A system and method for collecting and analyzing data related to pdu sessions

Abstract

The present disclosure relates to a system (102) for collecting and analyzing data related to Packet Data Unit (PDU) session establishments between User Equipments (UEs) and Data Network (DN) managed by a plurality of Access and Mobility Management Functions (AMFs) (404). The system (102) may comprise a memory (204) and one or more processors (202) configured to execute instructions stored in the memory (204). A Network Data Analytics Function (NWDAF) (208) may receive a request for network data analytics from a consumer network function (NF) (402), the request comprising a Single Network Slice Selection Assistance Information (S-NSSAI). The NWDAF (208) may extract the S-NSSAI from the request and subscribe to a plurality of AMFs (404) serving the S-NSSAI to obtain a number of User Equipment (UE) registrations and a number of established PDU sessions for individual Session Management Functions (SMFs) (406).

Description

A SYSTEM AND METHOD FOR COLLECTING AND ANALYZING DATA RELATED TO PDU SESSIONSRESERVATION OF RIGHTS

[0001] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as, but are not limited to, copyright, design, trademark, Integrated Circuit (IC) layout design, and / or trade dress protection, belonging to JIO PLATFORMS LIMITED or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.FIELD OF DISCLOSURE

[0002] The present disclosure relates to wireless cellular communications, and particularly relates to a system and method for collecting and analyzing data related to Packet Data Unit (PDU) session establishments between User Equipments (UEs) and a Data Network (DN) managed by a plurality of Access and Mobility Management Functions (AMFs).DEFINITION

[0003] As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used to indicate otherwise.

[0004] The expression 'Packet Data Unit (PDU) session' used hereinafter in the specification refers to a logical connection between a User Equipment (UE) and a Data Network (DN), providing a specific quality of service (QoS) and enabling data transfer in a 5G network.

[0005] The expression 'Access and Mobility Management Function (AMF)' used hereinafter in the specification refers to a network function in the 5G corenetwork responsible for handling UE registration, connection management, and mobility-related functions.

[0006] The expression 'Session Management Function (SMF)' used hereinafter in the specification refers to a network function in the 5G core network responsible for managing PDU sessions, including session establishment, modification, and release.

[0007] The expression 'Network Data Analytics Function (NWDAF)' used hereinafter in the specification refers to a centralized entity in the 5G core network that collects and analyzes data from various network functions to provide insights and support network optimization.

[0008] The expression 'Single Network Slice Selection Assistance Information (S-NSSAI)' used hereinafter in the specification refers to a parameter that uniquely identifies a network slice within a 5G network, consisting of a Slice / Service Type (SST) and a Slice Differentiator (SD).

[0009] The expression 'Public Eand Mobile Network (PEMN)' used hereinafter in the specification refers to a network operated by a single administration that provides land mobile telecommunications service to the public. It is uniquely identified by a combination of a Mobile Country Code (MCC) and a Mobile Network Code (MNC).

[0010] The expression 'consumer network function (NF)' used hereinafter in the specification refers to a network function in the 5G core network that requests and consumes data analytics services from the NWDAF.

[0011] The expression 'Policy Control Function (PCF)' used hereinafter in the specification refers to a network function responsible for policy control decision-making and for controlling policy rules in the 5G core network.

[0012] The expression 'Network Slice Selection Function (NSSF)' used hereinafter in the specification refers to a network function responsible for selectingthe set of network slice instances that will serve a particular UE in the 5G core network.

[0013] The expression 'Network Slice Subnet Management Function (NSSMF)' used hereinafter in the specification refers to a function responsible for management and orchestration of network slice subnets in the 5G core network.

[0014] The expression ‘Number of PDU Sessions establishment’ used hereinafter in the specification refers to a total number of established PDU sessions. In a packet-switched network, data is transmitted in discrete units (packets). These packets travel independently across the network and are reassembled at their destination. Establishing a PDU session involves configuring the network to recognize and handle packets associated with this specific session. The establishment of the PDU session ensures that the network infrastructure (routers, switches, etc.) is aware of the session’s existence and properly handle the data packets associated with the established session. To handle the established sessions, the network infrastructure maintains necessary routing tables, Quality of Service (QoS) parameters, security settings, and other configurations to ensure efficient and reliable data transmission.

[0015] The expression ‘data collection’ used hereinafter in the specification involves gathering and analyzing specific information about the number of Protocol Data Unit (PDU) sessions established for a given Single Network Slice Selection Assistance Information (S-NSSAI), with the data sourced from the Network Slice Assignment Control Function (NSACF). A PDU session represents a data connection between the User Equipment (UE) and the Data Network (DN). To collect data on the total number of PDU sessions established for a specific S- NSSAI, first identify the S-NSSAI of interest, such as one used for high-definition video streaming. Next, access the NSACF’s management interface or APIs to retrieve relevant data. Filter this data to extract records of PDU session establishments related to the chosen S-NSSAI. Aggregate the results by counting the unique PDU sessions established. Finally, analyze the data for trends or insightsand generate a comprehensive report summarizing the total number of PDU sessions and any observed patterns.

[0016] The expression ‘Network Slice Assignment Control Function (NSACF)’ used hereinafter in the specification refers to a network function that is responsible for the assignment and management of network slices for user sessions. It determines which network slice should be assigned to a particular user or service based on their subscription profile, service requirements, or current network conditions.

[0017] These definitions are in addition to those expressed in the art.BACKGROUND OF DISCLOSURE

[0018] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

[0019] In modem cellular networks, the concept of network slicing has emerged as a key technology to support diverse services and applications with varying requirements. Network slicing allows the creation of multiple virtual networks, each tailored to meet specific needs, on top of a shared physical infrastructure. The 3rd Generation Partnership Project (3GPP) has defined a framework for network slicing in the 5G system architecture, including introduction of the Single Network Slice Selection Assistance Information (S-NSSAI) to identify and select the appropriate network slice for a given service.

[0020] In the 3GPP 5G system architecture, the session management function (SMF) is responsible for managing the packet data unit (PDU) sessions within a network slice. A PDU session represents a logical connection between aUser Equipment (UE) and a Data Network (DN), providing a specific quality of service (QoS) and enabling data transfer. The Access and Mobility Management Function (AMF) is another critical component that handles UE registration, connection management, and mobility-related functions.

[0021] Efficient resource management and network optimization rely heavily on accurate data collection and analytics. Network operators and service providers require insights into the utilization of network slices, particularly the number of PDU sessions established within each slice. Such information helps in capacity planning, load balancing, and ensuring the desired QoS for each slice.

[0022] The current 3 GPP specifications provide a data collection mechanism for obtaining the total number of PDU sessions established in an S- NSSAI from the Network Slice Admission Control Function (NSACF). However, this mechanism falls short in providing granular information about the number of PDU sessions established in the S-NSSAI for individual SMFs. This limitation hinders the ability to make informed decisions and optimize resource allocation at the SMF level.

[0023] The lack of detailed insights into PDU session establishment at the individual SMF level within a network slice leads to several drawbacks: i. Inefficient resource allocation: Without knowing the specific load on each SMF, it becomes challenging to allocate resources effectively across the network slice. This can result in overprovisioning or underutilization of resources at the SMF level. ii. Eimited scalability: As the number of UEs and services grows, the absence of granular data on PDU sessions per SMF hinders the ability to scale the network slice dynamically based on actual demand. iii. Difficulty in identifying performance bottlenecks: When issues arise within a network slice, pinpointing the specific SMF(s) experiencing high load orperformance degradation becomes an arduous task without detailed PDU session data. iv. Suboptimal QoS assurance: Ensuring the desired QoS for each network slice requires a clear understanding of the PDU session distribution across SMFs. The current mechanism falls short in providing this level of visibility, making it challenging to meet QoS objectives consistently.

[0024] In light of these limitations, there is a need for a system and a method that enables the collection of PDU session establishment data at the individual SMF level within an S -NS SAI.SUMMARY

[0025] One embodiment of the present subject matter relates to a system for collecting and analyzing data related to Packet Data Unit (PDU) session establishments between User Equipments (UEs) and Data Network (DN) managed by a plurality of Access and Mobility Management Functions (AMFs). The system may comprise a memory and one or more processors configured to fetch and execute computer-readable instructions stored in the memory. The instructions, when executed, may cause the processor(s) to perform various operations.

