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 PERFORMING A COVERAGE IMPROVEMENT”
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 PERFORMING A COVERAGE
IMPROVEMENT
FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to network performance management systems. More particularly, embodiments of the present disclosure relate to method and system for performing a coverage improvement.
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 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 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. 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 connect multiple devices simultaneously. With each generation, wireless communication technology has become more advanced, sophisticated, and capable of delivering more services to its users.

[0004] In telecommunication network, it is extremely important to identify areas where user experience is poor. There may be various reasons for such poor user experience. The signal strength of a wireless network decreases as it travels through various materials such as walls, ceilings, and floors. This can result in areas with poor signal coverage or even complete dead zones. The wireless signals can also reflect off surfaces and create multipath interference, which can result in signal distortion, signal cancellation, and reduced coverage. Further, high density environments such as large buildings or crowded public areas can strain network capacity and lead to congestion, resulting in reduced coverage and slower data transfer speeds. It is also possible that limited budget has limited the number of antennae, resulting in areas with poor coverage. Coverage hole planning is done after network roll out based on field inputs i.e. drive test data and customer complaints and/or field optimization. This data for coverage planning is inadequate and often used without checking the consistency of issues. Therefore, it is pertinent to improve coverage in areas experiencing high levels of bad user experience.
[0005] Thus, there exists an imperative need in the art of a method and a system for performing a coverage improvement, which the present disclosure aims to address.
SUMMARY
[0006] 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.
[0007] An aspect of the present disclosure may relate to a method for performing a coverage improvement. The method includes receiving, by a receiving unit, at an analysis platform (AP) entity, a set of data related to one or more user(s), from at least one of a performance management (PM) entity and a fault management (FM) entity. The method further includes classifying, by a classification unit, at the AP entity, one or more geo-spatial grid on a network map, in one or more region(s) based on the set of data related to the one or more user(s). The method further

includes clustering, by a cluster unit, at the AP entity, one or more sites on the network map via a first trained model, wherein the one or more sites comprises one or more cell(s). The method further includes analysing, by an analysing unit, via the AP entity, the one or more sites based on the one or more geo-spatial grid. The method further includes detecting, by a detection unit, at the AP entity, a type of the cell(s). The method further includes determining, by a determination unit, at the AP entity, a set of remote electrical tilt (RET) data for each of the type of the cell(s) via a second trained model and a third trained model. The method further includes validating, by a validation unit, at the AP entity, the determined set of RET data based on a set of performance parameter(s) and the type of cell(s). The method further includes implementing, by an implementation unit, at the AP entity, the set of RET data for each type of the cell(s) through a Configuration Management System (CM) entity. Finally, the method includes triggering, by a trigger unit, at the AP entity, at least one action through the CM entity for a pre-defined degradation thresholds for the implemented set of RET data.
[0008] In an exemplary aspect of the present disclosure, the method further the set of data related to the one or more user(s) comprises at least one of a number of user(s) having satisfying experience, a number of users having unsatisfying user experience, a signal strength at a user device(s), and a network congestion(s) at the user device(s).
[0009] In an exemplary aspect of the present disclosure, the method further comprises the one or more geo-spatial grids are at least one of a good performance grid, a poor performance grid, a satisfying grid for a network service performance and an unsatisfying grid for the network service performance, and a satisfying grid for a user experience and an unsatisfying grid for the user experience.
[0010] In an exemplary aspect of the present disclosure, the first trained model is trained using at least one of a set of unsupervised machine learning algorithm, a K-means algorithm, and an artificial intelligence algorithm, the second trained model is trained based on a neural network based machine learning algorithm and, the third

trained model is trained based at least on a user dimensioning data, a path loss model data, a traffic modelling data, a normalization of channel quality indicator (CQI), an E-tilt history, a reflective quality of service attribute (RQA) associated with the one or more users, a net velocity Performance Management (NV-PM) data associated with the one or more users, a local service request (LSR) associated with the one or more users.
[0011] In an exemplary aspect of the present disclosure, the analysing the clustered one or more sites comprises: identifying, by an identification unit, the clustered one or more sites based on a predefined threshold limit for a performance data of the classified one or more geo-spatial grid.
[0012] In an exemplary aspect of the present disclosure, the type of cell is an overshooting cell, an overlapping cell and an undershooting cell.
[0013] In an exemplary aspect of the present disclosure, the set of performance parameters are at least one of a band wise harmonization, a final E- Tilt, an E-tilt implementation, and a load balance.
[0014] In an exemplary aspect of the present disclosure, performing, by a performing unit at the AP entity a cluster-wise pre-post analysis using the set of data related to one or more user(s) from at least one of the PM entity and FM entity.
[0015] In an exemplary aspect of the present disclosure, the triggering the at least one action by the CM entity comprises: reverting, by a reverting unit at the CM entity, the implemented set of RET data after detecting a degradation in any cluster performance by the AP entity using the set of data related to one or more user(s); repeating, by a repeater unit at the CM entity, the implementation of a new set of validated RET data for maintaining an identical performance or improving the performance of each cluster.
[0016] Another aspect of the present disclosure may relate to a system for performing a coverage improvement. The system comprises a receiving unit

configured to receive, at an analysis platform (AP) entity, a set of data related to one or more user(s), from at least one of a performance management (PM) entity and a fault management (FM) entity. The system further comprises a classification unit connected at least to the receiving unit. The classification unit is configured to classify, at the AP entity, one or more geo-spatial grid on a network map, in one or more region(s) based on the set of data related to the one or more user(s). The system further comprises a cluster unit connected at least to the classification unit. The cluster unit is configured to cluster, at the AP entity, one or more sites on the network map at a first trained model, wherein the one or more sites comprises one or more cell(s). The system further comprises an analysing unit connected at least to the cluster unit. The analysis unit is configured to analyse, at the AP entity, the one or more sites based on the one or more geo-spatial grid. The system further comprises a detection unit connected at least to the analysing unit. The detection unit is configured to detect, at the AP entity, a type of the cell(s). The system further comprises a determination unit connected at least to the detection unit. The determination unit is configured to determine, at the AP entity, a set of remote electrical tilt (RET) data for each of the type of the cell(s) via a second trained model and a third trained model. The system further comprises a validation unit connected at least to the determination unit. The validation unit is configured to validate, at the AP entity, the determined set of RET data based on a set of performance parameter(s) and the type of cell(s). The system further comprises an implementation unit connected at least to the validation unit. The implementation unit is configured to implement, at the AP entity, the set of RET data for each type of the cell(s) through a Configuration Management System (CM) entity. Finally, the system comprises a trigger unit connected at least to the implementation unit. The trigger unit is configured to trigger, at the AP entity, at least one action through the CM entity for a pre-defined degradation thresholds for the implemented set of RET data.
[0017] Yet another aspect of the present disclosure may relate to a non-transitory computer readable storage medium storing instructions for performing a coverage

improvement, the instructions include executable code which, when executed by one or more units of a system, causes a receiving unit to receive, at an analysis platform (AP) entity, a set of data related to one or more user(s), from at least one of a performance management (PM) entity and a fault management (FM) entity. The executable code when executed further causes a classification unit to classify, at the AP entity, one or more geo-spatial grid on a network map, in one or more region(s) based on the set of data related to the one or more user(s). The executable code when executed further causes a cluster unit to cluster, at the AP entity, one or more sites on the network map via a first trained model, wherein the one or more sites comprises one or more cell(s). The executable code when executed further causes an analysing unit to analyse, at the AP entity, the one or more sites based on the one or more geo-spatial grid. The executable code when executed further causes a detection unit to detect, at the AP entity, a type of the cell(s). The executable code when executed further causes a determination unit to determine, at the AP entity, a set of remote electrical tilt (RET) data for each of the type of the cell(s) at a second trained model and a third trained model. The executable code when executed further causes a validation unit to validate, at the AP entity, the determined set of RET data based on a set of performance parameter(s) and the type of cell(s). The executable code when executed further causes an implementation unit to implement, at the AP entity, the set of RET data for each type of the cell(s) through a Configuration Management System (CM) entity. The executable code when executed further causes a trigger unit to trigger, at the AP entity, at least one action through the CM entity for a pre-defined degradation thresholds for the implemented set of RET data.
OBJECTS OF THE DISCLOSURE
[0018] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below.
[0019] It is an object of the present disclosure to provide a system and a method for coverage improvement based on closed loop RET modification.

