Towards Green Future Cellular Network In Nigeria: Airtificial Intelligence Approach

Abstract

The information sector prioritizes green communications to cut energy costs and fossil fuel usage. The surge in terminals and network equipment with 5G and upcoming 6G drives up energy needs, emphasizing the need for greener solutions. However, 6G's advanced specifications like intelligence and security pose challenges to energy efficiency. Dynamic energy harvesting in 6G adds complexity to network management. AI is crucial for automation and reducing human intervention. This study explores AI's role in enhancing energy efficiency, network management, and energy harvesting control. It discusses primary factors driving green communications and reviews AI-based approaches. It focuses on AI's integration with Deep Learning (DL), Machine Learning (ML), Heuristic algorithms, Reinforcement Learning (RL), and Deep Reinforcement Learning (DRL) beyond 5G. It highlights Heuristic algorithms and ML for flexibility and efficiency in green cellular network communication (CNC). RL and DRL optimize resource allocation and power control but face challenges in training due to metric complexities.