System and method for monitoring subscriber experience indices

Abstract

The present disclosure relates to a system (108) for monitoring subscriber experience indices in a cellular network. The system (108) may comprise a memory (204) and one or more processors (202) communicatively coupled with the memory (204). The one or more processors (202) may be configured to receive, from a monitoring unit (114), a request for determining a set of subscriber experience indices of one or more subscribers. The one or more processors (202) may retrieve the set of subscriber experience indices and radio access network (RAN) logs comprising one or more attributes. An Artificial Intelligence (AI) engine (216) may compute correlation values between the one or more attributes of the RAN logs and the set of subscriber experience indices. The one or more processors (202) may transmit the set of subscriber experience indices and the correlation values to the monitoring unit (114) for further analysis and visualization.

Description

SYSTEM AND METHOD FOR MONITORING SUBSCRIBER EXPERIENCE INDICESRESERVATION OF RIGHTS

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

[0002] The embodiments of the present disclosure generally relate to communication networks. In particular, the present disclosure relates to a system and a method for monitoring subscriber experience indices.DEFINITION

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

[0004] The expression ‘subscriber experience indices’ used hereinafter in the specification refers to a set of metrics used to quantify and improve the overall satisfaction and quality of service for subscribers (customers) of a particular service provider, typically in industries such as telecommunications, media, and subscription-based services. These indices are crucial for understanding how well a company meets its customers' expectations and where improvements are needed.For example, a mobile network provider might utilize indices such as customer satisfaction (CSAT) surveys to gauge user satisfaction with network coverage and customer service, while tracking net promoter score (NPS) to measure customer loyalty and likelihood to recommend services. They might also monitor Chum Rate to assess subscriber retention, calculate average revenue per user (ARPU) to understand revenue trends per customer, and track metrics like first call resolution (FCR) to ensure efficient issue resolution. Service availability metrics help in measuring uptime and reliability, while adherence to service level agreements (SLAs) ensures consistent service delivery standards.

[0005] The expression ‘Radio Access Network (RAN) logs’ used hereinafter in the specification refers to detailed records or data generated by the components and devices within a Radio Access Network, which is a critical part of a mobile telecommunications system. The RAN logs capture various operational and performance-related information about the network elements, including base stations (NodeBs, eNodeBs in LTE / 4G, gNodeBs in 5G), antennas, and other equipment responsible for wireless communication with mobile devices.

[0006] The expression ‘one or more subscribers’ used hereinafter in the specification refers to at least one individual or entity that has subscribed to a particular service provided by a telecom operator or service provider. The one or more subscribers indicate that there is at least one user who has signed up for and is using the telecom service, such as a mobile phone network, internet service, or cable television service.

[0007] These definitions are in addition to those expressed in the art.BACKGROUND OF DISCLOSURE

[0008] 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 onlyto enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

[0009] In the rapidly evolving telecommunications industry, cellular network operators face the critical challenge of effectively monitoring and understanding the experience of their subscribers. Traditional methods of gauging subscriber satisfaction, such as direct interaction or surveys, prove inadequate due to the vast scale and complexity of modem cellular networks. Consequently, operators must rely on inferring subscriber experience from various network data, particularly Radio Access Network (RAN) logs, which contain valuable information about the performance and behaviour of the network. However, existing solutions often struggle to derive meaningful insights from this data, failing to establish strong correlations between network attributes and subscriber experience indices.

[0010] Current monitoring tools for subscriber experience in the market primarily focus on presenting raw subscriber usage data, such as call volumes or data consumption. While this information is valuable, it fails to provide a comprehensive understanding of subscribers' satisfaction with the network services. Some solutions attempt to bridge this gap by employing heuristics or combining various attributes of usage data to infer subscriber satisfaction. However, these approaches often fall short in terms of accuracy and meaningful insights, as they lack the ability to fully grasp the context and nuances of the heuristic functions used. Furthermore, these solutions do not effectively leverage the rich data available in RAN logs to establish strong correlations between network performance and subscriber experience.

[0011] Moreover, existing dashboards offer limited flexibility for network operators to monitor subscribers' experience and happiness index. Queries to extract subscriber-level records are often structured and restrictive, hindering the ability to gain a holistic view of the subscriber experience. The absence of a unified dashboard that captures and displays subscriber experience for comprehensivemonitoring and analysis further compounds the challenges faced by network operators. Additionally, the lack of advanced analytics capabilities, such as machine learning and artificial intelligence, in existing solutions hinders the ability to uncover complex patterns and correlations between network attributes and subscriber experience indices.

[0012] Prior art, such as TR2021020819A2, addresses the problem of determining patterns among customer problem solution methods in a call center setting. The prior art proposes a system that analyzes customer information, call records, and customer representative expressions to create a problem-solution dictionary using machine learning models. While the prior art aims to improve the resolution of customer problems in real-time, it does not specifically address the challenges of monitoring and understanding subscriber experience in a cellular network context. Moreover, the prior art does not focus on leveraging RAN logs and establishing correlations between network attributes and subscriber experience indices.

[0013] It is therefore a need of the present invention to provide a system and method that enables accurate and meaningful monitoring of subscriber experience indices in cellular networks.SUMMARY

[0014] The present disclosure discloses a system for monitoring subscriber experience indices. The system includes a memory and one or more processors. The one or more processors is communicatively coupled with the memory. The one or more processors are configured to execute instructions stored in the memory to receive, from a monitoring unit, a request for determining a set of subscriber experience indices of one or more subscribers. The one or more processors are configured to retrieve radio access network (RAN) logs from a second database, the RAN logs comprising one or more attributes. The one or more processors are configured to compute, using an Artificial Intelligence (Al) engine, one or more correlation values between the one or more attributes of the RAN logs and the setof subscriber experience indices. The one or more processors are configured to determine the set of subscriber experience indices by using the one or more computed correlation values. The one or more processors are configured to transmit the set of determined subscriber experience indices and the one or more computed correlation values to the monitoring unit.

[0015] In an embodiment, the system includes a request processing engine configured to determine whether the requested set of subscriber experience indices is available in a first database. The first database is configured to store a set of precomputed subscriber experience indices. The request processing engine is configured to retrieve the precomputed set of subscriber experience indices from the first database when the requested set of subscriber experience indices is available. The request processing engine is configured to compute, by a computation engine, the requested set of subscriber experience indices when the requested set of subscriber experience indices is not available in the first database.

[0016] In an embodiment, for computing the requested set of subscriber experience indices, the computation engine is configured to retrieve the set of RAN logs from a second database and derive the requested set of subscriber experience indices from radio frequency (RF) data in the retrieved set of RAN logs.

[0017] In an embodiment, the one or more attributes of the RAN logs include at least one of a timestamp, a unique identifier of the subscriber, a base station identifier, an event type, a signal strength, and quality metrics.

[0018] In an embodiment, the Al engine is further configured to analyze the one or more computed correlation values to identify one or more network issues and generate one or more recommendations for resolving the identified one or more network issues.

[0019] In an embodiment, the one or more processors are further configured to transmit the one or more generated recommendations to the monitoring unit andresolve the identified one or more network issues based on generated one or more recommendations .

[0020] In an embodiment, the set of subscriber experience indices includes at least one of a happiness score, top call release reasons, volume of services used, time spent by subscribers using services, and subscriber journey with base stations.

[0021] In an embodiment, the monitoring unit is configured to display the received set of subscriber experience indices on a user interface and provide an interactive interface for users to analyze and visualize the set of determined subscriber experience indices.

[0022] The present disclosure discloses a method for monitoring subscriber experience indices. The method includes receiving, from a monitoring unit, a request for determining a set of subscriber experience indices of one or more subscribers. The method includes retrieving radio access network (RAN) logs from a second database, the RAN logs comprising one or more attributes. The method includes computing, using an Artificial Intelligence (Al) engine, one or more correlation values between the one or more attributes of the RAN logs and the set of subscriber experience indices. The method includes determining the set of subscriber experience indices by using the one or more computed correlation values. The method includes transmitting the set of determined subscriber experience indices and the one or more computed correlation values to the monitoring unit.

[0023] In an embodiment, a step of retrieving the set of subscriber experience indices includes steps of determining, by a request processing engine, whether the requested set of subscriber experience indices is available in a first database. The first database is configured to store a set of precomputed subscriber experience indices. The step of retrieving the set of subscriber experience indices includes retrieving the precomputed set of subscriber experience indices from the first database when the requested set of subscriber experience indices is available. The step of retrieving the set of subscriber experience indices includes computing,by a computation engine, the requested set of subscriber experience indices when the requested set of subscriber experience indices is not available in the first database.

[0024] In an embodiment, a step of computing the requested set of subscriber experience indices by the computation engine further includes retrieving a set of RAN logs from a second database and deriving the requested set of subscriber experience indices from radio frequency (RF) data in the retrieved set of RAN logs.

[0025] In an embodiment, the method includes analyzing the one or more computed correlation values to identify one or more network issues and generating recommendations for resolving the identified one or more network issues.

[0026] In an embodiment, the method further includes transmitting the one or more generated recommendations to the monitoring unit and resolving the identified one or more network issues based on generated recommendations.

[0027] In an embodiment, the method further includes displaying the received set of subscriber experience indices on a user interface of the monitoring unit and providing an interactive interface for users to analyze and visualize the set of determined subscriber experience indices.

[0028] The present disclosure discloses a user equipment communicatively coupled to a system through a network. The user equipment is configured to monitor subscriber experience indices. The user equipment a memory and one or more processors coupled with the memory. The one or more processors are configured to execute instructions stored in the memory to perform steps of a method for monitoring subscriber experience indices. The method includes receiving, from a monitoring unit, a request for determining a set of subscriber experience indices of one or more subscribers. The method includes retrieving radio access network (RAN) logs from a second database, the RAN logs comprising one or more attributes. The method includes computing, using an Artificial Intelligence (Al)engine, one or more correlation values between the one or more attributes of the RAN logs and the set of subscriber experience indices. The method includes determining the set of subscriber experience indices by using the one or more computed correlation values. The method includes transmitting the set of determined subscriber experience indices and the one or more computed correlation values to the monitoring unit.

