DESC:
FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. TITLE OF THE INVENTION
SYSTEM AND METHOD FOR DETECTING ABNORMALITIES IN A NETWORK

2. APPLICANT(S)
NAME NATIONALITY ADDRESS
JIO PLATFORMS LIMITED INDIAN OFFICE-101, SAFFRON, NR. CENTRE POINT, PANCHWATI 5 RASTA, AMBAWADI, AHMEDABAD 380006, GUJARAT, INDIA
3.PREAMBLE TO THE DESCRIPTION

THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE NATURE OF THIS INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED.

FIELD OF THE INVENTION
[0001] The present invention relates to the field of telecommunications and network management, more particularly relates to a method and system for detecting abnormalities in a network.
BACKGROUND OF THE INVENTION
[0002] The development and deployment of 5G networks have ushered in a new era of telecommunications, providing significantly increased bandwidth, reduced latency, and enhanced connectivity for a wide range of applications. 5G networks are designed to support diverse services, including high-speed data transfer, IoT (Internet of Things) devices, autonomous vehicles, augmented reality, and more.
[0003] A typical 5G network consists of several components, including base stations, user equipment (UE), core network elements, and various network management systems. Base stations, also known as gNBs (Next Generation NodeBs), serve as access points to connect user devices with the network infrastructure. These base stations are responsible for transmitting and receiving data, managing network resources, and ensuring reliable communication within their coverage areas.
[0004] Network operators face significant challenges in maintaining optimal network coverage and performance due to the complex and dynamic nature of 5G networks. The large-scale deployment of base stations across vast geographical areas, the diverse radio frequency spectrum, and the varying environmental conditions all contribute to potential coverage gaps, interference, and suboptimal network performance.
[0005] Existing network management techniques often rely on manual monitoring, extensive hardware infrastructure, and time-consuming troubleshooting processes. These traditional approaches make it challenging to detect and address network issues promptly, resulting in degraded user experiences and increased operational costs for network operators.
[0006] The need exists for an efficient and automated solution that can proactively detect and optimize network coverage in 5G systems. There is a demand for a technique that can mitigate the complexities associated with traditional network monitoring approaches, reduce the dependency on extensive hardware resources and manpower, and enable early detection of network issues to prevent service degradation.
[0007] It is desirable to develop an AI/ML based approach that leverages advanced algorithms and machine learning models to analyse network parameters, identify patterns, and detect deviations from the expected behaviour. By harnessing the power of AI/ML, network operators can gain valuable insights into network performance, predict potential issues, and take proactive measures to optimize coverage, enhance service quality, and improve the overall user experience.
[0008] Therefore, there is a need for an invention that provides an AI/ML based network coverage analysis, monitoring, and optimization solution, which overcomes the limitations of the existing art and enables efficient management of 5G networks with reduced complexity, improved resource utilization, and enhanced network performance.
SUMMARY OF THE INVENTION
[0009] One or more embodiments of the present invention provides a method and a system for monitoring a network.
[0010] In one aspect of the present invention, a method for detecting abnormalities in a network is provided. The method includes the step of receiving one or more requests from a user via a User Interface (UE) for training a model. The method further includes the step of retrieving data pertaining to network parameters from a database in response to receipt of the one or more requests. The method further includes the step of training a model utilizing the retrieved data. The method further includes the step of detecting abnormalities in the network utilizing the trained model. Further, the method includes notifying the user in real time regarding the detected abnormalities in the network.
[0011] In an embodiment, the one or more requests includes user inputs for at least one of, training the model and monitoring network coverage.

