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 PERFORMING DATA ANALYTICS IN
A NETWORK”
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 PERFORMING DATA ANALYTICS IN A NETWORK
TECHNICAL FIELD
[0001] The present disclosure generally relates to field of wireless communication system. More
particularly, the present disclosure relates to system and method for performing data analytics in a network.
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 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] Wireless communication technology has rapidly evolved over the past few decades, with
each generation bringing significant improvements and advancements. The first generation of wireless communication technology was based on analog technology and offered only voice services. However, with the advent of the second-generation (2G) technology, digital communication and data services became possible, and text messaging was introduced. 3G technology marked the introduction of high¬speed internet access, mobile video calling, and location-based services. The fourth-generation (4G) technology revolutionized wireless communication with faster data speeds, better network coverage, and improved security. Currently, the fifth-generation (5G) technology is being deployed, promising even faster data speeds, low latency, and the ability to connect multiple devices simultaneously. With each generation, wireless communication technology has become more advanced, sophisticated, and capable of delivering more services to its users.
[0004] Existing Network Data Analytics Function (NWDAF) architectures consist of multiple
components and decision-making boxes. Due to the complexity of these architectures, a significant amount of bandwidth is required for communication between each component over different interfaces. This leads to the overuse of resources and creates challenges in the horizontal and vertical scaling of components. Moreover, the existing NWDAF architectures often lack a user-friendly interface for visualization and reporting. This absence of a dedicated visualization component makes it difficult for network operators to efficiently monitor network performance and make timely decisions based on the

analysed data. Additionally, the traditional NWDAF architectures do not fully leverage the potential of artificial intelligence and machine learning (AI/ML) for proactive network management. The integration of AI/ML components is typically limited, which restricts the ability of the system to predict network performance and user experience effectively.
[0005] Thus, there exists an imperative need in the art to provide a method and system for
performing data analytics in a network.
OBJECTS OF THE INVENTION
[0006] Some of the objects of the present disclosure, which at least one embodiment disclosed
herein satisfies are listed herein below.
[0007] It is an object of the present disclosure to provide method and system for performing data
analytics in a network.
[0008] It is another object of the present disclosure to provide a method and system for performing
data analytics in a network that simplifies the architecture by reducing the number of components and decision-making components, thereby minimizing the bandwidth requirements for communication between components.
[0009] It is another object of the present disclosure to provide a method and system for performing
data analytics in a network that enables efficient horizontal and vertical scaling of components to accommodate varying network demands.
[0010] It is another object of the present disclosure to provide a method and system for performing
data analytics in a network that includes a user-friendly visualization interface, allowing network operators or stake holders to easily monitor network performance and make informed decisions based on the analysed data.
[0011] It is yet another object of the present disclosure to provide a method and system for
performing data analytics in a network that integrates artificial intelligence and machine learning (AI/ML) components to proactively manage network performance and predict user experience, enhancing the overall efficiency and effectiveness of network operations.
SUMMARY OF THE DISCLOSURE

[0012] 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.
[0013] According to an aspect of the present disclosure, a method for performing data analytics in
a network is disclosed. The method includes receiving, by a Network data analytics function (NWDAF) front end module, a first set of data from one or more network functions of the network. The method further includes receiving, by a NWDAF back-end module, the first set of data from the NWDAF front end module. The method further includes computing, by the NWDAF back-end module, one or more parameters based on the first set of data. The method further includes generating, by the NWDAF back¬end module, a data analytics report based on the one or more parameters and at least one dashboarding request. The method further includes converting, by a NWDAF workflow module, the data analytics report into a visualization format. Thereafter, the method includes displaying, at a User Interface (UI) module, the visualization format of the data analytics report.
[0014] In an aspect, the method further includes storing, by the NWDAF back-end module, the data
analytics report at a Data Lake module; and retrieving, by the NWDAF workflow module, the data analytics report stored in the Data Lake module.
[0015] In an aspect, the method further includes sending, by the NWDAF back-end module, the
first set of data received from the NWDAF front end module to a NWDAF AI/ML module. The method further includes computing, by the NWDAF AI/ML module, one or more predicted parameters based on the first set of data using artificial intelligence. The method further includes generating, by the NWDAF AI/ML module, a predicted data analytics report based on the one or more predicted parameters. The method further includes sending, by the NWDAF AI/ML module, the predicted data analytics report to the NWDAF back-end module. The method further includes storing, by the NWDAF back-end module, the predicted data analytics report at the Data Lake module. The method further includes retrieving, by the NWDAF workflow module, the predicted data analytics report stored in the Data Lake module. The method further includes converting, by the NWDAF workflow module, the predicted data analytics report into a visualization format. Thereafter, the method includes displaying, at the User Interface (UI) module, the visualization format of the predicted data analytics report.
[0016] In an aspect, the method includes identifying, by the NWDAF back-end module, one or
more anomalies in the first set of data for the one or more network functions; and sending, by the

