FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
THE PATENTS RULES, 2003
COMPLETE
SPECIFICATION
(See section 10; rule 13)
TITLE OF THE INVENTION
SYSTEM AND METHOD FOR PERFORMING COVERAGE ANALYSIS IN A NETWORK
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
2
FIELD OF DISCLOSURE
[0001] The present disclosure relates generally to a field of network
optimization technology. In particular, the present disclosure pertains to a system
and a method for daily coverage analysis based on crowd source data in telecom
network. B 5 y collecting data from users across various locations, network operators
can identify areas with weak or no signal, enabling them to optimize coverage by
deploying additional infrastructure or adjusting antenna configurations.
DEFINITIONS
[0002] As used in the present disclosure, the following terms are generally
10 intended to have the meaning as set forth below, except to the extent that the context
in which they are used to indicate otherwise.
[0003] The expression ‘thematic map’ used hereinafter in the specification
refers to a map that contains one or more thematic layers.
[0004] These definitions are in addition to those expressed in the art.
15 [0005] A grid represents a plurality of areas covered by a network.
[0006] Noisy data points are a data set that contains extra meaningless data.
Almost all data sets will contain a certain amount of unwanted noise. Noisy data
can be filtered and processed into a higher quality data set.
BACKGROUND OF DISCLOSURE
20 [0007] 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,
25 and not as admissions of prior art.
[0008] The telecommunications industry has undergone significant changes in
recent years, with the rapid growth of mobile devices and the increasing demand
3
for high-speed data services. This growth has placed significant pressure on
network operators to provide reliable and consistent coverage to their customers.
However, network coverage is often impacted by various factors such as
topography, building materials, and interference from other sources. As a result,
network operators often face challenges 5 in providing consistent coverage across all
areas.
[0009] The fifth-generation (5G) network promises to revolutionize the
telecommunications industry by providing faster data speeds, lower latency, and
higher network capacity. However, the deployment of 5G networks has also posed
10 significant challenges, particularly in terms of coverage analysis. One of the biggest
challenges in 5G network coverage analysis is the limited coverage area. 5G
networks operate on high-frequency bands, which have shorter wavelengths and
limited range. As a result, 5G networks require more cell sites than 4G networks,
making it more challenging to provide consistent coverage across all areas. Another
15 challenge in 5G network coverage analysis is interference from other sources. 5G
networks operate on higher frequencies, which are more susceptible to interference
from buildings, trees, and other obstacles. This interference can result in
inconsistent coverage and poor network performance.
[0010] To provide consistent coverage across all areas, 5G networks require
20 greater network densification. This means more cell sites, more antennas, and more
infrastructure. However, network densification can be costly and time-consuming,
making it challenging for network operators to deploy 5G networks at scale. Unlike
4G networks, there is currently no standardized testing methodology for 5G
network coverage analysis. This makes it challenging for network operators to
25 compare network performance across different vendors and technologies.
[0011] Further, as of now, 6G and 7G networks are still in the conceptual stage,
and there is limited information available about the specific challenges that these
networks will face in terms of coverage analysis. However, based on the challenges
faced by previous generations of networks, we can anticipate some of the problems
30 that 6G and 7G networks may face in terms of coverage analysis.
4
[0012] Hence, the challenges faced by 6G and 7G networks in terms of
coverage analysis are likely to be similar to those faced by previous generations of
networks, albeit on a larger scale. Network operators will need to proactively
identify areas with weak or no signal coverage and optimize network performance
to provide reliable 5 and consistent coverage to their customers.
[0013] There is, therefore, a need for a system and a method for daily coverage
analysis based on crowd source data in telecom network.
SUMMARY
10 [0014] In an exemplary embodiment, a method for performing coverage
analysis in a network is described. The method comprises determining a grid of
cells representing a geographic area covered by a cellular network, and collecting,
by a data collection module, data associated with measurements from a plurality of
data sources across a grid of cells in the network. The method further comprises
15 obtaining, by a data analysis module, a plurality of network performance metrics
from analysis of the collected data. The method comprises enhancing, by a machine
learning (ML) module, the plurality of network performance metrics by evaluating
trends in the plurality of network performance metrics over a predefined period and
filtering network performance metric anomalies. The method includes analyzing,
20 by the data analysis module (122), the plurality of network performance metrics
associated with the grid of cells to determine one or more cells of the grid covering
portion of areas in the geographic area with a network coverage less than predefined
coverage. The method also includes identifying, by the data analysis module (122),
a predetermined number of user equipments (UEs) in the determined one or more
25 cells of the grid. The predefined number of user equipment (UE) in the grid is
randomly selected from the users who are located within the grid of cells
representing the geographic areas covered by the network. The method further
includes performing, by the data analysis module (122), a plurality of speed tests
for defined time intervals through the identified UEs of the grid to obtain speed test
30 results. The method includes analyzing the speed test results by comparing the
5
speed test results with the plurality of network performance metrics corresponding
to the one or more cells of the grid and determining if the speed test results
correspond to the one or more cells that lack network coverage in the one or more
cells of the grid.
[0015] In some embodiments, 5 the method further comprises identifying a cell
of the grid having inconsistent signal coverage.
[0016] In some embodiment, the method further comprises identifying a cell of
the grid based on a network coverage percentage of the cell being proximate to a
network coverage percentage median of the grid of cells.
10 [0017] In some embodiment, the method further comprises identifying a cell of
the grid having a network coverage percentage less than a predefined threshold. The
predefined threshold is a signal strength/power received below which there is no
network connectivity.
[0018] In some embodiment, the plurality of network performance metrics
15 comprise a reference signal received power (RSRP), a received signal strength
indicator (RSSI), a signal to interference and noise ratio (SINR), a reference signal
received quality (RSRQ), a channel quality index (CQI), a physical cell identity
(PCI), a block error ratio (BLER), and an uplink throughput and a downlink
throughput.
