Task Offloading and Resource Allocation Based on DQN-DDPG for Aerial-Ground Cooperative Mobile Edge Computing
In areas with limited infrastructure or emergency rescue scenarios,UAV assisted mobile edge computing is considered an effective solution,which can handle computing intensive tasks and delay sensitive computing tasks of resource constrained intelligent devices.Consider-ing the ground base station and multi UAV assisted multi-user air ground cooperative mobile edge computing scenario,a joint optimization method of user association,subchannel allocation and edge server computing resource allocation is proposed to minimize the long-term aver-age delay of task unloading and resource allocation.Firstly,generate a drone movement plan based on the user's random tasks,and establish offloading calculation models and local calculation models based on different offloading decisions.Then,optimize the problem with the objec-tive of minimizing long-term average latency.Finally,combining DQN and DDPG,a task offloading and resource allocation algorithm(HD-CR)based on hybrid deep reinforcement learning DQN-DDPG is proposed to solve the problems between discrete and continuous variables and mixed decision problems.Simulation results show that the proposed algorithm performs better in reducing average latency compared to al-gorithms such as DDCR based on discrete decision-making.
万方数据
维普
