The power requirements posed by the fifth-generation and beyond cellular
networks are an important constraint in network deployment and require
energy-efficient solutions. In this work, we propose a novel user load transfer
approach using airborne base stations (BS), mounted on drones, for reliable and
secure power redistribution across the micro-grid network comprising green
small cell BSs. Depending on the user density and the availability of an aerial
BS, the energy requirement of a cell with an energy deficit is accommodated by
migrating the aerial BS from a high-energy to a low-energy cell. The proposed
hybrid drone-based framework integrates long short-term memory with unique cost
functions using an evolutionary neural network for drones and BSs, and
efficiently manages energy and load redistribution. The proposed algorithm
reduces power outages at BSs and maintains consistent throughput stability,
thereby demonstrating its capability to boost the reliability and robustness of
wireless communication systems.

通过使用空中基站（由无人机搭载）进行可靠和安全的电力再分配，减少基站的停电，并保持一致的吞吐稳定性，从而提高无线通信系统的可靠性和稳健性。

DroneOptiNet: 5G 及更高的太阳能小型蜂窝网络优化无人机载荷转移机制的框架

DroneOptiNet: A Framework for Optimal Drone-based Load Redistribution  Mechanism for 5G and Beyond Solar Small Cell Networks

Artificial intelligence is bringing ever new functionalities to the realm of
mobile devices that are now considered essential (e.g., camera and voice
assistants, recommender systems). Yet, operating artificial intelligence takes
up a substantial amount of energy. However, artificial intelligence is also
being used to enable more energy-efficient solutions for mobile systems. Hence,
artificial intelligence has two faces in that regard, it is both a key enabler
of desired (efficient) mobile functionalities and a major power draw on these
devices, playing a part in both the solution and the problem. In this paper, we
present a review of the literature of the past decade on the usage of
artificial intelligence within the realm of green mobile computing. From the
analysis of 34 papers, we highlight the emerging patterns and map the field
into 13 main topics that are summarized in details.
Our results showcase that the field is slowly increasing in the past years,
more specifically, since 2019. Regarding the double impact AI has on the mobile
energy consumption, the energy consumption of AI-based mobile systems is
under-studied in comparison to the usage of AI for energy-efficient mobile
computing, and we argue for more exploratory studies in that direction. We
observe that although most studies are framed as solution papers (94%), the
large majority do not make those solutions publicly available to the community.
Moreover, we also show that most contributions are purely academic (28 out of
34 papers) and that we need to promote the involvement of the mobile software
industry in this field.

人工智能在移动设备领域带来了越来越多的新功能，但其操作也消耗了大量能源。本文回顾了过去十年在绿色移动计算领域中使用人工智能的文献，并总结了 13 个主要课题。研究结果表明，该领域近年来有所增长，但对于基于人工智能的移动系统能源消耗的研究相对较少，需要进一步探索。同时，研究还发现绝大多数文献只是学术贡献，需要促进移动软件行业的参与。