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
METHOD AND SYSTEM FOR ANALYSING WIRELESS LOCAL AREA NETWORK (WLAN) PERFORMANCE AND SUBSCRIPTION MANAGEMENT
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 analysing Wireless Local Area Network (WLAN) performance and subscription management
BACKGROUND OF THE INVENTION
[0002] A Telecommunications Service Provider (TSP) is a communications service provider that provides telephone and similar services. For example, local exchange carriers, mobile wireless communication companies, etc. with the exponential growth and demand in telecom, there are more service providers entering the market every day. A subscriber is free to choose a telecom service provider and with number portability coming into picture, it has become even easier and convenient for the subscriber to shift from one telecom service provider to another. Different service providers differ in terms of service quality, tariff, coverage, etc.
[0001] Interconnect is the process of handling calls for other service providers. This allows the customers of one service provider to communicate with the customers of another service provider. If two operators A and B are not interconnecting partners, then it would not be possible for a customer of Operator A to communicate with a customer of operator B. Usually, operators keep their agreements with each other to allow their customers to communicate with each other. This gives good business opportunity to all the operators engaged in interconnection. Any interconnection points at which the parties agree to connect their respective Networks is called "Interconnection Point".
[0002] Point of Interconnection (POI) in telecommunication is the physical interface between media gateways of two service providers, carriers, exchanges, enterprises. A trunk is a single channel of communication that allows multiple entities at one end to correspond with the correct entity at the other end. It is a “link” that carries many signals at the same time, creating more efficient network access between two nodes.
[0003] However, for all telecom service providers, a retail subscriber is bound by certain general conditions including the usage of services for personal use only, and commercial usage is not allowed generally. For commercial usage such as customer care calling services, toll free number, B2B calls, etc., the telecom service provider may have a different set of terms and conditions, charges, etc. A retail subscriber is therefore not allowed to use his number for commercial purposes such as call centre calling, dedicated customer service number and the like. It will be desired to identify such illegal usage and stop the same.
[0004] Besides, when a subscriber is using services, such as making call or sending messages within the network of his/her own telecom service provider, the terms and conditions and tariff etc. is easy to manage as it remains within the same network and no third party network is involved. However, in case of services when two or more different service providers are involved (inter-network), the revenue sharing becomes complex and depends upon the bi-lateral agreement between the service providers involved. Interconnect Usage Charge (IUC) is the cost that a mobile operator pays to another operator for carrying through/terminating a call. If a customer of Mobile Operator A calls a customer of Mobile Operator B and the call is completed, then A pays the IUC charge to B for carrying/facilitating the call.
[0005] Further, in the competitive telecom service provider market, the tariff plans of the service provider vary. It may happen so that the outgoing calls from a First Service Provider (FSP) may be cheaper than a Second Service Provider (SSP) or even free. The objective to provide cheaper and better services as compared to competition is generally to acquire new customers. In case of inter-network calls, the bilateral agreement between the service providers generally provides for revenue sharing, wherein the terminating network receives a part of the revenue. For example, if SSP is the terminating network, the FSP will pay interconnecting charges to the SSP.
[0006] It may happen so that some mischievous elements might try to take advantage of such arrangements and use machines, software such as dedicated bots to make calls to generate unfair revenues in favour of one of the service providers in a case of internetwork services. For example, the SSP may use bots to make short unfair calls from the FSP network to the SSP network just to generate revenues based upon the arrangement of the terminating network revenue sharing model.
[0007] Besides the illegal revenue loss to the telecom service provider, the bots make the network congested and overloaded affecting the experience and service quality for the actual users of the network. The bots choke the network, and the effect is more pronounced especially in the areas / cells from where these bots operate.
[0008] There is a need for a solution that overcomes the above challenges and unfair /illegal practices and provides a system and method for identifying such unfair practices and detecting the bots being used for making such illegal calls.
SUMMARY OF THE INVENTION
[0009] One or more embodiments of the present invention provides a method and a system for analysing Wireless Local Area Network (WLAN) performance and subscription management.
