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 IDENTIFICATION OF SERVING CELLS HAVING DEGRADED PERFORMANCE 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 identification of serving cells having degraded performance 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] There has been an ever-increasing demand for a reliable communication network as users of smart devices rely on the communication network to connect with each other across the globe and share content and data. Although network operators have tried to meet the increased demand by expanding network resources, still the users continue to experience a degradation in performance of the communication network in terms of higher call drops, longer duration of involuntary call mute, low internet speed, and higher latency in the communication network. Further, ongoing dynamic changes in network conditions due to variability in network traffic, environmental factors and complex network topologies have made it tougher for the network operators to monitor the performance of the communication network.
[0004] For enhancing user experience and optimizing the performance of the communication network, network operators rely on traditional methods of network optimization based on network performance reports which are generated based on data collected from transmission nodes serving a plurality of User Equipment (UEs). However, a significant drawback associated with the traditional methods is that an actual geographical location where the users face significant issues during calling cannot be identified. Therefore, the network operators face difficulties in identifying the transmission nodes facing high congestion, or frequent dropouts, thus impeding efforts to optimize the performance of the communication network effectively.
[0005] In light of the above-mentioned drawbacks and shortcomings of the traditional methods of network optimization, there lies a need for an improved system and method for accurate identification of the actual geographical location where the performance of the transmission nodes is compromised.
SUMMARY
[0006] 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.
[0007] In an embodiment, disclosed herein is a method for identification of serving cells having degraded performance in a communication network. The method comprises obtaining, by an acquisition module from a data collection entity, data including a plurality of parameters associated with a plurality of User Equipment (UEs) served and a plurality of serving cells serving the plurality of UEs in the communication network. Further, the method comprises identifying, by a processing module by applying one or more clustering techniques to the obtained data, one or more clusters of UEs among the plurality of UEs one or more clusters of UEs among the plurality of UEs (120) representing an area that is facing a degraded Quality of Service (QoS) associated with a call. Furthermore, the method comprises generating, by the processing module, a polygon based on the identified one or more clusters of the UEs and the plurality of parameters. Thereafter, the method comprises identifying, by the processing module, a group of serving cells with the degraded performance located within the generated polygon based on a cell identification parameter among the plurality of parameters.
[0008] In one or more embodiment, the polygon is generated based on a location parameter among the plurality of parameters. The generated polygon indicates a coverage boundary of the determined one or more cluster of the UEs.
[0009] In one or more embodiments, the method further comprises determining, by the processing module, a plurality of performance metrics corresponding to the identified group of serving cells based on the plurality of parameters. Thereafter, the method comprises generating, by the processing module, based on the determined plurality of performance metrics, a plan for performing a Root Cause Analysis (RCA) to optimize the identified group of serving cells.
[0010] In one or more embodiments, the degraded QoS associated with the call corresponds to an adverse event from one or more of a network failure, a call drop, a call mute, a reduced data throughput, or a service outage. The adverse event is identified from the data obtained from the data collection entity.
[0011] In one or more embodiments, the plurality of parameters includes the location parameter, the cell identification parameter, and a plurality of Key Performance Indicators (KPIs) associated with the plurality of serving cells and the plurality of the UEs. The location parameter includes positional information of each UE among the plurality of the UEs and positional information of the plurality of serving cells. The cell identification parameter includes identity information of the plurality of serving cells and a service area associated with the plurality of serving cells.
[0012] In one or more embodiments, the positional information corresponds to latitudinal and longitudinal coordinates of each UE among the plurality of UEs.
[0013] In another embodiment, disclosed herein is a system for identification of serving cells having degraded performance in a communication network. The system comprises an acquisition module and a processing module. The acquisition module is configured to obtain, from a data collection entity, data including a plurality of parameters associated with a plurality of User Equipment (UEs) and a plurality of serving cells serving the plurality of UEs in the communication network. The processing module is configured to identify, by applying one or more clustering techniques to the obtained data, one or more clusters of UEs among the plurality of UEs representing an area that is facing a degraded Quality of Service (QoS) associated with a call. Further, the processing module is configured to generate a polygon based on the identified one or more clusters of the UEs and the plurality of parameters. Thereafter, the processing module is configured to identify, a group of serving cells with the degraded performance located within the generated polygon based on a cell identification parameter among the plurality of parameters.
[0014] In one or more embodiments, the processing module is configured to determine a plurality of performance metrics corresponding to the identified group of serving cells based on the plurality of parameters. Thereafter, the processing module is configured to generate, based on the determined plurality of performance metrics, a plan for performing a Root Cause Analysis (RCA) to optimize the identified group of serving cells.
BRIEF DESCRIPTION OF DRAWINGS
[0015] 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:
[0016] FIG. 1 illustrates a block diagram depicting an exemplary environment of a wireless communication network, in accordance with an embodiment of the present disclosure.
[0017] FIG. 2 illustrates a system for identification of the serving cells having degraded performance the communication network, in accordance with an embodiment of the present disclosure.
[0018] FIG. 3 illustrates a block diagram depicting an example system architecture of a server, in accordance with an embodiment of the present disclosure.
[0019] FIG. 4 illustrates a block diagram depicting an example system architecture of a Network Management Console (NMC), in accordance with an embodiment of the present disclosure.
[0020] FIG. 5 illustrates an exemplary method for identification of serving cells having degraded performance in the communication network, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE INVENTION
[0021] 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.
[0022] 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.
[0023] 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.”
[0024] 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.”
[0025] 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.
[0026] 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.
[0027] 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.
[0028] 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.
[0029] In the disclosure, various embodiments are described using terms used in communication standards (e.g., 3rd Generation Partnership Project (3GPP), Extensible Radio Access Network (xRAN), and Open-Radio Access Network (O-RAN)), but these are merely examples for description. Various embodiments of the disclosure may also be modified and applied to other communication systems.
[0030] In order to facilitate an understanding of the disclosed invention, a number of terms are defined below.
[0031] Quality of Service (QoS) in the field of telecommunications can be defined as a set of specific requirements provided by a network to users, which are necessary in order to achieve the required functionality of an application (service). The users specify their performance requirements in form of QoS parameters such as delay or packet loss.
