当前位置: X-MOL 学术IEEE Veh. Technol. Mag. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Artificial Intelligence-Driven Fog Radio Access Networks: Integrating Decision Making Considering Different Time Granularities
IEEE Vehicular Technology Magazine ( IF 5.8 ) Pub Date : 2021-06-08 , DOI: 10.1109/mvt.2021.3078417
Jonathan M. DeAlmeida , Luiz DaSilva , Cristiano Bonato Bonato Both , Celia G. Ralha , Marcelo A. Marotta

Cloud- and fog-based networks are promising paradigms for vehicular and mobile networks. Fog radio access networks (F-RANs), in particular, can offload computation tasks to the network edge (i.e., in the fog) and reduce latency. Artificial intelligence (AI) techniques can be used in F-RANs to achieve, for example, enhanced energy efficiency and increased throughput. Nonetheless, the appropriate technique selection must consider the different time granularities at which decision making occurs in F-RANs.

中文翻译:


人工智能驱动的雾无线接入网络:考虑不同时间粒度的综合决策



基于云和雾的网络是车辆和移动网络的有前途的范例。特别是,雾无线接入网络(F-RAN)可以将计算任务卸载到网络边缘(即在雾中)并减少延迟。人工智能 (AI) 技术可用于 F-RAN,以实现提高能源效率和增加吞吐量等目的。尽管如此,适当的技术选择必须考虑 F-RAN 中决策发生的不同时间粒度。
更新日期:2021-06-08
down
wechat
bug