WO2025203080A1 - System and method for adjusting antenna azimuth angles at network sites - Google Patents
System and method for adjusting antenna azimuth angles at network sitesInfo
- Publication number
- WO2025203080A1 WO2025203080A1 PCT/IN2025/050448 IN2025050448W WO2025203080A1 WO 2025203080 A1 WO2025203080 A1 WO 2025203080A1 IN 2025050448 W IN2025050448 W IN 2025050448W WO 2025203080 A1 WO2025203080 A1 WO 2025203080A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- antenna azimuth
- azimuth angle
- antenna
- network
- data
- 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
Links
Classifications
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01Q—ANTENNAS, i.e. RADIO AERIALS
- H01Q1/00—Details of, or arrangements associated with, antennas
- H01Q1/12—Supports; Mounting means
- H01Q1/125—Means for positioning
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- 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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
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 or its affiliates (hereinafter referred as owner).
- 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 present disclosure generally relates to the field of wireless communication and network optimization. More particularly, the present disclosure relates to systems and methods for antenna azimuth angle optimization in cellular networks based on user geolocation data, network traffic statistics, and performance metrics to improve network coverage and network service quality.
- Geographical user data refers to location-based information collected from user devices, indicating where users are physically located within a given area or network.
- the location-based information includes geographic coordinates (such as latitude and longitude) and timestamps and may also include additional contextual information like user density, mobility patterns, or signal measurements at specific locations.
- antenna azimuth angle used hereinafter in the specification, refers to a horizontal pointing direction of an antenna’s radiation pattern with respect to a geographic reference (i.e., true north). The antenna azimuth angle is expressed in degrees within a 0° to 360° range, measured clockwise from the true north.
- true north refers to a direction along Earth’s surface towards a geographic north pole, which is a fixed point where all lines of longitude converge.
- sample count refers to the total number of measurement points collected in a specific area. For example, if a single user reports reference signal received power (RSRP) and signal-to- interference -plus-noise ratio (SINR) every second for 5 minutes, then a total of 300 samples are collected from the single user.
- RSRP reference signal received power
- SINR signal-to- interference -plus-noise ratio
- second antenna azimuth angle used hereinafter in the specification, refers to the current or existing azimuth angle of the antenna at the network site, prior to angle adjustment.
- the second antenna azimuth angle is used as a reference point to calculate the delta (difference) between the second (current) orientation and the first (newly) computed antenna azimuth angle.
- An objective of the present disclosure is to provide a system and a method for adjusting antenna azimuth configurations using geospatial user data to enhance network performance and network coverage efficiency.
- Yet another objective of the present disclosure is to reduce network performance issues, such as coverage gaps, overlapping signals, and interference, by automating an azimuth adjustment process based on data-driven insights rather than relying on static models or manual configurations.
- Yet another objective of the present disclosure is to provide a closed- loop system that continuously monitors network performance, validates implemented changes, and triggers corrective actions through workorders to ensure sustainable and optimal antenna configurations.
- Yet another objective of the present disclosure is to enhance customer experience, increase user data consumption, and improve return on investment (ROI) for telecom operators by deploying optimized azimuth configurations.
- the present invention discloses a method for adjusting an antenna azimuth angle at a network site.
- the method includes collecting, by a data collection unit, data associated with one or more antenna azimuth angles within a vicinity of one or more predefined locations.
- the method further includes calculating, by a processing unit, a weightage for each of the one or more antenna azimuth angles based on the collected data.
- the method further includes computing, by the processing unit, a first antenna azimuth angle based on the calculated weightage of a predefined number of the one or more antenna azimuth angles and one or more network site parameters.
- the method further includes calculating, by the processing unit, a delta value between the computed first antenna azimuth angle and a second antenna azimuth angle to determine an adjustment for the antenna azimuth angle at the network site.
- the data includes at least one of user location data, and network performance metrics.
- the data associated with the one or more antenna azimuth angles is collected and updated at least one of on-demand or during a configurable periodic interval.
- a system to adjust an antenna azimuth angle at a network site includes a data collection unit configured to collect data associated with one or more antenna azimuth angles within a vicinity of one or more predefined locations.
- the system further includes a processing unit communicatively coupled to the data collection unit.
- the processing unit is configured to receive the collected data from the receiving unit.
- the processing unit includes a weight calculation module configured to calculate a weightage for each of the one or more antenna azimuth angles based on the collected data.
- the processing unit also includes an angle adjustment module configured to compute a first antenna azimuth angle based on the calculated weightage of a predefined number of the one or more antenna azimuth angles and one or more network site parameters.
- the angle adjustment module is also configured to calculate a delta value between the computed first antenna azimuth angle and a second antenna azimuth angle to determine an adjustment for the antenna azimuth angle at the network site.
- the processing unit includes a report generation module configured to generate at least one of, a report, and a graphical representation of the computed first antenna azimuth angle.
- the processing unit includes a storage module configured to store the computed first antenna azimuth angle in a database.
- the stored first antenna azimuth angle is used as the second antenna azimuth angle for calculating the delta value in a next iteration.
- a user equipment (UE) communicatively coupled with a network includes steps of receiving, by the network, a connection request from the UE.
- the coupling further includes sending, by the network, an acknowledgment of the connection request to the UE.
- the coupling further includes transmitting a plurality of signals in response to the connection request.
- An antenna azimuth angle at a network site is adjusted by a method.
- the method includes collecting, by a data collection unit, data associated with one or more antenna azimuth angles within a vicinity of one or more predefined locations.
- the method further includes calculating, by a processing unit, a weightage for each of the one or more antenna azimuth angles based on the collected data.
- a computer program product including a non-transitory computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to execute a method for adjusting an antenna azimuth angle at a network site.
- the method includes collecting, by a data collection unit, data associated with one or more antenna azimuth angles within a vicinity of one or more predefined locations.
- the method further includes calculating, by a processing unit, a weightage for each of the one or more antenna azimuth angles based on the collected data.
- the method further includes computing, by the processing unit, a first antenna azimuth angle based on the calculated weightage of a predefined number of the one or more antenna azimuth angles and one or more network site parameters.
- the method further includes calculating, by the processing unit, a delta value between the computed first antenna azimuth angle and a second antenna azimuth angle to determine an adjustment for the antenna azimuth angle at the network site.
- FIG. 3 illustrates a flowchart of a method implemented by the system for adjusting the antenna azimuth angle at the network site, in accordance with an embodiment of the present disclosure.
- mobile device “user equipment”, “user device”, “communication device”, “device” and similar terms are used interchangeably for the purpose of describing the invention. These terms are not intended to limit the scope of the invention or imply any specific functionality or limitations on the described embodiments. The use of these terms is solely for convenience and clarity of description. The invention is not limited to any device or equipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the invention as defined herein.
- an “electronic device” or “portable electronic device” or “user device” or “communication device” or “user equipment” or “device” refers to any electrical, electronic, electromechanical, and computing device.
- the user device can receive and/or transmit one or parameters, performing function(s), communicating with other user devices, and transmitting data to the other user devices.
- the user equipment may have a processor, a display, a memory, a battery, and an input-means such as a hard keypad and/or a soft keypad.
- the user equipment may be capable of operating on any radio access technology including but not limited to IP-enabled communication, Zig Bee, Bluetooth, Bluetooth Low Energy, Near Field Communication, Z-Wave, Wi-Fi, Wi-Fi direct, etc.
- the user equipment may include, but not limited to, a mobile phone, a smartphone, 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 device as may be obvious to a person skilled in the art for implementation of the features of the present disclosure.
- the user device may also comprise a “processor” or “processing unit” includes processing unit, wherein the processor refers to any logic circuitry for processing instructions.
- the processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array (FPGA) circuits, any other type of integrated circuits, etc.
- the processor may perform signal coding data processing, input/output processing, and/or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor is a hardware processor.
- the present disclosure leverages a data-driven approach that processes the geospatial user data, along with physical, antenna, and terrain properties to dynamically compute optimal antenna azimuth configurations.
- the present disclosure continuously evaluates and updates azimuth settings for each nominal site location, ensuring maximum network coverage and improved signal quality in highly populated or high-demand areas.
- the disclosed approach enables adaptive and precise azimuth tuning based on actual user behaviour (e.g., user density distribution, mobility patterns, hotspot areas) and network performance metrics (e.g., signal strength, handover success rate, call drop rate, throughput, and user experience scores). This results in enhanced network capacity, reduced interference, improved user experience, and a more cost-effective deployment of network resources.
- FIG. 1 illustrates an exemplary network architecture (100) for implementing a system (102) to adjust an antenna azimuth angle at a network site, in accordance with embodiments of the present disclosure.
- the network architecture (100) may include one or more computing devices or one or more user equipment (UE) ( 104- 1 , 104-2...104- N) that may be associated with one or more users (106-1, 106-2...106-N) and the system (102) in an environment.
- the one or more UE (104-1, 104-2. . . 104-N) may be communicated to the system (102) through a network (108).
- UE user equipment
- UE user equipment
- UE user equipment
- 104- 1 , 104-2. . . 104-N may be communicated to the system (102) through a network (108).
- the one or more UE (104- 1 , 104-2. . .104-N) may be individually referred to as the UE ( 104) and collectively referred to as the UE (104).
- computing device(s) and “UE” may be used interchangeably throughout the disclosure. Although three UE (104) are depicted in the FIG. 1, however any number of the UE (104) may be included without departing from the scope of the ongoing description. Similarly, a person of ordinary skill in the art will understand that the one or more users (106-1, 106-2... 106-N) may be individually referred to as the user (106) and collectively referred to as the users (106).
- the UE (104) may include smart devices operating in a smart environment, for example, an internet of things (loT) system.
- the UE (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 systems, communication devices, networked vehicle accessories, networked vehicular devices, smart accessories, tablets, smart television (TV), computers, a smart security system, a smart home system, other devices for monitoring or interacting with or for the users (106) and/or entities, or any combination thereof.
- smartphones such as smartphones, smart watches, smart sensors (e.g., mechanical, thermal, electrical, magnetic, etc.), networked appliances, networked peripheral devices, networked lighting systems, communication devices, networked vehicle accessories, networked vehicular devices, smart accessories, tablets, smart television (TV), computers, a smart security system, a smart home system, other devices for monitoring or interacting with or for the users (106) and/or
- the UE (104) may include, but not be 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 UE (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 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, 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 UE (104) may communicate with the system (102) through a set of executable instructions residing on any operating system.
- the set of executable instructions may include a crowdsourcing application residing on the operating system of the UE (104), configured to collect the geospatial user data and the network performance metrics and transmit the collected geospatial user data and the network performance metrics to the system (102) for further processing.
- the UE (104) may communicate with the system (102) through the network (108) for sending or receiving various types of data.
- the network (108) may include at least one of a 5G network, a 6G network, or the like.
- the network (108) may enable the UE (104) to communicate with other devices in the network architecture (100) and/or with the system (102).
- the network (108) may include a wireless card or some other transceiver connection to facilitate this communication.
- the network (108) 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 or the like.
- WAN wide area network
- LAN local area network
- VPN virtual private network
- the network (108) may include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth.
- the UE (104) is communicatively coupled with the network (108).
- the network (108) may receive a connection request from the UE (104).
- the network (108) may send an acknowledgment of the connection request to the UE (104).
- the UE (104) may transmit a plurality of signals in response to the connection request.
- the signals may be, but are not limited to, the geospatial user data (e.g., global positioning system (GPS) coordinates), crowdsourced performance metrics (e.g., latency, dropped calls, or throughput data), and so forth.
- the signals may be utilized by the system (102) for further processing to assist in optimizing the antenna azimuth angle.
- the antenna azimuth angle determines how the antenna’s main lobe is oriented to cover a specific sector within a network site’s coverage area.
- a properly configured antenna azimuth angle ensures optimal distribution of radio frequency (RF) energy towards areas of high user density, thereby improving the network performance, network coverage uniformity, and overall system efficiency.
- RF radio frequency
- the system (102) may be configured for adjusting the antenna azimuth angle at the network site, such as, but not limited to, macrocell sites (e.g., large towers or rooftops), microcell sites (e.g., small towers), rooftop sites (e.g., antennas installed on the rooftops of commercial or residential buildings), small cell sites (e.g., pico cells and femto cells, often mounted on street furniture), and so forth to optimize signal coverage and the network performance.
