Method and system for performing root cause analysis in a network

Abstract

The present disclosure relates to a system and method for performing root cause analysis in a network The disclosure encompasses: receiving one or more KPIs of one or more network cells; monitoring the one or more KPIs of the one or more network cells; identifying one or more low- performing network cells based on predefined operational threshold of the one or more KPIs stored in a database; determining a time interval of the performance degradation of the one or more KPIs of the one or more low-performing network cells; identifying one or more root causes for degradation of the one or more KPIs of the one or more low-performing network cells; and generating a root cause analysis report for the one or more low-performing network cells wherein the generation of the root cause analysis report comprises compiling data on at least one of the identified performance degradation, one or more root causes, and proposed remediation steps.

Description

METHOD AND SYSTEM FOR PERFORMING ROOT CAUSE ANALYSIS IN A NETWORKFIELD OF INVENTION

[0001] The present disclosure relates generally to the field of wireless communication systems. More particularly, the present disclosure relates to method and system for performing root cause analysis in a network.BACKGROUND

[0002] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

[0003] Wireless communication technology has rapidly evolved over the past few decades, with each generation bringing significant improvements and advancements. The first generation of wireless communication technology was based on analog technology and offered only voice services. However, with the advent of the second-generation (2G) technology, digital communication and data services became possible, and text messaging was introduced. 3G technology marked the introduction of high-speed internet access, mobile video calling, and location-based services. The fourth-generation (4G) technology revolutionized wireless communication with faster data speeds, better network coverage, and improved security. Currently, the fifth-generation (5G) technology is being deployed, promising even faster data speeds, low latency, and the ability to connect multiple devices simultaneously. With each generation, wireless communication technology has become more advanced, sophisticated, and capable of delivering more services to its users.

[0004] In the 5G communication system, there is provided a plurality of network functions (NFs), for example an Access and Mobility Management Function (AMF), Session Management function (SMF), Authentication Server function (AUSF), Network Slice Selection Function (NSSF), Policy control function (PCF), a Network Repository Function (NRF), Network Data Analytics Function (NWDAF) and the like. One or more of the aforementioned NFs communicateswith each other, to implement multiple activities on the 5G communication system. For example, for data transfer, the AMF communicates with SMF, to initiate the communication. Accordingly, one or more connections are established between two peer NFs, to allow communication therebetween, and thus enable such activities there between. For providing network functions and services, network operators make deployment plan of network elements, devices and resources along with monitoring the performance of network elements and try to figure out causes of problems and issues during service delivery, so that network can be optimized, and users may get better service experience.

[0005] In a telecommunication network deployment, cell level optimization plays a significant role in improving the customer experience. The quick resolution of issues pertaining to cells is essential for improving overall end user experience. Many times, identification of the issues takes more time than actual time required for solving the issue. Traditionally, the analysis of the worst performing cells was done manually by radio optimization engineer.

[0006] Correlating the poor performance of the cell on various scenarios involving configuration parameter check, alarms etc. is a huge task for the network containing millions of nodes. Also, manual analysis is always prone to error and interpretation of the root cause varies on the basis of individual skill sets of the radio engineer.

[0007] Thus, there exists an imperative need in the art to provide an efficient system and method for performing root cause analysis in a network. The present disclosure provides a solution for identifying and resolution of poor performing cells in the network.OBJECTS OF THE INVENTION

[0008] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below.

[0009] It is an object of the present disclosure to provide a system and a method for automated detection of cell performance and provide remedial actions for network operation team.

[0010] It is another object of the present disclosure to provide a system and a method for detecting poor performance cell via Root Cause Analysis and providing curative action for network operation team.

[0011] It is yet another object of the present disclosure to provide a system and a method for providing Root Cause Analysis for violator cells based on detecting performance key performance indicators (KPIs) and degradation of KPIs.SUMMARY

[0012] This section is provided to introduce certain aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

[0013] According to an aspect of the present disclosure, a method for performing root cause analysis in a network is disclosed. The method includes receiving, at a transceiver unit, one or more key performance indicators (KPIs) of one or more network cells. Next, the method includes monitoring, by a monitoring unit, the one or more KPIs of the one or more network cells. Next, the method includes identifying, by an identifying unit, one or more low-performing network cells based on a predefined operational threshold of the one or more KPIs stored in a database. Next, the method includes determining, by a processing unit, a time interval of the performance degradation of the one or more KPIs of the one or more low-performing network cells. Next, the method includes identifying, by the identifying unit, one or more root causes for degradation of the one or more KPIs of the one or more low-performing network cells. Thereafter, the method includes generating, by a generating unit, a root cause analysis report for the one or more low- performing network cells, wherein the generation of the root cause analysis report comprises compiling data on at least one of the identified performance degradation, one or more root causes, and proposed remediation steps.

[0014] In an exemplary aspect of the present disclosure, the method includes triggering, by the processing unit, a work order for correcting the identified root cause for the one or more low- performing network cells.

[0015] In an exemplary aspect of the present disclosure, the work order for correcting the root cause comprises at least one of a timeline for remediation, necessary tools required, and resources required to correct the identified root cause for the one or more low-performing network cells.

[0016] In an exemplary aspect of the present disclosure, the method includes automatically updating, by a storing unit, network configuration parameters, based on the identified one or more root causes to prevent future performance degradation.

[0017] In an exemplary aspect of the present disclosure, one or more KPIs comprise at least one of a VoLTE drop rate, a mute call rate, an IP throughput, a cell effective throughput, a handover success rate, a session setup success rate, and an attach signalling failure rate.

[0018] In an exemplary aspect of the present disclosure, the method includes identifying by the identifying unit, via a trained model, a pattern of degradation of the one or more key performance indicators (KPIs) of the one or more low-performing network cells.

[0019] In an exemplary aspect of the present disclosure, the model is trained based on historical data of the one or more KPIs of one or more network cells.