[0029] The present disclosure discloses a computer program product comprising a non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one processor, cause the at least one processor to perform receiving, from a monitoring unit, a request for determining a set of subscriber experience indices of one or more subscribers. The method includes retrieving radio access network (RAN) logs from a second database, the RAN logs comprising one or more attributes. The method includes computing, using an Artificial Intelligence (Al) engine, one or more correlation values between the one or more attributes of the RAN logs and the set of subscriber experience indices. The method includes determining the set of subscriber experience indices by using the one or more computed correlation values. The method includes transmitting the set of determined subscriber experience indices and the one or more computed correlation values to the monitoring unit.OBJECTS OF THE PRESENT DISCLOSURE

[0030] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as listed herein below.

[0031] An object of the present disclosure is to provide a system and a method for monitoring subscriber experience indices.

[0032] Another object of the present disclosure is to provide a dashboard that displays a set of subscriber experience includes happiness score, type of failure faced, clear codes count, failed procedure, subscriber journey with a base station, distribution of call release reasons, distribution of services consumed, and the like.

[0033] Another object of the present disclosure is to provide a system and a method that determines subscriber experience indices using Radio Access Network (RAN) logs.

[0034] Another object of the present disclosure is to provide a system and a method that allows operators to identify and troubleshoot network issues if subscriber experience indices fall outside a predetermined range.

[0035] Another object of the present disclosure is to provide a system and a method with a flexible and interactive interface for visualizing and analyzing subscriber experience indices.

[0036] Another object of the present disclosure is to provide a system and method that computes correlation values between attributes of Radio Access Network (RAN) logs and subscriber experience indices. By analyzing these correlation values, the system may identify network issues impacting subscriber experience and generate recommendations for resolving them, enabling network operators to proactively manage and optimize network performance.BRIEF DESCRIPTION OF DRAWINGS

[0037] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes the disclosure of electrical components, electronic components or circuitry commonly used to implement such components.

[0038] FIG. 1 illustrates an exemplary network architecture for implementing a system for monitoring subscriber experience indices, in accordance with embodiments of the present disclosure.

[0039] FIG. 2 illustrates an exemplary block diagram of the system, in accordance with embodiments of the present disclosure.

[0040] FIG. 3 illustrates an exemplary implementation of the system, in accordance with embodiments of the present disclosure.

[0041] FIG. 4 illustrates an exemplary flowchart of a method for monitoring subscriber experience indices, in accordance with embodiments of the present disclosure.

[0042] FIG. 5 illustrates an exemplary computer system in which or with which embodiments of the present disclosure may be implemented.

[0043] FIG. 6 illustrates another exemplary flowchart of the method for monitoring subscriber experience indices, in accordance with embodiments of the present disclosure.

[0044] The foregoing shall be more apparent from the following more detailed description of the disclosure.LIST OF REFERENCE NUMERALS100 - Network Architecture102-1, 102-2, 102-3 - User (s)104-1, 104-2, 104-3 - User Equipment (s)106 -Network108 - System110-1- Network entity 1110-2- Network entity 2112-1- Base station-1112-2- Base station-2114- Monitoring unit202 - One or more processor(s)204 - Memory206 -Interface(s)210 -Database210-1- First Database210-2- Second Database212 - Request Processing engine214 - Computation engine216 - Al engine218 - Other unit(s)220- Distributed file system400 - Method flowchart510 - External Storage Device520 - Bus530 - Main Memory540 - Read Only Memory550 - Mass Storage Device560 - Communication Port570 - ProcessorBRIEF DESCRIPTION OF THE INVENTION

[0045] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not befully addressed by any of the features described herein. Example embodiments of the present disclosure are described below, as illustrated in various drawings in which like reference numerals refer to the same parts throughout the different drawings.

[0046] 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.

[0047] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0048] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0049] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive like the term “comprising” as an open transition word without precluding any additional or other elements.

[0050] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0051] The terminology used herein is to describe particular embodiments only and is not intended to be limiting the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any combinations of one or more of the associated listed items. It should be noted that the terms “mobile device”, “user equipment”, “user device”, “communication device”, “device” and similar termsare used interchangeably for the purpose of describing the invention. These terms are not intended to limit the scope of the invention or imply any specific functionality or limitations on the described embodiments. The use of these terms is solely for convenience and clarity of description. The invention is not limited to any particular type of device or equipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the invention as defined herein.

[0052] As used herein, an “electronic device”, or “portable electronic device”, or “user device”, or “communication device”, or “user equipment”, or “device” refers to any electrical, electronic, electromechanical, and computing device. The user device is capable of receiving and / or transmitting one or parameters, performing function / s, communicating with other user devices, and transmitting data to the other user devices. The user equipment may have a processor, a display, a memory, a battery, and an input-means such as a hard keypad and / or a soft keypad. The user equipment may be capable of operating on any radio access technology including but not limited to IP-enabled communication, Zig Bee, Bluetooth, Bluetooth Low Energy, Near Field Communication, Z-Wave, Wi-Fi, Wi-Fi direct, etc. For instance, the user equipment may include, but not limited to, a mobile phone, smartphone, virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other device as may be obvious to a person skilled in the art for implementation of the features of the present disclosure.

[0053] Further, the user device may also comprise a “processor” or “processing unit” includes processing unit, wherein processor refers to any logic circuitry for processing instructions. The processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing,input / output processing, and / or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor is a hardware processor.

[0054] As portable electronic devices and wireless technologies continue to improve and grow in popularity, the advancing wireless technologies for data transfer are also expected to evolve and replace the older generations of technologies. In the field of wireless data communications, the dynamic advancement of various generations of cellular technology are also seen. The development, in this respect, has been incremental in the order of second generation (2G), third generation (3G), fourth generation (4G), and now fifth generation (5G), and more such generations are expected to continue in the forthcoming time.

[0055] While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

[0056] In the rapidly evolving telecommunications industry, cellular network operators face the critical challenge of effectively monitoring and understanding the experience of their subscribers. Modem cellular networks' increasing complexity and scale have made it difficult for operators to gauge subscriber satisfaction using traditional methods such as surveys or direct interaction. Consequently, operators must infer subscriber experience from vast amounts of network data, particularly Radio Access Network (RAN) logs. However, existing solutions often struggle to derive meaningful insights from this data, providing only a superficial understanding of subscriber experience. Thepresent disclosure addresses these challenges by introducing a comprehensive system and method for monitoring subscriber experience indices, leveraging advanced techniques such as Artificial Intelligence (Al) and correlation analysis to derive actionable insights from RAN logs and subscriber usage data. In an example, the RAN logs may include:• Call Detail Records (CDRs): Information about calls made and received, including call duration, location, and quality metrics.• Signal Strength and Quality: Measurements of radio signal strength, Signal- to-Noise Ratio (SNR), and other RF (Radio Frequency) parameters.• Handover Events: Records of when a mobile device switches from one base station to another to maintain connectivity as it moves.• Alarms and Events: Notifications and alerts generated by network equipment for anomalies, faults, or performance issues.• Performance Metrics: Data on throughput, latency, packet loss, and other network performance indicators.• Subscriber Activity: Information on subscriber connections, session durations, and data usage patterns

[0057] The present disclosure aims to empower network operators with a powerful tool for monitoring and analyzing subscriber experience indices, enabling them to proactively manage and optimize network performance. By computing correlation values between attributes of RAN logs and subscriber experience indices using an Al engine, the system can identify network issues impacting subscriber satisfaction and generate recommendations for resolving them. This proactive approach to network management can lead to improved subscriber experience, reduced chum rates, and increased customer loyalty in the highly competitive telecommunications market. The system also provides a comprehensive dashboard that displays a range of metrics used for determiningsubscriber experience indices, offering network operators a holistic view of subscriber satisfaction and network performance.

[0058] The present disclosure relates to a system and method for monitoring subscriber experience indices in a cellular network. The system comprises a memory and one or more processors configured to execute instructions stored in the memory. The processors receive requests from a monitoring unit to determine subscriber experience indices, retrieve the indices and RAN logs from databases, and compute correlation values between attributes of the RAN logs and the indices using an Al engine. The computed subscriber experience indices and correlation values are then transmitted to the monitoring unit for display and analysis. The system and method may leverage various components such as a request processing engine, a computation engine, and databases storing precomputed indices and RAN logs to efficiently process and analyze the data.

[0059] The various embodiments throughout the disclosure will be explained in more detail with reference to FIG. 1- FIG. 6.

[0060] FIG. 1 illustrates an exemplary network architecture (100) for implementing a system (108) for monitoring subscriber experience indices in a network (106), in accordance with embodiments of the present disclosure.

[0061] Referring to FIG. 1, the network architecture (100) may include one or more computing devices or user equipments (104-1, 104-2, 104-3) associated with one or more users (102-1, 102-2, 102-3) in an environment. A person of ordinary skill in the art will understand that one or more users (102-1, 102-2, 102- 3) may be individually referred to as the user (102) and collectively referred to as the users (102). Similarly, a person of ordinary skill in the art will understand that one or more user equipments (104-1, 104-2, 104-3) may be individually referred to as the user equipment ( 104) and collectively referred to as the user equipment ( 104) . A person of ordinary skill in the art will appreciate that the terms “computing device(s)” and “user equipment” may be used interchangeably throughout the disclosure. Although three user equipments (104) are depicted in FIG. 1, howeverany number of the user equipment’s (104) may be included without departing from the scope of the ongoing description.

