Signal Modulation Identification Based on Deep Learning in FBMC/OQAM Systems

Jing Chen,Jianzhong Guo,Dejin Kong,Xin Shan,Han Wang

doi:10.1155/2021/4809699

Abstract

Signal modulation identification (SMI) has always been one of hot issues in filter-bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM), which is usually implemented by the machine learning-based feature extraction. However, it is difficult for conventional methods to extract the signal feature, resulting in a limited probability of correct classification (PCC). To tackle this problem, we put forward a novel SMI method based on deep learning to identify FBMC/OQAM signals in this paper. It is noted that the block repetition is employed in the FBMC/OQAM system to achieve the imaginary interference cancelation. In the proposed deep learning-based SMI technique, the in-phase and quadrature samples of FBMC/OQAM signals are trained by the convolutional neural network. Subsequently, the dropout layer is designed to prevent overfilling and improve the identification accuracy. To evaluate the proposed scheme, extensive experiments are conducted by employing datasets with different modulations. The results show that the proposed method can achieve better accuracy than conventional methods.

Highlights

Filter-bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) has been considered as one of the potential physical-layer techniques for future wireless communications [1,2,3,4]
We propose a novel signal modulation identification (SMI) technique based on the convolutional neural network (CNN) to make an identification on FBMC/OQAM signals
To evaluate the proposed scheme, extensive experiments are conducted by employing datasets with different modulations. e results show that the proposed method can achieve better accuracy than conventional methods

Summary

Introduction

Filter-bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) has been considered as one of the potential physical-layer techniques for future wireless communications [1,2,3,4]. At is to say, the probability of correct classification is not good enough, which indicates unpreferred in practical FBMC/OQAM systems To solve this issue, deep learning (DL) has been deemed to be one of effective techniques to deploy SMI [11]. We propose a novel SMI technique based on the convolutional neural network (CNN) to make an identification on FBMC/OQAM signals. It has been demonstrated that the imaginary interference factor symmetrical [18] On this basis of symmetric, the block repetition is designed in FBMC/OQAM to achieve the imaginary interference cancelation [18, 19]. The imaginary interferences of the original block and repeated block satisfy the following equation [18], i.e., jdcm,n + jdcm,2N− 1− n 0. When the factor κ goes to zero gradually, the Rician distribution will become a Rayleigh distribution

Proposed Deep Learning-Based SMI

Experiment Results

Conclusions