20 [0019] In some embodiment, the method further comprises determining, by the
data analysis module (120), at least one network performance attribute associated
with each cell of the grid of cells based on the enhanced plurality of network
performance metrics, the speed test results and the predefined number of UE, the
network performance attribute comprises a coverage area, a coverage percentage, a
25 network capacity, a data rate, a latency, a bandwidth, and a network energy usage.
[0020] In some embodiment, the method comprises optimizing, by a network
optimization module, the one or more serving cells by performing network
optimization steps. The network optimization comprises at least one of performing
adjustments in antenna configurations, network switching, and infrastructure
30 modification.
6
[0021] In some embodiment, the method comprises generating, by the network
optimization module, a work order to perform network optimization.
[0022] In some embodiment, the data sources include a plurality of network
speed monitoring applications, an operational support system (OSS), a unified data
5 repository (UDR), and a plurality of network functions.
[0023] In some embodiment, the method further comprises filtering the
network performance anomalies by identifying and filtering network performance
metrics that are outliers in the trend.
[0024] In some embodiment, the method further comprises evaluating, by the
10 data analysis module, network availability and quality of coverage of the network
by analyzing the network performance metrics.
[0025] In another exemplary embodiment, a system for performing coverage
analysis in a network is described. A data collection module configured to data
associated with measurements from a plurality of data sources across a grid of cells
15 in the network. A data analysis module configured to obtain a plurality of network
performance metrics from analysis of the collected data. A machine learning (ML)
module configured to enhance accuracy of the plurality of network performance
metrics by evaluating trends in the plurality of network performance metrics over a
predefined period and filtering network performance metric anomalies. The data
20 analysis module configured to perform a plurality of speed tests for every defined
time intervals in the grid through a predefined number of user equipment (UE) of
the grid to obtain speed test results. The predefined number of user equipment (UE)
in the grid is randomly selected from the users who are located within the grid of
cells representing the geographic areas covered by the network. The data analysis
25 module configured to analyze the speed test results by correlating the speed test
results with the enhanced plurality of network performance metrics and ascertain
the enhanced plurality of network performance metrics based on the correlation
between the speed test results and the network performance metrics. The data
analysis module configured to determine at least one network performance attribute
30 associated with each cell of the grid of cells based on the enhanced plurality of
7
network performance metrics, the speed test results and the predefined number of
UE.
[0026] In some embodiment, the data analysis module is configured to identify
a cell of the grid having inconsistent signal coverage.
[0027] In some embodiment, 5 the data analysis module is configured to is
configured to identify a cell of the grid based on a network coverage percentage of
the cell being proximate to a network coverage percentage median of the grid of
cells.
[0028] In some embodiment, the data analysis module is configured to identify
10 a cell of the grid having the network coverage percentage less than a predefined
threshold. The predefined threshold is a signal strength/power received below
which there is no network connectivity.
[0029] In some embodiment, the plurality of network performance metrics
comprise a reference signal received power (RSRP), a received signal strength
15 indicator (RSSI), a signal to interference and noise ratio (SINR), a reference signal
received quality (RSRQ), a channel quality index (CQI), a physical cell identity
(PCI), a block error ratio (BLER), and an uplink throughput and a downlink
throughput.
[0030] In some embodiment, the method further comprises determining, by the
20 data analysis module (120), at least one network performance attribute associated
with each cell of the grid of cells based on the enhanced plurality of network
performance metrics, the speed test results and the predefined number of UE, the
network performance attribute comprises a coverage area, a coverage percentage, a
network capacity, a data rate, a latency, a bandwidth, and a network energy usage.
25 [0031] In some embodiment, a network optimization module is configured to
optimize the one or more serving cells by performing network optimization steps.
The network optimization comprises at least one of performing adjustments in
antenna configurations, network switching, and infrastructure modification.
[0032] In some embodiment, the network optimization module is configured to
30 generate a work order to perform network optimization.
8
[0033] In some embodiment, the data sources include a plurality of network
speed monitoring applications, an operational support system (OSS), a unified data
repository (UDR), and a plurality of network functions.
[0034] In some embodiment, the ML module is configured to filter the network
performance metric 5 anomalies by identifying and filtering network performance
metrics that are outliers in the trend.
[0035] In some embodiment, the data analysis module is configured to evaluate
network availability and quality of coverage of the network by analyzing the
network performance metrics.
10 [0036] In some embodiment, the network optimization module configured to
generate a work order to optimize the determined one or more serving cells.
OBJECTS OF THE PRESENT DISCLOSURE
[0037] Some of the objects of the present disclosure, which at least one
15 embodiment herein satisfies are as listed herein below.
[0038] An object of the present disclosure is to provide a proactive approach to
coverage analysis that leverages crowd-sourced data to identify areas with weak or
no signal coverage.
[0039] An object of the present disclosure is to enable network operators to gain
20 insights into network performance and identify areas that require attention, thereby
optimizing coverage by deploying additional infrastructure or adjusting antenna
configurations.
[0040] An object of the present disclosure is to provide a comprehensive view
of network performance across all locations, enabling network operators to
25 proactively identify areas with inconsistent coverage and take immediate action to
optimize network performance.
[0041] An object of the present disclosure is to enhance the quality of service
for end-users by providing consistent coverage across all areas, resulting in a more
efficient and cost-effective network.
30 [0042] An object of the present disclosure is to prioritize network upgrades and
investments, resulting in a more efficient and cost-effective network.
9
[0043] An object of the present disclosure is to enable network operators to stay
ahead of the competition by providing reliable and consistent coverage to their
customers.
[0044] An object of the present disclosure is to provide a standardized testing
methodology for coverage analysis 5 that enables network operators to compare
network performance across different vendors and technologies.
