DESC:FORM 2
THE PATENTS ACT, 1970 (39 OF 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)

SYSTEM AND METHOD FOR ESTIMATING POPULATION COVERAGE IN A WIRELESS COMMUNICATION NETWORK

Jio Platforms Limited, an Indian company, having registered address at Office -101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India

The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD
The embodiments of the present disclosure generally relate to the field of wireless communication networks. More particularly, the present disclosure relates to a system and a method for estimating population coverage in a wireless communication network.
BACKGROUND OF THE INVENTION
The subject matter disclosed in the background section should not be assumed or construed to be prior art merely because of its mention in the background section. Similarly, any problem statement mentioned in the background section or its association with the subject matter of the background section should not be assumed or construed to have been previously recognized in the prior art.
In the realm of telecommunication networks, ensuring comprehensive network coverage is crucial for service providers seeking to deliver seamless services to users. To ensure the comprehensive network coverage, population coverage analysis stands out as a critical tool for enabling an optimization of resource allocation, an enhancement of a Quality of Service (QoS), and expansion of the telecommunication networks.
However, despite the importance of the population coverage analysis in the telecommunication networks, existing solutions suffer from several inherent limitations. One of the limitations of the existing solutions stems from fragmented nature of data sources leading to inconsistencies and inaccuracies in coverage assessment. Additionally, the existing solutions use manual processes for performing the population coverage analysis and thus are complex and inefficient in timely decision making and resource allocation.
Further, the existing solutions of the population coverage analysis uses static analysis and rely on disparate data sources. The existing solutions do not consider a dynamic nature of the telecommunication networks and behavior of the users in which network infrastructures evolve and the users demand shift. The Static analysis in the existing solutions fail to provide complete insights necessary to adapt and optimize process for performing the population coverage analysis.
Therefore, to overcome aforementioned challenges and limitations associated with the existing methodologies of the population coverage analysis, there lies a need for an improved system and method for estimating population coverage in a specific area within a wireless communication network.
SUMMARY
The following embodiments present a simplified summary in order to provide a basic understanding of some aspects of the disclosed invention. This summary is not an extensive overview, and it is not intended to identify key/critical elements or to delineate the scope thereof. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In an embodiment, a method for estimating population coverage in a wireless communication network is disclosed. The method includes partitioning, by a mapping module of a server, a coverage map of a coverage area associated with a plurality of cells into a plurality of grids. The method further includes determining, by a determination module of the server based on a mapping of trace data associated with a plurality of User Equipment (UEs) on the plurality of grids, a count of the plurality of UEs in each grid of the plurality of grids. Further, the method includes tagging, by a tagging module of the server based on overlapping a coverage footprint layer onto grid data associated with the plurality of grids, each grid of the plurality of grids with a corresponding coverage bucket among a plurality of coverage buckets. Furthermore, the method includes estimating, by a coverage estimation module of the server, the population coverage in a specific area within the coverage area based on a total count of the plurality of UEs in a specific coverage bucket among the plurality of coverage buckets in the specific area and a total count of the plurality of UEs in the specific area.
In some aspects of the present disclosure, the method further includes receiving, by a receiving module of the server, the trace data from a Trace Collection Entity (TCE). The trace data includes location information of the plurality of UEs and session information associated with the plurality of UEs. Further, the method includes mapping, by the mapping module, the trace data associated with the plurality of UEs on the plurality of grids.
In some aspects of the present disclosure, for determining the count of the UEs in each grid of the plurality of grids, the method includes identifying, by the determination module, based on the mapping of the trace data associated with the plurality of UEs on the plurality of grids, a corresponding grid among the plurality of grids for each UE of the plurality of UEs where a data usage of a corresponding UE of the plurality of UEs is maximum. Further, the method includes determining, by the determination module, the count of the plurality of UEs in each grid of the plurality of grids based on the identification of the corresponding grid for each UE of the plurality of UEs.
In some aspects of the present disclosure, the plurality of coverage buckets is classified based on Reference Signal Received Power (RSRP) of an overlapped coverage footprint on a corresponding grid among the plurality of grids.
In some aspects of the present disclosure, the specific area is within the coverage area and the plurality of UEs are served by the plurality of cells in the wireless communication network.
In some aspects of the present disclosure, the method further includes identifying, by the coverage estimation module, a set of grids among the plurality of grids in the specific area. Further, the method includes calculating, by the coverage estimation module, the total count of the plurality of UEs in the specific area by adding the count of the plurality of UEs in each grid of the set of grids. Furthermore, the method includes calculating, by the coverage estimation module, the total count of the plurality of UEs in the specific coverage bucket in the specific area by adding the count of the plurality of UEs in each grid of the set of grids which are tagged with the specific coverage bucket.
In some aspects of the present disclosure, the method further includes generating, by an output module of the server, a geographical map depicting a distribution of the plurality of UEs across the plurality of grids such that each UE of the plurality of UEs is tagged with one grid among the plurality of grids.
According to another aspect of the present disclosure, a system for estimating population coverage in a wireless communication network is disclosed. The system includes a mapping module configured to partition a coverage map of a coverage area associated with a plurality of cells into a plurality of grids. The system further includes a determination module configured to determine, based on a mapping of trace data associated with a plurality of User Equipment (UEs) on the plurality of grids, a count of the plurality of UEs in each grid of the plurality of grids. Further, the system includes a tagging module configured to tag, based on overlapping a coverage footprint layer onto grid data associated with the plurality of grids, each grid of the plurality of grids with a corresponding coverage bucket among a plurality of coverage buckets. Furthermore, the system includes a coverage estimation module configured to estimate the population coverage in a specific area within the coverage area based on a total count of the plurality of UEs in a specific coverage bucket among the plurality of coverage buckets in the specific area and a total count of the plurality of UEs in the specific area.
BRIEF DESCRIPTION OF DRAWINGS
