Research Article
Evaluating Deep Neural Networks to Classify Modulated and Coded Radio Signals
@INPROCEEDINGS{10.1007/978-3-030-05490-8_17, author={Phui Cheong and Miguel Camelo and Steven Latr\^{e}}, title={Evaluating Deep Neural Networks to Classify Modulated and Coded Radio Signals}, proceedings={Cognitive Radio Oriented Wireless Networks. 13th EAI International Conference, CROWNCOM 2018, Ghent, Belgium, September 18--20, 2018, Proceedings}, proceedings_a={CROWNCOM}, year={2019}, month={1}, keywords={Cognitive Radio Dynamic Spectrum Access Deep Neural Network Convolutional Neural Network Modulation and Coding Scheme}, doi={10.1007/978-3-030-05490-8_17} }- Phui Cheong
Miguel Camelo
Steven Latré
Year: 2019
Evaluating Deep Neural Networks to Classify Modulated and Coded Radio Signals
CROWNCOM
Springer
DOI: 10.1007/978-3-030-05490-8_17
Abstract
Cognitive Radio (CR) systems allow optimizing the use of the shared radio spectrum and enhancing the coexistence among different technologies by efficiently changing certain operating parameters of the radios such as transmit-power, carrier frequency, and modulation and coding scheme in real-time. Dynamic Spectrum Access (DSA), which allows radios to dynamically access and use the unused spectrum, is one of the tasks that are fundamental for a better use of the spectrum. In this paper, we extend the previous work on Automatic Modulation Classification (AMC) by using Deep Neural Network (DNNs) and evaluate the performance of these architectures on signals that are not only modulated but are also encoded. We call this the Automatic Modulation and Coding Scheme Classification problem, or . In this problem, radio signals are classified according to the Modulation and Coding Scheme (MCS) used during their transmission. Evaluations on a data set of 802.11 radio signals, transmitted with different MCS and Signal to Noise Ratio (SNR), provide important results on the impact of some DNN hyperparameters, e.g. number of layers, batch size, etc., in the classification accuracy.