Method and system for determining abnormalities in a wireless local area network (WLAN)

Abstract

The present disclosure relates to a method [400] and a system [300] for determining abnormalities in a wireless local area network (WLAN) In one example, the method [400] comprises: receiving, of a network data analytics function (NWDAF) module [302] from a session management function (SMF) module [306]. The method [400] comprises receiving, of the network data analytics function (NWDAF) module [302], a core network data collected by a virtual probe tool. The method [400] comprises analysing, the network data analytics function (NWDAF) module [302]. The method [400] comprises determining, 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. The method [400] comprises performing, of the NWDAF module [302]. The method [400] comprises displaying, by a user interface of the NWDAF module [302], a final result based on the performance of the at least one of the network related abnormalities procedures.

Description

METHOD AND SYSTEM FOR DETERMINING ABNORMALITIES IN A WIRELESS LOCAL AREA NETWORK (WLAN)TECHNICAL FIELD

[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).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 certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.

[0003] 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 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 identified, for example, network data analytics (NWDA) assisted QoS provisioning, NWDA-assisted determination of policy, and NWDA-assisted QoS adjustment.

[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 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 of the NWDAF to get the slice details..SUMMARY

[0006] 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.

[0007] An aspect of the present disclosure may relate to a method for determining 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 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 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 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 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 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 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 predefined geographical areas and one or more pre-defined periods of 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 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 module 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.

[0012] In an exemplary aspect of the present disclosure, the method further 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 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 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 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 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 userdevice related abnormalities. The system comprises a user interface connected to at least the performance unit, the user interface configured 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.

[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 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 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, 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 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 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

[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 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.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 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 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.

[0019] FIG. 1 illustrates an exemplary block diagram representation of 5thgeneration 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 exemplary implementation of the present disclosure.

[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.

[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 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.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 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 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 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.

[0027] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail.

[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 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 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 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.

[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 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 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 smartdevice”, “an electronic device”, “a mobile device”, “a handheld device”, “a wireless communication device”, “a mobile communication device”, “a communication device” may be any electrical, electronic and / or computing device or equipment, capable of implementing the features of the present disclosure. The user equipment / device may include, but is not limited to, a mobile phone, smart phone, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, wearable device or any other computing device which is capable of implementing the features of the present disclosure. Also, the user device may contain at least one input means configured to receive an input from unit(s) which are required to implement the features of the present disclosure.

[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 types of machine-accessible storage media. The storage unit stores at least the data that may be required by one or more units of the system to perform their respective functions.

[0033] As used herein “interface” or “user interface refers to a shared boundary 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.

[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 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 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 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-driven approaches for better service delivery and customer satisfaction in 5G networks.

[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 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.

[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.

[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 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 described with reference to the accompanying drawings.

[0040] FIG. 1 illustrates an exemplary block diagram representation of 5thgeneration core (5GC) network architecture, in accordance with exemplary implementation of the present disclosure. As shown in FIG. 1, the 5GC network architecture

[0100] includes a user equipment (UE)

[0102] , a radio access network (RAN)

[0104] , an access and mobility management function (AMF)

[0106] , a Session Management Function (SMF)

[0108] , a Service Communication Proxy (SCP)

[0110] , an Authentication Server Function (AUSF)

[0112] , a Network Slice Specific Authentication and Authorization Function (NSSAAF)

[0114] , a Network Slice Selection Function (NSSF)

[0116] , a Network Exposure Function (NEF)

[0118] , a Network Repository Function (NRF)

[0120] , a Policy Control Function (PCF)

[0122] , a Unified Data Management (UDM)

[0124] , an Application Function (AF)

[0126] , a User Plane Function (UPF)

[0128] , a data network (DN)

[0130] , and Network Data Analytics Function (NWDAF)

[0132] , wherein all the components are assumed to be connected to each other in a manner as obvious to the person skilled in the art for implementing features of the present disclosure.

[0041] Radio Access Network (RAN)

[0104] is the part of a mobile telecommunications system that connects user equipment (UE)

[0102] to the core network (CN) and provides access to different types of networks (e.g., 5G network). It consists of radio base stations and the radio access technologies that enable wireless communication.

[0042] Access and Mobility Management Function (AMF)

[0106] is a 5G core network function responsible for managing access and mobility aspects, such as UE registration, connection, and reachability. It also handles mobility management procedures like handovers and paging.