[0010] In one aspect of the present invention, the method for analysing Wireless Local Area Network (WLAN) performance and subscription management is disclosed. The method includes the step of collecting WLAN information corresponding to one or more User Equipment (UEs) connected with the WLAN and present at one or more locations. The method further includes the step of pre-processing the WLAN information for adhering to data sanitization. The method further includes the step of determining, by the one or more processors, using a pre-trained model, a need of scaling of resources provided to a system management unit for operating the WLAN. The method includes the step of reporting the need of scaling of the resources to a user over a user interface.
[0011] In one embodiment, the WLAN information comprises one of a Received Signal Strength Indicator (RSSI), Round Trip Time (RTT), Uplink/Downlink (UL/DL) data rate, and traffic volume.
[0012] In one embodiment, the AI/ML model being trained using historical WLAN information to identify patterns and connections between different variables including network performance.
[0013] In an embodiment, the AI/ML model is updated based on changing network conditions and user demands.
[0014] In an embodiment, the method includes triggering a Fulfilment Management System (FMS) by the system management unit for upscaling of the resources when the resources assigned by the system management unit are determined to be overloaded.
[0015] In an embodiment, the WLAN information corresponding to one or more User Equipment (UEs) is collected by the one or more processors from the system management unit.
[0016] In an embodiment, the model is at least one of, an Artificial Intelligence/Machine Learning (AI/ML) model.
[0017] In an embodiment, the data sanitization includes at least one of, but not limited to, data definition, data normalization, and data cleaning.
[0018] In an embodiment, the one or more processors determines using the pre-trained model a need of scaling of resources based on the load on the system management unit.
[0019] In an embodiment, reporting the need of scaling of the resources includes at least one of, providing alerts or notifications to the user.
[0020] In an embodiment, the system management unit is determined to be overloaded by comparing the load with one or more predefined thresholds
[0021] In another aspect of the present invention, the system for analysing Wireless Local Area Network (WLAN) performance and subscription management is disclosed. The system includes a data integration module configured to collect WLAN information corresponding to one or more UEs connected with the WLAN and present at one or more locations. The system further includes a data pre-processing module configured to pre-process the WLAN information for adhering to data sanitization. The system further includes a prediction unit configured to determine using a pre-trained model, a need of scaling of resources provided to a system management unit for operating the WLAN. Further the prediction unit is configured to report the need to scale the resources to a user over a user interface.
[0022] In another aspect of the present invention, a User Equipment (UE) is disclosed. One or more primary processors of the UE is 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 provide WLAN information including Received Signal Strength Indicator (RSSI), Round Trip Time (RTT), and UL/DL data rate.
[0023] In yet another aspect of the present invention, a non-transitory computer-readable medium having stored thereon computer-readable instructions that, when executed by a processor, causes the processor to collect WLAN information corresponding to one or more UEs connected with the WLAN and present at one or more locations. The processor is further configured to pre-process the WLAN information for adhering to data saitization. The processor is further configured to determine using a pre-trained model, a need of scaling of resources provided to a system management unit for operating the WLAN. The processor is further configured to report the need to scale the resources to a user over a user interface.
[0024] 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
[0025] 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.
[0026] FIG. 1 is an exemplary block diagram of a communication system for analysing Wireless Local Area Network (WLAN) performance and subscription management, according to one or more embodiments of the present disclosure;
[0027] FIG. 2 is an exemplary block diagram of a system for analysing Wireless Local Area Network (WLAN) performance and subscription management, according to one or more embodiments of the present disclosure;
[0028] 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
[0029] FIG. 4 is an exemplary diagram of an architecture of the system of the FIG. 2, according to one or more embodiments of the present disclosure;
[0030] FIG. 5 is a signal flow diagram for analysing Wireless Local Area Network (WLAN) performance and subscription management, according to one or more embodiments of the present disclosure; and
[0031] FIG. 6 is a flow chart illustrating a method for analysing Wireless Local Area Network (WLAN) performance and subscription management, according to one or more embodiments of the present disclosure.
[0032] The foregoing shall be more apparent from the following detailed description of the invention.

