or services operating at a specific network location.

[0038] Terms referring to signals, channels, control information, network entities, and device components used in the following description are examples provided for the convenience of explanation. Accordingly, the present disclosure is not limited to the terms described below, and other terms having equivalent technical meanings may be used.

[0039] Additionally, the present disclosure describes various embodiments using terms used in some communication standards (e.g., 3GPP (3rd Generation Partnership Project)), but this is merely illustrative. Various embodiments of the present disclosure can be easily modified and applied to other communication systems.

[0040] According to various embodiments of the present disclosure, a method is illustrated for utilizing analysis information and forecasted demand information regarding service demand occurring in a mobile communication network to automate the management of resources of an edge computing infrastructure connected to a core mobile communication network. Edge computing is located in close proximity to the user's location and provides a higher level of service than that provided by conventional centralized cloud computing. Service demand occurring in a mobile communication network can change dynamically depending on time and place due to the mobility characteristics of terminals, and the present disclosure illustrates a method for an automation technique that supports the efficient operation of resources by supporting edge computing optimized for dynamically changing service demand or resource demand.

[0041] A system according to the present disclosure exemplifies a method for analyzing and predicting the mobility and service demand of a terminal in a mobile communication network and providing this information to edge computing. The resources of edge computing may refer to a concept that includes both physical and logical resources for providing hosted services.

[0042] When service requests originating from a mobile communication network are not transmitted to a centralized internet or data center but are directly connected to services provided by edge computing, the wireless communication system can possess advantages such as low latency, high processing speed, and reduced load on the central cloud. To address situations where existing advantages cannot be provided due to unused idle resources arising from terminal mobility or a failure to pre-allocate resources, service providers can manage resources in an optimized manner by anticipating service demand for specific edge computing resources. This disclosure exemplifies a method in which a network data analytics function (NWDAF), an entity constituting a core network, analyzes terminal mobility and communication patterns to analyze and predict service demand arising from a specific service or a specific data network, and provides this information to edge computing. This disclosure exemplifies a method for managing edge computing resources that involves pre-provisioning resources, automating the operation of reclaiming idle resources, or assisting in resource management decisions.

[0043] A method according to various embodiments of the present disclosure includes a method for analyzing and predicting service demand for edge computing by considering the mobility and service usage patterns of a terminal. Through this, edge computing supports a method for recovering unused idle resources or allocating optimal resources according to service demand. A mobile communication system according to the present disclosure exemplifies a method for improving service quality by pre-provisioning edge computing resources in response to changes in demand, such as the occurrence, increase, or decrease of service demand. Through such resource optimization, the efficiency of resource operation for edge computing can be increased, and the effect of reducing management costs can be achieved through the application of automation.

[0045] FIG. 1 illustrates a wireless communication system according to various embodiments of the present disclosure.

[0046] Referring to FIG. 1, the wireless communication system includes a radio access network (RAN) 102 and a core network (CN) 104.

[0047] The wireless access network (102) is a network directly connected to a user device, for example, a terminal (120), and is an infrastructure that provides wireless access to the terminal (120). The wireless access network (102) includes a set of multiple base stations including a base station (110), and the multiple base stations can perform communication through interfaces formed between them. At least some of the interfaces between the multiple base stations may be wired or wireless. The base station (110) may have a structure separated into a central unit (CU) and a distributed unit (DU). In this case, one CU may control multiple DUs. In addition to being a base station, the base station (110) may be referred to as an 'access point (AP)', 'gNB (next generation node B)', '5G node (5th generation node)', 'wireless point', 'transmission / reception point (TRP)', or other terms having an equivalent technical meaning. The terminal (120) connects to a wireless access network (102) and communicates with a base station (110) via a wireless channel. The terminal (120) may be referred to as 'user equipment (UE)', 'customer premises equipment (CPE)', 'mobile station', 'subscriber station', 'remote terminal', 'wireless terminal', or 'user device', or other terms having an equivalent technical meaning.

[0048] The core network (104) is a network that manages the entire system, controls the wireless access network (102), and processes data and control signals for terminals (120) transmitted and received through the wireless access network (102). The core network (104) performs various functions such as controlling user planes and control planes, processing mobility, managing subscriber information, billing, and interoperability with other types of systems (e.g., LTE (long term evolution) systems). To perform the various functions described above, the core network (104) may include a plurality of functionally separated entities having different network functions (NFs). For example, the core network 104 includes an access and mobility management function (AMF) (130a), a session management function (SMF) (130b), a user plane function (UPF) (130c), a policy and charging function (PCF) (130d), a network repository function (NRF) (130e), a user data management (UDM) (130f), a network exposure function (NEF) (130g), It may include unified data repository (UDR) (130h) and network data analytics function (NWDAF).

[0049] The terminal (120) is connected to the wireless access network (102) and connects to an AMF (130a) that performs the mobility management function of the core network (104). The AMF (130a) is a function or device that handles both the connection to the wireless access network (102) and the mobility management of the terminal (120). The SMF (130b) is an NF that manages sessions. The AMF (130a) is connected to the SMF (130b), and the AMF (130a) routes session-related messages for the terminal (120) to the SMF (130b). The SMF (130b) is connected to the UPF (130c) to allocate user plane resources to be provided to the terminal (120) and establishes a tunnel for transmitting data between the base station (110) and the UPF (130c). The PCF (130d) controls information related to policy and charging for the session used by the terminal (120). The NRF (130e) stores information about NFs installed in the mobile operator network and performs the function of providing the stored information. The NRF (130e) can be connected to all NFs. When each NF starts operating in the operator network, it registers with the NRF (130e) to notify the NRF (130e) that the corresponding NF is operating within the network. The UDM (130f) is an NF that performs a role similar to the HSS (home subscriber server) of a 4G network and stores the subscription information of the terminal (120) or the context used by the terminal (120) within the network.

[0050] NEF (130g) is a third party (3 rdIt performs the role of connecting the server (party) and the NF within the 5G mobile communication system. It also performs the role of providing, updating, or acquiring data to the UDR (130h). The UDR (130h) stores subscription information of the terminal (120), stores policy information, stores data exposed to the outside, or stores data for a third-party application (3 rd It performs the function of storing information required by the party application. In addition, the UDR (130h) also performs the role of providing the stored data to other NFs.

