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 MONITORING SERVICE QUALITY IN THE NETWORK SLICES
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 networks more particularly relates to a method and a system for monitoring service quality in the network slices.
BACKGROUND OF THE INVENTION
[0002] In traditional telecommunications networks, managing and troubleshooting network issues, especially in complex and dynamic environments like 5G, can be challenging. Moreover, with massive increase in telecom users managing traffic in a network become a tedious job. To effectively manage traffic a network operators prefer using network slicing which enables the network operator to control traffic resources on a more granular level by dividing a single network as slices to cover many use cases based on customer demand and segmentation. Each slice of traffic can have its own resource requirements, Quality of Service (QoS), security configurations, and latency requirements. A network slice is a logical and isolated portion of a telecommunications network that is created to cater to the specific requirements of a particular service or application. The network slicing may be used for AR and VR applications that require low latency and high bandwidth to deliver immersive experiences and for this purpose the network slice is optimized for delivering real-time, high-definition content to headsets or devices. Applying the network slicing technology is also preferred for Voice over IP (VoIP) services rely on low latency and high-quality voice transmission to ensure that voice calls are crystal clear and free from interruptions; and for Ultra-Reliable Low-Latency Communications (URLLC) where the network slicing is designed to support mission-critical applications that require extremely low latency and high reliability such as autonomous vehicles, remote surgery, and industrial automation.
[0003] However, by deploying network slices in the telecom network may introduce additional complexity due to their diverse requirements and performance expectations. Traditional telecommunication networks lack robust trend analysis. In particular, the traditional telecom networks rely on reactive troubleshooting, where network issues are addressed after they occur. Due to the reactive approach of troubleshooting, there could be situations when the network issues are not addressed in a timely manner, or the network issues may go unnoticed. Due to this, there could be a chain of issues that may arise, which may interfere with the performance of the telecom network.
[0004] There is, therefore, a dire need for effective solutions for automatically detecting anomalies across multiple network slices in a telecommunication (telecom) network.
SUMMARY OF THE INVENTION
[0005] One or more embodiments of the present disclosure provide a method and system for monitoring service quality in network slices.
[0006] In one aspect of the present invention, the system for monitoring service quality in the network slices is disclosed. The system includes a retrieving unit configured to retrieve a first set of data pertaining to each of a plurality of network slices from one or more data sources. The system further includes an extracting unit configured to extract one or more features from the first set of data corresponding to network performance of each of the plurality of network slices. The system further includes a training unit configured to train a model utilizing the one or more features to identify at least one of, patterns and trends in each of the network slices. The system further includes a determining unit, configured to determine utilizing the trained model, one or more anomalies in the second set of data when a deviation is detected in the second set of data in comparison to at least one of, the trends or patterns of the first set of data.
[0007] In an embodiment, the first set of data includes information pertaining to at least one of traffic, signal strength, packet loss, latency and network statistics.
[0008] In an embodiment, the system further comprises a preprocessing unit configured to preprocess the first set of data.
[0009] In an embodiment, the one or more features extracted from the retrieved first set of data include at least one of, call parameters, geographic coordinates and network load metrics.
[0010] In an embodiment, the second set of data is received in real time from the one or more data sources.
[0011] In an embodiment, the system further comprises a depicting unit, configured to, depict the one or more anomalies on a user interface in at least one of, graphs, charts or dashboards.
[0012] In an embodiment, the system further comprises a transmitting unit, configured to, transmit at least one of, alerts, notifications and one or more actions to a user in response to determining the one or more anomalies.
[0013] In an embodiment, the one or more actions include at least one of recommending remedial actions to resolve the one or more anomalies.
[0014] In another aspect of the present invention, the method for monitoring service quality in the network slices is disclosed. The method includes the step of retrieving, by one or more processors, a first set of data pertaining to each of a plurality of network slices from one or more data sources. The method further includes the step of extracting by one or more processors, one or more features from the first set of data corresponding to network performance of each of the plurality of network slices. The method further includes the step of training, by one or more processors, a model utilizing the one or more features to identify at least one of, patterns and trends in each of the network slices. The method further includes the step of determining, by the one or more processors, utilizing the trained model, one or more anomalies in the second set of data when a deviation is detected in the second set of data in comparison to at least one of, the trends or patterns of the first set of data.
[0015] In another aspect of invention, User Equipment (UE) is disclosed. The UE includes one or more primary processors communicatively coupled to one or more processors, the one or more primary processors coupled with a memory. The processor causes the UE to transmit the first set of data pertaining to each of the plurality of network slices. The processor causes the UE to receive alerts to address the one or more detected anomalies. The processor further causes the UE to depict, the one more anomalies in at least one of, graphs, charts or dashboards on a user interface.
[0016] In another aspect of the invention, a non-transitory computer-readable medium having stored thereon computer-readable instructions is disclosed. The computer-readable instructions are executed by a processor. The processor is configured to retrieve a first set of data pertaining to each of a plurality of network slices from one or more data sources. The processor is configured to extract one or more features from the first set of data corresponding to network performance of each of the plurality of network slices. The processor is configured to train a model utilizing the one or more features to identify at least one of, patterns and trends in each of the network slices. The processor is configured to determine, utilizing the trained model, one or more anomalies in the second set of data when a deviation is detected in at least one of the second set of data in comparison to at least one of, the trends or patterns of the first set of data.
