activity
20182021
most citedData Augmentation for Deep Learning-based Radio Modulation Classification

13 citations · 15 across the 2 of their papers we have counts for

collaborators

6 papers

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.NI20212 cited

Stable Online Computation Offloading via Lyapunov-guided Deep Reinforcement Learning

Suzhi Bi, Liang Huang, Hui Wang +1

In this paper, we consider a multi-user mobile-edge computing (MEC) network with time-varying wireless channels and stochastic user task data arrivals in sequential time frames. In…

cs.LG2020

Visualizing Deep Learning-based Radio Modulation Classifier

Liang Huang, You Zhang, Weijian Pan +3

Deep learning has recently been successfully applied in automatic modulation classification by extracting and classifying radio features in an end-to-end way. However, deep learnin…

eess.SP201913 cited

Data Augmentation for Deep Learning-based Radio Modulation Classification

Liang Huang, Weijian Pan, You Zhang +3

Deep learning has recently been applied to automatically classify the modulation categories of received radio signals without manual experience. However, training deep learning mod…

cs.NI2019

Joint Optimization of Service Caching Placement and Computation Offloading in Mobile Edge Computing Systems

Suzhi Bi, Liang Huang, Ying-Jun Angela Zhang

In mobile edge computing (MEC) systems, edge service caching refers to pre-storing the necessary programs for executing computation tasks at MEC servers. At resource-constrained ed…

cs.NI2018

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

Liang Huang, Suzhi Bi, Ying-Jun Angela Zhang

Wireless powered mobile-edge computing (MEC) has recently emerged as a promising paradigm to enhance the data processing capability of low-power networks, such as wireless sensor n…