[0062] In an embodiment, the user equipment (104) may include, but is not limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a phablet device, and so on), a wearable computer device(e.g., a headmounted display computer device, a head-mounted camera device, a wristwatch computer device, and so on), a Global Positioning System (GPS) device, a laptop computer, a tablet computer, or another type of portable computer, a media playing device, a portable gaming system, and / or any other type of computer device with wireless communication capabilities, and the like. In an embodiment, the user equipment (104) may include, but is not limited to, any electrical, electronic, electro-mechanical, or an equipment, or a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device, where the user equipment (104) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, an audio aid, a microphone, a keyboard, and input devices for receiving input from the user (102) or the entity such as touch pad, touch enabled screen, electronic pen, and the like. A person of ordinary skill in the art will appreciate that the user equipment (104) may not be restricted to the mentioned devices and various other devices may be used. The architecture may include a monitoring unit (114) having a user interface that provides audio-visual indications to the user based on a set of signals transmitted by the system (108). In an embodiment, the monitoring unit (114) may be implemented on a UE (104) and may be used by operators of the network (106).

[0063] Referring to FIG. 1, the user equipment (104) may communicate with the system (108) through the network (106). In an embodiment, the network (106) may include at least one of a Fifth Generation (5G) network, 6G network, or the like. The network (106) may enable the user equipment (104) to communicate with other devices in the network architecture (100) and / or with the system (108).The network (106) may include a wireless card or some other transceiver connection to facilitate this communication. In another embodiment, the network (106) may be implemented as, or include any of a variety of different communication technologies such as a wide area network (WAN), a local area network (LAN), a wireless network, a mobile network, a Virtual Private Network (VPN), the Internet, the Public Switched Telephone Network (PSTN), or the like. In an embodiment, each of the UE (104) may have a unique identifier attribute associated therewith. In an embodiment, the unique identifier attribute may be indicative of Mobile Station International Subscriber Directory Number (MSISDN), International Mobile Equipment Identity (IMEI) number, International Mobile Subscriber Identity (IMSI), Subscriber Permanent Identifier (SUPI) and the like.

[0064] In an embodiment, the network (106) may include one or more base stations (112-1, 112-2), with which the UEs (104) may connect to and request services from. The base station (112-1, 112-2) may be a network infrastructure that provides wireless access to one or more terminals associated therewith. The base station (112-1, 112-2) may have coverage defined to be a predetermined geographic area based on the distance over which a signal may be transmitted. The base station (112-1, 112-2) may include, but not be limited to, wireless access point, evolved NodeB (eNodeB), 5G node or next generation NodeB (gNB), wireless point, transmission / reception point (TRP), and the like. In an embodiment, the base station (112-1, 112-2) may include one or more operational units that enable telecommunication between two or more UEs (104). In an embodiment, the one or more operational units may include, but not be limited to, transceivers, baseband unit (BBU), remote radio unit (RRU), antennae, mobile switching centres, radio network control units, one or more processors associated thereto, and a plurality of network entities (110-1, 110-2) such as Access and Mobility Management Function (AMF) unit, Session Management Function (SMF) unit, Network Exposure Function (NEF) units, or any custom built functions executing one or more processor-executable instructions, but not limited thereto.

[0065] In an embodiment, RAN logs may be generated as the operational units or network entities (110-1, 110-2) interact with each other and the UE (104) to provide services thereto. RAN logs refer to the records or data generated by various components of the radio access network, such as base stations, radio network controllers, or other network elements. These logs may contain detailed information about the performance and behaviour of the network, including timestamps, unique identifiers of subscribers, base station identifiers, event types, signal strengths, and quality metrics. RAN logs may serve as a valuable source of data for analyzing and deriving insights into the subscriber experience. RAN logs are essential for network monitoring, troubleshooting, optimization, and performance analysis. The RAN logs are used by network operators, engineers, and analysts to maintain network quality, identify and resolve issues promptly, optimize network resources, and improve the overall subscriber experience. In an embodiment, the RAN logs may include one or more attributes that may be used to derive performance and health metrics of the network (106). In an embodiment, the one or more attributes may include, but not be limited to, radio summary logs, timestamps, UE (104) information such as unique identifier attributes, configurations details, device type, etc., call event details, signal strength metrics, throughput metrics, unique attributes associated with the base stations (112), alarms and fault details, error codes, and the like. In an embodiment, the RAN logs may be used to compute the subscriber experience indices.

[0066] In an embodiment, the system (108) may be coupled to a monitoring unit (114) that may provide an audio-visual interface to the user (102) for monitoring and analyzing data. In an embodiment, the monitoring unit (114) may provide an interface, including, but not limited to, a Graphical User Interface (GUI), an Application Programming Interface (API) or a Command Line Interface (CLI). In an embodiment, the monitoring unit (114) may provide a dashboard for visualizing and monitoring the subscriber experience indices in real time. In an embodiment, the monitoring unit (114) may be used by users (102) or operators of the network (106).

[0067] In an embodiment, a user ( 102) or operator of the network (106) may use the monitoring unit (114) to transmit a request for determining a set of subscriber experience indices of one or more subscribers. In an example, the received request may be a query request (requesting specific information or metrics about subscriber experience), an analysis request (asking for an analysis of current subscriber experience indices), a report request (requesting a report on the latest subscriber experience metrics), a comparison request (requesting a comparison of subscriber experience indices over different time periods or between different subscriber groups), or a trend analysis request (requesting an analysis of trends in subscriber experience metrics). The request may be transmitted in the form of including, but not limited to, signals, data packets, and the like. In an embodiment, the system (108) may allow operators to analyze subscriber experience indices of one or more subscribers and identify factors that improve or reduce the same. In an embodiment, the system (108) may allow for monitoring the subscriber experience indices and take pre-emptive actions to ensure the indices remain in a predetermined range. In an embodiment, when the subscriber experience indices fall below the predetermined range, it may indicate that the network (106) is underperforming or malfunctioning. In an embodiment, when the subscriber experience indices exceed the predetermined range, it may indicate that the network (106) is operating with substantial costs. The system (108) may determine the subscriber experience indices based on whether a precomputed set of subscriber experience indices is stored in a first database. In an example, the precomputed set of subscriber experience indices is defined as a collection of the subscriber experience indices that have already been calculated or determined beforehand. In an example, the precomputed set of subscriber experience indices includes the subscriber experience indices that are recently generated. If the precomputed set of subscriber experience indices for the one or more subscribers is available in the first database, the system (108) retrieves and transmits the indices to the monitoring unit (114).

[0068] In an embodiment, if the precomputed set of subscriber experience indices is unavailable in the first database, the system (108) may retrieve the set ofRAN logs from a second database and determine the set of subscriber experience indices therewith. In an aspect, for retrieval of the set of subscriber experience indices, the system (108) may be configured to transmit a query specific to the first database to extract the set of subscriber experience indices. In an aspect, the query may include various subscriber experience indices that are needed, or a number of filters (for example, specific time range, geographical area, network conditions). In an aspect, the system may be configured to retrieve the data via a server, where the server is configured to receive the query from the system and provide a response by processing the query and retrieving the requested data from the first database. If the set of requested subscriber experience indices is not available in the first database, then the system may be configured to send a data retrieval request to the second database via the server. In an aspect, the data retrieval request may include various attributes of the RAN logs that are needed, or a number of filters (for example, specific time range, geographical area, network conditions). The system may be configured to determine a location and access method for the second database where the RAN logs are stored. The data retrieval request may include authentication credentials or permissions granted by an administrator. The system may be configured to execute the query against the second database using an application programming interface (API).

[0069] The set of subscriber experience indices may be derived from radio frequency (RF) data in RAN logs. In an embodiment, the system (108) may compute the subscriber experience indices based on the one or more attributes in the RAN logs. In an embodiment, the subscriber experience indices may be determined by an Al engine. In such embodiments, the Al engine may include a pre-trained ML model configured to take the one or more attributes of the RAN logs as input and provide the subscriber experience indices as output. The system (108) may store the subscriber experience indices in the first database so that subsequent requests for substantially similar subscriber experience indices may be retrieved from the first database instead of being recomputed.

[0070] In an embodiment, the subscriber experience indices may include one or more computed values that indicate the health and performance of the network, as well as a set of values indicating the satisfaction of the subscribers. In an example, the one or more computed values may include, but not be limited to, a happiness score, top call release reason (CRR) encountered, volume of services used, and time spent by subscribers using the services, subscriber journey maps with a plurality of base stations (112), and the like, which may be computed based on the one or more attributes of the RAN logs. In an embodiment, the network operators may use the subscriber experience indices to identify network issues causing the indices to fall outside the predetermined range.

[0071] In an embodiment, the system (108) may compute one or more correlation values between one or more attributes of the RAN logs and the subscriber experience indices. In an embodiment, the system (108) may use the Al engine to compute the correlation values. The one or more correlation values refer to a statistical measure that quantifies the degree to which two or more variables are related or move together in a linear fashion. It indicates the strength and direction of a linear relationship between variables. In an embodiment, the correlation values may allow network operators to identify the cause of the network issue. To compute correlation values between attributes of RAN logs and the set of subscriber experience indices, the Al engine, may configured to follow the given steps: o Collecting RAN logs, which may include information such as signal strength, network congestion, handover events, etc. o Calculating data on selected indices (for example CSAT, NPS, chum rate, etc.), which represent subscriber satisfaction and engagement. o preprocessing the data to handle missing values, outliers, and ensure consistency across datasets (RAN logs and subscriber indices). o Integrating the RAN log attributes with the subscriber experience indices in a format suitable for analysis.o Determining which attributes from the RAN logs are potentially correlated with the subscriber experience indices. This might involve domain expertise and initial exploratory data analysis. o Using one or many statistical methods such as Pearson correlation coefficient, Spearman rank correlation, or others to compute correlation values. o Utilizing machine learning models, particularly regression models or correlation-based feature selection methods, to identify significant relationships between RAN log attributes and subscriber indices.

