In this work, we study learning-aided adaptive control of optical beam alignment to maintain
a seamless connection with high communication performance in a point-to-point (P2P) …

In this work, an intelligent mobile edge computing (MEC) network is studied for Internet of
Things (IoT) in the presence of eavesdropping environments, where there are multiple users …

With the development of the Internet of Everything (IoE), huge amount of data is being
generated at network edge and needs to be processed in real time, such as edge video …

Mobile edge computing, as a formidable paradigm, sinks the computing and communication
resources from the centralized cloud to the edge of networks near to users, which meets the …

In recent years, numerous Deep Reinforcement Learning (DRL) neural network models
have been proposed to optimize computational offloading and resource allocation in Mobile …

Mobile edge computing (MEC) has been proposed as a promising paradigm to provide
mobile devices with both satisfactory computing capacity and task latency. One key issue in …

User activity analysis (UAA) is a promising technology for network management and network
security via network traffic. Recently, deep learning (DL) has been applied into network …

In the mobile edge computing (MEC) architecture, base stations with computational
capabilities are subject to service coverage limitations, and the mobility of devices leads to …

As users have higher and higher requirements for the quality of experience, traditional cloud
computing is gradually unable to meet the needs of user equipments. Hence mobile edge …

An effective task offloading strategy in the mobile edge computing enables terminals to
migrate their tasks to the edge server, accelerating the execution of terminal tasks. However …