FORM 2
THE PATENTS ACT, 1970 (39 OF 1970) & THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
“METHOD AND SYSTEM FOR ANOMALIES DETECTION IN COMMUNICATION NETWORK”
We, Jio Platforms Limited, an Indian National, of Office - 101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India.
The following specification particularly describes the invention and the manner in which it is to be performed.

METHOD AND SYSTEM FOR ANOMALIES DETECTION IN COMMUNICATION NETWORK
TECHNICAL FIELD
5
[0001] Embodiments of the present disclosure generally relate to network performance management systems. More particularly, embodiments of the present disclosure relate to anomalies detection in a communication network.
10 BACKGROUND
[0002] The following description of the related art is intended to provide
background information pertaining to the field of the disclosure. This section may
include certain aspects of the art that may be related to various features of the
15 present disclosure. However, it should be appreciated that this section is used only
to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
[0003] Wireless communication technology has rapidly evolved over the past few
20 decades, with each generation bringing significant improvements and
advancements. The first generation of wireless communication technology was
based on analog technology and offered only voice services. However, with the
advent of the second-generation (2G) technology, digital communication and data
services became possible, and text messaging was introduced. The third generation
25 (3G) technology marked the introduction of high-speed internet access, mobile
video calling, and location-based services. The fourth generation (4G) technology
revolutionized wireless communication with faster data speeds, better network
coverage, and improved security. Currently, the fifth generation (5G) technology is
being deployed, promising even faster data speeds, low latency, and the ability to
30 connect multiple devices simultaneously. With each generation, wireless
2

communication technology has become more advanced, sophisticated, and capable of delivering more services to its users.
[0004] The current state of wireless/mobile communication industry poses several
5 challenges for its users while utilizing mobile services from any operator, regardless
of the underlying technology. Some of these challenges include issues like
inadequate network coverage, low data throughput, call muting, call drops, inability
to place calls, calling cross-connection, and the like. The wireless/mobile
communication service providers rely on a range of cell-level Key Performance
10 Indicators (KPIs) to identify and address underperforming cells. However,
accurately pinpointing the exact locations where customer experience is
compromised is still a complex task. Typically, service providers/operators become
aware of these issues only when customers file complaints about specific problems
that negatively impact their overall experience. It can be even more difficult to pin-
15 point root-cause behind the issue reported by the user. This can result in a delayed
improvement of the identified issue leading to increase in the number of under-
performing cells and decreased overall efficiency of the communication network.
[0005] Further, over the period of time, various solutions have been developed to
20 improve the performance of communication network and to identify anomalies in
the communication network and improvement thereof to improve overall efficiency
of the communication network and enhanced user experience. However, there are
certain challenges with existing solutions. The existing solutions involve inefficient
manual analysis of trace records and user data to estimate user experience and
25 identify root causes of poor experience. These existing solutions can be time-
consuming and prone to human error. Further, existing solutions lack proactive
identification of users with poor user experience, whereas taking timely corrective
measures is crucial for maintaining customer satisfaction and loyalty. Also, manual
identification processes may be reactive and delay issue resolution. Additionally,
30 with existing solutions mapping customer samples on a grid and correlating them
3

with network performance data can be complex and time-consuming without automation.
[0006] Thus, there exists an imperative need in the art to provide a solution for
5 anomalies detection in a communication network for improved efficiency,
proactive issue resolution and enhanced customer satisfaction, which the present disclosure aims to address.
SUMMARY OF THE DISCLOSURE
10
[0007] This section is provided to introduce certain aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter. 15
[0008] An aspect of the present disclosure may relate to a method for anomalies
detection in a communication network. The method comprises receiving, by a
transceiver unit, a subscriber data from one or more subscribers, the subscriber data
comprising at least one of a Reference Signal Received Power (RSRP), a signal-to-
20 interference-plus-noise ratio (SINR), a Channel Quality Indicator (CQI), a
Reference Signal Received Quality (RSRQ), a downlink-to-uplink ratio (DL/UL)
Throughput, a Session release type (Normal/Abnormal), a service type. Further, the
method comprises determining, by a customer experience estimation (CEE)
module, a health score for each of the one or more subscribers, based on the
25 subscriber data and one or more predefined thresholds associated with the
subscriber data. Further, the method comprises identifying, by the CEE module,
one or more target subscribers from the one or more subscribers, based on a
matching of the health score for each subscriber with a pre-defined health score.
Further, the method comprises performing, by a Root Cause Analysis (RCA)
30 Module, a root cause analysis for each subscriber of the one or more target
subscribers. Further, the method comprises recommending, by a Mitigation
4

Module, one or more mitigation actions for each subscribers from the one or more target subscribers, based on the root cause analysis performed for each subscriber of the one or more target subscribers.
5 [0009] In an exemplary aspect of the present disclosure, for the determining, by the
CEE module, the health score for each of the one or more subscribers, the method further comprises receiving, by the transceiver unit from one or more subscribers, a subscriber trace data. Further, in this exemplary aspect, the method further comprises geographically mapping, by the CEE module, a set of one or more
10 subscriber samples in the subscriber trace data. The method further comprises
aggregating, by the CEE module, one or more subscriber samples at a pre-defined grid size. The method further comprises identifying, by the CEE module, a set of top serving cells associated with each subscriber based on the one or more subscriber samples. The method further comprises determining, by the CEE
15 module, the health score from each subscriber from the one or more subscribers,
based on the subscriber trace data and the set of top serving cells.
[0010] In an exemplary aspect of the present disclosure, for the performing, by the RCA Module, the root cause analysis for each subscriber of the one or more target
20 subscribers, the method further comprises fetching, by the RCA Module, a set of
new trace records for the one or more target subscribers. The method further comprises identifying, by the RCA Module, a set of parameters from the set of new trace records, for the one or more target subscribers, based on one or more predefined rules. The method further comprises checking, by the RCA Module, for
25 an issue for each subscriber, based on a comparison between the top serving cells
and a set of serving cells associated with a planning data. The method further comprises generating, by the RCA Module, one of a first positive result and a first negative result based on the checking of the issue, wherein the first positive result is generated in an event the issue is detected, and the first negative result is
30 generated in an event no issue is detected. The method further comprises
performing, by the RCA Module, a first procedure comprising checking, by the
5

RCA Module, for observation of the issue in a specific time period, wherein the
first procedure is performed in an event of generation of the first positive result.
The method further comprises generating, by the RCA Module, one of a positive
result of the first procedure and a negative result of the first procedure. The method
5 further comprises performing, by the RCA Module, one of a second procedure and
a fourth procedure, wherein the second procedure is performed in an event of generation of the negative result of the first procedure, and wherein the fourth procedure is performed in an event of generation of the positive result of the first procedure. The method further comprises generating, by the RCA Module, one of
10 a positive result of the second procedure and a negative result of the second
procedure in an event of performance of the second procedure. In this exemplary aspect, the method further comprises performing, by the RCA Module, one of a third procedure and a fifth procedure, wherein the third procedure is performed in an event of generation of the negative result of the second procedure, and wherein
15 the fifth procedure is performed in an event of generation of the positive result of
the second procedure. In this exemplary aspect, the method further comprises generating, by the RCA Module, one of a positive result of the third procedure and a negative result of the third procedure in an event of performance of the third procedure. The method further comprises performing, by the RCA Module, one of
20 a sixth procedure and work order procedure, wherein the sixth procedure is
performed in an event of generation of the positive result of the third procedure; and wherein the work order procedure is performed in an event of generation of the negative result of the third procedure.
25 [0011] In an exemplary aspect of the present disclosure, the method further
comprises performing, by the RCA Module, the work order procedure comprising raising a work order, by the mitigation module, for corrective actions, wherein the work order procedure is performed in an event of generation of: (a) the positive result of the fourth procedure, the fifth procedure, and the sixth procedure, and (b)
30 the negative result of the sixth procedure.
6

[0012] In an exemplary aspect of the present disclosure, the recommending, by the
Mitigation Module, one or more mitigation actions for each subscriber from the one
or more target subscribers comprises one of: raising a work order for corrective
actions, and sending a notification to the each subscriber based on the root cause
5 analysis performed for the each subscriber of the one or more target subscribers.
[0013] In an exemplary aspect of the present disclosure, the method further comprises generating, by the RCA Module, one of a positive result of the fourth procedure and a negative result of the fourth procedure in an event of performance
10 of the fourth procedure. Further, in this exemplary aspect, the method comprises
generating, by the RCA Module, one of a positive result of the fifth procedure and a negative result of the fifth procedure in an event of performance of the fifth procedure. Further, in this exemplary aspect, the method comprises generating, by the RCA Module, one of a positive result of the sixth procedure and a negative
15 result of the sixth procedure in an event of performance of the sixth procedure.
[0014] In an exemplary aspect of the present disclosure, the second procedure is
performed in an event of generation of the negative result of the fourth procedure,
and the third procedure is performed in an event of generation of the negative result
20 of the fifth procedure.
[0015] In an exemplary aspect of the present disclosure, the second procedure comprises checking, by the RCA Module, for observation of the issue in one or more specific cells, the third procedure comprising checking, by the RCA Module,
25 for observation of the issue in one or more specific user device models, the fourth
procedure comprises checking, by the RCA Module, for a presence of one or more of an alarms, an interference, an outage, and a service barring, the fifth procedure comprises checking, by the RCA Module, for a degradation of one or more performance management (PM) key performance indicators (KPIs), and the sixth
30 procedure comprises checking, by the RCA Module, for one or more issues related
to at least one of a user device capability information and software version.
7

