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 OPTIMIZING CELL PERFORMANCE FOR A GEO-SPATIAL GRID AREA NETWORK
We, Jio Platforms Limited, an Indian National, of Office - 101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India.
The following specification particularly eescribes the invention and the manner in which it is to be performed.

METHOD AND SYSTEM FOR OPTIMIZING CELL PERFORMANCE FOR A GEO-SPATIAL GRID AREA NETWORK
FIELD OF DISCLOSURE
[0001] Embodiments of the present disclosure generally relate to network performance management systems. More particularly, embodiments of the present disclosure relate to optimizing cell performance for a geo-spatial grid area network.
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] The need for implementation of optimization techniques within a network arises from the limitations of traditional network optimization methods as the traditional network optimization methods rely on static assumptions of network usage and network traffic. The static assumptions are derived from historical performance management (PM) data, which provides insights into past network performance but may not accurately reflect current or future conditions. This static approach can be effective in stable environments but in dynamic and rapidly changing network conditions, the approach may not be effective. The limitations of the traditional methods become evident when a need arises to predict and respond to dynamic shifts in network demands. Factors such as sudden increases in data usage, changes in user mobility patterns, and the introduction of new applications can significantly alter network traffic. Static optimization methods struggle to adapt

to these changes in real-time, leading to suboptimal network performance and user experience.
[0004] Thus, there exists an imperative need in the art to provide an efficient system and method for providing a good service experience to user(s) by identifying a dominant cell or best serving cell within a geographical grid and optimizing the best or dominant serving cell in a dynamic situation.
SUMMARY
[0005] 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.
[0006] An aspect of the present disclosure may relate to a method for optimizing cell performance for a geo-spatial grid area network. The method comprises collecting, by a transceiver unit via a network platform, a set of crowd source data from a crowd source data (CSD) entity. Further, the method includes computing, by a processing unit via the network platform, a dominance factor from the collected set of crowd source data. Furthermore, the method includes analysing, by the processing unit via the network platform, a dominant cell and related one or more neighbour cell(s) of the dominant cell based on the dominance factor. Hereinafter, the method comprises extracting, by the processing unit via the network platform, a set of physical and antenna parameters of the dominant cell and the related one or more neighbour cell(s) from a Master Database (MD) entity. The method further comprises triggering, by the processing unit via the network platform, a work order (WO) entity for making an optimization plan based on the extracted set of physical and antenna parameters and the computed dominance factor. The method further comprises receiving, by the processing unit via the network platform, the optimization plan via a configuration management (CM) entity for implementing in

a geo-spatial grid area. The method further comprises automatic triggering, by the processing unit via the network platform, an action through the CM entity for breaching a pre-defined degradation threshold via the received optimization plan.
[0007] In an exemplary aspect of the present disclosure, the set of crowd source data comprises at least one of tracing user sample, session data, call experience or call performance data.
[0008] In an exemplary aspect of the present disclosure, the dominance factor is computed from at least one of session count, session duration of each user in specific grid from specific cell, total session in grid from all serving cell, cell traffic, unique users count, or average CQI level of each cell in the grid.
[0009] In an exemplary aspect of the present disclosure, the extracting the set of physical and antenna parameters comprising executing, via an execution unit, at least one of antenna type, installed antenna height, tower height, cell azimuth, or Remote Electrical Tilt (RET) information.
[0010] In an exemplary aspect of the present disclosure, the triggering the WO entity for making the optimization plan comprises executing via an optimization unit, an algorithm for formulating the optimization plan via adjusting the set of physical and antenna parameters.
[0011] In an exemplary aspect of the present disclosure, the sending, by the processing unit, the optimization plan to an optimization team for evaluating and/or validating the optimization plan, and/or making any necessary modifications in the optimization plan.
[0012] In an exemplary aspect of the present disclosure, the automatic triggering the action for the received optimization plan comprises monitoring, by the processing unit via the network platform, through the CM entity the dominance

factor and the adjustment in the set of physical and antenna parameters. The automatic triggering the action for the received optimization plan further comprises generating, by the processing unit via the network platform, through the CM entity a statistical report for recommended adjustment in the set of physical and antenna parameters for the optimization plan and automatic triggering, by the processing unit via the network platform, the action through the CM entity if degradation factor or percentage of the adjustment of the set of physical and antenna parameters in the optimization plan breaches the pre-defined degradation threshold, a triggering criterion to revert the adjustment.
[0013] In an exemplary aspect of the present disclosure, the statistical report comprises one or more anomalies outcomes, wherein the one or more anomalies outcomes enable the optimization team to take actions for efficiently reverting the adjustments to the geo-spatial grid area network for precise and optimal performance.
[0014] In an exemplary aspect of the present disclosure, the statistical report is accessed by the optimization team via a performance assessment RF Analytics entity.
[0015] Another aspect of the present disclosure may relate to a system for optimizing cell performance for a geo-spatial grid area network. The system comprises a transceiver unit configured to collect, via a network platform, a set of crowd source data from a crowd source data (CSD) entity. The system further comprises a processing unit connected with at least the transceiver unit. The processing unit is configured to compute, via the network platform, a dominance factor from the collected set of crowd source data. The processing unit is further configured to analyse, via the network platform, a dominant cell and related one or more neighbour cell(s) of the dominant cell based on the dominance factor. Furthermore, the processing unit is configured to extract, via the network platform, a set of physical and antenna parameters of the dominant cell and the related one or