[0017] Other features and aspects of this invention will be apparent from the following description and the accompanying drawings. The features and advantages described in this summary and in the following detailed description are not all-inclusive, and particularly, many additional features and advantages will be apparent to one of ordinary skill in the relevant art, in view of the drawings, specification, and claims hereof. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes and may not have been selected to delineate or circumscribe the inventive subject matter, resort to the claims being necessary to determine such inventive subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[0019] FIG. 1 is an exemplary block diagram of an environment for monitoring service quality in network slices, according to one or more embodiments of the present invention;
[0020] FIG. 2 is an exemplary block diagram of a system for monitoring service quality in the network slices, according to one or more embodiments of the present invention;
[0021] FIG. 3 is a schematic representation of a workflow of the system of FIG. 1, according to the one or more embodiments of the present invention;
[0022] FIG. 4 is an exemplary block diagram of an architecture implemented in the system of the FIG.2, according to one or more embodiments of the present invention;
[0023] FIG. 5 is a signal flow diagram for monitoring service quality in the network slices, according to one or more embodiments of the present invention; and
[0024] FIG. 6 is a schematic representation of a method for monitoring service quality in the network slices, according to one or more embodiments of the present invention.
[0025] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0026] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise.
[0027] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure including the definitions listed here below are not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
[0028] A person of ordinary skill in the art will readily ascertain that the illustrated steps detailed in the figures and here below are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0029] FIG. 1 illustrates an exemplary block diagram of an environment 100 for monitoring service quality in the network slices, according to one or more embodiments of the present disclosure. In this regard, the environment 100 includes a User Equipment (UE) 110, a server 115, a network 105 and a system 120 communicably coupled to each other for monitoring service quality in the network slices. The network slices refer to a plurality of networks created virtually within a physical network infrastructure. Each of the plurality of the network slices serves different services utilizing resources dynamically based on the demand of each services within each network slice. The technology of network slices within a network enables the consumer to have reliable and optimized performance of services.
[0030] As per the illustrated embodiment and for the purpose of description and illustration, the UE 110 includes, but not limited to, a first UE 110a, a second UE 110b, and a third UE 110c, and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the UE 110 may include a plurality of UEs as per the requirement. For ease of reference, each of the first UE 10a, the second UE 110b, and the third UE 110c, will hereinafter be collectively and individually referred to as the “User Equipment (UE) 110”.
[0031] In an embodiment, the UE 110 is one of, but not limited to, any electrical, electronic, electro-mechanical or an equipment and a combination of one or more of the above devices such as a smartphone, virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device.
[0032] The environment 100 includes the server 115 accessible via the network 105. The server 115 may include, by way of example but not limitation, one or more of a standalone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof. In an embodiment, the entity may include, but is not limited to, a vendor, a network operator, a company, an organization, a university, a lab facility, a business enterprise side, a defense facility side, or any other facility that provides service.
[0033] The network 105 includes, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. The network 105 may include, but is not limited to, a Third Generation (3G), a Fourth Generation (4G), a Fifth Generation (5G), a Sixth Generation (6G), a New Radio (NR), a Narrow Band Internet of Things (NB-IoT), an Open Radio Access Network (O-RAN), and the like.
[0034] The network 105 may also include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network 105 may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, a VOIP or some combination thereof.
[0035] The environment 100 further includes the system 120 communicably coupled to the server 115 and the UE 110 via the network 105. The system 120 is configured for monitoring service quality in the network slices. As per one or more embodiments, the system 108 is adapted to be embedded within the server 115 or embedded as an individual entity.
[0036] Operational and construction features of the system 120 will be explained in detail with respect to the following figures.
[0037] FIG. 2 is an exemplary block diagram of the system 120 for monitoring service quality in the network slices, according to one or more embodiments of the present invention.
[0038] As per the illustrated embodiment, the system 120 includes one or more processors 205, a memory 210, a user interface 215, and a database 220. For the purpose of description and explanation, the description will be explained with respect to one processor 205 and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the system 120 may include more than one processor 205 as per the requirement of the network 105. The one or more processors 205, hereinafter referred to as the processor 205 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions.
[0039] As per the illustrated embodiment, the processor 205 is configured to fetch and execute computer-readable instructions stored in the memory 210. The memory 210 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 210 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.
[0040] In an embodiment, the user interface 215 includes a variety of interfaces, for example, interfaces for a graphical user interface, a web user interface, a Command Line Interface (CLI), and the like. The user interface 215 facilitates communication of the system 120. In one embodiment, the user interface 215 provides a communication pathway for one or more components of the system 120. Examples of such components include, but are not limited to, the UE 110 and the database 220.
[0041] The database 220 is one of, but not limited to, a centralized database, a cloud-based database, a commercial database, an open-source database, a distributed database, an end-user database, a graphical database, a No-Structured Query Language (NoSQL) database, an object-oriented database, a personal database, an in-memory database, a document-based database, a time series database, a wide column database, a key value database, a search database, a cache databases, and so forth. The foregoing examples of database 220 types are non-limiting and may not be mutually exclusive e.g., a database can be both commercial and cloud-based, or both relational and open-source, etc.
