System and method for triggering network performance-based alert notifications in a communication network

Abstract

Disclosed is a system (160) and a method (400) for triggering alert notification in a communication network (100). The method (400) involves acquiring performance metrics data including multiple Key Performance Indicators (KPIs) associated with network nodes. Further, over a defined time interval, one or more of current KPI values, moving averages, and variances of the KPIs are monitored. Furthermore, a deviation in a residual slope corresponding to the one or more of the current KPI values, the moving averages, and the variances of the plurality of KPIs is determined to detect anomalies. Upon identifying such deviations a an alert notification including information related to the anomalies is triggered and transmitted to a user device for further action or analysis.

Description

SYSTEM AND METHOD FOR TRIGGERING NETWORK PERFORMANCE-BASED ALERT NOTIFICATIONS INA COMMUNICATION NETWORKTECHNICAL FIELD

[0001] The embodiments of the present disclosure generally relate to the field of wireless communication networks and systems. More particularly, the present disclosure relates to a system and a method for triggering network performancebased alert notifications in a communication environment.BACKGROUND OF THE INVENTION

[0002] The subject matter disclosed in the background section should not be assumed or construed to be prior art merely because of its mention in the background section. Similarly, any problem statement mentioned in the background section or its association with the subject matter of the background section should not be assumed or construed to have been previously recognized in the prior art.

[0003] In the ever-expanding realm of wireless networks, efficient operation of communication networks is of utmost importance to ensure seamless communication and uninterrupted services to users. In general, a large wireless network comprises of millions of nodes, viz. fifth Generation (5G) and sixth Generation (6G) nodes. It is cumbersome to monitor the performance of such large wireless networks across a service provider’s area.

[0004] To this end, for ensuring a seamless Quality of Service (QoS) to the users, network operators often track Key Performance Indicators (KPIs) that are indicative of an operating state of the nodes connected to the wireless network. Using the KPIs, the network operators may implement optimization routines on the nodes in a hierarchy, estimate capacity requirements, prepare dashboards for business insights and capacity utilization, and proactively detect problems in the functioning of the nodes.

[0005] Heretofore, conventional network monitoring systems require the network operators to manually identify and address any significant deviations in the KPIs by monitoring a wide array of KPIs to identify potential issues. This process is not only labor intensive but also highly susceptible to human errors. Further, given the volume of data and complexity of the wireless networks, it is impractical to expect the network operators to detect every significant deviation in the KPIs consistently and accurately in real time. Consequently, minor issues may go unnoticed until they evolve into major disruptions affecting the QoS of the wireless networks and user satisfaction.

[0006] Moreover, the conventional network monitoring systems often suffer from inherent lag between occurrence of any issue and its detection owing to the significant deviation in the KPIs. During this period of lag, network performance may degrade further leading to prolonged service disruptions. This delay can be particularly detrimental in scenarios where rapid response is critical such as in financial services, healthcare, or emergency communication networks.

[0007] Additionally, lack of automated and real time notifications in the conventional network monitoring systems hampers efficiency of network management. Without immediate alerts, the network operators may not become aware of emerging issues until they receive complaints from the users or after conducting routine checks. This limitation not only increases time required to resolve the issues but also places unnecessary stress on network resources and personnel. The cumulative effect of these delays and inefficiencies leads to significant downtime, financial losses, and damage to the service provider’s reputation.

[0008] Therefore, to overcome aforementioned challenges associated with the conventional network monitoring systems, there lies a need for a system and a method that is capable of triggering automatic proactive alerts in case of significant deviations in the KPIs to provide seamless and reliable service to the users.SUMMARY

[0009] The following embodiments present a simplified summary to provide a basic understanding of some aspects of the disclosed invention. This summary is not an extensive overview, and it is not intended to identify key / critical elements or to delineate the scope thereof. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

[0010] In an embodiment, disclosed herein is a method for triggering alert notification in a communication network. The method comprises acquiring, by an acquisition unit, performance metrics data including a plurality of Key Performance Indicators (KPIs) associated with a plurality of network nodes. The method further comprises monitoring, by a monitoring unit, one or more of current KPI values, moving averages, and variances of the plurality of KPIs for a specific time interval. Furthermore, the method comprises determining, by a determination unit, a deviation in a residual slope corresponding to the one or more of the current KPI values, the moving averages, and the variances of the plurality of KPIs based on the monitoring. Thereafter, the method comprises triggering, by a notification engine based on the deviation, a transmission of an alert notification including information related to anomalies in one or more KPIs among the plurality of KPIs exhibiting the deviation to a user device.

[0011] In one or more aspects, the method further comprises displaying, by a display control unit on the user device, a visualization interface indicating the anomalies overlaid onto one or more geographic layers representing a network infrastructure of the communication network.

[0012] In one or more aspects, the method further comprises receiving, by a feedback collection unit, one or more user feedbacks from the user device in response to the visualization interface and the transmission of the alert notification to the user device. Furthermore, the method comprises evaluating, by a feedback evaluation unit, the one or more user feedbacks to assess an effectiveness of anomaly detection accuracy, a relevancy of the alert notification for a user of theuser device, and visualization clarity for the user. Thereafter, the method comprises adjusting, by the feedback evaluation unit based on a result of the evaluation, one or more operational parameters for one or more of detecting the anomalies, triggering the alert notification, and displaying the visualization interface indicating the anomalies.

[0013] In one or more aspects, the method further comprises determining, by the determination unit, whether the deviation in the residual slope is less than or greater than a predefined threshold range. Furthermore, the method comprises detecting, by the determination unit, at least one abnormal trend in the one or more of the current KPI values, moving averages, and variances of the plurality of KPIs based on a determination that the deviation in the residual slope is less than or greater than the predefined threshold range.

[0014] In one or more aspects, the performance metrics data is acquired from one or more network components selected from a group of network components including one or more base stations, switches, and routers. Further, the detection of the at least one abnormal trend comprises one or more of a detection of a sudden change in one or more of the current KPI values, the moving averages, and the variances of the plurality of KPIs or one of a gradual increase or decrease in the one or more of the current KPI values, the moving averages, and the variances of the plurality of KPIs.

[0015] In one or more aspects, the plurality of KPIs includes one or more of an accessibility of the communication network, an availability of the communication network, a payload in the communication network, mobility, traffic served by the plurality of network nodes in a pre-defined time period, a data throughput of the plurality of network nodes, a count of call drops and call mutes in a location served by the plurality of network nodes, a number of data calls and voice calls served by the plurality of network nodes, and a number of handovers performed by the plurality of network nodes.

[0016] In one or more aspects, the alert notification is transmitted to the user device via a notification channel in real-time.

