Deep Learning-Based Automatic Modulation Recognition Method in the Presence of Phase Offset

Jie Shi,Changxin Cai,Hao Huang,Sheng Hong,Yu Wang,Guan Gui

doi:10.1109/access.2020.2978094

Abstract

Automatic modulation recognition (AMR) plays an important role in various communications systems. It has the ability of adaptive modulation and can adapt to various complex environments. Automatic modulation recognition is also widely used in orthogonal frequency division multiplexing (OFDM) systems. However, because the recognition accuracy of traditional methods to extract the features of OFDM signals is very limited. In order to solve these problems, many deep learning based AMR methods have been proposed to improve the recognition performance. However, most of these AMR methods neglect the harmful effect by carrier phase offset (PO) which often appears in real communications systems. Hence it is required to consider the PO effect for designing the OFDM system. Unlike conventional methods, we propose a convolutional neural network (CNN) based AMR method for considering PO in the OFDM system. The proposed method is used to eliminate the PO to achieve the high classification accuracy. Experiment results are provided to confirm the proposed method when comparing to conventional methods.

Highlights

Blind signal recognition is considered as one of important techniques in many military and civilian applications [1]–[8], such as adaptive modulator, spectrum sensing and non-cooperative signal detection
Automatic modulation recognition (AMR) is a key step to realize the recognition technique and many AMR methods have been proposed in last decades
EXPERIMENT RESULTS AND DISCUSSIONS we performe several sets of comparative experiments to verify the performance of deep learning (DL)-based AMR in non-cooperative orthogonal frequency division multiplexing (OFDM) systems

Summary

Introduction

Blind signal recognition is considered as one of important techniques in many military and civilian applications [1]–[8], such as adaptive modulator, spectrum sensing and non-cooperative signal detection. Automatic modulation recognition (AMR) is a key step to realize the recognition technique and many AMR methods have been proposed in last decades Speaking, these traditional AMR methods can be developed based on two types, i.e., likelihood function and feature extraction [9]. The feature extraction based AMR methods are formulated as a pattern recognition problem and it can be realized by pre-processing, feature extraction, and classifier design. These feature extraction-based AMR methods are considered as instantaneous realization scenarios, such as instantaneous features, wavelet transform-based features, high-order statistics-based features, cyclic spectrum analysis-based features, and so on.

Methods

Results

Conclusion