System and method for site visualization and analysis using interface

Abstract

A system (102) and method (500) for site visualization and analysis using an interface is disclosed. The system is configured to determine a user type based on authentication information and detect a current location of a user equipment (UE) (108). Data corresponding to a plurality of key performance indicators (KPIs) of a site is obtained from a speed test server (112). Site visualization data and coverage data of the detected current location is fetched based on obtained data associated with the plurality of operators. The fetched site visualization data and the fetched coverage data are processed and plotted on a map. The system (102) is configured to generate recommendations based on the analysis of the site and the user type.

Description

SYSTEM AND METHOD FOR SITE VISUALIZATION AND ANALYSIS USING INTERFACERESERVATION 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 (JPL) 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.FIELD OF THE DISCLOSURE

[0002] The present disclosure relates generally to the field of communication systems. More particularly, the present disclosure relates to systems and methods for performing site visualization and analysis using an interface.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] RAN (Radio Access Network) refers to the part of a mobile telecommunications system that connects user equipment to the core network through radio connections.

[0005] RAN node configuration data refers to the configuration settings and parameters of network nodes within the radio access network, including a plurality of parameter values.

[0006] User Equipment (UE) refers to any device used directly by an enduser to communicate, including smartphones, tablets, or other mobile devices capable of connecting to the radio access network.

[0007] Reference Signal Received Power (RSRP) refers to a measurement of the power of cell-specific reference signals spread over the full bandwidth and narrowband, used by user equipment to determine cell selection and handover candidates.

[0008] Reference Signal Received Quality (RSRQ) refers to a ratio of the RSRP to the total received power, including interference and noise, providing a quality measurement of the received reference signal.

[0009] Signal-to-Interference-plus-Noise Ratio (SINR) refers to a measurement that compares the strength of a desired signal to the combined strength of interference from other signals and background noise, used to estimate the quality of a wireless connection.

[0010] Elastic Load Balancer (ELB) refers to a service that automatically distributes incoming application traffic across multiple targets, such as servers, to ensure optimal use of resources and maintain application availability.

[0011] Performance Management (PM) KPI refers to Key Performance Indicators used to measure and evaluate the performance of network elements and services, providing quantitative data on various aspects of network performance such as throughput, latency, and error rates.

[0012] Fault Management (FM) alarms refer to notifications or alerts generated by network elements or management systems to indicate abnormal conditions, failures, or potential issues in the network, helping operators quickly identify and respond to network problems.

[0013] Configuration Management (CM) data refers to information related to the settings, parameters, and operational characteristics of network elements,including hardware and software configurations, version information, and other attributes that define how network components are set up and operate.

[0014] Backend API calls refer to requests made to the server-side interfaces of the system, allowing the application to interact with databases, services, and other backend components to retrieve or manipulate data.

[0015] NV Coverage data refers to Network Virtual Coverage data, which represents the predicted or measured signal coverage of network services across geographical areas, often used to generate coverage maps and analyze network performance.

[0016] Heartbeat check refers to a periodic signal or message sent between system components to verify that they are operational and responsive, used to ensure continuous availability and proper functioning of the speed test infrastructure.

[0017] Framework server refers to a specialized server component within the system architecture responsible for managing and orchestrating specific tasks related to data processing, analysis, or visualization.BACKGROUND OF THE DISCLOSURE

[0018] The following description of the 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.

[0019] Wireless communication technology has undergone rapid evolution over the past few decades. From the first generation's analog voice services to the current fifth-generation (5G) technology, each advancement has brought significant improvements in speed, capacity, and functionality. The 5G technology promiseseven faster data speeds, lower latency, and the ability to connect multiple devices simultaneously, opening up new possibilities for various applications and services.

[0020] As wireless technologies advance, radio access networks (RANs) play a crucial role in connecting user equipment to core networks. RANs typically consist of radio base stations with large antennas that wirelessly connect user devices to the core network infrastructure. With the advent of 5G and increasing user demands, RANs are becoming increasingly complex, featuring higher speeds, more interconnected units, and the integration of various sub-networks into larger ones.

[0021] The evolution of wireless technologies has also led to changes in user behavior and expectations. Users now demand the ability to send different types of data simultaneously, including text, voice, video, and multimedia files. There is a growing demand for fast and reliable internet, especially for activities like gaming, audio, and video streaming on mobile devices. The users expect better network quality to minimize delays and ensure successful voice calls, leading to a heightened interest in real-time monitoring and optimization of RAN performance.

[0022] However, as networks become more complex, ensuring optimal performance becomes increasingly challenging. Network operators face significant difficulties in testing, troubleshooting, and identifying the causes of performance issues in both new and existing communication networks. Traditional techniques for monitoring and optimizing RAN performance often fail to accurately detect the root causes of performance degradation. These conventional methods typically require substantial manual effort from telecom operators, making them inefficient and time-consuming.

[0023] Moreover, the dynamic nature of modern networks, with frequent configuration changes and updates, adds another layer of complexity to performance management. Existing systems often struggle to keep track of these changes and their impact on network performance in real-time. This can lead todelays in identifying and resolving issues, potentially resulting in poor user experiences and increased customer complaints.

[0024] A significant challenge faced by network operators and field engineers is the lack of readily accessible, comprehensive site visualization and performance data on portable devices. Traditional methods often require engineers to carry multiple specialized devices or return to centralized control rooms to access critical information such as performance management (PM) KPIs and fault management (FM) alarms. This approach not only hampers the efficiency of field operations but also delays real-time decision-making and problem resolution. The inability to perform instant, on-site analysis using a single, portable device leads to increased response times, higher operational costs, and potentially prolonged service disruptions. Furthermore, end-users lack a user-friendly means to understand and compare network performance across different operators in their specific locations, hindering their ability to make informed decisions about service providers.

[0025] Conventional systems and methods face difficulty in efficiently monitoring, analyzing, and optimizing RAN performance, particularly in the context of rapidly evolving network configurations. There is, therefore, a need in the art to provide a method and a system that can overcome the shortcomings of the existing prior arts by offering real-time monitoring, automated analysis of configuration changes, and intelligent optimization of RAN performance.SUMMARY OF THE DISCLOSURE

[0026] In an exemplary embodiment, a system for visualization of a site using an interface is described. The system comprises a memory and one or more processors configured to execute a set of instructions stored in the memory. The instructions are for determining, by a User Identification Module, a user type based on authentication information received from a user equipment (UE). The instructions are for detecting, by a Location Detection Module, a current locationof the UE. The instructions are for obtaining, by a Data Request Module, data corresponding to a plurality of key performance indicators (KPIs) of a site and a plurality of operators on based on the detected current location from a speed test server. The instructions are for fetching, by the Data Request Module, site visualization data and coverage data of the plurality of operators at the detected current location based on the obtained data. The instructions are for processing and populating, by a Data Processing Module, the fetched site visualization data and the fetched coverage data on the UE. The instructions are for plotting, by the Map Visualization Module, the processed site visualization data and the processed coverage data on a map around the detected current location. The instructions are for analyzing, by an Analysis Module, the site based on the user input and the plotted site visualization data and coverage data. The instructions are for generating, by a Decision Support Module, a recommendation based on the analysis of the site and the determined user type.

[0027] In some embodiments, the one or more processors are further configured for providing, by the User Interaction Module, different levels of access to the plotted site visualization data and the plotted coverage data based on the determined user type. The user type is at least one of a field engineer or an end customer.

[0028] In some embodiments, the one or more processors are further configured for continuously updating, by a Real-time Update Module, the fetched site visualization data and the fetched coverage data in real-time based on changes in the detected current location of the UE.

[0029] In some embodiments, the one or more processors are further configured for aggregating, by a Data Aggregation Module, periodic data from active and passive web page testing on the speed test server. The one or more processors are further configured for processing, by the Data Aggregation Module, the aggregated periodic data. The one or more processors are further configured forstoring, by the Data Aggregation Module, the processed aggregated periodic data in a backend database for retrieval by the Data Request Module.

[0030] In some embodiments, the one or more processors are further configured for performing, by a Speed Test Module, a speed test for the detected current location of the UE. The one or more processors are further configured for incorporating, by the Speed Test Module, results of the performed speed test into the analysis of the site. The one or more processors are further configured for updating, by the Speed Test Module, the plotted coverage data based on the incorporated speed test results.

[0031] In some embodiments, the one or more processors are further configured for generating, by an Alert Module, an alert when the analysis of the site indicates a performance issue or an alarm condition at the site. The one or more processors are further configured for transmitting, by the Alert Module, the generated alert to the UE.

[0032] In another exemplary embodiment, a method for visualization of a site using an interface is described. The method comprises determining, by a User Identification Module, a user type based on authentication information received from a user equipment (UE). The method comprises detecting, by a Location Detection Module, a current location of the UE. The method comprises obtaining, by a Data Request Module, data corresponding to a plurality of key performance indicators (KPIs) of a site and a plurality of operators based on the detected current location from a speed test server. The method comprises fetching, by the Data Request Module, site visualization data and coverage data the plurality of operators at of the detected current location based on the obtained data. The method comprises processing and populating, by a Data Processing Module, the fetched site visualization data and the fetched coverage data on the UE. The method comprises plotting, by the Map Visualization Module, the processed site visualization data and the processed coverage data on a map based on the detected current location. The method comprises analyzing, by an Analysis Module, the site based on a user inputand the plotted site visualization data and coverage data. The method comprises generating, by a Decision Support Module, a recommendation based on the analysis of the site and the determined user type.

[0033] In some embodiments, the method further comprises providing, by a User Interaction Module, different levels of access to the plotted site visualization data and the plotted coverage data based on the determined user type. The user type is at least one of a field engineer or an end customer.

[0034] In some embodiments, the method further comprises continuously updating, by a Real-time Update Module, the fetched site visualization data and the fetched coverage data in real-time based on changes in the detected current location of the UE.

[0035] In some embodiments, the method further comprises aggregating, by a Data Aggregation Module, periodic data from active and passive web page testing on the speed test server. The method further comprises processing, by the Data Aggregation Module, the aggregated periodic data. The method further comprises storing, by the Data Aggregation Module, the processed aggregated periodic data in a backend database for retrieval by the Data Request Module.