NWDAF back-end module, a report of the identified one or more anomalies to the one or more network functions.
[0017] In an aspect, the one or more parameters comprise a total traffic data on the one or more
network functions, a real-time traffic on the one or more network functions, and a utilization of the one or more network functions.
[0018] In an aspect, the first set of data is part of one of a subscription request and an analytics
request.
[0019] In an aspect, the dashboarding request is received from a user.
[0020] Another aspect of the present disclosure provides a system for performing data analytics in
a network. The system includes a NWDAF front end module configured to receive a first set of data from one or more network functions of the network. The system includes a NWDAF back-end module configured to: receive the first set of data from the NWDAF front end module; compute, one or more parameters based on the first set of data; and generate a data analytics report based on the one or more parameters and at least one dashboarding request. The system further includes a NWDAF workflow module configured to convert the data analytics report into a visualization format. The system further includes a User Interface (UI) module configured to display the visualization format of the data analytics report.
[0021] Another aspect of the present disclosure provides a user equipment. The user equipment
comprises a processor, the processor configured to send, to the user interface (UI) module of a system, a dashboarding request; receive, from the user interface (UI) module of the system, a visualization format of the generated data analytics report based on the dashboarding request; wherein the data analytics report is generated based on receiving, by the system, a first set of data from one or more network functions of the network; receiving, by the system, the first set of data from the NWDAF front end module; computing, by the system, one or more parameters based on the first set of data; generating, by the system, a data analytics report based on the one or more parameters and at least one dashboarding request; converting, by the system, the data analytics report into a visualization format; displaying, at the system, the visualization format of the data analytics report.
[0022] Yet another aspect of the present disclosure may relate to a non-transitory computer-readable
storage medium storing instruction for performing data analytics in a network, the storage medium comprising executable code which, when executed by one or more units of a system, causes a Network

data analytics function (NWDAF) front end module of the system to receive by a first set of data from one or more network functions of the network. Further, the executable code when executed causes a NWDAF back-end module of the system to receive, the first set of data from the NWDAF front end module. Further, the executable code when executed causes the NWDAF back-end module of the system to compute, one or more parameters based on the first set of data. Further, the executable code when executed causes a the NWDAF back-end module of the system to generate, a data analytics report based on the one or more parameters and at least one dashboarding request. Further, the executable code when executed causes a NWDAF workflow module of the system to convert, the data analytics report into a visualization format. Further, the executable code when executed causes a User Interface (UI) module of the system to display, the visualization format of the data analytics report.
BRIEF DESCRIPTION OF THE DRAWINGS
[0023] 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. 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 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.
[0024] FIG. 1 (a) and FIG. 1 (b) illustrates multiple exemplary representations of block diagram of
a system for performing data analytics in a network, in accordance with exemplary embodiments of the present disclosure.
[0025] FIG. 2 illustrates an exemplary method flow diagram indicating the process for performing
data analytics in a network, in accordance with exemplary embodiments of the present disclosure.
[0026] FIG. 3 illustrates another exemplary method flow diagram indicating the process for
performing data analytics in a network, in accordance with exemplary embodiments of the present disclosure.
[0027] FIG. 4 illustrates an exemplary block diagram of a computing device upon which an
embodiment of the present disclosure may be implemented.

[0028] The foregoing shall be more apparent from the following more detailed description of the
disclosure.
5 DESCRIPTION
[0029] 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
10 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 only some of the problems discussed above.
[0030] The ensuing description provides exemplary embodiments only, and is not intended to limit
15 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. 20
[0031] 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, processes,
and other components may be shown as components in block diagram form in order not to obscure the
25 embodiments in unnecessary detail.
[0032] 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
30 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.
[0033] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an
35 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
7

“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. 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
5 be inclusive—in a manner similar to the term “comprising” as an open transition word—without
precluding any additional or other elements.
[0034] 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
10 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
15 system according to the present disclosure. More specifically, the processor or processing unit is a
hardware processor.
[0035] 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
20 device”, “a mobile communication device”, “a communication device” may be any electrical, electronic
and/or computing device or equipment, capable of 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,
25 the user device may contain at least one input means configured to receive an input 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.
[0036] As used herein, “storage unit” or “memory unit” refers to a machine or computer-readable
30 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, 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. 35
8

[0037] 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
5 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.
[0038] Radio Access Technology (RAT) refers to the technology used by mobile devices/user
10 equipment (UE) to connect to a cellular network. It refers to the specific protocol and standards that
govern the way devices communicate with base stations, which are responsible for providing the
wireless connection. Further, each RAT has its own set of protocols and standards for communication,
which define the frequency bands, modulation techniques, and other parameters used for transmitting
and receiving data. Examples of RATs include GSM (Global System for Mobile Communications),
15 CDMA (Code Division Multiple Access), UMTS (Universal Mobile Telecommunications System),
LTE (Long-Term Evolution), and 5G. The choice of RAT depends on a variety of factors, including the
network infrastructure, the available spectrum, and the mobile device's/device's capabilities. Mobile
devices often support multiple RATs, allowing them to connect to different types of networks and
provide optimal performance based on the available network resources. The invention herein relates to
20 the situations when the user equipment (UE) operates in the fifth generation (5G) communication
system.
[0039] As used herein, Network data analytics function (NWDAF) may refer to interfaces from the
service-based architecture to collect data by subscription or request model from other network functions
25 and similar procedures. This is to deliver analytics functions in the network for automation or reporting,
solving major custom interface or format challenges.
[0040] Further used herein, the NWDAF front end module as described in the present disclosure
clasp and manage subscription of network data consumers. 30
[0041] Also used herein, NWDAF back-end module may be utilised to determine network
analytics, network performance, user experience, security and work actively over closed loop network operations. It is also responsible for data collection from various data sources.
35 [0042] In an embodiment of the present disclosure, each of the NWDAF front-end module and
NWDAF back-end may be a module designed as individual micro services so that each of the services
9