[0016] Another aspect of the present disclosure may relate to a system for
anomalies detection in a communication network. The system comprises a
transceiver unit configured to receive a subscriber data from one or more
5 subscribers, the subscriber data comprising at least one of a Reference Signal
Received Power (RSRP), a signal-to-interference-plus-noise ratio (SINR), a Channel Quality Indicator (CQI), a Reference Signal Received Quality (RSRQ), a downlink-to-uplink ratio (DL/UL) Throughput, a Session release type (Normal/Abnormal), a service type. Further, the system comprises a customer
10 experience estimation (CEE) module connected to at least the transceiver unit. The
CEE module is configured to determine a health score for each of the one or more subscribers, based on the subscriber data and one or more predefined thresholds associated with the subscriber data. Further, the CEE module is configured to identify one or more target subscribers from the one or more subscribers, based on
15 a matching of the health score for each subscriber with a pre-defined health score.
Further, the system comprises a Root Cause Analysis (RCA) Module connected to at least the CEE module. The RCA module is configured to perform a root cause analysis for each subscriber of the one or more target subscribers. Further, the system comprises a Mitigation Module connected to at least the RCA module. The
20 mitigation module is configured to recommend one or more mitigation actions for
each subscriber from the one or more target subscribers, based on the root cause analysis for each subscriber of the one or more target subscribers.
[0017] Yet another aspect of the present disclosure may relate to a non-transitory
25 computer readable storage medium storing instructions for anomalies detection in
a communication network, the instructions include executable code which, when
executed by one or more units of a system, causes: a transceiver unit of the system
to receive a subscriber data from one or more subscribers, the subscriber data
comprising at least one of a Reference Signal Received Power (RSRP), a signal-to-
30 interference-plus-noise ratio (SINR), a Channel Quality Indicator (CQI), a
Reference Signal Received Quality (RSRQ), a downlink-to-uplink ratio (DL/UL)
8

Throughput, a Session release type (Normal/Abnormal), a service type. The
instructions further include executable code which, when executed causes a
customer experience estimation (CEE) module of the system to determine a health
score for each of the one or more subscribers, based on the subscriber data and one
5 or more predefined thresholds associated with the subscriber data; and to identify
one or more target subscribers from the one or more subscribers, based on a
matching of the health score for each subscriber with a pre-defined health score.
The instructions further include executable code which, when executed causes a
Root Cause Analysis (RCA) Module of the system to perform a root cause analysis
10 for each subscriber of the one or more target subscribers. The instructions further
include executable code which, when executed causes a Mitigation Module of the system to recommend one or more mitigation actions for each subscribers from the one or more target subscribers, based on the root cause analysis for each subscriber of the one or more target subscribers. 15
[0018] Yet another aspect of the present disclosure may relate to a user equipment
(UE). The UE comprises at least a transmitter unit and at least a receiver unit. The
transmitter unit is configured to send, to a system [300], a request for receiving one
or more mitigation actions related to anomalies detection in a communication
20 network. Further, the receiver unit configured to receive, from the system [300], the
one or more mitigation actions in response to the request. Also, the anomalies
detection in the communication network is performed based on receiving, by a
transceiver unit, a subscriber data from one or more subscribers. The subscriber
data comprises at least one of a Reference Signal Received Power (RSRP), a signal-
25 to-interference-plus-noise ratio (SINR), a Channel Quality Indicator (CQI), a
Reference Signal Received Quality (RSRQ), a downlink user equipment (DL UE)
Throughput, a Session release type (Normal/Abnormal), a service type. Further, the
anomalies detection in the communication network is performed based on
determining, by a customer experience estimation (CEE) module, a health score for
30 each of the one or more subscribers, based on the subscriber data and one or more
predefined thresholds associated with the subscriber data. anomalies detection in
9

the communication network is performed based on identifying, by the CEE module,
one or more target subscribers from the one or more subscribers, based on a
matching of the health score for each subscriber with a pre-defined health score.
anomalies detection in the communication network is performed based on
5 performing, by a Root Cause Analysis (RCA) Module, a root cause analysis for
each subscriber of the one or more target subscribers. anomalies detection in the
communication network is performed based on recommending, by a Mitigation
Module, one or more mitigation actions for each subscriber from the one or more
target subscribers, based on the root cause analysis performed for each subscriber
10 of the one or more target subscribers.
OBJECTS OF THE DISCLOSURE
[0019] Some of the objects of the present disclosure, which at least one
15 embodiment disclosed herein satisfies are listed herein below.
[0020] It is an object of the present disclosure to provide a system and a method for a trained model for anomalies detection in a communication network and improvement thereof to allow identifying the underperforming cells.
20
[0021] It is an object of the present disclosure to provide a system and a method for identifying users who encountered unsatisfactory user experiences, associating their data with various network and device-related concerns, and using the planning data for root cause analysis.
25
[0022] It is another object of the present disclosure to provide a solution that improves overall efficiency of the communication network and enhance user’s experience.
30 DESCRIPTION OF THE DRAWINGS
10

[0023] 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,
5 emphasis instead being placed upon clearly illustrating the principles of the present
disclosure. Also, the embodiments shown in the figures are not to be construed as
limiting the disclosure, but the possible variants of the method and system
according to the disclosure are illustrated herein to highlight the advantages of the
disclosure. It will be appreciated by those skilled in the art that disclosure of such
10 drawings includes disclosure of electrical components or circuitry commonly used
to implement such components.
[0024] FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture. 15
[0025] FIG. 2 illustrates an exemplary block diagram of a computing device upon which the features of the present disclosure may be implemented in accordance with exemplary implementation of the present disclosure.
20 [0026] FIG. 3 illustrates an exemplary block diagram of a system for anomalies
detection in a communication network, in accordance with exemplary implementations of the present disclosure.
[0027] FIG. 4 illustrates a method flow diagram depicting a method for anomalies
25 detection in a communication network, in accordance with exemplary
implementations of the present disclosure.
[0028] FIG. 5 illustrates exemplary experience categories for various parameters
based on their exemplary values, in accordance with exemplary implementations of
30 the present disclosure.
11

[0029] FIG. 6 illustrates an exemplary procedure for performing the root cause analysis for each subscriber of the one or more target subscribers, in accordance with exemplary implementations of the present disclosure.
5 [0030] FIG. 7 shows a UE being served by a set of serving cells.
[0031] The foregoing shall be more apparent from the following more detailed description of the disclosure.
10 DETAILED DESCRIPTION
[0032] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that
15 embodiments of the present disclosure may be practiced without these specific
details. Several features described hereafter may each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above.
20
[0033] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment.
25 It should be understood that various changes may be made in the function and
arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
[0034] Specific details are given in the following description to provide a thorough
30 understanding of the embodiments. However, it will be understood by one of
ordinary skill in the art that the embodiments may be practiced without these
12

specific details. For example, circuits, systems, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail.
5 [0035] Also, it is noted that individual embodiments may be described as a process
which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure
diagram, or a block diagram. Although a flowchart may describe the operations as
a sequential process, many of the operations may be performed in parallel or
concurrently. In addition, the order of the operations may be re-arranged. A process
10 is terminated when its operations are completed but could have additional steps not
included in a figure.
[0036] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the
15 subject matter disclosed herein is not limited by such examples. In addition, any
aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms
20 “includes,” “has,” “contains,” and other similar words are used in either the detailed
description or the claims, such terms are intended to be inclusive—in a manner similar to the term “comprising” as an open transition word—without precluding any additional or other elements.
25 [0037] As used herein, a “processing unit” or “processor” or “operating processor”
includes one or more processors, wherein processor refers to any logic circuitry for processing instructions. A processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a (Digital
30 Signal Processing) DSP core, a controller, a microcontroller, Application Specific
Integrated Circuits, Field Programmable Gate Array circuits, any other type of
13

integrated circuits, etc. The processor may perform signal coding data processing, input/output processing, and/or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor or processing unit is a hardware processor. 5
[0038] As used herein, “a user equipment”, “a user device”, “a smart-user-device”, “a smart-device”, “an electronic device”, “a mobile device”, “a handheld device”, “a wireless communication device”, “a mobile communication device”, “a communication device” may be any electrical, electronic and/or computing device
10 or equipment, capable of implementing the features of the present disclosure. The
user equipment/device may include, but is not limited to, a mobile phone, smart phone, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, wearable device or any other computing device which is capable of implementing the features of the present disclosure. Also, the user device may
15 contain at least one input means configured to receive an input from at least one of
a transceiver unit, a processing unit, a storage unit, a detection unit and any other such unit(s) which are required to implement the features of the present disclosure.
[0039] As used herein, “storage unit” or “memory unit” refers to a machine or
20 computer-readable medium including any mechanism for storing information in a
form readable by a computer or similar machine. For example, a computer-readable
medium includes read-only memory (“ROM”), random access memory (“RAM”),
magnetic disk storage media, optical storage media, flash memory devices or other
types of machine-accessible storage media. The storage unit stores at least the data
25 that may be required by one or more units of the system to perform their respective
functions.
[0040] As used herein “interface” or “user interface refers to a shared boundary
across which two or more separate components of a system exchange information
30 or data. The interface may also be referred to a set of rules or protocols that define
communication or interaction of one or more modules or one or more units with
14

each other, which also includes the methods, functions, or procedures that may be called.
[0041] All modules, units, components used herein, unless explicitly excluded
5 herein, may be software modules or hardware processors, the processors being a
general-purpose processor, a special purpose processor, a conventional processor,
a digital signal processor (DSP), a plurality of microprocessors, one or more
microprocessors in association with a DSP core, a controller, a microcontroller,
Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array
10 circuits (FPGA), any other type of integrated circuits, etc.
[0042] As used herein the transceiver unit includes at least one receiver and at least
one transmitter configured respectively for receiving and transmitting data, signals,
information or a combination thereof between units/components within the system
15 and/or connected with the system.
[0043] As used herein, performance management (PM) key performance indicators
(KPIs) are metrics used to assess and monitor network performance. These KPIs
may comprise, but not limited to, network availability (uptime), reliability
20 (frequency of disruptions), latency (delay in data transmission), packet loss
(percentage of lost data packets), throughput (data transfer rate), response time (time taken to respond to requests), etc. The PM KPIs help ensure efficient operation, reliability, and quality of service within the network.
25 [0044] As used herein, reference signal received power or RSRP is a measurement
of the signal strength received from a specific cell tower or base station. It indicates the power level of the reference signals sent by the base station, measured at the receiving device.
30 [0045] As used herein, signal-to-interference-plus-noise ratio or SINR is a measure
of the quality of the received signal, relative to the interference and noise present in the environment. A higher SINR indicates better signal quality and typically
15

