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

COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. TITLE OF THE INVENTION
SYSTEM AND METHOD FOR OPTIMIZING A NETWORK FOR IMPROVING QUALITY OF SERVICES (QoS)
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 wireless communication systems, more particularly relates to a method and a system for network optimization.
BACKGROUND OF THE INVENTION
[0002] In today's interconnected world, communication networks play a crucial role in delivering various services to users. Ensuring a high-quality experience for subscribers is a key objective for service providers. However, achieving and maintaining optimal QoS in communication networks can be challenging due to several factors.
[0003] One of the primary issues in QoS management is the dynamic nature of subscriber behavior and network conditions. Subscribers have diverse preferences, usage patterns, and requirements that continually change over time. Network conditions, such as congestion, bandwidth limitations, and varying traffic loads, further impact the QoS experience.
[0004] Traditional QoS management approaches often rely on predefined parameters and static thresholds. These methods may not effectively adapt to the dynamic nature of subscriber behavior and network demands. Consequently, service providers may struggle to allocate resources efficiently, leading to degraded QoS, service interruptions, and customer dissatisfaction.
[0005] There is therefore a need for a solution that overcomes the above challenges and provides a method and a system for optimizing a network for improving quality of services (QoS).
SUMMARY OF THE INVENTION
[0006] One or more embodiments of the present disclosure provides a method and a system for optimizing a network for improving quality of services (QoS).
[0007] In one aspect of the present invention, a method for optimizing the network for improving quality of services (QoS) is disclosed. The method includes the step of receiving, by one or more processors, data from a Network Function (NF), wherein the data is associated with a plurality of subscribers. The method further includes the step of grouping, by the one or more processors, the subscribers in one or more clusters based on one or more parameters pertaining to the subscribers identified from the analysis of the data. The method further includes the step of allocating, by the one or more processors, one or more network resources to a cluster of the one or more clusters for proper functioning of network slices for improving quality of services (QoS) for the plurality of subscribers.
[0008] In another embodiment, the data includes at least data usage and call summaries of each of the plurality of subscribers.
[0009] In yet another embodiment, the data is analysed using Artificial Intelligence (AI)/Machine Learning (ML) models for grouping the subscribers.
[0010] In yet another embodiment, the step of allocating the one or more network resources includes distributing network traffic of the cluster for proper functioning of the network slice.
[0011] In another aspect of the present invention a system for optimizing the network for improving quality of services (QoS) is disclosed. The system includes a transceiver unit configured to receive data from a Network Function (NF), wherein the data is associated with a plurality of subscribers. The system includes an analysis unit configured to analyse the received data. The system further includes a grouping unit configured to group the subscribers in one or more clusters based on one or more of parameters pertaining to the subscribers identified from the analysis of the data. The system further includes an allocation unit configured to allocate one or more network resources to a cluster of the one or more clusters for proper functioning of network slices for improving quality of services (QoS) for the plurality of subscribers.
[0012] 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. The processor is configured to receive data from a Network Function (NF), wherein the data is associated with a plurality of subscribers. The processor is further configured to group the subscribers in one or more clusters based on one or more parameters pertaining to the subscribers identified from analysis of the data. The processor is further configured to allocate one or more network resources to a cluster of the one or more clusters for proper functioning of network slices for improving quality of services (QoS) for the plurality of subscribers.
[0013] 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
[0014] 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.
[0015] FIG. 1 is an exemplary block diagram of an environment for optimizing a network for improving quality of services (QoS), according to one or more embodiments of the present invention;
[0016] FIG. 2 is an exemplary block diagram of a system for optimizing the network for improving quality of services (QoS), according to one or more embodiments of the present invention;
[0017] FIG. 3 is an exemplary architecture of the system of FIG. 2, according to one or more embodiments of the present invention;
[0018] FIG. 4 is an exemplary signal flow diagram illustrating the flow for optimizing the network for improving quality of services (QoS), according to one or more embodiments of the present disclosure; and
[0019] FIG. 5 is a flow diagram of a method for optimizing the network for improving quality of services (QoS), according to one or more embodiments of the present invention.