[0051] NWDAF (130l) analyzes and provides information collected from the network or external sources to NFs. NWDAF (130l) can collect information from OAM (operation, administration, and maintenance), NFs that constitute the 5G network, or AFs (application functions). NWDAF (130l) can collect information in various ways. A representative analysis function of NWDAF (130l) is to collect and analyze the load level of network slice instances and provide a function to NSSF (network slice selection function) to use for selecting specific terminals. In this case, when requesting analysis information or transmitting analysis result values ​​between a specific NF and NWDAF, a service-based interface defined in the 5G network may be used.

[0052] NWDAF (130l) can provide an analytics subscription service (Nnwdaf_analyticssubscription service). The analytics subscription service is intended to allow subscription to and unsubscription to events generated by NWDAF, and can be subdivided into receiving events periodically or receiving events when specific conditions are met. The analytics subscription service may be referred to as Nnwdaf_anlayticssubscription. Nnwdaf_anlayticssubscription provides three operations: subscribe, unsubscribe, and notify.

[0053] If a specific NF desires to subscribe (Nnwdaf_anlayticssubscription), the arguments transmitted by the specific NF to the NWDAF may be classified into required inputs and optional inputs. According to one embodiment of the present disclosure, the required inputs may include single network slice selection assistance information (S-NSSAI), an event identifier, a notification target address, and event reporting information. Additionally, according to one embodiment of the present disclosure, the optional inputs may include information additionally required for processing analysis information. Additionally, the optional inputs may include event filter information.

[0054] If NF wants to unsubscribe (Nnwdaf_anlayticssubscription_unsubscribe), NF passes subscription identifier information to NWDAF as required input, and NWDAF (130l) passes a message to the NF that requested the operation as output indicating that the unsubscribe has been confirmed.

[0055] The subscription notification operation (Nnwdaf_anlayticssubscription_notify) is NWDAF (103l) notifying NFs that are successfully subscribing to events about specific events periodically or when specific conditions are met. The required input information for the notification operation may include an event identifier, a notification target address, an identifier of a network slice instance, and load level information of a network slice instance, and there may be no required output information.

[0056] NWDAF (130l) can provide an analytics information request service (Nnwdaf_AnalyticsInfo service). The analytics information request service may refer to a service where an NF requests analysis of specific information and receives the result value as soon as the request is completed. The operations supported by the analytics information request service consist of requests and responses. An NF requesting analytics information can send an analytics information request to NWDAF.

[0057] NF sends network slice instance load level information, analytic ID, and additional parameters required for analysis to NWDAF as required inputs for the analysis information request. When NWDAF (130l) receives an analysis information request from NF, it sends the analysis results to NF as a response. NWDAF (130l) sends the load level information of the requested slice as a response.

[0058] The information provided by NWDAF in this disclosure is not limited to load information alone and may include terminal movement information, communication patterns of terminals / services, expected moving trajectories of terminal services, expected UE behavioral parameters, expected QoS (quality of Service), service experience, and expected network performance information. According to one embodiment of the present invention, the information provided by NWDAF may include, in addition to analysis information, analysis information related to services located outside or inside a mobile communication network. Service-related analysis information may be based on analysis values ​​or prediction values ​​at a specific point in time, and the types of information may include the number of terminals using the service, the number of QoS flows, the number of PDU (packet data unit) sessions, traffic volume, the number of packets, and the expected QoS level per service. Additionally, since the same service may be provided simultaneously at different locations in an edge computing environment, the analysis information of the service may be provided by using the address used in the network, the location of the DNN (data network name) or DNAI (DN access identifier) ​​connected to the core network, and the URL (uniform resource locator) / URI (uniform resource identifier) ​​together with the identifier that specifies the service. According to one embodiment of the present disclosure, when a service A providing streaming is provided at network addresses 1.1.1.1 and 1.1.2.1 and network addresses 2.2.2.2-2.2.2.10, (application ID=A, IP Address=1.1.1.1, 1.1.2.1), (application ID = A, IP Address=2.2.2.2-2.2.2.10) Analysis information can be provided by distinguishing the same service by location through combinations such as the above. For services providing streaming, network addresses, DNN or DNAI, URI / URL, etc. may be used in combination.

[0060] FIG. 2 illustrates objects of a mobile communication system and a service provider according to various embodiments of the present disclosure.

[0061] A unit that performs each function provided by the network system can be defined as a network function (NF). A network management system (201), a mobile communication system (203), and a service provider (205) can be represented as shown in FIG. 2. The mobile communication system (203) may include a user equipment (UE) (120), a radio access network (RAN) (102), a UPF (130c), an AMF (130a), an SMF (130b), a PCF (130d), a UDM / UDR (130m), and an NEF (130g). To communicate with the mobile communication system, the service provider (205) may include an application function (AF) and a data network (DN), such as the Internet, through which user data is transmitted. In addition to the NF, the system (201) for managing the network may include an operation, administration, and maintenance (OAM) (130k). Furthermore, external entities for providing services may include edge computing infrastructure, an infrastructure manager, and services. An edge computing infrastructure manager is an entity that performs the role of managing resources existing in the edge computing infrastructure, and refers to an entity that exists as an independent entity or exists on a specific service or platform to perform the same function. The edge computing infrastructure manager according to the present disclosure assumes a situation in which it can exchange information with a network through the NEF (130g), and is identical to or connected to the AF, enabling it to exchange information with the core network. The relationships between these entities are illustrated in FIG. 2. In FIG. 2, the OAM does not use a standardized interface and is capable of communicating with all entities of the mobile communication system in its own way.The system configuration of a service provider may vary depending on the service and can be distinguished into nodes that act as servers actually providing the service and management nodes, such as resource managers or orchestrators, that manage these servers. A clear distinction between these nodes may not exist depending on the service, but they can be logically separated.

[0063] FIG. 3 illustrates the configuration of a core network object in a wireless communication system according to various embodiments of the present disclosure. The configuration 130 exemplified in FIG. 4 can be understood as a configuration of a device having at least one function among 130a, 130b, 130c, 130d, 130e, 130f, 130g, 130h, 130i, 130j, 130l, and 130m of FIG. 1. Terms such as ‘… unit’, ‘… unit’ used below refer to a unit that processes at least one function or operation, and this may be implemented in hardware or software, or a combination of hardware and software.

[0064] Referring to FIG. 3 above, the core network object is configured to include a communication unit (310), a storage unit (320), and a control unit (330).

