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 IDENTIFICATION OF HIGH RANK NEIGHBOR CELLS
APPLICANT
JIO PLATFORMS LIMITED
of Office-101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi,
Ahmedabad - 380006, Gujarat, India; Nationality: India
The following specification particularly describes
the invention and the manner in which
it is to be performed
RESERVATION OF RIGHTS
[0001] A portion of the disclosure of this patent document contains
material, which is subject to intellectual property rights such as but are not limited
to, copyright, design, trademark, integrated circuit (IC) layout design, and/or trade
dress protection, belonging to Jio Platforms Limited 5 (JPL) or its affiliates
(hereinafter referred as owner). The owner has no objection to the facsimile
reproduction by anyone of the patent document or the patent disclosure, as it
appears in the Patent and Trademark Office patent files or records, but otherwise
reserves all rights whatsoever. All rights to such intellectual property are fully
10 reserved by the owner.
TECHNICAL FIELD
[0002] The present disclosure relates to the field of telecommunications and
network management. More precisely, it relates to a system for the identification of
15 high-ranking neighbor cells for handing over the user’s connection from the present
serving cell to the neighbor cell.
DEFINITION
[0003] As used in the present disclosure, the following terms are generally
intended to have the meaning as set forth below, except to the extent that the context
20 in which they are used to indicate otherwise.
[0004] The expression ‘handover” used hereinafter in the specification refers
to a process of transferring an ongoing communication session (such as a call or
data session) from one base station (eNodeB) to another as a user moves between
coverage areas. This process is crucial for maintaining seamless connectivity and
25 ensuring quality of service as users move within the network.
[0005] The expression ‘handover share’ used hereinafter in the specification
refers to an allocation or distribution of resources, such as spectrum or bandwidth,
among different network nodes (base stations or gNBs - gNodeBs). This allocation
2
determines how much capacity each node has for handling handover procedures
and ensuring smooth transitions for users moving between cells.
[0006] These definitions are in addition to those expressed in the art.
5 BACKGROUND
[0007] Background description includes information that may be useful in
understanding the present invention. It is not an admission that any of the
information provided herein is prior art or relevant to the presently claimed
invention, or that any publication specifically or implicitly referenced is prior art.
10 [0008] Whenever a mobile device moves from one cell to another, the network
needs to identify the most suitable neighboring cell for handover to maintain a
stable connection and deliver a high-quality user experience. Cellular networks are
composed of a grid of cells, each served by a base station or cell tower. These cells
collectively provide coverage to a specific geographical area. When a mobile device
15 moves from one cell to another, it needs to connect to a neighboring cell with a
stronger signal and better service quality. Handover is the process of transferring
an ongoing call or data session from one cell to another. It is initiated when the
signal strength of the serving cell weakens below a certain threshold or when a
neighboring cell provides a stronger signal. Handover aims to ensure uninterrupted
20 service and minimize call drops or data interruptions during the transition.
[0009] To identify high-ranking neighbor cells, the network collects
measurements from both the serving cell and neighboring cells. These
measurements include signal strength, signal quality, interference levels, cell load,
available capacity, and other performance metrics. These measurements help assess
25 the quality and suitability of neighboring cells for handover. Various algorithms
and techniques are used to evaluate the collected measurements and determine the
ranking or priority of neighboring cells. These algorithms may consider factors such
as signal strength, signal-to-interference ratio (SIR), quality of service
requirements, cell load balancing, and network policies. Machine learning
30 techniques can also be employed to improve the accuracy of handover decisions.
3
The identification of high-ranking neighbor cells plays a crucial role in network
optimization. By selecting the most suitable neighboring cells for handover, the
network can improve signal coverage, minimize call drops, balance network traffic,
and enhance overall network performance and capacity.
[0010] The identification of high-ranking 5 neighbor cells involves
understanding the principles of handover, the collection of network measurements,
the utilization of decision algorithms, and the overall goal of network optimization.
By effectively identifying and selecting high ranking neighbor cells,
telecommunications systems can ensure seamless handovers, provide better
10 coverage and service quality to mobile devices, and deliver an enhanced user
experience.
[0011] Existing systems for the identification of high-ranking neighbor cells in
telecommunications networks vary depending on the specific technology and
network infrastructure. The Received Signal Strength (RSS) Based Systems
15 determine the quality of neighboring cells based on their received signal strength.
The system selects the neighbor cell with the strongest signal as the high-ranking
neighbor cell. However, this approach has drawbacks as it may not consider other
important factors such as interference, signal quality, or network load, which can
impact the overall performance and suitability of the neighbor cell. The Signal-to-
20 Interference Ratio (SIR) Based Systems evaluate the SIR of neighboring cells to
identify high ranking neighbor cells. Higher SIR indicates better signal quality and
lower interference. However, SIR-based systems may not account for other crucial
factors such as cell load, traffic conditions, or specific quality of service
requirements, which can affect the selection of an optimal neighbor cell.
25 [0012] There is, therefore, a need to overcome the above drawbacks and
limitations in the current practices to provide an optimal solution for identifying
neighbor cells with a high rank to transfer the user’s connection. The system in the
present disclosure aims to leverage a combination of parameters, employ intelligent
algorithms, and consider real-time network conditions to accurately identify high
30 ranking neighbor cells for optimal handover decisions.
4
SUMMARY
[0013] The present disclosure discloses a method for identifying one or more
high rank neighbor cells in a network. The method includes collecting, by an
aggregation module, data corresponding to a plurality of parameters related to a
plurality of neighboring cells from an element management 5 system (EMS). The
method includes computing, by a performance module, one or more key
performance indicators (KPIs) for the plurality of neighbor cells based on the data
aggregated corresponding to each of the plurality of parameters over a first
predetermined time period. The method includes computing, by the performance
10 module, a plurality of KPIs for a plurality of source-target pairs. Each source-target
pair comprises a source cell and a target cell for handover. The method includes
computing, by the performance module, a total handover (HO) attempts over one
or more interfaces for a second predefined period for each source-target pair in a
service area, wherein each interface is a connection point between the source cell
15 and the target cell. The method includes calculate, by the performance module, a
percentage of HO share contributed by each source-target pair, wherein the
percentage of HO share contributed by each source-target pair is based upon a
number of HO attempts per source-target pair. The method includes identifying, by
a source-target module, one or more source-target pairs having the percentage of
20 HO share greater than a defined threshold. The method includes identifying, by the
source-target module, the one or more high rank neighbor cells by ranking, the
identified source-target pairs having the percentage of HO share greater than the
defined threshold and generating a list of the high ranked neighbor cells associated
with each source cell.
