System and method for evaluating performance of one or more fixed wireless devices

Abstract

The present disclosure relates to a system (108) and a method (400) for evaluating performance of one or more fixed wireless devices in a network (106). The method comprises receiving data from one or more data sources associated with the one or more fixed wireless devices. A processing unit (220) identifies one or more key performance indicators (KPIs) in the received data, aggregates at least one value of the one or more KPIs to generate aggregated data, and filters the aggregated data using predefined parameters to identify the fixed wireless devices. The processing unit (220) further identifies the one or more fixed wireless devices connected to one or more serving cells during a predefined time period by detecting multiple records in the aggregated data. The method also includes comparing one or more KPIs at a cell level across the identified cells to select a serving cell.

Description

SYSTEM AND METHOD FOR EVALUATING PERFORMANCE OF ONE OR MORE FIXED WIRELESS DEVICESRESERVATION 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 evaluating performance of one or more fixed wireless devices in a network.DEFINITIONS

[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 term “network management platform (NMP)” used hereinafter in the specification refers to a system that collects, aggregates, analyzes, and manages network data from multiple sources for planning, optimization, and decision-making purposes.

[0005] The term “call summary log (CSL)” used hereinafter in the specification refers to log data generated by network elements that summarize session-level information such as data usage, signal quality, and session events for fixed wireless devices.

[0006] The term “trace collection entity (TCE)” used hereinafter in the specification refers to a server or system responsible for collecting and storing CSL data and other performance metrics from network components.

[0007] The term “key performance indicator (KPI)” used hereinafter in the specification refers to a measurable value that indicates the performance of a network element or user session, such as throughput, signal strength, or call drop rate.

[0008] The term “type allocation code (TAC)” used hereinafter in the specification refers to the first eight digits of the International Mobile Equipment Identity (IMEI), identifying the device's manufacturer and model.

[0009] The term “reference signal received power (RSRP)” used hereinafter in the specification refers to the power level of a reference signal received by a device, used to measure signal strength in a long-term evolution (LTE) and 5G networks.

[0010] The term “channel quality indicator (CQI)” used hereinafter in the specification refers to an index that represents the communication channel quality as reported by a user device to the network.

[0011] The term “signal-to-interference-plus-noise ratio (SINR)” used hereinafter in the specification refers to a metric that quantifies the quality of a received signal by comparing it to the sum of interference and background noise.

[0012] The term “fixed wireless access (FWA)” used hereinafter in the specification refers to a type of wireless communication that provides broadband internet access to fixed locations using cellular network technologies like 4G or 5G.

[0013] These definitions are in addition to those expressed in the art.BACKGROUND OF DISCLOSURE

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

[0015] Wireless communication technology has rapidly evolved over the past few decades. The first generation of wireless communication technology was analog technology that offered only voice services. Further, when the second- generation (2G) technology was introduced, text messaging and data services became possible. The 3 G technology marked the introduction of high-speed internet access, mobile video calling, and location-based services. The fourth-generation (4G) technology revolutionized wireless communication with faster data speeds, improved network coverage, and security. Currently, the fifth-generation (5G) technology is being deployed, with even faster data speeds, low latency, and the ability to connect multiple devices simultaneously. The sixth generation (6G) technology promises to build upon these advancements, pushing the boundaries of wireless communication even further. The sixth generation (6G) technology promises to build upon these advancements, pushing the boundaries of wireless communication even further.

[0016] As wireless technologies are advancing, there is a need to cope with the 5G requirements and deliver a high level of service to the users / customers. Users can send different types of data simultaneously, such as text, voice, video, and multimedia files. The demand for fast and reliable internet is increasing, especially for activities like gaming, audio, and video streaming on mobile devices. Users want better network quality to minimize delays, successfully make voice calls, etc. Further, in the current wireless networks, fixed wireless devices are used to provide internet access to a specific location using wireless communication technologies, such as radio waves, instead of traditional wired connections like fiber optic cables or digital subscriber line (DSU). A wireless device is called a fixed wireless device / product in the context that the wireless device is stationary and provides communication capabilities to a fixed location like a home, office, shopping centre,etc. Fixed wireless devices may include an outdoor antenna / receiver installed at a fixed location (e.g., at a terrace or a rooftop), which communicates with a nearby wireless base station operated by an internet service provider (ISP). Therefore, the fixed wireless device uses air as a communication medium to deliver fiber-like performance over the air called ‘air fiber’ to connect two fixed locations such as a building to another building for communication, or a building to the ISP’s base station for internet communication, etc.

[0017] In an optimal deployment scenario, each air fiber device or fixed wireless device should ideally be connected to a single serving cell or server that provides the most favourable radio frequency (RF) conditions, such as high signal strength, low interference, and stable connectivity. This ensures the delivery of maximum throughput, low latency, and consistent user experience. However, several challenges may often arise due to the dynamic and complex nature of wireless environments. For instance, urban clutter, signal obstruction from buildings, fluctuating radio frequency (RF) conditions, overlapping cell coverage areas, and network load balancing strategies can lead to a situation where a fixed wireless device is handed over or simultaneously connects to multiple serving cells or sectors over a given period. In such cases, the fixed wireless device may intermittently associate with suboptimal cells, resulting in performance degradation and can manifest as reduced download / upload speeds, increased latency, dropped connections, or inconsistent service delivery. The issue becomes more noticeable in dense deployments where cells with overlapping coverage areas compete to serve the same fixed wireless device, often without a mechanism to evaluate which cell truly offers the best service quality under varying network conditions. The lack of alignment between device orientation, serving cell assignment, and real-time performance indicators contributes to a subpar user experience, especially in fixed deployments where the device is not mobile and thus relies heavily on optimal static connectivity.

[0018] However, current techniques fall short of offering a solution to identify the most suitable serving cell or server for a fixed wireless deviceconnected to multiple cells or servers. The current techniques rely on static signal metrics or configuration data, which do not reflect real-time performance or user experience. Moreover, such techniques are not equipped to handle data from multiple network vendors or technologies in a unified and scalable manner.

[0019] There is, therefore, a need in the art to provide a method and a system that can overcome the shortcomings of the existing prior arts.OBJECTIVES OF THE PRESENT DISCLOSURE

[0020] Some of the objectives of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.

[0021] An objective of the present disclosure is to provide a system and a method for identifying an optimal serving cell for the fixed wireless devices that are connected to multiple servers in a network.

[0022] Another objective of the present disclosure is to provide a system and a method that utilizes Key Performance Indicators (KPIs) and Radio Frequency (RF) performance metrics to evaluate the quality of service experienced by fixed wireless devices across various serving cells or servers.

[0023] Another objective of the present disclosure is to provide a system and a method that automates the process of selecting a serving cell and orientation adjustment for one or more fixed wireless devices, eliminating the need for manual configuration or physical intervention.

[0024] Another objective of the present disclosure is to provide a system and a method for aggregating and analyzing network data at multiple levels to identify performance trends and anomalies impacting fixed wireless device connectivity.

[0025] Another objective of the present disclosure is to provide a system and a method to enable actionable insights and recommendations for networkoptimization teams, including suggesting device orientation changes and providing evidence -based reasons for performance degradation.