[0042] In order for the system 120 to monitor service quality in the network slices, the processor 205 includes one or more modules. In one embodiment, the one or more modules includes, but not limited to, a retrieving unit 225, a preprocessing unit 230, an extracting unit 235, a training unit 240, a determining unit 245, a depicting unit 250 and a transmitting unit 255, communicably coupled to each other for monitoring service quality in the network slices.
[0043] In one embodiment, the one or more modules includes, but not limited to, the retrieving unit 225, the preprocessing unit 230, the extracting unit 235, the training unit 240, the determining unit 245, the depicting unit 250 and the transmitting unit 255 can be used in combination or interchangeably for monitoring service quality in the network slices.
[0044] The retrieving unit 225, the preprocessing unit 230, the extracting unit 235, the training unit 240, the determining unit 245, the depicting unit 250 and the transmitting unit 255 in an embodiment, may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 205. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 205 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 210 may store instructions that, when executed by the processing resource, implement the processor. In such examples, the system 120 may comprise the memory 210 storing the instructions and the processing resource to execute the instructions, or the memory 210 may be separate but accessible to the system 120 and the processing resource. In other examples, the processor 205 may be implemented by electronic circuitry. The retrieving unit 225 is configured to retrieve a first set of data pertaining to each of the plurality of network slices. The first set of data is received from one or more data sources including at least one of cell towers 405 (as shown in the FIG.4). The cell towers 405 are responsible for transmitting and receiving signals from at least one of mobile devices including but not limited to smartphones, tablets and Internet of Things (IoT) devices. The cell towers 405 monitor each of the network slice and provide data on signal strength, device location, network traffic data to the probing units. The data from cell towers 405 refers to the information collected from cellular network infrastructure. The data collected by the cell towers 405 provide insights into network health, traffic patterns and user experiences which enables troubleshooting and future expansions. In an alternate embodiment, the data sources include but not limited to user interface 215, the database 220, probing unit 415 (as shown in the FIG.4), and data consumers 410 (as shown in the FIG.4). In one embodiment, the one or more data sources may additionally include a user interface 215 which provides preferences in the network services which include but not limited to key performance indicators, and specific network slices to be focused. The network operators interact with the user interface 215 to define the preferable key performance indicators including but not limited to latency, packet loss, throughput, signal strength and traffic volume in the network slice so that there is stable performance of the network. The preferences change with time according to the customer demands and trends in the telecommunications industry. In another embodiment, the one or more data sources may additionally include the probing unit 415 which collects the raw data from the cell towers 405 which is to be utilized as the training dataset for the model. The probing unit 415 includes at least one of Next Generation Node B (gNodeB) in 5G, evolved Node B (eNodeB) in 4G and Node B in 3G telecommunications. The database 220, has the historical and real time data pertaining to performance of each of the plurality of network slices. The data consumers are telecom operators who monitor the analysed data provided by the probing unit 415. The data consumers generate traffic patterns based on at least one of usage of service and applications, customer feedback and real time performance metrics. The collected data from the one or more data sources consists of information pertaining to at least one of traffic, signal strength, packet loss, latency and other relevant network statistics. In an embodiment, the first set of data is the raw data.
[0045] Upon retrieving the first set of data in the raw format, the preprocessing unit 230 transforms the raw format data into standardized data. The raw data refers to the data with distinct and diverse measuring units of network information including traffic, signal strength, packet loss latency and network statistics. For example, when the latency is measured in milliseconds and the signal strength in decibels. The raw data further includes redundant and irrelevant information on the network parameters. The raw data leads to a model learning from diverse, redundant and irrelevant input data giving unreliable outcomes. Therefore, standard data is essential for training the model. The preprocessing unit 230 performs processing the raw data which includes but is not limited to removing irrelevant data, handling missing values, data cleaning and data normalization. For example, if the 5G network has a throughput of 1 Gbps, it would take approximately 800 seconds (or about 13.3 minutes) to transmit 100 GB. The data on the transmission of 100 GB is repeatedly received and then the raw data is said to possess redundant data. The preprocessing eliminates the redundant data and keeps the number of data required. The preprocessing unit is further configured to split the retrieved data into training data and test data. After preprocessing the raw data is ready for further processes to monitor service quality in the network slices.
[0046] Upon preprocessing the data, the extracting unit 235 is configured to extract the one or more features from the retrieved first set of data. The one or more features extracted from the retrieved data by the extracting unit 235 include at least one of the call parameters, geographic coordinates and network load metrics. The one or more features extracted are utilized for training models. The one or more features enable the model to understand the network characteristics in each of the plurality of network slices. For instance, the geographic coordinates or traffic volume explain how network load varies by location or peak usage times.