[0036] In some embodiments, the method further comprises performing, by a Speed Test Module, a speed test for the detected current location of the UE. The method further comprises incorporating, by the Speed Test Module, results of the performed speed test into the analysis of the site. The method further comprises updating, by the Speed Test Module, the plotted coverage data based on the incorporated speed test results.

[0037] In some embodiments, the method further comprises generating, by an Alert Module, an alert when the analysis of the site indicates a performance issue or an alarm condition at the site. The method further comprises transmitting, by the Alert Module, the generated alert to the UE.

[0038] In yet another exemplary embodiment, a User Equipment (UE) for facilitating visualization of a site is described. The UE is configured for generating a request for site visualization and analysis based on user inputs. The UE is configured for transmitting the generated request to a system. The UE is configured for receiving site visualization data and coverage data from the system. The UE is configured for displaying the received site visualization data and coverage data on a map. The UE is configured for receiving user input associated with the displayed site visualization data and coverage data. The UE is configured for transmitting the received user input to the system. The UE is configured for receiving a recommendation from the system based on the transmitted user input. The system is configured to perform steps as stated above.

[0039] In yet another exemplary embodiment, the present disclosure discloses a computer program product comprising a non-transitory computer- readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform a method for visualization of a site using an interface is described. The method comprises determining, by a User Identification Module, a user type based on authentication information received from a user equipment (UE). The method comprises detecting, by a Location Detection Module, a current location of the UE. The method comprises obtaining, by a Data Request Module, data corresponding to a plurality of key performance indicators (KPIs) of a site and a plurality of operators based on the detected current location from a speed test server. The method comprises fetching, by the Data Request Module, site visualization data and coverage data the plurality of operators at of the detected current location based on the obtained data. The method comprises processing and populating, by a Data Processing Module, the fetched site visualization data and the fetched coverage data on the UE. The method comprises plotting, by the Map Visualization Module, the processed site visualization data and the processed coverage data on a map based on the detected current location. The method comprises analyzing, by an Analysis Module, the site based on a user input and the plotted site visualization data and coverage data. Themethod comprises generating, by a Decision Support Module, a recommendation based on the analysis of the site and the determined user type.

[0040] The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of the present disclosure, and are not restrictive.OBJECTS OF THE DISCLOSURE

[0041] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.

[0042] An object of the present disclosure is to provide a system and a method for performing visualization and analysis of a site using an interface (e.g., application / application programming interface (API)) on a User Equipment (UE), thereby enabling real-time monitoring and optimization of network performance.

[0043] An object of the present disclosure is to provide a system and a method that determines user type and customizes access levels, thereby enhancing user experience for both field engineers and end customers.

[0044] An object of the present disclosure is to provide a system and a method that detects the current location of the UE and fetches relevant site data, thereby ensuring accurate and location-specific network analysis.

[0045] An object of the present disclosure is to provide a system and a method that monitors and obtains key performance indicators (KPIs) of a site and multiple operators, thereby enabling comprehensive network performance evaluation.

[0046] An object of the present disclosure is to provide a system and a method that processes and visualizes site data on a map, thereby facilitating intuitive understanding of network coverage and performance.

[0047] An object of the present disclosure is to provide a system and a method that analyzes site data and generates recommendations, thereby supporting informed decision-making for network optimization.

[0048] An object of the present disclosure is to provide a system and a method that enables field engineers to access current performance data and historical performance trends, thereby facilitating efficient troubleshooting and issue resolution.

[0049] An object of the present disclosure is to provide a system and a method that allows end customers to view signal strength and network type information, thereby empowering them to make informed choices about network operators.

[0050] An object of the present disclosure is to provide a system and a method that continuously updates site visualization and coverage data in real-time, thereby ensuring up-to-date network information for users.

[0051] An object of the present disclosure is to provide a system and a method that performs speed tests and incorporates results into site analysis, thereby providing a comprehensive view of network performance.

[0052] An object of the present disclosure is to provide a system and a method that generates alerts for performance issues or alarm conditions, thereby enabling proactive network management.

[0053] An object of the present disclosure is to provide a system and a method that enables field engineers to perform site inspections directly on their mobile devices, thereby saving time and reducing inspection costs.

[0054] An object of the present disclosure is to provide a system and a method that compares coverage and performance data of multiple operators, thereby assisting end customers in selecting the most suitable network operator.

[0055] Other objects 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.BRIEF DESCRIPTION OF DRAWINGS

[0056] The accompanying drawings, which are incorporated herein, and constitute a part of the present 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 the disclosure of electrical components, electronic components or circuitry commonly used to implement such components.

[0057] FIG. 1 illustrates an exemplary network architecture of a system for performing visualization and analysis of a site using an interface, in accordance with embodiments of the present disclosure.

[0058] FIG. 2 illustrates an exemplary micro service-based architecture of the system for performing visualization and analysis of the site using the interface, in accordance with embodiments of the present disclosure.

[0059] FIG. 3 illustrates an exemplary flow diagram of a method for visualizing and analyzing the site using the interface, in accordance with embodiments of the present disclosure.

[0060] FIG. 4 illustrates an exemplary flow diagram of a speed test process, integrated within the system for performing visualization and analysis of the site, in accordance with embodiments of the present disclosure.

[0061] FIG. 5 illustrates an exemplary flow diagram of a method for visualization of the site using the interface, in accordance with embodiments of the present disclosure.

[0062] FIG. 6 illustrates an exemplary block diagram of a computer system in which or with which embodiments of the present disclosure may be implemented.

[0063] The foregoing shall be more apparent from the following more detailed description of the disclosure.LIST OF REFERENCE NUMERALS100 - Network architecture102 - System108-1, 108-2... 108-N - User equipment110-1, 110-2...110-N - Users112 - Speed test server114 - Open-source framework server202 - One or more Processor(s)204 - Memory206 - Interfaces208 - Processing engine210 - Database212 - Location detection module214 - Data request module216 - Data processing module218 - Map visualization module220 - User interaction module222 - Analysis module224 - Decision support module226 - User identification module228 - Real-time Update module230 - Data aggregation module232 - Speed test module234 - Alert module236 - Other module(s)300 - Flow diagram302 to 316 - steps of flow diagram 300400 - Flow diagram402 - Application404 - Load balancer406 - Open-Source Frameworks412 - Distributed event streaming platform414 - Distributed file system (DFS)422 to 438 - steps of flow diagram 400500 - Method502 to 516 - steps of Method 500600 - Computer system610 - External storage device620 - Bus630 - Main memory640 - Read-only memory650 - Mass storage device660 - Communication port(s)670 - ProcessorDETAILED DESCRIPTION OF THE DISCLOSURE

[0064] 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 oneanother or with any combination of other features. An individual feature may not address all 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.

[0065] 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.

[0066] 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.

[0067] 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. 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.

[0068] 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.

[0069] 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.

[0070] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly 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 and all combinations of one or more of the associated listed items.

[0071] The number of communication devices using wireless networks is increasing rapidly, placing a growing burden on these networks and affecting their performance. Traditionally, field engineers had to physically visit affected sites with specialized equipment to diagnose and address performance issues. This approach is time-consuming, costly, and often impractical, especially when dealing with numerous locations across a wide area. Consequently, there is a pressing need for a solution that allows both field engineers and consumers to assess network performance factors without the necessity of on-site visits.

[0072] To address this challenge, the present disclosure introduces a system and method for visualization of a site using an interface (e.g., an application installed on a User Equipment (UE)). This innovative approach leverages the capabilities of modern smartphones and tablets to perform comprehensive network analysis and optimization tasks.

[0073] The system described in the present disclosure enables users, whether they are field engineers or end customers, to analyze the status of a currently connected site at their current location. It provides access to historical performance data, allowing users to check the last N days' performance of a site. The application facilitates performance monitoring (PM), retrieves key performance indicators (KPIs), and enables checking of fault management (FM) alarms and configuration management (CM) data, all without the need to log into a separate system.

[0074] The field engineers can directly assess site performance through their mobile devices, significantly reducing the time and cost associated with site inspections. The system provides current performance data, troubleshooting recommendations, and real-time updates, enhancing the efficiency of network management and issue resolution.

[0075] On the other hand, the consumer users get the ability to compare coverage and performance data of multiple operators at their current location. Thisempowers users to make informed decisions about selecting the best network operator based on actual signal strength and performance metrics in their area.

[0076] By determining the user type and customizing the information and functionality accordingly, the system caters to the specific needs of both technical professionals and general consumers. It incorporates features such as real-time data updates, speed testing, and alert generation for performance issues, providing a comprehensive tool for network analysis and optimization.

[0077] In essence, the present disclosure aims to revolutionize the way network performance is monitored, analyzed, and optimized, offering a versatile and user-friendly solution that addresses the evolving needs of modern wireless communication networks.

[0078] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGS. 1-6.

[0079] FIG. 1 illustrates a network architecture (100) of a system (102) for performing visualization and analysis of a site using an interface, in accordance with embodiments of the present disclosure.

[0080] In an embodiment, the system (102) may be configured to implement an Operation Support Systems / Business Support Systems (OSS / BSS) service. The system (102) is connected to a network (104), which is further connected to at least one computing devices 108-1, 108-2, ... 108-N (collectively referred as computing device 108, herein) associated with one or more users 110- 1, 110-2, ... 110-N (collectively referred as user (110), herein). The computing device (108) may be personal computers, laptops, tablets, wristwatch, or any custom-built computing device integrated within a modern diagnostic machine that can connect to a network as an loT (Internet of Things) device. In an embodiment, the computing device (108) may also be referred to as User Equipment (UE) or user device. Accordingly, the terms “computing device” and “User Equipment” may be used interchangeably throughout the disclosure. In an aspect, the user (110) is anetwork operator or a field engineer or an end customer. Further, the network (104) can be configured with a centralized server that stores and processes data related to site performance and network operations. The network (104) may include various types of wireless communication networks, including but not limited to cellular networks (2G, 3G, 4G, 5G), Wi-Fi networks, and other radio access networks (RANs).

