FORM 2
THE PATENTS ACT, 1970
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
PERFORMANCE
APPLICANT
JIO PLATFORMS LIMITED
of Office-101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India; Nationality: India
The following specification particularly describes
the invention and the manner in which
it is to be performed

RESERVATION OF RIGHTS
[0001] A portion of the disclosure of this patent document contains material,
which is subject to intellectual property rights such as but are not limited to, copyright, design, trademark, integrated circuit (IC) layout design, and/or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.
FIELD OF INVENTION
[0002] The embodiments of the present disclosure generally relate to
communication network planning. More particularly, the present disclosure relates to a system and a method for providing visual representation of network performance using best server plot.
DEFINITION
[0003] As used in the present disclosure, the following terms are generally
intended to have the meaning as set forth below, except to the extent that the context in which they are used to indicate otherwise.
[0004] The expression ‘Best Server Plot (BSP)’ used hereinafter in the
specification refers to a visual representation of a predicted best server (base station) that a mobile device would connect to at each location within a defined area. The BSP is a graphical representation of server performance, often used in computer systems and networks to monitor and analyze network traffic and identifying bottlenecks. The plot provides insights into server response time, throughput, and resource usage, helping IT professionals optimize server configurations, identify potential issues, and prevent downtime.

[0005] The expression ‘Uplink (UL) throughput’ used hereinafter in the
specification refers to an amount of data transmitted from a user device, such as a smartphone or computer, to a cellular tower. This measure of performance is typically reported in bits per second (bps) and represents the rate at which data is uploaded from the device to the network.
[0006] The expression ‘Downlink (DL) throughput’ used hereinafter in the
specification refers to an amount of data received by a user device from a cellular tower. It is used to measure the speed and efficiency of internet connections, especially in mobile environments where device capabilities, signal strength, and network traffic can impact network performance.
[0007] These definitions are in addition to those expressed in the art.
BACKGROUND OF THE INVENTION
[0008] The following description of 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 be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.
[0009] In the field of telecommunications and network planning, accurate
assessment of network performance and user experience is essential for providing optimal coverage and meeting user expectations. Network planning engineers rely on various tools and models to predict and plan network coverage, capacity, and performance. However, these tools often provide theoretical or simulated data that may not fully capture the real-world user experience.
[0010] Traditionally, network planning engineers have faced challenges in
obtaining reliable and up-to-date information about actual network performance as perceived by users. Without direct feedback from users, engineers struggle to

identify specific areas where network coverage may be lacking or where optimizations are required. This lack of accurate information can lead to inefficiencies in resource allocation and suboptimal network performance.
[0011] The advent of advanced mobile technologies and widespread
smartphone usage has created an opportunity to leverage crowd-sourced data for network optimization. Users can voluntarily contribute data about their network experiences, including coverage, signal strength, data speeds, and call quality. By aggregating and analyzing this crowd-sourced data, network planning engineers can obtain valuable insights into the actual user experience in different areas and identify specific cells or sites that require improvement.
[0012] The absence of a system that can inform network planning engineers
about the actual user experience poses challenges in accurately assessing network coverage and identifying areas for optimization. There is, therefore, a need in the art to provide a system and a method that can mitigate the problems associated with the prior arts.
OBJECTS OF THE INVENTION
[0013] Some of the objects of the present disclosure, which at least one
embodiment herein satisfies are as listed herein below.
[0014] An object of the present disclosure is to provide a system and a
method for leveraging real time user data to evaluate network performance as perceived by users.
[0015] An object of the present disclosure is to provide a system and a
method that determines coverage with the real-world network footprint.
[0016] An object of the present disclosure is to provide a system and a
method assessing the real time actual user experiences observed using the user data.

[0017] An object of the present disclosure is to provide a system and a
method consolidating the analyzed data into polygons allowing for a visual representation of network performance patterns.
[0018] An object of the present disclosure is to provide a system and a
method utilizing the insights gained from the user data to identify specific cells or sites requiring improvement based on user needs and experiences.
SUMMARY
[0019] The present disclosure discloses a system for providing visual
representation of network performance within a geographic area. The geographic area is defined by a plurality of grids. The system includes a receiving unit and a processing unit. The receiving unit is configured to receive a plurality of user records of a plurality of user equipments residing in the geographic area. The processing unit is configured to cooperate with the receiving unit. The processing unit is further configured to extract at least one location data from the plurality of received user records and aggregate the plurality of received user records corresponding to each grid based on the extracted location data. The processing unit is configured to identify one or more prominent serving cells in each grid based on one or more attributes. The processing unit is configured to consolidate the one or more grids having same identified prominent serving cell to generate one or more polygons and generate a visual representation corresponding to the generated polygons.
[0020] In an embodiment, the plurality of user records includes the at least
one location data, radio frequency (RF) condition, signal strength, a serving cell identifier (ID), and a serving sector identifier (ID).
[0021] In an embodiment, the processing unit is configured to identify one
or more serving sectors in each grid based on the one or more attributes.

[0022] In an embodiment, the processing unit is configured to assign same
color to the generated polygons having the same identified prominent serving cell.
[0023] In an embodiment, visual representation includes a cell-level visual
representation and a sector-level visual representation.
[0024] In an embodiment, for generating the sector-level visual
representation, the processing unit is configured to consolidate the one or more grids having same identified prominent serving cell and same identified sector to generate one or more sector-level polygons.
[0025] In an embodiment, the processing unit is configured to assign same
color to the generated sector-level polygons having the same identified prominent serving cell and the same identified sector.
[0026] In an embodiment, the processing unit is configured to utilize a
measured Best Server Plot (mBSP) approach for identifying the one or more prominent serving cells.
[0027] In an embodiment, the system further includes a display module
configured to present the generated visual representation.
[0028] In an embodiment, the one or more attributes include a number of
users attached to a cell, a number of records served by a cell or serving cell signal strength.
[0029] In an embodiment, the system is further configured to determine an
outage in each prominent serving cell based on the aggregated user records.
[0030] In an embodiment, the system is further configured to generate an
alarm based on the determined outage.
[0031] The present disclosure discloses a method for providing visual
representation of network performance within a geographic area defined by a plurality of grids. The method includes receiving a plurality of user records of a

plurality of user equipments residing in the geographic area. The method includes extracting at least one location data from the plurality of received user records. The method includes aggregating the plurality of received user records corresponding to each grid based on the extracted location data. The method includes identifying one or more prominent serving cells in each grid based on one or more attributes. The method includes consolidating the one or more grids having same identified prominent serving cell to generate one or more polygons. The method includes generating a visual representation corresponding to the generated polygons.
[0032] In an embodiment, the plurality of user records includes the at least
one location data, radio frequency (RF) condition, signal strength, a serving cell identifier (ID), and a serving sector identifier (ID).
[0033] In an embodiment, the method includes identifying one or more
serving sectors in each grid based on the one or more attributes.
[0034] In an embodiment, the method includes assigning same color to the
generated polygons having the same identified prominent serving cell.
[0035] In an embodiment, the visual representation includes a cell-level
visual representation and a sector-level visual representation.
[0036] In an embodiment, the method includes consolidating the one or
more grids having same identified prominent serving cell and same identified sector to generate one or more sector-level polygons to generate the sector-level visual representation.
[0037] In an embodiment, the method includes assigning same color to the
generated sector-level polygons having the same identified prominent serving cell and the same identified sector.
[0038] In an embodiment, the method includes utilizing a measured Best
Server Plot (mBSP) approach for identifying the one or more prominent serving cells.

