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WO2025008835A1 - Système et procédé d'optimisation des performances d'un réseau pour une zone cible - Google Patents

Système et procédé d'optimisation des performances d'un réseau pour une zone cible Download PDF

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Publication number
WO2025008835A1
WO2025008835A1 PCT/IN2024/050670 IN2024050670W WO2025008835A1 WO 2025008835 A1 WO2025008835 A1 WO 2025008835A1 IN 2024050670 W IN2024050670 W IN 2024050670W WO 2025008835 A1 WO2025008835 A1 WO 2025008835A1
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WIPO (PCT)
Prior art keywords
coverage
target area
processor
uncovered
grids
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/IN2024/050670
Other languages
English (en)
Inventor
Aayush Bhatnagar
Pradeep Kumar Bhatnagar
Manoj Shetty
Sneha VIRKAR
Dharmesh A CHITALIYA
Neelabh KRISHNA
Hanumant KADAM
Anshul Kothari
Nilesh KHANCHANDANI
Brijesh Shah
Nitesh Kumar Chourasia
Mayank Taran
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jio Platforms Ltd
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Jio Platforms Ltd
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Filing date
Publication date
Application filed by Jio Platforms Ltd filed Critical Jio Platforms Ltd
Publication of WO2025008835A1 publication Critical patent/WO2025008835A1/fr
Anticipated expiration legal-status Critical
Pending legal-status Critical Current

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/12—Discovery or management of network topologies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5003—Managing SLA; Interaction between SLA and QoS
    • H04L41/5009—Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic

Definitions

  • 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).
  • JPL Jio Platforms Limited
  • 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.
  • the embodiments of the present disclosure generally relate to communication network planning. More particularly, the present disclosure relates to a system and a method for optimizing a network performance for a target area.
  • a method for optimizing a network performance for a target area includes receiving a first set of information pertaining to a target area from one or more computing devices associated with one or more users.
  • the method includes sampling the target area into a plurality of segments to identify a first set of grids corresponding to the target area.
  • the method includes identifying a set of coverage parameters along the route based on the first set of grids.
  • the method includes aggregating the set of coverage parameters to generate one or more actionable insights.
  • the method includes identifying one or more uncovered patch locations along the route based on the one or more actionable insights in response to aggregating.
  • the method further includes performing a coverage planning and optimization for the one or more uncovered patch locations. [0009] In some embodiments, the method further includes performing a route coverage aggregation to estimate a coverage status on the target area.
  • the one or more actionable insights includes information associated with population distribution, area covered under different coverage categories, and details of the one or more uncovered patch locations.
  • the method further includes utilizing the one or more actionable insights for improving the network performance in the one or more uncovered patch locations.
  • the method further includes receiving a second set of information corresponding to a set of parameters, metrics, and Key Performance Indicators (KPIs) associated with a plurality of grids.
  • KPIs Key Performance Indicators
  • the first set of information includes a longitudinal and latitudinal data and a geographical data associated with the target area, and wherein the geographical data includes an altitude, terrain features, and land use classifications.
  • the method further includes receiving the first set of information in one of a real-time mode or a batch mode, from the one or more computing devices.
  • a system for optimizing a network performance for a target area includes receive a first set of information pertaining to a target area from one or more computing devices associated with one or more users.
  • the processor is configured to sample the target area into a plurality of segments to identify a first set of grids corresponding to the target area.
  • the processor is configured to identify a set of coverage parameters along a route based on the first set of grids.
  • the processor is configured to aggregate the set of coverage parameters to generate one or more actionable insights.
  • the processor is configured to identify one or more uncovered patch locations along the route based on the one or more actionable insights in response to aggregation.
  • the processor is further configured to perform a coverage planning and optimization for the one or more uncovered patch locations.
  • the processor is further configured to perform a route coverage aggregation to estimate a coverage status on the target area.
  • the one or more actionable insights comprise information associated with population distribution, area covered under different coverage categories, and details of the one or more uncovered patch locations.
  • the processor is configured to utilize the one or more actionable insights for improving the network performance in the one or more uncovered patch locations.
  • the processor is further configured to receive a second set of information corresponding to a set of parameters, metrics, and Key Performance Indicators (KPIs) associated with a plurality of grids.
  • KPIs Key Performance Indicators
  • the first set of information comprises a longitudinal and latitudinal data and a geographical data associated with the target area, and wherein the geographical data includes an altitude, terrain features, and land use classifications.