are individually scalable and maintainable. Further, multiple instances of each NWDAF front-end module and NWDAF back-end are module may exist to balance or distribute data collection from various data sources.
5 [0043] As discussed in the background section, existing Network Data Analytics Function
(NWDAF) architectures consist of multiple components and decision-making boxes. Due to the complexity of these architectures, a significant amount of bandwidth is required for communication between each component over different interfaces. This leads to the overuse of resources and creates challenges in the horizontal and vertical scaling of components. Moreover, the existing NWDAF
10 architectures often lack a user-friendly interface for visualization and reporting. This absence of a
dedicated visualization component makes it difficult for network operators to efficiently monitor network performance and make timely decisions based on the analysed data. Additionally, the traditional NWDAF architectures do not fully leverage the potential of artificial intelligence and machine learning (AI/ML) for proactive network management. The integration of AI/ML components
15 is typically limited, which restricts the ability of the system to predict network performance and user
experience effectively.
[0044] To overcome these and other inherent problems in the art, the present disclosure proposes a
solution of simplifying the architecture of the Network Data Analytics Function (NWDAF) by reducing
20 the number of components and decision-making components. The architecture minimizes bandwidth
requirements for communication between components, thereby addressing the issue of resource overuse and enabling easier horizontal and vertical scaling of components. Additionally, the present disclosure introduces a user-friendly interface for visualization and reporting. The interface, provided by the NWDAF workflow module, converts data analytics reports into a visualization format that can be
25 displayed on a User Interface (UI) module. The visualization format allows network operators to
efficiently monitor network performance and make timely decisions based on the analysed data, thus overcoming the lack of a dedicated visualization component in existing architectures. Furthermore, the present disclosure integrates artificial intelligence and machine learning (AI/ML) components more effectively into the NWDAF architecture. The NWDAF AI/ML module is designed to compute
30 predicted parameters based on network data and generate predicted data analytics reports. The reports
can be stored, retrieved, and visualized, enabling proactive management of network performance and user experience. This approach leverages the potential of AI/ML for network management, addressing the limitations of traditional NWDAF architectures in this regard.
10

[0045] It would be appreciated by the person skilled in the art that the present disclosure aims to
provide a more efficient, user-friendly, and intelligent solution for performing data analytics in a network, addressing the problems identified in the prior art.
5 [0046] Hereinafter, exemplary embodiments of the present disclosure will be described with
reference to the accompanying drawings.
[0047] Referring to FIG. 1 (a) and FIG. 1 (b), multiple exemplary representations of block diagram
of a system [100] for performing data analytics in a network, in accordance with exemplary
10 embodiments of the present disclosure is shown. The system [100] comprises one or more network
functions (NFs) (NF1 [102-1], NF2 [102-2]……….NFn [102-n]) (collectively referred to as NFs [102] or individually referred to as NF [102] herein). The system [100] further comprises NWDAF front-end module [104], NWDAF back-end module [106], NWDAF AI/ML module [108], a data lake module [110], a NWDAF workflow module [112], user interface (UI) module [114], at least one processing
15 unit [116], and at least one storage unit [118]. 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.
[0048] Also, in FIG. 1 (a) and FIG. 1 (b) only a few units are shown, however, the system [100]
20 may comprise multiple such units or the system [100] may comprise any such numbers of said units, as
required to implement the features of the present disclosure. Further, in an implementation, the system [100] may be present in a user device to implement the features of the present invention.
[0049] The system [100] for performing data analytics in a network comprises the NWDAF front
25 end module [104] configured to receive a first set of data from one or more network functions [102] of
the network. The NWDAF front end module [104] interfaces directly with the NFs [102]. The NFs
[102] includes the operational components of a telecommunications network that provide data regarding
a network usage, a load information, a congestion information, a fault data, a performance metrics,
subscriber information, or other relevant analytics data. Examples of the NFs [102] can include but not
30 limited only to an Access and Mobility Management Function (AMF), a Session Management Function
(SMF), a Network exposure function (NEF), a Network Slice Selection Function (NSSF) and the like.
The first set of data can include one of a subscription request and an analytics request. The subscription
request may involve the registration or a subscription of the NFs [102] to receive certain data or reports
at specified intervals or based on certain events. The subscription may correspond to data the NWDAF
35 front end module [104] should collect and forward for analysis based on the subscriber's needs. The
analytics request may include a demand for specific data analysis, which may be a one-time requirement
11

