FORM 2
THE PATENTS ACT, 1970 (39 OF 1970) & THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
“SYSTEM AND METHOD FOR FORECASTING COMMUNICATION TRAFFIC TRENDS WITHIN 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.

SYSTEM AND METHOD FOR FORECASTING COMMUNICATION TRAFFIC TRENDS WITHIN A NETWORK
FIELD OF INVENTION
[0001] The present disclosure relates generally to communication analytics in the field of network monitoring and optimization. In particular, the present disclosure relates to analysing User Equipment (UE) communication trends and traffic volume in Network Data Analytics Function (NWDAF) in a 5G communication system. More particularly, the present disclosure relates to system and method for UE communication analytics through predictive forecasting and network optimization.
BACKGROUND
[0002] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.
[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] As the existing network systems struggle with forecasting UE behaviour accurately which leads to an inability to proactively manage network resources or predict high-traffic periods. The inability to comprehensively map the user's journey from network connection to disconnection may limit understanding of UE behaviour and the overall network usage pattern. Conventionally available systems fail to provide real-time analytics and adaptive network responses to UE behaviour, potentially compromising network performance during high-traffic periods. Further, the existing or conventionally available solutions fail to accurately allocate network resources based on UE behaviour and traffic patterns, leading to network congestion and poor user experience. Further, the current systems struggle with seamless integration into the existing network infrastructure, which result in inefficiencies and errors in capturing and analysing UE communication trends. The lack of user-friendly interfaces and visual representations of data makes it challenging for network operators to comprehend and utilize the insights from UE communication analytics effectively.
[0005] Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks.
[0006] Thus, there exists an imperative need in the art to provide a method and system that can accurately and efficiently predict UE behaviour, manage network resources and load allocation, and enhance service provisioning.
OBJECTS OF THE INVENTION

[0007] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below.
[0008] It is an object of the present disclosure to provide a system and method for UE communication analytics through predictive forecasting and network optimization.
[0009] It is another object of the present disclosure to provide a system and method for UE communication analytics through predictive forecasting and network optimization that accurately predict UE behaviour, enabling more effective network management and service provisioning.
[0010] It is yet another object of the present disclosure to provide a system and method for UE communication analytics through predictive forecasting and network optimization that ensure optimal allocation and usage of network resources by understanding UE communication trends, thereby improving overall network performance.
[0011] It is yet another object of the present disclosure to provide a system and method for UE communication analytics through predictive forecasting and network optimization that offer real-time analytics and adaptive responses to changes in UE behaviour and communication patterns, minimizing network overload and enhancing user experience.
[0012] It is yet another object of the present disclosure to provide a system and method for UE communication analytics through predictive forecasting and network optimization. The UE communication analytics is performed by capturing the complete journey of a UE from connection to disconnection which provides comprehensive network visibility, aiding in strategic decision-making for network operations.

[0013] It is yet another object of the present disclosure to provide a system and method for UE communication analytics through predictive forecasting and network optimization that predict high traffic load trends in specific areas and timeframes, enabling pre-emptive actions for load balancing and reducing the chances of network congestion.
[0014] It is yet another object of the present disclosure to provide a system and method for UE communication analytics through predictive forecasting and network optimization that by leveraging artificial intelligence/machine learning (AI/ML) models, aims to train and refine its predictive capabilities continually, ensuring up-to-date, accurate insights for network management.
[0015] It is yet another object of the present disclosure to provide a system and method for UE communication analytics through predictive forecasting and network optimization that with its user interface, aims to visually represent UE communication journeys and network performance, making data more accessible and actionable for network operators/administrators.
[0016] It is yet another object of the present disclosure to provide a system and method for UE communication analytics through predictive forecasting and network optimization that is designed to handle increasing data volumes and user activity, ensuring the effectiveness and accuracy remain intact even under high-load conditions.
SUMMARY
[0017] 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.

[0018] An aspect of the present invention provides a method for forecasting communication traffic trends within a network. The method includes collecting, by a Network Data Analytics Function (NWDAF), user equipment (UE) communication data and access trends from a core Network Function (NF). The method further includes correlating, by the NWDAF, the collected UE communication data with Radio Access Network (RAN) data. Further, the method includes identifying, by the NWDAF, UE session behaviour, communication trends, and access behaviour trends based on the correlated UE communication data. The method further includes visualizing, by the NWDAF via a NWDAF user interface (UI), journey of the UE from network attachment to detachment. Thereafter, the method includes training, by the NWDAF, an NWDAF learning model with the visualized UE journey; and predicting, by the NWDAF using the trained NWDAF learning model, the UE behaviour in the network.
[0019] In an aspect, the method further comprises receiving, by the NWDAF, core network data from a probing agent.
[0020] In an aspect, the method comprises identifying, by the NWDAF, idle UE sessions of duration longer than a predefined threshold time period based on the received core network data.
[0021] In an aspect, the training the NWDAF learning model further comprises identifying, by the NWDAF, massive traffic volume trends within a specific area and time period.
[0022] In an aspect, the method further comprises enabling, by the NWDAF utilizing closed-loop feedback from data consumers to take pre-emptive actions to manage anticipated traffic volume higher than a predefined traffic threshold.
[0023] In an aspect, the journey of the UE comprises at least one of UE location presence, idle duration, or high traffic volume occurrences.

