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

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

FIELD OF THE INVENTION
[0001] The present invention relates to the field of wireless communication systems, more particularly relates to a method and system for network slice data analysis in a communication network.
BACKGROUND OF THE INVENTION
[0002] In general, Network analysis techniques are invaluable tools for identifying and resolving issues in communication networks. These techniques encompass various approaches, including performance monitoring, packet analysis, protocol analysis, and traffic flow analysis. By examining network data and traffic patterns, service providers can gain insights into network performance, identify anomalies, and pinpoint the root causes of issues.
[0003] However, most of the conventional techniques may be limited by geographical considerations and may not provide adequate or a true sense of analytical data for network issues resolution or network insights evaluation.
[0004] Therefore, there is a need for a solution that solves the aforementioned problem.
SUMMARY OF THE INVENTION
[0005] One or more embodiments of the present disclosure provide a method and system managing a southbound instance in a network.
[0006] In one aspect of the present invention, a method for network slice data analysis in a communication network is disclosed. The method includes the step of receiving, by one or more processors, a request from a user for obtaining network slicing data, the request corresponding to one or more issues faced by subscribers in a network slice of network resources. The method further includes the step of processing, by the one or more processors, the request through a computation engine using a computation layer. The method further includes the step of utilizing, by the one or more processors, at least one of, a database and a file distributed system within the computation layer to generate the network slicing data requested. The method further includes the step of presenting, by the one or more processors, the network slice data generated to the user for subscriber level analysis.
[0007] In one embodiment, the method comprising the step of directing, by the one or more processors, the network slice data request to a workflow component, which then forwards the request to the computation engine.
[0008] In another embodiment, the network slice data comprises Call Release Reason (CRR) distribution, worst International Mobile Subscriber Identity (IMSI) distribution, or top worst IMSIs within the network slice.
[0009] In yet another embodiment, the method comprising utilizing, by the one or more processors, the network slice data for optimizing each network slice separately.
[0010] In yet another embodiment, the method comprising analyzing, by the one or more processors, the network slice data for identifying subscriber behavior, tracking subscriber behavior and identifying the pattern of subscribers’ experience.
[0011] In yet another embodiment, the method comprising computing, by the one or more processors, slice-level statistics using Artificial Intelligence /Machine Learning (AI/ML) engine to extract slice-related data from gNodeB.
[0012] In yet another embodiment, the method comprising feeding the slice-level statistics to a 5G Network Data Analytics Function (NWDAF).
[0013] In yet another embodiment, the method comprising retrieving correlated data from the NWDAF based on slice-level experience of users associated with a particular network slice.
[0014] In another aspect of the present invention, a system for network slice data analysis in a communication network is disclosed. The system includes a user interface unit configured to configured to receive a request from a user for obtaining a network slicing data, wherein the request corresponds to one or more issues faced by subscribers in a network slice of network resources. The system further includes a workflow component configured to receive the request from the user interface unit and send the request to a computation engine. The system further includes the computation engine configured to submit the request to a computation layer to access at least one of, a database and a file distributed system for generating the network slice data requested, wherein the computation engine returns the network slice data generated to the user for subscriber level analysis.
[0015] 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 further configured to receive a request from a user for obtaining network slicing data, the request corresponding to one or more issues faced by subscribers in a network slice of network resources. The processor is further configured to process the request through a computation engine using a computation layer. The processor is further configured to utilize at least one of, a database and a file distributed system within the computation layer to generate the network slicing data requested. The processor is further configured to present, the network slice data generated to the user for subscriber level analysis.
[0016] 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
[0017] 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.
[0018] FIG. 1 is an exemplary block diagram of an environment for network slice data analysis in a communication network, according to one or more embodiments of the present invention;
[0019] FIG. 2 is an exemplary block diagram of a system for network slice data analysis in the communication network, according to one or more embodiments of the present invention;
[0020] FIG. 3 is an exemplary block diagram of architecture for network slice data analysis in the communication network, according to one or more embodiments of the present invention;
[0021] FIG. 4 is an exemplary signal flow diagram illustrating the flow for network slice data analysis in the communication network, according to one or more embodiments of the present disclosure; and
[0022] FIG. 5 is a flow diagram of a method for network slice data analysis in the communication network, according to one or more embodiments of the present invention.
[0023] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0024] 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.
[0025] 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.
[0026] 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.
[0027] The present disclosure describes network slice data analysis in a communication network. In a preferred embodiment, a user raise a request for network slice level data. The request is fed to a workflow component, which directs the same to a computation engine. In turn, the computation engine utilizes a computation layer that operates with at least one of, a database and a distributed file system, to generate the network slice level data as requested by the user. The network slice data is returned to the user, and the user subsequently perform data operations, for example, debugging, and correlations, etc.
