LightAMC: Lightweight Automatic Modulation Classification via Deep Learning and Compressive Sensing
preprintposted on 27.01.2020, 21:02 authored by Yu Wang, Jie Yang, Miao Liu, G G
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.
Email Address of Submitting Authorguiguan@njupt.edu.cn
Submitting Author's InstitutionNanjing University of Posts and Telecommunications
Submitting Author's CountryChina
Read the peer-reviewed publication
in IEEE Transactions on Vehicular Technology