Deep Reinforcement Learning Based Resource Management for Multi-Access Edge Computing in Vehicular Networks

• Published in: IEEE transactions on network science and engineering Vol. 7; no. 4; pp. 2416 - 2428
• Main Authors: Peng, Haixia, Shen, Xuemin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; DDPG; Deep learning; deep reinforcement learning; Edge computing; Mobile computing; multi-access edge computing; multi-dimensional resource management; Optimization; Problem solving; Quality of service; Radio spectrum management; Resource allocation; Resource management; Servers; Task analysis; Vehicles; Vehicular networks; Wireless fidelity

In this paper, we study joint allocation of the spectrum, computing, and storing resources in a multi-access edge computing (MEC)-based vehicular network. To support different vehicular applications, we consider two typical MEC architectures and formulate multi-dimensional resource optimization prob...