[0032] A serving cell may refer to a radio cell or an access point to which User Equipment (UE) is currently connected or from which the UE receives primary wireless communication services.
[0033] 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.
[0034] 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.
[0035] 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.
[0036] 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.
[0037] International Mobile Equipment Identity (IMEI) may refer to a unique numerical identifier assigned to mobile devices, used to identify valid devices on the network.
[0038] An object of the present disclosure is to provide a system and a method for identification of serving cells having degraded performance in a communication network using crowd source data collected from User Equipment’s (UEs). Another object of the present disclosure is to provide a system and a method for analyzing and assessing the QoS provided by the communication network based on user experience.
[0039] Another object of the present disclosure is to identify a root cause of degradation of the serving cells having degraded quality. Still another object of the present disclosure is to provide recommendations for improving the QoS provided by the communication network to the user. Yet another object of the present disclosure is to provide remedial actions for addressing the root cause of degradation of the serving cells having degraded quality.
[0040] The following description provides specific details of certain aspects of the disclosure illustrated in the drawings to provide a thorough understanding of those aspects. It should be recognized, however, that the present disclosure can be reflected in additional aspects and the disclosure may be practiced without some of the details in the following description.
[0041] 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.
[0042] FIG. 1 illustrates a block diagram depicting an exemplary environment 100 of a wireless communication network, in accordance with an embodiment of the present disclosure. The embodiment of the wireless communication network shown in FIG. 1 is for illustration only. Other embodiments of the wireless communication network may be used without departing from the scope of this disclosure.
[0043] The wireless communication network may comprise a plurality of nodes 110-1 through 110-n (cumulatively referred to as “nodes 110” and alternatively referred to as “serving cells 110” or “cells 110”) connected to a plurality of User Equipment (UEs) 120-1 through 120-n (cumulatively referred to as “UEs 120”) through a network 130. The nodes 110 and the UEs 120 also communicate with a server 140 through the network 130.
[0044] The nodes 110 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 provides coverage to a plurality of predetermined geographic areas based on distance over which a signal may be transmitted. Examples of the BS include, but are not limited to, a macro cell, a femtocell, wireless “Access Point (AP),” “evolved NodeB (eNodeB) (eNB),” “5th Generation (5G) node,” “next generation NodeB (gNB),” “wireless point,” “Transmission/Reception Point (TRP).” The BS may provide wireless access in accordance with wireless communication protocols, e.g., 5G/NR 3GPP New Radio interface/access (NR), LTE, LTE-A, High Speed Packet Access (HSPA), Wi-Fi 802.11a/b/g/n/ac, etc. Aspects of the present disclosure are intended to include, or otherwise cover, any technology (known or later developed) bearing same or similar characteristics as of the above-mentioned BS, without deviating from the scope of the present disclosure. The serving cells 110 may also include the small cells, the macro cells, the micro cells, the indoor serving cells, and the outdoor serving cells. The serving cells 110 may experience degraded performance when a value of one or more Key Performance Indicators (KPIs) corresponding to the serving cells 110 falls below a pre-defined threshold value of the KPI. The KPIs may refer to quantifiable measures that reflect a behavioral state of the UEs and serving cells 110.
[0045] Typically, the term “UE” can refer to any component such as “mobile station,” “subscriber station,” “remote terminal,” “wireless terminal,” “receive point,” or “end user device.” The UE 120 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 130. The UEs 120 may be served by one or more of the plurality of nodes 110.
[0046] The UEs 120 may communicate with the serving cells 110 to avail services of the serving cells 110 through the network 130. The network 130 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.
[0047] The communication network may be divided into different coverage regions. Each coverage region may comprise multiple serving cells 110 and UEs 120. The UEs 120 may be served by one or more serving cells 110 in one or more coverage regions. Extents of the coverage regions of the serving cells 110 may have shapes such as hexagonal and circular, including irregular shapes, depending upon the configuration of the serving cell 110, and variations in the radio environment associated with natural and man-made obstructions.
[0048] Although FIG. 1 illustrates one example of the communication environment 100, various changes may be made to FIG. 1. For example, the communication environment may include any number of serving cells 110 and any number of UEs 120 in any suitable arrangement. Further, the serving cells 110 may communicate directly with any number of UEs and provide the UEs with wireless broadband access to the network 130. Further, each of the serving cells 110 may also communicate directly with the server 140. Further, the serving cells 110 may provide access to other or additional external networks, such as external telephone networks or other types of data networks.
[0049] FIG. 2 illustrates an exemplary architecture of a system 200 for identification of the serving cells having degraded performance in the communication network, in accordance with an embodiment of the present disclosure. The embodiments of the system 200 shown in FIG. 2 are for illustration only. Other embodiments of the system 200 may be used without departing from the scope of this disclosure.
[0050] As shown in FIG. 2, the system 200 may include the plurality of serving cells 110, the plurality of UEs 120, the network 130, the server 140, a Network Management Console (NMC) 210, a data collection entity 220, and a Distributed File System (DFS) 230. The server 140 is configured to communicate with the UE 120, the NMC 210, the nodes 110, the data collection entity 220, and the database 230 via the network 130. It must be understood that there may be a plurality of UEs in the system 200, but for the sake of brevity only one UE 120 has been shown as an example in FIG. 2.
[0051] The UE 120 is configured to establish a session with the serving cells 110 to avail services of the serving cells 110 in the communication network. During the session, the UE 120 is configured to capture data including KPIs and metadata of the communication network. The UE 120 sends the data to the data collection entity 220, periodically, on demand or after an event. The data sent by each of the UE 120 may be referred to as crowd source data. The crowd source data (alternatively referred to as “the data”) may be related to the UE 120 latched to the serving cell during the session. The crowd source data includes data related to a plurality of parameters. The plurality of parameters further includes a location parameter including one or more of positional information of the plurality of the UEs 120 and positional information of the plurality of serving cells 110, a cell identification parameter including one or more of identity information of the plurality of serving cells 110 and serving area information associated with the plurality of serving cells 110, a plurality of KPIs associated with the serving cells 110, and the plurality of the UEs 120 in the communication network. The positional information comprises latitudinal and longitudinal coordinates of each UE among the plurality of UEs 120. The positional information may be captured by Global Positioning Sensors (GPS) provided in the UE 120, at a time of data collection. The crowd source data may additionally include hardware configuration of the UE 120 such as a model number of the UE 120, the IMEI of the UE 120, the IMSI of the UE 120.
