AI-Driven Cloud Networking Optimizations for Seamless LTE Connectivity

• Main Authors: AbdelRahman, Akram AbdelBaqi, Meftin, Noor Kadhim, Alak, Eman Khalil, Jawad, Haider Mahmood, Hatim, Qais Y., Khlaponin, Yurii, Al-Jawher, Waleed A. Mahmoud
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial intelligence; Bandwidth; bandwidth efficiency; cloud networking; deep learning (dl); Ethics; latency reduction; Long Term Evolution; lte; machine learning (ml); Optimization; predictive analysis; seamless connectivity; Spectral efficiency; Technological innovation; Telecommunication network reliability; Telecommunications; User experience
• ISSN: 2305-7254; 2305-7254; 2343-0737
• DOI: 10.23919/FRUCT64283.2024.10749959

Background: Relying on the growth of mobile data traffic and cloud service, it is essential to enhance network performance in the cloud domain to guarantee uninterrupted accessibility. Integrating long-term evolution (LTE) networks with cloud services presents its challenges, prompted by LTE's flighty bandwidth and concerns about latency. Some recent advancements in artificial intelligence (AI) seemingly can answer these problems, giving more efficient cloud networking over LTE.Objective: The article aims to analyze AI-based optimization techniques for the enhancement of cloud network performance in the realm of LTE, focusing on stable connectivity provision and reducing latency and bandwidth variation.Methods: The study proposes a multi-method approach to the prediction and evaluation of network quality based on machine learning algorithms, utilizing deep learning models for cloud networking configuration changes. These simulation tools can recreate LTE network conditions and assess the efficiency of AI-based optimization methods in different scenarios.Results: The preliminary data shows AI-driven optimizations at cloud networking outperform by almost 40% in reducing latency, and another 25% improvement should be achieved over LTE. By employing deep learning algorithms in predictive analysis and real-time changes, the sought-after goal of managing network uncertainty has been met.Conclusion: AI-based optimizations are a practical way to bridge the LTE gap in cloud networking, achieving significant gains across connectivity, bandwidth, and latency. This article showcases how AI can transform LTE cloud services, initiating a direction for stronger and smarter cloud networking.