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 CLASSIFYING USER ENVIRONMENT FOR DATA ANALYSIS IN A 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 describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD
[0001] The embodiments of the present disclosure generally relate to the field of communication networks. More particularly, the present disclosure relates to a system and a method for classifying a user environment for data analysis in a communication network.
BACKGROUND OF THE INVENTION
[0002] 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.
[0003] With an increasing demand for services of wireless communication networks around the world, network operators are striving to increase robustness of the communication network for providing support to a plurality of user devices on the communication network. To consistently improve services of the communication network in an entire coverage area within a geography, the network operators continuously monitor the communication network to improve the services of the network. The monitoring is performed for various reasons including utilization of network resources efficiently, detection of anomalies occurring in the network, detection of malfunctioning of nodes attached to the communication network.
[0004] For facilitating the monitoring, the network operators gather information on coverage of the communication network using crowd source data collected from the plurality of user devices in the communication network. The crowd source data comprises data corresponding to configurations of the user devices and radio parameters experienced by the users. However, the network operators are oblivious to a user environment and location of the user devices contributing the crowd source data. The user environment may be an indoor environment or an outdoor environment within the coverage area.
[0005] To this end, conventional methods for analyzing the crowd source data overlooks that the dynamic nature of network environments differs in the indoor environment and the outdoor environment. Further, remedial actions to be performed by the network operators are highly dependent upon whether the user device is being served by an indoor serving cell or an outdoor serving cell along with the radio parameters, thus prior knowledge of the user environment and the location of the user devices is crucial for the network operators.
[0006] In light of the above-mentioned limitations associated with the conventional methods for analyzing the crowd source data, there lies a need for a system and a method for determining the user environment to improve existing network services or provide new services in specific geographical locations.
SUMMARY
[0007] 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.
[0008] In an embodiment, a method for classifying a user environment in a communication network is described. The method comprises periodically collecting, by an acquisition module from a data collection entity, crowd source data associated with a plurality of User Equipment (UEs) served by a plurality of serving cells in the communication network. The crowd source data includes positional information of each of the plurality of the UEs, a timestamp indicating a time of capturing the positional information, a charging event in the plurality of the UEs, and an identifier of an at least one serving cell among the plurality of serving cells. Further, the method comprises estimating by a processing module, a presence of the at least one UE in one of an indoor environment or an outdoor environment based on the positional information of the at least one UE and the identifier of the at least one serving cell serving the at least one UE. Furthermore, the method comprises determining by the processing module, a certainty of the estimation by correlating one or more parameters corresponding to the crowd source data. Thereafter, the method comprises classifying, by the processing module, the user environment of the at least one UE based on the certainty of the estimation as one of the indoor environment or the outdoor environment.
[0009] In one aspect, the method further comprises determining, by the processing module, the certainty of the presence of the at least one UE in the indoor environment by correlating the one or more parameters corresponding to the positional information of the at least one UE and a geographical location of a building nearest to the positional information of the at least one UE. Further, for determining the certainty of the presence of the at least one UE in the indoor environment, the method comprises determining the accuracy of the positional information of the at least one UE based on the correlation of the positional information of the at least one UE and the geographical location of the building. Furthermore, the method comprises comparing the accuracy of the positional information of the at least one UE with a pre-defined threshold value.
[0010] Based on a result of the comparison, the method comprises performing a determination that the at least one UE is present in the indoor environment with high certainty when the accuracy of the positional information of the at least one UE is greater than the pre-defined threshold value. The method further comprises performing a determination whether the identifier of the at least one serving cell is an indoor serving cell, when the accuracy of the positional information of the at least one UE is lesser than or equal to the pre-defined threshold value. The method also comprises performing a determination that the at least one UE is present in the indoor environment with high certainty. Thereafter, the presence of the at least one UE is determined based on a determination that the identifier of the at least one serving cell is the indoor serving cell.
[0011] In one aspect, the method further comprises determining, by the processing module, the certainty of the presence of the at least one UE in the outdoor environment by determining a speed of movement of at least one UE among the plurality of the UEs based on a change in positional information of the at least one UE and a change in the timestamp of capturing the positional information of the at least one UE. Further, the method comprises correlating the one or more parameters corresponding to the positional information of the at least one UE and the speed of the movement of the at least one UE and comparing the speed of the movement of the at least one UE with a pre-defined threshold speed. Based on a result of the comparison, the method comprises performing one or more of a determination that the at least one UE is present in the outdoor environment with high certainty when the speed of the movement exceeds the pre-defined threshold speed.
[0012] The method further comprises performing a determination whether a charging event has occurred in the at least one UE during or outside the time of capturing the positional information when the speed of the movement of the at least one UE is less than or equal to the pre-defined threshold speed and a determination that the at least one UE is present in the outdoor environment with a low certainty or high certainty. Based on a determination that the charging event has occurred outside the change in the timestamp, the presence of the at least one UE is determined in the outdoor environment with the low certainty. Thereafter, the presence of the at least one UE is determined in the outdoor environment with the high certainty based on a determination that the charging event has occurred during the change in the timestamp.
[0013] In one aspect, for determining by the processing module, the certainty of the estimation, the method further comprises determining that the positional information of the at least one UE corresponds to a geographical location outside a building nearest to the at least one UE. Further, the method comprises determining a speed of movement of the at least one UE based on a change in the positional information of the at least one UE and a change in the timestamp of capturing the positional information of the at least one UE. Furthermore, the method comprises comparing the speed of the movement of the at least one UE with a pre-defined threshold speed. Thereafter, the method comprises performing, based on a result of the comparison, one or more of a determination that the user environment is the outdoor environment where the at least one UE is in transit with a high certainty when the speed of the movement of the at least one UE exceeds the pre-defined threshold speed. The method may also comprise performing a determination whether the accuracy of the positional information of the at least one UE is greater than or less than a pre-defined threshold value when the speed of the movement of the at least one UE is less than or equal to the pre-defined threshold speed. Furthermore, the method comprises performing a determination of whether a charging event has occurred in the at least one UE during the time of capturing the positional information upon a determination that the accuracy of the positional information of the at least one UE is greater than the pre-defined threshold value. Thereafter, the method comprises performing a determination that the user environment is the outdoor environment where the at least one UE is in transit with high certainty. Based on a determination that the charging event has occurred during the time of capturing the positional information, the user environment is determined as the outdoor environment.
[0014] In one aspect, for determining the certainty of the estimation, the method further comprises one or more of determining, by the processing module, based on a determination that the charging event has occurred outside the time of capturing the positional information, the user environment as the outdoor environment with high certainty. Further, the method comprises determining, by the processing module, whether the identifier of the at least one serving cell is an indoor serving cell upon a determination that the accuracy of the positional information of the at least one UE is less than or equal to the pre-defined threshold value. Based on a determination the identifier of the at least one serving cell is the indoor serving cell, the method comprises determining, by the processing module, the user environment as the indoor environment with low certainty.
[0015] In one aspect, the plurality of serving cells include a base station, a relay, a repeater, small cells, macro cells, micro cells, an indoor serving cell, an outdoor serving cells, and a wireless network cell identifiable using a Service Set Identifier (SSID).
[0016] In one aspect, the one or more parameters corresponding to the crowd source data include the positional information of the at least one UE, an accuracy of the positional information of the UE, the time of capturing the positional information, the charging event in the at least one UE, and a speed of the movement of the at least one UE.