[0028] Referring to FIG. 1, FIG. 1 illustrates an exemplary block diagram of an environment 100 for network slice data analysis in a communication network, according to one or more embodiments of the present invention. The environment 100 includes, a User Equipment (UE) 102, a server 104, a communication network 106, and a system 108. The UE 102 aids the user to interact with the system 108 for network slice data. In an embodiment, the user includes, at least one of, a network operator.
[0029] 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 communication network 106.
[0030] 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 Virtual Reality (VR) devices, Augmented Reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device.
[0031] The communication 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 communication 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 communication 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.
[0033] The environment 100 includes the server 104 accessible via the communication 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.
[0034] The environment 100 further includes the system 108 communicably coupled to the server 104, and the UE 102 via the communication network 106. The system 108 is adapted to be embedded within the server 104 or is embedded as the individual entity.
[0035] Operational and construction features of the system 108 will be explained in detail with respect to the following figures.
[0036] FIG. 2 is an exemplary block diagram of the system 108 for network slice data analysis in the communication network 106, according to one or more embodiments of the present invention.
[0037] As per the illustrated and preferred embodiment, the system 108 for network slice data analysis in the communication network 106, the system 108 includes one or more components such as one or more processors 202, a memory 204, a database 206, a file distribution system 224, and a User Interface (UI) 226. 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.
[0038] 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 for network slice data analysis in the communication network 106. 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.
[0039] As per the illustrated embodiment, the database 206 and the file distribution system 224 is configured to store data pertaining to a network slicing data. The database 206 and the file distribution system 224 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 the database 206 and the file distribution system 224 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.
[0040] In one embodiment, the file distribution system 224 is a file system that spans across multiple file servers or multiple locations, such as file servers that are situated in different physical places. Files are accessible just as if they were stored locally, from any device and from anywhere on the communication network 106.
[0041] In one embodiment, the UI 226 includes a variety of interfaces, for example, interfaces for a Graphical User Interface (GUI), a web user interface, a Command Line Interface (CLI), and the like. The UI 226 facilitates communication with the system 108. In one embodiment, the UI 226 provides a communication pathway between the user and the one or more components of the system 108.
[0042] In order for the system 108 for network slice data analysis in a communication network 106. The processor 202 includes a receiving unit 208, a workflow component 210, a computation engine 212, a computation layer 214, a display unit 216, an analyzing unit 218, an Artificial Intelligence/Machine Learning (AI/ML) engine 220, and a Network Data Analytics Function (NWDAF) 222. The processor 202 is communicably coupled to the one or more components of the system 108 such as the UI 226, database 206, the file distribution system 224, and the memory 204. In an embodiment, operations and functionalities of the receiving unit 208, the workflow component 210, the computation engine 212, the computation layer 214, the display unit 216, the analyzing unit 218, the Artificial Intelligence/Machine Learning (AI/ML) engine 220, the Network Data Analytics Function (NWDAF) 222, and the one or more components of the system 108 can be used in combination or interchangeably.
[0043] In an embodiment, the receiving unit 208 of the processor 202 is configured to receive a request from the UE 102 of the user via the UI 226 for obtaining network slicing data. The request corresponds to one or more issues faced by one or more subscribers in a particular network slice of network resources. In one embodiment, the receiving unit 208 of the processor 202 is configured to receive the request for subscriber level analysis. The subscriber level analysis request pertains to analyzing the one or more issues or data relating to the one or more issues faced by the one or more subscribers in the particular network slice. In particular, the one or more issues faced by the one or more subscribers includes at least one of, but not limited to, a call termination, and a poor call quality. For example, the one or more subscribers of a particular region are facing issues such as the poor call quality, so the users are transmitting request to the processor 202 in order to obtain data related to the one or more issues faced by the subscribers in the particular region.
[0044] 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 network resources. The network slicing is used most often in the 5G networks. Each slice of a network 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/customers. The management and orchestration of the network slices is key to network operators in support of their communication services.
[0045] The network slice data pertains to insights related to at least one of, but not limited to, a Call Release Reason (CRR) distribution, a worst International Mobile Subscriber Identity (IMSI) distribution, or a top worst IMSIs within the network slice.
[0046] In an embodiment, the CRR distribution insights within the network slice is the analysis and categorization of one or more reasons why calls are terminated within the particular network slice. While analyzing, the worst IMSI Distribution insights facilitates the user to identify and understand the distribution of the least performing IMSIs within the particular network slice. In particular, the worst IMSI Distribution insights is the analysis to check and categorize the performance IMSIs within the particular network slice. The least performing IMSIs are categorized as the worst IMSIs within the particular network slice. The top worst IMSIs is the analysis of top worst IMSIs distribution within the particular network slice, herein the value of N is user defined. In worst IMSIs, the IMSIs which are at the top as per the categorization of the worst IMSIs within the particular network slice are the top N worst IMSIs. In other words, the users identify and understands the top N worst performing IMSIs in the particular network slice.
