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Xu Feng
Researcher at Zhejiang University of Technology
Publications - 5
Citations - 390
Xu Feng is an academic researcher from Zhejiang University of Technology. The author has contributed to research in topics: Mobile edge computing & Enhanced Data Rates for GSM Evolution. The author has an hindex of 5, co-authored 5 publications receiving 189 citations.
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Journal ArticleDOI
Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing
TL;DR: A Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC system is proposed and extensive numerical results show that the proposed DQN-based approach can achieve the near-optimal performance.
Journal ArticleDOI
Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks
TL;DR: This paper proposes a distributed deep learning-based offloading (DDLO) algorithm for MEC networks, where multiple parallel DNNs are used to generate offloading decisions, and adopts a shared replay memory to store newly generated offload decisions.
Journal ArticleDOI
Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks
TL;DR: This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server and investigates low-complexity computation offloading policies to guarantee quality of service of the MEC network and to minimize WDs’ energy consumption.
Book ChapterDOI
Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Mobile Edge Computing
TL;DR: Simulation results show that the proposed deep Q-learning-based algorithm can achieve near-optimal performance in joint task offloading and resource allocation problems.
Proceedings ArticleDOI
Deep RL-Based Time Scheduling and Power Allocation in EH Relay Communication Networks
TL;DR: A novel deep reinforcement learning framework consisting of multiple computation units to obtain the online time scheduling and power allocation based on the current causal knowledge of energy arrivals and channel fading at each time slot is explored.