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 DETERMINING ABNORMALITIES IN A WIRELESS LOCAL AREA NETWORK (WLAN)”
We, Jio Platforms Limited, an Indian National, of Office - 101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India.
The following specifcaton partcularly describes the inventon and the manner in which it is to be performed.

METHOD AND SYSTEM FOR DETERMINING ABNORMALITIES IN A WIRELESS LOCAL AREA NETWORK (WLAN)
TECHNICAL FIELD
5
[0001] Embodiments of the present disclosure relate generally to the field of wireless communication systems. More particularly, embodiment of the present disclosure relates to a method and system for determining abnormalities in a wireless local area network (WLAN). 10
BACKGROUND
[0002] Following description of the related art is intended to provide background
information pertaining to the field of the disclosure. This section may include
15 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.
20 [0003] Network Data Analytics Function (NWDAF) (implemented by a server) is
a 5G node which serves other node function requests for use case analytics of said node function. While providing use case analytics for a node function, errors occur within NWDAF or while interfacing with other microservices, thereby disrupting the call flow. Disruption of call flow ultimately leads to NWDAF failing to provide
25 the required response to node function, which had requested a use case analysis
from NWDAF. Some exemplary reasons for errors are connectivity failure, insufficient memory in DB, invalid request from node function and so on. Further, NWDAF may serve use cases belonging to different domains, such as QoS, traffic steering, dimensioning, or security. New use cases related to 5G QoS have been
30 identified, for example, network data analytics (NWDA) assisted QoS provisioning,
NWDA-assisted determination of policy, and NWDA-assisted QoS adjustment.
2

[0004] Thus, new solutions are needed for NWDAF influencing QoS configuration
and adjustment. Further, over the period of time various solutions have been
developed where NWDAF requires slice details for use cases while serving
5 consumers. According to 3GPP specification, NWDAF has to get slice details from
NSSF. The NSSF will ingest the slice details from FMS. However, there are certain challenges with existing solutions.
[0005] Thus, there exists an imperative need in the art to improve the functionality
10 of the NWDAF to get the slice details..
SUMMARY
[0006] This section is provided to introduce certain aspects of the present disclosure
15 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.
[0007] An aspect of the present disclosure may relate to a method for determining
20 abnormalities in a wireless local area network (WLAN), the method comprising
receiving, by a transceiver unit of a network data analytics function (NWDAF)
module from a session management function (SMF) module, a data associated with
a connection of user device with a WLAN. The method comprises receiving, by the
transceiver unit of the network data analytics function (NWDAF) module, a core
25 network data collected by a virtual probe tool. The method comprises analysing, by
an analysis unit of the network data analytics function (NWDAF) module, the data
received from the SMF module and the core network data. The method comprises
determining, by a determination unit of the NWDAF module, one or more
abnormalities in the connection based on the analysis of the data received from the
30 SMF module and the core network data, wherein the one or more abnormalities
relate to at least one of a network related abnormalities and a user device related
3

abnormalities. The method comprises performing, by a performance unit of the
NWDAF module , at least one of a network related abnormalities procedure, and a
user device related abnormalities procedure, wherein the network related
abnormalities procedure is performed in an event at least a sub-set of the one or
5 more abnormalities comprises the network related abnormalities, and the user
device related abnormalities procedure is performed in an event at least a sub-set of
the one or more abnormalities comprises the user device related abnormalities. The
method comprises displaying, by a user interface of the NWDAF module, a final
result based on the performance of the at least one of the network related
10 abnormalities procedure, and the user device related abnormalities procedure.
[0008] In an exemplary aspect of the present disclosure, the method further
comprises predicting, by a processing unit of the network data analytics function
(NWDAF) module, one or more abnormality trends in the connection for one or
15 more predefined geographical areas and one or more pre-defined periods of time,
based on the analysis of the data received from the SMF module.
[0009] In an exemplary aspect of the present disclosure, the analysis is done for one
or more pre-defined geographical areas and one or more pre-defined periods of
20 time.
[0010] In an exemplary aspect of the present disclosure, the network related
abnormalities procedure comprises sending, by the transceiver unit of the NWDAF
module to a policy control function (PCF) module, a notification related to the
25 network related abnormalities; and adjusting, by the PCF module, one or more
parameters related to the WLAN for improving a performance of the WLAN.
[0011] In an exemplary aspect of the present disclosure, the user device related
abnormalities procedure comprises sending, by the transceiver unit of the NWDAF
30 module to the user device, a notification related to the user device related
4

abnormalities, wherein the notification comprises a suggestion related to correcting the user device related abnormalities.
[0012] In an exemplary aspect of the present disclosure, the method further
5 comprises after displaying the final result, notifying, by an updating unit of the
NWDAF module, a user about the one or more abnormalities in the connection.
[0013] Another aspect of the present disclosure may relate to a system for determining abnormalities in a wireless local area network (WLAN) performance
10 in a communication network, the system comprising a transceiver unit of a network
data analytics function (NWDAF) configured to receive, from a session management function (SMF) module, a data associated with a connection of user device with a WLAN, receive a core network data collected by a virtual probe tool. The system comprises an analysis unit connected to at least the transceiver unit, the
15 analysis unit configured to analyse the data received from the SMF module, and the
core network data. The system comprises a determination unit connected to at least the analysis unit, the determination unit configured to determine one or more abnormalities in the connection based on the analysis of the data received from the SMF module and the core network data, wherein the one or more abnormalities
20 relate to at least one of a network related abnormalities and a user device related
abnormalities. The system comprises a performance unit connected to at least the determination unit, the performance unit configured to perform at least one of a network related abnormalities procedure, and a user device related abnormalities procedure, wherein the network related abnormalities procedure is performed in an
25 event at least a sub-set of the one or more abnormalities comprises the network
related abnormalities, and the user device related abnormalities procedure is performed in an event at least a sub-set of the one or more abnormalities comprises the user device related abnormalities. The system comprises a user interface connected to at least the performance unit, the user interface configured to display
30 a final result based on the performance of the at least one of the network related
abnormalities procedure, and the user device related abnormalities procedure.
5