[0020] It is another object of the present disclosure to provide a solution that actively monitors and tracks problems and allows for timely intervention and prevents the problem from becoming more complex or causing more damage by identifying poor user experience polygons on a map.
5 [0021] It is yet another object of the present disclosure to provide a solution that
recognizes recurring problems or patterns and enables development of preventive measures, improved processes, and implementation of corrective actions to avoid future occurrence by identifying poor user experience polygons on a map.
[0022] It is yet another object of the present disclosure to provide a solution that
10 enables tracking and categorization of problems and enables efficient allocation of
resources, focusing on high-priority problems that have the most significant impact on goals or objectives, by identifying poor user experience polygons on a map.
[0023] It is yet another object of the present disclosure to provide a solution enables
provides valuable data and metrics for analysis, thereby enabling identification of
15 trends, root causes and underlying systemic issues by identifying poor user
experience polygons on a map.
DESCRIPTION OF THE DRAWINGS
[0024] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods
20 and systems in which like reference numerals refer to the same parts throughout the
different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. 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
25 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 drawings includes disclosure of electrical components or circuitry commonly used to implement such components.
8

[0025] FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture, in accordance with an exemplary implementation of the present disclosure.
[0026] FIG. 2 illustrates an exemplary block diagram of a computing device upon
5 which the features of the present disclosure may be implemented in accordance with
exemplary implementation of the present disclosure.
[0027] FIG. 3 illustrates an exemplary block diagram of a system for performing a coverage improvement, in accordance with exemplary implementations of the present disclosure.
10 [0028] FIG. 4 illustrates a method flow diagram for performing a coverage
improvement in accordance with exemplary implementations of the present disclosure.
[0029] FIG. 5 illustrates an exemplary block diagram of a system architecture for
performing a coverage improvement, in accordance with exemplary
15 implementations of the present disclosure.
[0030] FIG. 6 illustrates a process for performing a coverage improvement in accordance with exemplary implementations of the present disclosure.
[0031] FIG. 7 illustrates a process flow diagram for performing a coverage
improvement using remote electrical tilt (RET), in accordance with exemplary
20 implementations of the present disclosure.
[0032] The foregoing shall be more apparent from the following more detailed description of the disclosure.
DETAILED DESCRIPTION
[0033] In the following description, for the purposes of explanation, various
25 specific details are set forth in order to provide a thorough understanding of
embodiments of the present disclosure. It will be apparent, however, that
9

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
5 problems discussed above.
[0034] 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.
10 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.
[0035] Specific details are given in the following description to provide a thorough
understanding of the embodiments. However, it will be understood by one of
15 ordinary skill in the art that the embodiments may be practiced without these
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.
[0036] Also, it is noted that individual embodiments may be described as a process
20 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
is terminated when its operations are completed but could have additional steps not
25 included in a figure.
[0037] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the 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
10

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
“includes,” “has,” “contains,” and other similar words are used in either the detailed
5 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.
[0038] As used herein, a “processing unit” or “processor” or “operating processor” includes one or more processors, wherein processor refers to any logic circuitry for
10 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 Signal Processing) DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of
15 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.
[0039] As used herein, “a user equipment”, “a user device”, “a smart-user-device”,
20 “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
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
25 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
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
30 such unit(s) which are required to implement the features of the present disclosure.
11

[0040] As used herein, “storage unit” or “memory unit” refers to a machine or
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”),
5 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 that may be required by one or more units of the system to perform their respective functions.
[0041] As used herein “interface” or “user interface refers to a shared boundary
10 across which two or more separate components of a system exchange information
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 each other, which also includes the methods, functions, or procedures that may be called.
15 [0042] All modules, units, components used herein, unless explicitly excluded
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,
20 Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array
circuits (FPGA), any other type of integrated circuits, etc.
[0043] As used herein the transceiver unit include 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
25 and/or connected with the system.
[0044] As used herein, network coverage area describes the physical areas reached by a mobile network operator's radio signal. Further used herein, coverage improvement means improving the mobile network coverage range in the areas where the radio signals area weak.
12

[0045] As discussed in the background section, the current known solutions have several shortcomings. The present disclosure aims to overcome the above-mentioned and other existing problems in this field of technology by providing method and system for performing a coverage improvement.
5 [0046] FIG. 1 illustrates an exemplary block diagram representation of 5th
generation core (5GC) network architecture [100], in accordance with an 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
10 Management Function (SMF) [108], a Service Communication Proxy (SCP) [110],
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],
15 a Unified Data Management (UDM) [124], an application function (AF) [126], a
User Plane Function (UPF) [128], a data network (DN) [130], wherein all the 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.
[0047] The Radio Access Network (RAN) [104] is the part of a mobile
20 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.
[0048] The Access and Mobility Management Function (AMF) [106] is a 5G core
25 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.
[0049] The Session Management Function (SMF) [108] is a 5G core network function responsible for managing session-related aspects, such as establishing,
13

modifying, and releasing sessions. It coordinates with the User Plane Function (UPF) for data forwarding and handles IP address allocation and QoS enforcement.
[0050] The Service Communication Proxy (SCP) [110] is a network function in the
5G core network that facilitates communication between other network functions
5 by providing a secure and efficient messaging service. It acts as a mediator for
service-based interfaces.
[0051] The 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.
10 [0052] The Network Slice Specific Authentication and Authorization Function
(NSSAAF) [114] is a network function that provides authentication and authorization services specific to network slices. It ensures that UEs can access only the slices for which they are authorized.
[0053] The Network Slice Selection Function (NSSF) [116] is a network function
15 responsible for selecting the appropriate network slice for a UE based on factors
such as subscription, requested services, and network policies.
[0054] The Network Exposure Function (NEF) [118] is a network function that exposes capabilities and services of the 5G network to external applications, enabling integration with third-party services and applications.
20 [0055] The 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.
[0056] The Policy Control Function (PCF) [122] is a network function responsible
for policy control decisions, such as QoS, charging, and access control, based on
25 subscriber information and network policies.
14

[0057] The Unified Data Management (UDM) [124] is a network function that centralizes the management of subscriber data, including authentication, authorization, and subscription information.
[0058] The Application Function (AF) [126] is a network function that represents
5 external applications interfacing with the 5G core network to access network
capabilities and services.
[0059] The User Plane Function (UPF) [128] is a network function responsible for handling user data traffic, including packet routing, forwarding, and QoS enforcement.
10 [0060] The 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.
[0061] FIG. 2 illustrates an exemplary block diagram of a computing device [200]
15 (also referred to herein as computer system [200]) upon which the features of the
present disclosure may be implemented in accordance with exemplary implementation of the present disclosure.
[0062] In an implementation, the computing device [200] may also implement a
method for performing a coverage improvement utilising the system. In another
20 implementation, the computing device [200] itself implements the method for
performing a coverage improvement 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.
[0063] The computing device [200] may include a bus [202] or other
25 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
15

computing device [200] may also include a main memory [206], such as a random-
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
5 intermediate information during execution of the instructions to be executed by the
processor [204]. Such instructions, when stored in non-transitory storage media
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
10 (ROM) [208] or other static storage device coupled to the bus [202] for storing static
information and instructions for the processor [204].
[0064] 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
15 display [212], such as a cathode ray tube (CRT), Liquid crystal Display (LCD),
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
20 [204]. Another type of user input device may be a cursor controller [216], such as
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
25 the device to specify positions in a plane.
[0065] 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.
30 According to one implementation, the techniques herein are performed by the
16

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
5 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.
[0066] The computing device [200] also may include a communication interface
10 [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
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
15 telephone line. As another example, the communication interface [218] may be a
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
20 various types of information.
[0067] 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
25 ISP [226], the local network [222], a 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.
17

[0068] The computing device [200] encompasses a wide range of electronic
devices capable of processing data and performing computations. Examples of
computing device [200] include, but are not limited only to, personal computers,
laptops, tablets, smartphones, servers, and embedded systems. The devices may
5 operate independently or as part of a network and can perform a variety of tasks
such as data storage, retrieval, and analysis. Additionally, computing device [200] may include peripheral devices, such as monitors, keyboards, and printers, as well as integrated components within larger electronic systems, showcasing their versatility in various technological applications.
10 [0069] Referring to FIG. 3, an exemplary block diagram of a system [300] for
performing a coverage improvement, is shown, in accordance with the exemplary implementations of the present disclosure. The system [300] comprises at least one receiving unit [302], at least one analysis platform (AP) [304], at least one classification unit [306], at least one cluster unit [308], at least one analysing unit
15 [310], at least one detection unit [312], at least one determination unit [314], at least
one validation unit [316], at least one implementation unit [318], at least one configuration management system entity [320], at least one trigger unit [322], at least one identification unit [324], at least one performing unit [326], at least one reverting unit [328], and at least one repeater unit [330]. Also, all of the
20 components/ units of the system [300] are assumed to be connected to each other
unless otherwise indicated below. As shown in the figures all units shown within the system 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 [300] may comprise any such numbers of said units, as required
25 to implement the features of the present disclosure. Further, in an implementation,
the system [300] 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
18

network entity. In yet another implementation, the system [300] may reside partly in the server/ network entity and partly in the user device.
[0070] The system [300] is configured for performing a coverage improvement,
with the help of the interconnection between the components/units of the system
5 [300].
[0071] The receiving unit [302] is configured to receive, at an analysis platform (AP) entity [304], a set of data related to one or more user(s), from at least one of a performance management (PM) entity and a fault management (FM) entity.
[0072] The receiving unit [302] receives the set of data associated with one or more
10 user(s), at the (AP) platform entity [304] from at least one of the PM entity and FM
entity. In an exemplary aspect, the PM entity may include data of one or more
user(s) that includes such as but not limited to KPIs like call drop rate data, channel
quality data, downlink throughput data etc. Similarly, the FM entity may include
data of one or more user(s) that includes such that but not limited to resource data,
15 alarm performance degradation data, service impacting data etc.
[0073] In an exemplary aspect, one or more users are subscribers of the telecom service.
[0074] In an exemplary aspect, the set of data related to the one or more user(s)
comprises at least one of a number of user(s) having satisfying experience, a number
20 of users having unsatisfying user experience, a signal strength at a user device(s),
and a network congestion(s) at the user device(s).
[0075] In an exemplary aspect, the set of data comprises data related to user(s)
satisfaction experience i.e. user(s) having an overall positive experience with the
service and data related to user(s) unsatisfaction experience i.e. user(s) having
25 negative experience while using the service.
[0076] In an exemplary aspect, the set of data include signal strength data at the user device which is the transmitter power output as received by the cell site
19

antenna at a distance from the transmitting antenna of the user device. In an exemplary aspect, signal strength data may determine whether device is within network coverage area or not.
[0077] In an exemplary aspect, the network congestion(s) at the user device(s)
5 when a network is overrun with more data packet traffic than it can deal with. This
backup of data traffic may occur at the user device when too many communication
and data requests are made at the same time, over the network that doesn't have
enough network bandwidth to carry it causing issues like reduction in quality of
service (QOS) that causes packet loss, queueing delay, or the blocking of new
10 connections at the user device(s). In an exemplary aspect, network congestion may
relate to latency, throughput, and bandwidth issues.
[0078] The system [300] further comprises the classification unit [306] which
connected at least to the receiving unit [302]. The classification unit [306] is
configured to classify, at the AP entity [304], one or more geo-spatial grid on a
15 network map, in one or more region(s) based on the set of data related to the one or
more user(s).
[0079] The classification unit [306] classifies one or more geo-spatial grid on the
network map, in one or more regions (s) i.e. geographical regions based on the set
of data related to the one or more user(s) at the AP entity. In an exemplary aspect,
20 the classification of geo-spatial grid on the network map is done by categorizing
different areas having good or bad network coverage on a network map which helps in identifying regions where network coverage might need improvement.
[0080] In an exemplary aspect, the one or more geo-spatial grids are at least one of
a good performance grid, a poor performance grid, a satisfying grid for a network
25 service performance and an unsatisfying grid for the network service performance,
and a satisfying grid for a user experience and an unsatisfying grid for the user experience.
20