[0026] Other objectives and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.SUMMARY

[0027] In an exemplary embodiment, a method for evaluating performance of one or more fixed wireless devices in a network is disclosed. The method includes receiving by a receiving unit data associated with the one or more fixed wireless devices from one or more data sources. The method further includes identifying by a processing unit one or more key performance indicators (KPIs) associated with the one or more fixed wireless devices in the received data. The method includes aggregating by the processing unit at least one value of the one or more identified KPIs from the received data to generate an aggregated data. The method includes filtering by the processing unit the aggregated data based on one or more predefined parameters to identify the one or more fixed wireless devices. The method includes identifying by the processing unit the one or more fixed wireless devices connected to one or more serving cells within a first predefined time period based on the aggregated data. The identification is performed by detecting multiple records of the one or more fixed wireless devices corresponding to one or more serving cells in the aggregated data. The aggregated data is generated for a predefined time frame. The method further includes comparing by the processing unit the one or more KPIs at a cell level including at least one of a User Equipment (UE) throughput and one or more Radio Frequency (RF) parameters across the identified one or more serving cells over a second predefined time period to select a serving cell from the one or more serving cells for the one or more fixed wireless devices. The method also includes determining by the processing unit at least one action to optimize the performance of the one or more fixed wireless devices based on the selected serving cell.

[0028] In some embodiments, the at least one action includes adjusting an orientation of the one or more fixed wireless devices corresponding to the selected serving cell.

[0029] In some embodiments, the received data includes a call summary log also known as CSL data generated by one or more network elements of one or more network vendors. The one or more data sources include one or more trace collection entities (TCE) maintained by each network vendor of the one or more network vendors.

[0030] In some embodiments, aggregating the received data further includes computing aggregated at least one value of the one or more identified KPIs from the received data for each fixed wireless device at different levels including device level, cell level and network technology level, wherein the aggregated data is generated for a predefined time frame.

[0031] In some embodiments, the one or more parameters includes at least a Type Allocation Code also known as TAC associated with the one or more fixed wireless devices.

[0032] In another exemplary embodiment, a system for evaluating performance of one or more fixed wireless devices in a network is disclosed. The system includes a receiving unit configured to receive data associated with the one or more fixed wireless devices from one or more data sources. The system further includes a processing unit connected to the receiving unit. The processing unit is configured to identify one or more key performance indicators also known as KPIs associated with the one or more fixed wireless devices in the received data. The processing unit is configured to aggregate at least one value of the one or more identified KPIs from the received data to generate an aggregated data. The processing unit is also configured to filter the aggregated data based on one or more predefined parameters to identify the one or more fixed wireless devices. The processing unit is configured to identify the one or more fixed wireless devices connected to one or more serving cells within a predefined time period based on theaggregated data. The identification is performed by detecting multiple records of one or more fixed wireless devices corresponding to one or more serving cells in the aggregated data. The aggregated data is generated for a first predefined time frame. The processing unit is further configured to compare the one or more KPIs at a cell level including at least one of a User Equipment (UE) throughput and one or more Radio Frequency (RF) parameters across the identified one or more serving cells over a second predefined time period to select a serving cell from the one or more serving cells for the one or more fixed wireless devices. The processing unit is also configured to determine at least one action to optimize the performance of the one or more fixed wireless devices based on the selected serving cell.

[0033] In accordance with one embodiment of the present disclosure a computer program product comprising a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform a method for evaluating performance of one or more fixed wireless devices in a network. The method includes receiving by a receiving unit data associated with the one or more fixed wireless devices from one or more data sources. The method further includes identifying by a processing unit one or more key performance indicators (KPIs) associated with the one or more fixed wireless devices in the received data. The method includes aggregating by the processing unit at least one value of the one or more identified KPIs from the received data to generate an aggregated data. The method includes filtering by the processing unit the aggregated data based on one or more predefined parameters to identify the one or more fixed wireless devices. The method includes identifying by the processing unit the one or more fixed wireless devices connected to one or more serving cells within a first predefined time period based on the aggregated data. The identification is performed by detecting multiple records of the one or more fixed wireless devices corresponding to one or more serving cells in the aggregated data. The aggregated data is generated for a predefined time frame. The method further includes comparing by the processing unit the one or more KPIs at a cell level including at least one of a User Equipment (UE) throughput and one ormore Radio Frequency (RF) parameters across the identified one or more serving cells over a second predefined time period to select a serving cell from the one or more serving cells for the one or more fixed wireless devices. The method also includes determining by the processing unit at least one action to optimize the performance of the one or more fixed wireless devices based on the selected serving cell.

[0034] The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive.BRIEF DESCRIPTION OF DRAWINGS

[0035] 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.

[0036] FIG. 1 illustrates an exemplary network architecture for evaluating performance of one or more fixed wireless devices in a network, in accordance with embodiments of the present disclosure.

[0037] FIG. 2A illustrates an exemplary system architecture for evaluating performance of the one or more fixed wireless devices in the network, in accordance with embodiments of the present disclosure.

[0038] FIG. 2B illustrates a block diagram of a system for evaluating performance of the one or more fixed wireless devices in the network, in accordance with embodiments of the present disclosure

[0039] FIG. 3 illustrates an exemplary flow diagram of a method for evaluating performance of the one or more fixed wireless devices in the network, in accordance with embodiments of the present disclosure.

[0040] FIG. 4 illustrates another exemplary flow diagram of the method for evaluating performance of the one or more fixed wireless devices in the network, in accordance with embodiments of the present disclosure.

[0041] FIG. 5 illustrates an exemplary computer system in which or with which the system may be implemented in accordance with an embodiment of the present disclosure.

[0042] The foregoing shall be more apparent from the following more detailed description of the disclosure.LIST OF REFERENCE NUMERALS100 - Network Architecture102-1, 102-2. . . 102-N - A plurality of users104-1, 104-2. . . 104-N - User Equipments (UEs)106 - Network108 - System200A - System architecture202 - Network management platform (NMP)204 - Master database (MDB) server206 - Data storage server08 - Reporting server 10 - Radio optimization team 12 - Radio planning team 00B - Block diagram 14 - Receiving unit 16 - Memory 18 - Interface(s) 20 - Processing unit 22 - Database 00 - Flow Diagram 00 - Flow Diagram 00 - A computer system510 - External storage device520 - Bus530 - Main memory540 - Read only memory550 - Mass storage device560 - Communication port(s)570 - ProcessorDETAILED DESCRIPTION OF DISCLOSURE

[0043] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

[0044] 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.

[0045] 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.

[0046] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged.A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0047] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.

[0048] 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.

[0049] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in thisspecification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed items.

[0050] In an optimal deployment scenario, an air fiber device or a fixed wireless device should ideally be connected to a single server with favorable radio frequency (RF) conditions to ensure maximum performance. However, in real- world situations, some fixed wireless devices may be served by multiple cells / servers. In such situations, the fixed wireless devices may face an interference issue from the multiple cells / servers. Thus, such situations may lead to a deterioration in the performance of such fixed wireless devices / nodes. Therefore, there is a need for a system and a method that efficiently and accurately identifies the fixed wireless devices that are connected to multiple cells / servers.

[0051] In an aspect, the present disclosure provides a system and a method for identifying the optimal server or serving cell for the fixed wireless devices connected to multiple servers based on aggregation of the data (e.g., call summary log data) related with the fixed wireless devices. This helps in optimizing the network resources, elevating the overall service quality, and, consequently, fostering heightened customer satisfaction.

[0052] In the context of this description, and particularly when discussing the connection of one or more fixed wireless devices, the term ‘serving cell’ often refers to the specific sector of a base station that the device is actively communicating with. For simplicity and ease of understanding, the term ‘server’ may sometimes be used interchangeably with ‘serving cell’ to denote this connection point.