[0012] In an embodiment, the model continuously learns patterns and behavior pertaining to the network parameters of the retrieved data.
[0013] In an embodiment, at the step of detecting abnormalities in the network utilizing the trained model, includes the steps of monitoring utilizing the trained model the patterns and behavior of incoming data pertaining to the various network parameters stored in the database. The method further includes the step of applying by utilizing the trained model a logic to the incoming data while monitoring. Further in response to applying the logic detecting utilizing the trained model, the abnormalities in the network when there is a deviation in at least one of, the patterns and behavior of incoming data in comparison with the learned patterns and behavior of the retrieved data.
[0014] In an embodiment, the one or more notifications corresponds to a network health, a subscriber usage, and an overall service experience.
[0015] In another aspect of the present invention, the system for detecting abnormalities in a network is provided. The system includes the receiving unit configured to receive one or more requests from a user via a User Interface (UE) for training a model. The system further includes a retrieving unit configured to retrieve data pertaining to network parameters from a database in response to receipt of the one or more requests. Further the system includes a training unit configured to train a model utilizing the retrieved data. Further the system includes a detection unit configured to detect abnormalities in the network utilizing the trained model. Further the system includes a notification unit configured to notify the user in real time regarding the detected abnormalities in the network.
[0016] In yet another aspect of the invention, a non-transitory computer-readable medium having stored thereon computer-readable instructions is disclosed. The computer-readable instructions are executed by a processor. The processor is configured receive one or more requests received from a user via a User Interface (UE) for training a model, retrieve data pertaining to network parameters from a database in response to receipt of the one or more requests, train a model utilizing the retrieved data, detect abnormalities in the network utilizing the trained model and notify the user in real time regarding the detected abnormalities in the network.
[0017] In another aspect of the present invention, a User Equipment (UE) is disclosed. One or more primary processors communicatively coupled to one or more processors. The one or more primary processors are coupled with a memory. The memory stores instructions which when executed by the one or more primary processors causes the UE to transmit, one or more requests from a user to the one or more processors for training a model. Other features and aspects of this invention will be apparent from the following description and the accompanying drawings. The features and advantages described in this summary and in the following detailed description are not all-inclusive, and particularly, many additional features and advantages will be apparent to one of ordinary skill in the relevant art, in view of the drawings, specification, and claims hereof. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes and may not have been selected to delineate or circumscribe the inventive subject matter, resort to the claims being necessary to determine such inventive subject matter.
BRIEF 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 methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[0019] FIG. 1 is an exemplary block diagram of a communication system for detecting abnormalities in a network, according to one or more embodiments of the present disclosure;
[0020] FIG. 2 is an exemplary system diagram for detecting abnormalities in a network, according to one or more embodiments of the present disclosure;
[0021] FIG. 3 is a schematic representation of a workflow of the system of FIG. 2 communicably coupled with a User equipment (UE), according to one or more embodiments of the present disclosure;
[0022] FIG. 4 is an exemplary block diagram of an architecture of the system of the FIG. 2, according to one or more embodiments of the present disclosure;
[0023] FIG. 5 is a signal flow diagram for detecting abnormalities in a network, according to one or more embodiments of the present disclosure; and
[0024] FIG. 6 is a flow chart illustrating a method for detection of abnormalities in a network, according to one or more embodiments of the present disclosure.
[0025] The foregoing shall be more apparent from the following detailed description of the invention.

