Optimizing network performance and quality of service with AI-driven solutions for future telecommunications
1 Independent Researcher, London, United Kingdom.
2 Independent Telecommunications Engineer and Data Analyst, United Kingdom.
3 MTN, Lagos Nigeria.
4 Atlantic Technological University, Letterkenny, Ireland.
5 Independent Researcher, Lagos.
Review
International Journal of Frontiers in Engineering and Technology Research, 2024, 07(01), 073–092.
Article DOI: 10.53294/ijfetr.2024.7.1.0041
Publication history:
Received on 30 June 2024; revised on 05 August 2024; accepted on 08 August 2024
Abstract:
This paper investigates the application of AI-driven solutions to enhance network performance and Quality of Service (QoS) in future telecommunications. As the demand for higher bandwidth and seamless connectivity grows, traditional network management approaches face significant challenges in meeting these requirements. The study aims to address these challenges by leveraging artificial intelligence (AI) technologies, such as machine learning, neural networks, and predictive analytics.
The research methodology involves a comprehensive review of current literature, case studies, and experimental analysis of AI implementations in telecommunications. We explore various AI techniques for network optimization, including traffic prediction, anomaly detection, resource allocation, and automated network maintenance. Through these methods, the study identifies the key benefits and potential risks associated with AI-driven network management.
Key findings highlight the significant improvements in network efficiency, reduced latency, enhanced fault detection, and overall better QoS achieved through AI integration. AI-driven solutions enable dynamic and adaptive network configurations, ensuring optimal performance even under varying traffic conditions and unexpected disruptions. Additionally, the predictive capabilities of AI help in preemptively addressing network issues before they impact users, thus maintaining high QoS standards.
The paper concludes that AI-driven solutions present a promising avenue for the future of telecommunications, offering substantial enhancements in network performance and QoS. However, it also emphasizes the need for robust AI models, continuous monitoring, and ethical considerations to mitigate potential risks. The findings underscore the transformative potential of AI in shaping the next generation of telecommunications infrastructure, ensuring reliable and high-quality connectivity for users.
Keywords:
Artificial Intelligence (AI); Telecommunications; Network Performance; Quality of Service (QoS); Machine Learning; Predictive Maintenance; Data Privacy; Edge AI; 5G Networks; Autonomous Networks; Network Optimization; AI Integration; Network Scalability; Customer Satisfaction; Ethical AI; AI-Driven Solutions; Real-Time Data Analysis; Fault Detection; Middleware Solutions
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0