[0043] Session Management Function (SMF)

[0108] is a 5G core network function responsible for managing session-related aspects, such as establishing, modifying, and releasing sessions. It coordinates with the User Plane Function (UPF) for data forwarding and handles IP address allocation and QoS enforcement.

[0044] Service Communication Proxy (SCP)

[0110] is a network function in the 5G core network that facilitates communication between other network functions by providing a secure and efficient messaging service. It acts as a mediator for service-based interfaces.

[0045] Authentication Server Function (AUSF)

[0112] 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.

[0046] Network Slice Specific Authentication and Authorization Function (NSSAAF)

[0114] 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.

[0047] Network Slice Selection Function (NSSF)

[0116] 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.

[0048] Network Exposure Function (NEF)

[0118] 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)

[0120] is a network function that acts 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)

[0122] is a network function responsible for policy control decisions, such as QoS, charging, and access control, based on subscriber information and network policies.

[0051] Unified Data Management (UDM)

[0124] is a network function that centralizes the management of subscriber data, including authentication, authorization, and subscription information.

[0052] Application Function (AF)

[0126] is a network function that represents external applications interfacing with the 5G core network to access network capabilities and services.

[0053] User Plane Function (UPF)

[0128] is a network function responsible for handling user data traffic, including packet routing, forwarding, and QoS enforcement.

[0054] Data Network (DN)

[0130] refers to a network that provides data services to user equipment (UE) in a telecommunications system. The data services may include but are not limited to Internet services, private data network related services.

[0055] FIG. 2 illustrates an exemplary block diagram of a computing device

[0200] upon which the features of the present disclosure may be implemented in accordance with exemplary implementation of the present disclosure. In an implementation, the computing device

[0200] may also implement a method for determining abnormalities in a wireless local area network (WLAN), utilising the system. In another implementation, the computing device

[0200] itself implements the method for determining abnormalities in a wireless local area network (WLAN), using one or more units configured within the computing device

[0200] , wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.

[0056] The computing device

[0200] may include a bus

[0202] or other communication mechanism for communicating information, and a hardware processor

[0204] coupled with bus

[0202] for processing information. The hardware processor

[0204] may be, for example, a general-purpose microprocessor. The computing device

[0200] may also include a main memory

[0206] , such as a random-access memory (RAM), or other dynamic storage device, coupled to the bus

[0202] for storing information and instructions to be executed by the processor

[0204] , The main memory

[0206] also may be used for storing temporary variables or other intermediate information during execution of the instructions to be executed by the processor

[0204] , Such instructions, when storedin non-transitory storage media accessible to the processor

[0204] , render the computing device

[0200] into a special-purpose machine that is customized to perform the operations specified in the instructions. The computing device

[0200] further includes a read only memory (ROM)

[0208] or other static storage device coupled to the bus

[0202] for storing static information and instructions for the processor

[0204] ,

[0057] A storage device

[0210] , such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to the bus

[0202] for storing information and instructions. The computing device

[0200] may be coupled via the bus

[0202] to a display

[0212] , 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

[0214] , including alphanumeric and other keys, touch screen input means, etc. may be coupled to the bus

[0202] for communicating information and command selections to the processor

[0204] , Another type of user input device may be a cursor controller

[0216] , such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor

[0204] , and for controlling cursor movement on the display

[0212] , This input device

[0214] 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

[0200] 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

[0200] causes or programs the computing device

[0200] to be a special-purpose machine. According to one implementation, the techniques herein are performed by the computing device

[0200] in response to the processor

[0204] executing one or more sequences of one or more instructions contained in the main memory

[0206] , Such instructions may be read into the main memory

[0206] from another storage medium, such as the storage device

[0210] , Execution of the sequences of instructions contained in the main memory

[0206] causes the processor

[0204] 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 software instructions.

[0059] The computing device

[0200] also may include a communication interface

[0218] coupled to the bus

[0202] , The communication interface

[0218] provides a two-way data communication coupling to a network link

[0220] that is connected to a local network

[0222] , For example, the communication interface

[0218] may be an integrated services digital network (ISDN) card, cablemodem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface

[0218] 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

[0218] sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[0060] The computing device

[0200] can send messages and receive data, including program code, through the network(s), the network link

[0220] and the communication interface

[0218] , In the Internet example, a server

[0230] might transmit a requested code for an application program through the Internet

[0228] , the ISP

[0226] , the local network

[0222] , the host

[0224] and the communication interface

[0218] , The received code may be executed by the processor

[0204] as it is received, and / or stored in the storage device

[0210] , or other non-volatile storage for later execution.

