System and method for improving network performance

Abstract

A method (500) for identifying one or more attributes impacting performance of a network (106) is disclosed. The method (500) includes receiving data corresponding to a plurality of User Equipments (UEs) (104) from multiple data sources associated with various network vendors and Radio Access Technologies (RATs). The collected data is aggregated by a processing engine (208) at a user and cell level for each of the one or more RAT and the one or more network vendor. Analyzing, by the processing engine, the aggregated data to identify one or more of a geographical region experiencing high rates of Inter Radio Access Technology (IRAT) handover failures, guided by the UE location parameters retrieved from the data. The identified regions determine which cells contribute to the high failure rates. The method identifies at least one UE within the cells that exhibits a high count of the IRAT handover attempts and corresponding failures.

Description

SYSTEM AND METHOD FOR IMPROVING NETWORK PERFORMANCERESERVATION OF RIGHTS

[0001] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as, but are not limited to, copyright, design, trademark, Integrated Circuit (IC) layout design, and / or trade dress protection, belonging to Jio Platforms Limited or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.TECHNICAL FIELD

[0002] The present disclosure generally relates to the field of communication networks. More particularly, the present disclosure relates to a method and a system for improving network performance and user experience in a network.DEFINITION

[0003] As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used to indicate otherwise.

[0004] The expression ‘Inter-radio access technology (IRAT)’ used hereinafter in the specification refers to the capability of a mobile device or A user equipment (UE) to switch between different radio access technologies (RATs) seamlessly. In practical terms, IRAT involves the ability of a device to transition from one cellular network technology to another while maintaining an ongoing session or communication.

[0005] The expression ‘User (IMSI / IMEI) cell level’ used hereinafter in the specification refers to examining network data for each individual user and focusingon the specific cell they are connected to. International Mobile Subscriber Identity (IMSI)ZInternational Mobile Equipment Identity (IMEI) are unique identifiers associated with the user’s device within the network.

[0006] The expression ‘Inter-Radio Access Technology (IRAT)’ refers to the communication standards and technologies used by mobile devices to connect to a cellular network. In this case, it's about switching between 4G and 5G.

[0007] The expression ‘handover’ refers to a process when a user device seamlessly switches its connection from one base station (cell) to another within the network, typically to maintain a strong signal.

[0008] The above-mentioned definitions are in addition to those expresses in the art.BACKGROUND

[0009] 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 reader's understanding with respect to the present disclosure, and not as admissions of prior art.

[0010] Modern communication networks have evolved to include multiple generations of technology, such as fourth generation (4G) and fifth generation (5G), operating simultaneously. As user demand for data services grows, managing network congestion and ensuring seamless connectivity across these technologies has become increasingly complex. User equipment (UE) often transitions between different Radio Access Technologies (RAT), leading to challenges in maintaining optimal performance and service quality. The cellular network landscape is also undergoing a significanttransformation with the widespread deployment of the 5G technology alongside existing the 4G networks. This multi-generation environment offers users the potential to benefit from the speed and capacity enhancements of the 5G while seamlessly leveraging the established coverage of the 4G.

[0011] However, managing user experience and network performance becomes more complex in this multi -technology environment. One key challenge lies in an interradio access technology (IRAT) handover. This refers to the process of transitioning a UE between different radio access technologies (e.g., from the 4G to the 5G or vice versa) as it moves within the network.

[0012] Traditional network management systems struggle to handle the dynamic nature of multi-generation networks effectively. They often lack the capability to analyze and integrate data across different technologies, resulting in inefficient resource utilization and degraded user experience. Moreover, IRAT handovers or transitions between different RATs can lead to performance issues if not managed properly, causing dropped calls and poor connectivity.

[0013] Additionally, frequent or unsuccessful handovers can degrade user experience by causing call drops, data transfer interruptions, and overall service quality issues. Unnecessary handovers can strain network resources, reducing overall network capacity and efficiency. This can lead to user dissatisfaction and potential churn, as users experience poor service quality due to problematic handovers.

[0014] There is, therefore, a need in the art to provide a method and a system that can mitigate the disadvantages of the prior art.OBJECTIVE

[0015] Some of the objectives of the present disclosure, which at least one embodiment herein satisfies, are as follows:

[0016] An objective of the present disclosure is to provide a method and a system for identifying one or more attributes impacting performance of a network and user experience in a network.

[0017] Another objective of the present disclosure is to provide a system and a method for enabling effective analysis of an Inter-Radio Access Technology (IRAT) handovers by integrating data from different network technologies, thereby identifying handover success rates and users experiencing frequent or problematic handovers.

[0018] Yet another objective of the present disclosure is to provide a system and a method that improves overall service quality and user experience by ensuring efficient handovers between different Radio Access Technologies (RATs).

[0019] Another objective of the present disclosure is to provide a system and a method that automates the integration and analysis of session-level data from a fourth generation (4G) network and fifth generation (5G) network.

[0020] Other objectives and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.SUMMARY

[0021] In an exemplary embodiment, a method for identifying one or more attributes impacting performance of a network, is described. The method includes receiving, by a receiving module, data corresponding to a plurality of User Equipments (UEs) from a plurality of data sources associated with one or more network vendors and one or more Radio Access Technologies (RATs). The method includes aggregating, by a processing engine, the received data at a user level and a cell level for each of the one or more RATs and the one or more network vendors. The method includes analyzing, by the processing engine, the aggregated data to identify one ormore of a geographical region experiencing a high rate of Inter Radio Access Technology (IRAT) handover failure based on a UE location parameter retrieved from the aggregated data. The method includes one or more cells within the identified geographical region contributing to the high rate of IRAT handover failure based on the aggregated data at the cell level. The method includes at least one UE within the identified one or more cells having a high count of IRAT handover attempts and the high rate of IRAT handover failure, based on the aggregated data at the user level.

[0022] In an embodiment, the method includes the data comprises call summary log (CSL) data corresponding to the one or more RATs.

[0023] In an embodiment, the method includes the plurality of data sources comprises one or more trace collection entity (TCE) servers maintained by each network vendor of the one or more network vendors.

[0024] In an embodiment, the aggregating includes identifying, by the processing engine, one or more key performance indicators (KPIs) and associated network parameters in the CSL data received from the plurality of data sources associated with the one or more network vendors. The method includes aggregating, by the processing engine, the received data corresponding to each identified KPI to obtain aggregated data for each of the one or more RATs associated with each of the one or more network vendors.

[0025] In an embodiment, the method further includes storing, by the processing engine, the aggregated data at one or more predefined levels.

[0026] In an embodiment, the method includes the one or more predefined levels comprise at least one of a network technology level, a network vendor level, and a combination thereof.

[0027] In an embodiment, identifying the at least one UE includes detecting, by the processing engine, at least one of an International Mobile Equipment Identity (IMEI) or a Type Allocation Code (TAC) associated with the at least one UE to determine a UE type responsible for impacting the performance of the IRAT handover.