DETAILED DESCRIPTION OF THE INVENTION
[0026] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise.
[0027] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure including the definitions listed here below are not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
[0028] A person of ordinary skill in the art will readily ascertain that the illustrated steps detailed in the figures and here below are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0029] The present disclosure addresses the challenges faced in established technologies, where huge hardware infrastructure and manpower is required to monitor, track and correct the network systems. Artificial Intelligence and Machine Learning (AIML) solution reduces the complexity in several levels, which includes but not limited to, less hardware, less manpower, and facilitates to detect the issue in early stages. The present invention tracks the pattern and behavior of various network parameters and identifies if any change or deviation from the original pattern in last N days is required and notifies the end user, thereby contributing to a seamless and enhanced user experience in the 5G ecosystem.
[0030] Referring to FIG. 1, FIG. 1 illustrates an exemplary block diagram of a communication system 100 for detecting abnormalities in a network, according to one or more embodiments of the present disclosure. The communication system 100 includes a network 105, a User Equipment (UE) 110, a server 115, and a system 120. The UE 110 aids a user to interact with the system 120. In an embodiment, the UE 110 is one of, but not limited to, any electrical, electronic, electro-mechanical or an equipment and a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device.
[0031] For the purpose of description and explanation, the description will be explained with respect to the UE 110, or to be more specific will be explained with respect to a first UE 110a, a second UE 110b, and a third UE 110c, and should nowhere be construed as limiting the scope of the present disclosure. Each of the first UE 110a, the second UE 110b, and the third UE 110c is configured to connect to the server 115 via the network 105. As per the illustrated embodiment, the communication system 100 includes one or more base stations 125. In alternate embodiments, the UE 110 may include a plurality of UEs as per the requirement. For ease of reference, each of the first UE 110a, the second UE 110b, and the third UE 110c, will hereinafter be collectively and individually referred to as the “User Equipment (UE) 110”.
[0032] Further, the communication system 100 includes a base station 125. For the purpose of description and explanation, the description will be explained with respect to one or more base stations 125, or to be more specific will be explained with respect to a first base station 125a, a second base station 125b, and a third base station 125c, and should nowhere be construed as limiting the scope of the present disclosure. For ease of reference, each of the first base station 125a, the second base station 125b, and the third base station 125c, will hereinafter be collectively and individually referred to as the “base station 125”.
[0033] The first base station 125 includes, by way of example but not limitation, a cell site, cell phone tower, or cellular base station. Each of the first base station 125a, the second base station 125b, and the third base station 125c is a cellular-enabled mobile device site where antennas and electronic communications equipment are placed (typically on a radio mast, tower, or other raised structure) to create a cell, or adjacent cells, in the communication network. The structure typically supports an antenna and one or more sets of transmitters/receivers, digital signal processors, control electronics, a GPS receiver for timing, primary and backup electrical power sources, and sheltering.
[0034] The network 105 may include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network 105 may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof.
[0035] The network 105 includes, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. The network 105 may include, but is not limited to, a Third Generation (3G), a Fourth Generation (4G), a Fifth Generation (5G), a Sixth Generation (6G), a New Radio (NR), a Narrow Band Internet of Things (NB-IoT), an Open Radio Access Network (O-RAN), and the like.
[0036] The network 105 may also include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network 105 may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, a VOIP or some combination thereof.
[0037] The communication system 100 includes the server 115 accessible via the network 105. The server 115 may include by way of example but not limitation, one or more of a standalone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof. In an embodiment, the entity may include, but is not limited to, a vendor, a network operator, a company, an organization, a university, a lab facility, a business enterprise side, a defense facility side, or any other facility that provides service.
[0038] The communication system 100 further includes the system 120 communicably coupled to the server 115 and the UE 110 via the network 105. The system 120 is adapted to be embedded within the server 115 or is embedded as the individual entity. However, for the purpose of description, the system 120 is illustrated as remotely coupled with the server 115, without deviating from the scope of the present disclosure.
[0039] Operational and construction features of the system 120 will be explained in detail with respect to the following figures.
[0040] FIG. 2 illustrates an exemplary block diagram of the system 120 for detecting abnormalities in a network, according to one or more embodiments of the present disclosure.
[0041] As per the illustrated embodiment, the system 120 includes one or more processors 205, a memory 210, a user interface 215 and a database 220. For the purpose of description and explanation, the description will be explained with respect to one processor 205 and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the system 120 may include more than one processor 205 as per the requirement of the network. The one or more processors 205, hereinafter referred to as the processor 205 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions.
[0042] As per the illustrated embodiment, the processor 205 is configured to fetch and execute computer-readable instructions stored in the memory 210. The memory 210 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium which may be fetched and executed to display the enriched data to the user via the user interface in order to perform analysis. The memory 210 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.
[0043] In an embodiment, the user interface 215 includes a variety of interfaces, for example, interfaces for data input and output devices, referred to as Input/Output (I/O) devices, storage devices, and the like. The user interface 215 facilitates communication of the system 120. In one embodiment, the user interface 215 provides a communication pathway for one or more components of the system 120.
[0044] In an embodiment, the database 220 is one of, but not limited to, a centralized database, a cloud-based database, a commercial database, an open-source database, a distributed database, an end-user database, a graphical database, a No-Structured Query Language (NoSQL) database, an object-oriented database, a personal database, an in-memory database, a document-based database, a time series database, a wide column database, a key value database, a search database, a cache databases, and so forth. The foregoing examples of database 220 types are non-limiting and may not be mutually exclusive e.g., the database can be both commercial and cloud-based, or both relational and open-source, etc.