[0039] In an embodiment, the method includes presenting the generated
visual representation on a display module.
[0040] In an embodiment, the one or more attributes include a number of
users attached to a cell, a number of records served by a cell or serving cell signal strength.
[0041] In an embodiment, the method includes determining an outage in
each prominent serving cell based on the aggregated user records.
[0042] In an embodiment, the method includes generating an alarm based
on the determined outage.
[0043] The present disclosure discloses a user equipment configured to
provide visual representation of network performance within a geographic area defined by a plurality of grids. The user equipment includes a processor and a computer-readable storage medium storing programming instructions for execution by the processor. Under the programming instructions, the processor is configured to receive a plurality of user records of a plurality of user equipments residing in the geographic area defined. Under the programming instructions, the processor is configured to extract at least one location data from the plurality of received user records. Under the programming instructions, the processor is configured to aggregate the plurality of received user records corresponding to each grid based on the extracted location data. Under the programming instructions, the processor is configured to identify one or more prominent serving cells in each grid based on one or more attributes. Under the programming instructions, the processor is configured to consolidate the one or more grids having same identified prominent serving cell to generate one or more polygons. Under the programming instructions, the processor is configured to generate a visual representation corresponding to the generated polygons.
BRIEF DESCRIPTION OF DRAWINGS

[0044] The accompanying drawings, which are incorporated herein, and
constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes the disclosure of electrical components, electronic components, or circuitry commonly used to implement such components.
[0045] FIG. 1 illustrates an example network architecture for implementing
a system for providing visual representation of network performance within a geographic area defined by a plurality of grids, in accordance with an embodiment of the present disclosure.
[0046] FIG. 2 illustrates an example block diagram of the system, in
accordance with an embodiment of the present disclosure.
[0047] FIG. 3 illustrates an example flow diagram for aggregating data to
generate a cell-level layer and a sector-level layer, in accordance with an embodiment of the present disclosure.
[0048] FIG. 4 illustrates an example representation of the result of an actual
network footprint using a best server plot, in accordance with an embodiment of the present disclosure.
[0049] FIG. 5A shows a flow diagram illustrating a process of visualizing
outage or alarm information using network performance data and alarm details, in accordance with an embodiment of the present disclosure.

[0050] FIG. 5B illustrates an exemplary representation of an outage or
alarm visualization using the network performance data and alarm details, in accordance with an embodiment of the present disclosure.
[0051] FIG. 6 illustrates various steps of a method for providing visual
representation of network performance within a geographic area defined by a plurality of grids, in accordance with an embodiment of the present disclosure.
[0052] FIG. 7 illustrates an example computer system in which or with
which the embodiments of the present disclosure may be implemented.
[0053] The foregoing shall be more apparent from the following more
detailed description of the disclosure.
LIST OF REFERENCES
100 – Network Architecture
102, 102-1, 102-2…102-N – Users
104, 104-1, 104-2…104-N – User Equipments (UEs)
106 – Network
108 – System
202 – Receiving Unit
204 – Memory
206 – A Plurality of Interfaces
208 – Processing Unit
210 – Database
700 – Computer System
710 – External Storage Device
720 – Bus
730 – Main Memory
740 – Read-Only Memory
750 – Mass Storage Device
760 – Communication Port(s)

770 – Processor
DETAILED DESCRIPTION
[0054] In the following description, for explanation, various specific details
are outlined in order to provide a thorough understanding of embodiments of the
5 present disclosure. It will be apparent, however, that embodiments of the present
disclosure may be practiced without these specific details. Several features
described hereafter can each be used independently of one another or with any
combination of other features. An individual feature may not address all of the
problems discussed above or might address only some of the problems discussed
10 above. Some of the problems discussed above might not be fully addressed by any
of the features described herein.
[0055] 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
15 skilled in the art with an enabling description for implementing an exemplary
embodiment. 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.
[0056] Specific details are given in the following description to provide a
20 thorough understanding of the embodiments. However, it will be understood by one
of ordinary skill in the art that the embodiments may be practiced without these
specific details. For example, circuits, systems, networks, processes, and other
components may be shown as components in block diagram form in order not to
obscure the embodiments in unnecessary detail. In other instances, well-known
25 circuits, processes, algorithms, structures, and techniques may be shown without
unnecessary detail to avoid obscuring the embodiments.
[0057] Also, it is noted that individual embodiments may be described as a
process that is depicted as a flowchart, a flow diagram, a data flow diagram, a
11

structure diagram, or a block diagram. Although a flowchart may describe the
operations as a sequential process, many of the operations can 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
5 steps not included in a figure. A process may correspond to a method, a function, a
procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0058] The word “exemplary” and/or “demonstrative” is used herein to
10 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
necessarily to be construed as preferred or advantageous over other aspects or
designs, nor is it meant to preclude equivalent exemplary structures and techniques
15 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 description or the claims, such terms are intended to be inclusive like the term “comprising” as an open transition word without precluding any additional or other elements.
20 [0059] Reference throughout this specification to “one embodiment” or “an
embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout
25 this specification are not necessarily all referring to the same embodiment.
Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0060] The terminology used herein is to describe particular embodiments
only and is not intended to be limiting the disclosure. As used herein, the singular
12

forms “a”, “an”, and “the” are intended to include the plural forms as well, unless
the context indicates otherwise. It will be further understood that the terms
“comprises” and/or “comprising,” when used in this specification, specify the
presence of stated features, integers, steps, operations, elements, and/or
5 components, but do not preclude the presence or addition of one or more other
features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any combinations of one or more of the associated listed items.
[0061] Currently, there is no existing system that can inform a network
10 planning engineer about the actual user experience in terms of coverage penetration
in a specific area, as opposed to the planned or predicted data provided by the tools.
If network engineers were equipped with this information, it would significantly
facilitate their ability to prioritize network optimization activities for a particular
cell or site. The absence of such a system means that network planning engineers
15 are unable to accurately assess how well the network coverage is meeting the needs
of the users in real-world scenarios. The network planning engineers heavily rely on predicted or planned data, which may not accurately reflect the actual user experience. This limitation poses challenges when it comes to identifying areas that require immediate attention and optimization.
20 [0062] To overcome these limitations, the present disclosure relates to a
system and method for identifying network footprint areas for cells using measured Best Server Plot (mBSP). The present system identifies discrepancies between actual network performance, facilitating optimization and resource allocation for enhanced network capacity.
25 [0063] Additionally, the system receives data from user devices to reflect an
actual user experience of network performance, providing a more accurate representation of network conditions. The system utilizes a measured Best Server Plot (mBSP) to identify specific cells and sites that require adjustments.
13