Various embodiments disclosed herein will become better understood from the following detailed description when read with the accompanying drawings. The accompanying drawings constitute a part of the present disclosure and illustrate certain non-limiting embodiments of inventive concepts. Further, components and elements shown in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. For the purpose of consistency and ease of understanding, similar components and elements are annotated by reference numerals in the exemplary drawings.
FIG. 1 illustrates a diagram depicting an exemplary wireless communication network, in accordance with an embodiment of the present disclosure.
FIG. 2 illustrates a diagram depicting communication of one or more entities of the wireless communication network with a trace collection entity (TCE) system, in accordance with an embodiment of the present disclosure.
FIG. 3 illustrates a block diagram of a system for estimating population coverage in the wireless communication network, in accordance with an embodiment of the present disclosure.
FIG. 4 illustrates a flowchart depicting a method for estimating the population coverage in the wireless communication network, in accordance with an exemplary embodiment of the present disclosure.
FIG. 5 illustrates a schematic block diagram of a computing system for estimating the population coverage in the wireless communication network, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE INVENTION
Inventive concepts of the present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which examples of one or more embodiments of inventive concepts are shown. Inventive concepts may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Further, the one or more embodiments disclosed herein are provided to describe the inventive concept thoroughly and completely, and to fully convey the scope of each of the present inventive concepts to those skilled in the art. Furthermore, it should be noted that the embodiments disclosed herein are not mutually exclusive concepts. Accordingly, one or more components from one embodiment may be tacitly assumed to be present or used in any other embodiment.
The following description presents various embodiments of the present disclosure. The embodiments disclosed herein are presented as teaching examples and are not to be construed as limiting the scope of the present disclosure. The present disclosure should in no way be limited to the illustrative implementations, drawings, and techniques illustrated below, including the exemplary design and implementation illustrated and described herein, but may be modified, omitted, or expanded upon without departing from the scope of the present disclosure.
The following description contains specific information pertaining to embodiments in the present disclosure. The detailed description uses the phrases “in some embodiments” which may each refer to one or more or all of the same or different embodiments. The term “some” as used herein is defined as “one, or more than one, or all.” Accordingly, the terms “one,” “more than one,” “more than one, but not all” or “all” would all fall under the definition of “some.” In view of the same, the terms, for example, “in an embodiment” refers to one embodiment and the term, for example, “in one or more embodiments” refers to “at least one embodiment, or more than one embodiment, or all embodiments.”
The term “comprising,” when utilized, means “including, but not necessarily limited to;” it specifically indicates open-ended inclusion in the so-described one or more listed features, elements in a combination, unless otherwise stated with limiting language. Furthermore, to the extent that the terms “includes,” “has,” “have,” “contains,” and other similar words are used in either the detailed description, such terms are intended to be inclusive in a manner similar to the term “comprising.”
In the following description, for the purposes of explanation, various specific details are set forth 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.
The description provided herein discloses exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the present disclosure. Rather, the foregoing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing any of the exemplary embodiments. Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it may be understood by one of the ordinary skilled in the art that the embodiments disclosed herein may be practiced without these specific details.
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 herein the description, the singular forms "a", "an", and "the" include plural forms unless the context of the invention indicates otherwise.
The terminology and structure employed herein are for describing, teaching, and illuminating some embodiments and their specific features and elements and do not limit, restrict, or reduce the scope of the present disclosure. Accordingly, unless otherwise defined, all terms, and especially any technical and/or scientific terms, used herein may be taken to have the same meaning as commonly understood by one having ordinary skill in the art.
An aspect of the present disclosure is to provide a system and a method that facilitates estimation of a percentage of population covered in a specific geographical area within a communication network thereby enabling telecommunication operators to optimize their network coverage and to take necessary actions to cover maximum population within the communication network.
Another aspect of the present disclosure is to provide a system and a method for analyzing population coverage in the specific geographical area to enable the telecommunication operators to make informed decisions for optimizing network deployment strategies such as identifying underserved areas and enhancing customer retention initiatives.
Another aspect of the present disclosure is to provide the system and the method for estimating percentage population coverage in the specific geographical area under different coverage buckets classified based on coverage strength.
The term “Trace data” in the entire disclosure may represent log of detailed data of a user device at call level. The trace data is an additional source of information to performance measurements and allows going further in monitoring and optimization operations.
The term “coverage region” may refer to a geographical area where the plurality of cells provides services to users associated with a plurality of User Equipment (UEs).
The term “ coverage map” may refer to a map showing distribution and strength of network signal across the coverage region.
The term “grids” may refer to small areas of a fixed size of the coverage map used for analysis or visualization.
The term “network session information” may refer details of user session or data connection between a UE and a network infrastructure.
The term “Radio Frequency (RF) parameter” may refer to parameters used for determining radio link quality between the UE and a base station.
The term “Reference Signal Received Power (RSRP)” represents to a linear average of reference signal power (in Watts) in resource elements that carry cell-specific reference signals within considered measurement frequency bandwidth.
The term “Received Signal Strength Indicator (RSSI)” is a measurement of total received power observed by the UE over a specific bandwidth. The measurement includes the power of a desired signal, interference, and noise. RSSI is used as an indicator of signal strength in conjunction with performance metrics like RSRP and Reference Signal Receive Quality (RSRQ).
The term “RSRQ” is a quality metric represented as a ratio of the RSRP to the total RSSI in a measured bandwidth. In particular, the RSRQ indicates a quality of the signal relative to interference and noise.
The term “Signal-to-Interference-plus-Noise Ratio (SINR)” refers to a fundamental metric in wireless communication. The measurement indicates the RF channel quality. In particular, SINR refers to a ratio between the received power and the interference (plus noise).
Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings. FIG. 1 through FIG. 5, discussed below, and the one or more embodiments used to describe the principles of the present disclosure are by way of illustration only and should not be construed in any way to limit the scope of the present disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged system or device.