or a query for a detailed report on particular aspects of network performance. For example, the analytics request may be request from a network function (NF) to fetch a specific data analysis information based on the load information associated with a network slice.
5 [0050] The NWDAF back-end module [106] is communicatively coupled with the NWDAF front
end module [104] and/or the NFs [102]. The NWDAF back-end module [106] is configured to receive the first set of data from the NWDAF front end module [104], and any other such like data source such as network functions associated with the network that may be appreciated by a person skilled to implement the present disclosure. The NWDAF back-end module [106] is further configured to
10 compute one or more parameters based on the first set of data received from the NWDAF front end
module [104]. The computation process involves analysing the first set of data to compute, one or more parameters based on the first set of data. The one or more parameters may include but not limited only to a total traffic data on the one or more network functions [102], a real-time traffic on the one or more network functions [102], and a utilization of the one or more network functions [102]. The NWDAF
15 back-end module [106] is further configured to generate a data analytics report based on the one or
more parameters and at least one dashboarding request. The data analytics report may be generated to meet specific requirements indicated by the dashboarding request. The dashboarding request refers to a requisition or instruction for the creation of a dashboard, which is a visual representation of key data points and metrics. The dashboarding request prompts the system to convert the analysed data into a
20 dashboard, providing a user-friendly interface that encapsulates complex data in a simplified and
accessible manner. In an embodiment, the dashboarding request is received from a user (such as network administrators). In another embodiment, the dashboarding request is received from automated system prompts. The NWDAF back-end module [106] is further configured to store the data analytics report at the Data Lake module [110]. In an exemplary aspect, the Data Lake module [110] is further configured
25 to store a raw data received from one or more network function (NFs) [102].
[0051] The NWDAF workflow module [112] is communicatively coupled to the Data Lake module
[110]. The NWDAF workflow module [112] is configured to convert the data analytics report into a visualization format. In an exemplary aspect, the visualization format of the data analytics report
30 includes a traffic volume heatmap, a signal strength coverage map, tables, pie charts, a line graph and
any other visualization format that may be appreciated by a person skilled in the art as necessary to implement the present disclosure. After the data analytics report is generated by the NWDAF back-end module [106], it is stored at the Data Lake module [110]. The NWDAF workflow module [112] is further configured to retrieve the data analytics report stored in the Data Lake module [110]. Further,
35 the NWDAF workflow module [112], then processes the analytics report and converts the analytics into
graphical representations that includes but not limited only to charts, graphs, and other visual elements.
12

The graphical representations may be designed to be easily interpretable so that the users (such as network operators) can quickly and efficiently understand the insights and findings.
[0052] The UI module [114] is communicatively coupled to the NWDAF workflow module [112].
5 The UI module [114] is configured to display the visualization format of the data analytics report. After
the NWDAF workflow module [112] has converted the data analytics report into the visual format, then
the UI module [114] provides an intuitive and interactive interface, allowing the users (such as network
operators, administrators, or other stakeholders) to view, interpret, and analyse the network data in an
easily understandable manner. The display of data analytics reports in the visual format on the UI
10 module [114] facilitates quick decision-making and effective monitoring of network performance and
user experience.
[0053] In an embodiment, the NWDAF back-end module [106] is further configured to send the
first set of data received from the NWDAF front-end module [104] to the NWDAF AI/ML module
15 [108]. The NWDAF AI/ML module [108] is configured to apply artificial intelligence techniques for
analysing the received data and generating one or more predicted parameters or one or more statistics. The one or more predicted parameters could include forecasts about network traffic, network resources, thresholds, abnormal behaviour, user behaviour, service quality, and other relevant aspects of network performance. Subsequently, the NWDAF AI/ML module [108] generates a predicted data analytics
20 report based on the computed one or more predicted parameters. The predicted data analytics report
provides insights about future network conditions and potential areas for optimization, aiding in proactive decision-making. As used herein, artificial intelligence techniques may include one or mor techniques such as, but not limited to, a machine learning based technique, an Natural Language Processing (NLP) technique, a neural network-based technique, a decision tree-based technique and
25 any other such like technique that may be appreciated by a person skilled in the art to implement in the
present disclosure. Once the predicted data analytics report is generated, it is sent back to the NWDAF back-end module [106] for further processing and storage. The NWDAF back-end module [106] stores the predicted data analytics report in the Data Lake module [110]. Further, the NWDAF workflow module [112] retrieves the predicted data analytics report stored in the Data Lake module [110]. Finally,
30 the processed and visualized data analytics report is displayed at the User Interface (UI) module [114]
enabling users to easily interpret and understand the predictions and insights provided by the NWDAF AI/ML module [108]. The visualization format facilitates in the effective communication of complex data, facilitating informed decision-making and strategic planning in network management.
35 [0054] Referring to FIG. 2 an exemplary method flow diagram [200], for performing data analytics
in a network, in accordance with exemplary embodiments of the present disclosure is shown. In an
13