25 [0014] In an aspect, the percentage of HO share = {100 * [(total number of HO
attempts per source-target pair) / (total number of HO attempts for all interfaces for
the source cell)]}.
[0015] In an aspect, the plurality of parameters comprises one or more of a
signal strength, a signal quality, a plurality of interference levels, a data throughput,
30 call drop rates, a latency, and a capacity of the neighboring cell.
5
[0016] In an aspect, the first predefined time lies in a range of 15 to 30 minutes,
and the second predefined time lies in a range of one hour to two hours.
[0017] In an aspect, the method further includes a step of arranging, by the
source-target module, the plurality of source-target pairs in a descending order
based 5 on the percentage HO share.
[0018] In an aspect, the plurality of KPIs include one or more of signal
strength, signal quality, a plurality of interference levels, load balancing
requirements, a coverage area, a capacity, and a plurality of operator-defined
policies.
10 [0019] In an aspect, the method further includes a step of storing, by a database,
the generated list of high ranked neighbor cells associated with each of the source
cells.
[0020] In an aspect, the method further includes a step of analysing the one or
more high ranked neighbor cells associated with each source cell to enable handover
15 planning, cell compensation, and capacity planning.
[0021] The present disclosure discloses a system for identifying one or more
high rank neighbor cells in a network. The system includes an aggregation module,
a performance module, and a source-target module. The aggregation module is
configured to collect data corresponding to a plurality of parameters related to a
20 plurality of neighboring cells from an element management system (EMS). The
performance module is configured to compute one or more key performance
indicators (KPIs) for the plurality of neighbor cells based on the data aggregated
corresponding to each of the plurality of parameters over a first predetermined time
period. The performance module is configured to compute a plurality of KPIs for a
25 plurality of source-target pairs, wherein each source-target pair comprises a source
cell and a target cell for handover. The performance module is configured to
compute a total handover (HO) attempts towards over one or more interfaces for a
second predefined period for each source-target pair in the service area, wherein the
interface is a connection point between the source cell and the target cell. The
30 performance module is configured to calculate a percentage of HO share
contributed by each source-target pair, wherein the percentage of HO share
6
contributed by each source-target pair is based upon a number of HO attempts per
source-target pair. The source-target module is configured to identify one or more
source-target pairs having the percentage of HO share greater than a defined
threshold. The source-target module is configured to identify the one or more high
rank neighbor cells by ranking the identified source-t 5 arget pairs having the
percentage of HO share greater than the defined threshold and generate a list of the
high ranked neighbor cells associated with each source cell.
[0022] In an embodiment, the percentage of HO share = {100 * [(total number
of HO attempts per source-target pair) / (total number of HO attempts for all
10 interfaces for the source cell)]}.
[0023] In an embodiment, the plurality of parameters comprises one or more
of a signal strength, a signal quality, a plurality of interference levels, a data
throughput, call drop rates, a latency, and a capacity of the neighboring cells.
[0024] In an embodiment, the first predefined time lies in a range of 15 to 30
15 minutes, and the second predefined time lies in a range of one hour to two hours.
[0025] In an embodiment, the source-target module is configured to rank the
plurality of source-target pairs in descending order on basis of the percentage share
of HO attempts.
[0026] In an embodiment, the plurality of KPIs include one or more of signal
20 strength, signal quality, a plurality of interference levels, load balancing
requirements, a coverage area, a capacity, and a plurality of operator-defined
policies.
[0027] In an embodiment, the system includes a database (218) configured to
store the generated list of high ranked neighbor cells associated with each of the
25 source cells.
[0028] In an embodiment, the system is further configured to analyse the one
or more high ranked neighbor cells associated with each source cell to enable
handover planning, cell compensation, and capacity planning.
[0029] The present disclosure further discloses a user equipment which is
30 configured to identify one or more high rank neighbor cells in a network. The user
equipment includes a processor, and a computer readable storage medium storing
7
programming instructions for execution by the processor. Under the programming
instructions, the processor is configured to collect data corresponding to a plurality
of parameters related to a plurality of neighboring cells from an element
management system (EMS). Under the programming instructions, the processor is
configured to compute one or more key performance 5 indicators (KPIs) for the
plurality of neighbor cells based on the data aggregated corresponding to each of
the plurality of parameters over a first predetermined time period. Under the
programming instructions, the processor is configured to compute a plurality of
KPIs for a plurality of source-target pairs, wherein each source-target pair
10 comprises a source cell and a target cell for handover. Under the programming
instructions, the processor is configured to compute a total handover (HO) attempts
over one or more interfaces for a second predefined period for each source-target
pair in a service area, wherein each interface is a connection point between the
source cell and the target cell. Under the programming instructions, the processor
15 is configured to calculate a percentage of HO share contributed by each sourcetarget
pair, wherein the percentage of HO share contributed by each source-target
pair is based upon a number of HO attempts per source-target pair. Under the
programming instructions, the processor is configured to identify one or more
source-target pairs having the percentage of HO share greater than a defined
20 threshold. Under the programming instructions, the processor is configured to
identify the one or more high rank neighbor cells by ranking, the identified sourcetarget
pairs having the percentage of HO share greater than the defined threshold
and generate a list of the high ranked neighbor cells associated with each source
cell.
25
OBJECTS OF INVENTION
[0030] Some of the objects of the present disclosure, that at least one
embodiment herein satisfy are as listed herein below.
[0031] It is an object of the present disclosure to overcome the drawbacks
30 and limitations of the existing systems to identify a high-ranking cell to handover a
user’s connection.
8
[0032] It is an object of the present disclosure to accurately identify high
ranking neighbor cells, minimize call drops, reduce interruption in data sessions,
and ensure seamless handover transitions for mobile devices.
[0033] It is an object of the present disclosure to help maintain stronger and
more reliable connections, leading to improved voice call clarity, 5 faster data speeds,
and a better user experience.
[0034] It is an object of the present disclosure to distribute user traffic
evenly among neighboring cells, optimizing the utilization of network resources
and improving overall network capacity.
10 [0035] It is an object of the present disclosure to minimize the degradation
of signal quality and ensure a more stable and consistent connection for mobile
devices.