[0028] In an embodiment, identifying the geographical region includes dividing, by the processing engine, a network coverage area into a plurality of geographical grids having a predefined area, wherein each geographical grid is associated with the at least one UE.

[0029] In an embodiment, upon identifying the geographical region, the one or more cells, and the at least one UE, includes generating, by the processing engine, at least one report based on the identified geographical region, the one or more cells, and the at least one UE. The method includes transmitting, by the processing engine, the at least one report to one or more network operators to optimize the performance of the network.

[0030] In an exemplary embodiment, a system for identifying one or more attributes impacting performance of a network is disclosed. The system includes a receiving module configured to receive data corresponding to a plurality of User Equipments (UEs) from a plurality of data sources associated with one or more network vendors and one or more Radio Access Technologies (RATs). The system includes a processing engine configured to aggregate the received data at a user level and a cell level for each of the one or more RATs and the one or more network vendors. The system includes analyze the aggregated data to identify one or more of a geographical region experiencing a high rate of Inter Radio Access Technology (IRAT) handover failure based on a UE location parameter retrieved from the aggregated data. The system includes one or more cells within the identified geographical region contributing to the high rate of IRAT handover failure based on the aggregated data at the cell level. The system includes at least one UE within the identified one or morecells having a high count of IRAT handover attempts and the high rate of IRAT handover failure, based on the aggregated data at the user level.

[0031] In an embodiment, the system includes the data comprises call summary log (CSL) data corresponding to the one or more RATs.

[0032] In an embodiment, the system includes the plurality of data sources comprises one or more trace collection entity (TCE) servers maintained by each network vendor of the one or more network vendors.

[0033] In an embodiment, the system includes to aggregate the received data, the processing engine is configured to identify one or more key performance indicators (KPIs) and associated network parameters in the CSL data received from the plurality of data sources associated with the one or more network vendors. The system includes aggregate the received data corresponding to each identified KPI to obtain aggregated data for each of the one or more RATs associated with each of the one or more network vendors.

[0034] In an embodiment, the system includes the processing engine is further configured to store the aggregated data at one or more predefined levels.

[0035] In an embodiment, the system includes the one or more predefined levels comprise at least one of a network technology level, a network vendor level, and a combination thereof.

[0036] In an embodiment, the system includes to identify the at least one UE, the processing engine is configured to detect at least one of an International Mobile Equipment Identity (IMEI) or a Type Allocation Code (TAC) associated with the at least one UE to determine a UE type responsible for impacting the performance of the IRAT handover.

[0037] In an embodiment, the system includes to identify the geographical region, the processing engine is configured to divide a network coverage area into a plurality of geographical grids having a predefined area, wherein each geographical grid is associated with the at least one UE.

[0038] In an embodiment, the system includes upon identifying the geographical region, the one or more cells, and the at least one UE, the processing engine is configured to generate at least one report based on the identified geographical region, the one or more cells, and the at least one UE. The system includes transmit the at least one report to one or more network operators to optimize the performance of the network.

[0039] In an exemplary embodiment a User Equipment (UE) communicatively coupled with a network is disclosed. The coupling includes steps of sending, by the UE, a connection request to the network. The coupling includes receiving, by the UE, an acknowledgment of the connection request from the network. The coupling includes transmitting, by the UE, a plurality of signals in response to the acknowledgement, wherein based on the plurality of signals, identification of one or more attributes impacting performance of the network is performed by a method for identifying one or more attributes impacting performance of the network.

[0040] In an exemplary embodiment, a computer program product comprising a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors is disclosed. The method includes receive data corresponding to a plurality of User Equipments (UEs) from a plurality of data sources associated with one or more network vendors and one or more Radio Access Technologies (RATs). The method includes aggregating the received data at a user level and a cell level for each of the RATs and the one or more network vendors, method includes analyzing the aggregated data to identify one or more of a geographical region experiencing a high rate of Inter Radio Access Technology (IRAT)handover failure based on a UE location parameter retrieved from the aggregated data. The method includes one or more cells within the identified geographical region contributing to the high rate of IRAT handover failure based on the aggregated data at the cell level. The method includes at least one UE within the identified one or more cells having a high count of IRAT handover attempts and the high rate of IRAT handover failure, based on the aggregated data at the user level.BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING

[0041] 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.

[0042] FIG. 1 illustrates an exemplary network architecture for implementing a system for identifying one or more attributes impacting performance of a network, in accordance with an embodiment of the present disclosure.

[0043] FIG. 2 illustrates an exemplary block diagram of the system configured for identifying one or more attributes impacting performance of a network, in accordance with an embodiment of the present disclosure.

[0044] FIG. 3 illustrates an implementation of the system for identifying one or more attributes impacting performance of a network, in accordance with an embodiment of the disclosure.

[0045] FIG. 4 illustrates an exemplary process flow for identifying one or more attributes impacting performance of a network, in accordance with an embodiment of the disclosure.

[0046] FIG. 5 illustrates an exemplary flow diagram for identifying one or more attributes impacting performance of a network, in accordance with an embodiments of the present disclosure

[0047] FIG. 6 illustrates an exemplary computer system in which or with which the embodiments of the present disclosure may be implemented.

[0048] The foregoing shall be more apparent from the following more detailed description of the disclosure.LIST OF REFERENCE NUMERALS100 - Network architecture102-1, 102-2... 102-N - Plurality of Users104-1, 104-2... 104-N - Plurality of User Equipments106 - Network108 - System200 - Block Diagram202 - Processor(s)204 - Memory206 - Plurality of Interfaces208 - Processing engine210 - Database 212 - Data aggregation module214 - Analysis module216 - Optimizing module218 - Other engine(s)302 - Data lake 304 - Reporting server (RS)306 - Master database (MDB)308 - Radio planning team310 - Radio optimization team400 - Process flow diagram 500 - Flow diagram600 - Computer system610 - External storage device620 - Bus630 - Main memory640 - Read only memory650 - Mass storage device660 - Communication port(s)670 - ProcessorDETAILED DESCRIPTION

[0049] 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.

[0050] 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.

[0051] 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.

[0052] Also, it is noted that individual embodiments may be described as a process that 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. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0053] 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 like the term “comprising” as an open transition word without precluding any additional or other elements.

[0054] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0055] The terminology used herein is to describe particular embodiments only and is not intended to be limiting the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any combinations of one or more of the associated listed items. 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 invention. These terms are not intended to limit the scope of the invention 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 invention 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 invention as defined herein.

[0056] As used herein, an “electronic device”, or “portable electronic device”, or “user device” or “communication device” or “user equipment” or “device” refers toany electrical, electronic, electromechanical and computing device. The user device is capable of receiving and / or transmitting one or parameters, performing function / 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.

[0057] 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, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a Digital Signal Processing (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, 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.

[0058] 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 variousgenerations of cellular technology are also seen. The development, in this respect, has been incremental in the order of a second generation (2G), a third generation (3G), a fourth generation (4G), and now a fifth generation (5G), and more such generations are expected to continue in the forthcoming time.

