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 MANAGING 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 communication networks, more particularly relates to a method and a system for managing the communication networks.
BACKGROUND OF THE INVENTION
[0002] With the ever-increasing number of users in a communication network, users are facing issues such as calls not getting connected or call drop occurring during a conversation. The reasons may be due to the user being located in a location which has weak signal or high traffic being experienced in the location for that particular time.
[0003] In order to reduce these instances of call not getting connected or call drop situations, a dispersion analysis is generally performed to identify abnormalities in the communication network.
[0004] Generally, for dispersion analysis, initially the data pertaining to location/hotspot data of User Equipment (UE) is collected from consumers and dispersion analysis is performed on the same, and the analysis is provided back to the consumer. The consumers may be such as, but not limited to, AMF (Access and Mobility Management Function), PCF (Policy Control Function) or any other 5G NF (Network Function). This dispersion analysis performed is generally common for all consumers, hence the dispersion analysis data may be humungous in size. Each consumer is required to search and gather the relevant data specific to the consumer’s requirement from the dispersion analysis data common for all the consumers part of the communication network. The process of searching and gathering the relevant data specific to the consumer’s requirement is cumbersome and may utilize a lot of time. Furthermore, this also leads to utilization of substantial amount of memory space of the consumer, thereby creating latency in operation of the consumer and also decreasing the efficiency of the consumer.
[0005] In view of the above, there is a need for a system and method for dispersion analysis and prediction in order to provide the relevant dispersion analysis data specific to the consumer’s requirement.
SUMMARY OF THE INVENTION
[0006] One or more embodiments of the present disclosure provide a method and system for managing a network.
[0007] In one aspect of the present invention, the system for managing the network is disclosed. The system includes a receiving unit configured to receive a request for a dispersion analysis of the network via a User Equipment (UE). The system further includes a retrieving unit configured to retrieve data pertaining to a location of the UE from one or more network functions upon receipt of the request. The system further includes an identification unit configured to identify one or more hotspots in the network utilizing the data pertaining to the location of the UE. The system further includes a forecasting unit configured to forecast, one or more future hotspots utilizing at least one of data corresponding to the one or more identified hotspots and data pertaining to the historical location of the UE in the network. The system further includes a creation unit configured to create one or more policies to one of latch and release the UE at the forecasted hotspot.
[0008] In an embodiment, the one or more hotspots correspond to the one or more locations in the network where data pertaining to at least one of cell loading and data traffic consumption of the UE is higher than the data pertaining to at least one of the cell loading and the data traffic consumption of the UE at other locations of the network.
[0009] In an embodiment, the one or more network functions is at least one of an Access and Mobility management Function (AMF).
[0010] In an embodiment, the data pertaining to the location is retrieved via a location wise Tracking Area Identity (TAI) corresponding to one or more identifiers of the UE. The one or more identifier is at least one of a Subscription Permanent Identifier (SUPI) and International Mobile Subscriber Identifier (IMSI).
[0011] In another aspect of the present invention, the method of processing data in the network is disclosed. The method includes the step of receiving a request for a dispersion analysis of the network via a User Equipment (UE). The method further includes the step of retrieving data pertaining to a historical location of the UE from one or more network functions and a database upon receipt of the request. The method further includes the step of identifying one or more hotspots in the network utilizing the data pertaining to the location of the UE. The method further includes the step of forecasting one or more future hotspots utilizing at least one of data corresponding to the one or more identified hotspots and data pertaining to the historical location of the UE in the network. The method further includes the step of creating one or more policies to at least one of latch and release the UE at the forecasted hotspot.
[0012] In 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 to receive a request for a dispersion analysis of the network via a User Equipment (UE). The processor is configured to retrieve data pertaining to a location of the UE from one or more network functions upon receipt of the request. The processor is configured to identify one or more hotspots in the network utilizing the data pertaining to the location of the UE. The processor is configured to forecast one or more future hotspots utilizing at least one of data corresponding to the one or more identified hotspots and data pertaining to the historical location of the UE in the network. The processor is configured to create one or more policies to at least one of latch and release the UE at the forecasted hotspot.
[0013] In another aspect of invention, User Equipment (UE) is disclosed. The UE includes one or more primary processors communicatively coupled to one or more processors, the one or more primary processors coupled with a memory. The processor causes the UE to transmit a request for a dispersion analysis of the network to the one or more processors.