[0045] An object of the present disclosure is to provide a secure and reliable
system that protects against cyber threats and ensures the integrity of network
performance data.
10
BRIEF DESCRIPTION OF DRAWINGS
[0046] 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
15 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
20 drawings includes the disclosure of electrical components, electronic components
or circuitry commonly used to implement such components.
[0047] FIG. 1A illustrates an exemplary network architecture in which or with
which embodiments of the present disclosure may be implemented.
[0048] FIG. 1B illustrates an exemplary block diagram of a system for
25 performing coverage analysis in a network, in accordance with an embodiment of
the present disclosure.
[0049] FIG. 2 illustrates an exemplary method for daily coverage analysis
based on crowd source data, in accordance with an embodiment of the present
disclosure.
30 [0050] FIG. 3 illustrates a flow diagram of a method for performing coverage
analysis in a network; and
10
[0051] FIG. 4 illustrates an exemplary computer system in which or with which
embodiments of the present invention can be utilized, in accordance with
embodiments of the present disclosure.
[0052] The foregoing shall be more apparent from the following more detailed
5 description of the disclosure.
DETAILED DESCRIPTION OF DISCLOSURE
[0053] In the following description, for the purposes of explanation, various
specific details are set forth in order to provide a thorough understanding of
10 embodiments of the present disclosure. It will be apparent, however, that
embodiments of the present disclosure may be practiced without these specific
details. Several features described hereafter can each be used independently of one
another or with any combination of other features. An individual feature may not
address all of the problems discussed above or might address only some of the
15 problems discussed above. Some of the problems discussed above might not be
fully addressed by any of the features described herein.
[0054] 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
20 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.
[0055] Specific details are given in the following description to provide a
25 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
30 circuits, processes, algorithms, structures, and techniques may be shown without
unnecessary detail in order to avoid obscuring the embodiments.
11
[0056] 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 can be performed in
parallel or concurrently. In addition, the 5 order of the operations may be re-arranged.
A process is terminated when its operations are completed but could have additional
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
10 function or the main function.
[0057] The word “exemplary” and/or “demonstrative” is used herein to mean
serving as an example, instance, or illustration. For the avoidance of doubt, the
subject matter disclosed herein is not limited by such examples. In addition, any
aspect or design described herein as “exemplary” and/or “demonstrative” is not
15 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
description or the claims, such terms are intended to be inclusive—in a manner
20 similar to the term “comprising” as an open transition word—without precluding
any additional or other elements.
[0058] 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
25 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
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.
30 [0059] The terminology used herein is for the purpose of describing particular
embodiments only and is not intended to be limiting of the disclosure. As used
12
herein, the singular forms “a”, “an” and “the” are intended to include the plural
forms as well, unless the context clearly 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 components, but do not 5 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 and all combinations
of one or more of the associated listed items.
[0060] The present disclosure relates generally to network optimization
10 technology. In particular, the present disclosure pertains to a system and a method
for daily coverage analysis based on crowd source data in telecom network. The
system provides standardized testing methodology for coverage analysis that
enables network operators to compare network performance across different
vendors and technologies. This network optimization technology is a
15 groundbreaking innovation that aims to address the coverage issues and enhance
the overall user experience. It is a proactive approach to network optimization that
utilizes advanced algorithms and predictive analytics to identify potential coverage
gaps and take immediate action to optimize the network. This technology
revolutionizes the way network operators manage their networks by providing real20
time insights into network performance and identifying areas that require attention.
It enables operators to optimize network capacity, improve network efficiency, and
enhance the quality of service for end-users. The network optimization technology
also provides a comprehensive view of the network, allowing operators to identify
network issues before they impact users. It helps operators to prioritize network
25 upgrades and investments, resulting in a more efficient and cost-effective network.
[0061] The various embodiments throughout the disclosure will be explained
in more detail with reference to FIGs. 1-4.
[0062] FIG. 1A illustrates an exemplary network architecture (100-A) in which
or with which embodiments of the present disclosure may be implemented.
30 [0063] Referring to FIG. 1A, the network architecture (100-A) may include
one or more user equipments (104-1, 104-2…104-N) associated with one or more
13
users (102-1, 102-2…102-N) in an environment. A person of ordinary skill in the
art will understand that one or more users (102-1, 102-2…102-N) 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 one or
more user equipments (104-1, 104-5 2…104-N) may be individually referred to as
the user equipment (104) and collectively referred to as 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. 1A, however
10 any number of the user equipments (104) may be included without departing from
the scope of the ongoing description.
[0064] In an embodiment, the user equipment (104) may include 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
15 to, smart phones, smart watches, smart sensors (e.g., mechanical, thermal,
electrical, magnetic, etc.), networked appliances, networked peripheral devices,
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
20 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)
may include, but is not limited to, intelligent, multi-sensing, network-connected
devices, that 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.
25 [0065] In an embodiment, the user equipment (104) may include, but is not
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 headmounted
display computer device, a head-mounted camera device, a wristwatch
computer device, and so on), a Global Positioning System (GPS) device, a laptop
30 computer, a tablet computer, or another type of portable computer, a media playing
device, a portable gaming system, and/or any other type of computer device with
14
wireless communication capabilities, and the like. In an embodiment, the user
equipment (104) may include, 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 general-5 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)
10 or the entity 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.
[0066] Referring to FIG. 1A, the user equipment (104) may communicate with
15 a system (108) through a network (106). In an embodiment, the network (106) may
include at least one of a Fifth Generation (5G) network, a Sixth-Generation (6G)
network, or the like. The network (106) may enable the user equipment (104) to
communicate with other devices in the network architecture (100-A) and/or with
the system (108). The network (106) may include a wireless card or some other
20 transceiver connection to facilitate this communication. In another embodiment, the
network (106) may be implemented as, or include any of a variety of different
communication technologies such as a wide area network (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.