[0036] It is an object of the present disclosure to continuously monitors
network parameters, such as signal strength, signal quality, interference levels, and
15 cell load, to identify the most suitable high ranking neighbor cells based on the
prevailing conditions.
[0037] It is an object of the present disclosure to consider parameters and
thresholds set by the operators to prioritize certain neighbor cells over others,
aligning with the operator's network management objectives and ensuring
20 compliance with service level agreements.
[0038] It is an object of the present disclosure to improve the user
experience and satisfaction by providing seamless handover experiences, reliable
connections, and consistent service quality.
25 BRIEF DESCRIPTION OF DRAWINGS
[0039] The specifications of the present disclosure are accompanied with
drawings of the system and method to aid in better understanding of the said
invention. The drawings are in no way limitations of the present disclosure, rather
are meant to illustrate the ideal embodiments of the said disclosure.
30 [0040] In the figures, similar components and/or features may have the
same reference label. Further, various components of the same type may be
9
distinguished by following the reference label with a second label that distinguishes
among the similar components. If only the first reference label is used in the
specification, the description is applicable to any one of the similar components
having the same first reference label irrespective of the second reference label.
[0041] FIG. 1A illustrates a network architecture of 5 a system for identifying
one or more high rank neighbor cells in a network, in accordance with an
embodiment of the present invention.
[0042] FIG. 1B illustrates steps taken by the system for identification of high
ranked neighbor cells, in accordance with an embodiment of the present disclosure.
10 [0043] FIG. 2 illustrates an exemplary block diagram of the system, in
accordance with an embodiment of the present disclosure.
[0044] FIG. 3 illustrates an exemplary flow diagram of an identification of high
rank neighbor cells, in accordance with an embodiment of the present disclosure.
[0045] FIG. 4 illustrates an exemplary computer system in which or with which
15 embodiments of the present invention can be utilized, in accordance with an
embodiment of present disclosure.
[0046] FIG. 5 illustrates exemplary steps of a method identifying one or more
high rank neighbor cells in a network, in accordance with embodiments of the
present disclosure.
20 LIST OF REFERENCE NUMERALS
100-1 – Network Architecture
102 – Network
104 – Controller
106-1, 106-2…106-8 – Plurality of Base Stations
25 100-2 – System
202 – One or more processor(s)
204 – Memory
206 – A Plurality of Interfaces
208 – Processing Engine
30 210 – Performance Module
10
212 – Aggregation Module
214 – Source Target Module
216 – Other Module(s)
218 – Database
5 410 – External Storage Device
420 – Bus
430 – Main Memory
440 – Read Only Memory
450 – Mass Storage Device
10 460 – Communication Port
470 – Processor
DETAILED DESCRIPTION
[0047] In the following description, for explanation, various specific details
15 are outlined in order to provide a thorough understanding of 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
20 problems discussed above or might address only some of the problems discussed
above. Some of the problems discussed above might not be fully addressed by any
of the features described herein.
[0048] The ensuing description provides exemplary embodiments only and
is not intended to limit the scope, applicability, or configuration of the disclosure.
25 Rather, the ensuing description of the exemplary embodiments will provide those
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.
11
[0049] Specific details are given in the following description to provide a
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 5 diagram form in order not to
obscure the embodiments in unnecessary detail. In other instances, well-known
circuits, processes, algorithms, structures, and techniques may be shown without
unnecessary detail to avoid obscuring the embodiments.
[0050] Also, it is noted that individual embodiments may be described as a
10 process that 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 order of the operations may be re-arranged.
A process is terminated when its operations are completed but could have additional
15 steps not included in a figure. A process may correspond to a method, a function, a
procedure, a subroutine, a subprogram, etc. When a process corresponds to a
function, its termination can correspond to a return of the function to the calling
function or the main function.
[0051] The word “exemplary” and/or “demonstrative” is used herein to
20 mean serving as an example, instance, or illustration. For the avoidance of doubt,
the subject matter disclosed herein is not limited by such examples. In addition, any
aspect or design described herein as “exemplary” and/or “demonstrative” is not
necessarily to be construed as preferred or advantageous over other aspects or
designs, nor is it meant to preclude equivalent exemplary structures and techniques
25 known to those of ordinary skill in the art. Furthermore, to the extent that the terms
“includes,” “has,” “contains,” and other similar words are used in either the detailed
description or the claims, such terms are intended to be inclusive like the term
“comprising” as an open transition word without precluding any additional or other
elements.
30 [0052] Reference throughout this specification to “one embodiment” or “an
embodiment” or “an instance” or “one instance” means that a particular feature,
12
structure, or characteristic described in connection with the embodiment is included
in at least one embodiment of the present disclosure. Thus, the appearances of the
phrases “in one embodiment” or “in an embodiment” in various places throughout
this specification are not necessarily all referring to the same embodiment.
Furthermore, the particular features, structures, or characteristics 5 may be combined
in any suitable manner in one or more embodiments.
[0053] The terminology used herein is to describe particular embodiments
only and is not intended to be limiting the disclosure. As used herein, the singular
forms “a”, “an”, and “the” are intended to include the plural forms as well, unless
10 the context indicates otherwise. It will be further understood that the terms
“comprises” and/or “comprising,” when used in this specification, specify the
presence of stated features, integers, steps, operations, elements, and/or
components, but do not preclude the presence or addition of one or more other
features, integers, steps, operations, elements, components, and/or groups thereof.
15 As used herein, the term “and/or” includes any combinations of one or more of the
associated listed items.
[0054] The various embodiments throughout the disclosure will be
explained in more detail with reference to FIGS. 1-5.
[0055] The present disclosure relates to the field of telecommunications and
20 network management. More precisely, it relates to a system for the identification of
high-ranking neighbor cells for handing over the user’s connection from the present
serving cell to the neighbor cell.
[0056] FIG. 1A illustrates a network architecture (100- 1) of a system for
identifying one or more high rank neighbor cells in a network, in accordance with
25 an embodiment of the present invention.
[0057] The network architecture (100-1) comprises a controller (104), a
plurality of base stations (106-1, 106-2, 106-3, 106-4… 106-N) and at least one
user equipment (108) in a network (102). The controller (104) may be a system for
identification of high ranked neighbor cells in the network (102). The plurality of
30 base station may be communicatively coupled to the user equipment (108). The
plurality of base station is communicatively coupled to the controller (104).
13
[0058] FIG. 1B illustrates a block diagram for identification of high ranked
neighbor cells, in accordance with an embodiment of the present disclosure.
