Detection of Malicious Node in Centralized Cognitive Radio Networks Based on MLP Neural Network

2018 ◽  
pp. 865-877
Author(s):  
Zeynab Sadat Seyed Marvasti ◽  
Omid Abedi
Keyword(s):  
Neural Network ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Malicious Node ◽  
Mlp Neural Network

scholarly journals A PSD Maps Estimation Algorithm for Underlay Cognitive Radio Networks Based on the Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xu Han ◽  
Lei Xue ◽  
Ying Xu
Keyword(s):  
Neural Network ◽  
Cognitive Radio ◽  
Convolutional Neural Network ◽  
Cognitive Radio Networks ◽  
Estimation Algorithm ◽  
Radio Networks ◽  
Map Estimation ◽  
Color Mapping ◽  
Underlay Cognitive Radio ◽  
Conventional Methods

In the underlay cognitive radio networks (CRNs), the power spectral density (PSD) maps play a foundational role in detecting the idle radio resources. However, it is hard to get a high-accurate PSD map estimation result because of the complicated radio environment. For this reason, we propose a novel convolutional neural network- (CNN-) based PSD map estimation algorithm named map reconstruction CNN (MRCNN). Using the CNN to estimate PSD maps for underlay CRNs has not been reported until now. First, on the basis of the proposed color mapping process, we transform the PSD map estimation task to the image reconstruction task. Then, we train the MRCNN to learn the radio environment characteristics from the training data, rather than making direct biased or imprecise wireless environment hypotheses as in the conventional methods. We utilize the extracted knowledge in the training process to reconstruct the PSD map images. As demonstrated in the simulations, the proposed MRCNN method has a better PSD map estimation performance than the conventional methods.

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