[0020] In an exemplary aspect of the present disclosure, the identifying one or more root causes further comprises: performing, by the processing unit, an analysis of at least one of a set of Configuration Management (CM) based parameters, a set of Fault Management (FM) based parameters, and a set of Performance Management (PM) based parameters for each of the one or more low-performing network cells and corresponding one or more neighbouring cells.

[0021] In an exemplary aspect of the present disclosure, performing the analysis is based on a predefined priority matrix comprising a list of the set of CM-based parameters, the set of FM- based parameters, and the set of PM-based parameters and priority associated with each of the list of the set of CM based parameters, the set of FM based parameters, and the set of PM based parameters.

[0022] In an exemplary aspect of the present disclosure, the analysis is performed at the determined time interval.

[0023] In an exemplary aspect of the present disclosure, the identifying of the one or more low-performing network cells is based on comparing the one or more KPIs against predefined operational threshold for detecting deviation indicating performance degradation.

[0024] According to another aspect of the present disclosure, a system for performing root cause analysis in a network is disclosed. The system comprises a transceiver unit configured to receive one or more key performance indicators (KPIs) of one or more network cells. Further, the system comprises a monitoring unit configured to monitor the one or more KPIs of the one or more network cells. Further, the system comprises an identifying unit configured to identify one or more low-performing network cells based on predefined operational threshold of the one or more KPIs stored in a database. Furthermore, the system comprises a processing unit configured to determine a time interval of the performance degradation of the one or more KPIs of the one or more low- performing network cells. Furthermore, the system comprises the identifying unit further configured to identify one or more root causes for degradation of the one or more KPIs of the one or more low-performing network cells. Furthermore, the system comprises a generating unit configured to generate a root cause analysis report for the one or more low-performing network cells, wherein the generation of the root cause analysis report comprises compiling data on at least one of the identified performance degradation, one or more root causes, and proposed remediation steps.

[0025] According to yet another aspect of the present disclosure, a non-transitory computer-readable storage medium storing for performing root cause analysis in a network, the storage medium comprising executable code which, when executed by one or more units of a system, causes: a transceiver unit to receive one or more key performance indicators (KPIs) of one or more network cells; a monitoring unit to monitor the one or more KPIs of the one or more network cells; an identifying unit to identify one or more low-performing network cells based on predefined operational threshold of the one or more KPIs stored in a database; a processing unit to determine a time interval of the performance degradation of the one or more KPIs of the one or more low-performing network cells; the identifying unit to identify one or more root causes for degradation of the one or more KPIs of the one or more low-performing network cells; and a generating unit to generate a root cause analysis report for the one or more low-performing network cells, wherein the generation of the root cause analysis report comprises compiling data on at least one of the identified performance degradation, one or more root causes, and proposed remediation steps.

[0026] According to yet another aspect of the present disclosure, the identifying of the one or more low-performing network cells is based on comparing the one or more KPIs against predefined operational threshold for detecting deviation indicating performance degradation.DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electronic components or circuitry commonly used to implement such components.

[0028] FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture, in accordance with exemplary embodiment of the present disclosure.

[0029] FIG. 2 illustrates an exemplary block diagram of a computing device upon which an embodiment of the present disclosure may be implemented.

[0030] FIG. 3 illustrates an exemplary block diagram of a system for performing root cause analysis in a network, in accordance with exemplary embodiments of the present disclosure.

[0031] FIG. 4 illustrates an exemplary method flow diagram for performing root cause analysis in a network in accordance with exemplary embodiments of the present disclosure.

[0032] FIG. 5 illustrate an exemplary process flow diagram for performing root cause analysis in a network, in accordance with exemplary embodiments of the present disclosure.

[0033] FIG. 6 illustrate an exemplary process flow diagram for determining best neighbours for the cell, in accordance with exemplary embodiments of the present disclosure.

[0034] The foregoing shall be more apparent from the following detailed description of the disclosure.DESCRIPTION

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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 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.

[0041] As used herein, “a user equipment”, “a user device”, “a smart-user-device”, “a smart-device”, “an electronic device”, “a mobile device”, “a handheld device”, “a wireless communication device”, “a mobile communication device”, “a communication device” may be any electrical, electronic and / or computing device or equipment, capable of 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 at least one of a transceiver unit, a processing unit, a storage unit, a detection unit and any other such unit(s) which are required to implement the features of the present disclosure.

[0042] 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, opticalstorage 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.

[0043] As portable electronic devices and wireless technologies continue to improve and grow in popularity, the advancing wireless technologies for data transfer are also expected to evolve and replace the older generations of technologies. In the field of wireless data communications, the dynamic advancement of various generations of cellular technology are also seen. The development, in this respect, has been incremental in the order of second generation (2G), third generation (3G), fourth generation (4G), and now fifth generation (5G), and more such generations are expected to continue in the forthcoming time.

[0044] Radio Access Technology (RAT) refers to the technology used by mobile devices / user equipment (UE) to connect to a cellular network. It refers to the specific protocol and standards that govern the way devices communicate with base stations, which are responsible for providing the wireless connection. Further, each RAT has its own set of protocols and standards for communication, which define the frequency bands, modulation techniques, and other parameters used for transmitting and receiving data. Examples of RATs include GSM (Global System for Mobile Communications), CDMA (Code Division Multiple Access), UMTS (Universal Mobile Telecommunications System), LTE (Long-Term Evolution), and 5G. The choice of RAT depends on a variety of factors, including the network infrastructure, the available spectrum, and the mobile device's / device's capabilities. Mobile devices often support multiple RATs, allowing them to connect to different types of networks and provide optimal performance based on the available network resources.

[0045] 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 performing root cause analysis in a network. The present disclosure relates to method and system for identification and resolution of poor performing cells in the network. The present system and method provide automated root cause analysis which detects the cells performance problems based on monitoring KPIs with their configured performance threshold and provides remedial action directly for the operation team.