25 [0067] As illustrated in FIG. 1A, the user equipment (104) is communicatively
coupled with a system (108). The user equipment (104) may receive a connection
request. The user equipment (104) may send an acknowledgment of connection
request to the system (108). Data from a network monitoring application running in
the user equipment (104) is sent to the system (108). The system (108) performs a
30 coverage analysis in the network (106).
15
[0068] Although FIG. 1A shows exemplary components of the network
architecture (100-A), in other embodiments, the network architecture (100-A) may
include fewer components, different components, differently arranged components,
or additional functional components than depicted in FIG. 1A. Additionally, or
alternatively, one or more 5 components of the network architecture (100-A) may
perform functions described as being performed by one or more other components
of the network architecture (100-A).
[0069] FIG. 1B illustrates an exemplary block diagram (100-B) of the system
(108) for daily coverage analysis based on the crowd source data, in accordance
10 with an embodiment of the present disclosure.
[0070] Referring to FIG. 1B, where the block diagram (100-B) of the system
(108) is shown. The system includes a processor (112) and a memory (114). The
system comprises a processing engine (116). The processing engine (116) includes
a data collection module (118) configured to collect data from users across various
15 locations; a data analysis module (120) configured to analyze the collected data to
identify areas with weak or no signal coverage; and a network optimization module
(124) configured to optimize coverage by deploying additional infrastructure or
adjusting antenna configurations based on the analyzed data.
[0071] In an embodiment, the data collection module (118) is configured to
20 collect data in real-time, enabling network operators to proactively identify areas
with inconsistent coverage and take immediate action to optimize network
performance. The data collected by the data collection module (118) can come from
a variety of sources, including mobile devices, sensors, and other networkconnected
devices. For example, mobile devices can provide data on signal
25 strength, network speed, and other network performance metrics. Sensors can
provide data on environmental factors that may affect network performance, such
as temperature, humidity, and air quality. By collecting data in real-time, the data
collection module (118) enables network operators to identify areas with weak or
no signal coverage as soon as they occur. This enables network operators to take
30 immediate action to optimize network performance, such as deploying additional
infrastructure or adjusting antenna configurations.
16
[0072] In an embodiment, the data analysis module (120) is configured to
provide a comprehensive view of network performance across all locations,
enabling network operators to prioritize network upgrades and investments. The
data analysis module (120) uses advanced algorithms. A machine learning (ML)
module (122) is configured to analyze 5 the collected data. The machine learning
(ML) module (122) may be part of the data analysis module (120). It can identify
patterns and trends in the data that may indicate areas with weak or no signal
coverage. It can also identify environmental factors that may affect network
performance, such as buildings, trees, and other obstacles. The data collection
10 module (108) that collects data from a network speed monitoring application and
Passive SDK, which monitor users' network and capture network KPIs based on
events.
[0073] The data collection from, for example, a speed monitoring app and a
Passive SDK may refer to the process of gathering network performance data from
15 users' mobile devices. The speed monitoring app and the Passive SDK are software
tools that can be installed on mobile devices, such as smartphones and tablets. They
are designed to monitor users' network and capture network KPIs (Key
Performance Indicators) based on events. The speed monitoring app is a mobile
application that allows users to test the speed and quality of their network
20 connection. It provides real-time data on network performance, such as download
and upload speeds, latency, and jitter. This data can be collected by the system and
analyzed to gain insights into network performance across various locations. The
Passive SDK is a software development kit that can be integrated into mobile
applications to capture network performance data. It collects data on network KPIs
25 such as signal strength, network speed, and other network performance metrics.
This data can be collected in the background without user intervention and can
provide valuable insights into network performance across various locations. By
utilizing data collection from the speed monitoring app and the Passive SDK, the
system can monitor users' network and capture network KPIs based on events. This
30 data can be used to gain insights into network performance across various locations,
17
identify areas with weak or no signal coverage, and take immediate action to
optimize network performance.
[0074] In some embodiment, the network optimization module (124) is
configured to enhance the quality of service for end-users by providing consistent
coverage across all areas. The network optimization 5 module (124) uses the insights
gained from the data collection and data analysis modules to deploy additional
infrastructure or adjust antenna configurations to enhance network coverage. By
doing so, it can ensure that end-users receive consistent coverage across all areas.
For example, if the data analysis module (120) identifies that a particular area has
10 consistently poor signal coverage, the network optimization module (124) can
deploy additional infrastructure, such as a new cell tower or small cell, to improve
signal coverage in that area. Alternatively, it can adjust antenna configurations to
optimize network performance. By enhancing the quality of service for end-users,
the network optimization module (124) can improve the user experience and
15 increase customer satisfaction. This can result in increased customer loyalty and
retention, as well as attracting new customers.
[0075] In some embodiment, the data collection module (118) is configured to
collect data from a variety of sources, including mobile devices, sensors, and other
network-connected devices. Mobile devices, such as smartphones and tablets, are
20 ubiquitous and are often used to access telecom networks. These devices can
provide valuable data on signal strength, network speed, and other network
performance metrics. By collecting data from these devices, the data collection
module (118) can gain insights into network performance across various locations.
Sensors are another source of data that can be used to analyze network performance.
25 Sensors can collect data on environmental factors that may affect network
performance, such as temperature, humidity, and air quality. By collecting data
from sensors, the data collection module (118) can gain insights into how
environmental factors affect network performance. Other network-connected
devices, such as routers and switches, can also provide data on network
30 performance. By collecting data from these devices, the data collection module
(118) can gain a comprehensive view of network performance across all locations.
18
Further, the collected data is then analyzed using the machine learning module
(122) to identify and filter out outliers or noisy data points, improving the accuracy
of the grid analysis over N number of days.