[0053] The various embodiments throughout the disclosure will be explained in more detail with reference to FIG. 1- FIG. 5.

[0054] FIG. 1 illustrates an exemplary network architecture for evaluating performance of one or more fixed wireless devices in a network (106), in accordance with embodiments of the present disclosure.

[0055] Referring to FIG. 1, the network architecture (100) may include one or more computing devices or user equipments (104-1, 104-2. . . 104-N) associated with one or more users (102-1, 102-2... 102-N) in an environment. A person of ordinary skill in the art will understand that one or more users (102-1, 102-2... 102- N) may be individually referred to as the user (102) and collectively referred to as the users (102). Similarly, a person of ordinary skill in the art will understand that one or more user equipments (104-1, 104-2. . . 104-N) 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 two user equipments (104) are depicted in FIG. 1, however any number of the user equipments (104) may be included without departing from the scope of the ongoing description. In an embodiment, each of the user equipment (104) may have a first unique identifier attribute associated therewith. In an embodiment, the first 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.

[0056] In an embodiment, the user equipment (104) may include smart devices operating in a smart environment, for example, an Internet of Things (loT) system. In such an embodiment, the user equipment (104) may include but is not limited to, smartphones, smart watches, smart sensors (e.g., mechanical, thermal, electrical, magnetic, etc.), networked appliances, networked peripheral devices, networked lighting system, communication devices, networked vehicle accessories, networked vehicular devices, smart accessories, tablets, smart television (TV), computers, smart security system, smart home system, other devices for monitoring or interacting with or for the users ( 102) and / or entities, or any combination thereof.A person of ordinary skill in the art will appreciate that the user equipment (104) may include, but is not limited to, intelligent, multi-sensing, network-connected devices that can integrate seamlessly with each other and / or with a central server or a cloud-computing system or any other device that is network-connected.

[0057] 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 smartphone, 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, electromechanical, 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, wherein 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 touchpad, 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.

[0058] Referring to FIG. 1, the user equipment (104) may communicate with a system (108) via a network (106). The UE (104) may be communicatively coupled with the network (106). The communicative coupling comprises receiving, from the UE (104), a connection request by the network (106), sending an acknowledgment of the connection request to the UE (104), and transmitting a plurality of signals in response to the connection request. In an embodiment, the network (106) may include at least one of a Fourth Generation (4G) network, a FifthGeneration (5G) network, a Sixth Generation (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.

[0059] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).

[0060] FIG. 2A illustrates an exemplary system architecture (200A) of a system (108) for evaluating performance of the one or more fixed wireless devices in the network (106), in accordance with an embodiment of the present disclosure.

[0061] In an aspect, the system (200) includes a network management platform (NMP) (202), a master database server (MDB server) (204), a data storage server (206), and a reporting server (208). In an aspect, the system (108) may also include a radio optimization team (210) and a radio planning team (212).

[0062] In an aspect, the MDB server (204) may store information related to cell identifier (ID), global cell identifier (ID), and other telecommunication network operator-specific information stored in a specific nomenclature. In an aspect, the telecommunication network operator-specific nomenclature may include information such as geography name, geography center name, and geography cluster name of a specific eNodeB, etc. In an aspect, a call summary log (CSL) datamay include cell ID and global cell ID information. In an aspect, NMP (202) may map the global cell ID obtained from the CSL data to the operator-specific nomenclature information / data received from the MDB server (204). The mapping helps in aggregating the CSL data at a further granular level (e.g., at a specific geography center level or a specific geography cluster level, etc.) if required.

[0063] In an aspect, the CSL data may be obtained from the data storage server (206) every hour or as per a defined schedule automatically so that the obtained CSL data may be correlated for mapping to data obtained from the MDB server (204).

[0064] In an aspect, from the user-server information stored in the CSL data, the one or more fixed wireless devices latched to multiple servers (along with the cell -wise usage) in a pre-defined period of time may be identified.

[0065] In an aspect, the data storage server (206) may be a trace collection entity (TCE) server that is used for collecting and storing CSL data generated by network elements (e.g., routers, fixed wireless devices, etc.) of different vendors.

[0066] In an aspect, the reporting server (208) may generate statistics / reports to display them on a user interface (UI) dashboard. For example, the UI dashboard may display a list of one or more fixed wireless devices connected to multiple servers in a week or in a specific time period.

[0067] In an aspect, the UI dashboard may be further accessible to the radio optimization team (210) or the radio planning team (212) to perform further optimizations or take some corrective actions for the pinpointed or filtered fixed wireless devices.

[0068] In an aspect, the NMP (202) may communicate with the MDB server (204), the data storage server (206) and the reporting server (208) by using their corresponding interfaces. In an aspect, the NMP (202) may communicate with the MDB server (204) using a master database server (MDB) interface. The MDB interface allows the NMP (202) to interact with the central repository of networkconfiguration and subscriber information managed by the MDB server (204), enabling the NMP (202) to retrieve necessary data for analysis and management. In an aspect, the NMP (202) may communicate with the data storage server (206) (e.g., TCE server) with a TCE interface. The TCE interface facilitates the transfer of session-related data, such as trace records and performance measurements, from the Trace Collection Entity (TCE) server (206) to the NMP (202) for aggregation and analysis. In an aspect, the NMP (202) may communicate with the reporting server (208) with a reporting server (RS) interface. The RS interface enables the NMP (202) to send processed data and instructions to the reporting server (208) for the generation of network performance reports, visualizations, and alarms. In an aspect, the interfaces like the MDB interface, the TCE interface, and the RS interface may be implemented in the form of an application programming interface (API) that allows communication / interaction with the NMP (202).

[0069] FIG. 2A facilitates evaluation of performance of the one or more fixed wireless devices across multiple serving cells. The system (108) utilizes the network management platform (202) to retrieve, map, and correlate call summary log data with operator-specific nomenclature information, enabling identification of the optimal serving cell for each of the one or more fixed wireless devices.

[0070] The network management platform (202) interacts with the master database server (204), the trace collection entity server (206), and the reporting server (208) through respective interfaces to aggregate performance data and generate visualizations. These interactions enable comparison of KPIs and RF parameters across serving cells, supporting automated selection of the most suitable serving cell without manual intervention.

[0071] FIG. 2B illustrates an exemplary block diagram (200B) of a system (108) for evaluating the performance of the one or more fixed wireless devices in the network (106), in accordance with an embodiment of the present disclosure.

[0072] Referring to FIG. 2B, the system (108) may include an interface(s) (218) that may include a variety of interfaces, for example, interfaces for data inputand output devices, referred to as I / O devices, storage devices, and the like. The interface(s) (218) may facilitate communication to / from the system (108). The interface(s) (218) may also provide a communication pathway for one or more components of the system (108). Examples of such components include but are not limited to, a processing unit (220) and a database (222).

[0073] In an embodiment, the processing unit (220) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing unit (220). In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing unit (220) may be processor-executable instructions stored on a non- transitory machine-readable storage medium, and the hardware for the processing unit (220) 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 processing unit (220). In such examples, the system (108) may include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system (108) and the processing resource. In other examples, the processing unit (220) may be implemented by electronic circuitry.

[0074] Among other capabilities, the processing unit (220) may be configured to fetch and execute computer-readable instructions stored in a memory (216) of the system (108). The memory (216) 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 (216) 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.

[0075] In an embodiment, the database (222) may include data that may be either stored or generated as a result of functionalities implemented by the processing unit (220). In an embodiment, the database (222) may be separate from the system (108). In an embodiment, the database (222) may be indicative of including, but not limited to, a relational database, a distributed database, a cloudbased database, or the like.