[0047] In an embodiment, upon reception of the request from the user, the workflow component 210 of the processor 202 is configured to receive the request from the receiving unit 208. The workflow component 210 is a system for managing repetitive processes and tasks which occur in a particular order. The workflow component 210 allows the user to track progress and assess performance of the task. The workflow component 210 is further configured to forward the received request to the computation engine 212. In particular, the workflow component 210 directs the received request to the computation engine 212 responsible for handling the data processing tasks.
[0048] Upon reception of the request by computation engine 212, the computation engine 212 is configured to take charge of the request and submits the request to the computation layer 214. The computation engine 212 processes the request through the computation engine 212 using the computation layer 214. The processing of the request includes at least one of, but not limited to data computation. Data computation refers to the act of carrying out calculations or carrying out commands on the computation engine 212. The data computation includes several activities, including problem-solving, data processing, logic execution, and mathematical operations.Based on the request received, the computation engine 212 submits a job to the computation layer 214 for computing the required network slicing data.The computation layer 214 is the layer where the computation of network slice data takes place, the computation layer 214 computes data from at least one of, the database 206 and the file distribution system 224 based on the request coming from UI 226 for obtaining network slicing data. In particular, the computation layer 214 leverages the available resources, such as at least one of the database 206 and the file distribution system 224, to generate or compute the network slice data as requested by the user.
[0049] In one embodiment, at least one of, the database 206 and the file distribution system 224 includes raw data pertaining to each network slice. The raw data includes at least one of but not limited to, network slicing data for the one or more subscribers related to the particular network slice. Based on the request received from the user, at least one of, the computation layer 214 and the computation engine 212 utilizes at least one of, the database 206 and the file distribution system 224 in order to generate the network slicing data as requested by the user. For generating the network slicing data as requested by the user, at least one of, the computation layer 214 and the computation engine 212 fetches the network slicing data pertaining to one or more subscribers in the particular network slice from the at least one of, the database 206 and the file distribution system 224. In particular, the computation layer 214 generates or compute the fetched network slicing data as requested by the user. In one embodiment, subsequent to the generating/computing the fetched network slicing data, the generated/computed network slicing data is stored in at least one of, the database 206 and the file distribution system 224.
[0050] In one embodiment, utilizing the Artificial Intelligence/Machine Learning (AI/ML) engine 220 of the processor 202, the computation engine 212 and the computation layer 214 computes/generates the requested network slice data. In other words, the processor 202 continuously computes a slice-level statistics to extract the particular network slice related data. The slice-level statistics includes the data pertaining to the slice level experience of the one or more subscribers associated with the particular network slice. In other words, the slice level experience of the one or more subscribers pertains to an overall experience of the one or more subscribers in the particular network slice. For example, the slice level experience of the one or more subscribers includes at least one of but not limited to, facing one or more issues. The slice-level statistics are sliced and diced based on the user requirements received through the UI 226.
[0051] Further, the slice-level statistics are fed to the Network Data Analytics Function (NWDAF) 222 of the processor 202 by at least one of the computation engine 212, the computation layer 214 and the AI/ML engine 220. The NWDAF 222 is a network function that collects data from various 5G Core network functions. In one embodiment, data pertaining to the slice-level experience of one or more subscribers associated with the particular network slice is fed to the NWDAF 222. The NWDAF 222 is responsible for processing and analyzing at least one of, data fed to the NWDAF 222 and the massive amounts of data collected from various 5G Core network functions within the communication network 106. According to 3GPP specifications, The NWDAF 222 is designed as a stand-alone functional entity within the 5G Core network architecture
[0052] The NWDAF 222 correlates the slice-level statistics data with information received from various 5G Core network functions like the Network Slice Selection Function (NSSF) and other network functions related to the network slice usage. In one embodiment, the correlated data is generated by correlating the slice-level statistics data with the information received from various 5G Core network functions. Further the correlated data is retrieved by the computation engine 212. The NSSF is a network function which is used by the another network function such as Access and Mobility Management Function (AMF) in order to assist with the selection of the particular network slice instances that will serve a particular device.
[0053] Based on the correlated data, the NWDAF 222 derives the data pertaining to at least one of but not limited to, the network slice level experience of subscribers and the subscriber’s behavior associated with the particular network slice. Further, the computation engine 212 and the computation layer 214 retrieves the derived data from NWDAF 222 and provides the derived data to the user for proactive monitoring and troubleshooting the one or more issue faced by the user using the AI/ML engine 220 capabilities. Herein the data derived by the NWDAF 222 is the computed network slice data.
[0054] In one embodiment, once the computation pertaining to the generation of the network slice data as requested is completed, the computed network slice data is transmitted to the computation engine 212. Further, upon reception of the computed network slice data, the computation engine 212 provides the computed network slice data to the workflow component 210. Thereafter, the workflow component 210 transmits the computed network slice data to the UI 226 of the user.
