Spectrum sensing and power efficiency trade-off optimisation in cognitive radio networks over fading channels

2013 ◽  
Vol 7 (3) ◽  
pp. 198-205 ◽  
Author(s):  
Emad S. Hassan
Keyword(s):  
Cognitive Radio ◽  
Fading Channels ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Power Efficiency ◽  
Radio Networks ◽  
Trade Off

Energy Detection Based Spectrum Sensing Analysis of Cooperative Cognitive Radios under Different Fading Environments

2013 ◽  
pp. 50-73
Author(s):  
Deepti Kakkar ◽  
Mayank Gupta ◽  
Arun Khosla ◽  
Moin Uddin
Keyword(s):  
Cognitive Radio ◽  
Fading Channels ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Fusion Rule ◽  
Energy Detection ◽  
Radio Networks ◽  
Amplify And Forward ◽  
Comparative Performance

This chapter discusses the detection performance of relay based cognitive radio networks. Relays are assigned in cognitive radio networks to transmit the primary user’s signal to cognitive coordinators or CPUs, thus achieving cooperative spectrum sensing. The purpose of the chapter is to provide mathematical analysis of energy detectors for dual hop networks. The soft fusion rule is used at the relays which acts as amplify and forward relays. For the detection purpose, the energy detector is employed at the cognitive coordinator. In the ending sections, sensing performance is analyzed for different fading channels in the MATLAB environment and simulation results present comparative performance of various relay conditions with concluding remarks.

Robust cooperative spectrum sensing schemes for fading channels in cognitive radio networks

2010 ◽  
Vol 54 (2) ◽  
pp. 348-359 ◽  
Author(s):  
WenJing Yue ◽  
BaoYu Zheng ◽  
QingMin Meng ◽  
JingWu Cui ◽  
PeiZhong Xie
Keyword(s):  
Cognitive Radio ◽  
Fading Channels ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks

scholarly journals CSS using Energy Detector in AWGN and Flat-Fading Channels in Cognitive Radio Networks: A Complete Analysis

2020 ◽  
Vol 8 (6) ◽  
pp. 5042-5046
Keyword(s):  
Cognitive Radio ◽  
Fading Channels ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Complete Analysis ◽  
Flat Fading ◽  
Single User ◽  
Simulation Results

In this work, various spectrum sensing methods and algorithms are analyzed and their performance is been evaluated based on the different values of probabilities as obtained through MATLAB simulations. The work is been started from the analysis of the simplest single user sensing to advanced cooperative spectrum sensing and is further extended to CSS in AWGN noise and flat-fading channels. The results indicates that advanced cooperative spectrum sensing gives much better sensing decisions as compared to the results obtained by simulating single user sensing method. Simulation results obtained shows that Pd increases with Pf and also shows good values for SNR more than 0 dB. Also the Pd increases from 0.7 to 0.84 as we go from single user detection to CSS.

scholarly journals Spectrum sensing in cognitive radio networks: threshold optimization and analysis

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Kenan kockaya ◽  
Ibrahim Develi
Keyword(s):  
Online Learning ◽  
Cognitive Radio ◽  
Fading Channels ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Learning Algorithm ◽  
Radio Networks ◽  
Decision Threshold ◽  
Online Learning Algorithm ◽  
Sensing Performance

AbstractCognitive radio is a technology developed for the effective use of radio spectrum sources. The spectrum sensing function plays a key role in the performance of cognitive radio networks. In this study, a new threshold determination method based on online learning algorithm is proposed to increase the spectrum sensing performance of spectrum sensing methods and to minimize the total error probability. The online learning algorithm looks for the optimum decision threshold, which is the most important parameter to decide the presence or absence of the primary user, using historical detection data. Energy detection- and matched filter-based spectrum sensing methods are discussed in detail. The performance of the proposed algorithm was tested over non-fading and different fading channels for low signal-to-noise ratio regime with noise uncertainty. In the conclusion of the simulation studies, improvement in spectrum sensing performance according to optimal threshold selection was observed.

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