  • the processor is further configured to receive the first set of information in one of a real-time mode or a batch mode, from the one or more computing devices.
  • An object of the present disclosure is to provide a system and a method for optimizing a network performance for a taiget area by estimating network coverage parameters for the target area.
  • Another object of the present disclosure is to eliminate a need for a constant drive testing to be performed.
  • An object of the present disclosure is to allow to keep a track of a network coverage status on all roads of the target area, regularly.
  • An object of the present disclosure is to improve an accuracy of a network coverage analysis of the target area. [0029] An object of the present disclosure is to provide a system and a method that is economical and easy to implement for optimizing the network performance for the taiget area.
  • UEs User Equipments
  • FIG. 1 illustrates an example network architecture for implementing a proposed system optimizing a network performance of a target area, in accordance with an embodiment of the present disclosure.
  • FIG. 2 illustrates an example block diagram of a proposed system optimizing a network performance of a target area, in accordance with an embodiment of the present disclosure.
  • FIG. 3 illustrates an example flow diagram representing a method for optimizing a network performance of a target area, in accordance with an embodiment of the present disclosure.
  • FIG. 4A is an exemplary representation of a target area divided into a plurality of segments and a plurality of grids, in accordance with an embodiment of the present disclosure.
  • FIG. 4B is a pictorial representation of an information associated of a specific portion of the target area, in accordance with an embodiment of the present disclosure.
  • FIG. 4C illustrates a tabular representation of a coverage analysis of a target area in a particular site, in accordance with an embodiment of the present disclosure.
  • FIG. 4D illustrates a tabular representation of details associated with one or more uncovered patch locations, in accordance with an embodiment of the present disclosure.
  • FIG. 5 illustrates an example computer system in which or with which the embodiments of the present disclosure may be implemented.
  • individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged.
  • a process is terminated when its operations are completed but could have additional steps not included in a figure.
  • a process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
  • exemplary and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration.
  • the subject matter disclosed herein is not limited by such examples.
  • 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.
  • the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive like the term “comprising” as an open transition word without precluding any additional or other elements.
  • Embodiments herein relate to a method for optimizing a network performance for a target area by determining network metrics or network grid for the target area.
  • a first set of information pertaining to the taiget area for which a set of coverage parameters is to be determined may be received.
  • a second set of information corresponding to a set of parameters, metrics, and KPIs associated with a plurality of grids of a tile, or a site may be received.
  • the target area falls under a geographical region of the tile.
  • the target area is sampled so as to segment the target area into the plurality of segments. Then, among the plurality of segments, a first set of grids are identified, which correspond to the plurality of segments.
  • the first set of grids are identified by mapping the plurality of segments with the plurality of grids. In this manner, the first set of grids associated with the target area are identified. Then the set of coverage parameters are identified based on the first set of grids. Upon identification of the set of coverage parameters corresponding to the first set of grids, the network performance for the target area can be easily determined.
  • the network performance may include but not limited to the set of parameters (e.g., a network traffic), one or more metrics or key performance parameters (KPIs).
  • FIG. 1 illustrates an exemplary network architecture 100 for implementing a proposed system 108 for optimizing a network performance of a target area, in accordance with embodiments of the present disclosure.
  • the network architecture 100 includes one or more computing devices or user equipment’s 104-1, 104-2... 104-N associated with one or more users 102-1, 102-2... 102-N in an environment.
  • the one or more users 102-1, 102-2... 102-N may be collectively referred to as a user 102.
  • one or more user equipment’s 104-1, 104-2... 104-N may be collectively referred to as a user equipment 104.
  • the terms “computing device(s)” and “user equipment” may be used interchangeably throughout the disclosure . Although three user equipment’s 104 are depicted in FIG. 1, however any number of the user equipment’s 104 may be included without departing from the scope of the ongoing description.
  • the user equipment 104 may include smart devices operating in a smart environment, for example, an Internet of Things (loT) system.
  • 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 user 102 and/or entities (not shown), or any combination thereof.
  • smartphones 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
  • 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 .
  • the user equipment 104 includes, 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 head- mounted 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.
  • 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 head- mounted display computer device, a head-mounted camera device, a wristwatch computer device, and so on
  • GPS Global Positioning System
  • the user equipment 104 includes, 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, a laptop, a general -purpose computer, a desktop, a personal digital assistant, a tablet computer, a mainframe computer, or any other computing device.
  • VR virtual reality
  • AR augmented reality
  • a laptop a general -purpose computer
  • desktop a personal digital assistant
  • tablet computer a mainframe computer, or any other computing device.