[0020] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0021] 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.
[0022] 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.
[0023] 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.
[0024] The present invention discloses a system and a method for optimizing network for improving quality of services (QoS). More particularly, the system described herein offers a comprehensive approach for improving the QoS for subscribers within the network. By collecting and analyzing subscriber data, identifying characteristics, and creating clusters based on shared patterns and behaviors, the system optimizes service management. The system uses an Artificial Intelligence/Machine Learning (AI/ML) model to analyze the subscriber data. This leads to enhanced network performance, improved resource allocation, and ultimately, a better QoS experience for the subscribers.
[0025] Referring to FIG. 1, FIG. 1 illustrates an exemplary block diagram of an environment 100 for optimizing a network 106 for improving quality of services (QoS), according to one or more embodiments of the present invention. The environment 100 includes a User Equipment (UE) 102, a server 104, a network 106, a system 108, and one or more Network Functions (NFs) 110. A user interacts with the system 108 utilizing the UE 102.
[0026] For the purpose of description and explanation, the description will be explained with respect to one or more user equipment’s (UEs) 102, or to be more specific will be explained with respect 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. Each of the at least one UE 102 namely the first UE 102a, the second UE 102b, and the third UE 102c is configured to connect to the server 104 via the network 106.
[0027] In an embodiment, each of the first UE 102a, the second UE 102b, and the third UE 102c 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 smartphones, 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.
[0028] 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.
[0029] 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.
[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, a processor 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] In an embodiment, the one or more Network Functions (NFs) 110 are configured to perform tasks and provide services to enable network operations and communications. The one or more NFs 110 are responsible for performing tasks and providing services, such as routing, switching, authentication, security, billing, and more. The one or more NFs 110 play a crucial role in the efficient and reliable operation of networks 106. For example, the 5G NFs 110 includes at least one of, but not limited to, an Access and Mobility Management Function (AMF), a Session Management Function (SMF) and a User Plane Function (UPF).
[0032] The environment 100 further includes the system 108 communicably coupled to the server 104, the one or more NFs 110, and the UE 102 via the network 106. The system 108 is adapted to be embedded within the server 104 or is embedded as the individual entity.
[0033] Operational and construction features of the system 108 will be explained in detail with respect to the following figures.
[0034] FIG. 2 is an exemplary block diagram of the system 108 for optimizing the network 106 for improving quality of services (QoS), according to one or more embodiments of the present invention.
[0035] As per the illustrated and preferred embodiment, the system 108 for optimizing the network 106 for improving quality of services (QoS), the system 108 includes one or more processors 202, a memory 204, and a storage unit 206. 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. However, it is to be noted that the system 108 may include multiple processors as per the requirement and without deviating from the scope of the present disclosure. Among other capabilities, the processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204.
[0036] As per the illustrated embodiment, the processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204 as the memory 204 is communicably connected to the processor 202. The memory 204 is 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 optimize the network for improving quality of services (QoS). 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.
[0037] As per the illustrated embodiment, the storage unit 206 is configured to store data associated with a plurality of subscribers in the network 106. The storage unit 206 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 storage unit 206 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.
[0038] As per the illustrated embodiment, the system 108 includes the processor 202 to optimize the network 106 for improving quality of services (QoS). The processor 202 includes a transceiver unit 208, an analysis unit 210, a grouping unit 212, and an allocation unit 214. The processor 202 is communicably coupled to the one or more components of the system 108 such as the storage unit 206, and the memory 204. In an embodiment, operations and functionalities of the transceiver unit 208, the analysis unit 210, the grouping unit 212, the allocation unit 214, and the one or more components of the system 108 can be used in combination or interchangeably.
[0039] In one embodiment, the user such as a network operator provides various services to the plurality of subscribers which includes at least one of, but not limited to, voice services, data services, internet based services, and cloud based services. In particular, the user provides telephony services to the plurality of subscribers. In order to maintain high quality experience for the plurality of subscribers, the user optimizes the quality of services (QoS).