more neighbour cell(s) from a Master Database (MD) entity. The processing unit is further configured to trigger, via the network platform, a work order (WO) entity for making an optimization plan based on the extracted set of physical and antenna parameters and the computed dominance factor. Further, the processing unit is configured to receive, via the network platform, the optimization plan via a configuration management (CM) entity for implementing in a geo-spatial grid area. Further, the processing unit is configured to automatically trigger, via the network platform, an action through the CM entity for breaching a pre-defined degradation threshold via the received optimization plan.
[0016] Yet another aspect of the present disclosure may relate to a non-transitory computer readable storage medium, storing instructions for optimizing cell performance for a geo-spatial grid area network, the instructions include executable code which, when executed by one or more units of a system cause a transceiver unit to collect, via a network platform, a set of crowd source data from a crowd source data (CSD) entity. The instructions when executed by the system further cause a processing unit to compute, via the network platform, a dominance factor from the collected set of crowd source data. The instructions when executed by the system further cause the processing unit to analyse, via the network platform, a dominant cell and related one or more neighbour cell(s) of the dominant cell based on the dominance factor. The instructions when executed by the system further cause the processing unit to extract, via the network platform, a set of physical and antenna parameters of the dominant cell and the related one or more neighbour cell(s) from a Master Database (MD) entity. The instructions when executed by the system further cause the processing unit to trigger, via the network platform, a work order (WO) entity for making an optimization plan based on the extracted set of physical and antenna parameters and the computed dominance factor. The instructions when executed by the system further cause the processing unit to receive, via the network platform, the optimization plan via a configuration management (CM) entity for implementing in a geo-spatial grid area. The instructions when executed by the system further cause the processing unit to

automatically trigger, via the network platform, an action through the CM entity for breaching a pre-defined degradation threshold via the received optimization plan.
OBJECTS OF THE DISCLOSURE
[0017] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below.
[0018] It is an object of the present disclosure to provide a system and a method for optimizing cell performance for a geo-spatial grid area network for better service experience to user(s) in the network.
[0019] It is another object of the present disclosure to provide a system and a method for utilizing dominance factor to identify areas with suboptimal network coverage and performance of a cell by leveraging crowd sourced data to dynamically assess network conditions.
[0020] It is yet another object of the present disclosure to provide real-time fault monitoring, continuous monitoring of cell status, and potential reversion of changes when a temporary coverage issue is resolved.
[0021] It is yet another object of the present disclosure to provide intelligent selection of neighbouring cells for optimizing cell performance for the geo-spatial grid area network.
DESCRIPTION OF THE DRAWINGS
[0022] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale,

emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Also, the embodiments shown in the figures are not to be construed as limiting the disclosure, but the possible variants of the method and system according to the disclosure are illustrated herein to highlight the advantages of the disclosure. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components or circuitry commonly used to implement such components.
[0023] FIG. 1 illustrates an exemplary network platform (NP) architecture, in accordance with exemplary implementation of the present disclosure.
[0024] FIG. 2 illustrates an exemplary block diagram of a computing device upon which the features of the present disclosure may be implemented in accordance with exemplary implementation of the present disclosure.
[0025] FIG. 3 illustrates an exemplary block diagram of a system for optimizing cell performance for a geo-spatial grid area network, in accordance with exemplary implementations of the present disclosure.
[0026] FIG. 4 illustrates a method flow diagram for optimizing cell performance for a geo-spatial grid area network, in accordance with exemplary implementations of the present disclosure.
[0027] FIG. 5 illustrates an exemplary method flow for optimizing cell performance for a geo-spatial grid area network, in accordance with exemplary implementations of the present disclosure.
[0028] The foregoing shall be more apparent from the following more detailed description of the disclosure.
DETAILED DESCRIPTION

[0029] In the following description, for the purposes of explanation, various
specific details are set forth to provide a thorough understanding of embodiments
of the present disclosure. It will be apparent, however, that embodiments of the
5 present disclosure may be practiced without these specific details. Several features
described hereafter may each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above.
10
[0030] 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.
15 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.
[0031] Specific details are given in the following description to provide a thorough
understanding of the embodiments. However, it will be understood by one of
20 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.
25 [0032] Also, it is noted that individual embodiments may be described as a process
which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations may be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process
30 is terminated when its operations are completed but could have additional steps not
included in a figure.
9

[0033] 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
5 aspect or design described herein as “exemplary” and/or “demonstrative” is not
necessarily to be construed as preferred or advantageous over other aspects or
designs, nor is it meant to preclude equivalent exemplary structures and techniques
known to those of ordinary skill in the art. Furthermore, to the extent that the terms
“includes,” “has,” “contains,” and other similar words are used in either the detailed
10 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.
[0034] As used herein, a “processing unit” or “processor” or “operating processor”
15 includes one or more processors, wherein processor refers to any logic circuitry for
processing instructions. A processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a (Digital Signal Processing) DSP core, a controller, a microcontroller, Application Specific
20 Integrated Circuits, Field Programmable Gate Array circuits, any other type of
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.
25
[0035] As used herein, “a user equipment”, “a user device”, “a smart-user-device”, “a smart-device”, “an electronic device”, “a mobile device”, “a handheld device”, “a wireless communication device”, “a mobile communication device”, “a communication device” may be any electrical, electronic and/or computing device
30 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
10