[0076] The machine learning module (122) may use machine learning
algorithms that refer to a 5 set of algorithms and statistical models that enable
computers to learn and improve from experience without being explicitly
programmed. In the context of grid analysis based on crowd source data, machine
learning algorithms can be used to identify and filter out outliers or noisy data
points, improving the accuracy of the grid analysis over N number of days. Outliers
10 or noisy data points refer to data that does not conform to the expected pattern or
trend and can significantly affect the accuracy of the analysis. Machine learning
algorithms can be trained to identify these outliers or noisy data points and filter
them out from the analysis. By using machine learning algorithms to filter out
outliers or noisy data points, the accuracy of the grid analysis can be improved over
15 N number of days. This is because machine learning algorithms can learn from past
data and identify patterns and trends that are not immediately apparent to human
analysts.
[0077] In one embodiment, the system is designed to determine a grid of cells
representing the geographic areas covered by the network for poor and good
20 coverage. To determine this grid of cells, the system collects data from various
sources, such as speed monitoring app and Passive SDK, which monitor users'
network and capture network KPIs based on events. The data collection module
(118) collects data from these sources and prepares it for analysis. The data analysis
module (120) then analyzes the collected data to identify the areas with poor and
25 good network coverage. The analysis is based on various metrics such as signal
strength, network speed, and other network performance metrics. The grid of cells
is then divided into two categories - those with poor coverage and those with good
coverage - based on the analysis.
[0078] In an aspect, the network performance metrics may comprise a
30 reference signal received power (RSRP), a received signal strength indicator
(RSSI), a signal to interference and noise ratio (SINR), a reference signal received
19
quality (RSRQ), a channel quality index (CQI), a physical cell identity (PCI), a
block error ratio (BLER), and an uplink throughput and a downlink throughput.
Network anomalies are anomalies in network behavior deviate from what is normal,
standard, or expected. Detection of anomalies in network behavior may include
continuous m 5 onitoring of a network for unexpected trends or events.
[0079] The network performance metric anomalies may comprise poor
coverage, low quality, high packet loss, low throughput, high error rate, etc. In
current context, the anomalies may include outliers in the trends in trends in the
plurality of network performance metrics. The trend may refer to range of values
10 that are expected during a normal functioning of elements in the network. For
example, the RSRP range may be between -140dBm to -44 dBm. Any value beyond
this range may be identified ad an outlier or an anomaly. Similarly, the RSSI range
may be between -100 dBm to 0 dBm.
[0080] In an embodiment, identifying random N users for a grid refers to
15 randomly selecting N users who are located within the grid of cells representing the
geographic areas covered by the network. UE associated with the users are
identified and then subjected to active speed tests at different time intervals to
ascertain the data obtained from machine learning (ML). Active speed tests measure
the network performance by simulating the user's activity on the network. These
20 tests can be conducted using various tools and software applications, such as speed
test apps. By performing active speed tests on different time intervals, the system
can gather more data on network performance and validate the accuracy of the ML
algorithm's predictions. The different time intervals may refer to different time
periods, such as late in the night (00.00 hours), morning peak hours (8:30 AM to
25 9:30 AM), etc. Different time intervals may help analyze the speed test at different
time periods for a better understanding of network coverage and capacities. Based
on the test results and the number of users, the system can calculate the coverage
percentage for the grid. In examples, the analyzing the speed test results by
comparing the speed test results with the plurality of network performance metrics
30 corresponding to the one or more cells of the grid and determining if the speed test
results correspond to the one or more cells that lack network coverage in the one or
20
more cells of the grid. For example, a network performance metric, downlink
throughput provided by a UE may be 15 Mbps for a 4G LTE network indicating
poor coverage. The speed test using the same UE may show a speed test result of
18 Mbps, which when compared with the downlink throughput, ascertains that the
signal coverage is weak for that geographical area 5 covered by the cell in the grid.
The coverage percentage represents the percentage of users who are experiencing
good network performance within the grid. Based on this percentage, the system
can identify the poor grid, which represents the areas with poor network
performance.
10 [0081] In an embodiment, the system determines a serving cell for the
identified grid. The serving cell refers to the base station or cell tower that provides
the strongest signal to a mobile device within a particular geographic area. To
determine the serving cell for the identified grid, the system analyzes the data
collected from various sources, such as network logs and signal strength
15 measurements, and identifies the cell with the strongest signal within the grid. This
cell is considered to be the serving cell for the identified grid. Knowing the serving
cell for a particular grid can help network operators identify areas where network
coverage may be weak and take steps to optimize network performance and mobile
device users understand which cell tower is providing them with the strongest
20 signal, which can be useful when troubleshooting connectivity issues or trying to
improve network performance.
[0082] In an embodiment, the system also generates a work order to optimize
the serving cell. The work order is a document that outlines the tasks that need to
be performed to optimize the serving cell. The work order includes details such as
25 the location of the serving cell, the type of equipment required, and the specific
tasks that need to be performed to optimize the cell. The work order may include
tasks such as adjusting the antenna orientation, replacing faulty equipment, or
adding additional equipment to improve network coverage and performance. The
work order may also include a timeline for completing the tasks and a budget for
30 the required equipment and labor.
21
[0083] Further, the present disclosure provides a method for daily coverage
analysis based on crowd source data in telecom network.
[0084] In an aspect of the present invention, performing daily analysis of the
network coverage based on crowdsource data involves analyzing and evaluating the
availability and quality of network coverage 5 in different areas using data
contributed by users. The data contributed by the user is collected from various
speed testing application of the user devices. This approach provides insights into
the performance and reach of 4G/5G networks and helps the service providers to
optimize the network for a particular area.