[0059] As illustrated, in FIG. 1B, a block diagram of the system (100-2) for
identification of high ranked neighbors is disclosed.
[0060] At step (120), the system (100-2) in the present 5 disclosure collects
neighbor related statistics from vendor element management system (EMS). The
neighbor related statistics may include raw performance management counter data.
The neighbor related statistics comprises a signal strength, a signal quality, a
plurality of interference levels, a data throughput, call drop rates, a latency, and a
10 capacity.
[0061] At step (130), the system (100-2) in the present disclosure finds the
list of potential neighbor for all the cells on rolling weekly basis. The total handover
attempts towards all the interfaces over a rolling period of 7 days is counted for all
the cells in the service area. Similarly, total handover attempts towards all the
15 interfaces over a rolling period of 7 days is counted for each of the unique sourcetarget
pair. The percentage of contribution of attempts of each source-target pair in
overall Handover attempts is also computed.
[0062] At step (140), the handover share is computed as total HO attempts
per source-target pair to total HO attempts for each cell. The percentage of
20 contribution/share of attempts of each source-target pair in overall Handover
attempts is also computed.
[0063] At step (150), the source-target pairs are arranged in descending
order basis of a percentage share of handover attempts. Any source-target pair
having more than 10% (value is configurable) of a handover share is identified as a
25 high ranked neighbor. The system (100-2) computes key performance indictors
(KPIs) for source-target pair and assigns rank. The KPIs comprises signal strength,
signal quality, a plurality of interference levels, load balancing requirements, a
coverage area, a capacity, and a plurality of operator-defined policies.
[0064] At step (160), the high ranked neighbors are identified with these
30 features which are used in other algorithms like capacity planning and also for cell
compensation for mitigating the cell outage. The high ranked neighbor cells state
14
helps the operations team to identify the cause for the performance degradation of
the other cells.
[0065] In an embodiment, in a typical telecom network, as represented in the
system (100-2) in the present disclosure, maintaining service continuity and good
customer experience is of prime importance. Providing 5 good customer experience
in case of mobility scenario is a challenging task as the end user is not static. In
such a scenario, the user can be latched initially to one of the telecom nodes and
during the ongoing call/data session it might happen that the user’s connection is
handed over from the present serving cell to the neighbor cell. To ensure a seamless
10 experience it becomes essential to have proper handover related definitions between
the adjacent nodes and all the neighbor definitions which are properly audited and
optimized parameter settings are in place. With the introduction of self-optimization
feature in next generation networks (5G/6G), the system (100-2) adds the neighbors
automatically based on certain pre-defined criteria. Generally, one cell can have to
15 the tune of number of neighbor cells and the number of neighbor cells may vary
from case to case depending upon the Handover scenario. In such a case, it is
essential to identify the high ranked neighbor which are required for capacity
planning of the network. In the case of cell outage to improve the customer
experience, cell compensation is done by optimizing the high ranked neighbors.
20 Moreover, in most of the cases each of the source cell will be having very few
potential neighbors which will be contributing to 90-95 percentage of overall
handover attempts.
[0066] In an embodiment, the optimal strategy for identifying high ranked
neighbor cells in a telecommunications network may vary depending on network25
specific requirements and conditions. The system (100-2) chooses relevant
performance metrics that accurately reflect the quality and suitability of neighbor
cells. These metrics can include signal strength, signal quality, interference levels,
data throughput, call drop rates, latency, and capacity. The system (100) assigns
appropriate weights to performance metrics based on their significance in
30 determining the ranking of neighbor cells. It also sets threshold values for each
metric to define the desired performance level for high ranked neighbors and fine-
15
tunes the weights and thresholds based on network-specific requirements and
quality of service (QoS) targets. The system (100-2) can include weighted
averaging, fuzzy logic, machine learning, or other statistical methods. Consider
algorithms that can effectively handle large datasets, adapt to changing network
conditions, and provide accurate rankings based on the chosen 5 metrics. The system
(100-2) is designed to adapt dynamically to real-time network changes and
continuously monitor and update the performance metrics and rankings based on
the latest data.
[0067] FIG. 2 illustrates an exemplary block diagram of the system (100-2), in
10 accordance with an embodiment of the present disclosure.
[0068] As illustrated, in FIG. 2, an exemplary block diagram (200) of the
processing engine (208) and all its modules is disclosed. The processing engine
(208) for high ranked neighbor identification in telecommunications network
typically involves the use of advanced algorithms and computational techniques.
15 The processor (202), memory (204), and interface (206) are used for execution of
programming instructions. The processing engine (208) analyses collected data,
applies algorithms, and performs calculations to determine the ranking of neighbor
cells and the database (218) stores all data. The processing engine (208) collects
relevant network measurements and parameters from the serving cell and
20 neighboring cells. This data includes signal strength, signal quality, interference
levels, cell load, available capacity, and other performance metrics. The collected
data is pre-processed to ensure consistency, accuracy, and compatibility for further
analysis. The designated modules and the other executing modules (216) are used
for an execution of identification of high ranked cells.
25 [0069] The performance module is configured to compute one or more key
performance indicators (KPIs) for the plurality of neighbor cells based on the data
aggregated corresponding to each of the plurality of parameters over a first
predetermined time period. In an example, the plurality of parameters comprises
one or more of a signal strength, a signal quality, a plurality of interference levels,
30 a data throughput, call drop rates, a latency, and a capacity of the neighboring cells.
In an example, the first predefined time lies in a range of 15 to 30 minutes. The
16
performance module is configured to compute a plurality of KPIs for a plurality of
source-target pairs. Each source-target pair comprises a source cell and a target cell
for handover. The performance module is configured to compute a total handover
(HO) attempts towards over one or more interfaces for a second predefined period
for each source-target pair in the service area. The interface 5 is a connection point
between the source cell and the target cell. In an example, the second predefined
time lies in a range of one hour to two hours. The performance module is configured
to calculate a percentage of HO share contributed by each source-target pair,
wherein the percentage of HO share contributed by each source-target pair is based
10 upon a number of HO attempts per source-target pair. The percentage of HO share
is defined as {100 * [(total number of HO attempts per source-target pair) / (total
number of HO attempts for all interfaces for the source cell)]}.