[0046] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings.

[0047] FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture

[0100] , in accordance with exemplary embodiment 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] , a plurality if network functions or network entities such as, an access and mobility management function (AMF)

[0106] , a Session Management Function (SMF) unit

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

[0048] The User Equipment (UE)

[0102] interfaces with the network via the Radio Access Network (RAN)

[0104] ; the Access and Mobility Management Function (AMF)

[0106] manages connectivity and mobility, while the Session Management Function (SMF) unit

[0108] administers session control; the service communication proxy (SCP)

[0110] routes and manages communication between network services, enhancing efficiency and security, and the Authentication Server Function (AUSF)

[0112] handles user authentication; the NSSAAF

[0114] for integrating the 5G core network with existing 4G LTE networks i.e., to enable Non- Standalone (NSA) 5G deployments, the Network Slice Selection Function (NSSF)

[0116] , Network Exposure Function (NEF)

[0118] , and Network Repository Function (NRF)

[0120] enable network customization, secure interfacing with external applications, and maintain network function registries respectively; the Policy Control Function (PCF)

[0122] develops operational policies, and the Unified Data Management (UDM)

[0124] manages subscriber data; the Application Function (AF)

[0126] enables application interaction, the User Plane Function (UPF)

[0128] processes and forwards user data, and the Data Network (DN)

[0130] connects to external internet resources; collectively, these components are designed to enhance mobile broadband, ensure low-latency communication, and support massive machine-type communication, solidifying the 5GC as the infrastructure for next-generation mobile networks.

[0049] 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.

[0050] Access and Mobility Management Function (AMF)

[0106] (alternatively referred to as AMF unit

[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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] Unified Data Management (UDM)

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

[0060] Application Function (AF)

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

[0061] User Plane Function (UPF)

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

[0062] 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.

[0063] FIG. 2 illustrates an exemplary block diagram of a computing device

[0200] (also referred to herein as a computer system

[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 performing rootcause analysis in a network utilising the system. In another implementation, the computing device

[0200] itself implements the method for performing root cause analysis in a network using one or more units configured within computing device

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

[0064] The computing device

[0200] may include a bus

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

[0204] coupled with bus

[0202] for processing information. The 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 stored in 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] ,

[0065] 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 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.

[0066] 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.

[0067] 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, 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

[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.

[0068] 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] , 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.

[0069] The computing device

[0200] encompasses a wide range of electronic devices capable of processing data and performing computations. Examples of computing device

[0200] include, but are not limited only to, personal computers, laptops, tablets, smartphones, servers, andembedded systems. The devices may operate independently or as part of a network and can perform a variety of tasks such as data storage, retrieval, and analysis. Additionally, computing device

[0200] may include peripheral devices, such as monitors, keyboards, and printers, as well as integrated components within larger electronic systems, showcasing their versatility in various technological applications.

[0070] Referring to FIG. 3, an exemplary block diagram of a system

[0300] for performing of root cause analysis in a network is shown, in accordance with the exemplary embodiments of the present invention. The system

[0300] comprises at least one transceiver unit

[0302] , at least one monitoring unit

[0306] , at least one identifying unit

[0308] , at least one processing unit

[0310] , at least one generating unit

[0312] , at least one network

[0316] , at least one network cell

[0304] , and at least one storing unit

[0314] , The network cell

[0304] further comprises network cell 1 [304a], network cell 2 [304b] and network cell 3 [304c], Also, all of 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 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 to implement the features of the present disclosure. The system

[0300] may be a part of the user device / or may be independent of but in communication with the user device (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.

[0071] The system

[0300] is configured for performing root cause analysis in a network, with the help of the interconnection between the components / units of the system

[0300] ,

[0072] The system

[0300] comprises a transceiver unit

[0302] , which is configured to receive one or more key performance indicators (KPIs) of one or more network cells

[0304] , The transceiver unit

[0302] of the system

[0300] may receive the one or more KPIs of the one or more network cells

[0304] , The network cells

[0304] may be, for example, cells of 4G network, 5G network or 6G network, but not limited to, other communication network may also be possible. In an exemplary aspect, the network cell

[0304] further comprises network cell 1 [304a], network cell 2 [304b] andnetwork cell 3 [304c], In an exemplary aspect, the one or more KPIs comprise at least one of a VoLTE drop rate, a mute call rate, an IP throughput, a cell effective throughput, a handover success rate, a session setup success rate, and an attach signalling failure rate. The transceiver unit

[0302] of the system

[0300] may send the received one or more key performance indicators (KPIs) of one or more network cells

[0304] to a monitoring unit

[0306] for the further processing.

[0073] The system

[0300] comprises the monitoring unit

[0306] , which is configured to monitor the one or more KPIs of the one or more network cells

[0304] , After receiving the KPIs data from the transceiver unit

[0302] , the monitoring unit

[0306] of the system

[0300] monitors one or more network cells and their key performance indicators (KPIs) to know operational performance of cells for providing better and efficient services in the communication network. The monitoring unit

[0306] checks cell parameters and KPIs such as, but not limited to Call drop, Backhaul, Outage, Capacity, Interference, Coverage, Quality, IP throughput, Cell effective throughput, Handover success rate, Session setup success rate and Attach Signalling Failure. In an exemplary aspect, the monitoring unit

[0306] may also check or monitor physical parameters in cell such as antenna height, angle of orientation, electrical tilt etc.

[0074] The system

[0300] comprises an identifying unit

[0308] , which is configured to identify one or more low-performing network cells based on predefined operational threshold of the one or more KPIs stored in a database. The identifying unit

[0308] of the system

[0300] may communicatively attach with the monitoring unit

[0306] , The identifying unit

[0308] identifies one or more low-performing network cell based on the monitored key performance indicators (KPIs) values. The identifying unit

[0308] checks the monitored values with the stored threshold values in a database for identifying lower / poor / violating performance network cell. In an exemplary aspect, the predefined threshold values may be categorised based on different severity or optimum / alert levels.

