System and method for managing network performance

Abstract

The present disclosure provides a system (102) and method (500) for managing performance of a RAN node. The system (102) comprises a memory (204) and processors (202) configured to execute instructions. The system (102) receives a performance management request from a UE (108) or based on automatic triggers from predefined network conditions. The system (102) retrieves RAN node configuration data with parameter values. Further, the system (102) identifies associated cell identification and operational parameters and compares the retrieved data with previously stored data. The system (102) detects changes in parameter values and maps the detected changes to identified cell details. The system (102) implements performance adjustments for the RAN based on the mapping result. The system enables automated management without manual intervention based on learned patterns from historical configuration changes and their impacts, enhancing network efficiency and user experience.

Description

SYSTEM AND METHOD FOR MANAGING NETWORK PERFORMANCEFIELD OF THE DISCLOSURE

[0001] The embodiments of the present disclosure generally relate to telecommunications networks. In particular, the present disclosure relates to systems and methods for managing performance of a radio access network (RAN) node by analyzing configuration changes of the RAN node.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] RAN (Radio Access Network) refers to the part of a mobile telecommunications system that connects user equipment to the core network through radio connections.

[0004] RAN node configuration data refers to the configuration settings and parameters of network nodes within the radio access network, including a plurality of parameter values.

[0005] Elastic Load Balancer (ELB) refers to a service that automatically distributes incoming application traffic across multiple targets, such as servers, to ensure optimal use of resources and maintain application availability.

[0006] Distributed File System (DFS) refers to a file system designed to store and process large datasets across clusters of commodity hardware, providing high throughput access to application data.

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

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

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

[0010] Network management platform refers to a system for end-to-end telecom network management that incorporates various modules and servers for processing and analyzing RAN configuration data.

[0011] An API gateway refers to a server that acts as an entry point for client requests, routing them to appropriate backend services, handling authentication, and potentially performing other functions like request / response transformation and rate limiting.BACKGROUND OF THE DISCLOSURE

[0012] 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.

[0013] Wireless communication technology has undergone rapid evolution over the past few decades. From the first generation's analog voice services to the current fifth-generation (5G) technology, each advancement has brought significant improvements in speed, capacity, and functionality. The 5G technology promiseseven faster data speeds, lower latency, and the ability to connect multiple devices simultaneously, opening up new possibilities for various applications and services.

[0014] As wireless technologies advance, radio access networks (RANs) play a crucial role in connecting user equipment to core networks. RANs typically consist of radio base stations with large antennas that wirelessly connect user devices to the broader network infrastructure. With the advent of 5G and increasing user demands, RANs are becoming increasingly complex, featuring higher speeds, more interconnected units, and the integration of various sub-networks into larger ones.

[0015] The evolution of wireless technologies has also led to changes in user behavior and expectations. Users now demand the ability to send different types of data simultaneously, including text, voice, video, and multimedia files. There is a growing demand for fast and reliable internet, especially for activities like gaming, audio, and video streaming on mobile devices. Users expect better network quality to minimize delays and ensure successful voice calls, leading to a heightened interest in real-time monitoring and optimization of RAN performance.

[0016] However, as networks become more complex, ensuring optimal performance becomes increasingly challenging. Network operators face significant difficulties in testing, troubleshooting, and identifying the causes of performance issues in both new and existing communication networks. Traditional techniques for monitoring and managing RAN performance often fail to accurately detect the root causes of performance degradation. These conventional methods typically require substantial manual effort from telecom operators, making them inefficient and time-consuming.

[0017] Moreover, the dynamic nature of modern networks, with frequent configuration changes and updates, adds another layer of complexity to performance management. Existing systems often struggle to keep track of these changes and their impact on network performance in real-time. This can lead to delays in identifying and resolving issues, potentially resulting in poor user experiences and increased end-user complaints.

[0018] Conventional systems and methods face difficulty in efficiently monitoring, analyzing, and managing RAN performance, particularly in the context of rapidly evolving network configurations.

[0019] There is, therefore, a need in the art to provide a method and a system that can overcome the shortcomings of the existing prior arts by offering real-time monitoring, automated analysis of configuration changes, and intelligent optimization of RAN performance.SUMMARY OF THE DISCLOSURE

[0020] In an exemplary embodiment, a system for managing performance of a radio access network (RAN) is described. The system comprises a request processing module configured to receive a performance management request from a user equipment (UE) or based on automatic triggers from predefined network conditions. A data management module retrieves RAN node configuration data based on the received request. The RAN node configuration data includes one or more values of a plurality of parameters. The data management module identifies respective cell identification and operational parameters associated with the retrieved RAN node configuration data. A configuration analysis module compares the retrieved RAN node configuration data with previously stored RAN node configuration data. The configuration analysis module detects at least one change in one or more parameter values based on a result of the comparison. The configuration analysis module maps the detected at least one change in the one or more parameter values with the identified respective cell details. A RAN management module implements performance adjustments for the RAN based on a result of the mapping. In some embodiments, the data management module is further configured to periodically retrieve the RAN node configuration data, compare the retrieved RAN node configuration data with previously stored RAN node configuration data, detect changes in the parameter values based on thecomparison, and store the detected changes in the plurality of parameter values in a database when the comparison indicates a change.

[0021] In some embodiments, the database comprises a databasel for storing structured data. The database also includes a database2 for storing data with datewise partitioning. The database 2 is configured to operate with a Distributed File System (DFS).

[0022] In some embodiments, the mapping by the configuration analysis module further comprises associating the detected at least one change with at least one of credentials of an entity associated with the change, a parameter identifier, a parameter value, and a timestamp associated with the change implementation.

[0023] In some embodiments, a visualization and interface module is further configured for making the mapped at least one change available for access. The visualization and interface module provides a user interface for displaying the mapped at least one change.

[0024] In some embodiments, the visualization and interface module enables access to at least one of a configuration change history, cell optimization data, and end-user complaint resolution information.

[0025] In some embodiments, the values of the plurality of parameters in the RAN node configuration data include values of at least one of a signal strength, a reference signal received power (RSRP), a reference signal received quality (RSRQ), a signal-to-interference-plus-noise ratio (SINR), a cell identifier (Cell- ID), and other network performance indicators.

[0026] In some embodiments, a complaint processing module receives an enduser complaint related to network performance including slow data speeds, dropped calls, poor signal quality, or other network performance degradation indicators. The complaint processing module correlates the end-user complaint with the detected at least one change. The complaint processing module identifies a configurationchange related to the end-user complaint based on the correlation. The complaint processing module initiates a response to the identified configuration change. The response may include generating an alert for network operators, suggesting a rollback of the problematic configuration change, proposing an alternative optimization, or scheduling additional tests to assess and quantify the impact of the change.

[0027] In some embodiments, implementing performance adjustments for the RAN by the RAN management module comprises analyzing the detected at least one change in the values of the plurality of parameters. The RAN management module identifies patterns in the detected changes that impact network performance. The RAN management module adjusts RAN configuration parameters based on the identified patterns, wherein the adjusting can be performed automatically by an intelligent system such as a hot without manual intervention based on learned patterns from historical configuration changes and their impacts. In an aspect, the RAN management module identifies the detected changes in the at least one of the values of the plurality of parameters that impact network performance. The RAN management module adjusts RAN configuration parameters based on the identified at least one change in the values of the plurality of parameters that impact network performance.

[0028] In some embodiments, the system is implemented as a part of a network management platform for end-to-end telecom network management. The request processing module processes the performance management request received from the user equipment (UE) through a shared load balancer, a plurality of web servers, and a plurality of application programming interface (API) gateway servers. A plurality of first servers are utilized for processing the RAN node configuration data. A load balancing module employs an Elastic Load Balancer (ELB) to automatically distribute incoming application traffic across the plurality of first servers and a plurality of second servers implemented as drop wizard servers.

[0029] In some embodiments, the plurality of first servers is configured to compare the ingested data related to the RAN node configuration data received on a particular day with the data related to the RAN node configuration data retrieved or stored on a previous day. The plurality of second servers are configured to store, by the data management module, the detected changes in the node configuration data along with the username, the parameter name, the parameter value, the execution date, the execution time, and the details of the cell in either the database 1 or the database2.

