AI-Driven Cloud Networking Optimizations for Seamless LTE Connectivity

Abstract

Background: With the exponential development of mobile data traffic and the prevalence of cloud services, optimising cloud networking has become critical for ensuring continuous connectivity. Long-Term Evolution (LTE) network integration with cloud services poses unique hurdles owing to LTE's unpredictable capacity and latency concerns. Recent advances in artificial intelligence (AI) provide promising answers to these issues, allowing for more effective cloud networking via LTE. Objective: The article intends to investigate AI-driven optimisation strategies for improving cloud network performance over LTE networks, emphasising providing continuous connectivity and lowering latency and bandwidth fluctuation. Methods: To assess and anticipate network conditions, we employ a multimethod research strategy that includes machine learning algorithms and deep learning models, allowing us to adjust cloud networking settings dynamically. Simulation tools mimic LTE network conditions and evaluate the effectiveness of AI-powered optimisation methods in various scenarios. Results: Initial results show that AI-driven optimizations significantly outperform LTE in cloud networking, with up to a 40% decrease in latency and a 25% gain in bandwidth efficiency. Deep learning algorithms for predictive analysis and real-time modifications have successfully managed network unpredictability. Conclusion: AI-driven optimizations are a realistic approach for bridging the LTE gap in cloud networking, providing considerable increases in connection, bandwidth, and latency. The article highlights AI's potential to change LTE-enabled cloud services, opening the path for more resilient and efficient cloud networking solutions.

Keywords

• ai
• lte
• cloud networking
• optimization
• seamless connectivity
• bandwidth efficiency
• latency reduction
• machine learning (ml)
• deep learning (dl)
• predictive analysis.