[0014] Yet another aspect of the present disclosure may relate to a non-transitory
computer readable storage medium storing instructions for determining
abnormalities in a wireless local area network (WLAN), the instructions include
5 executable code which, when executed by one or more units of a system, causes a
transceiver unit of the system of a network data analytics function (NWDAF) module to receive, from a session management function (SMF) module, a data associated with a connection of user device with a WLAN, receive a core network data collected by a virtual probe tool. Further, the instructions include executable
10 code which, when executed causes an analysis unit of the system to analyse the data
received from the SMF module, and the core network data. Further, the instructions include executable code which, when executed causes a determination unit of the system to determine one or more abnormalities in the connection based on the analysis of the data received from the SMF module and the core network data,
15 wherein the one or more abnormalities relate to at least one of a network related
abnormalities and a user device related abnormalities. Further, the instructions include executable code which, when executed causes a performance unit of the system to perform at least one of a network related abnormalities procedure, and a user device related abnormalities procedure, wherein the network related
20 abnormalities procedure is performed in an event at least a sub-set of the one or
more abnormalities comprises the network related abnormalities, and the user device related abnormalities procedure is performed in an event at least a sub-set of the one or more abnormalities comprises the user device related abnormalities. Further, the instructions include executable code which, when executed causes a
25 user interface of the system to display a final result based on the performance of the
at least one of the network related abnormalities procedure, and the user device related abnormalities procedure.
OBJECTS OF THE DISCLOSURE
30
6

[0015] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below.
[0016] It is an object of the present disclosure to provide a system and a method for
5 determining abnormalities in a wireless local area network (WLAN).
[0017] It is yet another object of the present disclosure to provide a solution where the NWDAF exposes a handler to ingest slice data from Fulfilment Management System (FMS), NWDAF remains updated of any changes in slice provisioning. 10
DESCRIPTION OF THE DRAWINGS
[0018] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed method
15 and system 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
20 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.
25 [0019] FIG. 1 illustrates an exemplary block diagram representation of 5th
generation core (5GC) network architecture.
[0020] FIG. 2 illustrates an exemplary block diagram of a computing device upon
which the features of the present disclosure may be implemented in accordance with
30 exemplary implementation of the present disclosure.
7

[0021] FIG. 3 illustrates an exemplary block diagram of a system for determining abnormalities in a wireless local area network (WLAN), in accordance with exemplary implementations of the present disclosure.
5 [0022] FIG. 4 illustrates a method flow diagram for determining abnormalities in a
wireless local area network (WLAN), in accordance with exemplary implementations of the present disclosure.
[0023] FIG. 5 illustrates a system architecture diagram for determining
10 abnormalities in a wireless local area network (WLAN), in accordance with
exemplary implementations of the present disclosure.
[0024] The foregoing shall be more apparent from the following more detailed description of the disclosure. 15
DETAILED DESCRIPTION
[0025] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of
20 embodiments of the present disclosure. It will be apparent, however, that
embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter may each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the
25 problems discussed above.
[0026] The ensuing description provides exemplary embodiments only, and is not
intended to limit the scope, applicability, or configuration of the disclosure. Rather,
the ensuing description of the exemplary embodiments will provide those skilled in
30 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
8

arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
[0027] Specific details are given in the following description to provide a thorough
5 understanding of the embodiments. However, it will be understood by one of
ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail.
10
[0028] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations may be performed in parallel or
15 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.
[0029] The word “exemplary” and/or “demonstrative” is used herein to mean
20 serving as an example, instance, or illustration. For the avoidance of doubt, the
subject matter disclosed herein is not limited by such examples. In addition, any
aspect or design described herein as “exemplary” and/or “demonstrative” is not
necessarily to be construed as preferred or advantageous over other aspects or
designs, nor is it meant to preclude equivalent exemplary structures and techniques
25 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 be inclusive—in a manner
similar to the term “comprising” as an open transition word—without precluding
any additional or other elements.
30
9

[0030] As used herein, a “processing unit” or “processor” or “operating processor”
includes one or more processors, wherein processor refers to any logic circuitry for
processing instructions. A processor may be a general-purpose processor, a special
purpose processor, a conventional processor, a digital signal processor, a plurality
5 of microprocessors, one or more microprocessors in association with a Digital
Signal Processing (DSP) core, a controller, a microcontroller, Application Specific
Integrated Circuits, Field Programmable Gate Array circuits, any other type of
integrated circuits, etc. The processor may perform signal coding data processing,
input/output processing, and/or any other functionality that enables the working of
10 the system according to the present disclosure. More specifically, the processor or
processing unit is a hardware processor.
[0031] 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”,
15 “a wireless communication 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,
20 tablet computer, wearable device or any other computing device which is capable
of implementing the features of the present disclosure. Also, the user device may contain at least one input means configured to receive an input from unit(s) which are required to implement the features of the present disclosure.
25 [0032] As used herein, “storage unit” or “memory unit” refers to a machine or
computer-readable medium including any mechanism for storing information in a form readable by a computer or similar machine. For example, a computer-readable medium includes read-only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices or other
30 types of machine-accessible storage media. The storage unit stores at least the data
10

that may be required by one or more units of the system to perform their respective functions.
[0033] As used herein “interface” or “user interface refers to a shared boundary
5 across which two or more separate components of a system exchange information
or data. The interface may also be referred to a set of rules or protocols that define communication or interaction of one or more modules or one or more units with each other, which also includes the methods, functions, or procedures that may be called.
10
[0034] The Session Management Function (SMF) is a core network element responsible for managing the sessions between user devices and the network. Acting as a conductor of sorts, SMF orchestrates the flow of data traffic, ensuring seamless connectivity and efficient resource utilization. One of its primary tasks
15 involves managing the separation of the control plane, which handles connection
setup and management, and the user plane, which is responsible for actual data transmission. SMF plays a crucial role in enforcing various policies, such as data usage limits and quality of service requirements, to optimize network performance and adhere to service agreements. Additionally, SMF is tasked with dynamically
20 assigning IP addresses to user devices, enabling them to be identified and routed
effectively within the network. Overall, SMF acts as a cornerstone in 5G networks, facilitating robust session management and enhancing the overall user experience.
[0035] The Network Data Analytics Function (NWDAF) is a key component in 5G
25 networks that performs data analytics to provide valuable insights and intelligence.
NWDAF collects and analyses network data to enhance various aspects of the
network, such as performance, quality of service, and user experience. It helps in
making informed decisions for network optimization, predictive maintenance, and
overall network efficiency. NWDAF enables telecom operators to leverage data-
30 driven approaches for better service delivery and customer satisfaction in 5G
networks.
11

