8 citations · 14 across the 2 of their papers we have counts for
5 papers
Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing
Liang Wang, Kezhi Wang, Cunhua Pan +3
An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the…
Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing
Liang Wang, Kezhi Wang, Cunhua Pan +3
In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UAVs) serve as equipment providing computation resource, and they ena…
Learn to Compress CSI and Allocate Resources in Vehicular Networks
Liang Wang, Hao Ye, Le Liang +1
Resource allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. In this paper, we develop a hybrid architecture consisting of centr…
Learn to Allocate Resources in Vehicular Networks
Liang Wang, Hao Ye, Le Liang +1
Resource allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is app…
RL-Based User Association and Resource Allocation for Multi-UAV enabled MEC
Liang Wang, Peiqiu Huang, Kezhi Wang +4
In this paper, multi-unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC), i.e., UAVE is studied, where several UAVs are deployed as flying MEC platform to provide com…