[0052] The metadata corresponding to the KPIs may include information like type of the UE 120, traffic direction such as inbound or outbound, and Internet Protocol (IP) addresses of a source device and a destination device in the communication network. The KPIs may include, but not limited to, information associated with occurrence of a number of call drop events, information associated with a count of involuntary call mutes experienced by the plurality of the UEs 120 during a call in progress (henceforth referred to as call mute), information associated with a duration of involuntary call mutes experienced by the plurality of the UEs 120, information associated with volume of data transferred between the UEs 120 and the serving cell, information associated with nature of service availed by the UEs 120, information associated with feedback provided by the users regarding their experience with the network 130, information related to network performance metrics of the serving cells 110, and signal strength received by the UEs 120.
[0053] The data collection entity 220 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 data collection entity 220 may be a database configured to store the crowd source data and communicate with the server 140.
[0054] Further, the data collection entity 220 is communicatively coupled with the server 140. The data collection entity 220 sends the collected crowd source data periodically or on demand to the server 140. The server 140 obtains the collected crowd source data from the data collection entity 220, periodically at a pre-defined time period or on demand corresponding to a trigger such as an event or a user requirement. Upon obtaining the crowd source data from the data collection entity 220, the server 140 processes the crowd source data through a data model and identifies one or more clusters of UEs among the plurality of UEs 120 that are facing degraded QoS associated with a particular KPI. For an example, the one or more clusters of UEs 120 among the plurality of UEs may be identified that are facing degraded QoS associated with a call. The server 140 further identifies a group of serving cell having degradation in performance.
[0055] The server 140 is further configured to determine a plurality of performance metrics (cumulatively referred to as performance metrics) corresponding to the group of identified serving cells based on the KPIs collected from the crowd source data. The performance metrics may include calculated values derived from the KPIs to evaluate whether the communication network is meeting operational objectives and/or customer service objectives set by a network operator. Furthermore, based on an analysis of the determined plurality of performance metrics, the server 140 identifies a root cause of degradation in the performance of the group of serving cells.
[0056] The server 140 may further be connected to a storage medium for storing and managing the crowd source data collected from the data collection entity 220. 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 230. The DFS 230 may allow the server 140 seamless data access and retrieval as needed for processing and storage.
[0057] The DFS 230 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 140. In other embodiments, the DFS 230 may be integrated within the server 140 for storing records of the crowd source data. The DFS 230 may also contain records of the serving cells 110 deployed in a geographical region. The DFS 230 is further configured to be utilized for storage the plurality of performance metrics associated with the identified group of serving cells.
[0058] Furthermore, the server 140 is configured to determine the performance metrics corresponding to the group of serving cells based on the KPIs and transfer to the NMC 210. The NMC 210 may be managed by network administrators for taking a remedial action on the communication network based on the identified group of serving cells.
[0059] Although FIG. 2 illustrates one example of the system 200, various changes may be made to FIG. 2. Further, the system 200 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, for example the data collection entity 220 and the DFS 230 may be a single entity. A detailed description of the method for identification of the serving cells having degraded performance in the communication network is described further below.
[0060] FIG. 3 illustrates a block diagram depicting a detailed system architecture of the server 140, in accordance with an embodiment of the present disclosure. The embodiment of the server 140 as shown in Fig. 3 is for illustration only. However, the server 140 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 server 140.
[0061] As shown in Fig. 3, the server 140 includes an Input-Output (I/O) interface 302, one or more processors 304 (hereinafter may also be referred to as “processor 304” or “at least one processor 304”), a memory 306, a network communication manager 308, a communication interface 310, a database 312, and a plurality of modules/units 314 (collectively referred to as the modules 314). Components of the server 150 are coupled to each other via a communication bus 316.
[0062] The I/O interface 302 may include suitable logic, circuitry, interfaces, and/or codes that may be configured to receive input(s). For example, the I/O interface 302 may have an input interface and an output interface. The I/O interface 302 may be configured to enable the user to provide the user input(s) to trigger (or configure) the server 140 to perform various operations for identification of serving cells having degraded performance in the communication network. 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 302 including known, related art, and/or later developed technologies without deviating from the scope of the present disclosure.
[0063] The processor 304 may include processing circuitry, logic, interface(s), and/or code(s), and may be configured to communicate with the I/O interface 302, the memory 306, the network communication manager 308, the communication interface 310, the database 312, and the modules 314, via the communication bus 316. Examples of the communication bus 316 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 304 to the other subsystems of the server 140, as the communication bus 316, without deviating from the scope of the present disclosure.
[0064] The processor 304 may include various processing circuitry configured to execute instructions 306-1 (hereinafter also referred to as “a set of instructions 306-1”) stored in the memory 306 and to perform various processes. The processor 304 may also include a plurality of processing engines i.e., information processing units for identification of the serving cells 110 having degraded performance. The processor 304 may be configured to handle a set of tasks or computations executed by the processor 304 in a distributed computing environment. For an example, the processor 304 is configured to execute programs and processes to execute instruction(s) or code(s) stored in the memory 306 pertaining identification of the serving cells 110 having degraded performance in the communication network. The processor 304 is further configured to move data into or out of the memory 306 as required by an execution process of the server 140.
[0065] Examples of the processor 304 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.
[0066] The memory 306 is configured to store the set of instructions 306-1 required by the processor 304 for controlling overall operations of the server 140. A part of the memory 306 may include a Random-Access Memory (RAM), a cache memory, or a Read-Only Memory (ROM). 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 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 306 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 306 can be an internal storage unit or it can be an external storage unit of the server 140, cloud storage, or any other type of external storage. In some embodiments, when the memory 306 is external to the server 150, the memory 306 may be removably attached to the server 140. Aspects of the present disclosure are intended to include or otherwise cover any data storage medium as ‘the memory 306’, without deviating from the scope of the present disclosure.