[0072] For instance, if a location attribute in the RAN log has a negative correlation with the subscriber experience indices, then the network issue may be associated with underperformance or malfunctioning of base stations (112) in a location. In an embodiment, the system (108) may also generate one or more recommendations for performing preventative maintenance or pre-emptive network expansion. In an embodiment, the subscriber experience indices may be used to resolve network issues and appropriately upgrade specifications or configurations of the network (106). The system (108) may transmit the subscriber experience indices to the monitoring unit (114), where the indices may be displayed.

[0073] In accordance with embodiments of the present disclosure, the system (108) may be configured to provide monitoring subscriber experience indices. In an embodiment, the system (108) may also be configured to provide a real-time dashboard for monitoring subscriber experience indices.

[0074] FIG. 2 illustrates a block diagram (200) of the system (108), in accordance with embodiments of the present disclosure.

[0075] In an aspect, the system (108) may include one or more processor(s) (202). The one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog microcontrollers, digital signal processors, central processing units, logic circuitries, and / or anydevices that process data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the system (108). The memory (204) may be configured to store one or more computer- readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (204) may include any non-transitory storage device including, for example, volatile memory such as Random Access Memory (RAM), or non-volatile memory such as Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.

[0076] Referring to FIG. 2, the system (108) may include an interface(s) (206). The interface(s) (206) may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I / O devices, storage devices, and the like. The interface(s) (206) may facilitate communication to / from the system (108). The interface(s) (206) may also provide a communication pathway for one or more components of the system (108). Examples of such components include, but are not limited to, processing unit / engine(s) and a database (210).

[0077] In an embodiment, the one or more processors (202) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the one or more processors (202). In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the one or more processors (202) may be processorexecutable instructions stored on a non-transitory machine-readable storage medium, and the hardware for the one or more processors (202) may include 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 one or more processors (202). In such examples, the system (108) may include themachine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system (108) and the processing resource. In other examples, the one or more processors (202) may be implemented by electronic circuitry.

[0078] In an embodiment, the system (108) may include one or more databases, such as the first database (210-1) and the second database (210-2) (collectively referred to as a database or databases (210)). In an embodiment, the database (210) includes data that may be either stored or generated as a result of functionalities implemented by any of the components of the processor (202). In an embodiment, the database (210) may be separate from the system (108). In an embodiment, the database (210) may be indicative of including, but not limited to, a relational database, a distributed database, a distributed file sharing system, a cloud-based database, or the like.

[0079] In an embodiment, the first database (210-1) may be configured to store a set of precomputed subscriber experience indices. The set of precomputed subscriber experience indices typically refers to a predefined collection of metrics or measures that are systematically calculated or prepared to assess the satisfaction, behaviour, and interaction patterns of subscribers with a service or product. These indices are often used by companies to quickly evaluate and monitor subscriber experience without having to calculate them from raw data each time. In an embodiment, the subscriber experience indices may be associated with requests received from the monitoring unit (114). In an embodiment, the precomputed subscriber experience indices may be stored such that requests for substantially similar subscriber experience indices may be retrieved from the first database (210- 1) instead of being recomputed. In an embodiment, the RAN logs may be stored in the second database (210-2). The RAN logs may be retrieved by the system (108) to process the requests received from the monitoring unit (114). In an embodiment, the first database (210-1) and the second database (210-2) may be implemented in a single database.

[0080] In an exemplary embodiment, the one or more processors (202) may include one or more engines selected from any of a request processing engine (212), a computation engine (214), an Al engine (216), and other engines (218) having functions that may include, but are not limited to, testing, storage, and peripheral functions, such as wireless communication unit for remote operation, audio unit for alerts and the like.

[0081] The system (108) may comprise the memory (204) and one or more processors (202) communicatively coupled with the memory (204). The one or more processors (202) may be configured to execute instructions stored in the memory (204) to perform various functions associated with monitoring subscriber experience indices.

[0082] In one embodiment, the system (108) may receive, from the monitoring unit (114), the request for determining the set of subscriber experience indices of one or more subscribers. The set of subscriber experience indices typically includes a variety of metrics that collectively gauge the satisfaction, engagement, and overall experience of subscribers with a service or product. For example, the set of subscriber experience indices may include a Customer Satisfaction Score (CSAT), a Net Promoter Score (NPS), a Customer Effort Score (CES), a Retention Rate, a Chum Rate, an Average Resolution Time and Usage Metrics.

[0083] The CSAT measures overall satisfaction with a specific interaction, transaction, or experience. The NPS measures the likelihood of customers recommending the product or service to others, indicating loyalty and satisfaction. The CES measures the ease of which customers can interact with a service or complete a task. The chum rate refers to a percentage of subscribers who cancel or leave the service within a given period, indicating dissatisfaction or disengagement. The Retention Rate measures the percentage of customers who continue to use the service over a specified period, indicating satisfaction and loyalty. The Average Resolution Time measures how quickly customer issues or inquiries are resolved,indicating responsiveness and service efficiency. The usage metrics include metrics such as frequency of use, duration of use, and feature adoption rates, which reflect how actively subscribers are engaging with the service. These indices collectively provide a comprehensive view of subscriber experience, helping organizations identify strengths, weaknesses, and opportunities for improvement to enhance overall customer satisfaction and loyalty. Subscriber experience indices may refer to various metrics or indicators that quantify the quality of experience (QoE) of subscribers using the cellular network. These indices may provide valuable insights into the performance of the network from the perspective of the subscribers and may help network operators identify areas for improvement.

[0084] To determine the set of subscriber experience indices, the system may be configured to identify the goals of a network operator (marketing, customer service, product development, and management who are involved or impacted by subscriber experience). The goals of the network operator clarify the objectives of measuring subscriber experience indices. For example, improving customer satisfaction, reducing chum, increasing referrals, etc. Based on the goals, the system may be configured to define key metrics or review existing metrics. Further, the system may be configured to select one or more core metrics that directly align with the goals defined. In an example, the one or more core indices include CSAT, NPS, CES, chum rate, etc. Further, the system may be configured to identify critical touchpoints where subscriber experience can be measured, such as onboarding, support interactions, renewal processes, etc. The system is further configured to gather input and feedback. The system analyzes existing customer feedback, surveys, and reviews to understand what matters most to subscribers. The system may be configured to prioritize metrics based on their importance to achieving the goals set. In an aspect, the system establishes benchmarks or targets for each selected metric and defines specific quantitative goals for improving each metric overtime.

[0085] In an aspect, upon receiving the request, the system (108) may retrieve the set of subscriber experience indices. The retrieval process may involveaccessing one or more databases or data sources that store precomputed or real-time data related to subscriber experience. The system (108) may also retrieve RAN logs, which may contain valuable information about the performance of the network at the radio access level. RAN logs may include various attributes such as timestamps, unique identifiers of subscribers, base station identifiers, event types, signal strengths, and quality metrics.

[0086] To derive meaningful insights from the RAN logs and subscriber experience indices, the system (108) may use an Artificial Intelligence (Al) engine (216). The Al engine (216) refers to a platform that utilizes various algorithms, techniques, and data to simulate human intelligence and perform tasks traditionally requiring human cognition. The Al engine (216) is designed to analyze large volumes of data, make decisions, learn from patterns, and adapt to changing circumstances without explicit programming for each scenario. They typically incorporate machine learning models, natural language processing (NLP), computer vision, and other Al techniques to achieve tasks such as data analysis, pattern recognition, automation, and decision-making. The Al engine (216) may be configured to compute the one or more correlation values between the attributes of the RAN logs and the subscriber experience indices. Correlation values may indicate the strength and direction of the relationship between the attributes and the indices, helping to identify which network factors significantly impact subscriber experience.

[0087] After computing the correlation values, the system (108) may transmit the set of subscriber experience indices and the correlation values to the monitoring unit (114). The monitoring unit (114) may be a centralized entity responsible for overseeing the performance of the cellular network and making data-driven decisions to optimize network operations. By receiving the subscriber experience indices and correlation values, the monitoring unit (114) may gain valuable insights into the current state of the network and identify areas that require attention or improvement.

[0088] In one embodiment, the process of retrieving the set of subscriber experience indices may involve several steps. The system (108) may include a request processing engine (212) that determines whether the requested set of subscriber experience indices is available in a first database (210-1). The first database (210-1) may be a dedicated storage that contains precomputed subscriber experience indices, which may be regularly updated based on historical data or realtime network measurements.

[0089] If the requested set of subscriber experience indices is available in the first database (210-1), the request processing engine (212) may retrieve the precomputed indices directly from the database. This approach may be efficient and time-saving, as it eliminates the need for real-time computation of the indices. However, if the requested set of indices is not available in the first database (210- 1), the system (108) may use a computation engine (214) to calculate the indices on demand.

[0090] The computation engine (214) may derive the requested set of subscriber experience indices when they are not readily available in the first database (210-1). The computation engine (214) may retrieve the set of RAN logs from the second database (210-2) to perform this computation. The second database (210-2) may be a separate storage entity that specifically stores RAN logs collected from various elements of the cellular network, such as base stations, radio network controllers, or other network nodes.

[0091] Once the relevant RAN logs are retrieved from the second database (210-2), the computation engine (214) may process and analyze the logs to derive the requested set of subscriber experience indices. This derivation process may involve extracting relevant information from the RAN logs, such as radio frequency (RF) data, and applying various algorithms or models to calculate the indices. The specific algorithms or models used may depend on the nature of the subscriber experience indices being computed and the available data in the RAN logs.

