System and a method for visualizing timing advance data in a network

Abstract

The present disclosure may relate to a system (102) for visualizing timing advance (TA) data in a network (104). The system may receive, by a receiving unit (212), the TA data from at least two sources (302a, 302b), wherein the TA data may comprise time delay measurements. The system may store the received TA data in at least two databases (210a, 210b) in a unified format based on network type. The stored TA data may be categorized, by a processing module (216), into predetermined distance ranges. The system may generate at least one of a two- dimensional (2D) or three-dimensional (3D) visualization of the categorized TA data. A user interface module (214) may display the generated visualization.

Description

SYSTEM AND A METHOD FOR VISUALIZING TIMING ADVANCE DATA IN A NETWORKRESERVATION 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 DISCLOSUREThe embodiments of the present disclosure generally relate to the field of telecommunications. In particular, the present disclosure relates to a system and method for visualizing Timing Advance (TA) data in a network.DEFINITION

[0002] 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.

[0003] Timing Advance (TA) refers to a time delay experienced by a signal traveling between a mobile device or Customer Premises Equipment (CPE) and a base station in a cellular network.

[0004] Customer Premises Equipment (CPE) refers to any terminal and associated equipment located at a subscriber's premises and connected with a carrier's telecommunication channel at the demarcation point.

[0005] Unified format refers to a standardized data structure for storing TA data from different network types to facilitate consistent processing and visualization.

[0006] 2D visualization refers to a thematic representation using color gradients on a two-dimensional map to indicate density of TA data samples within each predetermined distance range.

[0007] 3D visualization refers to a map layer with three-dimensional representation areas where the height of each three-dimensional representation area corresponds to the percentage of TA data samples within each predetermined distance range.

[0008] Probability Density Function refers to a statistical function that describes the relative likelihood for a continuous random variable to take on a given value.

[0009] Cumulative Distribution Function refers to a function that gives the probability that a random variable is less than or equal to a certain value for a given distribution.

[0010] Network type refers to the generation of cellular network technology, such as 4G or 5G.

[0011] Thematic representation refers to a style of cartography in which map features are colored or patterned according to the value of a particular attribute or statistical variable being displayed on the map.

[0012] Base station refers to a fixed transceiver that serves as the main communication point for mobile devices within a cellular network.

[0013] Area of interest refers to a specific area or region that is the primary focus of investigation. The area of interest in a cellular network may be a specific area within a cell or may include entire cell or specific area from a plurality of cells.BACKGROUND OF THE DISCLOSURE

[0014] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

[0015] Cellular networks are the backbone of modem mobile communication, enabling critical services like voice calls, data transmission, and multimedia streaming. Efficient and reliable communication within these networks depends on precise synchronization between mobile devices and base stations.

[0016] A key metric for analyzing cellular network performance is Timing Advance (TA). TA represents the time delay experienced by a signal traveling between a mobile device and the base station. In networks using Time Division Multiple Access (TDMA) technology, like 4G LTE and 5G NR, the base station must calculate a TA value for each device. This value indicates the timing adjustment needed for the device's transmissions to arrive in sync with other users' signals, preventing jumbled data packets and reception errors caused by varying signal travel distances.

[0017] Traditionally, cellular network operators have stored TA data in tabular formats. Extracting actionable insights from this raw data is cumbersome, often demanding manual effort or additional tools to convert it into visual representations like charts, graphs, or coverage maps. Generating these visualizations to identify trends, signal coverage areas, and potential issues is timeconsuming and limits the ease of the TA data interpretation.

[0018] Furthermore, network operators frequently use separate, vendorspecific tools to visualize the TA data from different sources. This reliance on multiple isolated solutions hinders the ability to gain a unified, system-wide view of the cellular network's performance and make data-driven optimization decisions.

[0019] The lack of a streamlined, user-friendly solution for the TA data management and visualization poses several challenges:i. Manual data manipulation: Significant time and effort are expended on manually converting raw TA data into formats suitable for analysis and visualization. ii. Fragmented data sources: the TA data originating from different vendors or network technologies is often stored in disparate systems, complicating unified analysis. iii. Delayed issue identification: The difficulty in quickly visualizing and interpreting the TA data hinders the timely detection and resolution of network performance issues. iv. Suboptimal network optimization: Without a clear, comprehensive view of the TA data across the network, operators struggle to make informed decisions for performance optimization.

[0020] Conventional systems and methods face difficulty in visualizing Timing Advance (TA) data in a network. 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.

[0021] It is therefore an objective of the present invention to provide a system and method for automatically collecting, categorizing, and visualizing the TA data from diverse sources in a unified format. By generating user-friendly 2D and 3D visualizations that intuitively represent TA data across different distance ranges, the invention aims to streamline the TA data analysis, facilitate prompt issue identification, and enable data-driven optimization of cellular network performance, thereby overcoming the above-mentioned disadvantages in the field.SUMMARY OF THE DISCLOSURE

[0022] In an exemplary embodiment, a system for visualizing timing advance (TA) data in a network is described. The system comprises a memory and one or more processors configured to execute a set of instructions stored in the memory, a receiving unit, an user interface module, and a processing module. The system is configured to receive, by a receiving unit, TA data for a selected area ofinterest from at least two sources, wherein the TA data comprises time delay measurements experienced by signals traveling between the at least two sources and a base station in the network. The system is further configured to store by a processing module, in at least two databases, the received TA data in a unified format based on a network type, wherein the unified format comprises a standardized data structure for storing the received TA data from different network types. The system is also configured to categorize, by the processing module, the stored TA data into predetermined distance ranges and generate, by the processing module, at least one of a two-dimensional (2D) or three-dimensional (3D) visualization of the categorized TA data. Additionally, the processing module is configured to display, by a user interface module, the generated at least one of a 2D or 3D visualization of the categorized TA data.

[0023] In some embodiments, the network type comprises at least one of a 4G network or a 5G network.

[0024] In some embodiments, the system may be configured to determine whether the received TA data belongs to a 4G or 5G network. Based on the network type, the system may select one of at least two databases for storing the received TA data. A first database may be configured as a Long-Term Evolution (LTE) TA database specifically for storing the received TA data related to 4G network, while a second database may be configured as a New Radio (NR) TA database for storing the received TA data related to 5G network. This configuration allows for efficient organization and retrieval of TA data based on the network type.

[0025] In other embodiments, the process of categorizing the stored TA data into predetermined distance ranges may involve several steps. The system may define the predetermined distance ranges. Each TA data may then be sorted into the predetermined distance ranges based on its associated distance from the base station. Additionally, the system may calculate a percentage of TA data samples falling within each predetermined distance range. This categorization processenables the creation of meaningful visualizations that represent the distribution of TA data across different distances from the base station.

[0026] In some embodiments, the 2D visualization comprises a thematic representation using color gradients, wherein darker shades of a single color represent higher densities of the TA data samples within each predetermined distance range, and lighter shades of the same color represent lower densities of the TA data samples within each predetermined distance range.

[0027] In some embodiments, the 3D visualization comprises a map layer with three-dimensional representation areas , wherein the height of each three- dimensional representation area corresponds to the percentage of the TA data samples within each predetermined distance range, and higher elevations represent higher percentages of the TA data samples.

[0028] In some embodiments, the user interface module is further configured to allow selection of an area of interest for TA visualization and allow selection between the 2D and 3D visualization types.

[0029] In some embodiments, the user interface module is further configured to display a percentage and a number of total TA data samples for each predetermined distance range.

[0030] In some embodiments, the system is further configured to generate a visualization of Probability Density and Cumulative Distribution Function of the TA data on the map layer.

[0031] In another exemplary embodiment, a method for visualizing timing advance (TA) data in a network is described. The method comprises receiving, by a receiving unit, TA data for a selected area of interest from at least two sources, wherein the TA data comprises time delay measurements experienced by signalstraveling between the at least two sources and a base station in the network. The method further comprises storing, in at least two databases, the received TA data in a unified format based on a network type, wherein the unified format comprises a standardized data structure for storing received TA data from different network types. The method also comprises categorizing the stored TA data into predetermined distance ranges and generating at least one of a two-dimensional (2D) or three-dimensional (3D) visualization of the categorized TA data. Additionally, the method comprises displaying, by a user interface module, the generated at least one of the 2D or 3D visualization of the categorized TA data.

[0032] In some embodiments, the network type comprises at least one of a 4G network or a 5G network.

[0033] In some embodiments, the method may include determining whether the received TA data belongs to a 4G or 5G network. Based on this determination, the method may involve selecting one of at least two databases for storing the received TA data. A first database may be configured as a Long-Term Evolution (LTE) TA database specifically for storing the received TA data related to 4G network, while a second database may be configured as a New Radio (NR) TA database for storing the received TA data related to 5G network. This approach allows for efficient organization and retrieval of TA data based on the network type.

[0034] In other embodiments, the method of categorizing the stored TA data into predetermined distance ranges may involve several steps. The method may include defining the predetermined distance ranges. Each TA data may then be sorted into the predetermined distance ranges based on its associated distance from the base station. Additionally, the method may involve calculating the percentage of TA data samples falling within each predetermined distance range. This categorization process enables the creation of meaningful visualizations that represent the distribution of the TA data across different distances from the base station.

