An Integrated Framework for Energy-Efficient, Adaptive Wireless Network Management Using Hybrid Optimization and User-Centric Feedback Mechanisms

Abstract

This research proposes an innovative, adaptive framework for power management in wireless networks, emphasizing energy efficiency, user-centered optimization, and network sustainability. By integrating dynamic power management strategies with real-time user feedback, the framework minimizes energy consumption without compromising Quality of Service (QoS). Key components include hybrid optimization techniques, such as Dynamic Power Management (DPM) and Dynamic Voltage and Frequency Scaling (DVFS), which allow the system to respond dynamically to network demands. Novel metrics, including Waste Factor and percentile-based power efficiency measures, enable precise identification and reduction of energy waste across high-demand network areas, enhancing the framework’s adaptability. Machine learning and Big Data analytics further refine power management by adjusting network operations based on user behavior and demand patterns. Spatial and temporal demand shaping engages users to actively participate in the network's energy-saving initiatives. Scalable for next-generation networks, this approach supports sustainable 5G and 6G infrastructures, improving operational efficiency and environmental impact.