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
5 a transceiver unit, a processing unit, a storage unit, a detection unit and any other
such unit(s) which are required to implement the features of the present disclosure.
[0036] As used herein, “storage unit” or “memory unit” refers to a machine or computer-readable medium including any mechanism for storing information in a
10 form readable by a computer or similar machine. For example, a computer-readable
medium includes read-only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices or other types of machine-accessible storage media. The storage unit stores at least the data that may be required by one or more units of the system to perform their respective
15 functions.
[0037] As used herein “interface” or “user interface” refers to a shared boundary
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
20 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.
[0038] All modules, units, components used herein, unless explicitly excluded
25 herein, may be software modules or hardware processors, the processors being a
general-purpose processor, a special purpose processor, a conventional processor,
a digital signal processor (DSP), a plurality of microprocessors, one or more
microprocessors in association with a DSP core, a controller, a microcontroller,
Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array
30 circuits (FPGA), any other type of integrated circuits, etc.
11

[0039] 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 and/or connected with the system. 5
[0040] As discussed in the background section, the current known solutions have
various shortcomings. The present disclosure aims to overcome the problems
mentioned in the background and other existing problems in this field of technology
by providing method and system of optimizing cell performance for a geo-spatial
10 grid area network.
[0041] FIG. 1 illustrates an exemplary network platform (NP) architecture [100], in accordance with exemplary implementation of the present disclosure. As shown in FIG. 1, the exemplary block diagram [100] includes a network platform (NP)
15 [102], a crowd source data (CSD) entity [104], a Master Database (MD) Entity
[106], a work order (WO) Entity [108], a configuration management (CM) Entity [110] and a Radio Frequency (RF) Entity [112], 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.
20
[0042] The crowd source data (CSD) entity [104] refers to a system to collect information or data from a diverse set of users. The crowd source data entity [104] may collect data through internet, a user equipment, and the like. The data is collected from a diverse set of users. The users are referred to as the crowd. The
25 users may be a system operator, a network operator or the network consumers. The
CSD entity [104] interacts with the network platform [102] via an NP – CS interface. The NP CS interface is an interface between the network platform [102] and the crowd source data entity [104].
30 [0043] The Master Database (MD) Entity [106] refers to a database to store
physical parameters of each cell from a base grid data lake. The MD Entity [106]
12

stores physical parameters that includes but may not be limited to antenna type,
installed antenna height, tower height, cell azimuth, and Remote Electrical Tilt
(RET) information. The MD Entity [106] includes headers such as cell ID, cell
name, location coordinates, and the like. The cell ID refers to a unique identifier for
5 each network cell. In addition, the cell name refers to a name or label assigned to
the network cell. The location coordinates refer to latitude and longitude
coordinates defining geographic location of the cell. The MD entity [106] interacts
with the network platform [102] via an NP-MBD interface. The NP-MBD interface
is an interface between the Master Database (DB) Entity [106] and the network
10 platform [102].
[0044] The WO Entity [108] refers to an entity that stores information of one or more work orders (WO). The WO refers to a document that comprises tasks and procedure to perform the tasks for performing maintenance operations. The
15 information may include type of WO, procedure for each of the WO, start and end
time of completion of the WO, and the like. In one example, the WO Entity [108] may store information for making an optimization plan to adjust physical parameters of a cell in a base grid data lake. The WO Entity [108] may store the steps involved, duration, and the like related to the optimization plan.
20
[0045] The CM Entity [110] refers to an entity having a framework to manage and maintain performance of the network platform architecture [100]. The CM Entity [110] may comprise functional attributes, physical attributes and operational information of the network platform architecture [100]. The CM Entity [106]
25 interacts with the network platform [102] via an NP-CM interface. The NP-CM
interface is an interface between the CM Entity [110] and the network platform [102].
[0046] The RF Entity [112] refers to an entity that collects, processes, and analyses
30 radio frequency signals to extract valuable information. The RF Entity [112]
interacts with the network platform [102] via an NP-RF interface. The NP-RF
13

interface is an interface between the RF Entity [112] and the network platform [102].
[0047] FIG. 2 illustrates an exemplary block diagram of a computing device [200]
5 upon which the features of the present disclosure may be implemented in
accordance with exemplary implementation of the present disclosure. In an
implementation, the computing device [200] may also implement a method for
optimizing cell performance for a geo-spatial grid area network, utilising the
system. In another implementation, the computing device [200] itself implements
10 the method for optimizing cell performance for a geo-spatial grid area network,
using one or more units configured within the computing device [200], wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.
15 [0048] The computing device [200] may include a bus [202] or other
communication mechanism for communicating information, and a hardware
processor [204] coupled with bus [202] for processing information. The hardware
processor [204] may be, for example, a general-purpose microprocessor. The
computing device [200] may also include a main memory [206], such as a random-
20 access memory (RAM), or other dynamic storage device, coupled to the bus [202]
for storing information and instructions to be executed by the processor [204]. The
main memory [206] also may be used for storing temporary variables or other
intermediate information during execution of the instructions to be executed by the
processor [204]. Such instructions, when stored in non-transitory storage media
25 accessible to the processor [204], render the computing device [200] into a special-
purpose machine that is customized to perform the operations specified in the
instructions. The computing device [200] further includes a read only memory
(ROM) [208] or other static storage device coupled to the bus [202] for storing static
information and instructions for the processor [204].
30
14