[0035] In some embodiments, generating the 2D visualization comprises creating a thematic representation using color gradients, wherein darker shades of a single color represent higher densities of the TA data samples within each predetermined distance range, and lighter shades of the same color represent lower densities of TA data samples within each predetermined distance range.

[0036] In some embodiments, generating the 3D visualization comprises creating a map layer with three-dimensional representation areas , wherein the height of each three-dimensional representation area corresponds to the percentage of the TA data samples within each predetermined distance range, and higher elevations represent higher percentages of the TA data samples.

[0037] In some embodiments, the method further comprises allowing, via the user interface module, selection of an area of interest for the TA data visualization and allowing, via the user interface module, selection between the 2D and 3D visualization types.

[0038] In some embodiments, the method further comprises displaying, via the user interface module, a percentage and a number of total TA data samples for each predetermined distance range.

[0039] In some embodiments, the method further comprises generating a visualization of Probability Density and Cumulative Distribution Function of the TA data on the map layer.

[0040] In yet another exemplary embodiment, a non-transitory computer- readable medium storing instructions is described. When executed by a system for visualizing timing advance (TA) data in a network, the instructions cause the system to perform operations comprising receiving, by a receiving unit, TA data for a selected area of interest from at least two sources, wherein the TA data comprisestime delay measurements experienced by signals traveling between the at least two sources and a base station in the network. The operations further comprise storing, in at least two databases, the received TA data in a unified format based on a network type, wherein the unified format comprises a standardized data structure for storing received TA data from different network types. The operations also comprise categorizing the stored TA data into predetermined distance ranges and generating at least one of a two-dimensional (2D) or three-dimensional (3D) visualization of the categorized TA data. Additionally, the operations comprise displaying, by a user interface module, the generated at least one of the 2D or 3D visualization of the categorized TA data.

[0041] In yet another exemplary embodiment, a user equipment communicatively coupled to a system for visualizing timing advance (TA) data in a network is described. The system is configured to perform the method as described above.

[0042] The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive.OBJECTS OF THE DISCLOSURE

[0043] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.

[0044] An object of the present disclosure is to provide a system and a method that eliminates the need for manual conversion or additional tools by automating the process of transforming TA data into clear and informative visualizations.

[0045] An object of the present disclosure is to handle TA data from at least two sources within a single platform, storing the received TA data in a unified format based on a network type in at least two databases, allowing for acomprehensive view of network performance and eliminating the need for separate analyses of data from various sources.

[0046] An object of the present disclosure is to quickly identify areas with strong signal coverage, potential weak spots, and opportunities for network optimization by categorizing the stored TA data into predetermined distance ranges.

[0047] An object of the present disclosure is to develop intuitive visualizations, including both 2D and 3D visualizations, to facilitate easier analysis of TA data distribution across a network.

[0048] An object of the present disclosure is to provide a user interface module that allows selection of an area of interest for TA visualization, selection between 2D and 3D visualization types, and displays the generated visualizations along with the percentage and number of total TA data samples.BRIEF DESCRIPTION OF DRAWINGS

[0049] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes the disclosure of electrical components, electronic components or circuitry commonly used to implement such components.

[0050] FIG. 1 illustrates an exemplary network architecture of a system, in accordance with embodiments of the present disclosure.

[0051] FIG. 2 illustrates an exemplary micro service-based architecture of a system, in accordance with embodiments of the present disclosure.

[0052] FIG. 3 illustrates an exemplary block diagram of a system for visualizing the Timing Advance (TA) data in a network, in accordance with embodiments of the present disclosure.

[0053] FIG. 4 illustrates an exemplary a block diagram of a network and at least two databases, in accordance with embodiments of the present disclosure.

[0054] FIG. 5 illustrates an exemplary flow chart illustrating a method for visualizing the TA data, in accordance with embodiments of the present disclosure.

[0055] FIG. 6 illustrates an exemplary a spider view of the output of the system in which or with which, in accordance with embodiments of the present disclosure.

[0056] FIG. 7 illustrates an exemplary flowchart of a method, in accordance with embodiments of the present disclosure.

[0057] FIGs. 8 (A-C) illustrate an exemplary representations of User Interface (UI) module view of TA on 2D layer with which the embodiments of the present disclosure may be implemented.

[0058] FIG. 8D illustrates an exemplary representation of the UI module view of TA on the 3D layer with which the embodiments of the present disclosure may be implemented.

[0059] FIG. 8(E) illustrates a representation of UI module view of TA on 2D layer displaying the over shooter cell pattern, in accordance with embodiments of the present disclosure.

[0060] FIG. 8(F) illustrates a graphical representation of Cumulative Distribution Function (CDF) and Probability Density Function (PDF) of TA samples and percentage of samples across different distance ranges, in accordance with embodiments of the present disclosure.

[0061] FIG. 9 illustrates an exemplary computer system in which or with which embodiments of the present disclosure may be implemented.

[0062] The foregoing shall be more apparent from the following more detailed description of the disclosure.LIST OF REFERENCE NUMERALS100 - Network architecture102 - System104- Network106 - Centralized server108-1, 108-2... 108-N - User equipment110-1, 110-2... 110-N - Users202 - One or more processor(s)204- Memory206 - I / O interface(s)208 - Processing unit(s)210a, 210b - Databases212- Receiving unit214- User interface module216- Processing module218- Other module(s)910 - External Storage Device920 - Bus930 - Main Memory940 - Read Only Memory950 - Mass Storage Device960 - Communication Port970- ProcessorDETAILED DESCRIPTION OF THE DISCLOSURE

[0063] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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, anyaspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be constmed 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.

[0068] 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.

[0069] 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.

[0070] The aspects of the present disclosure are directed to a system and method for visualizing timing advance (TA) data in a network. The systemautomates the process of transforming the TA data into clear and informative 2D and 3D visualizations, eliminating the need for manual conversion or additional tools. It handles the TA data from at least two sources, storing the received data in a unified format based on the network type in at least two databases, allowing for a comprehensive view of network performance. The system categorizes the stored TA data into predetermined distance ranges, enabling quick identification of areas with strong signal coverage, potential weak spots, and opportunities for network optimization.

[0071] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGS. 1-9.

[0072] As illustrated in FIG. 1, one or more user equipment (108-1, 108- 2...108-N) may be connected to a system (102) for visualizing timing advance (TA) data through a network (106). A person of ordinary skill in the art will understand that the one or more user equipment (108-1, 108-2... 108-N) may be collectively referred to as computing devices (108) and individually referred to as a user equipment (108). One or more users (110-1, 110-2...110-N) may provide one or more requests to the system (102). A person of ordinary skill in the art will understand that the one or more users (110-1, 110-2... 110-N) may be collectively referred to as users (110) and individually referred to as a user (110).

[0073] In an embodiment, the user equipment (108) may include, but not be limited to, a mobile phone, a laptop, etc. Further, the user equipment (108) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, audio aid, microphone, or keyboard. Furthermore, the user equipment (108) may include a smartphone, virtual reality (VR) devices, augmented reality (AR) devices, a general-purpose computer, a desktop, a personal digital assistant, a tablet computer, and a mainframe computer. Additionally, input devices for receiving input from the user (110) such as a touchpad, touch-enabled screen, electronic pen, and the like may be used.

[0074] In an embodiment, the network (104) may include, by way of example but not limitation, 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. The network (104) may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a 4G network, a 5G network, or some combination thereof.

[0075] In an embodiment, the system (102) may continuously collect TA data for a selected area of interest from at least two sources. The user interface module may then allow selection of area of interest and the visualization type (2D or 3D). The processor may generate the TA data visualizations based on the collected data and the user-defined parameters. If updates are needed, the system may reconfigure the visualizations and provide these to the relevant network management systems for analysis. In an embodiment, the area of interest may be a specific area within a cell or may include entire cell or a specific area from a plurality of cells.

[0076] 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).

[0077] FIG. 2 illustrates an example block diagram (200) of a proposed system (102) for visualizing timing advance (TA) data, in accordance with an embodiment of the present disclosure.

[0078] Referring to FIG. 2, in an embodiment, the system ( 102) may include one or more processor(s) (202). The one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and / or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the system (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 visualize TA data and create 2D and 3D visualizations. 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.

[0079] In an embodiment, the system (102) may include an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output devices (RO), storage devices, and the like. The interface(s) (206) may facilitate communication through the system (102). The interface(s) (206) may also provide a communication pathway for one or more components of the system (102). Examples of such components include, but are not limited to, processing unit(s) (208), a first database (210a) for storing 4G the received TA data, and a second database (210b) for storing 5G the received TA data. Further, the processing unit(s) (208) may include a receiving unit (212), a user interface module (214), a processing module (216) and other module(s) (218). In an embodiment, the other module(s) (218) may include, but not be limited to, a datacategorization module, an input / output module, and a visualization generation module.