[0059] 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 a GSM (Global System for Mobile Communications), a Code Division Multiple Access (CDMA), a Universal Mobile Telecommunications System (UMTS), a Long-Term Evolution (LTE), the 5G technology, and a Sixth Generation (6G) technology. 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.

[0060] Wireless communication technology has rapidly evolved over the past few decades. The first generation of wireless communication technology was analog, offering only voice services. Further, text messaging and data services became possible when the 2G technology was introduced. The 3G technology marked the introduction of high-speed internet access, mobile video calling, and location-based services. The 4G technology revolutionized the wireless communication with faster data speeds, improved network coverage, and security. Currently, the 5G technology is being deployed, offering significantly faster data speeds, lower latency, and the ability to connect many devices simultaneously. These advancements represent a significant leapY1 forward from previous generations, enabling enhanced mobile broadband, improved Internet of Things (loT) connectivity, and more efficient use of network resources. The 6G technology promises to build upon these advancements, pushing the boundaries of wireless communication even further. While the 5G technology is still being rolled out globally, research and development into the 6G are rapidly evolving, with the aim of revolutionizing the way of connecting and interacting with technology.

[0061] While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

[0062] The present disclosure includes a system and a method for analyzing user equipment (UE) or user equipments (UEs) performance across multiple Radio Access Technologies (RATs) in a multi-generation network environment. The system and the method may integrate data from diverse sources linked to various network vendors and the RATs, aggregates the data at both user and cell levels, and conducts detailed analysis to identify network performance issues. The system and the method address Inter-RAT (IRAT) handover failures by pinpointing geographical regions, cells, and individual user equipments (UEs) contributing to the failures. Such insights are essential in diagnosing existing network issues but also in optimizing handover processes, by the means enhancing the overall quality of service. The present disclosure may provide a comprehensive tool for understanding UE behavior, performance metrics, and usage patterns, supporting strategic decision-making in network operations, customer service, and resource optimization.

[0063] The present disclosure discloses the method to identify attributes that impact network performance, focusing on the IRAT handover failures. The method begins with collecting data from multiple sources, linked to different network vendors and RATs, by the receiving module. The collected data from multiple source data is then aggregated by a processing engine at both the user and cell levels for each RAT and the network vendor. The aggregated data is analyzed to identify geographical regions with the high IRAT handover failure rates, based on the UE location parameters. Specific cells within these regions may be pinpointed as contributors to the high failure rates by examining cell-level data. Additionally, individual UEs within these identified cells are highlighted for their high counts of the IRAT handover attempts and failures, using the user-level data.

[0064] The present disclosure relates to a system and a method for improving network performance in a network. Various embodiments throughout the disclosure will be explained in more detail with reference to FIGS. (1-6).

[0065] FIG. 1 illustrates an exemplary network architecture (100) for implementing a system (108) for identifying one or more attributes impacting performance of a network (106), in accordance with an embodiment of the present disclosure. As illustrated in FIG. 1, the network architecture (100) may include one or more computing devices or User Equipments (UEs) (104-1, 104-2... 104-N) associated with one or more users ( 102- 1 , 102-2... 102-N) in an environment. A person of ordinary skill in the art will understand that one or more users (102-1, 102-2... 102-N) may be individually referred to as the user (102) and collectively referred to as the users (102). Similarly, a person of ordinary skill in the art will understand that one or more UEs (104-1, 104-2... 104-n) may be individually referred to as the UE (104) and collectively referred to as the UEs (104). A person of ordinary skill in the art will appreciate that the terms “computing device(s)” and “user equipment” may be used interchangeably throughout the disclosure. Although three UEs (104) are depicted in FIG. 1, anynumber of the UEs (104) may be included without departing from the scope of the ongoing description.

[0066] In an embodiment, the UE (104) may include smart devices operating in a smart environment, for example, an Internet of Things (loT) system. In such an embodiment, the UE (104) may include, but is not limited to, smartphones, smart watches, smart sensors (e.g., a mechanical sensor, a thermal sensor, an electrical sensor, a magnetic sensor, etc.), networked appliances, networked peripheral devices, networked lighting system, communication devices, networked vehicle accessories, networked vehicular devices, smart accessories, tablets, smart televisions (TVs), computers, smart security systems, smart home systems, other devices for monitoring or interacting with or for the user (102) and / or entities, or any combination thereof. A person of ordinary skill in the art will appreciate that the UE (104) may include, but is not limited to, intelligent, multi-sensing, network-connected devices, that can integrate seamlessly with each other and / or with a central server or a cloud-computing system or any other device that is network-connected.

[0067] In an embodiment, the UE (104) may include, but is not limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a phablet device, and so on), a wearable computer device (e.g., a head-mounted display computer device, a head-mounted camera device, a wristwatch computer device, and so on), a Global Positioning System (GPS) device, a laptop computer, a tablet computer, or another type of portable computer, a media playing device, a portable gaming system, and / or any other type of computer device with wireless communication capabilities, and the like. In an embodiment, the UE (104) may include, but is not limited to, any electrical, electronic, electro-mechanical, or an equipment, or a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, a laptop, a general-purpose computer, a desktop, a personal digital assistant, a tablet computer, a mainframe computer, or any othercomputing device. Further, the UE (104) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, an audio aid, a microphone, a keyboard, and input devices for receiving input from the user (102) or an entity such as a touch pad, a touch enabled screen, an electronic pen, and the like. A person of ordinary skill in the art will appreciate that the UE (104) may not be restricted to the mentioned devices and various other devices may be used.

[0068] In FIG. 1, the UE (104) may communicate with the system (108) through the network (106). In particular, the UE (104) may be communicatively coupled with the network (106). The coupling includes steps of receiving, by the network (106), a connection request from UE (104). Upon receiving the connection request, the coupling includes steps of sending, by the network (106), an acknowledgment of the connection request to the UE (104). Further, the coupling includes steps of transmitting a plurality of signals in response to the connection request.

[0069] In an embodiment, the network (106) may include at least one of the (4G) network, the 5G network, the 6G network, or the like. The network (106) may enable the UE (104) to communicate with other devices in the network architecture (100) and / or with the system (108). The network (106) may include a wireless card or some other transceiver connection to facilitate this communication. In another embodiment, the network (106) may be implemented as, or include any of a variety of different communication technologies such as a wide area network (WAN), a local area network (LAN), a wireless network, a mobile network, a Virtual Private Network (VPN), an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. In another embodiment, the network (106) includes, by way of example but not limitation, at leasta portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth.

[0070] In various embodiments, a UE (104) is communicatively coupled with a network. The coupling process involves the UE (104) sending a connection request to the network and receiving an acknowledgment of this request. Following the acknowledgment, the UE transmits multiple signals. Based on these signals, a method is applied to identify one or more attributes that affect the network's performance, as described by a method for identifying one or more attributes impacting performance of the network.