[0092] After the computation engine (214) derives the requested set of subscriber experience indices, the system (108) may store the computed indices in the first database (210-1) for future retrieval. This storage mechanism may optimize the system's performance by allowing quick access to previously computed indices, reducing the need for redundant computations. The stored indices may be updated periodically or whenever new data becomes available to ensure the accuracy and relevance of the information.

[0093] The RAN logs used by the system (108) may contain various attributes that provide valuable information about the performance of the cellular network. These attributes may include timestamps, which indicate the specific time at which certain events or measurements occurred. Timestamps may be crucial for understanding the temporal dynamics of network performance and identifying patterns or trends overtime.

[0094] Another important attribute of the RAN logs may be the unique identifier of the subscriber. This identifier may be used to track the experience of individual subscribers as they interact with the network. By analyzing subscriberspecific data, the system (108) may identify any issues or anomalies that affect particular users and take targeted actions to resolve them.

[0095] The RAN logs may also include base station identifiers, which specify the specific base station or cell site associated with each log entry. This information may be valuable for understanding the geographic distribution of network performance and identifying any location-specific issues. By correlating subscriber experience indices with base station identifiers, the system (108) may pinpoint areas of the network that require optimization or capacity enhancements.

[0096] Event types may be another crucial attribute captured in the RAN logs. These event types may indicate specific occurrences or conditions in the network, such as call drops, handover failures, or quality of service degradations. The system (108) may identify the most common or impactful issues affectingsubscriber experience by analyzing the frequency and distribution of different event types.

[0097] Signal strength and quality metrics may also be included in the RAN logs. These metrics may provide information about the strength and quality of the radio signals received by subscribers' devices. Poor signal strength or quality may lead to dropped calls, slow data speeds, or other issues that negatively impact subscriber experience. By monitoring these metrics, the system (108) may identify areas of the network that require coverage optimization or interference mitigation.

[0098] The Al engine (216) may compute the correlation values between the attributes of the RAN logs and the subscriber experience indices. By leveraging advanced machine learning algorithms and statistical techniques, the Al engine (216) may identify patterns, anomalies, and insights that may not be apparent through manual analysis.

[0099] One of the key functions of the Al engine (216) may be to identify network issues based on the computed correlation values. By examining the strength and direction of the correlations, the Al engine (216) may determine which attributes of the RAN logs have the most significant impact on subscriber experience indices. This analysis may reveal specific network issues, such as congested cells, faulty equipment, or suboptimal network configurations.

[0100] In addition to identifying network issues, the Al engine (216) may also generate one or more recommendations for resolving the identified issues. These one or more recommendations may be based on best practices, historical data, or machine learning models that predict the most effective actions to improve subscriber experience. For example, the Al engine (216) may suggest network parameter optimizations, capacity expansions, or maintenance activities to address the identified issues.

[0101] Once the Al engine (216) generates one or more recommendations, the system (108) may transmit these recommendations to the monitoring unit (114).The monitoring unit (114) may then review the recommendations and take appropriate actions to resolve the identified network issues. This may involve coordinating with network operations teams, dispatching field technicians, or making configuration changes to network elements.

[0102] By implementing the recommendations generated by the Al engine (216), the system (108) may proactively address network issues and improve subscriber experience. This proactive approach may help prevent or mitigate service disruptions, reduce customer complaints, and enhance overall network performance. The system (108) may continuously monitor the impact of the implemented recommendations and adjust its strategies based on the observed results.

[0103] The set of subscriber experience indices monitored by the system (108) may include various metrics that provide a comprehensive view of the quality of experience for subscribers. One such metric may be the happiness score, which may be a composite indicator that quantifies the overall satisfaction of subscribers with the network services. The happiness score may consider factors such as network availability, call quality, data speeds, and customer support interactions. The happiness score metric is used to quantify and evaluate the subjective wellbeing or satisfaction levels of individuals or groups. It typically involves asking respondents to rate their happiness or satisfaction on a numerical scale or through qualitative feedback. For example, in a customer satisfaction survey, a telecommunications company might ask subscribers to rate their overall happiness with the service received on a scale from 1 to 10. Another important subscriber experience indices may be the top call release reasons. This metric may identify the most frequent causes of call drops or disconnections, such as network congestion, coverage issues, or equipment failures. “Top call release reasons” are the primary reasons or causes for calls being disconnected or released by a telecommunications network or customer service center. These reasons are identified through analysis of call logs and may include technical issues, customer actions, or network-related problems. For example, the common top call release reasons could include networkcongestion, dropped calls due to poor signal strength, customer hang-ups, or issues with billing inquiries. By analyzing the top call release reasons, the system (108) may prioritize network improvements and troubleshooting efforts to address the most prevalent issues affecting subscriber experience.

[0104] The “volume of services” used by subscribers may also be a relevant subscriber experience index. This metric may track the usage patterns of various network services, such as voice calls, text messages, and data consumption. The system (108) may identify trends, preferences, and potential capacity constraints by monitoring service usage. This information may help network operators optimize their service offerings and ensure adequate resources are available to meet subscriber demands. The volume of services used may be defined as an amount or extent of services consumed or utilized by subscribers within a specified timeframe. This metric can encompass various services offered by a provider, such as data usage, voice calls, text messages, or additional features like streaming subscriptions. In an example, a mobile service provider measures the volume of services used by each subscriber monthly, including data usage in gigabytes, minutes of voice calls, and number of text messages sent.

[0105] The time spent by subscribers using different services may be another important subscriber experience index. This metric may provide insights into the engagement and satisfaction of subscribers with specific network services. For example, if subscribers spend a significant amount of time using data services, it may indicate a high level of satisfaction with the network's data performance. Conversely, if subscribers frequently experience long call setup times or interrupted sessions, it may suggest issues with network reliability or capacity. Time spent by subscribers using services may be defined as a duration or amount of time that subscribers engage with or utilize services provided by a company. This can include time spent actively using digital services, watching content, or interacting with customer support. In an example, a streaming platform tracks the average time subscribers spend watching videos or accessing content daily to gauge engagement levels and user behaviour patterns.

[0106] The subscriber journey with base stations may also be a valuable subscriber experience index. This metric may track the movement and interactions of subscribers across different base stations or cell sites. By analyzing subscriber journeys, the system (108) may identify coverage gaps, handover issues, or other network anomalies that impact subscriber experience. This information may help network operators optimize cell site placements, adjust network parameters, and ensure seamless connectivity for subscribers as they move within the network. Subscriber Journey with Base Stations may be defined as a path or sequence of interactions and connectivity experiences that subscribers undergo when connecting to and utilizing base stations within a telecommunications network. It includes aspects such as signal strength, handoff between base stations, and overall network performance. In an example, a mobile network operator maps the subscriber journey with base stations to analyze coverage gaps, optimize network efficiency, and improve service reliability for seamless connectivity across different geographical areas.

[0107] To facilitate the analysis and interpretation of subscriber experience indices, the monitoring unit (114) may provide a user interface that displays the received indices in a clear and intuitive manner. This user interface may include various visualizations, such as charts, graphs, and heatmaps, to help network operators quickly identify trends, patterns, and anomalies in the data.

[0108] In addition to displaying the subscriber experience indices, the monitoring unit (114) may also provide an interactive interface for users to analyze and visualize the data. This interactive interface may allow users to drill down into specific metrics, filter the data based on various criteria, and perform comparative analyses across different time periods or geographic regions. The monitoring unit (114) may empower network operators to gain deeper insights into subscriber experience and make data-driven decisions to optimize network performance by providing these interactive capabilities. Providing the interactive interface for users to analyze and visualize the set of determined subscriber experience indices involves creating a user-friendly platform that allows stakeholders to explore andderive insights from the data. To provide the interactive interface, the system may be configured to integrate the determined set of subscriber experience indices into a centralized data repository. The system may be configured to provide the user- friendly interface for data visualization and interactivity. In an example, the user- friendly interface is configured to provide:• various types of visualizations such as line charts, bar charts, heatmaps, scatter plots, and geographical maps.• Allow users to filter data based on different criteria (e.g., time period, geographic region, customer segment) and drill down into specific details.• Incorporate interactive elements (such as selection tools) for exploring data points in detail.• Offer customization options for users to adjust visualizations according to their preferences and specific analytical needs.

[0109] Overall, the system (108) for monitoring the subscriber experience indices may provide a comprehensive and proactive approach to ensuring high- quality network services for subscribers. By leveraging the power of Al and data analytics, the system (108) may identify network issues, generate actionable recommendations, and enable network operators to make informed decisions to enhance subscriber experience. The system's ability to process and analyze vast amounts of RAN logs and correlate them with subscriber experience indices may provide a deep understanding of the factors influencing network performance and subscriber satisfaction.

[0110] The potential benefits of the system (108) are numerous. By proactively monitoring and addressing network issues, the system (108) may help reduce the frequency and duration of service disruptions, leading to improvednetwork reliability and availability. This, in turn, may result in higher subscriber satisfaction, reduced chum rates, and increased customer loyalty.

[0111] Moreover, the insights provided by the system (108) may enable network operators to optimize their network investments and resource allocation. By identifying the most critical areas for improvement and prioritizing network upgrades or expansions based on subscriber experience indices, operators may achieve a higher return on investment and maximize the impact of their network enhancements.

[0112] FIG. 3 illustrates an exemplary implementation (300) of the system (108) for monitoring the subscriber experience indices, in accordance with embodiments of the present disclosure. The implementation (300) may involve various components of the system (108), such as the monitoring unit (114), the first database (210-1), the second database (210-2), the request processing engine (212), the computation engine (214), and the Al engine (216), working together to provide a comprehensive solution for monitoring and analyzing subscriber experience indices.