[0064] To facilitate network planning and optimization, the system
generates visual representations of network performance. These visual polygons
and analysis insights are presented to network planning engineers through a display
module, enabling them to make informed decisions regarding network
5 optimization.
[0065] Geographical visualization of alarms in a telecommunications
network provides operators with a powerful tool to understand the spatial
distribution and impact of network issues. By overlaying alarm information on a
map-based interface, operators gain a visual representation of alarms, helping them
10 identify geographic hotspots, assess the scale of the problem, and allocate resources
effectively.
[0066] By combining real-time alarm details (such as alarm location - site,
sector, and cell) with the base grid data (site/sector/cell coverage area), the present
system is configured to identify the location (grids) impacted by the specific alarm.
15 When visualized geographically along with the site information, the real-time alarm
details provide accurate information about the impacted area.
[0067] The present disclosure significantly improves customer service by
reducing the time taken to resolve issues, enhancing communication with
customers, and ultimately increasing overall customer satisfaction. Visual
20 representations of the affected areas allow customer care representatives to clearly
convey the impact of the issue, effectively manage customer expectations, and offer alternative solutions or workarounds if available.
[0068] The present disclosure collects large amounts of data from user
equipment and analyzes it at various levels of detail. This data contains valuable
25 information needed to capture and analyze the actual network situation from the
users' perspective. The present disclosure aims to facilitate a comparison between the predicted or planned coverage provided and the real field measurements or user experiences regarding network coverage.
14

[0069] The various embodiments throughout the disclosure will be
explained in more detail with reference to FIGS. 1-7.
[0070] FIG. 1 illustrates an exemplary network architecture (100) in which
or with which a system (108) for providing visual representation of network
5 performance within a geographic area defined by a plurality of grids is
implemented, in accordance with embodiments of the present disclosure.
[0071] Referring to FIG. 1, the network architecture (100) includes a
plurality of user equipments (104-1, 104-2…104-N) associated with a plurality of users (102-1, 102-2…102-N) in an environment. A person of ordinary skill in the
10 art will understand that one or more users (102-1, 102-2…102-N) (also known as a
plurality of users) may be individually referred to as the user (102) and collectively referred to as the users (102). Similarly, a person of ordinary skill in the art will understand that the plurality of user equipments (104-1, 104-2…104-N) may be individually referred to as the user equipment (104) and collectively referred to as
15 the user equipment (104). A person of ordinary skill in the art will appreciate that
the terms “computing device(s)” and “user equipment” may be used interchangeably throughout the disclosure. Although three user equipments (104) are depicted in FIG. 1, however any number of the user equipments (104) may be included without departing from the scope of the ongoing description.
20 [0072] In an embodiment, the user equipment (104) includes smart devices
operating in a smart environment, for example, an Internet of Things (IoT) system. In such an embodiment, the user equipment (104) may include, but is not limited to, smart phones, smart watches, smart sensors (e.g., mechanical, thermal, electrical, magnetic, etc.), networked appliances, networked peripheral devices,
25 networked lighting system, communication devices, networked vehicle accessories,
networked vehicular devices, smart accessories, tablets, smart television (TV), computers, smart security system, smart home system, other devices for monitoring or interacting with or for the users (102) and/or entities, or any combination thereof. A person of ordinary skill in the art will appreciate that the user equipment (104)
15

may include, but is not limited to, intelligent, multi-sensing, network-connected devices, which can integrate seamlessly with each other and/or with a central server or a cloud-computing system or any other device that is network-connected.
[0073] In an embodiment, the user equipment (104) includes, but is not
5 limited to, a handheld wireless communication device (e.g., a mobile phone, a smart
phone, a phablet device, and so on), a wearable computer device(e.g., a head-mounted display computer device, a head-mounted camera device, a wristwatch computer device, and so on), a Global Positioning System (GPS) device, a laptop computer, a tablet computer, or another type of portable computer, a media playing
10 device, a portable gaming system, and/or any other type of computer device with
wireless communication capabilities, and the like. In an embodiment, the user equipment (104) includes, but is not limited to, any electrical, electronic, electro-mechanical, or an equipment, or a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, laptop, a
15 general-purpose computer, desktop, personal digital assistant, tablet computer,
mainframe computer, or any other computing device, wherein the user equipment (104) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, an audio aid, a microphone, a keyboard, and input devices for receiving input from the user (102), or the entity
20 (110) such as touch pad, touch enabled screen, electronic pen, and the like. A person
of ordinary skill in the art will appreciate that the user equipment (104) may not be restricted to the mentioned devices and various other devices may be used.
[0074] Referring to FIG. 1, the user equipment (104) communicates with
the system (108). In an embodiment, the network (106) includes at least one of a
25 Fifth Generation (5G) network, 6G network, or the like. The network (106) enables
the user equipment (104) to communicate with other devices in the network architecture (100) and/or with the system (108). The network (106) includes a wireless card or some other transceiver connection to facilitate this communication. In another embodiment, the network (106) is implemented as, or include any of a
30 variety of different communication technologies such as a wide area network
16