10 [0085] In order to optimize the network performance in particular area
following steps are performed:
[0086] Performing a grid analysis based on crowd source data: Determine a
grid of cells representing the geographic areas covered by a cellular network for
poor and good coverage in the grid.
15 [0087] Collecting data from plurality of data sources: The data is collected
from the speed testing app and Passive SDK through which users' network is
monitored and network KPIs are captured based on events.
[0088] Performing data Analysis: Machine learning algorithms are used to
identify and filter out outliers or noisy data points, improving the accuracy of the
20 grid analysis over N number of days.
[0089] Identifying a bottom and median of the grid: The bottom of the grid that
is cells with the lowest network coverage or signal strength and the median of the
grid representing average network performance across the grid.
[0090] Performing active speed test: Identifying random N users for the grid
25 and performing the active speed test on different time interval to ascertain the data
obtained from the ML.
[0091] Calculating result: Based on the test results and number of users,
calculate the coverage percentage and accordingly identify the poor grid.
[0092] Serving Cell: Determining the serving cell for the identified grid.
30 [0093] Auto generate work order: Generating work order to optimize the
serving cell identified in above analysis.
22
[0094] In an aspect of the present invention, data corresponding to a plurality
of grids of cells in the network is collected from a plurality of data sources. The
data sources comprise plurality of network speed monitoring applications, an
operational support system (OSS), a unified data repository (UDR), and a plurality
of network functions. A machine learning (5 ML) technique is applied to the received
data to determine a plurality of features. The features comprise a reference signal
received power (RSRP), a received signal strength indicator (RSSI)a signal to
interference and noise ratio (SINR), a reference signal received quality (RSRQ), a
channel quality index (CQI), a physical cell identity (PCI), a block error ratio
10 (BLER), an uplink throughput and a downlink throughput. Attributes associated
with each grid from the plurality of grids are identified based on the plurality of
determined features to generate a list if grids. The list of grids is generated by
arranging each of the identified grids having a value corresponding to the at least
one identified attribute in a decreasing order. A bottom grid is identified from the
15 list of grids. The bottom of the grid represents to at least one cell having a lowest
value corresponding to the at least one identified attribute. Number of users for the
identified bottom grid is identified from the received data. A plurality of speed tests
is conducted on different time intervals to generate real time data corresponding to
the at least one attribute. A coverage percentage is calculated based on the generated
20 real time data corresponding to at least one attribute, and the defined number of
users. Serving cells are determined based on the calculated coverage percentage.
The serving cells are optimized by performing a plurality of network optimizations
steps. The plurality of network optimizations steps comprises adjustments in
antenna configurations, network switching, and infrastructure addition.
25 [0095] FIG. 2 where schematic flow diagram (200) of steps involved in the
method 200 for daily coverage analysis based on crowd source data in telecom
network is shown.
[0096] Referring to FIG. 2, the flow diagram (200) comprises of following
steps:
30 [0097] At step 202, the method 200 includes collecting data from users across
various locations.
23
[0098] At step 204, the method 200 includes analyzing the collected data to
identify areas with weak or no signal coverage.
[0099] At step 206, the method 200 includes optimizing coverage by deploying
additional infrastructure or adjusting antenna configurations based on the analyzed
5 data.
[00100] FIG. 3 illustrates a flow diagram (300) for a detailed method for daily
coverage analysis based on crowd source data.
[00101] Referring to FIG. 3, the flow diagram (300) comprises of following
steps:
10 [00102] At step 302, the process begins by determining a grid of cells
representing a geographic area covered by the network.
[00103] At step 304, the process includes collecting data from various sources,
including mobile devices, sensors, and other network-connected devices. Further,
the data sources include a plurality of network speed monitoring applications, an
15 operational support system (OSS), a unified data repository (UDR), and a plurality
of network functions. This data is collected in real-time and is used to identify areas
with weak or no signal coverage.
[00104] At step 306, the process includes obtaining, by a data analysis module
(120), a plurality of network performance metrics from analysis of the collected
20 data. The collected data is then analyzed using machine learning algorithms to
obtain the network performance metrics. The network performance metrics
comprise a reference signal received power (RSRP), a received signal strength
indicator (RSSI), a signal to interference and noise ratio (SINR), a reference signal
received quality (RSRQ), a channel quality index (CQI), a physical cell identity
25 (PCI), a block error ratio (BLER), and an uplink throughput and a downlink
throughput.
[00105] At step 308, the process includes enhancing the plurality of network
performance metrics by evaluating trends in the plurality of network performance
metrics over a predefined period and filtering network performance metric
30 anomalies. The machine learning algorithm analyzes the poor pattern of N days to
identify areas with inconsistent coverage. In this way, the network performance
24
metric anomalies are filtered by identifying and filtering network performance
metrics that are outliers in the trend A bottom and median of a grid is determined.
The bottom of grid representing the cells with the lowest network coverage or signal
strength (network coverage less than a predefined threshold, for example, <= -100
dBm for RSRP in 4G). The median 5 of a grid representing average network
performance across the grid (for example, -80 dBm to -90 dBM for RSRP in 4G
network). A predefined number of users is identified in the grid using data sources.
The predefined number of user equipment (UE) in the grid is randomly selected
from the users who are located within the grid of cells representing the geographic
10 areas covered by the network.
[00106] At step 310, the process includes analyzing, by the data analysis module
(122), the enhanced plurality of network performance metrics associated with the
grid of cells to determine one or more cells of the grid covering portion of areas in
the geographic area with a network coverage less than predefined coverage.
15 [00107] At step 312, the process includes identifying, by the data analysis
module (122), a predetermined number of user equipments (UEs) in the determined
one or more cells of the grid, , wherein the predefined number of user equipment
(UE) in the grid is randomly selected from the users who are located within the grid
of cells representing the geographic areas covered by the network.