[0055] In an alternate embodiment, the workflow component 210 transmits the computed network slice data to the display unit 216 of the processor which is configured to present the computed network slice data to the user. The user may then perform the necessary data operations on the computed network slice data to gain insights about the subscribers related issues.
[0056] In one embodiment, the analyzing unit 218 of the processor is configured to analyse the computed network slice data which facilitates the user in identifying the subscribers’ behavior, the tracking of the subscribers’ behavior and identifying the pattern of subscribers’ experience utilizing the AI/ML engine 220 which helps in improvising the communication network 106 before actual failures arises, providing a seamless usage of services.
[0057] The subscribers’ behavior pertains to at least one of, but not limited to, how the subscribers utilizes one or more services provided in the particular network slice. The subscribers’ behavior tracking involves gathering subscribers’ data to examine subscribers’ behaviors and extract valuable insights. For example, the user analyzes the subscribers’ behavior to identify subscribers’ behavior trends and discover most valuable subscribers utilizing the AI/ML engine 220. The identification of the pattern of subscribers’ experience is crucial for solving one or more issues faced by the one or more subscribers. The pattern of subscribers’ experience facilitate users to understand subscribers’ needs and how the subscribers’ are facing one or more issues.
[0058] In one embodiment, utilizing the computed network slice data and by identifying subscribers’ behavior, the tracking of the subscribers’ behavior and identifying the pattern of subscribers’ experience, the user utilizes the AI/ML engine 220 to resolve the one or more issues faced by the one or more subscribers in the particular network slice by debugging and troubleshooting the one or more issues.
[0059] In one embodiment, the AI/ML engine 220 learns at least one of, but not limited to, trends, behavior, patterns of the one or more subscribers from historical data pertaining to the one or more subscribers in the particular network slice, which facilitates the AI/ML engine 220 to improve the performance of the system 108 by troubleshooting and debugging the one or more issues faced by the one or more subscribers of the particular network slice. Subsequent to learning, the AI/ML engine 220 is trained on the historical data and extracts the particular network slice data from at least one of, but not limited to gNodeB. The extracted particular network slice data is fed to the NWDAF 222.
[0060] Further, the trained AI/ML engine 220 retrieves the correlated data 220 or derived data from the NWDAF 222 which facilitates troubleshooting the one or more issue faced by the one or more subscribers using the AI/ML engine 220 capabilities. In one embodiment, the correlated data 220 or derived data includes the network slice data generated for subscriber level analysis. Furthermore, the trained AI/ML engine 220 identifies at least one of the CRR distribution in the particular network slice, and worst IMSI distribution in the particular network slice by comparing the generated network slice data with the respective one or more thresholds predefined by the AI/ML engine 220. Thereafter, the user troubleshoots the one or more issues faced by the one or more subscribers using AI/ML engine 220. For example, the Call Release Reason (CRR) of the one or more subscribers are identified by the AI/ML engine 220, based on which the user will troubleshoot the one or more issues of the one or more subscribers related to the CRR.
[0061] The receiving unit 208, the workflow component 210, the computation engine 212, the computation layer 214, the display unit 216, the analyzing unit 218, the AI/ML engine 220, and the NWDAF 222 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.
[0062] FIG. 3 illustrates an exemplary block diagram of an architecture for network slice data analysis in the communication network 106, according to one or more embodiments of the present invention. More specifically, FIG. 3 illustrates the system 108 for network slice data analysis in the communication network 106. 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.
[0063] FIG. 3 shows communication between the UE 102, and the system 108. For the purpose of description of the exemplary embodiment as illustrated in FIG. 3, the UE 102 uses a 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 and the system 108 over the communication 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).
[0064] In an embodiment, the UE 102 includes a primary processor 302, and a memory 304 and a User Interface 306. In alternate embodiments, the UE 102 may include more than one primary processor 302 as per the requirement of the communication 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.
[0065] In one embodiment, the User Interface (UI) 306 may be integrated within the UE 102 and or may be integrated within the system 108 such as the UI 226. However, the user interact with the system 108 using any of the at least one of, the UI 306 and the UI 226 without limiting the scope of the invention.
[0066] 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 transmit the request via the UE 102 to the one or more processors 202 for obtaining network slicing data. 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.
[0067] For example, let us assume a group of subscribers are facing one or more issues and the group of subscribers belongs to a specific network slice. In such scenario, the user transmits the request to the system 108 via the UI 306 of the UE 102 in order to obtain network slicing data pertaining to the specific/particular slice to identify the one or more issues faced by the group of subscribers. In one embodiment, the one or more issues such as at least one of, but not limited to, a call getting terminated.
[0068] As mentioned earlier in FIG.2, the system 108 includes the processors 202, the memory 204, the database 206, the file distributed system 224 and the UI 226 for network slice data analysis in the communication network 106 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.
[0069] Further, the processor 202 includes the receiving unit 208, the workflow component 210, the computation engine 212, the computation layer 214, the display unit 216, the analyzing unit 218, the AI/ML engine 220, and the NWDAF 222 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.
[0070] Advantageously, the network slicing data facilitates the user in debugging or troubleshooting the one or more issues faced by the group of subscribers.
[0071] FIG. 4 is an exemplary architecture illustrating the flow for network slice data analysis in the communication network 106, according to one or more embodiments of the present disclosure.