[0061] Referring to FIG. 3, an exemplary block diagram of a system

[0300] for determining abnormalities in a wireless local area network (WLAN), is shown, in accordance with the exemplary implementations of the present disclosure. The system

[0300] may include a network data analytics function (NWDAF) module

[0302] , The NWDAF module

[0302] may comprise at least one transceiver unit

[0304] , at least one session management function (SMF) module

[0306] , at least one, at least one analysis unit

[0308] , at least one determination unit

[0310] , at least one performance unit

[0312] , at least one user interface

[0314] , at least one processing unit

[0316] , at least one policy control function (PCF) module

[0318] , at least one updating unit

[0320] and at least one storage unit

[0322] , Also, all the components / units of the system

[0300] are assumed to be connected to each other unless otherwise indicated below. As shown in the figures all units shown within the system

[0300] should also be assumed to be connected to each other. Also, in FIG. 3 only a few units are shown, however, the system

[0300] may comprise multiple such units or the system

[0300] may comprise any such numbers of said units, as required to implement the features of the present disclosure. Further, in an implementation, the system

[0300] may be present in a user device / user equipment

[0102] to implement the features of the present disclosure. The system

[0300] may be a part of the user device

[0102] / or may be independent of but in communication with the user device

[0102] (may also referred herein as a UE). In another implementation, the system

[0300] may reside in a server or a network entity. In yet another implementation, the system

[0300] may reside partly in the server / network entity and partly in the user device.

[0062] The system

[0300] 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

[0300] ,

[0063] In operation, in one example, the transceiver unit

[0304] of the network data analytics function (NWDAF) module

[0302] may receive a data associated with a connection of user device with a WLAN from the session management function (SMF) module

[0306] , The transceiver unit

[0304] may further receive a core network data collected by a virtual probe tool. The present disclosure encompasses for determining abnormalities in performance of the 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

[0304] is a part of the NWDAF module

[0302] and may receive data from various sources within the network, including the Session Management Function (SMF) module. The session management function (SMF) Module

[0306] 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 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.

[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 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. 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 abnormalities.

[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.

[0068] Thereafter, the analysis unit

[0308] may analyse the data received from the SMF module

[0306] , and the core network data. The present disclosure encompasses the analysis unit

[0308] used herein is responsible for processing the data received. It analyses both the data from the SMF module, which includes user device connection details.

[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 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.

[0070] Further, a processing unit

[0316] 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

[0306] , The processing unit

[0316] 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 Function (SMF) module

[0306] , The processing unit

[0316] uses data analytics techniques to identify patterns and trends of future abnormalities in the network connection.

[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 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 analysis is relevant to specific locations where the network performance needs to be monitored closely. Additionally, the analysisis conducted over one or more predefined periods of time (such as hourly interval, daily intervals, weekly intervals).

[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

[0310] may determine one or more abnormalities in the connection based on the analysis of the data received from the SMF module

[0306] 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

[0310] 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 failures.

[0075] Thereafter, the performance unit

[0312] , connected to at least the determination unit

[0310] , may 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 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

[0312] is configured to execute specific procedures to the type of abnormalities detected. When the determination unit

[0310] identifies that the abnormalities are network-related, the performance unit

[0312] 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 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.

[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.

[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.

[0078] In an example, if the determination unit

[0310] identifies that the abnormalities are related to user devices, the performance unit

[0312] 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).

[0079] In an example, for performance of the network related abnormalities procedure, the transceiver unit

[0304] is configured to send to a policy control function (PCF) module

[0318] , a notification related to the network related abnormalities, the PCF module

[0318] is further configured to adjust one or more parameters related to the WLAN for improving a performance of the WLAN. The transceiver unit

[0304] is configured to send a notification regarding the network- related abnormalities to the PCF module

[0318] , 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

[0318] 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 procedure, the transceiver unit

[0304] 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

[0312] identifies user device-related abnormalities, the transceiver unit

[0304] the affected user devices. It sends 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

[0314] may 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. The present disclosure encompasses the system involves displaying the final result. The user interface

[0314] is responsible for presenting the final outcomes related to identified abnormalities in network connections to the system user. It serves as the interfacethrough which users may view and understand the examined results of the system examines and actions taken.