[0081] In an exemplary aspect, the good performance grid signifies that how many
users are having a good experience with the network i.e. users are able use various
services such as but not limited to services like making calls, streaming videos, use
other services without any issues. Furthermore, good performance grid data may
5 suggest that a particular geospatial grid is providing seamless network coverage and
doesn’t need extra resources which may be utilized for other geospatial grid having network performance issue.
[0082] In an exemplary aspect, the bad performance grid signifies how many users are experiencing problems with the network. The bad performance data may
10 include such as but not limited to dropped calls, slow internet speeds, connection
failures etc. Furthermore, bad performance data suggest that a particular geospatial grid is providing bad network coverage and need more attention by providing extra resources for resolving the issues leading to its low performance. In an exemplary aspect, a high number of users reporting problems indicates that the network in that
15 particular geo spatial grid might need improvement.
[0083] In an exemplary aspect, the satisfying grid for a network service
performance signifies a geo spatial grid where network service performance is
satisfactory. Further, in the satisfying grid, users experience good call quality, fast
internet speeds, and the like. In addition, the satisfying grid for a user experience
20 signifies geo spatial grid areas where users report a satisfying experience with the
network.
[0084] In an exemplary aspect, the unsatisfying grid for the network service
performance signifies a geo spatial grid where network service performance is
unsatisfactory. The users in the unsatisfying grids may experience issues such as
25 but not limited to slow speeds, frequent dropped calls, or unreliable connections. In
addition, the unsatisfying grid for the user experience signifies areas where users report an unsatisfying experience i.e. users facing problems like low signal strength, frequent disconnections, and high latency contribute to a negative user experience etc.
21

[0085] The system [300] further comprises a cluster unit [308] connected at least to the classification unit [306]. The cluster unit [308] is configured to cluster, at the AP entity [304], one or more sites on the network map at a first trained model, wherein the one or more sites comprises one or more cell(s).
5 [0086] The cluster unit [308] clusters or group together one or more sites on the
network map at the first trained model at the AP entity [304]. The one or more sites include one or more cell(s). The clustering, by the cluster unit [308] helps in understanding which sites are related or should be grouped together for further analysis and processing.
10 [0087] The cluster unit [308] clusters one or more sites on the network map using
first trained model. In an exemplary aspect, trained model may include such as, but not limited to, a machine learning based model, an artificial intelligence-based model, a neural network-based model, a decision tree-based model and the like.
[0088] In an exemplary implementation, the first trained model is trained using at
15 least one of a set of unsupervised machine learning algorithm, a K-means algorithm,
and an artificial intelligence algorithm. Further, the first trained model includes set
of K-means algorithm which is also an unsupervised learning algorithm. There is
no labelled data for this clustering, unlike in supervised learning. K-Means
performs the division of objects into clusters that share similarities and are
20 dissimilar to the objects belonging to another cluster.
[0089] Furthermore, the first trained model includes set of unsupervised machine learning algorithm which refers to algorithm which is uses machine learning to analyse unlabelled datasets to discover patterns without human supervision,
[0090] In general, AI algorithms are instructions that enable machines to analyse
25 data, perform tasks, and make decisions. It's a subset of machine learning that tells
computers to learn and operate independently.
22

[0091] The system [300] further comprises an analysing unit [310] connected at least to the cluster unit [308]. The analysis unit [310] is configured to analyse, at the AP entity [304], the one or more sites based on the one or more geo-spatial grid.
[0092] The analysing unit [310] analyses the one or more sites based on the one or
5 more geo-spatial grid at the AP entity [304]. In an exemplary aspect, the analysis,
by the analysing unit [310], analyses how well one or more sites are performing in its designated area and if any specific adjustments are needed based on the geo spatial grid performance.
[0093] In an exemplary aspect, by analysing the one or more sites over a period of
10 time, the analysing unit [310] may identify historical trends, root causes, and
underlying systemic network issues enabling the network administrator to make informed decisions and implement improvements to enhance performance, productivity, and quality.
[0094] The system [300] further comprises the detection unit [312] connected at
15 least to the analysing unit [310]. The detection unit [312] is configured to detect, at
the AP entity [304], a type of the cell(s).
[0095] The detection unit [312] detects the type of the cell(s) which may include
detection of cells related to such as but not limited to 4G cells, 5G cells etc. In an
exemplary aspect, different types of cells might have different requirements for
20 optimization.
[0096] In addition, the type of cell is an overshooting cell, an overlapping cell and
an undershooting cell. The overshooting cell is a cell that extends its signal coverage
beyond its intended area which suggests that the cell’s signal strength is so strong
that it reaches areas outside its target zone, potentially interfering with neighbouring
25 cells. Further, the overshooting cell may cause interference with neighbouring cells,
leading to co-channel interference or a reduction in overall network efficiency.
23

[0097] In an exemplary aspect, the overlapping cell is a phenomenon which occurs when the coverage areas of adjacent cells overlap suggesting that there is some redundancy in the signal coverage where multiple cells cover the same geographic area.
5 [0098] Further, the undershooting cell is the cell that does not cover its intended
area adequately suggesting that signal strength is too weak to reach all parts of the targeted zone.
[0099] The system further comprises the determination unit [314] connected at
least to the detection unit [312]. The determination unit [314] is configured to
10 determine, at the AP entity [304], a set of remote electrical tilt (RET) data for each
of the type of the cell(s) at a second trained model and a third trained model.
[0100] The determination unit [314] determines the set of remote electrical tilt
(RET) for each of the type of the cell(s) at the second trained model and the third
trained model using the AP entity [304]. In an exemplary aspect, the determination
15 unit [314] is responsible for determining and computing the appropriate set of RET
settings for each type of cell.
[0101] In an exemplary aspect, RET makes it possible to adjust the electrical tilt of
an antenna remotely to optimize coverage and performance. RET mainly used for
mobile radio antennas, for example to optimise the alignment of the mobile radio
20 network at hotspots.
[0102] In an exemplary aspect, the second trained model is trained based on a neural network-based machine learning algorithm.
[0103] In addition, the second trained model is trained based on a neural network-
based machine learning algorithm which is deep learning, that uses interconnected
25 nodes or neurons in a layered structure that resembles the human brain.
[0104] In an exemplary aspect, the third trained model is trained based at least on a user dimensioning data, a path loss model data, a traffic modelling data, a
24

normalization of channel quality indicator (CQI), an E-tilt history, a reflective quality of service attribute (RQA) associated with the one or more users, a net velocity Performance Management (NV-PM) data associated with the one or more users, a local service request (LSR) associated with the one or more users.
5 [0105] In an exemplary aspect, the user dimension data refers to data of users
website visitors, such as their location, browser, device, and language.
[0106] In an exemplary aspect, the path loss model data is a model that describes the decrease in signal strength between a transmitter and a receiver in a wireless communication system.
10 [0107] Further, the CQI is a critical indicator used in wireless communication
systems, particularly in cellular networks like LTE (Long-Term Evolution) and 5G. Its primary purpose is to quantify the quality of the wireless channel at a specific point in time.
[0108] Further, the E-tilt history is historical data related to historical data related
15 to remote electrical tilt (RET).
[0109] In an exemplary aspect, a reflective quality of service attribute (RQA) that enables the user equipment (UE) to map Uplink user plane traffic to QoS flows without SMF [106]-provided QoS rules. This is achieved by creating UE-derived QoS rules on the received downlink (DL) traffic.
20 [0110] Also, net velocity Performance Management (NV-PM) data a data that
allows users to test, measure, compare and share their network performance -anytime, anywhere.
[0111] In an exemplary aspect, the Local Service Request (LSR) is a request used
by Competitive Local Exchange Carriers (CLECs) to request local exchange
25 services from an Incumbent Local Exchange Carrier (ILEC).
25