[0030] In another exemplary embodiment, a method for managing performance of a radio access network (RAN) is described. The method comprises receiving, by a request processing module, a performance management request from a user equipment (UE) or based on automatic triggers from predefined network conditions. A data management module retrieves a RAN node configuration data based on the received request. The RAN node configuration data includes one or more values of a plurality of parameters. The data management module identifies respective cell identification and operational parameters associated with the retrieved RAN node configuration data. A configuration analysis module compares the retrieved RAN node configuration data with a previously stored RAN node configuration data. The configuration analysis module detects at least one change in one or more values of the plurality of parameters based on a result of the comparison. The configuration analysis module maps the detected at least one change in one or more values of the plurality of parameters with the identified respective cell details. A RAN management module implements performance adjustments for the RAN based on a result of the mapping.

[0031] In some embodiments, the method further comprises periodically retrieving the RAN node configuration data, comparing the retrieved RAN node configuration data with previously stored RAN node configuration data, detecting changes in the parameter values based on the comparison, and storing the detected changes in the parameter values in a database when the comparison indicates a change.

[0032] In some embodiments, the database comprises a databasel for storing structured data. The database also includes a database2 for storing data with datewise partitioning. The database 2 is configured to operate with a Distributed File System (DFS).

[0033] In some embodiments, the mapping further comprises associating the detected at least one change with at least one of credentials of an entity associated with the change, a parameter identifier, a parameter value, and a timestamp associated with the change implementation.

[0034] In some embodiments, the method further comprises making, by a visualization and interface module, the detected at least one change available for access by a network management entity. The visualization and interface module provides a user interface for displaying the mapped detected at least one change.

[0035] In some embodiments, the method further comprises enabling the network management entity access to at least one of a configuration change history, cell optimization data, and end-user complaint resolution information, wherein the network management entity is a human operator or an automated system.

[0036] In some embodiments, the plurality of parameters in the RAN node configuration data include at least one of a signal strength, a Reference Signal Received Power (RSRP), a Reference Signal Received Quality (RSRQ), a Signal- to-Interference-plus-Noise Ratio (SINR), a Cell identifier (Cell-ID), and other network performance indicators.

[0037] In some embodiments, the method further comprises receiving, by a complaint processing module, an end-user complaint related to network performance including slow data speeds, dropped calls, poor signal quality, or other network performance degradation indicators. The complaint processing module correlates the end-user complaint with the mapped detected at least one change. The complaint processing module identifies a configuration change related to the enduser complaint based on the correlation. The complaint processing module initiatesa response to the identified configuration change or based on the correlation. The response may include generating an alert for network operators, suggesting a rollback of the problematic configuration change, proposing an alternative optimization, or scheduling additional tests to assess and quantify the impact of the change.

[0038] In some embodiments, implementing performance adjustments for the RAN comprises analyzing the mapped detected at least one change in the parameter values. The method includes identifying patterns in the detected changes that impact network performance. The method also includes adjusting RAN configuration parameters based on the identified patterns, wherein the adjusting can be performed automatically by an intelligent system without manual intervention based on learned patterns from historical configuration changes and their impacts In an aspect, the RAN management module identifies the detected changes in the at least one of the values of the plurality of parameters that impact network performance. The RAN management module adjusts RAN configuration parameters based on the identified at least one change in the values of the plurality of parameters that impact network performance.

[0039] In some embodiments, the method further comprises implementing the method as part of a network management platform for end-to-end telecom network management. The request processing module processes the performance management request received from the user equipment (UE) through a shared load balancer, a plurality of web servers, and a plurality of application programming interface (API) gateway servers. The method utilizes a plurality of first servers for processing the RAN node configuration data. A load balancing module employs an Elastic Load Balancer (ELB) to automatically distribute incoming application traffic across the plurality of first servers and a plurality of second servers implemented as drop wizard servers.

[0040] In some embodiments, the method further comprises comparing, by the configuration analysis module on the plurality of first servers, the ingested datarelated to the RAN node configuration data received on a particular day with the data related to the RAN node configuration data retrieved or stored on a previous day. The data management module on the plurality of second servers stores the detected changes in the node configuration data along with the username, the parameter name, the parameter value, the execution date, the execution time, and the details of the cell in either the database 1 or the database2.

[0041] In yet another exemplary embodiment, a User Equipment (UE) for facilitating management of performance of a radio access network (RAN) is described. The UE is configured for generating a performance management request related to RAN performance based on at least one of: user inputs, automated monitoring of network conditions, or predetermined performance thresholds being exceeded; and transmitting the generated performance management request to the system, wherein the system is configured to perform steps as stated above.

[0042] In yet another exemplary embodiment, a non-transitory computer- readable storage medium storing computer-executable instructions is described. When executed by one or more processors, the instructions cause the one or more processors to perform a method for managing performance of a radio access network (RAN). The method comprises receiving, by a request processing module, a performance management request from a user equipment (UE) or based on automatic triggers from predefined network conditions. A data management module retrieves a RAN node configuration data based on the received request. The RAN node configuration data includes one or more values of a plurality of parameters. The data management module identifies respective cell identification and operational parameters associated with the retrieved RAN node configuration data. A configuration analysis module compares the retrieved RAN node configuration data with a previously stored RAN node configuration data. The configuration analysis module detects at least one change in one or more values of the plurality of parameters based on a result of the comparison. The configuration analysis module maps the detected at least one change in the one or more values of the plurality of parameters with the identified respective cell details. A RANmanagement module implements performance adjustments for the RAN based on a result of the mapping.

[0043] 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

[0044] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.

[0045] An object of the present disclosure is to provide an improved system and a method for monitoring and managing a performance of a radio access network (RAN), thereby enhancing overall network efficiency and user experience.

[0046] An object of the present disclosure is to provide an improved system and a method for detecting configuration changes in a node of a radio access network (RAN), thereby enabling rapid identification and resolution of network issues.

[0047] An object of the present disclosure is to provide an improved system and a method for enhancing the user experience by managing the performance of the radio access network (RAN), thereby increasing end-user satisfaction and reducing complaints.

[0048] An object of the present disclosure is to provide a system and a method that automatically maps detected configuration changes to specific cell identification and operational parameters, thereby facilitating targeted network management efforts.

[0049] An object of the present disclosure is to provide a system and a method that periodically retrieves and compares RAN node configuration data, thereby ensuring up-to-date network performance monitoring.

[0050] An object of the present disclosure is to provide a system and a method that utilizes advanced database technologies for efficient storage and retrieval of configuration change data, thereby improving system scalability and performance.

[0051] An object of the present disclosure is to provide a system and a method that correlates end-user complaints with detected configuration changes, thereby expediting problem resolution and improving customer support.

[0052] An object of the present disclosure is to provide a system and a method that employs load balancing techniques for processing configuration data, thereby ensuring efficient utilization of system resources and improving response times.

[0053] An object of the present disclosure is to provide a system and a method that offers visualization and interface capabilities for configuration changes, thereby enhancing the ability of network management entities to understand and manage network performance.

[0054] An object of the present disclosure is to provide a system and a method that identifies patterns in configuration changes impacting network performance, thereby enabling proactive and automated network management strategies.

[0055] Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.BRIEF DESCRIPTION OF DRAWINGS

[0056] The accompanying drawings, which are incorporated herein, and constitute a part of 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 eachcomponent. It will be appreciated by those skilled in the art that disclosure of such drawings includes the disclosure of electrical components, electronic components or circuitry commonly used to implement such components.

[0057] FIG. 1 illustrates an exemplary network architecture of a system for managing performance of a radio access network (RAN), in accordance with embodiments of the present disclosure.

[0058] FIG. 2 illustrates an exemplary micro service-based architecture of the system for managing performance of a radio access network (RAN), in accordance with embodiments of the present disclosure.

[0059] FIG. 3 illustrates an exemplary flow diagram of a method for managing performance of the radio access network (RAN), in accordance with embodiments of the present disclosure.

[0060] FIG. 4 illustrates an exemplary architecture of the system for managing the performance of a radio access network (RAN), in accordance with embodiments of the present disclosure.

