Optimizing Power Efficiency in Cellular Networks through Adaptive Power Allocation Algorithm

Cellular networks present a method to improve energy efficiency in 5G millimeter wave (mm Wave) networks by using a genetic algorithm for user and power allocation. Since mm Wave networks consume high power, it is important to allocate resources efficiently. The proposed approach optimizes the assignment of users to base stations and adjusts power levels to reduce energy consumption while maintaining good network performance. The genetic algorithm helps find the best solution by simulating natural selection. Simulation results show that this method improves energy efficiency compared to traditional approaches. To address these issues, we propose the Power Genetic (GA) algorithm, which optimizes energy and radio resource allocation in hybrid energy-supplied networks while considering power degradation. By decomposing the problem into subproblems and integrating adaptive network slicing for energy efficiency, GA achieves close-to-optimal performance, outperforming benchmarks, reducing energy consumption and operational costs, and maintaining user Quality of Service (QoS). Moreover, advancements in base station technology are discussed, offering insights into reducing future investment costs in cellular networks. This abstract summarizes the challenges faced by cellular networks in managing energy demands and presents innovative solutions to enhance sustainability, efficiency, and cost-effectiveness.