[0047] Upon extracting the one or more features, the training unit 240 is configured to train a model slice. The model is selected by the user or the system 120. In an embodiment, the model is selected through automatic selection. In another embodiment, the model is selected by extracting one or more commands after parsing a request which is received from at least one of unit, microservice, service and software component. The model refers to at least one of machine learning models, statistical models or simulation models. The model is selected by the user or by the system 120 from the one or more models inserted by the user into the system 120 or available in the database 220. The selection of the model is based on the kind of output the system 120 has to provide including but not limited to clustering, regression, classification and pattern recognition. The training of the model refers to the process of applying a set of data on the model, where the model performs the computation on the data to provide the output, which the model was configured to perform. The training of the model is performed in methods including at least one of reinforcement learning, supervised and unsupervised learning. In the present embodiment, the model utilizes the first set of data and identifies at least one of patterns and trends in the one or more features pertaining to each of the plurality of network slices. The trends and patterns refer to values or range of values of one or more features in the first set of data. The values or range of values of one or more features are those that maintain a stable performance with seamless data transmission with negligible downtime and packet loss in each of the plurality of network slice. For example, for 200 GB data packet transmission the given network slice takes 10 milliseconds to 35 milliseconds for a stable performance with negligible downtime and packet loss, so the model learns the pattern of data transmission as between 10 milliseconds to 35 milliseconds for seamless transmission. For 150 GB or 300 GB the latency is different. Through reinforcement learning, the AI/ML model understands the variation in the latency according to the volume of data in transmission in real time. The model continuously learns the trends and patterns of network performance, network issues and user behaviour within each of the plurality of network slices in real time. In the present embodiment, the model under reinforcement learning learns from the dynamic environment data and continuously refines the decision-making ability of the model.
[0048] In an embodiment, the trained model utilizes the test data before utilizing the real time data. The test data is the immediate set of data which is unseen by the trained model. In the present embodiment, the test data pertains to each of the plurality of network slices but unseen or new to the trained model. The test data is used to evaluate the performance of the trained model. For example, the performance of the trained model is determined based on evaluating one or more performance metrics. In an embodiment, the one or more performance metrics include at least one of, but not limited to, precision, accuracy, mean absolute error and mean squared error. If it is determined that the model is not accurately trained based on evaluating the one or more performance metrics, then training of the model is to be performed again. If it is determined that the model is accurately trained based on evaluating the one or more performance metrics, then the trained model is used for further processes.
[0049] Upon training the model, the training unit 240 receives the second set of data pertaining to the each of the plurality of network slices. The second set of data refers to information about each of the plurality of network slices received continuously in real time. The data sources include at least one of the cell towers 405. In an embodiment, data sources further include but not limited to the probing unit 415, data consumers 410, user interface 215, and the database 220. The received second set of data includes at least one of traffic, signal strength, packet loss latency and network statistics of each of the plurality of network slices in real time. The trained model in the training unit 240, identifies one or more anomalies in the real time network in each of the plurality of network slices.
[0050] Upon receiving the second set of data, the determining unit 245 determines the one or more anomalies in the second set of data. The determination is performed by utilizing the trained model. The trained model compares the real time second set of data with the first set of data. The trained model which learnt the patterns and the trends in the one or more extracted features of the first set of data compares the learnt trends and the patterns against the second set of data. If the second set of data deviates from the trends and the patterns of the one or more extracted features in the first set of data learnt by the model, then the one or more deviations are determined as one or more anomalies by the determining unit 245. For the previous example, in a real time network when the learnt latency for 200 GB data packet is 10 milliseconds to 35 milliseconds for a stable performance in a given network slice, but when the latency observed by the model is less than or greater than 10 milliseconds to 35 milliseconds, then the determining unit 245 determines the deviation as an anomaly. The trained model utilizes processes including at least one of prediction, pattern matching, thresholding, mapping for determining the deviations. For example, if the model learns that the network traffic should be around 200 Mbpd during a particular time, but the real time data shows 2 Gbps, then the network traffic in the real time data is flagged as an anomaly.
[0051] In response to the determination of the one or more anomalies in each of the network slices, the depicting unit 250 is configured to depict the one or more anomalies on a user interface 215 in at least one of graphs, charts or dashboards. In the previously discussed example of the anomaly pertaining to latency, the anomaly is depicted at the user interface or on the user equipment of the traffic management team. The depiction of the anomaly is in the form of graphs, charts and dashboards. The visual depiction of one or more anomalies enhances the situation awareness. The users are able to easily grasp the status of each of the network slices enabling the network operators to make informed decisions based on real time information.
[0052] In an embodiment, upon determination of one or more anomalies by the determining unit 245, the transmitting unit 255, is configured to transmit at least one of alerts, notifications and one or more actions to a user. The alerts refer to warnings transmitted to the user pertaining to the event of anomalies in each of the network slices. The notifications refer to detailed explanations of the one or more determined anomalies so that the user can address the determined anomalies. The one or more actions include recommending remedial actions to resolve the one or more anomalies. For example, if the determining unit 245 detects an anomaly in one of the pluralities of network slices, attributing to sudden spike in latency and packet, the transmitting unit 255 transmits the alerts indicating the deviation in the latency trends of from the learnt trends as the anomaly. In an embodiment, the transmitting unit 255 further transmits detailed information including but not limited to the specific network slice affected, nature of the anomaly, impacted locations in the network 105 to the user interface 215 of the data consumers 410. The one or more actions pertain to recommending remedies to rectify the anomaly. In the present example, remedial actions recommended include at least one of rerouting traffic or deploying additional network resources temporarily. The system 120 is able to monitor the service quality of the real time network data in each of the plurality of network slices. In an embodiment, the database 220 stores the data pertaining to each of the network slices, the one or more anomalies detected, and actions initiated for each of the detected one or more anomalies. In the future if any similar one or more anomalies are detected by the trained model for at least one of the network slices, the determining unit 245 is configured to utilize the stored data in the database 220 pertaining to the one or more corresponding actions in order to resolve the one or more anomalies.