[0017] In an embodiment, disclosed herein is a system for classifying a user environment in a communication network. The system comprises an acquisition module, and a processing module. The acquisition module is configured to periodically collect, from a data collection entity, crowd source data associated with a plurality of User Equipment (UEs) served by a plurality of serving cells in the communication network. The crowd source data includes positional information of each of the plurality of the UEs, a timestamp indicating a time of capturing the positional information, a charging event in the plurality of the UEs, and an identifier of an at least one serving cell among the plurality of serving cells. The processing module is configured to estimate a presence of the at least one UE in one of an indoor environment or an outdoor environment based on the positional information of the at least one UE and the identifier of the at least one serving cell serving the at least one UE. Further, the processing module is configured to determine a certainty of the estimation by correlating one or more parameters corresponding to the crowd source data and classify, the user environment of the at least one UE based on the certainty of the estimation as one of the indoor environment or the outdoor environment.
BRIEF DESCRIPTION OF DRAWINGS
[0018] 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. In the drawings:
[0019] FIG. 1 illustrates a block diagram depicting an exemplary system for classification of crowd source data in the communication network, in accordance with an embodiment of the present disclosure.
[0020] FIG. 2 illustrates a block diagram depicting a detailed system architecture of a server, in accordance with an embodiment of the present disclosure.
[0021] FIG. 3 illustrates a method for classification of user environment of User Equipment (UEs) in the communication network based on a position parameter, in accordance with an embodiment of the present disclosure.
[0022] FIG. 4 illustrates a method for classification of the user environment of the UEs in the communication network based on a charging parameter, in accordance with an embodiment of the present disclosure.
[0023] FIG. 5 illustrates a method for classification of the user environment of the UEs in the communication network based on a speed parameter, in accordance with an embodiment of the present disclosure.
[0024] FIG. 6 illustrates a method for classification of the user environment of the UEs in the communication network, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE INVENTION
[0025] 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.
[0026] 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.
[0027] 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.”
[0028] 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 or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.”
[0029] 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.
[0030] 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.
[0031] 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 and in the appended claims, the singular forms "a", "an", and "the" include plural forms unless the context of the invention indicates otherwise.
[0032] 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 and the appended claims. 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.
[0033] An object of the present disclosure is to provide a system and a method for classifying a user environment for data analysis in a communication network by correlating crowd source data from User Equipment (UEs). Another object of the present disclosure is to provide the system and the method for analyzing network parameters based on the user environment. Still another object of the present disclosure is to provide the system and the method for optimizing quality of the communication network based on the user environment of the UE. Yet another object of the present disclosure is to provide the system and the method for dynamic handling of concerns in performance of the network based on the user environment of the UE.
[0034] In order to facilitate an understanding of the disclosed invention, a number of terms are defined below.
[0035] An indoor environment may refer to an enclosed space within a structure such as a building, home, office, or any similar area that is typically covered by a roof and enclosed by walls, which may affect propagation of radio signals and an accuracy of positional information.
[0036] An outdoor environment may refer to an open space external to buildings or structures, such as streets, parks, or any similar area where the radio signals generally propagate with minimal obstruction.
[0037] A small cell may refer to a low-power cellular radio access node with a limited coverage area, typically ranging from 10 meters to a few hundred meters.
[0038] A macro cell may refer to a high-power cellular radio access nodes that provide wide-area coverage, typically with a range of several kilometers, and are often mounted on towers or tall buildings.
[0039] A micro cell may refer to medium-power access nodes with coverage areas smaller than the macro cells but larger than femto or pico cells, generally used in urban areas to support a higher user density.
[0040] An indoor serving cell may refer to the serving cell that is physically located inside the building or structure and is configured to provide wireless coverage specifically to indoor areas.
[0041] An outdoor serving cell may refer to the serving cell located outside of the buildings and intended to provide wireless coverage to open or external areas.
[0042] A Service Set Identifier (SSID) refers to a unique alphanumeric identifier used to distinguish a specific Wireless Local Area Network (WLAN), most commonly associated with Wi-Fi networks, and broadcasted by a wireless network cell such as a wireless access point or a wireless router.
[0043] A coverage region may refer to a geographical area within which a wireless communication node, such as a Base Station (BS), the small cell, a repeater, or an access point, is capable of providing reliable wireless communication services to UE. The boundaries of the coverage region may vary based on factors such as transmission power, antenna configuration, environmental conditions, and network topology.
[0044] International Mobile Subscriber Identity (IMSI) may refer to a unique numerical identifier assigned to a mobile subscriber within a communication network. The IMSI may be utilized to associate the data collected from the UE with a particular subscriber.
[0045] International Mobile Equipment Identity (IMEI) may refer to a unique numerical identifier assigned to mobile devices, used to identify valid devices on the network.
[0046] Reference Signal Received Power (RSRP) may refer to an average power level of reference signals received from a specific serving cell, measured in dBm. The RSRP provides an indication of the signal strength and may be used to evaluate coverage quality. Value of the RSRP may assist in determining whether the UE is located in the indoor environment, or the outdoor environment based on signal attenuation patterns.
[0047] Reference Signal Received Quality (RSRQ) may refer to a measure of the quality of the received reference signal, calculated as a ratio of the RSRP to total received power.
[0048] Signal-to-Interference-plus-Noise Ratio (SINR) may represent a ratio of a power of a useful signal to a power of interference plus background noise, typically expressed in decibels (dB). The SINR may determine a signal quality experienced by the UE, depending on the indoor environment or the outdoor environment of the UE.
[0049] Microservices are independently deployable software in which complex applications are composed of small and independent processes. The distributed microservice may be developed as a suite of small services, each running in its own process and communicating with lightweight mechanisms such as Application Programming Interface(s) (API). Each microservice may adhere to a well-defined API.
[0050] 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.
[0051] FIG. 1 illustrates a block diagram depicting an exemplary system 100 for classification of crowd source data in the communication network, in accordance with an embodiment of the present disclosure. The embodiment of the system 100 shown in FIG. 1 is for illustration only. Other embodiments of the system 100 may be used without departing from the scope of this disclosure.
[0052] As shown in FIG. 1, the system 100 includes a plurality of User Equipment (UEs) (collectively referred to as UEs) 110, a network 120, a data collection entity 130, a load balancer 140, a server 150, one or more web servers 160 (alternatively may also be referred to as the “web servers 160”), a gateway server 170, and a Distributed File System (DFS) 180. Each component of the system 100 may be configured to communicate with each other via the network 120.
[0053] Typically, the term “user equipment” or “UE” may refer to any component such as “mobile station,” “subscriber station,” “remote terminal,” “wireless terminal,” “receive point,” or “user device.” The UE 110 may correspond to, but is not limited to, any of mobile devices, tablets, or other portable devices utilized by users to access services provided by the network 120. The UE 110 may be served by a plurality of nodes (alternatively referred to as “serving cells”). The nodes may include one of at least one Base Station (BS), at least one relay, and at least one Distributed Unit (DU). The BS may be a network infrastructure that provides wireless access to one or more terminals. The base station may also be referred to as the macro cell, a wireless “Access Point (AP),” “evolved NodeB (eNodeB) (eNB),” “5th Generation (5G) node,” “next generation NodeB (gNB),” “wireless point,” “Transmission/Reception Point (TRP),” or other terms having equivalent technical meanings. The serving cells provide coverage to the UEs 110 in a plurality of predetermined geographic areas based on distance over which a signal may be transmitted. The serving cell may correspond to any access point to which the UE 110 may be currently connected or from which the UE 110 may receive primary wireless communication services. The serving cells may also include the small cells, the macro cells, the micro cells, the indoor serving cells, and the outdoor serving cells. The serving cells may also include a wireless network cell identifiable by the SSID.