  • 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 an entity such as a touchpad, a touch-enabled screen, an electronic pen, and the like.
  • a visual aid device such as a camera
  • an audio aid such as a microphone
  • a keyboard for receiving input from the user 102
  • input devices for receiving input from the user 102
  • an entity such as a touchpad, a touch-enabled screen, an electronic pen, and the like.
  • the user equipment 104 may not be restricted to the above-mentioned devices and various other devices may be used.
  • the user equipment 104 communicates with a system 108, for example, a stale session management system, through a network 106.
  • the network 106 includes at least one of a Fourth Generation (4G) network, a Fifth Generation (5G) network, a Sixth Generation (6G) network, or the like.
  • the network 106 enables the user equipment 104 to communicate with other devices in the network architecture 100 and/or with the system 108.
  • the network 106 includes a wireless card or some other transceiver connection to facilitate this communication.
  • the network 106 is implemented as, or includes 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.
  • WAN wide area network
  • LAN local area network
  • VPN Virtual Private Network
  • PSTN Public Switched Telephone Network
  • a centralized server 110 may be associated with the system 108.
  • the centralized server 112 includes or comprise, by way of example but not limited to, one or more of: a stand-alone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, a hardware running on a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof.
  • the system (108) is configured to establish a plurality of sessions corresponding to various nodes, i.e., the user equipment 104.
  • the plurality of sessions includes an Access Management (AM) session, a Session Management (SM) session, and a Receiver (Rx) session.
  • the system 108 maintains a runtime user configurable stale session timer corresponding to each session of the plurality of sessions based on last message received.
  • a runtime user configurable stale session timer is stored in a shared data layer (SDL) database, or in first level (LI) cache.
  • the system (108) identifies at least one stale session based on the runtime user configurable stale session timer and generates session data corresponding to each session.
  • the session data includes a flag indicating whether a session is stale or not.
  • the system (108) transmits at least one update request and the session data to a specific node based on the determined session timer of the specific node.
  • the at least one update request is transmitted to the specific node through a hypertext transfer protocol 2 (HTTP2) interface.
  • the at least one update request is transmitted based on a configurable value.
  • the configurable value includes a predefined time, after the system is able to send the update request automatically.
  • the system 108 receives a response from the specific node.
  • the system 108 performs at least one operation on the session based on the received response.
  • the at least one operation includes termination of the session or retention of the session.
  • system 108 is configured to handle the stale sessions based on different error codes received from a service communication proxy (SCP) or timeouts received from a session management function (SMF).
  • SCP service communication proxy
  • SMF session management function
  • the system 108 is configured to hold a diameter session even after receiving a session termination request-session termination answer (STR-STA).
  • STR-STA session termination request-session termination answer
  • FIG. 1 shows exemplary components of the network architecture 100
  • 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.
  • FIG. 2 illustrates an exemplary block diagram 200 of the proposed system 108, in accordance with an embodiment of the present disclosure.
  • FIG. 2 is explained in conjunction with FIG. 1.
  • the system 108 includes a processor(s) 202.
  • the processor(s) 202 are implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions.
  • the processor(s) 202 are configured to fetch and execute computer-readable instructions stored in a memory 204 of the system 108.
  • the memory 204 is configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service.
  • the memory 204 includes any non-transitory storage device including, for example, a volatile memory such as a Random-Access Memory (RAM), or a nonvolatile memory such as an Erasable Programmable Read-Only Memory (EPROM), a flash memory, and the like.
  • a volatile memory such as a Random-Access Memory (RAM)
  • a nonvolatile memory such as an Erasable Programmable Read-Only Memory (EPROM), a flash memory, and the like.
  • EPROM Erasable Programmable Read-Only Memory
  • the system 108 includes an interface(s) 206.
  • the interface(s) 206 may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as Input/Output devices, storage devices, and the like.
  • the interface(s) 206 facilitates communication through the system 108.
  • the interface(s) 206 also provides a communication pathway for one or more components of the system 108. Examples of such components include, but are not limited to, processing unit/engine 208 and a database 210.
  • the processing engine 208 is implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine 208.
  • programming for the processing engine 208 is processor-executable instructions stored on a non- transitory machine-readable storage medium and the hardware for the processing engine 208 includes a processing resource (for example, one or more processors), to execute such instructions.
  • the machine-readable storage medium may store instructions that, when executed by processing resource (e.g., the one or more processor(s) 202), implement the processing engine 208.