[0040] In an embodiment, the transceiver unit 208 of the processor 202 is configured to receive data from the one or more NFs 110. In particular, the received data is associated with the plurality of subscribers. The data pertaining to each of the plurality of subscribers includes at least one of, but not limited to, a data usage, call summaries, a location information, a type of device, and one or more applications used by the plurality of subscribers. In an embodiment, the one or more NFs 110 serves as a data collector within the system 108. The one or more NFs 110 gathers various data associated with the plurality of subscribers of the network 106. Further, the transceiver unit 208 receives the data from the one or more NFs 110.
[0041] In an alternate embodiment, the system 108 also includes a probing agent. The probing agent acts as an intermediary between the one or more NFs 110 and the system 108. The probing agent receives the data associated with the plurality of subscribers collected by the one or more NFs 110 and provides the collected data to the processor 202 of the system 108. In one embodiment, the probing agent is responsible for storing the collected data in the storage unit 206, such as a database. By efficiently managing the storage of the data associated with the plurality of subscribers, the probing agent ensures that the system 108 can access and utilize the data effectively.
[0042] In an alternate embodiment, subsequent to receiving the data from the one or more NFs 110, the processor 202 is configured to normalize the data received at the transceiver unit 208 pertaining to plurality of subscribers. In particular, the processor 202 may include a normalizer to preprocess the received data. The normalizer performs at least one of, but not limited to, data normalization. The data normalization is the process of at least one of, but not limited to, reorganizing the received data, removing the redundant data within the received data, formatting the received data and removing null values from the received data. The main goal of the the normalizer is to achieve a standardized data format across the entire system 108. The normalizer ensures that the normalized data is stored appropriately in the storage unit 206 for subsequent retrieval and analysis. In one embodiment, the data received by the transceiver unit 208 is normalized by the normalizer of the processor 202.
[0043] In an embodiment, the analysis unit 210 of the processor 202 is configured to analyze the received data associated with the plurality of subscribers. In an alternate embodiment, the analysis unit 210 of the processor 202 is configured to analyze the normalized data associated with the plurality of subscribers. In one embodiment, the analysis unit 210 is at least one of, but not limited to, an Artificial Intelligence/Machine Learning (AI/ML) model. The analysis unit 210 tracks and monitors one or more parameters pertaining to the subscribers. The one or more parameters includes at least one of, but not limited to, characteristics, trends, patterns, and behavior of the plurality of subscribers.
[0044] In one embodiment, the characteristics of the plurality of subscribers are the qualities or features that belongs to the plurality of subscribers which make them special or different from the other subscribers. For example, subscriber A is the subscriber which always utilizes the highest subscription plan provided by the network operator. In one embodiment, the trend is a general change in one variable compared to another variable over a period of time. For example, the trends pertain to changes in the prices of the subscription plans for the plurality of subscribers provided by the network operator. In one embodiment, the pattern is a series of data that repeats in a recognizable way. For example, the pattern pertains to the data usage of the subscriber every day such as the subscriber is using at least 50% of their daily data limit. In one embodiment, the behavior of the plurality of the subscribers refers to subscribers interactions while utilizing the services. For example, the plurality of the subscribers actively or daily utilizing the services provided by the network operator.
[0045] Further, the normalized data associated with the plurality of subscribers is utilized by the analysis unit 210 in order to train itself. While training, the analysis unit 210 learns the one or more parameters such as at least one of, but not limited to, trends, patterns, behavior and characteristics of the plurality of subscribers. For example, the system 108 selects an appropriate analysis unit 210, such as at least one of, but not limited to, a neural network or a decision tree logic, from a set of available options of the analysis unit 210. Thereafter, the selected analysis unit 210 is trained using the normalized data. In one embodiment, the selected analysis unit 210 is trained on historical data pertaining to the plurality of subscribers. Based on training, the selected analysis unit 210 analyzes the received data and identifies the one or more parameters which includes at least one of, but not limited to, trends, patterns, behavior and characteristics of the plurality of subscribers by applying one or more logics.