correlates with improved communication performance and higher data rates in wireless networks.
[0046] As used herein, a channel quality indicator (CQI) is a metric used by the
5 mobile device to report the quality of the radio channel to the base station. It
provides information about the current channel conditions, including factors like
signal strength, interference, and noise. The base station may use this information
to optimize resource allocation, modulation, and coding schemes to maintain
efficient and reliable data transmission.
10
[0047] As used herein, reference signal received quality or RSRQ is a metric that
indicates the quality of the received reference signals relative to the interference
and noise level. It is calculated as the ratio of RSRP (Reference Signal Received
Power, representing the power of the serving cell's signals) to RSSI (Received
15 Signal Strength Indicator, representing the total received signal power including
noise and interference). RSRQ provides information about signal quality and helps
in optimizing handover decisions and resource allocation in the network.
[0048] As used herein, a downlink user equipment (DL UE) throughput refers to a
20 rate at which data is transmitted from base station to the user equipment (UE) (such
as, a mobile phone), over the downlink direction of a wireless communication channel.
[0049] As used herein, a session release type (normal/abnormal) refers to parameter
25 indicating expected (or normal) or unexpected (or premature/abnormal) termination
of a communication session between a user equipment (UE) and the network.
[0050] As used herein, a service type refers to a parameter indicating which
generation of network technology the user is latched to, for example, 4G network,
30 5G network, etc.
[0051] As used herein, the subscriber trace data refers to a set of detailed logs or records of events and/or activities, that take place within a communication network.
16

These may include, but not limited to, call detail records (such as, logs of individual
call sessions, including timestamps, caller identity, callee identity, duration, and
other relevant details), event logs (such as, records of system events such as network
outages, equipment failures, alarms, etc.), packet trace logs (such as, detailed
5 records of data packets transmitted over the network, packet headers, payload
details, and transmission timestamps), performance metrics (such as, logs of
network performance metrics such as latency, throughput, signal strength, and error
rates), etc. Subscriber trace data may provide a chronological record of network
activities and help operators and administrators understand, diagnose, and resolve
10 issues within the communication network.
[0052] As used herein, top serving cell(s) for a user device refers to the primary cell(s) or base station(s) that provides the strongest and most reliable signal to that user device.
15
[0053] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various components/units can be implemented interchangeably. While specific embodiments may disclose a particular functionality of these units for clarity, it is recognized that various
20 configurations and combinations thereof are within the scope of the disclosure. The
functionality of specific units as disclosed in the disclosure should not be construed as limiting the scope of the present disclosure. Consequently, alternative arrangements and substitutions of units, provided they achieve the intended functionality described herein, are considered to be encompassed within the scope
25 of the present disclosure.
[0054] Anomalies in a communication network refer to deviations or unexpected
events that differ from normal working of the system components. These anomalies
may be for example, inadequate coverage, low data throughput, call muting, and
30 call drops, etc. and may occur due to various factors such as, but not limited to,
security breaches, hardware failures, software errors, etc. Detecting and addressing anomalies promptly is important for maintaining network performance, security,
17

and reliability, and improving customer experience. As discussed in the background
section, there are several challenges in the currently existing mobile communication
industry for customers utilizing mobile services from any operator, like inadequate
coverage, low data throughput, call muting, and call drops. Operators rely on a
5 range of cell-level Key Performance Indicators (KPIs) to identify and address
underperforming cells. However, accurately pinpointing the exact locations where customer experience is compromised is still a complex task. Typically, operators become aware of these issues only when customers file complaints about specific problems that negatively impact their overall experience.
10
[0055] The solution as disclosed herein detects anomalies in a communication network and employs predefined logics and algorithms to assess the customer experience of users in a communication network, using trace recordings. The system automatically evaluates the user experience and assists operators in
15 identifying problematic areas along with associated user statistics. These findings
may then be correlated with network components and Key Performance Indicators (KPIs), providing insights for engineers to take timely corrective action. By leveraging the features as disclosed herein, operators can efficiently pinpoint and address the root causes of customer dissatisfaction, leading to improved service
20 quality and enhanced user satisfaction. Also, once customers with subpar user
experiences are identified, their data may be cross-referenced with other network and device-related issues. Planning data may then be utilized, taking into account the event's time stamp, to calculate Root Cause Analyses (RCAs) for engineering actions. The process may involve associating the data of the customers (who
25 encountered unsatisfactory user experiences) with various network and device-
related concerns and planning data. Furthermore, customer service representatives may be provided with explanations for the poor experiences, which may include factors like device incompatibility, network outages, service barring, inadequate coverage areas, low coverage zones, etc.
30
18

[0056] The system utilizes various metrics such as RSRP (Reference Signal
Received Power), SINR (Signal-to-Interference-plus-Noise Ratio), CQI (Channel
Quality Indicator), DL UE (downlink User Equipment) Throughput, and session
drop percentages to evaluate the quality of user experience. By processing these
5 input parameters from the trace records, the system may calculate a comprehensive
estimation of customer experience. In addition to evaluating user experience, using customer location data available in the trace records, the system may also perform mapping and plotting of customer samples, aggregating them on a geographical area grid size, for example, a 5m x 5m grid size. This mapping may provide a visual
10 representation of the customer distribution across the network. Furthermore, the
system may map the top serving cell against each customer, enabling network operators to identify the specific cell towers serving the customers. Also, once the customer experience estimation is done, the identification of customers who are experiencing poor customer experience is performed based on the calculated
15 estimation. The system analyses user experience values and proactively identifies
potential issues related to alarms, interference, network outages, service barring, poor network KPIs (Key Performance Indicators), UE device-related issues, and other factors that may impact customer satisfaction. By pinpointing the root causes of poor user experience, the system provides actionable insights to network
20 operators. The system may also suggest corrective steps and mitigation strategies
to address the identified issues effectively. These recommendations may include alarm resolution, interference mitigation techniques, network outage recovery measures, service barring adjustments, network KPI improvements, and guidance on resolving UE device-related issues. By automating the analysis and
25 identification of customer issues, network operators can swiftly respond to
problems, prioritize corrective actions, and ensure optimal network performance, leading to enhanced customer satisfaction and loyalty.
[0057] The solution of the present disclosure facilitates automatic identification of
30 areas where customer experience is adversely affected. By leveraging the features
of the disclosure as explained in the forthcoming description, users implementing
19

the features of the disclosure can proactively detect and address issues that impact
customer experience, without solely relying on customer complaints. The method
also offers a more efficient and effective way to improve the overall customer
experience in the mobile communication industry. The solution aims to address
5 several challenges including: Inefficient manual analysis (manual analysis of trace
records and customer data to estimate customer experience and identify root causes
of poor experience can be time-consuming and prone to human error), Lack of
proactive identification (identifying customers with poor user experience and taking
timely corrective measures is crucial for maintaining customer satisfaction and
10 loyalty. Manual identification processes may be reactive and delay issue
resolution), and Complex data mapping and visualization: Mapping customer samples on a grid and correlating them with network performance data can be complex and time-consuming without automation).
15 [0058] The present disclosure aims to overcome the above-mentioned and other
existing problems in this field of technology by providing method and system of anomalies detection in a communication network.
[0059] FIG. 1 illustrates an exemplary block diagram representation of 5th
20 generation core (5GC) network architecture, in accordance with exemplary
implementation of the present disclosure. As shown in FIG. 1, the 5GC network
architecture [100] includes a user equipment (UE) [102], a radio access network
(RAN) [104], an access and mobility management function (AMF) [106], a Session
Management Function (SMF) [108], a Service Communication Proxy (SCP) [110],
25 an Authentication Server Function (AUSF) [112], a Network Slice Specific
Authentication and Authorization Function (NSSAAF) [114], a Network Slice
Selection Function (NSSF) [116], a Network Exposure Function (NEF) [118], a
Network Repository Function (NRF) [120], a Policy Control Function (PCF) [122],
a Unified Data Management (UDM) [124], an application function (AF) [126], a
30 User Plane Function (UPF) [128], a data network (DN) [130], wherein all the
20

components are assumed to be connected to each other in a manner as obvious to the person skilled in the art for implementing features of the present disclosure.
[0060] Radio Access Network (RAN) [104] is the part of a mobile
5 telecommunications system that connects user equipment (UE) [102] to the core
network (CN) and provides access to different types of networks (e.g., 5G network). It consists of radio base stations and the radio access technologies that enable wireless communication.
10 [0061] Access and Mobility Management Function (AMF) [106] is a 5G core
network function responsible for managing access and mobility aspects, such as UE registration, connection, and reachability. It also handles mobility management procedures like handovers and paging.
15 [0062] Session Management Function (SMF) [108] is a 5G core network function
responsible for managing session-related aspects, such as establishing, modifying, and releasing sessions. It coordinates with the User Plane Function (UPF) for data forwarding and handles IP address allocation and QoS enforcement.
20 [0063] Service Communication Proxy (SCP) [110] is a network function in the 5G
core network that facilitates communication between other network functions by providing a secure and efficient messaging service. It acts as a mediator for service-based interfaces.
25 [0064] Authentication Server Function (AUSF) [112] is a network function in the
5G core responsible for authenticating UEs during registration and providing security services. It generates and verifies authentication vectors and tokens.
[0065] Network Slice Specific Authentication and Authorization Function
30 (NSSAAF) [114] is a network function that provides authentication and
21

authorization services specific to network slices. It ensures that UEs can access only the slices for which they are authorized.
[0066] Network Slice Selection Function (NSSF) [116] is a network function
5 responsible for selecting the appropriate network slice for a UE based on factors
such as subscription, requested services, and network policies.
[0067] Network Exposure Function (NEF) [118] is a network function that exposes
capabilities and services of the 5G network to external applications, enabling
10 integration with third-party services and applications.
[0068] Network Repository Function (NRF) [120] is a network function that acts as a central repository for information about available network functions and services. It facilitates the discovery and dynamic registration of network functions. 15
[0069] Policy Control Function (PCF) [122] is a network function responsible for policy control decisions, such as QoS, charging, and access control, based on subscriber information and network policies.
20 [0070] Unified Data Management (UDM) [124] is a network function that
centralizes the management of subscriber data, including authentication, authorization, and subscription information.
[0071] Application Function (AF) [126] is a network function that represents
25 external applications interfacing with the 5G core network to access network
capabilities and services.
[0072] User Plane Function (UPF) [128] is a network function responsible for
handling user data traffic, including packet routing, forwarding, and QoS
30 enforcement.
22