[0112] The system [300] comprises the validation unit [316] connected at least to the determination unit [314]. The validation unit [316] is configured to validate, at the AP entity [304], the determined set of RET data based on a set of performance parameter(s) and the type of cell(s).
5 [0113] The validation unit [316] validates the determined set of RET data based on
the set of performance parameter(s) and type of cell(s). In an exemplary aspect, the validation unit [316] ensures the proposed remote electrical tilt (RET) settings are effective by assessing them against set of performance parameters and the specific cell types at the AP entity [304].
10 [0114] Furthermore, the validation unit [316] validates the determined set of RET
data by ascertaining that they meet certain performance criteria such as signal strength and coverage quality etc. that are appropriate for resolving issues related to overshooting, overlapping, undershooting network cells.
[0115] In an exemplary aspect, the set of performance parameters are at least one
15 of a band wise harmonization, a final E- Tilt, an E-tilt implementation, and a load
balance.
[0116] In an exemplary aspect, the band wise harmonization refers to aligning the performance of different frequency bands used by the network ensuring that the RET adjustments improves overall network efficiency.
20 [0117] Also, the final E- tilt refers to an angle at which the antenna is tilted
electronically to enhance coverage. The final E-tilt setting needs to be optimal to ensure that the network provides adequate coverage and avoids interference.
[0118] In an exemplary aspect, E-tilt implementation refers to the actual application
of the final E-tilt settings to the network’s antennas. In an exemplary aspect, the E-
25 tilt implementation ensures that the theoretical final E-tilt adjustments translate into
actual improvements in network performance and coverage.
26

[0119] In an exemplary aspect, load balance ensures that network traffic is evenly distributed across multiple cells or sites. In an exemplary aspect, load balancing prevents any single site/cell from becoming overloaded while others are underutilized thereby reducing congestion.
5 [0120] The system [300] further comprises an implementation unit [318] connected
at least to the validation unit [316]. The implementation unit [318] is configured to implement, at the AP entity [304], the set of RET data for each type of the cell(s) through a Configuration Management System (CM) entity [320].
[0121] The implementation unit [318] implements the set of RET data for each of
10 the cell(s) through a CM entity [320] using the AP entity [304]. In an exemplary
aspect, the implementation unit [318] implement or execute the set of RET data that have been validated and finalized by the validating unit [316]. In an exemplary aspect, CM entity [320] includes data such as but not limited to threshold data, utilization data etc.
15 [0122] The system [300] further comprises a trigger unit [322] connected at least
to the implementation unit [318]. The trigger unit [322] is configured to trigger, at the AP entity [304], at least one action through the CM entity [320] for a pre-defined degradation thresholds for the implemented set of RET data.
[0123] The trigger unit [322] triggers at least one action through the CM entity
20 [320] for the pre-defined degradation thresholds for the implemented set of RET
data. In an exemplary aspect, the trigger unit [322] ensures network performance
remains within acceptable limits by monitoring the impact of RET adjustments and
comparing it to predefined degradation thresholds. If performance falls short, the
trigger unit [322] triggers corrective actions through the Configuration
25 Management System (CM) entity [320], to maintain efficient network operation. In
an example, the action may be managing network performance, monitoring the impact of RET adjustments, and the like.
27

[0124] In an exemplary aspect, pre-defined threshold refers to a specific
performance criterion set by the network administrator in advance to define
acceptable levels of network quality. The predefined thresholds may be based on
metrics such as signal strength, data throughput, or user experience, and are used to
5 monitor whether the implemented remote electrical tilt (RET) settings are effective.
If network performance falls below these predefined thresholds, the trigger unit [320] initiates corrective actions through the Configuration Management System (CM) entity [320], ensuring that network performance remains within acceptable limits thereby maintaining overall service quality.
10 [0125] The system [300] comprises the identification unit [324] connected at least
to the trigger unit [322]. The identification unit [324] is configured to identify the clustered one or more sites based on a predefined threshold limit for a performance data of the classified one or more geo-spatial grid.
[0126] The identification unit [324] identifies the clustered one or more sites based
15 on the predefined threshold limit for the performance data of the classified one or
more geo spatial grid. In an exemplary aspect, the identification unit [324] identifies
which clustered sites need attention by comparing their performance data against
predefined threshold limits. It uses performance metrics from classified geo-spatial
grids to identify sites whose performance falls below these set thresholds, thereby
20 highlighting areas that require further intervention or optimization to enhance
network quality.
[0127] The system [300] further comprise the performing unit [326] connected at
least to the identification unit [324]. The performing unit [326] is configured to
perform, at the AP entity [304], a cluster-wise pre-post analysis using the set of data
25 related to one or more user(s) from at least one of the PM entity and FM entity.
[0128] The performing unit [326] performs the cluster wise pre post analysis using the set of data related to one or more user(s) from at least one of the PM entity and FM entity to evaluate the impact of implemented changes on network performance. In an exemplary aspect, cluster-wise analysis signifies evaluating performance on
28

a per-cluster basis, which means it looks at groups of sites within specific geographic areas rather than individual sites alone.
[0129] In an exemplary aspect, pre-post analysis as the term suggests refers to
comparing network performance data before implementation and after the
5 implementation of changes to determine the effectiveness of those changes. The
pre-post analysis helps in understanding the impact of adjustments such as RET adjustments on user experience and network coverage.
[0130] The system [300] comprises the reverting unit [328] connected at least to
the performing unit [326]. The reverting unit [328] is configured to revert, at the
10 CM entity [320], the implemented set of RET data after detecting a degradation in
any cluster performance by the AP entity [304] using the set of data related to one or more user(s).
[0131] The reverting unit [328] reverts or rollback the implemented set of RET data
after detecting a degradation in any cluster performance by the AP entity [304]
15 using the set of data related to one or more user(s) at the CM entity [304] thereby
restoring the previous settings to address and mitigate the detected performance problems.
[0132] The system [300] comprises the repeater unit [330] connected at least to the
reverting unit [328]. The repeater unit [330] is configured to repeat, at the CM entity
20 [320], the implementation of a new set of validated RET data for maintaining an
identical performance or improving the performance of each cluster.
[0133] The repeater unit [330] repeats the implementation of the new set of
validated RET data for maintain the identical performance or improving the
performance of each cluster at the CM entity [320]. In an exemplary aspect, the
25 repeater unit [330] implements validated RET settings through the CM entity [320]
by maintaining consistent or improved performance across network clusters, leveraging data from previous performance evaluations to optimize network quality and user experience.
29

[0134] Referring to FIG. 4, an exemplary method flow diagram [400] for
performing a coverage improvement, in accordance with exemplary
implementations of the present disclosure is shown. In an implementation the
method [400] is performed by the system [300]. Further, in an implementation, the
5 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].
[0135] At step [404], the method [400] comprises receiving, by a receiving unit
[302], at an analysis platform (AP) entity [304], a set of data related to one or more
user(s), from at least one of a performance management (PM) entity and a fault
10 management (FM) entity.
[0136] The receiving unit [302] receives the set of data associated with one or more
user(s), at the (AP) platform from at least one of the PM entity and FM entity. In an
exemplary aspect, the PM entity may include data of one or more user(s) that
includes such as but not limited to a KPIs like call drop rate data, channel quality
15 data, downlink throughput data etc. Similarly, the FM entity may include data of
one or more user(s) that includes such that but not limited to resource data, alarm performance degradation data, service impacting data etc.
[0137] In an exemplary aspect, the set of data related to the one or more user(s)
comprises at least one of a number of user(s) having satisfying experience, a number
20 of users having unsatisfying user experience, a signal strength at a user device(s),
and a network congestion(s) at the user device(s).
[0138] In addition, the set of data comprises data related to user(s) satisfaction
experience i.e. user(s) having an overall positive experience with the service and
data related to user(s) unsatisfaction experience i.e. user(s) having negative
25 experience while using the service. Also, the set of data include signal strength data
at the user device which is the transmitter power output as received by the cell site antenna at a distance from the transmitting antenna of the user device. In an exemplary aspect, signal strength data may determine whether device is within network coverage area or not.
30

[0139] In an exemplary aspect, the network congestion(s) at the user device(s)
when a network is overrun with more data packet traffic than it can deal with. This
backup of data traffic may occur at the user device when too many communication
and data requests are made at the same time, over the network that doesn't have
5 enough network bandwidth to carry it causing issues like reduction in quality of
service (QOS) that causes packet loss, queueing delay, or the blocking of new connections at the user device(s). In an exemplary aspect, network congestion may relate to latency, throughput, and bandwidth issues.
[0140] At step [406], the method [400] comprises classifying, by a classification
10 unit [306], at the AP entity [304], one or more geo-spatial grid on a network map,
in one or more region(s) based on the set of data related to the one or more user(s).
[0141] The classification unit [306] classifies one or more geo-spatial grid on the
network map, in one or more regions (s) i.e. geographical regions based on the set
of data related to the one or more user(s) at the AP entity. In an exemplary aspect,
15 the classification of geo-spatial grid on the network map is done by categorizing
different areas having good or bad network coverage on a network map which helps in identifying regions where network coverage might need improvement.
[0142] In an exemplary aspect, the one or more geo-spatial grids are at least one of
a good performance grid, a poor performance grid, a satisfying grid for a network
20 service performance and an unsatisfying grid for the network service performance,
and a satisfying grid for a user experience and an unsatisfying grid for the user experience.
[0143] In an exemplary aspect, the good performance grid signifies that how many
users are having a good experience with the network i.e. users are able use various
25 services such as but not limited to services like making calls, streaming videos, use
other services without any issues. Furthermore, good performance grid data may suggest that a particular geospatial grid is providing seamless network coverage and doesn’t need extra resources which may be utilized for other geospatial grid having network performance issue.
31