[0081] In an embodiment, the system (102) may receive at least one input data from the user (110) via the at least one computing devices (108). In an aspect, the user (110) may be configured to initiate the process of authentication and authorization of subscribers at edge of the network (104), using an interface (e.g., application interface of a mobile application installed in the computing devices (108)). The mobile application may be configured to communicate with the network analysis server. In some examples, the mobile application may be a software or a mobile application from an application distribution platform. Examples of application distribution platforms include the App Store for iOS provided by Apple, Inc., Play Store for Android OS provided by Google Inc., and such application distribution platforms. In an embodiment, the computing device (108) may transmit the at least one captured data packet over a point-to-point or point-to-multipoint communication channel or network (104) to the system (102). In an embodiment, the computing device (108) may involve collection, analysis, and sharing of data received from the system (102) via the network (104). Furthermore, the system (102) may be connected to at least one speed test server (112) and at least one open- source framework server (114) via the network (104).

[0082] In an exemplary embodiment, the network (104) may include, but not be limited to, at least a 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. In an exemplary embodiment, the network 104 may include, but not be limited to, a wireless network, a wired network, an internet, an intranet, a public network, a privatenetwork, 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.

[0083] 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).

[0084] FIG. 2 with reference to FIG. 1, illustrates an exemplary micro- service-based architecture (200) of the system (102) for performing visualization and analysis of the site using the interface, in accordance with an embodiment of the present disclosure.

[0085] The system (102) includes one or more processor(s) (202), a memory (204), a processing engine (208), a database (210), and an interface(s) (206). In an exemplary embodiment, the processing engine (208) may include one or more modules / engines selected from any of a Location Detection Module (212), a Data Request Module (214), a Data Processing Module (216), a Map Visualization Module (218), a User Interaction Module (220), an Analysis Module (222), a Decision Support Module (224), a User Identification Module (226), a Real-time Update Module (228), a Data Aggregation Module (230), a Speed Test Module (232), an Alert Module (234), and other module(s) (236) having functions that may include but are not limited to receiving data, processing data, testing, storage, and peripheral functions, such as wireless communication unit for remote operation, audio unit for alerts and the like.

[0086] The one or more processor(s) (202) is configured to initiate the processor of authentication and authorization of subscribers at edge of the network using the interface (e.g., the application interface of the User Equipment (UE) (108)). In an embodiment, the application interface is configured to transmit one or more instructions to the one or more processor(s) (202).

[0087] In an embodiment, the one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog 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 the memory (204) of the system (102). 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 comprise any non-transitory storage device including, for example, volatile memory such as Random Access Memory (RAM), or non-volatile memory such as Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.

[0088] The interface(s) (206) is included within the system (102) to serve as a medium for data exchange, configured to facilitates user interaction with the mobile application. The interface(s) (206) may be composed of interfaces for data input and output devices, storage devices, and the like, providing a communication pathway for the various components of the system (102).

[0089] The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as RO devices, storage devices, and the like. The interface(s) (206) may facilitate communication to / from the system (102). The interface(s) (206) may also provide a communication pathway for one or more components of the system (102). Examples of suchcomponents include but are not limited to, the processing unit / engine(s) (208) and the database (210).

[0090] In an embodiment, the processing unit / engine(s) (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(s) (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processorexecutable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (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(s) (208). In such examples, the system (102) 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 (102) and the processing resource. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry.

[0091] In an embodiment, the database (210) is configured for serving as a centralized repository for storing and retrieving various operational data. The database (210) is designed to interact seamlessly with other components of the system (102) to support the system's functionality effectively. The database (210) may store data that may be either stored or generated as a result of functionalities implemented by any of the components of the one or more processor(s) (202) or the processing engines (208). In an embodiment, the database (210) may be separate from the system (102).

[0092] In an embodiment, the User Identification Module (226) may serve as the initial point of interaction for the system (102), determining whether the useris a field engineer or an end customer. This crucial differentiation may be based on authentication information received from the User Equipment (UE) (108). Authentication information may include, but is not limited to, username and password combinations, digital certificates, biometric data, or multi-factor authentication methods. For example, a field engineer might log in with special credentials, such as a company-issued ID and a temporary passcode generated by a secure token device, which would grant access to advanced features of the application. These advanced features might include detailed network diagnostics, equipment configuration options, and real-time performance metrics of network elements. On the other hand, an end customer might use a standard login process, perhaps utilizing their email address and a chosen password, or even access certain basic functions of the app without any authentication. These basic functions could include viewing general coverage maps or conducting simple speed tests. In some cases, the system might also employ role-based access control (RBAC), where different levels of access are granted based on predefined user roles within the organization.

[0093] Following the establishment of the user type, the Location Detection Module (212) may be tasked with accurately pinpointing the current location of the UE (108). The Location Detection Module (212) may employ various location determination technologies depending on the environment and the required level of precision. For outdoor locations, it could primarily rely on the Global Positioning System (GPS), a satellite-based navigation system that provides location and time information in all weather conditions. GPS typically offers accuracy within a few meters, which is crucial for field engineers working on specific network sites. For instance, if a field engineer is inspecting a cell tower in a rural area, the system (102) might use GPS to provide precise latitude and longitude coordinates, allowing the engineer to locate exact equipment positions or optimize antenna alignments. In contrast, for indoor environments or urban areas with poor GPS signals, the module might utilize Wi-Fi triangulation. This method determines location by measuring the signal strength of nearby Wi-Fi access points with known locations.

[0094] For example, an end customer checking their home internet speeds might have their location approximated based on the Wi-Fi networks detected by their device. The Location Detection Module (212) might also employ other technologies like cellular network triangulation, which uses signal strength from multiple cell towers to estimate location, or a hybrid approach combining multiple methods for improved accuracy. In more advanced scenarios, for very precise indoor positioning, the Location Detection Module (212) may integrate with Indoor Positioning Systems (IPS) that use technologies like Bluetooth beacons or Ultra- Wideband (UWB) for centimeter-level accuracy, which could be crucial for troubleshooting in complex indoor environments like shopping malls or airports.

[0095] The Data Request Module (214) may initiate its operation by monitoring and obtaining data corresponding to a plurality of key performance indicators (KPIs) of the site and multiple operators via an application programming interface (API). Key Performance Indicators (KPIs) are quantifiable measurements used to evaluate the performance of a network or service. For a field engineer, the Data Request Module (214) might collect detailed technical KPIs. These detailed technical / current performance KPIs may include:

[0096] Signal-to-Noise Ratio (SNR): A measure of signal strength relative to background noise, typically expressed in decibels (dB). For example, an SNR of 20 dB would indicate a good, clear signal, while an SNR of 5 dB might suggest a weak or noisy signal.

[0097] Bit Error Rate (BER): The percentage of bits that have errors relative to the total number of bits received in a transmission. For instance, a BER of lxl0A- 6 would mean one error bit for every million bits transmitted.

[0098] Channel Quality Indicator (CQI): A measure of the communication channel quality, usually on a scale from 1 to 15, where 15 represents the best quality.

[0099] Reference Signal Received Power (RSRP): The average power received from a single reference signal in cellular networks, typically measured in dBm.

[0100] Throughput: The actual amount of data transferred over a given period, often measured in bits per second (bps).

[0101] For an end customer, the Data Request Module (214) might collect simplified KPIs that are more readily understood by non-technical users. These simplified KPIs may include:

[0102] Download Speed: The rate at which data is transferred from the internet to the user's device, often measured in Megabits per second (Mbps).

[0103] Upload Speed: The rate at which data is transferred from the user's device to the internet, also typically measured in Mbps.

[0104] Latency: The time it takes for a data packet to travel from source to destination, usually measured in milliseconds (ms).

[0105] Jitter: The variation in latency over time, also measured in milliseconds (ms).

[0106] Packet Loss: The percentage of data packets that fail to reach their destination.

[0107] The Data Request Module (214) may also fetch site visualization data and coverage data specific to the detected location from a speed test server (112) via the API. The speed test server (112) may be a dedicated server that performs network speed tests and stores historical performance data. The site visualization data may include graphical representations of network performance and coverage.

[0108] The site visualization data may include a rich array of graphical representations of network performance and coverage, designed to provide intuitiveinsights at a glance. These visualizations encompass color-coded heat maps overlaid on geographical maps, illustrating signal strength distribution across different network technologies. The visualization data may include, cell tower locations, network topology diagrams, real-time and historical performance charts, signal strength heat maps, antenna radiation patterns, capacity utilization indicators, and alarm status dashboards. The cell tower locations may be represented by icons, with visual cues indicating their operational status. The system (102) may generate network topology diagrams to depict the interconnections between various network elements, including backhaul links. Real-time and historical performance charts may display crucial KPIs such as throughput, latency, and packet loss. The visualizations may also include 3D antenna radiation patterns and capacity utilization indicators. Alarm dashboards provide a quick overview of network issues, while user density maps help in understanding demand patterns. The system also offers comparative visualizations of competitor coverage and predictive quality indicators based on machine learning algorithms.

[0109] The coverage data may encompass information about signal strength and quality across different geographical areas. For example, the Data Request Module (214) might retrieve a heat map of signal strength for different operators in the area. This heat map could use color gradients to represent signal strength, with red indicating strong signals and blue indicating weak signals across a map of the area. For a field engineer, the Data Request Module (214) could fetch a detailed topology of network equipment in the vicinity. This network topology might include:

[0110] Cell Tower Locations: Precise coordinates of nearby cell towers.

[0111] Antenna Configurations: Details about antenna types, orientations, and transmission powers.

[0112] Backhaul Connections: Information about the connections between cell sites and the core network.

[0113] Network Element Status: Operational status of various network components like base stations, routers, and switches.

[0114] Frequency Allocations: Information about which frequency bands are used by different cells or operators in the area.

[0115] The Data Request Module (214) may use various API calls to retrieve this data, potentially including RESTful API requests, GraphQL queries, depending on the speed test server's (112) implementation.