20 [00108] At step 314, the process includes performing, by the data analysis
module (122), a plurality of speed tests for defined time intervals through the
identified UEs of the grid to obtain speed test results.
[00109] At step 316, the process includes analyzing the speed test results by
comparing the speed test results with the plurality of network performance metrics
25 corresponding to the one or more cells of the grid and determining if the speed test
results correspond to the one or more cells that lack network coverage in the one or
more cells of the grid.
[00110] The system also generates a work order to optimize and improve
coverage in the identified grid.
30 [00111] In an exemplary embodiment, a computer system in which or with
which embodiments of the present invention can be utilized is disclosed.
25
[00112] FIG. 4 illustrates an exemplary computer system (400) in which or with
which embodiments of the present disclosure may be implemented.
[00113] Referring to FIG. 4, the computer system (400) may include an external
storage device (410), a bus (420), a main memory (430), a read-only memory (440),
a mass storage device (450), 5 communication port(s) (460), and a processor (470).
A person skilled in the art will appreciate that the computer system may include
more than one processor and communication ports. The processor (470) may
include various modules associated with embodiments of the present disclosure.
The communication port(s) (460) may be any of an RS-232 port for use with a
10 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 port(s) (460) may be chosen depending on a network,
such a Local Area Network (LAN), Wide Area Network (WAN), or any network
to which the computer system connects.
15 [00114] The main memory (430) may be random access memory (RAM), or any
other dynamic storage device commonly known in the art. The read-only memory
(440) may be any static storage device(s) e.g., but not limited to, a Programmable
Read Only Memory (PROM) chips for storing static information e.g., start-up or
Basic Input/Output System (BIOS) instructions for the processor (470). The mass
20 storage device (450) may be any current or future mass storage solution, which can
be used to store information and/or instructions. Exemplary mass storage device
(450) includes, but is not limited to, Parallel Advanced 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)
25 and/or Firewire interfaces), one or more optical discs, Redundant Array of
Independent Disks (RAID) storage, e.g., an array of disks.
[00115] The bus (420) communicatively couples the processor (470) with the
other memory, storage, and communication blocks. The bus (420) may be, e.g., a
Peripheral Component Interconnect (PCI)/PCI Extended (PCI-X) bus, Small
30 Computer System Interface (SCSI), Universal Serial Bus (USB), or the like, for
connecting expansion cards, drives, and other subsystems as well as other buses,
26
such a front side bus (FSB), which connects the processor (470) to the computer
system.
[00116] Optionally, operator and administrative interfaces, e.g., a display,
keyboard, joystick, and a cursor control device, may also be coupled to the bus
(420) to support direct operator 5 interaction with the computer system. Other
operator and administrative interfaces can be provided through network
connections connected through the communication port(s) (460). Components
described above are meant only to exemplify various possibilities. In no way should
the aforementioned exemplary computer system limit the scope of the present
10 disclosure.
[00117] While considerable emphasis has been placed herein on the preferred
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
15 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
descriptive matter to be implemented merely as illustrative of the disclosure and not
as limitation.
20 ADVANTAGES OF THE PRESENT DISCLOSURE
[00118] The present disclosure provides proactive identification of areas with
weak or no signal coverage, as the system collects data from users in real-time,
allowing network operators to identify areas with inconsistent coverage and take
immediate action to optimize network performance.
25 [00119] The present disclosure provides a comprehensive view of network
performance across all locations, enabling network operators to prioritize network
upgrades and investments.
[00120] The present disclosure optimizes coverage to enhance the quality of
service for end-users by providing consistent coverage across all areas.
30 [00121] The present disclosure helps reducing costs by streamlining processes,
eliminating waste, and lowering the overall cost of production.
27
[00122] The present disclosure provides a competitive advantage by offering
unique features or capabilities that differentiate it from competitors.
[00123] The present disclosure promotes sustainability by reducing waste,
conserving resources, and minimizing environmental impact.
[00124] The present 5 disclosure allows network operators to quickly identify and
resolve network issues, reducing downtime and improving overall network
reliability.
[00125] The present disclosure helps network operators identify areas of high
demand and allocate network resources more efficiently, reducing congestion and
10 improving network performance.
[00126] The present disclosure shows system's ability to optimize coverage and
enhance the quality of service can lead to improved customer satisfaction, which
can help reduce churn and increase revenue.
[00127] The system provided by the present disclosure can be easily scaled to
15 accommodate growing network demands, making it a flexible and adaptable
solution for telecom operators.
[00128] The present disclosure provides valuable insights into network
performance and usage trends, helping network operators plan and prioritize
network upgrades and investments more effectively.
WE CLAIM:
1. A method for performing coverage analysis in a network (106), the method comprising:
determining a grid of cells representing a geographic area covered by the network;
collecting, by a data collection module (118), data associated with measurements from a plurality of data sources across a grid of cells in the network;
obtaining, by a data analysis module (120), a plurality of network performance metrics from analysis of the collected data;
enhancing, by a machine learning (ML) module (122), the plurality of network performance metrics by evaluating trends in the plurality of network performance metrics over a predefined period and filtering network performance metric anomalies;
analyzing, by the data analysis module (122), the enhanced plurality of network performance metrics associated with the grid of cells to determine one or more cells of the grid covering portion of areas in the geographic area with a network coverage less than predefined coverage;
identifying, by the data analysis module (122), a predetermined number of user equipments (UEs) in the determined one or more cells of the grid, , wherein the predefined number of user equipment (UE) in the grid is randomly selected from the users who are located within the grid of cells representing the geographic areas covered by the network;
performing, by the data analysis module (122), a plurality of speed tests for defined time intervals through the identified UEs of the grid to obtain speed test results; and
analyzing the speed test results by comparing the speed test results with the plurality of network performance metrics corresponding to the one or more cells of the grid and determining if the speed test results correspond

to the one or more cells that lack network coverage in the one or more cells of the grid.