[0070] In an embodiment, a performance module (210) is used for the
identification of high ranked neighbor cells in a telecommunications network and
15 is responsible for evaluating the performance metrics and determining the
suitability of neighboring cells for handover. It involves analysing various
performance indicators and applying algorithms to assess the quality and ranking
of neighbor cells. The performance module (210) collects and analyses performance
metrics related to neighboring cells, such as signal strength, signal quality,
20 interference levels, data throughput, call drop rates, latency, and other relevant
parameters. These metrics are used to evaluate the performance of neighbor cells
and determine their ranking. The performance module (210) sets threshold values
for performance metrics based on network operator policies, quality of service
(QoS) requirements, and industry standards. These thresholds define the acceptable
25 levels of performance for high ranked neighbor cells. Metrics that fall within or
exceed the defined thresholds are considered favourable for handover. The
performance module (210) evaluates the collected metrics against the predefined
thresholds and algorithmic rules. It assesses the performance of each neighbor cell
and assigns scores or rankings based on their compliance with the performance
30 criteria. The neighbor cells that exhibit superior performance characteristics are
assigned high rankings.
17
[0071] In an embodiment, an aggregation module (212) can be used to
consolidate and analyse data from multiple sources to determine the overall
ranking. The aggregation module is configured to collect data corresponding to a
plurality of parameters related to a plurality of neighboring cells from an element
management system (EMS). The aggregation module (212) 5 collects relevant data
from various sources, such as network measurement databases, performance
monitoring systems, network management systems, and other data repositories.
This data includes performance metrics, signal strength, signal quality, interference
levels, load information, and other parameters related to neighbor cells. It also
10 integrates the collected data from different sources and consolidates it into a unified
dataset. This integration process ensures that all relevant data points are considered
in the analysis. The aggregation module (212) may also perform data cleansing and
normalization to ensure consistency and accuracy. The aggregation module (212)
assigns weights to different performance metrics or parameters based on their
15 significance in determining the ranking of neighbor cells. For example, signal
quality may be given higher weightage compared to signal strength or interference
levels. The aggregation module (212) applies an aggregation algorithm to combine
the weighted performance metrics and parameters for each neighbor cell. This
algorithm calculates an aggregated score or rank based on the weighted values.
20 Various aggregation techniques can be used, such as weighted averages, weighted
sums, or more advanced methods like multi-criteria decision-making algorithms.
[0072] In an embodiment, the aggregation module (212) also calculates the
rankings of neighbor cells. Cells with higher aggregated scores are assigned higher
rankings, indicating their suitability as high ranked neighbors. The ranking
25 calculation process may involve normalization of scores to ensure fair comparison
across different metrics. The aggregation module (212) is designed to adapt to
dynamic network conditions and changing data. It continuously updates the
aggregated scores and rankings based on real-time data updates. This ensures that
the rankings remain up to date and reflective of the current network performance.
30 The aggregation module (212) integrates with the handover decision process,
providing the ranking information to the handover control system (100-2) or
18
network management systems. This enables the selection of high ranked neighbor
cells for handover decisions, considering the aggregated scores and rankings. The
aggregation module (212) plays a critical role in consolidating and analysing data
from multiple sources to determine the overall ranking of neighbor cells. By
considering various performance metrics and 5 applying appropriate aggregation
techniques, it provides a comprehensive evaluation of neighbor cell suitability for
handover, enabling the selection of higher ranked neighbors for improved network
performance and user experience.
[0073] In an embodiment, a source-target module (214) is used for determining
10 the potential neighbor cell candidates that have a high ranking compared to the
serving cell. The source-target module is configured to identify one or more sourcetarget
pairs having the percentage of HO share greater than a defined threshold. The
source-target module is configured to identify the one or more high rank neighbor
cells by ranking the identified source-target pairs having the percentage of HO share
15 greater than the defined threshold and generate a list of the high ranked neighbor
cells associated with each source cell. It analyses the relationship between the
serving cell and potential neighbor cells based on various criteria to identify high
ranked neighbors. It evaluates the performance and characteristics of the serving
cell, including signal strength, signal quality, interference levels, load, capacity, and
20 other relevant parameters. This evaluation serves as a reference point for comparing
potential neighbor cells. The source-target module (214) assesses the performance
and suitability of potential neighbor cells based on various criteria. These criteria
may include signal strength, signal quality, interference levels, load balancing
requirements, coverage area, capacity, and operator-defined policies. The source25
target module (214) also compares the ranking or scores of potential neighbor cells
with that of the serving cell. It identifies the neighbor cells that have a higher
ranking or score, indicating their potential to provide better performance or
coverage compared to the serving cell. It provides the ranking information to the
handover decision process or network management systems. Based on the ranking
30 results, the handover decision process can select the higher ranked neighbor cell as
the target for handover, considering other factors such as handover policies, QoS
19
requirements, and network conditions. The source-target module (214) adapts
dynamically to changing network conditions and real-time updates. It continuously
evaluates the performance of neighbor cells and updates the rankings based on the
latest measurements. This ensures that the identification of higher ranked neighbors
remains accurate and responsive to the dynamic nature 5 of the network. The sourcetarget
module is configured to rank the plurality of source-target pairs in descending
order on basis of the percentage share of HO attempts.
[0074] In an embodiment, the system is further configured to analyse the one
or more high ranked neighbor cells associated with each source cell to enable
10 handover planning, cell compensation, and capacity planning.
[0075] In an aspect of the present invention, the controller (104) is configured
as the system (100-2) for high ranked neighbor cells identification in
telecommunications network.
[0076] FIG. 3 illustrates an exemplary flow diagram (300) of an identification
15 of high ranked neighbor cells, in accordance with an embodiment of the present
disclosure.
[0077] As illustrated, in FIG. 3, an exemplary flow diagram (300) for the
identification of high ranked neighbor cells is disclosed.
[0078] At step (302), the system (100-2) collects relevant measurements
20 from the serving cell and neighboring cells. These measurements include signal
strength, signal quality, interference levels, cell load, available capacity, and other
performance metrics. The system (100-2) may also consider historical data and
trends to supplement the real-time measurements. All the parameters and statistics
are extracted from a Vendor Element Management System (EMS) for predefined
25 time (e.g., every fifteen minutes).