[0054] The network 120 enables communication between various components of the system 100. The UEs 110 and the serving cells communicate with the server 150 through the network 120. The network 120 may include wired connections, wireless connections such as a proprietary Internet Protocol (IP) network, Internet, or in accordance with other wireless communication standards such as Worldwide Interoperability for Microwave Access (WiMAX), Wi-Fi 802.11a/b/g/n/ac, or a combination of wired and wireless connections.
[0055] The communication network may be divided into different coverage regions. Each coverage region may comprise multiple serving cells and the UEs 110. The UEs 110 may be served by one or more serving cells in one or more coverage region defined to a predetermined geographic area based on a distance over which a signal may be transmitted. The coverage regions of the serving cells may have variations in radio environment associated with natural and man-made obstructions, and thus a variation in interference levels.
[0056] The UE 110 is configured to establish a session with the serving cells to avail services of the serving cells in the communication network. During the session, the UE 110 is configured to capture data including Key Performance Indicators and metadata of the communication network. The UE 110 sends the data to the data collection entity 130, periodically, on demand or after an event. The data sent by each of the UE 110 may be referred to as the crowd source data.
[0057] The data collection entity 130 may be a server or a group of servers configured to collect and store the crowd source data. The group of servers may be one or more of a cloud-based server, an application server, a content server, a host server, a web server, a database server, or a server hosted over a desktop computer. The group of servers may be hosted locally or over a cloud network. In one embodiment, the crowd source data collection entity 130 may be a database configured to store the crowd source data and communicate with the server 150.
[0058] The crowd source data may be related to the UE 110 latched to the serving cell during the session. The crowd source data may include hardware configuration of the UE 110 such as a model number of the UE 110, the IMEI of the UE 110, the IMSI of the UE 110, an identifier of the serving cell serving the UE 110, radio parameters including the RSRP, the RSRQ, the SINR, and parameters including position parameter (alternatively referred to as the positional information), time parameter and charging parameter.
[0059] The position parameter of the UE 110 may include one or more of the positional information such as latitude and longitude coordinates of the UE 110 indicating geographical location of the UE 110 and the accuracy of providing the latitude and longitude coordinates. The position parameter may be captured by a Global Positioning System (GPS) sensor installed in the UE 110, through Wi-Fi positioning, or other localization techniques. An accuracy of capturing the positional information may vary. The accuracy of capturing the positional information may refer to a degree of closeness of the captured positional information of the UE 110 to actual physical location of the UE 110. The accuracy of capturing the positional information may depend upon an accuracy of the GPS sensor or the technique utilized to capture the positioning information.
[0060] The time parameter may include a timestamp indicating a time of capturing one or more information of the crowd source data. The timestamp may refer to a recorded time value indicating a precise moment when a particular event or data measurement occurred, such as capturing of the positional information, detection of the charging event, or establishment of a network connection.
[0061] The charging parameter may include the charging event in the UE 110 indicating whether the UE 110 has been connected to a power source for battery charging or disconnected from the power source. The processor 202 may determine other attributes such as a speed parameter from the records of the crowd source data. The speed parameter may include speed of movement of the UE 110 determined from change in the position parameter and the time parameter of the UE 110.
[0062] The load balancer 140 is an intermediary between the network 120 and the server 150. The load balancer 140 is configured to distribute an incoming request for sending the crowd source data to the server 150, from the UE 110 to the server 150. Referring to Fig. 1, the load balancer 140 transmits the incoming request to the server 150 via the one or more web servers 160.
[0063] In one embodiment, the server 150 is communicatively coupled with the one or more web servers 160 for accessing the crowd source data from the UE 110. The web servers 160 may include a software application or a hardware device that processes incoming Hypertext Transfer Protocol (HTTP) requests or HyperText Transfer Protocol Secure (HTTPS) requests and serve Application Processing Interface (APIs) that collect the crowd source data from the users. The one or more web servers 160 may host Representational State Transfer (REST) based APIs, Hypertext Transfer Protocol (HTTP) based APIs or web applications that receive the crowd source data. The incoming requests to the one or more web servers 160 sent from the UEs 110 comprise logs related to signal strength, network drop, call quality, or network usage statistics. The incoming requests from the UEs 110 are parsed by the one or more web servers 160.
[0064] The incoming requests parsed by the one or more web servers 160 are forwarded by the one or more webservers 160 to the gateway server 170. The gateway server 170 may be an intermediary server for authenticating, rate limiting, for discovery of a plurality microservices among each other, and for dynamic routing of the parsed requests to the microservices for handling different tasks corresponding to the classification of crowd source data in the communication network. The gateway server 170 may utilize an authentication module for the authentication and authorization of the parsed incoming requests and requests of the microservices to access the one or more web servers 160.
[0065] The authentication module may run on a Lightweight Directory Access Protocol (LDAP). The authentication module may allow the parsed incoming requests to access the microservices or requests from the microservices to access the one or more web servers 160. The authentication module may determine whether the UE 110 is recognized and has permission to use the APIs. Upon determining that a parsed incoming request has failed the authentication, the parsed incoming request is denied access to the microservices and the server 150. Upon authentication, the requests of the microservices may be passed to the one or more web servers 160 for accessing the UE 110 or the parsed incoming requests may access the microservices and the server 150.
[0066] The server 150 may include a group of servers such as a cloud-based server, an application server, a content server, a host server, a web server, a database server, or a server hosted over a desktop computer. The server 150 is configured to process the crowd source data and determine a position of the UE 110 as one of a position in the indoor environment or position in the outdoor environment. The server 150 may utilize the microservices for fetching the crowd source data and performing a series of operations for processing the crowd source data. In one embodiment, the server 150 may function as a service registry for automatic detection of UE 110 and the microservices on the network 120. The instances of the microservices may register themselves on the server 150 and may facilitate discovery of the instances of the microservices.
[0067] The server 150 may further be connected to a storage medium for storing and managing the crowd source data collected from the data collection entity 130. Storage medium may generally be one or more of, without limitation, disk drives, hard-disk arrays, solid state storage devices, Network Attached Storage (NAS) devices, tape libraries or other magnetic, non-tape storage devices, and optical media storage devices. In one embodiment, the storage medium may form part of the DFS 180. The DFS 180 may allow the server 150 seamless data access and retrieval as needed for processing and storage. The DFS 180 is configured to provide a scalable and fault-tolerant storage system, capable of handling entire operation specific data across distributed clusters of files associated with the server 150. In other embodiments, the DFS 180 may be integrated within the server 150 for storing large volumes of the crowd source data and processing records of the crowd source data to determine whether the UE 110 is present in the indoor environment or the outdoor environment.
