FORM 2
THE PATENTS ACT, 1970
(39 OF 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
“METHOD AND SYSTEM FOR INGESTION OF SLICE DATA BY NETWORK DATA ANALYTICS FUNCTION (NWDAF)”
We, Jio Platforms Limited, an Indian National, of Office - 101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India.
The following specification particularly describes the invention and the manner in which it is to be performed.

METHOD AND SYSTEM FOR INGESTION OF SLICE DATA BY NETWORK DATA ANALYTICS FUNCTION (NWDAF)
FIELD OF INVENTION
5
[0001] Embodiments of the present disclosure generally relate to network performance management systems. More particularly, embodiments of the present disclosure relate to a method and system for ingestion of slice data by network data analytics function (NWDAF). 10
BACKGROUND
[0002] The following description of the related art is intended to provide background
information pertaining to the field of the disclosure. This section may include certain
15 aspects of the art that may be related to various features of the present disclosure. However,
it should be appreciated that this section is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
[0003] Network Data Analytics Function (NWDAF) (implemented by a server) is a 5G
20 node which serves other node function requests for use case analytics of said node function.
While providing use case analytics for a node function, errors occur within NWDAF or
while interfacing with other microservices, thereby disrupting the call flow. Disruption of
call flow ultimately leads to NWDAF failing to provide the required response to node
function, which had requested a use case analysis from NWDAF. Some exemplary reasons
25 for errors are connectivity failure, insufficient memory in DB, invalid request from node
function and so on. Further, NWDAF may serve use cases belonging to different domains,
such as QoS, traffic steering, dimensioning, or security. New use cases related to 5G QoS
have been identified, for example, network data analytics (NWDA) assisted QoS
provisioning, NWDA-assisted determination of policy, and NWDA-assisted QoS
30 adjustment. Thus, new solutions are needed for NWDAF influencing QoS configuration
and adjustment. Further, over the period of time various solutions have been developed where NWDAF requires a set of slice details for use cases while serving consumers. According to 3GPP specification, NWDAF must get the set of slice details from network
2

slice selection function (NSSF). The NSSF will ingest the set of slice details from fulfilment management system (FMS). However, there are certain challenges with existing solutions.
5 [0004] Thus, there exists an imperative need in the art to provide ingestion of slice data
by network data analytics function (NWDAF), which the present disclosure aims to address.
SUMMARY
10
[0005] This section is provided to introduce certain aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
15 [0006] An aspect of the present disclosure may relate to a method for ingestion of slice
data by network data analytics function (NWDAF). The method includes facilitating, by a NWDAF module, a set-up of a data handler in a data collection component of the NWDAF module. The method further includes setting-up, by the NWDAF module, a listening mode of the NWDAF module for one or more incoming connections. The method further includes
20 receiving, by the NWDAF module from a fulfilment management system (FMS) module,
a slice data management request. The method further includes generating, by the NWDAF module, a validation result based on the slice data management request, wherein the validation result is one of a valid request result and an invalid request result. The method further includes ingesting, by the NWDAF module using the data handler, a slice data in
25 an event of the generation of the valid request result, wherein the slice data is received from
the FMS module. Thereafter, the method includes sending, by the NWDAF module to the FMS module, a valid request indication in an event of the generation of the valid request result.
30 [0007] In an exemplary aspect of the present disclosure, prior to the generating, by the
NWDAF module, the validation result, the method comprises: validating, by the NWDAF module, the slice data management request.
3

[0008] In an exemplary aspect of the present disclosure, the method further comprises dynamically adjusting, by the NWDAF module, the slice data in real-time based on one or more changes in provisioned slices, provided by the FMS module.
5 [0009] In an exemplary aspect of the present disclosure, for the dynamically adjusting, by
the NWDAF module, the slice data, the method comprises implementing one or more machine learning based models.
[0010] In an exemplary aspect of the present disclosure, the method further comprises
10 sending, by the NWDAF module to the FMS module, a bad request indication in an event
of the generation of the invalid request result.
[0011] In an exemplary aspect of the present disclosure, the data handler is an intermediate
application programming interface (API) to receive the set of slice details from the FMS
15 module for the NWDAF module.
[0012] Another aspect of the present disclosure may relate to a system for ingestion of slice data by network data analytics function (NWDAF). The system comprises an NWDAF module configured to facilitate a set-up of a data handler in a data collection
20 component of the NWDAF module; set-up a listening mode of the NWDAF module for
one or more incoming connections; receive, from a fulfilment management system (FMS) module, a slice data management request; generate a validation result based on the slice data management request, wherein the validation result is one of a valid request result and an invalid request result; ingest a slice data using the data handler, in an event of the
25 generation of the valid request result, wherein the slice data is received from the FMS
module; and send, to the FMS module, a valid request indication in an event of the generation of the valid request result.
[0013] Yet another aspect of the present disclosure may relate to a non-transitory computer
30 readable storage medium storing instructions for ingestion of slice data by network data
analytics function (NWDAF), the instructions include executable code which, when executed by one or more units of a system, causes: an NWDAF module of the system to facilitate a set-up of a data handler in a data collection component of the NWDAF module;
4

the NWDAF module of the system to set-up a listening mode of the NWDAF module for
one or more incoming connections; the NWDAF module of the system to receive, from a
fulfilment management system (FMS) module, a slice data management request; the
NWDAF module of the system to generate a validation result based on the slice data
5 management request, wherein the validation result is one of a valid request result and an
invalid request result; the NWDAF module of the system to ingest a slice data using the
data handler, in an event of the generation of the valid request result, wherein the slice data
is received from the FMS module; and the NWDAF module of the system to send, to the
FMS module, a valid request indication in an event of the generation of the valid request
10 result.
OBJECTS OF THE INVENTION
[0014] Some of the objects of the present disclosure, which at least one embodiment
15 disclosed herein satisfies are listed herein below.
[0015] It is an object of the present disclosure to a method and system for ingestion of slice data by network data analytics function (NWDAF).
20 [0016] It is another object of the present disclosure to provide a solution where the
NWDAF exposes a handler to ingest slice data from Fulfilment Management System (FMS), NWDAF remains updated of any changes in slice provisioning.
[0017] It is yet another object of the present disclosure to provide a functionality to get the
25 set of slice details from FMS directly, by exposing an application programming interface
(API). In this way, NWDAF does not have to interact with NSSF, thereby reducing a communication hop that leads to network latency.
DESCRIPTION OF THE DRAWINGS
30
[0018] 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.
5