[0071] In another exemplary embodiment, the network architecture (100) may include a centralized server (not shown) may include or comprise, by way of example but not limitation, one or more of a stand-alone server, a server blade, a server rack, a bank of servers, a server farm, a hardware supporting a part of a cloud service or a system, a home server, a hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof.

[0072] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).

[0073] FIG. 2 illustrates an exemplary block diagram (200) of the system (108) configured for identifying one or more attributes impacting performance of the network (106) (e.g., the network (106), in accordance with an embodiment of the disclosure. FIG. 2 is explained in conjunction with FIG. 1. In an embodiment, the network may be, for example, the 4G network, the 5G network, the 6G network, and the like.

[0074] In an embodiment, the system (108) may include one or more processor(s) (202). The one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and / or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the system (108). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (204) may include any non-transitory storage device including, for example, volatile memory such as a Random-Access Memory (RAM), or a non-volatile memory such as an Erasable Programmable Read Only Memory (EPROM), a flash memory, and the like.

[0075] In an embodiment, the system (108) may include an interface(s) (206). The interface(s) (206) may include a variety of interfaces, for example, interfaces for data input and output devices (VO), storage devices, and the like. The interface(s) (206) may facilitate communication through the system (108). The interface(s) (206) may also provide a communication pathway for one or more components of the system (108). Examples of such components include, but are not limited to, a processing engine (208) and a database (210).

[0076] In an exemplary embodiment, the processing engine (208) may include one or more engines selected from any of a data aggregation module (212), an analysismodule (214), an optimizing module (216) and other engines (218) having functions that may include, but are not limited to, testing, storage, and peripheral functions, such as wireless communication unit for remote operation, and the like.

[0077] In an embodiment, the processing engine (208) may be triggered by an external request. The request may come from various sources like a central network management system (NMS) in response to user complaints or network alerts. Alternatively, an authorized remote monitoring tool could initiate analysis for specific network segments or user groups. Further, specific details for analysis, like which cells or timeframes to examine, could be pre-configured or included as additional parameters within the request message. This optional feature offers flexibility for on-demand analysis alongside the automated schedule.

[0078] In an embodiment, the data aggregation module (212) may be configured to receive and aggregate session data from the processing engine (208) to the user (IMSI / IMEI) cell level. The data aggregation module (212) is described as a component that performs several important functions related to gathering and processing session data within a telecommunications or network context. Here’s a breakdown of what it does based on the text:

[0079] Receives and Aggregates Session Data: The primary function of the data aggregation module (212) is to receive session data and aggregate it from the session level to the user (IMSI / IMEI) cell level. This means the data aggregation module (212) may take data that pertains to individual sessions and consolidates it to a higher level that identifies users (102) and their corresponding cell connections (IMSI / IMEI).

[0080] Identifies Data Sources: The data aggregation module (212) may collect session data from various network vendors, which indicates that it can interface withdifferent network technologies such as the 4G and the 5G. This ability allows it to gather comprehensive data from diverse sources within the network infrastructure.

[0081] Defines Key Performance Indicators (KPIs): Another critical function of the data aggregation module (212) is to identify relevant the KPIs. The KPIs are metrics used to evaluate the performance of network elements or services. The data aggregation module (212) may identify the KPIs based on the data it collects from various network vendors. Examples of the common KPIs may include handover rate per cell (overall frequency of handovers) and retained throughput post-handover (whether data transfer speed is maintained after switching technologies). By analyzing these KPIs, the system (108) may gather valuable insights into user behaviour and network performance, ultimately leading to better network optimization and user experience.

[0082] Extracts Data from Call Summary Log (CSL): The data aggregation module (212) may extract data fields from the CSL provided by various vendors. The CSL data typically contains summarized information about calls or sessions, which is crucial for performance monitoring and analysis.

[0083] Overall, the data aggregation module (212) plays a pivotal role in gathering, organizing, and preparing network data for further analysis and monitoring, ensuring that the KPIs are identified and utilized effectively.

[0084] In an embodiment, the analysis module (214) may be configured to analyze the aggregated data received from the data aggregation module (212) to obtain insights regarding various aspects of the IRAT handovers. The analysis module (214) may perform several functions to obtain insight, such as:

[0085] Lirst, the analysis module (214) may conduct user-level analysis of the received data to identify specific users (102) who experience a high count of the IRAT handovers. By analyzing the frequency and patterns of these handovers, the analysismodule (214) may pinpoints users who may be encountering performance issues. The analysis module (214) may further analyze the subsequent failures of these handovers due to Radio Frequency (RF) issues. By examining the causes and contexts of these failures, the analysis module (214) can highlight the users (102) most affected by network inefficiencies. Based on the insights gained, the analysis module (214) may enable preventive actions. For instance, network operators can adjust the RF parameters or provide targeted support to improve connectivity and reduce call drops for the specific users (102).

[0086] Second, the analysis module (214) may perform cell-level analysis to examine specific cells in the network (106) where the count of the IRAT handovers is significantly high. The analysis module (214) may assess the performance of the cells to understand why they might be triggering frequent handovers. By analyzing the RF thresholds at which the IRATs occur, the analysis module (214) may provide insights. For example, the analysis module (214) might identify that certain cells have suboptimal RF thresholds that cause unnecessary handovers, leading to performance degradation. The insights from cell analysis are used to optimize the network. Adjustments to the RF thresholds, power settings, or handover parameters can be made to minimize unnecessary handovers, thereby improving overall network efficiency and user experience.

[0087] Third, the analysis module (214) may perform geolocation-level analysis by utilizing the UE (104) geolocation data to map out regions with the high IRAT failures. The analysis module (214) may provide a spatial understanding of network performance issues by correlating handover failures with specific geographic locations. These geographical insights help in targeting specific areas for network improvements and optimizations. For example, regions identified with high failure rates might require infrastructure enhancements, such as additional base stations or improved antenna configurations. Network resources can be allocated more effectivelyby focusing on these problematic regions, ensuring that investment and optimization efforts yield the highest returns in terms of improved user experience and network reliability.

[0088] Overall, the analysis module (214) transforms aggregated session data into actionable insights. By focusing on the user-level, the cell-level, and the geolocation-level analyses, the analysis module (214) enables network operators to implement targeted optimizations.

[0089] In an embodiment, the optimizing module (216) may use the insights obtained from the analysis module (214) to optimize the network (106). The optimizing module (216) may translate data from the analysis module (214) to perform several functions to improve network performance and the user experience.

[0090] First, the optimizing module (216) may be responsible for network parameter adjustment. Using insights from the analysis module (214), the optimizing module (216) may adjust the RF thresholds and other critical network parameters. For example, if the analysis reveals that certain cells have suboptimal the RF thresholds causing frequent and unnecessary the IRAT handovers, the optimizing module (216) may be configured to recalibrate these thresholds to minimize these handovers. This adjustment helps to improve handover success rates and reduce service interruptions, leading to a more seamless and reliable user experience.