[0046] 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.
[0047] In an alternate embodiment, it is to be noted that the analysis unit 210 may be trained by a separate entity such as a training unit utilizing the normalized data without deviating from the scope of the present disclosure. In particular, the training unit receives the normalized data from the normalizer and trains the analysis unit 210 utilizing the normalized data.
[0048] In an embodiment, the grouping unit 212 of the processor 202 is configured to group the plurality of subscribers in one or more clusters based on one or more parameters pertaining to the subscribers identified from the analysis of the received data. The one or more clusters are the groups of plurality of subscribers. In particular, the grouping unit 212 creates the one or more clusters based on the one or more parameters which includes at least one of, the one or more of common patterns, the behavior and the characteristics of the plurality of subscribers. In particular, the grouping unit 212 divides the total number of the plurality of subscribers into smaller groups or one or more clusters based on similarities of the one or more parameters of the plurality of subscribers. For example, let us assume a subscriber A and a subscriber B among the plurality of subscribers are utilizing 100% of their data daily. Based on this behavior, the subscriber A and the subscriber B will be grouped within the same cluster.
[0049] In an embodiment, the allocation unit 214 of the processor 202 is configured to allocate one or more network resources to a cluster of the one or more clusters for proper functioning of network slices for improving quality of services (QoS) for the plurality of subscribers. The one or more network resources includes at least one of, but not limited to, memory, Central Processing Unit (CPU), nodes, server, and bandwidth. The quality of service (QoS) is a description or measurement of the overall performance of the services such as the telephony service and the cloud based service. The quality of service (QoS) guarantees a specific level of output for a specific connection, path, or type of traffic. In one embodiment, the plurality subscribers of the one or more clusters are related to a particular network slice. For example, if a specific cluster of plurality of subscribers experiences high traffic, then the allocation unit 214 allocates the required resources to that specific cluster in order to manage the high traffic by distributing the high traffic among the allocated resources of the specific cluster for proper functioning of the network slice.
[0050] In one embodiment, a network slicing is a technique that creates multiple virtual networks on top of a shared physical network to provide greater flexibility in the use and allocation of one or more network resources. The network slicing is used most often in the 5G networks. Each slice of the network 106 can have its own logical topology, security rules and performance characteristics. A network slice is a logical network that provides specific network capabilities and network characteristics, supporting various service properties for network slice subscribers.
[0051] The transceiver unit 208, the analysis unit 210, the grouping unit 212, and the allocation unit 214, in an exemplary embodiment, are 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 202. 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.
[0052] FIG. 3 illustrates an exemplary architecture for the system 108, according to one or more embodiments of the present invention. More specifically, FIG. 3 illustrates the system 108 for optimizing the network 106 for improving quality of services (QoS). It is to be noted that the embodiment with respect to FIG. 3 will be explained with respect to the UE 102 for the purpose of description and illustration and should nowhere be construed as limited to the scope of the present disclosure.
[0053] FIG. 3 shows communication between the UE 102, the system 108, and the one or more NFs 110. For the purpose of description of the exemplary embodiment as illustrated in FIG. 3, the UE 102, and the one or more NFs 110 uses network protocol connection to communicate with the system 108. In an embodiment, the network protocol connection is the establishment and management of communication between the UE 102, the one or more NFs 110 and system 108 over the network 106 (as shown in FIG. 1) using a specific protocol or set of protocols. The network protocol connection includes, but not limited to, Session Initiation Protocol (SIP), System Information Block (SIB) protocol, Transmission Control Protocol (TCP), User Datagram Protocol (UDP), File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP), Simple Network Management Protocol (SNMP), Internet Control Message Protocol (ICMP), Hypertext Transfer Protocol Secure (HTTPS) and Terminal Network (TELNET).
[0054] In an embodiment, the UE 102 includes a primary processor 302, and a memory 304 and a User Interface (UI) 306. In alternate embodiments, the UE 102 may include more than one primary processor 302 as per the requirement of the network 106. The primary processor 302, 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.