[0072] Initially, a request from the UI 226 for obtaining the network slice data for the particular network slice pertaining to the group of subscribers is received. Further, the request is transmitted to the workflow component 210. Furthermore, the workflow component 210 transmits the request to the computation engine 212.
[0073] Thereafter, the computation engine 212 submits a job to computation layer 214 for computing the required network slice data based on which the computation layer 214 computes data from the at least one of, the database 206 and the file distribution system 224. Once the computation is done by the computation layer 214, the computed network slice data is provided to the computation engine 212. Furthermore, the computation engine 212 provides the computed network slice data to the workflow component 210. Finally, the workflow component 210 provides the computed network slice data as a response for the request to the UI 226 and the user performs analysis utilizing the computed network slice data in order to debug or troubleshoot the one or more issues faced by the group of subscribers.
[0074] FIG. 5 is a flow diagram of a method 500 for network slice data analysis in the communication network 106, 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.
[0075] At step 502, the method 500 includes the step of receiving the request from the user for obtaining the network slicing data, the request corresponding to one or more issues faced by subscribers in the network slice of network resources. In one embodiment, the receiving unit 208 of the processor 202 is configured to receive the request from the user via the UE 102 for obtaining the network slicing data.
[0076] In an alternate embodiment, the UI 226 is configured to receive the request from the user via the UE 102 for obtaining the network slicing data.
[0077] For example, let us assume that subscribers of the particular network slice are facing one or more issues, in order to resolve the one or more issues faced by the subscribers, the user transmits the request to the system 108 to obtain the network slicing data of the particular network slice.
[0078] At step 504, the method 500 includes the step of processing the request through the computation engine 212 using the computation layer 214. In particular, the request for obtaining the network slicing data is received by the computation engine 212 from the workflow component 210. Based on the request received, the computation engine 212 submits a job to the computation layer 214 for computing the required network slicing data.
[0079] At step 506, the method 500 includes the step of utilizing, at least one of, the database 206 and the file distributed system 224 within the computation layer 214 to generate the network slicing data requested. Subsequent to submitting the job to the computation layer 214 by the computation engine 212, the computation layer 214 utilizes, at least one of, the database 206 and the file distributed system 224 to generate the network slicing data as requested by the user. In particular, the computation layer 214 utilizes the AI/ML engine 220 to generate the network slicing data by identifying the data pertaining to the network slice-level statistics based on the user requirements received through the UI 226. For example, computation layer 214 utilizing the AI/ML engine 220 identifies at least one of the CRR distribution in the particular network slice, and worst IMSI distribution in the particular network slice by comparing with the respective one or more thresholds.
[0080] Thereafter, the slice-level statistics are provided to the NWDAF 222 to correlate the slice-level statistics with the information collected by the NWDAF 222 from various 5G Core network functions. Based on the correlated information, the NWDAF 222 generates the network slicing data which is stored in at least one of, the database 206 and the file distributed system 224. Further, the at least one of, the computation layer 214 and the computation engine 212fetches the network slicing data from at least one of, the database 206 and the file distributed system 224. Furthermore, the fetched network slicing data is provided to the workflow component 210 by the computation engine 212.
[0081] At step 508, the method 500 includes the step of presenting, the network slice data generated to the user for subscriber level analysis. In particular, subsequent to the generation of the network slicing data as requested by the user, the workflow component 210 receives the generated network slicing data from the computation engine 212 and further transmits the generated network slicing data to the display unit 216 of the processor 202 which presents the generated network slicing data to the user. In an alternate embodiment, the workflow component 210 further transmits the generated network slicing data to the UI 226 which presents the generated network slicing data to the user. Then user perform the necessary data operations utilizing the generated network slicing data to debug or troubleshoot the one or more issues faced by the subscribers.
[0082] 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 from the user for obtaining the network slicing data, the request corresponding to one or more issues faced by subscribers in the network slice of network resources. The processor 202 is further configured to process, the request through the computation engine 212 using the computation layer 214. The processor 202 is further configured to utilize the database 206 and the file distributed system 224 within the computation layer 214 to generate the network slicing data requested. The processor 202 is further configured to present the network slice data generated to the user for subscriber level analysis.
[0083] 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.
[0084] The present disclosure provides technical advancement such enabling users to gain valuable insights and perform in-depth analysis based on a specific network slice. This capability allows for a targeted approach to troubleshoot and issue resolution, improving the overall subscriber experience. By analyzing data at the network slice level, the users understand the impact of slice performance on subscribers and tailor their optimizations accordingly. The invention facilitates debugging, troubleshooting, tracking, monitoring, and analysis of subscribers experience and behavior within the specific network slice. This comprehensive approach helps users to address issues efficiently, optimize network performance, and align their business objectives with network environment analysis. The invention empowers users with the ability to track subscriber movement across different cells and slices. This feature enables the identification of patterns and trends in subscriber experiences, helping users to identify whether issues are specific to certain slices or particular cells within those slices. This granular level of insight aids in targeted problem resolution and allows for proactive network enhancements.
[0085] 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