[0045] In order for the system 120 to detect abnormalities in a network , the processor 205 includes one or more modules. In one embodiment, the one or more modules includes, but not limited to, a receiving unit 225, a retrieving unit 230, a training unit 235, a detection unit 240, and a notification unit 245.
[0046] The receiving unit 225, the retrieving unit 230, the training unit 235, the detection unit 240 and the notification unit 245 in an embodiment, may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 205. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 205 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for processor 205 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 210 may store instructions that, when executed by the processing resource, implement the processor 205. In such examples, the system 120 may comprise the memory 210 storing the instructions and the processing resource to execute the instructions, or the memory 210 may be separate but accessible to the system 120 and the processing resource. In other examples, the processor 205 may be implemented by electronic circuitry.
[0047] In one embodiment, the receiving unit 225 is configured to receive, one or more requests from the user via the User Interface (UE) 110 for training a model. The one or more requests includes user inputs for at least one of, training the model and monitoring network coverage. The network coverage refers to the geographical area, where the radio signals are consistently available with adequate signal strength, and a quality to support various services.
[0048] Upon receiving the request from the user, the retrieving unit 230is configured to retrieve data pertaining to network parameters from a database 220 in response to receipt of the one or more requests. In an embodiment, the network parameters include, but are not limited to frequency bands, latency, throughput, quality of service, beamforming, network slicing, security protocols. Upon retrieving the data pertaining to the network parameters from the database 220, the training unit 235 is configured to train the model utilizing the retrieved data. In one embodiment, to train the model, the training unit 235 continuously learns patterns and behaviors pertaining to the network parameters of the retrieved data.
[0049] Further, the system 120 includes the detection unit 240 configured to detect the abnormalities, such as sudden drop in RF coverage, or network related issues etc. in the network 105 utilizing the trained model. The detection unit 240 monitors utilizing the trained model patterns and behavior of the incoming data pertaining to the various network parameters stored in the database 220. The incoming data refers to the data received by the receiving unit 225 form the at least one network node. The at least one network node may include but not limited to, gNodeBs, access gateway, policy control function, session management. In one embodiment, the AI/MLmodel can include, but not limited to, AI/ML techniques and AI/ML algorithms for data monitoring and training. Further the detection unit 240 is configured to apply a logic to the incoming data by utilizing the trained model, during the monitoring of the patterns and behavior of the incoming data pertaining to the various network. Further in response to applying the logic to the incoming data, the detection unit 240 is configured to detect the abnormalities in the network 105, when there is a deviation in at least one of, the patterns and behavior of the incoming data in comparison with the learnt patterns and behavior of the retrieved data.
[0050] Upon detecting the abnormalities in the network 105, the notification unit 245 is configured to notify the user in real time regarding the detected abnormalities in the network 105. The notifications include but are not limited to, RF planning, antenna tilt, network health, a subscriber usage, and an overall service experience. The subscriber usage includes, but not limited to, high speed data consumption, low latency applications, IOT connectivity, enhanced mobile experiences.
[0051] FIG. 3 describes an embodiment of the system 120 of FIG. 2, according to various embodiments of the present invention. It is to be noted that the embodiment with respect to FIG. 3 will be explained with respect to the first UE 110a and the system 120 for the purpose of description and illustration and should nowhere be construed as limited to the scope of the present disclosure.
[0052] As mentioned earlier in FIG. 1, the UE 110 may include an external storage device, a bus, a main memory, a read-only memory, a mass storage device, communication port(s), and a processor. The exemplary embodiment as illustrated in FIG. 3 will be explained with respect to the first UE 110a without deviating from the scope of the present disclosure and limiting the scope of the present disclosure. The first UE 110a includes one or more primary processors 305 communicably coupled to the one or more processors 205 of the system 120.
[0053] The one or more primary processors 305 are coupled with a memory 310 storing instructions which are executed by the one or more primary processors 305. Execution of the stored instructions by the one or more primary processors 305 enables the first UE 110a to transmit one or more requests from the user to the one or more processors for training a model. The one or more requests includes user inputs for at least one of, training the model and monitoring network coverage.
[0054] As per the illustrated embodiment, the system 120 includes the one or more processors 205, the memory 210, the user interface 215, and the database 220. The operations and functions of the one or more processors 205, the memory 210, the user interface 215, and the database 220 are already explained in FIG. 2. For the sake of brevity, a similar description related to the working and operation of the system 120 as illustrated in FIG. 2 has been omitted to avoid repetition.
[0055] FIG.4 is an exemplary architecture 400 which can be deployed in the system 120 for detecting abnormalities in a network, according to one or more embodiments of the present invention. The exemplary embodiment as illustrated in the FIG. 4 includes one or more GnodesBs 405, a probing agent 410, a conductor 415, a message broker unit 420, a normalizer 425, an Artificial Intelligence Data Records (AIDR) writer 430, a GUI 435, a workflow 440, the database, 220, an ingestion layer 445, a distributed file system 450, an Artificial Intelligence (AI)/Machine Learning (ML) module 455 and a Fulfilment Management System (FMS) 460. In an embodiment, one or more GnodeBs 405 serves as the primary data source in the network 105. Further the one or more GnodeBs can have different software versions. The one or more GnodeBs 405 is configured to generate call summary logs. The call summary logs refer to essential information about user sessions in the network 105. The information may include, but is not limited to, signal strength, network congestion, latency, and other Key Performance Indicators (KPIs). Further the GnodeBs 405 transmits the generated call summary logs to the probing agent 410 via a Transmission Control Protocol (TCP). The TCP refers to networking protocol configured to transmit the data packets between the GnodeBs 405 and the probing agent 410 over the network 105.
[0056] Upon receiving the call summary data generated by the one or more GnodeBs 405 of different versions via the TCP, the probing agent 410 validates and segregates the received data based on the version of the GnodeBs 405. The call summary data from the one or more GnodeBs 405 is received as hexadecimal dump stream over TCP. Further the probing agent 410 consumes the call summary data byte by byte and stores it in the message broker unit 420.
