With the development of new generation of information and communication technology,the Internet of Vehicles(IoV)/Vehicle-to-Everything(V2X),which realizes the connection between vehicle and X(i.e.,vehicles,pedestrians...With the development of new generation of information and communication technology,the Internet of Vehicles(IoV)/Vehicle-to-Everything(V2X),which realizes the connection between vehicle and X(i.e.,vehicles,pedestrians,infrastructures,clouds,etc.),is playing an increasingly important role in improving traffic operation efficiency and driving safety as well as enhancing the intelligence level of social traffic services.展开更多
To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services,the mobile edge computing(MEC)has been drawing increased attention from both industry and academia re...To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services,the mobile edge computing(MEC)has been drawing increased attention from both industry and academia recently.This paper focuses on mobile users’computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making policy.Since wireless network states and computing requests have stochastic properties and the environment’s dynamics are unknown,we use the modelfree reinforcement learning(RL)framework to formulate and tackle the computation offloading problem.Each mobile user learns through interactions with the environment and the estimate of its performance in the form of value function,then it chooses the overhead-aware optimal computation offloading action(local computing or edge computing)based on its state.The state spaces are high-dimensional in our work and value function is unrealistic to estimate.Consequently,we use deep reinforcement learning algorithm,which combines RL method Q-learning with the deep neural network(DNN)to approximate the value functions for complicated control applications,and the optimal policy will be obtained when the value function reaches convergence.Simulation results showed that the effectiveness of the proposed method in comparison with baseline methods in terms of total overheads of all mobile users.展开更多
文摘With the development of new generation of information and communication technology,the Internet of Vehicles(IoV)/Vehicle-to-Everything(V2X),which realizes the connection between vehicle and X(i.e.,vehicles,pedestrians,infrastructures,clouds,etc.),is playing an increasingly important role in improving traffic operation efficiency and driving safety as well as enhancing the intelligence level of social traffic services.
基金This work was supported by the National Natural Science Foundation of China(61571059 and 61871058).
文摘To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services,the mobile edge computing(MEC)has been drawing increased attention from both industry and academia recently.This paper focuses on mobile users’computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making policy.Since wireless network states and computing requests have stochastic properties and the environment’s dynamics are unknown,we use the modelfree reinforcement learning(RL)framework to formulate and tackle the computation offloading problem.Each mobile user learns through interactions with the environment and the estimate of its performance in the form of value function,then it chooses the overhead-aware optimal computation offloading action(local computing or edge computing)based on its state.The state spaces are high-dimensional in our work and value function is unrealistic to estimate.Consequently,we use deep reinforcement learning algorithm,which combines RL method Q-learning with the deep neural network(DNN)to approximate the value functions for complicated control applications,and the optimal policy will be obtained when the value function reaches convergence.Simulation results showed that the effectiveness of the proposed method in comparison with baseline methods in terms of total overheads of all mobile users.