[0086] Environment - 100;
[0087] User Equipment (UE) - 102;
[0088] Server - 104;
[0089] Network- 106;
[0090] System -108;
[0091] Processor - 202;
[0092] Memory - 204;
[0093] Database – 206;
[0094] Receiving unit – 208;
[0095] Workflow component– 210;
[0096] Computation engine – 212;
[0097] Computation layer - 214;
[0098] Display unit -216;
[0099] Analyzing unit – 218;
[00100] AI/ML engine – 220;
[00101] NWDAF – 222;
[00102] File distribution system - 224;
[00103] User Interface – 226/306;
[00104] Primary processor – 302;
[00105] Memory – 304
,CLAIMS:CLAIMS
We Claim:
1. A method (500) for network slice data analysis in a communication network (106), the method (500) comprising the steps of:
receiving, by one or more processors (202), a request from a user for obtaining a network slicing data, the request corresponding to one or more issues faced by subscribers in a network slice of network resources;
processing, by the one or more processors (202), the request through a computation engine (212) using a computation layer (214);
utilizing, by the one or more processors (202), at least one of, a database (206) and a file distributed system (224) within the computation layer (214) to generate the network slicing data requested; and
presenting, by the one or more processors (202), the network slice data generated to the user for subscriber level analysis.

2. The method (500) as claimed in claim 1, comprising directing, by the one or more processors (202), the network slice data request to a workflow component (210), which then forwards the request to the computation engine (212).

