Method and system for performing a service request-to-cell mapping in a network

Abstract

The present disclosure relates to a method and a system for performing a service request-to-cell mapping in a network is disclosed The method includes receiving, by a transceiver unit [102], a service request data associated with one or more service requests. Next, the method includes identifying, by an identification unit [104], a geo-location data associated with the service request based at least on one or more service request data. The method includes comparing, by an analyser unit [106], the service request with historical service requests to identify a serving cell from a plurality of serving cells corresponding to the service request. Thereafter, the method includes generating, by the generator unit [108], the service request-to-cell mapping list based at least one of the service request data, the geo-location data, and the identified serving cell.

Description

METHOD AND SYSTEM FOR PERFORMING A SERVICE REQUEST-TO-CELL MAPPING IN A NETWORKFIELD OF THE INVENTION

[0001] Embodiments of the present disclosure relates generally to the field of wireless communication systems. More particularly, embodiment of the present disclosure relates to a method and system for performing a service request-to-cell mapping in a network.BACKGROUND

[0002] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

[0003] Wireless communication technology has rapidly evolved over the past few decades, with each generation bringing significant improvements and advancements. The first generation of wireless communication technology was based on analog technology and offered only voice services. However, with the advent of the second-generation (2G) technology, digital communication and data services became possible, and text messaging was introduced. The third-generation 3G technology marked the introduction of high-speed internet access, mobile video calling, and location-based services. The fourth-generation (4G) technology revolutionized wireless communication with faster data speeds, better network coverage, and improved security. Currently, the fifth-generation (5G) technology is being deployed, promising even faster data speeds, low latency, and the ability to connect multiple devices simultaneously. With each generation, wireless communication technology has become more advanced, sophisticated, and capable of delivering more services to its users.

[0004] Existing solutions in the art of wireless communication systems, particularly in the context of handling customer complaints related to network issues, face several challenges. Firstly, there is a lack of efficient methods to accurately map customer complaints to specific cells or network technologies. This is crucial for identifying the root cause of the issue and implementing targeted solutions. Without precise complaint-to-cell mapping, network operatorsmay struggle to pinpoint the exact location or cell where the problem occurred, leading to inefficient troubleshooting and resolution processes. Secondly, existing systems often fail to leverage real-time or historical data effectively. The integration of crowd-sourced data, which can provide valuable insights into user experiences and network performance, is not adequately utilized in current approaches. This limitation hinders the ability to correlate user complaints with actual network conditions, resulting in less effective problem resolution. Furthermore, there is a lack of systems that can dynamically adapt to the evolving nature of wireless communication networks. As networks transition from 4G to 5 G and beyond, the complexity of managing and resolving customer complaints increases. Existing solutions may not be equipped to handle the intricacies of newer technologies, such as the increased number of cells, varying signal propagation characteristics, and the need for more sophisticated data analysis.

[0005] Further, over the period of time various solutions have been developed to improve the performance of communication devices and resolve customer service requests. However, these solutions are inefficient and fail to provide a service request to cells mapping. Thus, there exists an imperative need in the art to provide method and system for performing a service request-to-cell mapping.OBJECTS OF THE INVENTION

[0006] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below.

[0007] It is an object of the present disclosure to provide a method and system for performing a service request-to-cell mapping.

[0008] It is another object of the present disclosure to provide a solution that can identify a possible serving cell at accurate locations to further provide a service request to cells mapping.

[0009] It is another object of the present disclosure to provide a solution that is based on actual consumer problem and that may consider a consumer feedback to optimize or to improve the network.

[0010] It is another object of the present disclosure to provide a solution that can relate geographic information of customer’s problematic locations to a network cell ID.

[0011] It is yet another object of the present disclosure to provide a solution that can generate and provide a cell list to the network planning and engineering team to do the further planning and engineering.SUMMARY

[0012] This section is provided to introduce certain aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

[0013] According to an aspect of the present disclosure, a method for performing a service request-to-cell mapping in a network is disclosed. The method includes receiving, by a transceiver unit, a service request data associated with one or more service requests. Next, the method includes identifying, by an identification unit, a geo-location data associated with the service request based at least on one or more service request data. The method includes comparing, by an analyser unit, the service request with historical service requests to identify a serving cell from a plurality of serving cells corresponding to the service request. Thereafter, the method includes generating, by a generator unit, the service request-to-cell mapping list based at least one of the service request data, the geo-location data, and the identified serving cell.

[0014] In an aspect, in case the geo-location data is identified, the serving cell is associated based at least on a correlation of the service request data with a network data and a crowd-sourced data.

[0015] In an aspect, in case the geo-location data is not identified, the serving cell is associated based at least on a correlation of the service request data with the crowd-sourced data, wherein the correlation is identified based at least on a specified time window and a category of the service request.

[0016] In an aspect, the network data comprises a data related to at least one of the network traffic patterns, data transfer rates, and signal propagation characteristics.

