International Journal For Multidisciplinary Research

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AI-Driven Automation for Telecom Site Plumbing Diagrams: A Cloud Native, Multivendor Framework for Spectrum Utilization and E911 Compliance

Author(s) Mr. Balaji Chode
Country United States
Abstract The Radio Frequency Data Sheets (RFDS) Automation Platform is a cloud-native, AI-powered system designed to automate the generation of plumbing diagrams and streamline RF engineering documentation in the telecommunications industry [1]. Traditionally, creating Radio Frequency Data Sheets (RFDS) and their corresponding site schematics has required hours of manual effort from RF engineers, increasing the risk of error and inconsistency—especially in large-scale, multivendor deployments.
This platform leverages advanced parsing algorithms, a rule-based logic engine, and machine learning to extract data from structured and semi-structured inputs (e.g., Excel, CSV, XML), automatically apply vendor-specific configurations, and render standards aligned plumbing diagrams in real time. The system generates thousands of diagrams within minutes and continuously improves through AI learning from past errors, delivering optimal antenna–radio frequency configurations that maximize spectrum efficiency and ensure E911 compliance.
First deployed for ATT, this solution has reduced RF engineers’ manual workload by 60–70 percent, improved documentation accuracy, and significantly accelerated the pace of wireless network rollouts. Designed for extensibility, the platform supports integration with engineering toolchains via APIs and is adaptable to telecom carriers worldwide.
Keywords AI-powered RFDS automation platform, Telecom plumbing diagram generator, Radio frequency design automation, Cloud-native RF documentation system, RF engineering workflow automation, Auto-generated RF plumbing diagrams, Machine learning in telecom network design, Multivendor RF configuration tools, Intelligent telecom site documentation, E911-compliant network diagrams, Real-time RFDS parsing engine, Telecom infrastructure deployment automation, RF site diagram rendering software, Telecom AI/ML design optimization, 5G and LTE RF configuration automation, Field-ready RF engineering diagrams, AT&T network automation case study, Reinforcement learning for RF design, Digital twin for telecom infrastructure, Scalable RF design documentation platform
Field Computer Applications
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-06
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.47077
Short DOI https://doi.org/g9pzvp
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