(WAN), a local area network (LAN), a wireless network, a mobile network, a Virtual Private Network (VPN), the Internet, the Public Switched Telephone Network (PSTN), or the like.
[0075] In another exemplary embodiment, the system (108) may
5 communicate with a centralized server that includes or comprise, by way of
example but not limitation, one or more of: a stand-alone server, a server blade, a
server rack, a bank of servers, a server farm, hardware supporting a part of a cloud
service or system, a home server, hardware running a virtualized server, one or more
processors executing code to function as a server, one or more machines performing
10 server-side functionality as described herein, at least a portion of any of the above,
some combination thereof.
[0076] In an embodiment, the system (108) may receive a plurality of user
records of the one or more computing devices (104) associated with the one or more
users (102). Each user record may include information related to location, coverage,
15 signal strength, data speeds, serving cell, call quality and other relevant metrics.
The system (108) may be configured to analyze the received user records and generate insights into user experience and network optimization opportunities.
[0077] In an embodiment, the system (108) may identify specific cells and
sites requiring improvement based on the needs and experiences of one or more
20 users (102).
[0078] In an embodiment, the system (108) may allocate resources for fine-
tuning network capacity based on the identified improvement cells and sites.
[0079] In an embodiment, the system (108) may consolidate the analyzed
user records into one or more visual polygons representing dominant serving cell
17

names or cell ID + sector ID, and assign colors to one or more polygons with the same dominant serving cell name or cell ID + sector ID.
[0080] In an embodiment, the system (108) may map network details
against the one or more polygons, providing aggregated information for further
5 analysis.
[0081] In an embodiment, the system (108) may optimize network
performance based on the insights gained from the user records received from the plurality of users (102) and the analyzed user records.
[0082] The system (108) is configured to receive the user records data of
10 the plurality of user equipments residing in the geographic area. The user records
(also referred as user data) may include details such as network traffic patterns,
packet headers, throughput rates, latency measurements, error rates, device
configurations, routing tables, Quality of Service (QoS) parameters, network
topology maps, security logs, at least one location data, radio frequency (RF)
15 condition, signal strength, a serving cell identifier (cell ID), a serving sector
identifier (sector ID), and performance metrics like uptime and downtime. By
analyzing the received user records, the system enables network administrators and
engineers to identify bottlenecks, security threats, performance issues, and optimize
network efficiency and reliability. The system (108) is configured to aggregate the
20 received data to generate a plurality of polygons. In an example, the user equipment
is an indoor user equipment, and an outdoor user equipments.
[0083] FIG. 2 illustrates an example block diagram (200) of the system
(108), in accordance with an embodiment of the present disclosure.
[0084] The system (108) includes a receiving unit (202), a memory (204),
25 and a processing unit (208). The receiving unit (202) is configured to receive the
user records of the plurality of user equipments. In an aspect, the receiving unit (202) is configured to receive the user records directly from the user equipment, a plurality of network modules or any other sources (third party source). In an
18

example, the user records may also be stored in cloud-based services, either
provided by the network operator or third-party service providers. These could
include storage services, databases, and content delivery networks. In another
example, the user records may be received from subscriber data management
5 (SDM) systems. The SDM systems manage subscriber data across different
generations of networks and may integrate with 5G core network functions to ensure seamless service continuity.
[0085] The processing unit (208) is configured to cooperate with the
receiving unit (202) to receive the user records. In an aspect, the processing unit
10 (208) may include a mapping module and a data parameter engine.
[0086] The processing unit (208) is further configured to extract at least one
location data from the plurality of received user records. In an example, the plurality of user records includes the at least one location data, radio frequency (RF) condition, signal strength, a serving cell identifier (cell ID), and a serving sector
15 identifier (sector ID). In an example, the RF condition may include signal strength,
multipath fading, interference, shadowing, doppler shift and channel capacity. The signal quality refers to the power level of the radio signal received by the receiver. It is influenced by factors such as distance from the transmitter, obstacles in the propagation path, and interference from other sources. The signal quality also
20 includes parameters like Signal-to-Noise Ratio (SNR) and Signal-to-Interference-
plus-Noise Ratio (SINR). These metrics indicate the level of unwanted noise and interference present in the received signal, affecting the ability of the receiver to decode the transmitted data accurately. In wireless communication, signals can reach the receiver through multiple paths due to reflections, diffractions, and
25 scattering caused by obstacles in the propagation environment. Multipath fading
can lead to signal fading and distortion, impacting communication performance. In an example, the interference from other nearby transmitters operating on the same or adjacent frequencies can degrade the quality of the received signal. This interference can be caused by other cellular base stations, Wi-Fi routers, electronic
30 devices, etc. In an area, shadowing occurs when large objects such as buildings,
19

trees, or terrain block or attenuate the radio signal, causing variations in signal
strength and coverage in different areas. In wireless communication, the doppler
shift occurs when there is relative motion between the transmitter and the receiver,
causing a shift in the frequency of the received signal. This effect is especially
5 significant in mobile communication scenarios, where either the transmitter or the
receiver (or both) is in motion. Also, RF conditions determine the maximum data rate or channel capacity that can be achieved over the communication link. Factors such as bandwidth availability, modulation scheme, and coding rate influence the achievable data rate under given RF conditions.
10 [0087] Based on the extracted location data, the processing unit (208) is
further configured to aggregate the plurality of received user records corresponding to each grid. The processing unit (208) is configured to identify one or more prominent serving cells (also referred as dominant serving cells) in each grid based on one or more attributes. For example, the one or more attributes include a number
15 of users attached to a cell, a number of records served by a cell or serving cell signal
strength. Further, the processing unit (208) is configured to identify one or more serving sectors in each grid based on the one or more attributes.
[0088] The processing unit (208) is configured to consolidate the one or
more grids having same identified prominent serving cell to generate one or more
20 polygons. In an aspect, the processing unit (208) is configured to assign same color
to the generated polygons having the same identified prominent serving cell. The processing unit (208) is also configured to assign same color to the generated sector-level polygons having the same identified prominent serving cell and the same
20

identified sector. In an embodiment, visual representation includes a cell-level visual representation and a sector-level visual representation.
[0089] The processing unit (208) is configured to generate a visual
representation corresponding to the generated polygons. For example, the visual
5 representation includes a cell-level and a sector-level representation.
[0090] For generating the sector-level visual representation, the processing
unit (208) is configured to consolidate the one or more grids having same identified prominent serving cell and same identified sector to generate one or more sector-level polygons. For generating the cell-level layer visual representation, the
10 processing unit (208) is configured to group the plurality of generated polygons
having same dominant serving cell name. Further, for generating the sector level visual representation, the processing unit (208) is configured to group the plurality of generated polygons having same dominant serving cell Identifier (cell ID) (dominant serving cell name) and a sector ID. The sector ID refers to an
15 identification number assigned to a specific sector within a cell site. The cell site
often comprises multiple sectors, each covering a distinct portion of the area around the site. These sectors are equipped with antennas that transmit and receive signals. The sector ID helps in identifying and managing these individual sectors within the cell site. Sector IDs are important for network optimization, troubleshooting, and
20 resource allocation purposes. They help network operators monitor and manage the
performance of each sector independently, allowing for more efficient use of resources and better service quality for users.
[0091] In an embodiment, the system is further configured to determine an
outage in each prominent serving cell based on the aggregated user records. The
25 system is designed to identify any disruptions in the prominent serving cells or
prominent sectors by analyzing the user records. Upon detecting an outage, the system is configured to highlight the grids that will be impacted by the outage of the prominent serving cells/sectors. Furthermore, the system is configured to generate visual representations of these affected grids, providing a clear
21

