Edge-Efficient Deep Learning Models for Automatic Modulation Classification: A Performance Analysis

arXiv:2404.15343v1 Announce Type: cross
Abstract: The recent advancement in deep learning (DL) for automatic modulation classification (AMC) of wireless signals has encouraged numerous possible applications on resource-constrained edge devices. However, developing optimized DL models suitable for edge applications of wireless communications is yet to be studied in depth. In this work, we perform a thorough investigation of optimized convolutional neural networks (CNNs) developed for AMC using the three most commonly used model optimization techniques: a) pruning, b) quantization, and c) …

abstract advancement analysis applications arxiv classification communications cs.it cs.lg deep learning devices edge edge devices eess.sp however math.it performance performance analysis stat.ml type wireless wireless communications