[0057] Upon validating and segregating the call summary data, the probing agent 410 transmits it to the conductor 415. The conductor 415 ingests and decodes the call summary data of multiple versions received from the probing agent 410 before feeding it to the message broker unit 420. The conductor 415 is configured as the customized decoder component in the architecture 400. In an embodiment the conductor 415 operates on byte indices to decode the call summary data. The conductor 415 features configurable versions that facilitates adjustments to accommodate changes in the length and position of fields within the decoded data. The configurations are illustrated by the user via the GUI 435, facilitating the accessibility for the user. Further by handling the different data versions, the conductor 415 facilitates seamless processing and compatibility across the system 120.
[0058] The collected call summary data from the probing agent 410 and the decoded data from the conductor 415 are securely stored in the message broker unit 420. The message broker unit 420 is configured as the publisher subscriber service to store the data. The message broker unit 420 act as an intermediary for data transmission and storage.
[0059] In one embodiment, the architecture 400 includes the normalizer 425. The normalizer 425 receives the decoded data from the message broker unit 420. The normalizer 425 performs real time enrichment, stitching, and data correlation operations on the collected data and stores it in the database 220. Further the operations performed by the normalizer 425 facilitates the data by adding additional context, linking related information, and aligning the data for further analysis. Further the normalizer 425 optimizes the data for insights extraction and subsequent processing. In one embodiment, further the normalizer 425 is configured to receive rules from the user via the GUI 435. The rules may include, but are not limited to, data processing, data formatting and data transformation, data alignment based on the user request.
[0060] In one embodiment, the architecture 400 further includes Artificial Intelligence Data Records (AIDR) writer 430. The AIDR writer 430 continuously retrieves the data from the message broker unit 420 and stores it in the file system.
[0061] The GUI 435 facilitates an intuitive and user-friendly interface for end-users to interact with the collected call summary data. Further the GUI 435 facilitates users to visualize, analyse, and debug the data through reports, dashboards, and other visualizations. In an embodiment, the user can request for model training and configure specific network parameters to monitor the network 105 based on the call summary data via the GUI 435 to one or more processors. In one embodiment the GUI 435 facilitates the end user such as network operator to gain insights into network performance, troubleshoot issues, and make informed decisions based on the collected call summary data.
[0062] In an embodiment, the architecture 400 includes the workflow 440. The workflow 440 is configured for receiving the requests from the user via the GUI 435. The workflow 440 functions as the intermediary component for the end user to interact with the call summary data in the architecture 400. In one embodiment, the workflow 440 stores, but not limited to, SIM-related data in the database for the reference and further analysis.
[0063] In one embodiment, the architecture 400 includes the database 220. The database 220 is configured as the storage medium within the architecture. The database 220 stores various types of data, the various types of data may include, but not limited to, metadata, subscriber-level data, cell-level data, policy-related data, raw data, and other relevant information required for network management, analysis, and troubleshooting. The database 220 facilitates a centralized and organized repository for efficient data retrieval and management.
[0064] In one embodiment, the architecture 400 includes the ingestion layer 445. The ingestion layer 445 is configured to fetch the call summary data from the AIDR writer 430 and push it to the distributed file system 450. In one embodiment, further the ingestion layer 445 fetches the SIM details from the FMS 460 and transmits it to the workflow 440.
[0065] In one embodiment, architecture 400 further includes the distributed file system 450. The distributed file system 450 facilitates a scalable and distributed storage solution within the architecture. The distributed file system 450 is configured to store the collected call summary data and facilitates efficient data retrieval for subsequent analysis and processing by the AI/ML module 455. Further the distributed file system 450 ensures high availability, fault tolerance, and the ability to handle large volumes of data.
[0066] In one embodiment, the architecture 400 includes AI/ML module 450, the AI/ML module 455 retrieves the call summary data from the database 220 and distributed file system 450 based on the request received from the user via the GUI 435. The request includes, but not limited to, training the model and monitoring network coverage. In one embodiment, the model can include at least one of, AI/ML algorithms. In an embodiment, the AI/ML module 455 operates on the incoming data present in database 220 and the distributed file system 450 by applying the AI/ML algorithms based on the request received for the model training.
[0067] In one embodiment, the AI/ML algorithms may include but are not limited to, linear regression, decision trees, random forest, Recurrent Neural Networks (RNNs). Further the AI/ML module 455 continuously tracks and monitors the patterns and behaviours of various network parameters in the real-time, by comparing the retrieved data from the database 220 and the distributed file system 450 with learnt, patterns. In an embodiment, the model is configured to learn from its own findings based on the retrieved data from the database 220 and the distributed file system 450.
[0068] In an embodiment, the patterns and behaviours refer to the recurring trends, anomalies and/or regularities observed in various aspects of network operations and user behaviors. The network performance patterns include but are not limited to, traffic patterns, signal strength and quality, handover patterns. The user behavior may include, but not limited to, service usage patterns, mobility patterns, session patterns. Further the AI/ML algorithms operating on the incoming data identifies any deviations and/or abnormalities in the data pattern and provides real time notifications to the end-user via the GUI 435.
[0069] In one embodiment, the user for example, a subscriber sends a request such as a training request via the GUI 435 to the AI/ML module 455, specifying parameters of interest such as, at least one of, but not limited to, signal strength (RSRP), throughput, or latency. In response to receiving the training request, the AI/ML module 455 retrieves the call summary data from the database 220 and the distributed file system 450. Further the AI/ML module 455 trains a model using AI/ML algorithms to analyze historical data trends and current network conditions related to the specified parameters. The AI/ML module 455 continuously monitors network parameters in real-time, based on which, the AI/ML module 450 detects deviations from expected patterns, such as sudden drops in signal strength or increased latency. Upon identifying anomalies, the AI/ML module 455 generates real-time notifications via the GUI 435 to the subscriber. The notifications may include, but not limited to, alerts about potential service disruptions, recommendations for actions such as switching to a different cell tower, and insights into expected network performance based on current conditions.