implementation the method [200] is performed by the system [100]. As shown in FIG. 2, the method [200] starts at step [202].
[0055] Next, at step [204], the method [200] as disclosed in the present disclosure comprises
5 receiving, by a Network data analytics function (NWDAF) front end module [104], a first set of data
from one or more network functions [102] of the network. The NWDAF front end module [104] interfaces directly with the NFs [102]. The NFs [102] includes the operational components of a telecommunications network that provide data regarding network usage, performance metrics, subscriber information, or other relevant analytics data. Examples of the NFs [102] can include but not
10 limited only to an Access and Mobility Management Function (AMF), a Session Management Function
(SMF), Network Slice Selection Function (NSSF) and the like. The first set of data may be part of one of a subscription request and an analytics request. The subscription request may involve the registration or subscription of NFs [102] to receive certain data or reports at specified intervals or based on certain events. The subscription may correspond to data the NWDAF front end module should collect and
15 forward for analysis based on the subscriber's needs. The analytics request may include a demand for
specific data analysis, which may be a one-time requirement or a query for a detailed report on particular aspects of network performance.
[0056] Next, at step [206], the method [200] as disclosed in the present disclosure comprises
20 receiving, by a NWDAF back-end module [106], the first set of data from the NWDAF front end module
[104]. The NWDAF back-end module [106] is communicatively coupled with the NWDAF front end
module [104] and/or the NFs [102]. The NWDAF back-end module [106] receives the subscription
request and/or the analytics request from the NWDAF front end module [104].
25 [0057] Next, at step [208], the method [200] as disclosed in the present disclosure comprises
computing, by the NWDAF back-end module [106], one or more parameters based on the first set of data. The computation process involves analysing the first set of data to compute, one or more parameters based on the first set of data. The one or more parameters may include but not limited only to a total traffic data on the one or more network functions [102], a real-time traffic on the one or more
30 network functions [102], and a utilization of the one or more network functions [102].
[0058] Next, at step [210], the method [200] as disclosed in the present disclosure comprises
generating, by the NWDAF back-end module [106], a data analytics report based on the one or more
parameters and at least one dashboarding request. The data analytics report may be generated to meet
35 specific requirements indicated by the dashboarding request. The dashboarding request refers to a
requisition or instruction for the creation of a dashboard, which is a visual representation of key data
14

points and metrics. The dashboarding request prompts the system to convert the analysed data into a
dashboard, providing a user-friendly interface that encapsulates complex data in a simplified and
accessible manner. In an embodiment, the dashboarding request is received from a user (such as network
administrators). In another embodiment, the dashboarding request is received from automated system
5 prompts. After the data analytics report is generated by the NWDAF back-end module [106], it is stored
at the Data Lake module [110].
[0059] Next, at step [212], the method [200] as disclosed in the present disclosure comprises
converting, by a NWDAF workflow module [112], the data analytics report into a visualization format. The NWDAF workflow module [112] retrieves the data analytics report stored in the Data Lake module [110]. Thereafter, the NWDAF workflow module [112], then processes the analytics report and converts the analytics into graphical representations that includes but not limited only to charts, graphs, and other visual elements. The graphical representations may be designed to be easily interpretable so that the users (such as network operators) can quickly and efficiently understand the insights and findings.
[0060] Next, at step [214], the method [200] as disclosed in the present disclosure comprises
displaying, at a User Interface (UI) module [114], the visualization format of the data analytics report. After the NWDAF workflow module [112] has converted the data analytics report into the visual format, then the UI module [114] provides an intuitive and interactive interface, allowing the users (such as network operators, administrators, or other stakeholders) to view, interpret, and analyse the network data in an easily understandable manner. The display of data analytics reports in the visual format on the UI module [114] facilitates quick decision-making and effective monitoring of network performance and user experience.
25 [0061] Thereafter, the method terminates at step [216].
[0062] Referring to FIG. 3 an exemplary method flow diagram [300] for performing data analytics
in a network, in accordance with exemplary embodiments of the present disclosure is shown. In an
implementation the method [300] is performed by the system [100]. As shown in FIG. 3, the method
30 [300] starts at step [302].
[0063] Next, at step [304], the method [300] as disclosed in the present disclosure comprises
sending, by the NWDAF back-end module [106], the first set of data received from the NWDAF front
end module [104] to a NWDAF AI/ML module [108]. The first set of data may be part of one of a
35 subscription request and an analytics request. The subscription request may involve the registration or
subscription of NFs [102] to receive certain data or reports at specified intervals or based on certain
15