[0049] A storage device [210], such as a magnetic disk, optical disk, or solid-state
drive is provided and coupled to the bus [202] for storing information and
instructions. The computing device [200] may be coupled via the bus [202] to a
display [212], such as a cathode ray tube (CRT), Liquid crystal Display (LCD),
5 Light Emitting Diode (LED) display, Organic LED (OLED) display, etc. for
displaying information to a computer user. An input device [214], including alphanumeric and other keys, touch screen input means, etc. may be coupled to the bus [202] for communicating information and command selections to the processor [204]. Another type of user input device may be a cursor controller [216], such as
10 a mouse, a trackball, or cursor direction keys, for communicating direction
information and command selections to the processor [204], and for controlling cursor movement on the display [212]. The input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allow the device to specify positions in a plane.
15
[0050] The computing device [200] may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computing device [200] causes or programs the computing device [200] to be a special-purpose machine.
20 According to one implementation, the techniques herein are performed by the
computing device [200] in response to the processor [204] executing one or more sequences of one or more instructions contained in the main memory [206]. Such instructions may be read into the main memory [206] from another storage medium, such as the storage device [210]. Execution of the sequences of instructions
25 contained in the main memory [206] causes the processor [204] to perform the
process steps described herein. In alternative implementations of the present disclosure, hard-wired circuitry may be used in place of or in combination with software instructions.
30 [0051] The computing device [200] also may include a communication interface
[218] coupled to the bus [202]. The communication interface [218] provides a two-
15

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
5 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
10 various types of information.
[0052] 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
15 transmit a requested code for an application program through the Internet [228], the
ISP [226], the local network [222], the host [224] and the communication interface [218]. The received code may be executed by the processor [204] as it is received, and/or stored in the storage device [210], or other non-volatile storage for later execution.
20
[0053] The present disclosure is implemented by a system [300] (as shown in FIG. 3). In an implementation, the system [300] may include the computing device [200] (as shown in FIG. 2). It is further noted that the computing device [200] is able to perform the steps of a method [400] (as shown in FIG. 4).
25
[0054] Referring to FIG. 3, an exemplary block diagram of a system [300] for optimizing cell performance for a geo-spatial grid area network is shown, in accordance with the exemplary implementations of the present disclosure. The system [300] comprises at least one transceiver unit [302], at least one processing
30 unit [304], at least one execution unit [306] and at least one optimization unit [308].
Also, all of the components/ units of the system [300] are assumed to be connected
16

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
5 of said units, as required 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
10 [300] may reside in a server or a network entity. In yet another implementation, the
system [300] may reside partly in the server/ network entity and partly in the user device.
[0055] The system [300] is configured to optimize cell performance for a geo-
15 spatial grid area network, with the help of the interconnection between the
components/units of the system [300]. The geo-spatial grid area network refers to a
network that uses a grid-based system to manage and analyse spatial data.
[0056] The system [300] comprises a transceiver unit [302]. In one example, the
20 transceiver unit [302] may be configured to collect a set of crowd source data from
the crowd source data (CSD) entity [104]. The crowd source data entity [104] refers
to an entity comprising of information or data collected from a set of users. In one
example, the set of users may include network consumers. The set of crowd source
data comprises at least one of tracing user sample, session data, call experience data
25 or call performance data.
[0057] In one example, tracing user sample involves collecting data to track and
analyse user behaviour. The data for tracing user sample may include action
initiated by the user, time spent by the user on each action, and the like. The session
30 data refers to tracing activity of the user on an application or website. The call
17

experience data or the call performance data refers to monitoring the voice or video quality, clarity of voice, and the like.
[0058] The system [300] further comprises a processing unit [304]. The processing
5 unit [304] may be configured to compute a dominance factor. The dominance factor
refers to a value computed to indicate a data to be more prevalent compared to other data. In one example, the dominance factor may be computed from the collected set of crowd source data. In one example, the dominance factor may be computed from at least one of session count, session duration of each user in specific grid from
10 specific cell, total session in grid from all serving cell, cell traffic, unique users
count, or average Channel Quality Indicator (CQI) level of each cell in the grid. In one example, the processing unit [304] may identify the data from the crowd source data, where the dominance factor may be below a predefined threshold. The session count refers to a calculation of a number of times the website or application may be
15 accessed by the user. The session duration refers to a calculation of time duration
when the user accesses the session. The total session in grid from all serving cell refers to calculation of a total number of sessions across all cells of the grid. The cell traffic refers to a volume of data that a cell may handle at a particular instance. The unique user count refers to calculation of a total number of unique users
20 accessing the session. The CQI is a metric to determine the quality of the
communication channel. In one example, the predefined threshold may be configurable by a network operator.
[0059] The processing unit [304] may be further configured to analyse a dominant
25 cell and related one or more neighbour cell(s) of the dominant cell based on the
dominance factor. The dominant cell may represent a cell that may be dominant
over other cells in the grid. In general, dominant cell is the one that provides the
strongest signal within a given area and that covers the largest geographic area or
provides coverage in regions where other cells have weak or no signal. In an
30 example, to analyse the dominant cell and the one or more neighbouring cells, the
processing unit [304] may compute the dominance factor for each cell based on
18