[0014] 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
[0015] 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.
[0016] FIG. 1 is an exemplary block diagram of an environment for managing a network, according to one or more embodiments of the present invention;
[0017] FIG. 2 is an exemplary block diagram of a system for managing the network, according to one or more embodiments of the present invention;
[0018] FIG. 3 is a schematic representation of a workflow of the system of FIG. 1, according to the one or more embodiments of the present invention;
[0019] FIG. 4 is an exemplary block diagram of an architecture implemented in the system of the FIG. 2, according to one or more embodiments of the present invention;
[0020] FIG. 5 is a signal flow diagram for managing the network, according to one or more embodiments of the present invention; and
[0021] FIG. 6 is a schematic representation of a method of managing the network, according to one or more embodiments of the present invention.
[0022] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0023] 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.
[0024] 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.
[0025] 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.
[0026] The disclosed system and method aim at enhancing user experience identifying a hotspot/location of a User Equipment (UE) or a group of UEs while monitoring the dispersion data and maintaining a hotspot/ location data of the UEs collected over a long period of time and based on this historical data create a prediction model that create/modify policy for the UEs for latch/release process in that particular location.
[0027] FIG. 1 illustrates an exemplary block diagram of an environment 100 for managing a network, according to one or more embodiments of the present disclosure. In this regard, the environment 100 includes a User Equipment (UE) 102, a server 104, a network 106 and a system 108 communicably coupled to each other for managing the network 106. The managing the network 106 refers to the process of monitoring, controlling, and optimizing the performance of the network 106 through dynamic analysis and decision-making.
[0028] As per the illustrated embodiment and for the purpose of description and illustration, the UE 102 includes, but not limited to, a first UE 102a, a second UE 102b, and a third UE 102c, and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the UE 102 may include a plurality of UEs as per the requirement. For ease of reference, each of the first UE 102a, the second UE 102b, and the third UE 102c, will hereinafter be collectively and individually referred to as the “User Equipment (UE) 102”.
[0029] In an embodiment, the UE 102 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 a smartphone, 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.
[0030] The environment 100 includes the server 104 accessible via the network 106. The server 104 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.
[0031] The network 106 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 106 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.
[0032] The network 106 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 106 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.
[0033] The environment 100 further includes the system 108 communicably coupled to the server 104 and the UE 102 via the network 106. The system 108 is configured to manage the network 106. As per one or more embodiments, the system 108 is adapted to be embedded within the server 104 or embedded as an individual entity.
[0034] Operational and construction features of the system 108 will be explained in detail with respect to the following figures.
[0035] FIG. 2 is an exemplary block diagram of the system 108 for managing the network 106, according to one or more embodiments of the present invention.
[0036] As per the illustrated embodiment, the system 108 includes one or more processors 202, a memory 204, a user interface 206, and a database 208. For the purpose of description and explanation, the description will be explained with respect to one processor 202 and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the system 108 may include more than one processor 202 as per the requirement of the network 106. The one or more processors 202, hereinafter referred to as the processor 202 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.
[0037] As per the illustrated embodiment, the processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204. The memory 204 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 create or share data packets over a network service. The memory 204 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.
[0038] In an embodiment, the user interface 206 includes a variety of interfaces, for example, interfaces for a graphical user interface, a web user interface, a Command Line Interface (CLI), and the like. The user interface 206 facilitates communication of the system 108. In one embodiment, the user interface 206 provides a communication pathway for one or more components of the system 108. Examples of such components include, but are not limited to, the UE 102 and the database 208.
[0039] The database 208 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 208 types are non-limiting and may not be mutually exclusive e.g., a database can be both commercial and cloud-based, or both relational and open-source, etc.
[0040] In order for the system 108 to manage the network 106, the processor 202 includes one or more modules. In one embodiment, the one or more modules includes, but not limited to, a receiving unit 210, a retrieving unit 212, an identification unit 214, a forecasting unit 216, and a creation unit 218 communicably coupled to each other for managing the network 106.
[0041] In one embodiment, the one or more modules includes, but not limited to, the receiving unit 210, the retrieving unit 212, the identification unit 214, the forecasting unit 216, and the creation unit 218 can be used in combination or interchangeably for managing the network 106.