- macrocell sites e.g., large towers or rooftops
- microcell sites e.g., small towers
- rooftop sites e.g., antennas installed on the rooftops of commercial or residential buildings
- small cell sites e.g., pico cells and femto cells, often mounted on street furniture
- Embodiments of the present disclosure are intended to include or otherwise cover any network site, including known related art and/or later developed technologies.
- 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. 2A illustrates an exemplary system architecture (200) for azimuth computation based on a crowdsource, in accordance with an embodiment of the present disclosure.
- the platform (202) acts as a core command center.
- the platform (202) may include a processing unit (224) (as shown in FIG. 2B) that may be implemented using microprocessors, microcontrollers, digital signal processors, central processing units (CPUs), logic circuitries, and/or any devices that process data based on operational instructions.
- the processing unit (224) is configured to integrate and analyze the data from the upstream systems (data sources) and transmit insights or actions to the downstream systems (execution layers).
- the upstream systems include a master database (MDB) system (204), a crowdsourced (CS) data collection system (206), a performance management (PM) system (208) and a radio frequency (RF) analytics system (210).
- the data from the upstream systems may include, but is not limited to, MDB data (e.g., network topology, sector configurations, antenna azimuths, tilt values, site identifiers (IDs), cell parameters, azimuth angles, geolocation coordinates), crowdsourced data (e.g., user-reported coverage issues, signal-to-noise ratio (SINR) measurements, signal strength indicators, and user location data), PM data (e.g., call drop rates, throughput key performance indicators (KPIs), network outage alarms) and RF analytics data (e.g., antenna height, azimuth values).
- the downstream systems execution layers
- the downstream systems include a monitoring system (212) and a workorder system (214).
- the platform (202) may be connected to the MDB system (204) through a first API-based integration (i.e., platform-MDB interface), enabling the platform (202) to efficiently query and retrieve network topology (e.g., site geolocation data, sector configuration), cell site parameters (e.g., existing antenna azimuth angles, antenna height, antenna beamwidth, polarization information), inventory data (e.g., inventory status of antenna equipment, antenna model version), and so forth which are utilized by the platform (202) for computing optimal antenna azimuth adjustments.
- the MDB system (204) may include, but not be limited to, an online analytical processing (OLAP) database, structured query language (SQL) server analysis services, and the like.
- OLAP online analytical processing
- SQL structured query language
- the CS data collection system (206) functions as a data store configured to collect, store, and manage a variety of structured, semi-structured, and unstructured crowdsourced data obtained from end-user interactions and field measurements.
- the crowdsourced data includes mobile application data (e.g., application usage analytics, signal strength reports, call drop statistics), drive test data (e.g., reference signal received power (RSRP)/reference signal received quality (RSRQ) measurements, SINR values, throughput metrics collected through test equipment), user feedback (e.g., customer complaints regarding coverage issues, crowdsourced quality of experience (QoE) surveys), and other network performance data.
- mobile application data e.g., application usage analytics, signal strength reports, call drop statistics
- drive test data e.g., reference signal received power (RSRP)/reference signal received quality (RSRQ) measurements, SINR values, throughput metrics collected through test equipment
- user feedback e.g., customer complaints regarding coverage issues, crowdsourced quality of experience (QoE) surveys
- QoE quality of experience
- the platform (202) interacts with the CS data collection system (206) through a second API-based integration (platform-CS interface), enabling seamless collection, cleansing, normalization, and enrichment of the crowdsourced data by the platform (202).
- the platform (202) utilizes the crowdsourced data to derive the optimal antenna azimuth adjustments by correlating user-reported or measured RF issues (e.g., poor coverage zones, interference regions, overshooting or undershooting sectors) with existing antenna configurations.
- the PM system (208) is configured to collect, manage, and store KPIs, alarms, fault logs, and network counters from radio access networks (RANs) (e.g., macro cells, micro cells, small cells, distributed antenna systems) and core networks (e.g., long-term evolution (LTE), new radio (NR), voice over long-term evolution (VoLTE)).
- RANs radio access networks
- LTE long-term evolution
- NR new radio
- VoIP voice over long-term evolution
- the PM system (208) provides key metrics including, but not limited to, accessibility KPIs (e.g., call setup success rate, random access success rate), retainability KPIs (e.g., call drop rate, handover success rate), mobility KPIs (e.g., intra/inter-frequency handover success rate), user experience metrics (e.g., throughput, latency, packet loss, jitter), fault information (e.g., alarms, fault logs, network counters), and so forth.
- accessibility KPIs e.g., call setup success rate, random access success rate
- retainability KPIs e.g., call drop rate, handover success rate
- mobility KPIs e.g., intra/inter-frequency handover success rate
- user experience metrics e.g., throughput, latency, packet loss, jitter
- fault information e.g., alarms, fault logs, network counters
- the platform (202) interacts with the PM system (208) through a third API-based integration (platform-PM interface), which enables automated and realtime ingestion of performance data (key metrics).
- platform-PM interface third API-based integration
- the platform (202) analyzes post- adjustment KPIs and the network counters to validate the effectiveness of the azimuth angle adjustments (e.g., reduced call drops, improved accessibility, optimized handover success rates), detect performance degradation trends (e.g., cells still showing high interference levels or traffic congestion after adjustment), identify sectors requiring further fine-tuning or additional corrective actions (e.g., additional azimuth realignments, power control adjustments), and so forth.
- the azimuth angle adjustments e.g., reduced call drops, improved accessibility, optimized handover success rates
- performance degradation trends e.g., cells still showing high interference levels or traffic congestion after adjustment
- identify sectors requiring further fine-tuning or additional corrective actions e.g., additional azimuth realignments, power control adjustments
- the platform (202) may provide feedback to an optimization team (216) and other downstream systems for continuous RF optimization and closed-loop network performance improvement.
- the feedback loop ensures that antenna azimuth adjustments are systematically validated through the performance metrics and enables the platform (202) to enhance the overall RF performance based on data-driven insights from the PM system (208).
- the RF analytics system (210) is configured to collect, manage, and deliver RF parameters associated with each network cell and sector.
- the RF parameters may include, but not limited to, antenna orientation data (e.g., current antenna azimuth angles, electrical and mechanical tilt values), transmission characteristics (e.g., downlink and uplink power levels, reference signal power, antenna gain), sector-level configurations (e.g., frequency bands, supported technologies such as LTE or NR, carrier aggregation settings), cell adjacency information (e.g., neighbor cell relations, handover neighbor lists), and so forth.
- the platform (202) interacts with the RF analytics system (210) through a fourth API-based integration (platform-RF interface) to ingest the RF parameters.
- the platform (202) cross -validates the RF parameters with external data sources, such as the crowdsourced data, to enable precise azimuth correction recommendations (e.g., calculating the optimal antenna azimuth angles for the sites experiencing coverage gaps or interference hotspots).
- external data sources such as the crowdsourced data
- the platform (202) validates whether implemented changes have led to measurable improvements in the network performance (e.g., improved call setup success rate, reduced handover failures, enhanced throughput) and the user experience (e.g., reduced complaints, improved satisfaction scores).
- network performance e.g., improved call setup success rate, reduced handover failures, enhanced throughput
- user experience e.g., reduced complaints, improved satisfaction scores
- the ongoing validation process involves trend analysis (e.g., detecting KPI anomalies or performance regressions), geospatial heatmap generation (e.g., visualizing network coverage gaps or interference zones post-adjustment), and correlation of user-centric data (e.g., customer feedback, crowdsource application data) with network-side metrics.
- trend analysis e.g., detecting KPI anomalies or performance regressions
- geospatial heatmap generation e.g., visualizing network coverage gaps or interference zones post-adjustment
- correlation of user-centric data e.g., customer feedback, crowdsource application data
- the workorder system (214) interfaces with the platform (202) via a sixth API-based integration (platform-trouble ticket (TT) microservice interface), enabling automated transmission of the antenna azimuth adjustment recommendations.
- the workorder system (214) is configured to generate and manage workorders required to implement recommended corrective actions, such as the antenna azimuth adjustments (e.g., rotating the antenna to a new calculated azimuth angle based on optimization), mechanical or electrical tilt adjustments (e.g., modifying the antenna down-tilt to improve sector coverage), and so forth.
- the workorder system (214) routes the workorders to the optimization team (216) who possess local area knowledge (e.g., familiarity with nominal site constraints, urban vs. rural propagation environments, nearby obstructions such as buildings or terrain).
- the optimization team (216) acts as both a key stakeholder and an operational user of the platform (202), accessing outputs of the platform (202) via the API-based integration.
- the optimization team (216) consumes actionable insights provided by the platform (202) (e.g., antenna azimuth adjustment recommendations based on crowdsourced data validation) and coordinates closely with the workorder system (214) to initiate, validate, and execute the necessary corrective or preventive network actions.
- the optimization team (216) may include specialized personnel responsible for ensuring that the mobile network is performing at its best.
- the optimization team (216) includes, but not limited to, radio network engineers, drive test engineers, field technicians, performance analysts, machine, software, hots, and so forth.
- FIG. 2B illustrates an exemplary block diagram of the system (102) implemented with the platform (202) to adjust the antenna azimuth angle at the network site, in accordance with embodiments of the present disclosure.
- FIG. 2B illustrates the system (102), that includes a data collection unit (218), a memory (220), an interfacing unit (222), the processing unit (224), and a database (226).
- the processing unit (224) includes a storage module (228), a weight calculation module (230), an angle adjustment module (232), a report generation module (234), a workorder module (236) and a validation and monitoring module (238).
- the data collection unit (218) is configured to collect data associated with the antenna azimuth angles within the vicinity of predefined locations.
- the collected data includes, but is not limited to, user location data, the network performance metrics, and so forth.
- the user location data may be gathered from the crowdsourced data such as latitude and longitude (Lat- Long) coordinates reported by the UE (104) through mechanisms like measurement reports (e.g., RSRP/RSRQ measurements), application-level telemetry (e.g., location data from mobile applications), network signaling events (e.g., drive test logs or passive probing systems), and so forth.
- the Lat-Long coordinates may be timestamped and tagged with a serving cell ID (a unique identifier of a radio access node currently providing service to the UE (104)) to associate each data point with its corresponding network site accurately.
- the network performance metrics may include, but are not limited to, radio signal strength indicators (e.g., RSRP), signal quality metrics (e.g., SINR), throughput measurements (e.g., download/upload speeds in megabits per second (Mbps)), and additional KPIs such as call drop rates and latency statistics.
- the predefined locations may correspond to nominal points of interest, such as planned new site coordinates (e.g., a flagged location for future antenna installation) or coordinates of existing operational sites (e.g., macro cells or small cells).
- the collected data may first be transmitted to a server (i.e., cloud-based server) over, for example, secure communication protocols, such as, but not limited to, a hypertext transfer protocol secure (HTTPS), a message queuing telemetry transport (MQTT), and the like.
- the server may ingest and pre-process the data before storing the data in a centralized cloud storage system, such as an object storage service or a distributed database.
- the centralized cloud storage system aggregates the crowdsourced data from multiple UEs (104) across diverse geographic regions, enabling scalability and efficient data management.
- the data collection unit (218) may be configured to retrieve the crowdsourced data from the cloud storage system, providing access to a comprehensive set of timestamped Lat-Long coordinates, serving cell IDs, and the network performance metrics.
- the data collection unit (218) may be configured to collect and update the crowdsourced data during a configurable periodic interval (e.g., hourly, daily, or weekly) to ensure that the data reflects the latest network and user distribution dynamics.
- a configurable periodic interval may allow an operator to set the system (102) to collect the data every hour, every day, or every week, depending on how frequently updates are needed.
- the data collection unit (218) may be configured to collect and update the crowdsourced data on-demand.
- the crowdsourced data is subsequently used to analyze how various antenna azimuth angles influence the user experience and the network KPIs within the vicinity of each predefined location.
- the crowdsourced data is further processed by the data collection unit (218) to analyze parameters such as, but not limited to, user density distribution, signal propagation patterns, network quality trends and so forth within different azimuth sectors around the predefined locations.