understanding of the extent of the outage, and facilitating the planning of required actions or repairs.
[0092] The system analyzes the aggregated user records to identify any
anomalies or patterns indicative of service outages in prominent serving cells.
5 Outages could occur due to several reasons such as equipment failure, interference,
or network congestion. Upon detecting the outage in a prominent serving cell, the
system generates an alarm or notification to alert network operators or
administrators. In an example, the alarm may be a visual alert on a monitoring
dashboard, an email or SMS notification, or an automated ticket in a network
10 management system (NMS). Once alerted, network operators can take appropriate
actions to investigate and resolve the outage. This may involve troubleshooting the affected equipment, adjusting network configurations, reallocating resources, or implementing temporary mitigation measures to restore service to affected users.
[0093] In an example, the processing unit (208) is configured to utilize a
15 measured Best Server Plot (mBSP) approach for identifying the one or more
prominent serving cells. The mBSP technique allows the processing unit (208) to
thoroughly assess the efficiency and effectiveness of different servers in the
network. It examines important factors like latency, bandwidth, and signal strength
across various server setups. By analyzing these metrics, the system can identify
20 any irregularities that might impact the network's performance. This detailed
assessment helps the system to pinpoint areas for improvement, enhancing the
quality and stability of the 5G network. By utilizing mBSP, network operators can
accurately detect differences and make informed decisions to optimize network
performance, ensuring a seamless user experience in the rapidly evolving 5G
25 landscape.
[0094] Referring to FIG. 2, in an embodiment, the processing unit (208)
may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions.
22

Among other capabilities, the processing unit (208) may be configured to fetch and execute computer-readable instructions stored in the memory (204) of the system (108).
[0095] The memory (204) is configured to store the received set of user
5 records. The memory (204) may be configured to store one or more computer-
readable instructions or routines in a non-transitory computer readable storage
medium, which may be fetched and executed to create or share data packets over a
network service. The memory (204) may comprise any non-transitory storage
device including, for example, volatile memory such as random-access memory
10 (RAM), or non-volatile memory such as erasable programmable read only memory
(EPROM), flash memory, and the like.
[0096] In an embodiment, the system (108) may include an interface(s)
(206). The interface(s) (206) may comprise a variety of interfaces, for example,
interfaces for data input and output devices (I/O), storage devices, and the like. The
15 interface(s) (206) may facilitate communication through the system (108).
[0097] The interface(s) (206) may also provide a communication pathway
for one or more components of the system (108). Examples of such components
include, but are not limited to, the processing unit (208) and a database (210).
Further, the processing unit (208) may include the data parameter engine and other
20 engine(s). In an embodiment, the other engine(s) may include, but not limited to, a
data ingestion engine, an input/output engine, and a notification engine.
[0098] In an embodiment, the processing unit (208) may be implemented as
a combination of hardware and programming (for example, programmable
instructions) to implement one or more functionalities of the processing unit (208).
25 In examples described herein, such combinations of hardware and programming
may be implemented in several different ways. For example, the programming for the processing unit (208) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing unit (208) may comprise a processing resource (for example, one or more
23

processors), to execute such instructions. In the present examples, the machine-
readable storage medium may store instructions that, when executed by the
processing resource, implement the processing unit (208). In such examples, the
system may comprise the machine-readable storage medium storing the instructions
5 and the processing resource to execute the instructions, or the machine-readable
storage medium may be separate but accessible to the system and the processing resource. In other examples, the processing unit (208) may be implemented by electronic circuitry.
[0099] The processing unit (208) may perform specific functions required
10 for analysing network footprint areas. The processing unit (208) is implemented as
a combination of hardware and software. The software components include processor-executable instructions stored on a non-transitory machine-readable storage medium, which are executed by the hardware components to perform the required functions.
15 [00100] In an embodiment, the processing unit (208) may receive the user
records via the data parameter engine. The data parameter engine may be configured for receiving the user records of one or more computing devices associated with users. The data records include at least one location data, radio frequency (RF) condition, signal strength, a serving cell identifier (cell ID), and a
20 serving sector identifier (sector ID). For example, a user's smartphone may transmit
data regarding its signal strength and call quality to the system (108). The processing unit (208) may analyze the received user records and generate insights into user experience and network optimization opportunities.
[00101] The processing unit (208) may consolidate the user records into
25 visual polygons. In a cell-wise layer, all grids may be consolidated to form polygons
having the same dominant serving cell name, indicated with the same color, and network details may be mapped against these polygons. In a sector-level, all grids may be consolidated to form polygons having the same dominant cell ID and sector ID, indicated with the same color, and network details may be aggregated against
24

these polygons. For instance, if a certain area consistently shows poor signal strength, the polygons in this area may be marked in red, indicating a need for optimization.
[00102] In an embodiment, the processing unit (208) may be configured to
5 identify specific cells and sites requiring improvement based on the needs and
experiences of the one or more users (102). This identification process involves
analyzing the discrepancies between the predicted and actual user records to
pinpoint areas where the network does not meet user expectations. For example, if
multiple users report poor call quality in a specific cell, the processing unit (208)
10 can flag this cell for further investigation and optimization.
[00103] In an embodiment, the processing unit (208) may be configured to
allocate resources for fine-tuning network capacity based on the identified
improvement cells and sites. This resource allocation ensures that areas requiring
immediate attention receive the necessary resources for optimization. For example,
15 the system may prioritize upgrading network infrastructure or adjusting network
parameters in cells with significant performance issues to enhance user experience.
[00104] The system may assign colors to polygons with the same dominant
serving cell name or cell ID+ sector ID, creating a visual representation of network
performance. For example, polygons representing areas with excellent coverage
20 may be colored green, while those with poor coverage may be colored red,
providing a clear visual indication of network performance.
[00105] In an embodiment, the processing unit (208) may be configured to
map network details against the one or more polygons, providing aggregated
information for further analysis. This mapping process involves overlaying user
25 records onto the visual polygons, enabling a comprehensive view of network
performance across different areas. For example, the system may display metrics
25