  • system 108 includes a machine-readable storage medium storing instruction 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.
  • processing engine 208 may be implemented by an electronic circuitry.
  • the one or more processor(s) 202 may receive a first set of information pertaining to a target area for which a set of coverage parameters are to be determined.
  • the first set of information may be received from one or more computing devices, i.e., the user equipment 104 associated with one or more users, i.e., the user 102.
  • This first set of information may include a longitudinal and latitudinal data, i.e., a longitude and latitude coordinates, that precisely define boundaries or path of the target area.
  • the first set of information may include a geographical data such as an altitude, terrain features, and land use classifications, which could influence the coverage analysis.
  • the first set of information may be transmitted from the one or more computing devices in one of a real-time mode or a batch mode, ensuring that the processor 202 has the most up-to-date and accurate depiction of the target area for analysis.
  • the first set of information can be gathered through various means including Global Positioning System (GPS) devices, mobile applications, or uploaded files containing necessary coordinates and specifications associated with the one or more computing devices.
  • GPS Global Positioning System
  • the processor 202 may be configured to receive a second set of information corresponding to a set of parameters, metrics, and Key Performance Indicators (KPIs) associated with a plurality of grids of a tile or a site.
  • KPIs Key Performance Indicators
  • the target area falls under a geographical region of the tile, and each particular site or the tile may be segmented into a plurality of segments, or the plurality of grids.
  • This second set of information could include detailed metrics, associated with a signal strength, a data throughput, a latency, a coverage quality, and a user density within each grid.
  • the set of parameters may be a latency parameter, a signal strength parameter, a latency parameter, a coverage quality parameter, and the like.
  • the KPIs might encompass metrics such as call drop rates, data session success rates, and network congestion levels.
  • This information allows the processor(s) 202 to have a granular view of the network performance and coverage within the target area, segmented into specific, identifiable grids.
  • the information for each grid can be obtained from various network monitoring tools and sources, including base stations, network management systems, and the one or more computing devices.
  • the processor(s) 202 may be configured to sample the target area into a plurality of segments.
  • the sampling of the taiget area involves dividing the target area into smaller, uniform segments, which simplifies the analysis by focusing on discrete sections.
  • the processor 202 may be configured to identify a first set of grids among the plurality of grids, which correspond to these segments.
  • the first set of grids are specific grids through which the target area passes, ensuring that the coverage analysis is precisely targeted.
  • Each grid in the first set of grids can represent a specific portion of the target area, allowing for detailed examination and assessment of the set of coverage parameters within the plurality of segments.
  • the processor 202 may be configured to analyze the set of coverage parameters and generate one or more actionable insights into user experience and network optimization opportunities. This analysis might include assessing the signal strength, identifying areas with weak or no coverage, and determining an overall quality of the network within each grid in the first set of grids.
  • the processor(s) 202 can then aggregate the set of configuration parameters to generate the one or more actionable insights.
  • the one or more actionable insights may be generated in a form of a comprehensive report highlighting a coverage performance across the target area.
  • the processor 202 may perform a route coverage aggregation to estimate a coverage status on the target area. Based on the one or more actional insights, the processor 202 may identify one or more uncovered patch locations. In an embodiment, the one or more uncovered patch locations may correspond to locations having low coverage of the network (also referred as low network coverage locations).
  • the one or more actionable insights includes information associated with population distribution, area covered under different coverage categories (e.g., a high network coverage category, a medium network coverage category, a low network coverage category, and the like), and details of the one or more uncovered patch locations.
  • the processor(s) 202 may be configured to utilize the one or more actionable insights for improving the network performance in the one or more uncovered patch locations.
  • the processor(s) 202 may perform a coverage planning and optimization for the one or more uncovered patch locations.
  • the coverage planning and optimization may include recommendations for network enhancements, such as optimizing a placement of base stations, adjusting network configurations, or implementing new technologies to improve coverage of the network in the target area.
  • the analysis might identify specific areas where user experience is suboptimal, providing actionable data for network planning and optimization teams to address these issues effectively.
  • the one or more actionable insights can be crucial for strategic decision- making, enabling proactive measures to enhance the network and improve customer satisfaction.
  • FIG. 2 shows exemplary components of the system 108
  • the system 108 may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 2. Additionally, or alternatively, one or more components of the system 108 may perform functions described as being performed by one or more other components of the system 108.
  • FIG. 3 illustrates an example flow diagram 300 representing a method for optimizing a network performance of a target area, in accordance with an embodiment of the present disclosure.