[0017] According to another aspect of the present disclosure, disclosed is a system for triggering alert notifications in a communication network. The system comprises an acquisition unit configured to acquire performance metrics data including a plurality of Key Performance Indicators (KPIs) associated with a plurality of network nodes. The system further comprises a monitoring unit configured to monitor one or more of current KPI values, moving averages, and variances of the plurality of KPIs for a specific time interval. The system further comprises a determination unit configured to determine a deviation in a residual slope corresponding to the one or more of the current KPI values, the moving averages, and the variances of the plurality of KPIs based on the monitoring. Furthermore, the system comprises a notification engine configured to trigger, based on the deviation, a transmission of an alert notification including information related to anomalies in one or more KPIs among the plurality of KPIs exhibiting the deviation to a user device.BRIEF DESCRIPTION OF DRAWINGS

[0018] Various embodiments disclosed herein will become better understood from the following detailed description when read with the accompanying drawings. The accompanying drawings constitute a part of the present disclosure and illustrate certain non-limiting embodiments of inventive concepts. Further, components and elements shown in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. For consistency and ease of understanding, similar components and elements are annotated by reference numerals in the exemplary drawings.

[0019] FIG. 1 illustrates a block diagram depicting a communication environment, in accordance with an embodiment of the present disclosure.

[0020] FIG. 2 illustrates a block diagram depicting data flow for triggering network performance-based alert notifications, in accordance with an embodiment of the present disclosure.

[0021] FIG. 3 illustrates a process flow diagram for triggering the network performance-based alert notifications and displaying performance anomalies, in accordance with an embodiment of the present disclosure.

[0022] FIG. 4 illustrates a flowchart depicting a method for triggering the network performance-based alert notifications, in accordance with an embodiment of the present disclosure.

[0023] FIG. 5 illustrates a schematic architecture diagram depicting a computing system, in accordance with an embodiment of the present disclosure.DETAILED DESCRIPTION OF THE INVENTION

[0024] Aspects of the present disclosure will now be described in further detail with reference to the accompanying drawings, which illustrate one or more example embodiments. The embodiments are presented by way of example only and are not intended to limit the scope of the present disclosure. Rather, these descriptions are provided to ensure a clear and consistent understanding of the disclosed subject matter by those skilled in the art. It should be understood that the various embodiments described herein may be modified, combined, or adapted without departing from the overall scope and intent of the invention.

[0025] The following description presents various embodiments of the present disclosure. The embodiments disclosed herein are presented as teaching examples and are not to be construed as limiting the scope of the present disclosure. The present disclosure should in no way be limited to the illustrative implementations, drawings, and techniques illustrated below, including the exemplary design and implementation illustrated and described herein, but may be modified, omitted, or expanded upon without departing from the scope of the present disclosure.

[0026] The following description contains specific information pertaining to embodiments in the present disclosure. The detailed description uses the phrases “in some embodiments” or “some implementations” which may each refer to one or more or all of the same or different embodiments or implementations. The term “some” as used herein is defined as “one, or more than one, or all.” Accordingly, the terms “one,” “more than one,” “more than one, but not all” or “all” would all fall under the definition of “some.” In view of the same, the terms, for example, “in an embodiment” or “in an implementation” refers to one embodiment or one implementation and the term, for example, “in one or more embodiments” refers to “at least one embodiment, or more than one embodiment, or all embodiments”. Further, the term, for example, “in one or more implementations” refers to “at least one implementation, or more than one implementation, or all implementations.

[0027] The term “comprising,” when utilized, means “including, but not necessarily limited to;” it specifically indicates open-ended inclusion in the so-described one or more listed features, elements in a combination, unless otherwise stated with limiting language. Furthermore, to the extent that the terms “includes,” “has,” “have,” “contains,” and other similar words are used in either the detailed description, such terms are intended to be inclusive in a manner similar to the term “comprising.”

[0028] In the following description, for the purposes of explanation, various specific details are set forth to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features.

[0029] The description provided herein discloses exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the present disclosure. Rather, the foregoing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing anyof the exemplary embodiments. Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it may be understood by one of the ordinary skilled in the art that the embodiments disclosed herein may be practiced without these specific details.

[0030] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein the description, the singular forms "a", "an", and "the" include plural forms unless the context of the invention indicates otherwise.

[0031] The terminology and structure employed herein are for describing, teaching, and illuminating some embodiments and their specific features and elements and do not limit, restrict, or reduce the scope of the present disclosure. Accordingly, unless otherwise defined, all terms, and especially any technical and / or scientific terms, used herein may be taken to have the same meaning as commonly understood by one having ordinary skill in the art.

[0032] The present disclosure relates to a system and a method for triggering network performance-based alert notifications by utilizing major performance Key Performance Indicators (KPIs) and implementing a systematic monitoring process for pre-defined periods. By continuously monitoring the KPIs, trends of the KPIs that fall below predefined thresholds are detected, signaling potential issues or anomalies within the network. Leveraging a sophisticated notification engine, the system then triggers real-time notifications to alert users to these deviations. Upon receiving a notification, users can seamlessly navigate to the site layer and visualize the detected anomalies overlaid onto geographic layers. This allows for immediate contextualization of performance issues, empowering users to conduct further KPI analysis directly within layer interface. By integrating real-time monitoring, notification capabilities, and interactive visualization tools, proactive network management and rapid response to performance fluctuations is facilitated, ultimately enhancing operational efficiency and service quality.

[0033] In the disclosure, various embodiments are described using terms used in some communication standards (e.g., 3rd Generation Partnership Project (3GPP), 3rd Generation Partnership Project 2 (3GPP2), European Telecommunications Standards Institute (ETSI), International Telecommunication Union- Radiocommunication Sector (ITU-R), Extensible Radio Access Network (xRAN), and Open-Radio Access Network (O-RAN)), but these are merely examples for description. Various embodiments of the disclosure may also be easily modified and applied to other communication systems.

[0034] In order to facilitate an understanding of the disclosed invention, a number of terms are defined below.

[0035] In the present disclosure, the term “Key Performance Indicator (KPI)” refers to a measurable value used to evaluate performance, reliability, and Quality of Service (QoS) of a communication network.

[0036] Performance metrics data includes raw or derived measurements reflecting operational status of network components in the communication network. These may include KPIs and other derived indicators such as packet loss, latency, traffic volume, etc. collected over time for analytics and generating network alerts.

[0037] A “residual slope” refers to a rate of change of a residual error i.e., a difference between a predicted and actual KPI value over a time interval. The residual slope is used to detect abnormal drifts, drastic changes, or sudden deviations in KPI trends that may indicate network anomalies.

[0038] A “moving average” refers to a technique for smoothing time-series KPI data by averaging subsets over a rolling time window, as used in network monitoring and forecasting to reduce noise and identify KPI trend shifts.