[0068] Although Fig. 1 illustrates one example of the system 100, various changes may be made to Fig. 1. Further, the system 100 may include any number of components in addition to the components shown in Fig. 1. Further, various components in Fig. 1 may be combined, further subdivided, or omitted and additional components may be added according to particular needs. A detailed description of the method of determining interference in the communication network is described further below by way of several embodiments.
[0069] FIG. 2 illustrates a block diagram depicting a detailed system architecture of the server 150, in accordance with an embodiment of the present disclosure. The embodiment of the server 150 as shown in FIG. 2 is for illustration only. However, the server 150 may come in a wide variety of configurations, and FIG. 2 does not limit the scope of the present disclosure to any particular implementation of the server 150.
[0070] As shown in FIG. 2, the server 150 includes one or more processors 202 (hereinafter may also be referred to as “processor 202” or “at least one processor 202”), a memory 204, an Input-Output (I/O) interface 206, a communication interface 208, a database 210, a plurality of microservices 212 (alternatively referred to as the microservices 212), and a plurality of modules/units 214 (collectively referred to as the modules 214). Components of the server 150 are coupled to each other via a communication bus 216.
[0071] The I/O interface 206 may include suitable logic, circuitry, interfaces, and/or codes that may be configured to receive input(s). For example, the I/O interface 206 may have an input interface and an output interface. The I/O interface 206 may be configured to enable the user to provide the user input(s) to trigger (or configure) the server 150 to perform various operations for determining the position of the UE 110 in the outdoor environment or the indoor environment. Examples of the input interface may include, but are not limited to, a touch interface, a mouse, and a keyboard, and the output interface includes a digital display, an analog display, or a touch screen display. Aspects of the present disclosure are intended to include or otherwise cover any type of the I/O interface 206 including known, related art, and/or later developed technologies without deviating from the scope of the present disclosure.
[0072] The processor 202 may include processing circuitry, logic, interface(s), and/or code(s), and may be configured to communicate with the memory 204, the I/O interface 206, the communication interface 208, the database 210, the microservice(s) 212, and the modules 214 via the communication bus 216. Examples of the communication bus 216 may include, but are not limited to, a Peripheral Component Interconnect (PCI)/PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB), and a Front Side Bus (FSB). Aspects of the present disclosure are intended to include or otherwise cover any type of coupling means present or related to later developed technologies, that may be configured to for connect the processor 202 to the other subsystems of the server 150, as the communication bus 216, without deviating from the scope of the present disclosure.
[0073] The processor 202 may include various processing circuitry configured to execute instructions 204-1 (hereinafter also referred to as “a set of instructions 204-1”) stored in the memory 204 and to perform various processes. The processor 202 may also include a plurality of processing engines i.e., information processing units for processing the crowd source data and determine a position of the UE 110. The processor 202 may be configured to handle a set of tasks or computations executed by the processor 202 in a distributed computing environment. For an example, the processor 202 is configured to execute programs and processes to execute instruction(s) or code(s) stored in the memory 204 pertaining to classification of crowd source data in the communication network. The processor 202 is further configured to move data into or out of the memory 204 as required by an execution process of the server 150.
[0074] Examples of the processor 202 may include, but are not limited to, a Central Processing Unit (CPU), an Application Processor (AP), a dedicated processor, a graphics-only processing unit such as a Graphics Processing Unit (GPU), a programmable logic device, or any combination thereof.
[0075] The memory 204 is configured to store the set of instructions 204-1 required by the processor 202 for controlling overall operations of the server 150. A part of the memory 204 may include a Random-Access Memory (RAM), a cache memory, or a Read-Only Memory (ROM). The memory 204 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 204 may, in some examples, be implemented using 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 that the memory 204 is non-movable. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in RAM or cache). The memory 204 can be an internal storage unit or it can be an external storage unit of the server 150, cloud storage, or any other type of external storage. In some embodiments, when the memory 204 is external to the server 150, the memory 204 may be removably attached to the server 150. Aspects of the present disclosure are intended to include or otherwise cover any data storage medium as ‘the memory 204’, without deviating from the scope of the present disclosure.
[0076] Further, the server 150 may host the plurality of microservices 212 (alternatively referred to as the “microservices 212”). The server 150 may utilize a plurality of computing resources where the plurality of microservices 212 may be deployed, and a plurality of storage devices such as a database may be provided in the computing resource for each microservice.
[0077] The microservices 212 may correspond to a network architecture, where independent microservices communicate over APIs, enabling modular, scalable, and resilient network management applications. A microservice of the microservices 212 may refer to individual components that perform specific tasks within the system 100, for example, data ingestion microservice could be responsible for cleansing and queuing the crowd sourced data for further processing, user profiling microservice could be responsible for mapping data points in the crowd source data to the identifiers of the UE 110 and the serving cells of the communication network, location analytics microservice could be responsible for geotagging data points in the crowd source data, and log analysis microservice could be responsible for determining and average signal strength experienced by the UE 110.
[0078] It must be understood that the microservices platform may also be hosted outside the server 150 in a similar manner utilizing the resources of a computing device separate from the server 150 itself. The module(s) 214 may make a call to the microservices 212 for processing and analysis of the crowd source data by various components of the server(s) 150 and performing other tasks within the system 100.
[0079] The database 210 may store plurality of records of the classification of the user environment of the UE 110 as one of the indoor environment, and the outdoor environment. Furthermore, the database 210 may store one or more request configurations made to the server 150 via instances of the microservices 212 for retrieving the records of the crowd source data corresponding to the different parameters. The database 210 may be accessed and updated by the processor 202 via the microservices 212. The database 210 may be implemented as one or more of centralized database, Relational Database Management System (RDBMS), Non-Relational Database Management System, Hierarchical Database Management System, Network Database Management System, an in-memory database including a distributed in-memory data storage, distributed database, or a distributed file system.
[0080] The communication interface 208 may manage communications with the network 120 and the UEs 110. For example, the communication interface 208 may manage reception of the crowd source data from the UEs 110 by the server 150, directly or through the data collection entity 130. The communication interface 208 may include an electronic circuit specific to a standard that enables wired or wireless communication. The communication interface 208 is configured for communicating with external devices via one or more networks. Further, the communication interface 208 may also provide a communication pathway for one or more components of the server 150. Examples of such components include, but are not limited to, the module(s) 214 and the database 210.
[0081] In an embodiment, the module(s) 214 may be implemented as a combination of hardware and software programming (for example, programmable instructions) to implement one or more functionalities of the server 150. In non-limiting examples, described herein, such combinations of hardware and software programming may be implemented in several different ways, without deviating from the scope of the present disclosure. The module(s) 214 may include suitable logic, circuitry, interfaces, and/or codes. For example, the programming for the module(s) 214 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the module(s) 214 may comprise a processing resource (for example, one or more processors), to execute such instructions. In an embodiment, the module(s) 214 may be combined to a single module or each module of the module(s) 214 may be further subdivided into different modules.
[0082] In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the module(s) 214. In such examples, the server 150 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 150 and the processing resource. In other examples, the module(s) 214 may be implemented using an electronic circuitry.
[0083] In one or more embodiments, the module(s) 214 may include one or more modules such as an acquisition module 214-2, a processing module 214-4 and other modules (not shown in Fig. 2). The other modules may include a report generation module. Each of the module(s) 214 are communicatively coupled with each other.