[0042] The receiving unit 210, the retrieving unit 212, the identification unit 214, the forecasting unit 216, and the creation unit 218 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 202. 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 202 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 204 may store instructions that, when executed by the processing resource, implement the processor. In such examples, the system 108 may comprise the memory 204 storing the instructions and the processing resource to execute the instructions, or the memory 204 may be separate but accessible to the system 108 and the processing resource. In other examples, the processor 202 may be implemented by electronic circuitry.
[0043] In an embodiment, the receiving unit 210 is configured to receive a request for dispersion analysis of the network 106 via the UE 102. The dispersion analysis refers to the process of evaluating how data traffic, user load or signal strength is distributed across different areas within the network 106. The data traffic refers to the flow of digital information such as internet data, voice calls, video streaming and other type of data transmission that occur over the network between the UE 102 and the network infrastructure. The user load refers to the number of active UE 102 devices or users connected to the network 106 in a specific area. The signal strength refers to the power level of wireless signal received by the UE 102 from the network’s base station or access points.
[0044] In an embodiment, the request for dispersion analysis of the network 106 refers to an inquiry or action initiated by the UE 102 to obtain an understanding of how data traffic and user loads are dispersed across different locations within the network. The request includes, but is not limited to, current location of the UE 102, specific areas of interest, timeframes for analysis and any relevant network performance indicators like signal strength.
[0045] Upon receiving the request from the UE 102, the retrieving unit 212 is configured to retrieve the data pertaining to a location of the UE 102 from one or more network functions. The data is the information that relates to the geographical or network-based position of the UE 102.The data pertaining to the location of the UE 102 includes, but not limited to, Tracking Area Identity (TAI), cell identifier (ID), geographical coordinates, signal timing information. The TAI is a unique identifier used in the network 106 to track the location of the UE 102 within a specific tracking area. The TAI is composed of three parts such as Mobile Country Code (MCC), Mobile Network Code (MNC), Tracking Area Code (TAC). The MCC is a three-digit code that identifies the country of the network operator. The MNC is a two- or three-digit code that identifies the specific network operator within that country. The TAC is a unique code that identifies a specific tracking area within the operator's network. For example, suppose the TAC for a particular area in Berlin is 1234, in this case, the TAI for that tracking area would be: 262-01-1234. The cell ID is the identifier of the specific cell tower or base station that the UE 102 is connected to. The geographical coordinates are the latitude and longitude data that provide the exact physical location of the UE 102. The signal timing information includes data such as Time of Arrival (ToA) or Time Difference of Arrival (TDoA) which is used to triangulate the position of the UE 102.
[0046] The one or more network functions are specific functions within the network 106 that are responsible for managing and processing the location data. The one or more network functions include, but are not limited to, an Access and Mobility Management Function (AMF) 402 (shown in in FIG. 4), a Location Management Function (LMF), a base station or gNodeB. The AMF 402 manages the connection and mobility of the UE 102, including the tracking of the UE's 102 location. The LMF specifically handles the determination and management of the UE’s 102 location. The base station or gNodeB provides the cell ID and possible other location related information to the network 106. In an embodiment, in the present invention, the one or more network function is at least one of the AMF 402.
[0047] In an embodiment, the data pertaining to the location is retrieved via the location wise TAI corresponding to one or more identifiers of the UE 102. The location wise TAI is a unique identifier assigned to a specific tracking area within the network 106. The one or more identifier is at least one of, but not limited to, a Subscription Permanent Identifier (SUPI) and International Mobile Subscriber Identifier (IMSI). The SUPI is a unique identifier used in the 5th Generation (5G) networks 106 to permanently identify a subscriber. The subscriber refers to an individual or entity that has signed up for and is authorized to use the services provided by a network operator. The IMSI is a unique identifier to identify and authenticate the subscriber within the network 106. The IMSI includes three parts such as Mobile Country Code (MCC), Mobile Network Code (MNC) and Mobile Subscriber Identification Number (MSIN). The MCC identifies the country of the subscriber. The MNC identifies the mobile network operator within that country. The MSIN uniquely identifies the subscriber within the operator’s network 106. For example, an IMSI might be "310-260-1234567," where "310" is the MCC (USA), "260" is the MNC (AT&T), and "1234567" is the MSIN.