[0065] The communication unit (310) provides an interface for performing communication with other devices within the network. The communication unit (310) converts a bit sequence transmitted from a core network object to another device into a physical signal, and converts a physical signal received from another device into a bit sequence. That is, the communication unit (310) can transmit and receive signals. Accordingly, the communication unit (310) may be referred to as a modem, a transmitter, a receiver, or a transceiver. At this time, the communication unit (310) enables the core network object to communicate with other devices or systems via a backhaul connection (e.g., wired backhaul or wireless backhaul) or via a network.

[0066] The storage unit (320) stores data such as basic programs, application programs, and configuration information for the operation of core network objects. The storage unit (320) may be composed of volatile memory, non-volatile memory, or a combination of volatile memory and non-volatile memory. Additionally, the storage unit (320) provides the stored data upon request from the control unit (330).

[0067] The control unit (330) controls the overall operations of the core network object. For example, the control unit (330) transmits and receives signals through the communication unit (310). Additionally, the control unit (330) writes and reads data to and from the storage unit (320). To this end, the control unit (330) may include at least one processor. According to various embodiments, the control unit (330) may control the analysis of collected network data. The control unit (330) may control the analysis of data using intelligent technologies such as machine learning based on the collected network data. According to one embodiment of the present disclosure, the control unit (330) may determine statistical calculations or predictions of changes regarding information concerning end-to-end service satisfaction, network function load, network performance, terminal-related information, user data congestion, and changes in quality of service (QoS). The control unit (330) may control the core network object to perform operations according to various embodiments described below.

[0069] FIG. 4 illustrates a process of managing service resources according to the movement of a terminal in a wireless communication system according to various embodiments of the present disclosure.

[0070] Referring to FIG. 4, a terminal (120) within a first MEC (mobile edge computing) service area can connect to a network through the first MEC. Additionally, a terminal within a second MEC service area can connect to a network through the second MEC. According to FIG. 4, the terminal can move from the first MEC service area to the second MEC service area. When a terminal within the first MEC service area moves to the second MEC service area, service data within the first MEC area can be transmitted to a service provider within the second MEC area. According to one embodiment of the present disclosure, NWDAF (130l) can provide usage prediction information for a specific service to a service provider. According to one embodiment of the present disclosure, NWDAF (130l) can provide usage prediction information for a specific service within the first MEC service area to a service provider within the second MEC service area. By providing usage prediction information for a specific service to a service provider at NWDAF, the service provider can receive related data through the MEC service provider. In addition, if service data has been migrated, service resources can be managed through service resource provisioning or service resource scaling.

[0072] FIG. 5 illustrates an example of data collection for the use of service analysis information in a wireless communication system according to various embodiments of the present disclosure.

[0073] NWDAF (130l) receives service-related data to provide service-related analysis information. NWDAF (130l) may receive an identifier to distinguish a specific service from other services. For distinguishing specific service traffic, a set of packets for a specific service may be referred to as a service flow and may be represented as a set of packets or IP flows transmitted from UPF (130c). To obtain a method for detecting a specific service flow from other data flows, NWDAF (130l) may receive a method for detecting a specific service on a service-by-service basis using the received identifier and filter information.

[0074] The information that NWDAF (130l) collects to distinguish a specific service from another service may include a service data flow (SDF) description, a policy and charging control (PCC) rule, a service parameter, an application identifier, a packet flow description (PFD), application data, a QoS rule, an AF request, a packet detection rule (PDR), and a traffic report.

[0075] NWDAF (130l) can receive an SDF description from AF (130i). NWDAF (130l) can receive an SDF description from PCF (130d). NWDAF (130l) can receive an SDF description from SMF (130b). NWDAF (130l) can receive an SDF description from NEF (130g). An SDF description contains an SDF template, and each SDF template can be represented as a set of SDF filters. At least one filter within the set of SDF filters contains filter information for detecting packets for a specific service in UPF (130c). The filter information may include information for UL (uplink) / DL (downlink) directions. The filters may include filters for IP flows and Ethernet flows. An SDF template may include an application identifier. The SDF description may be transmitted from AF (130i) to PCF (130d) or may be configured internally in PCF according to an internal policy. NWDAF (130l) can collect the address of a specific service and the application identifier by collecting the SDF description transmitted from AF (130i) to PCF (130d).

[0076] NWDAF (130l) can receive PCC rules from SMF (130b) / PCF (130d). PCC rules are rules used by PCF (130d) to control policies or billing-related controls on SDF, control PDU (protocol data unit) session policies, and report PDU session-related events. PCC rules are generated by PCF (130d) and transmitted to SMF (130b), and can utilize information transmitted by AF (130i) or internal policies to generate them. PCC rules may include various information regarding policies and billing. They may include detection rules used in the data plane and policies and control methods applied to the detected data. Through this, NWDAF (130l) can collect rules that detect specific services.

[0077] NWDAF (130l) can receive service parameters from AF (130i). NWDAF (130l) can receive service parameters from PCF (130d). NWDAF (130l) can receive service parameters from UE (120). According to one embodiment of the present disclosure, AF (130i) can transmit parameters for a specific service being performed on UE (120) via NEF (130g). In this process, NWDAF (130l) can collect AF-service ID or application ID and information about the service provider.

[0078] NWDAF (130l) can receive an application identifier from AF (130i). NWDAF (130l) can receive an application identifier from PCF (130d). NWDAF (130l) can receive an application identifier from SMF (130b). NWDAF (130l) can receive an application identifier from NEF (130g). The application identifier is used to designate application detection filters for detecting specific services or applications. These filters may consist of SMF (130b) or parts of PCC rules passed into SMF.

[0079] NWDAF (130l) can receive a packet flow description (PFD) from AF (130i). NWDAF (130l) can receive a PFD from NEF (130g). NWDAF (130l) can receive a PFD from SMF (130b). The PFD contains information for detecting traffic for a specific application or service. A policy distribution function (PDF) corresponding to a specific application ID may consist of a PFD ID, the server's address and port, part of the URL, domain name matching criteria, and information regarding applicable protocols. The PFD can be stored in the UDR (130h) via the AF (130i) and the PFD stored in the UDR can be utilized by SMF (130b). NWDAF (130l) can collect an application identifier and information for detecting it through the collection of the PFD.

[0080] NWDAF (130l) can receive application data from AF (130i). NWDAF (130l) can receive application data from UDR (130h). NWDAF (130l) can receive application data from NEF (130g). Application data refers to information related to applications stored in UDR (130h), and requests related to PFD and AF influence traffic routing may be stored. Through the collection of this information, NWDAF (130l) can collect rules (PDF) that detect specific services or applications. In addition, through routing requests made by specific services, it can identify part of the traffic classification information corresponding to specific services.

