Comparison of CNN Architectures using RP Algorithm for Burst Signal Detection

Abstract

Recently, convolutional neural networks (CNNs) achieved remarkable success in various fields, especially computer vision and image processing. However, it is not known what type of CNN architecture is the best fit for the detection or classification of communication signals. In this work, we compare the three of CNN architecture in a burst signal detection task. The three CNN architectures are compared to their detection performance and computational complexity. The 9-layer CNN is shown to achieve a similar performance of 12-layer CNN on overall environments. The performance of the 7-layer CNN model is worse than that of the other two types of CNN architectures, except in terms of the computational complexity at low SNR.