[0084] In an embodiment, the processor 202, using the acquisition module 214-2, is configured to fetch, via the microservices 212, the position of the UE 110 including the latitude and the longitude coordinates of the UE 110, from the crowd source data. Based on the position of the UE 110, the processor 202, using the processing module 214-4, estimates a position of the UE 110 as one of a position in the indoor environment or the outdoor environment. Further, the processor 202, using the processing module 214-4, correlates the parameters of the crowd source data such as the charging parameter, the speed parameter, the accuracy of the position of the UE 110, and the time of capturing the position of the UE 110, for determining a certainty of the position of the UE 110 in one of the indoor environment or the outdoor environment.
[0085] In one scenario, the processor 202, using the processing module 214-4, determines a certainty of the estimated position of the UE 110 to be the indoor environment when the charging event in the UE 110 is detected, as a charging point is typically assumed to be available in an indoor environment.
[0086] Furthermore, the processor 202, using the processing module 214-4, estimates the position of the UE 110 to be in the outdoor environment when the speed of movement of the UE 110 is determined to be more than a pre-defined threshold value of the speed. The speed of movement may refer to a rate of displacement of the UE 110 over time, calculated based on changes in the positional information and associated timestamps of capturing the positional information. The pre-defined threshold value of the speed parameter may refer to a predefined numeric limit used as a reference for comparison. The processor 202, using the processing module 214-4, may thus determine the certainty of the UE 110 to be in outdoor environment where the UE 110 is in transit.
[0087] Similarly, the processor 202 using the processing module 214-4, determines whether the identifier of the serving cell belongs to one of an indoor serving cell and a wireless serving cell. Based on the identifier of the serving cell, the processor 202, using the processing module 214-4, may estimate the UE 110 to be present in the indoor environment as the indoor serving cell and the wireless serving cell typically serve an indoor location.
[0088] The processor 202, using the processing module 214-4, is configured to classify a user environment of the UE 110 as one of the indoor environment or an outdoor environment based on the certainty of the estimated position. The processor 202, using the processing module 214-4, may store the user environment of the UE 110 for further processing in the database 210.
[0089] The processor 202 stores in the database 210, a plurality of records of the classification of the user environment of the UE 110 as one of the indoor environment and the outdoor environment. The processor 202, using the processing module 214-4, may further analyze one or more performance metrics of the network 120 based on the user environment of the UE 110. The processor 202, using the report generation module, is configured to compile the classification of the user environment in a particular geographical region and the performance metrics of the network 120 in a network performance report, accessible by the network operators. Furthermore, the database 210 stores one or more request configurations made via instances of the microservices 212 for retrieving the records of the crowd source data corresponding to the different parameters. The database 210 may be accessed and updated by the processor 202 via the microservices 212.
[0090] Although FIG. 2 illustrates one example of server 150, various changes may be made to FIG. 2. For example, the server 150 may include any number of components in addition to the components shown in FIG. 2. Further, various components in FIG. 2 may be combined, further subdivided, or omitted and additional components may be added according to particular needs.
[0091] In an alternate embodiment, each module/unit of the module(s)/unit(s) 214 (i.e., the acquisition module 214-2 the processing module 214-4, and the report generation module) is configured to independently perform various operations of the processor 202, as described herein, without deviating from the scope of the present disclosure.
[0092] FIG. 3 illustrates a method 300 for classification of the user environment of the UEs 110 in the communication network based on the position parameter, in accordance with an embodiment of the present disclosure.
[0093] As illustrated in FIG. 3, at step 302, the server 150 fetches records of the crowd source data. From the records of the crowd source data, the processor 202, using the acquisition module 214-2, may fetch the position parameter of the UE 110 corresponding to a latitudinal and longitudinal position of the UE 110. From the latitudinal and longitudinal position of the UE 110, the processor 202, using the processing module 214-4, may determine the geographical location of the UE 110.
[0094] At step 304, the processor 202, using the processing module 214-4, may perform a geographical correlation to determine whether the geographical location corresponds to the location in the building or the structure. In one scenario, the processor 202, using the processing module 214-4, may determine the building or the structure nearest to the geographical location and determine whether the geographical location of the UE 110 corresponds to the location in the building or the structure.
[0095] If the processor 202, using the processing module 214-4, determines that the geographical location corresponds to the building or the structure, the processor 202, may determine from the records of the crowd source data, the accuracy of the positional information of the UE 110. The accuracy of the positional information of the UE 110 may be dependent on configuration of the GPS sensors installed in the UE 110 and may be determined in terms of the distance. The distance may specify correctness of the positional information and a pre-defined threshold value of the accuracy may be set. For an example, the pre-defined threshold value of the accuracy may correspond to the correctness of the positional information up to the distance of 30 meters. When the correctness of the positional information is determined to be within the specified distance, the GPS sensors may be highly accurate. The processor 202, at step 306, determines whether the accuracy of the GPS sensors in providing the positional information of the UE 110 is higher than the pre-defined threshold value of the positional accuracy by comparing the accuracy of the positional information of the at least one UE with the pre-defined threshold value.
[0096] When the accuracy of the GPS sensors is determined to be higher than the pre-defined threshold value, the processor 202, using the processing module 214-4, at step 308, determines that there is a high certainty that the latitudinal and longitudinal position of the UE 110 lies in the indoor environment. The processor 202, using the processing module 214-4, thus classifies the user environment to be the indoor environment of the UE 110 with the high certainty. The processor 202 may not perform any further operation.
[0097] However, if at step 306, the processor 202, using the processing module 214-4 determines that the accuracy of the GPS sensors is lesser than the pre-defined threshold value, the processor 202, using the acquisition module 214-2, fetches the identifier of the serving cell latched to the UE 110 from the records of the crowd source data.
[0098] At step 310, the processor 202, using the processing module 214-4, determines if the serving cell is at least one of the indoor serving cell and the wireless network cell. If the processor 202, using the processing module 214-4, determines an availability of at least one of the indoor serving cell and the wireless network cell, the method 300 may loop back to the step 308, and the processor 202, using the processing module 214-4, classifies the user environment of the UE 110 to be the indoor environment with high certainty.
[0099] Further, if at step 310, the processor 202, using the processing module 214-4, fails to determine the availability of the at least one of the indoor serving cell and the wireless network cell, then the method 300 may move to step 312.
[00100] At step 312, the processor 202 determines whether the speed of the movement of the UE 110 is greater than the pre-defined threshold value of speed of the UE 110. The processor 202 using the processing module 214-4 determines the speed of the movement of the UE 110 based on the change in the positional information of the UE 110 and change in the timestamp of capturing positional information of the UE 110. If the speed of the UE 110 is determined to be greater than the pre-defined threshold value of speed of the UE 110, the processor 202 using the processing module 214-4 may determine that the UE 110 is travelling in a vehicle. The processor 202, at step 314, determines that there is the high certainty that the latitudinal and longitudinal position of the UE 110 lies in the outdoor environment. In an embodiment, the processor 202, at step 314, determines that there is the high certainty that the latitudinal and longitudinal position of the UE 110 lies in the outdoor environment in transit in the vehicle. The processor 202 using the processing module 214-4 thus classifies the user environment status of the UE 110 to be in transit in the outdoor environment with the high certainty.
[00101] Referring back to step 312, if the processor 202 using the processing module 214-4 determines that the speed of the movement of the UE 110 is lesser than the pre-defined threshold value of speed of the UE 110, then the processor 202 triggers the initiation of method 400 with step A and perform operations depicted at steps 402 through 412 of the method 400 described below.