[0073] Data Network (DN) [130] refers to a network that provides data services to user equipment (UE) in a telecommunications system. The data services may include but are not limited to Internet services, private data network related services.
5 [0074] FIG. 2 illustrates an exemplary block diagram of a computing device [200]
upon which the features of the present disclosure may be implemented in
accordance with exemplary implementation of the present disclosure. In an
implementation, the computing device [200] may also implement a method for
anomalies detection in a communication network utilising the system. In another
10 implementation, the computing device [200] itself implements the method for
anomalies detection in a communication network using one or more units configured within the computing device [200], wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.
15 [0075] The computing device [200] may include a bus [202] or other
communication mechanism for communicating information, and a hardware
processor [204] coupled with bus [202] for processing information. The hardware
processor [204] may be, for example, a general-purpose microprocessor. The
computing device [200] may also include a main memory [206], such as a random-
20 access memory (RAM), or other dynamic storage device, coupled to the bus [202]
for storing information and instructions to be executed by the processor [204]. The
main memory [206] also may be used for storing temporary variables or other
intermediate information during execution of the instructions to be executed by the
processor [204]. Such instructions, when stored in non-transitory storage media
25 accessible to the processor [204], render the computing device [200] into a special-
purpose machine that is customized to perform the operations specified in the
instructions. The computing device [200] further includes a read only memory
(ROM) [208] or other static storage device coupled to the bus [202] for storing static
information and instructions for the processor [204].
30
23

[0076] A storage device [210], such as a magnetic disk, optical disk, or solid-state
drive is provided and coupled to the bus [202] for storing information and
instructions. The computing device [200] may be coupled via the bus [202] to a
display [212], such as a cathode ray tube (CRT), Liquid crystal Display (LCD),
5 Light Emitting Diode (LED) display, Organic LED (OLED) display, etc. for
displaying information to a computer user. An input device [214], including
alphanumeric and other keys, touch screen input means, etc. may be coupled to the
bus [202] for communicating information and command selections to the processor
[204]. Another type of user input device may be a cursor controller [216], such as
10 a mouse, a trackball, or cursor direction keys, for communicating direction
information and command selections to the processor [204], and for controlling
cursor movement on the display [212]. This input device typically has two degrees
of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allow
the device to specify positions in a plane.
15
[0077] The computing device [200] may implement the techniques described
herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware
and/or program logic which in combination with the computing device [200] causes
or programs the computing device [200] to be a special-purpose machine.
20 According to one implementation, the techniques herein are performed by the
computing device [200] in response to the processor [204] executing one or more sequences of one or more instructions contained in the main memory [206]. Such instructions may be read into the main memory [206] from another storage medium, such as the storage device [210]. Execution of the sequences of instructions
25 contained in the main memory [206] causes the processor [204] to perform the
process steps described herein. In alternative implementations of the present disclosure, hard-wired circuitry may be used in place of or in combination with software instructions.
30 [0078] The computing device [200] also may include a communication interface
[218] coupled to the bus [202]. The communication interface [218] provides a two-way data communication coupling to a network link [220] that is connected to a
24

local network [222]. For example, the communication interface [218] may be an
integrated services digital network (ISDN) card, cable modem, satellite modem, or
a modem to provide a data communication connection to a corresponding type of
telephone line. As another example, the communication interface [218] may be a
5 local area network (LAN) card to provide a data communication connection to a
compatible LAN. Wireless links may also be implemented. In any such
implementation, the communication interface [218] sends and receives electrical,
electromagnetic or optical signals that carry digital data streams representing
various types of information.
10
[0079] The computing device [200] can send messages and receive data, including
program code, through the network(s), the network link [220] and the
communication interface [218]. In the Internet example, a server [230] might
transmit a requested code for an application program through the Internet [228], the
15 ISP [226], the local network [222], the host [224], and the communication interface
[218]. The received code may be executed by the processor [204] as it is received,
and/or stored in the storage device [210], or other non-volatile storage for later
execution.
20 [0080] Referring to FIG. 3, an exemplary block diagram of a system [300] for
anomalies detection in a communication network, is shown, in accordance with the exemplary implementations of the present disclosure. The system [300] comprises at least one transceiver unit [302], at least one customer experience estimation (CEE) module [304], at least one Root Cause Analysis (RCA) Module [306], and
25 at least one Mitigation Module [308]. Also, all of the components/ units of the
system [300] are assumed to be connected to each other unless otherwise indicated below. As shown in the FIG. 3, all units shown within the system [300] should also be assumed to be connected to each other. Also, in FIG. 3 only a few units are shown, however, the system [300] may comprise multiple such units or the system
30 [300] may comprise any such numbers of said units, as required to implement the
features of the present disclosure. Further, in an implementation, the system [300]
25

may be present in a user device to implement the features of the present disclosure.
The system [300] may be a part of the user device / or may be independent of but
in communication with the user device (may also referred herein as a UE). In
another implementation, the system [300] may reside in a server or a network entity.
5 In yet another implementation, the system [300] may reside partly in the server/
network entity and partly in the user device.
[0081] The system [300] is configured for anomalies detection in a communication
network, with the help of the interconnection between the components/units of the
10 system [300].
[0082] The transceiver unit [302] is configured to receive a subscriber data from one or more subscribers, the subscriber data comprising at least one of a Reference Signal Received Power (RSRP), a signal-to-interference-plus-noise ratio (SINR), a
15 Channel Quality Indicator (CQI), a Reference Signal Received Quality (RSRQ), a
downlink user equipment (DL UE) Throughput, a Session release type (Normal/Abnormal), a service type. Here, Reference Signals Received Power (RSRP) and Reference Signal Received Quality (RSRQ) are key measures of signal level and quality for modern wireless communication networks, for example, long
20 term evolution (LTE) networks. RSRP is the power of the Reference Signals spread
over the full bandwidth and narrowband. An, RSRQ indicates the quality of the received reference signal. The RSRQ measurement provides additional information when RSRP is not sufficient to make a reliable handover or cell reselection decision. The CQI is used by a user device, such as a smartphone, to indicate the channel
25 quality to the base station.
[0083] In an implementation, the subscriber data is the subscriber trace data that
may be collected by a trace data collector. Collecting the subscriber trace data may
involve capturing various network events, signalling messages, call records, or any
30 other relevant information related to subscribers. Once the subscriber trace data is
collected, it may be processed for further analysis. This may involve filtering,
26

sorting, aggregating, or transforming the data to make it more suitable for analysis.
After analysis, the results or processed data may be stored for future reference or to
integrate with other systems. This may also involve storage of data in a suitable
format or database. Further, in an implementation, a network management system
5 (NMS) may be implemented that enables efficient control, monitoring, and
optimization of telecom networks. NMS may serve as a central hub for managing network elements, ensuring their smooth operation and performance. NMS may also help a network operator in fault, configuration, accounting, performance and security (FCAPS) management. Further, the NMS may also facilitate in key
10 performance indicators (KPI)/Alarms/Configurations Ingestion and Storage by
collecting, storing, and managing key performance indicators (KPIs), alarms, and configurations within a system or organization. Further, a database may also be implemented for storing and maintaining, for each user device for make, model, etc. details and band, technology supported along with various other details which can
15 further utilized for device level analytics. Further, a set of planning tool servers may
also be implemented using which a network operator may run and store a predicted data like RSRP (that is, a measurement of the signal strength received from a specific cell tower or base station), best server plot (BSP) (that is, a graphical representation to illustrate the performance of multiple servers within a system, and
20 helps analyse and optimize server utilization, response times, and overall system
efficiency in handling incoming traffic or requests), SINR (that is, a measure of the quality of the received signal, relative to the interference and noise present in the environment) etc. for all on-air and planned cells which gets updated in scheduled manner with updated network parameters.
25
[0084] In an implementation, the data from all these entities may be stored in a storage unit [310] and/or provided to the system [300] for further processing and usage. Further, a Front End UI/UX and Reporting Engine may also be implemented which, in an implementation, may be integrated with the system [300], using which
30 the network engineers, customer care executives, and/or any other relevant
authority can visualize the customer experience KPIs, subscriber sample plots and
27

download the details report generated from the module for all the poor experience users and their corresponding root cause analysis and mitigation plans.
[0085] Further, the customer experience estimation (CEE) module [304] is
5 connected to at least the transceiver unit [302]. The CEE module [304] is configured
to determine a health score for each of the one or more subscribers, based on the
subscriber data and one or more predefined thresholds associated with the
subscriber data. Further, the CEE module [304] is configured to identify one or
more target subscribers from the one or more subscribers, based on a matching of
10 the health score for each subscriber with a pre-defined health score. Here, the target
subscribers may refer to the subscribers with poor health score, that is, the subscribers with a health score less than a threshold health score.
[0086] In an implementation, the CEE module [304] for determining the health
15 score for each of the one or more subscribers, is further configured to
geographically map the set of one or more subscriber samples in the subscriber trace data. For this purpose, the CEE module [304] is configured to aggregate one or more subscriber samples at a pre-defined grid size, that is, the CEE module [304] may plot the subscriber samples aggregated on a specific grid size, for example, a
20 grid size of 5 metre by 5 metre. That is, the subscribers’ geographical location from
the subscriber trace data may be taken, and different sets/groups of subscribers may be created based on the location, wherein each group may contain, in accordance with above example, the subscribers located in a grid size of 5 metre by 5 metre on ground. Further, in this implementation, the CEE module [304] is configured to
25 identify a set of top serving cells associated with each subscriber based on the one
or more subscriber samples. Further, in this implementation, the CEE module [304] is configured to determine the health score from each subscriber from the one or more subscribers, based on the subscriber trace data and the set of top serving cells. Pertinently, for determining the health score from each subscriber from the one or
30 more subscribers, in an implementation, a count of total session for each IMSI (that
is, for each subscriber) is taken as an input, a count of samples in each bucket for
28

