activity
20182020
most citedFast Deep Learning for Automatic Modulation Classification

136 citations · 160 across the 4 of their papers we have counts for

collaborators

5 papers

eess.SP2020

Ensemble Wrapper Subsampling for Deep Modulation Classification

Sharan Ramjee, Shengtai Ju, Diyu Yang +3

Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms that rely on the output s…

stat.ML20191 cited

Histogram Transform Ensembles for Large-scale Regression

Hanyuan Hang, Zhouchen Lin, Xiaoyu Liu +1

We propose a novel algorithm for large-scale regression problems named histogram transform ensembles (HTE), composed of random rotations, stretchings, and translations. First of al…

stat.ML20191 cited

Best-scored Random Forest Classification

Hanyuan Hang, Xiaoyu Liu, Ingo Steinwart

We propose an algorithm named best-scored random forest for binary classification problems. The terminology "best-scored" means to select the one with the best empirical performanc…

eess.SP2019136 cited

Fast Deep Learning for Automatic Modulation Classification

Sharan Ramjee, Shengtai Ju, Diyu Yang +3

In this work, we investigate the feasibility and effectiveness of employing deep learning algorithms for automatic recognition of the modulation type of received wireless communica…

cs.LG201822 cited

Deep Neural Network Architectures for Modulation Classification

Xiaoyu Liu, Diyu Yang, Aly El Gamal

In this work, we investigate the value of employing deep learning for the task of wireless signal modulation recognition. Recently in [1], a framework has been introduced by genera…