Real-Time OFDM Signal Modulation Classification Based on Deep Learning and Software-Defined Radio

2021 ◽  
pp. 1-1
Author(s):  
Limin Zhang ◽  
Chong Lin ◽  
Wenjun Yan ◽  
Qing Ling ◽  
Yu Wang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Software Defined Radio ◽  
Modulation Classification ◽  
Signal Modulation ◽  
Ofdm Signal

Toward Polymorphic Internet of Things Receivers Through Real-Time Waveform-Level Deep Learning

2022 ◽  
Vol 25 (3) ◽  
pp. 28-33
Author(s):  
Francesco Restuccia ◽  
Tommaso Melodia
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Software Defined Radio ◽  
Wireless Systems ◽  
Physical World ◽  
Wireless Protocols ◽  
Key Innovation ◽  
Custom Made ◽  
Time Waveform

Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.

scholarly journals Automatic Modulation Classification Based on Deep Learning for Software-Defined Radio

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Peng Wu ◽  
Bei Sun ◽  
Shaojing Su ◽  
Junyu Wei ◽  
Jinhui Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Software Defined Radio ◽  
Radio Signal ◽  
Time Signal ◽  
Modulation Classification ◽  
Signal Identification ◽  
Automatic Modulation Classification ◽  
Neural Architecture ◽  
Average Accuracy ◽  
Artificial Intelligence Technology

With the development of artificial intelligence technology, deep learning has been applied to automatic modulation classification (AMC) and achieved very good results. In this paper, we introduced an improved deep neural architecture for implementing radio signal identification tasks, which is an important facet of constructing the spectrum-sensing capability required by software-defined radio. The architecture of the proposed network is based on the Inception-ResNet network by changing the several kernel sizes and the repeated times of modules to adapt to modulation classification. The modules in the proposed architecture are repeated more times to increase the depth of neural network and the model’s ability to learn features. The modules in the proposed network combine the advantages of Inception network and ResNet, which have faster convergence rate and larger receptive field. The proposed network is proved to have excellent performance for modulation classification through the experiment in this paper. The experiment shows that the classification accuracy of the proposed method is highest with the varying SNR among the six methods and it peaks at 93.76% when the SNR is 14 dB, which is 6 percent higher than that of LSTM and 13 percent higher than that of MentorNet, Inception, and ResNet purely. Besides, the average accuracy from 0 to 18 dB of the proposed method is 3 percent higher than that of GAN network. It will provide a new idea for modulation classification aiming at distraction time signal.

Deep learning based automatic signal modulation classification

2019 ◽  
Author(s):  
Jingyang Lu ◽  
Yi Li ◽  
Genshe Chen ◽  
Dan Shen ◽  
Xin Tian ◽  
...  
Keyword(s):  
Deep Learning ◽  
Modulation Classification ◽  
Signal Modulation

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Real-time Worker Safety Management System Using Deep Learning-based Video Analysis Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 25-30
Author(s):  
So Yeon Jeon ◽  
Jong Hwa Park ◽  
Sang Byung Youn ◽  
Young Soo Kim ◽  
Yong Sung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Analysis ◽  
Management System ◽  
Safety Management ◽  
Worker Safety ◽  
Safety Management System ◽  
Analysis Algorithm

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

Sign in / Sign up

Export Citation Format

Share Document