[0075] The identifying unit

[0308] is further configured to identify one or more root causes for degradation of the one or more KPIs of the one or more low-performing network cells. The identifying unit

[0308] is further configured to perform a root cause analysis (RCA) for identifying one or more root causes for degradation of the one or more KPIs. The RCA is the process of discovering the root causes of problems in one or more low performing network cells. The identifying unit

[0308] is further configured to identify, via a trained model, a pattern of degradation of the one or more KPIs of the one or more low-performing network cells. In an exemplary aspectof the present disclosure, the model is trained based on historical data of the one or more KPIs of one or more network cells. As used herein, the historical data is the past performance data of the one or more key performance indicators (KPIs) of one or more network cells that is stored in a database. The historical data may include data related to call drop, backhaul, outage, capacity, interference, coverage, quality, IP throughput, cell effective throughput, handover success rate, session setup success rate and attach signalling failure. The model such as but not limited to machine learning model or a neural network-based model, gets trained on the historical data in order to efficiently identify a pattern of degradation of the one or more KPIs of the one or more low-performing network cells.

[0076] The identifying of the one or more low-performing network cells is based on comparing the one or more KPIs against predefined operational threshold for detecting deviation indicating performance degradation. The identifying unit

[0308] identifies one or more low- performing network cell based on the monitored key performance indicators (KPIs) values. The identifying unit

[0308] checks the monitored values with the stored pre-defined threshold vales for identifying lower / poor / violator performance network cell. In an exemplary aspect, the one or more KPIs are compared with the pre-configured threshold for identifying where and when the problem is occurring. As used herein, the trained model may be such as, but not limited to, a machine learning based model, an artificial intelligence-based model, a neural network-based model, and a decision tree-based model and the like.

[0077] The system

[0300] comprises a processing unit

[0310] , which is configured to determine a time interval of the performance degradation of the one or more KPIs of the one or more low-performing network cells. The processing unit

[0310] may capture and determine time interval for degradation of the one or more key performance indicators (KPIs) of the one or more low-performing network cells. The time interval may be such as, but not limited to daily, weekday, weekend, pre-configured day / time interval / duration and etc. In an exemplary aspect of the present disclosure, the capturing time interval for capturing degradation of network cell may be scheduled / on-demand / real-time basis. The processing unit

[0310] is further configured to trigger a work order for correcting the identified root cause for the one or more low-performing network cells. In an exemplary aspect, if the parameter needs to be changed for correcting the identified root cause for one or more low performing network cells, the respective work order is raised to notify configuration management (CM). Similarly, if any alarm clearance is raised for correcting the identified root cause for one or more low performing network cells, the respective operations team is notified for taking corrective actions. After taking the required actions, the KPIs aremonitored again. As used herein, work order may comprise a network administrator defined sequence or order of tasks for correcting the identified root cause and further may sent for execution on the field by a network service operational team. In an exemplary aspect of the present disclosure, the work order for correcting the root cause comprises at least one of a timeline for remediation, necessary tools required, and resources required to correct the identified root cause for the one or more low-performing network cells. The necessary tools are referred to tools which are used to change the parameters of the one or more low performing network cells in order to rectify the performance one or more low performing network cell. In an exemplary aspect, the necessary tools may also refer to hardware tools which are used to change the hardware settings in order to rectify the performance of the one or more low performing network cell. As used herein, timeline for remediation is a time period to correct the identified root cause for the one or more low-performing network cells. Also used herein, resources required are resources allocated to correct the identified root cause for one or more low performing network cells.

[0078] In an exemplary aspect of the present disclosure, for identifying one or more root causes, the processing unit

[0310] is further configured to perform an analysis of at least one of a set of Configuration Management (CM) based parameters, a set of Fault Management (FM) based parameters, and a set of Performance Management (PM) based parameters for each of the one or more low-performing network cells and corresponding one or more neighbouring cells. As used herein, configuration management includes configuration of site / nodes and management of various parameters related to configuration. It includes changing of parameters, software updates, etc. Also used herein, fault management includes identification and management of hardware faults which can be service affecting / non-service affecting. Further used herein, Performance Management (PM) includes management and storing of key performance metrics of the nodes which is derived generally from the counter statistics. . The analysis is based on a predefined priority matrix comprising a list of the set of CM-based parameters, the set of FM-based parameters, and the set of PM-based parameters and priority associated with each of the list of the set of CM based parameters, the set of FM based parameters, and the set of PM based parameters. The analysis is performed at the determined time interval. The identified one or more root causes are analysed at determined time intervals of performance degradation of the one or more KPIs of the one or more low-performing network cells. The predefined priority matrix may represent priority of actions or tasks required on the basis of analysed parameters. In an exemplary aspect, processing unit

[0310] checks for alarm / outage alarm related information for both serving cell and neighbouring cells for any outage. RCA uses CM, FM and PM parameters for violator cell and its high ranked neighbours at the captured hour based on pre-defined priority to capture the root causeanalysis. In an exemplary aspect, each list of CM parameters includes such as but not limited to list of threshold parameters, utilization parameters etc. Furthermore, each list of FM parameters includes such as but not limited to list of resource parameters, alarm performance degradation parameters, service impacting parameters etc. Also, each list of PM parameters includes such as but not limited to list of KPIs like call drop rate parameters, channel quality parameters, downlink throughput parameters etc.

[0079] The process of obtaining Configuration Management (CM), Fault Management (FM), and Performance Management (PM) parameters includes analysing historical data, including past configurations, fault logs, and performance metrics, to identify impactful parameters. Advanced performance monitoring tools continuously track real-time data on utilization metrics and resource allocations, facilitating identifying key performance indicators (KPIs). A priority matrix is then developed, ranking parameters by their impact on network performance. The parameter list is dynamic, and updates based on continuous feedback from network assessments and new technological developments. Examples of parameters include CM (power levels, frequency settings), FM (alarms for equipment failures, performance degradation), and PM (KPIs like call drop rate, CQI, downlink throughput).