[0113] In one embodiment, the user (102) or an operator of the network (106) may initiate the process by using the monitoring unit (114) to transmit the request for determining the set of subscriber experience indices for one or more subscribers (at step 302). Upon receiving the request, the system (108) may first check whether the precomputed set of subscriber experience indices is already available in the first database (210-1) (at step 304). If the requested indices are found in the first database (210-1), the system (108) may retrieve them and transmit them directly to the monitoring unit (114), thereby providing a quick response to the user's request (at step 312).

[0114] However, if the requested subscriber experience indices are not available in the first database (210-1), the system (108) may invoke the computation engine (214) to calculate the indices in real time (at step 306). The computation engine (214) may be a separate component external to the system (108), as shownin FIG. 3, or it may be integrated within the one or more processors (202) of the system (108), as depicted in FIG. 2. Regardless of its location, the computation engine (214) may play a crucial role in deriving the subscriber experience indices when they are not readily available in the first database (210-1).

[0115] To compute the subscriber experience indices, the computation engine (214) may retrieve the set of RAN logs from the second database (210-2) (at step 308). The second database (210-2) may serve as the repository for storing RAN logs, which contain valuable information about the performance and behaviour of the radio access network. These RAN logs may include various attributes such as timestamps, unique identifiers of subscribers, base station identifiers, event types, signal strengths, and quality metrics. The computation engine (214) may derive meaningful insights into the subscriber experience by analyzing these attributes.

[0116] In some embodiments, the computation engine (214) may leverage the capabilities of the Al engine (216) to determine the subscriber experience indices based on the RAN logs. The Al engine (216) may employ advanced machine learning algorithms and statistical techniques to extract relevant patterns and correlations from the RAN data. By applying sophisticated data analytics and Al models, the Al engine (216) may accurately infer the subscriber experience indices, considering multiple factors and their complex interactions.

[0117] The Al engine (216) may utilize various machine learning and deep learning techniques to compute correlation values between the attributes of the RAN logs and the subscriber experience indices. The Al engine (216) may employ machine learning models such as linear regression, decision trees, random forests, or support vector machines to establish relationships between the input features (RAN log attributes) and the output variable (subscriber experience indices). Additionally, deep learning architectures such as feedforward neural networks, convolutional neural networks (CNNs), or recurrent neural networks (RNNs) may be used to capture complex patterns and dependencies in the RAN log data. The Al engine (216) may also involve data preprocessing techniques, including featureselection, data normalization, and handling of missing or noisy data, to ensure the quality and relevance of the input data. The training process of the Al models may involve collecting and labelling a dataset of RAN logs and corresponding subscriber experience indices, using techniques such as cross-validation and evaluation metrics to assess model performance. Furthermore, the Al engine (216) may incorporate methods for model interpretation and explainability, such as feature importance analysis and model visualization, to provide insights into the learned patterns and relationships between the RAN log attributes and the subscriber experience indices.

[0118] At step (310), the computation engine is configured to transmit the computed data to the system. Once the computation engine (214) has derived the requested set of subscriber experience indices, it may transmit them to the monitoring unit (114) for display and further analysis (at step 312). The monitoring unit (114) may provide a user-friendly interface that allows network operators to visualize the subscriber experience indices in various formats, such as charts, graphs, and heatmaps. This visual representation may enable operators to quickly identify trends, anomalies, and areas of concern, facilitating data-driven decisionmaking and proactive network management.

[0119] In addition to computing the subscriber experience indices, the Al engine (216) may also be configured to calculate correlation values between the attributes of the RAN logs and the subscriber experience indices. These correlation values may indicate the strength and direction of the relationships between specific network parameters and the overall subscriber experience. By examining these correlations, the system (108) may uncover key factors that have a significant impact on subscriber satisfaction and network performance.

[0120] The correlation values computed by the Al engine (216) may serve as valuable inputs for identifying and resolving network issues. The system (108) may continuously monitor the subscriber experience indices and compare them against predefined thresholds or ranges. If the indices fall outside the acceptablerange, it may trigger an alert to the network operators, prompting them to investigate and address the underlying issues. By proactively detecting and resolving network problems based on the insights derived from the correlation analysis, the system (108) may help maintain a high level of subscriber satisfaction and prevent potential service disruptions.

[0121] To further enhance the efficiency and responsiveness of the system (108), the computed set of subscriber experience indices may be stored in the first database (210- 1 ) for future retrieval . This caching mechanism may allow the system (108) to quickly serve subsequent requests for the same indices without the need for redundant computations. The system (108) may optimize its performance and reduce the response time for user queries by maintaining a repository of precomputed indices.

[0122] FIG. 4 illustrates an exemplary flowchart of a method (400) for monitoring subscriber experience indices, in accordance with embodiments of the present disclosure. The method (400) may encompass various steps and procedures that enable the system (108) to effectively monitor and analyze subscriber experience indices, leveraging the capabilities of its components such as the request processing engine (212), the computation engine (214), and the Al engine (216).

[0123] The method (400) may commence with the step of receiving (402) a request for determining the set of subscriber experience indices for one or more subscribers from the monitoring unit (114). This request may be initiated by the user (102) or the operator of the network (106) who seeks to gain insights into the quality of experience perceived by the subscribers. The request may specify the particular subscribers or groups of subscribers for whom the experience indices are to be determined.

[0124] Upon receiving the request, the method (400) may proceed to retrieve the requested set of subscriber experience indices. The retrieval process may involve a determination (404) by the request processing engine (212) regarding the availability of the requested indices in the first database (210-1). The firstdatabase (210-1) may serve as the repository for storing precomputed subscriber experience indices, which can be readily accessed and returned to the monitoring unit (114) if available. In an example, the first database (210-1) and the second database may be a MySQL Database, or a MongoDB. MySQL is a widely used open-source relational database management system (RDBMS). It can store structured data efficiently and support fast retrieval of precomputed subscriber experience indices. The MongoDB is a popular NoSQL database known for its flexibility and scalability. It is suitable for storing semi-structured or unstructured data, which can include subscriber experience indices in various formats.

[0125] If the requested set of subscriber experience indices is found in the first database (210-1), the method (400) may proceed to retrieve the precomputed indices and transmit them to the monitoring unit (114). This approach may provide a quick and efficient response to the user's request, as the indices are already calculated and stored in the database. By leveraging precomputed indices, the system (108) may reduce the computation overhead and improve the responsiveness of the monitoring process.

[0126] However, if the requested set of subscriber experience indices is not available in the first database (210-1), the method (400) may invoke the computation engine (214) to calculate the indices in real-time. The computation engine (214) may be responsible for deriving the subscriber experience indices based on the available data and the specific requirements of the request. This computation step (406) may involve various algorithms, models, and techniques to process the relevant data and generate meaningful insights into the subscriber experience.

[0127] To compute the requested set of subscriber experience indices, the computation engine (214) may retrieve the set of RAN logs from the second database (210-2). The second database (210-2) may serve as the repository for storing RAN logs, which capture detailed information about the performance and behaviour of the radio access network. These RAN logs may contain a wide rangeof atributes, such as timestamps, unique identifiers of subscribers, base station identifiers, event types, signal strengths, and quality metrics.

[0128] The computation engine (214) may analyze the retrieved RAN logs and derive the requested set of subscriber experience indices based on the radio frequency (RF) data contained within the logs. This derivation process may involve extracting relevant features, applying statistical methods, and utilizing machine learning algorithms to identify paterns and correlations that indicate the quality of the subscriber experience. By examining the RF data, the computation engine (214) may gain valuable insights into factors such as network coverage, signal quality, and service availability, which directly impact the subscriber experience.

[0129] Once the computation engine (214) has derived the requested set of subscriber experience indices, the method (400) may proceed to store (408) the computed indices in the first database (210-1) for future retrieval. This storage step may optimize the efficiency of the system (108) by allowing subsequent requests for the same indices to be served from the database without the need for redundant computations. By maintaining a cache of precomputed indices, the system (108) may improve its responsiveness and reduce the latency in providing results to the monitoring unit (114).

[0130] In addition to computing the subscriber experience indices, the method (400) may involve the use of the Artificial Intelligence (Al) engine (216) to calculate correlation values between the atributes of the RAN logs and the subscriber experience indices. This computation step (410) may leverage machine learning techniques and statistical analysis to uncover meaningful relationships and dependencies between specific network parameters and the overall subscriber experience.

[0131] The Al engine (216) may examine the RAN logs and the derived subscriber experience indices to identify paterns and correlations that provide insights into the factors influencing the quality of experience. For example, the Al engine (216) may discover that certain signal strength thresholds or networkcongestion levels have a significant impact on subscriber satisfaction. By calculating these correlation values, the Al engine (216) may help network operators understand the key drivers of subscriber experience and prioritize their efforts accordingly.

[0132] The correlation values computed by the Al engine (216) may be utilized to identify potential network issues and generate recommendations for resolving them. By analyzing the correlations, the system (108) may detect anomalies, performance degradations, or other problems that negatively impact the subscriber experience. The Al engine (216) may employ predictive models and intelligent algorithms to suggest appropriate actions or interventions that can address the identified issues and improve the overall network quality.

[0133] Once the subscriber experience indices and the correlation values have been computed, the method (400) may proceed to transmit (414) these results to the monitoring unit (114). The monitoring unit (114) may serve as the interface between the system (108) and the users (102) or network operators. It may provide a user-friendly dashboard or visualization tools that allow the users to explore and analyze the subscriber experience indices and the associated correlation values.

[0134] The transmitted results may be displayed on the user interface of the monitoring unit (114) in various formats, such as charts, graphs, heat maps, or tabular representations. These visualizations may help users quickly identify trends, patterns, and areas of concern within the subscriber experience data. The monitoring unit (114) may also provide interactive features that allow users to drill down into specific metrics, filter the data based on various criteria, and perform comparative analyses across different time periods or geographic regions.

