TY - JOUR
T1 - Deep Reinforcement Learning-Based Resource Management for Flexible Mobile Edge Computing
T2 - Architectures, Applications, and Research Issues
AU - Wang, Kezhi
AU - Wang, Liang
AU - Pan, Cunhua
AU - Ren, Hong
PY - 2022/6
Y1 - 2022/6
N2 - In this article, we introduce autonomous vehicleassisted mobile edge computing (AV-MEC), including unmanned ground vehicle (UGV)- and unmanned aerial vehicle (UAV)-assisted MEC, where the UAV/UGV can be deployed and carry the computing server to serve ground mobile devices (MDs). We first discuss applications and main research problems. Then, deep reinforcement learning (DRL)-based solutions are introduced, explored, and demonstrated. We also discuss challenges and future research directions for an AV-MEC system with DRL being applied to it.
AB - In this article, we introduce autonomous vehicleassisted mobile edge computing (AV-MEC), including unmanned ground vehicle (UGV)- and unmanned aerial vehicle (UAV)-assisted MEC, where the UAV/UGV can be deployed and carry the computing server to serve ground mobile devices (MDs). We first discuss applications and main research problems. Then, deep reinforcement learning (DRL)-based solutions are introduced, explored, and demonstrated. We also discuss challenges and future research directions for an AV-MEC system with DRL being applied to it.
KW - Computer architecture
KW - Q-learning
KW - Quality of service
KW - Resource management
KW - Servers
KW - Task analysis
KW - Trajectory
UR - http://www.scopus.com/inward/record.url?scp=85128586821&partnerID=8YFLogxK
U2 - 10.1109/mvt.2022.3156745
DO - 10.1109/mvt.2022.3156745
M3 - Article
AN - SCOPUS:85128586821
SN - 1556-6072
VL - 17
SP - 85
EP - 93
JO - IEEE Vehicular Technology Magazine
JF - IEEE Vehicular Technology Magazine
IS - 2
ER -