[0053] In an alternative embodiment, the detection and rectification of the one or more anomalies are performed in full automation. The automation is facilitated by extracting one or more commands related to one or more requests received from at least one of microservice, service, application, and software-component. The requests pertain to the recommendations for rectifying the one or more detected anomalies. Thereafter, one or more corresponding responses are transmitted to at least one of microservice, service, application, and software-component. The one or more responses pertain to the status of rectifying the one or more determined anomalies. In another embodiment, the request is to automate every process without manual interaction. The request received to the system 120 through an application or microservices and receiving at least one of result, analysis or recommendation generated by the system 120 are exchanged by Hypertext Transfer Protocol Representational State Transfer (HTTP REST) based communication. The complete automated system enables the rectification of the one or more determined anomalies without relying on the network operators avoiding at least one of downtime and data packet loss.
[0054] FIG. 3 describes a preferred embodiment of the system 120 of FIG. 2, according to various embodiments of the present invention. It is to be noted that the embodiment with respect to FIG. 3 will be explained with respect to the first UE 110a and the system 120 for the purpose of description and illustration and should nowhere be construed as limited to the scope of the present disclosure.
[0055] As mentioned earlier in FIG. 1, each of the first UE 102a, the second UE 110b, and the third UE 110c may include an external storage device, a bus, a main memory, a read-only memory, a mass storage device, communication port(s), and a processor. The exemplary embodiment as illustrated in FIG. 3 will be explained with respect to the first UE 110a without deviating from the scope of the present disclosure and the limiting the scope of the present disclosure. The first UE 110a includes one or more primary processors 305 communicably coupled to the one or more processors 205 of the system 120.
[0056] The one or more primary processors 305 are coupled with a memory 210 storing instructions which are executed by the one or more primary processors 305. Execution of the stored instructions by the one or more primary processors 305 enables the first UE 110a to transmit the first set of data pertaining to each of the plurality of network slices. The one or more primary processors 305 further enables the first UE 110a to receive alerts to address the one or more detected anomalies. The one or primary processors 305 further enables the first UE 110a to further depict, the one more anomalies in at least one of, graphs, charts or dashboards on a user interface.
[0057] As mentioned earlier in FIG. 2, the one or more processors 205 of the system 120 is configured to for monitoring service quality in the network slices. As per the illustrated embodiment, the system 120 includes the one or more processors 205, the memory 210, the user interface 215, and the database 220. The operations and functions of the one or more processors 205, the memory 210, the user interface 215, and the database 220 are already explained in FIG. 2. For the sake of brevity, a similar description related to the working and operation of the system 120 as illustrated in FIG. 2 has been omitted to avoid repetition.
[0058] Further, the processor 205 includes retrieving unit 225, the preprocessing unit 230, the extracting unit 235, the training unit 240, the determining unit 245, the depicting unit 250 and the transmitting unit 255. The operations and functions of the retrieving unit 225, the preprocessing unit 230, the extracting unit 235, the training unit 240, the determining unit 245, the depicting unit 250 and the transmitting unit 255 are already explained in FIG.2. Hence, for the sake of brevity, a similar description related to the working and operation of the system 120 as illustrated in FIG. 2 has been omitted to avoid repetition. The limited description provided for the system 120 in FIG. 3, should be read with the description provided for the system 120 in the FIG. 2 above, and should not be construed as limiting the scope of the present disclosure.
[0059] FIG. 4 is an exemplary block diagram of an architecture 400 implemented in the system of the FIG.2, according to one or more embodiments of the present invention.
[0060] The architecture 400 includes a cell tower 405, data consumers 410 a probing unit 415, the user interface 215, the database 220 and a network slice monitoring module 420. The network slice monitoring module 420 comprises a data preprocessing unit 430, a model training unit 435, a trend analysis unit 440, a data visualization unit 445 and an alerting and response unit 450.
[0061] In an embodiment, the first set of data pertaining to the network characteristics of each of the plurality of network slices is received by the network slice monitoring module 420. The first set of data refers to the network parameters relevant to data transmission received from one or more data sources. The data sources include at least one of the cell towers 405. In another embodiment, the data sources include but are not limited to user interfaces 215, the probing unit 415, the data consumers 410 and the data lake 425. The data lake 425 refers to the database 220 which has the historical and real time data pertaining to performance of each of the plurality of network slices. The first set of data includes at least one of the traffic, signal strength, packet loss latency and network statistics. The data pertaining to the network parameters from the radio base stations or cell towers 405 of diverse telecommunications networks is collected by the probing unit 415. The probing unit collects the raw data pertaining to at least one of the at least one of the traffic, signal strength, packet loss latency and network statistics. In an embodiment, the data consumers 410 including at least one of the network operators monitor the data received and analyzed by the probing unit 415.
[0062] As per the illustrated embodiment and for the purpose of description and illustration, the cell tower 405 includes, but not limited to, a first the cell tower 405a, a second cell tower 405b, and a third cell tower 405c, and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the cell tower 405 may include a plurality of cell towers 405 as per the requirement. For ease of reference, each of the first cell tower 405a, the second cell tower 405b, and the third cell tower 405c, will hereinafter be collectively and individually referred to as the “cell tower 405”.