FIG. 1 illustrates a diagram depicting an exemplary wireless communication network 100, in accordance with an embodiment of the present disclosure. The embodiment of the wireless communication network 100 shown in FIG. 1 is for illustration only. Other embodiments of the wireless communication network 100 may be used without departing from the scope of this disclosure.
As shown in FIG. 1, the wireless communication network 100 includes a plurality of base stations (BSs) 102-2 to 102-N (may also be referred to as “plurality of cells 102-2 to 102-N”). Each base station among the plurality of BSs 102-2 to 102-N may have same or similar configuration and cumulatively may referred to as “BSs 102” or “cells 102”. It is to be noted that the BSs 102 may also be referred to as “cells”, “gNBs”, or “nodes” interchangeably throughout this disclosure without departing from the scope of the invention. Further, the BSs 102 may also be referred to as “access point (AP)”, “evolved NodeB (eNodeB) (eNB)”, “5G node (5th generation node)”, “wireless point”, “transmission/reception point (TRP)”, “Radio Access Network (RAN)” or other terms having equivalent technical meanings.
The BSs 102 serve a plurality User Equipment (UEs) 104-2 to 104-N (hereinafter cumulatively referred to as UEs 104) in coverage regions 106-2 to 106-N (hereinafter cumulatively referred to as “coverage region 106” or “coverage area 106”). Each user equipment among the UEs 104 may have same or similar configuration. Typically, the term “user equipment” or “UE” can refer to any component such as “mobile station”, “subscriber station”, “remote terminal”, “wireless terminal”, “receive point”, “end user device”, or the like.
The BSs 102 are connected to a network 108 to provide one or more services to the UEs 104. The network 108 may include a proprietary Internet Protocol (IP) network, Internet, or other data network. In some embodiments, the BSs 102 may communicate with each other and with the UEs 104 using a communication technique, such as a 5th Generation 5G/ New Radio (NR), Long Term Evolution (LTE), Long Term Evolution Advanced (LTE-A), Worldwide Interoperability for Microwave Access (WiMAX), Wireless Fidelity (Wi-Fi), or other wireless communication techniques.
The network 108 may include suitable logic, circuitry, and interfaces that may be configured to provide several network ports and several communication channels for transmission and reception of data related to operations of various entities of the wireless communication network 100. Each network port may correspond to a virtual address (or a physical machine address) for transmission and reception of the communication data. For example, the virtual address may be an Internet Protocol Version 4 (IPV4) (or an IPV6 address) and the physical address may be a Media Access Control (MAC) address. The network 108 may be associated with an application layer for implementation of communication protocols based on one or more communication requests from the various entities of the wireless communication network 100. The communication data may be transmitted or received via the communication protocols. Examples of the communication protocols may include, but are not limited to, Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Simple Mail Transfer Protocol (SMTP), Domain Network System (DNS) protocol, Common Management Interface Protocol (CMIP), Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Long Term Evolution (LTE) communication protocols, or any combination thereof. In some aspects of the present disclosure, the communication data may be transmitted or received via at least one communication channel of several communication channels in the network 108. The communication channels may include, but are not limited to, a wireless channel, a wired channel, a combination of wireless and wired channel thereof. The wireless or wired channel may be associated with a data standard which may be defined by one of a Local Area Network (LAN), a Personal Area Network (PAN), a Wireless Local Area Network (WLAN), a Wireless Sensor Network (WSN), Wireless Area Network (WAN), Wireless Wide Area Network (WWAN), a metropolitan area network (MAN), a satellite network, the Internet, an optical fiber network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and a combination thereof. Aspects of the present disclosure are intended to include or otherwise cover any type of communication channel, including known, related art, and/or later developed technologies.
The BSs 102 also communicate with a server 110 configured to estimate population coverage in the wireless communication network 100. The server 110 may be a network of computers, a software framework, or a combination thereof, that may provide a generalized approach to create a server implementation. Examples of the server 110 may include, but are not limited to, personal computers, laptops, mini-computers, mainframe computers, any non-transient and tangible machine that can execute a machine-readable code, cloud-based servers, distributed server networks, or a network of computer systems. The server 110 may be realized through various web-based technologies such as, but not limited to, a Java web-framework, a .NET framework, a personal home page (PHP) framework, or any web-application framework.
Extents of the coverage region 106 are shown as approximately circular or elliptical for the purposes of illustration and explanation only. It should be clearly understood that the coverage region 106 associated with the BSs 102, such as coverage region 106-2, 106-4, may have other shapes, including irregular shapes, depending upon the configuration of the BSs 102, and variations in wireless communication network environment associated with natural and man-made obstructions.
Although FIG. 1 illustrates one example of the wireless communication network 100, various changes may be made to FIG. 1. For example, the wireless communication network 100 may include any number of BSs 102 in any suitable arrangement. Further, each BS among the BSs 102 may communicate directly with the server 110. Furthermore, the BSs 102 may provide access to other or additional external networks, such as external telephone networks or other types of data networks.
FIG. 2 illustrates a diagram depicting communication of entities of the wireless communication network 100 with a Trace Collection Entity (TCE) system 204, in accordance with an embodiment of the present disclosure. The TCE system 204 is a network entity of the wireless communication network 100 that manages collection and collation of UE measurements data received via the BSs 102. The UE measurement data is associated with the UEs 104 and is referred to as “trace data”. The TCE system 204 may be located within the network 108 or the server 110 or may be a separate entity in the wireless communication network 100.
The trace data may include one or more Key Performance Indicators (KPIs) (hereinafter referred to as “KPIs”) associated with session information of the UEs 104, Radio Frequency (RF) parameter details, and location information of the UEs 104. The KPIs associated with the session information of the UEs 104 include a total number of sessions, a volume of traffic consumed, timestamps of each of the sessions associated with the UEs 104, serving cell information of the UEs 104, location of the UEs 104, or any other relevant details. Further, the KPI associated with the RF parameter details may include the RSRP, the RSSI, RSRQ, SINR, and other RF parameters.
The collection of trace data by the TCE system 204 is controlled by a network management system 202 associated with the network 108. The network management system 202 includes an Element Manager (EM) which activates or deactivates collection of the trace data. When the EM activates the collection of the trace data, network elements of the wireless communication network 100 generate the trace data and transfers the trace data to the TCE system 204.
