14 citations · 42 across the 6 of their papers we have counts for
6 papers
Deep-Learning-Based Classification of Digitally Modulated Signals
John A. Snoap
This dissertation presents several novel deep-learning (DL)-based approaches for classifying digitally modulated signals, one method of which involves the use of capsule networks (…
Novel Nonlinear Neural-Network Layers for High Performance and Generalization in Modulation-Recognition Applications
John A. Snoap, Dimitrie C. Popescu, Chad M. Spooner
The paper presents a novel type of capsule network (CAP) that uses custom-defined neural network (NN) layers for blind classification of digitally modulated signals using their in-…
Physical Layer Secret Key Agreement Using One-Bit Quantization and Low-Density Parity-Check Codes
John A. Snoap
Physical layer approaches for generating secret encryption keys for wireless systems using channel information have attracted increased interest from researchers in recent years. T…
On Deep Learning Classification of Digitally Modulated Signals Using Raw I/Q Data
John A. Snoap, Dimitrie C. Popescu, Chad M. Spooner
The paper considers the problem of deep-learning-based classification of digitally modulated signals using I/Q data and studies the generalization ability of a trained neural netwo…
Robust Classification of Digitally Modulated Signals Using Capsule Networks and Cyclic Cumulant Features
John A. Snoap, James A. Latshaw, Dimitrie C. Popescu +1
The paper studies the problem of robust classification of digitally modulated signals using capsule networks and cyclic cumulant (CC) features extracted by cyclostationary signal p…
Using Capsule Networks to Classify Digitally Modulated Signals with Raw I/Q Data
James A. Latshaw, Dimitrie C. Popescu, John A. Snoap +1
Machine learning has become a powerful tool for solving problems in various engineering and science areas, including the area of communication systems. This paper presents the use…