[0048] Thereafter, the identification unit 214 is configured to identify one or more hotspots in the network 106 by utilizing the data pertaining to the location of the UE 102. The one or more hotspots refer to specific areas or locations within the network 106 where there is a significant concentration of network activity or usage. In an embodiment, the one or more hotspots correspond to the one or more locations in the network 106 where data pertaining to at least one of cell loading and data traffic consumption of the UE 102 is higher than the data pertaining to at least one of the cell loading and the data traffic consumption of the UE 102 at other locations of the network 106. The cell loading refers to the level of usage or demand on a cell within the network 106. The cell loading measures the number of active connections or the volume of data being handled by the cell. The cell is a distinct region within the network 106 served by a single base station or cell tower. The cell loading is measured based on at least one of, the number of users or UE 102 connected to the cell, the amount of data transmitted or received by the cell, and the percentage of the cell’s capacity that is being utilized. The data traffic consumption refers to the amount of data transmitted and received by the UE 102 within a particular area of the network 106 over a specified period. The data traffic consumption is measured based on at least on of, the volume of data transferred (measured in bytes, kilobytes, megabytes, etc.), the rate of data transmission, and the peak data usage times.
[0049] More specifically, the one or more hotspots are identified by collecting the data pertaining to the cell loading and the data traffic consumption from the various cells across the network 106. The data pertaining to the cell loading and the data traffic is retrieved from the AMF 402. Subsequently, the collected data is analyzed to compare the cell loading and the data traffic consumption across different locations in the network 106. The predefined thresholds are used to identify when the metrics for a particular location exceed normal levels, indicating potential hotspots. The predefined thresholds are specific values or limits set by network administrators that serve as benchmarks for evaluating network performance metrics. Based on the comparison, the locations where the cell loading, and the data traffic consumption is consistently higher than in other areas are flagged as hotspots. For example, in a cell covering a popular sports stadium, the cell loading reaches 75% during a major event. Since this exceeds the 70% predefined threshold, the network identifies this area as a hotspot, indicating the need for potential action such as load balancing or capacity enhancement.
[0050] Upon identifying the one or more hotspots, the forecasting unit 216 is configured to forecast one or more future hotspots. The one or more future hotspots are the locations within the network 106 where increased network usage, such as higher cell loading or data traffic consumption, is expected to occur. The one or more future hotspots are forecasted by utilizing at least one of data corresponding to the one or more identified hotspots and data pertaining to the historical location of the UE 102 in the network 106. The data corresponding to the one or more identified hotspots includes the data pertaining to the cell loading and the data traffic consumption in the one or more identified hotspots. The data pertaining to the historical location of the UE 102 includes the past location data of the UE 102 such as frequent movements and usage patterns.
[0051] For example, consider a city with a popular shopping mall that becomes a hotspot every weekend due to heavy foot traffic and high data usage. The forecasting unit 216 identifies this area as a current hotspot and examines historical data showing that users in this area tend to move towards a nearby park in the afternoons, where data traffic starts increasing. Based on this data, the forecasting unit 216 predicts that the nearby park is likely to become a future hotspot, especially on weekends, as more users move there after visiting the shopping mall.
[0052] In an embodiment, the forecasting unit 216 utilizes algorithms and AI/ML model to analyze patterns and trends in the data to forecast the one or more future hotspots. The patterns are specific, recognizable sequences or arrangements in the data that occur repeatedly. For example, if the system 108 detects that every weekday morning from 8 AM to 10 AM, a particular train station becomes a hotspot due to commuter data usage, this pattern can be used to predict future hotspots at the same station on weekdays. The trends refer to the overall direction in which certain metrics or behaviors are moving over time. For example, if there’s a trend showing that data usage in suburban areas is increasing every month, possibly due to more people moving there, the system might forecast that these areas will require more network resources in the future to handle the growing demand.
[0053] More specifically, the forecasting of the one or more future hotspots includes but is not limited to, data collection, data processing, algorithmic modeling. The data collection includes receiving the data pertaining to the historical location of the UE 102, the cell loading and the data traffic consumption. Further, the data collection includes accessing the historical data pertaining to the UE 102 movements, previous hotspot locations, and past network performance metrics, which are crucial for understanding patterns and trends over time. The data processing includes pattern recognition, time-series analysis, spatial analysis. The pattern recognition technique is to identify recurring patterns in the UE’s 102 behavior such as repeated visits to specific locations at particular times (For example, daily commute patterns, events etc.). The time series analysis is applied to understand trends over time, such as increasing data usage in a specific area or growing cell loading during particular hours or days. The spatial analysis is performed to understand how traffic is distributed across different areas and how it shifts over time. The algorithmic modeling includes machine learning models, predictive algorithms, anomaly detection. The machine learning models trained on historical data are utilized to forecast the one or more future hotspots. The machine learning models are designed to learn from past trends and apply the knowledge to predict future events. For example, a model might learn that a certain area tends to become a hotspot every weekday evening due to a regular influx of users. The predictive algorithms are used to extrapolate current trends into the future. For instance, if data traffic is increasing steadily in a certain area, the model can forecast when this area will reach the threshold to become the hotspot. The anomaly detection algorithm is used to identify unexpected surges in traffic that deviate from normal patterns, helping to forecast sudden, unanticipated hotspots.