[0091] Second, the optimizing module (216) may focus on resource allocation. Based on the detailed insights from the analysis, the optimizing module (216) may optimize the distribution and utilization of network resources. This may involve ensuring network capacity and coverage align with user demand and traffic patterns. For instance, if specific regions or cells are identified as having high the IRAT handover counts and subsequent failures, the optimizing module (216) may reallocate resources, such as bandwidth and signal power, to these areas. This targeted allocationensures efficient handling of user traffic, reduces congestion, and enhances overall network performance.

[0092] Third, the optimizing module (216) may incorporate performance monitoring as a continuous process. After implementing the optimizations, the optimizing module (216) may consistently monitor network performance to ensure that the changes deliver the intended benefits. This involves KPIs and user feedback to detect any new issues or areas where further adjustments may be needed. Continuous monitoring allows for adaptive optimization, where the network can be fine-tuned in response to evolving user behaviour and environmental conditions. By maintaining a proactive approach to performance management, the optimizing module (216) ensures sustained improvements in network efficiency and user satisfaction.

[0093] The system (108) is implemented to enhance network performance by identifying attributes that impact the network, with a detailed emphasis on the IRAT handover failures. The system (108) includes the receiving module and the processing engine, both integral to data management and analysis.

[0094] In an embodiment a receiving module gathers data from the multiple UEs linked through various data sources associated with the different network vendors and the RATs. For example, the receiving module may collect the CSL data from a trace collection entity (TCE) servers operated by the network vendors. The logs may provide comprehensive insights into network activities and performance metrics.

[0095] In the present disclosure the processing engine (208) aggregates the collected data at both the user and cell levels for each of the RAT and the vendor. The processing engine (208) identifies and uses the KPIs and associated network parameters embedded within the CSL data. For example, the system may aggregate data related to dropped calls or signal strength variations specific to each KPI, compiling datasets for every RAT and the vendor combination.

[0096] In an embodiment, upon aggregation, the processing engine (208) undertakes detailed analyses to identify issues. The processing engine (208) may identify geographical regions experiencing high IRAT handover failure rates by examining the UE location parameters from the aggregated data. For instance, the analysis may reveal that a downtown area of a city consistently experiences high handover failure rates due to the dense UE activity. Within the dense UE activity regions, the processing engine (208) identifies specific cells contributing to the high failure rates using cell-level aggregated data.

[0097] In an embodiment, the analysis extends to individual UEs within dense UE activity, identifying those with high counts of the IRAT handover attempts and failures. For example, the processing engine (208) may find that a particular model of smartphone frequently attempts handovers still records a high failure rate. The system (108) may utilize identifiers such as the IMEEIMSI or TAC to determine the types of the UEs impacting performance adversely.

[0098] To enhance geographical analysis, the network coverage area is divided into a series of geographical grids of predefined size, each associated with the UEs operating in that section. For instance, the grid may represent a specific square kilometer in a metropolitan area, facilitating precise identification of trouble spots within the network.

[0099] The processing engine ensures that aggregated data may be stored at predefined levels, which include network technology levels, vendor-specific levels, or a combination thereof. The structured storage allows systematic data access and organization.

[0100] In an embodiment, the system (108) may generate comprehensive reports based on geographical analysis, detailing identified geographical regions, problematic cells, and specific UEs. The reports are transmitted to network operators,offering essential insights that enable them to implement targeted optimization strategies to enhance network performance and reduce the IRAT handover failures. For example, if a report indicates persistent handover issues in a business district, operators can prioritize infrastructure upgrades in that area to address the problem.

[0101] In the present disclosure, the system (108) provides a robust solution for diagnosing and addressing network performance challenges across the varied RATs and the vendor environments, ensuring improved connectivity and reliability.

[0102] In an embodiment, the processing engine (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine (208). In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine (208) may be processor-executable instructions stored on a non- transitory machine-readable storage medium and the hardware for the processing engine (208) 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 engine (208). In such examples, the system (108) may 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 system (108) and the processing resource. In other examples, the processing engine (208) may be implemented by electronic circuitry.

[0103] In an embodiment, the database (210) may include data (e.g., the data associated with the at least one NF including the configuration data, the operational status data, and the service data, the log file, etc.) that may be either stored or generatedas a result of functionalities implemented by any of the components of the processing engine (208).

[0104] FIG. 3 illustrates an exemplary implementation (300) of the system (108), in accordance with embodiments of the present disclosure. The system (108) comprises a master database (MDB) (306), the processing engine (208), a data lake (302), and a reporting server (RS) (304). In an aspect, the system (108) may also include a radio optimization team (310) and a radio planning team (308).

[0105] In an aspect, the MDB (306) may store information related to various network sites divided into a plurality of sectors. In an aspect, each sector is served by a plurality of antennas. In a further aspect, each antenna may have a number of ports (bands) that are configured to operate on different technology. In an example, the MDB (306) may store a set of network site identifiers (I.D.s), a set of antenna identifiers (I.D.s) corresponding to each sector, and other telecommunication network operatorspecific information stored in a specific nomenclature. In an aspect, the telecommunication network operator-specific nomenclature may include information such as geography name, geography site name, and geography cluster name of a specific eNodeB, etc. In an aspect, the MDB (306) may serve as a central repository for all data within the network. The MDB (306) may store data collected from various sources.

[0106] The MDB (306) may store details concerning the physical elements of the network, encompassing information about towers, fiber optic cables, equipment locations, and their respective specifications. The MDB (306) may store configuration settings and parameters associated with network equipment, comprising data on cell tower configurations, routing protocols, and service parameters crucial for network functionality. The MDB (306) may store historical records of network performance metrics, such as call quality, data throughput, signal strength, and service availability across various locations, are stored within the MDB (306). The MDB (306) maymaintain data pertaining to subscribers, including their geographical locations, service subscriptions, and usage patterns, aiding in the provision of tailored services and targeted network optimizations.

[0107] In an embodiment, the MDB (306) may be configured to communicate with the processing engine (208) over the network (106) The master database (MDB) interface may be referred to as a logical or physical interface that facilitates communication between the MDB (306) as the centralized repository of subscriber, configuration, or operational data and the processing engine (208). The MDB interface performs real-time analysis, decision-making, or service execution based on the data retrieved. The MDB interface may provide structured queries, event-driven data access, and bulk data retrieval using standardized or proprietary protocols (e.g., REST, gRPC, Diameter, or proprietary APIs). The MDB interface may enable communication between the Unified Data Repository (UDR) and network functions such as the Policy Control Function (PCF), Unified Data Management (UDM), or external analytics platforms and the like, provides consistent and synchronized data flow. The MDB interface may support operations such as data synchronization, caching, update notifications, and access control to maintain integrity and performance of the processing engine (208) outputs.

[0108] The network (106) may enable the user equipment (104) to communicate between devices and / or with the network (106). As such, the network (106) may enable the UEs (104) to communicate with other UEs (104) via a wired or wireless network. The network ( 106) may include a wireless card or another transceiver connection to facilitate this communication. In an exemplary embodiment, the network (106) may incorporate one or more of a plurality of standard or proprietary protocols including, but not limited to, Wi-Fi, Zigbee, or the like. In another embodiment, the network (106) may be implemented as, or include any of a variety of different communication technologies such as a wide area network (WAN), a local area network(LAN), a wireless network, a mobile network, a Virtual Private Network (VPN), the Internet, the Public Switched Telephone Network (PSTN), or the like.