DETAILED DESCRIPTION OF THE INVENTION
[0033] 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.
[0034] 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.
[0035] 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.
[0036] The present disclosure addresses the challenges faced in established technologies for analysing Wireless Local Area Network (WLAN) performance and subscription management. The present invention provides the analytics on WLAN performance by examining metrics such as but not limited to, signal strength, latency, and traffic volume. The present invention utilizes the WLAN information from a system management unit based on parameters such as, but not limited to, a User Equipment (UE) location and Service Set Identifier (SSID), to assess and predict network quality, and to facilitate the optimization of the network management
[0037] Referring to FIG. 1, FIG. 1 illustrates an exemplary block diagram of a communication system 100 for analysing Wireless Local Area Network (WLAN) performance and subscription management, according to one or more embodiments of the present disclosure. The WLAN refers to a network architecture, which facilitates wireless communication and connectivity within a localized geographic area. The localized geographic area includes at least one of but not limited to, a home, school, office, campus, and the like.
[0038] 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.
[0039] 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. 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”.
[0040] 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.
[0041] 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, a VOIP or some combination thereof.
[0042] 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.
[0043] 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.
[0044] 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.
[0045] Operational and construction features of the system 120 will be explained in detail with respect to the following figures.
[0046] FIG. 2 illustrates an exemplary block diagram of the system 120 for analysing Wireless Local Area Network (WLAN) performance and subscription management, according to one or more embodiments of the present disclosure.
[0047] 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 105. 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.
[0048] 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. 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.
[0049] 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.
[0050] In an embodiment, the database 220 is one of, but not limited to, a Elasticsearch database, 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.
[0051] In order for the system 120 to analyse WLAN performance and subscription management, the processor 205 includes one or more modules. In one embodiment, the one or more modules includes, but not limited to, a data integration module 225, a data pre-processing module 230, a model training unit 235 and a prediction unit 240 communicably coupled to each other.
[0052] The data integration module 225, the data pre-processing module 230, the model training unit 235 and the prediction unit 240 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.
[0053] In an embodiment, the data integration module 225 is configured to collect WLAN information corresponding to the one or more UEs 110. The one or more UEs 110 are connected with the WLAN for the purpose of collecting WLAN information from a system management unit 405. Herein the system management unit 405 is at least one of, but not limited to, the System Management Unit (SMF) 405. Hereinafter, the system management unit 405 is referred to the SMF 405 and further information about the SMF 405 is provided in description related to the FIG. 4) The one or more UEs 110 are located at one or more locations. Herein, the one or more locations are at least one of, but not limited to location coordinates of the one or more UEs 110. For example, based on the location coordinates, the exact location of the one or more UEs 110 or at which cell the one or more UEs 110 is present is identified. The WLAN information includes at least one of, but not limited to, Received Signal Strength Indicator (RSSI), Round Trip Time (RTT), Upload /Download (UL/DL) data rate, and traffic volume.
[0054] In an embodiment, the RSSI is a quantifiable metric representing the power level of a signal received in the network 105. The RSSI is utilized to measure the signal strength in the network 105. The RTT is a performance metric of the network 105. The RTT quantifies the elapsed time required for the signal transmission from a source node to a destination node and subsequent return of the signal to the source node from the destination node. In an embodiment, the source node includes at least one of but not limited to, one or more UEs 110, router, server 115 and the like. The destination node includes at least one of, but not limited to, cloud service, web server, email server, network printer, and the like. The RTT is utilized to measure latency within the network 105 during the communications. The Upload (UL) data rate refers to the data transmission rate from the UE 110 to the network 105. The UL data rate is measured in bits per second (bps). The UL data rate quantifies the throughput capacity available for outbound data transfer from the UE 110 to the network 105. Similarly, the Download (DL) data rate refers to the received data rate by the UE 110 form the network 105. The DL data rate is measured in the bps. The DL data rate quantifies the throughput capacity available for inbound data transfer from the network to the client device. The UL/DL data rate serves as indicators of network throughput and bandwidth efficiency. The UL/DL data rates are essential for assessing the performance capabilities and overall capacity of the WLAN. The traffic volume refers to the aggregate amount of any data/information transmitted across the network 105 within a designated time interval. The traffic volume includes, but not limited to, the total volume of any data/information transmitted and received in the network 105.