[0054] Upon forecasting the one or more future hotspots, the creation unit 218 is configured to create one or more policies to at least one of latch and release the UE 102 at the forecasted hotspot. The one or more policies refer to specific rules or actions that are created and enforced by the network to manage the UE 102 at the forecasted hotspots. The one or more policies includes, but are not limited to, connection stability, load balancing, handover management, congestion avoidance, priority handling, resource allocation, access control, data protection. The connection stability includes policies that determine when the UE 102 should remain connected to a specific cell, particularly in areas where stable connectivity is critical, such as high-traffic zones or forecasted hotspots. The load balancing includes the rules that dictate how UEs 102 are distributed across cells to prevent overloading a single cell, ensuring an even distribution of network traffic. The handover management includes the policies that manage when and how the UE 102 should be released from a congested cell and handed over to a neighboring cell, based on factors like signal strength, cell loading, and UE mobility patterns. The congestion avoidance includes the strategies that trigger the release of UEs 102 from the hotspot when the cell reaches a certain threshold of congestion, directing them to less crowded cells to maintain service quality. The priority handling includes the policies that prioritize certain types of traffic (e.g., emergency services, VoIP calls) over others in the forecasted hotspots. The resource allocation includes the rules that dynamically allocate network resources (e.g., bandwidth, spectrum) to different UEs 102 based on their needs, ensuring efficient utilization of available resources in the hotspot. The access control includes the policies that enforce authentication and access control measures for the UEs 102 connecting to the network 106 in the forecasted hotspots. The data protection includes the rules that ensure the encryption and secure handling of data traffic in hotspots, particularly in areas with high user density where security risks may be higher.
[0055] In an embodiment, the latching of the UE 102 at the forecasted hotspot refers that the network 106 decides to keep the UE 102 connected to a specific cell within the forecasted hotspot. The network 106 may latch the UE 102 to a cell to prevent unnecessary handovers (switching from one cell to another), which could cause service interruptions or degrade the quality of the connection. For example, the user using UE 102 (like a smartphone) is in a busy shopping mall during the holiday season, at the forecasted hotspot. The network 106 might latch the UE 102 to a specific cell within the mall to ensure that the UE 102 stays connected to that cell, even if the user moves within the mall, thus providing a consistent and high-quality service. The release of the UE 102 at the forecasted hotspot refers to the network 106 deciding to disconnect the UE 102 from its current cell and either allow it to connect to a different cell or enter an idle state. The release action is used to manage network resources efficiently, especially when the cell within the forecasted hotspot becomes congested. By releasing some UEs 102, the network 106 can prevent overloading the cell and maintain overall network performance. For example, continuing with the shopping mall scenario, if the cell handling the UE 102 becomes too crowded, the network 106 might release the UE 102 from that cell and connect it to a nearby cell with less traffic. Alternatively, if the user using the UE 102 is leaving the mall, the network 106 might release the UE 102 to allow a smooth transition to an outdoor cell with better coverage for the new location.
[0056] Therefore, the system 108 predicts the hotspots in real time and helps in load balancing by allocating the UEs 102 from one cell to the another during peak hours. The system 108 optimized the network resource utilization by identifying and forecasting the hotspots. Further, the system 108 enhance the user experience and improves the network stability and performance.
[0057] FIG. 3 describes a preferred embodiment of the system 108 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 102a and the system 108 for the purpose of description and illustration and should nowhere be construed as limited to the scope of the present disclosure.
[0058] As mentioned earlier in FIG. 1, each of the first UE 102a, the second UE 102b, and the third UE 102c 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 102a without deviating from the scope of the present disclosure and limiting the scope of the present disclosure. The first UE 102a includes one or more primary processors 302 communicably coupled to the one or more processors 202 of the system 108.