  • FIG. 3 is explained in conjunction with FIGS. 1 and 2.
  • the first set of information pertaining to the taiget area from the one or more computing devices (same as the user equipment 104) associated with the one or more users (same as the user 102).
  • the first set of information may include, but is not limited to, the longitudinal and latitudinal data and the geographical data associated with the target area, and the geographical data includes the altitude, the terrain features, and the land use classifications.
  • the target area may be identified.
  • a second set of information corresponding to the set of parameters, metrics, and Key Performance Indicators (KPIs) associated with the plurality of grids may also be received.
  • the taiget area is sampled into the plurality of segments.
  • the sampling is done to improve an accuracy of measurements and identification of the one or more uncovered patch locations.
  • a route or the target area is sampled at 30 m intervals, and polygons are sampled at a lO x 10 m grid level to account for changes in the coverage of the network in the target area.
  • This step ensures that the target area is divided into the plurality of segments, facilitating a more precise analysis of the set of coverage parameters.
  • the first set of grids is identified corresponding to the target area.
  • the set of coverage parameters are identified along the route. This identification of each grid of the first set of grid involves determining various network performance metrics such as the signal strength, the data throughput, the latency, and the coverage quality within each grid in the first set of grids.
  • the set of coverage parameters for each of the first set of grids are aggregated to generate the one or more actionable insights.
  • the aggregation of the set of coverage parameters corresponds to combining and analyzing the set of coverage parameters associated with each of the first set of grids to derive meaningful information, i.e., the one or more actionable insights about the network performance within the target area.
  • the one or more actionable insights includes information associated with population distribution, area covered under different coverage categories, and details of the one or more uncovered patch locations.
  • the route coverage aggregation is performed to estimate the overall coverage on the taiget area.
  • This step involves compiling the aggregated set of coverage parameters to provide an overall estimate of the network coverage for the target area.
  • the route coverage aggregation considers a cumulative data from all the plurality of segments and the plurality of grids to generate a comprehensive coverage profile for the target area. This profile helps in understanding an extent of the network coverage, identifying any remaining gaps, and making informed decisions for future network improvements.
  • This aggregation process consolidates the first set of information to provide a comprehensive view of the coverage across the target area.
  • the one or more uncovered patch locations along the route may be identified based on the one or more actionable insights in response to aggregation. Further, the one or more actionable insights are utilized for improving the network performance in the one or more uncovered patch locations.
  • the one or more uncovered patch locations in the route or the polygon corresponds to patch locations where there is consistently low coverage are identified.
  • the one or more uncovered patch locations are areas that consistently report lower-than-expected network performance, indicating potential issues in the coverage of the network that need to be addressed.
  • the one or more uncovered patch locations may correspond to locations with low network coverage.
  • Such strategies may include adjusting placement and configuration of network infrastructure, deploying additional network resources, or optimizing existing network settings. The goal is to enhance the overall network performance and ensure better coverage in previously identified problematic areas, i.e., the one or more uncovered patch locations.
  • FIGS. 4A is an exemplary representation 400A of a target area divided into a plurality of segments and a plurality of grids, in accordance with an embodiment of the present disclosure.
  • FIG. 4A is explained in conjunction with FIGS. 1 - 3.
  • FIG. 4A a map of a target area divided into the plurality of segments depicted via lines and the plurality of grids (e.g., an exemplary grid 402A).
  • Each grid represents signal strength depicted via dash lines, allowing for detailed analysis of the set of coverage parameters within each grid.
  • the plurality grids are shown with various types of dash lines indicating different levels of signal strength to provide a coverage quality of the network, ranging from poor to excellent.
  • FIG, 4B is a pictorial representation 400B of an information associated of a specific portion of the target area, in accordance with an embodiment of the present disclosure.
  • FIG. 4B is explained in conjunction with FIGS. 1 - 4A.
  • FIG. 4B provides a detailed view of a specific portion, i.e., a macro site 404B of the target area with the first set of grids overlaid.
  • the target area includes a railway route 402b marked in special dashed lines, which traverses through the first set of grids.
  • the macro site 404B represented by icons, indicate locations of network infrastructure within the target area.
  • a legend 406B indicates the coverage quality in decibels (dBm), ranging from -140 dBm (poor coverage) to -40 dBm (excellent coverage).
  • the 402b shows the coverage quality across the legend 406B.
  • FIG. 4C illustrates a tabular representation 400C of a coverage analysis of a target area in a particular site, in accordance with an embodiment of the present disclosure.
