New Location Algorithm with Primary User Based on Scatter Information in Cognitive Radio

2021 ◽  
Author(s):  
Dan Ye
Keyword(s):  
Cognitive Radio ◽  
Primary User ◽  
Location Algorithm ◽  
New Location

CR–RSS location algorithm for primary user in cognitive radio

2014 ◽  
Vol 21 (1) ◽  
pp. 22-35 ◽  
Author(s):  
Yin-hui YU ◽  
Chen SUN ◽  
Nan-nan QIN ◽  
Ke GAO ◽  
Deng-zhao CHEN
Keyword(s):  
Cognitive Radio ◽  
Primary User ◽  
Location Algorithm

Cognitive Radio: Primary User Emulation Attacks and Remedies

2012 ◽  
Vol 5 (2) ◽  
pp. 103-108 ◽  
Author(s):  
Sachin Shetty ◽  
Meena Thanu ◽  
Ravi Ramachandran
Keyword(s):  
Cognitive Radio ◽  
Primary User ◽  
Primary User Emulation

scholarly journals Second order Kalman filtering channel estimation and machine learning methods for spectrum sensing in cognitive radio networks

2021 ◽  
Author(s):  
Olusegun Peter Awe ◽  
Daniel Adebowale Babatunde ◽  
Sangarapillai Lambotharan ◽  
Basil AsSadhan
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Clustering Algorithm ◽  
Polynomial Regression ◽  
Primary User ◽  
Radio Networks ◽  
Second Order

AbstractWe address the problem of spectrum sensing in decentralized cognitive radio networks using a parametric machine learning method. In particular, to mitigate sensing performance degradation due to the mobility of the secondary users (SUs) in the presence of scatterers, we propose and investigate a classifier that uses a pilot based second order Kalman filter tracker for estimating the slowly varying channel gain between the primary user (PU) transmitter and the mobile SUs. Using the energy measurements at SU terminals as feature vectors, the algorithm is initialized by a K-means clustering algorithm with two centroids corresponding to the active and inactive status of PU transmitter. Under mobility, the centroid corresponding to the active PU status is adapted according to the estimates of the channels given by the Kalman filter and an adaptive K-means clustering technique is used to make classification decisions on the PU activity. Furthermore, to address the possibility that the SU receiver might experience location dependent co-channel interference, we have proposed a quadratic polynomial regression algorithm for estimating the noise plus interference power in the presence of mobility which can be used for adapting the centroid corresponding to inactive PU status. Simulation results demonstrate the efficacy of the proposed algorithm.

scholarly journals Range-based Primary User Localization in Cognitive Radio Networks

2016 ◽  
Vol 93 ◽  
pp. 199-206 ◽  
Author(s):  
Awadhesh Kumar Singh ◽  
A.K. Singh
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Primary User ◽  
Radio Networks

Throughput performance under primary user emulation attack in cognitive radio networks

2017 ◽  
Vol 30 (18) ◽  
pp. e3371 ◽  
Author(s):  
Kuldeep Yadav ◽  
Binod Prasad ◽  
Abhijit Bhowmick ◽  
Sanjay Dhar Roy ◽  
Sumit Kundu
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Primary User ◽  
Radio Networks ◽  
Throughput Performance ◽  
Primary User Emulation Attack ◽  
Primary User Emulation
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