[0061] FIG. 5 illustrates an exemplary illustration of method managing the performance of the radio access network (RAN), in accordance with embodiments of the present disclosure.

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

[0063] The foregoing shall be more apparent from the following more detailed description of the disclosure.LIST OF REFERENCE NUMERALS100 - Network architecture102 - System104 - Network108, 108-1, 108-2...108-N - User equipment (UE)110, 110-1, 110-2...110-N - Users112 - Web servers114 - Application Program Interface (API) servers202 - Processor(s)204 - Memory206 - Interface(s)208 - Processing engine210 - Database212 - Request processing module214 - data management module216 - Configuration analysis module218 - RAN management module220 - Visualization and interface module222 - Complaint processing module224 - Load balancing module226 - Other module(s)300 - Method302, 304, 306, 308, and 310 - Steps of method 300400 - System architecture401 - Network management platform404 - Shared load balancer410 - First servers412 - Elastic Load Balancer (ELB)414 - Second servers416 - Database 1418 - Database 2500 - Method502, 504, 506, 508, 510, 512, 514 - Steps of method 500600 - Computer system610 - External storage device620 - Bus630 - Main memory640 - Read-only memory650 - Mass storage device660 - Communication port(s)670 - ProcessorDETAILED DESCRIPTION OF THE DISCLOSURE

[0064] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of 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.

[0065] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

[0066] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without thesespecific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0067] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0068] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.

[0069] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is includedin at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0070] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed items.

[0071] In the rapidly evolving landscape of wireless communications, the efficient management and optimization of radio access networks (RANs) have become increasingly critical. Traditional approaches to monitoring and managing RAN performance often struggle to keep pace with the complexity and dynamic nature of modern networks. These conventional methods frequently fall short in accurately identifying the root causes of performance degradation, leading to suboptimal network efficiency and potentially compromised user experiences.

[0072] The limitations of existing techniques are particularly evident in their inability to swiftly detect and respond to configuration changes within the RAN. Telecom operators often find themselves burdened with time-consuming manual processes to analyze network performance, troubleshoot issues, and implement management adjustments. This not only results in inefficient resource utilization but also delays critical improvements, potentially impacting service quality and user satisfaction.

[0073] Furthermore, as networks continue to grow in complexity, especially with the advent of 5G and beyond, the volume and variety of configuration data have increased exponentially. Traditional systems often lack the capability to effectively process, analyze, and derive actionable insights from this vast amount of information in real-time. This gap in capability leaves network operators ill- equipped to proactively manage network performance and respond swiftly to emerging issues. The actionable insights may include identification of optimal RAN parameter settings for specific traffic patterns or user densities, predictive maintenance alerts for network equipment based on performance degradation trends, real-time suggestions for load balancing across cells to improve overall network capacity, automated detection and resolution of inter-cell interference issues, recommendations for energy-saving measures during low-traffic periods without compromising service quality.

[0074] Recognizing these challenges, there is an urgent need for a system and method that can overcome the limitations of prior art approaches. Such a solution should not only efficiently monitor and manage RAN performance but also provide intelligent, automated analysis of configuration changes and their impact on network efficiency.

[0075] The present disclosure aims to address these critical needs by introducing an advanced system and method for RAN performance management. This innovative approach leverages cutting-edge technologies and methodologies to revolutionize how network performance is monitored, analyzed, and enhanced. By introducing a comprehensive suite of modules - including request processing, data management, configuration analysis, and RAN management - the system offers a holistic solution to the complex challenges of modern RAN management.

[0076] At its core, the present disclosure introduces a paradigm shift in RAN performance management. It moves beyond reactive troubleshooting to enable proactive, data-driven network management. The system's ability to automatically detect configuration changes, map them to specific cell identification andoperational parameters, and analyze their impact on network performance empowers telecom operators with unprecedented insights and control over their network infrastructure.

[0077] Moreover, by incorporating advanced features such as periodic data fetching, intelligent load balancing, and intuitive visualization interfaces, the system significantly reduces the manual effort required in network management. This not only improves operational efficiency but also allows network operators to focus on strategic improvements rather than routine troubleshooting.

[0078] In essence, the present disclosure aims to provide a comprehensive, efficient, and user-friendly solution for RAN performance management. By addressing the limitations of existing approaches and leveraging the power of automated analysis and intelligent management, this innovation promises to enhance network efficiency, improve user experiences, and equip telecom operators with the tools they need to manage the networks of today and tomorrow.

[0079] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGS. 1-6.

[0080] FIG. 1 illustrates a network architecture (100) of a system (102) for managing performance of a radio access network (RAN), in accordance with embodiments of the present disclosure.

[0081] In an embodiment, the system (102) is connected to a network (104), which is further connected to at least one computing devices 108-1, 108-2, . . . 108- N (collectively referred as computing device 108, herein) associated with one or more users 110-1, 110-2, ... 110-N (collectively referred as user (110), herein). The computing device (108) may be personal computers, laptops, tablets, wristwatch, or any custom-built computing device integrated within a modern diagnostic machine that can connect to a network as an loT (Internet of Things) device. In an embodiment, the computing device (108) may also be referred to as User Equipment (UE) or user device. Accordingly, the terms “computing device” and“User Equipment” may be used interchangeably throughout the disclosure. In an aspect, the user (110) is a network operator or a field engineer.

[0082] In an embodiment, the system (102) may receive at least one input data from the user (110) via the at least one computing devices (108). In an aspect, the user (110) may be configured to initiate the process of authentication and authorization of subscribers at edge of the network (104), through an application interface of a mobile application installed in the computing devices (108). In some examples, the mobile application may be a software or a mobile application from an application distribution platform. Examples of application distribution platforms include the App Store for iOS provided by Apple, Inc., Play Store for Android OS provided by Google Inc., and such application distribution platforms. In an embodiment, the computing device (108) may transmit the at least one captured data packet over a point-to-point or point-to-multipoint communication channel or network (104) to the system (102). In an embodiment, the computing device (108) may involve collection, analysis, and sharing of data received from the system (102) via the network (104). Furthermore, the system (102) may be connected to web servers (112) and Application Program Interface (API) servers (114) via the network (104).

[0083] In an exemplary embodiment, the network (104) may include, but not be limited to, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. In an exemplary embodiment, the network 104 may include, but not be limited to, a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet- switched network, a circuit- switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof.

[0084] 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).

[0085] FIG. 2 with reference to FIG. 1, illustrates an exemplary architecture (200) of the system (102) for managing performance of a radio access network (RAN), in accordance with an embodiment of the present disclosure.

[0086] The system (102) includes one or more processor(s) (202), a memory (204), a processing engine (208), a database (210), and an interface(s) (206). In an exemplary embodiment, the processing engine (208) may include one or more modules / engines selected from any of a request processing module (212), a data management module (214), a configuration analysis module (216), a RAN management module (218), a visualization and interface module (220), a complaint processing module (222), a load balancing module (224), and other module(s) (226) having functions that may include but are not limited to receiving data, processing data, testing, storage, and peripheral functions, such as wireless communication unit for remote operation, audio unit for alerts and the like.

[0087] In an embodiment, the one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog microcontrollers, digital signal processors, central processing units, logic circuitries, and / or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in the memory (204) of the system (102). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to createor share data packets over a network service. The memory (204) may comprise any non-transitory storage device including, for example, volatile memory such as Random Access Memory (RAM), or non-volatile memory such as Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.

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

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

[0090] In an embodiment, the processing unit / engine(s) (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processorexecutable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (208) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the system (102) may comprise the machine-readable storage medium storing the instructions and the processingresource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system (102) and the processing resource. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry.

[0091] In an embodiment, the database (210) is configured for serving as a centralized repository for storing and retrieving various operational data. The database (210) is designed to interact seamlessly with other components of the system (102) to support the system's functionality effectively. The database (210) may store data that may be either stored or generated as a result of functionalities implemented by any of the components of the one or more processor(s) (202) or the processing engines (208). In an embodiment, the database (210) may be separate from the system (102). The database (210) may be configured to maintain a databasel and a database2.

[0092] The system (102) for managing performance of a radio access network (RAN) may comprise a memory (204) and one or more processors (202) configured to execute a set of instructions stored in the memory (204). These instructions may enable various modules to work in concert to enhance RAN node performance through sophisticated analysis and management techniques.