[0017] In an aspect, the crowd-sourced data comprises at least one of a feedback data related to call quality, data speed, network reliability, and user equipment data.

[0018] In an aspect, the method further comprises classifying, by a classifier unit, the service request based at least on a pre-defined category.

[0019] In an aspect, the service request-to-cell mapping list is generated for one or more service requests, wherein the one or more service requests are organized in the service request- to-cell mapping list based at least on a severity score of the one or more service requests.

[0020] According to another aspect, a system for performing a service request-to-cell mapping in a network is disclosed. The system includes a transceiver unit, configured to receive a service request data associated with one or more service request; an identification unit, connected to the transceiver unit, wherein the identification unit is configured to identify a geolocation data associated with the service request based at least on one or more service request data; an analyser unit, connected to the identification unit, wherein the analyser unit is configured to compare the service request with historical service requests to identify a serving cell from a plurality of serving cells corresponding to the service request; and a generator unit, connected to the analyser unit, wherein the generator unit is configured to generate the service request-to-cell mapping list based at least on one of the service request data, the geo-location data, and identified serving cell.

[0021] Yet another aspect of the present disclosure relates to a user equipment (UE) for performing service request-to-cell mapping in a network. The UE may include a memory and a processor coupled to the memory. The processor is configured to: send a service request data associated with a service request; and receive a resolution of the service request based on the analysis of the service request data and the geo-location data. Further, to analyse the service request data and the geo-location data, the processor is configured to: receive a comparison of the service request with historical service requests to identify a serving cell from a plurality of serving cells corresponding to the service request; and generate a service request-to-cell mapping list based at least on one of the service request data, the geo-location data, or the identified serving cell.

[0022] Yet another aspect of the present disclosure relates to a non-transitory computer- readable storage medium storing instructions for performing a service request-to-cell mapping within a network. The instructions include executable code which, when executed by a processor, may cause the processor to receive a service request data associated with a service request;identify a geo-location data associated with the service request based at least on the service request data; compare the service request with historical service requests to identify a serving cell from a plurality of serving cells corresponding to the service request; and generate a service request-to-cell mapping list based at least on one of the service request data, the geo-location data, or the identified serving cell.BRIEF DESCRIPTION OF DRAWINGS

[0023] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.

[0024] FIG.l illustrates an exemplary block diagram of a system

[0100] performing a service request-to-cell mapping within a network in accordance with exemplary embodiments of the present disclosure.

[0025] FIG.2 illustrates an exemplary method

[0200] flow diagram indicating the process for performing a service request-to-cell mapping within a network, in accordance with exemplary embodiments of the present disclosure.

[0026] FIG.3 illustrates an exemplary process

[0300] for performing a service request-to- cell mapping within a network, in accordance with exemplary embodiments of the present disclosure.

[0027] FIG. 4 illustrates an exemplary block diagram of a computing device

[0400] upon which an embodiment of the present disclosure may be implemented.

[0028] FIG. 5 illustrates an exemplary block diagram of a user equipment (UE)

[0500] for performing a service request-to-cell mapping within a network.

[0029] The foregoing shall be more apparent from the following more detailed description of the disclosure.DETAILED DESCRIPTION

[0030] 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. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein. Example embodiments of the present disclosure are described below, as illustrated in various drawings in which like reference numerals refer to the same parts throughout the different drawings.

[0031] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

[0032] It should be noted that the terms "mobile device", "user equipment", "user device", “communication device”, “device” and similar terms are used interchangeably for the purpose of describing the disclosure. These terms are not intended to limit the scope of the disclosure or imply any specific functionality or limitations on the described embodiments. The use of these terms is solely for convenience and clarity of description. The disclosure is not limited to any particular type of device or equipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the disclosure as defined herein.

[0033] It should be noted that the terms "mobile device", "user equipment", "user device", “communication device”, “device” and similar terms are used interchangeably for the purpose ofdescribing the disclosure. These terms are not intended to limit the scope of the disclosure or imply any specific functionality or limitations on the described embodiments. The use of these terms is solely for convenience and clarity of description. The disclosure is not limited to any particular type of device or equipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the disclosure as defined herein.

[0034] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0035] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure.

[0036] In addition, each block may indicate some of modules, segments, or codes including one or more executable instructions for executing a specific logical fiinction(s). Further, functions mentioned in the blocks occur regardless of a sequence in some alternative embodiments. For example, two blocks that are contiguously illustrated may be simultaneously performed in fact or be performed in a reverse sequence depending on corresponding functions.