such as signal strength, data speeds, and call quality within each polygon, allowing network engineers to identify and address performance issues effectively.
[00106] In an embodiment, the processing unit (208) may be configured to
optimize network performance based on the insights gained from the user records
5 received from the one or more users (102) and the analyzed user records. This
optimization process involves adjusting network parameters, upgrading
infrastructure, and implementing other measures to enhance network performance.
For example, the system may recommend increasing the capacity of specific cells
or deploying additional network resources in areas with high user demand to
10 improve overall network quality.
[00107] In an embodiment, the system includes a database that periodically
receives user records to update the analysis and resource allocation accordingly.
[00108] The system (108) may include a display module configured to
present the generated visual representation, generated polygons, and analysis
15 insights to network planning engineers. The display module provides an intuitive
interface for viewing and interacting with user records, enabling engineers to make informed decisions regarding network optimization. For example, the display module may present a heatmap of signal strengths across different areas, allowing engineers to quickly identify problem zones and prioritize optimization efforts.
20 [00109] Although FIG. 2 shows exemplary components of the system (108),
in other embodiments, the system (108) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 2. Additionally, or alternatively, one or more components of
26

the system (108) may perform functions described as being performed by one or more other components of the system (108).
[00110] FIG. 3 illustrates an example flow diagram (300) aggregating data to
generate a cell-wise layer and a sector-level layer, in accordance with an
5 embodiment of the present disclosure.
[00111] The cell-wise layer (302) is primarily implemented for consolidating
all the grids and forming one or more polygons. In an example, the consolidated
grids have the same dominant serving cell name. The same color is applied to all
the polygons having the same dominant serving cell name. The processing is further
10 configured to map all other network details against these one or more polygons.
[00112] The sector-level (304) is primarily implemented for consolidating all
the grids and forming one or more polygons. In an example, the consolidated grids
have the same dominant cell ID+ sector ID. The same color is applied to all the
polygons having the same dominant cell ID+ sector ID. The processing is further
15 configured to aggregate all other details against these one or more polygons.
[00113] At step (302-1), the system (108) is configured to consolidate all the
grids and form one or more polygons having the same dominant serving cell name.
This step involves merging individual grids within the network into polygons based
on the dominant serving cell name. Each grid represents a specific geographical
20 area and includes user records such as signal strength, data speeds, and call quality.
For example, if several grids are primarily served by Cell A, these grids are consolidated into a single polygon labelled with Cell A. This process ensures that each polygon accurately represents the areas served by the same cell.
[00114] At step (302-2), the system (108) is configured to apply the same
25 color to all the polygons having the same dominant serving cell name. In this step,
the system assigns a unique color to each dominant serving cell name. All polygons representing areas served by the same cell are colored identically. For instance, polygons served by Cell A may be colored blue, while those served by Cell B may
27

be colored green. This color-coding helps network engineers quickly identify and differentiate between areas served by different cells.
[00115] At step (302-3), the system (108) is configured to map all other
network details against these one or more polygons. This step involves overlaying
5 additional network details onto the polygons. The system maps data such as signal
strength, call drop rates, data throughput, and user experience metrics against each
polygon. For example, a polygon representing Cell A's coverage area might show
an average signal strength of -70 dBm, a call drop rate of 1%, and an average data
throughput of 20 Mbps. This detailed information allows network engineers to
10 assess the performance of each cell and identify areas needing improvement.
[00116] At step (304-1), the system (108) is configured to consolidate all the
grids and form one or more polygons having the same dominant cell ID + sector
ID. Similar to step 302, this step involves merging individual grids within the
network into larger polygons based on the combination of the dominant cell ID and
15 sector ID. For example, if several grids are primarily served by cell ID 1234 and
sector ID 1, these grids are consolidated into a single polygon labelled with cell ID1234 and sector ID 1. This process ensures that each polygon accurately represents the areas served by the same service area and sector combination.
[00117] At step (304-2), the system (108) is configured to apply the same
20 color to all the polygons having the same dominant cell ID and sector ID. In this
step, the system assigns a unique color to each dominant cell ID and sector ID
combination. All polygons representing areas served by the same combination are
colored identically. For instance, polygons served by cell ID 1234 and sector ID 1
may be colored red, while those served by cell ID 1234 and sector ID 2 may be
25 colored yellow. This color-coding allows network engineers to easily differentiate
between areas served by different service areas and sector combinations.
[00118] At step (304-3), the system (108) is configured to aggregate all other
details against these one or more polygons. This step involves consolidating additional network details onto the polygons. The system aggregates data, such as
28

signal strength, call drop rates, data throughput, and user experience metrics for
each polygon. For example, a polygon representing cell ID 1234 and sector ID 1's
coverage area might show an aggregated signal strength of -65 dBm, a call drop
rate of 0.5%, and an average data throughput of 25 Mbps. This aggregated
5 information provides a comprehensive view of network performance for each
service area and sector combination, enabling network engineers to make data-driven decisions for optimization.
[00119] FIG. 4 illustrates an example representation (400) of the result of an
actual network footprint using the best server plot, in accordance with an
10 embodiment of the present disclosure.
[00120] The geographic area is divided into various polygons, each
representing a specific coverage area dominated by a particular serving cell. Different colors are used to indicate the areas served by different cells, facilitating easy identification and comparison of coverage areas.
15 [00121] In an exemplary illustration, a central polygon (402), highlighted
with an outline represents the coverage area served by the identified prominent serving cell identified as "A002." This visual representation aids in understanding the discrepancies between the predicted and actual coverage areas.
[00122] The system (108) visualizes the coverage areas served by different
20 serving cells using different polygons. Each polygon represents a specific
geographical area where a particular cell provides the best network service. For
29

example, the polygon (402) shows the area predominantly served by the identified prominent serving cell.
[00123] Distinct colors are used to distinguish between the areas served by
various cells. This color-coding helps in quickly identifying and differentiating the
5 coverage areas.
[00124] By highlighted, network engineers are able to identify areas where
the network performance does not meet expectations. The system (108) helps
network engineers identify specific cells and sites that require optimization. For
example, if the real time network performance of the prominent serving cell
10 identified as A002 is significantly smaller than a usual performance, this cell might
need adjustment or additional resources to improve coverage.
[00125] The visual representation aggregates user records such as signal
strength, data speeds, and call quality for each polygon. This detailed information
allows network engineers to assess the performance of each serving cell and
15 identify areas needing improvement.
[00126] FIGS. 5A-5B illustrate an example representation of the outage or
alarm visualization using the data and alarm details, in accordance with an embodiment of the present disclosure.
[00127] FIG. 5A shows a flow diagram (500) illustrating the process of
20 visualizing outage or alarm information using the user records and alarm details. In
an example, the user records are received from the various base stations in the
network. The process begins with receiving user records of one or more users,
represented by block (502). This data is collected and organized into a grid format
(504), where each grid represents a specific geographical area with network
25 performance metrics.
[00128] The grid data (504) is then aggregated by mapping (506). This step
involves consolidating the grid data based on prominent serving cells and prominent
30