[00102] Referring back to the step 304, if the processor 202, using the processing module 214-4, determines that the geographical location does not correspond to the building or the structure, then the processor 202 may trigger the initiation of method 500 with step B and perform operations depicted at steps 502 through 514 of the method 500 described below.
[00103] FIG. 4 illustrates the method 400 for classification of the user environment of the UEs 110 in the communication network based on the charging parameter, in accordance with an embodiment of the present disclosure.
[00104] When the processor 202, using the processing module 214-4, determines that the geographical location correspond to the building or the structure, then the processor 202 further determines whether the speed of movement of the UE 110 is greater than the pre-defined threshold value of speed of the UE 110. If it is determined that the speed of the movement of the UE 110 is not greater than the pre-defined threshold value of speed of the UE 110, then the processor 202 determines the timestamp of capturing the positional information of the UE 110. The timestamp of capturing the positional information of the UE 110 is fetched from the records of the crowd source data. The processor 202, using the processing module 214-4, performs a positional correlation by correlating two or more of positional information of the UE 110, the timestamp of capturing the positional information of the UE 110, and the speed of the movement of the UE 110. The method 400 starts at step 402.
[00105] At the step 402, the processor 202, using the processing module 214-4, determines whether the timestamp of capturing the positional information of the UE 110 indicates a night time or a day time. If it is determined that the timestamp indicates the night time, then at step 404, the processor 202 may determine whether the charging event has occurred in the UE 110 during the night time. If the charging event is determined to have taken place in the night time, the processor 202, at step 406, determines that there is the high certainty that the latitudinal and longitudinal position of the UE 110 lies in the indoor environment. The processor 202 may thus classify the user environment of the UE 110 to be the indoor environment with the high certainty. The processor 202 may not perform any further operation.
[00106] At step 408, if the charging event is determined to have taken place during the day time, the processor 202, using the processing module 214-4 determines that there is the low certainty that the latitudinal and longitudinal position of the UE 110 lies in the indoor environment. The processor 202 may thus classify the user environment of the UE 110 to be the indoor environment with the low certainty. The processor 202 may not perform any further operation.
[00107] Now referring back to step 402, if it is determined that the timestamp indicates the day time, the processor 202, at step 410, determines whether the charging event has occurred in the UE 110 during the change in the time of (i.e., the timestamp) of capturing the positional information of the UE 110. If the charging event has occurred during the change in the time of capturing the position of the UE 110, then the processor 202 loops to step 408, and determines that there is the low certainty that the latitudinal and longitudinal position of the UE 110 lies in the indoor environment. The determination uses the timestamp indicating the time of capturing the positional information. Further, based on the determination performed at step 408, the processor 202 classifies the user environment of the UE 110 to be the indoor environment with the low certainty.
[00108] At step 412, if the processor 202, using the processing module 214-4, determines that there is no charging event occurred outside the change in the time of capturing the positional information of the UE 110, then the processor 202 determines that there is the low certainty that the latitudinal and longitudinal position of the UE 110 lies in the outdoor environment. Further, based on the determination performed at step 412, The processor 202 thus classifies the user environment of the UE 110 to be the outdoor environment with the low certainty.
[00109] FIG. 5 illustrates the method 500 for classification of the user environment of the UEs 110 in the communication network based on the speed parameter, in accordance with an embodiment of the present disclosure.
[00110] With respect to step 304 in FIG. 3, if the processor 202, using the processing module 214-4, determines that the geographical location does not correspond to the location within the building or in any indoor environment, then the processor 202 determines that the user carrying the UE 110 is travelling or moving and there is the movement in the position of the UE 110. The processor 202 determines the speed of the movement of the UE 110 during the travel or the movement of the user.
[00111] At step 502, the processor 202, using the processing module 214-4, determines whether the speed of the movement of the UE 110 is greater than the pre-defined threshold value of speed of the UE 110 by comparing the speed of the movement of the UE 110 with the pre-defined threshold speed. If the speed of the movement of the UE 110 is determined to be greater than the pre-defined threshold value of speed of the UE 110, then the processor 202 determines that the user carrying the UE 110 is travelling in the vehicle.
[00112] At step 504, the processor 202 using the processing module 214-4 determines that there is the high certainty that the latitudinal and longitudinal position of the UE 110 lies in the outdoor environment i.e., the location during transit in the vehicle. The processor 202 using the processing module 214-4 thus classifies the user environment status of the UE 110 to be in transit in the outdoor environment with the high certainty.
[00113] At step 506, if the speed of the movement of the UE 110 is determined to be less than the pre-defined threshold value of speed of the UE 110, the processor 202, using the processing module 214-4, may determine whether the accuracy of the position of the UE 110 is high. When the accuracy of the position of the UE 110 is determined to be less than the pre-defined threshold value of the positional accuracy, the processor 202 using the acquisition module 214-2 fetches the identifier of the serving cell latched to the UE 110 from the records of the crowd source data.
[00114] At step 508, the processor 202, using the processing module 214-4, may determine if the serving cell is at least one of the indoor serving cell and the wireless network cell.

[00115] At step 510, if the processor 202 using the processing module 214-4 determines that the serving cell is at least one of the indoor serving cell and the wireless network cell, then the processor 202 may classify the user environment of the UE 110 to be the indoor environment with the low certainty.
[00116] At step 512, if the processor 202 using the processing module 214-4 determines that the serving cell is not the indoor serving cell or the wireless network cell, then the processor 202 may indicate the user environment of the UE 110 to be the outdoor environment with high certainty.
[00117] Referring back to step 506, when the accuracy of the position of the UE 110 is determined to be higher or greater than the pre-defined threshold value of the positional accuracy, the processor 202 at step 514, may determine if the charging event has occurred in the UE 110 during the change in the timestamp of capturing the position of the UE 110. If the charging event is determined to have taken place outside of the change in the timestamp of capturing the position of the UE 110, the method 500 may loop to step 512, and the processor 202 determines that there is the high certainty that the latitudinal and longitudinal position of the UE 110 lies in the outdoor environment. The processor 202 may classify the user environment of the UE 110 to be the outdoor environment with high certainty. The processor 202 may not perform any further operation.
[00118] If the charging event is determined to have taken place during the change in the timestamp of capturing the position of the UE 110, the method may loop to step 504. The processor 202 may thus classify the user environment of the UE 110 in the outdoor environment where the UE is in transit with the high certainty. The processor 202 may not perform any further operation.
[00119] In a non-limiting example, when the processor 202 determines a reduction in the value of the RSRP from -95 to -105 DBm, samples of the crowdsource data collected from the UE 110 within a pre-defined timestamp, let say 300 sec, latched to the serving cell lying within a pre-defined buffer distance, let say 50m from the positional parameter of the UE 110, the identifiers corresponding to the serving cell may be determined as a cell that belong to the indoor serving cell.
[00120] FIG. 6 illustrates a method 600 for classification of the user environment of the UEs 110 in the communication network, in accordance with an embodiment of the present disclosure.
