Deep Learning-Based Robust Automatic Modulation Classification for Cognitive Radio Networks

Seung-Hwan Kim,Dong-Seong Kim,Williams-Paul Nwadiugwu,Jae-Woo Kim

doi:10.1109/access.2021.3091421

Abstract

In this paper, a novel deep learning-based robust automatic modulation classification (AMC) method is proposed for cognitive radio networks. Generally, as network input of AMC convolutional neural networks (CNNs) images or complex signals are utilized in time domain or frequency domain. In terms of the image that contains RGB(Red, Green, Blue) levels the input size may be larger than the complex signal, which represents the increase of computational complexity. In terms of the complex signal it is normally used as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \times N$ </tex-math></inline-formula> size for the input, which is divided into in-phase and quadrature-phase (IQ) components. In this paper, the input size is extended as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times N$ </tex-math></inline-formula> size by copying IQ components and concatenating in reverse order to improve the classification accuracy. Since the increase in the amount of computation complexity due to the extended input size, the proposed CNN archiecture is designed to reduce the size from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times N$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \times N$ </tex-math></inline-formula> by an average pooling layer, which can enhence the classification accuracy as well. The simulation results show that the classification accuracy of the proposed model is higher than the conventional models in the almost signal-to-noise ratio (SNR) range.

Highlights

C OGNITIVE radio (CR) technology can make wireless devices connect one of unused spectrum subbands and exploit it [1], [2]
To show the performance in terms of the classification accuracy and the computational complexity the proposed model is compared with the related works that includes latest models such as MCNet [29], LCNN [26] and SCGNet [30]
To accurately evaluate the performance the approaches are categorized into machine learning techniques such as k-Nearest Neighbor (kNN), Decision Tree (DT), and Support Vector Machine (SVM) and deep learning technique

Summary

INTRODUCTION

C OGNITIVE radio (CR) technology can make wireless devices connect one of unused spectrum subbands and exploit it [1], [2]. One of the research works only considers a small number of modulation types, which is easier to achieve high classification performance. In another case, a simple channel condition for the wireless communication such as an additive white Gaussian noise (AWGN) channel is just considered, which can obtain relatively the clear feature compared with the Rayleigh fading channel. The proposed method shows the enhanced classification performance compared with the conventional CNN models. The expanded frame is implemented without high cost, but it can improve the accuracy This concept may have trade-off between classification accuracy and computational complexity, which should be considered

DATASET DESCRIPTION

NUMERICAL RESULTS

CONCLUSION