events. The subscription may correspond to data the NWDAF front-end module receives and forwards
for analysis based on the subscriber's needs. The analytics request may include a demand for specific
data analysis, which may be a one-time requirement or a query for a detailed report on particular aspects
of network performance. Once the first set of data is received, the NWDAF AI/ML module [108]
5 initiates the training of its machine learning models. The training process involves adjusting the model's
parameters to reduce errors and improve its ability to accurately predict future events. By learning from historical data stored at the data lake module [110], the NWDAF AI/ML module [108] can identify patterns and relationships that are indicative of future network behaviour and user experiences.
10 [0064] Next, at step [306], the method [300] as disclosed in the present disclosure comprises
computing, by the NWDAF AI/ML module [108], one or more predicted parameters based on the first set of data using artificial intelligence. The NWDAF AI/ML module [108] utilizes AI techniques to analyse the first set of data received from the NWDAF front end module [104]. The NWDAF AI/ML module [108] processes the first set of data and generate one or more predicted parameters about various
15 aspects of network performance, that includes traffic trends, potential network congestion points, user
behaviour, and service quality. The predicted one or more parameters provide insights into future network conditions, enabling proactive decision-making and optimization of network resources.
[0065] Next, at step [308], the method [300] as disclosed in the present disclosure comprises
20 generating, by the NWDAF AI/ML module [108], a predicted data analytics report based on the one or
more predicted parameters. The predicted data analytics report provides a comprehensive overview of
the expected future trends and behaviours within the network, based on the one or more predictive
parameters computed using the AI techniques. The predicted analytics report facilitates the user (such
as network operators and decision-makers) to proactively address potential issues, optimize network
25 performance, and enhance the overall user experience.
[0066] Next, at step [310], the method [300] as disclosed in the present disclosure comprises
sending, by the NWDAF AI/ML module [108], the predicted data analytics report to the NWDAF back¬
end module [106]. The NWDAF back-end module [106] processes and sends the predicted analytics
30 report as needed. Further, the predicted analytics report may be stored in the data lake module [110] for
future reference.
[0067] Next, at step [312], the method [300] as disclosed in the present disclosure comprises
storing, by the NWDAF back-end module [106], the predicted data analytics report at the Data Lake
35 module [110]. The Data Lake module [110] is a centralized repository for storing network-related data,
including raw data, processed information, and predictive analytics reports. The predicted data analytics
16

report stored in the data lake module [110] facilitates in making the predicted data analytics report accessible for further analysis, historical comparisons, or audit purposes.
[0068] Next, at step [314], the method [300] as disclosed in the present disclosure comprises
5 retrieving, by the NWDAF workflow module [112], the predicted data analytics report stored in the
Data Lake module [110]. The retrieval process is initiated by the NWDAF workflow module [112]
when there is a need to visualize, analyse, or present the predictive insights contained in the predicted
data analytics report.
10 [0069] Next, at step [316], the method [300] as disclosed in the present disclosure comprises
converting, by the NWDAF workflow module [112], the predicted data analytics report into a visualization format. The NWDAF workflow module [112] converts the retrieved predicted data analytics report into a more accessible and understandable format for users, utilizing graphical representations such as charts, graphs, and dashboards. The conversion process facilitates in making the
15 complex data and insights contained in the report easily interpretable by the user (such as network
operators, decision-makers, and other stakeholders).
[0070] Next, at step [318], the method [300] as disclosed in the present disclosure comprises
displaying, at the User Interface (UI) module [114], the visualization format of the predicted data
20 analytics report. The UI module [114] provides an intuitive and user-friendly experience, allowing users
to easily view and interpret the visualized data. The display of the visualization format enables users to quickly grasp the insights and predictions generated by the NWDAF AI/ML module [108], such as potential network performance issues, user experience forecasts, and resource utilization trends. By presenting the information in an accessible and visually appealing manner, the UI module [114]
25 facilitates effective communication of the predictive analytics, empowering users to make informed
decisions and take proactive measures to optimize network operations.
[0071] Thereafter, the method terminates at step [320].
30 [0072] FIG. 4 illustrates an exemplary block diagram of a computing device [400] (Also referred
to herein as computer system) upon which an embodiment of the present disclosure may be implemented. In an implementation, the computing device [400] implements the method for performing data analytics in a network using the system [100]. In another implementation, the computing device itself implements the method for performing data analytics in a network by using one or more units
35 configured within the computing device, wherein said one or more units are capable of implementing
the features as disclosed in the present disclosure.
17

[0073] The computing device [400] may include a bus [402] or other communication mechanism
for communicating information, and a processor [404] coupled with the bus [402] for processing
information. The processor [404] may be, for example, a general-purpose microprocessor. The
5 computing device [400] may also include a main memory [406], such as a random-access memory
(RAM), or other dynamic storage device, coupled to the bus [402] for storing information and
instructions to be executed by the processor [404]. The main memory [406] also may be used for storing
temporary variables or other intermediate information during execution of the instructions to be
executed by the processor [404]. Such instructions, when stored in non-transitory storage media
10 accessible to the processor [404], render the computing device [400] into a special-purpose machine
that is customized to perform the operations specified in the instructions. The computing device [400] further includes a read only memory (ROM) [408] or other static storage device coupled to the bus [402] for storing static information and instructions for the processor [404].
15 [0074] A storage device [410], such as a magnetic disk, optical disk, or solid-state drive is provided
and coupled to the bus [402] for storing information and instructions. The computing device [400] may be coupled via the bus [402] to a display [412], such as a cathode ray tube (CRT), for displaying information to a computer user. An input device [414], including alphanumeric and other keys, may be coupled to the bus [402] for communicating information and command selections to the processor
20 [404]. Another type of user input device may be a cursor controller [416], such as a mouse, a trackball,
or cursor direction keys, for communicating direction information and command selections to the processor [404], and for controlling cursor movement on the display [412]. This inputs 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 [400] may implement the techniques described herein using
customized hard-wired logic, one or more Application-Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs), firmware and/or program logic which in combination with the computing device [400] causes or programs the computing device [400] to be a special-purpose
30 machine. According to one embodiment, the techniques herein are performed by the computing device
[400] in response to the processor [404] executing one or more sequences of one or more instructions contained in the main memory [406]. Such instructions may be read into the main memory [406] from another storage medium, such as the storage device [410]. Execution of the sequences of instructions contained in the main memory [406] causes the processor [404] to perform the process steps described
35 herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with
software instructions.
18