[0076] In an embodiment, the system (108) is deployed within the network management platform (NMP) (202) that includes the processing unit (220) to perform the functionalities described herein.

[0077] In an embodiment, a receiving unit (214) is configured to receive data associated with the one or more fixed wireless devices from one or more data sources. In this embodiment, the data sources may include trace collection entities (TCEs) managed by various network vendors. The TCEs collect and store call summary log (CSL) data, which captures device-level and cell-level activity records including timestamps, radio signal metrics, and session summaries. The receiving unit (214) may connect to each TCE via secure application programming interfaces (APIs) and pull CSL data files periodically or in real-time, thereby providing an antecedent basis for the receiving unit (214) and the one or more data sources.

[0078] In another embodiment, the processing unit (220) is connected to the receiving unit (214). The processing unit (220) identifies one or more key performance indicators (KPIs) associated with the fixed wireless devices present in the CSL data. The one or more KPIs may include device throughput (downlink / uplink), radio signal strength (RSRP), radio signal quality (RSRQ), signal -to-noise ratio (SNR), and timing advance (TA). For each KPI, the processing unit (220) analyzes its value to assess the connection quality and performance of the one or more fixed wireless devices with the serving cell. For instance, a higher RSRP value generally indicates a stronger radio signal, suggesting a better connection. Similarly, higher downlink / uplink throughput values signify better data transmission rates. The processing unit (220) may employ a weighted scoringmechanism or predefined thresholds for each KPI to determine the overall quality of the connection with a particular cell. Cells with higher scores or KPI values exceeding certain thresholds are considered better-serving cells for the fixed wireless device. The processing unit (220) identifies each KPI by referencing its specific KPI field in the data, which may differ between one or more network vendors. To handle the vendor-specific variations, the processing unit (220) may be configured with a mapping database or a set of rules that correlate the KPI field names used by different vendors to a standardized set of KPI definitions allowing the processing unit (220) to correctly interpret and process the KPI data regardless of the vendor of the network equipment providing the CSL data.

[0079] In an embodiment, the downlink / uplink throughput KPI is used to determine the optimal serving cell for one or more fixed wireless devices. Throughput reflects the data transfer efficiency between the device and the network. The downlink throughput refers to the rate at which data is transferred from the network (106) to the one or more fixed wireless devices. Higher downlink throughput is crucial for activities like streaming video, downloading large files, and browsing websites. The uplink throughput, on the other hand, refers to the rate at which data is transferred from the fixed wireless device to the network. Higher uplink throughput is important for activities such as video conferencing, uploading files, and sending data. When a fixed wireless device is latched to multiple cells over a predefined period, the processing unit (220) compares the average or peak throughput values for each of those cells, considering both downlink and uplink performance. A cell that consistently provides higher downlink and / or uplink throughput, indicating a greater capacity for both receiving and transmitting data efficiently, is considered more capable of handling data-intensive services and thus is prioritized for selection as the serving cell with the optimal output.

[0080] In an embodiment, the reference signal received power (RSRP) is used to evaluate the signal strength received by the fixed wireless device from each of the serving cells it was latched to during the predefined period. The processing unit (220) compares the RSRP values across the cells, with a focus on selecting thecell that delivers the strongest (i.e., least negative) signal. A higher RSRP indicates a stronger and more stable connection, which directly contributes to better performance and reliability. Therefore, the cell with the highest RSRP is considered a strong candidate for the best serving cell, especially when throughput differences are marginal.

[0081] In an embodiment, the reference signal received quality (RSRQ) is analyzed to assess the quality of the signal received from the serving cells. RSRQ accounts for both signal strength and interference from surrounding cells. For example, the processing unit (220) selects the cell with the highest RSRQ (values closer to 0 dB), indicating less interference and better signal quality. This KPI becomes particularly significant in dense deployments where signal interference is common. A cell with superior RSRQ ensures better voice and data quality and is thus preferred when determining the optimal serving cell.

[0082] In an embodiment, the signal -to-noise ratio (SNR) is used to evaluate the clarity of the signal received by the fixed wireless device in relation to background noise. The processing unit (220) examines the SNR values for each cell to identify which cell offers the highest signal clarity. A higher SNR implies reduced transmission errors and higher data fidelity. Consequently, a cell with consistently higher SNR is likely to offer better overall service performance and is considered favorable during the cell selection process.

[0083] In an embodiment, the timing advance (TA) is used to infer the physical distance between the fixed wireless device and each of its serving cells. A lower TA value suggests that the device is closer to the base station, which generally correlates with lower latency and stronger signal conditions. The processing unit (220) uses TA to further refine the selection when other KPIs like throughput, RSRP, or RSRQ are comparable across multiple cells. A serving cell with the lowest TA value may be selected as the best candidate to ensure optimal network responsiveness and quality.

[0084] In a further embodiment, the processing unit (220) is configured to aggregate at least one value of the one or more identified KPIs from the received data to generate aggregated data. The aggregation is performed across various hierarchical levels, including device-level, cell-level, and technology-level (such as long-term evolution (LTE) or 5G new radio (5G NR)). The multi-level aggregation enables a comprehensive evaluation of network performance to facilitate optimal serving cell selection for the one or more fixed wireless devices.

[0085] At the device-level, the aggregation is performed per fixed wireless device based on a user-level identifier such as the International Mobile Subscriber Identity (IMS I) or the International Mobile Equipment Identity (IMEI). The raw call summary log (CSL) data, collected from network elements across multiple vendors and technologies, is aggregated per fixed wireless device to enable devicespecific performance analysis. For example, the processing unit (220) may calculate average downlink throughput, average uplink throughput, Reference Signal Received Power (RSRP), and Reference Signal Received Quality (RSRQ) for each fixed wireless device over a predefined time period (e.g., 24 hours). This devicelevel aggregation allows profiling of individual device performance to detect potential issues such as degraded performance caused by antenna misalignment or other network -impacting conditions.

[0086] At the cell-level, the processing unit (220) aggregates the values of the one or more identified KPIs for all fixed wireless devices served by a particular cell. For instance, the processing unit (220) may compute the RSRP, the SNR, or total cell throughput for each serving cell to identify the relative performance of each serving cell when the one or more fixed wireless devices are found to be latched to multiple server cells within a predefined time window. Such aggregation enables the system to determine the best serving cell based on comparative KPI performance.

[0087] At the technology-level, the aggregation of the one or more identified KPIs is performed across all one or more fixed wireless devices using aparticular radio access technology, such as LTE or 5G NR. For example, the processing unit (220) may calculate the average uplink throughput or average the TA across all fixed wireless devices utilizing LTE, and separately forthose utilizing 5G NR. This helps assess how each technology performs under similar usage patterns and whether a different technology may offer improved performance for the one or more fixed wireless devices.

[0088] In an embodiment, the processing unit (220) filters the aggregated data based on one or more parameters to identify the fixed wireless devices that are to be further analyzed. One such parameter may be a Type Allocation Code (TAC), which uniquely identifies the manufacturer and model of the fixed wireless device. Other parameters may include location identifiers, network technology type, or specific threshold values for selected KPIs. For example, the system may filter all devices with average RSRQ below a defined threshold in the last 24 hours and belonging to a specific TAC.

