A Novel Second-Order Perturbation Technique for Improving the Signal Processing in MIMO-NOMA Based Cognitive Radio Networks

2022 ◽  
Author(s):  
A. Hyils Sharon Magdalene ◽  
L. Thulasimani
Keyword(s):  
Signal Processing ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Perturbation Technique ◽  
Radio Networks ◽  
Second Order ◽  
Order Perturbation

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 Advanced Signal Processing for Cognitive Radio Networks

2009 ◽  
Vol 2010 (1) ◽  
Author(s):  
Ying-Chang Liang ◽  
Xiaodong Wang ◽  
Yonghong Zeng ◽  
Jinho Choi ◽  
Rui Zhang ◽  
...  
Keyword(s):  
Signal Processing ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Radio Networks

Exact Optimum RAN Selection Algorithm for Heterogeneous-Type Cognitive Radio Networks

2014 ◽  
Vol 1 ◽  
pp. 652-655
Author(s):  
Takumi.Matsui Takumi.Matsui ◽  
Mikio.Hasegawa Mikio.Hasegawa ◽  
Hiroshi.Hirai Hiroshi.Hirai ◽  
Kiyohito.Nagano Kiyohito.Nagano ◽  
Kazuyuki.Aihara Kazuyuki.Aihara
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Selection Algorithm
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