[0093] The request processing module (212) may be responsible for receiving performance management requests from a user equipment (UE) (108) or based on automatic triggers from predefined network conditions. For instance, a user experiencing slow internet speeds on their smartphone (an instance of UE (108)) may initiate a request through their device's network settings, or the system may automatically generate a request when network performance metrics fall below predetermined thresholds. This module (212) may act as the initial point of contact for any performance-related queries or issues reported by end-users. In an aspect the request processing module (212) may periodically check whether the performance metrics have fallen below the predetermined thresholds. If the request processing module (212) determines that the performance metrics have fallen belowthe predetermined thresholds, it automatically generates the request. If the request processing module (212) determines that the performance metrics have not fallen below the predetermined threshold, then it will not generate the request. Upon receiving a performance management request, the request processing module (212) may parse and categorize the incoming data, preparing it for further processing by other system components. Example of a typical performance management request may be: "ERROR_CODE: 3001, DESCRIPTION: Low download speed, CURRENT_SPEED: 1.5 Mbps, EXPECTED_SPEED: 10 Mbps". Upon receiving the request, the request processing module (212) may parse and categorize the incoming data, prepare it for further processing by the data management module (214) and configuration analysis module (216).

[0094] The data management module (214) may play a crucial role in retrieving and managing RAN node configuration data. Upon receiving a processed performance management request from the request processing module (212), this component may initiate the retrieval of relevant configuration data The request processing module (212) may periodically check whether the performance metrics have fallen below the predetermined thresholds. If the request processing module (212) determines that the performance metrics have fallen below the predetermined thresholds, it automatically generates the request. If the request processing module (212) determines that the performance metrics have not fallen below the predetermined threshold, then it will not generate the request..

[0095] In an embodiment, the RAN node configuration data may include a plurality of parameter values that are critical for network performance. These parameters may encompass various aspects of network operation, such as transmission power, frequency allocation, and antenna configurations. Specifically, the module may collect data on signal strength, reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus- noise ratio (SINR), and cell identifier (Cell-ID). For example, the data management module (214) might retrieve RSRP values for all cells in a specific area to analyze signal strength distribution and identify potential coverage gaps. A typical set ofretrieved data might look like: { "Cell-ID": "LAX_DOWNTOWN_5", "RSRP": - 85 dBm, "RSRQ": -10 dB, "SINR": 5 dB, "Transmission Power": 20 dBm } Where an RSRP of -85 dBm indicates good signal strength, while a value below -110 dBm might suggest poor coverage.

[0096] In addition to on-demand data retrieval, the data management module (214) may also be configured to perform periodic data collection. This proactive approach may involve regularly retrieving configuration data, comparing it with previously stored information, and recording any detected changes. The frequency of this periodic retrieving could be set to daily, hourly, or any other appropriate interval based on network requirements. For instance, the module might perform a daily sweep of all cell configurations at 3 AM when network traffic is typically at its lowest, minimizing the impact on active users. In an embodiment, the configuration data is retrieved by fetching a CM.XML file.

[0097] The system may employ a sophisticated database structure to manage the vast amounts of configuration data and change history. A database (210) may be implemented, comprising two distinct components. The first component, databasel, may be implemented as a structured database for storing structured data. This could include user account information, cell tower locations, or other relatively static network information. For example, it might store a table of all cell towers in the network, including their geographic coordinates, height, and type of equipment installed.

[0098] The second component, database2, may be implemented as an unstructured database for storing data with date-wise partitioning. This design allows for efficient retrieval of historical configuration changes, enabling network operators to quickly access and analyze trends over time. The unstructured database may be configured to operate with a Distributed File System (DFS), enabling scalable and distributed storage of large datasets. This setup could allow, for instance, an operator to quickly retrieve all configuration changes made to a specificcell tower over the past six months, facilitating long-term performance analysis and troubleshooting.

[0099] The configuration analysis module (216) may be tasked with the critical function of comparing newly retrieved RAN node configuration data with previously stored information. This comparison process may involve sophisticated algorithms designed to detect even subtle changes in network parameters. The module may not only identify changes but also quantify the magnitude and potential impact of these alterations. For example, it might detect a 2-degree change in antenna tilt for a particular cell, which could significantly impact coverage area and potentially affect thousands of users. A detected change might be represented as: { "Cell-ID": "LAX_D0WNT0WN_5", "Parameter": "Antenna Tilt", "Old Value": "5 degrees", "New Value": "3 degrees", "Change Magnitude": "2 degrees", "Potential Impact": "High - May affect coverage for up to 5000 users" }

[0100] Upon detecting changes, the configuration analysis module (216) may proceed to map these alterations to specific cell details. The term 'cell details' encompasses comprehensive information about a specific network cell, including cell identification information (Cell-ID, eNodeB ID, sector identifiers), geographical location data (latitude, longitude, altitude, coverage area boundaries), radio parameter configurations (frequency bands, bandwidth allocations, transmission power settings, antenna configurations), operational parameters (capacity settings, handover thresholds, neighbor cell relationships), and performance metrics. The geographical location data within these cell details enables spatial correlation of configuration changes with user-reported issues and facilitates visualization of network changes on geographical maps, allowing for more targeted and effective network management. This mapping process may involve associating each detected change with relevant contextual information, such as the credentials of the entity responsible for the change, specific parameter identifiers, updated values, and timestamps. For instance, if a network engineer named "John Doe" adjusts the power output of a cell tower in downtown Chicago, the module might record an entry: {"changeld": "CHG123456", "cellld":"CHI_D0WNT0WN_5", "parameter": "txPower", "oldValue": "18dBm", "newValue": "20dBm", "changedBy": "john.doe@network.com", "timestamp": "2023-06-15T14:30:00Z" }

[0101] This detailed mapping may provide a comprehensive audit trail of network configuration changes, enabling network operators to trace the evolution of their network setup over time and correlate changes with performance fluctuations.

[0102] The RAN management module (218) may represent the culmination of the system's analytical processes, translating insights into actionable management adjustments. This module may analyze the mapped changes provided by the configuration analysis module (216), potentially employing machine learning algorithms to identify patterns and trends that impact network performance. By correlating configuration changes with performance metrics, the RAN management module (218) may generate recommendations for parameter adjustments to enhance overall network efficiency. For example, if the module detects increased data traffic during a large sporting event, it might recommend: { "Cell-ID": "STADIUM_CELL_1", "Recommended Changes": [ {"Parameter": "Transmission Power", "New Value": "25 dBm", "Current Value": "20 dBm" }, {"Parameter": "Antenna Tilt", "New Value": "2 degrees", "Current Value": "5 degrees" } ], "Expected Outcome": "30% increase in data throughput, 20% reduction in dropped calls" }.

[0103] The management process may involve analysis of multiple parameters such as signal strength, RSRP, RSRQ, SINR, and Cell-ID. By analyzing these parameters in conjunction with detected configuration changes, the RAN management module (218) may propose targeted adjustments that address specific performance issues or pre-emptively improve network resilience. For example, if the RAN management module (218) detects that certain configuration changes consistently lead to improved data throughput during peak usage hours, it might automatically apply similar adjustments to other cells with comparable trafficpatterns. This could involve adjusting parameters like antenna tilt, transmission power, or handover thresholds to optimize coverage and capacity based on learned patterns.

[0104] A visualization and interface module (220) may be incorporated to make the system's insights accessible and actionable for network operators. The visualization and interface module (220) may be responsible for creating intuitive user interfaces that display mapped configuration changes, historical trends, and management recommendations. The visualizations may include interactive dashboards, allowing operators to drill down into specific cells, time periods, or parameter types for detailed analysis. For instance, it might generate a heat map showing areas of the network where recent configuration changes have led to improved or degraded performance, with the ability to overlay user complaint data or traffic patterns. A mock-up of the user interface might include:A map view of the network with color-coded cells indicating performance levelsA timeline showing configuration changes and corresponding performance metricsA table listing recent optimizations and their impactsA search function to quickly locate specific cells or parameters

[0105] The visualization and interface module (220) may also facilitate access to a configuration change history, providing a chronological view of network evolution. This historical perspective may be invaluable for understanding longterm trends and the cumulative impact of configuration changes. Additionally, the visualization and interface module (220) may offer access to cell optimization data, enabling operators to view the effects of implemented optimizations over time. For example, an operator could generate a report showing how a series of configuration changes over the past month has affected average data speeds in a particular neighborhood.