[0055] Upon collecting the WLAN information, the data pre-processing module 230 pre-processes the collected WLAN information for adhering to data sanitization. The data sanitization includes at least one of, but not limited to, data definition, data normalization, and data cleaning. The data definition compliance ensures whether the collected WLAN information adheres to the predefined standards and formats. The predefined standards and formats pertain to whether the collected WLAN information adhere to be in order to ensure consistency, accuracy, and compatibility for further processing and analysis. The predefined standards and formats are established by at least one of, but not limited to, industry organizations, technical specifications, and organizational policies. The data normalization refers to the process of adjusting and transforming the collected WLAN information to conform to a standard format. The data normalization includes at least one of, but not limited to, standardization of units, scaling the collected WLAN information, and the like. The data normalization is a fundamental step in the data pre-processing for standardizing and scaling the collected WLAN information to facilitate uniformity, consistency, and comparability. The data cleaning refers to the process of identifying, correcting and/or removing inaccuracies, inconsistencies, and missing values from the collected WLAN information. The data cleaning includes, but is not limited to, error detection and correction, handling missing values, consistency checks, duplicate removal. After performing all the preprocessing tasks, the data preprocessing module 230 transmits the preprocessed WLAN information to the model training unit 235.
[0056] Upon receiving the preprocessed WLAN information, the model training unit 235 is configured to train the model utilizing pre-processed WLAN information to identify patterns and connections between different variables including network performance. In an embodiment, the model is at least one of, but not limited to, an Artificial Intelligence/machine learning model (AI/ML). Hereinafter, the model is referred to the AI/ML model without limiting the scope of the invention. The AI/ML model includes, at least one of, but not limited to, regression models, classification models, time series models, and clustering models.
[0057] Initially the AI/ML model selection is performed by selecting the preferred AI/ML model. Further the selected AI/ML model is fed with the pre-processed WLAN information and the historical WLAN information. The historical WLAN information is retrieved from the DDL 440. The selected AI/ML model utilizes the fed pre-processed WLAN information and the historical WLAN information for training to identify patterns and connections between different variables of the network 105. The variables of the network 105 include at least one of, but not limited, network traffic, performance metrics, signal quality, load conditions, and the like.
[0058] In order to train the AI/ML model, the training unit 235 splits the pre-processed WLAN information into the training data and the testing data. For example, a ratio for splitting the training data and the testing data includes at least one of, but not limited to, 70:30, 80:20, or 90:10. In yet another example, let us consider list of numbers representing a dataset such as 1 to 100. Further, the dataset is randomly split into the training data and the testing data in the ratio of 80:20, such as the numbers 1 to 80 are training data and 81 to 100 are testing data. The training data is a subset of the WLAN information that is used to train the AI/ML model. The training data facilitates the AI/ML model to learn patterns and relationships within the WLAN information. In particular, the training data is fed to the AI/ML by the training unit 235 for training. Thereafter, the testing data is provided to the AI/ML model in order to evaluate the performance of the AI/ML model. In an alternate embodiment, the AI/ML model is trained on at least one of, but not limited to historical data pertaining to the WLAN information corresponding to one or more UEs 110.
[0059] In one embodiment, based on training, the AI/ML model learns at least one of, but not limited to, trends and patterns related to resources provided to a system management unit in past by applying one or more logics. In an alternate embodiment, the based on training, the AI/ML model learns at least one of, but not limited to, trends and patterns related to the user behavior. In one embodiment, the user is at least one of, but not limited to, a telecom operator. Herein, the user behavior pertains to when the user may likely require more or less resources for the system management unit. In one embodiment, the one or more logics may include at least one of, but not limited to, a k-means clustering, a hierarchical clustering, a Principal Component Analysis (PCA), an Independent Component Analysis (ICA), a deep learning logics such as Artificial Neural Networks (ANNs), a Convolutional Neural Networks (CNNs), a Recurrent Neural Networks (RNNs), a Long Short-Term Memory Networks (LSTMs), a Generative Adversarial Networks (GANs), a Q-Learning, a Deep Q-Networks (DQN), a Reinforcement Learning Logics, etc.
[0060] Herein, the learnt trends and patterns pertains to ideal one or more trends and patterns which can be used as a reference for the prediction. In one embodiment, the trends refer to a general direction or pattern of change observed over time within the WLAN information. For example, trends facilities in identifying load over time in the network 105. Herein, the patterns are recurring regularities observed in the WLAN information.
[0061] Upon training the AI/ML model, the prediction unit 240 of the system 120 determines using the pre-trained AI/ML model if there is a need of scaling of resources provided to the system management unit or the SMF 405. (the information about the SMF is 405 is illustrated in the FIG. 4) for operating the WLAN. The resources refer to various components and capabilities that can be accessed, utilized, or shared within the environment 100. The resources provided to the SMF 405 includes, at least one of but not limited to, network bandwidth, computational power, data storage, Virtual Machines (VMs) or containers, load balancers, and Central processing Unit (CPU). In one embodiment, AI/ML model monitors the load on the SMF 405. Based on monitoring when the load on the SMF 405 is increased as compared to one or more predefined thresholds, then there the prediction unit 240 determines that is a need of scaling the resources.
[0062] In one embodiment, in order to predict the need of scaling the resources provided to the SMF 405, the prediction unit 240 utilizes the pre-trained AI/ML model. Herein, the pre-trained AI/ML model had learnt the trends and patterns regarding resources provided to the SMF 405 based on the past behavior of the user. For example, let us consider that based on training the AI/ML model had learnt that in a particular time slot or a day, the user requires more or less resources for the SMF 405. So, based on the past behavior of the user, the prediction unit 240 predicts that the more or less resources are required by the user for the SMF 405. More particularly, based on the trends and patterns the prediction unit 240 predicts that in the time slot of 9AM to 11AM the user requires more resource such as the network bandwidth for the SMF 405. Further, the the prediction unit 240 predicts a value for the required resources. For example, the user requires 10 Gbps more bandwidth in the time slot of 9AM to 11AM. In another example, the user requires 50 GB more memory in the time slot of 9AM to 11AM.