In one or more embodiments, the EM notifies the BSs 102 of an activation message including configuration information (measurement configuration) measured by the UEs 104 and the location information of the UEs 104. The BSs 102 starts a trace session (Starting Trace Session) for collecting UE measurement information and transmits the configuration information measured by the UEs 104. The configuration information includes, for example, a measurement target and a measurement period, or instructions to report location information. The BSs 102 notifies an identifier of the trace session after collecting the UE measurement information. Thereafter, the BSs 102 reports to the TCE system 204, a trace record that records the collected UE measurement information.
FIG. 3 illustrates a block diagram depicting of a system 300 for estimating population coverage in the wireless communication network 100, in accordance with an embodiment of the present disclosure. The embodiment of the system 300 as shown in FIG. 3 is for illustration only. However, the system 300 may come in a wide variety of configurations, and FIG. 3 does not limit the scope of the present disclosure to any particular implementation of the system 300.
As shown in FIG. 3, the system 300 includes a server 110 which includes an Input-Output (I/O) interface 302, one or more processors 304 (hereinafter may also be referred to as “processor 304”), a memory 306, a network communication manager 308, a console host 310, a database 312, and one or more processing modules 314 (hereinafter may also be referred to as “processing modules 314”). Components of the server 110 are coupled to each other via a communication bus 328.
The I/O interface 302 may include suitable logic, circuitry, interfaces, and/or codes that may be configured to receive input(s) and present (or display) output(s) on the server 110. For example, the I/O interface may have an input interface and an output interface. The input interface may be configured to enable a user to provide input(s) to trigger (or configure) the server 110 to perform various operations for estimating the population coverage in the wireless communication network 100, such as but not limited to, configuring the server 110 to receive the trace data from the TCE system 204 and enabling the user to select an specific area within the coverage region 106. Examples of the input interface may include, but are not limited to, a touch interface, a mouse, a keyboard, a motion recognition unit, a gesture recognition unit, a voice recognition unit, or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the input interface including known, related art, and/or later developed technologies without deviating from the scope of the present disclosure. The output interface is configured to control a user device such to display a notification including a geographical map depicting a distribution of the UEs 104 across one or more grids in the coverage region 106. Examples of the output interface of the I/O interface 302 may include, but are not limited to, a digital display, an analog display, a touch screen display, an appearance of a desktop, and/or illuminated characters.
The processor 304 may include various processing circuitry and communicates with the memory 306, the network communication manager 308, the console host 310, and the database 312 via the communication bus 328. The processor 304 is configured to execute instructions 306A (hereinafter also referred to as “a set of instructions 306A”) stored in the memory 306 and to perform various processes. The processor 304 may include one or a plurality of processors, including a general-purpose processor, such as, for example, and without limitation, a central processing unit (CPU), an application processor (AP), a dedicated processor, a graphics-only processing unit such as a graphics processing unit (GPU) or the like, a programmable logic device, or any combination thereof.
The memory 306 stores the set of instructions 306A required by the processor 304 of the server 110 for controlling its overall operations. The memory 306 may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory 306 may, in some examples, be considered a non-transitory storage medium. The "non-transitory" storage medium is not embodied in a carrier wave or a propagated signal. However, the term "non-transitory" should not be interpreted as the memory 306 is non-movable. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache). The memory 306 may be an internal storage unit or an external storage unit of the server 110, cloud storage, or any other type of external storage. In certain examples, the memory 306 configured as the non-transitory storage medium may include hard drives, solid-state drives, flash drives, Compact Disk (CD), Digital Video Disk (DVD), and the like. Further, the memory 406 may include any type of non-transitory storage medium, without deviating from the scope of the present disclosure.
More specifically, the memory 306 may store computer-readable instructions 306 A including instructions that, when executed by a processor (e.g., the processor 304) cause the server 110 to perform various functions described herein. In some cases, the memory 306 may contain, among other things, a BIOS which may control basic hardware or software operation such as the interaction with peripheral components or devices.
The network communication manager 308 may manage communications with the BSs 102, the core network, or the UEs 104 (e.g., via one or more wired backhaul links). For example, the network communications manager 308 may manage the transfer of data communications for BSs 102 and client devices. The network communication manager 308 may include an electronic circuit specific to a standard that enables wired or wireless communication. The network communication manager 308 is configured for communicating with external devices via one or more networks.
The console host 310 may include suitable logic, circuitry, interfaces, and/or codes that may be configured to enable the I/O interface 302 to receive input(s) and/or render output(s). In some aspects of the present disclosure, the console host 310 may include suitable logic, instructions, and/or codes for executing various operations of one or more computer executable applications to host a console on an external user device, by way of which a user can trigger the server 110 to initiate one or more operations of the server 110. In some other aspects of the present disclosure, the console host 310 may provide a Graphical User Interface (GUI) for the server 110 for user interaction.
The database 312 is managed by the processor 304 and configured to store a coverage map of the coverage area 106 of the BSs 102. The database 312 may also store information of the distribution of the UEs 104 across the grids. The database 312 may also store the generated geographical map.
The processing module(s) 314 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the server 110. In non-limiting examples, described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing modules(s) 314 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor 304 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing module(s) 314. In such examples, the server 110 may also comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the server 110 and the processing resource. In other examples, the processing module(s) 314 may be implemented using an electronic circuitry.
In one or more embodiments, the processing module(s) 314 may include a receiving module 316, a mapping module 318, a determination module 320, a tagging module 322, a coverage estimation module 324, and an output module 326.
In an embodiment, the processor 304, using the receiving module 316, may be configured to receive the trace data from the TCE system 204. The trace data includes information of sessions associated with the UEs 104, location information of the UEs 104, serving cell information of the UEs 104, and the RF parameter details including information of the RSRP, RSRQ, RSSI, or other RF related information. For instance, the receiving module 316 may receive the trace data at predetermined time intervals, for example at each hour.
