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 IDENTIFYING DATA DISCREPANCY FOR TRACE DATA IN A WIRELESS COMMUNICATION NETWORK

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

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

TECHNICAL FIELD
[0001] The embodiments of the present disclosure generally relate to the field of wireless communication networks. More particularly, the present disclosure relates to a system and a method for identifying data discrepancy for trace data in a wireless 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] In networking environments, trace data includes network session information, which is required for effective operation and optimization of networking infrastructures. The networking infrastructures utilize the trace data for various purposes such as implementation of automation algorithms, capability planning and optimization, user experience tracing, network performance monitoring, and others. The networking infrastructures leverage enormous information within the trace data to manage network configurations, optimize routing protocols, and dynamically allocate resources in response to changing network conditions.
[0004] However, despite invaluable insights offered by the trace data, data integrity is compromised owing to technical challenges, leading to data loss during transmission from disparate sources. The data loss stems from various factors such as network congestion, packet drops, hardware failures, or communication errors, thereby impeding a seamless flow of information, which is critical for operation of dependent algorithms.
[0005] The data loss in the trace data have severe implications on performance and efficacy of the dependent algorithms. An inability to access complete and accurate trace data undermines reliability of algorithmic outputs, potentially yielding misleading results, and affects entire data management system.
[0006] As a consequence, addressing challenges associated with the data loss in the trace data emerged as an area of concern in the field of network management. Further, for upholding resilience and efficiency of the networking infrastructures, there is a requirement of developing robust mechanisms to mitigate the data loss, thereby ensuring integrity and availability of the trace data, and safeguarding efficacy of the dependent algorithms. To address the challenges associated with mitigation of the data loss, there is need to correctly identify the instances of the data loss and report those instances.
[0007] In light of the aforementioned challenges and considerations, there is a need for a reliable mechanism for identification of the data discrepancy for the trace data.
SUMMARY
[0008] 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.
[0009] In an embodiment, a method for identifying data discrepancy for trace data in a wireless communication network is disclosed. The method includes periodically collecting, by a data collection module of a server, the trace data including a plurality of Key Performance Indicators (KPIs) at a specific time interval. The method further includes periodically calculating, by a data calculation module of the server at the specific time interval, a moving average value of each KPI among the plurality of KPIs over a specific duration. Further, the method includes calculating, by the data calculation module, a delta percentage between a value of each KPI among the plurality of KPIs acquired periodically at the specific time interval and the moving average value of a corresponding KPI calculated periodically at the specific time interval. Furthermore, the method includes identifying, by a data discrepancy identification module of the server, the data discrepancy for the trace data if the calculated delta percentage between the value of at least one KPI among the plurality of KPIs and the moving average value of the corresponding KPI exceeds a specific threshold.
[0010] In some aspects of the present disclosure, the method further includes identifying, by the data discrepancy identification module, at least one time interval at which the delta percentage between the value of the at least one KPI and the moving average value of the corresponding KPI exceeds the specific threshold.
[0011] In some aspects of the present disclosure, the method further includes notifying, by an output module of the server, an end-user device about the at least one identified time interval and the at least one KPI in the at least one identified time interval.
[0012] In some aspects of the present disclosure, the method further includes performing, by a data correction module of the server, one or more corrective actions based on the identification of the data discrepancy for the trace data. The one or more corrective actions include recollecting the at least one KPI for the at least one identified time interval for which the data discrepancy is identified.
[0013] In some aspects of the present disclosure, the plurality of KPIs is associated with Radio Frequency (RF) parameter details and network session information associated with a User Equipment (UE).
[0014] In some aspects of the present disclosure, the plurality of KPIs includes one or more of a number of records, file size, a total number of sessions, and a volume of traffic consumed.
[0015] According to another aspect of the present disclosure, a system for identifying data discrepancy for trace data in a wireless communication network is disclosed. The system includes a data collection module, a data calculation module, and a data discrepancy identification module. The data collection module is configured to collect periodically, at a specific time interval, the trace data including a plurality of Key Performance Indicators (KPIs). The data calculation module is configured to calculate periodically, at the specific time interval, a moving average value of each KPI among the plurality of KPIs over a specific duration. The data calculation module is further configured to calculate a delta percentage between a value of each KPI among the plurality of KPIs acquired periodically at the specific time interval and the moving average value of a corresponding KPI calculated periodically at the specific time interval. The data discrepancy identification module is configured to identify the data discrepancy for the trace data if the calculated delta percentage between the value of at least one KPI among the plurality of KPIs and the moving average value of the corresponding KPI exceeds a specific threshold.