[0116] The Data Processing Module (216) may process and transmit the fetched data on the User Equipment (UE) (108) based on geographical location of the UE) 108 The Data Processing Module (216) may employ various data processing techniques to transform raw data into meaningful visualizations.

[0117] For field engineers, the Data Processing Module (216) may render complex 3D models of signal propagation. These 3D models may include: a. Signal Strength Heatmaps: Three-dimensional representations where signal strength is depicted by color gradients across different altitudes and distances from cell towers. b. Terrain-based Signal Propagation: Models that account for geographical features like hills, buildings, or vegetation and their impact on signal propagation. c. Frequency Band Visualizations: 3D representations showing how different frequency bands propagate through space.

[0118] For end customers, the Data Processing Module (216) may create simple color-coded maps. These color-coded maps may include: a. Coverage Quality Maps: Where green areas indicate strong coverage, yellow represents moderate coverage, and red shows poor coverage. b. Network Technology Maps: Displaying areas covered by different network technologies (e.g., 5G, 4G, 3G) using distinct colors.c. Speed Expectation Maps: Visualizations showing expected download or upload speeds in different areas.

[0119] The Data Processing Module (216) processes the raw site visualization and coverage data, preparing it for display. The Map Visualization Module (218) then uses this processed data to generate map-based visualizations. Specifically, the Map Visualization Module (218) creates map data centred on the detected location of the UE (108) and overlays it with the processed site visualization and coverage data. This map data is then transmitted to the UE (108) for display. For a field engineer, the Map Visualization Module (218) may generate a detailed overlay including: a. Cell Sectors: Visual representations of the coverage areas of individual antenna sectors, often displayed as triangular or pieshaped segments. b. Antenna Directions: Arrows or icons showing the orientation of antennas on cell towers. c. Interfering Signals: Highlighted areas or overlapping patterns indicating regions of signal interference. d. Network Element Locations: Icons representing the positions of various network elements like base stations, repeaters, or small cells. e. Backhaul Links: Lines showing the connections between cell sites and the core network.

[0120] For an end customer, the Map Visualization Module (218) may create a simpler view, such as: a. Coverage Quality Areas: Broad areas marked as having good, moderate, or poor coverage for different operators. b. Public Wi-Fi Hotspots: Icons showing locations of available publicWi-Fi networks.c. Expected Speed Zones: Areas indicating where users can expect certain download or upload speeds.

[0121] The User Interaction Module (220) may facilitate user interaction by receiving input from the UE (108) associated with the plotted data. The User Interaction Module (220) may interpret various types of user inputs and trigger appropriate responses. For a field engineer, the User Interaction Module (220) may enable the following interactions: a. Cell Tower Selection: When a field engineer taps on a specific cell tower icon, the User Interaction Module (220) may display detailed performance metrics for that tower, such as Current traffic load, Active alarms, Key Performance Indicators (KPIs) like dropped call rate or handover success rate, Equipment inventory and maintenance history. b. Sector Analysis: The field engineer may select individual sectors to view sector-specific data like Antenna tilt and azimuth, Transmit power, Frequency allocations, and Neighbor cell lists. c. Interference Investigation: The field engineer may use drawing tools to define areas and analyze potential sources of interference within those areas.

[0122] For an end customer, the User Interaction Module (220) may provide simpler interactions, such as: a. Address Search: The end customer may input a specific address to see coverage quality at that location. b. Operator Comparison: When the end customer taps on their home location, the User Interaction Module (220) may display a comparison of different operators, showing Expected signal strength, Typical download and upload speeds, Available networktechnologies (e.g., 5G, 4G), and Customer ratings or satisfaction scores. c. Speed Test Initiation: The end customer may tap a button to initiate a real-time speed test at their current location.

[0123] The Analysis Module (222) may perform in-depth analysis of the site based on user input and the processed data. For a field engineer, the Analysis Module (222) may employ complex algorithms to identify sources of interference or predict potential equipment failures. The Analysis Module (222) may utilize machine learning techniques to analyze patterns in signal propagation and network performance data. For instance, the Analysis Module (222) may use clustering algorithms to group cells with similar performance characteristics, enabling the identification of underperforming sectors. The Analysis Module (222) may also implement predictive maintenance algorithms that analyze equipment performance trends over time to forecast potential failures before they occur. These algorithms may consider factors such as equipment age, historical fault data, environmental conditions, and current performance metrics to generate accurate predictions. For example, the Analysis Module (222) might detect a gradual increase in the bit error rate of a specific network element, indicating a potential impending failure that requires preventive maintenance.

[0124] For an end customer, the Analysis Module (222) may focus on comparative analysis. The Analysis Module (222) may compare the end customer's current network performance against averages for their geographical area. This comparison may involve statistical analysis of key performance indicators such as download speed, upload speed, and latency. The Analysis Module (222) may calculate percentile rankings to show how the end customer's experience compares to other users in the vicinity. For example, the Analysis Module (222) might determine that an end customer's download speed is in the 75th percentile for their neighborhood, indicating above-average performance.

[0125] The Decision Support Module (224) may generate recommendations tailored to the user type based on the analysis performed by the Analysis Module (222). For a field engineer, the Decision Support Module (224) may provide specific, actionable suggestions for network optimization. These suggestions may be based on complex network planning algorithms and radio frequency (RF) optimization techniques. For example, the Decision Support Module (224) might recommend: "Adjust antenna azimuth on Tower As by 5 degrees eastward to reduce interference with Tower B. This adjustment is expected to improve the signal- to- interference ratio by 3 dB in the overlapping coverage area." The Decision Support Module (224) may also prioritize recommendations based on their expected impact on overall network performance, helping field engineers focus on the most critical issues first.

[0126] For an end customer, the Decision Support Module (224) may generate user-friendly recommendations aimed at improving the end customer's network experience. These recommendations may be based on comparative analysis of different operators' performance in the end customer's area. For instance, the Decision Support Module (224) might suggest: "Switch to Operator X for 20% faster download speeds at your location. Based on recent data, Operator X offers an average download speed of 100 Mbps in your area, compared to your current speed of 80 Mbps." The Decision Support Module (224) may also provide recommendations for optimal router placement or suggest upgrading to a higher- tier service plan if the end customer's usage patterns indicate a need for increased bandwidth.

[0127] The system (102) may implement role-based access control to provide different levels of access to the plotted data based on user type. For a field engineer, the system (102) may grant access to detailed technical or current performance information, including raw network logs and configuration files. These logs may contain low-level system events, error messages, and detailed performance metrics. The configuration files may include network element settings, routing tables, and security policies. Access to this level of detail allows field engineers to perform in-depth troubleshooting and make informed decisions about network optimizations. For example, a field engineer may access Signaling System 7 (SS7) logs to diagnose issues with call routing or examine Border Gateway Protocol (BGP) configurations to optimize internet routing.

[0128] For an end customer, the system (102) may restrict access to only user- friendly summaries of network performance. These summaries may be presented in easily understandable formats, such as graphical representations of speed tests or simple coverage maps. The system (102) may use data aggregation and simplification techniques to convert complex network metrics into intuitive ratings or scores. For example, instead of showing detailed signal strength measurements in dBm, the system (102) might display a simple five-bar indicator representing signal quality. Similarly, rather than providing access to raw throughput data, the system (102) may offer end customers a qualitative assessment of their connection speed, such as "Excellent for HD video streaming" or "Suitable for web browsing and email."

[0129] For field engineers, the system (102) may display detailed technical / current performance data including performance management (PM) KPIs, fault management (FM) alarms, and configuration management (CM) data when the field engineers select a specific site. Performance management KPIs may include metrics such as call setup success rate, handover success rate, and channel element utilization. Fault management alarms may encompass various severity levels of network issues, from critical hardware failures to minor software warnings. Configuration management data may include network element parameters, software versions, and hardware inventory information. For example, when a field engineer selects a particular cell site, the system (102) may present a comprehensive dashboard. This dashboard may show a list of recent alarms, such as "Critical: Power amplifier failure on Sector 1 " or "Warning: High temperature in equipment cabinet." The dashboard may also display current traffic load, indicating the number of active connections and the percentage of capacity utilization for voice and data services. Additionally, the system (102) may provide access to configurationparameters for each network element at the site, such as transmit power settings, frequency allocations, and neighbour cell lists.

[0130] The Analysis Module (222) may provide field engineers with access to historical performance data, typically covering the last 30 days. This historical data may be presented as trend lines, allowing field engineers to visualize how key performance indicators (KPIs) have changed over time. For instance, the Analysis Module (222) may generate a graph showing the daily average of dropped call rates over the past month. This visualization may help identify patterns or recurring issues, such as regular spikes in dropped calls during peak traffic hours or gradual degradation in performance. Such trends could indicate various network issues, including capacity constraints during high-traffic periods, potential interference from external sources, software configuration problems, and the need for network optimization or expansion.

[0131] For example, if the visualization shows a consistent pattern of increased dropped calls every weekday between 5 PM and 7 PM, it might suggest insufficient capacity during rush hour when many users are commuting. Similarly, a gradual increase in latency over weeks or months could indicate the need for infrastructure upgrades or optimization. The system's ability to present this data visually allows field engineers to quickly spot these trends and take appropriate action, such as adjusting network parameters, planning capacity upgrades, or scheduling preventive maintenance. The Analysis Module (222) may also offer the ability to overlay multiple KPIs on the same timeline, enabling field engineers to correlate different metrics and gain deeper insights into network behaviour.

[0132] The Data Request Module (214) may fetch details of nearby sites for field engineers, which the Analysis Module (222) then processes to identify active sites. The Data Request Module (214) may retrieve information such as site locations, equipment types, and current operational status. The Analysis Module (222) may then process this information to determine which sites are currently active and theirrelative performance. This processed data may be visualized as a network topology map, providing a comprehensive view of the local network infrastructure.

[0133] The network topology map may show the interconnections between sites, including backhaul links and hierarchical relationships between macro cells and small cells. The current operational status of each site may be indicated through color coding or icons, allowing field engineers to quickly identify sites that require attention. For example, green icons might represent fully operational sites, yellow icons may indicate sites with minor issues, and red icons could highlight sites experiencing critical problems.