Further, the processor 304, using the mapping module 318, may be configured to partition the coverage map of the coverage region 106 into a plurality of grids (herein after referred to as “grids”). In a non-limiting example, the processor 304, using the mapping module 318, may partition the coverage map into N uniform square grids of size AxA m. In another non-limiting example, the processor 304, using the mapping module 318 may partition the coverage map into M uniform rectangular grids of size AxB m. The coverage region 106 may corresponds to the geographical area served by the cells 102.
The processor 304, using the mapping module 318, may also be configured to map the trace data associated with UEs 104 on the grids using the location information of the UEs 104 included in the trace data.
In one or more aspects, the processor 304 may further map each UE among the UEs 104 to a single grid based on the mapping of the trace data associated with UEs 104 on the grids. For instance, the processor 304, using the determination module 320, may be identify a corresponding grid among the grids for each UE of the UEs 104 where a data usage of a corresponding UE is maximum.
In one or more aspects, the processor 304 may further determine grid wise unique user count. For instance, the processor 304, using the determination module 320, may determine the count of the UEs in each grid based on the identification of the corresponding grid for each UE of the UEs 104.
In one or more aspects, the processor 304, using the tagging module 322, may overlap a coverage footprint layer onto grid data associated with the grids. The coverage footprint layer may refer to a map layer where areas with different coverage are represented in different shades or color. The areas are classified in different coverage or in different coverage buckets based on the RSRP value received in the corresponding area. The processor 304, using the tagging module 322, overlaps the coverage footprint layer onto the grids to tag grids with a plurality of coverage buckets (hereinafter maybe referred to as “coverage buckets”). For instance, the processor 304, using the tagging module 322, may tag each grid of the grids with a corresponding coverage bucket among the coverage buckets.
In one or more aspects, the processor 304, using the coverage estimation module 324, may identify a set of grids among the grids in a specific area within the coverage area 106. In a non-limiting example, the specific area may be selected by a user associated with the end user device at the server side. Further, information the specific area may be preconfigured and stored in the database 312.
Further, the processor 304, using the coverage estimation module 324, may calculate a total count of the UEs in the specific area by adding the count of the UEs in each grid of the set of grids. Also, the processor 304, using the coverage estimation module 324, may calculate a total count of the UEs in a specific coverage bucket among the coverage buckets in the specific area by adding the count of the UEs in each grid of the set of grids which are tagged with the specific coverage bucket. Further, the processor 304, using the coverage estimation module 324, may estimate the population coverage in the specific area based on the total count of the UEs in the specific coverage bucket in the specific area and the total count of the UEs in the specific area.
For instance, the population coverage may be estimated using the formula as given below in equation (1):
Percentage population covered in a specific area for specific coverage bucket = ((Total user count in specific coverage bucket in the specific area)/(Total user count in the specific area))X100……….…….(1)
In one or more aspects, the processor 304, using the output module 326, may be configured to generate the geographical map depicting the distribution of the UEs 104 across the grids such that each UE of the UEs 104 is tagged only in the single grid among the grids.
Although FIG. 3 illustrates one example of the system 300/ the server 110, various changes may be made to FIG. 3. Further, the server 110 may include any number of components in addition to those shown in FIG. 3, without deviating from the scope of the present disclosure. Further, various components in FIG. 3 may be combined, further subdivided, or omitted and additional components may be added according to particular needs. For example, in some aspects of the present disclosure, the server 110 may be coupled to an external database that provides data storage space to the server 110.
FIG. 4 illustrates a flowchart depicting a method 400 for estimating population coverage in the wireless communication network 100, in accordance with an exemplary embodiment of the present disclosure. The method 400 comprises a series of operation steps indicated by blocks 402 through 408 performed by the system 300. The method 400 starts at block 402.
At block 402, the mapping module 318 may partition the coverage map of the coverage area 106 associated with the cells 102 into the grids. For instance, a size of each grid among the grids may be uniform. In a non-limiting example, the mapping module 318 may partition the coverage map into the grids of size AxA m. In another non-limiting example, the mapping module 318 may partition the coverage map into the grids of size AxB m. The coverage area 106 may corresponds to the geographical area served by the cells 102. The flow of the method 400 now proceeds to block 404.
At block 404, the determination module 320 may determine the count of the UEs 104 in each grid of the grids based on the mapping of the trace data on the grids. For instance, the receiving module 316 first receive the trace data with the UEs 104 and maps the trace data on the grids. The mapping of the trace data on the grids may corresponds to mapping of each UE of the UEs 104 with the single grid among the grids where the data usage of the corresponding UE of the UEs 104 is maximum. The mapping of each UE on the grids is performed on the basis of the location information of the UEs 104 and information of data usage of each UE in each grid is included in the trace data. Thereafter, the determination module 320 determines the count of the UEs 104 in each grid based on the mapping of each UE with one of the grids. The flow of the method 400 now proceeds to block 406.
At block 406, tagging module 322 may tag each grid of the grids with the corresponding coverage bucket among the coverage buckets based on the overlapping the coverage footprint layer onto the grid data associated with the grids. The coverage buckets are classified based on the RSRP of the overlapped coverage footprint on the corresponding grid among the grids. The flow of the method 400 now proceeds to block 408.
At block 408, the coverage estimation module 324 may estimate the population coverage in the specific area for the specific coverage bucket among the coverage buckets based on the total count of the UEs 104 in the specific coverage bucket in the specific area and the total count of the UEs 104 in the specific area.
In one or more embodiments, output module 326 may generate the geographical map depicting the distribution of the UEs 104 across the grids such that each UE of the UEs 104 is tagged only in the single grid among the grids.
In one or more embodiments, the output module 326 may report the estimated population coverage in the specific area for the specific coverage bucket to an external server for enabling telecommunication operators to access the population coverage in different regions with the wireless communication network 100.
FIG. 5 illustrates a schematic block diagram of a computing system 500 for estimating the population coverage in the wireless communication network 100, in accordance with an embodiment of the present disclosure.