BRIEF DESCRIPTION OF DRAWINGS
[0016] 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.
[0017] FIG. 1 illustrates a diagram depicting an exemplary wireless communication network, in accordance with an embodiment of the present disclosure.
[0018] FIG. 2 illustrates a diagram depicting communication of one or more entities of the wireless communication network with a trace collection entity (TCE) system, in accordance with an embodiment of the present disclosure.
[0019] FIG. 3 illustrates a block diagram of a system for identifying data discrepancy for trace data in the wireless communication network, in accordance with an embodiment of the present disclosure.
[0020] FIG. 4 illustrates a flowchart depicting a method for identifying the data discrepancy for the trace data in the wireless communication network, in accordance with an embodiment of the present disclosure.
[0021] FIG. 5 illustrates a schematic block diagram of a computing system for identifying the data discrepancy for the trace data in the wireless communication network, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE INVENTION
[0022] 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.
[0023] 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.
[0024] 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.”
[0025] The term “comprising,” when utilized, means “including, but not necessarily limited to;” it specifically indicates open-ended inclusion in the so-described one or more listed features, elements in a combination, unless otherwise stated with limiting language. Furthermore, to the extent that the terms “includes,” “has,” “have,” “contains,” and other similar words are used in either the detailed description, such terms are intended to be inclusive in a manner similar to the term “comprising.”
[0026] 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.
[0027] 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.
[0028] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein the description, the singular forms "a", "an", and "the" include plural forms unless the context of the invention indicates otherwise.
[0029] The terminology and structure employed herein are for describing, teaching, and illuminating some embodiments and their specific features and elements and do not limit, restrict, or reduce the scope of the present disclosure. Accordingly, unless otherwise defined, all terms, and especially any technical and/or scientific terms, used herein may be taken to have the same meaning as commonly understood by one having ordinary skill in the art.
[0030] An object of the present disclosure is to provide a system and a method for identifying data discrepancy for trace data and thereafter sending alert to a data operations team for taking corrective measures.
[0031] Another object of the present disclosure is to provide a system and a method that enables the data operations team to perform a Root Cause Analysis (RCA) for data loss in the trace data and based on the findings improving a data management system and fetching the lost trace data again, if available.
[0032] The term “Trace data” in the entire disclosure may represent log of detailed data of a user device at call level. The trace data is an additional source of information to performance measurements and allows going further in monitoring and optimization operations.
[0033] The term “data discrepancy for trace data” in the entire disclosure may refer to any inconsistency or mismatch in the trace data within specific period. The data discrepancy for trace data may be caused by at least one of the loss of the trace data, reception of incorrect trace data, alteration of the trace data, duplication of the trace data, or due to timestamp mismatch or format error in the trace data.
[0034] The term “network session information” may refer details of user session or data connection between a UE and a network infrastructure.
[0035] The term “Radio Frequency (RF) parameter” may refer to parameters used for determining radio link quality between the UE and a base station.
[0036] Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings. FIG. 1 through FIG. 4, 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.
[0037] FIG. 1 illustrates a diagram depicting an exemplary wireless communication network 100, in accordance with an embodiment of the present disclosure. The embodiment of the wireless communication network 100 shown in FIG. 1 is for illustration only. Other embodiments of the wireless communication network 100 may be used without departing from the scope of this disclosure.
[0038] As shown in FIG. 1, the wireless communication network 100 includes a plurality of base stations (BSs) 102-2 to 102-N (hereinafter referred to as BSs 102-2 to 102-N). Each base station among the BSs 102-2 to 102-N may have same or similar configuration and may also be referred to as “BS 102”. It is to be noted that the “base station” may also be referred to as “cell”, “gNB”, or “node” interchangeably throughout this disclosure without departing from the scope of the invention. Further, the “base station” may also be referred to as “access point (AP)”, “evolved NodeB (eNodeB) (eNB)”, “5G node (5th generation node)”, “wireless point”, “transmission/reception point (TRP)”, “Radio Access Network (RAN)” or other terms having equivalent technical meanings.
[0039] The BSs 102-2 to 102-N serve a plurality User Equipments (UEs) 104-2 to 104-N (hereinafter referred to as UEs 104-2 to 104-N) in coverage regions 106-2 to 106-N (hereinafter cumulatively referred to as coverage region 106). Each user equipment among the UEs 104-2 to 104-N may have same or similar configuration and may also be referred to as “UE 104”. Typically, the term “user equipment” can refer to any component such as “mobile station”, “subscriber station”, “remote terminal”, “wireless terminal”, “receive point”, “end user device”, or the like.
[0040] The BSs 102-2 to 102-N are connected to a network 108 to provide one or more services to the UEs 104-2 to 104-N. The network 108 may include a proprietary Internet Protocol (IP) network, Internet, or other data network. In some embodiments, the BSs 102-2 to 102-N may communicate with each other and with the UEs 104-2 to 104-N using a communication technique, such as a 5th Generation 5G/ New Radio (NR), Long Term Evolution (LTE), Long Term Evolution Advanced (LTE-A), Worldwide Interoperability for Microwave Access (WiMAX), Wireless Fidelity (Wi-Fi), or other wireless communication techniques.