Components in the drawings are not necessarily to scale, emphasis instead being placed
upon clearly illustrating the principles of the present disclosure. Also, the embodiments
shown in the figures are not to be construed as limiting the disclosure, but the possible
variants of the method and system according to the disclosure are illustrated herein to
5 highlight the advantages of the disclosure. It will be appreciated by those skilled in the art
that disclosure of such drawings includes disclosure of electrical components or circuitry commonly used to implement such components.
[0019] FIG. 1 illustrates an exemplary block diagram representation of 5th generation core
10 (5GC) network architecture.
[0020] FIG. 2 illustrates an exemplary block diagram of a computing device upon which the features of the present disclosure may be implemented in accordance with exemplary implementation of the present disclosure. 15
[0021] FIG. 3 illustrates an exemplary block diagram of a system for ingestion of slice data by network data analytics function (NWDAF), in accordance with exemplary implementations of the present disclosure.
20 [0022] FIG. 4 illustrates a method flow diagram for ingestion of slice data by network data
analytics function (NWDAF) in accordance with exemplary implementations of the present disclosure.
[0023] FIG. 5 illustrates an exemplary block diagram of a system architecture for
25 ingestion of slice data by network data analytics function (NWDAF) in accordance with
exemplary implementations of the present disclosure.
[0024] FIG. 6 illustrates a process flow diagram for ingestion of slice data by network data
analytics function (NWDAF) in accordance with exemplary implementations of the present
30 disclosure.
[0025] The foregoing shall be more apparent from the following more detailed description of the disclosure.
6

DETAILED DESCRIPTION
[0026] In the following description, for the purposes of explanation, various specific
5 details are set forth 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 may 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
10 only some of the problems discussed above.
[0027] 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
15 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.
[0028] Specific details are given in the following description to provide a thorough
20 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, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail.
25 [0029] Also, it is noted that individual embodiments may be described as a process which
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 may 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
30 completed but could have additional steps not included in a figure.
[0030] 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
7

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 techniques known to those of ordinary skill in the art.
5 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—in a manner similar to the term “comprising” as an open transition word— without precluding any additional or other elements.
10 [0031] As used herein, a “processing unit” or “processor” or “operating processor”
includes one or more processors, wherein processor refers to any logic circuitry for processing instructions. A 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 (Digital Signal
15 Processing) 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 or processing unit is a hardware processor.
20
[0032] As used herein, “a user equipment”, “a user device”, “a smart-user-device”, “a smart-device”, “an electronic device”, “a mobile device”, “a handheld device”, “a wireless communication device”, “a mobile communication device”, “a communication device” may be any electrical, electronic and/or computing device or equipment, capable of
25 implementing the features of the present disclosure. The user equipment/device may
include, but is not limited to, a mobile phone, smart phone, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, wearable device or any other computing device which is capable of implementing the features of the present disclosure. Also, the user device may contain at least one input means configured to receive an input
30 from at least one of a transceiver unit, a processing unit, a storage unit, a detection unit and
any other such unit(s) which are required to implement the features of the present disclosure.
8

[0033] As used herein, “storage unit” or “memory unit” refers to a machine or computer-
readable medium including any mechanism for storing information in a form readable by
a computer or similar machine. For example, a computer-readable medium includes read¬
only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media,
5 optical storage media, flash memory devices or other types of machine-accessible storage
media. The storage unit stores at least the data that may be required by one or more units of the system to perform their respective functions.
[0034] As used herein “interface” or “user interface refers to a shared boundary across
10 which two or more separate components of a system exchange information or data. The
interface may also be referred to a set of rules or protocols that define communication or interaction of one or more modules or one or more units with each other, which also includes the methods, functions, or procedures that may be called.
15 [0035] All modules, units, components used herein, unless explicitly excluded herein, may
be software modules or hardware processors, the processors being a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASIC),
20 Field Programmable Gate Array circuits (FPGA), any other type of integrated circuits, etc.
[0036] As used herein the transceiver unit include at least one receiver and at least one
transmitter configured respectively for receiving and transmitting data, signals,
information, or a combination thereof between units/components within the system and/or
25 connected with the system.
[0037] As used herein, hypertext transfer protocol (HTTP or HTTP 1) is an application protocol that usually contains a list of guidelines for transferring distributed data file systems and multimedia communication on the World Wide Web (WWW). 30
9

[0038] As used herein, an application programming interface (API) refers to a set of rules or protocols or interfaces that enables software applications or programs to communicate with each other to exchange data, features, and functionality.
5 [0039] As used herein, ‘ingestion of slice data’ (or take in) refers to collecting data related
to various network slices for analysis, monitoring of network performance, and resource optimization.
[0040] As used herein, ingestion of slice data by network data analytics function
10 (NWDAF) means the NWDAF receives the needed slice data directly from FMS module,
without going through any other intermediaries such as but not limited to Network Slice Selection Function (NSSF), thus reducing the number of communication hops.
[0041] As used herein, ‘machine learning’ (ML) refers to machines with the ability to
15 automatically learn from data and historical experiences while identifying patterns to make
predictions with minimal human intervention.
[0042] As used herein, listening mode refers to the state in which the NWDAF module
actively monitors for incoming connections and requests. In this mode, the NWDAF is
20 prepared to accept and process slice data management requests from the FMS module. The
listening mode ensures that the NWDAF can validate these requests in real time, respond with appropriate indications (such as valid or invalid request results), and ingest slice data directly from the FMS module.
25 [0043] As used herein, incoming connections refers to the network communication links
initiated by external systems, such as the FMS module, to interact with the NWDAF module. The incoming connections allow the FMS module to send slice data management requests to the NWDAF for validation and processing. The NWDAF, in its listening mode, actively monitors and accepts these connections to ensure timely ingestion and updating of
30 slice data, thereby maintaining optimal network performance and resource efficiency.
[0044] As used herein, a data handler refers to a component or interface within the NWDAF module that manages the ingestion and processing of slice data received from the
10

FMS module. The data handler facilitates the setup, validation, and updating of slice data
by interpreting incoming requests and ensuring they are correctly processed and stored.
The data handler acts as an intermediary, ensuring that NWDAF remains synchronized
with the latest slice provisioning information, thereby maintaining the efficiency and
5 accuracy of the network's data analytics functions.
[0045] As used herein, slice data refers to the information related to network slices within
a 5G network. The slice data includes details about the configuration, provisioning, and
status of individual network slices, which are distinct virtualized network partitions tailored
10 to meet specific service requirements. Slice data enables the network to allocate resources
efficiently and ensure optimal performance for various applications and services.
[0046] As used herein, slice data management request refers to a communication sent by the FMS module to the NWDAF module containing information about network slice
15 provisioning or updates. The slice data management request includes details necessary for
the NWDAF to validate and ingest slice data, ensuring that the network slices are accurately reflected in real-time. The slice data management request enables the NWDAF to stay updated with any changes in slice configurations, thus supporting efficient network operations and resource management.
20
[0047] As used herein, validation result refers to the outcome generated by the NWDAF module after assessing a slice data management request from the FMS module. This result determines whether the request is valid or invalid based on predefined criteria and validation processes. A valid request result indicates that the slice data can be ingested,
25 while an invalid request result signifies that the request does not meet the necessary
requirements, prompting the NWDAF to send a bad request indication back to the FMS module.
[0048] As used herein, valid request result refers to the outcome generated by the NWDAF
30 module when a slice data management request from the FMS module meets all the
necessary validation criteria. The valid request result indicates that the request is correctly formatted, contains the required information, and complies with the predefined protocols.
11

