The world today is experiencing an enormous increase in data traffic,coupled with demand for greater quality of experience(QoE)and performance.Increasing mobile traffic leads to congestion of backhaul networks.One pro...The world today is experiencing an enormous increase in data traffic,coupled with demand for greater quality of experience(QoE)and performance.Increasing mobile traffic leads to congestion of backhaul networks.One promising solution to this problem is the mo bile edge network(MEN)and consequently mobile edge caching.In this paper,a survey of mobile edge caching using machine learning is explored.Even though a lot of work and sur veys have been conducted on mobile edge caching,our efforts in this paper are rather focused on the survey of machine learning based mobile edge caching.Issues affecting edge caching,such as caching entities,caching policies and caching algorithms,are discussed.The ma chine learning algorithms applied to edge caching are reviewed followed by a discussion on the challenges and future works in this field.This survey shows that edge caching can reduce delay and subsequently the backhaul traffic of the network;most caching is conducted at the small base stations(SBSs)and caching at unmanned aerial vehicles(UAVs)is recently used to accommodate mobile users who dissociate from SBSs.This survey also demonstrates that machine learning approach is the state of the art and reinforcement learning is predominant.展开更多
The emerging unmanned aerial vehicle(UAV)technology and its applications have become part of the massive Internet of Things(mIoT)ecosystem for future cellular networks.Internet of things(IoT)devices have limited compu...The emerging unmanned aerial vehicle(UAV)technology and its applications have become part of the massive Internet of Things(mIoT)ecosystem for future cellular networks.Internet of things(IoT)devices have limited computation capacity and battery life and the cloud is not suitable for offloading IoT tasks due to the distance,latency and high energy consumption.Mobile edge computing(MEC)and fog radio access network(F-RAN)together with machine learning algorithms are an emerging approach to solving complex network problems as described above.In this paper,we suggest a new orientation with UAV enabled F-RAN architecture.This architecture adopts the decentralized deep reinforcement learning(DRL)algorithm for edge IoT devices which makes independent decisions to perform computation offloading,resource allocation,and association in the aerial to ground(A2G)network.Addi tionally,we summarized the works on machine learning approaches for UAV networks and MEC networks,which are related to the suggested architecture and discussed some technical challenges in the smart UAV-IoT,F-RAN 5G and Beyond 5G(6G).展开更多
文摘The world today is experiencing an enormous increase in data traffic,coupled with demand for greater quality of experience(QoE)and performance.Increasing mobile traffic leads to congestion of backhaul networks.One promising solution to this problem is the mo bile edge network(MEN)and consequently mobile edge caching.In this paper,a survey of mobile edge caching using machine learning is explored.Even though a lot of work and sur veys have been conducted on mobile edge caching,our efforts in this paper are rather focused on the survey of machine learning based mobile edge caching.Issues affecting edge caching,such as caching entities,caching policies and caching algorithms,are discussed.The ma chine learning algorithms applied to edge caching are reviewed followed by a discussion on the challenges and future works in this field.This survey shows that edge caching can reduce delay and subsequently the backhaul traffic of the network;most caching is conducted at the small base stations(SBSs)and caching at unmanned aerial vehicles(UAVs)is recently used to accommodate mobile users who dissociate from SBSs.This survey also demonstrates that machine learning approach is the state of the art and reinforcement learning is predominant.
文摘The emerging unmanned aerial vehicle(UAV)technology and its applications have become part of the massive Internet of Things(mIoT)ecosystem for future cellular networks.Internet of things(IoT)devices have limited computation capacity and battery life and the cloud is not suitable for offloading IoT tasks due to the distance,latency and high energy consumption.Mobile edge computing(MEC)and fog radio access network(F-RAN)together with machine learning algorithms are an emerging approach to solving complex network problems as described above.In this paper,we suggest a new orientation with UAV enabled F-RAN architecture.This architecture adopts the decentralized deep reinforcement learning(DRL)algorithm for edge IoT devices which makes independent decisions to perform computation offloading,resource allocation,and association in the aerial to ground(A2G)network.Addi tionally,we summarized the works on machine learning approaches for UAV networks and MEC networks,which are related to the suggested architecture and discussed some technical challenges in the smart UAV-IoT,F-RAN 5G and Beyond 5G(6G).