[0041] The network 108 may include suitable logic, circuitry, and interfaces that may be configured to provide several network ports and several communication channels for transmission and reception of data related to operations of various entities of the wireless communication network 100. Each network port may correspond to a virtual address (or a physical machine address) for transmission and reception of the communication data. For example, the virtual address may be an Internet Protocol Version 4 (IPV4) (or an IPV6 address) and the physical address may be a Media Access Control (MAC) address. The network 108 may be associated with an application layer for implementation of communication protocols based on one or more communication requests from the various entities of the wireless communication network 100. The communication data may be transmitted or received via the communication protocols. Examples of the communication protocols may include, but are not limited to, Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Simple Mail Transfer Protocol (SMTP), Domain Network System (DNS) protocol, Common Management Interface Protocol (CMIP), Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Long Term Evolution (LTE) communication protocols, or any combination thereof. In some aspects of the present disclosure, the communication data may be transmitted or received via at least one communication channel of several communication channels in the network 108. The communication channels may include, but are not limited to, a wireless channel, a wired channel, a combination of wireless and wired channel thereof. The wireless or wired channel may be associated with a data standard which may be defined by one of a Local Area Network (LAN), a Personal Area Network (PAN), a Wireless Local Area Network (WLAN), a Wireless Sensor Network (WSN), Wireless Area Network (WAN), Wireless Wide Area Network (WWAN), a metropolitan area network (MAN), a satellite network, the Internet, an optical fiber network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and a combination thereof. Aspects of the present disclosure are intended to include or otherwise cover any type of communication channel, including known, related art, and/or later developed technologies.
[0042] The BS 102 also communicates with a server 110 configured to identify data discrepancy for the trace data in the wireless communication network 100. The server 110 may be a network of computers, a software framework, or a combination thereof, that may provide a generalized approach to create a server implementation. Examples of the server 110 may include, but are not limited to, personal computers, laptops, mini-computers, mainframe computers, any non-transient and tangible machine that can execute a machine-readable code, cloud-based servers, distributed server networks, or a network of computer systems. The server 110 may be realized through various web-based technologies such as, but not limited to, a Java web-framework, a .NET framework, a personal home page (PHP) framework, or any web-application framework.
[0043] Extents of the coverage region 106 are shown as approximately circular or elliptical for the purposes of illustration and explanation only. It should be clearly understood that the coverage region 106 associated with the BSs 102-2 to 102-N, such as coverage region 106-2, 106-4, may have other shapes, including irregular shapes, depending upon the configuration of the BSs 102-2 to 102-N, and variations in wireless communication network environment associated with natural and man-made obstructions.
[0044] Although FIG. 1 illustrates one example of the wireless communication network 100, various changes may be made to FIG. 1. For example, the wireless communication network 100 may include any number of BSs in any suitable arrangement. Further, each BS 102 of the BSs 102-2 to 102-N may communicate directly with the server 110. Furthermore, the BSs 102-2 to 102-N may provide access to other or additional external networks, such as external telephone networks or other types of data networks.
[0045] FIG. 2 illustrates a diagram depicting communication of entities of the wireless communication network 100 with a Trace Collection Entity (TCE) system 204, in accordance with an embodiment of the present disclosure. The TCE system 204 is a network entity of the wireless communication network 100 that manages collection and collation of UE measurements data received via the BSs 102-2 to 102-N. The UE measurement data is associated with the UEs 104-2 to 104-N and is referred to as “trace data”. The TCE system 204 may be located within the network 108 or within the server 110 or may be a separate entity in the wireless communication network 100.
[0046] The trace data may include Key Performance Indicators (KPIs) (hereinafter referred to as “KPIs”) associated with network session information of the UEs 104-2 to 104-N. The KPIs associated with network session information may include a number of records, file size, a total number of sessions, or a volume of traffic consumed during each session. In a non-limiting example, the trace data may include International Mobile Subscriber Identity (IMSI) of the UE, cell ID of the BS to which the UE is connected during the session, session duration, Radio Access Technology (RAT) type, data volume received and sent, handover details, and session ID.
[0047] Further, the KPIs associated with network session information may also include timestamps of each of the sessions associated with the UEs 104-2 to 104-N, location information of the UEs 104-2 to 104-N, types of network activities, or any other relevant network usage details. Further, the trace data may also include Radio Frequency (RF) parameter details and the other performance metrics.
[0048] The collection of trace data by the TCE system 204 is controlled by a network management system 202 associated with the network 108. The network management system 202 includes an Element Manager (EM) which activates or deactivates collection of the trace data. When the EM activates the collection of the trace data, network elements of the wireless communication network 100 generate the trace data and transfers the trace data to the TCE system 204.