[0106] To enhance the system's responsiveness to user experiences, a complaint processing module (222) may be integrated. This module may receive and process end-user complaints related to network performance. By correlating these complaints with the mapped configuration changes, the module may identify potential links between specific network alterations and reported issues. For example, if multiple users in a specific area report dropped calls after a recent configuration change, the complaint processing module might automatically generate an alert for the network operations team, suggesting a rollback of the problematic change or proposing an alternative adjustment.

[0107] For example, the complaint processing module (222) may receive and process end-user complaints related to network performance. By correlating these complaints with the mapped configuration changes, the complaint processing module (222) may identify potential links between specific network alterations and reported issues. For example, if a user submits a complaint: { "User ID": "U123456", "Location": "Downtown LA", "Issue": "Frequent call drops", "Time": "2023-08-01 14:30 PST" } The module might correlate this with recent configuration changes: { "Cell-ID": "LAX_D0WNT0WN_5", "Recent Changes": [ {"Parameter": "Handover Threshold", "Old Value": "-100 dBm", "New Value": "-95 dBm", "Time": "2023-08-01 09:00 PST" } ], "Recommended Action": "Revert Handover Threshold to -100 dBm" }.

[0108] The complaint processing module (222) may also be responsible for initiating responses to identified issues. This may involve generating automated alerts for network operators, suggesting rollback of problematic configuration changes, or proposing targeted optimizations to address specific user complaints. By bridging the gap between user-reported issues and network configuration data, this module may significantly enhance the operator's ability to maintain high levels of service quality and user satisfaction.

[0109] To manage the computational demands of these processes, a load balancing module (224) may be employed. This load balancing module (224) mayutilize an Elastic Load Balancer (ELB) to distribute incoming application traffic across multiple servers efficiently. The system (102) may employ a plurality of first serversfor processing RAN node configuration data. These servers may handle the bulk of the data processing tasks, including the comparison of daily configuration data with historical records. For instance, these servers might handle the computationally intensive task of comparing daily configuration snapshots across thousands of network nodes, identifying changes, and preparing this data for further analysis.

[0110] A separate set of second servers, implemented as Drop wizard servers, may be utilized for storing processed data and handling other backend operations. These servers might manage the long-term storage of historical configuration data and handle API requests for data retrieval. The load balancing module (224) may ensure that the workload is evenly distributed across these server groups, preventing bottlenecks and ensuring optimal system performance even under high load conditions.

[0111] The entire system (102) may be implemented as part of a broader network management platform, providing end-to-end telecom network management capabilities. This integration may allow for seamless data flow between various network management functions, enabling a holistic approach to network optimization and maintenance.

[0112] In operation, the system (102) may process optimization requests from user equipment (UE) (108) through a multi-layered approach. Incoming optimization requests may first pass through a shared load balancer, which may distribute the traffic across a plurality of web servers (112). These web servers (112) may handle initial request processing before passing the data to a set of application programming interface (API) gateway servers (114). This tiered architecture may ensure efficient handling of high volumes of user requests while maintaining system responsiveness. For example, a user's request to run a network speed test might be initially received by the shared load balancer, routed to an available webserver (112) for processing, and then passed to an API gateway server (114) to initiate the actual speed test.

[0113] The configuration analysis module (216), communicating with the first set of servers, may perform daily comparisons of ingested RAN node configuration data. This daily analysis may involve comparing newly received data with information stored from the previous day, enabling the detection of recent changes that may impact network performance. For instance, it might identify that a particular cell's handover parameters were adjusted at 2 AM, potentially affecting users moving between cells during the morning commute.

[0114] The data management module (214), communicating with the second set of servers, may be responsible for retrieving and storing the detected changes in node configuration data. This storage process may involve recording detailed information about each change, including the username associated with the modification, the specific parameter name and its new value, the execution date and time of the change, and the details of the affected cell. For example, it might record that user "engineerjane" changed the "antennaDowntilt" parameter to "5 degrees" for cell "LAX_Downtown_3" on June 15, 2023, at 14:30 UTC.

[0115] Through this comprehensive approach to data collection, analysis, and management, the system (102) may provide network operators with powerful tools for maintaining and enhancing RAN performance. By automating many aspects of network configuration management, the system (102) may significantly reduce the manual effort required from telecom operators, leading to more efficient and effective network management practices. This could result in improved network performance, reduced downtime, and enhanced user satisfaction across the entire radio access network.

[0116] FIG. 3 illustrates a flow diagram of a method for managing performance of a radio access network (RAN), in accordance with an embodiment of the present disclosure.

[0117] At step 302, a network management platform may receive an application programming interface (API) request from a user equipment (UE) (108) or automatically based on predefined network conditions. The API request may be triggered when an event related to a network issue is detected from the UE (108) or when certain performance thresholds are exceeded. The network management platform serves as a comprehensive solution for end-to-end telecom network management.

[0118] At step 304, the network management platform may transmit the received API request to a plurality of application programming interface (API) gateway servers (114). These API gateway servers (114) function as a data-plane entry point for API calls representing user requests directed to target applications and services.

[0119] At step 306, the plurality of API gateway servers (114) may forward the received API request to a plurality of first servers.

[0120] The system may ingest data related to all RAN node configuration data from the UE (108) on a daily basis or at other scheduled intervals. Based on the API request, the plurality of first servers may compare the ingested RAN node configuration data received on a particular day with the data related to the RAN node configuration data retrieved or stored on a previous day.

[0121] The system may detect changes in the RAN node configuration data based on the comparison result. These changes may be directly associated with a cell based on a plurality of parameters. For example, the changes may be linked to a cell identification and operational parameters such as cell / sector, site, or node based on a username, parameter name, parameter value, execution date, and execution time. The parameters may include signal strength, reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to- interference -plus-noise ratio (SINR), cell identifier (Cell-ID), and other network performance indicators.

[0122] At step 308, the plurality of first servers may transmit the detected changes in the RAN node configuration data, along with associated metadata, to a plurality of second servers, which may be implemented as a centralized server.

[0123] At steps 310 the plurality of second servers may store the detected changes in the node configuration data, along with the username, parameter name, parameter value, execution date, execution time, and cell / sector / site / node details, in either a databasel or a database2.

[0124] The RAN node configuration data change may be mapped with the geographic location data of the user and other cell details. The term 'cell details' encompasses comprehensive information about a specific network cell, including cell identification information (Cell-ID, eNodeB ID, sector identifiers), geographical location data (latitude, longitude, altitude, coverage area boundaries), radio parameter configurations (frequency bands, bandwidth allocations, transmission power settings, antenna configurations), operational parameters (capacity settings, handover thresholds, neighbouring cell relationships), and performance metrics. The geographical location data within these cell details enables spatial correlation of configuration changes with user-reported issues and facilitates visualization of network changes on geographical maps, allowing for more targeted and effective network management. This mapping allows engineers or users to utilize the geographic location data and cell-wise configuration change history for resolving user-reported issues and managing network performance.

[0125] FIG. 4 illustrates an exemplary system architecture (400) for managing performance of a radio access network (RAN), in accordance with an embodiment of the present disclosure.

[0126] The system architecture (400), which represents a detailed implementation of system (102), may include a plurality of user equipment (UE) (108), a shared load balancer (404), and a network management platform (401).

[0127] The network management platform (401) may comprise a plurality of web servers (112), a plurality of application programming interface (API) gateway servers (114), a plurality of first servers (410), an Elastic Load Balancer (ELB) (412), a plurality of second servers (414), a databasel (416), and a database2 (418).

[0128] The shared load balancer (404) may receive API requests from the UE (108) when network issues are detected. The load balancer (404) then transmits these requests to the network management platform (401), effectively distributing the load among various servers within the platform.