[0134] To aid troubleshooting, the Decision Support Module (224) may generate specific recommendations for field engineers based on the analyzed data. These recommendations may be tailored to address identified issues or potential problems. For instance, if a site is experiencing high drop call rates, the Decision Support Module (224) might suggest: "Check feeder connections on sector 2, possible loose connection detected based on Voltage Standing Wave Ratio (VSWR) readings." The Decision Support Module (224) may provide additional context for its recommendations, such as historical data showing when the issue began or comparisons with similar sites that are performing optimally. The module may also prioritize recommendations based on their potential impact on overall network performance, helping field engineers focus on the most critical issues first.

[0135] For end customers, the system presents signal strength and network type information when they tap on a specific location on the map. This interaction is facilitated by the User Interaction Module (220), which handles user inputs and coordinates the display of relevant information. The actual visualization data is generated by the Map Visualization Module (218) and the Data Processing Module (216). When an end customer taps on a location, the User Interaction Module (220) captures this input and requests the relevant data from other modules. The Map Visualization Module (218) then generates a visual representation of the requested information, which may include:Signal bars to represent signal strength, with more bars indicating stronger signalIcons or text labels such as "4G" or "5G" to indicate the available mobile network technologyEstimated download and upload speedsSpecific frequency band information

[0136] This information is presented in a familiar format, similar to what end customers see on their device's status bar, making it easily understandable. The User Interaction Module (220) manages the display of this information on the user's device and handles any further interactions, such as requests for additional details.

[0137] The Decision Support Module (224) may compare coverage and performance data of multiple operators for end customers, generating recommendations for the best operator based on the end customer's location and needs. The Decision Support Module (224) may analyze factors such as signal strength, network technology availability, average speeds, and network reliability for each operator in the area. Based on this analysis, the Decision Support Module (224) may generate tailored recommendations. For example, the Decision Support Module (224) might suggest: "Operator A offers the fastest 5G speeds in your area, with average downloads of 300 Mbps, ideal for high-definition video streaming and online gaming. However, Operator B has more consistent 4G coverage, ensuring stable connections even in indoor locations, which may be preferable if you prioritize reliability over peak speeds."

[0138] To ensure data freshness, the Real-time Update Module (228) may continuously update the fetched data based on the User Equipment's (UE) (108) location changes. The Real-time Update Module (228) may employ background processes to monitor the UE's (108) location and trigger data updates when significant movement is detected. For field engineers driving between sites, thisfeature may provide up-to-date information about network performance along their route, potentially highlighting issues that require immediate attention. For end customers comparing network performance in different areas of their city, the Realtime Update Module (228) may refresh coverage and speed data as they move, allowing for real-time comparisons between neighborhoods or even specific buildings.

[0139] The Data Aggregation Module (230) may compile periodic data from network tests conducted on the speed test server (112), process this data, and store it for future use. The Data Aggregation Module (230) may orchestrate automated speed tests from various points in the network, collecting data on metrics such as download speed, upload speed, latency, and jitter. These tests may be conducted at regular intervals throughout the day to capture performance variations during peak and off-peak hours. The Data Aggregation Module (230) may then process this raw data, applying statistical analyses to identify trends, anomalies, or recurring issues. For example, the module may calculate daily, weekly, and monthly averages for each performance metric, allowing for the identification of long-term trends in network performance.

[0140] The system (102) may include a Speed Test Module (232) to perform on- demand tests at the UE's (108) location. When activated, the Speed Test Module (232) may initiate a series of data transfers between the UE (108) and the speed test server (112) to measure current network performance. The Speed Test Module (232) may measure metrics such as download speed, upload speed, latency, and packet loss. The results of these tests may be immediately incorporated into the site analysis, providing real-time data on network performance at the UE's (108) specific location. This data may be used to update the coverage maps and performance predictions in real-time, ensuring that both field engineers and end customers have access to the most current and accurate information about network performance at their location.

[0141] The Alert Module (234) may be designed to generate and transmit alerts for various performance issues or alarm conditions to the UE (108), tailoring the information to the specific user type. For field engineers, the Alert Module (234) may produce critical alarms indicating equipment failures or severe network degradations. These alerts may be highly technical and actionable. For instance, the Alert Module (234) might generate an alert stating: "Critical Alarm: Base Station Controller (BSC) 1234 experiencing high CPU utilization (95%) for the past 30 minutes. Potential risk of service disruption. Immediate investigation required." Such an alert may be accompanied by detailed logs, current performance metrics, and historical data to assist the field engineer in quickly diagnosing and resolving the issue.

[0142] For end customers, the Alert Module (234) may generate more user- friendly notifications focused on potential service impacts. These alerts may be less technical and more informative about how the network condition might affect the end customer's experience. For example, the Alert Module (234) might display a notification such as: "Scheduled Maintenance Alert: Network upgrades planned in your area on June 15th between 2 AM and 4 AM. You may experience temporary service interruptions during this time. We apologize for any inconvenience." The Alert Module (234) may also provide end customers with estimated resolution times for ongoing issues or suggestions for alternative services during planned outages.

[0143] The system (102) may provide this comprehensive toolset directly on the User Equipment (UE) (108), enabling field engineers to perform thorough site inspections and analyses without the need to carry additional specialized equipment or log into separate systems. This integration may significantly streamline the workflow of field engineers and reduce both the time and cost associated with site visits. For example, instead of carrying a laptop, spectrum analyzer, and various cables and adapters, a field engineer may only need their UE (108) to access all necessary tools and information.

[0144] The system (102) may offer features such as remote configuration capabilities, allowing field engineers to make changes to network elements directly from their UE (108). This may eliminate the need for physical access to equipment in many cases, further reducing the time required for site visits. For instance, a field engineer may be able to adjust the tilt of an antenna or modify the power output of a cell sector remotely using interface (e.g., the application on their UE (108)).

[0145] Furthermore, the system (102) may provide real-time collaboration tools, enabling field engineers to consult with remote experts or supervisors while on-site. This may involve features such as video calling, screen sharing, or augmented reality annotations, allowing for more efficient problem-solving and knowledge transfer. For example, a junior field engineer encountering a complex issue may initiate a video call with a senior engineer, sharing live video of the equipment and receiving guided instructions overlaid on their UE (108) screen.

[0146] By consolidating these diverse functions into a single, mobile platform, the system (102) significantly enhances the efficiency of field operations. This consolidation may lead to faster issue resolution times, reduced travel requirements, and more effective use of field engineer expertise. The resulting streamlined workflow may translate into substantial cost savings for network operators, as well as improved network performance and reliability for end customers.

[0147] FIG. 3 illustrates an exemplary flow diagram (300) of a method for visualizing and analyzing the site using the interface, in accordance with embodiments of the present disclosure.

[0148] Referring to FIG. 3, the flow diagram (300) describes the comprehensive process of site visualization and analysis using the interface (e.g., the application interface on the User Equipment (UE) (108)). The application, which may be developed for mobile platforms such as Android or iOS, is installed on the UE (108). This application serves as the primary interface for both field engineers and end customers to interact with the network visualization and analysis system. Uponinstallation, the user is prompted to grant necessary permissions for the application to function properly on the UE (108). These permissions may include access to location services, network state, and storage, which are crucial for the application's core functionalities.

[0149] The process initiates with the User Identification Module (226) determining the user type. This module authenticates the user and classifies them as either a field engineer or an end customer based on the provided credentials. This classification is crucial as it determines the level of access and the type of information presented to the user throughout their interaction with the application.

[0150] Following user identification, the Location Detection Module (212) activates to precisely detect the current location of the UE (108). This module may utilize various technologies such as GPS, Wi-Fi triangulation, or cellular networkbased positioning to accurately determine the user's location. The accuracy of this step is vital as it forms the basis for all subsequent location-based data retrieval and analysis.

[0151] At step 302, the system begins gathering data by obtaining information from passive tests. These passive tests are ongoing background processes that continuously collect network performance data. The data comprises performance factors of the site for a past predefined number (N) of days, typically ranging from 7 to 30 days. This historical data provides crucial context for analysis, allowing the system to identify trends, recurring issues, and baseline performance metrics for the site.

[0152] Step 304 involves acquiring data from active tests. Unlike passive tests, active tests are performed in real-time and provide current performance metrics of the site. This data represents the status of the currently connected site at the UE's (108) location, offering immediate insights into network performance. Active tests may include on-demand speed tests, latency checks, and signal strength measurements.

[0153] At step 306, the system expands its data collection by gathering information from additional sources. These sources may include open-source framework servers that provide publicly available network data, a distributed event streaming platform that offers real-time network event information, and a distributed file system (DFS) that stores large volumes of historical and aggregated network data. This step ensures a comprehensive dataset for analysis, combining both proprietary and public data sources.

[0154] Step 308 involves the Data Aggregation Module (230) compiling and processing the data from passive tests, active tests, and other sources. This module is responsible for normalizing the data from various sources, resolving any conflicts or discrepancies, and preparing the data for analysis. The aggregation process may involve complex algorithms to ensure data integrity and relevance.

[0155] In step 310, the processed data is stored in a backend database. This storage is organized according to specific logic and location parameters, allowing for efficient retrieval and analysis. The database schema may be optimized for quick spatial queries and time-series analysis, crucial for responsive application performance.

[0156] At step 312, if the user is authenticated in the application, the Data Request Module (214) retrieves site data from a master table containing all site- related information. This master table serves as a centralized repository of network infrastructure data, including details about cell towers, antennas, and other network elements.

[0157] At step 314, with the help of the API, the operator details are fetched on the user device. This step is crucial for multi-operator environments, allowing users to access and compare data across different network providers. The site data corresponding to the fetched operator is then requested from the speed test server (112). This process ensures that users have access to up-to-date performance metrics specific to their chosen operator.

[0158] At step 316, the Data Processing Module (216) populates the fetched site visualization and coverage data on the UE (108). This data is rendered based on geographical zoom levels, allowing users to view network information at various scales, from a broad regional view to a detailed street-level perspective. Concurrently, the Map Visualization Module (218) sets a map to the detected current location of the UE (108) and plots the processed site visualization and coverage data. This may involve rendering heat maps for signal strength, icons for network elements, and color-coded indicators for performance metrics.