[0067] The processor 304 is configured to utilize output data generated by a data model 306-2 stored in the memory 306. In an embodiment, the data model 306-2 may correspond to a cluster-based data model. The data model 306-2 may be a pre-trained model including an unsupervised, a semi-supervised or a supervised model. In one implementation, the data model 306-2 pre-trained over clustering techniques may be utilized.
[0068] In an embodiment, the module(s) 314 may be implemented as a combination of hardware and software programming (for example, programmable instructions) to implement one or more functionalities of the server 140. 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) 314 may include suitable logic, circuitry, interfaces, and/or codes. For example, the programming for the module(s) 314 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the module(s) 314 may comprise a processing resource (for example, one or more processors), to execute such instructions. In an embodiment, the module(s) 314 may be combined to a single module or each module of the module(s) 314 may be further subdivided into different modules.
[0069] In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the module(s) 314. In such examples, the server 140 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 140 and the processing resource. In other examples, the module(s) 314 may be implemented using an electronic circuitry.
[0070] In one or more embodiments, the module(s) 314 may include one or more modules such as an acquisition module 314-2, a processing module 314-4, and other modules (not shown in Fig. 3). The other modules may include a visualization generation module. Each of the module(s) 314 are communicatively coupled with each other.
[0071] In an embodiment, the processor 304, using the acquisition module 314-2, is configured to obtain, from the data collection entity 220, the crowd source data to initiate identification of serving cells 110 having degraded performance in the communication network. The crowd source data corresponds to the data including the plurality of parameters associated with the plurality of UEs 120 and the serving cells 110 serving the plurality of UEs 120. Further, the processor 304, using the acquisition module 314-4, is configured to utilize the data model 306-2 for processing the plurality of parameters in the crowd source data.
[0072] Before processing the crowd source data through the data model 306-2, the processor 304, using the processing module 314-4, is configured to pre-process the crowd source data by a series of operations. Through the series of operations, validated data is obtained. Further, the processor 304 is configured to compile a dataset of adverse events experienced by the UEs 120 from the validated crowd source data, suitable for identifying malfunctioning corresponding to the serving cells 110. The dataset may include samples of data corresponding to the plurality parameters. In one embodiment, the adverse events may correspond to service affecting events experienced by the user and may be determined through the KPIs associated with the plurality of UEs 120, the KPIs associated with the plurality of serving cells, or the data obtained from the data collection entity 220. Due to the adverse events faced by the plurality of the UEs 120, the QoS experienced by the plurality of UEs 120 may be degraded. Therefore, the degraded QoS associated with the call corresponds to the adverse event from one or more of a network failure, the call drop, the call mute, a reduced data throughput, or a service outage. For an example, adverse events faced during a call by the user such as a voice call, Voice over LTE (VoLTE), Voice over WiFi (VoWiFi), video call, or data call may be determined by the processor 304, using the KPIs from the validated crowd source data. The KPIs obtained from the data collection entity 220 may include the number of call drops and the information associated with the count of involuntary call mutes.
[0073] The processor 304 may process the dataset to identify the adverse events by comparing value of each KPI from a set of KPIs. collected from the validated crowd source data with a pre-defined threshold value of each KPI. The set of KPIs may be pre-defined from among the KPIs, relevant to a particular adverse event to be identified. The processor 304 may utilize the pre-defined threshold value of each KPI set by the network operator. The processor 304 may be configured to retrieve the location of the UE 120 from the location parameter when a sample of the validated crowd source data exceeds the pre-defined threshold value of each KPI.
[0074] The dataset may thus include locations of the UE 120 corresponding to the sample of the validated crowd source data exceeding the pre-defined threshold value of each KPI. At the time of execution, the processor 304, using the processing module 314-4, may load the data model 306-2 in the RAM and process the dataset using the data model 306-2. The data model 306-2 may comprise a machine learning component to perform a plurality of machine learning and deep learning operations on the dataset. The data model 306-2 may identify, based on the plurality of parameters including the positional information of the crowd source data and the KPIs collected from the crowd source data, one or more clusters of UEs among the plurality of UEs 120 that are facing degraded QoS associated with a call.
[0075] The processor 202, using the processing module 314-4 via the data model 306-2 identifies the one or more clusters of UEs among the plurality of UEs 120 using one or more machine learning based clustering techniques. The one or more clusters of the UEs represents an area facing a degraded Quality of Service (QoS) associated with a call. The one or more machine learning based clustering techniques may include, but not limited to, one or more of K-means clustering, hierarchical clustering, Density based Spatial Clustering of Applications with Noise (DSCAN), Gaussian Mixture clustering, density-based clustering, distribution-based clustering, and centroid-based clustering technique.
[0076] Although usage of a cluster-based data model is merely described as an example, it is possible to utilize other data models such as density-based data models, proximity-based data models, probability-based data model, and neural networks. The processor 304 is also configured to fine-tune the data model as per the requirement of a network operations team.
[0077] Furthermore, the processor 304, using the processing module 314-4, generates a polygon based on the identified one or more cluster of the UEs and the plurality of parameters. For generating the polygon, the processing module 314-4, may connect a set of samples among the plurality of samples indicating a coverage boundary of the determined one or more cluster of the UEs. In a non-limiting example, a set of outermost samples of the crowd source data in the one or more clusters of the UEs 120 may be connected to generate the polygon. The polygon may represent a geographical region of any size indicating a coverage boundary of the determined the one or more clusters of the UEs 120 are facing the adverse events. The geographical regions demarcated by the coverage boundary may be a part of a town, city, or a state. The geographical area represented by the generated polygon includes the plurality of the UEs facing the degraded QoS during the call. The processor 304, using the processing module 314-4, identifies a group of serving cells represented by the generated polygon based on the plurality of parameters. In another embodiment, the processor 304, using the processing module 314-4, may fetch the cell identification parameter of the plurality of serving cells 110 corresponding to the serving area information associated with the identified group of serving cells 110 from the DFS 230.
