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20112025
most citedOffloading and Resource Allocation with General Task Graph in Mobile Edge Computing: A Deep Reinforcement Learning Approach

7 citations · 40 across the 27 of their papers we have counts for

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10 papers · 1 filter

cs.IT2021

Energy-Efficient Online Data Sensing and Processing in Wireless Powered Edge Computing Systems

Xian Li, Suzhi Bi, Yuan Zheng +1

This paper focuses on developing energy-efficient online data processing strategy of wireless powered MEC systems under stochastic fading channels. In particular, we consider a hyb…

cs.IT2021★ 1 cited

Online Cognitive Data Sensing and Processing Optimization in Energy-harvesting Edge Computing Systems

Xian Li, Suzhi Bi, Zhi Quan +1

Mobile edge computing (MEC) has recently become a prevailing technique to alleviate the intensive computation burden in Internet of Things (IoT) networks. However, the limited devi…

cs.IT2021

An Integrated Optimization-Learning Framework for Online Combinatorial Computation Offloading in MEC Networks

Xian Li, Liang Huang, Hui Wang +2

Mobile edge computing (MEC) is a promising paradigm to accommodate the increasingly prosperous delay-sensitive and computation-intensive applications in 5G systems. To achieve opti…

cs.IT2021

Joint Resource Allocation and Cache Placement for Location-Aware Multi-User Mobile Edge Computing

Jiechen Chen, Hong Xing, Xiaohui Lin +2

With the growing demand for latency-critical and computation-intensive Internet of Things (IoT) services, the IoT-oriented network architecture, mobile edge computing (MEC), has em…

cs.IT2021

Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis

Hong Xing, Osvaldo Simeone, Suzhi Bi

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over siloed data centers is motivating renewed interest in the collaborative training of a sh…

cs.IT2020★ 7 cited

Decentralized Federated Learning via SGD over Wireless D2D Networks

Hong Xing, Osvaldo Simeone, Suzhi Bi

Federated Learning (FL), an emerging paradigm for fast intelligent acquisition at the network edge, enables joint training of a machine learning model over distributed data sets an…