[0159] The User Interaction Module (220) then activates, enabling users to interact with the plotted data. For field engineers, this module provides access to detailed technical information such as cell site parameters, network logs, and realtime performance metrics. They can perform virtual site inspections, accessing detailed information about network elements without physical presence at the site. End customers, on the other hand, are presented with a simplified interface showing relevant coverage data, expected performance metrics, and comparison tools for different operators.

[0160] The Analysis Module (222) continuously analyzes the site based on user input and processed data. This module may employ machine learning algorithms to identify patterns, predict potential issues, and suggest optimizations. For field engineers, it might highlight areas of concern or suggest configuration changes. For end customers, it might provide insights into expected service quality or suggest optimal locations for better network performance.

[0161] Finally, the Decision Support Module (224) generates recommendations tailored to the user type. For field engineers, these recommendations might include specific actions to optimize network performance or troubleshoot issues. For end customers, recommendations might focus on choosing the best operator for their location or optimizing their device settings for better connectivity.

[0162] Through this comprehensive process, both field engineers and end customers can perform real-time identification of network issues and conduct thorough site analysis directly through the application on their UE (108). This approach significantly reduces the need for physical site visits and additional equipment, streamlining network management and enhancing the user experience for both technical and non-technical users.

[0163] FIG. 4 illustrates an exemplary flow diagram (400) for a speed test process, integrated within the system for performing visualization and analysis of the site, in accordance with embodiments of the present disclosure.

[0164] The process begins at step 422, where an application (402) on the UE (108), driven by the Speed Test Module (232), sends a "GetscheduledSpeedTestServer" API call to a load balancer (404). This action is initiated based on the user's request or as part of automated performance monitoring.

[0165] At step 424, the load balancer (404) distributes this API call to multiple speed test open-source frameworks (406), typically to four different speed test servers (112). This distribution ensures efficient load management and system reliability.

[0166] In step 426, one of the speed test open-source frameworks may allocates a specific speed test server for the UE (108) based on its CelllD. If the CelllD is not in use, the nearest available server is selected. This allocation ensures that the speed test is conducted using a server optimally positioned relative to the user's location.

[0167] The application (402) may then send a speed test request to the allocated speed test server (112) at step 428. This request is formulated based on the current location detected by the Location Detection Module (212) and the user type determined by the User Identification Module (226).

[0168] At step 430, the speed test server (112) performs the actual speed test and returns the results to the application. These results typically include metrics such as download speed, upload speed, and latency.

[0169] The application (402) may process these results and, at step 432, sends synchronized active data corresponding to the speed test back to the load balancer (404). This data is crucial for real-time analysis and updating of network performance metrics.

[0170] At step 434, the load balancer (404) forwards the synchronized active data to one of the seventy-seven instances of the open-source framework servers (114).

[0171] At step 436, the open- source framework server (114) sends the data to a distributed event streaming platform (412).

[0172] At step 438, the distributed event streaming platform (412) transfers the data to a distributed file system (DFS) (414).

[0173] The data is then stored in the distributed file system (DFS) (414) for longterm storage and future analysis.

[0174] As a reliability measure, step 440 shows the speed test open-source framework (406) sending periodic heartbeat checks to the speed test server (112), ensuring continuous availability and proper functioning of the speed test infrastructure.

[0175] The Speed Test Module (232) integrates this process into the overall system functionality, working in concert with other modules to provide comprehensive network analysis. The Data Aggregation Module (230) compiles periodic data from both these active speed tests and passive web page testing, creating a rich dataset for analysis.

[0176] The Data Request Module (214) utilizes this aggregated data to fetch site and coverage details for the user's current location from a master table in the backend database. The Analysis Module (222) then processes this data, focusing on key performance indicators (KPIs) related to performance monitoring and fault management.

[0177] The Map Visualization Module (218) takes the processed data and plots it on a map, providing a visual representation of network performance at the user's location. This visualization differs based on the user type, as determined by the User Identification Module (226):

[0178] For end customers, the User Interaction Module (220) presents a user- friendly interface showing network coverage and throughput of different operators. This allows end customers to compare operators and make informed decisions about which service to use based on their specific location and nearby areas.

[0179] For field engineers, the system offers a more detailed and technical interface. After authentication, they can access comprehensive site details, including fault management alarms and detailed KPIs, through the Data Request Module (214). The Analysis Module (222) provides them with both real-time data about the currently connected site and historical performance data, typically for the last N days. This allows for trend analysis, predictive maintenance, and in-depth examination of performance monitoring (PM), fault management (FM) alarms, and configuration management (CM) data.

[0180] This integrated approach, combining real-time speed testing with comprehensive data analysis and visualization, enables both field engineers and end customers to perform real-time troubleshooting and network analysis. For field engineers, this means faster problem resolution and more efficient site inspections, as they can access all necessary tools and data directly on their UE (108) without needing additional equipment.

[0181] The system's ability to differentiate between user types and provide tailored interfaces and functionality demonstrates its versatility in addressing the needs of different user groups while utilizing the same underlying data and analysis capabilities. This not only enhances the efficiency of network management but also improves the overall user experience for both technical and non-technical users.

[0182] FIG. 5 illustrates an exemplary flow diagram of a method (500) for visualization of the site using the interface, in accordance with embodiments of the present disclosure.

[0183] At step 502, the method (500) includes determining, by a User Identification Module (226), a user type based on authentication information received from the UE (108). The user type is one of a field engineer or an end customer. This step is crucial for tailoring the user experience and functionality of the application. The User Identification Module (226) may utilize various authentication methods, such as username and password combinations, biometric data, or multi-factor authentication, to verify the user's identity and role. For field engineers, this may involve specialized credentials provided by their organization, while end customers might use more standard authentication methods. The determination of user type enables the system to provide different levels of access to the plotted site visualization data and coverage data. For instance, field engineers may be granted access to detailed technical information and advanced analysis tools, while end customers may see a simplified interface focused on coverage and performance comparisons.

[0184] At step 504, the method (500) includes detecting, by the Uocation Detection Module (212), a current location of the UE (108). This step is essential for providing location-specific data and analysis. The Eocation Detection Module (212) may employ various technologies such as GPS, Wi-Fi triangulation, or cellular network-based positioning to accurately determine the user's location. The precision of location detection may vary depending on the user type and the specific use case. For field engineers, highly accurate location data may be crucial forpinpointing specific network elements or troubleshooting location-dependent issues. For end customers, a more general location may be sufficient for providing relevant coverage and performance information.

[0185] At step 506, the method (500) includes obtaining, by the Data Request Module (214), data corresponding to a plurality of key performance indicators (KPIs) of the site and the plurality of operators based on the detected current location from the speed test server (112) via an application programming interface (API). This step involves collecting a wide range of network performance data to provide a comprehensive view of the network status. The KPIs may include metrics such as signal strength, data throughput, latency, error rates, and other relevant parameters. For field engineers, this step may involve fetching detailed technical / current performance data including performance management (PM) KPIs, fault management (FM) alarms, and configuration management (CM) data when the received input is associated with a specific site on the map. This detailed information allows field engineers to perform in-depth analysis and troubleshooting of network issues.

[0186] At step 508, the method (500) includes fetching, by the Data Request Module (214), site visualization data and coverage data of the detected current location based on the obtained data associated with the plurality of operators. This data is retrieved from different specialized servers via the API. The site visualization data and coverage data may be fetched from a dedicated network data server, which maintains up-to-date information on network topology, coverage areas, and site-specific details. The performance data, including real-time and historical metrics, may be obtained from a separate performance management server.

[0187] The speed test server (112) specifically provides data related to network speed tests, such as download and upload speeds, latency, and jitter. This multiserver approach enhances the user's understanding of network performance by providing comprehensive visual representations of coverage, site-specific data, andperformance metrics. For field engineers, this step may also involve fetching detailed site information of nearby locations from the network data server. The performance management server offers a wide range of network performance parameters, including but not limited to Key Performance Indicators (KPIs), Quality of Service (QoS) metrics, Resource utilization statistics, Fault and alarm data. By aggregating data from these specialized servers, the system provides a holistic view of network performance and characteristics at the user's location.

[0188] The Analysis Module (222) then analyzes these fetched site details to identify active sites, and the User Interaction Module (220) facilitates display of the identified active sites and their corresponding performance data on the map over the UE (108). This feature allows field engineers to gain a broader understanding of the network topology and performance in their vicinity.

[0189] At step 510, the method (500) includes processing and populating, by a Data Processing Module (216), the fetched site visualization data and the fetched coverage data. The processing is performed based on the geographical context of the UE's (108) location. This step involves Data normalization by standardizing data from various sources into a consistent format, statistical analysis by performing calculations on raw data to derive meaningful metrics, and applying visualization algorithms. The processed data is then transmitted to the UE (108) for local storage and use by the application. The Data Processing Module (216) prepares raw data for visualization and initial presentation, whereas the Analysis Module (222) performs more complex, interpretive analysis on the processed data, often in response to user interactions or specific queries. The actual display of data on the UE (108) is handled by the User Interaction Module (220), which takes the processed data and renders it into visual elements on the device's screen. This process ensures that data is efficiently prepared on the server-side, minimizing the computational load on the UE (108) while still providing rich, interactive visualizations.

[0190] The method (500) includes preparing, by the Map Visualization Module (218), map data centered on the detected current location of the UE (108). This step involves selecting the appropriate base map for the detected location, determining the optimal zoom level based on the type of data to be displayed and the user type (field engineer or end customer), and preparing the map layers that will hold the network performance data. The Map Visualization Module (218) then combines this map data with the processed network performance data from the Data Processing Module (216). This combined data package is transmitted to the UE (108) for rendering. It is important to note that the Map Visualization Module (218) and the Analysis Module (222) serve different functions. The Map Visualization Module (218) focuses on preparing geospatial data for visualization. On the other hand, the Analysis Module (222) performs in-depth analysis of network performance data, which may then be overlaid on the map prepared by the Map Visualization Module (218). This step provides the geographical context for the network performance data, allowing users to understand how network characteristics vary across different areas when the data is rendered on their device.