[0026] The system is configured to receive, by a Network Data Analytics Function (NWDAF), a request for analysis of PDU session establishment data from a consumer network function (NF). The request may comprise a Single Network Slice Selection Assistance Information (S-NSSAI). The NWDAF may extract the S-NSSAI from the request and subscribe to the plurality of AMFs serving the S- NSSAI for obtaining a number of User Equipment (UE) registrations and a number of established PDU sessions for each Session Management Functions (SMFs). Upon receiving the session information (number of UE registrations and the number of established PDU sessions) for the individual SMFs from the plurality of AMFs, the NWDAF may analyze the received data. The NWDAF may determine a total number of established PDU sessions between User Equipments (UEs) and DataNetwork (DN) for the S-NSSAI by aggregating the number of established PDU sessions managed by each SMF. The NWDAF may then transmit a response to the consumer NF, the response comprising the determined total number of established PDU sessions for the S-NSSAI.

[0027] The NWDAF may subscribe to the plurality of AMFs for obtaining a session information based on the extracted S-NSSAI for each session management function (SMF). The NWDAF may collect from the plurality of AMFs, the session information for each SMF. The NWDAF may analyze the collected session information to determine a total number of established PDU sessions for the extracted S-NSSAI by aggregating the number of established PDU sessions for the individual SMFs. The NWDAF may transmit a response to the consumer NF, the response comprising the determined total number of established PDU sessions for the extracted S-NSSAI.

[0028] In an embodiment, the NWDAF is further configured to perform analysis of the received session information by comparing current data with historical data stored in a database, to predict one or more recommendations based on the number of established PDU sessions for each SMF.

[0029] In an embodiment, the request further comprises Public Land Mobile Network (PLMN) information.

[0030] In an embodiment, the NWDAF is further configured to determine the total number of established PDU sessions based on the extracted S-NSSAI and the PLMN information.

[0031] In an embodiment, the NWDAF subscribes to the plurality of AMFs (404) using an AMF event subscription application programming interface (API) with a customized event type for the number of established PDU sessions.

[0032] In an embodiment, the NWDAF system is further configured to store the received number of UE registrations and the number of established PDU sessions for each SMF in a database.

[0033] In an embodiment, the NWDAF is further configured to validate the request received from the consumer NF by employing a set of predefined validation rules before processing the request.

[0034] In an embodiment, the consumer NF is one of a Policy Control Function (PCF), a Network Slice Selection Function (NSSF), or a Network Slice Subnet Management Function (NS SMF).

[0035] Another exemplary embodiment of the present subject matter relates to a method for collecting and analyzing data related to Packet Data Unit (PDU) session establishments between User Equipments (UEs) and Data Network (DN) managed by a plurality of Access and Mobility Management Functions (AMFs) The method includes receiving, by a network data analytics function (NWDAF), a request for analysis of PDU session establishment data from a consumer network function (NF). The method includes extracting, by the NWDAF, a single network slice selection assistance information (S-NSSAI) from the received request, wherein the S-NSSAI uniquely identifies a network slice. The method includes subscribing, by the NWDAF, to the plurality of AMFs for a session information based on the extracted S-NSSAI for each session management function (SMF). The method includes collecting, by the NWDAF from the plurality of AMFs, the session information for each SMF. The method includes analyzing, by the NWDAF, the collected session information to determine a total number of established PDU sessions between User Equipments (UEs) and Data Network (DN) for the extracted S-NSSAI by aggregating the number of established PDU sessions managed by each SMF. The method includes transmitting, by the NWDAF, a response to the consumer NF, the response comprising the determined total number of established PDU sessions for the extracted S-NSSAI.

[0036] In an embodiment, the method further includes performing, by the NWDAF, analysis of the received session information by comparing current data with historical data stored in a database, to predict one or more recommendations based on the number of established PDU sessions for each SMF.

[0037] In an embodiment, the method further includes determining, by the NWDAF, the total number of established PDU sessions based on the PLMN information.

[0038] In an embodiment, the method further includes subscribing to the plurality of AMFs using an AMF event subscription application programming interface (API) with a customized event type for the number of established PDU sessions.

[0039] In an embodiment, the method further includes storing the received number of UE registrations and the number of established PDU sessions for each SMF in a database.

[0040] In an embodiment, the method further includes validating, by the NWDAF, the request received from the consumer NF by employing a set of predefined validation rules before processing the request.

[0041] In an embodiment, the method further includes utilizing, by the consumer NF, the determined total number of established PDU sessions for the S- NSSAI to predict future resource allocations based on load patterns and utilization trends.

[0042] In an embodiment, the method further includes identifying, based on the extracted S-NSSAI, one or more Access and Mobility Management Function (AMF) that are serving the network slice associated with the S-NSSAI.

[0043] Yet another embodiment of the present subject matter relates to a computer program product comprising a non-transitory computer-readable medium having instructions stored thereon is described. The instructions, when executed byat least one processor of a Network Data Analytics Function (NWDAF), cause the at least one processor to perform operations. The operations comprise receiving, from a consumer Network Function (NF), a request for analysis of Packet Data Unit (PDU) session establishment data. The operations further comprise extracting a Single Network Slice Selection Assistance Information (S-NSSAI) from the received request, wherein the S-NSSAI uniquely identifies a network slice. The operations also include subscribing to a plurality of Access and Mobility Management Functions (AMFs) for session information based on the extracted S- NSSAI for each Session Management Function (SMF), wherein the session information includes a number of User Equipment (UE) registrations and a number of established PDU sessions. The operations further comprise receiving from the plurality of AMFs, the session information for each SMF. The operations also include analyzing, by the NWDAF, the received session information to determine a total number of established PDU sessions between User Equipments (UEs) and Data Network (DN) for the S-NSSAI by aggregating the number of established PDU sessions managed by each SMF. The operations further comprise transmitting, by the NWDAF, a response to the consumer NF, the response comprising the determined total number of established PDU sessions for the S-NSSAI.

[0044] Yet another embodiment relates to a user equipment (UE) that is communicatively coupled to a system for collecting and analyzing data related to Packet Data Unit (PDU) session establishments between User Equipments (UEs) and Data Network (DN) managed by a plurality of Access and Mobility Management Functions (AMFs) (404) via a network. The UE works in conjunction with the system, which comprises a memory and one or more processors configured to execute instructions stored in the memory. These instructions enable the system to perform the steps of the method for supporting data collection for PDU session establishment, including receiving requests from consumer NFs, extracting S- NSSAI information, subscribing to AMFs, analyzing session information, and providing responses with aggregated PDU session data.OBJECTS OF THE PRESENT DISCLOSURE

[0045] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as listed herein below.

[0046] It is an object of the present disclosure to determine a total number of established Packet Data Unit (PDU) sessions for individual Session Management Functions (SMFs) by utilizing a Network Data Analytics Function (NWDAF) that collects data from Access and Mobility Management Functions (AMFs) based on Single Network Slice Selection Assistance Information (S-NSSAI) and Public Land Mobile Network (PLMN) information.

[0047] It is an object of the present disclosure to utilize an existing AMF event subscription Application Programming Interface (API) with a customized event type for the number of established PDU sessions to facilitate the collection of data by the NWDAF from the AMFs.BRIEF DESCRIPTION OF DRAWINGS

[0048] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes the disclosure of electrical components, electronic components or circuitry commonly used to implement such components.

[0049] FIG. 1 illustrates an exemplary network architecture for implementing a system for collecting and analyzing data related to Packet Data Unit (PDU) session establishments between User Equipments (UEs) and Data Network (DN) managed by a plurality of Access and Mobility Management Functions (AMFs), in accordance with embodiments of the present disclosure.

[0050] FIG. 2 illustrates an exemplary architecture of the system, in accordance with embodiments of the present disclosure.

[0051] FIG. 3 illustrates a flowchart of a method for collecting and analyzing data related to Packet Data Unit (PDU) session establishments between the UEs and the DN managed by the plurality of Access and Mobility Management Functions (AMFs), in accordance with an embodiment of the disclosure.

[0052] FIG. 4 illustrates an exemplary representation of communication between a consumer Network Function (NF) and a network data analytics function (NWDAF) for receiving analytics and trends, in accordance with an embodiment of the disclosure.

[0053] FIG. 5 illustrates an exemplary flowchart with various steps followed by the system for fetching the data for an individual session management function (SMF) and correlating the data to generate a response, in accordance with an embodiment of the disclosure.