[0049] As used herein, invalid request result refers to the outcome generated by the
NWDAF module when a slice data management request from the FMS module does not
meet the required validation criteria. The invalid request result indicates that the request is
incorrect or malformed, prompting the NWDAF to send a bad request indication back to
5 the FMS module.
[0050] As used herein, event refers to any specific occurrence or change detected by the
NWDAF module that triggers a predefined response or action. The event includes receiving
a slice data management request from the FMS module, validating the request, and
10 determining whether it is valid or invalid. An event can also involve the ingestion of new
slice data or updates to existing slices.
[0051] As used herein, bad request indication refers to the response sent by the NWDAF
module to the FMS module when a slice data management request is deemed invalid. The
15 bad request indication notifies the FMS that the request cannot be processed due to errors
or inconsistencies in the data provided.
[0052] As used herein, valid request indication refers to a confirmation sent by the NWDAF module to the FMS module indicating that the slice data management request
20 received from the FMS module is deemed valid. The confirmation signifies that the request
has passed the validation checks and that the NWDAF module will proceed with ingesting the slice data. The valid request indication facilitates that the data exchange between the NWDAF module and the FMS module is synchronized and that the NWDAF module remains updated with the latest slice provisioning information.
25
[0053] As discussed in the background section, the current known solutions have several shortcomings. The present disclosure aims to overcome the above-mentioned and other existing problems in this field of technology by providing method and system for ingestion of slice data by network data analytics function (NWDAF).
30
[0054] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings.
12

[0055] FIG. 1 illustrates an exemplary block diagram representation of 5th generation
core (5GC) network architecture, in accordance with exemplary implementation of the
present disclosure. As shown in FIG. 1, the 5GC network architecture [100] includes a user
equipment (UE) [102], a radio access network (RAN) [104], an access and mobility
5 management function (AMF) [106], a Session Management Function (SMF) [108], a
Service Communication Proxy (SCP) [110], an Authentication Server Function (AUSF) [112], a Network Slice Specific Authentication and Authorization Function (NSSAAF) [114], a Network Slice Selection Function (NSSF) [116], a Network Exposure Function (NEF) [118], a Network Repository Function (NRF) [120], a Policy Control Function
10 (PCF) [122], a Unified Data Management (UDM) [124], an application function (AF)
[126], a User Plane Function (UPF) [128], a data network (DN) [130], network data analytics function (NWDAF) [132] and fulfilment management system (FMS) [134], wherein all the components are assumed to be connected to each other in a manner as obvious to the person skilled in the art for implementing features of the present disclosure.
15
[0056] Radio Access Network (RAN) [104] is the part of a mobile telecommunications system that connects user equipment (UE) [102] to the core network (CN) and provides access to different types of networks (e.g., 5G network). It consists of radio base stations and the radio access technologies that enable wireless communication.
20
[0057] Access and Mobility Management Function (AMF) [106] is a 5G core network function responsible for managing access and mobility aspects, such as UE registration, connection, and reachability. It also handles mobility management procedures like handovers and paging.
25
[0058] Session Management Function (SMF) [108] is a 5G core network function responsible for managing session-related aspects, such as establishing, modifying, and releasing sessions. It coordinates with the User Plane Function (UPF) for data forwarding and handles IP address allocation and QoS enforcement.
30
[0059] Service Communication Proxy (SCP) [110] is a network function in the 5G core network that facilitates communication between other network functions by providing a secure and efficient messaging service. It acts as a mediator for service-based interfaces.
13

[0060] Authentication Server Function (AUSF) [112] is a network function in the 5G core responsible for authenticating UEs during registration and providing security services. It generates and verifies authentication vectors and tokens. 5
[0061] Network Slice Specific Authentication and Authorization Function (NSSAAF) [114] is a network function that provides authentication and authorization services specific to network slices. It ensures that UEs can access only the slices for which they are authorized. 10
[0062] Network Slice Selection Function (NSSF) [116] is a network function responsible for selecting the appropriate network slice for a UE based on factors such as subscription, requested services, and network policies.
15 [0063] Network Exposure Function (NEF) [118] is a network function that exposes
capabilities and services of the 5G network to external applications, enabling integration with third-party services and applications.
[0064] Network Repository Function (NRF) [120] is a network function that acts as a
20 central repository for information about available network functions and services. It
facilitates the discovery and dynamic registration of network functions.
[0065] Policy Control Function (PCF) [122] is a network function responsible for policy
control decisions, such as QoS, charging, and access control, based on subscriber
25 information and network policies.
[0066] Unified Data Management (UDM) [124] is a network function that centralizes the management of subscriber data, including authentication, authorization, and subscription information. 30
[0067] Application Function (AF) [126] is a network function that represents external applications interfacing with the 5G core network to access network capabilities and services.
14

[0068] User Plane Function (UPF) [128] is a network function responsible for handling user data traffic, including packet routing, forwarding, and QoS enforcement.
5 [0069] Data Network (DN) [130] refers to a network that provides data services to user
equipment (UE) in a telecommunications system. The data services may include but are not limited to Internet services, private data network related services.
[0070] Network Data Analytics function (NWDAF) [132] (Also referred to herein as
10 NWDAF module [132]) refers to a component used to collect data from user equipment
(UE), network functions, and operations, administration, and maintenance (OAM) systems, etc. from the 5G core, cloud, and edge networks that can be used for analytics.
[0071] Fulfilment Management System (FMS) [134] (Also referred to herein as FMS
15 module [134]) facilitates functions like network inventory, service provisioning, network
configuration, and fault management. Furthermore, the FMS module [134] includes unified inventory management to execute provisioning workflows for products and services. Further, FMS module [134] enables the transition from independent processes to a dynamic lifecycle management system with closed-loop automated capabilities.
20
[0072] FIG. 2 illustrates an exemplary block diagram of a computing device [200] (also referred to herein as a computer system [200]) upon which the features of the present disclosure may be implemented in accordance with exemplary implementation of the present disclosure. In an implementation, the computing device [200] may also implement
25 a method for ingestion of slice data by network data analytics function (NWDAF) utilising
the system. In another implementation, the computing device [200] itself implements the method for ingestion of slice data by network data analytics function (NWDAF) using one or more units configured within the computing device [200], wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.
30
[0073] The computing device [200] may include a bus [202] or other communication mechanism for communicating information, and a processor [204] coupled with bus [202] for processing information. The processor [204] may be, for example, a general-purpose
15

microprocessor. The computing device [200] may also include a main memory [206], such
as a random-access memory (RAM), or other dynamic storage device, coupled to the bus
[202] for storing information and instructions to be executed by the processor [204]. The
main memory [206] also may be used for storing temporary variables or other intermediate
5 information during execution of the instructions to be executed by the processor [204].
Such instructions, when stored in non-transitory storage media accessible to the processor
[204], render the computing device [200] into a special-purpose machine that is customized
to perform the operations specified in the instructions. The computing device [200] further
includes a read only memory (ROM) [208] or other static storage device coupled to the bus
10 [202] for storing static information and instructions for the processor [204].
[0074] A storage device [210], such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to the bus [202] for storing information and instructions. The computing device [200] may be coupled via the bus [202] to a display [212], such as a
15 cathode ray tube (CRT), Liquid crystal Display (LCD), Light Emitting Diode (LED)
display, Organic LED (OLED) display, etc. for displaying information to a computer user. An input device [214], including alphanumeric and other keys, touch screen input means, etc. may be coupled to the bus [202] for communicating information and command selections to the processor [204]. Another type of user input device may be a cursor
20 controller [216], such as a mouse, a trackball, or cursor direction keys, for communicating
direction information and command selections to the processor [204], and for controlling cursor movement on the display [212]. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allow the device to specify positions in a plane.
25
[0075] The computing device [200] may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware, and/or program logic which in combination with the computing device [200] causes or programs the computing device [200] to be a special-purpose machine. According to one
30 implementation, the techniques herein are performed by the computing device [200] in
response to the processor [204] executing one or more sequences of one or more instructions contained in the main memory [206]. Such instructions may be read into the main memory [206] from another storage medium, such as the storage device [210].
16

