With the rapid development of vehicular networks, vehicle-to-everything (V2X) communications have huge number of tasks to be calculated, which brings challenges to the scarce network resources. Cloud servers can alleviate the terrible situation regarding the lack of computing abilities of vehicular user equipment (VUE), but the limited resources, t...
 A HYBRID CLOUD AND EDGE CONTROL STRATEGY FOR DEMAND RESPONSES USING DEEP REINFORCEMENT LEARNING AND TRANSFER LEARNING...
 A novel reconfigurable intelligent surfaces (RISs)- based transmission framework is proposed for downlink nonorthogonal multiple access (NOMA) networks. We propose a quality-of-service (QoS)-based clustering scheme to improve the resource efficiency and formulate a sum rate maximization problem by jointly optimizing the phase shift of the RIS and t...
 Offloading computation-intensive tasks (e.g., blockchain consensus processes and data processing tasks) to the edge/cloud is a promising solution for blockchain-empowered mobile edge computing. However, the traditional offloading approaches (e.g., auction-based and game-theory approaches) fail to adjust the policy according to the changing environ...
 We consider the downlink of an unmanned aerial vehicle (UAV) assisted cellular network consisting of multiple cooperative UAVs, whose operations are coordinated by a central ground controller using wireless fronthaul links, to serve multiple ground user equipments (UEs). A problem of jointly designing UAVs’ positions, transmit beamforming, as well ...
 This paper investigates a futuristic spectrum sharing paradigm for heterogeneous wireless networks with imperfect channels. In the heterogeneous networks, multiple wireless networks adopt different medium access control (MAC) protocols to share a common wireless spectrum and each network is unaware of the MACs of others. This paper aims to design a...
 In this paper, we propose DRL Track, a framework for target tracking with a collaborative deep reinforcement learning (C-DRL) in Edge-IoT with the aim to obtain high quality of tracking (QoT) and resource-efficient performance. In DRLTrack, a huge number of IoT devices are employed to collect data about a mobile target. One or two edge devices coor...
 We develop a novel framework for efficiently and effectively discovering crowdsourced services that move in close proximity to a user over a period of time. We introduce a moving crowdsourced service model which is modelled as a moving region. We propose a deep reinforcement learning-based composition approach to select and compose moving IoT servi...
 Non-orthogonal multiple access (NOMA) exploits the potential of power domain to enhance the connectivity for Internet of Things (IoT). Due to time-varying communication channels, dynamic user clustering is a promising method to increase the throughput of NOMA-IoT networks. This paper develops an intelligent resource allocation scheme for uplink NOM...
 Performing deep neural network (DNN) inference in real time requires excessive network resources, which poses a great challenge to the resource-limited industrial Internet of things (IIoT) networks. To address the challenge, in this paper, we introduce an end-edge-cloud orchestration architecture, in which the inference task assignment and DNN mode...

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