[0079] At step (304), based on the collected measurements, the system
(100-2) calculates various parameters that indicate the quality and suitability of
neighbor cells. These parameters may include signal-to-interference ratio (SIR),
signal-to-noise ratio (SNR), received power levels, cell load ratio, and other derived
30 metrics that reflect the performance of neighboring cells. The KPIs related to the
neighbor cells are computed by aggregation each hour. The system (100-2) assigns
20
appropriate weights to the calculated parameters based on their significance and
impact on the handover decision. The weights reflect the relative importance of
each parameter in determining the ranking of neighbor cells. Additionally, the
system (100-2) sets threshold values for each parameter to define the desired quality
or performance level for a high-ranking neighbor cell. KPIs 5 are also calculated for
the source-target pair which includes the source or the current cell and the highest
ranked neighboring cell.
[0080] As illustrated, in FIG. 3, at step (306), the system (100-2) computes
daily neighbor related KPIs by aggregation of hourly data. KPIs are computed for
10 all source-target pair.
[0081] At step (308), the total handover (HO) attempts towards all the
interfaces over a rolling period of 7 days are computed for each cell in service area.
Total HO attempts for each cell = A.
[0082] At step (310), the total handover (HO) attempts towards all the
15 interfaces over a rolling period of 7 days are computed for every source-target pair
in a given service area. Total HO attempts per source-target pair = B.
[0083] At step (312), the handover share is computed as total HO attempts
per source-target pair to total HO attempts for each cell. The percentage of handover
shares contributed by each target-source pair is calculated. The formula is given
20 by: percentage HO share = {100* [(total HO attempts per Source-Target pair (B))/
(total HO attempts for each cell (A))]}.
[0084] At step (314), determining whether percentage share of HO
contributed by a particular source-target pair > configurable number/certain
threshold (e.g., 10 percentage).
25 [0085] At step (316), if the calculated percentage is higher than a certain
threshold then the target cell is considered as a high ranked neighbor for the source
cell.
[0086] At step (318), if the calculated percentage is not higher than a certain
threshold then the target cell is not considered as a high ranked neighbor for the
30 source cell.
21
[0087] At step (320), all the high ranked neighbors are stored in for each
source cell is stored in the database (218). This can be used for a cell compensation
in the case of any source cell outage to improve a user experience.
[0088] At step (322), all the high ranked neighbors are stored in for each
source cell is stored in the database (218). The 5 high ranked neighbors can also be
used for capacity planning algorithms. This implementation will filter the potential
neighbor relations for each of the cells depending upon the criteria and hence field
team can focus their optimization activity on the cells which are having major share
of attempts. Load Shifting, Parameter Related changes, Azimuth and Tilt related
10 optimization activities can be planned easily depending on the potential neighbor
information.
[0089] As illustrated, in FIG. 3, a neighbor cell evaluation uses the calculated
parameters and defined thresholds to evaluate each neighboring cell to determine
its ranking. The system (100-2) compares the parameter values of each neighbor
15 cell against the thresholds and assigns a ranking score or priority based on the
compliance with the defined criteria. The system (100-2) also ranks the neighbor
cells based on their evaluation scores or priorities. Cells that meet or exceed the
threshold values for the defined parameters are assigned higher rankings. The
system (100-2) may also consider additional factors such as network policies, load
20 balancing requirements, or specific quality of service (QoS) criteria to further refine
the ranking. Based on the rankings, the system (100-2) makes a handover decision,
indicating the preferred high ranking neighbor cell for handover. The decision is
communicated to the network management systems or mobile devices, which
initiate the handover process to connect to the selected neighbor cell.
25 [0090] In an embodiment, the system (100-2) continuously monitors the
performance of the selected neighbor cell after handover. It gathers feedback on
signal quality, user experience, and network conditions to validate the accuracy of
the handover decision. This feedback is used to refine the parameters, thresholds,
and ranking algorithms in subsequent iterations. The system (100-2) is designed to
30 adapt dynamically to changing network conditions. It continuously updates the
measurements, recalculates the parameters, and adjusts the rankings based on real-
22
time data. This ensures that the identification of higher-ranking neighbor cells
remains accurate and responsive to the evolving network environment. By
following this process flow, the system (100-2) can effectively identify higher
ranking neighbor cells, enabling seamless handover and improving the overall
performance and user experience in a 5 telecommunications network.s
[0091] FIG. 4 illustrates an exemplary computer system (400) in which or with
which embodiments of the present invention can be utilized, in accordance with an
embodiment of present disclosure.
[0092] The computer system (400) includes input devices (402) connected
10 through I/O peripherals. The system (400) also includes a Central Processing Unit
(CPU) (404), and Output Devices (408), connected through the I/O peripherals. The
CPU (404) is also attached to a memory unit (416) along with an Arithmetic and
Logical Unit (ALU) (414), a control unit (412), along with secondary storage
devices (410) such as Hard Disks and a Secure Digital Card (SD). The data flow
15 and control flow (406) are indicated by a straight and dashed arrow respectively.
The CPU consists of data registers that hold the data bits, pointers, cache, Random
Access Memory (RAM) (204), and a main processing unit containing the
processing engine (208). The system (400) also consists of communication buses
that are used to transport the data internally in the system (400).
20 [0093] In an embodiment, a processor (202) of the system (100-2) is used to
process all the data that is required for identification of a higher ranked neighbor
cell. A person skilled in the art will appreciate that the system (100-2) may include
more than one processor (202) and communication ports for ease of function.
Examples of processors (202) include, but are not limited to, an Intel® Itanium®
25 or Itanium 2 processor (s), or AMD® Opteron® or Athlon MP® processor (s),
Motorola® lines of processors, FortiSOC™ system on a chip processor or other
future processors. The processor (202) may include various modules associated
with embodiments of the present invention. The input component can also include
communication ports, ethernet ports, gigabit ports, parallel port, or another
30 Universal Serial Bus (USB). The communication port can also be chosen depending
on a specific network such as a Wide Area Server (WAN), Local Area Network
23
LAN), or a Personal Area Network (PAN). The communication port can be a RS-
232 port that can be used with the remote dialling and internet connection options
of the system (400). A Gigabit port can be used to connect the system (400) to the
internet at all times. And the Gigabit port can use copper or fibre for connection.
[0094] FIG. 5 illustrates exemplary steps of 5 a method (500) identifying one or
more high rank neighbor cells in a network, in accordance with embodiments of the
present disclosure.
[0095] At step (502), an aggregation module collects the data corresponding to a
plurality of parameters related to a plurality of neighboring cells from an element
10 management system (EMS). In an example, the plurality of parameters comprises
one or more of a signal strength, a signal quality, a plurality of interference levels,
a data throughput, call drop rates, a latency, and a capacity of the neighboring cell.