[0109] In an embodiment, the MDB (306) may integrate data collected from the user (IMSI / IMEI) cell level, as handled by the data aggregation module (212). This may include comprehensive data sets aggregated from different network vendors and technologies, such as the 4G and the 5G. The MDB (306) ensures that all relevant data necessary for IRAT analysis and optimization is readily accessible to other modules within the system (108).

[0110] In an embodiment, the processing engine (208) may rely on the MDB (306) as a primary data source for improving network performance and user experience in a network (106). The processing engine (208) may execute the functionalities of the system (108). The processing engine (208) may fetch and process the aggregated data stored in the MDB (306), utilizing the computer-readable instructions stored in the memory (204). This includes performing the detailed IRAT analysis through algorithms and rules implemented within the analysis module (214). The processing engine (208) ensures efficient execution of tasks across the system (108), from data aggregation to optimization, by leveraging its computational capabilities and programming.

[0111] In an embodiment, the data lake (302) may act as a scalable repository for storing raw, unstructured data collected from various sources across the network (106). The data lake (302) supplements the MDB (306) by providing a flexible storage solution capable of handling large volumes of diverse data types, including logs, session data, and performance metrics. The data lake (302) may enable the system (108) to maintain a comprehensive archive of historical data, which is crucial for longitudinal analysis and trend identification related to IRAT handovers and network performance.

[0112] In an embodiment, a trace collection entity (TCE) interface may be provided for facilitating communication between the data lake (302) and the processing engine (208). The TCE interface is configured to ingest trace data from network elements into the data lake using protocols such as Kafka or REST and the like, and to send the data to the processing engine (208) for real-time or batch analysis. The TCE interface may support data normalization, metadata tagging, and streaming or querybased access, enabling functions such as anomaly detection, performance monitoring, and closed-loop automation in 5G or similar network environments.

[0113] In an embodiment, the RS (304) may propagate insights and analysis results derived from the processing engine (208). The RS (304) may generate comprehensive reports and visualizations that convey key findings related to IRAT handovers, RF thresholds, and network optimization efforts. These reports are instrumental in communicating actionable insights to stakeholders, such as network operators, radio optimization teams (310), and management, enabling informed decision-making and strategic planning.

[0114] In one embodiment, an reporting server (RS) interface may be provided between the processing engine (208) and the RS (304) to facilitate the transfer of processed analytics data. The RS interface may be configured to transmit data such as key performance indicators (KPIs), anomaly alerts, or policy outcomes using standardized protocols (e.g., REST or gRPC) and structured data formats (e.g., JSON or XML) and the like. For example, upon detecting a threshold-crossing event related to network latency in a specific slice, the processing engine (208) may transmit the event details via the RS interface to the RS (304), which generates a corresponding alert or report for operator review. The RS interface may support real-time or scheduled data transmission and includes metadata for timestamping, classification, and access control.

[0115] In an embodiment, the radio optimization team (310) may focus on optimizing radio communication networks, ensuring efficient spectrum use, minimizing interference, and maximizing coverage and capacity. The radio planning team (308) may plan the deployment of radio communication networks, including site selection, antenna placement, and frequency planning, to meet coverage and capacity requirements.

[0116] In an embodiment, the radio optimization team (310) may focus on implementing specific network optimizations based on identified IRAT handover issues and performance metrics. The radio optimization team (310) may adjust RF thresholds, allocate resources effectively, and optimize network parameters to enhance user experience and network efficiency. On the other hand, the radio planning team (308) may use the insights to strategize and plan future network expansions, upgrades, and deployments to accommodate growing demands and improve overall network performance.

[0117] FIG. 4 illustrates an exemplary process flow (400) for identifying one or more attributes impacting performance of a network.

[0118] At step (402), Initiate the procedure. The network performance and optimization of IRAT performance, a structured process is implemented, beginning with session data transfer.

[0119] At step (404), session data for the 4G and the 5G technologies is transferred from the TCE Server to the network management platform. This action centralizes all relevant data into one repository, forming the backbone for a comprehensive analysis that is may be important for identifying and addressing network performance issues.

[0120] At step (406), the aggregated data is processed at both the user and cell levels within the network management platform system. The data aggregation isimportant as it provides a granular view of performance metrics, enabling the identification of underlying trends and patterns that may affect network efficiency. By examining data at these levels, specific areas of concern are revealed, facilitating a more accurate diagnosis of network issues.

[0121] The IRAT analysis phase is carried out in a series of strategic steps. At step (408), the IRAT analysis step-(l), involves partitioning the entire network into grids, each associated with the specific UEs and their IRAT attempts and failures. The grid-based analysis is essential for mapping out geographic locations experiencing the high IRAT handover failure rates. Accordingly directing resources towards those areas that require focused troubleshooting efforts.

[0122] At step (410), the IRAT analysis step-(2), the process utilizes cell-level aggregated data to identify cells within these grids that are affecting the IRAT performance negatively. The step (410) may be important for isolating specific network components or areas that contribute to performance decline, enabling network operators to concentrate their efforts on the most impactful issues.

[0123] At step (412), the IRAT analysis step-(3) focuses on identifying specific users or handset models using the IMEI / TAC information that impact the overall network performance. By using the IMEI / TAC information, service providers may implement targeted interventions such as software updates, configuration adjustments, or direct communications with handset manufacturers.

[0124] At step (414), network optimization is achieved by applying the insights gathered from the preceding analyses. This phase may involve strategic adjustments, such as optimizing handover parameters, reallocating network resources, or implementing device-specific updates, all designed to optimize network performance.

[0125] The process concludes at step (416), after implementing data-driven changes, by thoroughly addressing network inefficiencies. The method may besupporting the claim of improved network performance through systematic analysis and targeted optimization actions.

[0126] FIG. 5 illustrates an exemplary flow diagram of a method (500) for identifying one or more attributes impacting performance of a network (i.e., the network (106), in accordance with an embodiment of the present disclosure. FIG. 5 is explained in conjunction with FIG. 1, FIG. 2, FIG. 3, and FIG. 4. Each step of the method (500) may be performed by various units (e.g., the data aggregation module (212), the analysis module (214), the optimizing module (216) and the other engine(s) (218) present within the processing engine (208) of the system (108).