serving sectors, creating a comprehensive map of network coverage and performance. The aggregated data helps in understanding the spatial distribution of network performance across different areas.
[00129] In an aspect, the system is configured to detect any outage related to
5 the prominent serving cell based on the user records. In another aspect, the system
is configured to detect any outage related to the prominent serving sector. Further, the system may be configured to detect an outage related to the serving cell by performing an internal analysis of a number of activities. In an example, the number of activities include signal strength and quality monitoring, handover failures
10 tracking, connection requests and failures monitoring, latency and throughput
analysis, or physical inspections. On detecting the outage, the system may be configured to detect an outage related to the serving cell by receiving at least one input from an external source. In an example, the external source may be Radio Resource Management (RRM), Cell Outage Detection Function (CODF), Fault
15 Management System (FMS), Service Quality Monitoring (SQM) and any other
information provider. The system is configured to determine which grids are going to be impacted by the outage of the serving cells. In an aspect, the system may be configured to generate a visual representation of the impacted grids. Visual representations of these impacted grids can help in understanding the extent of the
20 outage and planning for any necessary actions or repairs.
[00130] Next, the system identifies cells and sectors experiencing alarms, as
shown in block (510). The alarm cells (512) include site, sector, and cell-level
information for all service-affecting alarms. By combining the aggregated grid data
with the alarm details, the system can pinpoint the exact locations impacted by
25 specific alarms.
[00131] FIG. 5B shows a geographical visualization (514) of the impacted
area. The map highlights the regions affected by network outages or alarms, providing a clear view of the problem areas. The alarm visualization (514) includes site information, helping network engineers quickly identify and address the issues.
31

The use of color-coding or other visual indicators makes it easy to distinguish between different types of alarms and their severities.
[00132] The user records include information on cell coverage derived from
the summary log data of user sessions. This data is created using the dominant cell
5 logic, which takes into account session count, duration, traffic, and other relevant
metrics. By implementing this visualization, the system provides a comprehensive and real-time view of network performance and alarm conditions.
[00133] For example, in FIG. 5B, the red areas on the map indicate regions
affected by a specific alarm. The highlighted site within these areas helps network
10 engineers to focus their attention on resolving the issues. This visualization aids in
efficient network management and optimization by providing actionable insights into network performance and alarm conditions.
[00134] FIG. 6 illustrates various steps of a method (600) for providing visual
representation of network performance within a geographic area defined by a
15 plurality of grids. In an embodiment, the visual representation includes a cell-level
visual representation and a sector-level visual representation.
[00135] At step (602), the receiving unit receives a plurality of user records
of a plurality of user equipments residing in the geographic area. For example, the plurality of user records includes the at least one location data, radio frequency (RF)
32

condition, signal strength, a serving cell identifier (ID), and a serving sector identifier (ID).
[00136] At step (604), the processing unit is configured to extract at least one
location data from the plurality of received user records.
5 [00137] At step (606), the processing unit is configured to aggregate the
plurality of received user records corresponding to each grid based on the extracted location data.
[00138] At step (608), the processing unit is configured to identify one or
more prominent serving cells in each grid based on one or more attributes. In an
10 example, the method includes utilizing a measured Best Server Plot (mBSP)
approach for identifying the one or more prominent serving cells. In an example, the one or more attributes include a number of users attached to a cell, a number of records served by a cell or serving cell signal strength.
[00139] At step (610), the processing unit is configured to consolidate the
15 one or more grids having same identified prominent serving cell to generate one or
more polygons. In an embodiment, the method includes assigning same color to the generated polygons having the same identified prominent serving cell.
[00140] At step (612), the processing unit is configured to generate a visual
representation corresponding to the generated polygons.
20 [00141] In an embodiment, the method includes identifying one or more
serving sectors in each grid based on the one or more attributes.
[00142] In an embodiment, the method includes consolidating the one or
more grids having same identified prominent serving cell and same identified sector
to generate one or more sector-level polygons to generate the sector-level visual
25 representation. The method further includes a step of assigning same color to the
33

generated sector-level polygons having the same identified prominent serving cell and the same identified sector.
[00143] In an embodiment, the method includes presenting the generated
visual representation on a display module.
5 [00144] In an embodiment, the method includes determining an outage in
each prominent serving cell based on the aggregated user records.
[00145] In an embodiment, the method includes generating an alarm based
on the determined outage.
[00146] In an exemplary aspect, the present disclosure discloses a user
10 equipment configured to provide visual representation of network performance
within a geographic area defined by a plurality of grids. The user equipment
includes a processor and a computer-readable storage medium storing programming
instructions for execution by the processor. Under the programming instructions,
the processor is configured to receive a plurality of user records of a plurality of
15 user equipments residing in the geographic area defined. Under the programming
instructions, the processor is configured to extract at least one location data from
the plurality of received user records. Under the programming instructions, the
processor is configured to aggregate the plurality of received user records
corresponding to each grid based on the extracted location data. Under the
20 programming instructions, the processor is configured to identify one or more
prominent serving cells in each grid based on one or more attributes. Under the programming instructions, the processor is configured to consolidate the one or more grids having same identified prominent serving cell to generate one or more
34

polygons. Under the programming instructions, the processor is configured to generate a visual representation corresponding to the generated polygons.
[00147] FIG. 7 illustrates an example computer system (700) in which or
with which the embodiments of the present disclosure may be implemented.
5 [00148] As shown in FIG. 7, the computer system (700) may include an
external storage device (710), a bus (720), a main memory (730), a read-only memory (740), a mass storage device (750), a communication port(s) (760), and a processor (770). A person skilled in the art will appreciate that the computer system (700) may include more than one processor and communication ports. The
10 processor (770) may include various modules associated with embodiments of the
present disclosure. The communication port(s) (760) may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication ports(s) (760) may be chosen
15 depending on a network, such as a Local Area Network (LAN), Wide Area Network
(WAN), or any network to which the computer system (700) connects.
[00149] In an embodiment, the main memory (730) may be Random Access
Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (740) may be any static storage device(s) e.g., but not
20 limited to, a Programmable Read Only Memory (PROM) chip for storing static
information e.g., start-up or basic input/output system (BIOS) instructions for the processor (770). The mass storage device (750) may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced
25 Technology Attachment (PATA) or Serial Advanced Technology Attachment
(SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces).
[00150] In an embodiment, the bus (720) may communicatively couple the
processor(s) (570) with the other memory, storage, and communication blocks. The
35

bus (720) may be, e.g. a Peripheral Component Interconnect PCI) / PCI Extended
(PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus
(USB), or the like, for connecting expansion cards, drives, and other subsystems as
well as other buses, such a front side bus (FSB), which connects the processor (770)
5 to the computer system (700).
[00151] In another embodiment, operator, and administrative interfaces, e.g.,
a display, keyboard, and cursor control device may also be coupled to the bus (720)
to support direct operator interaction with the computer system 700). Other operator
and administrative interfaces can be provided through network connections
10 connected through the communication port(s) (760). Components described above
are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (700) limit the scope of the present disclosure.
[00152] While considerable emphasis has been placed herein on the preferred
15 embodiments, it will be appreciated that many embodiments can be made and that
many changes can be made in the preferred embodiments without departing from
the principles of the disclosure. These and other changes in the preferred
embodiments of the disclosure will be apparent to those skilled in the art from the
disclosure herein, whereby it is to be distinctly understood that the foregoing
20 descriptive matter is to be implemented merely as illustrative of the disclosure and
not as a limitation.
ADVANTAGES OF THE INVENTION
36