  • FIG. 4C is explained in conjunction with FIGS. 1 - 4B.
  • FIG. 4C shows results of the coverage analysis, highlighting the one or more uncovered patch locations along the railway route 402B via the tabular representation 400C.
  • These one or more uncovered patch locations are areas where the coverage quality is below an acceptable threshold (e.g., -105dBm), indicating poor or no network coverage.
  • a first column 402C depicts a route ID 402C of each route.
  • a second column 404C depicts a length greater 92.5% covered at -105dbm
  • a third column represents a length greater 95% covered at -105dbm.
  • other columns i.e., 406C, 408C, 410C, 412C, 414C, 416C, 418C, 420C, and 422C depict various information associated with the one or more uncovered patch locations.
  • FIG. 4D illustrates a tabular representation 400D of details associated with the one or more uncovered patch locations, in accordance with an embodiment of the present disclosure.
  • FIG. 4D is explained in conjunction with FIGS. 1 - 4C.
  • FIG. 4D a summary of the coverage analysis for the target area via the tabular representation 400C.
  • the tabular representation 400C represents the set of coverage parameters associated with the target area, such as, a patch ID 402D, a patch length 404D, a patch starts latitude 406D, a patch starts longitude 408D a patch end latitude 410D, a patch end longitude 412D, a nearest Service Access Point Identifier (SAP ID) 414D, a site category 416D, and a site status 418D.
  • SAP ID Service Access Point Identifier
  • the tabular representation 400C includes detailed information on population distribution, area covered under different coverage categories, and specific details about the one or more uncovered patch locations.
  • the tabular representation 400C helps in identifying the one or more uncovered patch locations that require network improvements and provides the one or more actionable insights for planning and optimization the overall network coverage status on the target area.
  • FIG. 5 illustrates an example computer system (500) in which or with which the embodiments of the present disclosure may be implemented.
  • 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, a communication port(s) 560, and a processor(s) 570.
  • 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 ports(s) (560) may be chosen depending on a network, such as a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system 500 connects.
  • LAN Local Area Network
  • WAN Wide Area Network
  • 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) chip 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 solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces).
  • PATA Parallel Advanced Technology Attachment
  • SATA Serial Advanced Technology Attachment
  • USB Universal Serial Bus
  • the bus 520 may be communicatively coupled with the processor(s) 570 with the other memory, storage, and communication blocks.
  • the bus 520 may be, e.g. a Peripheral Component Interconnect PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor 570 to the computer system 500.
  • PCI Peripheral Component Interconnect
  • PCI-X PCI Extended
  • SCSI Small Computer System Interface
  • USB Universal Serial Bus
  • operator and administrative interfaces e.g., a display, keyboard, and cursor control device may also be coupled to the bus 520 to support direct operator interaction with the computer system 500.
  • Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) 560.
  • the present disclosure provides a system and a method for optimizing a network performance of a target area by estimating coverage parameters for the target area.
  • the present disclosure provides the system and the method that eliminates a need for a constant drive testing to be performed.
  • the present disclosure provides the system and the method that allow to keep a track of a coverage status on all roads regularly. [0094] The present disclosure provides the system and the method that is economical and easy to implement for optimizing the network performance of the target area.
  • the present disclosure provides the system and the method to improve an accuracy of a coverage analysis of the target area.
  • the present disclosure provides the system and the method that provides network improvement and better customer experience.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Information Transfer Between Computers (AREA)

Abstract

L'invention concerne un système (108) et un procédé pour optimiser les performances d'un réseau dans une zone cible. Le système (108) est configuré pour recevoir, par un processeur (202), un premier ensemble d'informations comprenant des données longitudinales et latitudinales provenant d'un ou plusieurs dispositifs informatiques (104) associés à des utilisateurs (102). Le processeur (202) échantillonne la zone cible en segments et identifie un premier ensemble de grilles correspondant à la zone cible. Le processeur (202) identifie des paramètres de couverture le long de l'itinéraire sur la base du premier ensemble de grilles et agrège ces paramètres de couverture pour générer des aperçus exploitables. Le processeur (202) identifie un ou plusieurs emplacements de correctif non couverts sur la base des aperçus exploitables générés. Le système (108) effectue également une planification de couverture, une optimisation et une agrégation de couverture d'itinéraire pour une analyse complète.
PCT/IN2024/050670 2023-07-02 2024-06-03 Système et procédé d'optimisation des performances d'un réseau pour une zone cible Pending WO2025008835A1 (fr)

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