[0049] In one or more embodiments, the EM notifies the BSs 102-2 to 102-N of an activation message including configuration information (measurement configuration) measured by the UEs 104-2 to 104-N and the location information of the UEs 104-2 to 104-N. The BSs 102-2 to 102-N starts a trace session (Starting Trace Session) for collecting UE measurement information and transmits the configuration information measured by the UEs 104-2 to 104-N. The configuration information includes, for example, a measurement target and a measurement period, or instructions to report location information. The BSs 102-2 to 102-N notifies an identifier of the trace session after collecting the UE measurement information. Thereafter, the BSs 102-2 to 102-N reports to the TCE system 204, a trace record that records the collected UE measurement information.
[0050] FIG. 3 a block diagram of a system 300 for identifying the data discrepancy for trace data in the wireless communication network 100, in accordance with an embodiment of the present disclosure. The embodiment of the system 300 as shown in FIG. 3 is for illustration only. However, the system 300 may come in a wide variety of configurations, and FIG. 3 does not limit the scope of the present disclosure to any particular implementation of the system 300.
[0051] As shown in FIG. 3, the system 300 includes the server 110. The server 110 includes an Input-Output (I/O) interface 302, one or more processors 304 (hereinafter may also be referred to as “processor 304”), a memory 306, a network communication manager 308, a console host 310, and a database 312, and one or more processing modules 314 (hereinafter may also be referred to as “processing modules 314”). Components of the server 110 are coupled to each other via a communication bus 326.
[0052] The I/O interface 302 may include suitable logic, circuitry, interfaces, and/or codes that may be configured to receive input(s) and present (or display) output(s) on the server 110. For example, the I/O interface may have an input interface and an output interface. The input interface may be configured to enable a user to provide input(s) to trigger (or configure) the server 110 to perform various operations for identifying data discrepancy for the trace data, such as but not limited to, configuring the server 110 to receive the trace data from the TCE system 204. Examples of the input interface may include, but are not limited to, a touch interface, a mouse, a keyboard, a motion recognition unit, a gesture recognition unit, a voice recognition unit, or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the input interface including known, related art, and/or later developed technologies without deviating from the scope of the present disclosure. The output interface is configured to control a user device such as UE 104 to display a notification including time intervals in which data discrepancy for the trace data is identified and display information of the KPI included in the trace data for which the data discrepancy is identified. Examples of the output interface of the I/O interface 302 may include, but are not limited to, a digital display, an analog display, a touch screen display, an appearance of a desktop, and/or illuminated characters.
[0053] The processor 304 may include various processing circuitry and communicates with the memory 306, the network communication manager 308, the console host 310, and the database 312 via the communication bus 326. The processor 304 is configured to execute computer-readable instructions 306A (hereinafter also referred to as “a set of instructions 306A”) stored in the memory 306 and to cause the server 110 to perform various processes for identifying the data discrepancy. The processor 304 may include one or a plurality of processors, including a general-purpose processor, such as, for example, and without limitation, a central processing unit (CPU), an application processor (AP), a dedicated processor, a graphics-only processing unit such as a graphics processing unit (GPU) or the like, a programmable logic device, or any combination thereof.
[0054] The memory 306 stores the set of instructions 306A required by the processor 304 of the server 110 for controlling its overall operations. The memory 306 may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory 306 may, in some examples, be considered a non-transitory storage medium. The "non-transitory" storage medium is not embodied in a carrier wave or a propagated signal. However, the term "non-transitory" should not be interpreted as the memory 306 is non-movable. In some examples, the memory 306 may be configured to store larger amounts of information. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache). The memory 306 may be an internal storage unit or an external storage unit of the server 110, cloud storage, or any other type of external storage. In certain examples, the memory 306 configured as the non-transitory storage medium may include hard drives, solid-state drives, flash drives, Compact Disk (CD), Digital Video Disk (DVD), and the like. Further, the memory 306 may include any type of non-transitory storage medium, without deviating from the scope of the present disclosure.
[0055] More specifically, the memory 306 may store computer-readable instructions 306 A including instructions that, when executed by a processor (e.g., the processor 304) cause the server 110 to perform various functions described herein. In some cases, the memory 306 may contain, among other things, a BIOS which may control basic hardware or software operation such as the interaction with peripheral components or devices.
[0056] The network communication manager 308 may manage communications with the BSs 102-2 to 102-N, the core network, or the UEs 104-2 to 104-N (e.g., via one or more wired backhaul links). For example, the network communications manager 308 may manage the transfer of data communications for BSs 102-2 to 102-N and client devices. The network communication manager 308 may include an electronic circuit specific to a standard that enables wired or wireless communication. The network communication manager 308 is configured for communicating with external devices via one or more networks.