[0036] All modules, units, components used herein, unless explicitly excluded
herein, may be software modules or hardware processors, the processors being a
general-purpose processor, a special purpose processor, a conventional processor, a
5 digital signal processor (DSP), a plurality of microprocessors, one or more
microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array circuits (FPGA), any other type of integrated circuits, etc.
10 [0037] As used herein the transceiver unit include at least one receiver and at least
one transmitter configured respectively for receiving and transmitting data, signals, information or a combination thereof between units/components within the system and/or connected with the system.
15 [0038] As discussed in the background section, the current known solutions have
several shortcomings. The present disclosure aims to overcome the above-mentioned and other existing problems in this field of technology by providing method and system for determining abnormalities in a wireless local area network (WLAN). The present disclosure aims to overcome the above-mentioned and other
20 existing problems in this field of technology by ensuring direct communication
between the NWDAF and FMS, thereby eliminating the need for an additional communication hop i.e. NSSF.
[0039] Hereinafter, exemplary embodiments of the present disclosure will be
25 described with reference to the accompanying drawings.
[0040] FIG. 1 illustrates an exemplary block diagram representation of 5th
generation core (5GC) network architecture, in accordance with exemplary
implementation of the present disclosure. As shown in FIG. 1, the 5GC network
30 architecture [100] includes a user equipment (UE) [102], a radio access network
(RAN) [104], an access and mobility management function (AMF) [106], a Session
12

Management Function (SMF) [108], a Service Communication Proxy (SCP) [110],
an Authentication Server Function (AUSF) [112], a Network Slice Specific
Authentication and Authorization Function (NSSAAF) [114], a Network Slice
Selection Function (NSSF) [116], a Network Exposure Function (NEF) [118], a
5 Network Repository Function (NRF) [120], a Policy Control Function (PCF) [122],
a Unified Data Management (UDM) [124], an Application Function (AF) [126], a
User Plane Function (UPF) [128], a data network (DN) [130], and Network Data
Analytics Function (NWDAF) [132]. wherein all the components are assumed to
be connected to each other in a manner as obvious to the person skilled in the art
10 for implementing features of the present disclosure.
[0041] Radio Access Network (RAN) [104] is the part of a mobile
telecommunications system that connects user equipment (UE) [102] to the core
network (CN) and provides access to different types of networks (e.g., 5G network).
15 It consists of radio base stations and the radio access technologies that enable
wireless communication.
[0042] Access and Mobility Management Function (AMF) [106] is a 5G core
network function responsible for managing access and mobility aspects, such as UE
20 registration, connection, and reachability. It also handles mobility management
procedures like handovers and paging.
[0043] Session Management Function (SMF) [108] is a 5G core network function
responsible for managing session-related aspects, such as establishing, modifying,
25 and releasing sessions. It coordinates with the User Plane Function (UPF) for data
forwarding and handles IP address allocation and QoS enforcement.
[0044] Service Communication Proxy (SCP) [110] is a network function in the
5G core network that facilitates communication between other network functions
30 by providing a secure and efficient messaging service. It acts as a mediator for
service-based interfaces.
13

[0045] Authentication Server Function (AUSF) [112] is a network function in the 5G core responsible for authenticating UEs during registration and providing security services. It generates and verifies authentication vectors and tokens. 5
[0046] Network Slice Specific Authentication and Authorization Function (NSSAAF) [114] is a network function that provides authentication and authorization services specific to network slices. It ensures that UEs can access only the slices for which they are authorized. 10
[0047] Network Slice Selection Function (NSSF) [116] is a network function responsible for selecting the appropriate network slice for a UE based on factors such as subscription, requested services, and network policies.
15 [0048] Network Exposure Function (NEF) [118] is a network function that
exposes capabilities and services of the 5G network to external applications, enabling integration with third-party services and applications.
[0049] Network Repository Function (NRF) [120] is a network function that acts
20 as a central repository for information about available network functions and
services. It facilitates the discovery and dynamic registration of network functions.
[0050] Policy Control Function (PCF) [122] is a network function responsible for
policy control decisions, such as QoS, charging, and access control, based on
25 subscriber information and network policies.
[0051] Unified Data Management (UDM) [124] is a network function that centralizes the management of subscriber data, including authentication, authorization, and subscription information. 30
14

[0052] Application Function (AF) [126] is a network function that represents external applications interfacing with the 5G core network to access network capabilities and services.
5 [0053] User Plane Function (UPF) [128] is a network function responsible for
handling user data traffic, including packet routing, forwarding, and QoS enforcement.
[0054] Data Network (DN) [130] refers to a network that provides data services
10 to user equipment (UE) in a telecommunications system. The data services may
include but are not limited to Internet services, private data network related services.
[0055] FIG. 2 illustrates an exemplary block diagram of a computing device [200] upon which the features of the present disclosure may be implemented in
15 accordance with exemplary implementation of the present disclosure. In an
implementation, the computing device [200] may also implement a method for determining abnormalities in a wireless local area network (WLAN), utilising the system. In another implementation, the computing device [200] itself implements the method for determining abnormalities in a wireless local area network (WLAN),
20 using one or more units configured within the computing device [200], wherein said
one or more units are capable of implementing the features as disclosed in the present disclosure.
[0056] The computing device [200] may include a bus [202] or other
25 communication mechanism for communicating information, and a hardware
processor [204] coupled with bus [202] for processing information. The hardware
processor [204] may be, for example, a general-purpose microprocessor. The
computing device [200] may also include a main memory [206], such as a random-
access memory (RAM), or other dynamic storage device, coupled to the bus [202]
30 for storing information and instructions to be executed by the processor [204]. The
main memory [206] also may be used for storing temporary variables or other
15

intermediate information during execution of the instructions to be executed by the
processor [204]. Such instructions, when stored in non-transitory storage media
accessible to the processor [204], render the computing device [200] into a special-
purpose machine that is customized to perform the operations specified in the
5 instructions. The computing device [200] further includes a read only memory
(ROM) [208] or other static storage device coupled to the bus [202] for storing static information and instructions for the processor [204].
[0057] A storage device [210], such as a magnetic disk, optical disk, or solid-state
10 drive is provided and coupled to the bus [202] for storing information and
instructions. The computing device [200] may be coupled via the bus [202] to a
display [212], such as a cathode ray tube (CRT), Liquid crystal Display (LCD),
Light Emitting Diode (LED) display, Organic LED (OLED) display, etc. for
displaying information to a computer user. An input device [214], including
15 alphanumeric and other keys, touch screen input means, etc. may be coupled to the
bus [202] for communicating information and command selections to the processor
[204]. Another type of user input device may be a cursor controller [216], such as a
mouse, a trackball, or cursor direction keys, for communicating direction
information and command selections to the processor [204], and for controlling
20 cursor movement on the display [212]. This input device [214] 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.
[0058] The computing device [200] may implement the techniques described
25 herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware
and/or program logic which in combination with the computing device [200] causes
or programs the computing device [200] to be a special-purpose machine.
According to one implementation, the techniques herein are performed by the
computing device [200] in response to the processor [204] executing one or more
30 sequences of one or more instructions contained in the main memory [206]. Such
instructions may be read into the main memory [206] from another storage medium,
16