[0129] The network management platform (401) receives the API requests at the web servers (112), which then forward them to the API gateway servers (114). The API gateway servers (114) serve as a critical intermediary, performing several key functions. They handle request routing by directing incoming API calls to the appropriate backend services, manage authentication and authorization to verify the identity of the requester and ensure they have necessary permissions, implement rate limiting to control the number of requests a client can make in a given timeframe, perform request / response transformation to modify requests or responses as needed for compatibility, potentially cache responses to improve performance for frequently requested data, and collect analytics for monitoring and optimization purposes. After performing these essential functions, the API gateway servers (114) transmit the validated and processed requests to the first servers (410), ensuring that only properly vetted and formatted requests reach the core processing systems.

[0130] The first servers (410) ingest RAN node configuration data daily and compare it with previously stored data to detect changes. These changes are associated with specific cells / sectors, sites, or nodes, along with relevant metadata.

[0131] The detected changes are then transmitted to the second servers (414) via the Elastic Load Balancer (ELB) (412), which automatically distributes incoming application traffic across multiple targets.

[0132] Finally, the second servers (414) store the detected changes, along with all associated details, in either databasel (416) or database2 (418). Databasel (416) may be implemented as a structured database, while database2 (418) may utilize unstructured database for date-wise partitioned data storage.

[0133] This system architecture enables efficient monitoring, analysis, and optimization of RAN performance, allowing for quick identification and resolution of network issues, thereby enhancing overall user experience.

[0134] FIG. 5 illustrates an exemplary flow diagram of a method (500) for managing performance of a radio access network (RAN), in accordance with embodiments of the present disclosure.

[0135] At step (502), the method (500) includes receiving, by a request processing module (212), a performance management request from a user equipment (UE) (108) or based on automatic triggers from predefined network conditions. This step may involve the UE (108) initiating a request due to perceived network issues, such as slow internet speeds or dropped calls, or the request processing module (212) automatically generates a request when network performance metrics fall below predetermined thresholds. The request processing module (212) periodically checks whether the performance metrics have fallen below the predetermined thresholds. If the request processing module (212) determines that the performance metrics have fallen below the predetermined thresholds, it automatically generates the request. If the request processing module (212) determines that the performance metrics have not fallen below the predetermined threshold, then it will not generate the request. .

[0136] At step (504), the method (500) includes retrieving, by a data management module (214), a RAN node configuration data based on the received performance management request. The RAN node configuration data includes one or more values of a plurality of parameters. These parameters may encompass various aspects of network operation, such as transmission power, frequency allocation, and antenna configurations. The parameter values may include values ofat least one of signal strength, reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise ratio (SINR), cell identifier (Cell-ID), and other network performance indicators. For instance, the module might retrieve RSRP values for all cells in the vicinity of the UE (108) that sent the request.

[0137] At step (506), the method (500) includes identifying, by the data management module (214), respective cell identification and operational parameters associated with the retrieved RAN node configuration data. This step involves linking the configuration data to specific cells or sectors within the network, providing context for the subsequent analysis.

[0138] At step (508), the method (500) includes comparing, by a configuration analysis module (216), the retrieved RAN node configuration data with previously stored RAN node configuration data in database 1 or database 2. This comparison allows the system to identify any recent changes in network configuration that might impact performance.

[0139] At step (510), the method (500) includes detecting, by the configuration analysis module (216), at least one change in one or more values of the plurality of parameters based on a result of the comparison. For example, this step might detect that the antenna tilt of a nearby cell was adjusted from 5 degrees to 3 degrees within the last 24 hours.

[0140] At step (512), the method (500) includes mapping, by the configuration analysis module (216), the detected at least one change in the one or more values of the plurality of parameters with the identified respective cell details. This mapping may involve associating the detected change with at least one of identification information of an entity associated with the change, such as a username or employee ID of the network operator who made the change, a parameter identifier, a parameter value, and a timestamp associated with the change implementation. For instance, the configuration analysis module (216) may record31 that "engineerjohn" changed the "antennaDowntilt" parameter to "3 degrees" for cell "DOWNTOWN_1" on July 1, 2023, at 02:30 UTC.

[0141] At step (514), the method (500) includes implementing, by a RAN management module (218), performance adjustments for the RAN based on a result of the mapping. This management process may involve analyzing the detected changes in the plurality of parameter values, identifying patterns in the detected changes that impact network performance, and adjusting RAN configuration parameters based on the identified patterns. For example, if the system (102) identifies that reducing antenna tilt in urban areas during peak hours consistently improves data throughput, it might automatically apply similar adjustments to other urban cells during their respective peak usage times. In an aspect, the RAN management module (218) identifies the detected changes in the at least one of the values of the plurality of parameters that impact network performance. The RAN management module adjusts RAN configuration parameters based on the identified at least one change in the values of the plurality of parameters that impact network performance.

[0142] The method may further comprise retrieving the RAN node configuration data periodically at scheduled intervals such as hourly, weekly, daily etc. The method may further comprise comparing the retrieved RAN node configuration data with previously stored RAN node configuration data and detecting changes in the plurality of parameter values based on the comparison. Based on the comparison, the method may further comprise storing the detected changes in the plurality of parameter values in a database (210) when the comparison indicates a change and maintaining a historical record of the detected changes to establish a chronological trail of network configuration modifications. This proactive approach ensures that the system maintains an up-to-date view of the network configuration without relying solely on user-initiated requests.

[0143] The database (210) may comprise a database 1 (416) implemented as an structured database for storing structured data, and a database2 (418) implementedas an unstructured database for storing data with date-wise partitioning. The unstructured database may be configured to operate with a Distributed File System (DFS), enabling efficient storage and retrieval of large volumes of historical configuration data. The structured data may include organized information with a defined data model, such as user account details, cell tower locations, and equipment specifications. Further, the date- wise partitioning involves organizing and storing data based on specific dates or time periods to allow efficient retrieval of historical configuration changes.

[0144] The method may also include additional steps for enhancing user experience and network management. A visualization and interface module (220) may make the detected changes in the configuration data available for access and provide a user interface for displaying these changes. This module may also enable access to a configuration change history, cell optimization data, and end-user complaint resolution information, providing network management entities, which may be human operators or automated systems, with comprehensive tools for monitoring and managing network performance.

[0145] In addressing user complaints, the method may incorporate steps for receiving an end-user complaint related to network performance (slow data speeds, dropped calls, poor signal quality, or other network performance degradation indicators) by a complaint processing module (222), identifying configuration changes related to the complaint, and initiating a response based on the identified configuration change or based on the correlation. The response may include generating an alert for network operators, suggesting a rollback of the problematic configuration change, proposing an alternative optimization, or scheduling additional tests to assess and quantify the impact of the change. For instance, if a user reports poor call quality, the system might identify recent changes in voice codec settings for the relevant cell and suggest reverting these changes or applying alternative optimizations.

[0146] To support efficient data management, the method may include creating data structures for organizing the configuration change data. The data structures may include at least one of relational database tables for storing structured configuration data, key-value pairs for rapid retrieval of specific configuration parameters, time-series data structures for tracking changes over time, graph structures for representing relationships between different configuration elements, or JSON (JavaScript Object Notation) objects for flexible storage and exchange of configuration data. These data structures could be designed to optimize storage efficiency and query performance, enabling rapid analysis of historical configuration changes across the network.

[0147] The method may be implemented as part of a network management platform (401). This implementation involves processing requests through a multitiered architecture, utilizing a shared load balancer (404), web servers (112), and API gateway servers (114). The system may employ a plurality of first servers (410) for processing RAN node configuration data, and use an Elastic Load Balancer (ELB) (412) to distribute traffic from these servers to a set of second servers (414) implemented as a centralized server.

[0148] The method may involve comparing ingested RAN node configuration data of the current day and the previous day on the first servers (410). The detected changes, along with associated metadata such as username, parameter details, execution time, and cell information, may then be stored by the data management module (214) on the second servers (414) in either databasel (416) or database2 (418), ensuring comprehensive tracking of all network configuration changes.

[0149] In another exemplary embodiment, a user equipment (UE) (108) for facilitating management of performance of a radio access network (RAN) is described. The UE (108) may be configured to generate management requests related to RAN performance based on user inputs or automated monitoring of network conditions. These management requests serve as the initial trigger for themanagement process, enabling the system to maintain high levels of network performance and user satisfaction.