[0080] The receiving unit (212) may collect TA data for a selected area of interest from at least two sources. The "at least two sources" from which the receiving unit (212) collects TA data refer to various devices capable of connecting to and communicating with the cellular network. These sources may include, but are not limited to: i. Mobile phones: Smartphones and feature phones used by individual consumers for voice calls, data services, and various applications. ii. Tablets: Portable computing devices that may have cellular connectivity. iii. Internet of Things (loT) devices: Such as smart meters, connected vehicles, or environmental sensors that use cellular networks for data transmission. iv. Customer Premises Equipment (CPE): Fixed wireless terminals installed at a subscriber's location, often used in areas where traditional wired broadband is unavailable. v. Wearable devices: Such as smartwatches or fitness trackers with cellular connectivity. vi. Mobile hotspots: Portable devices that create Wi-Fi networks using cellular data connections. vii. Laptops with cellular modems: Computers equipped with built-in or external cellular modems for mobile internet access.

[0081] For example, in an urban environment, the receiving unit (212) might collect the TA data from hundreds of smartphones carried by pedestrians, tablets used in cafes, and loT sensors on traffic lights. In a rural setting, the sources might include fixed wireless CPEs on farms, connected agricultural equipment, and mobile phones in vehicles traveling through the area. By collecting data fromdiverse sources, the system can build a comprehensive picture of network performance across various use cases and environments.

[0082] The user interface module (214) may allow selection of an area of interest and the visualization type (2D or 3D). The processing module (216) ) may generate TA visualizations based on the collected data, categorized into predetermined distance ranges, and display the visualizations on the user interface.

[0083] In an embodiment, the processing unit(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 unit(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 unit(s) (208) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing unit(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 unit(s) (208). In such examples, the system 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 and the processing resource. In other examples, the processing unit(s) (208) may be implemented by electronic circuitry.

[0084] Although FIG. 2 shows exemplary components of the system (102), in other embodiments, the system (102) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 2. Additionally, or alternatively, one or more components of the system (102) may perform functions described as being performed by one or more other components of the system (102).

[0085] The system (102) for visualizing timing advance (TA) data in a network (104) is disclosed. Each TA data refers to a single instance of Timing Advance data collected from a source device. Each TA data includes the actual TA value, which represents the time delay measurement, along with associated metadata such as the timestamp and source device identifier. For example, the TA data might be {TA value: 15, Timestamp: 2024-07-04T10:30:00Z, DevicelD: DI 23}. The system (102) may comprise a memory (204) and one or more processors (202) configured to execute a set of instructions stored in the memory (204). The one or more processors (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and / or any devices that process data based on operational instructions.

[0086] The system (102) may include a receiving unit (212), which may collect the TA data for a selected area of interest from at least two sources (302a, 302b). The TA data may comprise time delay measurements experienced by signals traveling between the at least two sources and a base station in the network (104). These time delay measurements are crucial for synchronizing communication between mobile devices and base stations in cellular networks, particularly those employing Time Division Multiple Access (TDMA) technology, such as 4G and 5G networks.

[0087] In 4G Long-Term Evolution (LTE) networks, the base station, known as an eNodeB, may calculate the TA value for each connected device. This value represents the propagation delay between the eNodeB and the device, allowing the device to advance its uplink transmission timing to ensure synchronized arrival of signals at the eNodeB. Similarly, in 5G New Radio (NR) networks, the gNodeB may perform the same function, calculating TA values for connected devices to maintain synchronization.

[0088] The at least two sources from which the TA data is collected may include various network elements, such as base stations, mobile devices, or network monitoring probes. By gathering the TA data from multiple sources, the system (102) may provide a comprehensive view of the network's (104) timing synchronization performance. The TA data includes a plurality of TA data samples. In other words, the TA data can be sampled into a number of TA data samples.

[0089] The received TA data may be stored in at least two databases (210a, 210b) in a unified format based on the network type. This unified format may comprise a standardized data structure for storing the TA data from different network types, such as 4G and 5G networks. By employing a consistent data structure, the system (102) may streamline the storage, retrieval, and analysis of the TA data across diverse network technologies.

[0090] In an embodiment, the unified format comprises a standardized data structure designed to efficiently store and organize the TA data from different network types. This standardized data structure includes several key fields: a first field stores a unique identifier for each TA sample, ensuring that each TA data can be distinctly referenced; a second field records the network type (e.g., 4G or 5G) associated with each TA sample, allowing for easy filtering and analysis across different network generations; a third field contains the actual time delay measurement, which is the core TA data; a fourth field stores the geographic coordinates of the source of each TA sample, enabling spatial analysis of the data; a fifth field holds the identifier of the base station associated with each TA sample, facilitating cell-specific analysis; and a sixth field records the timestamp of when each TA sample was collected, allowing for temporal analysis of the TA data. This comprehensive data structure enables consistent processing and comparison of the TA data across different network types, enhancing the system's ability to provide meaningful visualizations and insights.

[0091] In an exemplary embodiment, the unified format and standardized data structure may include specific fields to ensure consistent storage and processing of the TA data across different network types . For example, the structure may include fields such as: a unique sample ID, network type (4G or 5G), TA value, source coordinates, base station ID, and timestamp. An example entry might look like: {ID: TA12345, NetworkType: 4G, TAValue: 50, SourceCoordinates: (40.7128, -74.0060), BaseStationlD: BS789, Timestamp: 2024-07-04T10:30:00Z}. This standardized structure allows for efficient data retrieval and analysis regardless of the original network type.

[0092] The system (102) determines whether the input data belongs to a 4G or 5G network through a multi-step process. First, it examines the metadata associated with each TA data. This metadata typically includes a network identifier field that explicitly states whether the data originates from a 4G LTE or 5G NR network. If this field is present and valid, the system uses it for classification. In cases where the metadata is inconclusive or missing, the system employs a secondary method. It analyzes the structural characteristics of the TA data itself, as 4G and 5G networks have distinct timing advance value ranges and resolutions. For 4G LTE, TA values typically range from 0 to 1282, corresponding to distances up to 100 km, with each unit representing about 78 meters. In contrast, 5G NR networks often use a finer resolution, with TA values potentially ranging from 0 to 3846, where each unit represents about 26 meters. By examining these value ranges and resolutions, the system can infer the network type. Additionally, the system may cross-reference the data with known cell IDs or frequency bands specific to 4G or 5G deployments in the network topology database. In cases of ambiguity, the system may employ machine learning algorithms trained on historical data to classify the network type based on patterns in the TA values and associated parameters.

[0093] The system (102) may categorize the stored TA data into predetermined distance ranges. These distance ranges may be based on the typicalcell sizes and propagation characteristics of the network (104). For example, the system (102) may define distance ranges such as 0 to 1.4 km, 1.4 to 2.9 km, 2.9 to 4.4 km, 4.4 to 5.9 km, and 5.9 km and above. Categorizing TA data into these ranges may allow for more granular analysis of timing synchronization performance across different areas of the network (104). The predetermined distance ranges (0 to 1.4 km, 1.4 to 2.9 km, 2.9 to 4.4 km, 4.4 to 5.9 km, and 5.9 km and above) are chosen based on typical cell sizes and signal propagation characteristics in urban, suburban, and rural environments. For instance, the 0 to 1.4 km range often corresponds to dense urban small cells, while the 5.9 km and above range may represent rural macro cells. This categorization allows network operators to quickly identify areas of varying network density and performance.

[0094] The predetermined distance ranges (0 to 1.4 km, 1.4 to 2.9 km, 2.9 to 4.4 km, 4.4 to 5.9 km, and 5.9 km and above) were specifically selected to optimize the visualization of cellular network coverage patterns. The 1.4 km incremental spacing corresponds to typical coverage zones in modem cellular network deployments, where signal strength experiences significant changes at these threshold distances. This specific categorization enables network operators to identify critical coverage transition points where signal quality may degrade and require optimization measures. Furthermore, these particular ranges align with standard cell planning parameters used in both 4G and 5G network deployments, making the visualization directly applicable to practical network optimization workflows.

[0095] The process of categorizing the TA data into predetermined distance ranges involves several steps designed to create meaningful and actionable visualizations. First, the system converts the raw TA values into distance measurements. For 4G LTE, each TA unit represents approximately 78 meters (twice the 39-meter resolution of LTE timing), while for 5G NR, each unit typically represents about 26 meters (twice the 13-meter resolution of NR timing). The system applies the appropriate conversion factor based on the identified networktype. Next, the system defines the predetermined distance ranges. These are typically set as 0 to 1.4 km, 1.4 to 2.9 km, 2.9 to 4.4 km, 4.4 to 5.9 km, and 5.9 km and above, based on common cell sizes and network planning considerations. However, these ranges are configurable and may be adjusted based on specific network deployments or analysis needs. The system then sorts each TA data into these ranges based on its calculated distance. To handle edge cases, the system employs a set of rules. For instance, a TA data exactly at 1.4 km would be categorized in the 0 to 1.4 km range. The system also implements error checking to flag any TA values that convert to distances beyond the theoretical maximum for the network type, which could indicate data corruption or equipment malfunction. Finally, the system calculates summary statistics for each range, including the count and percentage of TA data samples falling within each category. The percentage of TA data samples refers to the proportion of each TA data falling within each predetermined distance range relative to the total number of TA data collected for a given area of interest. For instance, if a cell has 1000 total TA data, and 250 of these fall within the 0 to 1.4 km range, the percentage of TA data samples for this range would be 25%. This percentage helps quantify the distribution of user devices across different distances from the base station. These statistics form the basis for the subsequent visualization steps, enabling the creation of meaningful 2D and 3D representations of the TA data distribution.