[0076] The computing device [400] also may include a communication interface [418] coupled to
the bus [402]. The communication interface [418] provides a two-way data communication coupling to
a network link [420] that is connected to a local network [422]. For example, the communication
5 interface [418] 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 [418] may be a local area network (LAN) card
to provide a data communication connection to a compatible LAN. Wireless links may also be
implemented. In any such implementation, the communication interface [418] sends and receives
10 electrical, electromagnetic or optical signals that carry digital data streams representing various types
of information.
[0077] The computing device [400] can send messages and receive data, including program code,
through the network(s), the network link [420] and the communication interface 418. In the Internet
15 example, a server [430] might transmit a requested code for an application program through the Internet
[428], the Internet Service Provider (ISP) [426], the local network [422], host [424] and the communication interface [418]. The received code may be executed by the processor [404] as it is received, and/or stored in the storage device [410], or other non-volatile storage for later execution.
20 [0078] The computing device [400] encompasses a wide range of electronic devices capable of
processing data and performing computations. Examples of computing device [400] include, but are not limited only to, personal computers, laptops, tablets, 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 [400] may include peripheral
25 devices, such as monitors, keyboards, and printers, as well as integrated components within larger
electronic systems, showcasing their versatility in various technological applications.
[0079] Yet another aspect of the present disclosure may relate to a user equipment (UE). The UE
comprises a processor, the processor is configured to send, to the user interface (UI) module [114] of a
30 system [100], a dashboarding request; receive, from the user interface (UI) module [114] of the system
[100], a visualization format of the generated data analytics report based on the dashboarding request; wherein the data analytics report is generated based on: receiving, by the system [100], a first set of data from one or more network functions [102] of the network; receiving, by the system [100], the first set of data from the NWDAF front end module [104]; computing, by the system [100], one or more
35 parameters based on the first set of data; generating, by the system [100], a data analytics report based
on the one or more parameters and at least one dashboarding request; converting, by the system [100],
19

the data analytics report into a visualization format; displaying, at the system [100], the visualization format of the data analytics report.
[0080] Yet another aspect of the present disclosure may relate to a non-transitory computer-readable
5 storage medium storing instruction for performing data analytics in a network, the storage medium
comprising executable code which, when executed by one or more units of a system [100], causes a Network data analytics function (NWDAF) front end module [104] of the system [100] to receive by a first set of data from one or more network functions [102] of the network. Further, the executable code when executed causes a NWDAF back-end module [106] of the system [100] to receive, the first set of
10 data from the NWDAF front end module [104]. Further, the executable code when executed causes the
NWDAF back-end module [106] of the system [100] to compute, one or more parameters based on the first set of data. Further, the executable code when executed causes a the NWDAF back-end module [106] of the system [100] to generate, a data analytics report based on the one or more parameters and at least one dashboarding request. Further, the executable code when executed causes a NWDAF
15 workflow module [112] of the system [100] to convert, the data analytics report into a visualization
format. Further, the executable code when executed causes a User Interface (UI) module [114] of the system [100] to display, the visualization format of the data analytics report.
[0081] As is evident from the above, the present disclosure provides a technically advanced solution
20 of a simplified NWDAF architecture for enhanced network data analytics. The invention significantly
reduces the complexity and bandwidth requirements of traditional NWDAF architectures by minimizing the number of components and interfaces involved. This streamlined architecture allows for efficient interaction among components, leading to lower latency and reduced resource usage. The technical solution encompasses a front-end module for managing subscriptions and analytics requests,
25 a back-end module for data collection and computation, an AI/ML module for applying artificial
intelligence to predict network performance, a data lake module for storing analysed network data, and a workflow module for converting data analytics reports into visual formats. The AI/ML module works in conjunction with the back-end module to proactively manage network performance and forecast user experiences based on the analysed data.
30
[0082] 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 these units for clarity, it is recognized that various configurations and combinations thereof are within the scope of the disclosure. The
35 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,
20

provided they achieve the intended functionality described herein, are considered to be encompassed within the scope of the present disclosure.
[0083] While considerable emphasis has been placed herein on the disclosed embodiments, it will
be appreciated that many embodiments can be made and that many changes can be made to the embodiments without departing from the principles of the present disclosure. These and other changes in the embodiments 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.