[0089] The aggregated data may be generated at predefined time intervals such as hourly or daily and may be stored in the database (222) in various configurations, such as:• Combined data from all vendors and technologies,• Data filtered by vendor (e.g., Vendor 1: 4G only; Vendor 2: 5G only),• Data filtered by technology (e.g., all 5G, all 4G), or• Custom combinations as defined by the network operator.

[0090] In another embodiment, the processing unit (220) identifies the one or more serving cells to which each of the one or more fixed wireless devices were connected during the predefined time period. The identification is achieved by detecting multiple CSL records in the aggregated data where the same fixed wireless device is associated with different cell IDs over time. The processing unit (220) examines the aggregated data and determines the frequency and duration ofconnection to each serving cell for a given device and enables the system to track cell switching behaviour and to identify if a device frequently moves between multiple serving cells to identify stable versus unstable device-cell pairings based on the aggregated data.

[0091] In yet another embodiment, the processing unit (220) compares the one or more KPIs across the identified serving cells for a given fixed wireless device. The comparison includes at least one of a User Equipment (UE) throughput and one or more Radio Frequency (RF) parameters such as RSRP and RSRQ. The processing unit (220) compares across the identified one or more serving cells over a second predefined time period to select a serving cell from the one or more serving cells for the one or more fixed wireless devices. The selected serving cell is the serving cell that provides efficient performance. The selected serving cell may be selected based on criteria such as highest average throughput (downlink and / or uplink), strongest RSRP (indicating better signal strength), and highest RSRQ (indicating better signal quality). In some cases, the processing unit (220) may also factor in additional KPIs such as the SNR or the TA to determine the proximity and interference conditions related to each serving cell.

[0092] The selected serving cell may be determined by optimizing the data usage requirements for the respective one or more fixed wireless devices. For example, if a fixed wireless device is used primarily for video streaming, throughput and signal stability may be prioritized; if used for latency-sensitive services like VoIP, signal quality may take precedence.

[0093] Subsequently, upon determining the selected serving cell, a manual adjustment is performed to reorient the antenna or directional unit (e.g., Air Fiber component) of the fixed wireless device. The manual realignment ensures that the one or more fixed wireless devices establish and maintain a stable connection to the single identified best-serving cell, thereby improving service reliability, optimizing network resource utilization, and enhancing end-user experience.

[0094] In an embodiment, the processing unit (220) is further configured to determine at least one action to optimize the performance of the fixed wireless device, based on the selected serving cell. In one example, the at least one action may include providing a recommendation to physically adjust the orientation of the fixed wireless device (e.g., realign a directional antenna or reposition the unit) towards the selected serving cell. The adjustment can help ensure the device has a stronger line-of-sight and better RF propagation with the chosen cell. The at least one action includes adjusting device orientation to improve performance.

[0095] In another embodiment, the adjustment of the orientation of the one or more fixed wireless devices to connect to the selected serving cell may be performed automatically instead of manually. In this embodiment, the fixed wireless device may be equipped with a motorized or electronically controlled directional antenna or alignment module, which can be remotely triggered by the processing unit (220) based on the serving cell selection outcome. Upon determining the selected serving cell using the comparison of one or more KPIs across identified serving cells, the processing unit (220) may transmit orientation instructions to the fixed wireless device to align its antenna with the best-serving cell. This automatic realignment reduces manual field intervention, enables faster optimization of connectivity, and supports dynamic adaptability in changing radio conditions or during cell load balancing scenarios.

[0096] In an alternative embodiment, the system (108) may be integrated with a network operations dashboard that visualizes KPIs and cell comparisons for each fixed wireless device. The processing unit (220) generates visual plots and tabular reports indicating the device’s connection history, KPI trends, and serving cell rankings. The dashboard can highlight underperforming devices and suggest corrective actions, such as recommending a different serving cell or escalating the case for field inspection. While not explicitly claimed, this embodiment demonstrates potential variations and additional features that support the broader scope of the invention and enhance usability.

[0097] In another variation of the embodiment, the processing unit (220) may be deployed in a cloud-native architecture using distributed data processing frameworks. This allows the system (108) to scale efficiently with large volumes of CSL data from multiple vendors. Additionally, the system may incorporate machine learning algorithms that analyze historical KPI patterns and automatically classify fixed wireless devices into categories such as "Optimal," "Misaligned," or "At-Risk." For example, a device consistently exhibiting high RSRP and throughput with infrequent cell changes might be classified as "Optimal." Conversely, a device showing low RSRP and frequent handovers between cells could be classified as "Misaligned," indicating a potential need for re-orientation. Devices exhibiting a gradual degradation in signal strength or an increasing frequency of cell changes might be classified as "At-Risk," suggesting a potential future issue. These classifications may serve as a basis for prioritized optimization actions.

[0098] In summary, the described embodiments collectively provide a comprehensive system (108) for evaluating fixed wireless device performance in a network (106). The system includes a receiving unit (214) that ingests vendorspecific performance data, and a processing unit (220) that performs KPI identification, aggregation, filtering, serving cell analysis, performance comparison, and action recommendation. Each claim element is directly supported and explicitly enabled by one or more embodiments described above. Variations and enhancements such as cloud-based deployment, visualization dashboards, and predictive analytics are also possible, ensuring the invention can be practiced across diverse network environments.

[0099] FIG. 3 illustrates an exemplary flow diagram of a method (300) for evaluating performance of the one or more fixed wireless devices in the network ( 106), in accordance with an embodiment of the present disclosure . Each step of the method (300) may be performed by various units (e.g., the network management platform (NMP) (202), the master database server (MDB server) (204), the data storage server (206), and the reporting server (208)) present within the processingunit (220) of the system (108). The method (300) aims to identify the most suitable serving cell for each fixed wireless device, ensuring that the device maintains stable connectivity with minimal interference, thereby enhancing overall network performance. The elements and steps described in the figure are part of a process implemented within the network management platform (NMP) (202), which acts as a central system for data aggregation, analysis, and decision-making.

[0100] In the first step (302) of this embodiment, session data is transferred from a Trace Collection Entity (TCE) Server to the network management platform (NMP) (202). The TCE Server is a network component responsible for collecting session-level data, including signalling and user-plane information, generated by the devices connected to the mobile network. The session-level data is crucial for analyzing the behaviour and performance of each device. The receiving unit (214) in the NMP receives this data to initiate the analysis process for performance optimization.

[0101] In the next step (304), the processing unit (220) performs data aggregation. Specifically, the session-level data is aggregated from the TCE server into a more useful format at the user and cell levels. Each session is associated with a particular user and device through the use of unique identifiers such as the international mobile subscriber identity (IMSI) and the international mobile equipment identity (IMEI). The IMSI serves as a unique identifier for the subscriber, while the IMEI is used to uniquely identify the one or more fixed wireless devices. The processing unit (220) maps each session to one or more fixed wireless devices based on these identifiers and further correlates them to the one or more serving cell(s) to which the devices were latched during the session to track the behaviour, movement, and performance of the one or more fixed wireless devices across the network (106). The aggregation simplifies further analysis by transforming raw, individual session records into a structured format organized per device and per serving cell.

[0102] In the third step (306), the aggregated data is filtered by the processing unit (220) to isolate data related specifically to the one or more fixed wireless devices. These devices are identified using a predefined list of Type Allocation Codes (TACs), which are embedded in the IMEI and correspond to specific device models known to be for the one or more fixed wireless devices . This ensures that only relevant devices, those intended for fixed wireless access, are selected for performance evaluation and optimization.