[0096] The process of categorizing stored TA data involves analyzing each TA value and assigning it to a predefined distance range. For example, a TA value of 10 in a 4G network, corresponding to about 780 meters, would be categorized in the 0 to 1.4 km range. This categorization allows for aggregated analysis and visualization of the TA data distribution.

[0097] The processing module (216) may generate at least one of a two- dimensional (2D) or three-dimensional (3D) visualization of the categorized TA data. These visualizations may provide intuitive representations of the TA data,enabling network operators to quickly identify areas with strong or weak timing synchronization performance.

[0098] In the case of 2D visualization, the system (102) may employ a thematic representation using color gradients. Darker shades of a single color may represent higher densities of the TA data samples within each predetermined distance range, while lighter shades of the same color may represent lower densities of TA data samples. This color-coded approach may allow for easy identification of areas with high or low concentrations of the TA data samples at specific distance ranges.

[0099] For example, if the system (102) uses a blue color gradient for the 2D visualization, dark blue regions on the map may indicate areas where a high percentage of the TA data samples fall within a particular distance range, such as 0 to 1.4 km. Conversely, light blue regions may represent areas where a lower percentage of the TA data samples fall within that same distance range. This thematic representation may help network operators quickly identify patterns and anomalies in timing synchronization performance across the network (104). The thematic representation using color gradients provides an intuitive visualization of the TA data density. For example, in a blue color scheme, dark blue might represent areas with 75-100% of the TA data samples, medium blue for 50-75%, light blue for 25-50%, and very light blue for 0-25%. This color-coding allows network operators to quickly identify hotspots of user activity or potential areas of network congestion.

[0100] The 3D visualization may comprise a map layer with three- dimensional representation areas , wherein the height of each three-dimensional representation area corresponds to the percentage of the TA data samples within each predetermined distance range. Higher elevations may represent higher percentages of the TA data samples, while lower elevations may indicate lowerpercentages. This 3D representation may provide a more immersive and informative view of the TA data distribution across the network (104).

[0101] For instance, if a particular area of the network (104) has a high percentage of the TA data samples falling within the 1.4 to 2.9 km distance range, the corresponding region on the 3D map may appear as a tall, elevated structure. This elevation may visually convey the concentration of the TA data samples within that specific distance range for the given area. By exploring the 3D map, network operators may gain insights into the spatial distribution of timing synchronization performance and identify areas that may require further investigation or optimization.

[0102] The system (102) may include a user interface module (214), which may be configured to display the generated 2D or 3D visualization of the categorized TA data. This user interface module (214) may provide an interactive and user-friendly means for network operators to explore and analyze the TA data visualizations. The user interface module may include interactive elements such as dropdown menus for selecting cell sites, toggle switches for choosing between 2D and 3D visualizations, and slider bars for adjusting color scales or elevation in 3D views. For example, a user might use a map-based interface to select a specific cell, then use a dropdown to choose between viewing the TA data as a 2D heat map or a 3D terrain-like visualization.

[0103] The user interface module (214) may allow selection of an area of interest for TA visualization. This feature may enable network operators to focus their analysis on specific regions of the network (104), such as individual cells or clusters of cells. By selecting a particular cell or area, the user interface module (214) may display the corresponding 2D or 3D visualization of the TA data for that specific region.

[0104] Additionally, the user interface module (214) may allow selection between the 2D and 3D visualization types. This flexibility may cater to different user preferences and analysis requirements. Some users may prefer the simplicity and clarity of 2D visualizations, while others may find the immersive and informative nature of 3D visualizations more valuable fortheir analysis.

[0105] The user interface module (214) may further display the percentage and number of total TA data samples. This information may provide context and support data-driven decision-making. By presenting the percentage and number of TA data samples, the user interface module (214) may help network operators assess the statistical significance and reliability of the visualized TA data.

[0106] For example, if the user interface module (214) displays that a particular cell has a high percentage of the TA data samples within the 0 to 1.4 km distance range, but the total number of TA data samples for that cell is relatively low, network operators may interpret the results with caution. In contrast, if a cell exhibits a similar percentage of the TA data samples within the same distance range but has a significantly higher total number of TA data samples, the findings may be considered more robust and actionable.

[0107] The processing module (216) of the system (102) may be further configured to generate a visualization of Probability Density and Cumulative Distribution Function (CDF) of the TA data on the map layer. These statistical measures may provide valuable insights into the distribution and variability of TA values across the network (104).

[0108] The Probability Density Function (PDF) may represent the likelihood of observing a particular TA value within a specific range. By visualizing the PDF on the map layer, network operators may identify the most frequently occurring TA values and detect any unusual patterns or shifts in the distribution. This information may help in understanding the typical timing synchronizationbehavior of the network ( 104) and identifying areas that deviate from the norm. For instance, the Probability Density Function might show that TA values between 10 and 20 (corresponding to distances of 780 to 1560 meters) are most common in a particular cell, indicating where the majority of users are located. The Cumulative Distribution Function could reveal that 80% of all TA values in the cell are below 30 (2340 meters), helping operators understand the coverage area serving most users.

[0109] The Cumulative Distribution Function (CDF) may depict the probability that a TA value falls below a certain threshold. By plotting the CDF on the map layer, network operators may determine the percentage of the TA data samples that meet specific timing synchronization criteria. This visualization may assist in setting appropriate thresholds for network performance and identifying areas that exceed or fall short of the desired timing synchronization levels.

[0110] For instance, if the CDF visualization reveals that a significant portion of the TA data samples in a particular area exceeds a predefined threshold, network operators may investigate the underlying causes and take corrective actions to improve timing synchronization in that region. Conversely, if the CDF indicates that most the TA data samples fall within acceptable ranges, network operators may conclude that the timing synchronization performance in that area is satisfactory.

[0111] The system's (102) ability to visualize the TA data in 2D and 3D formats, along with the Probability Density and Cumulative Distribution Function, may provide network operators with a comprehensive and intuitive tool for analyzing timing synchronization performance. By leveraging these visualizations, network operators may quickly identify areas of concern, prioritize optimization efforts, and make data-driven decisions to enhance the overall quality and reliability of the network (104).

[0112] Moreover, the system's (102) unified data storage format and categorization of the TA data into predetermined distance ranges may streamline the analysis process and enable consistent comparisons across different network types and regions. This standardization may facilitate collaboration among network operators and support the sharing of best practices and insights derived from the TA data visualizations.

[0113] In summary, the disclosed system (102) for visualizing timing advance data in a network (104) may offer a powerful and user-friendly solution for network operators to monitor, analyze, and optimize timing synchronization performance. By providing intuitive 2D and 3D visualizations, along with statistical measures such as Probability Density and Cumulative Distribution Function, the system (102) may enable data-driven decision-making and support the continuous improvement of network (104) quality and reliability.

[0114] In another embodiment, the present disclosure provides a non- transitory computer-readable medium storing instructions that, when executed by a system (102), enable the visualization of timing advance (TA) data in a network (104). The stored instructions cause the processors (202) to perform a series of operations, including: receiving TA data from multiple sources (302a, 302b) for a selected network area, where the TA data represents signal delay measurements; storing this data in a unified format across multiple databases (210a, 210b), accommodating different network types; categorizing the stored TA data into predefined distance ranges; generating either 2D or 3D visualizations of the categorized data; and displaying these visualizations through a user interface module (214). This computer-readable medium encapsulates the core functionality of the TA data visualization system, providing a portable and executable form of the invention that can be deployed across various computing platforms in telecommunications network management environments.

[0115] FIG. 3 illustrates a block diagram of a system (102) for generating and visualizing timing advance (TA) samples of a specific cell on 2D and 3D planes between sources (302a, 302b) and the base station (cell tower) in a network (104), in accordance with embodiments of the present disclosure. The sources (302a, 302b) may represent various devices capable of communicating with the cellular network. These may include mobile phones, which are typically handheld devices used by individual consumers for voice and data communication. In some cases, the sources may be Customer Premises Equipment (CPE), which refers to terminal equipment located at a subscriber's premises and connected with a carrier's telecommunication channel at the demarcation point. An example of CPE may be the Outdoor Customer Premises Equipment (OCPE) or Fixed wireless access (FWA), which is specifically designed for outdoor installation and may be used in fixed wireless access scenarios. These OCPEs may be particularly relevant in rural or suburban areas where traditional wired broadband may not be available or cost- effective. By incorporating diverse source types, the system (102) may provide a comprehensive view of the TA data across various usage scenarios, from mobile users to fixed wireless installations, enabling more accurate and versatile network optimization strategies. The "at least two sources" may include various devices capable of communicating with the cellular network, such as mobile phones, tablets, loT devices, or fixed wireless terminals. For example, in an urban setting, sources might include smartphones of pedestrians and vehicles, while in a rural area, sources could be fixed wireless CPEs on farms or homes.