such as the storage device [210]. Execution of the sequences of instructions
contained in the main memory [206] causes the processor [204] to perform the
process steps described herein. In alternative implementations of the present
disclosure, hard-wired circuitry may be used in place of or in combination with
5 software instructions.
[0059] The computing device [200] also may include a communication interface [218] coupled to the bus [202]. The communication interface [218] provides a two-way data communication coupling to a network link [220] that is connected to a
10 local network [222]. For example, the communication interface [218] may be an
integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface [218] may be a local area network (LAN) card to provide a data communication connection to a
15 compatible LAN. Wireless links may also be implemented. In any such
implementation, the communication interface [218] sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
20 [0060] The computing device [200] can send messages and receive data, including
program code, through the network(s), the network link [220] and the communication interface [218]. In the Internet example, a server [230] might transmit a requested code for an application program through the Internet [228], the ISP [226], the local network [222], the host [224] and the communication interface
25 [218]. The received code may be executed by the processor [204] as it is received,
and/or stored in the storage device [210], or other non-volatile storage for later execution.
[0061] Referring to FIG. 3, an exemplary block diagram of a system [300] for
30 determining abnormalities in a wireless local area network (WLAN), is shown, in
accordance with the exemplary implementations of the present disclosure. The
17

system [300] may include a network data analytics function (NWDAF) module
[302]. The NWDAF module [302] may comprise at least one transceiver unit [304],
at least one session management function (SMF) module [306], at least one, at least
one analysis unit [308], at least one determination unit [310], at least one
5 performance unit [312], at least one user interface [314], at least one processing unit
[316], at least one policy control function (PCF) module [318], at least one updating unit [320] and at least one storage unit [322]. Also, all the components/ units of the system [300] are assumed to be connected to each other unless otherwise indicated below. As shown in the figures all units shown within the system [300] should also
10 be assumed to be connected to each other. Also, in FIG. 3 only a few units are
shown, however, the system [300] may comprise multiple such units or the system [300] may comprise any such numbers of said units, as required to implement the features of the present disclosure. Further, in an implementation, the system [300] may be present in a user device/ user equipment [102] to implement the features of
15 the present disclosure. The system [300] may be a part of the user device [102]/ or
may be independent of but in communication with the user device [102] (may also referred herein as a UE). In another implementation, the system [300] may reside in a server or a network entity. In yet another implementation, the system [300] may reside partly in the server/ network entity and partly in the user device.
20
[0062] The system [300] is configured for determining abnormalities in a wireless local area network (WLAN), with the help of the interconnection between the components/units of the system [300].
25 [0063] In operation, in one example, the transceiver unit [304] of the network data
analytics function (NWDAF) module [302] may receive a data associated with a connection of user device with a WLAN from the session management function (SMF) module [306]. The transceiver unit [304] may further receive a core network data collected by a virtual probe tool. The present disclosure encompasses for
30 determining abnormalities in performance of the WLAN refers herewith to an
integrated mechanism designed to predict potential issues or irregularities in the
18

performance of a Wireless Local Area Network (WLAN) within a communication
network. The NWDAF module is for analysing network data. The transceiver unit
[304] is a part of the NWDAF module [302] and may receive data from various
sources within the network, including the Session Management Function (SMF)
5 module. The session management function (SMF) Module [306] manages session
related information for user devices, such as establishing, maintaining, and terminating connections.
[0064] In an example, the common abnormality is connection drops, where devices
10 frequently disconnect from the network or struggle to maintain a stable connection.
Another example is interference, where signals from other electronic devices or overlapping WLAN channels cause disruptions, leading to reduced network performance and increased error rates.
15 [0065] In an example data associated with a connection of a user device refers to
the various pieces of information that characterize the interaction between a user’s device and the WLAN. This data includes the connection status, signal strength, data transfer rates, device identifiers, and error rates. For example, connection status data may indicate whether the device is connected, disconnected, or attempting to
20 connect.
[0066] Further, the data provides includes information about the connection status of user devices with the WLAN. The core network data collected refers herewith is to analyse and expect potential abnormalities in performance of WLAN.
25 Furthermore, virtual probe tools are software-based instruments designed to collect
and monitor network data within a communication network, particularly within a WLAN (Wireless Local Area Network). The data collected by the virtual probe tools is necessary for the Network Data Analytics Function (NWDAF) module. This data helps the NWDAF module analyse network performance and predict potential
30 abnormalities.
19

[0067] In an example, the core network data in action could be the collection of performance metrics from a corporate network’s core routers, showing high latency during peak usage hours.
5 [0068] Thereafter, the analysis unit [308] may analyse the data received from the
SMF module [306], and the core network data. The present disclosure encompasses the analysis unit [308] used herein is responsible for processing the data received. It analyses both the data from the SMF module, which includes user device connection details.
10
[0069] In an example, the analysis of data received from the SMF module and core network data involves several steps to identify and understand abnormalities in the WLAN. Initially, the data is pre-processed to filter out irrelevant information and key metrics such as signal strength, error rates, and latency. The machine learning
15 algorithms are then applied to detect patterns and anomalies. The analysis also
involves correlation techniques to link user device data with core network data. By correlating the connection drops experienced by user devices with network performance metrics, the system can identify potential causes such as network congestion or hardware malfunctions.
20
[0070] Further, a processing unit [316] is configured to predict one or more abnormality trends in the connection for one or more predefined geographical areas and one or more pre-defined periods of time, based on the analysis of the data received from the SMF module [306]. The processing unit [316] is for determining
25 abnormal trends in network connections within specific geographical areas and
predefined time intervals. It achieves this by analysing data obtained from the Session Management Function (SMF) module [306]. The processing unit [316] uses data analytics techniques to identify patterns and trends of future abnormalities in the network connection.
30
20