[0076] Execution of the sequences of instructions contained in the main memory [206]
causes the processor [204] to perform the process steps described herein. In alternative
implementations of the present disclosure, hard-wired circuitry may be used in place of or
5 in combination with software instructions.
[0077] The computing device [200] also may include a communication interface [218] coupled to the bus [202]. The communication interface [218] provides a two-way data communication coupling to a network link [220] that is connected to a local network [222].
10 For example, the communication interface [218] may be an integrated services digital
network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface [218] may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be
15 implemented. In any such implementation, the communication interface [218] sends and
receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information.
[0078] The computing device [200] can send messages and receive data, including
20 program code, through the network(s), the network link [220] and the communication
interface [218]. In the Internet example, a server [230] might transmit a requested code for
an application program through the Internet [228], the ISP [226], the local network [222],
host [224] and the communication interface [218]. The received code may be executed by
the processor [204] as it is received, and/or stored in the storage device [210], or other non-
25 volatile storage for later execution.
[0079] The computing device [200] encompasses a wide range of electronic devices
capable of processing data and performing computations. Examples of computing device
[200] include, but are not limited only to, personal computers, laptops, tablets,
30 smartphones, servers, and embedded systems. The devices may operate independently or
as part of a network and can perform a variety of tasks such as data storage, retrieval, and analysis. Additionally, computing device [200] may include peripheral devices, such as
17

monitors, keyboards, and printers, as well as integrated components within larger electronic systems, showcasing their versatility in various technological applications.
[0080] Referring to FIG. 3, an exemplary block diagram of a system [300] for ingestion
5 of slice data by network data analytics function (NWDAF), is shown, in accordance with
the exemplary implementations of the present disclosure. The system [300] comprises at
least one NWDAF module [132] and a FMS module [134], data collection component [302]
and processing unit [304]. Also, all the components/ units of the system [300] are assumed
to be connected to each other unless otherwise indicated below. As shown in the figures all
10 units shown within the system should also be assumed to be connected to each other. Also,
in FIG. 3 only a few units are shown, however, the system [300] may comprise multiple such units or the system [300] may comprise any such numbers of said units, as required to implement the features of the present disclosure.
15 [0081] The system [300] is configured for ingestion of slice data by network data analytics
function (NWDAF), with the help of the interconnection between the components/units of the system [300]. The data collected related to network slices by the NWDAF module includes various details necessary for managing and optimizing network slices. The data encompasses slice provisioning information, such as slice identifiers, configuration
20 parameters, performance metrics, and real-time updates on the status of slices. It also
includes any changes in existing slice configurations or the provisioning of new slices, which are notified by the FMS module and ingested by the NWDAF. For example, the NWDAF module collects data such as slice identifiers (e.g., slice ID 1234), configuration parameters (e.g., bandwidth, latency requirements), and performance metrics (e.g.,
25 throughput, error rates). It also ingests real-time updates from the FMS, like the addition
of a new slice or modifications to an existing slice, ensuring NWDAF has the latest slice information for optimal network performance.
[0082] The system [300] comprises the NWDAF module [132] which is configured to
30 facilitate a set-up of a data handler in a data collection component [302] of the NWDAF
module [132]. In an exemplary aspect, the NWDAF module [132] is configured to collect data such as but not limited to slice data, from external network functions (NFs), by setting up or creating the data handler to facilitate the collection of slice data within its data
18

collection component [302]. The data handler is designed specifically to ingest (or take in)
slice data directly into the NWDAF module [132]. The data handler is an intermediate
application programming interface (API) to receive a set of slice details from the FMS
module [134] for the NWDAF module [132]. The external network functions are such as
5 but not limited to Access and Mobility Management Function (AMF) [106], Session
Management Function (SMF) [108], and Network Slice Selection Function (NSSF) [116] etc.
[0083] The NWDAF module [132] is further configured to set-up a listening mode of the
10 NWDAF module [132] for one or more incoming connections. In an exemplary aspect,
NWDAF module [132] is configured to listen one or more incoming connections via application protocol such as but not limited to hypertext transfer protocol (HTTP) interface. The HTTP interface is hosted on a specific internet protocol (IP) and port on the HTTP stack.
15
[0084] The NWDAF module [132] is further configured to receive, from the FMS module [134], a slice data management request. The NWDAF module [132] is further configured to receive, from the FMS module [134], the slice data management request corresponds to a command or inquiry sent by the FMS module [134] to the NWDAF module [132], seeking
20 to manage and update the network slice data. For example, if a new network slice is created
or if there is an update to an existing slice, the FMS module [134] will send a request to the NWDAF module [132] containing the details associated with the changes. In an implementation, the set of slice details may associate with such as, but not limited to, radio access network and core domain.
25
[0085] The set of Slice details can include a variety of specific parameters essential for network configuration and operation. For example, the set of slice details may encompass the slice ID, which uniquely identifies each network slice, and the allocated bandwidth, determining the amount of network capacity reserved for the slice. Additionally, Quality
30 of Service (QoS) parameters specify the performance characteristics required for the slice,
such as latency, jitter, and packet loss rates. Other details might include the geographical area covered by the slice, the duration for which the slice is provisioned, and the specific applications or services that the slice supports. Security policies, user access controls, and
19