[0037] Herein, the term "unit" indicates software or hardware components, such as a Field- Programmable Gate Array (FPGA) and an Application-Specific Integrated Circuit (ASIC). However, the meaning of the "unit" is not limited to software or hardware. For example, a "unit" may be configured to be in a storage medium that may be addressed and may also be configured to be reproduced one or more processor. Accordingly, a "unit" may include components such as software components, object oriented software components, class components, and task components and processors, functions, attributes, procedures, subroutines, segments of programcode, drivers, firmware, microcode, circuit, data, database, data structures, tables, arrays, and variables. The functions provided in the components and the "units" may be combined with a smaller number of components, and the "units" or may be further separated into additional components and "units". In addition, the components and the "units" may also be implemented to reproduce one or more central processing units (CPUs) within a device or a security multimedia card.

[0038] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive — in a manner similar to the term “comprising” as an open transition word — without precluding any additional or other elements.

[0039] As used herein, an “electronic device”, or “portable electronic device”, or “user device” or “communication device” or “user equipment” or “device” refers to any electrical, electronic, electromechanical and computing device. The user device is capable of receiving and / or transmitting one or parameters, performing fimction / s, communicating with other user devices and transmitting data to the other user devices . The user equipment may have a processor, a display, a memory, a battery and an input-means such as a hard keypad and / or a soft keypad. The user equipment may be capable of operating on any radio access technology including but not limited to IP-enabled communication, Zig Bee, Bluetooth, Bluetooth Low Energy, Near Field Communication, Z-Wave, Wi-Fi, Wi-Fi direct, etc. For instance, the user equipment may include, but not limited to, a mobile phone, smartphone, virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other device as may be obvious to a person skilled in the art for implementation of the features of the present disclosure.

[0040] Further, the user device may also comprise a “processor” or “processing unit” includes processing unit, wherein processor refers to any logic circuitry for processing instructions. The processor may be a general-purpose processor, a special purpose processor, aconventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing, in used herein, “a user equipment”, “a user device”, “a smart-user-device”, “a smart-device”, “an electronic device”, “a mobile device”, “a handheld device”, “a wireless communication device”, “a mobile communication device”, “a communication device” may be any electrical, electronic and / or computing device or equipment, capable of implementing the features of the present disclosure. The user equipment / device may include, but is not limited to, a mobile phone, smart phone, laptop, a general -purpose computer, desktop, personal digital assistant, tablet computer, wearable device or any other computing device which is capable of implementing the features of the present disclosure. Also, the user device may contain at least one input means configured to receive an input from at least one of a transceiver unit, a processing unit, a storage unit, a detection unit and any other such unit(s) which are required to implement the features of the present disclosure.

[0041] As used herein, “storage unit” or “memory unit” refers to a machine or computer- readable medium including any mechanism for storing information in a form readable by a computer or similar machine. For example, a computer-readable medium includes Read-Only Memory (“ROM”), Random Access Memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices or other types of machine-accessible storage media. The storage unit stores at least the data that may be required by one or more units of the system to perform their respective functions input / output processing, and / or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor is a hardware processor.

[0042] As portable electronic devices and wireless technologies continue to improve and grow in popularity, the advancing wireless technologies for data transfer are also expected to evolve and replace the older generations of technologies. In the field of wireless data communications, the dynamic advancement of various generations of cellular technology are also seen. The development, in this respect, has been incremental in the order of second generation (2G), third generation (3G), fourth generation (4G), and now fifth generation (5G), and more such generations are expected to continue in the forthcoming time.

[0043] Radio Access Technology (RAT) refers to the technology used by mobile devices / user equipment (UE) to connect to a cellular network. It refers to the specific protocol and standards that govern the way devices communicate with base stations, which are responsible for providing the wireless connection. Further, each RAT has its own set of protocols and standards for communication, which define the frequency bands, modulation techniques, and other parameters used for transmitting and receiving data. Examples of RATs include GSM (Global System for Mobile Communications), CDMA (Code Division Multiple Access), UMTS (Universal Mobile Telecommunications System), LTE (Long-Term Evolution), and 5G. The choice of RAT depends on a variety of factors, including the network infrastructure, the available spectrum, and the mobile device's / device's capabilities. Mobile devices often support multiple RATs, allowing them to connect to different types of networks and provide optimal performance based on the available network resources.

[0044] The term ‘Geo-location data’ refers to the data that identifies the geographical location of a device or user making the service request. It could include latitude and longitude coordinates or other location-based information.

[0045] The term ‘Historical service requests’ refers to the data or records of previous service requests made by users, which are used as reference points for comparison and analysis.

[0046] The term ‘Network data’ refers to the Information related to the operation and performance of the telecommunication network, such as traffic patterns, data transfer rates, and signal propagation characteristics.The term ‘Crowd-sourced data’ refers to the data collected from a large group of users or sources, often voluntarily contributed, which can include various types of information relevant to the network's operation or user experience.

[0047] All modules, units, components used herein may be software modules or hardware processors, the processors being a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. Furthermore, all the units in the system are interconnected, so they can communicate and work together smoothly.