[0082] Further, in one example, an updating unit

[0320] of the Network Data Analytics Function (NWDAF) module

[0302] may notify a user about one or more 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.

[0083] Referring to FIG. 4, an exemplary method flow diagram

[0400] for 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

[0400] is performed by the system

[0300] , Further, in an implementation, the system

[0300] may be present in a server device to implement the features of the present disclosure. Also, as shown in FIG. 4, the method

[0400] starts at step

[0402] ,

[0084] At step

[0404] , the method

[0400] comprises, receiving, by a transceiver unit

[0304] of a network data analytics function (NWDAF) module

[0302] from a session management function (SMF) module

[0306] , a data associated with a connection of 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

[0304] is a part of the 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

[0306] manages session related information for user devices, such as establishing, maintaining, and terminating connections.

[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 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 dataincludes 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 connect.

[0087] Further, the data provides information about the connection status of user devices with the WLAN.

[0088] At step

[0406] , the method

[0400] comprises, receiving, by the transceiver unit

[0304] of the network data analytics function (NWDAF) module

[0302] , a core network data collected by a virtual probe tool. The core network data collected 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) module

[0302] , This data helps the NWDAF module

[0302] 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 during peak usage hours.

[0090] At step

[0408] , the method

[0400] comprises, analysing, by an analysis unit

[0308] of the network data analytics function (NWDAF) module

[0302] , the data received from the SMF module

[0306] and the core network data. The present disclosure encompasses the analysis unit

[0308] used herein is responsible for processing the data received. It analyses both the data from the SMF module

[0306] , which includes user device connection details.

[0091] In an example, the analysis of data received from the SMF module

[0306] 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 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

[0300] can identify potential causes such as network congestion or hardware malfunctions.

[0092] Further predicting, by a processing unit

[0316] of the network data analytics function (NWDAF) module

[0302] , 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

[0306] , The processing unit

[0316] 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 Function (SMF) module

[0306] , The processing unit

[0316] 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 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 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.

[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 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.

[0096] At step

[0410] , the method

[0400] comprises, determining, by a determination unit

[0310] of the NWDAF module

[0302] , one or more abnormalities in the connection based on the analysis of the data received from the SMF module

[0306] 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

[0310] 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 failures.

[0097] At step

[0412] , the method

[0400] comprises, performing, by a performance unit

[0312] of the NWDAF module

[0302] , at least one of a network related abnormalities procedure, and a userdevice 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 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

[0312] is configured to execute specific procedures altered to the type of abnormalities detected. When the determination unit identifies that the abnormalities are network-related, the performance unit

[0312] 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 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 sending, by the transceiver unit

[0304] of the NWDAF module

[0302] 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

[0312] identifies user device-related abnormalities, the transceiver unit

[0304] the affected user devices. It sends 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 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 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.

[0101] Further, if the determination unit

[0310] identifies that the abnormalities are related to user devices, the performance unit

[0312] 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

[0304] of the NWDAF module

[0302] to apolicy control function (PCF) module

[0318] , a notification related to the network related abnormalities; and adjusting, by the PCF module

[0318] , one or more parameters related to the WLAN for improving the performance of WLAN. The transceiver unit

[0304] is configured to send a notification regarding the network-related abnormalities to the PCF module

[0318] , This notification includes details about the detected issues within the network infrastructure that are affecting performance of WLAN. Upon receiving the notification, the PCF module

[0318] 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.

[0102] At step

[0414] , the method

[0400] comprises, displaying, by a user interface

[0314] of the NWDAF module

[0302] , 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

[0400] involves displaying the final result. The user interface

[0314] 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.

[0103] Further, in one example, an updating unit

[0320] of the Network Data Analytics Function (NWDAF) module

[0302] may notify a user about one or more 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.

[0104] Thereafter, the method

[0400] terminates at step

[0416] ,

[0105] Referring to FIG. 5, a system architecture diagram for determining abnormalities in a wireless local area network (WLAN), in accordance with exemplary implementations of the present disclosure, is illustrated. The system

[0300] 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

[0300] ,

[0107] Session Management Function (SMF) Module

[0306] : This module collects UE WLAN connection information. It manages session related information for user devices, such as establishing, maintaining, and terminating connections.

