Deep Learning Based Sub-Nyquist Modulation Recognition

In this paper, we designed a Convolutional Neural Network (CNN) for Sub-Nyquist modulation recognition and compare the performance Long Short-Term Memory (LSTM) network and Convolutional Long Short-term Deep Neural Network (CLDNN) respectively. Unlike conventional modulation recognition task that operates with Nyquist sampled rate, the network architectures for Sub-Nyquist modulation recognition were specifically designed with a certain number of neurons, layers, and other hyperparameters to effectively extract key features from Sub-Nyquist sampled signals and process larger volumes of data. The simulation results demonstrate that the CNN network has the best recognition accuracy of 98.01% on the GBsense dataset, followed by the CLDNN of 96.81% and LSTM of 87.51% respectively

Automatic Modulation Recognition for Spectrum Sensing using Nonuniform Compressive Samples

the efficient acquisition of high-bandwidth (but sparse) signals via nonuniform low-rate sampling protocols. While most work in CS has focused on reconstructing the high-bandwidth signals from nonuniform low-rate samples, in this work, we consider the task of inferring the modulation of a communications signal directly in the compressed domain, without requiring signal reconstruction. We show that the N th power nonlinear features used for Automatic Modulation Recognition (AMR) are compressible in the Fourier domain, and hence, that AMR of M-ary Phase-Shift-Keying (MPSK) modulated signals is possible by applying the same nonlinear transformation on nonuniform compressive samples. We provide analytical support for the accurate approximation of AMR features from nonuniform samples, present practical rules for classification of modulation type using these samples, and validate our proposed rules on simulated data. A. Overview I