- the data collection unit (218) may be configured to perform angular segmentation of the crowdsourced data by dividing a surrounding area of each predefined location into fine-grained sectors with a l- degree azimuth resolution (i.e., from 0° to 359°). For each angular sector within a predefined distance (e.g., 500 meters (m) or 1 kilometre (km)), the data collection unit (218) may be configured to record a number of crowdsourced samples detected in the corresponding directional bin (i.e., each specific azimuth angle, such as 0°, 1°, 2°, ..., 359°, where data is collected and analyzed per individual degree around the antenna).
- a l- degree azimuth resolution i.e., from 0° to 359°.
- the data collection unit (218) may observe that 23 user samples fall within the 0° sector, 19 samples in the 1° sector, 15 samples in the 2° sector, continuing up to the 360° sector.
- the data collection unit (218) may also be configured to assign a weightage to each antenna azimuth angle based on the crowdsourced samples in the corresponding direction. In the above example, a higher weightage is assigned to the 0° sector as 23 (higher) user samples fall within the 0° sector.
- the memory (220) may be a non-transitory computer readable storage medium configured to store instructions or routines.
- the term “instructions” may refer to a sequence of commands that are written in a programming language and may be executed by the processing unit (224) to perform tasks associated with the system (102).
- the memory (220) may include any non-transitory storage device including, for example, but not limited to, a volatile memory such as a random-access memory (RAM), or a non-volatile memory such as an erasable programmable read only memory (EPROM), a flash memory, and the like.
- RAM random-access memory
- EPROM erasable programmable read only memory
- Embodiments of the present invention are intended to include or otherwise type of the memory (220) including known related art and/or later developed technologies.
- the interfacing unit (222) may include a variety of interfaces, for example, the interfaces for data input and output devices (I/O), storage devices, and the like.
- the interfacing unit (222) may facilitate communication through the system (102).
- the interfacing unit (222) may also provide a communication pathway for various other units/modules of the system (102).
- the database (226) may offer functionality to manage, capture, store, and retrieve antenna configuration data, such as but not limited to, an antenna ID, a physical antenna location, a current (second) antenna azimuth angle, a computed first antenna azimuth angle (latest computed value), the antenna type, the antenna model, a last adjustment timestamp, a delta value, and so forth.
- the database (226) is also configured to store a first table, a second table and a third table.
- the first table includes the number of crowdsourced samples (representing the count of user data samples collected from a network coverage area) and assigned weightage (the computed weightage assigned to the corresponding azimuth angles based on crowdsourced data analysis).
- the second table includes a mapping between each antenna azimuth angle and its corresponding calculated weightage, along with associated data collection parameters.
- the third table includes each triplet of the antenna azimuth angles along with their aggregated weightage, which may represent a cumulative weight of the corresponding three combined angles.
- the database (226) is designed to interact seamlessly with other modules of the system (102), such as the storage module (228), the weight calculation module (230), the angle adjustment module (232), the report generation module (234), the workorder module (236) and the validation and monitoring module (238), to support functionality of the system (102) effectively.
- the database (226) may store the data that may be generated as a result of functionalities implemented by any of the modules of the processing unit (224). In an embodiment, the database (226) may be separate from the system (102).
- the modules are controlled by the processing unit (224), which executes the instructions retrieved from the memory (220).
- the processing unit (224) further interacts with the interfacing unit (222) to facilitate user interaction and to provide options for managing and configuring the system (102).
- the processing unit (224) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions.
- the storage module (228) is communicatively coupled to the data collection unit (218) to receive the number of crowdsourced samples and the corresponding weightage assigned to each azimuth angle from the data collection unit (218).
- the storage module (228) is configured to store the number of crowdsourced samples and the corresponding weightage assigned to each azimuth angle (e.g., 0°, 1°, 2°, ..., 359°) in the first table of the database (226).
- the weight calculation module (230) is also communicatively coupled to the data collection unit (218) to receive the collected data from the data collection unit (218).
- the weight calculation module (230) is configured to calculate a weightage (new weightage) for each of the antenna azimuth angles based on the collected data.
- the collected data may include, but is not limited to, sample count (crowd-sourced samples in the corresponding direction), user count (number of distinct users (106) observed), the network performance metrics, and so forth.
- the weight calculation module (230) may be configured to compute the weightage for each antenna azimuth angle by applying a weightage calculation algorithm that factors in parameters (sample count, user count and network performance metrics) of the collected data.
- the weightage calculation algorithm may be, but not limited to, a weighted sum algorithm, a multicriteria decision analysis (MCDA), a z-score normalization, and so forth.
- MCDA multicriteria decision analysis
- Embodiments of the present invention are intended to include or otherwise cover any type of the weightage calculation algorithm, including known related art and/or later developed technologies.
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Abstract
The present disclosure relates to a system (102) and a method (300) for adjusting an antenna azimuth angle at a network site. Data associated with antenna azimuth angles within a vicinity of predefined locations is collected to calculate a weightage for each of the antenna azimuth angles. Further, a first antenna azimuth angle is computed based on the calculated weightage of a predefined number of the antenna azimuth angles and network site parameters. Also, a delta value between the computed first antenna azimuth angle and a second antenna azimuth angle is calculated to determine an adjustment for the antenna azimuth angle at the network site.
Description
SYSTEM AND METHOD FOR ADJUSTING ANTENNA AZIMUTH ANGLES AT NETWORK SITES
RESERVATION 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 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 present disclosure generally relates to the field of wireless communication and network optimization. More particularly, the present disclosure relates to systems and methods for antenna azimuth angle optimization in cellular networks based on user geolocation data, network traffic statistics, and performance metrics to improve network coverage and network service quality.
DEFINITION
[0003] As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used to indicate otherwise.
[0004] The term “Geospatial user data,” as used hereinafter in the specification, refers to location-based information collected from user devices, indicating where users are physically located within a given area or network. The location-based information includes geographic coordinates (such as latitude and longitude) and timestamps and may also include additional contextual information like user density, mobility patterns, or signal measurements at specific locations.
[0005] The term “antenna azimuth angle”, used hereinafter in the specification, refers to a horizontal pointing direction of an antenna’s radiation pattern with respect to a geographic reference (i.e., true north). The antenna azimuth angle is expressed in degrees within a 0° to 360° range, measured clockwise from the true north.
[0006] The term ‘true north’ used hereinafter in the specification, refers to a direction along Earth’s surface towards a geographic north pole, which is a fixed point where all lines of longitude converge.
[0007] The term “sample count,” used hereinafter in the specification, refers to the total number of measurement points collected in a specific area. For example, if a single user reports reference signal received power (RSRP) and signal-to- interference -plus-noise ratio (SINR) every second for 5 minutes, then a total of 300 samples are collected from the single user.
[0008] The term “network performance metrics” used hereinafter in the specification, refers to quantifiable indicators used to assess the efficiency, quality, and reliability of a network. These metrics help evaluate how well the network is performing and identify areas for improvement. Examples of the network performance metrics include RSRP, SINR, reference signal received quality (RSRQ), and so forth.
[0009] The term “RSRP,” used hereinafter in the specification, refers to a measurement of the power of cell-specific reference signals spread over the full bandwidth and narrowband, used by user equipment to determine cell selection and handover candidates.
[0010] The term “SINR” used hereinafter in the specification, refers to a measurement that compares the strength of a desired signal to the combined strength of interference from other signals and background noise, used to estimate the quality of a wireless connection.
[0011] The term “RSRQ,” used hereinafter in the specification, refers to a ratio of the RSRP to the total received power, including interference and noise, providing a quality measurement of the received reference signal.
[0012] The term ‘user count’ used hereinafter in the specification, refers to the number of unique users that contributed samples in the corresponding area. For example, even if a single user contributes 100 samples, the user is still counted as one unique user.
[0013] The term “first antenna azimuth angle” used hereinafter in the specification, refers to a new optimal directional orientation (measured in degrees) at which the antenna of the network site should be positioned to achieve maximum network coverage and performance for a given area.
[0014] The term ‘second antenna azimuth angle’ used hereinafter in the specification, refers to the current or existing azimuth angle of the antenna at the network site, prior to angle adjustment. The second antenna azimuth angle is used as a reference point to calculate the delta (difference) between the second (current) orientation and the first (newly) computed antenna azimuth angle.
[0015] The term ‘predefined performance criteria” used hereinafter in the specification, refers to a set of threshold values for key performance indicators (KPIs) that are established prior to performance evaluation. The predefined performance criteria define acceptable performance levels a network must meet or exceed to be considered optimized or functioning as expected.
[0016] The term “workorder” used hereinafter in the specification, refers to an official document or electronic record that authorizes and outlines specific tasks or activities to be performed. The work order includes details such as the nature of the task (antenna azimuth adjustment), location of the task (specific network site or tower), responsible team, and so forth.
[0017] These definitions are in addition to those expressed in the art.
BACKGROUND OF DISCLOSURE
[0018] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.
[0019] Wireless communication technology has rapidly evolved over the past few decades. The first generation (1G) of wireless communication was based on analog technology and offered only voice services. The introduction of a second- generation (2G) enabled text messaging and basic data services. Third generation (3G) marked a shift toward high-speed internet access, mobile video calls, and location-based services. Fourth generation (4G) revolutionized wireless communication with significantly faster data speeds, enhanced network coverage, and stronger security protocols. Presently, ongoing deployment of a fifth generation (5G) offers even higher data rates, ultra-low latency, and a capacity to connect a massive number of devices simultaneously.
[0020] In light of these technological advancements and growing network complexity, telecom operators continuously seek to optimize network infrastructure to ensure superior network coverage and user experience. One of the fundamental aspects of radio network planning is a precise configuration of antenna azimuth angles, i.e., a horizontal orientation of antennas relative to true north. Accurate azimuth configurations are vital in determining how well a cell site serves users within its coverage area.
[0021] Conventionally, determining antenna azimuth settings has relied heavily on manual and heuristic-based approaches, including site engineer expertise, local topographical knowledge, or predictive outputs from radio frequency (RF) planning tools. However, such conventional techniques often lack accuracy due to their dependence on subjective judgment or static modeling assumptions. Moreover, misconfigured azimuth angles can result in coverage gaps,
overlapping signals, or interference, leading to degraded network quality, reduced data throughput, and user dissatisfaction. Furthermore, improper antenna orientation can force operators to incur additional costs to resolve service degradation, undermining the overall return on investment (ROI) for the deployed infrastructure.
[0022] Thus, there is a need for an improved system and method that overcomes the limitations of conventional azimuth configuration techniques and enables accurate and data-driven optimization of the antenna azimuth angles to ensure enhanced network coverage and performance.
OBJECTIVES OF THE PRESENT DISCLOSURE
[0023] Some of the objectives of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0024] An objective of the present disclosure is to provide a system and a method for adjusting antenna azimuth configurations using geospatial user data to enhance network performance and network coverage efficiency.
[0025] Another objective of the present disclosure is to dynamically calculate a precise antenna orientation by analyzing real-world crowd-sourced user distribution data, ensuring maximum coverage in areas with high user concentration.
[0026] Yet another objective of the present disclosure is to reduce network performance issues, such as coverage gaps, overlapping signals, and interference, by automating an azimuth adjustment process based on data-driven insights rather than relying on static models or manual configurations.
[0027] Yet another objective of the present disclosure is to provide a closed- loop system that continuously monitors network performance, validates implemented changes, and triggers corrective actions through workorders to ensure sustainable and optimal antenna configurations.
[0028] Yet another objective of the present disclosure is to enhance customer experience, increase user data consumption, and improve return on investment (ROI) for telecom operators by deploying optimized azimuth configurations.
[0029] 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
[0030] In an exemplary embodiment, the present invention discloses a method for adjusting an antenna azimuth angle at a network site. The method includes collecting, by a data collection unit, data associated with one or more antenna azimuth angles within a vicinity of one or more predefined locations. The method further includes calculating, by a processing unit, a weightage for each of the one or more antenna azimuth angles based on the collected data. The method further includes computing, by the processing unit, a first antenna azimuth angle based on the calculated weightage of a predefined number of the one or more antenna azimuth angles and one or more network site parameters. The method further includes calculating, by the processing unit, a delta value between the computed first antenna azimuth angle and a second antenna azimuth angle to determine an adjustment for the antenna azimuth angle at the network site.