resource prioritization settings are also critical components of the set of slice details, ensuring that the slice meets the required operational and security standards.
[0086] In an exemplary aspect, the NWDAF module [132] is configured to receive, from
5 the FMS module [134], the slice data management request which is used for adding the
new slice data or changing in existing slice data.
[0087] The NWDAF module [132] receives the set of slice details directly from the FMS
module [134] through the data handler which acts as a bridge between the NWDAF module
10 [132] and FMS module [134], by exposing data handler’s application programming
interface (API). In this way, NWDAF module [132] does not have to interact with NSSF [116] for receiving the set of slice details, thereby reducing a communication hop.
[0088] The NWDAF module [132] is further configured to generate a validation result
15 based on the slice data management request, wherein the validation result is one of a valid
request result and an invalid request result. The NWDAF module [132], prior to generating
a validation result, is configured to validate the slice data management request. These
requests pertain to the provisioning (or setup) or management of the network slice data.
After validating the slice data management request received from the FMS module [134],
20 the NWDAF module [132] generates a validation result based on that slice data
management request. In an exemplary aspect, the validation result generates the valid request result if the request is correct and generates an invalid request result if the request is incorrect or invalid.
25 [0089] Validation within the NWDAF module [132], based on the slice data management
request, is performed on several key criteria. First, the request is checked for adherence to a predefined format and structure, ensuring that all required fields are present and correctly formatted. This step verifies that the data adheres to the pre-defined protocol between the NWDAF module [132] and FMS module [134]. Second, the authenticity and authorization
30 of the request are verified by examining digital signatures and authentication tokens,
confirming that the request comes from a legitimate and trusted source. Third, the content of the slice data is scrutinized for consistency with existing network configurations and operational parameters, ensuring there are no conflicts or discrepancies with current slice
20

provisions. Lastly, the contextual relevance of the request is assessed by analyzing whether
the slice data management request is necessary and appropriate given the current network
state, load, and recent changes. Through these validation steps, the NWDAF module [132]
effectively filters out invalid or unauthorized data, maintaining the integrity and reliability
5 of the network data analytics function.
[0090] The NWDAF module [132] is further configured to ingest a slice data using the data handler, in an event of the generation of the valid request result, wherein the slice data is received from the FMS module [134]. The NWDAF module [132], upon validating a
10 correct request, is configured to ingest slice data using the data handler which is designed
specifically to ingest (or take in) slice data directly from the FMS module [134]. This means that the NWDAF module [132] receives the needed slice data directly from the FMS module [134], without going through any other intermediaries such as but not limited to NSSF [116], thus reducing the number of communication hops making the system [300]
15 more efficient and less resource intensive. Slice data includes various parameters that
define a network slice, such as slice ID, bandwidth allocation, Quality of Service (QoS) parameters, and resource allocation details. For example, slice data might specify a slice ID of "slice123", a bandwidth of "100 Mbps", and QoS parameters like latency and jitter specifications. When it comes to ingestion by the NWDAF module [132], either the
20 complete slice data or a portion of it can be fed. This means that if there is a need to update
only specific parameters, such as the bandwidth allocation or QoS parameters, the FMS module [134] can send just those portions of the slice data. The NWDAF module [132] will then ingest and validate only the provided updates, allowing for efficient and focused data processing without the need to handle the entire slice configuration each time.
25
[0091] The NWDAF module [132] is further configured to dynamically adjust the slice data in real-time based on one or more changes in provisioned slices, provided by the FMS module [134]. In an exemplary aspect, the NWDAF module [132] dynamically adjusts the network slice data in real-time in response to one or more changes in the provisioned slices
30 by the FMS module [134], thereby keeping the NWDAF module [132] updated with any
modifications in slice provisioning.
21

[0092] In the older system, NWDAF had to rely on NSSF [116] to obtain the set of slice details. This dependency increased complexity and could also impact the speed and accuracy of information transfer, especially if NSSF [116] was not operating optimally.
5 [0093] The NWDAF module [132] for dynamically adjusting the slice data, is configured
to implement one or more machine learning based models (such as one or more artificial intelligence or machine learning (AI/ML) models). In an exemplary aspect, the NWDAF module [132] utilizes one or more artificial intelligence or machine learning (AI/ML) models to adjust slice data dynamically. This means that the AI/ML models would
10 continuously monitor the state of the network and the performance of individual slices and
adjust as needed. For example, if the AI/ML model detects that a particular slice is under heavy load, it could automatically allocate more resources to that slice to maintain performance. Conversely, if a slice is underutilized, the AI/ML model could reduce its resource allocation, thus optimizing the overall network resource usage. An AI/ML model
15 uses artificial intelligence and machine learning techniques to make complex decisions and
adapt to changing situations. In the context of network slice management, it could use a variety of techniques such as predictive analysis, pattern recognition, and reinforcement learning to make optimal decisions about slice provisioning and management. The AI/ML model would be trained on a large amount of historical data related to network data,
20 enabling it to recognize patterns and trends and predict future network conditions. This
allows it to make more accurate and effective decisions than a traditional, static network management system. Predictive analysis involves using historical data and statistical algorithms to forecast future events, helping to anticipate network issues before they occur. Pattern recognition identifies recurring patterns and trends in data, which can be used to
25 detect anomalies or optimize network performance. Reinforcement learning, a type of
machine learning technique, involves training algorithms through trial and error to make decisions that maximize long-term rewards, such as improving network efficiency and reducing latency in real-time.
30 [0094] First, the AI/ML model collects historical and real-time data related to network
slices from the NWDAF's data handler. The data includes various parameters such as network traffic, user activity patterns, resource utilization, and any recent changes in slice configurations. The historical data helps in understanding long-term patterns and trends,
22

while the real-time data provides a snapshot of the current network state. Next, the AI/ML
model processes the data to detect anomalies, identify patterns, and predict future states of
the network. The AI/ML can utilise technique such as, but not limited only to simple
statistical techniques to more complex deep learning techniques, depending on the
5 requirements and the amount of data available. For instance, time-series analysis might be
used to forecast traffic loads, while classification algorithms could predict potential faults or degradations in service quality.
[0095] Once the model has processed the data, it generates predictions about future
10 network conditions. The predictions can include anticipated increases in traffic, potential
bottlenecks, or even the likelihood of service disruptions. The model might also provide
recommendations for optimizing resource allocation to maintain service quality and
efficiency. For example, if the model predicts a surge in network traffic, it could suggest
pre-emptively allocating more resources to certain slices to handle the expected load. The
15 NWDAF module [132] then utilises the predictions to adjust the slice data dynamically. If
the AI/ML model forecasts a change in network conditions, the NWDAF module [132] can
modify the slice configurations in real-time to accommodate these changes. This might
involve reallocating resources, changing traffic routing paths, or adjusting other network
parameters to maintain optimal performance. By continuously learning from new data and
20 refining its predictions, the AI/ML model helps the NWDAF module [132] stay responsive
to the ever-changing network environment. This capability not only improves the efficiency and reliability of the network but also enhances the overall user experience by minimizing disruptions and maintaining high service quality.
25 [0096] It would be appreciated by the person skilled in the art that the use of AI/ML for
dynamic slice provisioning and management enables a more flexible, efficient, and responsive network that can better meet the demands of various services and use cases.
[0097] The NWDAF module [132] is further configured to send to the FMS module [134],
30 a valid request indication in an event of the generation of the valid request result. In an
exemplary aspect, the NWDAF module [132] is configured to responds with a predetermined valid request indication, notifying the FMS module [134] that the valid request result has been generated. For example, the NWDAF module [132] may send a 200
23

