V2E Association and Resource Allocation via Deep Reinforcement Learning in MEC-based HetVNets

IEEE Vehicular Technology Conference, pp. 1–7

Abstract

Mobile edge computing (MEC) based heterogeneous vehicular networks (HetVNets) can interwork between IEEE 802.11p-based vehicular networks and cellular-assisted vehicular networks for vehicle-to-everything (V2X) communications. It is an attractive technology for supporting low latency applications for vehicles. However, in the practical system without precise prior knowledge of the dynamic wireless environment, solving joint vehicle-to-edge (V2E) association and resource allocation problem is a challenge. In this paper, first, we use stochastic geometry to model a real scenario. Specifically, the intersection area is modeled as two perpendicular streets, the spatial distribution of vehicle nodes on each street is modeled as an independent one-dimensional (1D) homogeneous Poisson Point Process (PPP), the spatial distribution of different types of edge nodes is modeled as different and independent PPPs. We consider the service time during which a vehicle node with different types of network interfaces gets a service from an edge node. Then, a deep reinforcement learning (DRL) based method is proposed to solve the uplink-and-downlink V2E association problem minimizing the service time while ensuring the computation resource allocation constraints. Simulation results illustrate the better performance of our solution than that of other traditional methods.

Authors 5

1. Yuying Wu

   Southeast University

   Affiliation as printed

   Southeast University,National Mobile Communications Research Laboratory,Nanjing,Jiangsu,China,210096

2. Zhengming J. Zhang

   Southeast University

   Affiliation as printed

   Southeast University,National Mobile Communications Research Laboratory,Nanjing,Jiangsu,China,210096

3. Paul Zheng Aachen Chair of Information Theory and Data Analytics

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Information Theory and Data Analytics,Aachen,Germany,D-52074

4. Yulin Hu Aachen Chair of Information Theory and Data Analytics

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Information Theory and Data Analytics,Aachen,Germany,D-52074

5. Anke Schmeink Aachen Chair of Information Theory and Data Analytics

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Information Theory and Data Analytics,Aachen,Germany,D-52074