[0135] In addition to displaying the subscriber experience indices, the monitoring unit (114) may present the generated recommendations for resolving network issues. These recommendations may be based on the correlation values and the insights derived from the Al engine (216). By providing actionable suggestions,the monitoring unit (114) may assist network operators in making informed decisions and taking proactive measures to enhance the subscriber experience.

[0136] The method (400) may further include the step of resolving (412) the identified network issues based on the generated recommendations. Network operators may review the recommendations provided by the system (108) and take appropriate actions to address the underlying problems. This may involve optimizing network parameters, reallocating resources, upgrading infrastructure, or implementing targeted solutions to improve the quality of service delivered to subscribers.

[0137] By continuously monitoring the subscriber experience indices and taking prompt actions based on the insights and recommendations provided by the system (108), network operators may proactively manage the network and ensure a high level of subscriber satisfaction. The method (400) may enable operators to identify and resolve issues before they escalate into major problems, thereby reducing the impact on subscribers and minimizing service disruptions.

[0138] The set of subscriber experience indices monitored by the system (108) may encompass a wide range of metrics that provide a comprehensive view of the subscriber experience. These indices may include but are not limited to, a happiness score, top call release reasons, volume of services used, time spent by subscribers using services, and subscriber journey with base stations. Each of these indices may offer unique insights into different aspects of the subscriber experience, allowing network operators to gain a holistic understanding of the quality of service provided.

[0139] The happiness score may serve as an overall indicator of subscriber satisfaction, considering various factors such as network performance, service reliability, and customer support. By tracking the happiness score over time, network operators may gauge the general sentiment of subscribers and identify trends or fluctuations that warrant attention. A declining happiness score may signalthe need for proactive measures to address underlying issues and improve the subscriber experience.

[0140] The top call release reasons may provide valuable information about the most common causes of call drops or disconnections experienced by subscribers. By analyzing these reasons, network operators may identify specific network issues or areas of weakness that require focus and optimization. For example, if a high percentage of call releases are attributed to poor signal quality or network congestion, operators may take steps to enhance coverage, upgrade infrastructure, or optimize resource allocation in affected areas.

[0141] The volume of services used by subscribers may offer insights into the usage patterns and preferences of different subscriber segments. By monitoring the usage of various services, such as voice calls, data services, and value-added offerings, network operators may gain a better understanding of the demand and popularity of specific services. This information may guide marketing strategies, service provisioning, and capacity planning to ensure that the network is equipped to meet the evolving needs of subscribers.

[0142] The time spent by subscribers using different services may provide an indication of the engagement and satisfaction levels associated with each service. By analyzing the duration and frequency of service usage, network operators may identify services that are highly valued by subscribers and allocate resources accordingly. Conversely, services with low usage or short engagement times may require further investigation to understand the reasons behind their lack of popularity and take corrective measures.

[0143] The subscriber journey with base stations may offer valuable insights into the mobility patterns and network performance experienced by subscribers as they move across different geographic areas. By tracking the handover processes, signal strengths, and quality metrics associated with different base stations, network operators may identify coverage gaps, capacity constraints, or other issues that impact the seamless connectivity and quality of service. Thisinformation may guide network planning, optimization, and expansion strategies to ensure a consistent and reliable subscriber experience across the network.

[0144] The method (400) may also involve providing an interactive interface within the monitoring unit (114) to enable users to analyze and visualize the subscriber experience indices. This interface may offer advanced features such as data exploration, filtering, and drill-down capabilities, allowing users to delve deeper into specific metrics, time periods, or subscriber segments. By providing intuitive and user-friendly tools for data analysis, the monitoring unit (114) may empower network operators to derive actionable insights and make data-driven decisions to enhance the subscriber experience.

[0145] The method (400) may enable network operators to proactively monitor and manage the subscriber experience, taking timely actions to address problems and optimize network performance. By providing a data-driven approach to understanding the factors influencing subscriber satisfaction, the method (400) may help operators prioritize their efforts, allocate resources efficiently, and deliver a superior quality of service to their customers.

[0146] By continuously monitoring subscriber experience indices, network operators may gain real-time visibility into the performance of their network and the satisfaction levels of their subscribers. This proactive monitoring approach may allow operators to detect and resolve issues promptly, minimizing the impact on subscribers and preventing potential chum.

[0147] Moreover, the method (400) may provide valuable insights into the key drivers of subscriber experience, enabling operators to focus their efforts on the areas that matter most to their customers. By analyzing the correlation values between network attributes and subscriber experience indices, operators may identify the critical factors that influence subscriber satisfaction and take targeted actions to optimize those aspects of the network.

[0148] The Al-powered analysis and recommendation capabilities of the method (400) may further enhance the efficiency and effectiveness of network management. By leveraging advanced machine learning algorithms and intelligent automation, the system (108) may provide accurate and timely recommendations for resolving network issues, reducing the reliance on manual troubleshooting and enabling operators to take proactive measures to prevent future problems.

[0149] Furthermore, the interactive interface provided by the monitoring unit (114) may empower network operators with the tools and insights they need to make informed decisions and drive continuous improvement. By providing a user- friendly platform for data exploration and visualization, the method (400) may facilitate collaboration, knowledge sharing, and data-driven decision-making across different teams and departments within the organization.

[0150] FIG. 5 illustrates an exemplary computer system (500) in which or with which embodiments of the present disclosure may be implemented. As shown in FIG. 5, the computer system (500) may include an external storage device (510), a bus (520), amain memory (530), a read only memory (540), amass storage device (550), a communication port (560), and a processor (570). A person skilled in the art will appreciate that the computer system (500) may include more than one processor (570) and communication ports (560). Processor (570) may include various modules associated with embodiments of the present disclosure.

[0151] In an embodiment, the communication port (560) 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 port (560) may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (500) connects.

[0152] In an embodiment, the memory (530) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read-only memory (540) may be any static storage device(s) e.g., but not limitedto, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or Basic Input / Output System (BIOS) instructions for the processor (570).

[0153] In an embodiment, the mass storage (550) may be any current or future mass storage solution, which may 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), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g., an array of disks (e.g., SATA arrays).

[0154] In an embodiment, the bus (520) communicatively couples the processor(s) (570) with the other memory, storage and communication blocks. The bus (520) may be, e.g., a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB) or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (570) to the computer system (500).

[0155] Optionally, operator and administrative interfaces, e.g., a display, keyboard, joystick, and a cursor control device, may also be coupled to the bus (520) to support direct operator interaction with the computer system (500). Other operator and administrative interfaces may be provided through network connections connected through the communication port (560). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (500) limit the scope of the present disclosure.

[0156] The method and system of the present disclosure may be implemented in a number of ways. For example, the methods and systems of the present disclosure may be implemented by software, hardware, firmware, or anycombination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure. The programs for executing the method according to the present disclosure can be recorded on various types of recording media, including, but not limited to, magnetic storage media (e.g., hard disks, floppy disks, magnetic tapes), optical storage media (e.g., CD-ROMs, DVDs, Blu-ray discs), solid-state storage media (e.g., USB flash drives, SD cards, solid-state drives), and any other non- transitory computer-readable storage media. These recording media can store the programs in the form of machine-readable instructions, which can be executed by a computer or other processing device to implement the methods described in the present disclosure.

[0157] FIG. 6 illustrates another exemplary flowchart of the method (600) for monitoring subscriber experience indices, in accordance with embodiments of the present disclosure.

[0158] Step (602) involves receiving the request from the monitoring unit to determine the set of subscriber experience indices for one or more subscribers. In an example, the received request may be a query request (requesting specific information or metrics about subscriber experience), an analysis request (asking for an analysis of current subscriber experience indices), a report request (requesting a report on the latest subscriber experience metrics), a comparison request (requesting a comparison of subscriber experience indices over different time periods or between different subscriber groups), or a trend analysis request (requesting an analysis of trends in subscriber experience metrics).

[0159] In an aspect, step (602) involves processing, which typically includes gathering, processing, and analyzing relevant data to generate meaningful metrics that reflect the quality of service and customer satisfaction.

[0160] Step (604) involves retrieving radio access network (RAN) logs from a secondary database. In an example, the RAN logs contain various attributes that are crucial for understanding and analyzing the performance of the radio access network within a telecommunications system. At step (604), the system initiates a query to the secondary database where RAN logs are stored. This database typically stores detailed operational data from the network elements involved in radio access, such as base stations (NodeBs, eNodeBs in LTE / 4G, gNodeBs in 5G), antennas, and related equipment. The system processes the retrieved RAN logs, potentially aggregating, filtering, or analyzing them to extract relevant insights and prepare them for further analysis.

[0161] At step (606), the method involves using the Artificial Intelligence (Al) engine to compute one or more correlation values between the attributes of the RAN logs and the set of subscriber experience indices. The Al engine is employed to analyze the relationship between the attributes of the RAN logs and the subscriber experience indices. In an example, the Al engine is configured to:• Identify which attributes of the RAN logs are most relevant or influential in predicting or explaining variations in the subscriber experience indices.• Compute correlation values (e.g., Pearson correlation coefficient, Spearman's rank correlation) between pairs of attributes from RAN logs and subscriber indices. These correlation values quantify the strength and direction of relationships.• Employ machine learning techniques to discover patterns, dependencies, or causal relationships between RAN attributes and subscriber indices. This may include regression models, classification models, or clustering algorithms, depending on the nature of the analysis.

[0162] At step (608), the system is configured to determine the set of subscriber experience indices by using the one or more computed correlation values obtained from the previous step (606). Based on the computed correlation values, the system sets thresholds or criteria to determine which RAN attributes significantly impact subscriber experience indices. The system is configured to identify correlation values that exceed predefined thresholds, indicating a meaningful relationship (e.g., correlation coefficient above a certain value). The system is configured to consider both positive and negative correlations to understand how variations in RAN performance affect subscriber behaviours or perceptions. Using the established thresholds or criteria, the system determines a subset of RAN attributes that most strongly correlate with the set of subscriber experience indices.