[0081] NWDAF (130l) can receive QoS rules from SMF (130b). NWDAF (130l) can receive QoS rules from UE (120). QoS rules are used to classify QoS flows in UE (120) and include a set of packet filters. Since packet filters are generated based on parts of an SDF template, NWDAF (130l) can collect specific QoS flow matching rules to collect flow matching rules.

[0082] NWDAF (130l) can receive AF requests from AF (130i). NWDAF (130l) can receive AF requests from NEF (130g). NWDAF (130l) can receive AF requests from UDR (130h). NWDAF (130l) can receive AF requests from PCF (130d). AF (130i) can send a request (AF request on traffic routing) to apply a specific routing rule to specific traffic. The AF request is stored in UDR (130h) via NEF (130g), and the request stored in UDR can be used by PCF (130d). The AF request includes the target UE, the forwarding location (DN access identifier, DNAI), and the detection method. Through this, information related to the UE using a specific service, the service detection method, and the service location can be obtained. Additionally, tunneling-related information can be obtained through N6 traffic information collection.

[0083] NWDAF (130l) can receive PDR (packet detection rule) from SMF (130b). NWDAF (130l) can receive PDR from UPF (130c). PDR is a rule for detecting specific user data traffic in the data plane and can be composed of IP packet filters and Ethernet packet filters. Specific data traffic cannot be mapped 1:1 to a specific service, but can be used to detect a specific service because it is based on SDF.

[0084] NWDAF (130l) can receive traffic reports from UPF (130c). NWDAF (130l) can receive traffic reports from SMF (130b). SMF (130b) can request reports on traffic usage from UPF (130c) for billing or traffic control. The traffic report may include reports related to PDU sessions or traffic flows. Filter information corresponding to traffic flows is provided by SMF (130b) and can be derived by utilizing parts of PCC rules. Through this, NWDAF (130l) can collect information related to traffic flow detection rules and traffic usage.

[0086] NWDAF (130l) can obtain a method to detect a specific service or application through collected information, and the service can be expressed in detail as a combination of a specific application and location information providing it. According to one embodiment of the present disclosure, even services providing the same application can be distinguished as separate services depending on the location and the service provider. Accordingly, the service can be expressed using an application identifier and an application location together. In this case, the application location can be expressed as an AF ID, network address, network URL, etc.

[0087] In addition to using application identifiers and location information together, NWDAF (130l) may assign application identifiers to each service to distinguish specific services. The same application is used, but the application ID is assigned differently depending on the service provider or location. According to one embodiment of the present disclosure, when the same application A is provided by different providers, the services can be distinguished through separate application identifiers such as A1 and A2.

[0088] In order to analyze and predict the demand for a specific service, NWDAF (130l) may receive past service provision information and corresponding traffic information. The entity responsible for collecting traffic information is UPF (130c), and UPF may report usage information for each traffic to SMF (130b) that requested the report of traffic usage information. NWDAF (130l) may receive traffic usage information directly from UPF (130c) or collect traffic usage information through SMF (130b). By utilizing the traffic report information, NWDAF (130l) may collect usage information for each service, the number of UEs currently using a specific service in the entire core network, the number of PDU sessions, the number of service flows, and the amount of traffic by time period. Depending on the form in which analysis information is provided, NWDAF (130l) may collect traffic for a specific service occurring at a specific terminal or in a specific region based on terminal location information or traffic location information from AMF or SMF.

[0089] In the present disclosure, the method for collecting service-specific traffic is based on a report generated based on information collected by the UPF (130c), but the embodiments of the present disclosure are not limited thereto. For example, the function of enabling the UPF (130c) to directly perform service-specific traffic monitoring and report may be provided through an independent interface. Additionally, for example, the functions of the UPF (130c) may be performed in the same way through any traffic collection device and reporting device on the internal user data path of the core network, and devices collecting user data in the external network.

[0091] FIG. 6 illustrates a flowchart (600) of a method of operation of a network data analytics function (NWDAF) in a wireless communication system according to various embodiments of the present disclosure. FIG. 6 illustrates the operation process of an NWDAF, which is an entity including the configuration of FIG. 3.

[0092] Referring to FIG. 6, in step (601), the NWDAF (130l) receives information used for service analysis. The NWDAF (130l) receives network information related to service usage. The information related to service usage may include subscription information, mobility information, routing policy information, session modification event information, traffic reports, and application information.

[0093] Subscription information represents information for correlation between a specific terminal and other information. Mobility information represents information regarding the terminal's location at a specific point in time. Routing policy information represents information to be used by the terminal at a specific location. Session modification event information represents information for predicting the patterns of sessions using the application. Traffic reporting represents information for identifying the application's traffic patterns. Application information represents information for traffic correlation.

[0094] According to one embodiment of the present disclosure, the information received by NWDAF may include a service data flow (SDF) description, a policy and charging control (PCC) rule, a service parameter, an application identifier, a packet flow description (PFD), application data, a QoS rule, an AF request, a packet detection rule (PDR), and a traffic report.

[0095] In step (603), the NWDAF (130l) receives service analysis request information related to service usage. The service analysis request information specifies the type of analysis information as service analysis information. Although the description in this disclosure is named "Service Usage," the name may be changed by the provider or developer. In the request related to service analysis information, the identifier of the target service is requested as the target for analysis. The service identifier may be expressed as a combination of an application identifier, a network address, and an OSappID. The service analysis request information may include at least one of filter information for classification by terminal, terminal group, region, specific DNN (data network name), specific DNAI (DN access identifier), slice, and time.

[0096] In step (605), NWDAF (130l) generates service analysis information related to service demand forecasting based on the received network information.

[0097] NWDAF (130l) analyzes past and present service usage for specific services based on received information. NWDAF (130l) analyzes information related to service usage by applying methods used in the fields of artificial intelligence (AI) or machine learning to learn past patterns or by applying patterns of similar services. The information analyzed may include service traffic usage, number of service users, number of service packets, number of service flows, traffic patterns (relationship between UL and DL data), average traffic size, minimum bandwidth, bursty traffic, average latency, minimum latency, average flow duration, average flow size, usage cycle, periodicity, and ON / OFF pattern (whether traffic occurs for a certain period after traffic occurs at a specific time). Factors used to distinguish specific services may be expressed as a combination of application identifiers, network addresses, OSappID (operating system specific Application Identifier), etc. The information analyzed in this way can be provided by applying filters, categorized by service-using terminal, terminal group, region, specific DNN, specific DNAI, slice, and time. The filters may include conditions for cases where specific criteria are satisfied. These specific conditions may include information regarding specific times or time intervals, or thresholds for specific analysis values.