[0063] Upon collection by the probing unit 415, the data is thereafter received by the network slice monitoring module 420 from the probing unit 415. The received data is raw data which is not suitable for training a model due to the heterogeneity of the raw data. The data preprocessing unit 430 transforms the raw data into standardized format. The heterogenous character of network data is converted to uniform nature. From the standardized data, the one or more features are extracted. The one or more features refer to the significant information that uniquely characterizes each of the network slices within the network 105. The one or more extracted features include but are not limited to the call parameters, geographic coordinates and network load metrics. The data pertaining to one or more features are provided to the training a model.
[0064] Upon receiving and preprocessing the retrieved first set of raw data and extracting the one or more features from the standardized data, the data pertaining to one or more features is utilized for training a model by the model training unit 435. The model training unit 435 utilizes the data to learn trends and patterns in each of the plurality of network slices. The trends and patterns refer to the values or range of values in each of the plurality of network slices pertaining to a stable network performance attributed by negligible latency and data packet loss. In an embodiment the trends and patterns learnt by the model are further stored in a database 220 called data lake 425. The stored data pertaining to the learnt trends and patterns are utilized by the training model later in the process.
[0065] Upon training the model, the network slice monitoring module 420 receives the second set of data from the probing unit 415. The second set of data pertains to at least one of the traffic, signal strength, packet loss, latency and network statistics of each of the network slices in real time. The received second set of data is delivered to the model training unit 435.
[0066] Upon receiving the second set of data pertaining to the each of the network slices in real time, the trend analysis unit 440 compares the learnt trends and patterns in the first set of data against the second set of data. Upon comparing the learnt trends and patterns in the first set of data against the second set of data, the trend analysis unit 440 determines the one or more anomalies in the second set of data. Whenever the one or more network characteristics of the second set of data deviates from the trends and patterns in the one or more network characteristics of the first set of data, the one or more deviations are determined as one or more anomalies by the trend analysis unit 440.
[0067] Upon determining the one or more anomalies by the trend analysis unit 440, the data visualization unit 445 decides to depict the determined one or more anomalies on a user interface 215 of the data consumers 410 . The depiction of one or more anomalies is in at least one of graphs, charts or dashboards. The data visualization unit 445 enables user to have situational awareness of the one or more determined anomalies.
[0068] In an embodiment, the alerting and responses unit 450 within the network slice monitoring module 420 transmits at least one of alerts, notifications and one or more actions to the user interface 215 of the data consumers 410. The alerts are warnings and notifications are detailed explanation on the one or more determined anomalies transmitted to the user interface 215 of the network operators. The one or more actions are one or more remedies recommended by the alerts and responses unit to the user interface 215 of the network operators. Utilizing the received visual depiction, alerts, notifications and recommendations on remedies recommended at the user interface 215, the network operators rectify the one or more determined anomalies. The automated determining of one or more anomalies and corresponding actions to rectify the anomalies enable optimized service quality to the users.
[0069] FIG. 5 is a signal flow diagram for monitoring service quality in the network slices, according to one or more embodiments of the present invention. The
[0070] At step 505, the system 120 collects the first set of data pertaining to each of the plurality of network slices from one or more data sources. The data sources include at least one of the cell towers 405. In another embodiment, the data sources include but are not limited to the user interface 215, the probing unit 415, the data consumers 410 and the data lake 425. The first set of data includes but is not limited to the traffic, signal strength, packet loss latency and network statistics. At step 510, the first set of data which is raw data and not suitable for training a model is preprocessed. The preprocessing includes but is not limited to data cleaning and data normalization. The raw data is transformed into standard format by the data preprocessing unit 430. The standard data is suitable for training a model.
[0071] At step 515, the one or more features pertaining to the network characteristics of each network slice are extracted from the standard data using the extracting unit 235. The one or more features include at least one of the call parameters, geographic coordinates and network load metrics. The one or more extracted features is significant to understand how the network 105 is distinct for each of the one or more features.
[0072] At step 520, the data of the one or more extracted features pertaining to the network characteristics of each network slice is provided for model training. The one or more extracted features is significant to understand how the network 105 is distinct for each of the one or more features.
[0073] At step 525, the model identifies trends and patterns within the extracted data and stores the learnt patterns in the database 220. The trends and patterns refer to values or range of values of network characteristics in each of the network slices that maintain a stable network performance without downtime and data packet loss.
[0074] In an embodiment, the system 120 receives the second set of data pertaining to the at least one of the traffic, signal strength, packet loss latency and network statistics of each of the network slices. The second set of data is received by the system 120 in real time.
[0075] At step 530, the determining unit 245 compares the learnt trends and patterns of the network slices from the first set of data against the second set of data. Whenever the second set of data deviates, from the trends and patterns of one or more network characteristics in the first set of data, the determining unit 245 considers the one or more deviations as one or more anomalies in the given network slice.
[0076] At step 535, upon determination of one or more anomalies, the system 120 depicts the one or more anomalies visually on the user interface 215 of the data consumers 410. The visual depiction enables data consumers 410 to quickly understand the scenario of affected network slice.
[0077] At step 540, the system 120 transmits alerts, notifications and one or more recommendations to the user interface 215 of the data consumers 410. The alerts are warnings, and the notifications are detailed descriptions, corresponding to the one or more anomalies in the given network slice. The one or more actions are one or more recommendations to the user interface 215 of the data consumers 410 corresponding to remedial actions to be taken to rectify the one or more anomalies.
[0078] FIG. 6 is a flow diagram of a method 600 for monitoring service quality in the network slices, according to one or more embodiments of the present invention. For the purpose of description, the method 600 is described with the embodiments as illustrated in FIG. 2 and should nowhere be construed as limiting the scope of the present disclosure.