[0054] FIG. 6 illustrates an exemplary block diagram of a computer system in which or with which embodiments of the present disclosure may be implemented.

[0055] The foregoing shall be more apparent from the following more detailed description of the disclosure.LIST OF REFERENCE NUMERALS100 - Network Architecture102 - System104 - Network106 - Centralized server108-1, 108-2. . . 108-N - User Equipment (s)110-1, 110-2... 110-N - User (s)202 - One or more processor(s)204 - Memory206 - I / O Interface(s)208 - Network Data Analytics Function (NWDAF)210 - Other Module (s)212 - Database402 - Consumer NF404 - AMF406 - SMF610 - External Storage Device620 - Bus630 - Main Memory640 - Read Only Memory650 - Mass Storage Device660 - Communication Port670 - ProcessorBRIEF DESCRIPTION OF THE INVENTION

[0056] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein. Example embodiments of the present disclosure are described below, as illustrated in various drawings in which like reference numerals refer to the same parts throughout the different drawings.

[0057] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure.Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

[0058] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0059] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0060] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniquesknown to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive like the term “comprising” as an open transition word without precluding any additional or other elements.

[0061] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0062] The terminology used herein is to describe particular embodiments only and is not intended to be limiting the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any combinations of one or more of the associated listed items. It should be noted that the terms “mobile device”, “user equipment”, “user device”, “communication device”, “device” and similar terms are used interchangeably for the purpose of describing the invention. These terms are not intended to limit the scope of the invention or imply any specific functionality or limitations on the described embodiments. The use of these terms is solely for convenience and clarity of description. The invention is not limited to any particular type of device or equipment, and it should be understood that otherequivalent terms or variations thereof may be used interchangeably without departing from the scope of the invention as defined herein.

[0063] As used herein, an “electronic device”, or “portable electronic device”, or “user device” or “communication device” or “user equipment” or “device” refers to any electrical, electronic, electromechanical, and computing device. The user device is capable of receiving and / or transmitting one or parameters, performing function / s, communicating with other user devices, and transmitting data to the other user devices. The user equipment may have a processor, a display, a memory, a battery, and an input-means such as a hard keypad and / or a soft keypad. The user equipment may be capable of operating on any radio access technology including but not limited to IP-enabled communication, Zig Bee, Bluetooth, Bluetooth Low Energy, Near Field Communication, Z-Wave, Wi-Fi, Wi-Fi direct, etc. For instance, the user equipment may include, but not limited to, a mobile phone, smartphone, virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other device as may be obvious to a person skilled in the art for implementation of the features of the present disclosure.

[0064] Further, the user device may also comprise a “processor” or “processing unit” includes processing unit, wherein processor refers to any logic circuitry for processing instructions. The processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing, input / output processing, and / or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor is a hardware processor.

[0065] As portable electronic devices and wireless technologies continue to improve and grow in popularity, the advancing wireless technologies for data transfer are also expected to evolve and replace the older generations of technologies. In the field of wireless data communications, the dynamic advancement of various generations of cellular technology are also seen. The development, in this respect, has been incremental in the order of second generation (2G), third generation (3G), fourth generation (4G), and now fifth generation (5G), and more such generations are expected to continue in the forthcoming time.

[0066] While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

[0067] At present, the collection of granular data on the number of established Packet Data Unit (PDU) sessions for individual Session Management Function (SMF) within a specific network slice is a challenging task. Traditional methods rely on the Network Slice Admission Control Function (NSACF) to provide the total number of PDU sessions established in a Single Network Slice Selection Assistance Information (S-NSSAI). However, these methods fail to offer insights into the distribution of PDU sessions across individual SMF, hindering effective resource management and quality of service optimization. The present disclosure addresses these challenges by introducing a system and method that leverages a Network Data Analytics Function (NWDAF) to collect and analyze data from the Access and Mobility Management Functions (AMFs) based on S-NSSAI and Public Land Mobile Network (PLMN) information. By enabling the collection of fine-grained data on PDU session establishment at the SMF level, the presentdisclosure empowers network operators to make informed decisions and optimize network performance.

[0068] The present disclosure serves the purpose of enhancing the efficiency and effectiveness of network resource management in 5G networks. By providing a system and method for collecting and analyzing data on the number of established PDU sessions for individual SMFs within a specific S-NSSAI, the present disclosure enables network operators to gain valuable insights into the utilization patterns and trends of network slices. This information can be leveraged to optimize resource allocation, identify performance bottlenecks, and ensure the desired quality of service for end-users.

[0069] The present disclosure relates to a system and method for collecting and analyzing data related to Packet Data Unit (PDU) session establishments between User Equipments (UEs) and Data Network (DN) managed by a plurality of Access and Mobility Management Functions (AMFs). The system comprises an NWDAF equipped with a memory and one or more processors, configured to receive a request for network data analytics from a consumer network function (NF). The request includes S-NSSAI information, which the NWDAF extracts and uses to subscribe to multiple AMFs serving the specific S-NSSAI. The AMFs provide the NWDAF with the number of UE registrations and the number of established PDU sessions for individual SMFs. The NWDAF analyses the received data, determines the total number of established PDU sessions for the S-NSSAI by aggregating the data from individual SMFs, and transmits a response containing the calculated total to the consumer NF. The method involves the steps of receiving a request, extracting S-NSSAI information, subscribing to AMFs, receiving data from AMFs, analyzing the data, determining the total number of established PDU sessions, and transmitting a response to the consumer NF.

[0070] The various embodiments throughout the disclosure will be explained in more detail with reference to FIG. 1- FIG. 6.

[0071] FIG. 1 illustrates an exemplary network architecture (100) for implementing a system (102) for collecting and analyzing data related to Packet Data Unit (PDU) session establishments between User Equipments (UEs) and Data Network (DN) managed by a plurality of Access and Mobility Management Functions (AMFs), in accordance with embodiments of the present disclosure.

[0072] Referring to FIG. 1, the system (102) is connected to the network (104), which is further connected to one or more user equipments (108-1, 108-2, ... 108-N). A person of ordinary skill in the art will understand that the one or more user equipments may be collectively referred as user equipment 108). One or more users (110-1, 110-2. . . 110-N) may provide one or more requests to the system (102). A person of ordinary skill in the art will understand that the one or more users (110- 1, 110-2... 110-N) may be collectively referred as users (110) and individually referred as a user (110).

[0073] In an embodiment, the user equipment (108) may include, but not be limited to, a mobile, a laptop, etc. Further, the user equipment (108) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, audio aid, microphone, or keyboard. Furthermore, the user equipment (108) may include a mobile phone, smartphone, virtual reality (VR) devices, augmented reality (AR) devices, a laptop, a general- purpose computer, a desktop, a personal digital assistant, a tablet computer, and a mainframe computer. Additionally, input devices for receiving input from the user (110) such as a touchpad, touch-enabled screen, electronic pen, and the like may be used.

[0074] In an embodiment, the network (104) may include at least one of a Fifth Generation (5G) network, Sixth Generation (6G) network, or the like. The network (104) may enable the user equipment (108) to communicate with other devices in the network architecture (100) and / or with the system (102). The network (104) may include a wireless card or some other transceiver connection to facilitate this communication. In another embodiment, the network (104) may beimplemented as, or include any of a variety of different communication technologies such as a wide area network (WAN), a local area network (LAN), a wireless network, a mobile network, a Virtual Private Network (VPN), the Internet, the Public Switched Telephone Network (PSTN), or the like.

[0075] In an embodiment, the network (104) may include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network (104) may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit- switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof.

[0076] In an embodiment, the system (102) is configured to collect, analyze, and share the data received from user equipment 108 via the communication network 104.

[0077] In an embodiment, the network 104 is further configured with a centralized server 106 including a database, where all data related to the deployment and operation of the unmanned vehicle wireless backhaul system is stored. This data can be retrieved whenever there is a need to reference it in the future.

[0078] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).

[0079] FIG. 2 illustrates an exemplary architecture (200) of the system (102) comprising various modules and components, such as a memory 204 and one or more processor(s) 202, in accordance with embodiments of the present disclosure. The exemplary architecture (200) enables the system (102) to efficiently collecting and analyzing data related to PDU session establishments between the UEs and the data network managed by a plurality of Access and Mobility Management Functions (AMFs). In an aspect, the system (102) is configured to collecting data associated with PDU session establishment requests and responses exchanged between UEs and DN(s). The data collection is performed at multiple points within the network, including but not limited to AMFs, UEs, and DNs. In an aspect, each AMF is configured to manage a subset of UEs and is responsible for processing PDU session establishment requests and coordinating with DNs.

