Abstract:

This paper explores the integration of Big Data analytics and Multi-RAT (Radio Access Technologies) in the development of energy-saving systems for modern telecommunications networks. As the demand for high-speed mobile data and connectivity increases, network operators are confronted with the dual challenge of reducing energy consumption while maintaining or improving service quality. By leveraging Big Data analytics, we propose a cooperative energy-saving framework that dynamically selects and coordinates multiple RATs (e.g., 4G, 5G, Wi-Fi) to optimize power usage based on real-time traffic demands, network load, and environmental conditions. Our approach incorporates machine learning techniques to predict traffic patterns and optimize energy efficiency at both the network and device levels. Experimental results demonstrate the potential of such systems to achieve significan In this paper, we examine the integration of big data analytics with 5G networks and how the synergy between the two technologies is poised to reshape industries such as healthcare, manufacturing, transportation, and urban management. We explore the driving forces behind this integration, highlight key trends, and discuss the challenges that must be overcome to fully realize the potential of this convergence.

Description:

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