[0024] In an aspect, the method further comprises the step of notifying, by the NWDAF to the core NF in cases of predicted network overload to maintain uninterrupted user mobility experience.
[0025] In an aspect, notifying the core NF includes auto-detection of traffic load capacity breaches and provision of real-time notifications.
[0026] In an aspect, the UE communication data is received from a trace collection entity (TCE).
[0027] Another aspect of the present invention provides a system for forecasting communication traffic trends within a network. The system includes a Network Data Analytics Function (NWDAF). The NWDAF includes a collecting unit, a correlating unit, an identifying unit, a visualizing unit, a training unit, and a predicting unit. The collecting unit is configured to collect user equipment (UE) communication data and access trends from a core Network Function (NF). Next, the correlating unit is configured to correlate the collected UE communication data with Radio Access Network (RAN) data. Next, the identifying unit is configured to identify UE session behaviour, communication trends, and access behaviour trends based on the correlated UE communication data. Next, the visualizing unit is configured to visualize, via a NWDAF user interface (UI), journey of the UE from network attachment to detachment. Next, the training unit is configured to train a NWDAF learning model with the visualized UE journey; and the predicting unit is configured to predict, using the trained NWDAF learning model, the UE behaviour in the network.
[0028] Yet another aspect of the present disclosure, a non-transitory computer-readable storage medium storing instructions for forecasting communication traffic trends within a network is disclosed. The instructions include executable code which, when executed by one or more units of a system, causes: a collecting unit of

the system to collect user equipment (UE) communication data and access trends from a core network function; a correlating unit of the system to correlate the collected UE communication data with Radio Access Network (RAN) data; an identifying unit of the system to identify UE session behaviour, communication trends, and access behaviour trends based on the correlated UE communication data; a visualizing unit of the system to visualize, via a NWDAF user interface (UI), journey of the UE from network attachment to detachment; a training unit of the system to train a NWDAF learning model with the visualized UE journey; and a predicting unit of the system to predict, using the trained NWDAF learning model, the UE behaviour in the network.
DESCRIPTION OF THE DRAWINGS
[0029] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[0030] FIG. 1 illustrates an exemplary block diagram of architecture of a NWDAF, in accordance with exemplary embodiments of the present disclosure.
[0031] FIG. 2A illustrates an exemplary system architecture for forecasting communication traffic trends within a network, in accordance with exemplary embodiments of the present disclosure.

[0032] FIG. 2B illustrates an exemplary NWDAF with various functional units or modules, in accordance with an embodiment of the present disclosure.
[0033] FIG. 3 illustrates an exemplary method flow diagram indicating process for
5 forecasting communication traffic trends within a network, in accordance with
exemplary embodiments of the present disclosure.
[0034] FIG. 4 illustrates an exemplary block diagram of a computing device upon which an embodiment of the present disclosure may be implemented. 10
[0035] The foregoing shall be more apparent from the following more detailed description of the disclosure.
DETAILED DESCRIPTION
15
[0036] 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
20 details. Several features described hereafter can each be used independently of one
another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein. Example embodiments of
25 the present disclosure are described below, as illustrated in various drawings in
which like reference numerals refer to the same parts throughout the different drawings.
[0037] The ensuing description provides exemplary embodiments only, and is not
30 intended to limit the scope, applicability, or configuration of the disclosure. Rather,
the ensuing description of the exemplary embodiments will provide those skilled in
9

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. 5
[0038] It should be noted that the terms "mobile device", "user equipment", "user device", “communication device”, “device” and similar terms are used interchangeably for the purpose of describing the invention. These terms are not intended to limit the scope of the invention or imply any specific functionality or
10 limitations on the described embodiments. The use of these terms is solely for
convenience and clarity of description. The invention is not limited to any particular type of device or equipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the invention as defined herein.
15
[0039] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other
20 components may be shown as components in block diagram form in order not to
obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
25 [0040] 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 can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process
30 is terminated when its operations are completed but could have additional steps not
included in a figure.
10

[0041] The word “exemplary” and/or “demonstrative” is used herein to mean
serving as an example, instance, or illustration. For the avoidance of doubt, the
subject matter disclosed herein is not limited by such examples. In addition, any
5 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. Furthermore, to the extent that the terms
“includes,” “has,” “contains,” and other similar words are used in either the detailed
10 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.
[0042] As used herein, an “electronic device”, or “portable electronic device”, or
15 “user device” or “communication device” or “user equipment” or “device” refers
to any electrical, electronic, electromechanical and computing device. The user
device is capable of receiving and/or transmitting one or parameters, performing
function/s, communicating with other user devices and transmitting data to the
other user devices. The user equipment may have a processor, a display, a memory,
20 a battery and an input-means such as a hard keypad and/or a soft keypad. The user
equipment may be capable of operating on any radio access technology including
but not limited to IP-enabled communication, Zig Bee, Bluetooth, Bluetooth Low
Energy, Near Field Communication, Z-Wave, Wi-Fi, Wi-Fi direct, etc. For
instance, the user equipment may include, but not limited to, a mobile phone,
25 smartphone, virtual reality (VR) devices, augmented reality (AR) devices, laptop,
a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other device as may be obvious to a person skilled in the art for implementation of the features of the present disclosure.
30 [0043] Further, the user device may also comprise a “processor” or “processing
unit” includes processing unit, wherein processor refers to any logic circuitry for
11

processing instructions. The processor may be a general-purpose processor, a
special purpose processor, a conventional processor, a digital signal processor, a
plurality of microprocessors, one or more microprocessors in association with a
DSP core, a controller, a microcontroller, Application Specific Integrated Circuits,
5 Field Programmable Gate Array circuits, any other type of integrated circuits, etc.
The processor may perform signal coding data processing, input/output processing, and/or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor is a hardware processor.
10 [0044] 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
15 development, in this respect, has been incremental in the order of second generation
(2G), third generation (3G), fourth generation (4G), and now fifth generation (5G), and more such generations are expected to continue in the forthcoming time.
[0045] Radio Access Technology (RAT) refers to the technology used by mobile
20 devices/ user 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
25 transmitting and receiving data. Examples of RATs include GSM (Global System
for Mobile Communications), 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.
30 Mobile devices often support multiple RATs, allowing them to connect to different
12