[0071] In an example, one or more pre-defined periods of time refers to specific
time intervals that have been identified and set for monitoring or analysis purposes.
These periods can range from minutes to hours, days, or even longer. For example,
a network administrator may define periods such as peak usage hours (e.g., 6 PM
5 to 9 PM), non-peak hours (e.g., 2 AM to 5 AM), or specific days of the week when
network traffic is historically high or low.
[0072] In an example, the analysis is done for one or more pre-defined geographical
areas and one or more pre-defined periods of time. The geographical focus that the
10 analysis is relevant to specific locations where the network performance needs to
be monitored closely. Additionally, the analysis is conducted over one or more predefined periods of time (such as hourly interval, daily intervals, weekly intervals).
15 [0073] In an example, one or more pre-defined geographical areas refers to regions
within the WLAN's coverage area that have been identified for targeted monitoring and analysis.
[0074] Thereafter, the determination unit [310] may determine one or more
20 abnormalities in the connection based on the analysis of the data received from the
SMF module [306] and the core network data, wherein the one or more
abnormalities relate to at least one of a network related abnormalities and a user
device related abnormalities. The present disclosure encompasses the determination
unit [310] is to determine abnormalities based on the analysed data. It evaluates the
25 analysis results to identify specific issues affecting the network. The abnormalities
issues that originate from the network itself, such as signal interference, bandwidth congestion, or hardware failures.
[0075] Thereafter, the performance unit [312], connected to at least the
30 determination unit [310], may perform at least one of a network related
abnormalities procedure, and a user device related abnormalities procedure,
21

wherein the network related abnormalities procedure is performed in an event at
least a sub-set of the one or more abnormalities comprises the network related
abnormalities, and the user device related abnormalities procedure is performed in
an event at least a sub-set of the one or more abnormalities comprises the user
5 device related abnormalities. The present disclosure encompasses, the performance
unit [312] is configured to execute specific procedures to the type of abnormalities detected. When the determination unit [310] identifies that the abnormalities are network-related, the performance unit [312] initiates a network-related abnormalities procedure. This procedure encompasses actions and measures
10 expected at resolving issues originating from the network itself, such as signal
interference, bandwidth congestion, or hardware failures within the network. The user device-related abnormalities are issues that originate from the devices connected to the WLAN. These abnormalities can be due to hardware, software, or configuration problems on the user devices.
15
[0076] In an example (network related abnormalities), if the analysis identifies high interference levels affecting the network, the procedure might include adjusting the channel settings on the router or adding additional access points to improve coverage and reduce congestion.
20
[0077] In an example, (user device related abnormalities) if a user’s device shows determined connection drops due to outdated network drivers, the procedure may involve sending a notification to the user suggesting an update of their network drivers or adjusting device settings.
25
[0078] In an example, if the determination unit [310] identifies that the abnormalities are related to user devices, the performance unit [312] executes a user device-related abnormalities procedure. This procedure involves addressing issues specific to the user devices connected to the network (such as device
30 misconfigurations, hardware malfunctions).
22

[0079] In an example, for performance of the network related abnormalities
procedure, the transceiver unit [304] is configured to send to a policy control
function (PCF) module [318], a notification related to the network related
abnormalities, the PCF module [318] is further configured to adjust one or more
5 parameters related to the WLAN for improving a performance of the WLAN. The
transceiver unit [304] is configured to send a notification regarding the network-
related abnormalities to the PCF module [318]. This notification includes details
about the detected issues within the network infrastructure that are affecting a
performance of the WLAN. Upon receiving the notification, the PCF module [318]
10 takes active measures to address these abnormalities. It is to adjust one or more
parameters related to the WLAN. These adjustments can involve various actions, such as modifying bandwidth allocation, altering network traffic priorities.
[0080] In an example, the performance of the user device related abnormalities
15 procedure, the transceiver unit [304] is further configured to send, to the user
device, a notification related to the user device related abnormalities, wherein the
notification comprises a suggestion related to correcting the user device related
abnormalities. When the performance unit [312] identifies user device-related
abnormalities, the transceiver unit [304] the affected user devices. It sends
20 notifications directly to the user devices, informing them of the detected
abnormalities. These notifications include suggestions or instructions to correct the identified issues, such as changing settings, or addressing hardware problems.
[0081] Thereafter, the user interface [314] may display a final result based on the
25 performance of the at least one of the network related abnormalities procedure, and
the user device related abnormalities procedure. The present disclosure
encompasses the system involves displaying the final result. The user interface
[314] is responsible for presenting the final outcomes related to identified
abnormalities in network connections to the system user. It serves as the interface
30 through which users may view and understand the examined results of the system
examines and actions taken.
23

[0082] Further, in one example, an updating unit [320] of the Network Data
Analytics Function (NWDAF) module [302] may notify a user about one or more
abnormalities in the network connection. This means users are promptly informed
5 of any issues, allowing for timely action or troubleshooting. It shows outcomes
from either the network-related abnormalities procedure or the user device-related abnormalities procedure.
[0083] Referring to FIG. 4, an exemplary method flow diagram [400] for
10 determining abnormalities in a wireless local area network (WLAN), in accordance
with exemplary implementations of the present disclosure is shown. In an
implementation the method [400] is performed by the system [300]. Further, in an
implementation, the system [300] may be present in a server device to implement
the features of the present disclosure. Also, as shown in FIG. 4, the method [400]
15 starts at step [402].
[0084] At step [404], the method [400] comprises, receiving, by a transceiver unit [304] of a network data analytics function (NWDAF) module [302] from a session management function (SMF) module [306], a data associated with a connection of
20 user device with a WLAN. The present disclosure encompasses for determining
abnormalities in performance of WLAN refers herewith to an integrated mechanism designed to predict potential issues or irregularities in the performance of a Wireless Local Area Network (WLAN) within a communication network. The NWDAF module is for analysing network data. The transceiver unit [304] is a part of the
25 NWDAF module that serves both as a transmitter and receiver of data. It is
configured to receive data from various sources within the network, including the Session Management Function (SMF) module. The session management function (SMF) Module [306] manages session related information for user devices, such as establishing, maintaining, and terminating connections.
30
24