[0048] As discussed in the background section, existing solutions in the art of wireless communication systems, particularly in the context of handling customer complaints related to network issues, face several challenges. Firstly, there is a lack of efficient methods to accurately map customer complaints to specific cells or network technologies. This is crucial for identifying the root cause of the issue and implementing targeted solutions. Without precise complaint-to- cell mapping, network operators may struggle to pinpoint the exact location or cell where the problem occurred, leading to inefficient troubleshooting and resolution processes. Secondly, existing systems often fail to leverage real-time or historical data effectively. The integration of crowd-sourced data, which can provide valuable insights into user experiences and network performance, is not adequately utilized in current approaches. This limitation hinders the ability to correlate user complaints with actual network conditions, resulting in less effective problem resolution. Furthermore, there is a lack of systems that can dynamically adapt to the evolving nature of wireless communication networks. As networks transition from 4G to 5G and beyond, the complexity of managing and resolving customer complaints increases. Existing solutions may not be equipped to handle the intricacies of newer technologies, such as the increased number of cells, varying signal propagation characteristics, and the need for more sophisticated data analysis.

[0049] The present disclosure provides a method and system for performing service request-to-cell mapping in a network that addresses the problems in the art by introducing several innovative features. Firstly, it offers an efficient way to accurately map customer complaints to specific cells or network technologies by receiving service request data and identifying geolocation data associated with the service request. This precise mapping enables network operators to pinpoint the exact location or cell where the problem occurred, leading to more efficient troubleshooting and resolution processes. Secondly, the present disclosure leverages real-time or historical data effectively by comparing the service request with historical service requests to identify a serving cell. It also considers the integration of crowd-sourced data, which provides valuable insights into user experiences and network performance. This approach enhances the ability to correlate user complaints with actual network conditions, resulting in more effective problem resolution. Furthermore, the present disclosure addresses the need for systems that can dynamically adapt to the evolving nature of wireless communication networks. It does so by providing a method and system that can accommodate the complexities of newer technologies, such as 5G. The system is designed to handle the increased number of cells, varying signal propagation characteristics, and the need for more sophisticated data analysis.

[0050] It would be appreciated by the person skilled in the art that the present disclosure provides a technically advanced solution that overcomes the shortcomings of existing solutions in the field of wireless communication systems, particularly in handling customer complaints related to network issues. It offers a more efficient, effective, and adaptable approach to complaint-to-cell mapping, which is crucial for improving network performance and customer satisfaction.

[0051] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings.

[0052] Referring to FIG. 1, an exemplary block diagram depicting a system

[0100] for performing a service request-to-cell mapping within a network is shown, in accordance with the exemplary embodiments of the present disclosure. The system

[0100] comprises at least one transceiver unit

[0102] , at least one identification unit

[0104] , at least one analyser unit

[0106] and at least one generator unit

[0108] , and at least one classifier unit

[0110] , Also, all of the components / units of the system

[0100] are assumed to be connected to each other unless otherwise indicated below.

[0053] The system comprises a transceiver unit

[0102] , configured to receive a service request data associated with one or more service request. In an implementation of the present disclosure, the term ‘transceiver unit’ serves as a component of the system responsible for both transmitting and receiving signals within the telecommunication network. The transceiver unit

[0102] receives service request data associated with user requests within the telecommunication network. It serves as the entry point for the system, capturing the necessary data to initiate the mapping process.

[0054] The identification unit

[0104] is connected to the transceiver unit

[0102] , The identification unit

[0104] is configured to identify a geo-location data associated with the service request based on at least one or more service request data. In an implementation of the present disclosure, the term ‘identification unit’ refers to a component of the system responsible for analysing service request data to identify the geographical location associated with the request. Once the transceiver unit receives service request data, the identification unit analyses this data to extract relevant information about the geographical location of the user making the request. Further, once the geo-location data is identified, the serving cell is identified based at least one of a correlation of the service request data with a network data and a crowd-sourced data.

[0055] The identification unit

[0104] is further configured to determine a correlation between a network-based server plot and a crowd source information, wherein the network data comprises data related to at least one of a network traffic pattern, data transfer rates, and signal propagation characteristics and the crowd source information comprises one of a feedback data related to call quality, data speed, or network reliability, and user equipment data. Further, in case no location information data is identified, the identification unit

[0104] is configured to determine correlation with the crowd source database based on a time window and a category of a service request.

[0056] The analyser unit

[0106] is connected to the identification unit

[0104] , The analyser unit

[0106] is configured to compare the service request with historical service requests to identify a serving cell from a plurality of serving cells corresponding to the service request. In an implementation of the present disclosure, the term ‘analyser unit’ refers to the system tasked with comparing the current service request with historical service requests to determine the serving cell within the network. After identifying the geographical location associated with the service request, the analyser unit performs a comparison with historical service requests.