types of networks and provide optimal performance based on the available network resources.
[0046] gNodeB (gNB) refers to the base station component in 5G (fifth generation)
5 wireless networks. It is an essential element of the Radio Access Network (RAN)
responsible for transmitting and receiving wireless signals to and from user devices, such as smartphones, tablets, and Internet of Things (IoT) devices. In 5G networks, there are similar components in other generations of wireless networks. Here are a few examples: Base Transceiver Station (BTS): In 2G (second-generation)
10 networks, the BTS serves as the base station responsible for transmitting and
receiving wireless signals. It connects mobile devices to the cellular network infrastructure. NodeB: In 3G (third generation) networks, the NodeB is the base station component that enables wireless communication. It facilitates the transmission and reception of signals between user devices and the network.
15 eNodeB: In 4G (fourth generation) LTE (Long-Term Evolution) networks, the
eNodeB serves as the base station. It supports high-speed data transmission, low latency, and improved network capacity. Access Point (AP): In Wi-Fi networks, an access point functions as a central hub that enables wireless devices to connect to a wired network. It provides a wireless interface for devices to access the network
20 and facilitates communication between them. The examples illustrate the base
station components in different generations of wireless networks, such as BTS in 2G, NodeB in 3G, eNodeB in 4G LTE, and gNodeB in 5G. Each component plays a crucial role in facilitating wireless connectivity and communication between user devices and the network infrastructure.
25
[0047] As discussed in the background section, existing systems might struggle with forecasting UE behaviour accurately. This could lead to an inability to proactively manage network resources or predict high-traffic periods. The inability to comprehensively map the user's journey from network connection to
30 disconnection can limit understanding of UE behaviour and the overall network
usage pattern. Some systems may not provide real-time analytics and adaptive
13

network responses to UE behaviour, potentially compromising network
performance during high-traffic periods. Existing solutions might fail to accurately
allocate network resources based on UE behaviour and traffic patterns, leading to
network congestion and poor user experience. Current systems also struggle with
5 seamless integration into the existing network infrastructure, which can result in
inefficiencies and errors in capturing and analysing user communication trends. The
lack of user-friendly interfaces and visual representations of data may make it
challenging for network operators to comprehend and utilize the insights from UE
communication analytics effectively. Further, existing solutions may rely on such
10 traditional techniques, which might not effectively learn and predict from the data,
compared to advanced AI/ML models used in the invention.
[0048] Thus, there exists an imperative need in the art to provide a method and system for automated network parameter configuration and audit across diverse
15 network devices. The proposed invention addresses these issues by introducing
advanced features for network overload detection and user communication trend forecasting. These advancements enable network operators and consumers to optimize network performance, enhance user experiences, and effectively manage network resources.
20
[0049] The present disclosure relates generally to communication analytics in the field of network monitoring and optimization. In particular, the present disclosure relates to analysing UE communication trends and traffic volume. More particularly, the present disclosure relates to system and method for UE
25 communication analytics through predictive forecasting and network optimization.
[0050] The present disclosure addresses the problems in the prior art by introducing
a method and system for forecasting communication traffic trends within a network
using a Network Data Analytics Function (NWDAF). The method involves
30 collecting user equipment (UE) communication data and access trends from a core
Network Function (NF), correlating this data with Radio Access Network (RAN)
14

data, and identifying UE session behaviour, communication trends, and access
behaviour trends. The NWDAF visualizes the journey of the UE from network
attachment to detachment and trains an NWDAF learning model with this
visualized journey. This trained model is then used to predict UE behaviour in the
5 network, facilitating network load management and service optimization. The
method overcomes existing problems by providing real-time analytics and adaptive network responses to UE behaviour, which helps maintain network performance during high-traffic periods. It also accurately maps the user's journey, allowing for a better understanding of UE behaviour and overall network usage patterns. The
10 method enables proactive management of network resources and prediction of high-
traffic periods, addressing the issue of network congestion and poor user experience. Additionally, the use of advanced AI/ML models in the NWDAF learning model enhances the ability to learn and predict from the data, overcoming the limitations of traditional algorithms. Furthermore, the method is designed for
15 seamless integration into the existing network infrastructure, ensuring efficiency
and accuracy in capturing and analysing user communication trends.
[0051] As used herein, network data analytics function (NWDAF) analyses data
collected from network functions (NFs) and user equipment(s) (UEs) and publishes
20 the results to subscribing data analytics consumers. The NWDAF subscribes to
uniquely identified events published by the NFs in order to acquire the data that it then goes on to analyse and expose to its subscribers.
[0052] Hereinafter, exemplary embodiments of the present disclosure will be
25 described with reference to the accompanying drawings.
[0053] Referring to FIG. 1, an exemplary block diagram [100] of architecture of
NWDAF [104], in accordance with exemplary embodiments of the present
disclosure, is shown. The block diagram [100] comprises of various network
30 functions (NFs) [NFa-NFn] [102a-102n] (collectively referred to as NFs [102] or
individually referred to as NF [102] hereinafter). The NFs [102] may include but
15