[0078] The processor 304, using the processing module 314-4, determines the plurality of performance metrics corresponding to the group of identified serving cells based on the one or more of the plurality of parameters obtained from the crowd source data. Further, the processor 304, using the processing module 314-4, generate a plan for performing a Root Cause Analysis (RCA) to optimize the identified group of serving cells. The RCA may be performed for identifying a root cause of degradation in performance of the identified group of serving cells or service-disrupting incidents in the serving cells causing degraded QoS in the one or more clusters of the UEs in the geographical region represented by the polygon. Based on the RCA, the processor 304, using the processing module 314-4, is configured to include in the plan, one or more recommendations for resolving the root cause of degradation corresponding to the group of identified serving cells.
[0079] In a non-limiting example, the processor 304, may utilize the KPIs such as call drops and call mutes to determine performance metrics such as call quality and network stability. In a non-limiting example, the performance metrics may include call setup success rate, call drop rate, coverage gaps in the communication network, and session drop rates for data sessions. To identify the root cause of degradation from the determined performance metrics, the processor 304, may perform a series of operations. The series of operations may include analyzing trends, identification of anomalies, and correlation of the performance metrics with potential factors that may contribute to the identified anomalies.
[0080] Through the series of operations, the processor 304 may determine the root cause of degradation in the identified group of serving cells causing the degradation in the services or non-service affecting issue. The root cause of degradation in the performance of the group of serving cells may correspond to an underlying problem or issue with the serving cells 110 causing the service disruption or non-service affecting problems in the serving cells 110, and eventually causing adverse events for the UEs 120. The root cause of degradation may thus include hardware issues in the serving cells 110, software issues in the serving cells 110, network congestion, transmission and/or backhaul issues with the serving cells 110, and radio frequency issues such as interference from neighboring serving cells, improper antenna tilt in the serving cells 110 or low signal quality in the serving cells 110.
[0081] The processor 304, may further refer to a repository of possible resolutions for the identified root cause of degradation in the serving cells stored in the database 312. Based on the possible resolutions, the processor 304 may provide the one or more recommendations to the user for corrective actions that may be performed on the identified group of serving cells. The generated plan may include, but not limited to, recommendations related to configuration parameter adjustments, hardware upgrades, antenna reorientation, and software upgrades associated with the identified serving cells. The processor 304, may further monitor performance of the identified group of serving cells and based on the performance of the identified group of serving cells, the processor 304 may validate that the root cause of degradation has been resolved and that the corrective actions are effective.
[0082] Furthermore, the processor 304, using the visualization generation module, is configured to generate visualization data based on compilation of the performance metrics corresponding to the group of identified serving cells. The visualization data may include the identity information of the group of serving cells and the serving area information associated with the plurality of group of serving cells having degraded performance, the plan including the root cause of degradation in the performance of the group of serving cells, and the recommendations for resolving the root cause of degradation in the identified group of serving cells. The network operator, through the visualization data, may easily identify the root cause of degradation in the performance of the group of serving cells. In another embodiment, the processor 304 may compile the performance metrics corresponding to the group of identified serving cells in form of a report. The report may include the identity information of the group of serving cells and the serving area information associated with the plurality of group of serving cells having degraded performance, the root cause of degradation in the performance of the group of serving cells, and the plan or suggestions for resolving the root cause of degradation in the identified group of serving cells.
[0083] The processor 304, using the processing module 314-4, is configured to provide, via a Graphical User Interface (GUI) or a user interface of the NMC 210, the visualization data or the report including the one or more suggestions to an end user for optimizing the performance of the one or more identified serving cells.
[0084] In another embodiment, the processor 304, using the network communication manager 308, is configured to generate visualization data based on the root cause of degradation in the performance of the group of serving cells and the one or more recommendations to the network operator for optimizing the performance of the one or more identified serving cells.
[0085] The network communication manager 308 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 network communication manager 308 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 140 to identify the group of serving cells in the geographical area represented by the generated polygon.
[0086] The communication interface 310 may manage communications with the NMC 210, the network 130, the data collection entity 220, and the DFS 230. For example, the communication interface 310 may manage the reception of the crowd source data directly from the UEs 120 or through the data collection entity 130. The communication interface 310 may include an electronic circuit specific to a standard that enables wired or wireless communication. The communication interface 310 is configured for communicating with external devices via one or more networks. Further, the communication interface 310 may also provide a communication pathway for one or more components of the server 140. Examples of such components include, but are not limited to, the module(s) 314, the database 312, and the DFS 230.
[0087] The database 312 may store the pre-processed crowd source data fed to the data model 306-2. Furthermore, the database 210 may store one or more a result of the processing obtained from the data model 306-2 including information corresponding to the identified group of serving cells and the root cause of degradation. The database 312 may be accessed and updated by the processor 304. The database 312 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.
[0088] Although FIG. 3 illustrates one example of server 140, various changes may be made to FIG. 3. For example, the server 140 may include any number of components in addition to the components shown in FIG. 3. Further, various components in FIG. 3 may be combined, further subdivided, or omitted and additional components may be added according to particular needs.
[0089] In an alternate embodiment, each module/unit of the module(s)/unit(s) 314 (i.e., the acquisition module 314-2, the processing module 314-4, and the visualization generation module) is configured to independently perform various operations of the processor 304, as described herein, without deviating from the scope of the present disclosure.
[0090] FIG. 4 illustrates a block diagram depicting an example system architecture of the NMC 210, in accordance with an embodiment of the present disclosure. The embodiment of the system architecture of the of the NMC 210 as shown in FIG. 4 is for illustration only. However, the NMC 210 may come in a wide variety of configurations, and FIG. 4 does not limit the scope of the present disclosure to any particular system architecture of the NMC 210.
[0091] As shown in FIG. 4, the NMC 210 (alternatively referred to as “client device” or “user device”) includes one or more processors 402 (hereinafter also referred to as “processor 402”), a memory 404, an interface(s) 406, a communication unit 408, and a processing engine(s)/unit(s) 410. These components may be in electronic communication via one or more buses (e.g., communication bus 412). Although not shown in FIG. 4, the NMC 210 may also include a touchscreen, and a display. The term “NMC 210” may refer to any electronic device such as “user device”, “User Equipment (UE)”, “mobile station,” “subscriber station,” “remote terminal,” “wireless terminal,” or “receive point”, a desktop computer, a portable computing devices such as laptops, tablet computers, handheld computer, mobile phones, wearable computers, or any other device suitable to provide front end services. For the sake of convenience, the term “user device” used herein refers to an electronic device such as the NMC 210 that wirelessly accesses the server 140 via the network 130.