[0080] The system (102) includes various components, such as the memory 204, the one or more processor(s) 202, and the Network Data Analytics Function (NWDAF) 208, to collect and analyze data on the number of established PDU sessions for individual Session Management Function (SMF) within a specific network slice.

[0081] The one or more processors 202 are configured to fetch and execute computer-readable instructions stored in the memory 204. The processor(s) 202 may be implemented as microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and / or any devices that process data based on operational instructions. The processor(s) 202 may be configured to fetch and execute computer-readable instructions stored in the memory 204 of the system (102). The memory 204 may store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed for collecting and analyzing data related to PDU session establishments between the UEs and the data network managed by the plurality of Access and Mobility Management Functions (AMFs). The memory 204 may comprise volatile memory such as Random Access Memory(RAM), or non-volatile memory such as Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.

[0082] Referring to FIG. 2, the system (102) may include various input output (VO) interface(s) 206. The interface(s) 206 may comprise a variety of interfaces, such as interfaces for data input and output devices (VO devices), storage devices, and the like. The interface(s) 206 may facilitate communication to / from the system (102) and provide a communication pathway for one or more components of the system (102). Examples of such components include, but are not limited to, the NWDAF 208, other modules 210, and a database 212.

[0083] In an embodiment, the database 212 may comprise data that may be either stored or generated as a result of functionalities implemented by any of the components of the processor 202 or the NWDAF 208. The database 212 may store data related to the number of UE registrations and the number of established PDU sessions for individual SMF, which is collected and analyzed by the NWDAF 208. In an embodiment, the database 212 may be separate from the system (102).

[0084] The AMF is responsible for handling the registration and connection management of User Equipment (UE) devices. When a UE registers with the network, it establishes a connection with an AMF, which then coordinates with a SMF to set up a PDU session for the UE. The PDU session enables the UE to communicate with the data network and access various services.

[0085] The NWDAF 208 is a centralized entity that collects and analyzes data from various network functions to provide insights and support network optimization. The NWDAF 208 may receive a request for network data analytics from a consumer network function (NF). The consumer NF may be a network entity that requires information about the number of PDU sessions established in a specific network slice to make informed decisions and optimize network performance.

[0086] The request received by the NWDAF 208 may include a Single Network Slice Selection Assistance Information (S-NSSAI), which identifies the network slice for which the data analytics are requested. A network slice is a logically isolated network partition that provides specific network capabilities and characteristics to serve a particular service or use case. The S-NSSAI (acting as NSSAI identifier) is a parameter that uniquely identifies a network slice within the 5G network. The S-NSSAI is a key parameter in 5G networks that uniquely identifies a specific network slice. S-NSSAI consists of a Slice / Service Type (SST) and a Slice Differentiator (SD). The SST refers to the expected network slice behaviour in terms of features and services, while the SD allows differentiation between multiple network slices of the same Slice / Service Type. The NWDAF (208) uses the S-NSSAI to identify the relevant network slice for which data analytics are requested.

[0087] The NWDAF (208) extracts the S-NSSAI identifier from the received request by parsing the request message structure. Typically, the request includes a dedicated field or parameter for S-NSSAI. The NWDAF (208) examines the headers and body of the request message, identifies the specific field containing the S-NSSAI information, and retrieves the S-NSSAI value. This extraction process may involve decoding the message format (e.g., JSON or XML), locating the S- NSSAI field within the message structure, and validating the extracted S-NSSAI against the expected format, which consists of a Slice / Service Type (SST) and an optional Slice Differentiator (SD).

[0088] The request received by the NWDAF 208 may include not only the S-NSSAI but also Public Land Mobile Network (PLMN) information. PLMN information is an identifier in mobile networks that uniquely identifies a specific mobile network operator's network. It typically consists of a Mobile Country Code (MCC) and a Mobile Network Code (MNC). The MCC identifies the country in which the network operates, while the MNC identifies the specific network operator within that country. By including PLMN information in the request, the consumer NF can specify the particular network operator's domain for which the PDU sessiondata is required. This is especially valuable in scenarios involving network sharing, roaming, or multi-operator deployments.

[0089] When the NWDAF 208 receives the request containing both S- NSSAI and PLMN information, the NWDAF 208 uses this combined data to provide more granular and context- specific analytics. The NWDAF 208 filters and processes the PDU session data based on both the network slice (identified by the S-NSSAI) and the specific operator's network (identified by the PLMN information). This allows for a more precise determination of the total number of established PDU sessions within a particular network slice for a specific operator's network.

[0090] The inclusion of PLMN information enables several advanced uses. For instance, the PLMN information allows the network operators to compare PDU session establishment patterns across different geographical regions or between different network operators sharing the same infrastructure. The NWDAF 208 may also use the PLMN information to correlate PDU session data with other network parameters specific to that operator's network, such as radio access network (RAN) performance metrics or core network capacity. This correlation can lead to more comprehensive and actionable insights, enabling network operators to make informed decisions about network slice management, capacity planning, and quality of service optimization on a per-operator basis.

[0091] Moreover, the combination of S-NSSAI and PLMN information allows for more sophisticated trend analysis and forecasting. The NWDAF 208 may track and predict PDU session establishment patterns for specific network slices within particular operator networks, accounting for factors such as local events, regional trends, or operator- specific service offerings that may influence network usage.

[0092] Upon receiving the request, the NWDAF 208 may extract the S- NSSAI from the received request. This extraction process may involve parsing the request message and retrieving the S-NSSAI parameter from the appropriate fieldor data structure. The extracted S-NSSAI may be used by the NWDAF 208 to identify the relevant AMFs and SMFs associated with the specified network slice.

[0093] To collect the necessary data, the NWDAF 208 may subscribe to the plurality of AMFs serving the network slice identified by the S-NSSAI. The subscription process involves using an AMF event subscription Application Programming Interface (API) to establish a communication channel between the NWDAF 208 and the AMFs. The NWDAF 208 may use an AMF event subscription Application Programming Interface (API) to subscribe to the AMFs. The subscription request may also specify the granularity of the data required by the NWDAF 208. The subscription request may include a customized event type specifically designed to obtain the number of PDU sessions established by the AMFs. The NWDAF (208) utilizes a customized event type named "Number_of_Est_PDU_Sessions" when subscribing to AMFs (404) via the AMF event subscription API. This custom event type is specifically designed to efficiently collect PDU session establishment data. The "Number_of_Est_PDU_Sessions" event type allows the NWDAF (208) to request notifications from AMFs (404) whenever there are changes in the number of established PDU sessions. This customized event type includes parameters such as the S-NSSAI, SMF identifier, threshold values for triggering notifications, and reporting frequency. By using this tailored event type, the NWDAF (208) can receive targeted, relevant data about PDU session establishments, enabling timely and efficient analytics. By subscribing to this custom event, the NWDAF 208 can receive notifications or updates from the AMFs whenever there is a change in the number of PDU sessions. The NWDAF 208 subscribes to the plurality of AMFs using an AMF event subscription Application Programming Interface (API) with a customized event type. In an example, an event subscription API, named 'Number_of_Est_PDU_Sessions', is specifically designed to efficiently collect PDU session establishment data. The 'Number_of_Est_PDU_Sessions' event type allows the NWDAF 208 to request notifications from the AMFs whenever there are changes in the number of established PDU sessions. These notifications include theupdated number of established PDU sessions. The customized event type may include parameters such as the S-NSSAI, SMF identifier, threshold values for triggering notifications, and reporting frequency. The S-NSSAI parameter identifies the network slice associated with the event, enabling targeted monitoring and reporting within the specified network slice. The SMF identifier allows for precise tracking and analysis of events associated with a particular SMF instance. The threshold values parameter defines specific conditions or limits that, when exceeded or met, trigger notifications. These thresholds can be based on various metrics, such as session establishment times, failure rates, or other performance indicators relevant to the event. The reporting frequency specifies the interval or rate at which reports regarding the event are generated and delivered. The reporting frequency may be configured to provide real-time updates, periodic summaries, or other reporting intervals as required by the system. By utilizing this tailored event type, the NWDAF 208 can receive targeted, relevant data about PDU session establishments, enabling timely and efficient analytics.