[0085] In an example, the common abnormality is connection drops, where devices
frequently disconnect from the network or struggle to maintain a stable connection.
Another example is interference, where signals from other electronic devices or
overlapping WLAN channels cause disruptions, leading to reduced network
5 performance and increased error rates.
[0086] In an example data associated with a connection of a user device refers to
the various pieces of information that characterize the interaction between a user’s
device and the WLAN. This data includes the connection status, signal strength,
10 data transfer rates, device identifiers, and error rates. For example, connection status
data may indicate whether the device is connected, disconnected, or attempting to connect.
[0087] Further, the data provides information about the connection status of user
15 devices with the WLAN.
[0088] At step [406], the method [400] comprises, receiving, by the transceiver unit [304] of the network data analytics function (NWDAF) module [302], a core network data collected by a virtual probe tool. The core network data collected
20 refers herewith is to analyse and expect potential abnormalities in performance of
WLAN. Furthermore, virtual probe tools are software-based instruments designed to collect and monitor network data within a communication network, particularly within a WLAN (Wireless Local Area Network). The data collected by the virtual probe tools is necessary for the Network Data Analytics Function (NWDAF)
25 module [302]. This data helps the NWDAF module [302] analyse network
performance and predict potential abnormalities.
[0089] In an example, the core network data in action could be the collection of
performance metrics from a corporate network’s core routers, showing high latency
30 during peak usage hours.
25

[0090] At step [408], the method [400] comprises, analysing, by an analysis unit
[308] of the network data analytics function (NWDAF) module [302], the data
received from the SMF module [306] and the core network data. The present
disclosure encompasses the analysis unit [308] used herein is responsible for
5 processing the data received. It analyses both the data from the SMF module [306],
which includes user device connection details.
[0091] In an example, the analysis of data received from the SMF module [306] and core network data involves several steps to identify and understand
10 abnormalities in the WLAN. Initially, the data is pre-processed to filter out
irrelevant information and key metrics such as signal strength, error rates, and latency. The machine learning algorithms are then applied to detect patterns and anomalies. The analysis also involves correlation techniques to link user device data with core network data. By correlating the connection drops experienced by user
15 devices with network performance metrics, the system [300] can identify potential
causes such as network congestion or hardware malfunctions.
[0092] Further predicting, by a processing unit [316] of the network data analytics function (NWDAF) module [302], one or more abnormality trends in the
20 connection for one or more predefined geographical areas and one or more pre-
defined periods of time, based on the analysis of the data received from the SMF module [306]. The processing unit [316] is for determining abnormal trends in network connections within specific geographical areas and predefined time intervals. It achieves this by analysing data obtained from the Session Management
25 Function (SMF) module [306]. The processing unit [316] uses data analytics
techniques to identify patterns and trends of future abnormalities in the network connection.
[0093] In an example, one or more pre-defined periods of time refers to specific
30 time intervals that have been identified and set for monitoring or analysis purposes.
These periods can range from minutes to hours, days, or even longer. For example,
26

a network administrator may define periods such as peak usage hours (e.g., 6 PM to 9 PM), non-peak hours (e.g., 2 AM to 5 AM), or specific days of the week when network traffic is historically high or low.
5 [0094] In an example, the analysis is done for one or more pre-defined geographical
areas and one or more pre-defined periods of time. The geographical focus ensures
that the analysis is relevant to specific locations where the network performance
needs to be monitored closely. Additionally, the analysis is conducted over one or
more predefined periods of time (such as hourly interval, daily intervals, weekly
10 intervals).
[0095] In an example, one or more pre-defined geographical areas refers to regions within the WLAN's coverage area that have been identified for targeted monitoring and analysis.
15
[0096] At step [410], the method [400] comprises, determining, by a determination unit [310] of the NWDAF module [302], one or more abnormalities in the connection based on the analysis of the data received from the SMF module [306] and the core network data, wherein the one or more abnormalities relate to at least
20 one of a network related abnormalities and a user device related abnormalities. The
present disclosure encompasses the determination unit [310] is to determine abnormalities based on the analysed data. It evaluates the analysis results to identify specific issues affecting the network. The abnormalities issues that originate from the network itself, such as signal interference, bandwidth congestion, or hardware
25 failures.
[0097] At step [412], the method [400] comprises, performing, by a performance
unit [312] of the NWDAF module [302], at least one of a network related
abnormalities procedure, and a user device related abnormalities procedure,
30 wherein the network related abnormalities procedure is performed in an event at
least a sub-set of the one or more abnormalities comprises the network related
27

abnormalities, and the user device related abnormalities procedure is performed in
an event at least a sub-set of the one or more abnormalities comprises the user
device related abnormalities. The present disclosure encompasses, the performance
unit [312] is configured to execute specific procedures altered to the type of
5 abnormalities detected. When the determination unit identifies that the
abnormalities are network-related, the performance unit [312] initiates a network-
related abnormalities procedure. This procedure encompasses actions and measures
expected at resolving issues originating from the network itself, such as signal
interference, bandwidth congestion, or hardware failures within the network. The
10 user device-related abnormalities are issues that originate from the devices
connected to the WLAN. These abnormalities can be due to hardware, software, or configuration problems on the user devices.
[0098] In an example, the user device related abnormalities procedure comprises
15 sending, by the transceiver unit [304] of the NWDAF module [302] to the user
device, a notification related to the user device related abnormalities, wherein the
notification comprises a suggestion related to correcting the user device related
abnormalities. When the performance unit [312] identifies user device-related
abnormalities, the transceiver unit [304] the affected user devices. It sends
20 notifications directly to the user devices, informing them of the detected
abnormalities. These notifications include suggestions or instructions to correct the identified issues, such as changing settings, or addressing hardware problems.
[0099] In an example (network related abnormalities), if the analysis identifies high
25 interference levels affecting the network, the procedure might include adjusting the
channel settings on the router or adding additional access points to improve coverage and reduce congestion.
[0100] In an example, (user device related abnormalities) if a user’s device shows
30 determined connection drops due to outdated network drivers, the procedure may
28

involve sending a notification to the user suggesting an update of their network drivers or adjusting device settings.
[0101] Further, if the determination unit [310] identifies that the abnormalities are
5 related to user devices, the performance unit [312] executes a user device-related
abnormalities procedure. This procedure involves addressing issues specific to the user devices connected to the network (such as device misconfigurations, hardware malfunctions) wherein the network related abnormalities procedure comprises sending, by the transceiver unit [304] of the NWDAF module [302] to a policy
10 control function (PCF) module [318], a notification related to the network related
abnormalities; and adjusting, by the PCF module [318], one or more parameters related to the WLAN for improving the performance of WLAN. The transceiver unit [304] is configured to send a notification regarding the network-related abnormalities to the PCF module [318]. This notification includes details about the
15 detected issues within the network infrastructure that are affecting performance of
WLAN. Upon receiving the notification, the PCF module [318] takes active measures to address these abnormalities. It is to adjust one or more parameters related to the WLAN. These adjustments can involve various actions, such as modifying bandwidth allocation, altering network traffic priorities.
20
[0102] At step [414], the method [400] comprises, displaying, by a user interface [314] of the NWDAF module [302], a final result based on the performance of the at least one of the network related abnormalities procedure, and the user device related abnormalities procedure. The present disclosure encompasses the method
25 [400] involves displaying the final result. The user interface [314] is responsible for
presenting the final outcomes related to identified abnormalities in network connections to the system user. It serves as the interface through which users may view and understand the examined results of the system examines and actions taken.
30 [0103] Further, in one example, an updating unit [320] of the Network Data
Analytics Function (NWDAF) module [302] may notify a user about one or more
29