2. The method as claimed in claim 1, further comprising identifying a cell of the grid having inconsistent signal coverage.
3. The method as claimed in claim 1, further comprising identifying a cell of the grid based on a network coverage percentage of the cell being proximate to a network coverage percentage median of the grid of cells.
4. The method as claimed in claim 1, further comprising identifying a cell of the grid having the network coverage percentage less than a predefined threshold, wherein the predefined threshold is a signal strength/power received, below which there is no network connectivity.
5. The method as claimed in claim 1, wherein the plurality of network performance metrics comprise a reference signal received power (RSRP), a received signal strength indicator (RSSI), a signal to interference and noise ratio (SINR), a reference signal received quality (RSRQ), a channel quality index (CQI), a physical cell identity (PCI), a block error ratio (BLER), and an uplink throughput and a downlink throughput.
6. The method as claimed in claim 1, , further comprising: determining, by the data analysis module (120), at least one network performance attribute associated with each cell of the grid of cells based on the enhanced plurality of network performance metrics, the speed test results and the predefined number of UE, wherein the network performance attribute comprises a coverage area, a coverage percentage, a network capacity, a data rate, a latency, a bandwidth, and a network energy usage.
7. The method as claimed in claim 1 further comprising:

optimizing, by a network optimization module (124), the one or more serving cells by performing network optimization steps, wherein the network optimization comprises at least one of performing adjustments in antenna configurations, network switching, and infrastructure modification.
8. The method as claimed in claim 7 further comprising: generating, by the network optimization module (124), a work order to perform network optimization.
9. The method as claimed in claim 1, wherein the data sources include a plurality of network speed monitoring applications, an operational support system (OSS), a unified data repository (UDR), and a plurality of network functions.
10. The method as claimed in claim 1, wherein the network performance metric anomalies are filtered by identifying and filtering network performance metrics that are outliers in the trend.
11. The method as claimed in claim 1 further comprising: evaluating, by the data analysis module (120), network availability and quality of coverage of the network by analyzing the network performance metrics.
12. A system (108) for performing coverage analysis in a network (106) comprising:
a data collection module (118) configured to data associated with measurements from a plurality of data sources across a grid of cells in the network;
a data analysis module (120) configured to obtain a plurality of network performance metrics from analysis of the collected data;
a machine learning (ML) module (122) configured to enhance accuracy of the plurality of network performance metrics by evaluating

trends in the plurality of network performance metrics over a predefined period and filtering network performance metric anomalies; the data analysis module (120) configured to:
perform a plurality of speed tests for every defined time intervals in the grid through a predefined number of user equipment (UE) of the grid to obtain speed test results, wherein the predefined number of user equipment (UE) in the grid are randomly selected from the users who are located within the grid of cells representing the geographic areas covered by the network;
analyze the speed test results by correlating the speed test results with the enhanced plurality of network performance metrics;
ascertain the enhanced plurality of network performance metrics by comparing the speed test results with the plurality of network performance metrics corresponding to the one or more cells of the grid and determining if the speed test results correspond to the one or more cells that lack network coverage in the one or more cells of the grid.
13. The system as claimed in claim 12, wherein the data analysis module (120) is configured to identify a cell of the grid having inconsistent signal coverage.
14. The system as claimed in claim 12, wherein the data analysis module (120) is configured to identify a cell of the grid based on a network coverage percentage of the cell being proximate to a network coverage percentage median of the grid of cells.
15. The system claimed as in claim 12, wherein the data analysis module (120) is configured to identify a cell of the grid having the network coverage percentage less than a predefined threshold, wherein the predefined

threshold is a signal strength/power received below which there is no network connectivity.
16. The system as claimed in claim 12, wherein the plurality of network performance metrics comprise a reference signal received power (RSRP), a received signal strength indicator (RSSI), a signal to interference and noise ratio (SINR), a reference signal received quality (RSRQ), a channel quality index (CQI), a physical cell identity (PCI), a block error ratio (BLER), and an uplink throughput and a downlink throughput.
17. The system as claimed in claim 12, wherein the data analysis module (120) is configured to determine at least one network performance attribute associated with each cell of the grid of cells based on the enhanced plurality of network performance metrics, the speed test results and the predefined number of UE, and wherein the network performance attribute comprises a coverage area, a coverage percentage, a network capacity, a data rate, a latency, a bandwidth, and a network energy usage.
18. The system as claimed in claim 12, wherein a network optimization module (124) is configured to optimize the one or more serving cells by performing network optimization steps, wherein the network optimization comprises at least one of performing adjustments in antenna configurations, network switching, and infrastructure modification.
19. The system as claimed in claim 12, wherein the network optimization module (124) is configured to generate a work order to perform network optimization.
20. The system as claimed in claim 12, wherein the data sources include a plurality of network speed monitoring applications, an operational support

system (OSS), a unified data repository (UDR), and a plurality of network functions.
21. The system as claimed in claim 12, wherein the ML module (122) is configured to filter the network performance metric anomalies by identifying and filtering network performance metrics that are outliers in the trend.
22. The system as claimed in claim 12, wherein the data analysis module (120) is configured to evaluate network availability and quality of coverage of the network by analyzing the network performance metrics.
23. The system claimed as in claim 12, wherein the network optimization module (124) configured to generate a work order to optimize the determined one or more serving cells.
24. A user equipment (104) communicatively coupled with a system (108), the coupling comprises steps of:
receiving a connection request;
sending an acknowledgment of connection request to the system (108); and
transmitting data from a network monitoring application running in the user equipment (104) to the system (108), wherein the system (108) is configured for performing coverage analysis in a network (106) as claimed in claim 12.