[0098] In step (607), NWDAF (130l) transmits service analysis information including service demand forecasting information.

[0099] Service analysis information includes analysis information by terminal, analysis information by application, composite analysis information, and expected service usage information. Analysis information by terminal may include at least one of a terminal identifier, GPSI (general public subscription identifier), DNN, DNAI, information regarding expected residual time, and information regarding expected traffic volume. Analysis information by application may include at least one of an application identifier, DNN, DNAI, information regarding expected residual time, and information regarding expected traffic volume. Expected service usage information may include at least one of information regarding expected traffic volume, the number of expected sessions, the number of expected users, and the number of expected packets.

[0100] Analysis information may be provided to NFs constituting the mobile communication system or to external AFs (130i) via NEF (130g). NWDAF (130l) may provide analysis information via NEF if it trusts AF (130i), and may provide analysis information via NEF if it does not trust AF. NWDAF (130l) can provide service analysis information regarding service prediction based on analyzed past or present information. Information regarding the increase or decrease of each analysis value can be provided by providing the value of analysis information expected at a specific future time. At this time, the specific future time may refer to a specific point in time, and the analysis information may include analysis values ​​for a specific future time slot.

[0101] Figure 6 illustrates that NWDAF receives service analysis request information after receiving information used for service analysis, but NWDAF may receive information used for service analysis after receiving service analysis request information.

[0103] FIG. 7 illustrates a process (700) in which an NWDAF provides service analysis information to a network function (NF) located within a core network in a wireless communication system according to various embodiments of the present disclosure.

[0104] In step (701), the NWDAF (130l) receives information necessary for service analysis. The information necessary for service analysis may refer to information used by the NWDAF for service provider to forecast service demand. According to one embodiment of the present disclosure, the information necessary for service analysis may include at least one of subscriber information, mobility information, routing policy, session modification event, traffic report, and application information.

[0105] Subscriber information represents information for correlation between a specific terminal and other information. Mobility information represents information regarding the terminal's location at a specific point in time. Routing policy information represents information to be used by the terminal at a specific location. Session change events represent information for predicting the patterns of sessions using the application. Traffic reporting represents information for identifying the application's traffic patterns. Application information represents information for traffic correlation.

[0106] In step (703), NF transmits service analysis request information to NWDAF for using service analysis information. According to one embodiment of the present disclosure, NF transmits a subscription request or a temporary request to NWDAF. The service analysis request information specifies the type of analysis information as service analysis information. Although the description in the present disclosure is named "Service Usage," the name may be changed by the provider or developer. In the request related to service analysis information, the identifier of the target service is requested as the analysis target. According to the information transmitted by NF (Nnwdaf_analyticexposure_subscribe / request), the analyticID may include service usage, the target may include an application identifier, and the filter may include at least one of GPSI, area of ​​interest, and S-NSSAI. The service identifier may be expressed as a combination of an application identifier, a network address, and an OSappID. The service analysis request information may include at least one of filter information for classification by terminal, terminal group, region, specific DNN, specific DNAI, slice, and time.

[0107] In step (705), the NWDAF (130l) generates service analysis information. The NWDAF (130l) generates service analysis information for use in accordance with the received analysis request. According to one embodiment of the present disclosure, the NWDAF (130l) analyzes information related to service usage by applying methods used in the fields of artificial intelligence or machine learning to learn past patterns or by applying patterns of similar services. The information to be analyzed may include service traffic usage, number of service users, number of service packets, number of service flows, traffic pattern (relationship between UL and DL data), average traffic size, minimum bandwidth, bursty traffic, average latency, minimum latency, average flow duration, average flow size, usage cycle, periodicity, and ON / OFF pattern (whether traffic occurs for a certain period after traffic occurs at a specific time).

[0108] In step (707), NWDAF (130l) transmits service analysis information to NF. If the analysis information cannot be expressed as a single piece of analysis information according to the service analysis request filter, NWDAF (130l) may generate and transmit multiple responses. The information (Nnwdaf_analyticexposure_notify / response) transmitted by NWADF (130l) may include at least one of appID, DNN, DNAI, expected residual time, and expected traffic volume.

[0109] NWDAF (130l) generates service analysis information based on service analysis request information received from NEF and transmits it to NEF. The service analysis information may include terminal-specific analysis information, application-specific analysis information, composite analysis information, and expected service usage information.

[0110] Analysis information by terminal may include at least one of a terminal identifier, GPSI, DNN, DNAI, information regarding expected residual time, and information regarding expected traffic volume. Analysis information by application may include at least one of an application identifier, DNN, DNAI, information regarding expected residual time, and information regarding expected traffic volume. Service usage prediction information may include at least one of information regarding expected traffic volume, the number of predicted sessions, the number of predicted users, and the number of predicted packets.

[0111] FIG. 7 illustrates that service analysis request information is received after information necessary for service analysis is received, but this is merely an example, and information necessary for service analysis may be received after service analysis request information is received.

[0113] FIG. 8 illustrates a process (800) in which an NWDAF provides service analysis information to an application function (AF) via an NEF in a wireless communication system according to various embodiments of the present disclosure. The NWDAF (130l) may use the NEF to provide service analysis information to an AF located outside the network, rather than to the NF. The NEF (130g) may mediate messages between the AF and the NWDAF.

[0114] Referring to FIG. 8, in step (801), NWDAF (130l) receives information necessary for service analysis. Information necessary for service analysis may mean information used by NWDAF to forecast service demand for a service provider. According to one embodiment of the present disclosure, information necessary for service analysis may include at least one of subscriber information, mobility information, routing policy, session modification event, traffic report, and application information.

[0115] Subscriber information represents information for correlation between a specific terminal and other information. Mobility information represents information regarding the terminal's location at a specific point in time. Routing policy information represents information to be used by the terminal at a specific location. Session change events represent information for predicting the patterns of sessions using the application. Traffic reporting represents information for identifying the application's traffic patterns. Application information represents information for traffic correlation.

[0116] In step (803), AF (130i) transmits a first service request information to NEF for using service analysis information. The type of analysis information in the first service request information may be specified as Service usage, and an interface for external exposure provided by NEF is used to generate a second service analysis request information to be transmitted to NWDAF. NEF (130g) generates the second service analysis request information by converting the terminal identifier external information into information used internally. According to the information (Nnef_analyticexposure_subscribe / request) transmitted by AF, the analyticID may include Expected service usage, the target may include an application identifier, and the filter may include at least one of GPSI, area of ​​interest, and S-NSSAI.