[0150] The method may employ sophisticated techniques for analyzing the mapped detected changes in the plurality of parameter values. This analysis may involve machine learning algorithms that can identify patterns in the network configuration changes and their impact on performance metrics. For example, the system might recognize that certain combinations of parameter changes, such as adjustments to both antenna tilt and transmission power, have a more significant impact on network performance than changes to individual parameters in isolation.

[0151] In managing the RAN performance, the method may utilize predictive modelling to anticipate the effects of potential configuration changes before they are implemented. This could involve running simulations based on historical data and current network conditions to estimate the impact of proposed changes. For instance, before adjusting the handover parameters for a group of cells, the system might simulate the expected effect on call drop rates and data throughput, allowing network operators to make informed decisions.

[0152] The visualization and interface module (220) may provide advanced visualization tools to help network operators understand the complex relationships between configuration changes and network performance. This could include interactive heatmaps overlaying configuration changes with performance metrics, time-series graphs showing the evolution of key parameters, and network topology diagrams highlighting areas of recent changes or ongoing issues.

[0153] The method may incorporate real-time monitoring capabilities, allowing for immediate detection and response to configuration changes that negatively impact network performance. For example, if a configuration change results in a sudden increase in dropped calls in a particular area, the system could automatically alert operators and suggest immediate corrective actions.

[0154] To enhance the efficiency of the management process, the method may implement a prioritization system for addressing detected issues. This could involve assigning severity levels to different types of performance problems and configuration changes, ensuring that the most critical issues are addressed first. For instance, changes affecting emergency services communications might be given the highest priority for analysis and management.

[0155] The complaint processing module (222) may utilize natural language processing techniques to analyze user complaints and correlate them more accurately with specific network issues and configuration changes. This could allow the system to interpret free-form text complaints and automatically categorize them based on the likely underlying network issues.

[0156] The method may also include steps for automated testing and validation of configuration changes. After implementing management, the system could automatically initiate a series of network tests to verify the effectiveness of the changes and ensure that they haven't introduced new issues. This could involve scheduled drive tests, automated call quality assessments, or triggering test routines on a sample of user devices in the affected area.

[0157] To support long-term network planning and management, the method may include functionality for generating comprehensive reports and trend analyses. These reports could provide insights into the most frequent types of configuration changes, their typical impacts on network performance, and recommendations for future network upgrades or expansions based on observed patterns.

[0158] The method may also incorporate external data sources to provide information for network performance management. This could include integrating weather data, local event schedules, or traffic information to anticipate and proactively address potential network congestion or coverage issues.

[0159] In addressing security concerns, the method may include steps for validating and authorizing configuration changes. This could involve implementinga multi-level approval process for significant changes, ensuring that all modifications to the network configuration are properly vetted and documented. The multi-level approval process refers to a structured system where proposed changes must be reviewed and approved by multiple authorized individuals or teams before implementation. This typically involves a hierarchical approach, where initial approval might come from a technical expert, followed by a team lead, and finally a senior manager or network administrator. Each level of approval may focus on different aspects such as technical feasibility, security implications, operational impact, and alignment with organizational policies. This layered approach helps minimize the risk of unauthorized or potentially harmful changes to the network configuration, enhances accountability, and ensures that changes are thoroughly evaluated from various perspectives before being executed.

[0160] The UE (108) involved in this process may be equipped with specialized software or firmware that enables more detailed reporting of network performance issues. This could include the ability to log and report specific types of connection problems, signal quality measurements, or application performance metrics, providing richer data for the management process. The connection problems may correspond to frequent disconnections, failed handovers between cells, or inability to establish initial network connection. The signal quality measurements may include detailed reference signal received power (RSRP), reference signal received quality (RSRQ), and signal-to-interference-plus-noise ratio (SINR) values over time. Further, the application performance metrics may include video streaming bitrates, gaming latency, or voice call quality scores.

[0161] In yet another embodiment, the User Equipment (UE) (108) facilitates management of performance of the radio access network (RAN). The UE (108) is configured for generating a management request related to RAN performance based on user inputs. The UE (108) transmits the generated management request to the system (102). The system (102) is configured to retrieve a RAN node configuration data based on the received management request. The RAN node configuration data includes one or more values of a plurality of parameters. The system (102) isconfigured to identify respective cell details associated with the retrieved RAN node configuration data. Further, the system (102) is configured to compare the retrieved RAN node configuration data with a previously stored RAN node configuration data. Further, the system (102) is configured to detect at least one change in one or more values of the plurality of parameters based on a result of the comparison. Further, the system (102) is configured to map the detected at least one change in the one or more values with the identified respective cell details. Further, the system (102) is configured to optimize the performance of the RAN based on a result of the mapping.

[0162] In yet another exemplary embodiment, a non-transitory computer- readable storage medium storing computer-executable instructions is described. When executed by one or more processors, the instructions cause the one or more processors to perform a method for managing performance of a radio access network (RAN). The method comprises receiving a management request from a user equipment (UE) (108). The method comprises retrieving a RAN node configuration data based on the received management request. The RAN node configuration data includes one or more values associated with a plurality of parameters. The method comprises identifying respective cell details associated with the retrieved RAN node configuration data. The method comprises comparing the retrieved RAN node configuration data with a previously stored RAN node configuration data. The method comprises detecting at least one change in one or more values of the plurality of parameters based on a result of the comparison. The method comprises mapping the detected at least one change in the one or more values with the identified respective cell details. Finally, the method comprises managing the performance of the RAN based on a result of the mapping.

[0163] FIG. 6 illustrates an example computer system (600) in which or with which the embodiments of the present disclosure may be implemented.

[0164] As shown in FIG. 6, the computer system (600) may include an external storage device (610), a bus (620), a main memory (630), a read-only memory (640),a mass storage device (650), a communication port(s) (660), and a processor (670). A person skilled in the art will appreciate that the computer system (600) may include more than one processor and communication ports. The processor (670) may include various modules associated with embodiments of the present disclosure. The communication port(s) (660) may be any of an RS-232 port for use with a modem-based dialup connection, a 10 / 100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication ports(s) (660) may be chosen depending on a network, such as a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (600) connects.

[0165] In an embodiment, the main memory (630) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (640) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chip for storing static information e.g., start-up or basic input / output system (BIOS) instructions for the processor (670). The mass storage device (650) may be any current or future mass storage solution, which can be used to store information and / or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and / or Firewire interfaces).

[0166] In an embodiment, the bus (620) may communicatively couple the processor(s) (670) with the other memory, storage, and communication blocks. The bus (620) may be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCLX) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (670) to the computer system (600).

[0167] In another embodiment, operator, and administrative interfaces, e.g., a display, keyboard, and cursor control device may also be coupled to the bus (620) to support direct operator interaction with the computer system (600). Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) (660). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (600) limit the scope of the present disclosure.

[0168] The present disclosure provides technical advancement related to radio access network (RAN) management. This advancement addresses the limitations of existing solutions by introducing an automated, data-driven approach to detecting, analyzing, and responding to configuration changes in RAN nodes. The disclosure involves a sophisticated system architecture that integrates real-time data collection, intelligent analysis, and proactive management techniques, which offer significant improvements in network performance, operational efficiency, and user experience. By implementing dedicated modules for request processing, data management, configuration analysis, and RAN management, the disclosed invention enhances the ability of network operators to maintain optimal network performance in the face of constant changes and increasing complexity. This results in reduced network issues, faster problem resolution, and improved overall quality of service for end-users, while simultaneously reducing the manual effort required from network engineers. The system's ability to correlate configuration changes with performance metrics and user complaints provides insights into network behavior, enabling more informed decision-making and strategic network planning.

[0169] 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 foregoingdescriptive matter to be implemented merely as illustrative of the disclosure and not as limitation.ADVANTAGES OF THE PRESENT DISCLOSURE

[0170] The present disclosure provides a system and method that monitors and optimizes the performance of a radio access network (RAN) accurately, efficiently, and conveniently. By implementing a comprehensive approach that includes periodic data retrieving, intelligent analysis of configuration changes, and automated management processes, the system enables network operators to maintain peak performance with reduced manual intervention.