[0057] The classifier unit

[0110] is connected to the identification unit

[0104] , The classifier unit

[0110] is further configured to classify the service request based at least on a pre-defined category. In an implementation of the present disclosure, the term "predefined categories" could be defined internally by the network operators or may adhere to industry standards. Examples of such predefined categories may include, but are not limited to, accessibility issue, coverage issue, voice related, data related, and SMS related. The service requests are mapped / translated to each of these predefined categories by the classifier unit

[0110] , In one example, the classifier unit

[0110] operates automatically using a machine learning model. Examples of such machine learning models may include, but are not limited to, supervised learning model, unsupervised learning model, semi-supervised learning model, and reinforcement learning model. Any other machine learning models may also be used, and would lie within the scope of the present subject matter.

[0058] As would be appreciated, by classifying service requests into predefined categories, the method outlined in the invention can effectively organize, analyze, and respond to different types of requests more efficiently. This classification helps in streamlining the service request-to-cell mapping process, ensuring that each request is handled appropriately based on its category and priority level within the network infrastructure.

[0059] The method for categorizing service requests using a classifier unit. This classifier unit

[0110] employs a machine learning model to automatically map or translate service requests into various categories.

[0060] For example, accessibility issues may include requests related tonetwork availability, or signal strength, or handover measures. Coverage issues may include requests regarding network availability or low signal strength. Voice-related issues may involve requests related to call-drop, or sound distorted, or voice delay. Data-related issues may pertain to data usage, speeds, or billing concerns. SMS-related issues may involve problems with text messaging functionality.

[0061] The classifier unit

[0110] utilizes various machine learning models for classification purposes. Examples of such models include support vector machines (SVM), decision trees, neural networks, k-nearest neighbours (KNN), and logistic regression, among others.

[0062] The generator unit

[0108] is connected to the analyser unit

[0106] , The generator unit

[0108] is configured to generate the service request-to-cell mapping list based at least on one of the service request data, the geo-location data, and or identified serving cell. In an implementation of the present disclosure the serving cell is identified, the generator unit

[0108] compiles this information along with other relevant data into a service request-to-cell mapping list wherein the service request-to-cell mapping list is generated for one or more service requests, wherein the one or more service requests are organized in the service request-to-cell mapping list based at least on a severity score of the one or more service requests. The term ‘Severity score’ refers to a measure or rating indicating the level of urgency or importance assigned to a service request, which helps prioritize handling and response.

[0063] This list provides a comprehensive overview of each service request and its corresponding serving cell within the telecommunication network.

[0064] Referring to FIG. 2 an exemplary method flow diagram

[0200] , for performing a service request-to-cell mapping within a network, in accordance with exemplary embodimentsof the present disclosure is shown. In an implementation the method

[0200] is performed by the system

[0100] , As shown in FIG. 2, the method

[0200] starts at step

[0202] ,

[0065] At step

[0204] , the method

[0200] as disclosed by the present disclosure comprises receiving, by a transceiver unit

[0102] , a service request data associated with one or more service request. In an implementation of the present disclosure, the term the transceiver unit

[0102] receives service request data associated with user requests within the telecommunication network. It serves as the entry point for the system, capturing the necessary data to initiate the mapping process.

[0066] Next, at step

[0206] , the method

[0200] as disclosed by the present disclosure comprises identifying, by an identification unit

[0104] , a geo-location data associated with the service request based on at least one or more service request data. In an implementation of the present disclosure, Once the transceiver unit receives service request data, the identification unit analyses this data to extract relevant information about the geographical location of the user making the request. Further, once the geo-location data is identified, the serving cell is identified based at least one of a correlation of the service request data with a network data and a crowdsourced data. Further, the network data comprises data related to at least one of a network traffic pattern, data transfer rates, and signal propagation characteristics and the crowd source information comprises one of a feedback data related to call quality, data speed, or network reliability, and user equipment data. Further, in case no location information data is identified, the

[0104] unit is configured to determine correlation with the crowd source database based on a time window and a category of a service request.

[0067] Further, at step

[0208] , the method

[0200] as disclosed by the present disclosure comprises comparing, by an analyser unit

[0106] , the service request with historical service requests to identify a serving cell from a plurality of serving cells corresponding to the service request. In an implementation of the present disclosure, the term ‘analyser unit’ refers to the system tasked with comparing the current service request with historical service requests to determine the serving cell within the network. After identifying the geographical location associated with the service request, the analyser unit performs a comparison with historical service requests.

[0068] Further, a classifier unit

[0110] is to classify the service request based at least on a pre-defined category. In an implementation of the present disclosure, the term "predefinedcategories" could be defined internally by the network operators or may adhere to industry standards. By classifying service requests into predefined categories, the method outlined in the invention can effectively organize, analyze, and respond to different types of requests more efficiently. This classification helps in streamlining the service request-to-cell mapping process, ensuring that each request is handled appropriately based on its category and priority level within the network infrastructure.

