Spectrum sensing using two-stage detection to compensate for reduced primary user duty cycle

2011 ◽  
Author(s):  
K. Chang ◽  
B. Senadji
Keyword(s):  
Spectrum Sensing ◽  
Duty Cycle ◽  
Primary User ◽  
Two Stage

Noise Uncertainty Effect on a Modified Two-Stage Spectrum Sensing Technique

2016 ◽  
Vol 1 (2) ◽  
pp. 341 ◽  
Author(s):  
Heba A.Tag El-Dien ◽  
Rokaia M. Zaki ◽  
Mohsen M. Tantawy ◽  
Hala M. Abdel-Kader
Keyword(s):  
Spectrum Sensing ◽  
Energy Detection ◽  
Primary User ◽  
Probability Of Detection ◽  
Minimum Eigenvalue ◽  
Two Stage ◽  
Noise Uncertainty ◽  
Second Stage ◽  
Single Detector ◽  
Maximum Minimum

Detecting the presence or absence of primary user is the key task of cognitive radio networks. However, relying on single detector reduces the probability of detection and increases the probability of missed detection. Combining two conventional spectrum sensing techniques by integrating their individual features improves the probability of detection especially under noise uncertainty. This paper introduces a modified two-stage detection technique that depends on the energy detection as a first stage due to its ease and speed of detection, and the proposed Modified Combinational Maximum-Minimum Eigenvalue based detection as a second stage under noise uncertainty and comperes it with the case of using Maximum-Minimum Eigenvalue and  Combinational Maximum-Minimum Eigenvalue as a second stage.

scholarly journals Impact of Primary User Duty Cycle in Generalized Fading Channels on Spectrum Sensing in Cognitive Radio

2015 ◽  
Vol 46 ◽  
pp. 1196-1202 ◽  
Author(s):  
Vaibhav Kumar ◽  
Achyut Sharma ◽  
Soumitra Debnath ◽  
Ranjan Gangopadhyay
Keyword(s):  
Cognitive Radio ◽  
Fading Channels ◽  
Spectrum Sensing ◽  
Duty Cycle ◽  
Primary User ◽  
Generalized Fading Channels

Two-Stage Spectrum Sensing for Cognitive Radio Using Eigenvalues Detection

2020 ◽  
Vol 12 (4) ◽  
pp. 18-36
Author(s):  
Faten Mashta ◽  
Wissam Altabban ◽  
Mohieddin Wainakh
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Primary User ◽  
Detection Performance ◽  
Probability Of Detection ◽  
Energy Detector ◽  
Detection Methods ◽  
Two Stage ◽  
Low Snr ◽  
Good Detection

Spectrum sensing in cognitive radio has difficult and complex requirements, requiring speed and good detection performance at low SNR ratios. As suggested in IEEE 802.22, the primary user signal needs to be detected at SNR = -21dB with a probability of detection exceeds 0.9. Conventional spectrum sensing methods such as the energy detector, which is characterized by simplicity with good detection performance at high SNR values, are ineffective at low SNR values, whereas eigenvalues detection methods have good detection performance at low SNR ratios, but they have high complexity. In this paper, the authors investigate the process of spectrum sensing in two stages: in the first stage (coarse sensing), the energy detector is adopted, while in the second stage (fine sensing), eigenvalues detection methods are used. This method improves performance in terms of probability of detection and computational complexity, as the authors compared the performance of two-stage sensing scheme with ones where only energy detection or eigenvalues detection is performed.

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.

Energy Detection Spectrum Sensing with Discontinuous Primary User Signal

2009 ◽  
Author(s):  
F. Penna ◽  
C. Pastrone ◽  
M. A. Spirito ◽  
R. Garello
Keyword(s):  
Spectrum Sensing ◽  
Energy Detection ◽  
Primary User
Sign in / Sign up

Export Citation Format

Share Document