not limited only to, an Access and Mobility Management Function (AMF), and a Network Slice Selection Function (NSSF), and/or the like Additionally, the block diagram [100] comprises an NWDAF [104] and is responsible for performing data analytics on the network function data received from the NFs [102]. 5
[0054] The NWDAF [104] receives network function data from the NFs [102], performs data analytics function on the network function data received from the NFs [102], generates a data analytics visualization of the data analytics performed on the network function data received from the NFs [102]; and displays the data
10 analytics visualization to the user. The architecture of the NWDAF [104] will now
be explained in detail hereinafter. The NWDAF [104] includes a NWDAF front end module [106], a NWDAF back end module [108], a NWDAF learning model [110] (alternatively referred to herein as a NWDAF AI/ML model [110]), a database [112], a NWDAF workflow module [114], and a NWDAF User Interface (UI)
15 module [116]. One or more of the NWDAF front end module [106], the NWDAF
back end module [108], the NWDAF learning model [110], the database [112], the NWDAF workflow module [114], and the NWDAF UI module [116], are in communication with each other to perform the data analytics operations by the NWDAF [104].
20
[0055] As is shown in FIG. 1, the NWDAF front end module [106] is in communication with the NFs [102]. Further, the NWDAF back end module [108] is in communication with the NWDAF front end module [106]. The NWDAF learning model [110] and database [112] are in communication with the NWDAF
25 back end module [108]. The NWDAF workflow module [114] is in communication
with the database [112]. The NWDAF UI Module [116] is in communication with the NWDAF workflow module [114].
[0056] An operation and process for performing data analytics function by various
30 aforementioned modules of the NWDAF [104], will now be explained in detail
hereinafter. The NWDAF front end module [106] receives subscriptions or
16

analytics request from NFs [102]. The NWDAF front end module [106]
communicates with NWDAF back end module [108] for the received requests. The
NWDAF back end module [108] performs computation as well as data collection
job from various data sources. It provides analysed data to NWDAF learning model
5 [110]. The NWDAF learning model [110] trains its model over the feed provided
by the NWDAF back end module [108] and proactively take part in network performance and user experience forecasting. The NWDAF back end module [108] and NWDAF learning model [110] perform computation and analysis in a collaboration manner for analysing the request and captured data information. The
10 NWDAF back end module [108] provides analysed data to the database [112],
which stores the analytics output related to several network use-cases. The NWDAF back end module [108] may also fetch analytics results data from the database [112]. The NWDAF workflow module [114] fetches the analytics output according to the dash-boarding requirement. The NWDAF workflow module [114] provides
15 network performance and user experience visualization dashboards to end users.
An operation and process for performing data analytics function by various aforementioned modules of the NWDAF [104], will now be explained in detail hereinafter.
20 [0057] FIG. 2A illustrates an exemplary architecture [200a] of a system [200b] for
forecasting communication traffic trends within a network, in accordance with exemplary embodiments of the present disclosure. Further, FIG. 2B illustrates an exemplary system [200b] with various functional units or modules, in accordance with an embodiment of the present disclosure. As shown in FIG. 2A, the
25 architecture [200a] may include a core network function [202], a trace collection
entity [204], a probing agent [206], a NWDAF UI module [116], a NWDAF learning Model [110], Data Consumers [212]. Further, as disclosed in FIG. 2B, NWDAF [104] may include a collecting unit [104a], a correlating unit [104b], an identifying unit [104c], a visualizing unit [104d], a training unit [104e], a predicting
30 unit [104f], a processing unit [104g], a notifying unit [104h] and a storage unit
[104i]. Herein all the components/modules or units are assumed to be connected to
17

each other in a manner as obvious to the person skilled in the art for implementing
features of the present disclosure. In an implementation, the collecting unit [104a]
may be associated with NWDAF Front End Module [106] and correlating unit
[104b], identifying unit [104c], visualizing unit [104d], training unit [104e],
5 predicting unit [104f], processing unit [104g], notifying unit [104h] and storage unit
[104i] may be associated with the NWDAF back end module [108]. In an implementation, the one or more units may be associated in any combination with the NWDAF front end module [106]/ NWDAF back end module [108].
10 [0058] As illustrated in FIG. 2A and FIG. 2B, the NWDAF [104] may be used for
forecasting communication traffic trends within a network. The NWDAF [104] may include the collecting unit [104a]. The collecting unit [104a] is configured to collect User Equipment (UE) communication data and access trends from the core network function [202] that includes gathering data that reflects how the UE uses the
15 network services, which may include call logs, data usage statistics, session
durations, types of services accessed (voice or data), and other relevant information that reveals the UE's behaviour on the network. The collecting unit [104a] is further configured to receive Radio Access Network (RAN) data from a trace collection entity (TCE) [204] that includes information about the UE's interactions with the
20 cellular radio components of the network, such as signal quality, cell transitions,
handovers, congestion, cell data, and other metrics that can indicate the UE's movement and radio environment. The TCE [204] refers to a component responsible for gathering and storing traces or logs generated by base station elements (RAN) within a network or system. The collecting unit [104a] also
25 receives core network data from a probing agent [206] that includes acquiring
detailed network performance metrics and other diagnostic data such as counters, alarms, configuration, fault, and procedures from the virtualized elements of the core network, which are essential for assessing the network's ability to handle traffic and for identifying potential issues that could affect service quality. In an
30 implementation, the probing agent [206] is such as, but not limited to, Virtual Probe.
The Virtual Probe collects probing data (Streaming Data Record-SDR) from 4G or
18

