ARTIFICIAL INTELLIGENCE-DRIVEN FOG RADIO ACCESS NETWORKS Integrating Decision Making Considering Different Time Granularities

被引：2

作者：

DeAlmeida, Jonathan M. ^{[1
,2
]}

DaSilva, Luiz A. ^{[3
]}

Both, Cristiano B. ^{[4
]}

Ralha, Celia G. ^{[5
]}

Marotta, Marcelo A. ^{[1
]}

机构：

[1] Univ Brasilia, Comp Sci, BR-70910900 Brasilia, DF, Brazil

[2] Virginia Tech, Cybersecur, Arlington, VA 22203 USA

[3] IEEE, Commun Soc, Piscataway, NJ USA

[4] Univ Vale Rio dos Sinos, Appl Comp Grad Program, BR-93022000 Sao Leopoldo, RS, Brazil

[5] Univ Brasilia, Informat Grad Program, BR-70910900 Brasilia, DF, Brazil

来源：

IEEE VEHICULAR TECHNOLOGY MAGAZINE | 2021年 / 16卷 / 03期

关键词：

ALLOCATION;

D O I：

10.1109/MVT.2021.3078417

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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. © 2005-2012 IEEE.

引用

页码：137 / 148

页数：12

共 17 条

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