abnormalities in the network connection. This means users are promptly informed of any issues, allowing for timely action or troubleshooting It shows outcomes from either the network-related abnormalities procedure or the user device-related abnormalities procedure. 5
[0104] Thereafter, the method [400] terminates at step [416].
[0105] Referring to FIG. 5, a system architecture diagram for determining abnormalities in a wireless local area network (WLAN), in accordance with 10 exemplary implementations of the present disclosure, is illustrated. The system [300] comprises the following units and modules:
[0106] Virtual Probe Tool (VPROBE): This unit is responsible for collecting core network data. It serves as the initial point of data collection within the system [300]. 15
[0107] Session Management Function (SMF) Module [306]: This module collects UE WLAN connection information. It manages session related information for user devices, such as establishing, maintaining, and terminating connections.
20 [0108] Network Data Analytics Function Backend (NWDAF BE): This module plays a central role in the system [300]. It performs multiple functions:
• Transceiver Unit [304]: Configured to receive data from the SMF module
[306] and core network data collected by the VPROBE.
• Analysis Unit [308]: Connected to the transceiver unit [304], it analyses the
25 data received from the SMF module [306] and core network data to identify
potential abnormalities.
• Determination Unit [310]: Connected to the analysis unit [308], it
determines one or more abnormalities in the connection based on the
analysis of the data.
30

• Performance Unit [312]: Connected to the determination unit [310], it performs procedures to address the identified abnormalities, whether they are network related or user device related.
5 [0109] NWDAF AI/ML Model: This model is used to train and enhance the accuracy of abnormality prediction within the WLAN. This model provides UE WLAN connection analytics for training the models.
[0110] The AI/ML model in the NWDAF BE analyses historical and real-time data 10 to identify and predict abnormalities in the WLAN. Using machine learning algorithms, it learns from data patterns and trends to detect deviations from normal network behaviour. The model continually updates its knowledge base with new data, in forecasting potential network issues. The system [300] also features closed-loop forecasting, where abnormal UE WLAN connection trends are analysed for 15 predefined geographical areas and time slots.
[0111] In an example, the data is gathered from the VPROBE (core network data) and the SMF module [306] (user device connection information). The collected data is cleaned and normalized to ensure consistency. The key metrics indicative of 20 network performance, such as signal strength and latency, are identified and extracted. The machine learning algorithms are applied to the extracted features to train the model, using a portion of the data for training and another for validation. The model’s performance is evaluated using metrics like accuracy.
25 [0112] Policy Control Function (PCF) Module [318]: Connected to the transceiver unit [304], it receives notifications related to network abnormalities and adjusts one or more parameters related to the WLAN to improve performance.
[0113] NWDAF User Interface (NWDAF UI) [314]: Connected to the performance
30 unit [312], it displays the final results based on the performance of the abnormalities
procedures, providing a visual representation of UE WLAN connection
31

abnormality trends. It also provides closed loop reporting in case of issue at user device end.
[0114] Data Consumers: These entities consume the subscribed UE WLAN
5 connection analytics provided by the NWDAF BE. They receive closed loop
reporting in case of issues at the user device end.
[0115] Updating Unit [320]: It presents the final outcomes related to identified abnormalities in network connections to the system user, facilitating user
10 understanding of the system’s analysis and actions taken. The Updating Unit [320]
ensures the user is informed of the outcomes, facilitating understanding of the system's analysis and actions taken. Data Consumers receive subscribed UE WLAN connection analytics and closed-loop reporting in case of issues at the user device end.
15
[0116] Processing Unit [316]: Configured to predict abnormality trends in connection for predefined geographical areas and periods, based on the analysis of data from the SMF module [306].
20 [0117] The interactions among these components are as follows:
[0118] Data Collection and Analysis: The VPROBE collects core network data and the SMF collects UE WLAN connection information, which are then received by the NWDAF BE’s transceiver unit [304]. 25
[0119] Abnormality Detection: The analysis unit [308] processes the collected data to identify abnormalities, which the determination unit [310] evaluates to categorize as network related or user device related.
32

[0120] Procedure Execution: Based on the determination, the performance unit [312] executes relevant procedures to address the abnormalities. Notifications are sent to the PCF module [318] or directly to the user devices as needed.
5 [0121] User Interface and Reporting: The NWDAF UI [314] visualizes the trends
and final results, while the updating unit [320] ensures the user is informed of the outcomes. The data consumers receive analytics and closed loop reports as applicable.
10 [0122] The present disclosure further discloses a non-transitory computer readable
storage medium storing instructions for determining abnormalities in a wireless local area network (WLAN), the instructions include executable code which, when executed by one or more units of a system [300], causes a transceiver unit [304] of the system [300] of a network data analytics function (NWDAF) module [302] to
15 receive, from a session management function (SMF) module [306], a data
associated with a connection of user device with a WLAN, receive a core network data collected by a virtual probe tool. Further, the instructions include executable code which, when executed causes an analysis unit [308] of the system [300] to analyse the data received from the SMF module [306], and the core network data.
20 Further, the instructions include executable code which, when executed causes a
determination unit [310] of the system [300] to determine one or more abnormalities in the connection based on the analysis of the data received from the SMF module [306] and the core network data, wherein the one or more abnormalities relate to at least one of a network related abnormalities and a user
25 device related abnormalities. Further, the instructions include executable code
which, when executed causes a performance unit [312] of the system [300] to perform at least one of a network related abnormalities procedure, and a user device related abnormalities procedure, wherein the network related abnormalities procedure is performed in an event at least a sub-set of the one or more
30 abnormalities comprises the network related abnormalities, and the user device
related abnormalities procedure is performed in an event at least a sub-set of the
33

