Work

Deep Learning for Modulation Recognition: A Survey With a Demonstration

Year: 2020

Type: article

Abstract: In this paper, we review a variety of deep learning algorithms and models for modulation recognition and classification of wireless communication signals. Specifically, deep learning (DL) has shown overwhelming advantages in computer vision, robotics, and voice recognition. Recently, DL has been proposed to apply to wireless communications for signal detection and classification in order to better learn the active users for electromagnetic spectrum sharing purposes. Therefore, we aim to provide a survey on the most recent techniques which use DL for recognizing and classifying a wireless signal. We focus on the most widely used DL models, emphasize the advantages and limitations, and discuss the challenges as well as future directions. In addition, we also apply a DL algorithm, convolutional neural network (CNN), to demonstrate the feasibility of using CNN to recognize and classify the over-the-air wireless signals using Mathworks DL toolbox with PlutoSDR and Universal Software Radio Peripheral (USRP), respectively.

Source: IEEE Access

DOI: 10.1109/access.2020.2986330

Authors: Ruolin Zhou, Fugang Liu, Christopher W. Gravelle

Institutions: University of Massachusetts Dartmouth, Heilongjiang University of Science and Technology

Language: English

Topic: Wireless Signal Modulation Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,750

Funders: Dartmouth College, University of Massachusetts Dartmouth, Office of Naval Research