[0031] In some embodiments, the data includes at least one of user location data, and network performance metrics.
[0032] In some embodiments, the data associated with the one or more antenna azimuth angles is collected and updated at least one of on-demand or during a configurable periodic interval.
[0033] In some embodiments, the one or more network site parameters include at least one of one or more physical characteristics of the one or more predefined locations, one or more antenna characteristics of the one or more predefined locations and terrain information of the one or more predefined locations.
[0034] In some embodiments, the method includes generating, by the processing unit, at least one of a report, and a graphical representation of the computed first antenna azimuth angle.
[0035] In some embodiments, the method includes triggering, by the processing unit, at least one workorder to validate the computed first antenna azimuth angle and adjust the antenna azimuth angle based on the calculated delta value.
[0036] In some embodiments, the method includes storing, by the processing unit, the computed first antenna azimuth angle in a database. The stored first antenna azimuth angle is used as the second antenna azimuth angle for calculating the delta value in a next iteration.
[0037] In an exemplary embodiment, a system to adjust an antenna azimuth angle at a network site is disclosed. The system includes a data collection unit configured to collect data associated with one or more antenna azimuth angles within a vicinity of one or more predefined locations. The system further includes a processing unit communicatively coupled to the data collection unit. The processing unit is configured to receive the collected data from the receiving unit. The processing unit includes a weight calculation module configured to calculate a weightage for each of the one or more antenna azimuth angles based on the collected data. The processing unit also includes an angle adjustment module configured to compute a first antenna azimuth angle based on the calculated weightage of a predefined number of the one or more antenna azimuth angles and one or more network site parameters. The angle adjustment module is also configured to calculate a delta value between the computed first antenna azimuth angle and a second antenna azimuth angle to determine an adjustment for the antenna azimuth angle at the network site.
[0038] In some embodiments, the data include at least one of user location data, and network performance metrics.
[0039] In some embodiments, the data associated with the one or more antenna azimuth angles is collected and updated at least one of on-demand or during a configurable periodic interval.
[0040] In some embodiments, the one or more network site parameters include at least one of one or more physical characteristics of the one or more predefined locations, one or more antenna characteristics of the one or more predefined locations and terrain information of the one or more predefined locations.
[0041] In some embodiments, the processing unit includes a report generation module configured to generate at least one of, a report, and a graphical representation of the computed first antenna azimuth angle.
[0042] In some embodiments, the processing unit includes a workorder module configured to trigger at least one workorder to validate the computed first antenna azimuth angle and adjust the antenna azimuth angle based on the calculated delta value.
[0043] In some embodiments, the processing unit includes a storage module configured to store the computed first antenna azimuth angle in a database. The stored first antenna azimuth angle is used as the second antenna azimuth angle for calculating the delta value in a next iteration.
[0044] In an exemplary embodiment, a user equipment (UE) communicatively coupled with a network is disclosed. The coupling includes steps of receiving, by the network, a connection request from the UE. The coupling further includes sending, by the network, an acknowledgment of the connection request to the UE. The coupling further includes transmitting a plurality of signals in response to the connection request. An antenna azimuth angle at a network site is adjusted by a method. The method includes collecting, by a data collection unit, data associated with one or more antenna azimuth angles within a vicinity of one or more predefined locations. The method further includes calculating, by a processing unit, a weightage for each of the one or more antenna azimuth angles based on the collected data. The method further includes computing, by the processing unit, a
first antenna azimuth angle based on the calculated weightage of a predefined number of the one or more antenna azimuth angles and one or more network site parameters. The method further includes calculating, by the processing unit, a delta value between the computed first antenna azimuth angle and a second antenna azimuth angle to determine an adjustment for the antenna azimuth angle at the network site.
[0045] In an exemplary embodiment, a computer program product including a non-transitory computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to execute a method for adjusting an antenna azimuth angle at a network site. The method includes collecting, by a data collection unit, data associated with one or more antenna azimuth angles within a vicinity of one or more predefined locations. The method further includes calculating, by a processing unit, a weightage for each of the one or more antenna azimuth angles based on the collected data. The method further includes computing, by the processing unit, a first antenna azimuth angle based on the calculated weightage of a predefined number of the one or more antenna azimuth angles and one or more network site parameters. The method further includes calculating, by the processing unit, a delta value between the computed first antenna azimuth angle and a second antenna azimuth angle to determine an adjustment for the antenna azimuth angle at the network site.
[0046] 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
[0047] 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.
[0048] FIG. 1 illustrates an exemplary network architecture for implementing a system to adjust an antenna azimuth angle at a network site, in accordance with embodiments of the present disclosure.
[0049] FIG. 2A illustrates an exemplary system architecture for azimuth computation based on a crowdsource, in accordance with an embodiment of the present disclosure.
[0050] FIG. 2B illustrates an exemplary block diagram of the system implemented with a platform to adjust the antenna azimuth angle at the network site, in accordance with embodiments of the present disclosure.
[0051] FIG. 3 illustrates a flowchart of a method implemented by the system for adjusting the antenna azimuth angle at the network site, in accordance with an embodiment of the present disclosure.
[0052] FIG. 4 illustrates an exemplary computer system in which, or with which, the system and the method of the present disclosure may be implemented.
[0053] The foregoing shall be more apparent from the following more detailed description of the disclosure.
LIST OF REFERENCE NUMERALS
100 - Network architecture
102 - System
104-1, 104-2. . . 104-N - User Equipment
106-1, 106-2...106-N - Users
108 -Network
200 - System Architecture
202 - Platform
204 - Master Database System
206 - Crowdsourced Data Collection System
208 - Performance Management System
210 - Radio Frequency Analytics System
212 - Monitoring System
214 - Workorder System
216 - Optimization Team
218 - Data Collection Unit
220 - Memory
222 - Interfacing Unit
224 - Processing Unit
226 - Database
228 - Storage Module
230 - Weight Calculation Module
232 - Angle Adjustment Module
234 - Report Generation Module
236 - Workorder Module
238 - Validation and Monitoring Module
300 - Method
400 - Computer system
410 - External storage device
420 - Bus
430 - Main memory
440 - Read only memory
450 - Mass storage device
460 - Communication port(s)
470 - Processor
DETAILED DESCRIPTION OF DISCLOSURE
[0054] In the following description, for the purposes of explanation, various specific details are set forth to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein. Example embodiments of the present disclosure are described below, as illustrated in various drawings in which like reference numerals refer to the same parts throughout the different drawings.
[0055] 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.
[0056] 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 to avoid obscuring the embodiments.
[0057] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the
operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0058] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive like the term “comprising” as an open transition word without precluding any additional or other elements.
[0059] 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 features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0060] The terminology used herein is to describe embodiments only and is not intended to be limiting the disclosure. As used herein, the singular forms “a” “an”, and “the” are intended to include the plural forms as well, unless the context
indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any combinations of one or more of the associated listed items. It should be noted that the terms “mobile device”, “user equipment”, “user device”, “communication device”, “device” and similar terms are used interchangeably for the purpose of describing the invention. These terms are not intended to limit the scope of the invention or imply any specific functionality or limitations on the described embodiments. The use of these terms is solely for convenience and clarity of description. The invention is not limited to any device or equipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the invention as defined herein.
[0061] As used herein, an “electronic device” or “portable electronic device” or “user device” or “communication device” or “user equipment” or “device” refers to any electrical, electronic, electromechanical, and computing device. The user device can receive and/or transmit one or parameters, performing function(s), communicating with other user devices, and transmitting data to the other user devices. The user equipment may have a processor, a display, a memory, a battery, and an input-means such as a hard keypad and/or a soft keypad. The user equipment may be capable of operating on any radio access technology including but not limited to IP-enabled communication, Zig Bee, Bluetooth, Bluetooth Low Energy, Near Field Communication, Z-Wave, Wi-Fi, Wi-Fi direct, etc. For instance, the user equipment may include, but not limited to, a mobile phone, a smartphone, 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 device as may be obvious to a person skilled in the art for implementation of the features of the present disclosure.
[0062] Further, the user device may also comprise a “processor” or “processing unit” includes processing unit, wherein the processor refers to any logic circuitry for processing instructions. The processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array (FPGA) circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing, input/output processing, and/or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor is a hardware processor.
[0063] Aspects of this disclosure are directed to a system and method for adjusting antenna azimuth angles in mobile network infrastructures (e.g., macro cells, small cells, distributed antenna systems) based on geospatial user data in a vicinity of a network site. Conventional antenna azimuth configuration approaches often depend on manual engineering practices, heuristic models, or static radio frequency (RF) planning tools, which are inadequate in addressing the dynamic nature of user distributions and environmental factors in real-world deployments. These conventional approaches result in suboptimal antenna orientations, leading to network inefficiencies, network coverage gaps, interference, and increased operational costs.
[0064] The present disclosure leverages a data-driven approach that processes the geospatial user data, along with physical, antenna, and terrain properties to dynamically compute optimal antenna azimuth configurations. The present disclosure continuously evaluates and updates azimuth settings for each nominal site location, ensuring maximum network coverage and improved signal quality in highly populated or high-demand areas. Unlike conventional methods that rely on static models or subjective assessments, the disclosed approach enables adaptive and precise azimuth tuning based on actual user behaviour (e.g., user density distribution, mobility patterns, hotspot areas) and network performance metrics
(e.g., signal strength, handover success rate, call drop rate, throughput, and user experience scores). This results in enhanced network capacity, reduced interference, improved user experience, and a more cost-effective deployment of network resources.
[0065] The various embodiments throughout the disclosure will be explained in more detail with reference to FIG. 1- FIG. 4.
[0066] FIG. 1 illustrates an exemplary network architecture (100) for implementing a system (102) to adjust an antenna azimuth angle at a network site, in accordance with embodiments of the present disclosure.
[0067] Referring to FIG. 1, the network architecture (100) may include one or more computing devices or one or more user equipment (UE) ( 104- 1 , 104-2...104- N) that may be associated with one or more users (106-1, 106-2...106-N) and the system (102) in an environment. In an embodiment, the one or more UE (104-1, 104-2. . . 104-N) may be communicated to the system (102) through a network (108). A person of ordinary skill in the art will understand that the one or more UE (104- 1 , 104-2. . .104-N) may be individually referred to as the UE ( 104) and collectively referred to as the UE (104). A person of ordinary skill in the art will appreciate that the terms “computing device(s)” and “UE” may be used interchangeably throughout the disclosure. Although three UE (104) are depicted in the FIG. 1, however any number of the UE (104) may be included without departing from the scope of the ongoing description. Similarly, a person of ordinary skill in the art will understand that the one or more users (106-1, 106-2... 106-N) may be individually referred to as the user (106) and collectively referred to as the users (106).
[0068] In an embodiment, the UE (104) may include smart devices operating in a smart environment, for example, an internet of things (loT) system. In such embodiment, the UE (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 systems, communication devices, networked vehicle accessories, networked vehicular
devices, smart accessories, tablets, smart television (TV), computers, a smart security system, a smart home system, other devices for monitoring or interacting with or for the users (106) and/or entities, or any combination thereof. A person of ordinary skill in the art will appreciate that the UE (104) may include, but not be 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.
[0069] In an embodiment, the UE (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 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, 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.
[0070] In an embodiment, the UE (104) may include, but is not limited to, any electrical, electronic, electro-mechanical, or equipment, or a combination of one or more of the above devices, such as virtual reality (VR) devices, augmented reality (AR) devices, a general-purpose computer, a desktop, a personal digital assistant, a mainframe computer, or any other computing device. In another embodiment, the UE (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 (106) or the entity such as a touchpad, a touch-enabled screen, an electronic pen, and the like. A person of ordinary skill in the art will appreciate that the UE (104) may not be restricted to the mentioned devices and various other devices may be used.