[0117] In step (805), the NEF (130g) transmits the second service request information to the NWDAF. The type of analysis information of the second service request information is specified as service analysis information. For the request related to the analysis information, the identifier of the target service is requested as the analysis target. The service identifier can be expressed as a combination of an application identifier, a network address, and an OSappID. The information received by the NWDAF may be referred to as (Nnwdaf analyticexposure subscribe / request) and may include at least one of filter information for classification by terminal, terminal group, region, specific DNN, specific DNAI, slice, and time.

[0118] In step (807), NWDAF (130l) generates second service analysis information based on the second service analysis request information, and in step (809), NWDAF (130l) transmits the generated second service analysis information to NEF. NEF (130g) generates first service analysis information by converting information used internally into information that can be exposed externally in order to generate first service analysis information to be transmitted to AF. The information (Nnwdaf_analyticexposure_notify / response) transmitted by NWDAF (103l) may include at least one of AppID, DNN, expected residual time, and expected traffic volume.

[0119] In step (811), NEF (130g) transmits the first service analysis information to AF. The information transmitted by NEF (130g) can be expressed as Nnef_analyticexposure_notify / response.

[0120] FIG. 8 illustrates that service analysis request information is received after information necessary for service analysis is received, but this is merely an example, and information necessary for service analysis may be received after service analysis request information is received.

[0121] In the request related to the first service analysis information transmitted by AF, the identifier of the target service is requested as the target for analysis. The service identifier may be expressed as a combination of an application identifier, a network address, and an OSappID. The service analysis request information may include at least one of filter information for classification by terminal, terminal group, region, specific DNN, specific DNAI, slice, or time.

[0122] Since AF (130i) can use identifiers used externally in addition to identifiers used within the network, NEF (130g) transmits second service analysis request information to be transmitted to NWDAF. According to one embodiment of the present disclosure, when NEF (130g) refers to a specific terminal to NWDAF, it may use GPSI (general public subscription identifier), external identifier, MSISDN (mobile station international subscriber directory number), etc., instead of using UE ID or SUPI (subscription permanent identifier). NEF (130g) performs the role of converting external identifiers used externally into internal identifiers. According to one embodiment of the present disclosure, regarding location information, if AF (130i) requests general location information including longitude and latitude for a specific area, NEF (130g) can convert it into information for internal location management, such as Cell ID. If multiple NWDAFs are deployed, NEF (130g) can transmit the request to the NWDAF that provides service-specific analysis information.

[0123] Network information related to service usage received by NWDAF may include subscription information, mobility information, routing policy information, session modification event information, traffic reports, and application information.

[0124] NWDAF (130l) generates second service analysis information based on second service analysis request information received from NEF and transmits it to NEF. According to one embodiment of the present disclosure, the second service analysis information includes terminal-specific analysis information, application-specific analysis information, composite analysis information, and expected service usage information.

[0125] Analysis information by terminal may include at least one of a terminal identifier, GPSI, DNN, DNAI, information regarding expected residual time, and information regarding expected traffic volume. Analysis information by application may include at least one of an application identifier, DNN, DNAI, information regarding expected residual time, and information regarding expected traffic volume. Service usage prediction information may include at least one of information regarding expected traffic volume, the number of predicted sessions, the number of predicted users, and the number of predicted packets.

[0126] NEF (130g) transmits to AF the first service analysis information generated by converting the internal identifier in the second service analysis information received from NWDAF into an identifier that can be exposed externally.

[0128] FIG. 9 illustrates a process (900) in which an NWDAF provides service analysis information to a trusted AF in a wireless communication system according to various embodiments of the present disclosure. The information collected, analyzed, and predicted by the NWDAF can be transmitted to an OAM managing a mobile communication system or to an entity managing a specific application. At this time, the interface used to transmit the analysis information is not limited to using a standardized NWDAF interface.

[0129] Referring to FIG. 9, in step (901), NWDAF (130l) receives information necessary for service analysis. Information necessary for service analysis may mean information used by NWDAF to forecast service demand for a service provider. According to one embodiment of the present disclosure, information necessary for service analysis may include at least one of subscriber information, mobility information, routing policy, session modification event, traffic report, and application information.

[0130] Subscriber information represents information for correlation between a specific terminal and other information. Mobility information represents information regarding the terminal's location at a specific point in time. Routing policy information represents information to be used by the terminal at a specific location. Session change events represent information for predicting the patterns of sessions using the application. Traffic reporting represents information for identifying the application's traffic patterns. Application information represents information for traffic correlation.

[0131] In step (903), the OAM or AF (130i) sends a subscription request or a temporary request to the NWDAF to use service analysis information. This request information specifies the type of analysis information as service analysis information. Although the description in this disclosure is named "Service Usage," the name may be changed by the provider or developer. Additionally, in the request related to analysis information, the identifier of the target service is requested as the target of analysis. The service identifier may be expressed as a combination of an application identifier, network address, OSappID, etc., as described above. The request may additionally include at least one of filter information for classification by terminal, terminal group, region, specific DNN, specific DNAI, slice, or time. According to the information (Nnwdaf_analyticexposure_subscribe / request) transmitted by OAM or AF (130i), the analyticID may include service usage, the target may include an application identifier, and the filter may include at least one of GPSI, area of ​​interest, and S-NSSAI.

[0132] In step (905), NWDAF (130l) generates service analysis information based on service analysis request information received from OAM or AF, and in step (907), NWDAF (130l) transmits the generated analysis information to the OAM or AF that requested the service analysis. The information (Nnwdaf_analyticexposure_notify / response) transmitted by NWDAF (130l) may include at least one of AppID, DNN, DNAI, expected residual time, and expected traffic volume. The service analysis information transmitted by NWDAF to OAM or AF includes terminal-specific analysis information, application-specific analysis information, composite analysis information, and service usage prediction information (expected service usage) information.

[0133] Analysis information by terminal may include at least one of a terminal identifier, GPSI, DNN, DNAI, information regarding expected residual time, and information regarding expected traffic volume. Analysis information by application may include at least one of an application identifier, DNN, DNAI, information regarding expected residual time, and information regarding expected traffic volume. Service usage prediction information may include at least one of information regarding expected traffic volume, the number of predicted sessions, the number of predicted users, and the number of predicted packets.