[0103] In the fourth step (308), the processing unit (220) identifies the one or more fixed wireless devices latched to multiple server cells during the first predefined time period for example 24 hours. Latching refers to the process by which a device connects and remains attached to a particular cell for service. The one or more fixed wireless devices that frequently switch between multiple cells or are simultaneously reported across several cells may suffer from degraded performance due to interference, instability, or suboptimal RF conditions. Identifying the one or more fixed wireless devices allows the system to target them for optimization.

[0104] In the fifth step (310), the processing unit (220) compares the one or more key performance indicators (KPIs) at the cell level for each of the serving cells associated with the identified one or more fixed wireless devices. The one or more KPIs include, but are not limited to, the User Equipment (UE) throughput and the Radio Frequency (RF) parameters such as the Reference Signal Received Power (RSRP), the Reference Signal Received Quality (RSRQ), and the Signal-to- Interference-plus-Noise Ratio (SINR). By evaluating the one or more KPIs across the one or more server cells, the system selects the best serving cell for each fixed wireless device typically the one offering the highest throughput and optimal RF conditions.

[0105] In the final step (312), a manual adjustment of the Air Fiber orientation is carried out. Here, “Air Fiber” refers to the antenna or transceiver unit of the one or more fixed wireless devices. The transceiver unit of the one or morefixed wireless devices is typically mounted on a fixed structure such as a rooftop or pole. The adjustment involves physically aligning the antenna toward the identified best serving cell to establish a stable and direct connection, eliminating interference from other cells and ensuring consistent high-performance connectivity.

[0106] This process then concludes with the fixed wireless device optimally oriented toward a single, best serving cell, thereby improving performance and resource utilization in the network (106).

[0107] FIG. 4 illustrates an exemplary flow diagram of a method (400) for evaluating performance of the one or more fixed wireless devices in the network (106), in accordance with an embodiment of the present disclosure.

[0108] At step 402, the receiving unit (214) receives data associated with the one or more fixed wireless devices from the one or more data sources. In an aspect, the one or more data sources may be a plurality of trace collection entity (TCE) servers (e.g., data storage servers). In an aspect, the data associated with the one or more fixed wireless devices may be a call summary log (CSL) data generated by various network elements (e.g., routers, fixed wireless devices, etc.) of different vendors. In an aspect, the CSL data may include session data (e.g., subscriber / user activities like session creation / deletion, etc.) along with other information related to the plurality of fixed wireless devices. For example, for a specific user in a specific session, the CSL data may include information related to the amount of data transferred by the UE (104), reference signal received power (RSRP) for that specific session, channel quality indicator (CQI), total handovers made in a specific cell, etc.

[0109] In an aspect, the CSL data (e.g., session data) may be received from a plurality of TCE servers to the network management platform (NMP) (202). In an aspect, the NMP may enable the network planning and management.

[0110] At step 404, the processing unit (220) inside the network management platform (NMP) (202) may perform identification of one or more key performance indicators (KPIs) (e.g., throughput, voice call drop rates, etc.)associated with the one or more fixed wireless devices in the received CSL data and the KPI field of the CSL data obtained from the different vendors.

[0111] At step 406, the processing unit (220) inside the NMP (202) may perform the data aggregation of at least one value of the one or more identified KPIs from the received data to generate an aggregated data. For example, the NMP (202) may aggregate the CSL data, available in the network from one or more vendors, and for different technologies (4G, 5G, etc.), at international mobile subscriber identity (IMSI) / intemational mobile equipment identity (IMEI) (user) level. In an aspect, the NMP (202) may perform data aggregation from the session level to a user (IMSI / IMEI) cell level.

[0112] In an aspect, aggregating the received data further comprises computing aggregated one or more values of the one or more identified KPIs from the received data for each fixed wireless device at different levels, including device level, cell level, and network technology level, the aggregated data is generated for a predefined time frame. In an embodiment, at the device level, the processing unit (220) aggregates all the session data and the at least one value of the one or more identified KPIs associated with a specific device, irrespective of the number of cells or servers it connects to during a defined time interval. For example, if a fixed wireless device with IMSI 123456789012345 establishes fifty sessions in one hour across three different serving cells, the system computes aggregated values such as total data usage (e.g., 2 GB), average throughput (e.g., 40 Mbps), average Reference Signal Received Power (RSRP) (e.g., -85 dBm), and average Signal-to- Interference-plus-Noise Ratio (SINR) (e.g., 15 dB). Such aggregation enables peruser analysis and tracking of service quality and device behaviour across different network elements. Further at the cell level, for example, if a serving cell with Cell ID ABC123 handles traffic from 100 unique devices within a one -hour interval, the system aggregates the total uplink and downlink data (e.g., 150 GB), computes the average throughput per user (e.g., 30 Mbps), and evaluates cell-level parameters such as average RSRP (e.g., -90 dBm), average number of handovers (e.g., 25), and voice call drop rates (e.g., 1.2%). The aggregation allows for performancebenchmarking and identifying underperforming or congested cells in the network. At the network technology level, for instance, across the 5G network, the processing unit (220) may compute the total 5G data throughput (e.g., 2 Tbps), determine the average RSRP (e.g., -88 dBm), calculate the average Channel Quality Indicator (CQI) (e.g., 11), and identify the number of active devices using 5G services (e.g., 25,000 devices). Such insights help network operators evaluate the adoption and efficiency of new-generation networks and plan upgrades or optimization strategies accordingly.

[0113] In an aspect, the calculation of aggregation of the at least one value of the one or more identified KPIs may be performed by using raw values for numerator and denominators. In an aspect, the calculation of the aggregated values may be performed at an hourly level, daily level, or as per a pre-defined schedule (e.g., user defined).

[0114] In an exemplary implementation, the Throughput Success Rate KPI may be calculated using the total successful throughput sessions as the numerator and the total attempted throughput sessions as the denominator. If the data received over a period (e.g., 1 hour) includes:• Total Successful Throughput Sessions = 850• Total Throughput Session Attempts = 1000Then, the Throughput Success Rate = (850 / 1000) x 100 = 85%In another example, for a Cell Attach Success Rate KPI:• Successful Attach Requests = 950• Total Attach Requests = 980Then, Attach Success Rate = (950 / 980) x 100 = 96.9%Similarly, for a Call Drop Rate KPI, the raw values may be:Dropped Calls = 20Total Established Calls = 1000Then, Call Drop Rate = (20 / 1000) x 100 = 2%

[0115] In an aspect, the data aggregation performed by the NMP (202) may include storing the aggregated data at the IMSI and other levels of vendor / technology (e.g., according to 4G or 5G technologies). In an aspect, the aggregated data may be stored separately as the one or more network vendors or their technologies combined (e.g., 4G and 5G), all 5G combined, vendor 1 (only 4G), vendor 2 (only 5G), vendor 3 (only 5G) and vendor 4 (only 5G), or any other combination thereof.

[0116] In an aspect, the aggregated data (e.g., final backend data) may provide cell level usage information for each user along with the various RF KPIs for different technologies like 4G, or 5G, etc. In an aspect, the aggregated data may be further easily ingested in a user interface (UI) of the NMP (202).

[0117] In an aspect, for example, if a userl had “100” sessions with a wireless network in an hour and connected to “10” different cells / servers / base stations, the present disclosure aggregates data at the user level for each connected cell / server / base station. In an aspect, the method (400) allows aggregating the CSL data based on assigned weights and may record information like {userl, connected to cell 1, ‘x’ total number of sessions established with cell 1, KPIs like RSRP, total traffic, etc.}, {userl, connected to cell2, ‘x’ total number of sessions established with cell2, KPIs like RSRP, total traffic, etc.} ... {userl, connected to cell 10, ‘x’ total number of sessions established with cell 10, KPIs like RSRP, total traffic, etc.} .