[0071] Referring to FIG. 1, the UE (104) may communicate with the system (102) through a set of executable instructions residing on any operating system. In an embodiment, the set of executable instructions may include a crowdsourcing
application residing on the operating system of the UE (104), configured to collect the geospatial user data and the network performance metrics and transmit the collected geospatial user data and the network performance metrics to the system (102) for further processing.
[0072] In an embodiment, the UE (104) may communicate with the system (102) through the network (108) for sending or receiving various types of data. In an embodiment, the network (108) may include at least one of a 5G network, a 6G network, or the like. The network (108) may enable the UE (104) to communicate with other devices in the network architecture (100) and/or with the system (102). The network (108) may include a wireless card or some other transceiver connection to facilitate this communication. In another embodiment, the network (108) 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 or the like.
[0073] In an embodiment, the network (108) may include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network (108) may also include, by way of example but not limitation, one or more of a radio access network (RAN), a wireless network, a wired network, the internet, the intranet, a public network, a private network, a packet-switched network, a circuit- switched network, an ad hoc network, an infrastructure network, a public- switched telephone network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. In an embodiment, the system (102) may be connected to backend servers (not shown).
[0074] In an embodiment, the UE (104) is communicatively coupled with the network (108). The network (108) may receive a connection request from the UE
(104). The network (108) may send an acknowledgment of the connection request to the UE (104). The UE (104) may transmit a plurality of signals in response to the connection request. In an embodiment, the signals may be, but are not limited to, the geospatial user data (e.g., global positioning system (GPS) coordinates), crowdsourced performance metrics (e.g., latency, dropped calls, or throughput data), and so forth. The signals may be utilized by the system (102) for further processing to assist in optimizing the antenna azimuth angle. The antenna azimuth angle determines how the antenna’s main lobe is oriented to cover a specific sector within a network site’s coverage area. In an embodiment, a properly configured antenna azimuth angle ensures optimal distribution of radio frequency (RF) energy towards areas of high user density, thereby improving the network performance, network coverage uniformity, and overall system efficiency.
[0075] The system (102) may be configured for adjusting the antenna azimuth angle at the network site, such as, but not limited to, macrocell sites (e.g., large towers or rooftops), microcell sites (e.g., small towers), rooftop sites (e.g., antennas installed on the rooftops of commercial or residential buildings), small cell sites (e.g., pico cells and femto cells, often mounted on street furniture), and so forth to optimize signal coverage and the network performance. Embodiments of the present disclosure are intended to include or otherwise cover any network site, including known related art and/or later developed technologies.
[0076] Although the FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).
[0077] FIG. 2A illustrates an exemplary system architecture (200) for azimuth computation based on a crowdsource, in accordance with an embodiment of the present disclosure.
[0078] FIG. 2A with reference to FIG. 1, illustrates the system architecture (200) for azimuth computation based on the crowdsource. The system architecture (200), which represents a detailed implementation of the system (102), may include a platform (202). The platform (202) may be communicatively coupled to various upstream systems and downstream systems through an application programming interface (API)-based integration to enable automated data processing, analysis, and operational decision-making.
[0079] The platform (202) acts as a core command center. The platform (202) may include a processing unit (224) (as shown in FIG. 2B) that may be implemented using microprocessors, microcontrollers, digital signal processors, central processing units (CPUs), logic circuitries, and/or any devices that process data based on operational instructions. In an exemplary embodiment, the processing unit (224) is configured to integrate and analyze the data from the upstream systems (data sources) and transmit insights or actions to the downstream systems (execution layers).
[0080] The upstream systems (data sources) include a master database (MDB) system (204), a crowdsourced (CS) data collection system (206), a performance management (PM) system (208) and a radio frequency (RF) analytics system (210). In an exemplary embodiment, the data from the upstream systems may include, but is not limited to, MDB data (e.g., network topology, sector configurations, antenna azimuths, tilt values, site identifiers (IDs), cell parameters, azimuth angles, geolocation coordinates), crowdsourced data (e.g., user-reported coverage issues, signal-to-noise ratio (SINR) measurements, signal strength indicators, and user location data), PM data (e.g., call drop rates, throughput key performance indicators (KPIs), network outage alarms) and RF analytics data (e.g., antenna height, azimuth
values). Further, the downstream systems (execution layers) include a monitoring system (212) and a workorder system (214).
[0081] The MDB system (204) serves as a centralized repository that stores detailed information about network infrastructure, including, but not limited to, antenna types, antenna manufacturers, antenna configurations, tower types (e.g., rooftop towers common in urban or city areas, ground-based lattice towers used in rural areas, and other deployment structures), and so forth. The MDB system (204) also records physical parameters such as antenna height and supports the characterization of deployment scenarios (e.g., antennas mounted on building rooftops versus ground-based installations). The data may be collected and maintained for the antennas deployed across the network (108), providing essential information to support various operational and planning activities such as radio network optimization and asset management. In an embodiment, the platform (202) may be connected to the MDB system (204) through a first API-based integration (i.e., platform-MDB interface), enabling the platform (202) to efficiently query and retrieve network topology (e.g., site geolocation data, sector configuration), cell site parameters (e.g., existing antenna azimuth angles, antenna height, antenna beamwidth, polarization information), inventory data (e.g., inventory status of antenna equipment, antenna model version), and so forth which are utilized by the platform (202) for computing optimal antenna azimuth adjustments. In an embodiment, the MDB system (204) may include, but not be limited to, an online analytical processing (OLAP) database, structured query language (SQL) server analysis services, and the like.
[0082] The CS data collection system (206) functions as a data store configured to collect, store, and manage a variety of structured, semi-structured, and unstructured crowdsourced data obtained from end-user interactions and field measurements. The crowdsourced data includes mobile application data (e.g., application usage analytics, signal strength reports, call drop statistics), drive test data (e.g., reference signal received power (RSRP)/reference signal received quality (RSRQ) measurements, SINR values, throughput metrics collected through test
equipment), user feedback (e.g., customer complaints regarding coverage issues, crowdsourced quality of experience (QoE) surveys), and other network performance data. In an embodiment, the platform (202) interacts with the CS data collection system (206) through a second API-based integration (platform-CS interface), enabling seamless collection, cleansing, normalization, and enrichment of the crowdsourced data by the platform (202). The platform (202) utilizes the crowdsourced data to derive the optimal antenna azimuth adjustments by correlating user-reported or measured RF issues (e.g., poor coverage zones, interference regions, overshooting or undershooting sectors) with existing antenna configurations.
[0083] The PM system (208) is configured to collect, manage, and store KPIs, alarms, fault logs, and network counters from radio access networks (RANs) (e.g., macro cells, micro cells, small cells, distributed antenna systems) and core networks (e.g., long-term evolution (LTE), new radio (NR), voice over long-term evolution (VoLTE)). In an embodiment, the PM system (208) provides key metrics including, but not limited to, accessibility KPIs (e.g., call setup success rate, random access success rate), retainability KPIs (e.g., call drop rate, handover success rate), mobility KPIs (e.g., intra/inter-frequency handover success rate), user experience metrics (e.g., throughput, latency, packet loss, jitter), fault information (e.g., alarms, fault logs, network counters), and so forth.
[0084] The platform (202) interacts with the PM system (208) through a third API-based integration (platform-PM interface), which enables automated and realtime ingestion of performance data (key metrics). Once ingested, the platform (202) analyzes post- adjustment KPIs and the network counters to validate the effectiveness of the azimuth angle adjustments (e.g., reduced call drops, improved accessibility, optimized handover success rates), detect performance degradation trends (e.g., cells still showing high interference levels or traffic congestion after adjustment), identify sectors requiring further fine-tuning or additional corrective actions (e.g., additional azimuth realignments, power control adjustments), and so forth. In an embodiment, the platform (202) may provide feedback to an
optimization team (216) and other downstream systems for continuous RF optimization and closed-loop network performance improvement. The feedback loop ensures that antenna azimuth adjustments are systematically validated through the performance metrics and enables the platform (202) to enhance the overall RF performance based on data-driven insights from the PM system (208).
[0085] The RF analytics system (210) is configured to collect, manage, and deliver RF parameters associated with each network cell and sector. In an embodiment, the RF parameters may include, but not limited to, antenna orientation data (e.g., current antenna azimuth angles, electrical and mechanical tilt values), transmission characteristics (e.g., downlink and uplink power levels, reference signal power, antenna gain), sector-level configurations (e.g., frequency bands, supported technologies such as LTE or NR, carrier aggregation settings), cell adjacency information (e.g., neighbor cell relations, handover neighbor lists), and so forth. In an embodiment, the platform (202) interacts with the RF analytics system (210) through a fourth API-based integration (platform-RF interface) to ingest the RF parameters. Once ingested, the platform (202) cross -validates the RF parameters with external data sources, such as the crowdsourced data, to enable precise azimuth correction recommendations (e.g., calculating the optimal antenna azimuth angles for the sites experiencing coverage gaps or interference hotspots).
[0086] The monitoring system (212) interacts with the platform (202) through a fifth API-based integration (platform-MD interface), enabling automated, API- driven, and real-time data exchange. The platform (202), in conjunction with the monitoring system (212), performs continuous and periodic monitoring and validation activities to ensure sustained effectiveness of RF optimization actions, such as the antenna azimuth angle adjustments. By utilizing real-time and historical data streams (e.g., post-adjustment KPIs from the PM system (208), user experience metrics from the CS data collection system (206), RF parameter feedback from the RF analytics system (210)), the platform (202) validates whether implemented changes have led to measurable improvements in the network performance (e.g.,
improved call setup success rate, reduced handover failures, enhanced throughput) and the user experience (e.g., reduced complaints, improved satisfaction scores).
[0087] The ongoing validation process involves trend analysis (e.g., detecting KPI anomalies or performance regressions), geospatial heatmap generation (e.g., visualizing network coverage gaps or interference zones post-adjustment), and correlation of user-centric data (e.g., customer feedback, crowdsource application data) with network-side metrics. The results are then used to iteratively refine the antenna azimuth recommendations and other RF optimization strategies, creating a dynamic and closed-loop system.
[0088] The workorder system (214) interfaces with the platform (202) via a sixth API-based integration (platform-trouble ticket (TT) microservice interface), enabling automated transmission of the antenna azimuth adjustment recommendations. The workorder system (214) is configured to generate and manage workorders required to implement recommended corrective actions, such as the antenna azimuth adjustments (e.g., rotating the antenna to a new calculated azimuth angle based on optimization), mechanical or electrical tilt adjustments (e.g., modifying the antenna down-tilt to improve sector coverage), and so forth. Upon generation of the workorders, the workorder system (214) routes the workorders to the optimization team (216) who possess local area knowledge (e.g., familiarity with nominal site constraints, urban vs. rural propagation environments, nearby obstructions such as buildings or terrain).
[0089] The optimization team (216) acts as both a key stakeholder and an operational user of the platform (202), accessing outputs of the platform (202) via the API-based integration. The optimization team (216) consumes actionable insights provided by the platform (202) (e.g., antenna azimuth adjustment recommendations based on crowdsourced data validation) and coordinates closely with the workorder system (214) to initiate, validate, and execute the necessary corrective or preventive network actions. In an embodiment, the optimization team (216) may include specialized personnel responsible for ensuring that the mobile
network is performing at its best. The optimization team (216) includes, but not limited to, radio network engineers, drive test engineers, field technicians, performance analysts, machine, software, hots, and so forth.
[0090] FIG. 2B illustrates an exemplary block diagram of the system (102) implemented with the platform (202) to adjust the antenna azimuth angle at the network site, in accordance with embodiments of the present disclosure.
[0091] FIG. 2B with reference to FIG. 1 and FIG. 2A, illustrates the system (102), that includes a data collection unit (218), a memory (220), an interfacing unit (222), the processing unit (224), and a database (226). The processing unit (224) includes a storage module (228), a weight calculation module (230), an angle adjustment module (232), a report generation module (234), a workorder module (236) and a validation and monitoring module (238).