OK response to the FMS module [134] as valid request indication in the event of the
generation of the valid request result. The NWDAF module [132] is further configured to
send to the FMS module [134] a bad request indication in an event of the generation of the
invalid request result. In an exemplary aspect, the NWDAF module [132] is configured to
5 respond bad request indication using the predetermined error message notifying the FMS
module [134] that the invalid request result has been generated. For example, the NWDAF module [132] may send a 404 bad response to the FMS module [134] as bad request indication in the event of the generation of the invalid request result.
10 [0098] In an exemplary aspect, the system [300] comprises a processing unit [304] which
is configured to implement various aspects of the system [300]. In an exemplary aspect, NWDAF module [132] processes the sliced data received from the FMS module [134] using the processing unit [304].
15 [0099] Referring to FIG. 4, an exemplary method flow diagram [400] for ingestion of slice
data by network data analytics function (NWDAF) in accordance with exemplary implementations of the present disclosure is shown. In an implementation the method [400] is performed by the system [300]. Further, in an implementation, the system [300] may be present in a server device to implement the features of the present disclosure. Also, as
20 shown in FIG. 4, the method [400] starts at step [402].
[0100] At step [404], the method [400] as disclosed by the present disclosure comprises facilitating, by a NWDAF module [132], a set-up of a data handler in a data collection component [302] of the NWDAF module [132]. In an exemplary aspect, the NWDAF
25 module [132] is configured to collect data such as but not limited to slice data, from external
network functions (NFs), by setting up or creating the data handler to facilitate the collection of slice data within its data collection component [302]. The data handler is designed specifically to ingest (or take in) slice data directly into the NWDAF module [132]. The data handler is an intermediate application programming interface (API) to
30 receive the set of slice details from the FMS module [134] for the NWDAF module [132].
The external network functions are such as but not limited to Access and Mobility Management Function (AMF) [106], Session Management Function (SMF) [108], and NSSF [116] etc.
24

[0101] In an exemplary aspect, the data handler within the NWDAF module [132] is set
up through a series of configuration steps to facilitate the ingestion of slice data from the
FMS module. Initially, the NWDAF module [132] facilitates the setup by deploying the
5 data handler within its data collection component [302]. This involves configuring the data
handler to operate as an intermediate API, capable of receiving slice details. The NWDAF module [132] is then sets in a listening mode, ready to accept incoming connections and data requests from the FMS module [134]. The setup process includes defining the parameters and protocols for data communication, such as specifying the HTTP interface,
10 host IP, and port details. Once configured, the data handler operates by receiving slice data
management requests, validating them based on predefined criteria, and processing valid requests to update the network slice information in real-time. The setup allows the NWDAF module [132] to stay updated with the latest slice data without requiring direct interaction with the NSSF, thus streamlining the data ingestion process.
15
[0102] At step [406], the method [400] as disclosed by the present disclosure comprises setting-up, by the NWDAF module [132], a listening mode of the NWDAF module [132] for one or more incoming connections. In an exemplary aspect, NWDAF module [132] may listen one or more incoming connections via an application protocol such as but not
20 limited to hypertext transfer protocol (HTTP) interface. The HTTP interface is hosted on a
specific internet protocol (IP) and port on the HTTP stack.
[0103] At step [408], the method [400] as disclosed by the present disclosure comprises receiving, by the NWDAF module [132] from a FMS module [134], a slice data
25 management request. The NWDAF module [132] is further may receive, from the FMS
module [134], the slice data management request corresponds to a command or inquiry sent by the FMS module [134] to the NWDAF module [132], seeking to manage and update the network slice data. For example, if a new network slice is created or if there is an update to an existing slice, the FMS module [134] will send a request to the NWDAF module [132]
30 containing the details associated with the changes. In an implementation, the set of slice
details may associate with such as, but not limited to, radio access network and core domain.
25

[0104] The NWDAF module [132] receives the set of slice details directly from FMS
module [134] through the data handler which acts as a bridge between the NWDAF module
[132] and FMS module [134], by exposing data handler’s application programming
interface (API). In this way, NWDAF module [132] does not have to interact with NSSF
5 [116] for receiving the set of slice details, thereby reducing a communication hop.
[0105] At step [410], the method [400] as disclosed by the present disclosure comprises generating, by the NWDAF module [132], a validation result based on the slice data management request, wherein the validation result is one of a valid request result and an
10 invalid request result. The NWDAF module [132], prior to generating a validation result,
is configured to validate the slice data management request. These requests pertain to the provisioning (or setup) or management of the network slice data. After validating the slice data management request received from the FMS module [134], the NWDAF module [132] generates a validation result based on that slice data management request. In an exemplary
15 aspect, the validation result generates the valid request result if the request is correct and
generates an invalid request result if the request is incorrect or invalid.
[0106] At step [412], the method [400] as disclosed by the present disclosure comprises ingesting, by the NWDAF module [132] using the data handler, a slice data in an event of
20 the generation of the valid request result, wherein the slice data is received from the FMS
module [134]. The NWDAF module [132], upon validating a correct request, is configured to ingest slice data using the data handler which is designed specifically to ingest (or take in) slice data directly from the FMS module [134]. This means that the NWDAF module [132] receives the needed slice data directly from FMS module [134], without going
25 through any other intermediaries such as but not limited to NSSF [116], thus reducing the
number of communication hops. This makes the method more efficient and less resource intensive. Slice data typically includes various parameters that define a network slice, such as slice ID, bandwidth allocation, Quality of Service (QoS) parameters, and resource allocation details. For example, slice data might specify a slice ID of "slice123", a
30 bandwidth of "100 Mbps", and QoS parameters like latency and jitter specifications. When
it comes to ingestion by the NWDAF module [132], either the complete slice data or a portion of it can be fed. This means that if there is a need to update only specific parameters, such as the bandwidth allocation or QoS parameters, the FMS module [134] can send just
26

those portions of the slice data. The NWDAF module [132] will then ingest and validate only the provided updates, allowing for efficient and focused data processing without the need to handle the entire slice configuration each time.
5 [0107] In an exemplary aspect, the method [400] comprises dynamically adjusting, by the
NWDAF module [132], the slice data in real-time based on one or more changes in
provisioned slices, provided by the FMS module [134]. In an exemplary aspect, the
NWDAF module [132] dynamically adjusts the network slice data in real-time or
instantaneously in response to one or more changes in the provisioned slices by the FMS
10 module [134], thereby keeping the NWDAF module [132] updated with any modifications
in slice provisioning.
[0108] In the older method, NWDAF had to rely on NSSF [116] to obtain the set of slice
details. This dependency increased complexity and could also impact the speed and
15 accuracy of information transfer, especially if NSSF [116] was not operating optimally.
[0109] Furthermore, for the dynamically adjusting, by the NWDAF module [132], the slice data, the method [400] comprises implementing one or more machine learning based models. In an exemplary aspect, the NWDAF module [132] utilizes one or more artificial
20 intelligence/machine learning (AI/ML) models to adjust slice data dynamically. This means
that the AI/ML models would continuously monitor the state of the network and the performance of individual slices and adjust as needed. For example, if the AI/ML model detects that a particular slice is under heavy load, it could automatically allocate more resources to that slice to maintain performance. Conversely, if a slice is underutilized, the
25 AI/ML model could reduce its resource allocation, thus optimizing the overall network
resource usage. An AI/ML model uses artificial intelligence and machine learning techniques to make complex decisions and adapt to changing situations. In the context of network slice management, it could use a variety of techniques such as predictive analysis, pattern recognition, and reinforcement learning to make optimal decisions about slice
30 provisioning and management. The AI/ML model would be trained on a large amount of
network data, enabling it to recognize patterns and trends and predict future network conditions. This allows it to make more accurate and effective decisions than a traditional, static network management system.
27