[0144] In an exemplary aspect, the bad performance grid signifies how many users
are experiencing problems with the network. The bad performance data may
include such as but not limited to dropped calls, slow internet speeds, connection
failures etc. Furthermore, bad performance data suggest that a particular geospatial
5 grid is providing bad network coverage and need more attention by providing extra
resources for resolving the issues leading to its low performance. In an exemplary aspect, a high number of users reporting problems indicates that the network in that particular geo spatial grid might need improvement.
[0145] In an exemplary aspect, a satisfying grid for a network service performance
10 signifies a geo spatial grid where network service performance is satisfactory. It
suggests that users in these areas are experiencing acceptable levels of service, such as good call quality, fast internet speeds etc.
[0146] In an exemplary aspect, the unsatisfying grid for a network service
performance signifies a geo spatial grid where network service performance is
15 seems unsatisfactory. The users in these grids might experience issues such as but
not limited to slow speeds, frequent dropped calls, or unreliable connections.
[0147] At step [408], the method [400] comprises clustering, by a cluster unit [308], at the AP entity [304], one or more sites on the network map at a first trained model, wherein the one or more sites comprises one or more cell(s).
20 [0148] The cluster unit [308] clusters or group together one or more sites on the
network map at the first trained model at the AP entity [304]. The one or more sites include one or more cell(s). The clustering, by the cluster unit [308] helps in understanding which sites are related or should be grouped together for further analysis and processing.
25 [0149] The cluster unit [308] clusters one or more sites on the network map using
first trained model. In an exemplary aspect, trained model may include such as, but not limited to, a machine learning based model, an artificial intelligence-based model, a neural network-based model, a decision tree-based model and the like.
32

[0150] In an exemplary aspect, the first trained model is trained using at least one of a set of unsupervised machine learning algorithm, a K-means algorithm, and an artificial intelligence algorithm.
[0151] In an exemplary aspect, the first trained model includes set of unsupervised
5 machine learning algorithm which refers to algorithm which is uses machine
learning to analyse unlabelled datasets to discover patterns without human supervision,
[0152] In an exemplary aspect, the first trained model includes set of K-means
algorithm which is also an unsupervised learning algorithm. There is no labelled
10 data for this clustering, unlike in supervised learning. K-Means performs the
division of objects into clusters that share similarities and are dissimilar to the objects belonging to another cluster.
[0153] In an exemplary aspect, AI algorithms are instructions that enable machines
to analyse data, perform tasks, and make decisions. It's a subset of machine learning
15 that tells computers to learn and operate independently.
[0154] At step [410], the method [400] comprises analysing, by an analysing unit [310], at the AP entity [304], the one or more sites based on the one or more geo-spatial grid.
[0155] The analysing unit [310] analyses the one or more sites based on the one or
20 more geo-spatial grid at the AP entity [304]. In an exemplary aspect, the analysis,
by the analysing unit [310], analyses how well one or more sites are performing in its designated area and if any specific adjustments are needed based on the geo spatial grid performance.
[0156] In an exemplary aspect, by analysing the one or more sites over a period of
25 time, the analysing unit may identify historical trends, root causes, and underlying
systemic network issues enabling the network administrator to make informed
33

decisions and implement improvements to enhance performance, productivity, and quality.
[0157] At step [412], the method [400] comprises detecting, by a detection unit [312], at the AP entity [304], a type of the cell(s).
5 [0158] The detection unit [312] detects the type of the cell(s) which may include
detection of cells related to such as but not limited to 4G cells, 5G cells etc. In an exemplary aspect, different types of cells might have different requirements for optimization.
[0159] In an exemplary aspect, the type of cell is an overshooting cell, an
10 overlapping cell and an undershooting cell.
[0160] In an exemplary aspect, the overshooting cell is a cell that extends its signal coverage beyond its intended area. In an exemplary aspect, an overshooting cell may cause interference with neighbouring cells, leading to co-channel interference or a reduction in overall network efficiency.
15 [0161] In an exemplary aspect, the overlapping cell is a phenomenon which occurs
when the coverage areas of neighbouring cells overlap suggesting that there is some redundancy in the signal coverage where multiple cells cover the same geographic area.
[0162] In an exemplary aspect, the undershooting cell is the cell that does not cover
20 its intended area suggesting that signal strength is too weak to reach all parts of the
targeted zone.
[0163] At step [414], the method [400] comprises determining, by a determination unit [314], at the AP entity [304], a set of remote electrical tilt (RET) data for each of the type of the cell(s) via a second trained model and a third trained model.
25 [0164] The determination unit [314] determines the set of remote electrical tilt
(RET) for each of the type of the cell(s) at the second trained model and the third
34

trained model using the AP entity [304]. In an exemplary aspect, the determination unit [314] is responsible for determining and computing the appropriate set of RET settings for each type of cell.
[0165] In an exemplary aspect, RET that makes it possible to adjust the electrical
5 tilt of an antenna remotely to optimize coverage and performance. RET mainly used
for mobile radio antennas, for example to optimise the alignment of the mobile radio network at hotspots.
[0166] In an exemplary aspect, the second trained model is trained based on a neural network-based machine learning algorithm.
10 [0167] In an exemplary aspect, the second trained model is trained based on a
neural network-based machine learning algorithm which is deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.
[0168] In an exemplary aspect, the third trained model is trained based at least on
15 a user dimensioning data, a path loss model data, a traffic modelling data, a
normalization of channel quality indicator (CQI), an E-tilt history, a reflective quality of service attribute (RQA) associated with the one or more users, a net velocity Performance Management (NV-PM) data associated with the one or more users, a local service request (LSR) associated with the one or more users.
20 [0169] In an exemplary aspect, the user dimension data refers to data of users or
website visitors, such as their location, browser, device, and language.
[0170] In an exemplary aspect, the path loss model data is a model that describes the decrease in signal strength between a transmitter and a receiver in a wireless communication system.
25 [0171] In an exemplary aspect, the CQI is a critical indicator used in wireless
communication systems, particularly in cellular networks like LTE (Long-Term
35

Evolution) and 5G. Its primary purpose is to quantify the quality of the wireless channel at a specific point in time.
[0172] In an exemplary aspect, the E-tilt history is historical data related to remote
electrical tilt (RET). Further, a reflective quality of service attribute (RQA) that
5 enables the user equipment (UE) to map Uplink user plane traffic to quality of
service (QoS) flows without SMF [106]-provided QoS rules. This is achieved by creating UE-derived QoS rules on the received downlink (DL) traffic.
[0173] In an exemplary aspect, net velocity Performance Management (NV-PM)
data refers to a data that allows users to test, measure, compare and share their
10 network performance.
[0174] In an exemplary aspect, the Local Service Request (LSR) is a request used by Competitive Local Exchange Carriers (CLECs) to request local exchange services from an Incumbent Local Exchange Carrier (ILEC).
[0175] At step [416], the method [400] comprises validating, by a validation unit
15 [316], at the AP entity [304], the determined set of RET data based on a set of
performance parameter(s) and the type of cell(s). The validation unit [316] validates
the determined set of RET data based on the set of performance parameter(s) and
type of cell(s). In an exemplary aspect, the validation unit [316] ensures the
proposed remote electrical tilt (RET) settings are effective by assessing them
20 against set of performance parameters and the specific cell types at the AP entity
[304].
[0176] Furthermore, the validation unit [316] validates the determined set of RET
data by ascertaining that they meet certain performance criteria such as but not
limited to signal strength and coverage quality etc. that are appropriate for resolving
25 issues related to overshooting, overlapping, undershooting network cells.
36

[0177] In an exemplary aspect, the set of performance parameters are at least one of a band wise harmonization, a final E- Tilt, an E-tilt implementation, and a load balance.
[0178] In an exemplary aspect, the band wise harmonization refers to aligning the
5 performance of different frequency bands used by the network ensuring that the
RET adjustments improve overall network efficiency.
[0179] In an exemplary aspect, the final E- tilt refers to an angle at which the
antenna is tilted remotely to enhance coverage. The final E-tilt setting needs to be
optimal to ensure that the network provides adequate coverage and avoids
10 interference.
[0180] In an exemplary aspect, E-tilt implementation refers to the actual application of the final E-tilt settings to the network’s antennas. In an exemplary aspect, the E-tilt implementation ensures that the theoretical final E-tilt adjustments translate into actual improvements in network performance and coverage.
15 [0181] In an exemplary aspect, load balance ensures that network traffic is evenly
distributed across multiple cells or sites. In an exemplary aspect, load balancing prevents any single site/cell from becoming overloaded while others are underutilized thereby reducing congestion.
[0182] At step [418], the method [400] comprises implementing, by an
20 implementation unit [318], at the AP entity [304], the set of RET data for each type
of the cell(s) through a Configuration Management System (CM) entity [320]. The
implementation unit [318] implements the set of RET data for each of the cell(s)
through a CM entity [320] using the AP entity [304]. In an exemplary aspect, the
implementation unit [318] implement or execute the set of RET data that have been
25 validated and finalized by the validating unit [316]. In an exemplary aspect, CM
entity [320] includes data such as but not limited to threshold data, utilization data etc.
37

