Machine Learning-Based Algorithm for Channel Selection Utilizing Preemptive Resume Priority in Cognitive Radio Networks Validated by NS-2

2019 ◽  
Vol 39 (2) ◽  
pp. 1038-1058
Author(s):  
D. Sumathi ◽  
S. S Manivannan
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Channel Selection ◽  
Radio Networks ◽  
Preemptive Resume ◽  
Preemptive Resume Priority

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.

On Fuzzy Logic-Based Channel Selection in Cognitive Radio Networks

2015 ◽  
pp. 255-274 ◽  
Author(s):  
Yong Yao ◽  
Alexandru Popescu ◽  
Adrian Popescu
Keyword(s):  
Fuzzy Logic ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Environmental Changes ◽  
Radio Channel ◽  
New Technology ◽  
Channel Selection ◽  
Decision Criterion ◽  
Radio Networks ◽  
Competition Level

Cognitive radio networks are a new technology based on which unlicensed users are allowed access to licensed spectrum under the condition that the interference perceived by licensed users is minimal. That means unlicensed users need to learn from environmental changes and to make appropriate decisions regarding the access to the radio channel. This is a process that can be done by unlicensed users in a cooperative or non-cooperative way. Whereas the non-cooperative algorithms are risky with regard to performance, the cooperative algorithms have the capability to provide better performance. This chapter shows a new fuzzy logic-based decision-making algorithm for channel selection. The underlying decision criterion considers statistics of licensed user channel occupancy as well as information about the competition level of unlicensed users. The theoretical studies indicate that the unlicensed users can obtain an efficient sharing of the available channels. Simulation results are reported to demonstrate the performance and effectiveness of the suggested algorithm.

scholarly journals Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks

2019 ◽  
Vol 15 (9) ◽  
pp. 155014771986036 ◽  
Author(s):  
Sundar Srinivasan ◽  
KB Shivakumar ◽  
Muazzam Mohammad
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Primary User ◽  
Radio Networks ◽  
Attack Detection ◽  
Supervised Machine Learning ◽  
Machine Language ◽  
Detection Accuracy ◽  
Primary User Emulation

Cognitive radio networks are software controlled radios with the ability to allocate and reallocate spectrum depending upon the demand. Although they promise an extremely optimal use of the spectrum, they also bring in the challenges of misuse and attacks. Selfish attacks among other attacks are the most challenging, in which a secondary user or an unauthorized user with unlicensed spectrum pretends to be a primary user by altering the signal characteristics. Proposed methods leverage advancement to efficiently detect and prevent primary user emulation future attack in cognitive radio using machine language techniques. In this paper novel method is proposed to leverage unique methodology which can efficiently handle during various dynamic changes includes varying bandwidth, signature changes etc… performing learning and classification at edge nodes followed by core nodes using deep learning convolution network. The proposed method is compared with that of two other state-of-art machine learning-based attack detection protocols and has found to significantly reduce the false alarm to secondary network, at the same time improve the overall detection accuracy at the primary network.

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