[0108] Network Data Analytics Function Backend (NWDAF BE): This module plays a central role in the system

[0300] , It performs multiple functions:• Transceiver Unit

[0304] : Configured to receive data from the SMF module

[0306] and core network data collected by the VPROBE.• Analysis Unit

[0308] : Connected to the transceiver unit

[0304] , it analyses the data received from the SMF module

[0306] and core network data to identify potential abnormalities.• Determination Unit

[0310] : Connected to the analysis unit

[0308] , it determines one or more abnormalities in the connection based on the analysis of the data.• Performance Unit

[0312] : Connected to the determination unit

[0310] , it performs procedures to address the identified abnormalities, whether they are network related or user device related.

[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 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

[0300] also features closed-loop forecasting, where abnormal UE WLAN connection trends are analysed for predefined geographical areas and time slots.[OHl] In an example, the data is gathered from the VPROBE (core network data) and the SMF module

[0306] (user device connection information). The collected data is cleaned and normalized to ensure consistency. The key metrics indicative of 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.

[0112] Policy Control Function (PCF) Module

[0318] : Connected to the transceiver unit

[0304] , 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)

[0314] : Connected to the performance unit

[0312] , it displays the final results based on the performance of the abnormalities procedures, providing a visual representation of UE WLAN connection 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 connection analytics provided by the NWDAF BE. They receive closed loop reporting in case of issues at the user device end.

[0115] Updating Unit

[0320] : It presents the final outcomes related to identified abnormalities in network connections to the system user, facilitating user understanding of the system’s analysis and actions taken. The Updating Unit

[0320] 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.

[0116] Processing Unit

[0316] : Configured to predict abnormality trends in connection for predefined geographical areas and periods, based on the analysis of data from the SMF module

[0306] ,

[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

[0304] ,

[0119] Abnormality Detection: The analysis unit

[0308] processes the collected data to identify abnormalities, which the determination unit

[0310] evaluates to categorize as network related or user device related.

[0120] Procedure Execution: Based on the determination, the performance unit

[0312] executes relevant procedures to address the abnormalities. Notifications are sent to the PCF module

[0318] or directly to the user devices as needed.

[0121] User Interface and Reporting: The NWDAF UI

[0314] visualizes the trends and final results, while the updating unit

[0320] ensures the user is informed of the outcomes. The data consumers receive analytics and closed loop reports as applicable.

[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

[0300] , causes a transceiver unit

[0304] of the system

[0300] of a network data analytics function (NWDAF) module

[0302] to receive, from a session management function (SMF) module

[0306] , 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

[0308] of the system

[0300] to analyse the data received from the SMF module

[0306] , and the core network data. Further, the instructions include executable code which, when executed causes a determination unit

[0310] of the system

[0300] to determine one or more abnormalities in the connection based on the analysis of the data received from the SMF module

[0306] 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. Further, the instructions include executable code which, when executed causes a performance unit

[0312] of the system

[0300] 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 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 user interface

[0314] of the system

[0300] 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.

[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 Function (NWDAF) to directly access slice details from (FMS) through an exposed Application Programming Interface (API). This directinteraction 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 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 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.

[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 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.

Claims

We Claim:

1. A method [400] for determining abnormalities in a wireless local area network (WLAN), the method [400] comprising: 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 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; 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; 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, 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 related abnormalities procedure, and the user device related abnormalities procedure.

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 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 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 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.

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 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.

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) 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; 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 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 [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 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 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 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 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, 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 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 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 unit [320] configured to notify a user about the one or more abnormalities in the connection.

13. A non-transitory computer-readable storage medium storing instructions for determining abnormalities in a wireless local area network (WLAN), the instructions comprising executable code which, when executed by one or more units of a system [300], causes: a transceiver unit [304] of a network data analytics function (NWDAF) module [302] to receive, from a session management function (SMF) module [306], a data associated with a connection of user device with a WLAN; the transceiver unit [304] of the network data analytics function (NWDAF) module [302] to receive a core network data collected by a virtual probe tool; an analysis unit [308] of the network data analytics function (NWDAF) module [302] to analyse the data received from the SMF module [306] and the core network data; a determination unit [310] of the NWDAF module [302] 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 device related abnormalities; a performance unit [312] of the NWDAF module [302] 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 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] of the NWDAF module [302] 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.

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