[0183] At step [420], the method [400] comprises triggering, by a trigger unit [322],
at the AP entity [304], at least one action through the CM entity [320] for a pre¬
defined degradation thresholds for the implemented set of RET data. The trigger
unit [322] triggers at least one action through the CM entity [320] for the pre-
5 defined degradation thresholds for the implemented set of RET data. In an
exemplary aspect, the trigger unit [322] ensures network performance remains
within acceptable limits by monitoring the impact of RET adjustments and
comparing it to predefined degradation thresholds. If performance falls short, the
trigger unit [322] triggers corrective actions through the Configuration
10 Management System (CM) entity [320], to maintain optimal network operation. In
an example, the action may be managing network performance, monitoring the
impact of RET adjustments, and the like.
[0184] In an exemplary aspect, pre-defined threshold refers to a specific performance criterion set by the network administrator in advance to define
15 acceptable levels of network quality. The predefined thresholds may be based on
metrics such as signal strength, data throughput, or user experience, and are used to monitor whether the implemented remote electrical tilt (RET) settings are effective. If network performance falls below these predefined thresholds, the trigger unit [320] initiates corrective steps through the Configuration Management System
20 (CM) entity [320], ensuring that network performance remains within acceptable
limits thereby maintaining overall service quality.
[0185] In an exemplary aspect, the analysing the clustered one or more sites
comprises: identifying, by an identification unit [324], the clustered one or more
sites based on a predefined threshold limit for a performance data of the classified
25 one or more geo-spatial grid.
[0186] The identification unit [324] identifies the clustered one or more sites based on the predefined threshold limit for the performance data of the classified one or more geo spatial grid. In an exemplary aspect, the identification unit [324] identifies which clustered sites need attention by comparing their performance data against
38

predefined threshold limits. It uses performance metrics from classified geo-spatial grids to identify sites whose performance falls below these set thresholds, thereby highlighting areas that require further intervention or optimization to enhance network quality.
5 [0187] The method [400] comprises performing, by the performing unit [326] at
the AP entity [304] a cluster-wise pre-post analysis using the set of data related to one or more user(s) from at least one of the PM entity and FM entity. The performing unit [326] performs the cluster wise pre post analysis using the set of data related to one or more user(s) from at least one of the PM entity and FM entity
10 to evaluate the impact of implemented changes on network performance. In an
exemplary aspect, cluster-wise analysis signifies evaluating performance at groups of sites within specific geographic areas rather than individual sites alone. Further, pre-post analysis refers to comparing network performance data before implementation and after the implementation of changes to determine effectiveness
15 of the said changes.
[0188] The triggering the at least one action by the CM entity [320] comprises reverting, by a reverting unit [328] at the CM entity [320], the implemented set of RET data after detecting a degradation in any cluster performance by the AP entity [304] using the set of data related to one or more user(s).
20 [0189] The reverting unit [328] reverts or rollback the implemented set of RET data
after detecting a degradation in any cluster performance by the AP entity [304] using the set of data related to one or more user(s) at the CM entity [304] thereby restoring the previous settings to address and mitigate the detected performance problems.
25 [0190] In an exemplary aspect, the triggering the at least one action by the CM
entity [320] further comprises repeating, by a repeater unit [330] at the CM entity [320], the implementation of a new set of validated RET data for maintaining an identical performance or improving the performance of each cluster.
39

[0191] The repeater unit [330] repeats the implementation of the new set of
validated RET data for maintain the identical performance or improving the
performance of each cluster at the CM entity [320]. In an exemplary aspect, the
repeater unit [330] implements validated RET settings through the CM entity [320]
5 to ensure consistent or improved performance across network clusters, leveraging
data from previous performance evaluations to optimize network quality and user experience.
[0192] At step [422], the method [400] terminates.
[0193] Referring to FIG. 5, an exemplary system architecture diagram [500] for
10 performing a coverage improvement, in accordance with exemplary
implementations of the present disclosure is shown. In an implementation the method [500] is performed by the system [300].
[0194] The system architecture [500] comprises AP entity [304] configured to receive the set of data associated with one or more user(s), at the (AP) platform
15 entity [304] from at least one of the PM system [502] (also referred to herein as PM
entity) and FM system [504] (also referred to herein as FM entity). In an exemplary aspect, the PM entity may include data of one or more user(s) that includes such as but not limited to KPIs like call drop rate data, channel quality data, downlink throughput data etc. In an exemplary aspect, PM system [502] interacts with AP
20 entity [304] through PM interface. Similarly, the FM entity may include data of one
or more user(s) that includes such that but not limited to resource data, alarm performance degradation data, service impacting data etc. Similarly, FM system [504] interacts with AP entity [304] through FM interface.
[0195] The AP entity [304] further receives data from the MDB system [506] which
25 may store predefined threshold data. In an exemplary aspect, MDB server may also
include historical data or past performance data. Also, MDB system [506] interacts with AP entity [304] through MDB interface.
40

[0196] The AP entity [304] further receives data from CM system [320]. In an
exemplary aspect, if performance falls short, the Configuration Management
System (CM) entity [320] triggers or initiates corrective measures to maintain
efficient network operation. Furthermore, CM system [320] interacts with AP entity
5 [304] through CM interface.
[0197] Referring to FIG. 6, an exemplary process [600] for performing a coverage improvement, in accordance with exemplary implementations of the present disclosure is shown.
[0198] At step [602], the process [600] comprises identifying the user location
10 experiencing poor network coverage. In an exemplary aspect, the receiving unit
[302] receives the set of data associated with one or more user(s), at the (AP) platform entity [304] from at least one of the PM entity and FM entity.
[0199] At step [604], the process [600] further comprises classifying grids based on user experience. In an exemplary aspect, the classification unit [306] classifies
15 one or more geo-spatial grid on the network map, in one or more regions (s) i.e.
geographical regions based on the set of data related to the one or more user(s) at the AP entity. In an exemplary aspect, the classification of geo-spatial grid on the network map is done by categorizing different areas having good or bad network coverage on a network map which helps in identifying regions where network
20 coverage might need improvement.
[0200] At step [606], the process [600] comprises clustering of sights using a first
trained model such as but not limited to K - means algorithm model. The cluster
unit [308] clusters one or more sites on the network map using first trained model.
In an exemplary aspect, trained model may also include such as, but not limited to,
25 a machine learning based model, an artificial intelligence-based model, a neural
network-based model, a decision tree-based model and the like.
[0201] At step [608], the process [600] comprises checking intersections of grids with clusters based on number of clusters present.
41

[0202] At step [610], the process [600] comprises identification of clusters having greater than 50 % unsatisfied grid. In an exemplary aspect, after identifying clusters having greater than 50 % unsatisfied grid, the data related to it is sent further for processing using machine learning algorithm.
5 [0203] At step [612], the process [600] comprises implementing tilt
recommendations based on ML algorithms.
[0204] At step [614], the process [600] comprises imputing the ML algorithm.
[0205] At step [616], the process [600] comprises calculating traffic modelling value.
10 [0206] At step [618], normalization of CQI occurs. In an exemplary aspect, the CQI
is a critical indicator used in wireless communication systems, particularly in cellular networks like LTE (Long-Term Evolution) and 5G. Its primary purpose is to quantify the quality of the wireless channel at a specific point in time.
[0207] At step [620], checking E-tilt history which refers to historical data related
15 to remote electrical tilt (RET). In an exemplary aspect, E tilt history may of range
between 6 months to 1 year.
[0208] At step [622], user data such as but not limited to RQA, NV-PM, LSR is checked and calculated.
[0209] In an exemplary aspect, a reflective quality of service attribute (RQA) that
20 enables the user equipment (UE) to map Uplink user plane traffic to quality of
service (QoS) flows without SMF [106]-provided QoS rules. This is achieved by creating UE-derived QoS rules on the received downlink (DL) traffic.
[0210] In an exemplary aspect, net velocity Performance Management (NV-PM)
data is a data that allows users to test, measure, compare and share their network
25 performance- anytime, anywhere.
42

[0211] In an exemplary aspect, the Local Service Request (LSR) is a request used by Competitive Local Exchange Carriers (CLECs) to request local exchange services from an Incumbent Local Exchange Carrier (ILEC).
[0212] At step [624], process [600] comprises user dimensioning through path loss
5 propagation model.
[0213] At step [626], process [600] comprises detecting the type of cell(s) which
may include overshooting cell, an overlapping cell and an undershooting cell. The
detection unit detects the type of the cell(s) which may include detection of cells
related to such as but not limited to 4G cells, 5G cells etc. In an exemplary aspect,
10 different types of cells might have different requirements for optimization.
[0214] In an exemplary aspect, the overshooting cell is a cell that extends its signal coverage beyond its intended area. In an exemplary aspect, an overshooting cell may cause interference with neighbouring cells, leading to co-channel interference or a reduction in overall network efficiency.
15 [0215] In an exemplary aspect, the overlapping cell is a phenomenon which occurs
when the coverage areas of neighbouring cells overlap suggesting that there is some redundancy in the signal coverage where multiple cells cover the same geographic area. Further, the undershooting cell is the cell that does not cover its intended area suggesting that signal strength is too weak to reach all parts of the targeted zone.
20 [0216] At step [628], the process [600] comprises aggregating, at the machine
learning algorithm (neural network) module, data related to identified clusters having greater than 50 % unsatisfied grid and data related to detected cell(s) type which may include overshooting cell, an overlapping cell and an undershooting cell.
[0217] At step [630], the process [600] further comprises validation of band wise
25 E – tilt for harmonization. In an exemplary aspect, the band wise harmonization
refers to aligning the performance of different frequency bands used by the network
ensuring that the RET adjustments improve overall network efficiency and
43

coverage area. In an exemplary aspect, the validation unit [316] validates the
determined set of RET data based on the set of performance parameter(s) and type
of cell(s). In an exemplary aspect, the validation unit [316] ensures the proposed
remote electrical tilt (RET) settings are effective by assessing them against set of
5 performance parameters and the specific cell types at the AP entity [304].
Furthermore, the validation unit [316] validates the determined set of RET data by ascertaining that they meet certain performance criteria such as but not limited to signal strength and coverage quality etc.
[0218] At step [632], the process [600] further comprises validation of band wise
10 E – tilt for load balancing. In an exemplary aspect, load balance ensures that
network traffic is evenly distributed across multiple cells or sites. In an exemplary aspect, load balancing prevents any single site/cell from becoming overloaded while others are underutilized thereby reducing congestion.
[0219] At step 634, the process [600] further comprises final E – tilt
15 recommendations. In an exemplary aspect, the final E- tilt refers to an angle at
which the antenna is tilted remotely to enhance coverage. The final E-tilt setting needs to be optimal to ensure that the network provides adequate coverage and avoids interference
[0220] At step [636], the process [600] further comprises E – tilt implementation.
20 In an exemplary aspect, E-tilt implementation refers to the actual application of the
final E-tilt settings to the network’s antennas. It involves configuring the antennas according to the determined E-Tilt values. In an exemplary aspect, the E-tilt implementation ensures that the theoretical final E-tilt adjustments reflect into actual improvements in network performance and coverage.
25 [0221] At step [638], the process [600] further comprises cluster wise pre-post
analysis. In an exemplary aspect, the performing unit [326] performs the cluster wise pre post analysis using the set of data related to one or more user(s) from at least one of the PM entity and FM entity to evaluate the impact of implemented changes on network performance. In an exemplary aspect, cluster-wise analysis
44