[0055] In an embodiment, the primary processor 302 is configured to fetch and execute computer-readable instructions stored in the memory 304. The memory 304 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 discovery of at least one asset in the network 106. The memory 304 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.
[0056] In an embodiment, the User Interface (UI) 306 includes a variety of interfaces, for example, a graphical user interface, a web user interface, a Command Line Interface (CLI), and the like. The User Interface (UI) 306 allows the user to request the system 108 for optimizing the network 106 for improving quality of services (QoS). In one embodiment, the user may include at least one of, but not limited to, a network operator.
[0057] For example, the system 108 accurately analyzes and tracks the plurality of subscribers behavior which allows the system 108 to identify the one or more parameters of the plurality of subscribers. This enables the system 108 for creation of one or more clusters of subscribers with similar one or more parameters. With the ability to identify the specific cluster with high traffic among the one or more clusters, the system 108 allocates additional resources to the specific cluster, resulting in improved QoS for the plurality of subscribers within the specific cluster.
[0058] As mentioned earlier in FIG.2, the system 108 includes the processors 202, the memory 204, and the storage unit 206, for optimizing the network 106 for improving quality of services (QoS) 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.
[0059] Further, as mentioned earlier the processor 202 includes the transceiver unit 208, the analysis unit 210, the grouping unit 212, and the allocation unit 214, which 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 provided for the system 108 in the FIG. 2 above, and should not be construed as limiting the scope of the present disclosure.
[0060] FIG. 4 is a signal flow diagram illustrating the flow for optimizing the network 106 for improving quality of services (QoS), according to one or more embodiments of the present disclosure.
[0061] At step 402, the one or more NFs 110 transmits the data related to the plurality of subscribers to the system 108.
[0062] At step 404, the system 108 analyses the data subsequent to receiving the data related to the plurality of subscribers from the one or more NFs 110. The system 108 analyses the data in order to identify the one or more parameters such as the trends, the patterns, the behavior and the characteristics of the plurality of subscribers.
[0063] At step 406, the system 108 groups the plurality of subscribers in one or more clusters based the identified one or more parameters of the plurality of subscribers based on the analysis of the data.
[0064] At step 408, the system 108 allocates one or more network resources to a cluster of the one or more clusters for improving quality of services (QoS) for the plurality of subscribers.
[0065] FIG. 5 is a flow diagram of a method 500 for optimizing the network 106 for improving quality of services (QoS), according to one or more embodiments of the present invention. For the purpose of description, the method 500 is described with the embodiments as illustrated in FIG. 2 and should nowhere be construed as limiting the scope of the present disclosure.
[0066] At step 502, the method 500 includes the step of receiving, the data from the one or more Network Functions (NFs) 110, wherein the data is associated with the plurality of subscribers. In one embodiment, the transceiver unit 208 receives the data from the one or more Network Functions (NFs) 110. For example, the transceiver unit 208 receives the data pertaining to the subscriber which include at least one of, but not limited to, the subscriber location, the call summaries, the device type, the data consumption and the application usage.
[0067] Subsequent to receiving the data from the one or more NFs 110, the analysis unit 210 trains itself utilizing the received data to identify the one or more parameters pertaining to the plurality subscribers. For example, the analysis unit 210 applies the one or more logics on the received data for identifying one or more parameters associated with the received data. The analysis unit 210 utilizes the one or more logics and identifies the one or more parameters such as at least one of, but not limited to, the data usage patterns of the plurality of subscribers.
[0068] At step 504, the method 500 includes the step of grouping the subscribers in the one or more clusters based on the one or more parameters pertaining to the plurality of subscribers identified from the analysis of the data. In one embodiment, the grouping unit 212 is configured to group the subscribers in the one or more clusters based on the one or more parameters. For example, the clusters are formed by grouping subscribers together who exhibit similar patterns and behaviors. In particular, the plurality of subscribers with similar data usage, behavior or call summaries are placed within the same cluster.