[00153] The present disclosure provides a system and a method that
leverages user records to provide accurate information on network performance as perceived by the users.
[00154] The present disclosure provides a system and a method for
5 evaluating network coverage based on actual user experiences in terms of network
footprint and coverage area.
[00155] The present disclosure provides a system and a method for analyzing
the received user records and generating insights into user experiences and network optimization opportunities.
10 [00156] The present disclosure provides a system and a method that utilizes
user needs and experiences to identify specific cells or sites requiring improvement.
[00157] The present disclosure provides a system and a method that provides
visual representation of user records to identify patterns and areas of concern more easily, facilitating targeted optimization efforts.
15 [00158] The present disclosure provides a system and a method that maps
additional network details against the visual representation of network performances to provide aggregated information for further analysis.
[00159] The present disclosure provides a system and a method for
optimizing network performance by utilizing the insights gained from the network
20 performance data user records and the analyzed network details.
37

WE CLAIM:
1. A system (108) for providing visual representation of network performance
within a geographic area defined by a plurality of grids, the system (108)
comprising:
a receiving unit (202) configured to receive a plurality of user records of a plurality of user equipments residing in the geographic area;
a processing unit (208) configured to cooperate with the receiving unit (202) and is further configured to:
extract at least one location data from the plurality of received user records;
aggregate the plurality of received user records corresponding to each grid based on the extracted location data;
identify one or more prominent serving cells in each grid based on one or more attributes;
consolidate the one or more grids having same identified prominent serving cell to generate one or more polygons; and
generate a visual representation corresponding to the generated polygons.
2. The system (108) as claimed in claim 1, wherein the plurality of user records includes the at least one location data, radio frequency (RF) condition, signal strength, a serving cell identifier (cell ID), and a serving sector identifier (sector ID).
3. The system (108) as claimed in claim 1, wherein the processing unit (208) is configured to identify one or more serving sectors in each grid based on the one or more attributes.

4. The system (108) as claimed in claim 1, wherein the processing unit (208) is configured to assign same color to the generated polygons having the same identified prominent serving cell.
5. The system (108) as claimed in claim 1, wherein the visual representation includes a cell-level visual representation and a sector-level visual representation.
6. The system (108) as claimed in claim 5, wherein for generating the sector-level visual representation, the processing unit (208) is configured to consolidate the one or more grids having same identified prominent serving cell and same identified sector to generate one or more sector-level polygons.
7. The system (108) as claimed in claim 6, wherein the processing unit (208) is configured to assign same color to the generated sector-level polygons having the same identified prominent serving cell and the same identified sector.
8. The system (108) as claimed in claim 1, wherein the processing unit (208) is configured to utilize a measured Best Server Plot (mBSP) approach for identifying the one or more prominent serving cells.
9. The system (108) as claimed in claim 1, further includes a display module configured to present the generated visual representation.
10. The system (108) as claimed in claim 1, wherein the one or more attributes include a number of users attached to a cell, a number of records served by a cell or serving cell signal strength.
11. The system (108) as claimed in claim 1, is further configured to determine an outage in each prominent serving cell based on the aggregated user records.
12. The system (108) as claimed in claim 11, is further configured to generate an alarm based on the determined outage.
13. A method (600) for providing visual representation of network performance
within a geographic area defined by a plurality of grids, the method
comprising:

receiving (602) a plurality of user records of a plurality of user equipments residing in the geographic area;
extracting (604) at least one location data from the plurality of received user records;
aggregating (606) the plurality of received user records corresponding to each grid based on the extracted location data;
identifying (608) one or more prominent serving cells in each grid based on one or more attributes;
consolidating (610) the one or more grids having same identified prominent serving cell to generate one or more polygons; and
generating (612) a visual representation corresponding to the generated polygons.
14. The method (600) as claimed in claim 13, wherein the plurality of user records includes the at least one location data, radio frequency (RF) condition, signal strength, a serving cell identifier (cell ID), and a serving sector identifier (sector ID).
15. The method (600) as claimed in claim 13, further comprising identifying one or more serving sectors in each grid based on the one or more attributes.
16. The method (600) as claimed in claim 13, further comprising assigning same color to the generated polygons having the same identified prominent serving cell.
17. The method (600) as claimed in claim 13, wherein the visual representation includes a cell-level visual representation and a sector-level visual representation.
18. The method (600) as claimed in claim 17, further comprising consolidating the one or more grids having same identified prominent serving cell and same identified sector to generate one or more sector-level polygons to generate the sector-level visual representation.

19. The method (600) as claimed in claim 17, further comprising assigning same color to the generated sector-level polygons having the same identified prominent serving cell and the same identified sector.
20. The method (600) as claimed in claim 13, further comprising utilizing a measured Best Server Plot (mBSP) approach for identifying the one or more prominent serving cells.
21. The method (600) as claimed in claim 17, further comprising presenting the generated visual representation on a display module.
22. The method (600) as claimed in claim 17, wherein the one or more attributes include a number of users attached to a cell, a number of records served by a cell or serving cell signal strength.
23. The method (600) as claimed in claim 13, further comprising determining an outage in each prominent serving cell based on the aggregated user records.
24. The method (600) as claimed in claim 23, further comprising generating an alarm based on the determined outage.
25. A user equipment (104) configured to provide visual representation of network performance within a geographic area defined by a plurality of grids, the user equipment (104) comprising:
a processor; and
a computer readable storage medium storing programming instructions for execution by the processor, the programming instructions to:
receive a plurality of user records of a plurality of user
equipments residing in the geographic area defined;
extract at least one location data from the plurality of received user records;
aggregate the plurality of received user records corresponding to each grid based on the extracted location data;

identify one or more prominent serving cells in each grid based on one or more attributes;
consolidate the one or more grids having same identified prominent serving cell to generate one or more polygons; and
generate a visual representation corresponding to the generated polygons.