signifies evaluating performance at groups of sites within specific geographic areas rather than individual sites.
[0222] In an exemplary aspect, pre-post analysis refers to comparing network
performance data before implementation and after the implementation of changes
5 to determine the effectiveness of the said changes.
[0223] At step [640], the process [600] further comprises a closed looping process using pre-post analysis and model correction based on new data.
[0224] At step [642], the process [600] further comprises checking if any cluster performance is degraded or not.
10 [0225] At step [644], the process [600] further comprises reverting the E Tilt
corresponding to cells of degraded cluster. In an exemplary aspect, the reverting unit [328] reverts or rollback the implemented set of RET data after detecting a degradation in any cluster performance by the AP entity [304] using the set of data related to one or more user(s) at the CM entity [304] thereby restoring the previous
15 settings to address and mitigate the detected performance problems.
[0226] In an exemplary aspect, the machine learning algorithm (neural network)
module includes a traffic modelling data, a normalization of channel quality
indicator (CQI), an E-tilt history, a reflective quality of service attribute (RQA)
associated with the one or more users, a net velocity Performance Management
20 (NV-PM) data associated with the one or more users, a local service request (LSR)
associated with the one or more users.
[0227] In an exemplary aspect, the process [600] further comprises selecting one or more regions i.e. geographical region (i.e. city JC, Band).
[0228] In an exemplary aspect, the process [600] further comprises a receiving, at
25 front end parser, geographical data related to the one or more regions.
45

[0229] In an exemplary aspect, the process [600] further comprises ascertaining whether 95 % of the cells are available which are greater than 95 % RAN availability for last 3 days average.
[0230] In an exemplary aspect, if 95 % of the cells available are greater than 95 %
5 RAN availability for last 3 days average then the process [600] comprises which
cells which are not meeting 95 % RAN criteria should be excluded from analysis along with its respective clusters which will be formed in later stage.
[0231] In an exemplary aspect, the process [600] further comprises sorting lowest
to highest handover attempts and assign rank starting from 1 (same value cell and
10 same rank) to max number for each source cell and respective neighbors.
[0232] In an exemplary aspect, if 95 % of the cells available are not greater than 95% RAN availability for last 3 days average then the process [700] the includes rolling back based on based on the last 3 days average threshold data if meeting it is meeting threshold criteria.
15 [0233] In an exemplary aspect, the process further [600] further comprises
capturing attempts in log reports and again initiating process defined in step [608].
[0234] In an exemplary aspect, the process [600] the process [600] further comprises finding max handover rank from handover matrix.
[0235] In an exemplary aspect, the process [600] the process [600] further
20 comprises finding max distance rank from inter site distance matrix.
[0236] In an exemplary aspect, the process [600] the process [600] further
comprises training for each grid to identify the users in the grid (LSR data). In an
exemplary aspect, the Local Service Request (LSR) is a request used by
Competitive Local Exchange Carriers (CLECs) to request local exchange services
25 from an Incumbent Local Exchange Carrier (ILEC).
46

[0237] In an exemplary aspect, the process [600] the process [600] further
comprises calculating the actual reference signal received power (RSRP) by
averaging of all the RSRPs of the user’s environment combination in that grid and
calculating actual SINR by averaging of all the signal to noise ratios (SINRs) of the
5 user’s environment combination in that grid. As used herein, signal-to-noise ratio
(SNR) is a measure used in telecom that compares the level of a desired signal to the level of background noise.
[0238] In an exemplary aspect, the process [600] further comprises training for
each grid with the RSRP of the cell providing coverage to maximum number of
10 users.
[0239] In an exemplary aspect, the process [600] further comprises tagging the grid with whether it is hilly or not based on the trained data from above process steps.
[0240] In an exemplary aspect, if the grid elevation is greater than the average elevation of all the grids for example +25 to + 30 meters, then the elevation is hilly.
15 [0241] In an exemplary aspect, for each of the grid environment tagging and each
cell the process [600] comprises calculating distance, diffraction, loss, transmission power, direction gain, effective user height, effective tower height, effective grid elevation.
[0242] The process [600] comprises calculating distance between grid center
20 identity under consideration environment using the below mentioned parameters.
• Environment Tagging – indoor, outdoor, Mobile (one hot encoding).
• Clutter category – Small Unit, Medium Unit, Dense Unit (one Hot encoding).
• Diffraction Loss – Knife – Deygout Method. As used herein, Deygout
25 Method is a method to calculate the diffraction loss of the major or dominant
obstacles.
• Transmission Power – master database (DB File).
• Direction Gain – Antenna Files.
47

• Effective user height –is calculated based on specialized resource functions (SRFS) of effective tower height.
[0243] In an exemplary aspect, the process [600] further comprises training the
neural network on the above parameters + one encoded environment, clutter
5 allegory and hilly (0 or 1).
[0244] In an exemplary aspect, the process [600] further comprises training for each grid to identify the users in the grid (LSR data).
[0245] In an exemplary aspect, for each of the grid, environment tagging and each
cell the process [600] further comprises calculating distance, diffraction loss,
10 transmission power, direction gain, effective user height, effective tower height,
effective grid elevation, clutter category.
[0246] In an exemplary aspect, the process [600] further comprises predicting RSRP for each of the cells using the model trained before.
[0247] In an exemplary aspect, the process [600] further comprises tagging the grid
15 with cell giving max RSRP for unique combination of grid and environment (Refer
to this as source cell, and the remaining as neighbors. It will be used in calculating SINR).
[0248] Referring to FIG. 7, an exemplary process flow diagram [700] for
performing a coverage improvement using remote electrical tilt (RET), in
20 accordance with exemplary implementations of the present disclosure is shown.
Also, as shown in FIG. 7, the process [700] starts at step [702].
[0249] At step [704], the process [700] comprises dumping required RET tilt change in assemble to order’s (ATO’s) oracle table as per standard format.
[0250] At step [706], the process [700] further comprises approving and executing,
25 at ATO module, of dumped required RET tilt change.
48

[0251] At step [708], the process [700] further comprises reading feedback on implementation.
[0252] At step [710], the process [700] further comprises checking for Src whether the implementation is successful or not.
5 [0253] At step [712], if the implementation is succeeded it is denoted by the letter
“S”. If the implementation does not fail, the process [700] moves to step [716].
[0254] At step [714], the process [700] further comprises checking for Nbrs
whether the implementation is partially or fully successful. In an exemplary aspect,
for partial implementation, out of 3 Nbrs, if two or one Nbrs’s tilt got changed then
10 process doesn’t reattempt from the flow. If the implementation is not successful,
the process [700] moves to the step [716].
[0255] At step [716], failed implementation of both Src and Nbrs are denoted as “X” and combined to further proceed to the next step.
[0256] At step [718], the process [700] comprises retrying if the implementation is
15 not successful.
[0257] At step [720], if the retry counter is not greater than 1, then the process [700] proceeds again to the step [704]. In an exemplary aspect, if the retry counter is not greater than 1, 2nd retry reattempt will not be allowed hence exits would happen proceeding to the next process [700] step.
20 [0258] At step [722], maximum retried attempts are done for tilt changes.
[0259] At step [724], the process [700] involves manual intervention and capturing. In an exemplary aspect, the process may involve directly exiting from the tilt optimization and follow steps as per master flow.
[0260] At step [726], if the implementation as discussed in the step [714] is fully
25 successful then passing it on to the next step.
49