[0096] At step (504), a performance module computes one or more key
performance indicators (KPIs) for the plurality of neighbor cells based on the data
15 aggregated corresponding to each of the plurality of parameters over a first
predetermined time period. In an example, the first predefined time lies in a range
of 15 to 30 minutes.
[0097] At step (506), the performance module computes a plurality of KPIs for a
plurality of source-target pairs. Each source-target pair comprises a source cell and
20 a target cell for handover. In an aspect, the plurality of KPIs include one or more of
signal strength, signal quality, a plurality of interference levels, load balancing
requirements, a coverage area, a capacity, and a plurality of operator-defined
policies.
[0098] At step (508), the performance module computes a total handover (HO)
25 attempts over one or more interfaces for a second predefined period for each sourcetarget
pair in a service area, wherein each interface is a connection point between
the source cell and the target cell. In an example, the second predefined time lies in
a range of one hour to two hours.
[0099] At step (510), the performance module calculates a percentage of HO share
30 contributed by each source-target pair, wherein the percentage of HO share
24
contributed by each source-target pair is based upon the number of HO attempts per
source-target pair.
[0100] At step (512), a source-target module identifies one or more source-target
pairs having the percentage of HO share greater than a defined threshold.
[0101] At step (514), the source-target module identifies 5 the one or more high rank
neighbor cells by ranking, the identified source-target pairs having the percentage
of HO share greater than the defined threshold and generates a list of the high
ranked neighbor cells associated with each source cell.
[0102] In an aspect, the percentage of HO share = {100 * [(total number of HO
10 attempts per source-target pair) / (total number of HO attempts for all interfaces for
the source cell)]}.
[0103] In an aspect, the method further includes a step of arranging, by the sourcetarget
module, the plurality of source-target pairs in a descending order based on
the percentage HO share.
15 [0104] In an aspect, the method further includes a step of storing, by a database
(218), the generated list of high ranked neighbor cells associated with each of the
source cells.
[0105] In an aspect, the method further includes a step of analysing the one or more
high ranked neighbor cells associated with each source cell to enable handover
20 planning, cell compensation, and capacity planning.
[0106] In an exemplary aspect, the present disclosure discloses a user equipment
which is configured to identify one or more high rank neighbor cells in a network.
The user equipment includes a processor, and a computer readable storage medium
storing programming instructions for execution by the processor. Under the
25 programming instructions, the processor is configured to collect data corresponding
to a plurality of parameters related to a plurality of neighboring cells from an
element management system (EMS). Under the programming instructions, the
processor is configured to compute one or more key performance indicators (KPIs)
for the plurality of neighbor cells based on the data aggregated corresponding to
30 each of the plurality of parameters over a first predetermined time period. Under
the programming instructions, the processor is configured to compute a plurality of
25
KPIs for a plurality of source-target pairs, wherein each source-target pair
comprises a source cell and a target cell for handover. Under the programming
instructions, the processor is configured to compute a total handover (HO) attempts
over one or more interfaces for a second predefined period for each source-target
pair in a service area, wherein each interface is a 5 connection point between the
source cell and the target cell. Under the programming instructions, the processor
is configured to calculate a percentage of HO share contributed by each sourcetarget
pair, wherein the percentage of HO share contributed by each source-target
pair is based upon a number of HO attempts per source-target pair. Under the
10 programming instructions, the processor is configured to identify one or more
source-target pairs having the percentage of HO share greater than a defined
threshold. Under the programming instructions, the processor is configured to
identify the one or more high rank neighbor cells by ranking, the identified sourcetarget
pairs having the percentage of HO share greater than the defined threshold
15 and generate a list of the high ranked neighbor cells associated with each source
cell.
[0107] It is to be appreciated by a person skilled in the art that while
various embodiments of the present disclosure have been elaborated for
identification of a higher ranked neighbor cell. However, the teachings of the
20 present disclosure are also applicable for other types of applications as well, and all
such embodiments are well within the scope of the present disclosure. However,
the system (100-2) and method for sign language conversion is also equally
implementable in other industries as well, and all such embodiments are well within
the scope of the present disclosure without any limitation.
25 [0108] Moreover, in interpreting the specification, all terms should be
interpreted in the broadest possible manner consistent with the context. In
particular, the terms “comprises” and “comprising” should be interpreted as
referring to elements, components, or steps in a non-exclusive manner, indicating
that the referenced elements, components, or steps may be present, or utilized, or
30 combined with other elements, components, or steps that are not expressly
referenced. Where the specification claims refer to at least one of something
26
selected from the group consisting of A, B, C….and N, the text should be
interpreted as requiring only one element from the group, not A plus N, or B plus
N, etc.
[0109] While considerable emphasis has been placed herein on the
preferred embodiments it will be appreciated that 5 many embodiments can be made
and that many changes can be made in the preferred embodiments without departing
from the principles of the disclosure. These and other changes in the preferred
embodiments of the disclosure will be apparent to those skilled in the art from the
disclosure herein, whereby it is to be distinctly understood that the foregoing
10 descriptive matter is to be implemented merely as illustrative of the disclosure and
not as a limitation.
ADVANTAGES OF THE INVENTION
[0110] The present invention provides a system for efficiently executing an
15 identification of highest-ranking neighbor cell.
[0111] The present invention provides a system that ensures that handovers
occur to cells that provide superior performance to improve network performance
in terms of call quality, data transfer rates, reduced call drops, and minimized
latency.
20 [0112] The present invention provides a system that enables seamless
handovers with minimal disruptions, leading to a more satisfactory and
uninterrupted communication experience.
[0113] The present invention provides a system that facilitates load balancing
by distributing traffic among neighboring cells, thereby reducing congestion on
25 specific cells, and maximizing the overall network capacity.
[0114] The present invention provides a system that employs advanced
algorithms and performance metrics to evaluate the performance of neighbor cells
objectively.
[0115] The present invention provides a system that incorporates dynamic
30 adaptation techniques, continuously monitoring and updating the performance
rankings based on real-time network conditions.
27
[0116] The present invention provides a system that allows for quick
adjustment to changes in signal strength, interference levels, and other performance
indicators, ensuring the rankings remain up to date.
[0117] The present invention provides a system that can be customized to fit
specific network requirements, including different 5 technologies, deployment
scenarios, and operator-defined policies.
[0118] The present invention provides a system that provides a clear ranking of
neighbor cells, and assists in the handover decision-making process.