[0080] The system

[0300] comprises a generating unit

[0312] that is configured to generate a root cause analysis report for the one or more low-performing network cells, wherein the generation of the root cause analysis report comprises compiling data on at least one of the identified performance degradation, one or more root causes, and proposed remediation steps. In an exemplary aspect, the compiled data is aggregated data of key performance KPIs such as but not limited to VoLTE drop rate, a mute call rate, an IP throughput, a cell effective throughput, a handover success rate, a session setup success rate, and an attach signalling failure rate. The proposed remediation steps are performed when the performance degradation is identified. After the identification of performance degradation, the remediation steps are performed depending on the type of issues identified. For example, if there is a performance degradation issue in the network hardware, an appropriate action of hardware repair / replacement is performed to rectify the issue. Similarly, if there is a performance degradation issue in some specific network cells then the parameters related to those network cells are changed or adjusted to rectify the issue.

[0081] The generating unit

[0312] automatically generates a root cause analysis report for the one or more low-performing network cells and provides resolution in the form of a detailed report, which is further sent as a work order for execution on field (i.e., work order is applied onthe one or more low performing network cells) for correcting root cause for the one or more low- performing network cells.

[0082] The system

[0300] further comprises a storing unit

[0314] , which is configured to automatically update network configuration parameters, based on the identified one or more root causes to prevent future performance degradation. When the network configuration gets updated based on the identified root cause it prevents future performance degradation by foreseeing performance degradation related equipment failures and network issues before they occur, scheduling maintenance to prevent service disruptions. The network configuration parameters are related to threshold values of configurable parameters. For example, if a particular configuration parameter is found to contribute to frequent call drops or reduced throughput in certain cells, the storing unit

[0314] will adjust this parameter across the affected network cells. The adjustment could involve changing settings related to signal strength, bandwidth allocation, or other critical parameters that impact network performance. The storing unit

[0314] receives network configuration data from the generating unit

[0312] , In an exemplary aspect, the storing unit

[0314] stores threshold values of operational key performance indicators or configured parameters for different services and attributes.

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

[0400] , indicating the method for performing root cause analysis in a network, in accordance with exemplary embodiments of the present disclosure is shown. In an implementation, the method

[0400] is performed by the system

[0300] , As shown in FIG. 4, the method

[0400] starts at step

[0402] ,

[0084] Next, at step

[0404] , the method

[0400] as disclosed by the present disclosure comprises receiving, at a transceiver unit

[0302] , one or more key performance indicators of one or more network cells

[0304] , The transceiver unit

[0302] of the system

[0300] may receive the one or more KPIs of the one or more network cells

[0304] , The one or more KPIs comprise at least one of a VoLTE drop rate, a mute call rate, an IP throughput, a cell effective throughput, a handover success rate, a session setup success rate, and an attach signalling failure rate. The network cells

[0304] may be, for example, cells of 4G network, 5G network or 6G network, but not limited to, other communication network may also be possible. In an exemplary aspect, the network cell

[0304] further comprises network cell 1 [304a], network cell 2 [304b] and network cell 3 [304c], The transceiver unit

[0302] of the system

[0300] may send the received one or more key performance indicators (KPIs) of one or more network cells

[0304] to a monitoring unit

[0306] for the further processing.

[0085] Next, at step

[0406] , the method

[0400] as disclosed by the present disclosure comprises monitoring, by the monitoring unit

[0306] , the one or more key performance indicators of the one or more network cells

[0304] , In an exemplary aspect, the monitoring unit

[0306] monitors one or more network cells and their key performance indicators to know operational performance of cells for providing services in the communication network. After receiving the KPIs data from the transceiver unit

[0302] , the monitoring unit

[0306] of the system

[0300] monitors one or more network cells and their key performance indicators (KPIs) to know operational performance of cells for providing better and efficient services in the communication network. The monitoring unit

[0306] checks cell parameters and KPIs such as, but not limited to Call drop, Backhaul, Outage, Capacity, Interference, Coverage, Quality, IP throughput, Cell effective throughput, Handover success rate, Session setup success rate and Attach Signalling Failure. In an exemplary aspect, the monitoring unit

[0306] may also check or monitor physical parameters in cell such as antenna height, angle of orientation, electrical tilt etc.

[0086] Next, at step

[0408] , the method

[0400] as disclosed by the present disclosure comprises identifying, by an identifying unit

[0308] , one or more low-performing network cells based on predefined operational threshold of the one or more key performance indicators KPIs stored in a database. The identifying unit

[0308] of the system

[0300] may communicatively attach with the monitoring unit

[0306] , In an exemplary aspect, the predefined threshold values may be categorised based on different severity or optimum / alert levels. In an exemplary aspect, the identifying of the one or more low-performing network cells is based on comparing the one or more KPIs against predefined operational threshold for detecting deviation indicating performance degradation. In an event the monitored key performance indicators (KPIs) values associated with a specific network cell exceed the predefined operational threshold, it may be concluded that this specific cell is a low performing network cell. The identifying unit

[0308] identifies one or more low-performing network cell based on the monitored key performance indicators (KPIs) values. The identifying unit

[0308] checks the monitored values with the stored pre-defined threshold values for identifying lower / poor / violator performance network cell.

[0087] Next, at step

[0410] , the method

[0400] as disclosed by the present disclosure comprises determining, by a processing unit

[0310] , a time interval of the performance degradation of the one or more key performance indicators of the one or more low-performing network cells. The processing unit

[0310] may capture and determine time interval for degradation of the one or more key performance indicators (KPIs) of the one or more low-performing network cells. The time interval may be such as, but not limited to daily, weekday, weekend, pre-configured day / timeinterval / duration and etc. In an exemplary aspect of the present disclosure, the time interval for capturing degradation of network cell may be scheduled / on-demand / real-time basis. In an exemplary aspect, a pattern of performance degradation of the identified low performing network cells is also identified based on the monitored KPIs over a period of time / the determined time interval.

