International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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AI in Network Infrastructure: Transforming Telecommunications with Intelligent Systems

Author(s) Ramanathan Sekkappan
Country United States
Abstract AI technologies are drastically changing the backbone of telecommunications networks and bringing
unheard-of security, network management, and optimization capabilities. From dynamic resource
allocation to predictive maintenance and intelligent traffic management, this article shows how artificial
intelligence and machine learning technologies are being used to solve important problems in modern
telecommunications. By means of a thorough investigation of AI-driven solutions, this article shows that
technologies are offering real-time network optimization, automatic threat identification, and improved
service dependability while drastically lowering operating costs. While addressing important technical
issues and operational considerations, the conversation covers pragmatic solutions across several network
domains, including 5G infrastructure, edge computing, and autonomous networking systems. Examining
real-world case studies and developing trends helps this article show how artificial intelligence is not only
transforming present network operations but also opening the path for future telecommunications
innovations, enabling more resilient, efficient, and intelligent network infrastructure that can adapt to
changing technological demand and user expectations.
Keywords Keywords: Network Automation, Artificial Intelligence in Telecommunications, Intelligent Traffic Management, Predictive Network Maintenance, AI-powered Network Security
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-05
Cite This AI in Network Infrastructure: Transforming Telecommunications with Intelligent Systems - Ramanathan Sekkappan - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.32482
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.32482
Short DOI https://doi.org/g8t3jz
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