[0116] The network (104) includes, but is not 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, one or more messages, packets, signals, waves, voltage or current levels, or combinations thereof. The network (104) may also include one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cablenetwork, a cellular network, a satellite network, a fiber optic network, or combinations thereof.

[0117] The system (102) comprises a memory (204), at least two databases (210a, 210b), and one or more processors (202). The one or more processors (202) are configured to execute a set of instructions stored in the memory (204) to perform various functions.

[0118] A receiving unit (212) is configured to receive the TA data transmitted by the at least two sources (302a, 302b). The receiving unit (212) may include at least one antenna for transmitting and receiving communications packets or records to / from the sources (302a, 302b) via a wireless access node. These antennas may include near-field antennas, WiFi antennas, and radio frequency antennas. The receiving unit (212) may include a wireless-frequency transceiver having a variable gain amplifier that generates radio-frequency signals for transmission. A wireless amplifier circuit may be used to amplify the radiofrequency signals at the output of the variable gain amplifier for transmission through a plurality of antennas.

[0119] The at least two databases (210a, 210b) are configured to store the TA data in a unified format based on network type. These databases may include a Long-Term Evolution (LTE) TA database for storing data related to 4G network and a New Radio (NR) TA database for storing data related to 5G network. The databases (210a, 210b) are configured to store and manage information regarding the sources (302a, 302b), their sessions, serving cells, and other pertinent network data. They may also store program instructions, pre-processed data, and predefined sets of parameters. The databases may include various types of computer-readable media, such as volatile memory (e.g., SRAM, DRAM) and non-volatile memory (e.g., ROM, flash memories, hard disks, optical disks, magnetic tapes).

[0120] The primary function of the databases (210a, 210b) is to serve as a centralized repository for the TA data collected from various sources within the respective network (5G or 4G). By storing the TA data in a unified format, the system (102) simplifies data management and facilitates easier processing and analysis for visualization.

[0121] The processing module (216) is configured to perform processing and categorization to prepare the TA data for visualization. They categorize the received TA data into predetermined distance ranges (e.g., 0 to 1.4 km, 1.4 to 2.9 km, 2.9 to 4.4 km, 4.4 to 5.9 km, and 5.9 km and above). This categorization is crucial for generating visualizations that depict the distribution of the TA data across distances within the selected cell area. The processors (202) may perform processing specific to the network technology (4G or 5G) based on information from the retrieved TA data.

[0122] The system (102) generates at least one of a two-dimensional (2D) or three-dimensional (3D) visualization of the categorized TA data. The 2D visualization comprises a thematic map with color gradients representing sample distribution across distance ranges. The 3D visualization uses both color and height to depict distance alongside sample distribution.

[0123] A user interface module (214) is configured to display the generated visualizations. It provides a gateway for users or subscribers to engage with the TA data. Users can navigate the selection of the specific technology (4G or 5G) and choose areas of interest for visualization. The UI module (214) offers a selection between 2D and 3D visualizations, generating a thematic gradient view in the case of 2D visualization, utilizing data from the LTE TA Database for 4G or NR TA Database for 5G.

[0124] The UI module (214) integrates interactive elements for single-click actions, categorizes samples based on distance, and employs thematic visualizationfor effective representation. It displays essential information such as the percentage of total samples and sample counts, enhancing the user's ability to gain insights from the TA data visualization. The UI module (214) includes interfaces for data input and output devices (I / O), storage devices, and provides communication pathways for various components of the system (102).

[0125] In addition to the basic visualizations, the system (102) is capable of generating visualizations of Probability Density and Cumulative Distribution Functions of the TA data on the map layer, providing a comprehensive view of the TA data distribution in the network (104).

[0126] This system (102) ensures an intuitive and informative user experience, facilitating efficient analysis of timing advance information across different network technologies and geographical areas. It provides network operators with a powerful tool for understanding and optimizing their network performance based on timing advance data.

[0127] FIG. 4 illustrates an exemplary block diagram (400) of a system (102) for processing and categorizing Timing Advance (TA) data from both 5G and 4G technologies, in accordance with one or more embodiments of the present disclosure. The system (102) may employ a unified distance categorization and combined database approach, which may foster improved analysis and visualization capabilities.

[0128] In one embodiment, the system (102) may comprise a raw TA data input block (402). This block (402) may represent the initial TA data received by the system (102), which may encapsulate the time delay experienced by signals traveling between mobile devices or Customer Premises Equipment (CPE) and base stations. The TA data may be a critical metric in mobile communication systems, potentially serving crucial functions such as synchronization and distance estimation. Time delay measurements refer to the round-trip time for a signal to travel between the source device and the base station. For instance, in a 4G LTEnetwork, a time delay measurement of 5 units would correspond to approximately 390 meters (5 * 78 meters), accounting for the round-trip distance.

[0129] The system (102) may further include a network type differentiation module, represented by blocks (404) and (406). This module may be capable of distinguishing between the TA data originating from 5G and 4G networks respectively. Such differentiation may be essential as it may allow the system (102) to handle the TA data appropriately based on the specific technology used.

[0130] In another embodiment, the system (102) may incorporate two distinct databases: a first database (210a) and a second database (210b). The first database (210a) may be configured as an NR (New Radio) TA combined database (408) for 5G networks, specifically designed for storing and managing the TA data collected within a 5G network. It may be capable of storing the TA data from various sources within the 5G network, not limited to a single vendor or type of base station. Similarly, the second database (210b) may be configured as an LTE (Long-Term Evolution) TA Combined Database (410) for 4G networks, mirroring the structure and functionality of the NR TA combined database. The second database (210b) may be designed to store the TA data from various sources within the 4G network. This dual -database approach allows the system (102) to efficiently manage and analyze TA data across different network generations, providing a comprehensive view of network performance in mixed 4G / 5G environments.

[0131] The system (102) may further comprise a TA sample categorization module, represented by blocks (412) and (414). This module may categorize TA data samples based on predetermined distance ranges, which may typically include 0 to 1.4 km, 1.4 to 2.9 km, 2.9 to 4.4 km, 4.4 to 5.9 km, and 5.9 km and above. This categorization may be applied regardless of the source within the network and may play a crucial role in generating visualizations that depict the distribution of the TA data samples across distances.

[0132] In yet another embodiment, the system (102) may include a visualization generation module (not shown in FIG. 4). This module may use the categorized data to create either 2D or 3D representations. In 2D visualizations, the module may employ color gradients to represent the density of TA data samples within each distance range. Darker shades may typically indicate higher densities, while lighter shades may represent lower densities. For 3D visualizations, the module may utilize both color and height to represent the distribution and percentage of the TA data samples across the distance ranges.

[0133] The system (102) may also incorporate a user interface module (not shown in FIG. 4) that may allow network operators to interact with the visualizations. This module may provide functionalities such as zooming, panning, and selecting specific areas for detailed analysis.

[0134] In operation, the raw TA data may first enter the system through block (402). The network type differentiation module (404, 406) may then determine whether the data originates from a 5G or 4G network. Based on this determination, the data may be routed to either the NR TA combined database (408) or the LTE TA Combined Database (410) for storage and initial processing.

[0135] Subsequently, the TA sample categorization module (412, 414) may access the stored data and categorize it into the predetermined distance ranges. This categorized data may then be passed to the visualization generation module for creation of 2D or 3D representations.

[0136] The structured approach to data processing and visualization employed by the system (102) may enable network operators to gain valuable insights into their network performance. They may easily identify areas of high user concentration, potential coverage issues, or anomalies in signal propagation. Such information may be invaluable for network planning, optimization of base station placements, and overall improvement of service quality.

[0137] Moreover, by providing a unified platform for both 4G and 5G TA data, the system (102) may facilitate comparative analysis between the two technologies. This can be particularly useful in areas where both networks coexist, potentially allowing operators to make informed decisions about network upgrades or technology transitions.

[0138] The system's (102) ability to handle data from various sources and present it in a standardized format may also promote interoperability and consistency in network analysis across different equipment vendors and network configurations. This standardization may represent a significant step towards more efficient and effective network management practices in the rapidly evolving telecommunications landscape.

[0139] In additional embodiments, the system (102) may incorporate machine learning algorithms to predict network performance based on historical TA data. It may also include features for real-time data processing, allowing for immediate visualization of network changes or anomalies.

[0140] The present disclosure thus provides a comprehensive system for processing, categorizing, and visualizing the TA data from both 4G and 5G networks, potentially offering significant advantages in network management and optimization.

[0141] FIG. 5 illustrates an exemplary flow chart depicting a method (500) for visualizing Timing Advance (TA) data in a network catering to both 4G and 5G technologies, in accordance with one or more embodiments of the present disclosure. The method (500) may be implemented by the system (102) described earlier, which may include one or more processors (202), a memory (204), and at least two databases: a first database (210a) and a second database (210b).