3. The method (500) as claimed in claim 1, comprising utilizing, by the one or more processors (202), the network slice data for optimizing each network slice separately.

4. The method (500) as claimed in claim 1, wherein the network slice data comprises Call Release Reason (CRR) distribution, worst International Mobile Subscriber Identity (IMSI) distribution, or top worst IMSIs within the network slice.

5. The method (500) as claimed in claim 4, comprising analysing, by the one or more processors (202), the network slice data for identifying subscriber behaviour, tracking subscriber behaviour and identifying the pattern of subscribers’ experience.

6. The method (500) as claimed in claim 1, comprising computing, by the one or more processors (202), slice-level statistics using Artificial Intelligence /Machine Learning (AI/ML) engine (220) to extract slice-related data from gNodeB.

7. The method (500) as claimed in claim 6, comprising feeding the slice-level statistics to a 5G Network Data Analytics Function (NWDAF) (222).

8. The method (500) as claimed in claim 7, comprising retrieving correlated data from the NWDAF (222) based on slice-level experience of users associated with a particular network slice.

9. A system (108) for network slice data analysis in a communication network (106), the system (108) comprising:
a user interface (226) configured to receive a request from a user for obtaining a network slicing data, wherein the request corresponds to one or more issues faced by subscribers in a network slice of network resources; and
a workflow component (210) configured to receive the request from the user interface unit and send the request to a computation engine (212); and
the computation engine (212) configured to submit the request to a computation layer (214) to access at least one of, a database (206) and a file distributed system (224) for generating the network slice data requested, wherein the computation engine (212) returns the network slice data generated to the user for subscriber level analysis.

10. The system (108) as claimed in claim 9, wherein the network slice data comprises Call Release Reason (CRR) distribution, worst International Mobile Subscriber Identity (IMSI) distribution, or top worst IMSIs within the network slice.

11. The system (108) as claimed in claim 9, wherein the computation engine (212) analyses the network slice data for identifying subscriber behaviour, tracking subscriber behaviour and identifying the pattern of subscribers’ experience.

12. The system (108) as claimed in claim 9, wherein the computation engine (212) computes slice-level statistics using Artificial Intelligence /Machine Learning (AI/ML) engine (220) to extract slice-related data from gNodeB.

13. The system (108) as claimed in claim 12, wherein the computation engine (212) feeds the slice-level statistics to a 5G Network Data Analytics Function (NWDAF) (222).

14. The system (108) as claimed in claim 13, wherein the computation engine (212) retrieves correlated data from the NWDAF (222) based on slice-level experience of users associated with a particular network slice.