[0134] FIG. 9 illustrates that service analysis request information is received after information necessary for service analysis is received, but this is merely an example, and information necessary for service analysis may be received after service analysis request information is received.

[0135] FIG. 10 illustrates a process (1000) in which a service provider provides service-related analysis information through an intermediate entity in a wireless communication system according to various embodiments of the present disclosure.

[0136] When a specific service provider leases specific resources, such as a cloud environment or MEC, to provide services, the service provider and the resource management entity may be different. If the service operated by the service provider cannot perform the role of an AF, the resource management entity communicates with the mobile communication network on its behalf to transmit network analysis information. FIG. 10 includes a method for a service provider to obtain service analysis information when the service provider of a specific application running on a terminal and the entity managing the server resources providing the service externally are different.

[0137] Referring to FIG. 10, in step (1001), NWDAF (130l) receives information necessary for service analysis.

[0138] Information required for service analysis may refer to information used by NWDAF for service providers to forecast service demand. According to one embodiment of the present disclosure, information required for service analysis may include at least one of subscriber information, mobility information, routing policy, session modification event, traffic report, and application information.

[0139] Subscriber information represents information for correlation between a specific terminal and other information. Mobility information represents information regarding the terminal's location at a specific point in time. Routing policy information represents information to be used by the terminal at a specific location. Session change events represent information for predicting the patterns of sessions using the application. Traffic reporting represents information for identifying the application's traffic patterns. Application information represents information for traffic correlation.

[0140] In step (1003), the application (app, 130n) may transmit first service analysis request information for a specific application to AF using its own request from a platform provided by the cloud or MEC. AF (130i) may include a MEC or cloud platform, and the identifiers used in the first service analysis request information may be determined separately between the service provider and the platform provider. The information (request_service_demand) transmitted by the App (130n) may include information regarding an external UE ID, appID, target, and filter.

[0141] In step (1005), AF (130i) converts the first service analysis request information so that it can be processed by NEF. AF (130i) generates second service analysis request information based on an identifier used in the mobile communication network. According to one embodiment of the present disclosure, the UE identifier was assigned and used by the service itself, but the first service analysis request information must be converted into an identifier that can be processed by NEF in order to request service analysis information from NWDAF. Accordingly, the internal UE identifier is converted into an identifier such as GPSI or External ID. Likewise, in the case of the application identifier, the identifier internally assigned to the application or service can be converted into a combination of the above-mentioned application identifier, network address, OSappID, etc. The request may additionally include at least one of filter information for classification by terminal, terminal group, region, specific DNN, specific DNAI, slice, and time.

[0142] In step (1007), AF (130i) transmits second service analysis request information to NEF. The request transmitted to NEF is made using a standard interface. The type of analysis information can be specified as "Service Usage". According to the information (Nnef_analyticexposure_subscribe / request) transmitted by AF (130i), analyticID may include service usage, target may include application identifier, and filter may include at least one of GPSI, Area of ​​Interest, and S-NSSAI.

[0143] NEF (130g) converts external terminal identifier information into information used internally to generate third-party service analysis request information.

[0144] In step (1009), the NEF (130g) transmits third service analysis request information (Nnwdaf_analyticexposure_subscribe / request) generated based on an internal identifier to the NWDAF. The type of analysis information in the third service analysis request information is specified as "service analysis information." Although the description in this disclosure is named "Service Usage," the name may be changed by the provider or developer. In the request related to analysis information, the identifier of the target service is requested as the analysis target. The service identifier may be expressed as a combination of an application identifier, a network address, and an OSappID, and the third service analysis request information received by the NWDAF may include at least one of filter information for classification by terminal, terminal group, region, specific DNN, specific DNAI, slice, and time.

[0145] In step (1011), NWDAF (130l) generates third service analysis information based on the third service analysis request information.

[0146] In step (1013), NWDAF (130l) transmits the generated third service analysis information to NEF. NWDAF (130l) generates the third service analysis information based on the third service analysis request information received from NEF and transmits it to NEF. The third service analysis information may be represented as Nnwdaf_analyticexposure_notify / response and may include a list of service usage analytics. According to one embodiment of the present disclosure, the third service analysis information includes terminal-specific analysis information, application-specific analysis information, composite analysis information, and expected service usage information.

[0147] Analysis information by terminal may include at least one of a terminal identifier, GPSI, DNN, DNAI, information regarding expected residual time, and information regarding expected traffic volume. Analysis information by application may include at least one of an application identifier, DNN, DNAI, information regarding expected residual time, and information regarding expected traffic volume. Service usage prediction information may include at least one of information regarding expected traffic volume, the number of predicted sessions, the number of predicted users, and the number of predicted packets.

[0148] The NEF (130g) that receives the third service analysis information generates the second service analysis information to be transmitted to the AF by converting the identifier used internally into information that can be exposed externally.

[0149] In step (1015), NEF (130g) transmits the second service analysis information (Nnef_analyticexposure_notify / response) to AF.

[0150] In step (1017), AF (130i) converts the second service analysis information so that it can be processed by the application. AF (130i) changes the identifiers used in mobile communication to identifiers used by the application or service, contrary to what was done in step (1005). AF (130i) generates the first service analysis information through the identifier change.

[0151] In step (1019), AF (130i) transmits first service analysis information to the application (130n). The first service analysis information may be represented as response_service_demand and may include service usage analytics information.

[0152] In step (1021), the App (130n) that received the first service analysis information or the AF (130i) that received the second service analysis information manages the resources. The resource management method may include at least one of scaling the resources, provisioning the resources, or migrating service data from the first MEC service area to the second MEC service area.

[0153] FIG. 10 illustrates that service analysis request information is received after information necessary for service analysis is received, but this is merely an example, and information necessary for service analysis may be received after service analysis request information is received.

[0155] FIG. 11 illustrates a flowchart (1100) of a method of operation of a service provider in a wireless communication system according to various embodiments of the present disclosure. FIG. 11 illustrates a method of operation of a service provider. The service provider may include an App (130n) or an AF (130i).

[0156] Referring to FIG. 11, in step (1101), the service provider transmits information used for service analysis. The service provider may represent an object that provides a service for resource allocation, including an application or AF. According to one embodiment of the present disclosure, the AF (130i) may transmit at least one of an AF request, application data, PFD, application identifier, service parameter, and SDF description.

[0157] In step (1103), the service provider transmits service analysis request information related to service usage. In the request related to the first service analysis information transmitted by AF, the identifier of the target service is requested as the target for analysis. The service identifier may be expressed as a combination of an application identifier, a network address, and an OSappID. The service analysis request information may include at least one of filter information for classification by terminal, terminal group, region, specific DNN, specific DNAI, slice, and time.