performance metrics like signal strength and traffic load and then identify the dominant cell as the one with the highest factor. Further, the processing unit [304] may then evaluate the performance and interactions of the related one or more neighbouring cells to detect any issues for optimization. 5
[0060] The processing unit [304] may be further configured to extract a set of physical and antenna parameters of the dominant cell and the related one or more neighbour cell(s) present in the Master Database (MD) entity [106]. In one example, the processing unit [304] may extract the set of physical and antenna parameters. In
10 an embodiment, to extract the set of physical and antenna parameters, the
processing unit [304] is configured to execute, via an execution unit [306], at least one of antenna type, installed antenna height, tower height, cell azimuth, or Remote Electrical Tilt (RET) information. In one example, the set of physical and antenna parameters may be extracted based on extraction of at least one of antenna type,
15 installed antenna height, tower height, cell azimuth, or Remote Electrical Tilt (RET)
information. In an example, the set of physical parameters may be associated with the cell. The set of physical parameters may include identifier of the cell, longitudinal and latitudinal coordinates of the cell, and the like.
20 [0061] Furthermore, the processing unit [304] may be configured to trigger the WO
entity [108] for making an optimization plan. In one example, the optimization plan refers to a plan to adjust the physical parameters and antenna parameters. The optimization plan may be based on the extracted set of physical and antenna parameters and the computed dominance factor. In one example, to make the
25 optimization plan, the processing unit [304] may be configured to execute an
algorithm for formulating the optimization plan by adjusting the set of physical and antenna parameters. In one example, the algorithm may be executed by an optimization unit [308]. The processing unit [304] may further be configured to send the optimization plan to an optimization team for evaluating and/or validating
30 the optimization plan, and/or making any necessary modifications in the
optimization plan.
19

[0062] The processing unit [304] may be further configured to receive the
optimization plan for implementing in the geo-spatial grid area. In an example, the
geo-spatial grid area refers to a specific geographic region or zone within a network
5 where various spatial and environmental factors can affect network performance.
The geo-spatial grid area is defined by coordinates or boundaries that allow for analysis and management of network cells within that region. In one example, the processing unit [304] may receive the optimization plan via the configuration management (CM) entity [110].
10
[0063] The processing unit [304] may be further configured to automatically trigger an action through the CM entity [110]. In one example, the processing unit [304] may automatically trigger the action in an event of breach of a pre-defined degradation threshold. The action may be triggered via the received optimization
15 plan. In one example, to automatically trigger the action for the received
optimization plan, the processing unit [304] may be configured to monitor the dominance factor and the adjustment in the set of physical and antenna parameters through the CM entity [110].
20 [0064] The processing unit [304] may be further configured to generate a statistical
report for recommended adjustment in the set of physical and antenna parameters for the optimization plan. In one example, the statistical report comprises one or more anomalies outcomes. The one or more anomalies outcomes or unexpected outcome may enable the optimization team to take actions. In one example, the
25 actions may include recommending adjustment to the set of physical and antenna
parameters for efficiently reverting the adjustments to the geo-spatial grid area network for precise and optimal performance. The statistical report is accessed by the optimization team via a performance assessment RF Analytics entity (same as RF entity [112] of FIG. 1).
30
20

[0065] Further the processing unit [304] may be further configured to automatically
trigger the action through the CM entity [110]. In one example, the automatic
trigger may be initiated if degradation factor or percentage of the adjustment of the
set of physical and antenna parameters in the optimization plan breaches the pre-
5 defined degradation threshold. In one example, the automatic trigger may be to
revert the adjustment. The pre-defined degradation threshold may be set by the
network operator or the system operator. Based on the automatic trigger, the
adjustment may be reverted by the processing unit [304].
10 [0066] Referring to FIG. 4, an exemplary method flow diagram [400] for
optimizing cell performance for a geo-spatial grid area network, 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 system [300] may be present in a server device to implement
15 the features of the present disclosure. Also, as shown in FIG. 4, the method [400]
starts at step [402].
[0067] At step [404], the method comprises collecting, by a transceiver unit [302]
via a network platform, a set of crowd source data from a crowd source data (CSD)
20 entity [104]. The crowd source data entity [104] refers to an entity comprising of
information or data collected from a set of users. In one example, the set of crowd source data comprises at least one of tracing user sample, session data, call experience or call performance data.
25 [0068] In one example, tracing user sample involves collecting data to track and
analyze user behaviour. The data for tracing user sample may include action initiated by the user, time spent by the user on each action, and the like. The session data refers to tracing activity of the user on an application or website. The call experience data or the call performance data refers to monitoring the voice or video
30 quality, clarity of voice, and the like.
21