[0059] The one or more primary processors 302 are coupled with a memory 304 storing instructions which are executed by the one or more primary processors 302. Execution of the stored instructions by the one or more primary processors 302 enables the first UE 102a to transmit the request for the dispersion analysis of the network to the one or more processors 202.
[0060] As mentioned earlier in FIG. 2, the one or more processors 202 of the system 108 is configured to manage the network 106. As per the illustrated embodiment, the system 108 includes the one or more processors 202, the memory 204, the user interface 206, and the database 208. The operations and functions of the one or more processors 202, the memory 204, the user interface 206, and the database 208 are already explained in FIG. 2. For the sake of brevity, a similar description related to the working and operation of the system 108 as illustrated in FIG. 2 has been omitted to avoid repetition.
[0061] Further, the processor 202 includes the receiving unit 210, the retrieving unit 212, the identification unit 214, the forecasting unit 216, and the creation unit 218. The operations and functions of the receiving unit 210, the retrieving unit 212, the identification unit 214, the forecasting unit 216, and the creation unit 218 are already explained in FIG. 2. Hence, for the sake of brevity, a similar description related to the working and operation of the system 108 as illustrated in FIG. 2 has been omitted to avoid repetition. The limited description provided for the system 108 in FIG. 3, should be read with the description as provided for the system 108 in the FIG. 2 above, and should not be construed as limiting the scope of the present disclosure.
[0062] FIG. 4 is an exemplary block diagram of an architecture 400 of the system 108 for managing the network 106, according to one or more embodiments of the present invention.
[0063] The architecture 400 includes the AMF 402, a Network Data Analytics Function (NWDAF) backend (BE) 404, a NWDAF Artificial Intelligence/Machine learning (AI/ML) model 406, and a data consumers 408. In an embodiment, the NWDAF represents operator managed network analytics logical function. The NWDAF BE is the decision-making block i.e., backend determines network performance, user experience, security and work actively over closed loop network operations. The NWDAF BR is also responsible for data collection from various data sources. The AI/ML models within the NWDAF are key components that enable advanced data-driven analytics and decision-making. The NWDAF AI/ML model works closely with decision-making blocks, proactively manages network performance and train its ML model with the analyzed data.
[0064] In an embodiment, the request for dispersion analysis of the network 106 is received from the UE 102. Upon receiving the request, the data pertaining to the location of the UE 102 is retrieved from the AMF 402. The AMF 402 is responsible for managing UE's 102 access and mobility within the network 106. More specifically, the AMF 402 collects the data related to the UE 102 location, the cell loading and the data traffic consumption and transmits the data to the NWDAF BE 404. The data pertaining to the location is retrieved via the location-wise TAI corresponding to one or more identifiers of the UE 102. The one or more identifiers is at least one of the SUPI and the IMSI.
[0065] Upon receiving the data pertaining to the UE 102 location, the cell loading and the data traffic consumption from the AMF 402, the NWDAF BE 404 analyzes the data. Thereafter, the NWDAF BE 404 identifies the one or more hotspots in the network 106 with the help of the NWDAF AI/ML model 406. The one or more hotspots correspond to the one or more locations in the network 106 where data pertaining to at least one of cell loading and data traffic consumption of the UE 102 is higher than the data pertaining to at least one of the cell loading and the data traffic consumption of the UE 102 at other locations of the network 106.
[0066] Based on the at least one of data corresponding to the one or more identified hotspots and data pertaining to the historical location of the UE 102 in the network 106, the NWDAF AI/ML model 406 forecast the one or more future hotspots. The NWDAF AI/ML model 406 are trained on historical data to improve their accuracy in predicting events like hotspot formation or congestion in the network 106. The historical data refers to the collection of past information and records related to network performance, user behavior, and other relevant metrics that have been gathered over time. The historical data include, but not limited to, the UE 102 location data, cell loading data, data traffic consumption data, event logs, Quality of Service (QoS) metrics, environmental data.
[0067] Subsequently, the data consumers 408 creates the one or more policies to manage the UEs 102 at the forecasted hotspots. The one or more policies is involved in determining whether to latch or release the UE 102 at the forecasted hotspots.
[0068] FIG. 5 is a signal flow diagram for managing the network 106, according to one or more embodiments of the present invention.