[0092] The one or more components of the NMC 210 are communicatively coupled with the processor 402 (described below) for accessing different functionalities of the system 200. The processor 402 may include various processing circuitry and configured to execute programs or computer readable instructions stored in the memory 404. The processor 402 may also include an intelligent hardware device including a general-purpose processor, such as, for example, and without limitation, a Central Processing Unit (CPU), an Application Processor (AP), a dedicated processor, or the like, a microcontroller, a Field-Programmable Gate Array (FPGA), a programmable logic device, a discrete hardware component, or any combination thereof. In some cases, the processor 402 may be configured to operate a memory array using a memory controller. In some cases, a memory controller may be integrated into the processor 402. The processor 402 may be configured to execute computer-readable instructions stored in a memory (e.g., the memory 404) to cause the NMC 210 to perform various functions (e.g., for display the visualization data or report received from the server 140).
[0093] The memory 404 is communicatively coupled to the processor 402. A part of the memory 404 may include a RAM, and another part of the memory 404 may include a flash memory or other ROM. The memory 404 is configured to store a set of instructions required by the processor 402 for controlling overall operations of the NMC 210. The memory 404 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 EPROM or EEPROM memories. In addition, the memory 404 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 that the memory 404 is non-movable. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in the RAM or cache). The memory 404 can be an internal storage unit or it can be an external storage unit of the NMC 210, cloud storage, or any other type of external storage.
[0094] More specifically, the memory 404 may store computer-readable instructions including instructions that, when executed by a processor (e.g., the processor 402) cause the NMC 210 to perform various functions described herein. In some cases, the memory 404 may contain, among other things, a BIOS which may control basic hardware or software operation such as the interaction with peripheral components or devices.
[0095] The interface 406 (same as the GUI) may include suitable logic, circuitry, a variety of interfaces, and/or codes that may be configured to receive input(s) and present output(s) on the application interface of the NMC 210. The variety of interfaces may include interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. For example, the I/O interface may have an input interface and an output interface. The interface 406 may facilitate communication of the NMC 210 with various devices and systems connected to it. The interface 406 may also provide a communication pathway for one or more components of the NMC 210. Examples of such components include, but are not limited to, the processing Engine(s)/Unit(s) 410.
[0096] In one or more embodiments, processing Engine(s)/Unit(s) 410 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the NMC 210. 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 Engine(s)/Unit(s) 410 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor 402 may comprise a processing resource (for example, one or more processors), to execute such instructions.
[0097] In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing Engine(s)/Unit(s) 410. In such examples, the NMC 210 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 NMC 210 and the processing resource. In other examples, the processing Engine(s)/Unit(s) 410 may be implemented using an electronic circuitry.
[0098] In one or more embodiments, processing Engine(s)/Unit(s) 410 may include one or more Engine(s)/Unit(s) selected from any of an input unit 410-2, a display control unit 410-4, and other Engines/Unit(s) (not shown).
[0099] In an embodiment, the processor 402, using the input unit 410-2, is configured to receive, from the server 140, the report or the visualization data. The processor 402 may control, via the display control unit 410-4, the interface 406 of the NMC 210 to display visualization generated by the processor 402 based on the visualization data or the report received from the server 140.
[0100] In one embodiment, the processor 402, using the display control unit 410-4, renders the visualization data or the report in one or more pictorial format, graphical format, and tabular format. The processor 402, may also control, via the display unit 410-4, the interface 406 to display the visualization data or the report including the identity information of the group of serving cells and the serving area information associated with the plurality of group of serving cells having degraded performance, the plan including the root cause of degradation in the performance of the group of serving cells, and the recommendations for optimization of the group of serving cells corresponding to the RCA.
[0101] The processor 402 is configured to control the interface 406 to provide an intuitive control to the network administrators to manage display of graphical elements such as charts, diagrams, or highlighted text in the visualization data or the report that enhance the comprehension and presentation of the visualization data or report. Further, the processor 402 controls the interface 406 to display the retrieved report in a desired format along with selectable options for sorting, filtering, downloading, and sharing the analysis of the report.
[0102] In another embodiment, the processor 402, using the display control unit 410-4, controls to display the visualization data generated by the server 140. The visualization data may include pop-up notifications corresponding to the identified group of serving cells, the root cause of degradation in the group of serving cells, and the one or more recommendations to the end user for improving performance of the group of serving cells.
[0103] The communication unit 408 may include one or more antennas, one or more of Radio Frequency (RF) transceivers, a transmit processing circuitry, and a receive processing circuitry. The communication unit 408 may be configured to receive incoming signals, such as signals transmitted by the server 140, and the NMC 210. The communication unit 408 may down-convert the incoming signals to generate baseband signals which may be sent to the receiver processing circuitry. The receiver processing circuitry may transmit the processed baseband signals to the processor 402 for further processing. The transmit processing circuitry may receive analog or digital data from the processor 402 and may encode, multiplex, and/or digitize the outgoing baseband data to generate processed baseband signals. The communication unit 408 may further receive the outgoing processed baseband from the transmit processing circuitry and up-converts the baseband signals to Radio Frequency (RF) signals that may be transmitted to the server 140.
[0104] The NMC 210 may be deployed as a software application on a dedicated server, a cloud-based solution, or a hybrid system, depending on the communication network requirements. The NMC 210 may be utilized by network administrators of a network operations team for receiving the root cause analysis of degradation in the performance of the group of serving cells. Additionally, the NMC 210 may be integrated with network monitoring tools, database management systems, and security modules to provide a holistic view of the performance metrics analysis of the crowd source data in form of the visualization data or the report.
