CNN and DCGAN for Spectrum Sensors over Rayleigh Fading Channel

Mu, Junsheng; Tan, Yun; Xie, Dong; Zhang, Fangpei; Jing, Xiaojun

doi:10.1155/2021/9970600

Cited by 9 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main way this loss function works is to determine the model at the decision boundary, but once it is determined, it stops and does not continue, which results in the model crashing. In order to solve the problem of model collapse due to perceptrons and loss functions, an improved algorithm using a convolutional neural network instead of a multi-layer perceptron is used, also known as DCGAN [18]. The ReLU activation function is used between the convolutional layers, which are then processed using a pooling layer.…”

Section: Improved Generative Adversarial Network Structurementioning

confidence: 99%

The application of big data technology in the art of visual communication design with information technology

Tao,

Wang

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

Based on big data technology, this paper firstly proposes that visual image generation design based on the improved adversarial generative network becomes one of the ideas to solve the current dilemma of visual communication design, which is not only a convenient, creative means but also changes the traditional way of visual communication design. Secondly, in the extension of visual image samples, in addition to using some operations such as rotation, translation and scale transformation of visual images, an improved DCGAN adversarial generative network algorithm is also used. The improved DCGAN network is then used to augment the number of visual images while maintaining the quality of visual image generation. Finally, intelligent generation models based on visual communication design processes and thinking are investigated, focusing on intelligent data sets and retrieval, image generation techniques, and intelligent solution recommendations. The results show that DCGAN has a lower value of IS = 1.5, FID = 249.39, IS and a higher value of FID, indicating that it generates better quality visual images and greatly improves the efficiency and accuracy of visual communication design. This study can extract effective information and generate visual images more accurately and efficiently, and provide technical support for visual communication designers.

show abstract

Section: Improved Generative Adversarial Network Structurementioning

confidence: 99%

The application of big data technology in the art of visual communication design with information technology

Tao,

Wang

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

“…With the great success of deep learning in natural image recognition, wireless signal processing technology based on deep learning has received more and more attention [15]. In [16], an SNR estimation scheme was proposed based on deep learning (DL), where a one-dimensional convolutional neural networks (CNN) is used for accurate SNR estimation.…”

Section: Related Workmentioning

confidence: 99%

Non-Cooperative UAV Detection with Semi-Adaptive Sampling of Control Signal and SNR Estimation

et al. 2022

Self Cite

View full text Add to dashboard Cite

This paper proposes a non-cooperative unmanned aerial vehicle (UAV) signal detection strategy based on a multichannel control signal with an energy detector (ED), wherein the sampling point of the control signal on each subchannel is adjusted with environmental signal-to-noise (SNR) in a semi-adaptive manner. In order to estimate the SNR in the environment, not only is a convolutional neural network (CNN) applied in the proposed signal detection strategy, but a long shor-term memory network (LSTM) network is also included; in terms of features, it combines deep features and time-dimension features. The numbers of layers of the CNN and LSTM impact the performance of the algorithm. The decision on the presence or absence of a control signal is made at the fusion center (FC) based on the majority voting rule. This paper shows that the network with a two-layer CNN and a two-layer LSTM can achieve high estimation accuracy of environmental SNR. Simultaneously, the detection accuracy is improved by about 1 dB compared with the classical multichannel detection schemes.

show abstract

“…Modern electronic warfare is rapidly developing with the rise of electronic information technology. Electromagnetic signal identification becomes a critical part of a cognitive radio (CR) [1]. However, electromagnetic signal waveforms appear to be agile and heavily interfered in a diverse electromagnetic environment.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Electromagnetic Signals Based on the Spiking Convolutional Neural Network

Tao

Xiao

Gong

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Feature extraction and recognition of signals are the bases of a cognitive radio. Traditional manual extraction for signals’ features becomes difficult in the complex electromagnetic environment. Although convolutional neural networks (CNNs) can extract signal features automatically, they have low accuracy in recognizing electromagnetic signals at low signal-to-noise ratios (SNRs) due to the agility of signals. Considering the great potential of spiking neural networks (SNNs) in classification, a spiking convolution neural network (SCNN) for the recognition of electromagnetic signals is proposed in this paper. The SCNN effectively integrates the extraction ability of spatial features in CNNs and temporal features in SNNs. Since the SCNN is difficult to train, the strategy of surrogate gradient is proposed to train it. By taking the 2-dimensional time-frequency distribution of 6 signals as input, the SCNN can effectively identify different signals at low SNRs. The method proposed in this paper contributes to promote the research and application of SNNs in the recognition of electromagnetic signals.

show abstract

CNN and DCGAN for Spectrum Sensors over Rayleigh Fading Channel

Cited by 9 publications

References 23 publications

The application of big data technology in the art of visual communication design with information technology

The application of big data technology in the art of visual communication design with information technology

Non-Cooperative UAV Detection with Semi-Adaptive Sampling of Control Signal and SNR Estimation

Recognition of Electromagnetic Signals Based on the Spiking Convolutional Neural Network

Contact Info

Product

Resources

About