[0039] A “variance” quantifies a variability of a KPI over time, reflecting a degree to which KPI data points deviate from the average. A high variance may indicate instability or inconsistent network behavior.

[0040] An “anomaly detection” refers to an identification of patterns in network performance data that deviate significantly from normal behavior. This includes both sudden (e.g., spikes) and gradual (e.g., drifts) deviations that may suggest faults, congestion, or degradation in telecommunications services.

[0041] An “alert notification” refers to a message that is automatically triggered and transmitted to a user device to notify network operators or monitoring systems about the deviations in network performance parameters. The alert notification may be prioritized and sent via predefined channels (e.g., SMS, email, push notification) based on a severity and type of detected anomaly.

[0042] A “cell topology” refers to a logical and a geographical structure of cells in the communication network, including parameters like cell identifiers, coverage boundaries, and neighboring cell relationships.

[0043] A “severity scoring logic” refers to a rule-based or Artificial Intelligence (Al)-driven framework used to assign criticality levels (e.g., minor, major, critical) to detected anomalies or faults based on the deviations in the KPIs and historical network data.

[0044] Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings. FIG. 1 through FIG. 5, discussed below, and the one or more embodiments used to describe the principles of the present disclosure are by way of illustration only and should not be construed in any way to limit the scope of the present disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged system or device.

[0045] FIG. 1 illustrates a communication environment 100 (hereinafter may also be referred to as a communication network 100 or communication system 100 ), in accordance with an embodiment of the present disclosure. The embodiment of the communication environment 100 shown in FIG. 1 is for illustration only. Otherembodiments of the communication environment 100 may be used without departing from the scope of the present disclosure.

[0046] As shown in FIG. 1, the communication environment 100 includes a server 160 (hereinafter interchangeably referred to and designated as System 160, a network 110 (hereinafter also referred to as the “network 110”), a node 120 such as a gNB (e.g., base station) and a user device 130.

[0047] The node 120 communicates with the network 110, such as Internet, a proprietary Internet Protocol (IP) network, or other data network. Further, the node 120 communicates with the server 160 via the network 110.

[0048] The server 160 is configured to triggering the alert notifications in the communication network 100. Furthermore, the embodiment of the server 160 as shown in FIG. 1 is for illustration only. However, the server 160 may come in a wide variety of configurations, and FIG. 1 does not limit the scope of the present disclosure to any particular implementation of the server 160. The server 160 includes a memory 102, a processor 104, a database 140, a communication interface 106 including communication circuitry, and a processing Unit(s) / Engine(s) 108.

[0049] The memory 102 stores a set of instructions required by one or more processing engines Specifically, the memory 102 may stores a UI framework via which the processor 104 may control the UI 130-1 of the user device 130. The UI framework may be depicted as a separate component within the server 160. The memory 102 may include non-volatile storage elements. Examples of such nonvolatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of Electrically Programmable Memories (EPROM) or Electrically Erasable and Programmable Memories (EEPROM). In addition, the memory 102 may, in some examples, be considered a non-transitory storage medium. The "non-transitory" storage medium is not embodied in a carrier wave or a propagated signal. However, the term "non-transitory" should not be interpreted as the memory 102 is non-movable. In some examples, the memory 102 may be configured to store larger amounts of information. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache). The memory 102 may be an internal storage unit or an external storage unit of the server 160, cloud storage, or any other type of external storage.

[0050] The processor 104 is configured to execute programs and other processes stored in the memory 102. The processor 104 is further configured to store data into or fetch the data from the memory 102 as required by one or more execution process disclosed herein for triggering the alert notifications in the communication network 100. The processor 104 may also be coupled to a network interface that may allow the server 160 to communicate with other devices or systems over a network. The network interface may support communications over any suitable wired or wireless connect! on(s). The processor 104 may include various processing circuitry and communicates with the memory 102, the communication interface 106, and the database 140. The processor 104 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, processing units, or a general -purpose processor, such as, for example, and without limitation, a Central Processing Unit (CPU), an Application Processor (AP), a dedicated processor, a graphics-only processing unit such as a Graphics Processing Unit (GPU) or the like, a programmable logic device, or any combination thereof that manipulate data based on operational instructions. Among other capabilities, the processor 104 is configured to fetch and execute computer-readable instructions stored in the memory 102 to perform various processes.

[0051] The communication interface 106 includes an electronic circuit specific to a standard that enables wired or wireless communication. The communication interface 106 is configured to communicate internally between internal hardware components. The communication interface 106 may be further configured to communicate with external devices via one or more networks.

[0052] In an embodiment, the processing units(s) / engine(s) 108 may be implemented as a combination of hardware and programming (for example,programmable instructions) to implement one or more functionalities of the processing units(s) / engine(s) 108. In non-limiting examples, described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing units(s) / engine(s) 108 may be processor-executable instructions stored on a non -transitory machine- readable storage medium and the hardware for the processor 104 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing units(s) / engine(s) 108. In such examples, the server 160 may also comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the server 160 and the processing resource. In other examples, the processing units(s) / engine(s) 108 may be implemented using an electronic circuitry.

[0053] In an example embodiment, the processing units(s) / engine(s) 108 may include one or more units or engines selected from any of an acquisition unit 108- 1, a monitoring unit 108-2, a determination unit 108-3, a notification engine 108-4, a display control unit 108-5, a feedback collection unit 108-6, and a feedback evaluation unit 108-7. The processing units(s) / engine(s) 108 may include other units / engines (shown in FIG. 2) such as, but are not limited to, a threshold setting unit 220, a drastic change notifier 230, and the like.

[0054] In an embodiment, the processor 104, using the acquisition unit 108-1, is configured to acquire performance metrics data including Key Performance Indicators (KPIs) associated with network nodes in a communication network 100. In an example, the performance metrics data may be acquired from one or more network nodes selected from a group of network nodes or components including one or more base stations, switches, and routers that are present in the communication network 100. The one or more network nodes, including base stations, switches, and routers, may be selected by the network operator formonitoring based on one or more selection criteria such as geographic relevance, service priority, fault history, or traffic density. In one embodiment, a monitoring policy may designate all base stations within urban zones to be included for KPI collection, whereas only critical aggregation routers may be monitored in a core segment of the communication network 100.

[0055] In another example, the KPIs may include one or more of an accessibility of the communication network 100, an availability of the communication network 100, a payload in the communication network 100, mobility, traffic served by the network nodes in a pre-defined time period, a data throughput of the network nodes, a count of call drops and call mutes in a location served by the network nodes, a number of data calls and voice calls served by the network nodes, and a number of handovers performed by the network nodes.

[0056] Further, the processor 104, using the monitoring unit 108-2, monitors one or more of current KPI values, moving averages, and variances of the KPIs for a specific time interval, for example, hourly, daily, weekly, or monthly basis.