[0127] In step (502), the method (500), includes the receiving module that collects data related to multiple the UEs. The UEs may interact with various data sources, which are linked to different network vendors and utilize multiple RATs. The step (502) may be significant for aggregating and synthesizing data from the diverse UEs, which may include information on network performance, user location, signal strength, and other metrics relevant to network management and optimization and the like. The diversity of vendors and the RATs may imply the method is designed to work with heterogeneous networks, supporting a range of technologies, for example, the LTE, the 5G, or Wi-Fi, and potentially integrating proprietary vendor technologies and the like. In an embodiment, the data comprises the CSL data corresponding to the one or more RATs. In an embodiment, the plurality of data sources comprises one or more trace TCE servers maintained by each network vendor of the one or more network vendors. For example, a telecom operator working with various vendors, may be utilizing distinct RATs such as the LTE and the 5G. LTE, known for its reliability, and the 5G, noted for its speed, represent a mix of technologies that require careful monitoring. May be each vendor employs the TCE servers to capture data such as CSL information. The CSL data may include detailed records of calls, such as duration, drop rates, and network stability metrics. The receiving module aggregates the CSL datamay be from the TCE servers, creating a comprehensive dataset that reflects the performance across different RATs and the vendors. The step (502) may form a complete picture of the network's operation, allowing for the identification of key performance attributes and disparities across different regions and technologies.

[0128] At step (504), the method (500) includes the aggregation of data by a processing engine (208). This aggregation occurs at both the user level and the cell level for each RAT and the network vendor. In one embodiment, the aggregation process includes identifying the KPIs and associated network parameters from the CSL data, which is received from multiple data sources affiliated with network vendors. The processing engine (208) aggregates the data related to each identified KPI, producing aggregated data for each RAT pertaining to each network vendor. In an embodiment, the method (500) includes storing the aggregated data at one or more predefined levels, which is managed by the processing engine (208). In an embodiment, the method (500) includes the predefined levels, at which the aggregated data may be stored, include the network technology level, the network vendor level, or a combination of these.

[0129] At step (506), the method (500) includes analyzing, by the processing engine (208), the aggregated data to identify one or more of a geographical region experiencing a high rate of Inter Radio Access Technology (IRAT) handover failure based on a UE location parameter retrieved from the aggregated data. One or more cells within the identified geographical region contributing to the high rate of IRAT handover failure based on the aggregated data at the cell level. At least one UE within the identified one or more cells having a high count of IRAT handover attempts and the high rate of IRAT handover failure, based on the aggregated data at the user level

[0130] At step (506), method (500) involves the processing engine (208) analyzing aggregated data to identify one or more of the geographical regions where a high rate of the IRAT handover failures occurs. The identification may rely on the UE location parameters. The method (500) also determines specific cells within theseidentified regions that contribute to the elevated failure rates, using data aggregated at the cell level. Additionally, it identifies at least one UE within these cells that has a high count of handover attempts and failures, based on user-level aggregated data. In one embodiment, method (500) includes identifying at least one UE by having the processing engine detect either the IMEI or the TAC associated with the UE. The detection helps determine the UE type responsible for affecting the IRAT handover performance.

[0131] In another embodiment, method (500) includes dividing the network coverage area into multiple geographical grids with predefined areas, accomplished by the processing engine (208). Each geographical grid may be linked with at least one UE, which aids in identifying the regions experiencing issues. In another embodiment, after identifying the geographical region, the contributing cells, and the UEs, method (500) also includes generating a report. The report, may be created by the processing engine (208), is based on the identified geographical regions, the cells, and the UEs. The report may be then transmitted by the processing engine (208) to network operators for network performance optimization.

[0132] FIG. 6 illustrates an exemplary computer system (600) in which or with which embodiments of the present disclosure may be implemented. As shown in FIG. 6, the computer system (600) may include an external storage device (610), a bus (620), a main memory (630), a read-only memory (640), a mass storage device (650), communication port(s) (660), and a processor (670). A person skilled in the art will appreciate that the computer system (600) may include more than one processor and communication ports. The processor (670) may include various modules associated with embodiments of the present disclosure. The communication port(s) (660) may be any of an RS-(232) port for use with a modem-based dialup connection, a (10) / (l 00) Ethernet port, a Gigabit or (10) Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port(s) (660) maybe chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (600) connects.

[0133] The main memory (630) may be Random- Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (640) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or Basic Input / Output System (BIOS) instructions for the processor (670). The mass storage device (650) may be any current or future mass storage solution, which can be used to store information and / or instructions. The mass storage device (650) includes, but is not limited to, Parallel Advanced Technology Attachment (PAT A) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and / or Firewire interfaces), one or more optical discs, a Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks.

[0134] The bus (620) communicatively couples the processor (670) with the other memory, storage, and communication blocks. The bus (620) may be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCLX) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (670) to the computer system (600).

[0135] Optionally, operator and administrative interfaces, e.g. a display, keyboard, joystick, and a cursor control device, may also be coupled to the bus (620) to support direct operator interaction with the computer system (600). Other operators and administrative interfaces can be provided through network connections connected through the communication port(s) (660). The components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (600) limit the scope of the present disclosure.

[0136] In another exemplary embodiment, the present disclosure discloses the exemplary computer system (600) is configured to execute a computer program product comprising a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors (202), cause the one or more processors (202) to perform the method (500) for identifying one or more attributes impacting performance of a network is disclosed. The method (500) includes receiving data corresponding to a plurality of the UEs (104) from a plurality of data sources associated with one or more network vendors and one or more RATs. The method (500) includes aggregating the received data at a user level and a cell level for each of the RATs and the one or more network vendors. The method (500) includes analyzing the aggregated data to identify one or more of a geographical region experiencing a high rate of the IRAT handover failure based on the UE location parameter retrieved from the aggregated data. The method (500) includes one or more cells within the identified geographical region contributing to the high rate of the IRAT handover failure based on the aggregated data at the cell level. The method (500) includes at least one UE within the identified one or more cells having a high count of the IRAT handover attempts and the high rate of the IRAT handover failure, based on the aggregated data at the user level.

[0137] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

[0138] The method and system of the present disclosure may be implemented in a number of ways. For example, the methods and systems of the present disclosuremay be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0139] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be implemented merely as illustrative of the disclosure and not as a limitation.

[0140] The present disclosure offers significant technical advancements for identifying one or more attributes impacting performance of the network, network performance and improving the user experience in a network. The advancement addresses the limitations of existing solutions including problems of dynamic nature of multi-generation networks, and lack of capabilities to analyze and integrate data across different technologies, resulting in inefficient resource utilization and degraded user experience. The disclosure involves managing the multi-generation networks by aggregating and analysing the session data at user level and obtaining insights for one or more parameters. The insights may be used for tuning the network to perform better.

[0141] The present disclosure discloses the system and the method for improving the networks by tackling the IRAT handovers. The present disclosureleverages automation for continuous data collection and scalable processing, analyzing user behavior and cell performance for the 4G and the 5G. By utilizing user geolocation data (if available), the system identifies problematic regions and suggests data-driven optimization strategies like dynamic resource allocation. The present disclosure focuses on the user experience and the network efficiency and offers the significant technical advancement in managing the multi-generation cellular networks.ADVANTAGES OF THE PRESENT DISCLOSURE

[0142] The present disclosure provides a method and a for identifying one or more attributes impacting performance of a network.