[0069] At step [406], the method comprises computing, by a processing unit [304],
a dominance factor from the collected set of crowd source data. The dominance
factor refers to a value computed to indicate a data to be more prevalent compared
to other data. In one example, the dominance factor may be computed from at least
5 one of session count, session duration of each user in specific grid from specific
cell, total session in grid from all serving cell, cell traffic, unique users count, or average CQI level of each cell in the grid. The session count refers to a calculation of a number of times the website or application may be accessed by the user. The session duration refers to a calculation of time duration when the user accesses the
10 session. The total session in grid from all serving cell refers to calculation of a total
number of sessions across all cells of the grid. The cell traffic refers to a volume of data that a cell may handle at a particular instance. The unique user count refers to calculation of a total number of unique users accessing the session. The CQI is a metric to determine the quality of the communication channel.
15
[0070] Next at step [408], the method comprises analysing, by the processing unit [304] via the network platform, a dominant cell and related one or more neighbour cell(s) of the dominant cell based on the dominance factor. The dominant cell may represent a cell that may be dominant over other cells in the grid.
20
[0071] Further, at step [410], the method comprises extracting, by the processing unit [304] via the network platform, a set of physical and antenna parameters of the dominant cell and the related one or more neighbour cell(s) from a Master Database (MD) entity. In one example, the extracting the set of physical and antenna
25 parameters comprises executing, via an execution unit [306], at least one of antenna
type, installed antenna height, tower height, cell azimuth, or Remote Electrical Tilt (RET) information. The RET information refers to calculation of antenna tilt. In an example, the set of physical parameters may be associated with the cell. The set of physical parameters may include identifier of the cell, longitudinal and latitudinal
30 coordinates of the cell, and the like.
22

[0072] Further at step [412], the method comprises triggering, by the processing
unit [304], a work order (WO) entity for making an optimization plan. The
optimization plan may be based on the extracted set of physical and antenna
parameters and the computed dominance factor. In one example, the optimization
5 plan refers to a plan to adjust the physical parameters and antenna parameters. In
one example, the triggering the WO entity for making the optimization plan
comprises executing via an optimization engine an algorithm for formulating the
optimization plan via adjusting the set of physical and antenna parameters. The
method further comprises sending, by the processing unit [304], the optimization
10 plan to an optimization team for evaluating and/or validating the optimization plan,
and/or making any necessary modifications in the optimization plan.
[0073] Next at step [414], the method comprises receiving, by the processing unit
[304], the optimization plan via a configuration management (CM) entity [110] for
15 implementing in the geo-spatial grid area.
[0074] Next at step [416], the method comprises automatically triggering, by the
processing unit [304], an action through the CM entity [110] for breaching a pre¬
defined degradation threshold via the received optimization plan. The automatic
20 triggering the action for the received optimization plan comprises monitoring, by
the processing unit [304], through the CM entity [110], the dominance factor and the adjustment in the set of physical and antenna parameters.
[0075] The automatic triggering further comprises generating, by the processing
25 unit [304], through the CM entity [110], a statistical report for recommended
adjustment in the set of physical and antenna parameters for the optimization plan.
In one example, the statistical report comprises one or more anomalies outcomes.
In one example, the one or more anomalies outcomes or unexpected outcomes
enable the optimization team to take actions for efficiently reverting the adjustments
30 to the geo-spatial grid area network for precise and optimal performance. The
23

statistical report is accessed by the optimization team via the performance assessment RF Analytics entity [112].
[0076] Further, the automatic triggering comprises automatic triggering, by the
5 processing unit [304] the action through the CM entity [110]. In one example, the
automatic trigger may be initiated if degradation factor or percentage of the
adjustment of the set of physical and antenna parameters in the optimization plan
breaches the pre-defined degradation threshold. The automatic trigger may be to
revert the adjustment. The pre-defined degradation threshold may be set by the
10 network operator or the system operator. Based on the automatic trigger, the
adjustment may be reverted by the processing unit [304].
[0077] The method may terminate at step [418].
15 [0078] Referring to FIG.5, an exemplary method flow [500] for optimizing cell
performance for a geo-spatial grid area network, in accordance with exemplary implementations of the present disclosure. In an example, the exemplary method [500] may start at step [502].
20 [0079] At step [504], the set of crowd source data may be collected from a crowd
source data (CSD) entity [104]. In an example, the collected set of crowd source data may be used to create a base grid data lake. The base grid data lake may include one or more grids.
25 [0080] Next at step [506], the dominance factor may be computed from the
collected set of crowd source data. The dominance factor may be computed from at least one of session count, session duration of each user in specific grid from specific cell, total session in grid from all serving cell, cell traffic, unique users count, or average CQI level of each cell in the grid. In an example, the system [300]
30 may further analyse the dominant cell based on the computed dominance factor.
24

[0081] In one example, the system [300] may identify one or more grids from the
base grid data where the dominance factor is lower than a first predefined threshold
"Y %". Further, the system [300] may identify the dominant cell where the
distribution of dominance cells in a grid is lower than a second predefined threshold
5 "Z %”. The first predefined threshold and the second predefined threshold may be
configurable by the network operator.
[0082] Next at step [508], based on the computed dominance factor and the analysis
of the dominant cell, the one or more neighbour cell(s) of the dominant cell may be
10 analysed by the system [300].
[0083] Next at step [510], the set of physical and antenna parameters of the
dominant cell and the related one or more neighbour cell(s) may be extracted from
the MD entity [106]. The set of physical and antenna parameters may be extracted
15 based on extraction of at least one of antenna type, installed antenna height, tower
height, cell azimuth, or Remote Electrical Tilt (RET) information.
[0084] Further at step [512], an algorithm to formulate optimization plans may be
executed by the execution unit [306]. The optimization plan may include a plan to
20 adjust the physical parameters and antenna parameters. The optimization plans may
be sent to a relevant optimization team for approval.
[0085] Further at step [514], the WO entity [108] may be triggered to send the
optimization plans to the optimization team. The optimization team may evaluate
25 the optimization plans. In one example, the optimization team may make
modifications. In another example, the optimization team may not make any modifications.
[0086] Further at step [516], the validated optimization plans may be integrated to
30 the CM entity [110]. The CM entity [110] may execute the validated optimization
25