[0070] In an alternate embodiment, the end user such as for example, the network operator may utilize the GUI 435 to send a training request to the AI/ML module 455 specifying parameters of interest such as, but not limited to, handover success rates, load balancing efficiency, QoS metrics. Upon receiving the request, from the network operator, the AI/ML module 455 retrieves the call summary data from database 220 and distributed file system 450. Further the AI/ML module 455 applies AI/ML algorithms to analyze historical data and current network conditions, identifying patterns and correlations related to the specified parameters. The AI/ML module 455 using learnt patterns, continuously monitors network health, subscriber usage patterns, and service experiences in real-time. Further the AI/ML module 455 detects anomalies such as persistent congestion in certain cells, unusually high call drop rates, or deviations from QoS thresholds. Upon detecting anomalies, the AI/ML module 455 provides real-time notifications to the network operator via the GUI 435, highlighting critical issues, recommending optimizations, or suggesting adjustments to network configurations.
[0071] In one embodiment, the architecture 400 further includes the FMS 460. The FMS 460 is configured as the inventory system, designed to capture and store subscriber level details.
[0072] FIG. 5 is a signal flow diagram for monitoring a network according to one or more embodiments of the present invention. For the purpose of description, the signal flow diagram is described with the embodiments as illustrated in FIG. 2 and should nowhere be construed as limiting the scope of the present disclosure.
[0073] At step 505, the user transmits the request to the GUI 435 for model training and to configure specific network parameters to monitor the network 105. The user provides the necessary inputs to initiate the network coverage analysis and optimization of the network parameters in the network 105.
[0074] At step 510, upon receiving the request from the user, the GUI 435 directs the user requests to the AI/ML module 455. The GUI 435 serves as the interface between the user and the system 120.
[0075] At step 515, upon receiving the user inputs and configurations form the user via the GUI 435, the AI/ML module 455 initiates the data acquisition and training process for detecting abnormalities in the network 105. The AI/ML module 440 retrieves the network data from the database 220 and distributed file system 445. Further the AI/ML 455 module utilizes the retrieved data to train its advanced AI/ML model, where the AI/ML models continuously learns patterns and behavior pertaining to the network parameters of the retrieved data. Further the AI/ML module 455 facilitates the real-time pattern monitoring and analysis after completing the training process.
[0076] At step 520, the AI/ML module 455 detects significant deviations and abnormalities in the network 105, during the real-time pattern monitoring and analysis. When deviations and abnormalities are identified, the AI/ML module 455 generates real-time notifications. The generated notification are transmitted to the GUI 435 for the user reference and analysis.
[0077] At step 525, the user receives the generated notifications through the GUI 435. The notifications facilitate the user to have an insights into network health, subscriber usage, and the overall service experience.
[0078] FIG. 6 is a flow chart illustrating a method 600 for detecting abnormalities in a network.
[0079] At step 605, the method 600 includes the step of receiving one or more requests from a user via the User Interface (UE) for training the model. The one or more requests includes user inputs for at least one of training the model and monitoring network coverage.
[0080] At step 610, the method 600 includes the step of retrieving data pertaining to network parameters from a database in response to receipt of the one or more requests. The network parameters include, but are not limited frequency bands, latency, throughput, quality of service, beamforming, network slicing, security protocols.
[0081] At step 615, the method 600 includes the step of training the model utilizing the retrieved data.
[0082] At step 620, the method 600 includes the step of detecting, by the one or more processors, abnormalities in the network utilizing the trained model. The detecting of abnormalities is performed by monitoring by utilizing the trained model, the patterns and behavior of the incoming data pertaining to the various network parameters stored in the database 220. Thereafter, applying, utilizing the trained model, a logic to the incoming data while monitoring. In response to applying the logic, detecting, utilizing the trained model, the abnormalities in the network whenever there is a deviation in at least one of, the patterns and behaviour of the incoming data in comparison with the learned patterns and behaviour of the retrieved data.
[0083] At step 625, the method 600 includes the step of notifying the user in real time regarding the detected abnormalities in the network. The notifications include but are not limited to, network health, a subscriber usage, and an overall service experience.
[0084] The present invention discloses a non-transitory computer-readable medium having stored thereon computer-readable instructions. The computer-readable instructions are executed by the processor 205. The processor 205 is configured to receive data pertaining to a user session in the network from one or more network elements. The processor 205 is configured to validate the received data. Further, the processor 205 is configured to segregate the validated data. Further, the processor 205 is configured to decode the validated data pertaining to the user session. Further the processor 205 is configured to parse the decoded data to identify irregularities in the decoded data. Further the processor 205 is configured to generate reports and insights based on parsing of the decoded data.
[0085] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIG.1-6) are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0086] The present disclosure incorporates technical advancement that facilitates AI/ML based approach that leverages advanced machine learning models to analyse network parameters. By eliminating huge hardware infrastructure and manpower required to monitor, track and correct the network systems, and enhances data security, provides near real-time network insights. This facilitates the efficient troubleshooting, optimization, and proactive network management, contributing to a seamless and enhanced user experience in the 5G ecosystem. The invention provides the network operators with near real-time network intelligence, empowering them to promptly identify and resolve network degradation or failure occurrences. Further the present invention supports the automation and predictive capabilities. Further the invention facilitates user to have insights of the network health as well as subscriber usage and experience of services.
[0087] The present invention provides various advantages, including optimal resource utilization and reduced execution time. The system eliminates the huge hardware infrastructure and manpower required to monitor, track and correct the network systems. The solution minimizes time and challenges as it involves intensive debugging, and the problem gets detected once after any network issue. Further the system provides near real-time network intelligence, facilitating proactive identification and resolution of network issues, resulting in a seamless user experience. The solution further leverages advanced AI/ML modules and anomaly detection techniques to enhance network monitoring and troubleshooting capabilities, facilitating efficient data analysis and faster issue resolution.
[0088] The present invention offers multiple advantages over the prior art and the above listed are a few examples to emphasize on some of the advantageous features. The listed advantages are to be read in a non-limiting manner.