[0105] Although FIG. 4 illustrates one example of NMC 210, various changes may be made to FIG. 4. For example, various components in FIG. 4 could be combined, further subdivided, or omitted, and additional components could be added according to particular needs. As a particular example, the processor 402 may be divided into multiple processors, such as one or more CPUs and one or more GPUs. Further, while FIG. 4 illustrates the NMC 210 configured as a mobile telephone or smartphone, the NMC 210 may also be configured to operate as other types of mobile or stationary devices.
[0106] In an alternate embodiment, each engine/module of the processing Engine(s)/module(s) 410 is configured to independently perform various operations of the processor 402, as described herein, without deviating from the scope of the present disclosure. Additionally, different engines/modules shown in Fig. 4 may be split into two or more engines/modules each operating independently in communication with one another, optionally in a distributive manner, with shared responsibilities. Furthermore, multiple instances of the engines/modules may be implemented for identification of serving cells having degraded performance or multiple modules can be combined into a single engine/module to perform all corresponding functions described herein.
[0107] FIG. 5 illustrates an exemplary method 500 for performing performance metrics analysis corresponding to serving cells in the communication network, in accordance with an embodiment of the present disclosure. The method 500 comprises a series of operations indicated by steps 502 through 512. Although method 500 shows example blocks of steps 502 to 512, in some embodiments, the method 500 may include additional steps, fewer steps or steps in different order than those depicted in Fig. 5. In other embodiments, the steps 502-512 may be combined or may be performed in parallel.
[0108] At step 502, the processor 304, using the acquisition module 314-2, obtains the data including the plurality of parameters associated with the UEs 120 and the serving cells 110 serving the UEs 120, from the UE 120 via the data collection entity 220. In a non-limiting example, the data may include information about the UE 120 and event-based information experienced by the user while accessing services of the network 130. The plurality of parameters include the location parameter corresponding to one or more of positional information of the plurality of the UEs 120 and positional information of the plurality of serving cells 110, the cell identification parameter corresponding to one or more of identity information of the plurality of serving cells 110 and serving area information associated with the plurality of serving cells 110, and the KPIs associated with the serving cells 110, and the plurality of the UEs 120 in the communication network.
[0109] The KPIs may include information associated with occurrence of a number of call drop events, information associated with a count of involuntary call mutes experienced by the plurality of the UEs 120, information associated with a duration of involuntary call mutes experienced by the plurality of the UEs 120 during a call.
[0110] At step 504, the processor 304, using the processing module 314-4, performs pre-processing operations over the crowd source data, for preparing the dataset of the crowd source data for the data model 306-2. The pre-processing operations may include operations such as data cleaning and data validation. The crowd source data may be cleaned to remove inconsistent, incomplete, duplicate, and inaccurate samples of the crowd source data. The processor 304 may then validate the crowd source data for ensuring consistency of the samples of the crowd source data. The processor 304 prepares the dataset from the pre-processed crowd source data for further analysis of the dataset using the data model 306-3. The dataset may comprise the data including the plurality of parameters, indicating adverse events such as the call drop and the call mute experienced by the users. The adverse events may be identified from the pre-processed samples of the crowd source data by comparing values of each KPI identified through validating the crowd source data with a pre-defined threshold value of each parameter. The dataset may also include the location of the UE 120 when a sample of the validated crowd source data exceeds the pre-defined threshold value of each parameter.
[0111] In one embodiment, the samples of the crowd source data in the dataset may be plotted in a map layer or a graphical manner, based on the positional information of the crowd source data and the KPIs obtained in the plurality of parameters. At step 506, the processor 304, using the processing module 314-4, identifies the one or more clusters of UEs among the plurality of UEs 120 representing the area facing degraded QoS associated with a call. The one or more clusters may signify a large group or a large number of UEs 120 in a region experiencing the adverse events at a same duration of time-period, indicating a degrading quality of the communication network during the call. The one or more cluster of the UEs among the UEs 120 representing an area that is facing a degraded Quality of Service (QoS) associated with a call. The one or more clusters may be identified by applying the one or more clustering techniques to the obtained data. The one or more clusters are identified based on the location parameter of the UEs 120 and the KPIs associated with the UEs 120 from the plotted dataset.
[0112] At step 508, the processor 304, using the processing module 314-4, generates the polygon connecting outermost samples of the dataset in the one or more clusters of the UEs. The polygon is generated based on the identified one or more cluster of the UEs and the location parameter among the plurality of parameters. The polygon may be generated using one or more of Voronoi algorithm, a convex hull technique, and triangulation method. The generated polygon indicates the coverage boundary of the determined one or more cluster of the UEs.
[0113] In one embodiment, the polygon may be generated by creating a bounding box connecting the set of samples among the plurality of samples indicating the coverage boundary of the determined one or more cluster of the UEs. The set of samples may correspond to outermost samples of the crowd source data in the one or more clusters, lying at the coverage boundary of the one or more clusters excluding outliers. The processor 304 may be configured to identify any outliers from the one or more clusters and exclude the outliers from the bounding box. The bounding box may then be utilized to extract the polygon covering the one or more clusters. The polygon may represent the coverage boundary of the geographical region of any size facing the adverse events such as a part of a town, city, or a state. The processor 304, using the processing module 314-4, may retrieve a latitudinal and longitudinal extent of the geographical region represented by the polygon.
[0114] At step 510, the processor 304, using the processing module 314-4, identifies a group of serving cells in the geographical area represented by the generated polygon based on the cell identification parameter among the plurality of parameters. The group of serving cells may be identified on basis of the identity information of the plurality of serving cells and the serving area information associated with the plurality of serving cells collected from the crowd source data. The processor 304 may also access other information from the DFS 230 or the database 312 for identifying the group of serving cells in the generated polygon.
[0115] At step 512, the processor 304 may then determine performance metrics corresponding to the identified group of serving cells. Based on the performance metrics, the processor 304 may then generate a plan for performing the RCA corresponding to service-disrupting incidents in the group of serving cells in the geographical region represented by the generated polygon. The processor 304 may further identify service disrupting problems and non-service affecting problems in the serving cells 110 to identify the root cause of degradation in the performance of the group of serving cells causing adverse events in the UEs 120. Further, the generated plan also includes one or more recommendations to enable the network operator to take remedial as well as pre-emptive actions for optimizing the performance of the one or more identified serving cells by minimizing number of call drops and involuntary call mutes. The processor 304 transmits the generated plan to the NMC 210 for display to the network operator.
