LightAMC: Lightweight Automatic Modulation Classification via Deep
Learning and Compressive Sensing
Abstract
Automatic modulation classification (AMC) is an promising technology for
non-cooperative communication systems in both military and civilian
scenarios. Recently, deep learning (DL) based AMC methods have been
proposed with outstanding performances. However, both high computing
cost and large model sizes are the biggest hinders for deployment of the
conventional DL based methods, particularly in the application of
internet-of-things (IoT) networks and unmanned aerial vehicle
(UAV)-aided systems. In this correspondence, a novel DL based
lightweight AMC (LightAMC) method is proposed with smaller model sizes
and faster computational speed. We first introduce a scaling factor for
each neuron in convolutional neural network (CNN) and enforce scaling
factors sparsity via compressive sensing. It can give an assist to
screen out redundant neurons and then these neurons are pruned.
Experimental results show that the proposed LightAMC method can
effectively reduce model sizes and accelerate computation with the
slight performance loss.