[0057] The console host 310 may include suitable logic, circuitry, interfaces, and/or codes that may be configured to enable the I/O interface 302 to receive input(s) and/or render output(s). In some aspects of the present disclosure, the console host 310 may include suitable logic, instructions, and/or codes for executing various operations of one or more computer executable applications to host a console on an external user device, by way of which a user can trigger the server 110 to identify home and work locations of the user. In some other aspects of the present disclosure, the console host 310 may provide a Graphical User Interface (GUI) for the server 110 for user interaction.
[0058] The processing module(s) 314 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the server 110. In non-limiting examples, described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing modules(s) 314 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor 304 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing module(s) 314. In such examples, the server 110 may also comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the server 110 and the processing resource. In other examples, the processing module(s) 314 may be implemented using an electronic circuitry.
[0059] In one or more embodiments, the processing module(s) 314 may include a data collection module 316, a data calculation module 318, a data discrepancy identification module 320, an output module 322, and a data correction module 324.
[0060] In an embodiment, the processor 304, using the data collection module 316, may be configured to collect or acquire the trace data from the TCE system 204 periodically at a specific time interval. The specific time interval may be a predefined time interval. In another embodiment, the specific time interval may be a configurable time interval configurable by the data operations team at operator side or at the server 110 side. In a non-limiting example, the processor 304 may collect the trace data at each hour. The trace data includes the KPIs associated with Radio Frequency (RF) parameter details and network session information of the UEs 104-2 to 104-N. The KPIs may include at least one of the number of records, the file size, the total number of sessions, and the volume of traffic consumed at the UEs 104-2 to 104-N.
[0061] In an embodiment, the processor 304, using the data calculation module 318, may be configured to calculate, periodically at the specific time interval, a moving average value of each KPI among the plurality of KPIs over a specific duration. The specific duration may be a predefined time duration. In another embodiment, the specific duration may be a configurable time duration configurable by the data operations team at operator side or at the server 110 side.
[0062] In a non-limiting example, if the specific duration corresponds to last 7 days and the specific time interval correspond to one hour, the processor 304 collects the trace data at each hour (periodically at one hour) and calculates hourly last 7 days moving average value of each KPI among the plurality of KPIs included in the trace data. In another non-limiting example, if the specific duration corresponds to last 7 days and the specific time interval correspond to two hours, the processor 304 collects the trace data at each two-hour interval and calculates last 7 days moving average value of each KPI at each two-hour interval.
[0063] In one or more embodiments, the moving average value is a statistical measure that calculates average of set of datapoints in a dataset over a specific period, then shifts forward one step at a time across the dataset. The moving average may be performed to smooth out short term variation in the dataset. In a non-limiting example, if the total number of sessions included in the KPI over 5 days is as follows: Total number of sessions: [9, 5, 7, 6, 2],
moving average over the specific period (for example 3 days) would be,
Day 3: (9+5+7)/3 = 7
Day 4: (5+7+6)/3 = 6.66
Day 5: (7+6+2)/3 = 5.
[0064] In an embodiment, the processor 304, using the data calculation module 318, may be further configured to calculate a delta percentage between a value of each KPI among the plurality of KPIs acquired periodically at the specific time interval and the moving average value of a corresponding KPI calculated periodically at the specific time interval.
[0065] For instance, the processor 304 may compare the value of each KPI acquired at each of the specific time interval with the corresponding moving average value. In a non-limiting example, if the specific duration corresponds to last 7 days and the specific time interval correspond to one hour, the processor 304 compares the hourly value of the trace data for each KPI with the last 7 days moving average value of the corresponding hour. Thereafter, the processor 304 may calculate, for each KPI among the plurality of KPIs, a percentage of difference between the value of each KPI and a corresponding moving average value. The percentage of difference between two values is known as the delta percentage between the two values.
[0066] In an embodiment, the processor 304, using the data discrepancy identification module 320, may be configured to identify the data discrepancy for the trace data if the calculated delta percentage between the value of at least one KPI among the plurality of KPIs and the moving average value of the corresponding KPI exceeds a specific threshold. The processor 304 may mark the corresponding KPI for which the delta percentage exceeds the specific threshold. The processor 304 may identify the time interval at which the delta percentage exceeds the specific threshold. In an embodiment, the specific threshold may be predetermined threshold.
[0067] In one or more embodiments, a value of the specific threshold may be dynamically calculated by the processor 304 using a machine learning model. The machine learning model may be included in the data discrepancy identification module 320. Further, the machine learning model may be a pre trained model which uses values of KPI and a type of KPI as an input and calculate the specific threshold using the input values.
[0068] In an embodiment, the processor 304, using the output module 322, may be configured to notify an end-user device about the identified time interval and the at least one KPI for which the delta percentage exceeds the specific threshold in the identified time interval. The end-user device may correspond to a device associated with the data operations team. For instance, the processor 304 may be communicatively coupled with the end-user device and is configured to send information of the identified time intervals and the at least one KPI to the data operations team for taking corrective measures.