[0261] At step [728], the process [700] end.
[0262] The present disclosure further discloses a non-transitory computer readable
storage medium storing instructions for performing a coverage improvement, the
instructions include executable code which, when executed by one or more units of
5 a system, causes: a receiving unit [302] to receive, at an analysis platform (AP)
entity [304], a set of data related to one or more user(s), from at least one of a performance management (PM) entity and a fault management (FM) entity. The executable code when executed further causes a classification unit [306] to classify, at the AP entity [304], one or more geo-spatial grid on a network map, in one or
10 more region(s) based on the set of data related to the one or more user(s). The
executable code when executed further causes a cluster unit [308] to cluster, at the AP entity [304], one or more sites on the network map at a first trained model, wherein the one or more sites comprises one or more cell(s). the executable code when executed further causes an analysing unit [310] to analyse, at the AP entity
15 [304], the one or more sites based on the one or more geo-spatial grid. The
executable code when executed further causes a detection unit [312] to detect, at the AP entity [304], a type of the cell(s). The executable code when executed further causes a determination unit [314] to determine, at the AP entity [304], a set of remote electrical tilt (RET) data for each of the type of the cell(s) at a second trained
20 model and a third trained model. The executable code when executed further causes
a validation unit [316] to validate, at the AP entity [304], the determined set of RET data based on a set of performance parameter(s) and the type of cell(s). The executable code when executed further causes an implementation unit [318] to implement, at the AP entity [304], the set of RET data for each type of the cell(s)
25 through a Configuration Management System (CM) entity [320]. The executable
code when executed further causes a trigger unit [322] to trigger, at the AP entity [304], at least one action through the CM entity [320] for a pre-defined degradation thresholds for the implemented set of RET data.
[0263] As is evident from the above, the present disclosure provides a technically
30 advanced solution for performing a coverage improvement. The key advantages of
50

the present invention are early detection by actively monitoring and tracking
problems, we can identify issues in their early stages before they escalate into more
significant challenges. This allows for timely intervention and prevents the problem
from becoming more complex or causing further damage. Furthermore, proactive
5 problem-solving: By recognizing recurring problems or patterns, network
administrator develops preventive measures, improve processes, and implement corrective actions to avoid future occurrences. Furthermore, the present solution provides efficient resource allocation by tracking and categorizing problems, network administrator gains insights into the frequency and impact of different
10 types of issues. This information enables network administrator to allocate
resources more efficiently, focusing on high-priority problems that have the most significant impact on network administrator goals or objectives. Furthermore, the continuous improvement by identification tracking provides valuable data and metrics for analysis. By reviewing and analysing problem data over time, network
15 administrator may identify trends, root causes, and underlying systemic issues. This
knowledge enables network administrator to make informed decisions and implement improvements to enhance performance, productivity, and quality.
[0264] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various the components/units can be
20 implemented interchangeably. While specific embodiments may disclose a
particular functionality of these units for clarity, it is recognized that various 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
25 arrangements and substitutions of units, provided they achieve the intended
functionality described herein, are encompassed within the scope of the present disclosure
[0265] While considerable emphasis has been placed herein on the disclosed
implementations, it will be appreciated that many implementations can be made and
30 that many changes can be made to the implementations without departing from the
51

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 and non-limiting.

We Claim:
1. A method for performing a coverage improvement, the method comprising:
- receiving, by a receiving unit [302], at an analysis platform (AP) entity [304], a set of data related to one or more user(s), from at least one of a performance management (PM) entity and a fault management (FM) entity;
- classifying, by a classification unit [306], at the AP entity [304], one or more geo-spatial grid on a network map, in one or more region(s) based on the set of data related to the one or more user(s);
- clustering, by a cluster unit [308], at the AP entity [304], one or more sites on the network map at a first trained model, wherein the one or more sites comprises one or more cell(s);
- analysing, by an analysing unit [310], at the AP entity [304], the one or more sites based on the one or more geo-spatial grid;
- detecting, by a detection unit [312], at the AP entity [304], a type of the cell(s);
- determining, by a determination unit [314], at the AP entity [304], a set of remote electrical tilt (RET) data for each of the type of the cell(s) via a second trained model and a third trained model;
- validating, by a validation unit [316], at the AP entity [304], the determined set of RET data based on a set of performance parameter(s) and the type of cell(s);
- implementing, by an implementation unit [318], at the AP entity [304], the set of RET data for each type of the cell(s) through a Configuration Management System (CM) entity [320]; and
- triggering, by a trigger unit [322], at the AP entity [304], at least one action through the CM entity [320] for a pre-defined degradation thresholds for the implemented set of RET data.

2. The method as claimed in claim 1, wherein the set of data related to the one or more user(s) comprises at least one of a number of user(s) having satisfying experience, a number of users having unsatisfying user experience, a signal strength at a user device(s), and a network congestion(s) at the user device(s).
3. The method as claimed in claim 1, wherein the one or more geo-spatial grids are at least one of a good performance grid, a poor performance grid, a satisfying grid for a network

service performance and an unsatisfying grid for the network service performance, and a satisfying grid for a user experience and an unsatisfying grid for the user experience.
4. The method as claimed in claim 1, wherein the first trained model is trained using at least one of a set of unsupervised machine learning algorithm, a K-means algorithm, and an artificial intelligence algorithm, the second trained model is trained based on a neural network based machine learning algorithm and, the third trained model is trained based at least on a user dimensioning data, a path loss model data, a traffic modelling data, a normalization of channel quality indicator (CQI), an E-tilt history, a reflective quality of service attribute (RQA) associated with the one or more users, a net velocity Performance Management (NV-PM) data associated with the one or more users, a local service request (LSR) associated with the one or more users.
5. The method as claimed in claim 1, wherein the analysing the clustered one or more sites comprises: identifying, by an identification unit [324], the clustered one or more sites based on a predefined threshold limit for a performance data of the classified one or more geo-spatial grid.
6. The method as claimed in claim 1, wherein the type of cell is an overshooting cell, an overlapping cell and an undershooting cell.
7. The method as claimed in claim 1, wherein the set of performance parameters are at least one of a band wise harmonization, a final E- Tilt, an E-tilt implementation, and a load balance.
8. The method as claimed in claim 1, wherein the method further comprises performing, by a performing unit [326] at the AP entity [304] a cluster-wise pre-post analysis using the set of data related to one or more user(s) from at least one of the PM entity and FM entity.
9. The method as claimed in claim 1, wherein the triggering the at least one action by the CM entity [320] comprises:

- reverting, by a reverting unit [328] at the CM entity [320], the implemented set of RET data after detecting a degradation in any cluster performance by the AP entity [304] using the set of data related to one or more user(s); and
- repeating, by a repeater unit [330] at the CM entity [320], the implementation of a new set of validated RET data for maintaining an identical performance or improving the performance of each cluster.
10. A system for performing a coverage improvement, the system comprises:
- a receiving unit [302] configured to receive, at an analysis platform (AP) entity [304], a set of data related to one or more user(s), from at least one of a performance management (PM) entity and a fault management (FM) entity;
- a classification unit [306] connected at least to the receiving unit [302], the classification unit [306] is configured to classify, at the AP entity [304], one or more geo-spatial grid on a network map, in one or more region(s) based on the set of data related to the one or more user(s);
- a cluster unit [308] connected at least to the classification unit [306], the cluster unit [308] is configured to cluster, at the AP entity [304], one or more sites on the network map at a first trained model, wherein the one or more sites comprises one or more cell(s);
- an analysing unit [310] connected at least to the cluster unit [308], the analysis unit [310] is configured to analyse, at the AP entity [304], the one or more sites based on the one or more geo-spatial grid;
- a detection unit [312] connected at least to the analysing unit [310], the detection unit [312] is configured to detect, at the AP entity [304], a type of the cell(s);
- a determination unit [314] connected at least to the detection unit [312], the determination unit [314] is configured to determine, at the AP entity [304], a set of remote electrical tilt (RET) data for each of the type of the cell(s) at a second trained model and a third trained model;
- a validation unit [316] connected at least to the determination unit [314], the validation unit [316] is configured to validate, at the AP entity [304], the determined set of RET data based on a set of performance parameter(s) and the type of cell(s);
- an implementation unit [318] connected at least to the validation unit [316], the implementation unit [318] is configured to implement, at the AP entity [304], the

set of RET data for each type of the cell(s) through a Configuration Management System (CM) entity [320]; and - a trigger unit [322] connected at least to the implementation unit [318], the trigger unit [322] is configured to trigger, at the AP entity [304], at least one action through the CM entity [320] for a pre-defined degradation thresholds for the implemented set of RET data.
11. The system as claimed in claim 10, wherein the set of data related to the one or more user(s) comprises at least one of a number of user(s) having satisfying experience, a number of users having unsatisfying user experience, a signal strength at a user device(s), and a network congestion(s) at the user device(s).
12. The system as claimed in claim 10, wherein the one or more geo-spatial grids are at least one of a good performance grid, a poor performance grid, a satisfying grid for a network service performance and an unsatisfying grid for the network service performance, and a satisfying grid for a user experience and an unsatisfying grid for the user experience.
13. The system as claimed in claim 10, wherein the first trained model is trained using at least one of a set of unsupervised machine learning algorithm, a K-means algorithm, and an artificial intelligence algorithm, the second trained model is trained based on a neural network based machine learning algorithm and, the third trained model is trained based at least on a user dimensioning data, a path loss model data, a traffic modelling data, a normalization of channel quality indicator (CQI), an E-tilt history, a reflective quality of service attribute (RQA) associated with the one or more users, an NV-PM data associated with the one or more users, a local service request (LSR) associated with the one or more users.
14. The system as claimed in claim 10, wherein the system further comprises an identification unit [324] connected at least to the trigger unit [322], the identification unit [324] is configured to identify the clustered one or more sites based on a predefined threshold limit for a performance data of the classified one or more geo-spatial grid.

15. The system as claimed in claim 10, wherein the type of cell is an overshooting cell, an overlapping cell and an undershooting cell.
16. The system as claimed in claim 10, wherein the set of performance parameters are at least one of a band wise harmonization, a final E- Tilt, an E-tilt implementation, and a load balance.
17. The system as claimed in claim 10, wherein the system further comprises a performing unit [326] connected at least to the identification unit [324], wherein the performing unit [326] is configured to perform, at the AP entity [304], a cluster-wise pre-post analysis using the set of data related to one or more user(s) from at least one of the PM entity and FM entity.
18. The system as claimed in claim 17, wherein the system further comprises:

- a reverting unit [328] connected at least to the performing unit [326], wherein the reverting unit [328] is configured to revert, at the CM entity [320], the implemented set of RET data after detecting a degradation in any cluster performance by the AP entity [304] using the set of data related to one or more user(s); and
- a repeater unit [330] connected at least to the reverting unit [328], wherein the repeater unit [330] is configured to repeat, at the CM entity [320], the implementation of a new set of validated RET data for maintaining an identical performance or improving the performance of each cluster.