plans in the geo-spatial grid area. The CM entity [110] may ensure that the optimization plans are accurately applied to the geo-spatial grid area.
[0087] Further at step [518], based on the implementation of the optimization plans
5 by the CM entity [110], the system [300] may continuously monitors the dominance
factor in the dominant cell and related one or more neighbour cell(s) of the dominant cell. Based on the continuous monitoring, the system [300] may generate the statistical report. The statistical report comprises one or more anomalies outcomes. In one example, the statistical report may be accessible by the optimization team. 10
[0088] At step [520], based on the statistical report, the optimization team may address the anomalies.
[0089] Further at step [522], the pre-defined degradation threshold may be set at
15 the CM entity [110]. The system [300] may trigger an action for breaching the pre-
defined degradation threshold based on the received optimization plan. The
automatic trigger may be initiated if degradation factor or percentage of the
adjustment of the set of physical and antenna parameters in the optimization plan
breaches the pre-defined degradation threshold, a triggering criterion to revert the
20 adjustment.
[0090] At step [524], based on the automatic trigger, the adjustment may be reverted by the system [300].
25 [0091] The exemplary method flow [500] may terminate at step [526].
[0092] The present disclosure further discloses a non-transitory computer readable
storage medium, storing instructions for optimizing cell performance for a geo-
spatial grid area network, the instructions include executable code which, when
30 executed by one or more units of a system, cause a transceiver unit [302] to collect,
via a network platform, a set of crowd source data from a crowd source data (CSD)
26

entity [104]. The instructions when executed by the system further cause a
processing unit [304] to compute, via the network platform, a dominance factor
from the collected set of crowd source data. The instructions when executed by the
system further cause the processing unit [304] to analyse, via the network platform,
5 a dominant cell and related one or more neighbour cell(s) of the dominant cell based
on the dominance factor. The instructions when executed by the system further cause the processing unit [304] to extract, via the network platform, a set of physical and antenna parameters of the dominant cell and the related one or more neighbour cell(s) from a Master Database (MD) entity [106]. The instructions when executed
10 by the system further cause the processing unit [304] to trigger, via the network
platform, a work order (WO) entity [108] for making an optimization plan based on the extracted set of physical and antenna parameters and the computed dominance factor. The instructions when executed by the system further cause the processing unit [304] to receive, via the network platform, the optimization plan via a
15 configuration management system and Integration (CM) entity [110] for
implementing in a geo-spatial grid area. The instructions when executed by the system further cause the processing unit [304] to automatically trigger, via the network platform, an action through the CM entity [110] for breaching a pre-defined degradation threshold via the received optimization plan.
20
[0093] As is evident from the above, the present disclosure provides a technically advanced solution for optimizing cell performance for a geo-spatial grid area network. The present solution provides a system and a method for optimizing cell performance for a geo-spatial grid area network for better service experience to
25 user(s) in the network. The present disclosure further provides a system and a
method for utilizing dominance factor to identify areas with sub-optimal network coverage and performance of a cell by leveraging crowd sourced data to dynamically assess network conditions. The present disclosure provides real-time fault monitoring, continuous monitoring of cell status, and potential reversion of
30 changes when a temporary coverage issue is resolved. The present disclosure also
27

provides for intelligent selection of neighbouring cells for optimizing cell performance for the geo-spatial grid area network
[0094] While considerable emphasis has been placed herein on the disclosed
5 implementations, it will be appreciated that many implementations can be made and
that many changes can be made to the implementations without departing from the
principles of the present disclosure. These and other changes in the implementations
of the present disclosure will be apparent to those skilled in the art, whereby it is to
be understood that the foregoing descriptive matter to be implemented is illustrative
10 and non-limiting.
[0095] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various components/units can be implemented interchangeably. While specific embodiments may disclose a
15 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 arrangements and substitutions of units, provided they achieve the intended
20 functionality described herein, are considered to be encompassed within the scope
of the present disclosure
28

We Claim:
1. A method for optimizing cell performance for a geo-spatial grid area
network, the method comprising:
- collecting, by a transceiver unit [302] via a network platform, a set of crowd source data from a crowd source data (CSD) entity;
- computing, by a processing unit [304] via the network platform, a dominance factor from the collected set of crowd source data;
- analysing, by the processing unit [304] via the network platform, a dominant cell and related one or more neighbour cell(s) of the dominant cell based on the dominance factor;
- extracting, by the processing unit [304] via the network platform, a set of physical and antenna parameters of the dominant cell and the related one or more neighbour cell(s) from a Master Database (MD) entity [106];
- triggering, by the processing unit [304] via the network platform, a work order (WO) entity [108] for making an optimization plan based on the extracted set of physical and antenna parameters and the computed dominance factor;
- receiving, by the processing unit [304] via the network platform, the optimization plan via a configuration management (CM) entity [110] for implementing in a geo-spatial grid area; and
- automatically triggering, by the processing unit [304] via the network platform, an action through the CM entity [110] for breaching a pre-defined degradation threshold via the received optimization plan.
2. The method as claimed in claim 1, wherein the set of crowd source data
comprises at least one of tracing user sample, session data, call experience
or call performance data.

