Convolutional Neural Networks for Pilot-Induced Cyclostationarity Based OFDM Signals Spectrum Sensing in Full-Duplex Cognitive Radio

2020 ◽  
Vol E103.B (1) ◽  
pp. 91-102
Author(s):  
Hang LIU ◽  
Xu ZHU ◽  
Takeo FUJII
Keyword(s):  
Neural Networks ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Convolutional Neural Networks ◽  
Full Duplex ◽  
Ofdm Signals

scholarly journals Cooperative Spectrum Sensing Based on Convolutional Neural Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4440
Author(s):  
Youheng Tan ◽  
Xiaojun Jing
Keyword(s):  
Neural Networks ◽  
Spectrum Sensing ◽  
Convolutional Neural Networks ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Majority Voting ◽  
Signal To Noise ◽  
Hidden Terminal ◽  
Simulation Results ◽  
Sensing Performance

Cooperative spectrum sensing (CSS) is an important topic due to its capacity to solve the issue of the hidden terminal. However, the sensing performance of CSS is still poor, especially in low signal-to-noise ratio (SNR) situations. In this paper, convolutional neural networks (CNN) are considered to extract the features of the observed signal and, as a consequence, improve the sensing performance. More specifically, a novel two-dimensional dataset of the received signal is established and three classical CNN (LeNet, AlexNet and VGG-16)-based CSS schemes are trained and analyzed on the proposed dataset. In addition, sensing performance comparisons are made between the proposed CNN-based CSS schemes and the AND, OR, majority voting-based CSS schemes. The simulation results state that the sensing accuracy of the proposed schemes is greatly improved and the network depth helps with this.

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.

Spectrum Sensing in Full-Duplex Cognitive Radio Networks Under Hardware Imperfections

2017 ◽  
Vol 66 (3) ◽  
pp. 2072-2084 ◽  
Author(s):  
Alexandros-Apostolos A. Boulogeorgos ◽  
Haythem A. Bany Salameh ◽  
George K. Karagiannidis
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Full Duplex
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