[0094] Once the subscription is established, the AMFs may send the requested data (session information) to the NWDAF 208. The data may include the number of UE registrations handled by each AMF and the corresponding number of PDU sessions established by the associated SMFs. The NWDAF 208 may receive this data periodically or whenever there is a significant change in the PDU session count.

[0095] The session information obtained by the NWDAF (208) from the AMFs (404) includes various data points related to PDU sessions and UE registrations. Examples of such session information include, but are not limited to: the number of active PDU sessions per SMF, the types of PDU sessions (e.g., IPv4, IPv6, Ethernet), the Quality of Service (QoS) parameters associated with each session, the duration of active sessions, the frequency of session establishments and releases, the number of UEs registered with each AMF, and the distribution of UEs across different SMFs. This granular session information allows the NWDAF (208)to perform comprehensive analysis and generate insights into network slice utilization and performance.

[0096] Upon receiving the data from the AMFs, the NWDAF 208 may store the received information in the database 212. The database 212 may be a centralized repository that maintains historical records of the PDU session establishment data. Storing the data in the database 212 allows the NWDAF 208 to perform various analyses and generate insights over time.

[0097] The NWDAF 208 performs an analysis of the received session information to gain valuable insights into the PDU session establishment patterns and resource utilization within the specified network slice. At a first step, the NWDAF 208 may be configured for data aggregation, combining the session information received from the multiple AMFs for each SMF associated with the specified S -NS SAI. After combining the session information received from the multiple AMFs, the NWDAF 208 calculates the total number of established PDU sessions by summing up the number reported by each SMF. The NWDAF 208 yields a comprehensive figure representing the entire network slice identified by the S-NSSAI, representing the overall session load.

[0098] In addition to the overall PDU session count, the NWDAF 208 may generate various analytics and insights based on the collected data. These analytics may include real-time monitoring of PDU session establishment trends, historical analysis of PDU session patterns over time, and predictive modelling to forecast future resource requirements.

[0099] In an aspect, the present disclosure provides analysis of the received session information as it is received that may provide an up-to-date view of the current PDU session establishment status within the network slice. This information may help the consumer NF make quick decisions and respond to any sudden changes or anomalies in the network behaviour. For example, if the real-time analytics indicate a sudden surge in PDU session establishments, the consumer NF may proactively allocate additional resources to handle the increased load.

[0100] Once the analysis is complete, the NWDAF 208 may generate a response containing the requested data analytics and insights. The response may include the total number of established PDU sessions for the specified network slice, as well as any additional analytics such as real-time monitoring data, historical trends, and predictive forecasts.

[0101] The NWDAF (208) may use machine learning algorithms to analyze patterns in the number of established PDU sessions for each SMF (406) over time. By comparing current session data with historical trends, the NWDAF (208) may identify potential capacity issues, predict future resource requirements, and generate recommendations for load balancing across SMFs. For example, if the analysis reveals a consistent increase in PDU sessions for a particular SMF, the NWDAF (208) may recommend allocating additional resources to that SMF or suggest redistributing traffic to underutilized SMFs to optimize network performance.

[0102] The NWDAF 208 may transmit the response back to the consumer NF that initiated the request. The transmission may occur over a secure communication channel to protect the confidentiality and integrity of the data. Upon receiving the response from the NWDAF 208, the consumer NF may utilize the provided data analytics to make informed decisions and optimize network operations. For example, if the consumer NF is a Policy Control Function (PCF), it may use the PDU session establishment data to adjust policies and rules for resource allocation and quality of service management. If the consumer NF is a Network Slice Selection Function (NSSF), it may leverage the insights to make optimal slice selection decisions based on the current and predicted resource utilization.

[0103] The system (102) may provide several advantages over traditional approaches to data collection and analysis in 5G networks. By leveraging the NWDAF 208 as a centralized entity for collecting and analyzing PDU session establishment data, the system (102) enables a more efficient and streamlined process for obtaining valuable insights.

[0104] The granular data collection at the SMF level allows for a more detailed understanding of resource utilization and performance within each network slice. By analyzing the PDU session establishment patterns for individual SMFs, the NWDAF 208 can identify potential bottlenecks, imbalances, or optimization opportunities specific to each SMF. This level of granularity enables targeted interventions and fine-grained resource management decisions.

[0105] By analyzing the PDU session establishment data in conjunction with the PLMN information, the NWDAF 208 can identify any variations or patterns specific to each PLMN.

[0106] For example, the NWDAF 208 may detect that a particular network slice experiences higher PDU session establishment rates in one PLMN compared to another. This insight can be valuable for network operators to understand the usage patterns and resource requirements across different PLMNs. It may also help in identifying any regional- specific factors that influence the PDU session establishment behaviour.

[0107] The PLMN information can also be used by the consumer NF to make PLMN-specific decisions or optimizations. For instance, a PCF may define different policies or resource allocation strategies for the same network slice based on the PLMN in which it is deployed. Similarly, an NSSF may consider the PLMN information when selecting the most appropriate network slice instance for a UE based on factors such as location, network load, or service requirements.

[0108] To ensure the reliability and accuracy of the collected data, the NWDAF 208 may implement data validation mechanisms. The NWDAF 208 may validate the received request to ensure that it complies with the expected format, contains all the necessary information, and originates from an authorized consumer NF.

[0109] The NWDAF (208) may employs a set of predefined validation rules to ensure the integrity and validity of requests received from consumer NFs (402). These validation rules include, but are not limited to: v. Syntax validation: Ensuring the request adheres to the expected format and structure. vi. Authentication and authorization: Verifying that the consumer NF (402) has the necessary permissions to access the requested data. vii. Parameter validation: Checking the validity and format of included parameters such as S -NS SAI and PLMN information. viii. Service- specific validation: Confirming that the requested analytics service is supported and accessible to the consumer NF (402). ix. Resource availability check: Verifying that the NWDAF (208) has sufficient resources to process the request.

[0110] By applying these validation rules, the NWDAF (208) can prevent processing of malformed or unauthorized requests, ensuring the security and efficiency of the data analytics service.

[0111] The validation process may involve checking the syntax and semantics of the received request, verifying the authenticity of the consumer NF through authentication mechanisms, and ensuring that the requested data analytics are within the scope of the NWDAF's capabilities and permissions. By validating the request, the NWDAF 208 can prevent any malformed, unauthorized, or malicious requests from being processed, thereby maintaining the integrity and security of the data collection and analysis process. In an aspect, the validation process involves several steps: (1) Syntax validation ensures the request adheres to the expected format and structure. (2) Authentication and authorization verify the consumer NF's identity and permissions. (3) Parameter validation checks the validity of included parameters like S-NSSAI and PLMN information. (4) Servicespecific validation confirms that the requested analytics service is supported and accessible to the consumer NF.

[0112] As 5G networks continue to evolve and new services emerge, the ability to effectively collect and analyze PDU session establishment data becomes increasingly critical. The system (102) presented in the present disclosure addresses this need by providing a scalable, flexible, and intelligent solution that can adapt to the dynamic requirements of 5G networks. By adopting the system (102), network operators and service providers can gain a deeper understanding of their network slices, make informed decisions, and continuously optimize their network performance. This, in turn, can lead to improved service quality, enhanced user experience, and more efficient utilization of network resources.

[0113] In another exemplary embodiment the present subject matter relates to a computer program product embodied in a non-transitory computer-readable medium contains instructions that, when executed by a processor (202) of a Network Data Analytics Function (NWDAF) (208), enable efficient collection and analysis of Packet Data Unit (PDU) session establishment data in 5G networks. The NWDAF (208) receives requests from consumer Network Functions (NFs) (402) for PDU session analysis, extracts Single Network Slice Selection Assistance Information (S-NSSAI) from these requests, and subscribes to multiple Access and Mobility Management Functions (AMFs) (404) to gather session information for each Session Management Function (SMF) (406). This session information, including User Equipment (UE) registrations and established PDU sessions, is then analyzed to determine the total number of PDU sessions between UEs and Data Networks (DNs) for the specified S-NSSAI. The NWDAF (208) aggregates data from each SMF (406) and transmits a comprehensive response back to the consumer NF (402), providing valuable insights into network slice utilization and performance.