[0158] According to one embodiment of the present disclosure, AF (130i) transmits service analysis request information to NEF. According to another embodiment of the present disclosure, if NWDAF is a trusted service provider, AF (130i) transmits a subscription request or a temporary request to NWDAF to use the service analysis information.

[0159] In step (1105), the service provider receives service analysis information including service demand forecasting information. According to one embodiment of the present disclosure, AF (130i) may receive service analysis information converted from NEF, or if AF is a trusted service provider, may receive service analysis information directly from NWDAF.

[0160] In step (1107), the service provider can manage resources based on the received analysis information. The service provider, having received service analysis information including service demand forecast information, manages resources according to the service forecast analysis information.

[0161] Since the amount of resources required changes depending on service demand, service providers can manage resources flexibly to optimize unused resources allocated to specific applications or adjust the allocation so that the minimum resources necessary for the current service are provided. Service demand forecasts can be predicted based on service analysis information, and service providers can reduce the amount of allocated resources if demand is expected to decrease. Conversely, if demand is expected to increase, service providers can allocate more resources to ensure smooth service provision.

[0162] When a service provider anticipates that a request for a specific service will occur in a new region due to terminal mobility, it can reduce the latency required for initial service provision by pre-transmitting the context and stored information necessary for service provision to a server or MEC supporting that specific location. Alternatively, by utilizing service usage analysis information of a specific UE, the latency required for service provision can be reduced by pre-migrating or copying the service context or virtual image of the specific terminal based on analysis of service requests made by the UE in a specific region.

[0164] Methods according to the embodiments described in the claims or specification of the present disclosure may be implemented in the form of hardware, software, or a combination of hardware and software.

[0165] When implemented in software, a computer-readable storage medium may be provided for storing one or more programs (software modules). One or more programs stored in the computer-readable storage medium are configured for execution by one or more processors within an electronic device. One or more programs include instructions that cause the electronic device to execute methods according to the embodiments described in the claims or specification of this disclosure.

[0166] Such programs (software modules, software) may be stored in random access memory, non-volatile memory including flash memory, read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic disc storage devices, compact disc-ROM (CD-ROM), digital versatile discs (DVDs), or other forms of optical storage devices, magnetic cassettes. Alternatively, they may be stored in memory composed of some or all of these. Additionally, each constituent memory may include multiple units.

[0167] Additionally, the program may be stored on an attachable storage device that can be accessed via a communication network such as the Internet, Intranet, LAN (local area network), WAN (wide area network), or SAN (storage area network), or a combination thereof. Such a storage device may be connected to a device performing an embodiment of the present disclosure through an external port. Additionally, a separate storage device on a communication network may be connected to a device performing an embodiment of the present disclosure.

[0168] In the specific embodiments of the present disclosure described above, the components included in the disclosure are expressed in a singular or plural form according to the specific embodiments presented. However, the singular or plural expression is selected to suit the situation presented for convenience of explanation, and the present disclosure is not limited to singular or plural components; even if a component is expressed in the plural form, it may be composed of a singular form, and even if a component is expressed in the singular form, it may be composed of a plural form.

[0169] Meanwhile, although the detailed description of the present disclosure has described specific embodiments, it is understood that various modifications are possible within the scope of the present disclosure. Therefore, the scope of the present disclosure should not be limited to the described embodiments, but should be defined by the claims set forth below as well as equivalents thereof.

Claims

Claim 1 A method performed by a network data analytics function (NWDAF) device in a wireless communication system, comprising: receiving a first message from a network function (NF) device requesting a subscription for analytics, wherein the first message includes filter information including area of ​​interest and slice information; generating analytics information based on the first message, wherein the analytics information includes an identifier for an application to which the analytics information is provided, network slice information for the application, a data network name (DNN) to which the analytics information is provided, a data network access identifier (DNAI) associated with the analytics information, traffic rate information for the application, and delay information for the application; and transmitting a second message containing the analytics information to the NF device in response to the first message. Claim 2 A method according to claim 1, wherein the filter information further includes information on a time interval and information on a threshold value of an analysis value. Claim 3 A method according to paragraph 2, wherein, if the information regarding the time interval indicates a future time interval, the analysis information further includes predicted traffic rate information for the application and predicted delay information for the application. Claim 4 A method according to claim 1, wherein the analysis information is for a specific UE (user equipment) or group of UEs. Claim 5 In paragraph 4, the method further comprises the analysis information including location information related to the specific UE or the group of UEs. Claim 6 A method according to claim 1, wherein the analysis information is related to a mobile edge computing (MEC) service. Claim 7 In a network data analytics function (NWDAF) device of a wireless communication system, at least one processor; A NWDAF device comprising at least one memory that is communiquently coupled to the at least one processor and stores instructions, wherein the instructions are executed by the at least one processor individually or in any combination, and wherein the NWDAF device receives a first message from a network function (NF) device requesting a subscription for analytics, wherein the first message includes filter information including area of ​​interest and slice information, and generates analytics information based on the first message, wherein the analytics information includes an identifier for an application to which the analytics information is provided, network slice information for the application, a data network name (DNN) to which the analytics information is provided, a data network access identifier (DNAI) associated with the analytics information, traffic rate information for the application, and delay information for the application, and is configured to transmit a second message containing the analytics information to the NF device in response to the first message. Claim 8 In claim 7, the NWDAF device further comprises filter information including information on time intervals and information on threshold values ​​of analysis values. Claim 9 In claim 8, if the information regarding the time interval indicates a future time interval, the analysis information further includes predicted traffic rate information for the application and predicted delay information for the application, an NWDAF device. Claim 10 In paragraph 7, the NWDAF device, wherein the analysis information is for a specific UE (user equipment) or group of UEs. Claim 11 In paragraph 10, the above analysis information further includes location information related to the specific UE or the group of UEs, an NWDAF device. Claim 12 In paragraph 7, the NWDAF device, wherein the analysis information is related to MEC (mobile edge computing) services. Claim 13 delete Claim 14 delete Claim 15 delete Claim 16 delete Claim 17 delete Claim 18 delete Claim 19 delete Claim 20 delete Claim 21 delete Claim 22 delete Claim 23 delete Claim 24 delete Claim 25 delete Claim 26 delete Claim 27 delete Claim 28 delete Claim 29 delete Claim 30 delete

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