RSRP, RSRQ, SINR, CQI, DL UE Throughput, and a percentage of drop sessions
is taken as an input, and a count of drop sessions (that is, abnormally released
sessions) for each IMSI (that is, for each subscriber) is taken as an input. Further, a
user score for each user is calculated based on the above inputs, using a set of
5 computations. An exemplary set of computations for this purpose is given below.
o DL UE Throughput Score = 5* (Number of excellent sessions/Total
sessions) +4* (Number of good Sessions/Total Sessions) + 3* (Number of
Average Sessions/Total Sessions) + 2* (Number of Poor Sessions/Total
Sessions) +1* (Number of Worst Sessions/Total Sessions)
10 o RSRP Score = 5* (Number of Excellent Sessions/Total Sessions) + 4*
(Number of good Sessions/Total Sessions) + 3* (Number of Average
Sessions/Total Sessions) + 2* (Number of Poor Sessions/Total Sessions) +
1* (Number of Worst Sessions/Total Sessions)
o RSRQ Score = 5* (Number of Excellent Sessions/Total Sessions) + 4*
15 (Number of good Sessions/Total Sessions) + 3* (Number of Average
Sessions/Total Sessions) + 2* (Number of Poor Sessions/Total Sessions) +
1* (Number of Worst Sessions/Total Sessions)
o SINR Score = 5* (Number of Excellent Sessions/Total Sessions) + 4*
(Number of good Sessions/Total Sessions) + 3* (Number of Average
20 Sessions/Total Sessions) + 2* (Number of Poor Sessions/Total Sessions) +
1* (Number of Worst Sessions/Total Sessions)
o CQI Score = 5* (Number of Excellent Sessions/Total Sessions) + 4*
(Number of good Sessions/Total Sessions) + 3* (Number of Average
Sessions/Total Sessions) + 2* (Number of Poor Sessions/Total Sessions) +
25 1* (Number of Worst Sessions/Total Sessions)
o Percentage Drop Session Score = 5* (Number of Excellent Sessions/Total Sessions) + 4* (Number of good Sessions/Total Sessions) + 3* (Number of Average Sessions/Total Sessions) + 2* (Number of Poor Sessions/Total Sessions) + 1* (Number of Worst Sessions/Total Sessions) 30
29

[0087] Further, a health score is calculated for each subscriber/user based on a health score computation. An exemplary health score computation is given below: Health Score = (DL UE Throughput Score + RSRP Score + RSRQ Score +SINR Score + CQI Score + Percentage Drop Session Score) 5
[0088] Further, the Root Cause Analysis (RCA) Module [306] is connected to at
least the CEE module [304]. The RCA module [306] is configured to perform a root
cause analysis for each subscriber of the one or more target subscribers or a subset
of the one or more target subscribers. For clear understanding on the root cause
10 analysis procedure, reference may be made to explanation with respect to FIG. 6 in
this disclosure, which illustrates an exemplary procedure for performing the root cause analysis for each subscriber of the one or more target subscribers.
[0089] Further, the Mitigation Module [308] connected to at least the RCA module
15 [306], is configured to recommend one or more mitigation actions for each
subscriber from the one or more target subscribers, based on the root cause analysis for each subscriber of the one or more target subscribers. In an implementation, the Mitigation Module [308], for recommending, one or more mitigation actions for each subscriber from the one or more target subscribers is configured to perform
20 one of: raising a work order for corrective actions, and sending a notification to
each subscriber based on the root cause analysis performed for each subscriber of the one or more target subscribers. For example, if the issue is at user-end, the Mitigation Module [308] may send notification along with recommendation of one or more mitigation actions or corrective actions to the user. In another example, if
25 the issue is at network-end, the Mitigation Module [308] may send notification
along with recommendation of one or more mitigation actions or corrective actions to the network operator. In another example, the Mitigation Module [308] may send notification indicating the issue and no recommendations for corrective actions to the user or the network operator. In an implementation, the work orders may be
30 raised using a separate entity such as, a work order management system (WMS),
which may be an entity responsible for facilitating the generation of all work orders.
30

[0090] Referring to FIG. 4, an exemplary method flow diagram [400] for anomalies
detection in a communication network, in accordance with exemplary
implementations of the present disclosure is shown. In an implementation, the
5 method [400] is performed by the system [300]. Further, in an implementation, the
system [300] may be present in a server device to implement the features of the present disclosure. Also, as shown in FIG. 4, the method [400] starts at step [402].
[0091] At step 402, the method comprises receiving, by a transceiver unit [302], a
10 subscriber data from one or more subscribers, the subscriber data comprising at
least one of a Reference Signal Received Power (RSRP), a signal-to-interference-
plus-noise ratio (SINR), a Channel Quality Indicator (CQI), a Reference Signal
Received Quality (RSRQ), a downlink-to-uplink ratio (DL/UL) Throughput, a
Session release type (Normal/Abnormal), a service type. Here, Reference Signals
15 Received Power (RSRP) and Reference Signal Received Quality (RSRQ) are key
measures of signal level and quality for modern wireless communication networks,
for example, long term evolution (LTE) networks. RSRP is the power of the
Reference Signals spread over the full bandwidth and narrowband. An, RSRQ
indicates the quality of the received reference signal. The RSRQ measurement
20 provides additional information when RSRP is not sufficient to make a reliable
handover or cell reselection decision. The CQI is used by a user device, such as a smartphone, to indicate the channel quality to the base station.
[0092] At step 404, the method comprises determining, by a customer experience
25 estimation (CEE) module [304], a health score for each of the one or more
subscribers, based on the subscriber data and one or more predefined thresholds associated with the subscriber data.
[0093] In an implementation, the determining, by the CEE module [304], the health
30 score for each of the one or more subscribers, further comprises geographically
mapping, by the CEE module [304], a set of one or more subscriber samples in the
31

subscriber trace data. For this purpose, the method comprises aggregating, by the
CEE module [304], one or more subscriber samples at a pre-defined grid size, that
is, the CEE module [304] may plot the subscriber samples aggregated on a specific
grid size, for example, a grid size of 5 metre by 5 metre. That is, the subscribers’
5 geographical location from the subscriber trace data may be taken and different
sets/groups of subscribers may be created based on the location, wherein each group
may contain, in accordance with above example, the subscribers located in a grid
size of 5 metre by 5 metre on ground. Further, in this implementation, the method
comprises identifying, by the CEE module [304], a set of top serving cells
10 associated with each subscriber based on the one or more subscriber samples.
Further, in this implementation, the method comprises determining, by the CEE
module [304], the health score from each subscriber from the one or more
subscribers, based on the subscriber trace data and the set of top serving cells.
Pertinently, for determining the health score from each subscriber from the one or
15 more subscribers, in an implementation, a count of total session for each IMSI (that
is, for each subscriber) is taken as an input, a count of samples in each bucket for
RSRP, RSRQ, SINR, CQI, DL UE Throughput, and a percentage of drop sessions
is taken as an input, and a count of drop sessions (that is, abnormally released
sessions) for each IMSI (that is, for each subscriber) is taken as an input. Further, a
20 user score for each user is calculated based on the above inputs, using a set of
computations. An exemplary set of computations for this purpose is given below.
o DL UE Throughput Score = 5* (Number of excellent sessions/Total
sessions) + 4* (Number of good Sessions/Total Sessions) + 3* (Number of
Average Sessions/Total Sessions) + 2* (Number of Poor Sessions/Total
25 Sessions) +1* (Number of Worst Sessions/Total Sessions)
o RSRP Score = 5* (Number of Excellent Sessions/Total Sessions) + 4*
(Number of good Sessions/Total Sessions) + 3* (Number of Average
Sessions/Total Sessions) + 2* (Number of Poor Sessions/Total Sessions) +
1* (Number of Worst Sessions/Total Sessions)
30 o RSRQ Score = 5* (Number of Excellent Sessions/Total Sessions) + 4*
(Number of good Sessions/Total Sessions) + 3* (Number of Average
32