[0163] At step (610), the system transmits the set of determined subscriber experience indices and the one or more computed correlation values to the monitoring unit. The transmission may occur in real-time or at scheduled intervals, depending on the monitoring unit's requirements and capabilities. Real-time updates allow for immediate monitoring and responsiveness to changing network conditions or subscriber feedback. Upon receiving the transmitted data, the monitoring unit displays the subscriber experience indices and correlation values in the user-friendly interface.

[0164] The present disclosure discloses a user equipment that is communicatively coupled to a system through a network. The user equipment is configured to monitor subscriber experience indices. The user equipment a memory and one or more processors coupled with the memory. The one or more processors are configured to execute instructions stored in the memory to perform steps of a method for monitoring subscriber experience indices. The method includes receiving, from the monitoring unit, the request to determine the set of subscriber experience indices of one or more subscribers. The method includes retrieving radio access network (RAN) logs from a second database, the RAN logs comprising one or more attributes. The method includes computing, using the Artificial Intelligence(Al) engine, one or more correlation values between the one or more attributes of the RAN logs and the set of subscriber experience indices. The method includes determining the set of subscriber experience indices by using the one or more computed correlation values. The method includes transmitting the set of determined subscriber experience indices and the one or more computed correlation values to the monitoring unit.

[0165] In another embodiment, the present subject matter relates to a computer program product comprising a non-transitory computer-readable medium may provide a convenient and efficient means for implementing the method (600) for monitoring subscriber experience indices. The non-transitory computer- readable medium may have instructions stored thereon that, when executed by at least one processor (202), cause the processor (202) to perform a series of operations. These operations may include receiving (604) a request from a monitoring unit (114) for determining a set of subscriber experience indices of one or more subscribers. Upon receiving the request, the processor (202) may retrieve the set of subscriber experience indices from a database or other storage medium. Additionally, the processor (202) may retrieve radio access network (RAN) logs, which comprise various attributes related to the performance and behaviour of the network. The retrieved RAN logs and subscriber experience indices may be utilized by the Artificial Intelligence (Al) engine (216) to compute correlation values between the attributes of the RAN logs and the subscriber experience indices. These correlation values may provide insights into the relationship between specific network parameters and the overall subscriber experience. Finally, the processor (202) may transmit the set of subscriber experience indices and the computed correlation values to the monitoring unit (114) for further analysis and visualization.

[0166] The present disclosure introduces a significant technological advancement by consolidating subscriber experience metrics into a single dashboard. The present disclosure addresses existing limitations in analyzing subscriber experience and happiness scores within telecommunications services. Previously, end-users lacked the flexibility to readily assess subscriber experiencesand happiness indices, only being able to extract subscriber-level data through specific UI queries. One potential advantage of the present system and method is the ability to derive actionable insights from subscriber usage data and RAN logs, empowering network operators with a comprehensive view of subscriber experience. This may facilitate proactive management and enhancement of network services, ultimately leading to improved subscriber satisfaction in the dynamic telecommunications landscape.

[0167] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the disclosure and not as limitation.ADVANTAGES OF THE PRESENT DISCLOSURE

[0168] The present disclosure provides a system and a method for monitoring subscriber experience indices.

[0169] The present disclosure provides a dashboard that displays a plurality of metrics used for determining subscriber experience indices such as happiness score, type of failure faced, clear codes count, failed procedure, subscriber journey with a base station, distribution of call release reasons, distribution of services consumed, and the like.

[0170] The present disclosure provides a system and a method that determines subscriber experience indices using Radio Access Network (RAN) logs.

[0171] The present disclosure provides a system and a method that allows operators to identify and troubleshoot network issues if subscriber experience indices fall outside a predetermined range.

[0172] The present disclosure provides a system and a method with a flexible and interactive interface for visualizing and analyzing subscriber experience indices.

[0173] The present disclosure is to provide a system and method that computes correlation values between attributes of Radio Access Network (RAN) logs and subscriber experience indices. By analyzing these correlation values, the system may identify network issues impacting subscriber experience and generate recommendations for resolving them, enabling network operators to proactively manage and optimize network performance.

Claims

CLAIMSWe Claim:

1. A system (108) for monitoring subscriber experience indices, comprising: a memory (204); and one or more processors (202) communicatively coupled with the memory (204), wherein the one or more processors (202) are configured to execute instructions stored in the memory (204) to: receive, from a monitoring unit (114), a request for determining a set of subscriber experience indices of one or more subscribers; retrieve radio access network (RAN) logs from a second database, the RAN logs comprising one or more attributes; compute one or more correlation values between the one or more attributes of the RAN logs and the set of subscriber experience indices; determine the set of subscriber experience indices by using the one or more computed correlation values; and transmit the set of determined subscriber experience indices and the one or more computed correlation values to the monitoring unit (114).

2. The system (108) of claim 1, includes a request processing engine (212) configured to: determine whether the requested set of subscriber experience indices is available in a first database (210-1), the first database (210-1) is configured to store a set of precomputed subscriber experience indices;retrieve the precomputed set of subscriber experience indices from the first database (210-1) when the requested set of subscriber experience indices is available; and compute, by a computation engine (214), the requested set of subscriber experience indices when the requested set of subscriber experience indices is not available in the first database (210-1).

3. The system (108) of claim 2, wherein for computing the requested set of subscriber experience indices, the computation engine (214) is configured to: retrieve the set of RAN logs from the second database (210-2); and derive the requested set of subscriber experience indices from radio frequency (RF) data in the retrieved set of RAN logs.

4. The system (108) of claim 1, wherein the one or more attributes of the RAN logs include at least one of a timestamp, a unique identifier of the subscriber, a base station identifier, an event type, a signal strength, and quality metrics.

5. The system (108) of claim 1, wherein an Artificial Intelligence (Al) engine (216) is further configured to: analyze the one or more computed correlation values to identify one or more network issues; and generate one or more recommendations for resolving the identified one or more network issues.

6. The system (108) of claim 5, wherein the one or more processors (202) are further configured to: transmit the one or more generated recommendations to theresolve the identified one or more network issues based on generated one or more recommendations.

7. The system (108) of claim 1, wherein the set of subscriber experience indices includes at least one of a happiness score, top call release reasons, volume of services used, time spent by subscribers using services, and subscriber journey with base stations.

8. The system (108) of claim 1, wherein the monitoring unit (114) is configured to: display the received set of subscriber experience indices on a user interface; and provide an interactive interface for users to analyze and visualize the set of determined subscriber experience indices.

9. A method (600) for monitoring subscriber experience indices, the method comprising: receiving (602), from a monitoring unit (114), a request for determining a set of subscriber experience indices of one or more subscribers; retrieving (604) radio access network (RAN) logs from a second database, the RAN logs comprising one or more attributes; computing (606), using one or more processors (202), one or more correlation values between the one or more attributes of the RAN logs and the set of subscriber experience indices; determining (608) the set of subscriber experience indices by using the one or more computed correlation values; andtransmitting (610) the set of determined subscriber experience indices and the one or more computed correlation values to the monitoring unit (114).

10. The method (600) of claim 9, further comprising retrieving the set of subscriber experience indices including steps of: determining, by a request processing engine (212), whether the requested set of subscriber experience indices is available in a first database (210-1), the first database (210-1) is configured to store a set of precomputed subscriber experience indices; retrieving the precomputed set of subscriber experience indices from the first database (210-1) when the requested set of subscriber experience indices is available; and computing, by a computation engine (214), the requested set of subscriber experience indices when the requested set of subscriber experience indices is not available in the first database (210-1).

11. The method (600) of claim 9, wherein computing the requested set of subscriber experience indices by the computation engine (214) further comprises: retrieving a set of RAN logs from a second database (210-2); and deriving the requested set of subscriber experience indices from radio frequency (RF) data in the retrieved set of RAN logs.

12. The method (600) of claim 9, wherein the one or more attributes of the RAN logs include at least one of a timestamp, a unique identifier of the subscriber, a base station identifier, an event type, a signal strength, and quality metrics.

13. The method (600) of claim 9, further comprising:analyzing the one or more computed correlation values to identify one or more network issues; and generating recommendations for resolving the identified one or more network issues.

14. The method (600) of claim 13, further comprising: transmitting the one or more generated recommendations to the monitoring unit (114); and resolving the identified one or more network issues based on generated recommendations.

15. The method (600) of claim 9, wherein the set of subscriber experience indices includes at least one of a happiness score, top call release reasons, volume of services used, time spent by subscribers using services, and subscriber journey with base stations.

16. The method (600) of claim 9, further comprising: displaying the received set of subscriber experience indices on a user interface of the monitoring unit (114); and providing an interactive interface for users to analyze and visualize the set of determined subscriber experience indices.

17. A user equipment (104) communicatively coupled to a system (108) through a network (106), wherein the system (108) for monitoring subscriber experience indices, comprising: a memory; and one or more processors coupled with the memory, wherein the one or more processors are configured to execute instructions stored in the memory to perform steps of a method (400) as claimed in claim 9.

18. A computer program product comprising a non-transitory computer- readable medium having instructions stored thereon that, when executed by at least one processor, cause the at least one processor to perform operations comprising: receiving (602), from a monitoring unit (114), a request for determining a set of subscriber experience indices of one or more subscribers; retrieving (604) radio access network (RAN) logs from a second database, the RAN logs comprising one or more attributes; computing (606) one or more correlation values between the one or more attributes of the RAN logs and the set of subscriber experience indices; determining (608) the set of subscriber experience indices by using the one or more computed correlation values; and transmitting (610) the set of determined subscriber experience indices and the one or more computed correlation values to the monitoring unit (114).

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