[0088] In an exemplary aspect, the method further comprising triggering, by the processing unit

[0310] , a work order for correcting the identified root cause for the one or more low-performing network cells. In an exemplary aspect, if the parameter needs to be changed for correcting the identified root cause for one or more low performing network cells, the respective work order is raised to notify configuration management (CM). Similarly, if any alarm clearance is raised for correcting the identified root cause for one or more low performing network cells, the respective operations team is notified for taking corrective actions. After taking the required actions, the KPIs are monitored again. Furthermore, the work order for correcting the root cause comprises at least one of a timeline for remediation, necessary tools required, and resources required to correct the identified root cause for the one or more low-performing network cells. The identifying one or more root causes further comprises performing, by the processing unit

[0310] , an analysis of at least one of a set of Configuration Management (CM) based parameters, a set of Fault Management (FM) based parameters, and a set of Performance Management (PM) based parameters for each of the one or more low-performing network cells and corresponding one or more neighbouring cells. Furthermore, performing the analysis is based on a predefined priority matrix comprising a list of the set of CM-based parameters, the set of FM-based parameters, and the set of PM-based parameters and priority associated with each of the list of the set of CM based parameters, the set of FM based parameters, and the set of PM based parameters. The analysis is performed at the determined time interval. The predefined priority matrix may represent priority of actions or tasks required on the basis of analysed parameters.

[0089] In an exemplary aspect, processing unit

[0310] checks for alarm / outage alarm related information for both serving cell and neighbouring cells for any outage. In an exemplary aspect, RCA uses CM, FM and PM parameters for violator cell and its high ranked neighbours at the captured hour based on pre-defined priority to capture the root cause analysis. In an exemplary aspect, each list of CM parameters includes such as but not limited to list of threshold parameters, utilization parameters etc. Furthermore, each list of FM parameters includes such as but not limited to list of resource parameters, alarm performance degradation parameters, service impactingparameters etc. Also, each list of PM parameters includes such as but not limited to list of KPIs like call drop rate parameters, channel quality parameters, downlink throughput parameters etc.

[0090] Next, at step

[0412] , the method

[0400] as disclosed by the present disclosure comprises identifying, by the identifying unit

[0308] , one or more root causes for degradation of the one or more key performance indicators KPIs of the one or more low-performing network cells. The identifying unit

[0308] identifies a pattern of degradation of the one or more key performance indicators of the one or more low-performing network cells based on the captured time interval, such as, daily, weekday, weekend, on-demand, etc. In an exemplary aspect, the method further comprises identifying, by the identifying unit

[0308] , via a trained model, a pattern of degradation of the one or more KPIs of the one or more low-performing network cells. The model is trained based on historical data of the one or more KPIs of one or more network cells. As used herein, the trained model may be such as, but not limited to, a machine learning based model, an artificial intelligence-based model, a neural network-based model, and a decision tree-based model and the like.

[0091] Next, at step

[0414] , the method

[0400] as disclosed by the present disclosure comprises generating, by a generating unit

[0312] , a root cause analysis report for the one or more low-performing network cells. The generation of the root cause analysis report comprises compiling data on at least one of the identified performance degradation, one or more root causes, and proposed remediation steps. The generating unit

[0312] automatically generates a root cause analysis report for the one or more low-performing network cells and provides resolution in the form of a detailed report, which is further sent as a work order for execution on field (i.e. work order is applied on the one or more low performing network cells) for correcting root cause for the one or more low-performing network cells.

[0092] In an aspect of the present of the present disclosure, the system

[0300] comprises a storing unit

[0314] , which is configured to automatically update network configuration parameters, based on the identified one or more root causes to prevent future performance degradation. When the network configuration gets updated based on the identified root cause it prevents future performance degradation by foreseeing performance degradation related equipment failures and network issues before they occur, scheduling maintenance to prevent service disruptions. The network configuration parameters are related to threshold values of configurable parameters. The storing unit

[0314] receives network configuration data from the generating unit

[0312] , In anexemplary aspect, the storing unit

[0314] stores threshold values of operational key performance indicators or configured parameters for different services and attributes.

[0093] Thereafter, the method

[0400] terminates at step

[0416] ,

[0094] Referring to FIG. 5, an exemplary process

[0500] flow diagram, indicating the process

[0500] for performing root cause analysis in a network, in accordance with exemplary embodiments of the present disclosure is shown. In an implementation, the process

[0500] is performed by the system

[0300] , As shown in FIG. 5 the process

[0500] starts at step

[0502] ,

[0095] At step

[0504] , the process

[0500] involves calculating aggregate of one or more key KPIs for each network cell and filter cells based on violation of threshold values for these KPIs. The one or more KPIs include such as but not limited to at least VoLTE drop rate, muting call rate, IP throughput, cell effective throughput, mobility handover success rate (HOSR), session set up success rate, attach failure signalling rate etc.

[0096] At step

[0506] , the process

[0500] involves checking hourly the values of one or more KPIs of the one or more network cells which are violating the threshold values.

[0097] At step

[0508] , the process

[0500] involves checking whether one or more KPIs related to one or more network cells violating the threshold throughout the day.