[0057] In an embodiment, the processor 104, using the determination unit 108-3, determines a deviation in a residual slope corresponding to the one or more of the current KPI values, the moving averages, and the variances of the KPIs based on a result of the monitoring process. The current KPI values refers to instantaneous or real-time measurements of performance parameters captured during ongoing network operations. For instance, the current KPI values may include instantaneous Reference Signal Received Power (RSRP), signal throughput in Mbps, handover success rate, packet drop rate, or active session count, obtained at periodic intervals such as every 5 seconds or every minute from the network nodes.

[0058] In particular, the monitoring unit 108-2 continuously or periodically tracks and analyze the KPIs associated with the network nodes in the communication network 100. For each KPI, the monitoring may include tracking of the current KPI values, calculating the moving average over a predefined number of time intervals, and determining the variance to measure deviation in the current KPI values. In anon-limiting example, the KPI (packet drop rate) is monitored for one or more network nodes over a sequence of six sampling intervals, each interval may span a duration of ten minutes. The current values of the packet drop rate during the six intervals indicate a progressive increase from approximately 1.2% to 3.1%, thereby reflecting a degraded trend in performance of the communication network 100. The moving average of the packet drop rate across the six intervals may be computed to be approximately 2.1%, which exceeds a predefined threshold of 2% set for initiating anomaly detection. Additionally, the variance of the packet drop rate across the six samples may be observed to be moderate, suggesting increased inconsistency in the behavior of the communication network 100.

[0059] Further, the processor 104, using the determination unit 108-3, determines whether the deviation in the residual slope is less than or greater than a predefined threshold range. Furthermore, the processor 104, using the determination unit 108- 3 detects at least one abnormal trend in the one or more of the current KPI values, moving averages, and variances of the plurality of KPIs based on a determination that the deviation in the residual slope is less than or greater than the predefined threshold range. Based on the detection of the at least one abnormal trend, the processor 104, using the notification engine 108-4, triggers the transmission of the alert notification including information related to the anomalies to the user device 130. In a non-limiting example, deviations with a residual slope beyond ±5% (predefined threshold range) are classified as abnormal trend including anomalies and real-time alert is triggered upon identification or detection of such anomalies via an integrated notification engine (i.e., notification engine 108-4). In another nonlimiting example, a scenario where the call drop rate within a service area gradually increases from 0.5% to 2.5% over the span of 30 minutes can be classified as the abnormal trend.

[0060] In a non-limiting example, the detection of the at least one abnormal trend comprises one or more of a detection of a sudden change in one or more of the current KPI values, the moving averages, and the variances of the plurality of KPIsor one of a gradual increase or decrease in the one or more of the current KPI values, the moving averages, and the variances of the KPIs.

[0061] In an embodiment, the processor 104, using the notification engine 108-4 is configured to trigger the transmission of the alert notification to the user device 130 based on the deviation. The alert notification may include, but not limited to, information related to the anomalies in one or more of the KPIs exhibiting the deviation. The information related to the anomalies may include, but is not limited to, a description of anomaly type, impacted network node(s), KPI names and corresponding threshold violations, severity grading, associated timestamps, and the like. The KPI exhibiting the deviation may correspond to any KPI whose trend line, moving average, or variance demonstrates a significant deviation from predefined thresholds. For example, if an uplink throughput (KPI) exhibits a downward deviation of more than 15% from the moving average, the uplink throughput may be classified as the KPI exhibiting the deviation and may be flagged for anomaly alerting. More specifically, the notification engine 108-4 plays a critical role in ensuring that network operators are promptly informed of any issues, enabling swift action to maintain optimal network performance and reliability.

[0062] In an example, the alert notification may be transmitted to the user device 130 via a notification channel in real-time. The notification channel may correspond to at least one of a mobile application, a web application, a Short Message Service (SMS), and an email application running on the user device 130. Upon receiving such alert notification, a user of the user device 130 can seamlessly navigate to a site layer to visualize the detected anomalies overlaid on geographic representations of the communication network 100.

[0063] Thereafter, the processor 104, using the display control unit 108-5, controls the user device 130 to display a visualization interface indicating the anomalies overlaid onto one or more geographic layers representing a network infrastructure of the communication network 100. The user device 130 may be controlled usingan Application Programming Interface (API) call triggered through the communication interface 106 to the user device 130.

[0064] In one or more embodiments, the processor 104, using the feedback collection unit 108-6, may collect or receive one or more user feedbacks from the user device 130 in response to the visualization interface displayed on the user device 130 and the transmission of the alert notification to the user device 130. The user feedbacks, as received by the feedback collection unit 108-6 in response to the display of the visualization interface or triggered alert notifications may be explicit (e.g., “false positive” or “useful alert”) or implicit (e.g., click -through rates or time spent on alert details), and may be analyzed using scoring algorithms or machine learning classifiers to compute effectiveness scores.

[0065] In one or more embodiments, the processor 104, using the feedback evaluation unit 108-7, evaluates the one or more user feedbacks received from the user device 130 to assess an effectiveness of anomaly detection accuracy, a relevancy of the alert notification for the user of the user device 130 and a visualization clarity for the user of the user device 130. For instance, the user feedbacks, as received by the feedback collection unit 108-6 in response to the display of the visualization interface or triggered alert notifications, are further evaluated based on predefined metrics (i.e., configured by network operator) for the anomaly detection accuracy, relevancy, and usability.

[0066] Based on a result of the evaluation, the processor 104, using the feedback evaluation unit 108-7, may adjust one or more operational parameters for one or more of detecting the anomalies, triggering the alert notification, and displaying the visualization interface indicating the anomalies. The one or more operational parameters refers to configurable thresholds and logic variables used by the feedback evaluation unit 108-7 to monitor the anomaly detection, alerting sensitivity, and visualization behavior. Such operational parameters may include, but are not limited to, threshold ranges for residual slope detection, alert notification frequency, KPI sampling intervals, aggregation windows for the moving averages,and the like. These parameters are termed “operational” as they directly affect runtime behavior of the anomaly detection and alerting mechanism of the above described system 100. In an example, the adjustment of the one or more operational parameters may include, but not limited to, updating one or more of predefined thresholds for detecting the anomalies, a notification frequency of notification alerts, a sampling frequency, the moving averages, and the predefined threshold range for the residual slope, a cell topology, or a severity scoring logic.

[0067] In another example, the anomalies may be dynamically detected based on the adjustment of the one or more operational parameters. For instance, upon reducing the residual slope detection threshold from ±5% to ±3% based on user feedback indicating missed alerts, the determination unit 108-3 may detect additional performance degradations which previously went unreported or undetected.