[0092] The data collection unit (218) is configured to collect data associated with the antenna azimuth angles within the vicinity of predefined locations. In an embodiment, the collected data includes, but is not limited to, user location data, the network performance metrics, and so forth. For example, the user location data may be gathered from the crowdsourced data such as latitude and longitude (Lat- Long) coordinates reported by the UE (104) through mechanisms like measurement reports (e.g., RSRP/RSRQ measurements), application-level telemetry (e.g., location data from mobile applications), network signaling events (e.g., drive test logs or passive probing systems), and so forth. In an embodiment, the Lat-Long coordinates may be timestamped and tagged with a serving cell ID (a unique identifier of a radio access node currently providing service to the UE (104)) to associate each data point with its corresponding network site accurately. Further, the network performance metrics may include, but are not limited to, radio signal strength indicators (e.g., RSRP), signal quality metrics (e.g., SINR), throughput measurements (e.g., download/upload speeds in megabits per second (Mbps)), and additional KPIs such as call drop rates and latency statistics. In an embodiment, the predefined locations may correspond to nominal points of interest, such as planned
new site coordinates (e.g., a flagged location for future antenna installation) or coordinates of existing operational sites (e.g., macro cells or small cells).
[0093] In an exemplary embodiment, the collected data may first be transmitted to a server (i.e., cloud-based server) over, for example, secure communication protocols, such as, but not limited to, a hypertext transfer protocol secure (HTTPS), a message queuing telemetry transport (MQTT), and the like. In such embodiment, the server may ingest and pre-process the data before storing the data in a centralized cloud storage system, such as an object storage service or a distributed database. The centralized cloud storage system aggregates the crowdsourced data from multiple UEs (104) across diverse geographic regions, enabling scalability and efficient data management. The data collection unit (218) may be configured to retrieve the crowdsourced data from the cloud storage system, providing access to a comprehensive set of timestamped Lat-Long coordinates, serving cell IDs, and the network performance metrics. In an embodiment, the data collection unit (218) may be configured to collect and update the crowdsourced data during a configurable periodic interval (e.g., hourly, daily, or weekly) to ensure that the data reflects the latest network and user distribution dynamics. For example, the configurable periodic interval may allow an operator to set the system (102) to collect the data every hour, every day, or every week, depending on how frequently updates are needed. In another embodiment, the data collection unit (218) may be configured to collect and update the crowdsourced data on-demand. The crowdsourced data is subsequently used to analyze how various antenna azimuth angles influence the user experience and the network KPIs within the vicinity of each predefined location. Upon retrieval, the crowdsourced data is further processed by the data collection unit (218) to analyze parameters such as, but not limited to, user density distribution, signal propagation patterns, network quality trends and so forth within different azimuth sectors around the predefined locations.
[0094] In an exemplary embodiment, the data collection unit (218) may be configured to perform angular segmentation of the crowdsourced data by dividing a surrounding area of each predefined location into fine-grained sectors with a l-
degree azimuth resolution (i.e., from 0° to 359°). For each angular sector within a predefined distance (e.g., 500 meters (m) or 1 kilometre (km)), the data collection unit (218) may be configured to record a number of crowdsourced samples detected in the corresponding directional bin (i.e., each specific azimuth angle, such as 0°, 1°, 2°, ..., 359°, where data is collected and analyzed per individual degree around the antenna). For instance, within a 1 km radius around the predefined location, the data collection unit (218) may observe that 23 user samples fall within the 0° sector, 19 samples in the 1° sector, 15 samples in the 2° sector, continuing up to the 360° sector. In an embodiment, the data collection unit (218) may also be configured to assign a weightage to each antenna azimuth angle based on the crowdsourced samples in the corresponding direction. In the above example, a higher weightage is assigned to the 0° sector as 23 (higher) user samples fall within the 0° sector.
[0095] In an embodiment, the data collection unit (218) is configured to transmit both the number of crowdsourced samples and the corresponding weightage assigned to each azimuth angle to the storage module (228) of the processing unit (224). In another embodiment, the data collection unit (218) is configured to transmit the number of crowdsourced samples and the corresponding antenna azimuth angles to the storage module (228).
[0096] The memory (220) may be a non-transitory computer readable storage medium configured to store instructions or routines. As used herein, the term “instructions” may refer to a sequence of commands that are written in a programming language and may be executed by the processing unit (224) to perform tasks associated with the system (102). The memory (220) may include any non-transitory storage device including, for example, but not limited to, a volatile memory such as a random-access memory (RAM), or a non-volatile memory such as an erasable programmable read only memory (EPROM), a flash memory, and the like. Embodiments of the present invention are intended to include or otherwise type of the memory (220) including known related art and/or later developed technologies.
[0097] In an embodiment, the interfacing unit (222) may include a variety of interfaces, for example, the interfaces for data input and output devices (I/O), storage devices, and the like. The interfacing unit (222) may facilitate communication through the system (102). The interfacing unit (222) may also provide a communication pathway for various other units/modules of the system (102).
[0098] In an embodiment, the database (226) may offer functionality to manage, capture, store, and retrieve antenna configuration data, such as but not limited to, an antenna ID, a physical antenna location, a current (second) antenna azimuth angle, a computed first antenna azimuth angle (latest computed value), the antenna type, the antenna model, a last adjustment timestamp, a delta value, and so forth. The database (226) is also configured to store a first table, a second table and a third table. The first table includes the number of crowdsourced samples (representing the count of user data samples collected from a network coverage area) and assigned weightage (the computed weightage assigned to the corresponding azimuth angles based on crowdsourced data analysis). The second table includes a mapping between each antenna azimuth angle and its corresponding calculated weightage, along with associated data collection parameters. The third table includes each triplet of the antenna azimuth angles along with their aggregated weightage, which may represent a cumulative weight of the corresponding three combined angles.
[0099] The database (226) is designed to interact seamlessly with other modules of the system (102), such as the storage module (228), the weight calculation module (230), the angle adjustment module (232), the report generation module (234), the workorder module (236) and the validation and monitoring module (238), to support functionality of the system (102) effectively. The database (226) may store the data that may be generated as a result of functionalities implemented by any of the modules of the processing unit (224). In an embodiment, the database (226) may be separate from the system (102).
[00100] The modules are controlled by the processing unit (224), which executes the instructions retrieved from the memory (220). The processing unit (224) further interacts with the interfacing unit (222) to facilitate user interaction and to provide options for managing and configuring the system (102). The processing unit (224) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions.
[00101] The storage module (228) is communicatively coupled to the data collection unit (218) to receive the number of crowdsourced samples and the corresponding weightage assigned to each azimuth angle from the data collection unit (218). The storage module (228) is configured to store the number of crowdsourced samples and the corresponding weightage assigned to each azimuth angle (e.g., 0°, 1°, 2°, ..., 359°) in the first table of the database (226).
[00102] Further, the weight calculation module (230) is also communicatively coupled to the data collection unit (218) to receive the collected data from the data collection unit (218). The weight calculation module (230) is configured to calculate a weightage (new weightage) for each of the antenna azimuth angles based on the collected data. In an embodiment, the collected data may include, but is not limited to, sample count (crowd-sourced samples in the corresponding direction), user count (number of distinct users (106) observed), the network performance metrics, and so forth.
[00103] In an exemplary embodiment, the weight calculation module (230) may be configured to compute the weightage for each antenna azimuth angle by applying a weightage calculation algorithm that factors in parameters (sample count, user count and network performance metrics) of the collected data. The weightage calculation algorithm may be, but not limited to, a weighted sum algorithm, a multicriteria decision analysis (MCDA), a z-score normalization, and so forth. Embodiments of the present invention are intended to include or otherwise cover
any type of the weightage calculation algorithm, including known related art and/or later developed technologies.
[00104] In an embodiment, the weightage may be calculated as a composite score, wherein each parameter in the collected data is assigned a corresponding weighted value. The weight calculation module (230) is further configured to aggregate weighted values of each parameter to derive a final weightage score for each antenna azimuth angle. For example, the weight calculation module (230) may assign 50% weight to the sample count, 30% to the user count, and 20% to the average SINR. If the antenna azimuth angle of 90° has 150 samples, 40 users, and the average SINR of 20 dB, the final weightage score is computed by applying the weights to the corresponding parameters and summing the weights to determine the composite weightage for each antenna azimuth angle.
[00105] In an aspect, if the 90° azimuth sector has 150 samples, 40 unique users, an average RSRP of -85 dBm, and the SINR of 20 dB, it may result in a higher weightage. In contrast, the 210° azimuth sector may show only 50 samples, 10 users, the average RSRP of -100 dBm, and SINR of 8 dB, leading to a lower weightage. This highlights that the 90° sector has both higher user activity and better radio conditions, making it a stronger candidate for the optimal antenna azimuth adjustment. In certain embodiments, the weight calculation module (230) may be configured to normalize values of the weightage to ensure consistency across azimuths. The weight calculation module (230) is configured to transmit the calculated weightage of the antenna azimuth angles to the storage module (228).
[00106] In an alternative embodiment, the weight calculation module (230) may be configured to calculate the weightage for triplet azimuth locations. In other words, the weight calculation module (230) may be configured to generate multiple groups of three azimuth angles (triplets) across an azimuth spectrum and compute an aggregated weightage for each triplet using aggregation techniques. In one exemplary embodiment, the triplet may be a sector-based triplet azimuth, where three angles correspond to fixed sector boundaries (e.g., Sector 1: 0°-120°, Sector
2: 120°-240°, Sector 3: 240°-360°). In another exemplary embodiment, the triplet may be formed using a sliding window approach, where the triplet shifts by one azimuth angle at a time (e.g., (0°, 1°, 2°), (1°, 2°, 3°), (2°, 3°, 4°), etc.) to cover the entire spectrum. For instance, if the antenna azimuth angles range from 0° to 360°, the weight calculation module (230) may sequentially create triplet groups such as (0°, 1°, 2°), (1°, 2°, 3°), (2°, 3°, 4°), and so forth, and compute a combined weightage for each triplet by aggregating the individual weightage of the three constituent antenna azimuth angles. The aggregation techniques may include, but are not limited to, summation, averaging, mean calculation, or other statistical methods. The weight calculation module (230) may be configured to transmit the aggregated weightage of each triplet to the storage module (228).
[00107] The weight calculation module (230) is further configured to select a predefined number of antenna azimuth angles from the second or third table of the database (226) based on the calculated weightage. In an embodiment, the predefined number of antenna azimuth angles may include the top N antenna azimuth angles, where N = 1, 2, 3, ... n, ranked based on their assigned weightage. In a preferred embodiment, N is 3, referring to the top 3 antenna azimuth angles with the highest weightage values. In an embodiment, the weight calculation module (230) is configured to select the predefined number of individual antenna azimuth angles with the highest weightage (e.g., 90°, 150°, and 270°) from the second table of the database (226), treating each antenna azimuth angle separately. To select the highest weightage, the weight calculation module (230) may be configured to apply sorting algorithms, such as, but not limited to, a quick sort algorithm, a merge sort algorithm, a bubble sort algorithm and so forth, to arrange the antenna azimuth angles in a descending or increasing order based on their weightage, and top N antenna azimuth angles are then selected. In another embodiment, the weight calculation module (230) is configured to select the predefined number of azimuth triplet groupings with the highest weightage (e.g., 90°-92°, 150°-152°, and 270°-272°) from the third table of the database (226).
Similar sorting algorithms may be used to order the azimuth triplet groupings, selecting the top N triplets based on the corresponding combined weightage values.
[00108] The storage module (228) is also communicatively coupled to the weight calculation module (230), to receive the calculated weightage of the antenna azimuth angles along with the parameters of the collected data and the aggregated weightage of each triplet from the weight calculation module (230). The storage module (228) is configured to store the calculated weightage of the antenna azimuth angles along with the parameters of the collected data in the second table of the database (226), where each antenna azimuth angle is mapped to its corresponding weightage. In an embodiment, the calculated weightage may be used to identify optimal azimuth configurations or to select top-ranking azimuth sectors for further analysis. The storage module (228) is configured to store the aggregated weightage of each triplet in the third table of the database (226).