[0098] At step

[0510] , if one or more KPIs related to one or more network cells violate the threshold though out the day then the process

[0500] involves:• Checking one or more KPI values of the one or more network cell hourly, for previous day and• Flagging the first hour as the time of issue when one or more KPIs related to one or more network cells violate the threshold. In an aspect, details such as but not limited to date and time, of the flagged issue are stored in the storing unit

[0314] ,

[0099] At step

[0512] , if one or more KPIs related to one or more network cells does not violate the threshold though out the day then the process

[0500] involves checking whether the one or more KPIs violates threshold from one particular hour and continuing till the end of the day, if one or more KPI of the one or more network cells does not violate the pre-defined threshold though out the day.[000100] At step

[0514] , if the KPIs violate the predefined threshold from one particular hour and continue until the end of the day, the process

[0500] involves checking the first hour of the day during which the issue(s) have cropped up, if one or more KPIs violates pre-defined threshold from one particular hour, continuing till the end of the day. In an exemplary aspect, the details such as but not limited to date and time, of the first hour of the day when the issue first occurred are stored in a storing unit

[0314] ,[000101] At step

[0516] , the process

[0500] involves checking the worst hour of the day, if one or more KPIs does not violate pre-defined threshold from one particular hour and continuing till the end of the day. In an exemplary aspect, details such as but not limited date and time, of worst hour of the day is stored in the storing unit

[0314] ,[000102] At step

[0518] , the process

[0500] involves checking the pattern of issues in the past 15 days with a buffer of + / - 1 hour of the first hour.[000103] At step

[0520] , the process

[0500] involves storing, in the storing unit

[0314] , the pattern for example, daily, weekdays, weekends, particular day of the week etc.[000104] At step

[0522] , the process

[0500] involves storing, in the storing unit

[0314] , the date and time for each one or more network cells.[000105] At step

[0524] , the process

[0500] involves checking configuration management (CM) parameters change, fault management (FM) parameter (traps), performance management (PM) parameters for one or more network cells which are violating threshold value and its high ranked neighbour as per the resolution matrix. The resolution matrix refers to a structured framework used within network management systems to prioritize and resolve network issues effectively. The resolution matrix comprises various parameters and metrics related to network performance, including configuration management (CM) parameters, fault management (FM) parameters (traps), and performance management (PM) parameters.[000106] Referring to FIG. 6, an exemplary process

[0600] flow diagram, indicating the process

[0600] for determining best neighbours for the cell, in accordance with exemplary embodiments of the present disclosure is shown. In an implementation, the process

[0600] is performed by the system

[0300] ,[000107] At step

[0602] , the process

[0600] involves capturing the high ranked neighbour based on the best neighbour logic.[000108] At step

[0604] , the process

[0600] involves capturing the list of unique target cells in a span of 7 days for each source and calculating total handover (HO) attempts (intra + X2+ SI) with each target cell in 7 days.[000109] At step

[0606] , the process

[0600] involves calculating the percentage of handover (HO) attempts of each target cell = (HO attempts with each target cells* 100 (Total HO attempts) and capture the list of target cells with HO attempts > 10% which will be the best neighbours for that cell.[000110] In an aspect of the present of the present disclosure, a non-transitory computer- readable storage medium storing for performing root cause analysis in a network, the storage medium comprising executable code which, when executed by one or more units of a system

[0300] , causes: a transceiver unit

[0302] to receive one or more key performance indicators (KPIs) of one or more network cells

[0304] ; a monitoring unit

[0306] to monitor the one or more KPIs of the one or more network cells

[0304] ; an identifying unit

[0308] to identify one or more low-performing network cells based on predefined operational threshold of the one or more KPIs stored in a database; a processing unit

[0310] to determine a time interval of the performance degradation of the one or more KPIs of the one or more low-performing network cells; the identifying unit

[0308] to identify one or more root causes for degradation of the one or more KPIs of the one or more low-performing network cells; and a generating unit

[0312] to generate a root cause analysis report for the one or more low-performing network cells, wherein the generation of the root cause analysis report comprises compiling data on at least one of the identified performance degradation, one or more root causes, and proposed remediation steps. Further, the identifying of the one or more low-performing network cells is based on comparing the one or more KPIs against predefined operational threshold for detecting deviation indicating performance degradation.[000111] As is evident from the above, the present disclosure provides a technically advanced solution for providing automated root cause analysis for identifying violator cell(s) in 4G / 5G / 6G mobile network system. The present disclosure provides a method and system for identifying the cells which cross KPIs thresholds such as daily and drills down to the granularity of an hour to capture the time of KPI degradation. The present solution identifies the pattern of the degraded KPIs i.e., daily, weekday, weekend, etc. The present solution checks for configuration parameterschange, incident(s) of the outage alarm and performance KPIs of the violator cells as well as its neighbour cells through an extensive resolution matrix and provides resolution in the form of a detailed report, which is further sent as a work order for execution on field. The present solution with route cause analysis helps in better tracking and speedy resolution of violator cells, thus maintaining a healthy network.[000112] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various the components / units can be implemented interchangeably. While specific embodiments may disclose a particular functionality of these units for clarity, it is recognized that various configurations and combinations thereof are within the scope of the disclosure. The functionality of specific units as disclosed in the disclosure should not be construed as limiting the scope of the present disclosure. Consequently, alternative arrangements and substitutions of units, provided they achieve the intended functionality described herein, are considered to be encompassed within the scope of the present disclosure.[000113] While considerable emphasis has been placed herein on the disclosed embodiments, it will be appreciated that many embodiments can be made and that many changes can be made to the embodiments without departing from the principles of the present disclosure. These and other changes in the embodiments of the present disclosure will be apparent to those skilled in the art, whereby it is to be understood that the foregoing descriptive matter to be implemented is illustrative and non-limiting.

Claims

We Claim:

1. A method for performing root cause analysis in a network, the method comprising: receiving, at a transceiver unit [302], one or more key performance indicators (KPIs) of one or more network cells [04]; monitoring, by a monitoring unit [306], the one or more KPIs of the one or more network cells [304]; identifying, by an identifying unit [308], one or more low-performing network cells based on predefined operational threshold of the one or more KPIs stored in a database; determining, by a processing unit [310], a time interval of performance degradation of the one or more KPIs of the one or more low-performing network cells; identifying, by the identifying unit [308], one or more root causes for performance degradation of the one or more KPIs of the one or more low-performing network cells; and generating, by a generating unit [312], a root cause analysis report for the one or more low- performing network cells, wherein the generation of the root cause analysis report comprises compiling data on at least one of identified performance degradation, one or more root causes, and proposed remediation steps.