The computing system 500 includes a network 502, a network interface 504, a processor 506 (similar in functionality to the processor 304 of FIG. 3), an Input/Output (I/O) interface 508 (similar in functionality to the I/O interface 302 of FIG. 3), and a non-transitory computer readable storage medium 510 (hereinafter may also be referred to as the “storage medium 510” or the “storage media 510”). The network interface 504 includes wireless network interfaces such as Bluetooth, Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), General Packet Radio Service (GPRS), or Wideband Code Division Multiple Access (WCDMA) or wired network interfaces such as Ethernet, Universal Serial Bus (USB), or Institute of Electrical and Electronics Engineers-864 (IEEE-864).
The processor 506 may include various processing circuitry/modules and communicate with the storage medium 510 and the I/O interface 508. The processor 506 is configured to execute instructions stored in the storage medium 510 and to perform various processes. The processor 506 may include an intelligent hardware device including a general-purpose processor, such as, for example, and without limitation, the CPU, the AP, the dedicated processor, or the like, the graphics-only processing unit such as the GPU, the microcontroller, the FPGA, the programmable logic device, the discrete hardware component, or any combination thereof. The processor 506 may be configured to execute computer-readable instructions 510-1 stored in the storage medium 510 to cause the system 300 to perform various functions disclosed throughput the disclosure.
The storage medium 510 stores a set of instructions i.e., computer program instructions 510-1 (hereinafter may also be referred to as instructions 510-1) required by the processor 506 for controlling its overall operations. The storage media 510 may include an electronic storage medium, a magnetic storage medium, an optical storage medium, a quantum storage medium, or the like. For example, the storage media 510 may include, but are not limited to, hard drives, floppy diskettes, optical disks, ROMs, RAMs, EPROMs, EEPROMs, flash memory, magnetic or optical cards, solid-state memory devices, or other types of physical media suitable for storing electronic instructions. In one or more embodiments, the storage media 510 includes a Compact Disk-Read Only Memory (CD-ROM), a Compact Disk-Read/Write (CD-R/W), and/or a Digital Video Disc (DVD). In one or more implementations, the storage medium 510 stores computer program code configured to cause the computing system 500 to perform at least a portion of the processes and/or methods disclosed herein throughput the disclosure.
Embodiments of the present disclosure have been described above with reference to flowchart illustrations of methods and systems according to embodiments of the disclosure, and/or procedures, algorithms, steps, operations, formulae, or other computational depictions, which may also be implemented as computer program products. In this regard, each block or step of the flowchart, and combinations of blocks (and/or steps) in the flowchart, as well as any procedure, algorithm, step, operation, formula, or computational depiction can be implemented by various means, such as hardware, firmware, and/or software including one or more computer program instructions embodied in computer-readable program code. As will be appreciated, any such computer program instructions may be executed by one or more computer processors, including without limitation a general-purpose computer or special purpose computer, or other programmable processing apparatus to perform a group of operations comprising the operations or blocks described in connection with the disclosed method.
Further, these computer program instructions, such as embodied in computer-readable program code, may also be stored in one or more computer-readable memory or memory devices (for example, the memory 306 or the storage medium 510) that can direct a computer processor or other programmable processing apparatus to function in a particular manner, such that the instructions 510-1 stored in the computer-readable memory or memory devices produce an article of manufacture including instruction means which implement the function specified in the block(s) of the flowchart(s).
It will further be appreciated that the term “computer program instructions” as used herein refer to one or more instructions that can be executed by the one or more processors (for example, the processor 304 or the processor 506) to perform one or more functions as described herein. The instructions 510-1 may also be stored remotely such as on a server, or all or a portion of the instructions can be stored locally and remotely.
Referring to the technical abilities and advantageous effect of the present disclosure, operational advantages that may be provided by one or more embodiments may include providing the system and the method that facilitates the telecommunication operators of the communication network to accurately estimate the percentage of population covered in a specific geographical area within the communication network, and further help the telecommunication operators in audit and to take necessary actions to cover maximum population within the communication network. A further potential advantage of the one or more embodiments disclosed herein may include enabling the telecommunication operators to make informed decisions for optimizing network deployment strategies such as identifying underserved areas and enhancing customer retention initiatives. Further, analyzing the population coverage enable the telecommunication operators to make informed decisions for optimizing resource use, expanding market presence, and improve overall business strategy.
Those skilled in the art will appreciate that the methodology described herein in the present disclosure may be carried out in other specific ways than those set forth herein in the above disclosed embodiments without departing from essential characteristics and features of the present invention. The above-described embodiments are therefore to be construed in all aspects as illustrative and not restrictive.
The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Any combination of the above features and functionalities may be used in accordance with one or more embodiments.
In the present disclosure, each of the embodiments has been described with reference to numerous specific details which may vary from embodiment to embodiment. The foregoing description of the specific embodiments disclosed herein may reveal the general nature of the embodiments herein that others may, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications are intended to be comprehended within the meaning of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and is not limited in scope.
LIST OF REFERENCE NUMERALS
The following list is provided for convenience and in support of the drawing figures and as part of the text of the specification, which describe innovations by reference to multiple items. Items not listed here may nonetheless be part of a given embodiment. For better legibility of the text, a given reference number is recited near some, but not all, recitations of the referenced item in the text. The same reference number may be used with reference to different examples or different instances of a given item. The list of reference numerals is:
100 - Wireless communication network
102 - Base Stations (BSs)
102-2 to 102-N - Plurality of BSs
104 - User Equipment (UEs)
104-2 to 104-N - Plurality of UEs
106 - Coverage region
108 - Network
110 - Server
200 - Communication of network entities with a TCE system 204
202 - Network management system
204 - Trace Collection Entity (TCE) system
300 - System for estimating population coverage
302 - Input-Output (I/O) interface
304 - Processor
306 - Memory
306 A - Set of instructions
308 - Network communication manager
310 - Console host
312 - Database
314 - Processing unit(s)/modules(s)
316 - Receiving module
318 - Mapping module
320 - Determination module
322 - Tagging module
324 - Coverage estimation module
326 – Output Module
328 - Communication bus
400 - Method for estimating the population coverage
402-408 - Operation steps of the method 400
500 – Block diagram of a computing system
502 – Network
504 – Network interface
506 – Processor
508 – Input/Output (I/O) interface
510 – Non-transitory computer readable storage medium
510-1 - Set of instructions
,CLAIMS:I/We Claim:

1. A method (400) for estimating population coverage in a wireless communication network (100), the method (400) comprising:
partitioning (402), by a mapping module (318) of a server (110), a coverage map of a coverage area (106) associated with a plurality of cells (102) into a plurality of grids;
determining (404), by a determination module (320) of the server (110) based on a mapping of trace data associated with a plurality of User Equipment (UEs) on the plurality of grids, a count of the plurality of UEs in each grid of the plurality of grids;
tagging (406), by a tagging module (322) of the server (110) based on overlapping a coverage footprint layer onto grid data associated with the plurality of grids, each grid of the plurality of grids with a corresponding coverage bucket among a plurality of coverage buckets; and
estimating (408), by a coverage estimation module (324) of the server (110), the population coverage in a specific area within the coverage area (106) based on a total count of the plurality of UEs in a specific coverage bucket among the plurality of coverage buckets in the specific area and a total count of the plurality of UEs in the specific area.

2. The method (400) as claimed in claim 1, further comprising:
receiving, by a receiving module (316) of the server (110), the trace data from a Trace Collection Entity (TCE) (204), wherein the trace data includes location information of the plurality of UEs and session information associated with the plurality of UEs; and
mapping, by the mapping module (318), the trace data associated with the plurality of UEs on the plurality of grids.

3. The method (400) as claimed in claim 2, wherein for determining the count of the UEs in each grid of the plurality of grids, the method (400) comprising:
identifying, by the determination module (320), based on the mapping of the trace data associated with the plurality of UEs on the plurality of grids, a corresponding grid among the plurality of grids for each UE of the plurality of UEs where a data usage of a corresponding UE of the plurality of UEs is maximum; and
determining, by the determination module (320), the count of the plurality of UEs in each grid of the plurality of grids based on the identification of the corresponding grid for each UE of the plurality of UEs.