Sessions/Total Sessions) + 2* (Number of Poor Sessions/Total Sessions) +
1* (Number of Worst Sessions/Total Sessions)
o SINR Score = 5* (Number of Excellent Sessions/Total Sessions) + 4*
(Number of good Sessions/Total Sessions) + 3* (Number of Average
5 Sessions/Total Sessions) + 2* (Number of Poor Sessions/Total Sessions) +
1* (Number of Worst Sessions/Total Sessions)
o CQI Score = 5* (Number of Excellent Sessions/Total Sessions) + 4*
(Number of good Sessions/Total Sessions) + 3* (Number of Average
Sessions/Total Sessions) + 2* (Number of Poor Sessions/Total Sessions) +
10 1* (Number of Worst Sessions/Total Sessions)
o Percentage Drop Session Score = 5* (Number of Excellent Sessions/Total Sessions) + 4* (Number of good Sessions/Total Sessions) + 3* (Number of Average Sessions/Total Sessions) + 2* (Number of Poor Sessions/Total Sessions) + 1* (Number of Worst Sessions/Total Sessions) 15
[0094] Further, a health score is calculated for each subscriber/user based on a health score computation. An exemplary health score computation is given below: Health Score = (DL UE Throughput Score + RSRP Score + RSRQ Score +SINR Score + CQI Score + Percentage Drop Session Score) 20
[0095] Further, at step 406, the method comprises identifying, by the CEE module
[304], one or more target subscribers from the one or more subscribers, based on a
matching of the health score for each subscriber with a pre-defined health score.
Here, the target subscribers may refer to the subscribers with poor health score, that
25 is, the subscribers with a health score less than a threshold health score.
[0096] Further, at step 408, the method comprises performing, by a Root Cause
Analysis (RCA) Module [306], a root cause analysis for each subscriber of the one
or more target subscribers. Also, the root cause analysis procedure is performed as
30 explained in this disclosure with reference to FIG. 6.
33

[0097] Further, at step 410, the method comprises recommending, by a Mitigation
Module [308], one or more mitigation actions for each subscriber from the one or
more target subscribers, based on the root cause analysis performed for each
subscriber of the one or more target subscribers. In an implementation, the
5 recommending, by the Mitigation Module [308], one or more mitigation actions for
each subscriber from the one or more target subscribers, comprises one of: raising a work order for corrective actions, and sending a notification to the each subscriber based on the root cause analysis performed for the each subscriber of the one or more target subscribers. For example, if the issue is at user-end, the Mitigation
10 Module [308] may send notification along with recommendation of one or more
mitigation actions or corrective actions to the user. In another example, if the issue is at network-end, the Mitigation Module [308] may send notification along with recommendation of one or more mitigation actions or corrective actions to the network operator. In another example, the Mitigation Module [308] may send
15 notification indicating the issue and no recommendations for corrective actions to
the user or the network operator. In an implementation, the work orders may be
raised using a separate entity such as, a work order management system (WMS),
which may be an entity responsible for facilitating the generation of all work orders.
Further, the method [300] ends here.
20
[0098] Referring to FIG. 5, which illustrates exemplary experience categories for
various parameters based on their exemplary values. For example, as shown in FIG.
5, the DL UE Throughput score is considered excellent if the value is greater than
or equal to 20. Similarly, the DL UE Throughput score is considered worst if the
25 value is below 5. Similarly, the percentage drop session score is considered
excellent if the value is below 1% (that is, less than 1% of the sessions drop/release abnormally), and the DL UE Throughput score is considered good for a value between 1% and 2%, average for a value between 2% and 3%, poor for a value between 3% and 4%, and worst for a value greater than 4% (that is, more than 4%
30 of the sessions drop/release abnormally).
34

[0099] Referring to Fig. 6, in an implementation, the root cause analysis procedure
is performed for each subscriber of the one or more target subscribers or the subset
of the one or more target subscriber, one by one or in parallel for a plurality of the
target subscribers. Thus, the root cause analysis procedure first comprises
5 identifying one or more subscribers with a health score less than a pre-defined
threshold health score. This may include selecting a subscriber among the target subscribers for performing the root cause analysis procedure (see block 601 in FIG. 6). Further, as shown in FIG. 6, in an implementation, for performing the root cause analysis for each subscriber of the one or more target subscribers, the RCA module
10 [306] is configured to fetch a set of new trace records for a target subscriber among
the one or more target subscribers (see block 602 in FIG. 6). That is, the set of new trace records comprises the raw trace records of the target subscriber (that is, the raw trace records of the subscriber with poor health score are taken), and the target subscribers are the ones for which he analysis for performance of network is done.
15 Further, in this implementation, the RCA module [306] is configured to identify a
set of parameters from the set of new trace records, for the target subscriber among the one or more target subscribers, based on one or more predefined rules (see block 603 in FIG. 6). This set of parameters includes those parameters which are causing the low/ poor health score for this target subscriber. For a clear understanding on
20 this set of parameters, reference may be made to explanation with respect to Fig. 5
in this disclosure, which illustrates exemplary experience categories for various parameters based on their exemplary values. Further, in an implementation, the RCA module [306] is configured to calculate the total session duration and a total traffic consumption by the target subscriber (see block 604 in FIG. 6). Further, in
25 this implementation, the RCA module [306] is configured to check for an issue for
each subscriber, based on a comparison between the top serving cells (as available in the new trace records) and a set of serving cells associated with a planning data (see block 605 in FIG. 6). For this purpose, top serving cells are identified based on a planning data, and anomaly(ies) is detected by comparing the parameters (such as
30 RSRP, RSRQ, etc.) of subscriber data with other serving cells as per the planning
data. In an exemplary implementation, for the above purpose of detecting the
35

anomaly(ies), a pre-stored database is used in which an information is available for
radio access network (RAN) nodes including a latitude and longitude details (i.e.,
geolocation details), an antenna azimuth details, an antenna height details, etc.
Further, the trace records of the subscribers also comprise the geolocation details
5 along with serving cell details for each user equipment (UE) for each call/data
session. By using cell identity (cell ID) in trace records for each location, Top N serving cells may be identified for each UE. For clear understanding, reference may be made to the explanation provided in reference to Fig. 7 in this disclosure. Further, in this implementation, the RCA module [306] is configured to generate one of a
10 first positive result and a first negative result based on the checking of the issue,
wherein the first positive result is generated in an event the issue is detected, and the first negative result is generated in an event no issue is detected. Further, in this implementation, the RCA module [306] is configured to perform a first procedure in an event the first positive result is generated, wherein for performing the first
15 procedure, the RCA module [306] is configured to check for observation of the
issue in a specific time duration only (see block 606 in FIG. 6). Further, in this implementation, the RCA module [306] is configured to generate one of a positive result of the first procedure and a negative result of the first procedure. Here, the positive result of the first procedure means that an issue was observed by the
20 subscriber during a specific time duration only, and negative result of the first
procedure means that an issue was not observed by the subscriber during a specific time duration only, that is during a specific time period.
[0100] Further, in this implementation, the RCA module [306] is configured to
25 perform one of a second procedure and a fourth procedure. The second procedure
is performed in an event of generation of the negative result of the first procedure,
and the fourth procedure is performed in an event of generation of the positive result
of the first procedure. Further, in this implementation, the RCA module [306] is
configured to generate one of a positive result of the second procedure and a
30 negative result of the second procedure in an event of performance of the second
procedure. Here, the second procedure comprises checking, by the RCA Module
36

[306], for observation of the issue in one or more specific cells (see block 607 in
FIG. 6), and the fourth procedure comprising checking, by the RCA Module [306],
for a presence of one or more of alarms, an interference, an outage, and a service
barring (see block 609 in FIG. 6). Also here, the positive result of the second
5 procedure means that an issue was observed when the subscriber was being served
by a specific cell or a specific group of cells only, and negative result of the second procedure means that an issue was not observed when the subscriber was being served by a specific cell or a specific group of cells only. Further, in this implementation, the RCA module [306] is configured to perform one of a third
10 procedure and a fifth procedure. Here, the third procedure is performed in an event
of generation of the negative result of the second procedure, and the fifth procedure is performed in an event of generation of the positive result of the second procedure. Here, the third procedure comprises checking, by the RCA Module [306], for observation of the issue in one or more specific user device models (see block 608
15 in FIG. 6), and the fifth procedure comprises checking, by the RCA Module [306],
for a degradation of one or more performance management (PM) key performance indicators (KPIs) (see block 610 in FIG. 6). Further, in this implementation, the RCA module [306] is configured to generate one of a positive result of the third procedure and a negative result of the third procedure in an event of performance
20 of the third procedure. Also here, the positive result of the third procedure means
that an issue was observed by subscribers using specific device model only, and negative result of the third procedure means that an issue was not observed by subscribers using specific device model only. Further, in this implementation, the RCA module [306] is configured to perform one of: a sixth procedure, and work
25 order procedure. Here, the sixth procedure is performed in an event of generation
of the positive result of the third procedure, and the work order procedure is performed in an event of generation of the negative result of the third procedure (see block 612 in FIG. 6). This work order procedure comprises raising a work order, by the mitigation module [308], for corrective actions (see blocks 612, 613,
30 614, and 615 in FIG. 6). Also, the work order procedure is also performed in an
event of generation of the positive result of the fourth procedure (see block 613 in
37