[0191] At step 512, the method (500) involves plotting, by the Map Visualization Module (218), the processed site visualization data and the processed coverage data on the map based on the detected current location. The plotting includes the coordination of multiple modules to create a comprehensive visual representation of network performance and coverage around the detected current location. The Data Processing Module (216) provides the processed site visualization and coverage data. The Map Visualization Module (218) integrates this data with the previously prepared map data, creating a layered data structure suitable for visualization. This includes determining how to represent different data types visually (e.g., color-coded overlays for signal strength, icons for network elements), and organizing the data for efficient rendering on the UE (108), The User Interaction Module (220) receives this integrated data package and manages the actual rendering of the visualization on the UE's (108) display. This includes plotting the data on the map, handling user interactions with the plotted elements,and managing the display of additional information when users interact with specific elements. Rather than having overlapping functionalities, each module plays a distinct role. The Data Processing Module (216) prepares raw data for visualization, the map Visualization Module (218) integrates processed data with map information, the User Interaction Module (220) manages the actual display and user interactions on the UE (108). This division of responsibilities allows for efficient data processing, seamless integration of different data types, and responsive user interactions.

[0192] The method (500) includes receiving, by a User Interaction Module (220), an input from the UE (108) associated with the plotted site visualization data and the plotted coverage data. This step allows users to interact with the displayed information, enabling them to explore specific aspects of network performance or request additional details about particular locations or network elements. For end customers, this step may involve displaying signal strength and network type information when the received input is associated with a specific location on the map. This feature allows end customers to quickly assess the quality of service they can expect at different locations.

[0193] At step 514, the method (500) includes analyzing, by an Analysis Module (222), the site based on the received input and the plotted site visualization data and coverage data. This step involves applying advanced analytical techniques to interpret the data and derive meaningful insights. Examples of these techniques and resulting insights include:

[0194] Trend Analysis: The Analysis Module (222) may examine historical data to identify patterns. For instance, it might detect a recurring drop in network performance every weekday at 5 PM, suggesting a need for capacity enhancement during peak hours.

[0195] Anomaly Detection: By comparing current performance metrics with historical baselines, the module can identify unusual events. For example, it mightflag a sudden increase in packet loss rate, indicating a potential equipment failure or network congestion.

[0196] Predictive Analytics: Using machine learning algorithms, the module can forecast future network conditions. It might predict, based on current trends, that a particular cell tower will reach capacity within the next month, allowing for proactive upgrades.

[0197] Comparative Analysis: The module can compare performance across different network elements or operators. For instance, it might reveal that Operator A consistently outperforms Operator B in download speeds within a specific area.

[0198] Root Cause Analysis: When issues are detected, the module can correlate various metrics to suggest probable causes. For example, if users are experiencing slow speeds, the analysis might point to interference from a newly installed nearby transmitter.

[0199] Coverage Gap Identification: By analyzing signal strength data across the mapped area, the module can pinpoint locations with suboptimal coverage, helping prioritize areas for network expansion.

[0200] Capacity Planning: The module can analyze user density and traffic patterns to recommend optimal locations for new cell sites or small cells.

[0201] For field engineers, the method (500) includes providing access to historical performance data of the site for a predefined number (N) of past days. The Analysis Module (222) analyzes trends in this historical performance data, and the User Interaction Module (220) displays the analyzed trends to facilitate realtime troubleshooting of network issues. This feature enables field engineers to identify patterns, recurring problems, and potential areas for improvement in network performance.

[0202] At step 516, the method (500) includes generating, by a Decision Support Module (224), a recommendation based on the analysis of the site and thedetermined user type. This step leverages the insights gained from the analysis to provide actionable recommendations tailored to the user's role and needs. For field engineers, this may involve generating troubleshooting recommendations based on the analysis of the site. The User Interaction Module (220) then displays these generated troubleshooting recommendations to facilitate quick resolution of network issues. This feature supports field engineers in efficiently diagnosing and addressing network problems.

[0203] For end customers, this step may involve comparing coverage and performance data of multiple operators at the detected current location. The Decision Support Module (224) generates recommendations for selecting an operator based on this comparison, helping end customers choose the best service provider for their needs.

[0204] Further, the method (500) includes facilitating, by the Decision Support Module (224), an action on the UE (108) based on the generated recommendation and the determined user type. This step enables users to take immediate action based on the insights and recommendations provided by the system. For field engineers, this might involve initiating remote configuration changes or scheduling maintenance tasks. For end customers, this step may involve facilitating the connection of the UE (108) to the selected operator, streamlining the process of switching to a better-performing network.

[0205] The method (500) may also include continuously updating, by a Realtime Update Module (228), the fetched site visualization data and the fetched coverage data in real-time based on changes in the detected current location of the UE (108). This ensures that users always have access to the most current and relevant information as they move through different areas.

[0206] Furthermore, the method may include aggregating, by a Data Aggregation Module (230), periodic data from active and passive web page testing on the speed test server (112). The Data Aggregation Module (230) processes thisaggregated periodic data and stores it in a backend database for retrieval by the Data Request Module (214). This feature enables the system to provide historical context and trend analysis, enhancing the depth of insights available to users.

[0207] The method may include performing, by a Speed Test Module (232), a speed test for the detected current location of the UE (108). Once the speed test is completed, the results are sent to the Data Processing Module (216), which integrates this new data with existing performance metrics. The Analysis Module (222) then reassesses the site's performance, taking into account the fresh speed test data. Simultaneously, the Map Visualization Module (218) updates the plotted coverage data on the map, adjusting color-coded overlays or numerical indicators to reflect the new speed information.

[0208] For instance, if the speed test reveals higher than expected download speeds, the coverage map might be updated to show an improved performance zone around the user's location. This real-time integration of speed test results enhances the accuracy and relevance of the information provided to users, offering an up-to- the-minute view of network performance at their specific location.

[0209] To ensure users are promptly informed of critical issues, the method may include generating, by an Alert Module (234), an alert when the analysis of the site indicates a performance issue or an alarm condition at the site. The generated alert is then transmitted to the UE (108), allowing users to quickly identify and respond to potential problems. For example, the Alert Module might generate warnings such as "Cell Tower B3 is experiencing unusually high traffic, potential congestion detected" for field engineers, or "Network performance in your area may be degraded due to ongoing maintenance work" for end-users.

[0210] Other examples could include alerts for sudden drops in signal strength, detection of interference from new sources, or notifications of impending weather conditions that might affect network performance. For field engineers, the alerts might also include more technical details like "Sector 2 of Cell Site A12 showingabnormal SINR levels, possible antenna misalignment detected." These timely, context-specific alerts enable users to take prompt action, whether it's a field engineer prioritizing a site visit or an end-user choosing to switch to a different network temporarily.

[0211] For field engineer user types, the method includes providing access to site visualization, analysis of KPIs of performance monitoring and fault management on the UE (108) without requiring the field engineer to carry additional systems to the site location. This feature significantly enhances the mobility and efficiency of field engineers, allowing them to perform comprehensive site analyses using only their UE (108).

[0212] In another exemplary embodiment, a User Equipment (UE) (108) for facilitating visualization, real-time analysis, and operator selection for a site is described. The UE (108) is configured to execute the application that implements the method described above. It includes hardware and software components necessary to perform location detection, data processing, user interface rendering, and network communication functions required by the application. The UE (108) may be a smartphone, tablet, or other mobile device capable of running the application and connecting to cellular and data networks. Its capabilities enable users, whether field engineers or end customers, to access powerful network analysis tools and make informed decisions about network performance and operator selection directly from their mobile devices.

[0213] In yet another exemplary embodiment, a User Equipment (UE) (108) for facilitating visualization, real-time analysis, and operator selection for a site is described. The UE is configured for generating a request for site visualization and analysis based on user inputs. The UE (108) is configured for transmitting the generated request to a system (102). The UE (108) is configured for receiving site visualization data and coverage data from the system (102). The UE (108) is configured for displaying the received site visualization data and coverage data ona map. The UE (108) is configured for receiving user input associated with the displayed site visualization data and coverage data.

[0214] The UE (108) is configured for transmitting the received user input to the system (102) and receiving recommendations based on this input. For example, if a field engineer inputs a query about a specific cell tower's performance, the system might recommend: "Adjust antenna azimuth by 5 degrees east to optimize coverage" or "Schedule maintenance for Sector 2 due to increasing error rates." For an end-user comparing network providers, the system could recommend: "Switch to Operator B for 20% faster download speeds in your area" or "Stay with current provider; they offer the best indoor coverage for your location."

[0215] The UE (108) then facilitates actions based on these recommendations, such as providing a one-click option for field engineers to log the recommended maintenance task or offering end-users a simple interface to initiate a switch to the recommended network provider. These targeted recommendations and facilitated actions streamline decision-making and enhance user experience, whether for technical optimizations or consumer choices. The system (102) is configured to perform the analytical and data processing steps necessary to generate these tailored recommendations .

[0216] FIG. 6 illustrates an example computer system (600) in which or with which the embodiments of the present disclosure may be implemented.

[0217] 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), a 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 / 100 Ethernet port, a Gigabit or 10Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication ports(s) (660) may be chosen depending on a network, such as a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (600) connects.

[0218] In an embodiment, 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) chip 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. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) 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).

[0219] In an embodiment, the bus (620) may communicatively couple the processor(s) (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).

[0220] In another embodiment, operator, and administrative interfaces, e.g., a display, keyboard, and cursor control device may also be coupled to the bus (620) to support direct operator interaction with the computer system (600). Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) (660). Components described above are meant only to exemplify various possibilities. In no way shouldthe aforementioned exemplary computer system (600) limit the scope of the present disclosure.