[0069] In an embodiment, the processor 304, using the data correction module 324, may be configured to perform one or more corrective actions based on the identification of the data discrepancy for the trace data. In a non-limiting example, the one or more corrective actions may include recollecting the at least one KPI for the at least one identified time interval for which the data discrepancy is identified. In another non-limiting example, the one or more corrective actions may also include revalidating the at least one KPI collected the at least one identified time interval or improving data collection methodology at the TCE system 204.
[0070] The database 312 is managed by the processor 304 and configured to store the calculated moving average values and delta percentage value. The data base 312 may also store information of KPI among the KPIs for which the data discrepancy is identified. The data base 312 may also store information of the time interval associated with the trace data collection during which the data discrepancy for the trace data is identified.
[0071] Although FIG. 3 illustrates one example of the server 110, various changes may be made to FIG. 3. Further, the server 110 may include any number of components in addition to those shown in FIG. 3, without deviating from the scope of the present disclosure. Further, various components in FIG. 3 may be combined, further subdivided, or omitted and additional components may be added according to particular needs. For example, in some aspects of the present disclosure, the server 110 may be coupled to an external database that provides data storage space to the server 110.
[0072] FIG. 4 illustrates a flowchart depicting a method 400 for identifying the data discrepancy for the trace data in the wireless communication network 100, in accordance with an embodiment of the present disclosure. The method 400 comprises a series of operation steps indicated by blocks 402 through 408 performed by the system 300 for identifying the data discrepancy for the trace data. The method 400 starts at block 402.
[0073] At block 402, the data collection module 316 may collect or acquire the trace data from the TCE system 204 periodically at the specific time interval. The trace data includes the KPIs associated with the RF parameter details and the network session information of the UEs 104-2 to 104-N. The KPIs may include at least one of the number of records, the file size, the total number of sessions, and the volume of traffic consumed at the UEs 104-2 to 104-N. The flow of the method 400 now proceeds to block 404.
[0074] At block 404, the data calculation module 318 may calculate the moving average value of each KPI among the plurality of KPIs over a specific duration. The data calculation module 318 calculates the moving average value periodically at the specific time interval. The specific time interval may be the predefined time interval or the configurable time interval configurable by the data operations team at operator side or at the server 110 side. Further, the specific duration may be the predefined time duration or the configurable time duration configurable by the data operations team at operator side or at the server 110 side.
[0075] For instance, if the specific duration corresponds to last 7 days and the specific time interval correspond to one hour, the data calculation module 318 collects the trace data at each hour (periodically at one hour) and calculates hourly last 7 days moving average value of each KPI among the plurality of KPIs included in the trace data.
[0076] At block 406, the data calculation module 318 may further calculate the delta percentage between the value of each KPI among the plurality of KPIs acquired periodically at the specific time interval and the moving average value of the corresponding KPI.
[0077] For instance, if the specific duration corresponds to last 7 days and the specific time interval correspond to one hour, the data calculation module 318 compares the hourly value of the trace data for each KPI with the last 7 days moving average value of the corresponding hour. Thereafter, the data calculation module 318 may calculate, as the delta percentage, a percentage of difference between the hourly value of the trace data for each KPI and the last 7 days moving average value of the corresponding hour.
[0078] At block 408, the data discrepancy identification module 320 may identify the data discrepancy for the trace data if the calculated delta percentage between the value of at least one KPI among the plurality of KPIs and the moving average value of the corresponding KPI exceeds the specific threshold. The data discrepancy identification module 320 may identify the corresponding KPI for which the delta percentage exceeds the specific threshold and the time interval at which the delta percentage exceeds the specific threshold.
[0079] Thereafter, the output module 322, may notify the end-user device about the identified time interval and the at least one KPI for which the delta percentage exceeds the specific threshold in the identified time interval for taking corrective measures.
[0080] FIG. 5 illustrates a schematic block diagram of a computing system 500 for identifying the data discrepancy for the trace data in the wireless communication network 100, in accordance with an embodiment of the present disclosure.
[0081] The computing system 500 includes a network 502, a network interface 504, a processor 506 (similar in functionality to the processor 304 of FIG. 3), an Input/Output (I/O) interface 508 (similar in functionality to the I/O interface 302 of FIG. 3), and a non-transitory computer readable storage medium 510 (hereinafter may also be referred to as the “storage medium 510” or the “storage media 510”). The network interface 504 includes wireless network interfaces such as Bluetooth, Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), General Packet Radio Service (GPRS), or Wideband Code Division Multiple Access (WCDMA) or wired network interfaces such as Ethernet, Universal Serial Bus (USB), or Institute of Electrical and Electronics Engineers-864 (IEEE-864).