FIG. 6), the fifth procedure (see block 614 in FIG. 6), the sixth procedure (see block
615 in FIG. 6), and the negative result of the sixth procedure (see block 612 in FIG.
6). In an implementation, the work order for corrective actions may be raised using
a work order management system. However, the steps of the work order procedure
5 in one or more of these events may differ. For example, in the event of positive
result of the sixth procedure, workorder may be raised to a device team to take corrective actions. However, in the event of negative result of the sixth procedure, the workorder may be raised to a field team for field visit and taking corrective actions. Also, the negative result of the sixth procedure may be due to some
10 uncategorized issue (due to which the subscribers may be having poor health score)
which is required to be analysed manually, and thus workorder is raised to field team for field visit and take corrective action. A person skilled in the art would appreciate that the above example is for the purpose of understanding only and does not restrict or limit the present disclosure in any possible manner. Also, the sixth
15 procedure comprises checking for one or more issues related to at least one of a user
device/ user equipment (UE) capability information and software version (see block 611 in FIG. 6). Also, in this implementation, the RCA Module [306] is further configured to generate one of a positive result of the fourth procedure and a negative result of the fourth procedure in an event of performance of the fourth procedure.
20 Here, the positive result of the fourth procedure means that an issue was observed
related to a presence of alarms, interference, outage, service barring, etc. against the top serving cells for the subscriber/user, and negative result of the second procedure means that an issue was not observed related to a presence of alarms, interference, outage, service barring, etc. against the top serving cells for the subscriber/user.
25 Also, in this implementation, the RCA Module [306] is further configured to
generate one of a positive result of the fifth procedure and a negative result of the fifth procedure in an event of performance of the fifth procedure. Here, the positive result of the fifth procedure means that performance management KPIs have been degraded, and negative result of the fifth procedure means that the performance
30 management KPIs have not been degraded. Also, in this implementation, the RCA
Module [306] is further configured to generate one of a positive result of the sixth
38

procedure and a negative result of the sixth procedure in an event of performance
of the sixth procedure. Here, the positive result of the sixth procedure means that
one or more issues are found related to at least one of a user device/ user equipment
(UE) capability information and software version, and negative result of the fifth
5 procedure means that no issue is found related to the user device/ user equipment
(UE) capability information and the software version. Also, in another implementation, the second procedure is performed in an event of generation of the negative result of the fourth procedure, and the third procedure is performed in an event of generation of the negative result of the fifth procedure.
10
[0101] For clarity purpose, it is re-iterated that the RCA Module [306]: for performing the second procedure, is configured to check for observation of the issue in one or more specific cells; for performing the third procedure, is configured to check for observation of the issue in one or more specific user device models; for
15 performing the fourth procedure, is configured to check for a presence of one or
more of an alarms, an interference, an outage, and a service barring; for performing the fifth procedure, is configured to check for a degradation of one or more performance management (PM) key performance indicators (KPIs); for performing the sixth procedure, is configured to check for one or more issues related to at least
20 one of a user device capability information and software version.
[0102] Referring to Fig. 7, which shows a UE being served by a set of serving cells. In an example, say the Top N serving cells for a UE, say UE1, are from following nodes, say for example, Node A, Node B, Node C and Node D as shown in Fig. 7.
25 In this example, if UE1 is being served by desired serving cells (i.e., Node A, Node
B, Node C and Node D), then no anomaly is detected. However, in case the Top N serving cells for the UE1 are from other nodes, for example, Node W, Node X, Node Y and Node Z as shown in Fig. 7 (or any node other than Node A, Node B, Node C and Node D) then this UE1 is being served from cells which are not desired
30 or may be overshooting, which may be detected as an anomaly. A person skilled in
the art would appreciate that the above example is provided for understanding
39

purposes only and does not limit or restrict the present disclosure in any possible manner.
[0103] The present disclosure further discloses a non-transitory computer readable
5 storage medium storing instructions for anomalies detection in a communication
network, the instructions include executable code which, when executed by a one
or more units of a system, causes: a transceiver unit [302] of the system to receive
a subscriber data from one or more subscribers, the subscriber data comprising at
least one of a Reference Signal Received Power (RSRP), a signal-to-interference-
10 plus-noise ratio (SINR), a Channel Quality Indicator (CQI), a Reference Signal
Received Quality (RSRQ), a downlink-to-uplink ratio (DL/UL) Throughput, a
Session release type (Normal/Abnormal), a service type. The instructions further
include executable code which, when executed causes a customer experience
estimation (CEE) module [304] of the system to determine a health score for each
15 of the one or more subscribers, based on the subscriber data and one or more
predefined thresholds associated with the subscriber data; and to identify one or
more target subscribers from the one or more subscribers, based on a matching of
the health score for each subscriber with a pre-defined health score. The instructions
further include executable code which, when executed causes a Root Cause
20 Analysis (RCA) Module [306] of the system to perform a root cause analysis for
each subscriber of the one or more target subscribers. The instructions further
include executable code which, when executed causes a Mitigation Module [308]
of the system to recommend one or more mitigation actions for each subscribers
from the one or more target subscribers, based on the root cause analysis for each
25 subscriber of the one or more target subscribers.
[0104] The present disclosure further discloses a user equipment (UE). The UE
comprises at least a transmitter unit and at least a receiver unit. The transmitter unit
is configured to send, to a system [300], a request for receiving one or more
30 mitigation actions related to anomalies detection in a communication network.
Further, the receiver unit configured to receive, from the system [300], the one or
40

more mitigation actions in response to the request. Also, the anomalies detection in
the communication network is performed based on receiving, by a transceiver unit
[302], a subscriber data from one or more subscribers. The subscriber data
comprises at least one of a Reference Signal Received Power (RSRP), a signal-to-
5 interference-plus-noise ratio (SINR), a Channel Quality Indicator (CQI), a
Reference Signal Received Quality (RSRQ), a downlink user equipment (DL UE)
Throughput, a Session release type (Normal/Abnormal), a service type. Further, the
anomalies detection in the communication network is performed based on
determining, by a customer experience estimation (CEE) module [304], a health
10 score for each of the one or more subscribers, based on the subscriber data and one
or more predefined thresholds associated with the subscriber data. anomalies
detection in the communication network is performed based on identifying, by the
CEE module [304], one or more target subscribers from the one or more
subscribers, based on a matching of the health score for each subscriber with a pre-
15 defined health score. anomalies detection in the communication network is
performed based on performing, by a Root Cause Analysis (RCA) Module [306], a
root cause analysis for each subscriber of the one or more target subscribers.
anomalies detection in the communication network is performed based on
recommending, by a Mitigation Module [308], one or more mitigation actions for
20 each subscriber from the one or more target subscribers, based on the root cause
analysis performed for each subscriber of the one or more target subscribers.
[0105] As is evident from the above, the present disclosure provides a technically advanced solution for anomalies detection in a communication network. The
25 present solution enables a user to attain a comprehensive understanding of the
diverse network characteristics that impact the overall customer experience, encompassing aspects such as data usage, voice usage, network quality, and device compatibility. Further, the features of the present disclosure facilitate improved efficiency. The automation of the anomalies-detection process streamlines the
30 process of estimating customer experience, identifying root causes, and suggesting
corrective steps, reducing manual effort and increasing productivity. Also, the
41

features of the present disclosure may also be used to obtain a trained model for
anomalies detection in a communication network and improvement thereof to allow
identify the underperforming cells. Further, the features of the present disclosure
facilitate proactive issue resolution. By automating the identification of customers
5 with poor user experience, the system can trigger timely actions to mitigate issues
before they significantly impact customer satisfaction. Further, the features of the
present disclosure facilitate accurate data analysis. Automation eliminates the
possibility of human errors in data analysis, ensuring more reliable and consistent
results. Further, the features of the present disclosure facilitate enhanced customer
10 satisfaction. Further, the features of the present disclosure facilitate better resource
allocation, that is, by facilitating users to allocate resources efficiently by focusing on the areas that require immediate attention.
[0106] While considerable emphasis has been placed herein on the disclosed
15 implementations, it will be appreciated that many implementations can be made and
that many changes can be made to the implementations without departing from the
principles of the present disclosure. These and other changes in the implementations
of the present disclosure will be apparent to those skilled in the art, whereby it is to
be understood that the foregoing descriptive matter to be implemented is illustrative
20 and non-limiting.
42

We Claim:
1. A method for anomalies detection in a communication network, the method
comprising:
5 - receiving, by a transceiver unit [302], a subscriber data from one or more
subscribers, the subscriber data comprising at least one of a Reference
Signal Received Power (RSRP), a signal-to-interference-plus-noise
ratio (SINR), a Channel Quality Indicator (CQI), a Reference Signal
Received Quality (RSRQ), a downlink user equipment (DL UE)
10 Throughput, a Session release type (Normal/Abnormal), a service type;
- determining, by a customer experience estimation (CEE) module [304],
a health score for each of the one or more subscribers, based on the
subscriber data and one or more predefined thresholds associated with
the subscriber data;
15 - identifying, by the CEE module [304], one or more target subscribers
from the one or more subscribers, based on a matching of the health score for each subscriber with a pre-defined health score;
- performing, by a Root Cause Analysis (RCA) Module [306], a root
cause analysis for each subscriber of the one or more target subscribers;
20 and
- recommending, by a Mitigation Module [308], one or more mitigation
actions for each subscriber from the one or more target subscribers,
based on the root cause analysis performed for each subscriber of the
one or more target subscribers.
25
2. The method as claimed in claim 1 wherein the determining, by the CEE
module [304], the health score for each of the one or more subscribers,
further comprises:
- receiving, by the transceiver unit [302] from one or more subscribers, a
30 subscriber trace data;
43

5
10

-
-
-
-

geographically mapping, by the CEE module [304], a set of one or more
subscriber samples in the subscriber trace data;
aggregating, by the CEE module [304], one or more subscriber samples
at a pre-defined grid size;
identifying, by the CEE module [304], a set of top serving cells
associated with each subscriber based on the one or more subscriber
samples; and
determining, by the CEE module [304], the health score from each
subscriber from the one or more subscribers, based on the subscriber
trace data and the set of top serving cells.