[0079] At step 605, the method 600 includes the step of retrieving the first set of data pertaining to each of the plurality of network slices. The first set of data is received from the one or more data sources, which include at least one of the cell towers 405. In another embodiment, the data sources include but not limited to the user interface 215, the probing unit 415, the data consumer 410 and the data lake 425. The retrieving process further includes preprocessing the retrieved raw data into standard format enabling the data to be utilized for model training.
[0080] At step 610, the method 600 includes the step of extracting the one or more features from the standardized first set of data. The one or more features correspond to the network performance of each of the plurality of network slices. The one or more features extracted include at least one of the call parameters, geographic coordinates and network load metrics of each of the plurality of network slices.
[0081] At step 615, the method 600 includes the step of training the model utilizing the extracted data pertaining to the one or more features from the first set of data. The model analyses the data and identifies at least one of patterns and trends of network characteristics in each of the given network slices. The patterns and trends are values or range of values pertaining to stable network performance without data packet loss and negligible downtime.
[0082] At step 620, the method 600 includes the step of determining one or more anomalies in the second set of data. The determination is done by utilizing the trained model. The trained model compares the learnt trends and patterns of network characteristics of the first set of data against the second set of data. Whenever there are one or more deviations in the one or more network characteristics in the second set of data from the trends and patterns of the network characteristics in the first set of data, the determining unit 245 determines the one or more deviations as one or more anomalies. In the present invention, the information regarding the one or more determined anomalies is depicted on the user interface 215 of the data consumers 410. In the present invention, the information regarding the one or more determined anomalies is further notified to the user interface 215 in the form of alerts, notifications and one or more actions. The alerts, warnings, notifications as detailed descriptions of the one or more determined anomalies in each of the network slices are transmitted to the user interface 215 of the data consumers 410. Upon receiving the visual depictions, alerts, notifications and one or more recommendations, the data consumers 410 take appropriate remedial actions to rectify the one or more determined anomalies. The optimization in resource allocation within the network slices based on the identified trends and patterns in performance data improves efficiency in the usage of network resources. The proactive approach to rectify the anomalies occurring in each of the network slices resolves the issues before they affect the service quality.
[0083] The present invention further discloses a non-transitory computer-readable medium having stored thereon computer-readable instructions. The computer-readable instructions are executed by the processor 205. The processor 205 is configured to retrieve the first set of data pertaining to each of the plurality of network slices from the one or more data sources. The processor 205 is further configured to extract the one or more features from the first set of data corresponding to the network performance of each of the plurality of network slices. The processor 205 is further configured to train the model utilizing the one or more features to identify the at least one of, patterns and trends in each of the network slices. The processor 205 is further configured to receive the second set of data pertaining to each of the plurality of network slices. The processor 205 is further configured to determine, utilizing the trained model, one or more anomalies in the second set of data when a deviation is detected in the second set of data in comparison to at least one of, the trends or patterns of the first set of data.
[0084] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIG.1-6) are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0085] The present disclosure incorporates technical advancement in solving the problem of maintaining stable network performance in multiple network slices within a network. The present invention conducts real time monitoring of the network performance continuously. The training model is deployed to analyze the trends and patterns of the network performance in each of the network slices in real time. The automated detection of anomalies, visual depiction of anomalies and transmission of alerts and recommendations of remedial actions reduces the time to rectify anomalies in each network slice. The present invention optimizes resource allocation based on the determined anomalies in each of the network slices. The proactive issue resolution of the network anomalies before the anomalies impacts the service quality or customer satisfaction enhances the efficiency and reliability of the network. With the increased reliability of the network services, the customer experience is also improved. With the automated proactive approach in maintaining the service quality in each of the network slices within the network, the present invention brings significant technical and economic advancements in the modern telecommunications networks.
[0086] 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
[0087] Environment- 100
[0088] Network- 105
[0089] User Equipment (UE)- 110
[0090] Server- 115
[0091] System -120
[0092] Processor- 205
[0093] Memory- 210
[0094] User interface- 215
[0095] Database- 220
[0096] Retrieving unit-225
[0097] Preprocessing unit- 230
[0098] Extracting unit- 235
[0099] Training unit- 240
[00100] Determining unit- 245
[00101] Depicting unit- 250
[00102] Transmitting unit- 255
[00103] Primary Processor- 305
[00104] Memory- 310
[00105] Cell Towers- 405
[00106] Probing Unit- 415
[00107] Network Slice Monitoring Module- 420
[00108] Data Lake- 425
[00109] Data Preprocessing Unit- 430
[00110] Model Training Unit- 435
[00111] Trend Analysis Unit- 440
[00112] Data Visualization Unit- 445
[00113] Alerting and Responses Unit- 450 ,CLAIMS:CLAIMS
We Claim:
1. A method (600) for monitoring service quality in network slices, the method comprises the steps of:
retrieving (605), by one or more processors, a first set of data pertaining to each of a plurality of network slices from one or more data sources;
extracting (610) by one or more processors, one or more features from the first set of data corresponding to network performance of each of the plurality of network slices;
training (615), by one or more processors, a model utilizing the one or more features to identify at least one of, patterns and trends in each of the plurality of network slices;
determining (620), by the one or more processors, utilizing the trained model, one or more anomalies in the second set of data when a deviation is detected in the second set of data in comparison to at least one of, the trends or patterns of the first set of data.

