AI Models for Green Communications Towards 6G

Abstract

Green communications have always been a target for the information industry to alleviate energy overhead and reduce fossil fuel usage. In the current 5G and future 6G eras, there is no doubt that the volume of network infrastructure and the number of connected terminals will keep exponentially increasing, which results in the surging energy cost. It becomes growing important and urgent to drive the development of green communications. However, there is no doubt that 6G will have increasingly stringent and diversified requirements for Quality of Service (QoS), security, flexibility, and intelligence, all of which challenge the improvement of energy efficiency. Moreover, the dynamic energy harvesting process, which will be widely adopted in 6G, further complicates the power control and network management. To address these challenges and reduce human intervention, Artificial Intelligence (AI) has been extensively recognized and acknowledged as the only solution. Academia and industry have conducted extensive research to alleviate energy demand, improve energy efficiency, and manage energy harvesting in various communication scenarios. In this paper, we present main considerations for green communications and survey related research on AI-based green communications. We focus on how AI techniques are adopted to manage networks and improve energy efficiency towards the green era. We analyze how Machine Learning (ML) techniques including state-of-the-art Deep Learning (DL) can cooperate with conventional AI methods and mathematical models to reduce the algorithm complexity and improve the accuracy rate in 6G. Finally, we discuss the existing problems and envision the open research issues of AI models towards green 6G.