  Deep neural networks (DNNs) designed for computer vision and natural language
processing tasks cannot be directly applied to the radio frequency (RF)
datasets. To address this challenge, we propose to convert the raw RF data to
data types that are suitable for off-the-shelf DNNs by introducing a
convolutional transform technique. In addition, we propose a simple 5-layer
convolutional neural network architecture (CONV-5) that can operate with raw RF
I/Q data without any transformation. Further, we put forward an RF dataset,
referred to as RF1024, to facilitate future RF research. RF1024 consists of 8
different RF modulation classes with each class having 1000/200 training/test
samples. Each sample of the RF1024 dataset contains 1024 complex I/Q values.
Lastly, the experiments are performed on the RadioML2016 and RF1024 datasets to
demonstrate the improved classification performance.