[0171] The present disclosure offers a sophisticated mechanism for detecting configuration changes in RAN nodes in an optimized way. Through the use of dedicated modules for data management and configuration analysis, coupled with advanced database technologies, the system can quickly identify and categorize even subtle changes in network parameters, providing a robust foundation for performance optimization and management.

[0172] The present disclosure significantly enhances user experience by continuously managing the performance of the radio access network (RAN). The configuration changes are correlated with performance metrics (throughput, latency, signal strength, error rates, and resource utilization) and user complaints. Furthermore, the visualization and interface capabilities allow for rapid identification and resolution of performance problems, ensuring a consistently high-quality network experience for end-users.

[0173] The present disclosure introduces an approach to RAN management by implementing a multi-tiered architecture This multi-tiered architecture typically includes web servers for handling user requests, application servers for business logic processing, and database servers for data storage and retrieval. For example, incoming requests are first processed by load balancers, then routed to appropriate application servers for analysis, and finally to database servers for data storage orretrieval. This architecture, combined with load balancing techniques, ensures scalability and responsiveness, even in large and complex network environments.

[0174] The present disclosure provides a unique solution for mapping configuration changes to specific cell details and geographical locations. This feature enables network operators to gain deeper insights into the relationship between network configuration and performance in different areas, facilitating more targeted and effective management strategies. For example, operators can visualize how changes in antenna tilt angles or transmission power levels correlate with data throughput or call drop rates in specific locations. This mapping capability facilitates targeted management strategies such as adaptive coverage management, interference management, capacity planning, and event-based management. These strategies allow operators to fine-tune their networks more precisely, improving overall performance, user experience, and resource efficiency.

Claims

CLAIMS1. A system (102) for managing performance of a radio access network (RAN) node, the system (102) comprising: a request processing module (212) configured to receive a performance management request ; a data management module (214) configured to: retrieve RAN node configuration data based on the received performance management request; and identify respective cell identification and operational parameters associated with the retrieved RAN node configuration data; a configuration analysis module (216) configured to: compare the retrieved RAN node configuration data with previously stored RAN node configuration data; detect at least one change in the one or more parameter values based on the comparison; and map the detected at least one change in the one or more parameter values with the identified respective cell details; and a RAN management module (218) configured to implement performance adjustments for the RAN node based on the mapping.

2. The system (102) of claim 1, wherein the performance management request is initiated by a user equipment (UE) (108).

3. The system (102) of claim 1, wherein the performance management request is automatically triggered based on predefined network conditions by the request processing module (212).

4. The system (102) of claim 1, wherein the data management module (214)is further configured to: retrieve, at scheduled intervals, the RAN node configuration data; store the detected at least one change in the one or more parameter values in a database (210); and maintain a historical record of the detected changes to establish a chronological trail of network configuration modifications.

5. The system (102) of claim 1 further comprising a visualization and interface module (220) configured to provide a user interface for displaying the one or more parameter values of the plurality of parameters and the detected change in the one or more parameter values.

6. The system (102) of claim 1 wherein a network management entity is enabled to access at least one of a configuration change history, cell optimization data, and end-user complaint resolution information, wherein the network management entity is a human operator or an automated system.

7. The system (102) of claim 1, wherein the RAN node configuration data includes one or more values associated with a plurality of parameters, wherein the plurality of parameters in the RAN node configuration data include at least a signal strength, a reference signal received power (RSRP), a reference signal received quality (RSRQ), a signal-to-interference-plus-noise ratio (SINR), a cell identifier (Cell-ID), and other network performance indicators.

8. The system (102) of claim 1, further comprising a complaint processing module (222) configured to: receive the end-user complaint related to network performance including at least one of slow data speeds, dropped calls, poor signal quality, and other network performance degradation indicators; correlate the user complaint with the detected at least one change; and initiate a response based on the correlation, wherein the response includes generating an alert for network operators, suggesting a rollback of a problematic configuration change, proposing an alternative optimization, or scheduling additional tests to assess and quantify the impact of the detected at least one change.

9. The system (102) of claim 1, wherein managing the performance of the RAN node comprises steps of: analyzing the mapped at least one change in the one or more parameter values; identifying the at least one change in the one or more parameter values that impact network performance based on the analysis; and adjusting the one or more parameter values based on the identified at least one change in the one or more parameter values.

10. A method (500) for managing performance of a radio access network (RAN) node, the method comprising: receiving (502), by a request processing module (212), a performance management request;retrieving (504), by a data management module (214), RAN node configuration data based on the received performance management request; identifying (506), by the data management module (214), respective cell identification and operational parameters associated with the retrieved RAN node configuration data; comparing (508), by a configuration analysis module (216), the retrieved RAN node configuration data with previously stored RAN node configuration data; detecting (510), by the configuration analysis module (216), at least one change in the one or more parameter values based on the comparison; mapping (512), by the configuration analysis module (216), the detected at least one change in the one or more parameter values with the identified respective cell details; and managing (514), by a RAN management module (218), the performance of the RAN node based on the mapping.

11. The method (500) of claim 10, wherein the performance management request is initiated by a user equipment (UE) (108).

12. The method (500) of claim 10, wherein the performance management request is automatically triggered based on predefined network conditions by the request processing module (212).

13. The method (500) of claim 10, further comprising: retrieving, at scheduled intervals, the RAN node configuration data;storing the detected at least one change in the one or more parameter values in a database (210); and maintaining a historical record of the detected changes to establish a chronological trail of network configuration modifications.

14. The method (500) of claim 10, further comprising: providing, by a visualization and interface module (220), a user interface for displaying the one or more parameter values of the plurality of parameters and the detected change in the one or more parameter values.

15. The method (500) of claim 10, further comprising enabling a network management entity to access at least one of a configuration change history, cell optimization data, and end-user complaint resolution information, wherein the network management entity is a human operator or an automated system.

16. The method (500) of claim 10, wherein the RAN node configuration data includes one or more values associated with a plurality of parameters, wherein the plurality of parameters in the RAN node configuration data include at least a signal strength, a reference signal received power (RSRP), a reference signal received quality (RSRQ), a signal-to-interference-plus- noise ratio (SINR), a cell identifier (Cell- ID), and other network performance indicators.

17. The method (500) of claim 10, further comprising: receiving, by a complaint processing module (222), the end-user complaint related to network performance including at least one of slow data speeds, dropped calls, poor signal quality, and other network performance degradation indicators;correlating, by the complaint processing module (222), the user complaint with the detected at least one change; and initiating, by the complaint processing module (222), a response based on the correlation, wherein the response includes generating an alert for network operators, suggesting a rollback of a problematic configuration change, proposing an alternative optimization, or scheduling additional tests to assess and quantify the impact of the detected at least one change.

18. The method (500) of claim 10, wherein managing the performance of the RAN node comprises: analyzing the mapped at least one change in the one or more parameter values; identifying the at least one change in the one or more parameter values that impact network performance based on the analysis; and adjusting the one or more parameter values based on the identified at least one change in the one or more parameter values.

19. A User Equipment (UE) (108) for facilitating management of performance of a radio access network (RAN), the UE (108) is configured for: generating a performance management request related to RAN performance based on at least one of: user inputs, automated monitoring of network conditions, or predetermined performance thresholds being exceeded; and transmit the generated performance management request to a system (102), wherein the system (102) is configured to perform steps as claimed in claim 1.

20. A non-transitory computer-readable storage medium storing computerexecutable instructions that, when executed by one or more processors, cause the one or more processors to perform a method for managing performance of a radio access network (RAN) node, the method comprising: receiving, by a request processing module (212), a performance management request; retrieving, by a data management module (214), RAN node configuration data based on the received performance management request; identifying, by the data management module (214), respective ell identification and operational parameters associated with the retrieved RAN node configuration data; comparing, by a configuration analysis module (216), the retrieved RAN node configuration data with previously stored RAN node configuration data; detecting, by the configuration analysis module (216), at least one change in the one or more parameter values based on the comparison; mapping, by the configuration analysis module (216), the detected at least one change in the one or more parameter values with the identified respective cell details; and implementing, by a RAN management module (218), performance adjustments for the RAN node based on the mapping.

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