[0142] At step 502, the process may initiate, marking the commencement of the TA data visualization. This step may involve comprehensive system initialization, including loading necessary software libraries and modules for data processing and visualization, establishing secure connections to the first database (210a) and the second database (210b), initializing the user interface module (214) for user interaction, and verifying system resources and optimizing performance for subsequent data processing tasks.

[0143] At step 504, the system (102) may perform a critical check to determine whether the input data belongs to a 4G or 5G network. This determination may be crucial for subsequent processing steps, as the system may need to handle data differently based on the network type. The system may employ various methods to make this determination, including analyzing metadata associated with the TA data, querying the appropriate database based on predefined data structures, examining specific fields within the TA data that indicate the network generation, and utilizing machine learning algorithms trained to classify network data types.

[0144] At step 506, if the technology is determined to be 4G, the system (102) may proceed to a "Cell Selection" stage. This step may involve a high degree of user interaction with the system (102) to select a specific area of interest for visualization within the 4G network. The system (102) may provide a sophisticated interface allowing the user to specify geographical coordinates for the area of interest, input cell identifiers directly, select from an interactive map-based interface, apply filters based on various criteria such as performance metrics, geographical regions, or specific network parameters, and utilize saved presets or favourites for quick access to frequently analyzed areas. Following this selection, the process may branch into two potential visualization paths: 2D Plane or 3D Plane, offering users flexibility in how they wish to analyze the data.

[0145] At step 508, if 2D visualization is chosen, the system (102) may generate a detailed 2D map with thematic coloring. The visualization process may involve retrieving relevant TA data from the first database (210a), which in this case may be configured as an "LTE TA Database" specifically optimized for 4G networks. The system may analyze each TA data sample to categorize it based on predefined distance ranges, calculate the percentage of the TA data samples within each distance range, generate a base map of the selected area, potentially incorporating geographical features or satellite imagery, and overlay the TA data using a carefully designed color scheme. The system may implement a dynamic color gradient system that adjusts based on the data distribution, ensuring optimal visibility of patterns, and add interactive elements for detailed data views.

[0146] At step 510, the system (102) may perform a focused retrieval of the TA data relevant to the selected cell from the first database (210a). This database, configured for 4G networks, may employ advanced data structures and indexing methods to ensure rapid and efficient data access, even when dealing with large volumes of the TA data samples.

[0147] At step 512, if 3D visualization is chosen, the system (102) may generate a sophisticated 3D map with thematic coloring. This process may involve utilizing the same TA data retrieved from the first database (210a), creating a three- dimensional model of the selected area, potentially incorporating terrain data for added context, and positioning the TA data samples in the 3D space based on their distance from the base station. The system may apply color coding similar to the 2D visualization and incorporate height variations to provide an additional dimension of data representation. Advanced rendering techniques may be implemented to ensure smooth performance even with large datasets, and interactive elements may be added for enhanced analysis capabilities.

[0148] At step 514, the system (102) may present the generated 2D and 3D visualizations for the 4G network on the user interface module (214). This interfacemay be highly interactive, allowing users to switch between views, zoom in and out of specific areas, apply data filters in real-time, overlay additional network information, and export visualizations or raw data for further analysis or reporting.

[0149] At step 516, if the network is determined to be 5G, the process may move to a "Cell Selection" stage tailored for 5G networks. This step may be similar to step 506 but may incorporate 5G-specific elements such as options to select mmWave frequency bands, filters for beamforming configurations, and consideration of small cell deployments characteristic of 5G networks.

[0150] At steps 518 and 520, the system (102) may generate 2D and 3D visualizations for 5G data, respectively. These steps may follow processes similar to steps 508 and 512, but with important adaptations for 5G network, such as adjusting distance ranges to account for the typically smaller cell sizes in 5G networks, incorporating visualizations of beamforming patterns and their impact on the TA data, representing multi-path propagation effects more prominently, and visualizing the impact of higher frequency signals on building penetration and overall coverage.

[0151] At step 519, the system (102) may retrieve TA data relevant to the selected 5G cell from the second database (210b), which may be configured as an "NR TA Database." This database may be optimized for the unique characteristics of 5G TA data, including potentially higher resolution timing information and beamforming-related data.

[0152] At step 520, the system (102) may present the generated 2D and 3D visualizations for the 5G network on the user interface module (214). This interface may include all the features available for 4G visualizations, with additional tools specific to 5G analysis, such as beamforming efficiency analysis tools, inter-cell interference visualization in dense small-cell deployments, and comparative views between 4G and 5G coverage in overlapping areas.

[0153] The flowchart (500) provides a comprehensive, flexible, and insightful approach to visualizing TA data for both 4G and 5G networks. By leveraging the capabilities of the first database (210a) for 4G data and the second database (210b) for 5G data, the system (102) ensures efficient data management and retrieval specific to each network technology. This sophisticated visualization method enables network operators to identify areas of strong signal presence, potential coverage issues, and optimize network performance based on the spatial distribution of TA data samples across different network generations.

[0154] Furthermore, the flowchart (500) may be designed with extensibility in mind, allowing for the incorporation of future advancements in cellular technology. The system may be easily adapted to include additional databases for new network types as they emerge. The method also supports time-based analysis capabilities, enabling users to visualize changes in the TA data distribution over time. This feature is particularly valuable for identifying long-term trends, assessing the impact of network modifications, and planning for future network evolutions across both 4G and 5G technologies.

[0155] FIG. 6 illustrates a spider view of the output of the system (102) in accordance with one or more embodiments of the present disclosure. The spider view, also referred to as the spider view menu, is an innovative feature that may automatically extract and centralize information from various sources within the telecommunications network, presenting it on a unified user interface (UI) that facilitates easy navigation and comprehensive analysis.

[0156] The spider view menu may be designed to provide network operators with a convenient and efficient way to access relevant and up-to-date information from multiple sources in a single, intuitive interface. This centralized approach may significantly reduce the time and effort typically required to gather informationfrom disparate systems or applications, enabling operators to stay informed about network status and trends with minimal effort.

[0157] Referring to FIG. 6, a visual representation (600) of a data management and visualization interface is displayed. This interface may be employed in a telecommunications network to facilitate the monitoring and administration of wireless cellular network nodes. The interface may be structured around a central node identifier, which may serve as the focal point from which all data categories radiate.

[0158] The node identifier may provide essential details of the network node under examination, such as its frequency band (e.g., "2300 MHz"), sector (e.g., "Sec4_Cl"), and aunique identifying code (e.g., "I-MU-MUMB-ENB-0001"). This identifier may act as a central hub, allowing quick access to various data segments related to the specific node.

[0159] Radiating from the central node identifier, the interface may include several key segments, each designed to provide specific insights into different aspects of the network node's operation and performance: i. Alarms Segment: This segment may be tailored to present a comprehensive overview of current and historical alarm data for the node. It may be designed to alert network operators to any immediate or past issues that require attention, offering a streamlined approach to fault management. The alarms segment may include color-coded indicators for different severity levels and interactive elements for detailed alarm information. ii. Performance Segment: This segment may include Key Performance Indicators (KPIs) and timing advance data. It may be configured to display various performance metrics vital for assessing the health and efficiency of the network node. The timing advance subsegment may specifically focus on the delay experienced by signalstraveling between multiple sources (such as mobile phones or Outdoor Customer Premises Equipment) and the base station. iii. Properties Segment: This segment may provide detailed information on the node's characteristics, encompassing a broad range of data including the node's physical attributes, operational settings, and other pertinent properties that define its functionality within the network. This may include information such as hardware specifications, software versions, and configuration parameters. iv. Capacity Segment: This segment may be configured to monitor the network node's usage statistics and future capacity requirements. It may provide insights into the network's current load and assist in forecasting future demands to ensure optimal performance. The capacity segment may include graphical representations of usage trends and predictive analytics for capacity planning. v. Configuration Segment: This segment may offer an interface through which the network's technical settings can be viewed and adjusted. These settings may be crucial for maintaining the node's performance and for implementing changes to its operational parameters. The configuration segment may include safeguards to prevent unauthorized or potentially harmful changes to critical settings. vi. Coverage Segment: This segment may offer an interface through which the coverage provided by cells, including their settings and performance metrics, can be viewed. It may include visual representations of coverage areas, potentially overlaid on geographical maps for context. vii. Neighbour Segment: This segment may provide information about neighboring cells and their performance. It may be crucial for understanding inter-cell relationships, handover performance, and potential interference issues.

[0160] Each segment within the interface may be designed for ease of use, with visual and interactive elements that allow network operators to quickly navigate and interpret the data presented. The interface may aim to consolidate complex data into a single, accessible point, thus simplifying the task of network management and enhancing operational efficiency within the telecommunications network.

[0161] The unified UI may be configured to generate the visual representation of the pre-processed data in each segment of the spider view menu. This may involve sophisticated data processing and visualization techniques to ensure that large volumes of complex network data are presented in a clear, intuitive manner.