5G network nodes. The SDR can be a transaction or procedure in 5G core network (CN) or a call flow in 4G network. The SDR can also be a call detailed record (CDR) written in network nodes or a debugging record (can be logs as well).
5 [0059] The NWDAF [104] may further include the correlating unit [104b]
configured to correlate the collected UE communication data with RAN data. The correlation may include analysing and finding relationships between the behaviour of the UE as it communicates through the network and the data pertaining to the network's radio components. By performing the correlation, the correlating unit
10 [104b] aims to identify how the UE's communication patterns affect and are
affected by the RAN environment, such as signal quality, network load, and handover rates to predict network behaviour, manage loads, and optimize the user's experience on the network. For example, if the collecting unit [104a] gathers data showing that a particular UE frequently experiences call drops in a specific area,
15 the correlating unit [104b] might analyse the RAN data to see if the call drops
correlate with poor signal quality or high traffic in that area at certain times. If there’s a consistent pattern, such as call drops occurring during peak traffic hours, the NWDAF [104] might then use this insight to predict network overload and take pre-emptive measures to improve service in that area.
20
[0060] The NWDAF [104] may include the identifying unit [104c]. The identifying unit is configured to analyse the correlated UE communication data. The identifying unit [104c] identifies distinctive patterns and trends in the session behaviour of the UE, how the UE communicates over time, and how it accesses the network. For
25 example, the identifying unit [104c] might identify a trend where a significant
number of UEs are inactive for long periods and then suddenly become active, possibly indicating a patterned usage at certain times of day or in specific locations. Another example could be identifying a trend of increased data usage during specific events or identifying a frequent drop in service quality, which could trigger
30 further network optimization actions. These insights facilitate in understanding
19

network demand and ensuring that resources are allocated efficiently to maintain a high quality of service.
[0061] The identifying unit [104c] is further configured to identify idle User
5 Equipment (UE) sessions that last for a duration exceeding a predetermined
threshold based on the core network data to detect UEs that are connected to the
network but not actively using services for extended periods, which could indicate
potential network inefficiencies or areas for optimization. The identifying unit
[104c] is further configured to detect significant trends in traffic volume within a
10 certain area and timeframe to recognize patterns of high network usage for
anticipating and managing network congestion, ensuring adequate resource allocation, and maintaining optimal service quality in areas experiencing heavy traffic.
15 [0062] The NWDAF [104] may include the visualizing unit [104d]. The visualizing
unit [104d] may facilitate in creating a visual representation of the User Equipment's (UE's) activity within the network. The visualizing unit [104d] takes the information from the correlating unit [104b], the identifying unit [104c] and translates it into a format that can be easily understood and interpreted through the
20 NWDAF UI module [116]. Essentially, the visualizing unit [104d] charts the entire
lifecycle of a UE's interaction with the network, from the moment it connects ("network attachment") to when it leaves the network or turns off ("network detachment"). This visual journey can reveal patterns such as the most frequented locations, times of high or low activity, and the quality of network service
25 experienced by the UE throughout its daily operation. Such visuals are instrumental
in helping network operators and analysts to quickly assess and respond to UE and network behaviours. In an implementation, journey data or visual journey data may comprise, such as, but not limited to, mobility, handover, session services (voice or data), location and quality of service (QoS).
30
20

[0063] The NWDAF [104] includes a training unit [104e]. The training unit [104e]
is configured to receive the visualized journey of the UE created by the visualizing
unit [104d] and then using this information to train a machine learning model (such
as the NWDAF learning model [110]). This training involves feeding the journey
5 data into the NWDAF learning model [110] so that the NWDAF learning model
[110] can detect, learn from, and adapt to the patterns and behaviours of UEs within
the network. It would be appreciated by the person skilled in the art that through
this process, the NWDAF learning model [110] becomes better at predicting future
behaviours and trends based on historical data, thereby enhancing its capability to
10 forecast network demands and potential issues. This predictive power is central to
the NWDAF's [104] ability to proactively manage network resources and optimize the network's performance.
[0064] The NWDAF [104] may include the predicting unit [104f]. The predicting
15 unit [104f] may be configured to use the insights gained from the trained NWDAF
learning model [110] to anticipate User Equipment (UE) behaviour in future within
the network. By applying the patterns learned from past UE behaviour for group of
UEs, the predicting unit [104f] may forecast potential high-traffic areas, times of
increased or decreased network usage, and even predict when and where service
20 quality may degrade. This foresight allows network operators to adjust resources in
real-time, improve network efficiency, and enhance the overall user experience by preventing issues before they impact users.
[0065] The NWDAF [104] may include a processing unit [104g] that utilizes
25 closed-loop feedback from data consumers [212] to take proactive measures for
managing anticipated traffic volumes that exceed a predefined threshold to enable
the processing unit [104g] to automatically adjust network resources based on real¬
time data analysis and feedback to prevent congestion and ensure smooth network
operation. As used herein, data consumers [212] are network functions [102] that
30 subscribes to analytics at the NWDAF [104]. For example, a large sports event is
scheduled in a city, attracting thousands of spectators. The NWDAF [104]
21

anticipates a significant increase in mobile data traffic in the area surrounding the
stadium on the day of the event. The processing unit [104g] receives closed-loop
feedback from data consumers [212], such as network operators or automated
systems, indicating that the current network resources might not be sufficient to
5 handle the expected surge in traffic. Using this feedback, the processing unit [104g]
proactively allocates additional bandwidth or scale resources and optimizes the
configuration of nearby cell towers to accommodate the anticipated increase in
demand. This pre-emptive adjustment of network resources helps to prevent
congestion and ensures that attendees can use their mobile devices without
10 experiencing any drop in service quality during the event.
[0066] The NWDAF [104] includes a notifying unit [104h] for alerting the core network function [202] when there is a prediction of network overload. This notification mechanism helps in taking timely measures to avoid service disruption
15 and maintain a seamless user experience by ensuring that the network can handle
the anticipated increase in traffic. The process of notifying the core network function [202] involves the automatic detection of situation or event when the network's traffic load capacity is exceeded and the provision of real-time notifications. This feature allows for immediate awareness and response to potential
20 network capacity issues, enabling rapid adjustments to manage the network load
and prevent degradation of service quality.
[0067] The NWDAF [104] includes a storage unit [104i] for storing collected data
by the NWDAF [104]. Further, the storage unit [104i] may also store the analysed
25 data, UE behaviour data, trends data, training data (previous data and current data
for further training), and operational data as per implemented by the present disclosure.
[0068] Referring to FIG. 3, exemplary method flow diagram [300], for forecasting
30 communication traffic trends within a network, in accordance with exemplary
embodiments of the present disclosure is shown. In an implementation, the method
22