15
20
25
30

3. The method as claimed in claim 1, wherein the performing, by the RCA Module [306], the root cause analysis for each subscriber of the one or more target subscribers, further comprises:
- fetching, by the RCA Module [306], a set of new trace records for the one or more target subscribers;
- identifying, by the RCA Module [306], a set of parameters from the set of new trace records, for the one or more target subscribers, based on one or more predefined rules;
- checking, by the RCA Module [306], for an issue for each subscriber, based on a comparison between a top serving cells and a set of serving cells associated with a planning data;
- generating, by the RCA Module [306], one of a first positive result and a first negative result based on the checking of the issue, wherein the first positive result is generated in an event the issue is detected, and the first negative result is generated in an event no issue is detected;
- performing, by the RCA Module [306], a first procedure comprising checking, by the RCA Module [306], for observation of the issue in a specific time period, wherein the first procedure is performed in an event of generation of the first positive result;

44

- generating, by the RCA Module [306], one of a positive result of the first procedure and a negative result of the first procedure;
- performing, by the RCA Module [306], one of a second procedure and a fourth procedure,
5 wherein the second procedure is performed in an event of generation
of the negative result of the first procedure, and
wherein the fourth procedure is performed in an event of generation of the positive result of the first procedure;
- generating, by the RCA Module [306], one of a positive result of the
10 second procedure and a negative result of the second procedure in an
event of performance of the second procedure;
- performing, by the RCA Module [306], one of a third procedure and a
fifth procedure,
wherein the third procedure is performed in an event of generation
15 of the negative result of the second procedure, and
wherein the fifth procedure is performed in an event of generation of the positive result of the second procedure;
- generating, by the RCA Module [306], one of a positive result of the
third procedure and a negative result of the third procedure in an event
20 of performance of the third procedure; and
- performing, by the RCA Module [306], one of a sixth procedure and a
work order procedure,
wherein the sixth procedure is performed in an event of generation
of the positive result of the third procedure; and
25 wherein the work order procedure is performed in an event of
generation of the negative result of the third procedure.
4. The method as claimed in claim 1, wherein the recommending, by the
Mitigation Module [308], one or more mitigation actions for each subscriber
30 from the one or more target subscribers comprises one of: raising a work
order for corrective actions, and sending a notification to the each subscriber
45

based on the root cause analysis performed for the each subscriber of the one or more target subscribers.
5. The method as claimed in claim 3, the method further comprising:
5 - generating, by the RCA Module [306], one of a positive result of the
fourth procedure and a negative result of the fourth procedure in an
event of performance of the fourth procedure; generating, by the RCA
Module [306], one of a positive result of the fifth procedure and a
negative result of the fifth procedure in an event of performance of the
10 fifth procedure; generating, by the RCA Module [306], one of a positive
result of the sixth procedure and a negative result of the sixth procedure in an event of performance of the sixth procedure.
6. The method as claimed in claim 5, the method further comprising:
15 - performing, by the RCA Module [306], the work order procedure
comprising raising a work order, by the mitigation module [308], for corrective actions, wherein the work order procedure is performed in an event of generation of: the positive result of the fourth procedure, the fifth procedure, and the sixth procedure, and the negative result of the
20 sixth procedure.
7. The method as claimed in claim 5, wherein the second procedure is
performed in an event of generation of the negative result of the fourth
procedure, and the third procedure is performed in an event of generation of
25 the negative result of the fifth procedure.
8. The method as claimed in claim 3, wherein atleast one of:
the second procedure comprises checking, by the RCA Module [306],
for observation of the issue in one or more specific cells,
30 the third procedure comprises checking, by the RCA Module [306], for
observation of the issue in one or more specific user device models,

the fourth procedure comprises checking, by the RCA Module [306], for
a presence of one or more of an alarm, an interference, an outage, and a
service barring,
the fifth procedure comprises checking, by the RCA Module [306], for
a degradation of one or more performance management (PM) key
performance indicators (KPIs), and
the sixth procedure comprises checking, by the RCA Module [306], for
one or more issues related to at least one of a user device capability
information and software version.
9. A system for anomalies detection in a communication network, the system comprising:
- a transceiver unit [302] configured to receive a subscriber data from one or more subscribers, the subscriber data comprising at least one of a Reference Signal Received Power (RSRP), a signal-to-interference-plus-noise ratio (SINR), a Channel Quality Indicator (CQI), a Reference Signal Received Quality (RSRQ), a downlink user equipment (DL UE) Throughput, a Session release type (Normal/Abnormal), a service type;
- a customer experience estimation (CEE) module [304] connected to at least the transceiver unit [302], the CEE module [304] configured to:
o determine a health score for each of the one or more subscribers, based on the subscriber data and one or more predefined thresholds associated with the subscriber data; and
o identify one or more target subscribers from the one or more subscribers, based on a matching of the health score for each subscriber with a pre-defined health score;
- a Root Cause Analysis (RCA) Module [306] connected to at least the
CEE module [304], the RCA module [306] configured to perform a root
cause analysis for each subscriber of the one or more target subscribers;
and

- a Mitigation Module [308] connected to at least the RCA module [306],
the mitigation module [308] configured to recommend one or more
mitigation actions for each subscribers from the one or more target
subscribers, based on the root cause analysis for each subscriber of the
5 one or more target subscribers.
10. The system as claimed in claim 9, wherein the CEE module [304], for determining the health score for each of the one or more subscribers, is further configured to:
- geographically map a set of one or more subscriber samples in a subscriber trace data;
- aggregate one or more subscriber samples at a pre-defined grid size;
- identify a set of top serving cells associated with each subscriber based on the one or more subscriber samples; and
- determine the health score from each subscriber from the one or more subscribers, based on the subscriber trace data and the set of top serving cells.
The system as claimed in claim 10, wherein prior to the CEE module [304] geographically mapping a set of one or more subscriber samples in the subscriber trace data, the transceiver unit [302] is configured to:
- receive, from one or more subscribers, the subscriber trace data.
12. The system as claimed in claim 9, wherein the RCA Module [306], for
25 performing the root cause analysis for each subscriber of the one or more
target subscribers, is further configured to:
- fetch a set of new trace records for the one or more target subscribers;
- identify a set of parameters from the set of new trace records, for the one or more target subscribers, based on one or more predefined rules;

- check for an issue for each subscriber, based on a comparison between
a top serving cells and a set of serving cells associated with a planning
data;
- generate one of a first positive result and a first negative result based on
5 the checking of the issue, wherein the first positive result is generated in
an event the issue is detected, and the first negative result is generated in an event no issue is detected;
- perform a first procedure, wherein for performing the first procedure,
the RCA module [306] is configured to check for observation of the
10 issue in a specific time period, wherein the first procedure is performed
in an event of generation of the first positive result;
- generate one of a positive result of the first procedure and a negative result of the first procedure;
- perform one of a second procedure and a fourth procedure,
15 wherein the second procedure is performed in an event of generation
of the negative result of the first procedure, and
wherein the fourth procedure is performed in an event of generation of the positive result of the first procedure;
- generate one of a positive result of the second procedure and a negative
20 result of the second procedure in an event of performance of the second
procedure;
- perform one of a third procedure and a fifth procedure,
wherein the third procedure is performed in an event of generation
of the negative result of the second procedure, and
25 wherein the fifth procedure is performed in an event of generation
of the positive result of the second procedure;
- generate one of a positive result of the third procedure and a negative
result of the third procedure in an event of performance of the third
procedure;
30 - perform one of a sixth procedure and work order procedure,

wherein the sixth procedure is performed in an event of generation of the positive result of the third procedure; and
wherein the work order procedure is performed in an event of generation of the negative result of the third procedure. 5
13. The system as claimed in claim 9, wherein the Mitigation Module [308], for
recommending, one or more mitigation actions for each subscriber from the
one or more target subscribers is configured to perform one of: raising a
work order for corrective actions, and sending a notification to the each
10 subscriber based on the root cause analysis performed for the each
subscriber of the one or more target subscribers.
14. The system as claimed in claim 12, the RCA Module [306] is further
configured to:
15 - generate one of a positive result of the fourth procedure and a negative
result of the fourth procedure in an event of performance of the fourth procedure; generate one of a positive result of the fifth procedure and a negative result of the fifth procedure in an event of performance of the fifth procedure; generate one of a positive result of the sixth procedure
20 and a negative result of the sixth procedure in an event of performance
of the sixth procedure.
15. The system as claimed in claim 12, wherein the RCA module [306] is further
configured to:
25 - perform the work order procedure comprising raising a work order, by
the mitigation module [308], for corrective actions, wherein the work order procedure is performed in an event of generation of: the positive result of the fourth procedure, the fifth procedure, and the sixth procedure, and the negative result of the sixth procedure.
30

16. The system as claimed in claim 15, wherein the second procedure is
performed in an event of generation of the negative result of the fourth
procedure, and the third procedure is performed in an event of generation of
the negative result of the fifth procedure.
5
17. The system as claimed in claim 12, wherein the RCA Module [306]:
for performing the second procedure is configured to check for
observation of the issue in one or more specific cells,
for performing the third procedure is configured to check for
10 observation of the issue in one or more specific user device models,
for performing the fourth procedure is configured to check for a presence of one or more of an alarm, an interference, an outage, and a service barring, for performing the fifth procedure is configured to check for a
15 degradation of one or more performance management (PM) key
performance indicators (KPIs), and
for performing the sixth procedure is configured to check for one or more issues related to at least one of a user device capability information and software version.
20
18. A user equipment comprising:
- at least a transmitter unit configured to send to a system [300], a request
for receiving one or more mitigation actions related to anomalies
detection in a communication network; and
25 - at least a receiver unit configured to receive, from the system [300], the
one or more mitigation actions in response to the request,
wherein the anomalies detection in the communication network is performed based on:
o receiving, by a transceiver unit [302], a subscriber data from one
30 or more subscribers, the subscriber data comprising at least one
of a Reference Signal Received Power (RSRP), a signal-to-

interference-plus-noise ratio (SINR), a Channel Quality
Indicator (CQI), a Reference Signal Received Quality (RSRQ),
a downlink user equipment (DL UE) Throughput, a Session
release type (Normal/Abnormal), a service type;
5 o determining, by a customer experience estimation (CEE) module
[304], a health score for each of the one or more subscribers, based on the subscriber data and one or more predefined thresholds associated with the subscriber data;
o identifying, by the CEE module [304], one or more target
10 subscribers from the one or more subscribers, based on a
matching of the health score for each subscriber with a pre-defined health score;
o performing, by a Root Cause Analysis (RCA) Module [306], a
root cause analysis for each subscriber of the one or more target
subscribers; and
o recommending, by a Mitigation Module [308], one or more mitigation actions for each subscriber from the one or more target subscribers, based on the root cause analysis performed for each subscriber of the one or more target subscribers.