[0063] Further the prediction unit 240 performs a close loop reporting to the user over the UI 215 to scale the resources based on the need. Herein, the user is reported regarding the need of scaling of the resources by at least one of, but not limited to, alerts or notifications to the user. Due to which, the system 120 facilitates closed loop reporting by continuously monitoring the WLAN information to detect breaches such as, but not limited to overload and/or underload from the normal performance of the network 105. If the issues, anomalies and deviations are detected, the system 120 triggers closed loop actions to adjust resources accordingly. The closed loop action refers to the automated response initiated based on the detected anomalies from closed loop reporting.
[0064] In one embodiment, the data pertaining to the scaling of resources such as the value associated to the required resources are visualized on the UI 215 by the user. In other words, the user is provided with the report regarding need of scaling of the resources on the UI 215. For example, the user can view the value on the UI 225 such as the 50 GB more memory is required for the SMF 405 in the time slot of 9AM to 11AM.
[0065] In one embodiment, the AI/ML model monitors the load on the SMF 405 and as the load on the SMF 405 is increased based on comparison with the one or more predefined thresholds, the AI/ML model infers that the SMF 405 is overload and there is the need of scaling the resources. Similarly, based on monitoring when the load on the SMF 405 is less as compared to the one or more predefined thresholds, then the AI/ML model infers that the SMF 405 is underload.
[0066] In one embodiment, the SMF 405 triggers closed loop actions when then SMF 405 is overload. Herein a Fulfilment Management System (FMS) is upscaling of the resources by allocating the resources to the SMF 405. In one embodiment, based on the triggers, the SMF 405 communicates with the user to take action to scale in/out resources. In one example, if the SMF 405 is overload and requires more 50 GB memory in the time slot of 9AM to 11AM, then the FMS provides more 50 GB memory to the SMF 405.
[0067] Further the system 120 facilitates continuous learning and optimization by continuous adaptation and evolution corresponding to the changing network conditions and user demands. In particular, the AI/ML model is being updated based on based on changing network conditions and user demands. Herin, based on changing network conditions and user demands the AI/ML model trains itself. For example, based on new WLAN information the AI/ML model updates itself. In one embodiment, based on the past prediction, an output of the past prediction is again provided as an input to the AI/ML model to refine the predictions of the AI/ML model which ensures that the system 120 remains efficient and effective over time. In one embodiment, the system 120 facilitates AI/ML model forecasting for future resource needs and to identify patterns and connections between different variables including network 105 performance based on historical WLAN information.
[0068] FIG. 3 is a schematic representation of a workflow of the system of FIG. 2 communicably coupled with the User equipment (UE) 110, according to one or more embodiments of the present disclosure. 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.
[0069] As mentioned earlier in FIG. 1, the first UE 110a 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.
[0070] The first UE 110a includes one or more primary processors 305 communicably coupled to the one or more processors 205 of the system 120.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 provide WLAN information to the system 120. The WLAN information includes at least one of but limited to, RSSI, RTT, UL/DL data rate and the like.
[0071] As mentioned earlier in FIG. 2, the one or more processors 202 of the system 120 is configured to analyse WLAN performance and subscription management. 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.
[0072] Further, the processor 205 includes the data integration module 225, the data pre-processing module 230, the model training unit 235, the prediction unit 240. The operations and functions of the data integration module 225, the data pre-processing module 230, the model training unit 235, the prediction unit 240. are already explained in FIG. 2. Hence, 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. The limited description provided for the system 120 in FIG. 3, should be read with the description provided for the system 120 in the FIG. 2 above, and should not be construed as limiting the scope of the present disclosure.
[0073] FIG. 4 is an exemplary architecture 400 which can be implemented in the system 120 of the FIG. 2) for analysing WLAN performance and subscription management, according to one or more embodiments of the present invention.
[0074] The architecture 400 includes the SMF 405, a Network Data Analytics Function (NWDAF) 410, network operator 1, network operator 2, an operational unit 415, a data integration unit 420, a data processing unit 425, a training unit 430, a forecast unit 430, an user interface 215, Distributed data lake (DDL) 445.
[0075] The SMF 405 collects raw WLAN information from the network 105. The collected raw WLAN information includes, at least one of but not limited to, Received Signal Strength Indicator (RSSI), Round Trip Time (RTT), UL/DL data rate, and traffic volume. Further the SMF 405 transmits the collected raw WLAN information to the NWDAF 410. The NWDAF 410 is a component within the network 105 that is responsible for performing data analytics and providing insights into network behavior and performance. The NWDAF 410 collects and analyzes data from various sources within the network 105.
[0076] On the receipt of the raw WLAN information from the SMF 405, the NWDAF 410 analyses the received raw WLAN information via processing. The processing is performed by evaluating various metrics to assess the received raw WLAN information related to the quality and performance of the WLAN connections. The metrics include, at least one of but not limited to, RSSI, RTT, Uplink Data Rate, Signal-to-Noise Ratio (SNR), packet loss, connection duration, error rates. Upon analysis, the NWDAF 410 transmits the analyzed WLAN information to the operational unit 415.
[0077] The operational unit 415 is an AI/ML based system configured to perform multiple tasks. The tasks include, but are not limited to, executing a range of logics, performing predictive analysis, detecting anomalies, and generating outputs driven by artificial intelligence utilizing Large Language Models (LLM). The operational unit 415 functions include, at least one of but is not limited to, analyzing the network data and operational data, and leveraging machine learning techniques for in-depth analysis of the network 105 data. The operational unit 415 is incorporated with multiple sub- units for performing functions and multiple tasks as mentioned above. The sub-units are but not limited to, the data integration unit 420, the data processing unit 425, the training unit 430, and the forecast unit 435.