[0118] At step 408, the processing unit (220) inside the NMP (202) may perform filtration on the aggregated data to identify the one or more fixed wireless devices that are connected to the multiple servers (serving cells) based on one or more parameters for each device. In an aspect, the TAC may include an 8-digit number that allows for identifying the device's manufacturer, model number, and the regulating body that approved it. For example, in the aggregated data received from step (406), device make and model along with the device features like devicetype, technology supported (e.g., 4G or 5G), frequency band supported, carrier aggregation information, etc. may be included or mapped for each TAC. In an aspect, the aggregated data including TAC information may allow further aggregation of the data at the one or more KPIs.

[0119] At step 410, the processing unit (220) may perform identification of the one or more fixed wireless devices connected to one or more serving cells within the first predefined time period based on the aggregated data, the identification is performed by detecting multiple records of a fixed wireless device corresponding to one or more serving cells in the aggregated data. The processing unit (220) may further determine how the one or more fixed wireless devices establish / latch a session with more than one servers (along with cell-wise usage) or servers or base stations within the pre-defined period of time.

[0120] At step 412, the processing unit (220) may perform comparison of the one or more KPIs at a cell level, including at least one User Equipment (UE) throughput and one or more Radio Frequency (RF) parameters, across the identified one or more serving cells over a predefined time period to select a serving cell from the one or more serving cells for the one or more fixed wireless devices. The processing unit (220) compares the one or more KPIs and the RF parameters across the identified one or more serving cells over a predefined time period (e.g., last 24 hours, last 7 days, or a user-defined window) to evaluate which serving cell has demonstrated better overall performance and reliability, especially under similar network loads or environmental conditions. Based on the comparative analysis, the processing unit (220) selects the serving cell among the available options for the one or more fixed wireless devices, thereby improving service experience, ensuring better load distribution across the network, and enhancing the stability and quality of the fixed wireless connection.

[0121] At step 414, the processing unit (220) may determine at least one action to optimize the performance of the one or more fixed wireless device based on the selected serving cell. The at least one action comprises adjusting anorientation of the at least one fixed wireless device corresponding to the selected serving cell. In an aspect, the at least one action may be determined for the one or more fixed wireless devices to ensure that the one or more fixed wireless devices may connect to a single optimal server or serving cell. In an aspect, the at least one action may include adjusting an orientation of the one or more fixed wireless devices for ensuring connection to the identified optimal / best server. In an aspect, the orientation of the one or more fixed wireless devices may be adjusted manually.

[0122] In an aspect, the raw data from user-server information may be utilized for identifying the optimal server for the one or more fixed wireless devices connected to multiple servers. In an aspect, the one or more KPIs at the cell level may be analyzed across different servers over a predefined period. In an aspect, the one or more KPIs may be a throughput or a plurality of RF parameters. For example, the plurality of RF parameters may be the RSRP or a signal-to-interference-plus- noise ratio (SINR) etc. The RSRP is a measure of the received power level in a telecommunication network. The SINR quantifies the quality of a received signal by considering the strength of the desired signal, interference from other sources, and background noise.

[0123] In an aspect, the selection of the optimal / best server may be determined by optimizing the data usage requirements for the one or more fixed wireless devices.

[0124] In an aspect, upon identifying the optimal serving cell, the at least one action, such as manual adjustments, is made to the orientation of the fixed wireless devices, thus ensuring that one or more fixed wireless devices may get connected to the selected serving cell.

[0125] Thus, the present disclosure ensures that the one or more data sources may get connected to the optimal / best server.

[0126] FIG. 5 illustrates a computer system (500) in which or with which the embodiments of the present disclosure may be implemented.

[0127] As shown in FIG. 5, the computer system (500) may include an external storage device (510), a bus (520), a main memory (530), a read-only memory (540), a mass storage device (550), communication port(s) (560), and a processor (570) . A person skilled in the art will appreciate that the computer system may include more than one processor and communication ports. The processor (570) may include various modules associated with embodiments of the present disclosure. The communication port(s) (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(s) (560) may be chosen depending on a network (106), such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system connects.

[0128] The main memory (530) may be random access memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (540) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or Basic Input / Output System (BIOS) instructions for the processor (570). The mass storage device (550) may be any current or future mass storage solution which can be used to store information and / or instructions. Exemplary mass storage device (550) includes, but is 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.

[0129] The bus (520) communicatively couples the processor (570) with the other memory, storage, and communication blocks. The bus (520) may be, e.g., a Peripheral Component Interconnect / Peripheral Component Interconnect Extended bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as otherbuses, such a front side bus (FSB), which connects the processor (570) to the computer system.

[0130] 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. Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) (560). The components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure

[0131] In another exemplary embodiment, a system for evaluating performance of one or more fixed wireless devices in a network is disclosed. The system includes a receiving unit configured to receive data associated with the one or more fixed wireless devices from one or more data sources. The system further includes a processing unit connected to the receiving unit. The processing unit is configured to identify one or more key performance indicators also known as KPIs associated with the one or more fixed wireless devices in the received data. The processing unit is configured to aggregate at least one value of the one or more identified KPIs from the received data to generate an aggregated data. The processing unit is also configured to filter the aggregated data based on one or more predefined parameters to identify the one or more fixed wireless devices. The processing unit is configured to identify the one or more fixed wireless devices connected to one or more serving cells within a predefined time period based on the aggregated data. The identification is performed by detecting multiple records of one or more fixed wireless devices corresponding to one or more serving cells in the aggregated data. The aggregated data is generated for a first predefined time frame. The processing unit is further configured to compare the one or more KPIs at a cell level including at least one of a User Equipment (UE) throughput and one or more Radio Frequency (RF) parameters across the identified one or more serving cells over a second predefined time period to select a serving cell from the one ormore serving cells for the one or more fixed wireless devices. The processing unit is also configured to determine at least one action to optimize the performance of the one or more fixed wireless devices based on the selected serving cell.

[0132] In accordance with one embodiment of the present disclosure a computer program product comprising a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform a method for evaluating performance of one or more fixed wireless devices in a network. The method includes receiving by a receiving unit data associated with the one or more fixed wireless devices from one or more data sources. The method further includes identifying by a processing unit one or more key performance indicators (KPIs) associated with the one or more fixed wireless devices in the received data. The method includes aggregating by the processing unit at least one value of the one or more identified KPIs from the received data to generate an aggregated data. The method includes filtering by the processing unit the aggregated data based on one or more predefined parameters to identify the one or more fixed wireless devices. The method includes identifying by the processing unit the one or more fixed wireless devices connected to one or more serving cells within a first predefined time period based on the aggregated data. The identification is performed by detecting multiple records of the one or more fixed wireless devices corresponding to one or more serving cells in the aggregated data. The aggregated data is generated for a predefined time frame. The method further includes comparing by the processing unit the one or more KPIs at a cell level including at least one of a User Equipment (UE) throughput and one or more Radio Frequency (RF) parameters across the identified one or more serving cells over a second predefined time period to select a serving cell from the one or more serving cells for the one or more fixed wireless devices. The method also includes determining by the processing unit at least one action to optimize the performance of the one or more fixed wireless devices based on the selected serving cell.

[0133] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made, and many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be implemented merely as illustrative of the disclosure and not as a limitation.