We Claim:
1. A method for performing data analytics in a network, the method comprising:
receiving, by a Network data analytics function (NWDAF) front end module [104], a first set of data from one or more network functions [102] of the network;
receiving, by a NWDAF back-end module [106], the first set of data from the NWDAF front end module [104];
computing, by the NWDAF back-end module [106], one or more parameters based on the first set of data;
generating, by the NWDAF back-end module [106], a data analytics report based on the one or more parameters and at least one dashboarding request;
converting, by a NWDAF workflow module [112], the data analytics report into a visualization format; and
displaying, at a User Interface (UI) module [114], the visualization format of the data analytics report.
2. The method as claimed in claim 1, the method further comprising:
storing, by the NWDAF back-end module [106], the data analytics report at a Data Lake module [110]; and
retrieving, by the NWDAF workflow module [112], the data analytics report stored in the Data Lake module [110].
3. The method as claimed in claim 2, further comprising:
sending, by the NWDAF back-end module [106], the first set of data received from the NWDAF front end module [104] to a NWDAF AI/ML module [108];
computing, by the NWDAF AI/ML module [108], one or more predicted parameters based on the first set of data using artificial intelligence;
generating, by the NWDAF AI/ML module [108], a predicted data analytics report based on the one or more predicted parameters;
sending, by the NWDAF AI/ML module [108], the predicted data analytics report to the NWDAF back-end module [106];
storing, by the NWDAF back-end module [106], the predicted data analytics report at the Data Lake module [110];
retrieving, by the NWDAF workflow module [112], the predicted data analytics report stored in the Data Lake module [110];
converting, by the NWDAF workflow module [112], the predicted data analytics report into a visualization format; and

displaying, at the User Interface (UI) module [114], the visualization format of the predicted data analytics report.
4. The method as claimed in claim 1, the method comprising:
identifying, by the NWDAF back-end module [106], one or more anomalies in the first set of data for the one or more network functions [102]; and
sending, by the NWDAF back-end module [106], a report of the identified one or more anomalies to the one or more network functions [102].
5. The method as claimed in claim 1, wherein the one or more parameters comprise a total traffic data on the one or more network functions [102], a real-time traffic on the one or more network functions [102], and a utilization of the one or more network functions [102].
6. The method as claimed in claim 1 wherein the first set of data is part of one of a subscription request and an analytics request.
7. The method as claimed in claim 1, wherein the dashboarding request is received from a user.
8. A system for performing data analytics in a network, the system comprising:
a NWDAF front end module [104] configured to receive a first set of data from one or more network functions [102] of the network;
a NWDAF back-end module [106] configured to:
receive the first set of data from the NWDAF front end module [104]; compute, one or more parameters based on the first set of data;
generate a data analytics report based on the one or more parameters and at least one dashboarding request;
a NWDAF workflow module [112] configured to convert the data analytics report into a visualization format; and
a User Interface (UI) module [114] configured to display the visualization format of the data analytics report.
9. The system as claimed in claim 8, wherein:
the NWDAF back-end module [106] is further configured to store the data analytics report at a Data Lake module [110]; and
the NWDAF workflow module [112] is further configured to retrieve the data analytics report stored in the Data Lake module [110].

10. The system as claimed in claim 9, wherein:
the NWDAF back-end module [106] is further configured to send the first set of data received from the NWDAF front end module [104] to a NWDAF AI/ML module [108]; the NWDAF AI/ML module [108] is configured to:
one or more predicted parameters based on the first set of data using artificial intelligence, and
generate a predicted data analytics report based on the one or more predicted parameters;
send the predicted data analytics report to the NWDAF back-end module [106]; the NWDAF back-end module [106] is further configured to store the predicted data analytics report at the Data Lake module [110];
the NWDAF workflow module [112] is further configured to:
retrieve the predicted data analytics report stored in the Data Lake module [110], and
convert the predicted data analytics report into a visualization format; and the User Interface (UI) module [114] is further configured to display the visualization format of the predicted data analytics report.
11. The system as claimed in claim 8, wherein the NWDAF back-end module [106] is further
configured to:
identify one or more anomalies in the first set of data for the one or more network functions [102]; and
send a report of the identified one or more anomalies to the one or more network functions [102].
12. The system as claimed in claim 8, wherein the one or more parameters comprise a total traffic data on the one or more network functions [102], a real-time traffic on the one or more network functions [102], and a utilization of the one or more network functions [102].
13. The system as claimed in claim 8 wherein the first set of data is part of one of a subscription request and an analytics request.
14. The system as claimed in claim 8, wherein the dashboarding request is received from a user.
15. A user equipment (UE) comprising:
a processor, the processor configured to:
send, to the user interface (UI) module [114] of a system [100], a dashboarding request;

receive, from the user interface (UI) module [114] of the system [100], a visualization format of a generated data analytics report based on the dashboarding request; wherein the data analytics report is generated based on
receiving, by the system [100], a first set of data from one or more network functions [102] of a network;
receiving, by the system [100], the first set of data from a NWDAF front end module [104];
computing, by the system [100], one or more parameters based on the first set of data;
generating, by the system [100], a data analytics report based on the one or more parameters and at least one dashboarding request;
converting, by the system [100], the data analytics report into a visualization format; and
displaying, at the system [100], the visualization format of the data analytics report.