[00121] At step 602, the processor 202, using the acquisition module 214-2, collects crowd source data periodically from the data collection entity 130. The acquisition module 214-2 collects the crowd source data after a pre-defined frequency from the data collection entity 130, for example, on a daily, weekly, or fortnightly basis. The crowd source data is associated with the UEs 110 connected to one or more serving cells in the communication network. The collected crowd source data may include the positional information (such as latitude and longitude) of each UE, the timestamp indicating the time of data capture, the charging event associated with the UE, and the identifier corresponding to at least one serving cell among the plurality of serving cells.
[00122] At step 604, the processor 202, using the processing module 214-4, estimates the presence of at least one UE in either the indoor or the outdoor environment based on the positional information of the UE and the identifier of the serving cell currently serving that UE. The estimation may help infer the likely environment in which the user is present, for instance, by correlating the identifiers of the serving cells or GPS locations with the indoor or the outdoor environment.
[00123] At step 606, the processor 202, using the processing module 214-4, determines the certainty level of the environment estimation by correlating one or more parameters derived from the crowd source data. The parameters may include, but are not limited to, the positional information of the at least one UE, the accuracy of the positional information of the UE, the time of capturing the positional information, the charging event in the at least one UE, and the speed of the movement of the at least one UE.
[00124] At step 608, the processor 202, using the processing module 214-4, may classify the environment of the UE as either the indoor or the outdoor environment based on the certainty level obtained at step 606.
[00125] In an embodiment, the method 600 represents an overall method performed by the processor 202 for classification of the user environment of the UEs 110 in the communication network. The method 600 includes compiled operation steps from the method 300, the method 400, and the method 500.
[00126] Now, 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 classification of the crowd source data based on the user environment of the UE. By accurately categorizing the estimated position of the UE along with the certainty of the estimation, the present disclosure allows the network operators to decide whether the user device is in the indoor environment or the outdoor environment. The understanding of the user location and the environment facilitates dynamic adjustments to network resource allocation, thereby enhancing the overall quality of service.
[00127] Further, certain embodiments of the present disclosure describes the classification of the user environment which not only improves the positional accuracy and tracking but also enables a more reliable estimation of network performance metrics, which is critical for optimizing connectivity in diverse coverage areas. Furthermore, the present disclosure empowers the network operators to perform detailed analyses of user experience by correlating performance metrics with environmental conditions. With the ability to differentiate and process data based on the indoor and the outdoor environments, the network operators may optimize strategies to better address issues such as signal attenuation, network congestion, or interference common to specific environments.
[00128] Embodiments of the present technology may be described herein with reference to flowchart illustrations of methods and systems according to embodiments of the technology, 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 methods.
[00129] 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 204) that can direct a computer processor or other programmable processing apparatus to function in a particular manner, such that the instructions 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).
[00130] 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 202) to perform one or more functions as described herein. The instructions may also be stored remotely such as on a server, or all or a portion of the instructions can be stored locally and remotely.
[00131] 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.
[00132] 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.
[00133] 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
[00134] 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 – Communication system for classification of crowd source data in a communication network
110 - User Equipment
120 – Network
130 – Data collection entity
140 – Load Balancer
150 – Server
160 - One or more webservers
170 – Gateway server
180 - Distributed File System
202 – Processor
204 – Memory
204-1 – Instructions
206 – I/O Interface
208 – Communication Interface
210 – Database
212 – Microservice(s)
214 – Module(s)
214-2 – Acquisition module
214-4 – Processing module
216 – Communication Bus
300 - Method for classification of user environment of UEs in the communication network based on the position parameter
302- 312- Operation steps of the method 300
400 - Method for classification of user environment of the UEs in the communication network based on the charging parameter
402-412 - Operation steps of the method 400
500 - Method for classification of the user environment of the UEs in the communication network based on the speed parameter
502- 512- Operation steps of the method 500
600 - Method for classification of the user environment of the UEs in the communication network
602- 608- Operation steps of the method 600
,CLAIMS:We Claim:

1. A method (600) for classifying a user environment in a communication network, the method (600) comprising:
periodically collecting, by an acquisition module (214-2) from a data collection entity (130), crowd source data associated with a plurality of User Equipment (UEs) (110) served by a plurality of serving cells in the communication network, wherein the crowd source data includes positional information of each of the plurality of the UEs (110), a timestamp indicating a time of capturing the positional information, a charging event in the plurality of the UEs, and an identifier of an at least one serving cell among the plurality of serving cells;
estimating, by a processing module (214-4), a presence of the at least one UE in one of an indoor environment or an outdoor environment based on the positional information of the at least one UE and the identifier of the at least one serving cell serving the at least one UE;
determining, by the processing module (214-4), a certainty of the estimation by correlating one or more parameters corresponding to the crowd source data; and
classifying, by the processing module (214-4), the user environment of the at least one UE based on the certainty of the estimation as one of the indoor environment or the outdoor environment.
2. The method (600) as claimed in claim 1, further comprising determining, by the processing module (214-4), the certainty of the presence of the at least one UE in the indoor environment by:
correlating the one or more parameters corresponding to the positional information of the at least one UE and a geographical location of a building nearest to the positional information of the at least one UE;
determining the accuracy of the positional information of the at least one UE based on the correlation of the positional information of the at least one UE and the geographical location of the building;
comparing the accuracy of the positional information of the at least one UE with a pre-defined threshold value; and
performing, based on a result of the comparison, one or more of:
a determination that the at least one UE is present in the indoor environment with high certainty when the accuracy of the positional information of the at least one UE is greater than the pre-defined threshold value; or
a determination whether the identifier of the at least one serving cell is an indoor serving cell, when the accuracy of the positional information of the at least one UE is lesser than or equal to the pre-defined threshold value; and
a determination that the at least one UE is present in the indoor environment with high certainty, wherein the presence of the at least one UE is determined based on a determination that the identifier of the at least one serving cell is the indoor serving cell.
3. The method (600) as claimed in claim 1, further comprising determining, by the processing module (214-4), the certainty of the presence of the at least one UE in the outdoor environment by:
determining a speed of movement of at least one UE among the plurality of the UEs (110) based on a change in positional information of the at least one UE and a change in the timestamp of capturing the positional information of the at least one UE;
correlating the one or more parameters corresponding to the positional information of the at least one UE and the speed of the movement of the at least one UE;
comparing the speed of the movement of the at least one UE with a pre-defined threshold speed; and
performing, based on a result of the comparison, one or more of:
a determination that the at least one UE is present in the outdoor environment with high certainty when the speed of the movement exceeds the pre-defined threshold speed; or
a determination whether a charging event has occurred in the at least one UE during or outside the time of capturing the positional information when the speed of the movement of the at least one UE is less than or equal to the pre-defined threshold speed; and
a determination that the at least one UE is present in the outdoor environment with a low certainty or high certainty, wherein the presence of the at least one UE is determined in the outdoor environment with the low certainty based on a determination that the charging event has occurred outside the change in the timestamp,
and wherein the presence of the at least one UE is determined in the outdoor environment with the high certainty based on a determination that the charging event has occurred during the change in the timestamp.