[0110] It would be appreciated by the person skilled in the art that the use of AI/ML model for dynamic slice provisioning and management enables a more flexible, efficient, and responsive network that can better meet the demands of various services and use cases. 5
[0111] At step [414], the method [400] as disclosed by the present disclosure comprises sending, by the NWDAF module [132] to the FMS module [134], a valid request indication in an event of the generation of the valid request result. In an exemplary aspect, the NWDAF module [132] responds with a predetermined valid request indication, notifying
10 the FMS module [134] that the valid request result has been generated. For example, the
NWDAF module [132] may send a 200 OK response to the FMS module [134] as valid request indication in the event of the generation of the valid request result. In an exemplary aspect, the method further comprises sending, by the NWDAF module [132] to the FMS module [134], a bad request indication in an event of the generation of the invalid request
15 result. In an exemplary aspect, the NWDAF module [132] may respond bad request
indication using the predetermined error message notifying the FMS module [134] that the invalid request result has been generated. For example, the NWDAF module [132] may send a 404 bad response to the FMS module [134] as bad request indication in the event of the generation of the invalid request result.
20
[0112] Thereafter, the method [400] terminates at step [416].
[0113] The present disclosure further discloses a non-transitory computer readable storage medium storing instructions for ingestion of slice data by network data analytics function
25 (NWDAF), the instructions include executable code which, when executed by one or more
units of a system, causes: an NWDAF module [132] of the system to facilitate a set-up of a data handler in a data collection component [302] of the NWDAF module [132]; the NWDAF module [132] of the system to set-up a listening mode of the NWDAF module [132] for one or more incoming connections; the NWDAF module [132] of the system to
30 receive, from the FMS module [134], a slice data management request; the NWDAF
module [132] of the system to generate a validation result based on the slice data management request, wherein the validation result is one of a valid request result and an invalid request result; the NWDAF module [132] of the system to ingest a slice data using
28

the data handler, in an event of the generation of the valid request result, wherein the slice data is received from the FMS module [134]; and the NWDAF module [132] of the system to send, to the FMS module [134], a valid request indication in an event of the generation of the valid request result. 5
[0114] Referring to FIG. 5, an exemplary block diagram of a system architecture [500]
for ingestion of slice data by network data analytics function (NWDAF), is shown, in
accordance with the exemplary implementations of the present disclosure. The system
architecture [500] comprises the NWDAF module [132], a data consumer [502], external
10 NFs [504] for collecting data (especially slice data), a database (DB) [506], and a NWDAF
AI/ML model [508]. As shown in FIG. 5 the system architecture [500] may comprise other devices/ units/ modules also. All devices/ units/ modules may connect with each other in any combination.
15 [0115] The system architecture [500] comprises a NWDAF module [132] which is
configured to receive subscription request from the data consumers [502]. The data consumers [502] then receives notification from the NWDAF module [132] confirming that the subscription request has been accepted. In an exemplary aspect, the data consumers [502] may be such as, but not limited to, policy control function (PCF) [122].
20
[0116] After receiving subscription request from the data consumers [502], the NWDAF module [132] receives collected slice data from the external NFs [504] (e.g., AMF [106] and SMF [108]). The slice data is collected from external NFs [504] by setting up the data handler to facilitate the collection of slice data in NWDAF module [132] and storing the
25 same in the DB [506]. In an exemplary aspect, the NWDAF module [132] could retrieve
slices data stored from the DB [506] for further processing.
[0117] The NWDAF module [132] is further connected to NWDAF artificial
intelligence/machine learning (AI/ML) model [508]. The NWDAF AI/ML model [508]
30 retrieves the stored slice data from the DB [506] to continuously monitor the performance
of individual slices and dynamically adjust the same. For example, if the AI/ML model detects that a particular slice is under heavy load, it could automatically allocate more resources to that slice to maintain performance. Conversely, if a slice is underutilized, the
29

AI/ML model could reduce its resource allocation, thus optimizing the overall network
resource usage. In an exemplary aspect, the NWDAF AI/ML model [508] feeds adjusted
sliced data for predicting exceptions trends and receiving predicted trends related to sliced
data from the NWDAF module [132]. After receiving the predicted exception trends, the
5 AI model can be trained for predicting the future trends. Further it improves (training of
the model) the accuracy of AI/ML model.
[0118] First, the AI/ML model collects historical and real-time data related to network slices from the NWDAF's data handler. The data includes various parameters such as
10 network traffic, user activity patterns, resource utilization, and any recent changes in slice
configurations. The historical data helps in understanding long-term patterns and trends, while the real-time data provides a snapshot of the current network state. Next, the AI/ML model processes the data to detect anomalies, identify patterns, and predict future states of the network. The AI/ML can utilise technique such as, but not limited only to simple
15 statistical techniques to more complex deep learning techniques, depending on the
requirements and the amount of data available. For instance, time-series analysis might be used to forecast traffic loads, while classification algorithms could predict potential faults or degradations in service quality.
20 [0119] Once the model has processed the data, it generates predictions about future
network conditions. The predictions can include anticipated increases in traffic, potential bottlenecks, or even the likelihood of service disruptions. The model might also provide recommendations for optimizing resource allocation to maintain service quality and efficiency. For example, if the model predicts a surge in network traffic, it could suggest
25 pre-emptively allocating more resources to certain slices to handle the expected load. The
NWDAF module [132] then utilises the predictions to adjust the slice data dynamically. If the AI/ML model forecasts a change in network conditions, the NWDAF module [132] can modify the slice configurations in real-time to accommodate these changes. This might involve reallocating resources, changing traffic routing paths, or adjusting other network
30 parameters to maintain optimal performance. By continuously learning from new data and
refining its predictions, the AI/ML model helps the NWDAF module [132] stay responsive to the ever-changing network environment. This capability not only improves the
30