[0221] In yet another exemplary embodiment, a non-transitory computer- readable storage medium storing computer-executable instructions is described. When executed by one or more processors, the instructions cause the one or more processors to perform a method for visualization of a site using an interface is described. The method comprises determining, by a User Identification Module, a user type based on authentication information received from a user equipment (UE). The method comprises detecting, by a Location Detection Module, a current location of the UE. The method comprises obtaining, by a Data Request Module, data corresponding to a plurality of key performance indicators (KPIs) of a site and a plurality of operators based on the detected current location from a speed test server. The method comprises fetching, by the Data Request Module, site visualization data and coverage data the plurality of operators at of the detected current location based on the obtained data. The method comprises processing and populating, by a Data Processing Module, the fetched site visualization data and the fetched coverage data on the UE. The method comprises plotting, by the Map Visualization Module, the processed site visualization data and the processed coverage data on a map based on the detected current location. The method comprises analyzing, by an Analysis Module, the site based on a user input and the plotted site visualization data and coverage data. The method comprises generating, by a Decision Support Module, a recommendation based on the analysis of the site and the determined user type.

[0222] The present disclosure provides technical advancement related to radio access network (RAN) visualization and analysis. This advancement addresses the limitations of existing solutions by introducing a comprehensive, mobile-based system for real-time network performance monitoring and optimization. The disclosure involves a sophisticated application that runs on a user's mobile device, integrating location-aware data collection, advanced analytics, and user-specific interfaces, which offer significant improvements in network managementefficiency and user experience. By implementing a modular architecture with specialized components for user identification, data processing, analysis, and decision support, the disclosed invention enhances the ability of both field engineers and end customers to understand and interact with complex network environments. This results in faster problem resolution, more informed decisionmaking for network selection, and improved overall network performance. The system's capability to provide tailored experiences based on user type, coupled with its real-time data processing and visualization features, represents a significant leap forward in making advanced network analytics accessible and actionable directly from a mobile device. This not only streamlines the work of field engineers but also empowers end customers with unprecedented insights into their network options, ultimately contributing to more efficient network operations and higher customer satisfaction.

[0223] 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 to be implemented merely as illustrative of the disclosure and not as limitation.ADVANTAGES OF THE PRESENT DISCLOSURE

[0224] The present disclosure provides a system and method for real-time visualization and analysis of network sites through a mobile application, enabling field engineers and network operators to efficiently identify and address performance issues without the need for physical site visits or accessing separate systems. This significantly reduces the time and cost associated with site inspections and troubleshooting.

[0225] The present disclosure offers a user-type specific interface, allowing both field engineers and end customers to access tailored information and functionality. Field engineers can view current performance data, including KPIs, alarms, and configuration information, while end customers can easily compare network coverage and performance across different operators.

[0226] The present disclosure enables continuous, location-based updates of network performance data, ensuring that users always have access to the most current information as they move through different areas. This real-time capability enhances the accuracy of network analysis and decision-making processes.

[0227] The present disclosure incorporates a sophisticated analysis module that processes historical data and identifies trends, allowing for predictive maintenance and proactive network optimization. This feature helps prevent potential issues before they impact user experience.

[0228] The present disclosure includes a decision support system that generates tailored recommendations based on user type and analyzed data. This assists field engineers in quickly resolving network issues and helps end customers select the most suitable network operator for their location.

[0229] The present disclosure integrates speed testing functionality directly into the application, providing real-time performance data that is immediately incorporated into the site analysis. This feature offers users an up-to-date and comprehensive view of network performance.

[0230] The present disclosure allows end customers to make informed decisions about network operator selection based on actual performance data and coverage information specific to their location. This empowers users to choose the best service provider for their needs.

[0231] The present disclosure eliminates the need for field engineers to carry additional equipment or log into separate systems for site inspections. All necessarytools and data are accessible through a single mobile application, improving efficiency and mobility.

[0232] The present disclosure features an alert system that notifies users of critical performance issues or alarm conditions, enabling quick response to potential network problems and minimizing service disruptions.

[0233] The present disclosure provides a comprehensive data aggregation and storage system, allowing for long-term trend analysis and historical performance review. This feature supports strategic network planning and continuous improvement initiatives.

Claims

CLAIMS1. A system (102) for visualization of a site using an interface, the system (102) comprising: a memory (204); and one or more processors (202) configured to execute a set of instructions stored in the memory (204) for: determining, by a User Identification Module (226), a user type based on authentication information received from a user equipment (UE) (108); detecting, by a Location Detection Module (212), a current location of the UE (108); obtaining, by a Data Request Module (214), data corresponding to a plurality of key performance indicators (KPIs) of the site and a plurality of operators based on the detected current location from a speed test server (112); fetching, by the Data Request Module (214), site visualization data and coverage data of the plurality of operators at the detected current location based on the obtained data; processing and populating, by a Data Processing Module (216), the fetched site visualization data and the fetched coverage data on the UE (108); plotting, by the Map Visualization Module (218), the processed site visualization data and the processed coverage data on a map based on the detected current location;analyzing, by an Analysis Module (222), the site based on a user input, the plotted site visualization data and the plotted coverage data; and generating, by a Decision Support Module (224), a recommendation based on the analysis of the site and the determined user type.

2. The system (102) of claim 1, wherein the one or more processors (202) are further configured for: providing, by a User Interaction Module (220), different levels of access to the plotted site visualization data and the plotted coverage data based on the determined user type, and wherein the user type is at least one of a field engineer or an end customer.

3. The system (102) of claim 1, wherein the one or more processors (202) are further configured for: continuously updating, by a Real-time Update Module (228), the fetched site visualization data and the fetched coverage data in real-time based on changes in the detected current location of the UE (108).

4. The system (102) of claim 1, wherein the one or more processors (202) are further configured for: aggregating, by a Data Aggregation Module (230), periodic data from active and passive web page testing on the speed test server (112); processing, by the Data Aggregation Module (230), the aggregated periodic data; and storing, by the Data Aggregation Module (230), the processed aggregated periodic data in a backend database for retrieval by the Data Request Module (214).

5. The system (102) of claim 1, wherein the one or more processors (202) are further configured for: performing, by a Speed Test Module (232), a speed test for the detected current location of the UE (108); incorporating, by the Speed Test Module (232), results of the performed speed test into the analysis of the site; and updating, by the Speed Test Module (232), the plotted coverage data based on the incorporated speed test results.

6. The system (102) of claim 1, wherein the one or more processors (202) are further configured for: generating, by an Alert Module (234), an alert when the analysis of the site indicates a performance issue or an alarm condition at the site; and transmitting, by the Alert Module (234), the generated alert to the UE (108).

7. A method (500) for visualization of a site using an interface, the method (500) comprising: determining (502), by a User Identification Module (226), a user type based on authentication information received from a user equipment (UE) (108); detecting (504), by a Location Detection Module (212), a current location of the UE (108); obtaining (506), by a Data Request Module (214), data corresponding to a plurality of key performance indicators (KPIs) of the site and a plurality of operators based on the detected current location from a speed test server (112);fetching (508), by the Data Request Module (214), site visualization data and coverage data of the plurality of operators at the detected current location based on the obtained data; processing and populating (510), by a Data Processing Module (216), the fetched site visualization data and the fetched coverage data on the UE (108); plotting (512), by the Map Visualization Module (218), the processed site visualization data and the processed coverage data on a map based on the detected current location; analyzing (514), by an Analysis Module (222), the site based on a user input, the plotted site visualization data and the plotted coverage data; and generating (516), by a Decision Support Module (224), a recommendation based on the analysis of the site and the determined user type-8. The method (500) of claim 7, further comprising: providing, by a User Interaction Module (220), different levels of access to the plotted site visualization data and the plotted coverage data based on the determined user type, and wherein the user type is at least one of a field engineer or an end customer.

9. The method (500) of claim 7, further comprising: continuously updating, by a Real-time Update Module (228), the fetched site visualization data and the fetched coverage data in real-time based on changes in the detected current location of the UE (108).

10. The method (500) of claim 7, further comprising:aggregating, by a Data Aggregation Module (230), periodic data from active and passive web page testing on the speed test server (112); processing, by the Data Aggregation Module (230), the aggregated periodic data; and storing, by the Data Aggregation Module (230), the processed aggregated periodic data in a backend database for retrieval by the Data Request Module (214).

11. The method (500) of claim 7, further comprising: performing, by a Speed Test Module (232), a speed test for the detected current location of the UE (108); incorporating, by the Speed Test Module (232), results of the performed speed test into the analysis of the site; and updating, by the Speed Test Module (232), the plotted coverage data based on the incorporated speed test results.

12. The method (500) of claim 7, further comprising: generating, by an Alert Module (234), an alert when the analysis of the site indicates a performance issue or an alarm condition at the site; and transmitting, by the Alert Module (234), the generated alert to the UE (108).

13. A User Equipment (UE) (108) for facilitating visualization of a site, the UE (108) is configured for: generating a request for site visualization and analysis based on user inputs; transmitting the generated request to a system (102);receiving site visualization data and coverage data from the system(102); displaying the received site visualization data and coverage data on a map; receiving user input associated with the displayed site visualization data and coverage data; transmitting the received user input to the system (102); and receiving a recommendation from the system (102) based on the transmitted user input, wherein the system (102) is configured to perform steps as claimed in claim 1.

14. A computer program product comprising a non- transitory computer- readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform a method (500) for visualization of a site using an interface, the method (500) comprising: determining (502), by a User Identification Module (226), a user type based on authentication information received from a user equipment (UE) (108); detecting (504), by a Location Detection Module (212), a current location of the UE (108); obtaining (506), by a Data Request Module (214), data corresponding to a plurality of key performance indicators (KPIs) of the site and a plurality of operators based on the detected current location from a speed test server (112);fetching (508), by the Data Request Module (214), site visualization data and coverage data of the plurality of operators at the detected current location based on the obtained data; processing and populating (510), by a Data Processing Module (216), the fetched site visualization data and the fetched coverage data on the UE (108); plotting (512), by the Map Visualization Module (218), the processed site visualization data and the processed coverage data on a map based on the detected current location; analyzing (514), by an Analysis Module (222), the site based on a user input, the plotted site visualization data and the plotted coverage data; and generating (516), by a Decision Support Module (224), a recommendation based on the analysis of the site and the determined user type-

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