[00109] The angle adjustment module (232) is communicatively coupled to the weight calculation module (230). The angle adjustment module (232) is configured to receive the selected predefined number of antenna azimuth angles from the weight calculation module (230). The angle adjustment module (232) is configured to compute the first antenna azimuth angle based on the calculated weightage of the predefined number of the antenna azimuth angles and network site parameters. The network site parameters include, but are not limited to, physical characteristics of the predefined locations, antenna characteristics of the predefined locations, terrain information of the predefined locations and so forth. In an exemplary embodiment, the physical characteristics, the antenna characteristics and the terrain information of the predefined locations may be fetched from the MDB system (204). The physical and antenna characteristics may include, but are not limited to, the antenna type, the antenna manufacturer, the mechanical tilt, electrical tilt configuration, power configuration, the antenna height, the tower type (e.g., rooftop tower, ground-based lattice tower), and other site-specific parameters relevant for azimuth computation. The terrain information may include, but is not limited to, an elevation of the site (e.g., height above sea level), a type of surrounding environment (e.g.,
forest, buildings, open fields), a classification of the region as urban (e.g., city, dense population) or rural (e.g., village, low population density), and other geographical or environmental characteristics that may affect signal propagation. In an embodiment, the angle adjustment module (232) may be configured to utilize an optimization algorithm, such as, but not limited to, a weighted scoring algorithm, a rule-based logic, a machine learning model, and so forth, to compute a combined score and determine the optimal antenna azimuth angle from the selected top N (e.g., top 3) antenna azimuth angles. In an embodiment, the combined score may be derived by considering the weightage of the predefined number of the antenna azimuth angles, the physical characteristics, the antenna characteristics, and the terrain characteristics at the network site. For example, although Azimuth A = 45° may have a high weightage due to high user density, but if the Azimuth A points toward an obstructive terrain feature such as a hill or dense foliage, it may be deprioritized in a scoring process. On the other hand, if Azimuth B = 90° may exhibit a slightly lower initial weightage, but if the Azimuth B points toward an open urban corridor with favorable antenna orientation (e.g., proper antenna tilt, height, and beamwidth) and clear terrain, it may be assigned a higher combined score. The antenna azimuth angle with the highest combined score is then selected as the first antenna azimuth angle for the network site, ensuring an optimal balance between the user distribution and environmental factors. The angle adjustment module (232) is configured to transmit the computed first antenna azimuth angle to the storage module (228).
[00110] Further, the angle adjustment module (232) is configured to calculate a delta value between the computed first antenna azimuth angle and the second (current) antenna azimuth angle to determine the adjustment for the antenna azimuth angle at the network site.
[00111] The angle adjustment module (232) is configured to calculate the delta value between the computed first antenna azimuth angle and the second antenna azimuth angle by comparing the first antenna azimuth angle with the second (current) antenna azimuth that is stored in the database (226). In one embodiment,
if the first antenna azimuth angle is equal to the second antenna azimuth angle (i.e., delta = 0), the angle adjustment module (232) may be configured to determine that no antenna adjustment is required, and the delta value is either discarded or recorded as zero in the database (226). In another embodiment, if the first antenna azimuth angle is different from the second antenna azimuth angle (i.e., delta! = 0), the angle adjustment module (232) may be configured to store the delta value in the database (226) for further action.
[00112] For instance, if the top three angles are 90°, 100°, and 95°, and 90° has the maximum weightage (e.g., the highest combination of the sample count, the user count, and the network performance metrics), then 90° is selected as the first or corrected antenna azimuth angle. The first antenna azimuth angle is then compared with a nominal azimuth angle (i.e., second antenna azimuth angle) available in the database (226) (e.g., if the database (226) records 45°, but the angle adjustment module (232) identifies 90°, a delta of 45° is detected).
[00113] In an embodiment, the angle adjustment module (232) may also be configured to transmit the computed first antenna azimuth angle and the delta value to the report generation module (234). In another embodiment, when the delta is non-zero, the angle adjustment module (232) is configured to generate a validation signal. The angle adjustment module (232) is configured to transmit the generated validation signal to the workorder module (236).
[00114] The storage module (228) is communicatively coupled to the angle adjustment module (232), to receive the computed first antenna azimuth angle from the angle adjustment module (232). The storage module (228) is configured to store the computed first antenna azimuth angle in the database (226). The stored first antenna azimuth angle is used as the second antenna azimuth angle for calculating the delta value in a next iteration. The next iteration refers to a subsequent execution of the entire optimization (adjustment) and validation cycle after the completion of a previous cycle. For example, once the first antenna azimuth angle is computed, stored in the database (226), and corresponding adjustments and validations are
completed, the system (102) re-initiates the process in a future cycle. During the next iteration, updated crowdsourced data is collected, and a new first antenna azimuth angle is computed based on current network conditions. The previously stored first antenna azimuth angle (from the prior iteration) now serves as the second antenna azimuth angle for comparison. The system (102) then calculates a new delta value by comparing the new first antenna azimuth angle with the stored first antenna azimuth angle to determine if further adjustments are needed. This cyclical approach ensures that the antenna alignment and the network performance are continuously optimized based on dynamic environmental and user distribution changes.
[00115] In an embodiment, the report generation module (234) is communicatively coupled to the angle adjustment module (232), to receive the computed first antenna azimuth angle and the delta value from the angle adjustment module (232). The report generation module (234) is configured to generate a report containing details such as the computed first antenna azimuth angle, the second antenna azimuth angle retrieved from the database (226), and the delta value indicating the discrepancy between the computed first antenna azimuth angle and the second antenna azimuth angle. The report may be generated in multiple formats, including, but not limited to, tabular summaries, structured portable document formats (PDFs), and comma separated value (CSV) files, and so forth for further analysis or archiving. In another embodiment, the report generation module (234) is configured to provide an output in a graphical representation format. For instance, the graphical representation format may include map layer visualizations, where directional arrows are overlaid on geospatial maps to depict both the second antenna azimuth angle and the computed first antenna azimuth angle. As used herein, the term “map layer visualization” refers to the process of overlaying graphical data, such as directional indicators, heatmaps, or other visual markers, on top of a geospatial map to represent specific information spatially. For example, the map layer visualization involves displaying antenna orientations, azimuth angles,
network coverage areas, and network performance metrics directly on the map to provide intuitive and location-based insights for the optimization team (216).
[00116] The workorder module (236) is communicatively coupled to the angle adjustment module (232) for receiving the validation signal from the angle adjustment module (232). The workorder module (236) is configured to trigger a workorder based on the received validation signal. The workorder module (236) is configured to trigger the workorder for validating the computed first antenna azimuth angle and adjusting the antenna azimuth angle based on the calculated delta value. The workorder prompts the optimization team (216) to physically visit the network site to validate and inspect the second antenna azimuth angle. During an inspection, possible scenarios may arise: the antenna may be found positioned at an outdated angle (e.g., 45°), requiring reorientation to the computed first antenna azimuth angle (e.g., 90°); the antenna may already be physically aligned to the computed first antenna azimuth angle (e.g., 90°) but is inaccurately recorded as 45° in the database (226), requiring correction in the database (226); or the antenna may be found at an intermediate or unexpected angle (e.g., 70°), requiring appropriate adjustments, such as physical antenna adjustments, correction in the database (226), or both. Based on the validation, the optimization team (216) executes recommended changes, ensuring the antenna orientation is optimized and accurately documented.
[00117] Following the successful execution of the antenna azimuth angle adjustments by the optimization team (216), the validation and monitoring module (238) is communicatively coupled with the PM system (208) (as explained above in FIG. 2A) and configured to initiate post-implementation performance analysis using the PM system (208). The validation and monitoring module (238) is configured to collect and analyze the network KPIs related to the implemented antenna azimuth changes. The validation and monitoring module (238) may be configured to assess the network KPIs such as, but not limited to, network coverage improvement, signal quality, the call drop rates, the handover success, and the user experience metrics to validate the effectiveness of the antenna azimuth angle
31 adjustments. In an embodiment, based on the assessment, if the KPIs meet predefined performance criteria, the changes are confirmed as successful.
[00118] In another embodiment, when the assessed KPIs fail to meet the predefined performance criteria, this indicates the presence of performance deficiencies such as unexpected signal degradation, poor handover success rates, increased call drop rates, or network coverage gaps. In such embodiment, the validation and monitoring module (238) may be configured to initiate troubleshooting workflows (i.e., structured sequence of diagnostic and corrective actions) to address the identified performance deficiencies. The corrective actions may include, but are not limited to, additional antenna reorientation, fine-tuning of network parameters, and so forth to restore the network performance to acceptable levels.
[00119] The validation and monitoring module (238) is configured to conduct periodic monitoring and validation, performing continuous checks on the network KPIs to proactively detect any new or recurring abnormalities over time. Upon detecting the abnormalities, the validation and monitoring module (238) may be configured to generate a signal for the workorder module (236) to trigger a new workorder, prompting the optimization team (216) to perform further investigation and remediation. This feedback mechanism ensures that the corrective actions are executed promptly to maintain optimal network performance. The entire process is designed as a closed-loop system, where a cycle of the crowdsourced data collection, user density weightage calculation, antenna azimuth computation, antenna adjustment, performance monitoring through the PM system (208), anomaly detection, work order triggering, physical inspection, and corrective actions is repeated iteratively to achieve continuous improvement in the network optimization and maintain alignment with network performance objectives.
[00120] FIG. 3 illustrates a flowchart of a method (300) implemented by the system (102) for adjusting the antenna azimuth angle at the network site, according to certain embodiments.
[00121] FIG. 3 with reference to FIG. 2B, illustrates the method (300) for adjusting the antenna azimuth angle at the network site by using data collection unit (218) and the processing unit (224) of the system (102).
[00122] At step (302), the method (300) includes collecting, by the data collection unit (218), the data associated with the antenna azimuth angles within the vicinity of the predefined locations. In an embodiment, the collected data includes the user location data and the network performance metrics. In an exemplary embodiment, the location data may be gathered from the crowdsourced data, such as the Lat-Long coordinates reported by the UE (104). The network performance metrics may include, but are not limited to, the radio signal strength indicators (e.g., RSRP), the signal quality metrics (e.g., SINR), the throughput measurements (e.g., download/upload speeds in megabits per second (Mbps)), and the additional KPIs such as the call drop rates and the latency statistics. In an embodiment, the predefined locations correspond to the nominal points of interest, such as planned new site coordinates (e.g., a flagged location for future antenna installation) or coordinates of the existing operational sites (e.g., macro cells or small cells). Step (302) further includes performing the angular segmentation of the crowdsourced data by dividing the surrounding area of each predefined location into fine-grained sectors. In an embodiment, step (302) further includes recording the number of crowdsourced samples detected in the corresponding directional bin and assigning the weightage to the antenna azimuth angles based on the crowdsourced samples.
[00123] At step (304), the method (300) includes calculating, by the processing unit (224), the weightage for each antenna azimuth angle based on the collected data. In an embodiment, the weightage for each antenna azimuth angle is calculated by applying the weightage calculation algorithm. In an embodiment, the weightage may be calculated as the composite score, where each parameter (sample count, user count and network performance metrics) in the collected data is assigned a corresponding weighted value. Further, the weighted values may be aggregated to derive the final weightage score for each antenna azimuth angle, which may be stored in the second table of the database (226). In another embodiment, the
weightage for triplet azimuth locations may be calculated, which may be stored in the third table of the database (226). Step (304) further includes selecting the predefined number of antenna azimuth angles from the second table or the third table of the database (226) based on the calculated weightage. In an embodiment, the predefined number of azimuth angles may include the top 3 antenna azimuth angles having the highest weightage.
[00124] At step (306), the method (300) includes computing, by the processing unit (224), the first antenna azimuth angle based on the calculated weightage of the predefined number of the antenna azimuth angles and the network site parameters. The network site parameters may include, but are not limited to, the physical characteristics of the predefined locations, the antenna characteristics of the predefined locations, the terrain information of the predefined locations and so forth. In an exemplary embodiment, the combined score may be computed by considering the weightage of the predefined number of the antenna azimuth angles along with the physical characteristics, antenna characteristics, and terrain characteristics of the network site. The antenna azimuth angle with the highest combined score is selected as the first antenna azimuth angle for the network site.