[0162] The spider view may also incorporate customizable features, allowing operators to tailor the interface to their specific needs or preferences. This may include options to rearrange segments, adjust data update frequencies, or set personalized alerts for specific network conditions.

[0163] Furthermore, the spider view may be designed with scalability in mind, capable of handling data from multiple nodes simultaneously. This may allow for comparative analysis across different nodes or regions of the network, facilitating more comprehensive network management strategies.

[0164] The spider view illustrated in FIG. 6 represents a powerful tool for network operators, consolidating critical information from various sources into a single, intuitive interface. By providing a holistic view of network node performance and status, it may significantly enhance the efficiency and effectiveness of network management tasks in complex telecommunications environments.

[0165] FIG. 7 illustrates an exemplary flow diagram of a method (700) for visualizing timing advance (TA) data in a network (104), in accordance with embodiments of the present disclosure.

[0166] At step (702), the method (700) includes receiving, by a receiving unit (212), TA data for a selected area of interest from at least two sources (302a, 302b), wherein the TA data comprises time delay measurements experienced by signals traveling between the at least two sources (302a, 302b) and abase station in the network (104). This step involves the collection of raw TA data from multiple sources, which may include mobile devices, Customer Premises Equipment (CPE), or other network-connected devices. The TA data provides crucial information about signal propagation times, which is essential for network synchronization and performance optimization.

[0167] At step (704), the method (700) includes storing, in at least two databases (210a, 210b), the received TA data in a unified format based on a network type, wherein the unified format comprises a standardized data structure for storing the received TA data from different network types. This step ensures that TA data from various network technologies, such as 4G and 5G, is stored in a consistent and easily accessible format. The unified format facilitates efficient data retrieval and analysis across different network generations.

[0168] At step (706), the method (700) includes categorizing the stored TA data into predetermined distance ranges. This categorization step is crucial for creating meaningful visualizations. The predetermined distance ranges may comprise 0 to 1.4 km, 1.4 to 2.9 km, 2.9 to 4.4 km, 4.4 to 5.9 km, and 5.9 km and above. By grouping TA data into these ranges, the method enables a clear representation of signal propagation patterns at various distances from the base station.

[0169] At step (708), the method (700) includes generating at least one of a two-dimensional (2D) or three-dimensional (3D) visualization of the categorized TA data. For 2D visualization, this involves creating a thematic representation using color gradients, wherein darker shades of a single color represent higher densities of the TA data samples within each predetermined distance range, and lighter shades of the same color represent lower densities of the TA data samples. For 3D visualization, the method creates a map layer with three-dimensional representation areas , wherein the height of each three-dimensional representation area corresponds to the percentage of the TA data samples within each predetermined distance range, and higher elevations represent higher percentages of the TA data samples. In the 3D visualization, three-dimensional representation areas represent areas with higher concentrations of the TA data samples. For instance, a tall peak might represent an area where 30% of all TA data samples in the cell are located, while a lower plateau might represent an area with 10% of samples. This allows for intuitive identification of user clustering and potential network congestion points.

[0170] At step (710), the method (700) includes displaying, by a user interface module (214), the generated at least one of a 2D or 3D visualization ofthe categorized TA data. This step provides network operators with an intuitive visual representation of the TA data distribution.

[0171] In additional embodiments, the method (700) may further include allowing, via the user interface module (214), selection of an area of interest for TA visualization, and selection between the 2D and 3D visualization types. This feature enhances the flexibility and usability of the visualization tool.

[0172] The method (700) may also include displaying, via the user interface module (214), the percentage and number of total TA data samples, providing quantitative insights alongside the visual representation.

[0173] Furthermore, the method (700) may include generating a visualization of Probability Density and Cumulative Distribution Function of the TA data on the map layer, offering additional statistical perspectives on the TA data distribution.

[0174] The present disclosure provides technical advancement related to network performance analysis and optimization in telecommunications. This advancement addresses the limitations of existing solutions by offering a unified approach to visualizing the TA data across different network generations. The disclosure involves innovative data categorization and visualization techniques, which offer significant improvements in network performance analysis efficiency and accuracy. By implementing adaptive 2D and 3D visualization methods, the disclosed invention enhances network operators' ability to identify signal propagation patterns and potential coverage issues, resulting in improved network planning and optimization capabilities. The standardized data structure and unified visualization approach enable seamless analysis across 4G and 5G networks, facilitating more effective decision-making in multi-generation network environments.

[0175] FIGS. 8(A) to 8(D) illustrate various representations of the User Interface (UI) module view for visualizing Timing Advance (TA) data in both 2D and 3D layers, in accordance with one or more embodiments of the present disclosure. These figures demonstrate the system's capability to present the TA data distributions across different distance ranges, providing network operators with intuitive visual representations for analysis and optimization.

[0176] FIG. 8(A) presents a 2D layer view of the TA data where the majority of samples fall within the 0 to 1.4 km range from the base station. This visualization may employ a color gradient scheme, where the intensity or hue of the color represents the density of the TA data samples. In this case, the area closest to the base station (represented by the center of the image) may show the highestconcentration of samples, indicated by the most intense or darkest color. This representation may suggest a scenario where most user equipment or Customer Premises Equipment (CPE) are located in close proximity to the base station, potentially indicating a dense urban environment or a small cell deployment.

[0177] FIG. 8(B) illustrates a more complex distribution of TA data on a 2D layer. In this scenario, the highest concentration of samples still falls within the 0 to 1.4 km range, but there is also a significant secondary concentration in the 1.4 to 2.9 km range. This visualization may use a dual -peak color intensity distribution, where the primary peak (darkest or most intense color) is near the center (0 to 1.4 km), and a secondary peak of slightly less intensity appears in the surrounding area (1.4 to 2.9 km). Such a distribution might be indicative of a suburban environment or a situation where there are two distinct clusters of users at different distances from the base station.

[0178] FIG. 8(C) depicts a 2D layer view where the majority of TA data samples fall within the 1.4 to 2.9 km range. In this representation, the area of highest color intensity or saturation may form a ring-like structure around the center, rather than being concentrated at the center itself. This distribution could represent a scenario where the immediate vicinity of the base station is less populated or has fewer active users, while the bulk of the network traffic comes from slightly further away. Such a pattern might be seen in areas with a central business district surrounded by residential areas, or in scenarios where geographic features influence user distribution.

[0179] FIG. 8(D) presents a 3D layer view of the TA data, focusing on a distribution where most samples fall within the 1.4 to 2.9 km range. Unlike the 2D representations, this 3D visualization may incorporate height as an additional dimension to represent data density. The 3D view may show a raised ring or plateau structure corresponding to the 1.4 to 2.9 km range, with the height of this structure indicating the concentration of samples. Areas closer to or further from the basestation may be represented as lower elevations. This 3D representation may offer a more intuitive understanding of the TA data distribution, allowing network operators to visually "see" the concentrations of users at different distances from the base station.

[0180] FIG. 8(E) depicts a 2D layer visualization of TA data for an over shooter cell scenario. In this representation, the system displays a distinctive pattern characterized by a concentration of TA samples extending well beyond the typical coverage boundaries of the cell, creating an elongated coverage footprint. The visualization employs a blue color gradient where the darkest shade indicates the highest concentration of TA samples, gradually transitioning to lighter shades representing decreasing sample densities. This over shooter cell pattern is particularly valuable for network optimization as it identifies situations where a cell's signal propagates significantly beyond its intended coverage area, potentially causing interference with neighboring cells. Network operators can leverage this visualization to detect and address antenna misalignment, excessive transmission power, or terrain-related signal propagation anomalies that contribute to the overshooting phenomenon. By identifying these issues, operators can implement targeted adjustments to transmission parameters, antenna tilt, or azimuth to optimize coverage boundaries and minimize inter-cell interference, ultimately improving overall network performance and user experience.

[0181] FIG. 8(F) presents a comprehensive graphical representation of TA data distribution through dual visualization of Probability Density Function (PDF) and Cumulative Distribution Function (CDF). The visualization employs a structured chart interface with distance ranges (in kilometers) plotted along the x- axis, sample count represented by blue histogram bars (PDF) on the left y-axis, and cumulative percentage of samples shown as a blue line graph (CDF) on the right y- axis. This dual representation enables network operators to simultaneously analyze both the absolute distribution of samples across distance ranges and the cumulative percentage distribution. In this particular example, the highest concentration ofsamples falls within the 2.9 to 4.4 km range, with approximately 2,300 samples, while the CDF curve shows that approximately 50% of all samples fall within distances up to 2.9 km, reaching nearly 100% coverage at distances beyond 5.9 km. The interface includes selector fields for SAP ID, Sector / Cell identification, and date selection, allowing operators to perform temporal and cell-specific analysis. This statistical representation complements the spatial visualizations by providing quantitative metrics for TA distribution, enabling precise network planning decisions and performance optimization based on actual user distribution patterns across different distance ranges from the base station.