[0069] At step 502, the request for the dispersion analysis of the network 106 is received from the UE 102.
[0070] At step 504, upon receiving the request, the AMF 402 retrieves the data pertaining to the location of the UE 102. The AMF 402 collects the data pertaining to the UE 102 location, the cell loading and the data traffic consumption and transmits the data to the NWDAF BE 404.
[0071] At step 506, based on receiving the data pertaining to the UE 102 location, the cell loading and the data traffic consumption, the NWDAF BE 404 identifies the one or more hotspots in the network 106 by utilizing the data pertaining to the location of the UE 102. The one or more hotspots correspond to the one or more locations in the network 106 where data pertaining to at least one of cell loading and data traffic consumption of the UE 102 is higher than the data pertaining to at least one of the cell loading and the data traffic consumption of the UE 102 at other locations of the network 106.
[0072] At step 508, based on the at least one of data corresponding to the one or more identified hotspots and data pertaining to the historical location of the UE 102 in the network 106, the NWDAF AI/ML model 406 forecast the one or more future hotspots. In an embodiment, the AI/ML model within NWDAF uses the identified hotspots and historical data of the UE's 102 locations to predict future hotspots. The NWDAF AI/ML model analyzes patterns and trends in the data to forecast the one or more future hotspots. The NWDAF AI/ML model can consider various factors, such as time of day, mobility patterns, and past traffic data, to make accurate predictions.
[0073] At step 510, subsequently, the data consumers 408 creates one or more policies to manage UEs 102 at the forecasted hotspots. The one or more policies is involved in determining whether to latch or release the UE 102 at the forecasted hotspots.
[0074] FIG. 6 is a flow diagram of a method 600 for managing the network 106, according to one or more embodiments of the present invention. For the purpose of description, the method 600 is described with the embodiments as illustrated in FIG. 2 and should nowhere be construed as limiting the scope of the present disclosure.
[0075] At step 602, the method 600 includes the step of receiving the request for the dispersion analysis of the network via the UE 102 by the receiving unit 210.
[0076] At step 604, the method 600 includes the step of retrieving the data pertaining to the location of the UE 102 from one or more network functions upon receipt of the request by the retrieving unit 212. The one or more network functions is at least one of the AMF 402. The data pertaining to the location is retrieved via the location-wise TAI corresponding to one or more identifiers of the UE 102. The one or more identifier is at least one of the SUPI and the IMSI.
[0077] At step 606, the method 600 includes the step of identifying the one or more hotspots in the network 106 by utilizing the data pertaining to the location of the UE 102 by the identification unit 214. The one or more hotspots correspond to the one or more locations in the network 106 where data pertaining to at least one of cell loading and data traffic consumption of the UE 102 is higher than the data pertaining to at least one of the cell loading and the data traffic consumption of the UE 102 at other locations of the network 106.
[0078] At step 608, the method 600 includes the step of forecasting the one or more future hotspots by utilizing the at least one of data corresponding to the one or more identified hotspots and data pertaining to the historical location of the UE 102 in the network 106 by forecasting unit 216.
[0079] At step 610, the method 600 includes the step of creating the one or more policies to at least one of latch and release the UE 102 at the forecasted hotspot by the creation unit 218.
[0080] 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 202. The processor 202 is configured to receive the request for the dispersion analysis of the network via the UE 102. The processor 202 is further configured to retrieve data pertaining to the location of the UE 102 from one or more network functions upon receipt of the request. The processor 202 is further configured to identify the one or more hotspots in the network 106 utilizing the data pertaining to the location of the UE 102. The processor 202 is further configured to forecast the one or more future hotspots utilizing at least one of data corresponding to the one or more identified hotspots and data pertaining to the historical location of the UE 102 in the network 106. The processor 202 is further configured to create the one or more policies to at least one of latch and release the UE 102 at the forecasted hotspot.
[0081] 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.
[0082] The present disclosure incorporates technical advancement of predicting the one or more hotspots in real time. Thus, the present invention reduces congestion, improves user experience and maximizes network performance particularly in high-density or high- traffic areas. The present invention helps in load balancing by allocating the UEs from one location to another during peak hours. Further, user experience is improved by latching and releasing the UE in the hotspots in real time. The present invention helps in maintaining a consistent quality of service (QoS) and enhances the overall reliability of the network. Further, the present invention leads to better utilization of network infrastructure, reducing operational costs for network operators while improving service quality for end-users. Furthermore, the present invention results in better network stability and efficiency, as the network can take preemptive actions to prevent or mitigate potential issues before they impact the user experience.