[0069] At step 506, the method 500 includes the step of allocating the one or more network resources to a cluster of the one or more clusters for proper functioning of the network slices for improving quality of services (QoS) for the plurality of subscribers. In one embodiment, the allocation unit 214 allocates the one or more network resources to the cluster among the one or more clusters. In particular, if a specific cluster is experiencing high traffic, then the allocation unit 214 performs one or more operations such as at least one of but not limited to, allocating the one or more resources to the specific clusters to ensure an improved QoS experience for plurality of subscribers within that specific cluster. For example, the allocation unit 214 allocates the CPU to the specific cluster and distributes the high traffic to the allocated CPU which ultimately results in improved QoS for the plurality of subscribers within the specific cluster.
[0070] 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 data from a network function (NF) 110, wherein the data is associated with a plurality of subscribers. The processor 202 is further configured to group the subscribers in one or more clusters based on one or more parameters of the subscribers identified from the analysis of the data. The processor 202 is further configured to allocate one or more network resources to a cluster of the one or more clusters for proper functioning of network slices for improving quality of services (QoS) for the plurality of subscribers.
[0071] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIG.1-5) 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.
[0072] The present disclosure provides technical advancement. By dynamically adapting and optimizing service provision based on subscriber characteristics, the system enhances resource allocation, reduces congestion, and ultimately enhances the overall network performance. These advancements lead to increased customer satisfaction, improved network efficiency, and a higher quality of service experience for the plurality of subscribers in the network.
[0073] 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

[0074] Environment - 100;
[0075] User Equipment (UE) - 102;
[0076] Server - 104;
[0077] Network- 106;
[0078] System -108;
[0079] One or more Network Functions (NFs) – 110;
[0080] Processor - 202;
[0081] Memory - 204;
[0082] Transceiver unit – 208;
[0083] Analysis unit – 210;
[0084] Grouping unit – 212;
[0085] Allocation unit – 214;
[0086] Primary Processor – 302;
[0087] Memory – 304;
[0088] User Interface (UI) – 306.

,CLAIMS:CLAIMS
We Claim:
1. A method (500) of optimizing a network (106) for improving quality of services (QoS), the method (500) comprising the steps of:
receiving, by one or more processors (202), data from a Network Function (NF) (110), wherein the data is associated with a plurality of subscribers;
grouping, by the one or more processors (202), the subscribers in one or more clusters based on one or more parameters pertaining to the subscribers identified from analysis of the data; and
allocating, by the one or more processors (202), one or more network resources to a cluster of the one or more clusters for proper functioning of network slices for improving quality of services (QoS) for the plurality of subscribers.

2. The method (500) as claimed in claim 1, wherein the data includes at least data usage and call summaries of each of the plurality of subscribers.

3. The method (500) as claimed in claim 1, wherein the data is analysed using artificial intelligence (AI)/ machine learning (ML) models for grouping the subscribers.

4. The method (500) as claimed in claim 1, the step of allocating the one or more network resources comprising: distributing network traffic of the cluster for proper functioning of the network slice.

5. A system (108) for optimizing a network (106) for improving quality of services (QoS), the system (108) comprising:
a transceiver unit (208) configured to receive, data from a Network Function (NF) (110), wherein the data is associated with a plurality of subscribers;
an analysis unit (210) configured to analyse, the received data;
a grouping unit (212) configured to group, the subscribers in one or more clusters based on one or more parameters pertaining to the subscribers identified from the analysis of the data; and
an allocation unit (214) configured to allocate, one or more network resources to a cluster of the one or more clusters for proper functioning of network slices for improving quality of services (QoS) for the plurality of subscribers.

6. The system (108) as claimed in claim 5, wherein the data includes at least data usage and call summaries of each of the plurality of subscribers.

7. The system (108) as claimed in claim 5, wherein the data is analysed using Artificial Intelligence (AI)/Machine Learning (ML) models for grouping the subscribers.

8. The system (108) as claimed in claim 5, wherein the allocation unit (214) allocates the one or more network resources by distributing network traffic of the cluster for proper functioning of the network slice.