one or more abnormalities comprises the user device related abnormalities. Further,
the instructions include executable code which, when executed causes a user
interface [314] of the system [300] to display a final result based on the performance
of the at least one of the network related abnormalities procedure, and the user
5 device related abnormalities procedure.
[0123] As is evident from the above, the present disclosure provides a technically advanced solution for determining abnormalities in a wireless local area network (WLAN). The present solution involves enhancing the Network Data Analytics
10 Function (NWDAF) to directly access slice details from (FMS) through an exposed
Application Programming Interface (API). This direct interaction eliminates the need for NWDAF to communicate with the Network Slice Selection Function (NSSF) to obtain slice details, thereby reducing the number of communication hops. As a result, network latency is minimized, and the efficiency of NWDAF in
15 performing use case analytics, particularly related to Quality of Service (QoS)
provisioning, policy determination, and QoS adjustment, is significantly improved.
[0124] While considerable emphasis has been placed herein on the disclosed implementations, it will be appreciated that many implementations can be made and
20 that many changes can be made to the implementations without departing from the
principles of the present disclosure. These and other changes in the implementations of the present disclosure will be apparent to those skilled in the art, whereby it is to be understood that the foregoing descriptive matter to be implemented is illustrative and non-limiting.
25
[0125] 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
30 configurations and combinations thereof are within the scope of the disclosure. The

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.

We Claim:
1. A method [400] for determining abnormalities in a wireless local area
network (WLAN), the method [400] comprising:
5 - receiving, by a transceiver unit [304] of a network data analytics
function (NWDAF) module [302] from a session management function (SMF) module [306], a data associated with a connection of user device with a WLAN;
- receiving, by the transceiver unit [304] of the network data analytics
10 function (NWDAF) module [302], a core network data collected by a
virtual probe tool;
- analysing, by an analysis unit [308] of the network data analytics
function (NWDAF) module [302], the data received from the SMF
module [306] and the core network data;
15 - determining, by a determination unit [310] of the NWDAF module
[302], one or more abnormalities in the connection based on the analysis of the data received from the SMF module [306] and the core network data, wherein the one or more abnormalities relate to at least one of a network related abnormalities and a user device related abnormalities;
20 - performing, by a performance unit [312] of the NWDAF module [302],
at least one of a network related abnormalities procedure, and a user device related abnormalities procedure, wherein the network related abnormalities procedure is performed in an event at least a sub-set of the one or more abnormalities comprises the network related abnormalities,
25 and the user device related abnormalities procedure is performed in an
event at least a sub-set of the one or more abnormalities comprises the user device related abnormalities; and
- displaying, by a user interface [314] of the NWDAF module [302], a
final result based on the performance of the at least one of the network
30 related abnormalities procedure, and the user device related

2. The method [400] as claimed in claim 1, further comprising: predicting, by
a processing unit [316] of the network data analytics function (NWDAF) module
[302], one or more abnormality trends in the connection for one or more predefined
5 geographical areas and one or more pre-defined periods of time, based on the
analysis of the data received from the SMF module [306].
3. The method [400] as claimed in claim 1, wherein the analysis is performed
for one or more pre-defined geographical areas and one or more pre-defined periods
10 of time.
4. The method [400] as claimed in claim 1, wherein the network related
abnormalities procedure comprises:
- sending, by the transceiver unit [304] of the NWDAF module [302] to a
15 policy control function (PCF) module [318], a notification related to the
network related abnormalities; and
- adjusting, by the PCF module [318], one or more parameters related to
the WLAN for improving a performance of the WLAN.
20 5. The method [400] as claimed in claim 1, wherein the user device related
abnormalities procedure comprises:
- sending, by the transceiver unit [304] of the NWDAF module [302] to
the user device, a notification related to the user device related
abnormalities, wherein the notification comprises a suggestion related
25 to correcting the user device related abnormalities.
6. The method [400] as claimed in claim 1, further comprising: after displaying
the final result, notifying, by an updating unit [320] of the NWDAF module [302], a user about the one or more abnormalities in the connection. 30
37

7. A system [300] for determining abnormalities in a wireless local area
network (WLAN) performance in a communication network, the system [300] comprising
- a transceiver unit [304] of a network data analytics function (NWDAF)
5 module [302] configured to:
o receive, from a session management function (SMF) module
[306], a data associated with a connection of user device with a
WLAN, and
o receive a core network data collected by a virtual probe tool;
10 - an analysis unit [308] connected to at least the transceiver unit [304], the
analysis unit [308] configured to analyse the data received from the SMF module [306], and the core network data;
- a determination unit [310] connected to at least the analysis unit [308],
the determination unit [310] configured to determine one or more
15 abnormalities in the connection based on the analysis of the data
received from the SMF module [306] and the core network data, wherein the one or more abnormalities relate to at least one of a network related abnormalities and a user device related abnormalities;
- a performance unit [312] connected to at least the determination unit
20 [310], the performance unit [312] configured to perform at least one of
a network related abnormalities procedure, and a user device related
abnormalities procedure, wherein the network related abnormalities
procedure is performed in an event at least a sub-set of the one or more
abnormalities comprises the network related abnormalities, and the user
25 device related abnormalities procedure is performed in an event at least
a sub-set of the one or more abnormalities comprises the user device related abnormalities; and
- a user interface [314] connected to at least the performance unit [312],
the user interface [314] configured to display a final result based on the
30 performance of the at least one of the network related abnormalities
procedure, and the user device related abnormalities procedure.
8. The system [300] as claimed in claim 7, wherein a processing unit [316] is
configured to:
- predict one or more abnormality trends in the connection for one or more
5 predefined geographical areas and one or more pre-defined periods of
time, based on the analysis of the data received from the SMF module [306].
9. The system [300] as claimed in claim 7, wherein the analysis is performed
10 for one or more pre-defined geographical areas and one or more pre-defined periods
of time.
10. The system [300] as claimed in claim 7, wherein for performance of the
network related abnormalities procedure,
15 - the transceiver unit [304] is configured to send, to a policy control
function (PCF) module [318], a notification related to the network related abnormalities.
- the PCF module [318] is further configured to adjust one or more
parameters related to the WLAN for improving a performance of the
20 WLAN.
11. The system [300] as claimed in claim 7, wherein for the performance of the
user device related abnormalities procedure,
- the transceiver unit [304] is further configured to send, to the user
25 device, a notification related to the user device related abnormalities,
wherein the notification comprises a suggestion related to correcting the user device related abnormalities.
12. The system [300] as claimed in claim 7, further comprising an updating
30 unit [320] configured to notify a user about the one or more abnormalities in the
connection.