Cooperative Spectrum Sensing using Kalman Filter based Adaptive Fuzzy System for Cognitive Radio Networks

2012 ◽  
Author(s):  
Thuc Kieu-Xuan
Keyword(s):  
Kalman Filter ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Fuzzy System ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Adaptive Fuzzy ◽  
Adaptive Fuzzy System

scholarly journals Unscented Kalman Filter Based on Spectrum Sensing in a Cognitive Radio Network Using an Adaptive Fuzzy System

2018 ◽  
Vol 2 (4) ◽  
pp. 39 ◽  
Author(s):  
Md Amin ◽  
Md Rahman ◽  
Mohammad Hossain ◽  
Md Islam ◽  
Kazi Ahmed ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Fuzzy System ◽  
Cognitive Radio Network ◽  
Unscented Kalman Filter ◽  
Fusion Rule ◽  
Radio Network ◽  
Adaptive Fuzzy ◽  
Adaptive Fuzzy System

In this paper, we proposed the unscented Kalman filter (UKF) based on cooperative spectrum sensing (CSS) scheme in a cognitive radio network (CRN) using an adaptive fuzzy system—in this proposed scheme, firstly, the UKF to apply the nonlinear system which is used to minimize the mean square estimation error; secondly, an adaptive fuzzy logic rule based on an inference engine to estimate the local decisions to detect a licensed primary user (PU) that is applied at the fusion center (FC). After that, the FC makes a global decision by using a defuzzification procedure based on a proposed algorithm. Simulation results show that the proposed scheme achieved better detection gain than the conventional schemes like an equal gain combining (EGC) based soft fusion rule and a Kalman filter (KL) based soft fusion rule under any conditions. Moreover, the proposed scheme achieved the lowest global probability of error compared to both the conventional EGC and KF schemes.

scholarly journals Optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Haiyan Ye ◽  
Jiabao Jiang
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Spectrum Utilization ◽  
Channel Characteristics ◽  
Optimal Linear ◽  
Static Division ◽  
Cooperative Nodes

AbstractThe lack of spectrum resources restricts the development of wireless communication applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.

Cooperative Spectrum Sensing with Entropy for Mobile Cognitive Radio Networks

2020 ◽  
Author(s):  
Cadena Munoz Ernesto ◽  
Julian Andres Rodriguez Martinez ◽  
Luis Fernando Pedraza Martinez ◽  
Ingrid Patricia Paez Parra
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks

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.

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