[0082] The processor 506 may include various processing circuitry/modules and communicate with the storage medium 510 and the I/O interface 508. The processor 506 is configured to execute instructions stored in the storage medium 510 and to perform various processes. The processor 506 may include an intelligent hardware device including a general-purpose processor, such as, for example, and without limitation, the CPU, the AP, the dedicated processor, or the like, the graphics-only processing unit such as the GPU, the microcontroller, the FPGA, the programmable logic device, the discrete hardware component, or any combination thereof. The processor 506 may be configured to execute computer-readable instructions 510-1 stored in the storage medium 510 to cause the system 300 to perform various functions disclosed throughput the disclosure.
[0083] The storage medium 510 stores a set of instructions i.e., computer program instructions 510-1 (hereinafter may also be referred to as instructions 510-1) required by the processor 506 for controlling its overall operations. The storage media 510 may include an electronic storage medium, a magnetic storage medium, an optical storage medium, a quantum storage medium, or the like. For example, the storage media 510 may include, but are not limited to, hard drives, floppy diskettes, optical disks, ROMs, RAMs, EPROMs, EEPROMs, flash memory, magnetic or optical cards, solid-state memory devices, or other types of physical media suitable for storing electronic instructions. In one or more embodiments, the storage media 510 includes a Compact Disk-Read Only Memory (CD-ROM), a Compact Disk-Read/Write (CD-R/W), and/or a Digital Video Disc (DVD). In one or more implementations, the storage medium 510 stores computer program code configured to cause the computing system 500 to perform at least a portion of the processes and/or methods disclosed herein throughput the disclosure.
[0084] Embodiments of the present disclosure have been described above with reference to flowchart illustrations of methods and systems according to embodiments of the disclosure, and/or procedures, algorithms, steps, operations, formulae, or other computational depictions, which may also be implemented as computer program products. In this regard, each block or step of the flowchart, and combinations of blocks (and/or steps) in the flowchart, as well as any procedure, algorithm, step, operation, formula, or computational depiction can be implemented by various means, such as hardware, firmware, and/or software including one or more computer program instructions embodied in computer-readable program code. As will be appreciated, any such computer program instructions may be executed by one or more computer processors, including without limitation a general-purpose computer or special purpose computer, or other programmable processing apparatus to perform a group of operations comprising the operations or blocks described in connection with the disclosed method.
[0085] Further, these computer program instructions, such as embodied in computer-readable program code, may also be stored in one or more computer-readable memory or memory devices (for example, the memory 306 or the storage medium 510) that can direct a computer processor or other programmable processing apparatus to function in a particular manner, such that the instructions 510-1 stored in the computer-readable memory or memory devices produce an article of manufacture including instruction means which implement the function specified in the block(s) of the flowchart(s).
[0086] It will further be appreciated that the term “computer program instructions” as used herein refer to one or more instructions that can be executed by the one or more processors (for example, the processor 304 or the processor 506) to perform one or more functions as described herein. The instructions 510-1 may also be stored remotely such as on a server, or all or a portion of the instructions can be stored locally and remotely.
[0087] Referring to the technical abilities and advantageous effect of the present disclosure, operational advantages that may be provided by one or more embodiments may include providing the system and the method for identifying the data loss or inconsistency of the trace data which helps the data operations team to perform a root cause analysis for the lost or inconsistent trace data. The root cause analysis enables data operations team to make informed decisions on optimization strategies and fetching the lost or inconsistent trace data.
[0088] 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.
[0089] 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.
[0090] 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
[0091] The following list is provided for convenience and in support of the drawing figures and as part of the text of the specification, which describe innovations by reference to multiple items. Items not listed here may nonetheless be part of a given embodiment. For better legibility of the text, a given reference number is recited near some, but not all, recitations of the referenced item in the text. The same reference number may be used with reference to different examples or different instances of a given item. The list of reference numerals is:
100 - Wireless communication network
102 - Base Station (BS)
102-2 to 102-N - Plurality of BSs
104 - User Equipment (UE)
104-2 to 104-N - Plurality of UEs
106 - Coverage region
108 - Network
110 - Server
200 - Communication of network entities with a Trace Collection Entity (TCE) system 204
202 - network management system
204 - Trace Collection Entity (TCE) system
300 - System for identifying data discrepancy
302 - Input-Output (I/O) interface
304 - Processor
306 - Memory
306 A - Set of instructions
308 - Network communication manager
310 - Console host
312 - Database
314 - Processing unit(s)/modules(s)
316 - Data collection module
318 - Data calculation module
320 - Data discrepancy identification module
311 - Output module
324 - Data correction module
326 - Communication bus
400 - Method for identifying data discrepancy
402-408 - Operation steps of the method 400
500 – Block diagram of a computing system
502 – Network
504 – Network interface
506 – Processor
508 – Input/Output (I/O) interface
510 – Non-transitory computer readable storage medium
510-1 - Set of instructions
,CLAIMS:We Claim