[0114] FIG. 3 illustrates a flowchart of a method (300) for collecting and analyzing data related to the PDU session establishments between the UEs and the data network managed by the plurality of Access and Mobility Management Functions (AMFs) (404), in accordance with an embodiment of the presentdisclosure. The method 300 may be performed by the system (102), such as the one depicted in FIG. 2, comprising the NWDAF 208.

[0115] The method (300) facilitates to obtain the total number of PDU sessions established based on the S-NSSAI for all network slices available in the network. The disclosed system and method utilize existing an AMF event subscription Application Programming Interface (API) to send a customized API event (For example “Number of Est PDU Sessions”) towards the AMF.

[0116] In step 302, the NWDAF 208 may receive a request for analysis of PDU session establishment data from a consumer network function (NF). The consumer NF may be a network entity, such as a Policy Control Function (PCF), a Network Slice Selection Function (NSSF), or a Network Slice Subnet Management Function (NSSMF), which requires insights into the number of established PDU sessions for individual Session Management Functions (SMFs) within a specific network slice. The request may include Single Network Slice Selection Assistance Information (S-NSSAI), which identifies the network slice for which the data analytics are sought.

[0117] Upon receiving the request, in step 304, the NWDAF 208 may extract the S-NSSAI from the received request. The S-NSSAI may serve as a key parameter for identifying the relevant network slice and the associated AMFs and SMFs regarding which the PDU session data needs to be collected. In an aspect, the S-NSSAI is configured to include a plurality of data fields that facilitate network slice identification and service differentiation. The S-NSSAI may include an identifier. The S-NSSAI comprises at least two components: a Slice / Service type and a Slice Differentiation information. The slice / service type denotes the specific type of service or application for which the network slice is optimized. Examples of Slice / Service Types include Enhanced Mobile Broadband (eMBB), UltraReliable Low Latency Communication (URLLC), or Massive Machine-Type Communications (mMTC). Each type is tailored to meet the requirements of distinct service classes. The slice differentiation information provides additionalgranularity within the Slice / Service Type to differentiate between multiple instances or variations of the same service type. It enables further segmentation of network slices to cater to specific application demands or customer requirements.

[0118] In step 306, the NWDAF 208 may subscribe to the plurality of AMFs based on the extracted S-NSSAI to receive a session information. The session information includes a number of UE registrations and a number of established PDU sessions. A plurality of subscribed AMFs serves the network slice identified by the extracted S-NSSAI. The subscription may be performed by using an AMF event subscription API with a customized event type specifically. In an example, the customized event type is designed for obtaining the number of established PDU sessions. By subscribing to the AMFs, the NWDAF 208 may establish a communication channel to receive updates on the number of UE registrations and the number of established PDU sessions for individual SMFs 406 associated with the identified network slice.

[0119] When the UE registers with the network, the UE may first connect to the AMF, which may then coordinate with an appropriate SMF to establish the PDU session for the UE. By subscribing to the AMFs serving the identified network slice, the NWDAF 208 may gain access to the most up-to-date information on the number of UE registrations and established PDU sessions for each SMF within that slice.

[0120] In step 308, the NWDAF 208 receives, from the subscribed AMFs, the session information having the number of UE registrations and the number of established PDU sessions for the individual SMFs associated with the network slice. The received session information may provide a granular view of the PDU session establishment status for each SMF 06, enabling the NWDAF 208 to perform detailed analysis and derive valuable insights.

[0121] Upon receiving the session information from the AMFs, in step 310, the NWDAF 208 may analyze the received number of UE registrations and the number of established PDU session establishments between the UEs and the datanetwork managed by each SMF to determine a total number of established PDU sessions for the S-NSSAI. The step of analyzing may involve various techniques, such as statistical analysis, trend analysis, and machine learning algorithms, to identify patterns, correlations, and anomalies in the PDU session establishment data.

[0122] The analysis performed by the NWDAF 208 to provide valuable insights into the performance and utilization of each SMF within the network slice. For example, the NWDAF 208 may identify SMFs that are experiencing a high load of PDU session establishments, indicating potential congestion or resource constraints. On the other hand, SMFs with a low number of established PDU sessions may suggest underutilization of resources or potential opportunities for optimization.

[0123] In step 310, the NWDAF 208 may determine the total number of established PDU sessions for the network slice identified by the S-NSSAI. This may be achieved by aggregating the number of established PDU sessions reported by each individual SMF associated with the slice. The aggregation may involve simple summation or more complex calculations, depending on the specific requirements and algorithms employed by the NWDAF 208.

[0124] In addition to determining the total number of established PDU sessions, the NWDAF 208 may also generate and provide a real-time, historical, and predictive analytics on the number of established PDU sessions for the individual SMFs. These analytics may offer valuable insights into the trends, patterns, and forecasts of PDU session establishments over time, enabling proactive management and optimization of the network slice resources.

[0125] In step 312, the NWDAF 208 may transmit a response to the consumer NF, containing the determined total number of established PDU sessions for the network slice identified by the S-NSSAI. The response may also include the real-time, historical, and predictive analytics generated by the NWDAF 208,providing a comprehensive and multi-dimensional view of the PDU session establishment data.

[0126] Upon receiving the response from the NWDAF 208, the consumer NF may utilize the provided data and analytics to make informed decisions and take appropriate actions. For example, a PCF may utilize the total number of established PDU sessions and the associated analytics to optimize policies and rules for resource allocation and quality of service management within the network slice. An NSSF may leverage the data to make intelligent decisions on network slice selection and assignment based on the current and predicted utilization levels. The NSSMF may use the insights to dynamically adjust the resources allocated to individual SMFs within the slice based on their PDU session establishment trends and requirements.

[0127] In addition to the S-NSSAI, the request received by the NWDAF 208 in step 302 may also include Public Land Mobile Network (PLMN) information. The PLMN information may specify the mobile network operator or the specific network region for which the PDU session establishment data is requested. In such cases, the NWDAF 208 may consider both the S-NSSAI and the PLMN information when determining the total number of established PDU sessions and generating the associated analytics.

[0128] The inclusion of PLMN information in the request may enable the NWDAF 208 to provide more granular and operator- specific insights. It may allow the consumer NF to compare and benchmark the PDU session establishment trends across different PLMNs or network regions, facilitating optimization and resource allocation strategies tailored to specific operator requirements.

[0129] To ensure the reliability and consistency of the collected data, the NWDAF 208 may implement data validation mechanisms. In step 306, before subscribing to the AMFs, the NWDAF 208 may validate the request received from the consumer NF. The validation process may involve checking the correctness and completeness of the provided S-NSSAI and PLMN information, verifying theauthorization and authentication of the requesting entity, and ensuring that the requested data analytics are within the scope of the NWDAF's capabilities and permissions.

[0130] The validation step may help prevent unauthorized or malicious requests from being processed, protecting the security and integrity of the network slice data. It may also help identify and handle any errors or inconsistencies in the request parameters, reducing the risk of collecting and analyzing invalid or misleading data.

[0131] In step 308, upon receiving the number of UE registrations and the number of established PDU sessions from the AMFs, the NWDAF 208 may store this data in the database 212. The database 212 may be designed to handle large volumes of data and support fast querying and analysis.

[0132] The storage of the collected data in the database 212 may enable the NWDAF 208 to maintain a historical record of the PDU session establishment trends over time. It may allow for efficient retrieval and analysis of the data whenever required, without the need to repeatedly subscribe to and collect data from the AMFs. The database 212 may also facilitate the generation of historical and predictive analytics by providing a rich dataset for machine learning algorithms to train on.

[0133] For example, by identifying SMFs experiencing a high load of PDU session establishments, the consumer NF may proactively allocate additional resources to those SMFs to prevent congestion and ensure smooth service delivery. Conversely, SMF 406, with a low number of established PDU sessions, may be identified as a candidate for resource optimization, allowing the reallocation of underutilized resources to other critical areas of the network.

[0134] The predictive analytics generated by the NWDAF 208 may enable the consumer NF to anticipate future trends and patterns in PDU session establishments. This may facilitate proactive capacity planning and resourceallocation strategies, ensuring that the network slice is well-prepared to handle upcoming demands and peak periods. By staying ahead of the curve, network operators can avoid potential service disruptions and maintain a high level of service quality for their users.

[0135] In an aspect, the NWDAF 208 may have machine learning capabilities, enabling the continuous improvement and refinement of the data analytics over time. As more data is collected and analyzed, the NWDAF 208 may learn and adapt its algorithms to provide increasingly accurate and relevant insights. This iterative learning process may lead to the development of more sophisticated and effective network optimization strategies, ultimately benefiting the overall performance and efficiency of the 5G network.