is performed by the system [200b]. The method [300] may be implemented by the system [200b]. The method begins at step [302] and proceeds to step [304].
[0069] At step 304, the method [300] comprises collecting, by a Network Data
5 Analytics Function (NWDAF) [104], user equipment (UE) communication data and
access trends from a core network function [202] i.e., Access management and mobility function (AMF). The NWDAF [104] may be configured to collect User Equipment (UE) communication data and access trends from the core network function [202] that includes gathering data that reflects how the UE uses the
10 network services, which may include call logs, data usage statistics, session
durations, types of services accessed, and other relevant information that reveals the UE's behaviour on the network. The NWDAF [104] is configured for receiving Radio Access Network (RAN) data from a trace collection entity (TCE) [204] that includes information about the UE's interactions with the cellular radio components
15 of the network, such as signal quality, cell transitions, handovers, and other metrics
that can indicate the UE's movement and radio environment. The TCE [204] refers to a component responsible for gathering and storing traces or logs generated by base station elements (RAN) within a network or system. NWDAF [104] also receives core network data from a probing agent [206] that includes acquiring
20 detailed network performance metrics and other diagnostic data from the virtualized
elements of the core network, which are essential for assessing the network's ability to handle traffic and for identifying potential issues that could affect service quality. In an aspect, the probing agent [206] may be a Virtual Probe, which may collect probing data (Streaming Data Record-SDR) from 4G or 5G network nodes. The
25 SDR can be a transaction or procedure in 5G core network (CN) or a call flow in
4G network. SDR can also be a call detailed record (CDR) written in network nodes or a debugging record (can be logs as well).
[0070] At step 306, the method [300] comprises correlating, by the NWDAF [104],
30 the collected (UE) communication data with Radio Access Network (RAN) data.
The correlation may include analysing and finding relationships between the
23

behaviour of the UE as it communicates through the network and the data pertaining
to the network's radio components. By performing the correlation, the NWDAF
[104] aims to identify how the UE's communication patterns affect and are affected
by the RAN environment, such as signal quality, network load, and handover rates.
5 This understanding is critical for the NWDAF [104] to predict network behaviour,
manage loads, and optimize the user's experience on the network.
[0071] At step 308, the method [300] comprises identifying, by the NWDAF [104], at least one of UE session behaviour, communication trends, and access behaviour
10 trends based on the correlated UE communication data. The NWDAF [104] is
configured to analyse the UE communication data that has been correlated with RAN data. The NWDAF [104] identifies distinctive patterns and trends in the session behaviour of the UE, how the UE communicates over time, and how it accesses the network. For example, the NWDAF [104] might identify a trend where
15 a significant number of UEs are inactive for long periods and then suddenly become
active, possibly indicating a patterned usage at certain times of day or in specific locations.
[0072] The NWDAF [104] is further configured to identify idle User Equipment
20 (UE) sessions that last for a duration exceeding a predetermined threshold based on
the core network data to detect UEs that are connected to the network but not
actively using services for extended periods, which could indicate potential network
inefficiencies or areas for optimization. The NWDAF [104] is further configured to
detect significant trends in traffic volume within a certain area and timeframe to
25 recognize patterns of high network usage for anticipating and managing network
congestion, ensuring adequate resource allocation, and maintaining optimal service quality in areas experiencing heavy traffic.
[0073] At step 310, the method [300] comprises visualizing, by the NWDAF [104]
30 via a NWDAF UI module [116], journey of the UE from network attachment to
detachment. The NWDAF [104] may facilitate in creating a visual representation
24

of the User Equipment's (UE's) activity within the network. The NWDAF [104]
takes the information relating to correlating, the identifying and translates it into a
format that can be easily understood and interpreted through the NWDAF UI
module [116]. The network charts the entire lifecycle of a UE's interaction with the
5 network, from the moment it connects ("network attachment") to when it leaves the
network or turns off ("network detachment"). This visual journey can reveal
patterns such as the most frequented locations, times of high or low activity, and
the quality of network service experienced by the UE throughout its daily operation.
Such visuals (e.g., in form of charts, graphs, bars) are instrumental in helping
10 network operators, analysts or network management team to quickly assess and
respond to UE and network behaviours. In an implementation, journey data or visual journey data may comprise, such as, but not limited to, mobility, handover, session services (voice or data), location and quality of service (QoS).
15 [0074] At step 312, the method [300] comprises training, by the NWDAF [104], an
NWDAF learning model [110] with the visualized UE journey. The NWDAF [104] is configured to receive the visualized journey of the User Equipment (UE) and using this information to train a machine learning (ML) based model (such as the NWDAF learning model [110]). The training involves feeding the journey data into
20 the NWDAF learning model [110] so that the NWDAF learning model [110] can
detect, learn from, and adapt to the patterns and behaviours of UEs within the network. It would be appreciated by the person skilled in the art that through this process, the NWDAF learning model [110] becomes better at predicting future behaviours and trends based on historical data related to UE and network behaviour
25 in past, thereby enhancing its capability to forecast network demands and potential
issues. This predictive power is central to the NWDAF's [104] ability to proactively manage network resources and optimize the network's performance.
[0075] At step 314, the method [300] comprises predicting, by the NWDAF [104]
30 using the trained NWDAF learning model [110], the UE behaviour in the network.
The NWDAF [104] may be configured to use the insights gained from the trained
25