3. The method as claimed in claim 1, wherein the dominance factor is computed from at least one of session count, session duration of each user in specific grid from specific cell, total session in grid from all serving cell, cell traffic, unique users count, or average CQI level of each cell in the grid.
4. The method as claimed in claim 1, wherein the extracting the set of physical and antenna parameters comprising: executing, via an execution unit [306], at least one of antenna type, installed antenna height, tower height, cell azimuth, or Remote Electrical Tilt (RET) information.
5. The method as claimed in claim 1, wherein the triggering the WO entity for making the optimization plan comprises: executing via an optimization unit [308], an algorithm for formulating the optimization plan via adjusting the set of physical and antenna parameters.
6. The method as claimed in claim 5 further comprises sending, by the processing unit [304], the optimization plan to an optimization team for evaluating and/or validating the optimization plan, and/or making any necessary modifications in the optimization plan.
7. The method as claimed in claim 1, wherein the automatic triggering the action for the received optimization plan comprises:

- monitoring, by the processing unit [304] via the network platform, through the CM entity the dominance factor and the adjustment in the set of physical and antenna parameters;
- generating, by the processing unit [304] via the network platform, through the CM entity [110] a statistical report for recommended adjustment in the set of physical and antenna parameters for the optimization plan; and
- automatic triggering, by the processing unit [304] via the network platform, the action through the CM entity [110] if degradation factor

or percentage of the adjustment of the set of physical and antenna parameters in the optimization plan breaches the pre-defined degradation threshold, a triggering criterion to revert the adjustment.
8. The method as claimed in claim 7, wherein the statistical report comprises one or more anomalies outcomes, wherein the one or more anomalies outcomes enable the optimization team to take actions for efficiently reverting the adjustments to the geo-spatial grid area network for precise and optimal performance.
9. The method as claimed in claim 7, wherein the statistical report is accessed by the optimization team via a performance assessment RF Analytics entity [112].
10. A system for optimizing cell performance for a geo-spatial grid area network, the system comprising:
- a transceiver unit [302] configured to:
o collect, via a network platform, a set of crowd source data from a crowd source data (CSD) entity [104];
- a processing unit [304] connected with at least the transceiver unit
[302], the processing unit [304] is configured to:
o compute, via the network platform, a dominance factor from the
collected set of crowd source data; o analyse, via the network platform, a dominant cell and related one
or more neighbour cell(s) of the dominant cell based on the
dominance factor; o extract, via the network platform, a set of physical and antenna
parameters of the dominant cell and the related one or more
neighbour cell(s) from a Master Database (MD) entity [106]; o trigger, via the network platform, a work order (WO) entity [108]
for making an optimization plan based on the extracted set of

physical and antenna parameters and the computed dominance
factor; o receive, via the network platform, the optimization plan via a
configuration management (CM) entity [110] for implementing in a
geo-spatial grid area; and o automatically trigger, via the network platform, an action through
the CM entity [110] for breaching a pre-defined degradation
threshold via the received optimization plan.
11. The system as claimed in claim 10, wherein the set of crowd source data comprises at least one of tracing user sample, session data, call experience or call performance data.
12. The system as claimed in claim 10, wherein the dominance factor is computed from at least one of session count, session duration of each user in specific grid from specific cell, total session in grid from all serving cell, cell traffic, unique users count, or average CQI level of each cell in the grid.
13. The system as claimed in claim 10, wherein the processing unit [304] extracts the set of physical and antenna parameters by:
executing, via an execution unit [306], at least one of antenna type, installed antenna height, tower height, cell azimuth, or Remote Electrical Tilt (RET) information.
14. The system as claimed in claim 10, wherein to make the optimization plan, the processing unit [304] is configured to execute, via an optimization unit [308], an algorithm for formulating the optimization plan via adjusting the set of physical and antenna parameters.
15. The system as claimed in claim 14, wherein the processing unit [304] is further configured to send the optimization plan to an optimization team for

evaluating and/or validating the optimization plan, and/or making any necessary modifications in the optimization plan.
16. The system as claimed in claim 10, wherein to automatically trigger the
action for the received optimization plan, the processing unit [304] is
configured to:
- monitor, via the network platform, through the CM entity [110] the dominance factor and the adjustment in the set of physical and antenna parameters;
- generate, via the network platform, through the CM entity [110] a statistical report for recommended adjustment in the set of physical and antenna parameters for the optimization plan; and
- automatic trigger, via the network platform, the action through the CM entity [110] if degradation factor or percentage of the adjustment of the set of physical and antenna parameters in the optimization plan breaches the pre-defined degradation threshold, a triggering criterion to revert the adjustment.

17. The system as claimed in claim 16, wherein the statistical report comprises one or more anomalies detection, wherein the anomalies or unexpected outcomes enable the optimization team to take actions for efficiently reverting the adjustments to the geo-spatial grid area network for precise and optimal performance.
18. The system as claimed in claim 16, wherein the statistical report is accessed by the optimization team via a performance assessment RF Analytics entity [112].