2. The method as claimed in claim 1, the method further comprising triggering, by the processing unit [310], a work order for correcting the identified one or more root causes for the one or more low-performing network cells.

3. The method as claimed in claim 2, wherein the work order for correcting the one or more root causes comprise at least one of a timeline for remediation, necessary tools required, and resources required to correct the identified one or more root causes for the one or more low- performing network cells.

4. The method as claimed in claim 1, further comprising automatically updating, by a storing unit [314], network configuration parameters, based on the identified one or more root causes to prevent future performance degradation.

5. The method as claimed in claim 1, wherein the one or more KPIs comprise at least one of a VoLTE drop rate, a mute call rate, an IP throughput, a cell effective throughput, a handover success rate, a session setup success rate, and an attach signalling failure rate.

6. The method, as claimed in claim 1, wherein the method comprises identifying, by the identifying unit [310], via a trained model, a pattern of degradation of the one or more KPIs of the one or more low-performing network cells.

7. The method as claimed in claim 6, wherein the model is trained based on historical data of the one or more KPIs of one or more network cells [304],8. The method as claimed in claim 1, wherein the identifying one or more root causes further comprises performing, by the processing unit [310], an analysis of at least one of a set of Configuration Management (CM) based parameters, a set of Fault Management (FM) based parameters, and a set of Performance Management (PM) based parameters for each of the one or more low-performing network cells and corresponding one or more neighbouring cells.

9. The method as claimed in claim 8, wherein performing the analysis is based on a predefined priority matrix comprising a list of the set of CM-based parameters, the set of FM-based parameters, and the set of PM-based parameters and priority associated with each of the list of the set of CM based parameters, the set of FM based parameters, and the set of PM based parameters.

10. The method as claimed in claim 8, wherein the analysis is performed at the determined time interval.

11. The method as claimed in claim 1, wherein the identifying of the one or more low- performing network cells is based on comparing the one or more KPIs against predefined operational threshold for detecting deviation indicating performance degradation.

12. A system [300] for performing root cause analysis in a network, the system comprising: a transceiver unit [302] configured to receive one or more key performance indicators (KPIs) of one or more network cells [304];a monitoring unit [306] configured to monitor the one or more KPIs of the one or more network cells [304]; an identifying unit [308] configured to identify one or more low-performing network cells based on predefined operational threshold of the one or more KPIs stored in a database; a processing unit [310] configured to determine a time interval of performance degradation of the one or more KPIs of the one or more low-performing network cells; the identifying unit [308] further configured to identify one or more root causes for performance degradation of the one or more KPIs of the one or more low-performing network cells; and a generating unit [312] configured to generate a root cause analysis report for the one or more low- performing network cells, wherein the generation of the root cause analysis report comprises compiling data on at least one of identified performance degradation, one or more root causes, and proposed remediation steps.

13. The system as claimed in claim 12, the processing unit [310] is further configured to trigger a work order for correcting the identified one or more root causes for the one or more low- performing network cells.

14. The system as claimed in claim 13, wherein the work order for correcting the one or more root cause comprise at least one of a timeline for remediation, necessary tools required, and resources required to correct the identified one or more root causes for the one or more low- performing network cells.

15. The system as claimed in claim 12, wherein the system [300] comprises a storing unit [314] configured to automatically update network configuration parameters, based on the identified one or more root causes to prevent future performance degradation.

16. The system as claimed in claim 12, wherein the one or more KPIs comprise at least one of a VoLTE drop rate, a mute call rate, an IP throughput, a cell effective throughput, a handover success rate, a session setup success rate, and an attach signalling failure rate.

17. The system as claimed in claim 12, wherein the identifying unit [308] is further configured to identify, via a trained model, a pattern of degradation of the one or more KPIs of the one or more low-performing network cells.

18. The system as claimed in claim 17, wherein the model is trained based on historical data of the one or more KPIs of one or more network cells [304],19. The system as claimed in claim 12, wherein for identifying one or more root causes further comprises the processing unit [310] is configured to perform an analysis of at least one of a set of Configuration Management (CM) based parameters, a set of Fault Management (FM) based parameters, and a set of Performance Management (PM) based parameters for each of the one or more low-performing network cells and corresponding one or more neighbouring cells.

20. The system as claimed in claim 19, wherein performing the analysis is based on a predefined priority matrix comprising a list of the set of CM-based parameters, the set of FM- based parameters, and the set of PM-based parameters and priority associated with each of the list of the set of CM based parameters, the set of FM based parameters, and the set of PM based parameters.

21. The system as claimed in claim 19, wherein the analysis is performed at the determined time interval.

22. The system as claimed in claim 12, wherein the identifying of the one or more low- performing network cells is based on comparing the one or more KPIs against predefined operational threshold for detecting deviation indicating performance degradation.

23. A non-transitory computer-readable storage medium storing for performing root cause analysis in a network, the storage medium comprising executable code which, when executed by one or more units of a system, causes: a transceiver unit [302] to receive one or more key performance indicators (KPIs) of one or more network cells [304]; a monitoring unit [306] to monitor the one or more KPIs of the one or more network cells [304]; an identifying unit [308] to identify one or more low-performing network cells based on predefined operational threshold of the one or more KPIs stored in a database; a processing unit [310] to determine a time interval of performance degradation of the one or more KPIs of the one or more low-performing network cells;the identifying unit [308] to identify one or more root causes for performance degradation of the one or more KPIs of the one or more low-performing network cells; and a generating unit [312] to generate a root cause analysis report for the one or more low-performing network cells, wherein the generation of the root cause analysis report comprises compiling data on at least one of identified performance degradation, one or more root causes, and proposed remediation steps.

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