NWDAF learning model [110] to anticipate future User Equipment (UE) behaviour
within the network. By applying the patterns learned from past UE behaviour for
group of UEs, the NWDAF [104] can forecast potential high-traffic areas, times of
increased or decreased network usage, and even predict when and where service
5 quality may degrade. This foresight allows network operators to adjust resources in
real-time, improve network efficiency, and enhance the overall user experience by preventing issues before they impact users.
[0076] In an exemplary aspect, enabling, by the NWDAF [104] utilizing closed-
10 loop feedback from Data Consumers [212] to take pre-emptive actions to manage
anticipated traffic volume higher than a predefined traffic threshold. The NWDAF
[104] via a processing unit [104g] may utilize the closed-loop feedback from data
consumers [212] to take proactive measures for managing anticipated traffic
volumes that exceed a predefined threshold to enable the processing unit [104g] to
15 automatically adjust network resources based on real-time data analysis and
feedback to prevent congestion and ensure smooth network operation. As used
herein, data consumers [212] are network functions [102] that subscribes to
analytics at the NWDAF [104]. For example, a large sports event is scheduled in a
city, attracting thousands of spectators. The NWDAF [104] anticipates a significant
20 increase in mobile data traffic in the area surrounding the stadium on the day of the
event. The processing unit [104g] receives closed-loop feedback from data
consumers [212], such as network operators or automated systems, indicating that
the current network resources might not be sufficient to handle the expected surge
in traffic. Using this feedback, the processing unit [104g] proactively allocates
25 additional bandwidth or scale resources and optimizes the configuration of nearby
cell towers to accommodate the anticipated increase in demand. This pre-emptive
adjustment of network resources helps to prevent congestion and ensures that
attendees can use their mobile devices without experiencing any drop in service
quality during the event.
30
26

[0077] In an exemplary aspect, notifying, by the NWDAF [104] to the core network
function [202] in cases of predicted network overload to maintain uninterrupted
user mobility experience. The NWDAF [104] via a notifying unit [104h] may alert
the core network function [202] when there is a prediction of network overload.
5 This notification mechanism helps in taking timely measures to avoid service
disruption and maintain a seamless user experience by ensuring that the network
can handle the anticipated increase in traffic. The process of notifying the core
network function [202] involves the automatic detection of situation or event when
the network's traffic load capacity is exceeded and the provision of real-time
10 notifications. This feature allows for immediate awareness and response to potential
network capacity issues, enabling rapid adjustments to manage the network load and prevent degradation of service quality.
[0078] The method [300] terminates at step [316].
15
[0079] In an example, a city experiencing a sudden natural disaster, like an earthquake or flood. The Network Data Analytics Function (NWDAF) quickly collects and correlates communication data received from user equipment (UE) and the Radio Access Network (RAN). It identifies trends in UE behaviour, such as
20 increased call volumes and data usage as people seek information and communicate
with their relatives or emergency services. The NWDAF visualizes the movement of UEs, highlighting areas with exceptionally high traffic. It receives additional data from a virtual Probe or probing agent to refine its analysis. The NWDAF detects idle sessions and massive traffic volume trends, enabling it to anticipate network
25 congestion. It uses closed-loop feedback from data consumers to take pre-emptive
actions, like reallocating resources or enhancing network capacity. In case of predicted network overload, the NWDAF notifies the core Network Function to maintain seamless connectivity. This swift response helps ensure uninterrupted communication during the critical period following the disaster, aiding in
30 emergency response and recovery efforts.
27

[0080] Referring to FIG. 4, which illustrates an exemplary block diagram of a
computing device [400] (also referred herein as a computer system [400]) upon
which an embodiment of the present disclosure may be implemented. In an
implementation, the computing device [400] implements the method for forecasting
5 communication traffic trends within a network using the system [200b]. In another
implementation, the computing device [400] itself implements the method for forecasting communication traffic trends within a network using one or more units configured within the computing device [400], wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.
10
[0081] The computing device [400] may include a bus [402] or other communication mechanism for communicating information, and a hardware processor [404] coupled with bus [402] for processing information. The hardware processor [404] may be, for example, a general purpose microprocessor. The
15 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
20 processor [404]. Such instructions, when stored in non-transitory storage media
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
25 information and instructions for the processor [404].
[0082] 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
30 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
28

be coupled to the bus [402] for communicating information and command selections to the processor [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 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.
[0083] The computing device [400] 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 [400] causes or programs the computing device [400] to be a special-purpose 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 herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.
[0084] 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 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 electrical,

electromagnetic, or optical signals that carry digital data streams representing various types of information.
[0085] 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 example, a server [430] might transmit a requested code for an application program through the Internet [428], the 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.
[0086] Yet another aspect of the present disclosure, a non-transitory computer-readable storage medium storing instructions for forecasting communication traffic trends within a network is disclosed. The instructions include executable code which, when executed by one or more units of a system, causes: a collecting unit [104a] of the system to collect user equipment (UE) communication data and access trends from a core network function [202]; a correlating unit [104b] of the system to correlate the collected UE communication data with Radio Access Network (RAN) data; an identifying unit [104c] of the system to identify UE session behaviour, communication trends, and access behaviour trends based on the correlated UE communication data; a visualizing unit [104d] of the system to visualize, via a NWDAF UI module [116], journey of the UE from network attachment to detachment; a training unit [104e] of the system to train a NWDAF learning model [110] with the visualized UE journey; and a predicting unit [104f] of the system to predict, using the trained NWDAF learning model [110], the UE behaviour in the network.
[0087] As is evident from the above, the present disclosure provides a technically advanced solution for forecasting communication traffic trends within a network. The present disclosure provides a solution for UE communication analytics through