4. The method (400) as claimed in claim 1, wherein the plurality of coverage buckets is classified based on Reference Signal Received Power (RSRP) of an overlapped coverage footprint on a corresponding grid among the plurality of grids.

5. The method (400) as claimed in claim 1, wherein the specific area is within the coverage area (106) and the plurality of UEs are served by the plurality of cells (102) in the wireless communication network (100).

6. The method (400) as claimed in claim 1, further comprising:
identifying, by the coverage estimation module (324), a set of grids among the plurality of grids in the specific area;
calculating, by the coverage estimation module (324), the total count of the plurality of UEs in the specific area by adding the count of the plurality of UEs in each grid of the set of grids; and
calculating, by the coverage estimation module (324), the total count of the plurality of UEs in the specific coverage bucket in the specific area by adding the count of the plurality of UEs in each grid of the set of grids which are tagged with the specific coverage bucket.

7. The method (400) as claimed in claim 1, further comprising generating, by an output module (326) of the server (110), a geographical map depicting a distribution of the plurality of UEs across the plurality of grids such that each UE of the plurality of UEs is tagged with one grid among the plurality of grids.

8. A system (300) for estimating population coverage in a wireless communication network (100), the system (300) comprising:
a mapping module (318) configured to partition a coverage map of a coverage area (106) associated with a plurality of cells (102) into a plurality of grids;
a determination module (320) configured to determine, based on a mapping of trace data associated with a plurality of User Equipment (UEs) on the plurality of grids, a count of the plurality of UEs in each grid of the plurality of grids;
a tagging module (322) configured to tag, based on overlapping a coverage footprint layer onto grid data associated with the plurality of grids, each grid of the plurality of grids with a corresponding coverage bucket among a plurality of coverage buckets; and
a coverage estimation module (324) configured to estimate the population coverage in a specific area within the coverage area (106) based on a total count of the plurality of UEs in a specific coverage bucket among the plurality of coverage buckets in the specific area and a total count of the plurality of UEs in the specific area.

9. The system (300) as claimed in claim 8, further comprising a receiving module (316) configured to receive the trace data from a Trace Collection Entity (TCE) (204), wherein
the trace data includes location information of the plurality of UEs, and session information associated with the plurality of UEs; and
the mapping module (318) is further configured to map the trace data associated with the plurality of UEs on the plurality of grids.

10. The system (300) as claimed in claim 9, wherein, to determine the count of the UEs in each grid of the plurality of grids, the determination module (320) is configured to:
identify, based on the mapping of the trace data associated with the plurality of UEs on the plurality of grids, a corresponding grid among the plurality of grids for each UE of the plurality of UEs where a data usage of a corresponding UE of the plurality of UEs is maximum; and
determine the count of the plurality of UEs in each grid of the plurality of grids based on the identification of the corresponding grid for each UE of the plurality of UEs.

11. The system (300) as claimed in claim 8, wherein the plurality of coverage buckets is classified based on Reference Signal Received Power (RSRP) of an overlapped coverage footprint on a corresponding grid among the plurality of grids.

12. The system (300) as claimed in claim 8, wherein the specific area is within the coverage area (106) and the plurality of UEs are served by the plurality of cells (102) in the wireless communication network (100).

13. The system (300) as claimed in claim 8, wherein the coverage estimation module (324) is further configured to:
identify a set of grids among the plurality of grids in the specific area;
calculate the total count of the plurality of UEs in the specific area by adding the count of the plurality of UEs in each grid of the set of grids; and
calculate the total count of the plurality of UEs in the specific coverage bucket in the specific area by adding the count of the plurality of UEs in each grid of the set of grids which are tagged with the specific coverage bucket.

14. The system (300) as claimed in claim 8, further comprising an output module (326) configured to generate a geographical map depicting a distribution of the plurality of UEs across the plurality of grids such that each UE of the plurality of UEs is tagged with one grid among the plurality of grids.