In this paper, we review a variety of deep learning algorithms and models for modulation
recognition and classification of wireless communication signals. Specifically, deep learning …

With the continuous development of communication technology, the wireless communication
environment becomes more and more complex with various intentional and unintentional …

Automatic modulation classification (AMC) is an important stage in intelligent wireless
communication receivers. It is a necessary process after signal detection, and before …

This paper proposes a deep learning based intelligent method for detecting and identifying
radio signals considering two applications: first, cognitive radar for identifying micro …

In this work, a novel model for recognition of handwritten Kannada characters using transfer
learning from Devanagari handwritten recognition system is presented. The objective is to …

In a cognitive radio network (CRN), spectrum sensing is an important prerequisite for
improving the utilization of spectrum resources. In this paper, we propose a novel spectrum …

In this paper, we demonstrate a software defined radio (SDR) prototype with the capability of
signal classification in real-time. Detection, classification, and characterization of wireless …

Recently, Deep Neural Networks (DNNs) have been used extensively for Automatic
Modulation Classification (AMC). Due to their high complexity, DNNs are typically unsuitable …

Machine learning offers performance improvements and novel functionality, but its life cycle
performance is understudied. In areas like cognitive communications, where systems are …

Recently, combinatorial interaction testing (CIT) has been applied to machine learning.
Recent results demonstrate that combinatorial coverage metrics can correlate with …