predictive forecasting and network optimization. The present disclosure provides a solution for UE communication analytics through predictive forecasting and network optimization that accurately predict User Equipment (UE) behaviour, enabling more effective network management and service provisioning. Further, the present disclosure provides a solution for UE communication analytics through predictive forecasting and network optimization that ensure optimal allocation and usage of network resources by understanding UE communication trends, thereby improving overall network performance. Further, the present disclosure provides a solution for UE communication analytics through predictive forecasting and network optimization that offer real-time analytics and adaptive responses to changes in UE behaviour and communication patterns, minimizing network overload and maintaining consistent user experience. Further, the present disclosure provides a solution for UE communication analytics through predictive forecasting and network optimization that by capturing the complete journey of a UE from connection to disconnection, provides comprehensive network visibility, aiding in strategic decision-making for network operations. Furthermore, the present disclosure provides a solution for UE communication analytics through predictive forecasting and network optimization that predict high traffic load trends in specific areas and timeframes, enabling pre-emptive actions for load balancing and reducing the chances of network congestion. Further, the present disclosure provides a solution for UE communication analytics through predictive forecasting and network optimization that by leveraging AI/ML models, aims to train and refine its predictive capabilities continually, ensuring up-to-date, accurate insights for network management. Further, the present disclosure provides a solution for UE communication analytics through predictive forecasting and network optimization that with its user interface, aims to visually represent UE communication journeys and network performance, making data more accessible and actionable for network operators/administrators. Furthermore, the present disclosure provides a solution for UE communication analytics through predictive forecasting and network optimization that is designed to handle increasing data volumes and user activity,

ensuring the effectiveness and accuracy remain intact even under high-load conditions.
[0088] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various the 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 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 considered to be encompassed within the scope of the present disclosure.
[0089] 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.

I/We Claim:
1. A method for forecasting communication traffic trends within a network,
comprising:
collecting, by a Network Data Analytics Function (NWDAF) [104], user equipment (UE) communication data and access trends from a core network function [202];
correlating, by the NWDAF [104], the collected UE communication data with Radio Access Network (RAN) data;
identifying, by the NWDAF [104], at least one of UE session behaviour, communication trends, and access behaviour trends based on the correlated UE communication data;
visualizing, by the NWDAF [104] via a NWDAF user interface (UI) module [116], journey of the UE from network attachment to detachment;
training, by the NWDAF [104], an NWDAF learning model [110] with the visualized UE journey; and
predicting, by the NWDAF [104] using the trained NWDAF learning model [110], the UE behaviour in the network.
2. The method as claimed in claim 1, wherein the method further comprises receiving, by the NWDAF [104], core network data from a probing agent [206].
3. The method as claimed in claim 2, further comprises identifying, by the NWDAF [104], idle UE sessions of duration longer than a predefined threshold time period based on the received core network data.
4. The method as claimed in claim 1, wherein the training further comprises identifying, by the NWDAF [104], massive traffic volume trends within a specific area and time period.
5. The method as claimed in claim 4, further comprising enabling, by the NWDAF [104] utilizing closed-loop feedback from Data Consumers [212] to

take pre-emptive actions to manage anticipated traffic volume higher than a predefined traffic threshold.
6. The method as claimed in claim 1, wherein the journey of the UE comprises at least one of UE location presence, idle durations, or high traffic volume occurrences.
7. The method as claimed in claim 1, further comprising the step of notifying, by the NWDAF [104] to the Core Network Function [202] in cases of predicted network overload to maintain uninterrupted user mobility experience.
8. The method as claimed in claim 7, wherein notifying the Core Network Function [202] comprises auto-detection of traffic load capacity breaches and provision of real-time notifications.
9. The method as claimed in claim 1, wherein the UE communication data is received from a trace collection entity (TCE) [204].
10. A system for forecasting communication traffic trends within a network, the system comprises:
a Network Data Analytics Function (NWDAF) [104] comprises:
a collecting unit [104a] configured to collect user equipment (UE) communication data and access trends from a Core network function [202];
a correlating unit [104b] configured to correlate the collected UE communication data with Radio Access Network (RAN) data;
an identifying unit [104c] configured to identify UE session behaviour, communication trends, and access behaviour trends based on the correlated UE communication data;
a visualizing unit [104d] configured to visualize, via a NWDAF user interface (UI) module [116], journey of the UE from network attachment to detachment;

a training unit [104e] configured to train a NWDAF learning model [110] with the visualized UE journey; and
a predicting unit [104f] configured to predict, using the trained NWDAF learning model [110], the UE behaviour in the network.
11. The system as claimed in claim 10, wherein the collecting unit is configured to receive core network data from a probing agent [206].
12. The system as claimed in claim 11, wherein the identifying unit [104c] configured to identify idle UE sessions of duration longer than a predefined threshold time period based on the received core network data.
13. The system as claimed in claim 10, wherein the identifying unit [104c] is configured to identify massive traffic volume trends within a specific area and time period.
14. The system as claimed in claim 13, wherein the NWDAF [104] further comprises a processing unit [104g] configured to enable, utilizing closed-loop feedback from Data Consumers [212] to take pre-emptive actions to manage anticipated traffic volume higher than a predefined traffic threshold.
15. The system as claimed in claim 10, wherein the journey of the UE comprises at least one of UE location presence, idle duration, and high traffic volume occurrences.
16. The system as claimed in claim 10, wherein the NWDAF [104] further comprises a notifying unit [104h] configured to notify the Core Network Function [202] in cases of predicted network overload to maintain uninterrupted user mobility experience.
17. The system as claimed in claim 16, wherein notifying the Core Network Function [202] comprises auto-detection of traffic load capacity breaches and provision of real-time notifications.
18. The system as claimed in claim 10, wherein the UE communication data is received from a trace collection entity (TCE) [204].