[0134] The present disclosure provides a technical advancement in optimizing the connectivity of fixed wireless devices across multiple serving cells or servers in a telecommunications network. The present disclosure addresses limitations in existing network performance monitoring solutions by enabling intelligent selection of the optimal serving cell using cross-vendor Call Summary Log (CSL) data and radio frequency (RF) KPIs. The present disclosure provides key inventive aspects, including aggregation of session-level data from multiple technologies and vendors, type allocation code (TAC)-based device classification, and KPI-based comparative analysis for optimal serving cell identification to significantly improve the accuracy of network diagnostics and corrective decisionmaking, enhancing the overall performance and user experience of fixed wireless devices. The present disclosure is especially valuable in high-density deployments of 4G / 5G Fixed Wireless Access (FWA) solutions where users may frequently connect to suboptimal cells due to misaligned device orientation or radio conditions.ADVANTAGES OF THE PRESENT DISCLOSURE

[0135] The present disclosure provides a system and a method that identifies the most suitable server for the fixed wireless devices by evaluating the performance of the fixed wireless devices across various servers.

[0136] The present disclosure provides a system and a method that identifies the optimal serving cell for the fixed wireless devices, without any manualintervention in the server selection process itself. The system analyzes network performance data to automatically pinpoint the best connection.

[0137] The present disclosure provides a system and a method that identifies the optimal serving cell for the fixed wireless devices connected with multiple servers by utilizing the real-time network performance data.

[0138] The present disclosure provides a system and a method that supports improvement of overall service quality by identifying performance-related issues of fixed wireless devices across servers.

[0139] The present disclosure provides a system and a method that helps improve customer satisfaction and has the potential to enhance business revenues.

[0140] The present disclosure provides a system and a method that allows optimization of the fixed wireless device’s orientation and selecting the optimal / best server. This allows the network optimization team to take corrective actions to enhance / improve the end-user experience.

[0141] The present disclosure provides a system and a method for identifying the optimal / best server for the fixed wireless devices connected to multiple servers based on aggregation of the data (e.g., call summary log data) related to the fixed wireless devices. This helps optimize the network resources, elevating the overall service quality, and, consequently, fostering heightened customer satisfaction.

Claims

CLAIMS1. A method (400) for evaluating performance of one or more fixed wireless devices in a network (106), the method (300) comprising: receiving (402), by a receiving unit (214), data associated with the one or more fixed wireless devices from one or more data sources; identifying (404), by a processing unit (220), one or more key performance indicators (KPIs) associated with the one or more fixed wireless devices in the received data; aggregating (406), by the processing unit (220), at least one value of the one or more identified KPIs from the received data to generate an aggregated data; filtering (408), by the processing unit (220), the aggregated data based on one or more parameters to identify the one or more fixed wireless devices; identifying (410), by the processing unit (220), the one or more fixed wireless devices connected to one or more serving cells within a first predefined time period based on the aggregated data, wherein the identification is performed by detecting multiple records of the one or more fixed wireless devices corresponding to one or more serving cells in the aggregated data; comparing (412), by the processing unit (220), the one or more KPIs at a cell level, including at least one of a User Equipment (UE) throughput and one or more Radio Frequency (RF) parameters, across the identified one or more serving cells over a second predefined time period to select a serving cell from the one or more serving cells for the one or more fixed wireless devices; anddetermining (414), by the processing unit (220), at least one action to optimize the performance of the one or more fixed wireless devices based on the selected serving cell.

2. The method (400) as claimed in claim 1, wherein the at least one action comprises adjusting an orientation of the one or more fixed wireless devices corresponding to the selected serving cell.

3. The method (400) as claimed in claim 1, wherein the received data comprises a call summary log (CSL) data generated by one or more network elements of one or more network vendors, and wherein the one or more data sources comprises one or more trace collection entity (TCE) maintained by each network vendor of the one or more network vendors.

4. The method (400) as claimed in claim 1, wherein aggregating the received data further comprises computing aggregated at least one value of the one or more identified KPIs from the received data for each fixed wireless device at different levels, including device level, the cell level, and network technology level, wherein the aggregated data is generated for a predefined time frame.

5. The method (400) as claimed in claim 1 , wherein the one or more parameters includes at least one of a Type Allocation Code (TAC) associated with the one or more fixed wireless devices.

6. A system (108) for evaluating performance of one or more fixed wireless devices in a network (106), the system (108) comprising: a receiving unit (214) configured to receive data associated with the one or more fixed wireless devices from one or more data sources; a processing unit (220) connected to the receiving unit (214), configured to:identify one or more key performance indicators (KPIs) associated with the one or more fixed wireless devices in the received data; aggregate at least one value of the one or more identified KPIs from the received data to generate an aggregated data; filter the aggregated data based on one or more predefined parameters to identify the one or more fixed wireless devices; identify the one or more fixed wireless devices connected to one or more serving cells within a first predefined time period based on the aggregated data, wherein the identification is performed by detecting multiple records of the one or more fixed wireless devices corresponding to one or more serving cells in the aggregated data; compare the one or more KPIs at a cell level, including at least one User Equipment (UE) throughput and one or more Radio Frequency (RF) parameters, across the identified one or more serving cells over a second predefined time period to select a serving cell from the one or more serving cells for the one or more fixed wireless devices; and determine at least one action to optimize the performance of the one or more fixed wireless devices based on the selected serving cell.

7. The system (108) as claimed in claim 6, wherein the at least one action comprises adjusting an orientation of the one or more fixed wireless devices corresponding to the selected serving cell.

8. The system ( 108) as claimed in claim 6, wherein the received data comprises a call summary log (CSL) data generated by one or more network elements of one or more network vendors, and wherein the one or more data sources comprises one or more trace collection entity (TCE) maintained by each network vendor of the one or more network vendors.

9. The system (108) as claimed in claim 6. wherein aggregating the received data further comprises computing aggregated one or more values of the one or more identified KPIs from the received data for each fixed wireless device at different levels, including device level, the cell level, and network technology level, wherein the aggregated data is generated for a predefined time frame.

10. The system (108) as claimed in claim 6, wherein the one or more parameters include at least a Type Allocation Code (TAC) associated with the one or more fixed wireless devices.

11. A computer program product comprising a non-transitory computer- readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to execute a method (400) for evaluating performance of one or more fixed wireless devices in a network, the method (400) comprising: receiving (402), by a receiving unit (214), data associated with the one or more fixed wireless devices from one or more data sources; identifying (404), by a processing unit (220), one or more key performance indicators (KPIs) associated with the one or more fixed wireless devices in the received data; aggregating (406), by the processing unit (220), at least one value of the one or more identified KPIs from the received data to generate an aggregated data; filtering (408), by the processing unit (220), the aggregated data based on one or more parameters to identify the one or more fixed wireless devices; identifying (410), by the processing unit (220), the one or more fixed wireless devices connected to one or more serving cells within a firstpredefined time period based on the aggregated data, wherein the identification is performed by detecting multiple records of the one or more fixed wireless devices corresponding to one or more serving cells in the aggregated data; comparing (412), by the processing unit (220), the one or more KPIs at a cell level, including at least one of a User Equipment (UE) throughput and one or more Radio Frequency (RF) parameters, across the identified one or more serving cells over a second predefined time period to select a serving cell from the one or more serving cells for the one or more fixed wireless devices; and determining (414), by the processing unit (220), at least one action to optimize the performance of the one or more fixed wireless devices based on the selected serving cell.

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