REFERENCE NUMERALS
[0089] Communication system – 100
[0090] Network – 105
[0091] User Equipment – 110
[0092] Server – 115
[0093] System – 120
[0094] Processor -205
[0095] Memory – 210
[0096] User Interface– 215
[0097] Database- 220
[0098] Receiving module - 225
[0099] Retrieving unit -230
[00100] Training model -235
[00101] Detection unit -240
[00102] Notification unit – 245
[00103] GnodeBs - 405
[00104] Probing Agent - 410
[00105] Conductor – 415
[00106] Message broker unit 420
[00107] Normalizer - 425
[00108] Artificial Intelligence Data Records (AIDR) writer -430
[00109] GUI – 435
[00110] Workflow – 440
[00111] Ingestion layer - 445
[00112] Distributed file system - 450
[00113] Artificial Intelligence (AI)/Machine Learning (ML) module – 455
[00114] Fulfillment management system (FMS) - 460

,CLAIMS:CLAIMS
We Claim:
1. A method (600) for detecting abnormalities in a network, the method comprising the steps of:
receiving (605), by one or more processors, one or more requests from a user via a User Interface (UE) for training a model;
retrieving (610), by the one or more processors, data pertaining to network parameters from a database in response to receipt of the one or more requests;
training (615), by the one or more processors, a model utilizing the retrieved data;
detecting (620), by the one or more processors, abnormalities in the network utilizing the trained model; and
notifying (625), by the one or more processors, the user in regarding the detected abnormalities in the network.