[0078] The data integration unit 420 of the operational unit 415 is configured to collect the analyzed WLAN information, which is received from the NWDAF 410. Further the data integration unit 420 integrates the collected analyzed WLAN information with the corresponding one or more UE 110 connected with the WLAN and present at one or more locations. Upon integration, the data integration unit 420 transmits the collected analyzed WLAN information to the data processing unit 425.
[0079] On the receipt of the analyzed WLAN information, the data processing unit 425 pre-process the analyzed WLAN information for adhering to data definition, data normalization, data cleaning, and data splitting. The data definition compliance ensures whether the collected WLAN information adheres to the predefined standards and formats. The predefined standards and formats pertain to whether the collected WLAN information are in order to ensure consistency, accuracy, and compatibility for further processing and analysis. The predefined standards and formats are established by at least one of, but not limited to, industry organizations, technical specifications, and organizational policies. The data normalization is performed for standardizing and scaling the collected WLAN information to facilitate uniformity, consistency, and comparability. The data cleaning is performed to remove and correct inaccuracies and inconsistences in the WLAN information. The data cleaning includes the tasks, such as but not limited to, removing data points with missing values (NaN values) to prevent errors in analysis, removing redundant data. After performing all the preprocessing tasks, the data processing unit 425 transmits the preprocessed WLAN information to the training unit 430.
[0080] Upon receiving the preprocessed WLAN information, the training unit 430 trains the AI/ML model utilizing the pre-processed WLAN information to identify patterns and connections between different variables including network performance. The AI/ML model includes, at least one of, but not limited to, regression models, classification models, time series models, and clustering models.
[0081] Further upon training the AI/ML model, the forecast unit 435 determines using the pre-trained AI/ML model whether there is a need of scaling of resources provided to the SMF 405 for operating the WLAN. Further the forecast unit 240 reports the telecom operator over the UI 215 to scale the resources based on the need.
[0082] FIG. 5 is a signal flow diagram analysing WLAN performance and subscription management, 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 FIG. 4 and should nowhere be construed as limiting the scope of the present disclosure.
[0083] At step 505, the SMF 405 collects raw WLAN information from the network 105. The collected raw WLAN information includes, at least one of but not limited to, Received Signal Strength Indicator (RSSI), Round Trip Time (RTT), UL/DL data rate, and traffic volume.
[0084] At step 510, the SMF 405 transmits the collected raw WLAN information to the NWDAF 410.
[0085] At step 515, on the receipt of the raw WLAN information from the SMF 405, the NWDAF 410 analyses the received raw WLAN information via processing. The NWDAF 410 transmits the analyzed WLAN information to the data integration unit 420 of the operational unit 415.
[0086] At step 520, the data integration unit 420 integrates the collected analyzed WLAN information with the corresponding one or more UEs 110 connected with the WLAN and present at one or more locations. Upon integration, the data integration unit 420 transmits the collected analyzed WLAN information to the data pre- processing unit 425.
[0087] At step 525, the data processing unit 430 pre-process the analyzed WLAN information for adhering to data definition, data normalization, data cleaning, and data splitting. After performing all the preprocessing tasks, the data processing unit 425 transmits the preprocessed WLAN information to the training unit 430.
[0088] At step 530, the training unit 430 trains the AI/ML model utilizing pre-processed WLAN information to identify patterns and connections between different variables including network performance.
[0089] At step 535, the forecast unit 435 determines using the pre-trained AI/ML model whether there is a need of scaling of resources provided to the SMF 405 for operating the WLAN. Further the forecast unit 435 reports the telecom operator over the user interface 215 to scale the resources based on the need.
[0090] At step 540, the forecast unit 435 performs closed loop reporting performed by continuously monitoring network performance, forecasting incoming load in the SMF 405.
[0091] At step 545, if the forecast unit 435 detects an overload condition or anticipates a need for additional resources based on the WLAN performance, the forecast unit 435 triggers the FMS 440 to perform closed loop action.
[0092] At step 550, the FMS 440 performs automated resource provisioning by dynamically allocating additional resources to the SMF 405.
[0093] FIG. 6 is a flow diagram illustrating a method of analysing Wireless Local Area Network (WLAN) performance and subscription management, according to one or more embodiments of the present disclosure. 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.
[0094] At step 605, the method 600 includes the step collecting WLAN information corresponding to one or more User Equipment (UEs) (110) connected with the WLAN and present at one or more locations. The one or more UEs 110 are connected with the WLAN for the purpose of collecting WLAN information. The one or more UEs 110 are located at one or more locations. The WLAN information includes at least one of, but not limited to, Received Signal Strength Indicator (RSSI), Round Trip Time (RTT), Upload /Download (UL/DL) data rate, and traffic volume.