[0068] The node 120 provides wireless broadband access to the network 110. The node 120 may communicate with the user device 130 or the server 160 using any one of a 5th Generation 5G / New Radio (NR), Long Term Evolution (LTE), Long Term Evolution Advanced (LTE-A), Worldwide Interoperability for Microwave Access (WiMAX), Wireless Fidelity (Wi-Fi), or other wireless communication techniques. In an example embodiment, the node 120 may be referred to as “base station”.

[0069] The term “base station” may refer to any component (or collection of components) configured to provide wireless access to a network, such as Transmit Point (TP), Transmit-Receive point (TRP), an Evolved Base Station (eNodeB or eNB), a 5G / NR base station (gNB), a macrocell, a femtocell, a WiFi Access Point (AP), or other wirelessly enabled devices. The base stations may provide wireless access in accordance with one or more wireless communication protocols, e.g., 5G / NR 3GPP new radio interface / access (NR), LTE, LTE-A, High Speed Packet Access (HSPA), Wi-Fi 802.11a / b / g / n / ac, etc.

[0070] The user device 130 includes the UI 130-1 (also referred to as a “display interface 130-1”) and a communication unit 130-2. The display interface 130-1 may correspond to a UI module (i.e., a visualization interface) capable of graphically presenting the performance metrics or anomalies using maps, charts, or tables, enabling operators to interpret network states effectively. In particular, the display interface 130-1 facilitates display of the visualization data indicating network performance anomalies in a spatial context and overlays detected anomalies onto the geographic layers representing the network infrastructure of the communication network 100. Users can interact with the display interface 130-1 to drill down into specific areas or network elements, conduct further analysis, and explore potential root causes of performance issues.

[0071] The communication unit 130-2 may include a plurality of antennas, a plurality of Radio Frequency (RF) transceivers, a transmit processing circuitry, and a receive processing circuitry. Additionally, the user device 130 may further include circuitry, programing, applications, or a combination thereof. Further, depending on the network type, the term “user device” may refer to any component such as “mobile station,” “subscriber station,” “remote terminal,” “wireless terminal,” “receive point,” or “end user device,”. For the sake of convenience, the term “user device” used in this disclosure refers to a remote wireless equipment that wirelessly accesses the node 120 and the server 160 via the network 110.

[0072] The database 140 may correspond to a centralized database configured to store and manage structured data, such as network-related data and configurations. The database 140 may be a relational database organizing related data such as in a table, or a non-relational database organizing graphical and time series data.

[0073] Although FIG. 1 illustrates one example of the communication environment 100, various changes may be made to FIG. 1. For example, the communication environment 100 may include any number of databases and may include any number of user devices in any suitable arrangement. Further, the server 160 may include any number of components in addition to the components shown in FIG. 1 and variouscomponents in FIG. 1 may be combined, further subdivided, or omitted and additional components may be added according to particular needs.

[0074] FIG. 2 illustrates a block diagram 200 depicting data flow for triggering network performance-based alert notifications, in accordance with an embodiment of the present disclosure. The flow of data takes place between a Performance Metrics (PM) data source 210, the threshold setting unit 220, the drastic change notifier 230 and the notification engine 108-4. The PM data source 210 may be an external unit or module to the server 160 and may transfer the performance metrics data to the server 160. Further, the threshold setting unit 220 and the drastic change notifier 230 are integrated in the server 160 in addition to the notification engine 108-4.

[0075] The acquisition unit 108-1 fetches, from the PM data source 210, the performance metrics data including the KPIs associated with the nodes in the communication network 100. In one or more implementations, the PM data source 210 may correspond to network nodes or components such as base stations, switches, and routers.

[0076] The threshold setting unit 220 sets pre-defined threshold value for each KPI of the KPIs. The predefined threshold value refers to a benchmark value assigned to KPI trends for anomaly identification. In a non-limiting example, a predefined threshold for the packet drop may be set to 2%, a predefined threshold range for slope sensitivity / residual error bounds may be set to ±5%. Similarly, a threshold may be set to configure the sampling frequency and moving average window size per KPI (i.e., for respective KPI a respective threshold may be set by the threshold setting unit 220). The determination unit 108-3 compares the current KPI values of the corresponding KPI with the pre-defined threshold value for the corresponding KPI. In an operational example, any monitored value exceeding the threshold value for the packet drop (for example, greater than 2%) may be treated as a performance anomaly, depending on the deviation.

[0077] The notification engine 108-4 generates the alert notification and communicates the alert to network administrators through various notification channels, including, but not limited to, a web-based application, mobile application, Short Message Service (SMS), or email interface, upon identifying any deviation from the pre-defined threshold value or range set by the threshold setting unit 220. If any KPI falls below the pre-defined threshold value or range, indicating a deviation in the residual slope corresponding to the one or more of the current KPI values, the notification engine 108-4 flags it as an anomaly. The alert notification corresponds to a system-generated message transmitted to the user device 130 indicating occurrence of detected anomalies in the network performance. In a nonlimiting example, the alert notification may include metadata such as timestamp, affected KPI, network element identity, and a severity level.

[0078] The drastic change notifier 230 notifies detection of a sudden or a drastic change in network performance to the user device 130 in order to notify the user (i.e., the network administrators) to take corrective actions timely.

[0079] FIG. 3 illustrates a process flow diagram 300 for triggering the network performance-based alert notifications and displaying performance anomalies, in accordance with an embodiment of the present disclosure. The process flow diagram 300 includes steps 302 through 312.

[0080] At step 302, the acquisition unit 108-1 acquires, from the PM data source 210, the performance metrics data including the KPIs associated with the nodes in the communication network 100.

[0081] At step 304, the determination unit 108-3 compares the current values of the corresponding KPI with the pre-defined threshold value for the corresponding KPI set by the threshold setting unit 220.

[0082] At step 306, the determination unit 108-3 triggers the notification engine 108-4 to send an alert message (i.e., notification alert) to the user device 130 via the communication interface 106. The notification engine 108-4 may be triggered bythe determination unit 108-3 when the abnormal trend comprising the detection of the sudden change or the gradual increase or decrease in one or more of the current KPI values, the moving averages, and the variances of the plurality of KPIs is detected.

[0083] At step 308, the notification engine 108-4 sends the alert message to the user device 130.

[0084] At step 310, the visualization data indicating the detected anomalies is displayed on the UI 130-1 using which users can analyse or identify the performance anomalies in the spatial context.

[0085] At step 312, the displayed visualization data may be further analysed by the network administrator or the user of the user device to explore potential root causes of performance issues in the communication network 100.