efficiency and reliability of the network but also enhances the overall user experience by minimizing disruptions and maintaining high service quality.
[0120] In an exemplary aspect, NWDAF AI/ML model [508] is communicatively coupled
5 with the data consumers [502] for closed loop forecasting of requested subscription for
further analysis.
[0121] Referring to FIG. 6, an exemplary process flow diagram [600] for ingestion of slice data by network data analytics function (NWDAF) in accordance with exemplary
10 implementations of the present disclosure is shown. In an implementation the process [600]
is performed by the server. The process [600] comprises a process flow [606a] associated with prior art scenario with NSSF [116] and a process flow [606b] associated without NSSF [116] as per the present disclosure. The process flow diagram [600] illustrates slice data ingestion by NWDAF module [132] through several specific steps:
15
[0122] The consumer NF [602] sends subscription request to NWDAF module [132]. The consumer NF [602] then receives notification from NWDAF module [132] confirming that the subscription request has been accepted. In an exemplary aspect, NWDAF module [132] is configured to store subscription request data in database [604a] and database [604b].
20
[0123] As shown in older process flow (such as [606a]), the NWDAF module [132] communicates and proceeds the process flow [606a] with the FMS module [134] via NSSF [116].
25 [0124] As shown in process flow [606b] as per implementation of the present disclosure,
the NWDAF module [132], after receiving subscription request from the consumer NF [602], further receives from the FMS module [134], slice load provisioning data i.e. slice data management request on an application-level protocol such as but not limited to HTTP 1 handler. Examples of slice load provisioning data include metrics like the number of
30 active users per slice, current data throughput, resource allocation (CPU, memory), and
latency measurements. The NWDAF module [132] is configured to collect data from FMS module [134] using the data handler. In an exemplary aspect, NWDAF module [132] is configured to listen for one or more incoming connections via hypertext transfer protocol
31

(HTTP) 1. The NWDAF module [132] then generates validation results based on slice data management request. In an exemplary aspect, the validation result generates the valid request result if the request is correct and generates an invalid request result if the request is incorrect or invalid. 5
[0125] The generated validation results are then stored in the databases [604b] for further analysis. In an exemplary aspect, the NWDAF module [132] is configured to retrieve stored validation results for further processing.
10 [0126] As is evident from the above, the present disclosure provides a technically
advanced solution for ingestion of slice data by network data analytics function (NWDAF). The present solution bypasses the need for NSSF [116], making the process of slice data ingestion by NWDAF module [132] more direct and efficient. The present disclosure provides a solution such that NWDAF exposes a handler to ingest slice data from FMS
15 module [134] and NWDAF module [132] remains updated of any changes in slice
provisioning. The present disclosure provides a solution which reduces a communication hop and thereby decreases the resource utilization.
[0127] An aspect of the present disclosure may relate to a non-transitory computer
20 readable storage medium storing instructions for ingestion of slice data by network data
analytics function (NWDAF), the instructions include executable code which, when
executed by one or more units of a system, causes: an NWDAF module of the system to
facilitate a set-up of a data handler in a data collection component of the NWDAF module;
the NWDAF module of the system to set-up a listening mode of the NWDAF module for
25 one or more incoming connections; the NWDAF module of the system to receive, from a
fulfilment management system (FMS) module, a slice data management request; the
NWDAF module of the system to generate a validation result based on the slice data
management request, wherein the validation result is one of a valid request result and an
invalid request result; the NWDAF module of the system to ingest a slice data using the
30 data handler, in an event of the generation of the valid request result, wherein the slice data
is received from the FMS module; and the NWDAF module of the system to send, to the FMS module, a valid request indication in an event of the generation of the valid request result.
32

[0128] Further, in accordance with the present disclosure, it is to be acknowledged that
the functionality described for the various components/units can be implemented
interchangeably. While specific embodiments may disclose a particular functionality of
5 these units for clarity, it is recognized that various configurations and combinations thereof
are within the scope of the disclosure. The functionality of specific units as disclosed in the
disclosure should not be construed as limiting the scope of the present disclosure.
Consequently, alternative arrangements and substitutions of units, provided they achieve
the intended functionality described herein, are encompassed within the scope of the
10 present disclosure.
[0129] While considerable emphasis has been placed herein on the disclosed
implementations, it will be appreciated that many implementations can be made and that
many changes can be made to the implementations without departing from the principles
15 of the present disclosure. These and other changes in the implementations of the present
disclosure will be apparent to those skilled in the art, whereby it is to be understood that the foregoing descriptive matter to be implemented is illustrative and non-limiting.
33

We Claim:
1. A method for ingestion of slice data by network data analytics function (NWDAF),
the method comprising:
5 - facilitating, by a NWDAF module [132], a set-up of a data handler in a data
collection component [302] of the NWDAF module [132];
- setting-up, by the NWDAF module [132], a listening mode of the NWDAF
module [132] for one or more incoming connections;
- receiving, by the NWDAF module [132] from a fulfilment management
10 system (FMS) module [134], a slice data management request;
- generating, by the NWDAF module [132], a validation result based on the slice
data management request, wherein the validation result is one of a valid request
result and an invalid request result;
- ingesting, by the NWDAF module [132] using the data handler, a slice data in
15 an event of the generation of the valid request result, wherein the slice data is
received from the FMS module [134]; and
- sending, by the NWDAF module [132] to the FMS module [134], a valid
request indication in an event of the generation of the valid request result.
20 2. The method as claimed in claim 1, wherein prior to the generating, by the NWDAF
module [132], the validation result, the method comprises:
- validating, by the NWDAF module [132], the slice data management request.
3. The method as claimed in claim 1, the method comprising:
25 - dynamically adjusting, by the NWDAF module [132], the slice data in real-
time based on one or more changes in provisioned slices, provided by the FMS module [134].
4. The method as claimed in claim 3, wherein for the dynamically adjusting, by the
30 NWDAF module [132], the slice data, the method comprises implementing one
or more machine learning based models.
5. The method as claimed in claim 1, the method comprising:
34

- sending, by the NWDAF module [132] to the FMS module [134], a bad
request indication in an event of the generation of the invalid request result.
6. The method as claimed in claim 1, wherein the data handler is an intermediate
5 application programming interface (API) to receive a set of slice details from the
FMS module [134] for the NWDAF module [132].
7. A system for ingestion of slice data by network data analytics function (NWDAF),
the system comprising:
10 - an NWDAF module [132] configured to:
o facilitate a set-up of a data handler in a data collection component
[302] of the NWDAF module [132];
o set-up a listening mode of the NWDAF module [132] for one or more
incoming connections;
15 o receive, from a fulfilment management system (FMS) module [134],
a slice data management request;
o generate a validation result based on the slice data management
request, wherein the validation result is one of a valid request result
and an invalid request result;
20 o ingest a slice data using the data handler, in an event of the generation
of the valid request result, wherein the slice data is received from the FMS module [134]; and o send, to the FMS module [134], a valid request indication in an event of the generation of the valid request result. 25
8. The system as claimed in claim 7, wherein the NWDAF module [132], prior to
generating the validation result, is configured to:
- validate the slice data management request.
30 9. The system as claimed in claim 7, the NWDAF module [132] is configured to:
- dynamically adjust the slice data in real-time based on one or more changes in
provisioned slices, provided by the FMS module [134].
35

10. The system as claimed in claim 9, wherein the NWDAF module [132] for dynamically adjusting the slice data, is configured to implement one or more machine learning based models.
5 11. The system as claimed in claim 7, wherein the NWDAF module [132] is configured
to:
- send to the FMS module [134] a bad request indication in an event of the
generation of the invalid request result.
10 12. The system as claimed in claim 7, wherein the data handler is an intermediate
application programming interface (API) to receive a set of slice details from the FMS module [134] for the NWDAF module [132].