[0143] The present disclosure provides a system and a method for improving network performance and user experience in a network.

[0144] The present disclosure provides the system and the method that effectively addressing local network challenges and improves service reliability.

[0145] The present disclosure provides the system and the method that reduces unnecessary handovers and optimizes network efficiency.

[0146] The present disclosure provides the system and the method that enhances user experience. The users may benefit from reduced call drops, improved data transfer rates, and overall better service quality.

[0147] The present disclosure provides the system and the method that enables informed decision-making and proactive network management.

Claims

CLAIMS1. A method (500) for identifying one or more attributes impacting performance of a network (106), the method (500) comprising: receiving, by a receiving module, data corresponding to a plurality of User Equipments (UEs) (104) from a plurality of data sources associated with one or more network vendors and one or more Radio Access Technologies (RATs); aggregating, by a processing engine (208), the received data at a user level and a cell level for each of the one or more RATs and the one or more network vendors; and analyzing, by the processing engine (208), the aggregated data to identify one or more of: a geographical region experiencing a high rate of Inter Radio Access Technology (IRAT) handover failure based on a UE (104) location parameter retrieved from the aggregated data; one or more cells within the identified geographical region contributing to the high rate of IRAT handover failure based on the aggregated data at the cell level; and at least one UE (104) within the identified one or more cells having a high count of IRAT handover attempts and the high rate of IRAT handover failure, based on the aggregated data at the user level.

2. The method (500) as claimed in claim 1, wherein the data comprises call summary log (CSL) data corresponding to the one or more RATs.

3. The method (500) as claimed in claim 1, wherein the plurality of data sources comprises one or more trace collection entity (TCE) servers maintained by each network vendor of the one or more network vendors.

4. The method (500) as claimed in claim 1, wherein the aggregating comprises: identifying, by the processing engine (208), one or more key performance indicators (KPIs) and associated network parameters in the CSL data received from the plurality of data sources associated with the one or more network vendors; and aggregating, by the processing engine (208), the received data corresponding to each identified KPI to obtain aggregated data for each of the one or more RATs associated with each of the one or more network vendors.

5. The method (500) as claimed in claim 4, further comprising: storing, by the processing engine (208), the aggregated data at one or more predefined levels.

6. The method (500) as claimed in claim 5 wherein the one or more predefined levels comprise at least one of a network technology level, a network vendor level, and a combination thereof.

7. The method (500) as claimed in claim 1, wherein identifying the at least one UE (104) comprises: detecting, by the processing engine (208), at least one of an International Mobile Equipment Identity (IMEI) or a Type Allocation Code (TAC) associated with the at least one UE (104) to determine a UE (104) type responsible for impacting the performance of the IRAT handover.

8. The method (500) as claimed in claim 1, wherein identifying the geographical region comprises:dividing, by the processing engine (208), a network coverage area into a plurality of geographical grids having a predefined area, wherein each geographical grid is associated with the at least one UE (104).

9. The method (500) as claimed in claim 1, wherein upon identifying the geographical region, the one or more cells, and the at least one UE (104), comprising: generating, by the processing engine (208), at least one report based on the identified geographical region, the one or more cells, and the at least one UE (104); and transmitting, by the processing engine (208), the at least one report to one or more network operators to optimize the performance of the network.

10. A system (108) for identifying one or more attributes impacting performance of a network, the system comprising: a receiving module configured to receive data corresponding to a plurality of User Equipments (UEs) (104) from a plurality of data sources associated with one or more network vendors and one or more Radio Access Technologies (RATs); and a processing engine (208) configured to: aggregate the received data at a user level and a cell level for each of the one or more RATs and the one or more network vendors; and analyze the aggregated data to identify one or more of: a geographical region experiencing a high rate of Inter Radio Access Technology (IRAT) handover failure based on a UE (104) location parameter retrieved from the aggregated data;one or more cells within the identified geographical region contributing to the high rate of IRAT handover failure based on the aggregated data at the cell level; and at least one UE (104) within the identified one or more cells having a high count of IRAT handover attempts and the high rate of IRAT handover failure, based on the aggregated data at the user level.

11. The system (108) as claimed in claim 10, wherein the data comprises call summary log (CSL) data corresponding to the one or more RATs.

12. The system (108) as claimed in claim 10, wherein the plurality of data sources comprises one or more trace collection entity (TCE) servers maintained by each network vendor of the one or more network vendors.

13. The system (108) as claimed in claim 10, wherein to aggregate the received data, the processing engine (208) is configured to: identify one or more key performance indicators (KPIs) and associated network parameters in the CSL data received from the plurality of data sources associated with the one or more network vendors; and aggregate the received data corresponding to each identified KPI to obtain aggregated data for each of the one or more RATs associated with each of the one or more network vendors.

14. The system (108) as claimed in claim 13, wherein the processing engine (208) is further configured to: store the aggregated data at one or more predefined levels.

15. The system (108) as claimed in claim 14, wherein the one or more predefined levels comprise at least one of a network technology level, a network vendor level, and a combination thereof.

16. The system (108) as claimed in claim 10, wherein to identify the at least one UE (104), the processing engine (208) is configured to: detect at least one of an International Mobile Equipment Identity (IMEI) or a Type Allocation Code (TAC) associated with the at least one UE (104) to determine a UE (104) type responsible for impacting the performance of the IRAT handover.

17. The system (108) as claimed in claim 10, wherein to identify the geographical region, the processing engine (208) is configured to: divide a network coverage area into a plurality of geographical grids having a predefined area, wherein each geographical grid is associated with the at least one UE (104).

18. The system (108) as claimed in claim 10, wherein upon identifying the geographical region, the one or more cells, and the at least one UE (104), the processing engine (208) is configured to: generate at least one report based on the identified geographical region, the one or more cells, and the at least one UE (104); and transmit the at least one report to one or more network operators to optimize the performance of the network.

19. A User Equipment (UE) (104) communicatively coupled with a network, the coupling comprises steps of: sending, by the UE (104), a connection request to the network;receiving, by the UE (104), an acknowledgment of the connection request from the network; and transmitting, by the UE (104), a plurality of signals in response to the acknowledgement, wherein based on the plurality of signals, identification of one or more attributes impacting performance of the network is performed by a method (500) as claimed in claim 1.

20. A computer program product comprising a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors (202), cause the one or more processors (202) to: receive data corresponding to a plurality of User Equipments (UEs) (104) from a plurality of data sources associated with one or more network vendors and one or more Radio Access Technologies (RATs); aggregate the received data at a user level and a cell level for each of the RATs and the one or more network vendors; and analyze the aggregated data to identify one or more of: a geographical region experiencing a high rate of Inter Radio Access Technology (IRAT) handover failure based on a UE (104) location parameter retrieved from the aggregated data; one or more cells within the identified geographical region contributing to the high rate of IRAT handover failure based on the aggregated data at the cell level; and at least one UE (104) within the identified one or more cells having a high count of IRAT handover attempts and the high rate of IRAT handover failure, based on the aggregated data at the user level.

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