4. The method (600) as claimed in claim 1, wherein for determining, by the processing module (214-4), the certainty of the estimation, the method further comprises:
determining that the positional information of the at least one UE corresponds to a geographical location outside a building nearest to the at least one UE;
determining a speed of movement of the at least one UE based on a change in the positional information of the at least one UE and a change in the timestamp of capturing the positional information of the at least one UE;
comparing the speed of the movement of the at least one UE with a pre-defined threshold speed; and
performing, based on a result of the comparison, one or more of:
a determination that the user environment as the outdoor environment where the at least one UE is in transit with a high certainty when the speed of the movement of the at least one UE exceeds the pre-defined threshold speed; or
a determination whether the accuracy of the positional information of the at least one UE is greater than or less than a pre-defined threshold value when the speed of the movement of the at least one UE is less than or equal to the pre-defined threshold speed;
a determination whether a charging event has occurred in the at least one UE during the time of capturing the positional information upon a determination that the accuracy of the positional information of the at least one UE is greater than the pre-defined threshold value; and
a determination that the user environment is the outdoor environment where the at least one UE is in transit with high certainty, wherein the user environment is determined as the outdoor environment based on a determination that the charging event has occurred during the time of capturing the positional information.
5. The method (600) as claimed in claim 4, wherein for determining the certainty of the estimation, the method further comprises one or more of:
determining, by the processing module (214-4), based on a determination that the charging event has occurred outside the time of capturing the positional information, the user environment as the outdoor environment with high certainty; or
determining, by the processing module (214-4), whether the identifier of the at least one serving cell is an indoor serving cell upon a determination that the accuracy of the positional information of the at least one UE is less than or equal to the pre-defined threshold value; and
determining, by the processing module (214-4), based on a determination the identifier of the at least one serving cell is the indoor serving cell, the user environment as the indoor environment with low certainty.
6. The method (600) as claimed in claim 1, wherein the plurality of serving cells include a base station, a relay, a repeater, small cells, macro cells, micro cells, an indoor serving cell, an outdoor serving cells, and a wireless network cell identifiable using a Service Set Identifier (SSID).
7. The method (600) as claimed in claim 1, wherein the one or more parameters corresponding to the crowd source data include the positional information of the at least one UE, an accuracy of the positional information of the UE, the time of capturing the positional information, the charging event in the at least one UE, and a speed of the movement of the at least one UE.
8. A system (100) for classifying a user environment in a communication network, the system (100) comprising:
an acquisition module (214-2) configured to:
periodically collect, from a data collection entity (130), crowd source data associated with a plurality of User Equipment (UEs) (110) served by a plurality of serving cells in the communication network, wherein the crowd source data includes positional information of each of the plurality of the UEs, a timestamp indicating a time of capturing the positional information, a charging event in the plurality of the UEs, and an identifier of an at least one serving cell among the plurality of serving cells; and
a processing module (214-4) configured to:
estimate a presence of the at least one UE in one of an indoor environment or an outdoor environment based on the positional information of the at least one UE and the identifier of the at least one serving cell serving the at least one UE;
determine a certainty of the estimation by correlating one or more parameters corresponding to the crowd source data; and
`classify, the user environment of the at least one UE based on the certainty of the estimation as one of the indoor environment or the outdoor environment.
9. The system (100) as claimed in claim 8, wherein to determine the certainty of the presence of the at least one UE in the indoor environment, the processing module (214-4) is configured to:
correlate the one or more parameters corresponding to the positional information of the at least one UE and a geographical location of a building nearest to the positional information of the at least one UE;
determine the accuracy of the positional information of the at least one UE based on the correlation of the positional information of the at least one UE and the geographical location of the building;
compare the accuracy of the positional information of the at least one UE with a pre-defined threshold value; and
perform, based on a result of the comparison, one or more of:
a determination that the UE is present in the indoor environment with high certainty when the accuracy of the positional information of the at least one UE is greater than the pre-defined threshold value; or
a determination whether the identifier of the at least one serving cell is an indoor serving cell, when the accuracy of the positional information of the at least one UE is lesser than or equal to the pre-defined threshold value; and
a determination that the at least one UE is present in the indoor environment with high certainty, wherein the presence of the at least one UE is determined based on a determination that the identifier of the at least one serving cell is the indoor serving cell.
10.The system (100) as claimed in claim 8, wherein to determine the certainty of the presence of the at least one UE in the outdoor environment, the processing module (214-4) is further configured to:
determine a speed of movement of at least one UE among the plurality of the UEs based on a change in positional information of the at least one UE and a change in the timestamp of capturing the positional information of the at least one UE;
correlate the one or more parameters corresponding to the positional information of the at least one UE and the speed of the movement of the at least one UE;
compare the speed of the movement of the at least one UE with a pre-defined threshold speed; and
perform, based on a result of the comparison, one or more of:
a determination that the at least one UE is present in the outdoor environment with high certainty when the speed of the movement exceeds the pre-defined threshold speed; or
a determination whether a charging event has occurred in the at least one UE during or outside the time of capturing the positional information when the speed of the movement of the at least one UE is less than or equal to the pre-defined threshold speed; and
a determination that the at least one UE is present in the outdoor environment with a low certainty or high certainty, wherein the presence of the at least one UE is determined in the outdoor environment with the low certainty based on a determination that the charging event has occurred outside the change in the timestamp,
and wherein the presence of the at least one UE is determined in the outdoor environment with the high certainty based on a determination that the charging event has occurred during the change in the timestamp.
11. The system (100) as claimed in claim 8, wherein to determine the certainty of the estimation, the processing module (214-4) is further configured to:
determine that the positional information of the at least one UE corresponds to a geographical location outside a building nearest to the at least one UE;
determine a speed of movement of the at least one UE based on a change in the positional information of the at least one UE and a change in the timestamp of capturing the positional information of the at least one UE;
compare the speed of the movement of the at least one UE with a pre-defined threshold speed; and
performing, based on a result of the comparison, one or more of:
a determination that the user environment as the outdoor environment where the at least one UE is in transit with a high certainty when the speed of the movement of the at least one UE exceeds the pre-defined threshold speed; or
a determination whether the accuracy of the positional information of the at least one UE is greater than or less than a pre-defined threshold value when the speed of the movement of the at least one UE is less than or equal to the pre-defined threshold speed;
a determination whether a charging event has occurred in the at least one UE during the time of capturing the positional information upon a determination that the accuracy of the positional information of the at least one UE is greater than the pre-defined threshold value; and
a determination that the user environment is the outdoor environment where the at least one UE is in transit with high certainty, wherein the user environment is determined as the outdoor environment based on a determination that the charging event has occurred during the time of capturing the positional information.
12. The system (100) as claimed in claim 11, wherein to determine the certainty of the estimation, the processing module (214-4) is further configured to perform one or more of:
determine, based on a determination that the charging event has occurred outside the time of capturing the positional information, the user environment as the outdoor environment with high certainty; or
determine whether the identifier of the at least one serving cell is an indoor serving cell upon a determination that the accuracy of the positional information of the at least one UE is less than or equal to the pre-defined threshold value; and
determine, based on a determination the identifier of the at least one serving cell is the indoor serving cell, the user environment as the indoor environment with low certainty.
13. The system (100) as claimed in claim 8, wherein the plurality of serving cells include a base station, a relay, a repeater, small cells, macro cells, micro cells, an indoor serving cell, an outdoor serving cells, and a wireless network cell identifiable using a Service Set Identifier (SSID).
14. The system (100) as claimed in claim 8, wherein the one or more parameters corresponding to the crowd source data include the positional information of the at least one UE, an accuracy of the positional information of the UE, the time of capturing the positional information, the charging event in the at least one UE, and a speed of the movement of the at least one UE.