TY - JOUR
T1 - Edge Intelligence for Multi-Dimensional Resource Management in Aerial-Assisted Vehicular Networks
AU - Peng, Haixia
AU - Wu, Huaqing
AU - Shen, Xuemin Sherman
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/10/1
Y1 - 2021/10/1
N2 - A new architecture with drone-assisted multi-access edge computing (MEC) is proposed for vehicular networks to support computation-intensive and delay-sensitive applications and services. Artificial intelligence (AI)-based resource management schemes are developed such that terrestrial and aerial spectrum, computing, and storage resources can be cooperatively allocated for guaranteeing the quality of service requirements from different applications. A case study on the joint management of the spectrum and computing resources is presented to demonstrate the effectiveness of AI-based resource management schemes.
AB - A new architecture with drone-assisted multi-access edge computing (MEC) is proposed for vehicular networks to support computation-intensive and delay-sensitive applications and services. Artificial intelligence (AI)-based resource management schemes are developed such that terrestrial and aerial spectrum, computing, and storage resources can be cooperatively allocated for guaranteeing the quality of service requirements from different applications. A case study on the joint management of the spectrum and computing resources is presented to demonstrate the effectiveness of AI-based resource management schemes.
UR - https://www.scopus.com/pages/publications/85120003632
U2 - 10.1109/MWC.101.2100056
DO - 10.1109/MWC.101.2100056
M3 - 文章
AN - SCOPUS:85120003632
SN - 1536-1284
VL - 28
SP - 59
EP - 65
JO - IEEE Wireless Communications
JF - IEEE Wireless Communications
IS - 5
ER -