[0095] At step 610, the method 600 includes the step of pre-processing the WLAN information for adhering to data sanitization which includes at least one of, but not limited to, the data definition, the data normalization, and the data cleaning. The data definition compliance ensures whether the collected WLAN information adheres to the predefined standards and formats. The predefined standards and formats pertain to whether the collected WLAN information adhere to be in order to ensure consistency, accuracy, and compatibility for further processing and analysis. The predefined standards and formats are established by at least one of, but not limited to, industry organizations, technical specifications, and organizational policies. The data normalization refers to the process of adjusting and transforming the collected WLAN information to conform to a standard format. The data normalization includes at least one of, but not limited to, standardization of units, scaling the collected WLAN information, and the like. The data normalization is a fundamental step in the data pre-processing for standardizing and scaling the collected WLAN information to facilitate uniformity, consistency, and comparability. The data cleaning refers to the process of identifying, correcting and/or removing inaccuracies, inconsistencies, and missing values from the collected WLAN information. The data cleaning includes, but is not limited to, error detection and correction, handling missing values, consistency checks, duplicate removal. After performing all the preprocessing tasks, the data preprocessing module 230 transmits the preprocessed WLAN information to the model training unit 235.
[0096] At step 615, the method 600 includes the step of determining using the pre-trained model, whether there is a need of scaling of resources provided to the system management unit for operating the WLAN. The resources provided to the SMF 405 includes, at least one of but not limited to, network bandwidth, computational power, data storage, Virtual Machines (VMs) or containers, load balancers.
[0097] At step 620, the method 600 includes the step of reporting the need of scaling of the resources to the user such as the telecom operator over the UI 215. Based on determining the need of scaling of resources, the data pertaining to the scaling of resources such as the value associated to the required resources are visualized on the UI 215 by the user. Here based on the visualization the user compares the value associated to the required resources with an actual value that may be a threshold.
[0098] At step 625, the method 600 includes the step of closed loop reporting. Due to reporting the need of scaling of the resources, the system 120 facilitates closed loop reporting by continuously monitoring the WLAN information to detect breaches such as, but not limited to overload and/or underload from the normal performance of the network 105. For example, the AI/ML model monitors the load on the SMF 405 by comparing the load with the one or more predefined thresholds. Thereafter, the AI/ML model infers that the SMF 405 is overloaded when the load breaches the one or more predefined thresholds. Similarly, the AI/ML model infers that the SMF 405 is underload when the load does not breaches the minimum value of the one or more predefined thresholds. Based on the comparisons, the AI/ML model identifies whether is the need of scale in/out resources.
[0099] At step 625, the method 600 includes the step of performing closed loop actions. In one embodiment, if the issues, anomalies and deviations are detected, the system 120 triggers closed loop actions to adjust resources accordingly. The closed loop action refers to the automated response initiated based on the detected anomalies from closed loop reporting. For example, the system 120 triggers closed loop actions when then SMF 405 is overload or underload. In particular, the system 120 takes action to scale in/out resources based on the overload or underload on the SMF 405. In one example, if the SMF 405 is underload and do not require 10 GB memory, then the the system 120 deallocates the 10 GB GB memory to the SMF 405. In other example, if the SMF 405 is overload and requires 10 GB memory, then the the system 120 allocates the 10 GB GB memory to the SMF 405.
[00100] The present invention further 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 collect WLAN information corresponding to one or more UEs connected with the WLAN and present at one or more locations. The processor 205 is further configured to pre-process the WLAN information for adhering to data sanitization. The processor 205 is further configured to determine using a pre-trained model, a need of scaling of resources provided to a system management unit for operating the WLAN. The processor 205 is further configured to report the need to scale the resources to a user over a user interface.
[00101] 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.
[00102] The present disclosure incorporates technical advancement by facilitating network management via real-time data visualization, automated resource scaling, predictive analytics, continuous learning, and efficient data management and the like. The present invention facilitates dynamic real-time insights into WLAN performance metrics and load analytics. Further the present invention facilitates Artificial Intelligence /Machine Learning model forecasting for future resource needs based on historical and real-time data.
[00103] The present invention provides various advantages, including optimal resource utilization, reduced execution time, improved network performance and operational efficiency. The present invention facilitates the automated resource scaling by utilizing a closed-loop reporting mechanism to automatically adjust resources in the System Management Facility (SMF) without manual intervention. Further the present invention facilitates Integrated Artificial Intelligence/Machine Learning capabilities for enhancing the accuracy of resource predictions. Further the present invention optimizes network management via a sophisticated data analysis.
[00104] 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
[00105] Communication system – 100
[00106] Network – 105
[00107] User Equipment – 110
[00108] Server – 115
[00109] System – 120
[00110] Processor -205
[00111] Memory – 210
[00112] User Interface– 215
[00113] Database- 220
[00114] Data integration module - 225
[00115] Data pre –processing module unit - 230
[00116] Model training unit - 235
[00117] Prediction unit – 240
[00118] System Management unit or SMF - 405
[00119] Network Data Analytics Function (NWDAF) - 410
[00120] Operational unit - 415
[00121] Data integration unit 420
[00122] Data processing unit – 425
[00123] Training unit – 430
[00124] Forecast unit - 435
[00125] Fulfilment Management System - 440
[00126] Distributed Data Lake - 445
,CLAIMS:CLAIMS
We Claim
1. A method (600) of analysing Wireless Local Area Network (WLAN) performance and subscription management, the method comprising the steps of:
collecting (605), by one or more processors (205), WLAN information corresponding to one or more User Equipment (UEs) (110) connected with the WLAN and present at one or more locations;
pre-processing (610), by the one or more processors (205), the WLAN information for adhering to data sanitization;
determining (615), by the one or more processors (205), using a pre-trained model, a need of scaling of resources provided to a system management unit for operating the WLAN; and
reporting (620), by the one or more processors (205), the need of scaling of the resources to a user over a user interface.