[00125] At step (308), the method (300) includes calculating, by the processing unit (224), the delta value between the computed first antenna azimuth angle and the second antenna azimuth angle to determine the adjustment for the antenna azimuth angle at the network site. In an embodiment, if the first antenna azimuth angle is equal to the second antenna azimuth angle (i.e., delta = 0), indicating that no antenna adjustment is required, then the delta value is discarded. In another embodiment, if the first antenna azimuth angle is different from the second antenna azimuth angle (i.e., delta! = 0), then the delta value between the first antenna azimuth angle and the second antenna azimuth angle may be stored in the database (226) for further action. In an embodiment, step (308) further includes storing the computed first antenna azimuth angle in the database (226). The stored first antenna azimuth angle is used as the second antenna azimuth angle for calculating the delta value in the next iteration.
[00126] Step (308) further includes generating, by the processing unit (224), the report or the graphical representation (map layer visualizations) of the computed first antenna azimuth angle. Step (308) also includes triggering, by the processing unit (224), the workorder for validating the computed first antenna azimuth angle and adjusting the antenna azimuth angle based on the calculated delta value. The workorder prompts the optimization team (216) to physically visit the network site to validate and inspect the existing second antenna azimuth angle for executing the antenna azimuth angle adjustments.
[00127] Following the successful execution of the antenna azimuth angle adjustments by the optimization team (216), the method (300) includes initiating the post-implementation performance analysis using the PM system (208) to assess the impact of the implemented changes. The method (300) includes monitoring and validating the network KPIs such as, but not limited to, the network coverage, the signal quality, the handover success, and the user experience metrics to confirm optimization success or detect performance deficiencies. If deficiencies are identified, corrective actions are triggered to resolve the deficiencies. This forms a continuous closed-loop system where the antenna adjustments, KPI monitoring, anomaly detection, and corrective actions are iteratively performed to sustain optimal network performance.
[00128] FIG. 4 illustrates an exemplary computer system (400) in which, or with which, the system (102) and the method (300) of the present disclosure may be implemented. As shown in FIG. 4, the computer system (400) may include an external storage device (410), a bus (420), a main memory (430), a read only memory (440), a mass storage device (450), a communication port (460), and a processor (470). A person skilled in the art will appreciate that the computer system (400) may include more than one processor (470) and the communication ports (460). The processor (470) may include various modules associated with embodiments of the present disclosure.
[00129] In an embodiment, the external storage device (410) may be any device that is commonly known in the art such as, but not limited to, a memory card, a memory stick, a solid-state drive, a hard disk drive (HDD), and so forth.
[00130] In an embodiment, the bus (420) may be communicatively coupled with the processor(s) (470) with the other memory, storage, and communication blocks. The bus (420) may be, e.g., a Peripheral Component Interconnect (PCI)/PCI Extended (PCI-X) bus, a Small Computer System Interface (SCSI), a 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 (470) to the computer system (400).
[00131] In an embodiment, the main memory (430) may be a Random-Access Memory (RAM), or any other dynamic storage device commonly known in the art. The Read-only memory (440) 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 (470).
[00132] In an embodiment, the mass storage device (450) may be any current or future mass storage solution, which may be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, a Parallel Advanced Technology Attachment (PATA) or a Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g., an array of disks (e.g., SATA arrays).
[00133] Further, the communication port (460) 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 (460) may be chosen depending
on the network (108), such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (400) connects.
[00134] Optionally, operator and administrative interfaces, e.g., a display, a keyboard, a joystick, and a cursor control device, may also be coupled to the bus (420) to support a direct operator interaction with the computer system (400). Other operator and administrative interfaces may be provided through network connections connected through the communication port (460). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (400) limit the scope of the present disclosure.
[00135] The exemplary computer system (400) is configured to execute 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 adjusting an antenna azimuth angle at a network site. The method (300) includes collecting, by a data collection unit (218), data associated with one or more antenna azimuth angles within a vicinity of one or more predefined locations. The method (300) further includes calculating, by a processing unit (224), a weightage for each of the one or more antenna azimuth angles based on the collected data. The method (300) further includes computing, by the processing unit (224), a first antenna azimuth angle based on the calculated weightage of a predefined number of the one or more antenna azimuth angles and one or more network site parameters. The method (300) further includes calculating, by the processing unit (224), a delta value between the computed first antenna azimuth angle and a second antenna azimuth angle to determine an adjustment for the antenna azimuth angle at the network site.
[00136] In an exemplary embodiment, a user equipment (UE) (104) communicatively coupled with a network (108) is disclosed. The coupling includes steps of receiving, by the network (108), a connection request from the UE (104). The coupling further includes sending, by the network (108), an acknowledgment
of the connection request to the UE (104). The coupling further includes transmitting a plurality of signals in response to the connection request. An antenna azimuth angle at a network site is adjusted by a method (300). The method (300) includes collecting, by a data collection unit (218), data associated with one or more antenna azimuth angles within a vicinity of one or more predefined locations. The method (300) further includes calculating, by a processing unit (224), a weightage for each of the one or more antenna azimuth angles based on the collected data. The method (300) further includes computing, by the processing unit (224), a first antenna azimuth angle based on the calculated weightage of a predefined number of the one or more antenna azimuth angles and one or more network site parameters. The method (300) further includes calculating, by the processing unit (224), a delta value between the computed first antenna azimuth angle and a second antenna azimuth angle to determine an adjustment for the antenna azimuth angle at the network site.
[00137] The present disclosure provides a technical advancement in the field of antenna azimuth angle adjustment for mobile networks such as 4G and 5G. This advancement overcomes the limitations of existing solutions by utilizing user distribution data along with physical and antenna properties of nearby sites to compute optimal azimuth angles. The disclosure involves processing user density patterns, terrain information, and network parameters to recommend precise antenna orientations, thereby enhancing network coverage, reducing interference, and improving service quality. By implementing the disclosure, operators benefit from optimized network performance, improved resource utilization, and increased return on investment (ROI), while also enabling continuous monitoring and adaptive adjustments through a closed-loop system to maintain consistent network stability and efficiency.
[00138] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or
examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.
ADVANCEMENTS OF THE PRESENT DISCLOSURE
[00139] The present disclosure described herein above has several technical advantages as follows:
[00140] The present disclosure provides a system and method for adjusting antenna azimuth angles based on user distribution data, addressing inefficiencies and inaccuracies associated with conventional manual and static model-based approaches.
[00141] The present disclosure enables precise computation of antenna azimuth directions by analyzing user density, physical site properties, antenna characteristics, and terrain information, resulting in improved network coverage and reduced signal interference.
[00142] The present disclosure ensures dynamic adjustment recommendations that align antenna orientations with real-time user distribution patterns, thus supporting enhanced user experience and higher data throughput.
[00143] The present disclosure facilitates better resource utilization by reducing the need for costly field adjustments and manual site surveys, ultimately improving operational efficiency and return on investment (ROI) for telecom operators.
[00144] The present disclosure implements a closed-loop system that continuously monitors performance metrics and supports iterative adjustments to maintain optimal network performance over time.
Claims
1. A method (300) for adjusting an antenna azimuth angle at a network site, the method (300) comprising steps: collecting, by a data collection unit (218), data associated with one or more antenna azimuth angles within a vicinity of one or more predefined locations; calculating, by a processing unit (224), a weightage for each of the one or more antenna azimuth angles based on the collected data; computing, by the processing unit (224), a first antenna azimuth angle based on the calculated weightage of a predefined number of the one or more antenna azimuth angles and one or more network site parameters; and calculating, by the processing unit (224), a delta value between the computed first antenna azimuth angle and a second antenna azimuth angle to determine an adjustment for the antenna azimuth angle at the network site.
2. The method (300) as claimed in claim 1 , wherein the data comprises at least one of user location data, and network performance metrics.
3. The method (300) as claimed in claim 1, wherein the data associated with the one or more antenna azimuth angles is collected and updated at least one of on-demand or during a configurable periodic interval.
4. The method (300) as claimed in claim 1 , wherein the one or more network site parameters comprises at least one of one or more physical characteristics of the one or more predefined locations, one or more antenna characteristics of the one or more predefined locations and terrain information of the one or more predefined locations.
5. The method (300) as claimed in claim 1, comprising generating, by the processing unit (224), at least one of a report, and a graphical representation of the computed first antenna azimuth angle.
6. The method (300) as claimed in claim 1, comprising triggering, by the processing unit (224), at least one workorder to validate the computed first antenna azimuth angle and adjust the antenna azimuth angle based on the calculated delta value.
7. The method (300) as claimed in claim 1, comprising storing, by the processing unit (224), the computed first antenna azimuth angle in a database (226), wherein the stored first antenna azimuth angle is used as the second antenna azimuth angle for calculating the delta value in a next iteration.
8. A system (102) to adjust an antenna azimuth angle at a network site, the system (102) comprising: a data collection unit (218) configured to collect data associated with one or more antenna azimuth angles within a vicinity of one or more predefined locations; and a processing unit (224) communicatively coupled to the data collection unit (218), wherein the processing unit (224) is configured to receive the data from the data collection unit (218), wherein the processing unit (224) comprises: a weight calculation module (230) configured to calculate a weightage for each of the one or more antenna azimuth angles based on the collected data; and an angle adjustment module (232) configured to: compute a first antenna azimuth angle based on the calculated weightage of a predefined number of the one or more antenna azimuth angles and one or more network site parameters; and
calculate a delta value between the computed first antenna azimuth angle and a second antenna azimuth angle to determine an adjustment for the antenna azimuth angle at the network site.
9. The system (102) as claimed in claim 8, wherein the data comprises at least one of user location data, and network performance metrics.
10. The system (102) as claimed in claim 8, wherein the data associated with the one or more antenna azimuth angles is collected and updated at least one of on-demand or during a configurable periodic interval.
11. The system (102) as claimed in claim 8, wherein the one or more network site parameters comprises at least one of one or more physical characteristics of the one or more predefined locations, one or more antenna characteristics of the one or more predefined locations and terrain information of the one or more predefined locations.
12. The system (102) as claimed in claim 8, wherein the processing unit (224) comprises a report generation module (234) configured to generate at least one of, a report, and a graphical representation of the computed first antenna azimuth angle.
13. The system (102) as claimed in claim 8, wherein the processing unit (224) comprises a workorder module (236) configured to trigger at least one workorder to validate the computed first antenna azimuth angle and adjust the antenna azimuth angle based on the calculated delta value.
14. The system (102) as claimed in claim 8, wherein the processing unit (224) comprises a storage module (228) configured to store the computed first antenna azimuth angle in a database (226), wherein the stored first antenna
azimuth angle is used as the second antenna azimuth angle for calculating the delta value in a next iteration.
15. A user equipment (UE) (104) communicatively coupled with a network (108), the coupling comprises steps of: receiving, by the network (108), a connection request from the UE (104); sending, by the network (108), an acknowledgment of the connection request to the UE (104); and transmitting a plurality of signals in response to the connection request, wherein an antenna azimuth angle at a network site is adjusted by a method (300) as claimed in claim 1.
16. 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 (300) for adjusting an antenna azimuth angle at a network site, the method (300) comprising: collecting, by a data collection unit (218), data associated with one or more antenna azimuth angles within a vicinity of one or more predefined locations; calculating, by a processing unit (224), a weightage for each of the one or more antenna azimuth angles based on the collected data; computing, by the processing unit (224), a first antenna azimuth angle based on the calculated weightage of a predefined number of the one or more antenna azimuth angles and one or more network site parameters; and calculating, by the processing unit (224), a delta value between the computed first antenna azimuth angle and a second antenna azimuth angle to determine an adjustment for the antenna azimuth angle at the network site.
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|---|---|---|---|---|
| US20170093033A1 (en) * | 2014-06-04 | 2017-03-30 | Fasmetrics S.A. | Dynamic antenna azimuth adjustment |
| CN109429249A (en) * | 2017-09-04 | 2019-03-05 | 中国移动通信集团浙江有限公司 | A kind of antenna azimuth optimization method and equipment based on MR location data |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170093033A1 (en) * | 2014-06-04 | 2017-03-30 | Fasmetrics S.A. | Dynamic antenna azimuth adjustment |
| CN109429249A (en) * | 2017-09-04 | 2019-03-05 | 中国移动通信集团浙江有限公司 | A kind of antenna azimuth optimization method and equipment based on MR location data |
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