2. The method (600) as claimed in claim 1, wherein the one or more requests includes user inputs for at least one of, training the model and monitoring network coverage.

3. The method (600) as claimed in claim 1, wherein in order to train the model, the model continuously learns patterns and behaviour pertaining to the network parameters of the retrieved data.

4. The method (600) as claimed in claim 1, wherein the step of detecting, by the one or more processors, abnormalities in the network utilizing the trained model, includes the steps of:
monitoring, by the one or more processors, utilizing the trained model, the patterns and behaviour of incoming data pertaining to the various network parameters stored in the database;
applying, by the one or more processors, utilizing the trained model, a logic to the incoming data while monitoring; and
in response to applying the logic, detecting, by the one or more processors, utilizing the trained model, the abnormalities in the network whenever there is a deviation in at least one of, the patterns and behaviour of the incoming data in comparison with the learned patterns and behaviour of the retrieved data.

5. The method (600) as claimed in claim 1, wherein the one or more notifications corresponds to a network health, a subscriber usage, and an overall service experience.

6. A system (120) for detecting abnormalities in a network, the system comprising:
a receiving unit (225) configured to receive, one or more requests from a user via a User Interface (UE) (110) for training a model;
a retrieving unit (230) configured to retrieve, data pertaining to network parameters from a database in response to receipt of the one or more requests;
a training unit (235) configured to train, a model utilizing the retrieved data;
a detection unit (240) configured to detect, abnormalities in the network utilizing the trained model; and
a notification unit (245) configured to notify, the user in real time regarding the detected abnormalities in the network.

7. The system (120) as claimed in claim 6, wherein the one or more requests includes user inputs for at least one of, training the model and monitoring network coverage.

8. The system (120) as claimed in claim 6, wherein to train the model, the model continuously learns patterns and behaviour pertaining to the network parameters of the retrieved data.

9. The system (120) as claimed in claim 6, wherein in order to detect abnormalities in the network, the detection unit is configured to:
monitor, utilizing the trained model, the patterns and behaviour of incoming data pertaining to the various network parameters stored in the database;
apply, utilizing the trained model, a logic to the incoming data while monitoring; and
in response to applying the logic, detect, utilizing the trained model, the abnormalities in the network whenever there is a deviation in at least one of, the patterns and behaviour of the incoming data in comparison with the learned patterns and behaviour of the retrieved data.

10. The system (120) as claimed in claim 6, wherein the one or more notifications corresponds to a network health, a subscriber usage, and an overall service experience.

11. A User Equipment (UE) (110), comprising:
one or more primary processors (305) communicatively coupled to one or more processors (205), the one or more primary processors (305) coupled with a memory (310), wherein said memory stores instructions which when executed by the one or more primary processors causes the UE (110) to:
transmit, one or more requests from a user to the one or more processors for training a model;
wherein the one or more processors is configured to perform the steps as claimed in claim 1.