Levenberg marquedet lion based artificial neural network for cooperative spectrum sensing in cognitive radio

2019 ◽  
Vol 14 (4) ◽  
pp. 321-336
Author(s):  
Rajendra Yelalwar ◽  
Yerram Ravinder
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Artificial Neural

Artificial neural network design for improved spectrum sensing in cognitive radio

2020 ◽  
Vol 26 (8) ◽  
pp. 6155-6174
Author(s):  
Dhaval K. Patel ◽  
Miguel López-Benítez ◽  
Brijesh Soni ◽  
Ángel F. García-Fernández
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cognitive Radio ◽  
Network Design ◽  
Spectrum Sensing ◽  
Artificial Neural

Deep Cooperative Spectrum Sensing Utilizing Recurrent Convolutional Neural Networks

2020 ◽  
Author(s):  
Rahil Sarikhani ◽  
Farshid Keynia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Recurrent Neural Network ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Secondary Users ◽  
Spectrum Holes

Abstract Cognitive Radio (CR) network was introduced as a promising approach in utilizing spectrum holes. Spectrum sensing is the first stage of this utilization which could be improved using cooperation, namely Cooperative Spectrum Sensing (CSS), where some Secondary Users (SUs) collaborate to detect the existence of the Primary User (PU). In this paper, to improve the accuracy of detection Deep Learning (DL) is used. In order to make it more practical, Recurrent Neural Network (RNN) is used since there are some memory in the channel and the state of the PUs in the network. Hence, the proposed RNN is compared with the Convolutional Neural Network (CNN), and it represents useful advantages to the contrast one, which is demonstrated by simulation.

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