[0069] Next, at step

[0210] , the method

[0200] as disclosed by the present disclosure comprises generating, by the generator unit

[0108] , a service request-to-cell mapping list based at least on one of the service request data, the geo-location data, and or the identified serving cell. In an implementation of the present disclosure, the term ‘generator unit’ refers to the generator unit is a component of the system responsible for generating a service request-to-cell mapping list based on various factors, including service request data, geographical location data, and the identified serving cell. Once the serving cell is identified, the generator unit compiles this information along with other relevant data into a service request-to-cell mapping list wherein the service request-to-cell mapping list is generated for one or more service requests, wherein the one or more service requests are organized in the service request-to-cell mapping list based at least on a severity score of the one or more service requests. The term ‘Severity score’ refers to a measure or rating indicating the level of urgency or importance assigned to a service request, which helps prioritize handling and response.

[0070] Thereafter, the method terminates at step

[0212] ,

[0071] Furthermore, referring to FIG.3 that illustrates an exemplary process

[0300] for providing service request to cells mapping based on actual issue location face by user for telecom, in accordance with exemplary embodiments of the present disclosure.

[0072] SI: Start - The process begins with the initiation of the complaint handling procedure.

[0073] S2: Customer complaints inputs - User complaints are received and logged into the system. These complaints are the initial data points for the mapping process.

[0074] S3: Classify network complaints as per defined category - The complaints are then categorized according to predefined criteria, which might include the type of service issue, the urgency, etc.

[0075] S4: Check for geolocation data availability for requested users - The system checks whether there is geolocation data available for the users who have logged the complaints, which is important for accurately mapping the complaint to a specific network cell.

[0076] S5: Check for relevant events per user complaint type from the database - If geolocation data is available, the system looks for network events i.e. user sessions associated with that category that are relevant to the category logged and occurred within the same timeframe and location as the complaint. For example, if the complaint logged by user, is categorized as “data related”, then all browsing & streaming events are checked.

[0077] S6: Get serving cell ID from identified event to tag complaints to cell - Once an event that matches the complaint in terms of type, time, and location is found, the cell that served the user at the time of the event is identified, and the complaint is tagged accordingly.

[0078] S7: Is user record available in crowdsource data for complaint time period? -If there is no geolocation data, the system checks whether there is crowdsourced data available for the user for the time period in which the complaint was made.

[0079] S8: Change time window as per algorithm inputs for further check - If no relevant event is found in the crowdsourced data, the system may extend the search period based on algorithmic inputs and check again.

[0080] S9: Tag complaint to serving cell based on crowdsourced - If the crowdsourced data identifies a relevant event, the complaint is tagged to the cell serving the user during the identified event period.

[0081] S10: Create all complaint to serving cell list - A comprehensive list of all complaints and their corresponding serving cells is created, which can be used for further analysis and troubleshooting.

[0082] Sil: Is event of same category visible in geolocation for defined period? - If there is no user record in the crowdsourced data, the system checks for events of the same category in the geolocation data for a defined period.

[0083] S12: Tag complaints to cell based on geolocation events - If such events are found, complaints are tagged based on geolocation events.

[0084] S13: Category classification and alternate category inputs - If an event in the same category is not found in the geolocation data, the system may perform a category reclassification or consider alternative categories for the complaint.

[0085] S14: Tag complaint to serving cell based on crowdsourced - If the user record is available in crowdsourced data, the complaint is tagged to the relevant serving cell based on this data.

[0086] S15: Correlations between complaint location and best serving plot - When no direct data is available to link the complaint to a serving cell, the system makes a correlation between the location of the complaint and the best available data on serving cells.

[0087] At S16: Tag complaint to serving cell based on correlation results - Based on the correlation performed in S15, the system tags the complaint to the most probable serving cell.

[0088] The endpoint S10 indicates the completion of the process, where the resulting list is presumably used for network optimization and addressing customer issues. This systematic approach aims to improve the accuracy of complaint handling by considering various data sources, including direct geolocation, crowdsourced data, and algorithm-driven correlations.

[0089] The described steps in the figure ensure that the disclosed method provides a comprehensive approach to identify the root causes of customer complaints in the telecommunications network, taking into account both real-time and historical data, as well as crowdsourced information, thereby addressing the problems in the art.

[0090] FIG. 4 illustrates an exemplary block diagram of a computing device

[0400] upon which an embodiment of the present disclosure may be implemented. In an implementation, the computing device implements the method for scaling up network nodes using the system

[0100] ,In another implementation, the computing device itself implements the method for scaling up network nodes in 5G core (5GC) network by using one or more units configured within the computing device, wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.

[0091] The computing device

[0400] may include a bus

[0402] or other communication mechanism for communicating information, and a hardware processor

[0404] coupled with bus

[0402] for processing information. The hardware processor

[0404] may be, for example, a general- purpose microprocessor. The computing device

[0400] may also include a main memory

[0406] , such as a random-access memory (RAM), or other dynamic storage device, coupled to the bus

[0402] for storing information and instructions to be executed by the processor

[0404] , The main memory

[0406] also may be used for storing temporary variables or other intermediate information during execution of the instructions to be executed by the processor

[0404] . Such instructions, when stored in non-transitory storage media accessible to the processor

[0404] , render the computing device

[0400] into a special-purpose machine that is customized to perform the operations specified in the instructions. The computing device

[0400] further includes a read only memory (ROM)

[0408] or other static storage device coupled to the bus

[0402] for storing static information and instructions for the processor

[0404] .