[0086] FIG. 4 illustrates a flowchart depicting a method 400 for triggering the network performance-based alert notifications, in accordance with an embodiment of the present disclosure. The method 400 comprises a series of operation steps indicated by blocks 402 through 418. The method 400 starts at block 402. The method 400 described herein is a process executed by the processor 104 utilizing the processing unit(s) / engine(s) 108 to trigger the notification alerts for evaluating the performance of the communication network 100, and to visualize the anomalies that are root cause of performance related issues in the communication network 100.

[0087] At block 402, the acquisition unit 108-1 acquires the performance metrics data including the KPIs associated with the network nodes. The performance metrics data is acquired in real time from the network nodes such as the base stations, the switches, and the routers in the communication network 100.

[0088] At block 404, the monitoring unit 108-2 monitors one or more of the current KPI values, the moving averages, and the variances of the KPIs for the specific time interval. In a non-limiting example, the specific time interval of monitoring may bedaily and hourly basis. The performance metrics data is aggregated and analysed to derive meaningful insights on overall performance of the communication network 100. The monitoring of the current KPI values may be performed by the monitoring unit 108-2 by continuously acquiring raw telemetry data from network nodes and applying analytical algorithms to derive trends, detect spikes, and compute derivatives such as the moving averages and the residual slopes. For example, a 5- minute moving average may be calculated over the last ten KPI values captured at 30-second intervals, and variance may be computed to quantify data dispersion.

[0089] At block 406, the determination unit 108-3 determines the deviation in the residual slope corresponding to the one or more of the current KPI values, the moving averages, and the variances of the KPIs based on the monitoring. The determination unit 108-3 further determines whether the deviation in the residual slope is less than or greater than the predefined threshold range. Furthermore, based on the determination that the deviation in the residual slope is less than or greater than the predefined threshold range, the determination unit 108-3 detects the at least one abnormal trend in the one or more of the current KPI values, the moving averages, and the variances of the KPIs.

[0090] At block 408, based on the determined deviation, the notification engine 108-4 triggers the transmission of the alert notification exhibiting the deviation to the user device 130. The alert notification includes information related to the anomalies in the one or more current KPIs among the KPIs. The alert notification is transmitted to the user device 130 via the notification channel in real-time.

[0091] At block 410, the display control unit 108-5 sends the control signal as the API call to the user device 130 to display the visualization interface indicating the anomalies overlaid onto one or more geographic layers representing the network infrastructure of the communication network 100.

[0092] At block 412, the feedback collection unit 108-6 receives the user feedbacks from the user device 130 in response to the display of the visualization interface at the user device 130 and the transmission of the alert notification to the user device130. In a non-limiting example, the user feedbacks may include, but not limited to, an indication whether an alert was helpful, too frequent, or irrelevant (e.g., false positive i.e., no issue observed on ground), a recommendation emphasizing certain KPIs over others based on operational needs ((e.g., a regional operator may prioritize call mute rate over throughput due to recurring VoLTE issues), information on how and where the user wants to receive the alerts (e.g., field network teams may prefer push notifications via mobile for real-time response), a request to overlay certain KPIs on specific map layers (e.g., elevation, customer density, fiber routes), information on whether the alert notification led to meaningful intervention or if it was premature / inaccurate, and the like.

[0093] At block 414, the feedback evaluation unit 108-7 evaluates the one or more user feedbacks to assess an effectiveness of anomaly detection accuracy, the relevancy of the alert notification for the user of the user device 130, and the visualization clarity for the user. In particular, the feedback evaluation unit 108-7 may determine whether the one or more user feedbacks received from the user device includes any suggestion, requests, indication, recommendation, correct, or any modification in the displayed visualization data or the provided alert notification. For example, the user feedback may indicate false positive indicating that no issue was observed on the ground in response to the provided alert notification. Also, the user feedback may include indications categorizing whether the alert was helpful, too frequent, or irrelevant. Based on the analysis of the information included in the user feedbacks, the feedback evaluation unit 108-7 may perform adjustments in the one or more operational parameters (at block 416).

[0094] Specifically, at the block 416, the feedback evaluation unit 108-7 adjusts the one or more operational parameters for detecting the anomalies. The adjustment of the one or more operational parameters comprises updating one or more of the predefined thresholds for detecting the anomalies, the notification frequency of alerts, the sampling frequency, the moving averages, and the predefined threshold range for the residual slope, the cell topology, or the severity scoring logic. In particular, the feedback evaluation unit 108-7 may dynamically adjust theoperational parameters to optimize the detection of the anomalies and triggering the alert notification. In a non-limiting example, the feedback evaluation unit 108-7 may adjust the threshold values per KPI (e.g., packet drop threshold moved from 2% to 1.5%), the slope sensitivity / residual error bounds (e.g., ±5% changed to ±3% for more sensitive anomaly detection), the sampling frequency and the moving average window size, the notification frequency to avoid alert fatigue, a geographic or topological granularity (e.g., cell-sector vs. site-level vs. cluster-level alerts), the severity scoring logic based on user impact, recurrence, and cross correlation between the KPIs.

[0095] At block 418, the notification engine 108-4 triggers the alert notification when the at least one abnormal trend or any deviation in the one or more of the current KPI values, the moving averages, and the variances of the KPIs is detected. Further, the visualization data including the detected anomalies is displayed on the display interface 130-1 when the determination unit 108-3 triggers the user device 130 using the communication interface 106.

[0096] FIG. 5 illustrates a schematic architecture diagram depicting a computing system 500 in which embodiments of the present disclosure may be implemented. The computing system 500 can be any commercially available and well-known computer capable of performing the functions described herein. The computing system 500 may be any type of computer, including a server, a web server, a cloud server, etc. The one or more components of the computing system 500 may perform the operational steps of the method 400 and the functions similar to the components of the server 160 as disclosed herein with respect to FIG. 1 through FIG. 4.

[0097] The computing system 500 includes a network 502, a network interface 504, a processor 506, an Input / Output (I / O) interface 508 and a non-transitory computer readable storage medium 510 (hereinafter may also be referred to as the “storage medium 510” or the “storage media 510”).

[0098] The network interface 504 includes wireless network interfaces such as Bluetooth, Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX),General Packet Radio Service (GPRS), or Wideband Code Division Multiple Access (WCDMA) or wired network interfaces such as Ethernet, Universal Serial Bus (USB), or Institute of Electrical and Electronics Engineers-864 (IEEE-864).

[0099] The processor 506 may include various processing circuitry and communicate with the storage medium 510 and the VO interface 508. The processor 506 is configured to execute instructions stored in the storage medium 510 and to perform various processes. The processor 506 may include an intelligent hardware device including a general -purpose processor, such as, for example, and without limitation, a Central Processing Unit (CPU), an Application Processor (AP), a dedicated processor, or the like, a graphics-only processing unit such as a Graphics Processing Unit (GPU), a microcontroller, a Field-Programmable Gate Array (FPGA), a programmable logic device, a discrete hardware component, or any combination thereof. The processor 506 may be configured to execute computer- readable instructions 510-1 (hereinafter also referred to as “instructions 510-1) stored in the storage medium 510 to cause the server 160 to perform various functions.