[0182] In all these representations, the UI module may offer interactive features such as zoom, pan, and rotate (especially for the 3D view), allowing operators to examine the data from various angles and focus on specific areas of interest. Color legends or scales may be provided to help interpret the meaning of different color intensities or heights. Additionally, the UI may allow for switching between these different views or overlaying them with other network data for comprehensive analysis.

[0183] These visualizations serve as powerful tools for network operators, enabling them to quickly identify patterns in user distribution, potential coverage issues, or areas that may require capacity upgrades. By providing both 2D and 3D representations, the system caters to different analytical preferences and use cases, enhancing the overall effectiveness of network planning and optimization efforts.

[0184] FIG. 9 illustrates an example computer system (900) in which or with which the embodiments of the present disclosure may be implemented.

[0185] As shown in FIG. 9, the computer system (900) may include an external storage device (910), a bus (920), a main memory (930), a read-only memory (940), a mass storage device (950), a communication port(s) (960), and a processor (970). A person skilled in the art will appreciate that the computer system(900) may include more than one processor and communication ports. The processor (970) may include various modules associated with embodiments of the present disclosure. The communication port(s) (960) may be any of an RS-232 port for use with a modem-based dialup connection, a 10 / 100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication ports(s) (960) 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 (900) connects.

[0186] In an embodiment, the main memory (930) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (940) 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 (970). The mass storage device (950) 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 Lirewire interfaces).

[0187] In an embodiment, the bus (920) may communicatively couple the processor(s) (970) with the other memory, storage, and communication blocks. The bus (920) may be, e.g. a Peripheral Component Interconnect PCI) / PCI Extended (PCI-X) 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 (970) to the computer system (900).

[0188] In another embodiment, operator and administrative interfaces, e.g., a display, keyboard, and cursor control device may also be coupled to the bus (920)to support direct operator interaction with the computer system (900). Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) (960). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (900) limit the scope of the present disclosure.

[0189] The method and system of the present disclosure may be implemented in a number of ways. For example, the methods and systems of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0190] 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

[0191] The present disclosure automatically collects TA data from various sources (network elements and external providers) and transforms it into a unified format, eliminating the need for manual data manipulation and streamlining the analysis process.

[0192] The present disclosure generates user-friendly 2D maps with thematic coloring based on sample percentages, and potentially 3D visualizations that incorporate height to represent distance, allowing for a more intuitive understanding of signal distribution and network performance variations.

[0193] The present disclosure visualizes TA data across different distances, providing valuable insights into signal propagation within the network, which can be used to optimize base station placement, adjust antenna configurations, and address potential signal degradation issues.

[0194] The present disclosure streamlines data analysis and facilitates faster identification of network issues, leading to cost savings in network management and optimization processes.

Claims

CLAIMS1. A system (102) for visualizing timing advance (TA) data in anetwork (104), comprising: receive, by a receiving unit (212), TA data for an area of interest from at least two sources (302a, 302b), wherein the TA data comprises time delay measurements experienced by signals traveling between the at least two sources (302a, 302b) and a base station in the network (104); store, by a processing module (216), in at least two databases (210a, 210b), the received TA data in a unified format based on a network type, wherein the unified format comprises a standardized data structure for storing the received TA data based on the network type; categorize, by the processing module (216), the stored TA data based on predetermined distance ranges; generate, by the processing module (216), at least one of a two-dimensional (2D) or three-dimensional (3D) visualization of the categorized TA data; and display, by a user interface module (214), the generated at least one of the 2D or 3D visualization of the categorized TA data.

2. The system (102) as claimed in claim 1, wherein the network type comprises at least one of a 4G network or a 5G network.

3. The system (102) as claimed in claim 1, wherein the receiving unit (212) is configured to: determine whether the received TA data belongs to a 4G or 5G network; and select one of the at least two databases (210a, 210b) for storing the received TA data based on the network type, wherein: a first database (210a) is configured as a Long-Term Evolution (LTE) TA database for storing the received TA data related to 4G network, and a seconddatabase (210b) is configured as a New Radio (NR) TA database for storing the received TA data related to 5G network.

4. The system (102) as claimed in claim 1, wherein the processing module (216) is configured to categorize the stored TA data based on the predetermined distance ranges comprises: defining the predetermined distance ranges; sorting each TA data into the predetermined distance ranges based on its associated distance from the base station; and calculating a percentage of TA data samples falling within each predetermined distance range.

5. The system (102) as claimed in claim 4, wherein the processing module (216) is configured to implement the 2D visualization, wherein the 2D visualization comprises: a thematic representation using color gradients, wherein darker shades of a single color represent higher densities of the TA samples within each predetermined distance range, and lighter shades of the same color represent lower densities of the TA data samples within each predetermined distance range.

6. The system (102) as claimed in claim 4, wherein the processing module (216) is configured to implement the 3D visualization, wherein the 3D visualization comprises: a map layer with three-dimensional representation areas, wherein a height of each three-dimensional representation area corresponds to the percentage of the TA data samples within each predetermined distance range, and higher elevations represent higher percentages of the TA data samples.

7. The system (102) as claimed in claim 1, wherein the user interface module (214) is configured to: allow selection of the area of interest for TA visualization; and allow selection between the 2D and 3D visualization types.

8. The system (102) as claimed in claim 1, wherein the user interface module (214) is configured to display a percentage and a number of total TA data samples for each predetermined distance range.

9. The system (102) as claimed in claim 6, wherein the processing module (216) is configured to generate a visualization of Probability Density and Cumulative Distribution Function of the TA data on the map layer.

10. A method (700) for visualizing timing advance (TA) data in a network (104), the method comprising: receiving (702), by a receiving unit (212), TA data for area of interest from at least two sources (302a, 302b), wherein the TA data comprises time delay measurements experienced by signals traveling between the at least two sources (302a, 302b) and a base station in the network (104); storing (704), by a processing module (216) in at least two databases (210a, 210b), the received TA data in a unified format based on a network type, wherein the unified format comprises a standardized data structure for storing the received TA data based on the network type; categorizing (706), by the processing module (216) , the stored TA data based on predetermined distance ranges; generating (708), by the processing module (216), at least one of a two-dimensional (2D) or three-dimensional (3D) visualization of the categorized TA data; and displaying (710), by a user interface module (214), the generated at least one of the 2D or 3D visualization of the categorized TA data.

11. The method (700) as claimed in claim 10, wherein the network type comprises at least one of a 4G network or a 5G network.

12. The method as claimed in claim 10, further comprising: determining whether the received TA data belongs to a 4G or 5 G network; and selecting one of the at least two databases for storing the received TA data based on the network type, wherein: a first database is configured as a Long-Term Evolution (LTE) TA database for storing the received TA data related to 4G network, and a second database is configured as a New Radio (NR) TA database for storing the received TA data related to 5G network.

13. The method as claimed in claim 10, wherein categorizing the stored TA data into predetermined distance ranges comprises: defining the predetermined distance ranges; sorting each TA data into the predetermined distance ranges based on its associated distance from the base station; and calculating a percentage of TA data samples falling within each predetermined distance range.

14. The method (700) as claimed in claim 13, wherein generating the 2D visualization comprises: creating a thematic representation using color gradients, wherein darker shades of a single color represent higher densities of the TA data samples within each predetermined distance range, and lighter shades of the same color represent lower densities of the TA data samples within each predetermined distance range.

15. The method (700) as claimed in claim 13, wherein generating the 3D visualization comprises: creating a map layer with three-dimensional representation areas , wherein height of each three-dimensional representation area corresponds to a percentage of TA data samples withineach predetermined distance range, and higher elevations represent higher percentages of the TA data samples.

16. The method (700) as claimed in claim 10, further comprising: allowing, via the user interface module (214), selection of the area of interest for TA data visualization; and allowing, via the user interface module (214), selection between the 2D and 3D visualization.

17. The method (700) as claimed in claim 10, further comprising displaying, via the user interface module (214), a percentage and a number of total TA data samples for each predetermined distance range.

18. The method (700) as claimed in claim 15, further comprising generating a visualization of Probability Density and Cumulative Distribution Function of the TA data on the map layer.

19. A non-transitory computer-readable medium storing instructions that, when executed a system (102) for visualizing timing advance (TA) data in a network (104), cause the system (102) to perform operations comprising: receiving, by a receiving unit (212), TA data for an area of interest from at least two sources (302a, 302b), wherein the TA data comprises time delay measurements experienced by signals traveling between the at least two sources (302a, 302b) and a base station in the network (104); storing, by the processing module (216) in at least two databases (210a, 210b), the received TA data in a unified format based on a network type, wherein the unified format comprises a standardized data structure for storing received TA data based on the network type; categorizing, by the processing module (216), the stored TA data based on predetermined distance ranges;generating, by the processing module (216), at least one of a two- dimensional (2D) or three-dimensional (3D) visualization of the categorized TA data; and displaying, by a user interface module (214), the generated at least one of the 2D or 3D visualization of the categorized TA data.

20. A user equipment (108) communicatively coupled to a system (102) for visualizing timing advance (TA) data in a network (104), wherein the system (102) is configured to perform the method (700) as claimed in claim 10.

Images