RF Signal Transformation and Classification using Deep Neural Networks
This work addresses the problem of RF signal classification for researchers and practitioners in wireless communications, but it is incremental as it builds on existing DNN methods with adaptations for RF data.
The paper tackled the challenge of applying deep neural networks to radio frequency (RF) data by proposing a convolutional transform technique to make raw RF data suitable for off-the-shelf DNNs and introducing a simple 5-layer CNN (CONV-5) that works directly with raw RF I/Q data, resulting in improved classification performance on datasets like RadioML2016 and RF1024.
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.