[0092] A storage device

[0410] , such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to the bus

[0402] for storing information and instructions. The computing device

[0400] may be coupled via the bus

[0402] to a display

[0412] , such as a cathode ray tube (CRT), for displaying information to a computer user. An input device

[0414] , including alphanumeric and other keys, may be coupled to the bus

[0402] for communicating information and command selections to the processor

[0404] . Another type of user input device may be a cursor controller

[0416] , such as a mouse, atrackball, or cursor direction keys, for communicating direction information and command selections to the processor

[0404] , and for controlling cursor movement on the display

[0412] , This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allow the device to specify positions in a plane.

[0093] The computing device

[0400] may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and / or program logic which in combination with the computing device

[0400] causes or programs the computing device

[0400] to be a special-purpose machine. According to one embodiment, the techniques herein areperformed by the computing device

[0400] in response to the processor

[0404] executing one or more sequences of one or more instructions contained in the main memory

[0406] , Such instructions may be read into the main memory

[0406] from another storage medium, such as the storage device

[0410] , Execution of the sequences of instructions contained in the main memory

[0406] causes the processor

[0404] to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.

[0094] The computing device

[0400] also may include a communication interface

[0418] coupled to the bus

[0402] , The communication interface

[0418] provides a two-way data communication coupling to a network link

[0420] that is connected to a local network

[0422] , For example, the communication interface

[0418] may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface

[0418] may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, the communication interface

[0418] sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[0095] The computing device

[0400] can send messages and receive data, including program code, through the network(s), the network link

[0420] and the communication interface

[0418] , In the Internet example, a server

[0430] might transmit a requested code for an application program through the Internet

[0428] , the Internet Service Provider (ISP)

[0426] , the host

[0424] , the local network

[0422] , and the communication interface

[0418] , The received code may be executed by the processor

[0404] as it is received, and / or stored in the storage device

[0410] , or other nonvolatile storage for later execution.

[0096] The computing device

[0400] encompasses a wide range of electronic devices capable of processing data and performing computations. Examples of computing device

[0400] include, but are not limited only to, personal computers, laptops, tablets, smartphones, servers, and embedded systems. The devices may operate independently or as part of a network and can perform a variety of tasks such as data storage, retrieval, and analysis. Additionally, computing device

[0400] may include peripheral devices, such as monitors, keyboards, and printers, as wellas integrated components within larger electronic systems, showcasing their versatility in various technological applications.

[0097] FIG. 5 illustrates an exemplary block diagram

[0500] of a user equipment (UE) for performing a service request-to-cell mapping within a network, in accordance with exemplary embodiments of the present disclosure. In an embodiment, the UE

[0502] comprises a memory

[0504] and a processor

[0506] coupled to the memory

[0504] ,

[0098] As illustrated, the processor

[0506] is configured send a service request data associated with a service request; receive a resolution of the service request based on the analysis of the service request data and the geo-location data, wherein to analyse the service request data and the geo-location data, the processor

[0506] is configured to: receive a comparison of the service request with historical service requests to identify a serving cell from a plurality of serving cells corresponding to the service request; and generate a service request-to-cell mapping list based at least on one of the service request data, the geo-location data, or the identified serving cell.

[0099] Yet another aspect of the present disclosure relates to a non-transitory computer- readable storage medium storing instructions for performing a service request-to-cell mapping within a network. These instructions entail executable code that, when executed by one or more units of the system the instructions, facilitates: receiving, by a transceiver unit

[0102] , a service request data associated with a service request Next, the method includes identifying, by an identification unit

[0104] , a geo-location data associated with the service request based at least on the service request data. The method includes comparing, by an analyser unit

[0106] , the service request with historical service requests to identify a serving cell from a plurality of serving cells corresponding to the service request. Thereafter, the method includes; and generating, by the generator unit

[0108] , the service request-to-cell mapping list based at least one of the service request data, the geo-location data, and the identified serving cell.

[0100] As is evident from the above, the present disclosure provides a technically advanced solution for providing service request to cells mapping based on actual issue location face by user for telecom. Mainly, solution as disclosed in the present disclosure encompasses identifying a possible serving cell at accurate locations to further provide service request to cells mapping. Also, in an implementation the solution is based on actual consumer problem and considers a consumer feedback to optimize or to improve the network. Moreover, the solutioncan relate geographic information of customer’s problematic locations to a network cell ID, and can generate and provide a cell list to the network planning and engineering team to do the further planning and engineering. Therefore, the present solution provides various technical advantages over the existing arts.