2. The method (600) in claim 1 wherein, the first set of data includes information pertaining to at least one of traffic, signal strength, packet loss, latency and network statistics.

3. The method (600) as claimed in claim 1, wherein the step of, retrieving, a first set of data pertaining to each of a plurality of network slices from one or more data sources, further includes the step of, preprocessing the first set of data

4. The method (600) in claim 1, wherein the one or more features extracted from the retrieved data include at least one of, call parameters, geographic coordinates and network load metrics

5. The method (600) as claimed in claim 1, wherein the second set of data is received in real time from the one or more data sources.

6. The method (600) as claimed in claim 1, wherein the method further comprises the steps of:
depicting, by the one or more processors, the one or more anomalies on a user interface in at least one of, graphs, charts, or dashboards.

7. The method (600) as claimed in claim 1, wherein the method further comprises the step of:
transmitting, by the one or more processors, at least one of, alerts, notifications and one or more actions to a user in response to determining the one or more anomalies.

8. The method (600) as claimed in claim 7, wherein the one or more actions include at least one of recommending remedial actions to resolve the one or more anomalies.

9. A system (120) for monitoring service quality in network slices, the system comprising:
a retrieving unit (225), configured to, retrieve a first set of data pertaining to each of a plurality of network slices from one or more data sources;
an extracting unit (235), configured to, extract one or more features from the first set of data corresponding to network performance of each of the plurality of network slices;
a training unit (240), configured to, train a model utilizing the one or more features to identify at least one of, patterns and trends in each of the plurality of network slices;
determining unit (245), by the one or more processors, utilizing the trained model, one or more anomalies in the second set of data when a deviation is detected in the second set of data in comparison to at least one of, the trends or patterns of the first set of data.

10. The system (120) as claimed in claim 9, wherein the first set of data includes information pertaining to at least one of traffic, signal strength, packet loss, latency and network statistics.

11. The system (120) as claimed in claim 9, wherein the system (120) further comprises of a preprocessing unit (230) configured to preprocess the first set of data.

12. The system (120) as claimed in claim 9, wherein the one or more features extracted from the retrieved first set of data include at least one of, call parameters, geographic coordinates and network load metrics.

13. The system (120) as claimed in claim 9, wherein the second set of data is received in real time from the one or more data sources.

14. The system (120) as claimed in claim 9, wherein the system further comprising:
a depicting unit (250), configured to, depict the one or more anomalies on a user interface in at least one of, graphs, charts, or dashboards.

15. The system (120) as claimed in claim 9, wherein the system further comprising:
a transmitting unit (255), configured to, transmit at least one of, alerts, notifications and one or more actions to a user in response to determining the one or more anomalies.

16. The system (120) as claimed in claim 15, wherein the one or more actions include at least one of recommending remedial actions to resolve the one or more anomalies.

17. A User Equipment (UE) (110), comprising:
one or more primary processors (305) communicatively coupled to one or more processors (205), the one or more primary processors (305) coupled with a memory (310), wherein said memory (310) stores instructions which when executed by the one or more primary processors (305) causes the UE (110) to:
transmit, the first set of data pertaining to each of the plurality of network slices.
receive, alerts to address the one or more detected anomalies;
depict, the one more anomalies in at least one of, graphs, charts or dashboards on a user interface (215);
wherein the one or more processors (205) is configured to perform the steps as claimed in claim 1.