1. A method (400) for identifying data discrepancy for trace data in a wireless communication network (100), the method (400) comprising:
periodically collecting (402), by a data collection module (316) of a server (110), the trace data including a plurality of Key Performance Indicators (KPIs) at a specific time interval;
periodically calculating (404), by a data calculation module (318) of the server (110) at the specific time interval, a moving average value of each KPI among the plurality of KPIs over a specific duration;
calculating (406), by the data calculation module (318), a delta percentage between a value of each KPI among the plurality of KPIs acquired periodically at the specific time interval and the moving average value of a corresponding KPI calculated periodically at the specific time interval; and
identifying (408), by a data discrepancy identification module (320) of the server (110), the data discrepancy for the trace data if the calculated delta percentage between the value of at least one KPI among the plurality of KPIs and the moving average value of the corresponding KPI exceeds a specific threshold.

2. The method (400) as claimed in claim 1, further comprising identifying, by the data discrepancy identification module (320), at least one time interval at which the delta percentage between the value of the at least one KPI and the moving average value of the corresponding KPI exceeds the specific threshold.

3. The method (400) as claimed in claim 2, further comprising notifying, by an output module (322) of the server (110), an end-user device about the at least one identified time interval and the at least one KPI in the at least one identified time interval.

4. The method (400) as claimed in claim 2, further comprising performing, by a data correction module (324) of the server (110), one or more corrective actions based on the identification of the data discrepancy for the trace data, wherein the one or more corrective actions include recollecting the at least one KPI for the at least one identified time interval for which the data discrepancy is identified.

5. The method (400) as claimed in claim 1, wherein the plurality of KPIs is associated with Radio Frequency (RF) parameter details and network session information associated with a User Equipment (UE).

6. The method (400) as claimed in claim 5, wherein the plurality of KPIs includes one or more of a number of records, file size, a total number of sessions, and a volume of traffic consumed.

7. A system (300) for identifying data discrepancy for trace data in a wireless communication network, the system (300) comprising:
a data collection module (316) configured to collect periodically, at a specific time interval, the trace data including a plurality of Key Performance Indicators (KPIs);
a data calculation module (318) configured to:
calculate periodically, at the specific time interval, a moving average value of each KPI among the plurality of KPIs over a specific duration; and
calculate a delta percentage between a value of each KPI among the plurality of KPIs acquired periodically at the specific time interval and the moving average value of a corresponding KPI calculated periodically at the specific time interval; and
a data discrepancy identification module (320) configured to identify the data discrepancy for the trace data if the calculated delta percentage between the value of at least one KPI among the plurality of KPIs and the moving average value of the corresponding KPI exceeds a specific threshold.

8. The system (300) as claimed in claim 7, wherein the data discrepancy identification module (320) is further configured to identify at least one time interval at which the delta percentage between the value of the at least one KPI and the moving average value of the corresponding KPI exceeds the specific threshold.

9. The system (300) as claimed in claim 8, further comprising an output module (322) configured to notify an end-user device about the at least one identified time interval and the at least one KPI in the at least one identified time interval.

10. The system (300) as claimed in claim 8, further comprising a data correction module (324) configured to perform one or more corrective actions based on the identification of the data discrepancy for the trace data, wherein the one or more corrective actions include recollecting the at least one KPI for the at least one identified time interval for which the data discrepancy is identified.

11. The system (300) as claimed in claim 7, wherein the plurality of KPIs is associated with Radio Frequency (RF) parameter details and network session information associated with a User Equipment (UE).

12. The system (300) as claimed in claim 11, wherein the plurality of KPIs includes one or more of a number of records, file size, a total number of sessions, and a volume of traffic consumed.