[0116] 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 identifying and optimizing the serving cells undergoing a service or a non-service affecting issue in the communication of network. The present disclosure provides an easy solution for identification of the group of serving cells facing degraded performance based on the user experience.
[0117] The present disclosure further enables the network operation team to analyze and perform a quick corrective action on the serving cells having degraded performance for enhancement of the user experience while availing services of the communication network. Another notable advantage offered by the present disclosure is that the disclosed system and the method provides recommendations or suggestions to the user corresponding to the corrective actions to be taken for improvement of performance of the identified group of serving cell.
[0118] 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.
[0119] 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) 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).
[0120] 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) 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.
[0121] 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.
[0122] 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.
[0123] 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
[0124] 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 – Exemplary environment of a wireless communication network
110 – Plurality of nodes
120 – Plurality of User Equipment (UE)
130 – Network
140 – Server
200 – System for identification of serving cells having degraded performance in the communication network
210 – Network Management Console (NMC)
220 – Data Collection Entity
230 – Distributed File System
302 – Input/Output (I/O) Interface
304 – Processor(s)
306 – Memory
306-1 – Set of instructions
306-2 – Data Model
308 – Network Communication Manager
310 – Communication Interface
312 - Database
314 – Module(s)/Unit(s)
316 – Communication Bus
400 – System architecture of Network Management Console (NMC)
402 – Processor
404 – Memory
406 – Interface
408 – Communication unit
410 – Processing Engine(s)/Unit(s)
410-2 – Input Unit
410-4 – Display Control Unit
412 – Communication Bus
500 – Method
502-512 – Operation steps of the method 500
,CLAIMS:WE CLAIM:
1. A method (500) for identification of serving cells having degraded performance in a communication network, the method (500) comprising:
obtaining, by an acquisition module (314-2) from a data collection entity (220), data including a plurality of parameters associated with a plurality of User Equipment (UEs) (120) and a plurality of serving cells (110) serving the plurality of UEs (120) in the communication network;
identifying, by a processing module (314-4) by applying one or more clustering techniques to the obtained data, one or more clusters of UEs among the plurality of UEs (120) representing an area that is facing a degraded Quality of Service (QoS) associated with a call;
generating, by the processing module (314-4), a polygon based on the identified one or more clusters of the UEs and the plurality of parameters; and
identifying, by the processing module (314-4), a group of serving cells with the degraded performance located within the generated polygon based on a cell identification parameter among the plurality of parameters.
2. The method (700) as claimed in claim 1, wherein the polygon is generated based on a location parameter among the plurality of parameters, and wherein the generated polygon indicates a coverage boundary of the determined one or more cluster of the UEs.
3. The method (500) as claimed in claim 1, further comprising:
determining, by the processing module (314-4), a plurality of performance metrics corresponding to the identified group of serving cells based on the plurality of parameters; and
generating, based on the determined plurality of performance metrics, a plan for performing a Root Cause Analysis (RCA) to optimize the identified group of serving cells.
4. The method as claimed in claim 1, wherein the degraded QoS associated with the call corresponds to an adverse event from one or more of a network failure, a call drop, a call mute, a reduced data throughput, or a service outage, and wherein the adverse event is identified from the data obtained from the data collection entity (220).
5. The method (700) as claimed in claim 1, wherein
the plurality of parameters includes the location parameter, the cell identification parameter, and a plurality of Key Performance Indicators (KPIs) associated with the plurality of serving cells (110) and the plurality of the UEs (120),
the location parameter includes positional information of each UE among the plurality of the UEs (120) and positional information of the plurality of serving cells (110), and
the cell identification parameter includes identity information of the plurality of serving cells (110) and a service area associated with the plurality of serving cells (110).
6. The method (500) as claimed in claim 5, wherein the positional information corresponds to latitudinal and longitudinal coordinates of each UE among the plurality of UEs (120).
7. A system (200) of identification of serving cells having degraded performance in a communication network, the system (200) comprising:
an acquisition module (314-2) configured to obtain, from a data collection entity (220), data including a plurality of parameters associated with a plurality of User Equipment (UEs) (120) and a plurality of serving cells (110) serving the plurality of UEs (120) in the communication network; and
a processing module (314-4) configured to:
identify, by applying one or more clustering techniques to the obtained data, one or more clusters of UEs among the plurality of UEs (120) representing an area that is facing a degraded Quality of Service (QoS) associated with a call;
generate a polygon based on the identified one or more clusters of the UEs and the plurality of parameters; and
identify a group of serving cells with the degraded performance located within the generated polygon based on a cell identification parameter among the plurality of parameters.
8. The system (200) as claimed in claim 7, wherein
the polygon is generated based on a location parameter among the plurality of parameters, wherein the generated polygon indicates a coverage boundary of the determined one or more cluster of the UEs.
9. The system (200) as claimed in claim 7, wherein the processing module (314-4) is further configured to:
determine a plurality of performance metrics corresponding to the identified group of serving cells based on the plurality of parameters; and
generate, based on the determined plurality of performance metrics, a plan for performing a Root Cause Analysis (RCA) to optimize the identified group of serving cells.
10. The system (200) as claimed in claim 7, wherein the degraded QoS associated with the call corresponds to an adverse event from one or more of a network failure, a call drop, a call mute, a reduced data throughput, or a service outage, and wherein the adverse event is identified from the data obtained from the data collection entity (220).
11. The system (200) as claimed in claim 7, wherein
the plurality of parameters includes the location parameter, the cell identification parameter, and a plurality of Key Performance Indicators (KPIs) associated with the plurality of serving cells (110) and the plurality of the UEs (120),
the location parameter includes positional information of each UE among the plurality of the UEs (120) and positional information of the plurality of serving cells (110), and
the cell identification parameter includes identity information of the plurality of serving cells (110) and a service area associated with the plurality of serving cells (110).
12. The system (200) as claimed in claim 11, wherein the positional information corresponds to latitudinal and longitudinal coordinates of each UE among the plurality of UEs (120).