[0100] The storage medium 510 stores a set of instructions 510-1 required by the processor 506 for controlling its overall operations. The storage medium 510 further stores a microservices framework 510-2.

[0101] The storage media 510 may include one or more of an electronic storage medium, a magnetic storage medium, an optical storage medium, a quantum storage medium, or the like. For example, the storage media 510 may include, but are not limited to, hard drives, floppy diskettes, optical disks, ROMs, RAMs, EPROMs, EEPROMs, flash memory, magnetic or optical cards, solid-state memory devices, or other types of physical media suitable for storing electronic instructions. In one or more implementations, the storage media 510 includes a Compact Disk -Read Only Memory (CD-ROM), a Compact Disk-Read / Write (CD-R / W), and / or a Digital Video Disc (DVD).T1

[0102] In one or more embodiments, the storage medium 510 stores computer program code configured to cause the computing system 500 to perform at least a portion of the processes and / or methods. Accordingly, in at least one embodiment, the computing system 500 performs the method for triggering the network performance-based alert notifications.

[0103] Now, referring to the technical abilities and advantageous effect of the present disclosure, various operational advantages may be provided by one or more embodiments described herein. In certain embodiments, the disclosed method and system describes the process of triggering the network performance-based alert (push) notifications based on the detection of the drastic or sudden changes in the KPIs by automatically monitoring the KPIs and enabling proactive issue detection, thereby allowing the network operators to identify emerging problems in real-time, before escalation of the same into significant service disruptions or a negative impact on users’ experience.

[0104] A further potential advantage of the one or more embodiments disclosed herein may include timely resolution of detected issue owing to instant push notifications alerting the network operators regarding the KPI deviations. The network operators can thus promptly investigate for determining root causes of the detected issue and take corrective actions, minimizing downtime and reducing the potential for widespread service outages.

[0105] Another potential advantage of the one or more embodiments disclosed herein may include enabling streamlining of resource allocation by providing targeted notifications for specific KPI deviations. The network operators can thus allocate resources more efficiently, directing efforts and the resources towards addressing critical issues identified through the notifications, thus maximizing effectiveness of network management activities.

[0106] Yet another potential advantage of the one or more embodiments disclosed herein may include enhanced users’ experience owing to timely detection and resolution of network issues thereby improving service reliability and performance.The users experience fewer disruptions, faster response times, and overall better service quality, fostering greater satisfaction and loyalty.

[0107] Furthermore, another potential advantage of the one or more embodiments disclosed herein may include facilitating cost savings by minimizing impact of network downtime and reducing need for reactive troubleshooting measures. By addressing the issues proactively, organizations can avoid potential revenue losses associated with the service disruptions and optimize operational efficiency.

[0108] Another noteworthy advantage offered by the one or more embodiments in the present disclosure may include, but not limited thereto, empowering the network operators with actionable insights derived from real-time KPI data. By analysing historical trends and patterns in the KPI deviations and user feedbacks in response to the alert notifications, the network operators can make informed decisions regarding network optimization strategies, infrastructure investments, and performance improvements.

[0109] Yet another potential advantage of the one or more embodiments disclosed herein may include facilitating the organizations to meet regulatory requirements and maintain accountability by ensuring adherence to predefined performance standards and service level agreements. Push notifications provide a transparent mechanism fortracking and documenting the KPI deviations, and the corresponding actions taken to address them.

[0110] Overall, the present disclosure relating to push notifications based on drastic or sudden changes in KPIs significantly offers advantages such as enhancing network management capabilities, enabling organizations to maintain high levels of service quality, responsiveness, and reliability.

[0111] Those skilled in the art will appreciate that the methodology described herein in the present disclosure may be carried out in other specific ways than those set forth herein in the above disclosed embodiments without departing from essential characteristics and features of the present invention. The above-describedembodiments are therefore to be construed in all aspects as illustrative and not restrictive.

[0112] The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Any combination of the above features and functionalities may be used in accordance with one or more embodiments.

[0113] In the present disclosure, each of the embodiments has been described with reference to numerous specific details which may vary from embodiment to embodiment. The foregoing description of the specific embodiments disclosed herein may reveal the general nature of the embodiments herein that others may, by applying current knowledge, readily modify and / or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications are intended to be comprehended within the meaning of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and is not limited in scope.LIST OF REFERENCE NUMERALS

[0114] The following list is provided for convenience and in support of the drawing figures and as part of the text of the specification, which describe innovations by reference to multiple items. Items not listed here may nonetheless be part of a given embodiment. For better legibility of the text, a given reference number is recited near some, but not all, recitations of the referenced item in the text. The same reference number may be used with reference to different examples or different instances of a given item. The list of reference numerals is:100 - Communication environment / communication system / communi cation network102 - Memory104 - Processor106 - Communication Interface108-1 - Acquisition Unit108-2 - Monitoring Unit108-3 - Determination Unit108-4 - Notification Engine108-5 - Display Control Unit108-6 - Feedback Collection Unit108-7 - Feedback Evaluation Unit110 - Network120 - Node / gNB / Base station130 - User Device130-1 - User Interface130-2 - Communication Unit140 - Database160 - Server / System200 - Data flow for triggering alert notification210 - Performance Metrics (PM) data source220 - Threshold Setting Unit230 - Drastic Change Notifier300- Flow diagram for triggering alert notifications and displaying performance anomalies400 - Method for triggering alert notification500 - Computing system502 - Network504 - Network Interface506 - Processor508 - I / O Interface510 - Non-Transitory Computer Readable Storage Medium / StorageMedium / Storage Media510-1 - Instructions / Computer-readable Instructions510-2 - Microservices Framework

Claims

CLAIMSWe claim:

1. A method (400) for triggering alert notifications in a communication network (100), the method comprising: acquiring, by an acquisition unit (108-1), performance metrics data including a plurality of Key Performance Indicators (KPIs) associated with a plurality of network nodes; monitoring, by a monitoring unit (108-2), one or more of current KPI values, moving averages, and variances of the plurality of KPIs for a specific time interval; determining, by a determination unit (108-3), a deviation in a residual slope corresponding to the one or more of the current KPI values, the moving averages, and the variances of the plurality of KPIs based on the monitoring; and triggering, by a notification engine (108-4) based on the deviation, a transmission of an alert notification including information related to anomalies in one or more KPIs among the plurality of KPIs exhibiting the deviation to a user device (130).