[0101] While considerable emphasis has been placed herein on the disclosed embodiments, it will be appreciated that many embodiments can be made and that many changes can be made to the embodiments without departing from the principles of the present disclosure. These and other changes in the embodiments of the present disclosure will be apparent to those skilled in the art, whereby it is to be understood that the foregoing descriptive matter to be implemented is illustrative and non-limiting.

[0102] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various the components / units can be implemented interchangeably. While specific embodiments may disclose a particular functionality of these units for clarity, it is recognized that various configurations and combinations thereof are within the scope of the disclosure. The functionality of specific units as disclosed in the disclosure should not be construed as limiting the scope of the present disclosure. Consequently, alternative arrangements and substitutions of units, provided they achieve the intended functionality described herein, are considered to be encompassed within the scope of the present disclosure.

Claims

We Claim1. A method for performing service request-to-cell mapping in a network, comprising: receiving, by a transceiver unit [102], a service request data associated with a service request; identifying, by an identification unit [104], a geo-location data associated with the service request based at least on the service request data; comparing, by a processing unit [106], the service request with historical service requests to identify a serving cell from a plurality of serving cells corresponding to the service request; and- generating, by the processing unit [106], a service request-to-cell mapping list based at least on one of the service request data, the geo-location data, or the identified serving cell.

2. The method as claimed in claim 1, wherein in case the geo-location data is identified, the serving cell is identified based at least on a correlation of the service request data with a network data or a crowd-sourced data.

3. The method as claimed in claim 2, wherein in case the geo-location data is not identified, the serving cell is identified based at least on a correlation of the service request data with the crowd-sourced data, wherein the correlation is identified based at least on a specified time window and a category of the service request.

4. The method as claimed in claim 2, wherein the network data comprises data related to at least network traffic patterns, data transfer rates, or signal propagation characteristics.

5. The method as claimed in claim 2, wherein the crowd-sourced data comprises at least feedback data related to call quality, data speed, network reliability, or user equipment data.

6. The method as claimed in claim 1, further comprises classifying, by the processing unit [106], the service request based at least on a pre-defined category.

7. The method as claimed in claim 1, wherein the service request-to-cell mapping list is generated for one or more service requests, wherein the one or more service requests are organized in the service request-to-cell mapping list based at least on a severity score of the one or more service requests.

8. A system for performing service request-to-cell mapping in a network, comprising: a transceiver unit, configured to receive a service request data associated with a service request; an identification unit, configured to identify a geo-location data associated with the service request based at least on the service request data; a processing unit, configured to: compare the service request with historical service requests to identify a serving cell from a plurality of serving cells corresponding to the service request; and generate a service request-to-cell mapping list based at least on one of the service request data, the geo-location data, or identified serving cell.

9. The system as claimed in claim 8, wherein in case the geo-location data is identified, the serving cell is identified based at least on a correlation of the service request data with a network data or a crowd-sourced data.

10. The system as claimed in claim 9, wherein in case the geo-location data is not identified, the serving cell is identified based at least on a correlation of the service request data with the crowd-sourced data, wherein the correlation is identified based at least on a specified time window and a category of the service request.

11. The system as claimed in claim 9, wherein the network data comprises data related to at least network traffic patterns, data transfer rates, or signal propagation characteristics.

12. The system as claimed in claim 9, wherein the crowd-sourced data comprises at least feedback data related to call quality, data speed, or network reliability, or user equipment data.

13. The system as claimed in claim 8, wherein the processing unit [106] is further configured to classify the service request based at least on a pre-defined category.

14. The system as claimed in claim 8, wherein the service request-to-cell mapping list is generated for one or more service requests, wherein the one or more service requests are organized in the service request-to-cell mapping list based at least on a severity score of the one or more service requests.

15. A user equipment (UE) [502] for performing service request-to-cell mapping in a network, the UE comprises: a memory [504]; and a processor [506] coupled to the memory [504], the processor [506] configured to: send a service request data associated with a service request; and receive a resolution of the service request based on an analysis of the service request data and a geo-location data, wherein to analyse the service request data and the geo-location data, the processor [506] is configured to: receive a comparison of the service request with historical service requests to identify a serving cell from a plurality of serving cells corresponding to the service request; and generate a service request-to-cell mapping list based at least on one of the service request data, the geo-location data, or the identified serving cell.

16. A non-transitory computer-readable storage medium storing instructions for performing a service request-to-cell mapping within a network, the storage medium comprising executable code which, when executed by one or more units of a system, causes: a transceiver unit [102] to receive a service request data associated with a service request; an identification unit [104] to identify a geo-location data associated with the service request based at least on the service request data; an analyser unit [106] to compare the service request with historical service requests to identify a serving cell from a plurality of serving cells corresponding to the service request; and a generator unit [108] to generate a service request-to-cell mapping list based at least on one of the service request data, the geo-location data, or the identified serving cell.

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