[0136] FIG. 4 illustrates an exemplary representation (400) of communication between the consumer NF (402) and the NWDAF (208) for receiving analytics and trends, in accordance with an embodiment of the disclosure.

[0137] The NWDAF 208 collects and analyzes data from various network functions to provide insights and support network optimization. The NWDAF 208 may receive a request for network data analytics from the consumer network function (NF) 402. The consumer NF 402 may be a network entity that requires information about the number of PDU sessions established in a specific network slice to make informed decisions and optimize network performance. The request received by the NWDAF 208 may include not only the S-NSSAI but also Public Land Mobile Network (PLMN) information. When the NWDAF 208 receives the request containing both S-NSSAI and PLMN information, the NWDAF 208 filters and processes the PDU session data based on both the network slice (identified by the S-NSSAI) and the specific operator's network (identified by the PLMN information). This allows for a more precise determination of the total number of established PDU sessions within a particular network slice for a specific operator's network.

[0138] In an operative aspect, the NWDAF may subscribe to the plurality of AMFs (404) to obtain session information based on the extracted S-NSSAI for each SMF. The AMF 404 is responsible for handling the registration and connection management of User Equipment (UE) devices. When the UE registers with the network, it establishes a connection with an AMF 404, which then coordinates with a Session Management Function (SMF) 406 to set up a PDU session for the UE. The PDU session enables the UE to communicate with the data network and access various services.

[0139] The NWDAF 208 is configured for data aggregation, combining the session information received from multiple AMFs 404 for each SMF 406 associated with the specified S-NSSAI. The NWDAF may analyze the collected session information to determine the total number of established PDU sessions for the extracted S-NSSAI by aggregating the number of established PDU sessions for the individual SMFs. The NWDAF 208 calculates the total number of established PDU sessions by summing up the number reported by each individual SMF 406. The calculation yields a comprehensive figure representing the entire network slice identified by the S-NSSAI, offering a clear picture of the overall session load. The NWDAF 208 may be further configured to conduct a statistical analysis, which may include determining average, median, or peak numbers of PDU sessions per SMF 406 or AMF 404. These statistical measures provide insights into the typical load distribution across different network functions and help identify any imbalances or bottlenecks in resource allocation. To gain a temporal perspective, the NWDAF 208 compares the current data with historical data stored in the database 212. This comparison enables the identification of trends or patterns in PDU session establishment over time, which can be crucial for capacity planning and predicting future network requirements.

[0140] The NWDAF may transmit a response to the consumer NF. The response includes the determined total number of established PDU sessions for the extracted S-NSSAI.

[0141] In an aspect, the NWDAF 208 may perform a correlation analysis between the number of UE registrations and the number of established PDU sessions. This analysis can reveal important relationships between user activity and network resource consumption, providing insights into network slice utilization efficiency. For instance, it might uncover scenarios where an increase in UE registrations doesn't lead to a proportional increase in PDU sessions, indicating potential issues with session establishment or user behaviour.

[0142] In an aspect, the NWDAF 208 not only provides a comprehensive view of the current state of the network slice but also enables predictive insights and proactive management. The NWDAF 208 can offer valuable, actionable intelligence to the consumer NF 402 by synthesizing data from various sources and applying advanced analytical techniques. This information can be used to optimize resource allocation, improve quality of service, and make informed decisions about network slice management and expansion. The depth and breadth of this analysis underscore the critical role of the NWDAF 208 in enhancing the efficiency and performance of 5G networks.

[0143] FIG. 5 illustrates an exemplary flowchart (500) with various steps followed by the system for fetching the data for the individual SMF and correlating the data to generate the response, in accordance with an embodiment of the disclosure.

[0144] At step (502), the consumer NF subscribes to the NWDAF (208) to perform network data analytics. In an example, the consumer NF sends the request to the NWDAF (208). In an example, the request includes specific parameters or criteria that the consumer NF wants analyzed. For example, the specific parameters relate to network performance, service quality, traffic patterns, security metrics, or any other relevant aspect of the network. The request may include (S-NSSAI. The request further comprises PEMN information.

[0145] At step (504), the NWDAF (208) validates the received request and fetches parameters associated with the received request. NWDAF (208) validatesthe request to ensure that it is well-formed, authenticated, and authorized. Once the request is validated, NWDAF (208) retrieves the parameters specified in the request. If the request is not valid, the NWDAF (208) may send an error response to the consumer NF (step 506).

[0146] At step (508), if the request is not valid, the NWDAF (208) may send a subscription request to all AMFs serving that slice. In an aspect, the NWDAF (208) may send a customized event ("Number_of_Est_PDU_Sessions" and "UES_IN_AREA_REPORT"), including in the subscription request. In an aspect, the subscription request may include the fetched parameters.

[0147] At step (510), upon receiving the subscription request from the NWDAF (208), the AMF collects the requested data. For example, the requested data may include information related to user sessions, mobility events, authentication details, QoS parameters, and other relevant metrics. The AMF processes the collected data for transmission to NWDAF (208). Once the data is prepared, the AMF sends a notification to NWDAF (208) indicating that the requested data is ready for transmission. The AMF sends a notification to the NWDAF with the requested data. The notification includes data for different SMF (Session Management Function) instances (step 512).

[0148] Once data is fetched from individual SMF instances, the NWDAF (208) aggregates the fetched data into a unified dataset (step 514). Aggregation is essential to create a comprehensive view that encompasses data from all relevant SMF instances. After aggregation, NWDAF (208) correlates the data from different SMF instances. For example, the correlation involves identifying relationships and patterns across the datasets. For instance, correlating session duration with QoS metrics can help determine if longer sessions experience lower latency or higher throughput, indicating potential optimization opportunities.

[0149] At step (516), the NWDAF (208) processes the data and send notification to the consumer NF. In an example, the NWDAF applies various analytical techniques to the correlated data to derive insights. In an example, theanalytical techniques may include statistical analysis, trend identification, anomaly detection, and predictive modelling. In another example, the NWDAF is configured to process the data to understand network performance, identify bottlenecks, predict future network demands, and optimize resource allocation.

[0150] In another embodiment, the present disclosure also encompasses a user equipment (UE) 108 that is communicatively coupled to the system (102) for collecting and analyzing data related to the PDU session establishments between the UEs and the data network managed by the plurality of Access and Mobility Management Functions (AMFs) (404) via the network 104. The system (102) comprises the memory 204 and one or more processors 202 configured to fetch and execute computer-readable instructions stored in the memory 204. These instructions, when executed, cause the processor(s) 202 to perform the steps of the method 300. This method includes the NWDAF 208 receiving a request for network data analytics from a consumer NF 402, extracting the S-NSSAI from the request, subscribing to AMFs 404 serving the S-NSSAI to obtain the number of UE registrations and established PDU sessions for individual SMFs 406, analyzing the received data, determining the total number of established PDU sessions for the S- NSSAI, and transmitting a response with the determined total to the consumer NF 402. By incorporating the UE 108 into the system (102), this embodiment enables the collection of PDU session establishment data directly from the source, i.e., the UEs, thereby enhancing the accuracy and completeness of the data used for analysis and decision-making.

[0151] FIG. 6 illustrates an exemplary computer system 600 in which or with which embodiments of the present disclosure may be implemented. As shown in FIG. 6, the computer system may include an external storage device 610, a bus 620, a main memory 630, a read-only memory 640, a mass storage device 650, communication port(s) 660, and a processor 670. A person skilled in the art will appreciate that the computer system may include more than one processor and communication ports. The processor 670 may include various modules associated with embodiments of the present disclosure. The communication port(s) 660 maybe any of an RS-232 port for use with a modem-based dialup connection, a 10 / 100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port(s) 660 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system connects.

[0152] The main memory 630 may be random access memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory 640 may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or Basic Input / Output System (BIOS) instructions for the processor 670. The mass storage device 650 may be any current or future mass storage solution, which can be used to store information and / or instructions. Exemplary mass storage device 650 includes, but is not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and / or Firewire interfaces), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks.

[0153] The bus 620 communicatively couples the processor 670 with the other memory, storage, and communication blocks. The bus 620 may be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCLX) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor 670 to the computer system.