2. The method (400) as claimed in claim 1, further comprising: displaying, by a display control unit (108-5) on the user device (130), a visualization interface indicating the anomalies overlaid onto one or more geographic layers representing a network infrastructure of the communication network (100); receiving, by a feedback collection unit (108-6), one or more user feedbacks from the user device (130) in response to the visualization interface and the transmission of the alert notification to the user device (130); evaluating, by a feedback evaluation unit (108-7), the one or more user feedbacks to assess an effectiveness of anomaly detection accuracy, a relevancy of the alert notification for a user of the user device (130), and visualization clarity for the user; and adjusting, by the feedback evaluation unit (108-7) based on a result of the evaluation, one or more operational parameters for one or more of detecting theanomalies, triggering the alert notification, and displaying the visualization interface indicating the anomalies.

3. The method (400) as claimed in claim 2, wherein the adjustment of the one or more operational parameters comprises updating one or more of predefined thresholds for detecting the anomalies, a notification frequency of alerts, a sampling frequency, the moving averages, and the predefined threshold range for the residual slope, a cell topology, or a severity scoring logic.

4. The method (400) as claimed in claim 1, further comprising: determining, by the determination unit (108-3), whether the deviation in the residual slope is less than or greater than a predefined threshold range; and detecting, by the determination unit (108-3), at least one abnormal trend in the one or more of the current KPI values, moving averages, and variances of the plurality of KPIs based on a determination that the deviation in the residual slope is less than or greater than the predefined threshold range, wherein the transmission of the alert notification is further triggered based on the detection of the least one abnormal trend.

5. The method (400) as claimed in claim 4, wherein the performance metrics data is acquired from one or more network nodes selected from a group of network nodes comprising one or more base stations, switches, and routers, and the detection of the at least one abnormal trend comprises one or more of a detection of a sudden change in one or more of the current KPI values, the moving averages, and the variances of the plurality of KPIs or one of a gradual increase or decrease in the one or more of the current KPI values, the moving averages, and the variances of the plurality of KPIs.

6. The method (400) as claimed in claim 1, wherein the plurality of KPIs includes one or more of an accessibility of the communication network, anavailability of the communication network, a payload in the communication network, mobility, traffic served by the plurality of network nodes in a pre-defined time period, a data throughput of the plurality of network nodes, a count of call drops and call mutes in a location served by the plurality of network nodes, a number of data calls and voice calls served by the plurality of network nodes, and a number of handovers performed by the plurality of network nodes.

7. The method (400) as claimed in claim 1, wherein the alert notification is transmitted to the user device (130) via a notification channel in real-time, and wherein the notification channel corresponds to at least one of a mobile application, a web application, a Short Message Service (SMS), and an email application installed on the user device.

8. A system (160) for triggering alert notifications in a communication network (100), the system (160) comprising: an acquisition unit (108-1) configured to acquire performance metrics data including a plurality of Key Performance Indicators (KPIs) associated with a plurality of network nodes; a monitoring unit (108-2) configured to monitor one or more of current KPI values, moving averages, and variances of the plurality of KPIs for a specific time interval; a determination unit (108-3) configured to determine a deviation in a residual slope corresponding to the one or more of the current KPI values, the moving averages, and the variances of the plurality of KPIs based on the monitoring; and a notification engine (108-4) configured to trigger, based on the deviation, a transmission of an alert notification including information related to anomalies in one or more KPIs among the plurality of KPIs exhibiting the deviation to a user device (130).

9. The system (160) as claimed in claim 8, further comprising:a display control unit (108-5) configured to display, on the user device (130), a visualization interface indicating the anomalies overlaid onto one or more geographic layers representing a network infrastructure of the communication network (100); a feedback collection unit (108-6) configured to receive one or more user feedbacks from the user device (130) in response to the visualization interface and the transmission of the alert notification to the user device (130); and a feedback evaluation unit (108-7) configured to: evaluate the one or more user feedbacks to assess an effectiveness of anomaly detection accuracy, a relevancy of the alert notification for a user of the user device (130), and visualization clarity for the user; and adjust, based on a result of the evaluation, one or more operational parameters for one or more of detecting the anomalies, triggering the alert notification, and displaying the visualization interface indicating the anomalies.

10. The system (160) as claimed in claim 9, wherein the adjustment of the one or more operational parameters comprises updating one or more of predefined thresholds for detecting the anomalies, a notification frequency of alerts, a sampling frequency, the moving averages, and the predefined threshold range for the residual slope, a cell topology, or a severity scoring logic.

11. The system (160) as claimed in claim 8, further comprising: determining, by the determination unit (108-3), whether the deviation in the residual slope is less than or greater than a predefined threshold range; and detecting, by the determination unit (108-3), at least one abnormal trend in the one or more of the current KPI values, moving averages, and variances of the plurality of KPIs based on a determination that the deviation in the residual slope is less than or greater than the predefined threshold range, wherein the transmission of the alert notification is further triggered based on the detection of the at least one abnormal trend.

12. The system (160) as claimed in claim 11, wherein the performance metrics data is acquired from one or more network nodes selected from a group of network nodes comprising one or more base stations, switches, and routers, and the detection of the at least one abnormal trend comprises one or more of a detection of a sudden change in one or more of the current KPI values, the moving averages, and the variances of the plurality of KPIs or one of a gradual increase or decrease in the one or more of the current KPI values, the moving averages, and the variances of the plurality of KPIs.

13. The system (160) as claimed in claim 8, wherein the plurality of KPIs includes one or more of an accessibility of the communication network, an availability of the communication network, a payload in the communication network, mobility, traffic served by the plurality of network nodes in a pre-defined time period, a data throughput of the plurality of network nodes, a count of call drops and call mutes in a location served by the plurality of network nodes, a number of data calls and voice calls served by the plurality of network nodes, and a number of handovers performed by the plurality of network nodes.

14. The system (160) as claimed in claim 8, wherein the alert notification is transmitted to the user device (130) via a notification channel in real-time, and wherein the notification channel corresponds to at least one of a mobile application, a web application, a Short Message Service (SMS), and an email application running on the user device (130).

15. A computer program product (500) comprising computer-executable instructions (510-1) that are stored on a non-transitory computer-readable storage medium (510) and that, when executed by at least one processor performs operations comprising:acquiring performance metrics data including a plurality of Key Performance Indicators (KPIs) associated with a plurality of network nodes in a communication network; monitoring one or more of current KPI values, moving averages, and variances of the plurality of KPIs for a specific time interval; determining a deviation in a residual slope corresponding to the one or more of the current KPI values, the moving averages, and the variances of the plurality of KPIs based on the monitoring; and triggering, based on the deviation, a transmission of an alert notification including information related to anomalies in one or more KPIs among the plurality of KPIs exhibiting the deviation to a user device.

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