[0119] The present invention provides a system that provides informed
10 decisions based on objective performance metrics, leading to more efficient and
accurate handovers.
[0120] The present invention provides a system that helps minimize handover
failures and unsuccessful handover attempts and reduces call drops and improves
overall network reliability.
We claim:
1. A method (500) for identifying one or more high rank neighbor cells in a network, the method comprising:
collecting (502), by an aggregation module (212), data corresponding to a plurality of parameters related to a plurality of neighboring cells from an element management system (EMS);
computing (504), by a performance module (210), one or more key performance indicators (KPIs) for the plurality of neighbor cells based on the data aggregated corresponding to each of the plurality of parameters over a first predetermined time period;
computing (506), by the performance module (210), a plurality of KPIs for a plurality of source-target pairs, wherein each source-target pair comprises a source cell and a target cell for handover;
computing (508), by the performance module (210), a total handover (HO) attempts over one or more interfaces for a second predefined period for each source-target pair in a service area, wherein each interface is a connection point between the source cell and the target cell;
calculating (510), by the performance module (210), a percentage of HO share contributed by each source-target pair, wherein the percentage of HO share contributed by each source-target pair is based upon a number of HO attempts per source-target pair;
identifying (512), by a source-target module (214), one or more source-target pairs having the percentage of HO share greater than a defined threshold; and
identifying (514), by the source-target module (214), the one or more high rank neighbor cells by ranking, the the identified source-target pairs having the percentage of HO share greater than the defined threshold and generating a list of the high ranked neighbor cells associated with each source cell.

2. The method (500) as claimed in claim 1, wherein the percentage of HO share = {100 * [(total number of HO attempts per source-target pair) / (total number of HO attempts for all interfaces for the source cell)]}.
3. The method (500) as claimed in claim 1, wherein the plurality of parameters comprises one or more of a signal strength, a signal quality, a plurality of interference levels, a data throughput, call drop rates, a latency, and a capacity of the neighboring cell.
4. The method (500) as claimed in claim 1, wherein the first predefined time lies in a range of 15 to 30 minutes, and the second predefined time lies in a range of one hour to two hours.
5. The method (500) as claimed in claim 1, further comprising arranging, by the source-target module (214), the plurality of source-target pairs in a descending order based on the percentage HO share.
6. The method (500) as claimed in claim 1, wherein the plurality of KPIs include one or more of signal strength, signal quality, a plurality of interference levels, load balancing requirements, a coverage area, a capacity, and a plurality of operator-defined policies.
7. The method (500) as claimed in claim 1, further comprising storing, by a database (218), the generated list of high ranked neighbor cells associated with each of the source cells.
8. The method (500) as claimed in claim 1, further comprising analysing the one or more high ranked neighbor cells associated with each source cell to enable handover planning, cell compensation, and capacity planning.

9. A system (100-2) for identifying one or more high rank neighbor cells in a network, the system comprising:
an aggregation module (212) configured to collect data corresponding to a plurality of parameters related to a plurality of neighboring cells from an element management system (EMS); a performance module (210) configured to:
compute one or more key performance indicators (KPIs) for the plurality of neighbor cells based on the data aggregated corresponding to each of the plurality of parameters over a first predetermined time period;
compute a plurality of KPIs for a plurality of source-target pairs, wherein each source-target pair comprises a source cell and a target cell for handover;
compute a total handover (HO) attempts towards over one or more interfaces for a second predefined period for each source-target pair in the service area, wherein the interface is a connection point between the source cell and the target cell;
calculate a percentage of HO share contributed by each source-target pair, wherein the percentage of HO share contributed by each source-target pair is based upon a number of HO attempts per source-target pair; and a source-target module (214) configured to:
identify one or more source-target pairs having the percentage of HO share greater than a defined threshold; and
identify the one or more high rank neighbor cells by ranking the identified source-target pairs having the percentage of HO share greater than the defined threshold and generate a list of the high ranked neighbor cells associated with each source cell.

10. The system (100-2) as claimed in claim 9, wherein the percentage of HO share = {100 * [(total number of HO attempts per source-target pair) / (total number of HO attempts for all interfaces for the source cell)]}.
11. The system (100-2) as claimed in claim 9, wherein the plurality of parameters comprises one or more of a signal strength, a signal quality, a plurality of interference levels, a data throughput, call drop rates, a latency, and a capacity of the neighboring cells.
12. The system (100-2) as claimed in claim 9, wherein the first predefined time lies in a range of 15 to 30 minutes, and the second predefined time lies in a range of one hour to two hours.
13. The system (100-2) as claimed in claim 9, wherein the source-target module (214) is configured to rank the plurality of source-target pairs in descending order on basis of the percentage share of HO attempts.
14. The system (100-2) as claimed in claim 9, wherein the plurality of KPIs include one or more of signal strength, signal quality, a plurality of interference levels, load balancing requirements, a coverage area, a capacity, and a plurality of operator-defined policies.
15. The system (100-2) as claimed in claim 9, includes a database (218) configured to store the generated list of high ranked neighbor cells associated with each of the source cells.
16. The system (100-2) as claimed in claim 9, is further configured to analyse the one or more high ranked neighbor cells associated with each source cell to enable handover planning, cell compensation, and capacity planning.
17. A user equipment configured to identify one or more high rank neighbor cells in a network, the user equipment comprising:

a processor; and
a computer readable storage medium storing programming instructions for execution by the processor, the programming instructions to:
collect data corresponding to a plurality of parameters related to a plurality of neighboring cells from an element management system (EMS);
compute one or more key performance indicators (KPIs) for the plurality of neighbor cells based on the data aggregated corresponding to each of the plurality of parameters over a first predetermined time period;
compute a plurality of KPIs for a plurality of source-target pairs, wherein each source-target pair comprises a source cell and a target cell for handover;
compute a total handover (HO) attempts over one or more interfaces for a second predefined period for each source-target pair in a service area, wherein each interface is a connection point between the source cell and the target cell;
calculate a percentage of HO share contributed by each source-target pair, wherein the percentage of HO share contributed by each source-target pair is based upon a number of HO attempts per source-target pair;
identify one or more source-target pairs having the percentage of HO share greater than a defined threshold; and
identify the one or more high rank neighbor cells by ranking, the identified source-target pairs having the percentage of HO share greater than the defined threshold and generate a list of the high ranked neighbor cells associated with each source cell.