[0154] Optionally, operator and administrative interfaces, e.g., a display, keyboard, joystick, and a cursor control device, may also be coupled to the bus 620 to support direct operator interaction with the computer system. Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) 660. Components described above are meantonly to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.

[0155] The method and system of the present disclosure may be implemented in a number of ways. For example, the methods and systems of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0156] The present disclosure provides a technical advancement in countingPDU sessions for each SMF. This addresses the current limitations in providing information on “PDU sessions established / released on a network slice”. The disclosure meets the immediate need to provide PDU sessions established / released on a network slice for each SMF directly from the NWDAF. This enables the NWDAF to retrieve the “number of PDU sessions established” and “number of UE registrations” directly from the AMF for individual SMFs. This ensures that by receiving data for individual SMFs and using utilization-based decision-making, operators can make informed choices and predict future resource allocations based on load patterns and utilization trends.

[0157] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from thedisclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the disclosure and not as limitation.ADVANTAGES OF THE PRESENT DISCLOSURE

[0158] The present disclosure provides technical advancement related to determining a total number of established Packet Data Unit (PDU) sessions for individual Session Management Functions (SMFs) by utilizing a Network Data Analytics Function (NWDAF) that collects data from Access and Mobility Management Functions (AMFs) based on Single Network Slice Selection Assistance Information (S-NSSAI) and Public Land Mobile Network (PLMN) information.

[0159] The present disclosure provides technical advancement related to utilizing an existing AMF event subscription Application Programming Interface (API) with a customized event type for the number of established PDU sessions to facilitate the collection of data by the NWDAF from the AMFs.

Claims

CLAIMS1. A system (102) for collecting and analyzing data related to Packet Data Unit (PDU) session establishments between User Equipments (UEs) and Data Network (DN) managed by a plurality of Access and Mobility Management Functions (AMFs) (404), the system (102) comprising: a memory (204); and one or more processor(s) (202) configured to fetch and execute instructions stored in the memory (204) for: receiving, by a Network Data Analytics Function (NWDAF) (208), a request for analysis of PDU session establishment data from a consumer network function (NF) (402); extracting, by the NWDAF (208), a Single Network Slice Selection Assistance Information (S-NSSAI) from the received request, wherein the S-NSSAI uniquely identifies a network slice; subscribing, by the NWDAF (208), to the plurality of AMFs (404) for obtaining a session information, based on the extracted S- NSSAI, for each session management function (SMF) (406); collecting, by the NWDAF (208) from the plurality of AMFs (404), the session information for each SMF (406); analyzing, by the NWDAF (208), the collected session information to determine a total number of established PDU sessions between the User Equipments (UEs) and the Data Network (DN) for the extracted S-NSSAI, by aggregating the number of established PDU sessions managed by each SMF (406); andtransmitting, by the NWDAF (208), a response to the consumer NF (402), the response comprising the determined total number of established PDU sessions for the extracted S-NSSAI.

2. The system (102) as claimed in claim 1, wherein the session information includes a number of user equipment (UE) registrations and a number of established PDU sessions.

3. The system (102) as claimed in claim 1, wherein the NWDAF (208) is further configured to perform analysis of the received session information by comparing current data with historical data stored in a database (212), to predict one or more recommendations based on the number of established PDU sessions for each SMF (406).

4. The system (102) as claimed in claim 1, wherein the request further comprises Public Land Mobile Network (PLMN) information.

5. The system (102) as claimed in claim 1, wherein the NWDAF (208) subscribes to the plurality of AMFs (404) using an AMF event subscription application programming interface (API) with a customized event type for the number of established PDU sessions.

6. The system (102) as claimed in claim 1, is further configured to store the received number of UE registrations and the number of established PDU sessions for each SMF (406) in a database (212).

7. The system (102) as claimed in claim 1, wherein the NWDAF (208) is further configured to validate the request received from the consumer NF (402) by employing a set of predefined validation rules before processing the request.

8. The system (102) as claimed in claim 1, wherein the consumer NF (402) is one of a Policy Control Function (PCF), a Network Slice Selection Function (NSSF), or a Network Slice Subnet Management Function (NSSMF).

9. A method (300) for collecting and analyzing data related to Packet Data Unit (PDU) session establishments between User Equipments (UEs) and Data Network (DN) managed by a plurality of Access and Mobility Management Functions (AMFs) (404), the method (300) comprising: receiving (302), by a Network Data Analytics Function (NWDAF) (208), a request for analysis of PDU session establishment data from a consumer network function (NF) (402); extracting (304), by the NWDAF (208), a Single Network Slice Selection Assistance Information (S-NSSAI) from the received request, wherein the S-NSSAI uniquely identifies a network slice; subscribing (306), by the NWDAF (208), to the plurality of AMFs (404) for a session information based on the extracted S-NSSAI for each session management function (SMF) (406); collecting (308), by the NWDAF (208) from the plurality of AMFs (404), the session information for each SMF (406); analyzing (310), by the NWDAF (208), the collected session information to determine a total number of established PDU sessions between the User Equipments (UEs) and the Data Network (DN) for the extracted S-NSSAI by aggregating the number of established PDU sessions managed by each SMF (406); and transmitting (312), by the NWDAF (208), a response to the consumer NF (402), the response comprising the determined total number of established PDU sessions for the extracted S-NSSAI.

10. The method (300) as claimed in claim 9, wherein the session information includes a number of user equipment (UE) registrations and a number of established PDU sessions.

11. The method (300) as claimed in claim 9, further comprising performing, by the NWDAF (208), analysis of the received session information by comparing current data with historical data stored in a database (212), to predict one or more recommendations based on the number of established PDU sessions for each SMF (406).

12. The method (300) as claimed in claim 9, wherein the request further comprises Public Land Mobile Network (PLMN) information.

13. The method (300) as claimed in claim 9, further comprising subscribing to the plurality of AMFs (404) using an AMF event subscription application programming interface (API) with a customized event type for the number of established PDU sessions.

14. The method (300) as claimed in claim 9, further comprising storing the received number of UE registrations and the number of established PDU sessions for each SMF (406) in a database (212).

15. The method (300) as claimed in claim 9, further comprising validating, by the NWDAF (208), the request received from the consumer NF by employing a set of predefined validation rules before processing the request.

16. The method (300) as claimed in claim 9, wherein the consumer NF is one of a Policy Control Function (PCF), a Network Slice Selection Function (NSSF), or a Network Slice Subnet Management Function (NSSMF).

17. The method (300) as claimed in claim 9, further comprising utilizing, by the consumer NF (402), the determined total number of established PDU sessions for the S-NSSAI to predict future resource allocations based on load patterns and utilization trends.

18. The method (300) as claimed in claim 9, further comprising identifying, based on the extracted S-NSSAI, one or more Access and MobilityManagement Function (AMF) (404) that are serving the network slice associated with the S -NS SAI.

19. A computer program product comprising a non-transitory computer- readable medium having instructions stored thereon that, when executed by at least one processor (202) of a network data analytics function (NWDAF) (208), cause the at least one processor (202) to perform operations comprising: receiving (302), by a Network Data Analytics Function (NWDAF) (208), a request for analysis of PDU session establishment data from a consumer network function (NF) (402); extracting (304), by the NWDAF (208), a Single Network Slice Selection Assistance Information (S-NSSAI) from the received request, wherein the S-NSSAI uniquely identifies a network slice; subscribing (306), by the NWDAF (208), to the plurality of AMFs (404) for a session information based on the extracted S-NSSAI for each session management function (SMF) (406); collecting (308), by the NWDAF (208) from the plurality of AMFs (404), the session information for each SMF (406); analyzing (310), by the NWDAF (208), the collected session information to determine a total number of established PDU sessions between the User Equipments (UEs) and the Data Network (DN) for the extracted S-NSSAI by aggregating the number of established PDU sessions managed by each SMF (406); and transmitting (312), by the NWDAF (208), a response to the consumer NF (402), the response comprising the determined total number of established PDU sessions for the extracted S-NSSAI.

20. A user equipment (108) communicatively coupled to a system (102) for collecting and analyzing data related to Packet Data Unit (PDU) session establishments between User Equipments (UEs) and Data Network (DN) managed by a plurality of Access and Mobility Management Functions (AMFs) (404) via a network (104), wherein the system (102) comprising: a memory (204); and one or more processor(s) (202) configured to fetch and execute instructions stored in a memory (204) to perform steps of a method (300) as claimed in claim 9.

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