[0083] 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

[0084] Environment- 100
[0085] User Equipment (UE)- 102
[0086] Server- 104
[0087] Network- 106
[0088] System -108
[0089] Processor- 202
[0090] Memory- 204
[0091] User Interface- 206
[0092] Database- 208
[0093] Receiving Unit- 210
[0094] Retrieving Unit- 212
[0095] Identification unit- 214
[0096] Forecasting Unit- 216
[0097] Creation Unit- 218
[0098] One or more primary processor- 302
[0099] Memory- 304
[00100] AMF- 402
[00101] NWDAF BE- 404
[00102] NWDAF AI/ML model -406
[00103] Data consumers- 408
,CLAIMS:CLAIMS:
We Claim:
1. A method (600) of managing a network (106), the method (600) comprising the steps of:
receiving, by one or more processors (202), a request for a dispersion analysis of the network (106) via a User Equipment (UE) (102);
retrieving, by the one or more processors (202), data pertaining to a location of the UE (102) from one or more network functions upon receipt of the request;
identifying, by the one or more processors (202), one or more hotspots in the network utilizing the data pertaining to the location of the UE (102);
forecasting, by the one or more processors (202), one or more future hotspots utilizing at least one of data corresponding to the one or more identified hotspots and data pertaining to the historical location of the UE (102) in the network (106); and
creating, by the one or more processors (202), one or more policies to at least one of latch and release the UE (102) at the forecasted hotspot.

2. The method (600) as claimed in claim 1, wherein one or more hotspots correspond to one or more locations in the network (106) where data pertaining to at least one of cell loading and data traffic consumption of the UE (102) is higher than the data pertaining to at least one of the cell loading and the data traffic consumption of the UE (102) at other locations of the network (106) .

3. The method (600) as claimed in claim 1, wherein the one or more network functions is at least one of an Access and Mobility management Function (AMF) (402).

4. The method (600) as claimed in claim 1, wherein the data pertaining to the location is retrieved via a location wise Tracking Area Identity (TAI) corresponding to one or more identifiers of the UE (102), wherein the one or more identifier is at least one of a Subscription Permanent Identifier (SUPI) and International Mobile Subscriber Identifier (IMSI).

5. A system (108) for managing a network, the system (108) comprising:
a receiving unit (210) configured to receive, a request for a dispersion analysis of the network via a User Equipment (UE) (102);
a retrieving unit (212) configured to retrieve, data pertaining to a location of the UE (102) from one or more network functions upon receipt of the request;
an identification unit (214) configured to identify, one or more hotspots in the network utilizing the data pertaining to the location of the UE (102);
a forecasting unit (216) configured to forecast, one or more future hotspots utilizing at least one of data corresponding to the one or more identified hotspots and data pertaining to the historical location of the UE (102) in the network (106); and
a creation unit (218) configured to create, one or more policies to at least one of latch and release the UE (102) at the forecasted hotspot.

6. The system (108) as claimed in claim 5, wherein the one or more hotspots correspond to the one or more locations in the network (106) where data pertaining to at least one of cell loading and data traffic consumption of the UE (102) is higher than the data pertaining to at least one of the cell loading and the data traffic consumption of the UE (102) at other locations of the network (106).

7. The system (108) as claimed in claim 5, wherein the one or more network functions is at least one of an Access and Mobility management Function (AMF) (402).

8. The system (108) as claimed in claim 5, wherein the data pertaining to the location is retrieved via a location wise Tracking Area Identity (TAI) corresponding to one or more identifiers of the UE, wherein the one or more identifier is at least one of a Subscription Permanent Identifier (SUPI) and International Mobile Subscriber Identifier (IMSI).

9. A User Equipment (UE) (102) comprising:
one or more primary processors (302) communicatively coupled to one or more processors (202), the one or more primary processors (302) coupled with a memory (304), wherein said memory (304) stores instructions which when executed by the one or more primary processors (302) causes the UE (102) to:
transmit, a request for a dispersion analysis of the network (106) to the one or more processors (202), wherein the one or more processors (202) is configured to perform the steps as claimed in claim 1.