2. The method (600) as claimed in claim 1, the WLAN information comprising one of a Received Signal Strength Indicator (RSSI), Round Trip Time (RTT), Uplink/Downlink (UL/DL) data rate, and traffic volume.

3. The method (600) as claimed in claim 1, the AI/ML model being trained using historical WLAN information to identify patterns and connections between different variables including network performance.

4. The method (600) as claimed in claim 3, comprising updating the AI/ML model based on changing network conditions and user demands.

5. The method (600) as claimed in claim 1, comprising triggering a Fulfilment Management System (FMS) by the system management unit for upscaling of the resources when the resources assigned by the system management unit are determined to be overloaded.

6. The method (600) as claimed in claim 1, wherein the WLAN information corresponding to one or more User Equipment (UEs) (110) is collected by the one or more processors (205) from the system management unit.

7. The method (600) as claimed in claim 1, wherein the model is at least one of, an Artificial Intelligence/Machine Learning (AI/ML) model.

8. The method (600) as claimed in claim 1, wherein the data sanitization includes at least one of, but not limited to, data definition, data normalization, and data cleaning.

9. The method (600) as claimed in claim 1, wherein the one or more processors (205) determines using the pre-trained model a need of scaling of resources based on the load on the system management unit.

10. The method (600) as claimed in claim 1, wherein reporting the need of scaling of the resources includes at least one of, providing alerts or notifications to the user.

11. The method (600) as claimed in claim 5, the system management unit is determined to be overloaded by comparing the load with one or more predefined thresholds.

12. A system (120) for analysing Wireless Local Area Network (WLAN) performance and subscription management, the system comprising:
a data integration module (225) configured to collect WLAN information corresponding to one or more UEs connected with the WLAN and present at one or more locations;
a data pre-processing module (230) configured to pre-process the WLAN information for adhering to data sanitization; and
a prediction unit (240) configured to:
determine using a pre-trained model, a need of scaling of resources provided to a system management unit for operating the WLAN; and
report the need of scaling of the resources to a user over a user interface.

13. The system (120) as claimed in claim 12, wherein the WLAN information includes Received Signal Strength Indicator (RSSI), Round Trip Time (RTT), UL/DL data rate, and traffic volume.

14. The system (120) as claimed in claim 12, wherein the system comprises a model training unit (235) configured to train the AI/ML model using historical WLAN information to identify patterns and connections between different variables including network performance.

15. The system (120) as claimed in claim 14, wherein the AI/ML model is updated based on changing network conditions and user demands.

16. The system (120) as claimed in claim 12, wherein the system management unit triggers a Fulfilment Management System (FMS) for upscaling of the resources when the resources assigned by the system management unit are determined to be overloaded.

17. The system (120) as claimed in claim 12, wherein, the WLAN information corresponding to one or more User Equipment (UEs) (110) is collected by the data integration module (225) from the system management unit.

18. The system (120) as claimed in claim 12, wherein the model is at least one of, an Artificial Intelligence/Machine Learning (AI/ML) model.

19. The system (120) as claimed in claim 12, wherein the data sanitization includes at least one of, but not limited to, data definition, data normalization, and data cleaning.

20. The system (120) as claimed in claim 12, wherein the prediction unit (240) determines using a pre-trained model a need of scaling of resources based on the load on the system management unit.

21. The system (120) as claimed in claim 12, wherein reporting the need of scaling of the resources includes at least one of, providing alerts or notifications to the user.

22. The system (120) as claimed in claim 16, the system management unit is determined to be overloaded by comparing with one or more predefined thresholds.

23. A User Equipment (UE) (110), comprising:
a primary processor (305) coupled with a primary memory (310), wherein said primary memory (310) stores instructions which when executed by the primary processor (305) causes the UE (110) to:
provide WLAN information including Received Signal Strength Indicator (RSSI), Round Trip Time (RTT), and UL/DL data rate,
wherein the processor (205) is configured to perform the steps as claimed in claim 1.