scholarly journals Performance Evaluation of Cluster Based Cooperative Cognitive Radio Network

2019 ◽  
Vol 9 (1) ◽  
pp. 13
Author(s):  
Masoud Moradkhani ◽  
Zeynab Zalani
Keyword(s):  
Energy Consumption ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Fading Channel ◽  
Cognitive Radio Network ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Radio Network ◽  
Network Efficiency ◽  
Simulation Results

Cognitive radio is regarded as one of the transformational technologies that play a fundamental role in establishment and development of next generation wireless networks. Clustering based cognitive radio networks can reduce the energy consumption and increase the spectrum sensing accuracy. In this paper, we investigate the performance of a cognitive radio network with a clustering architecture for cooperative spectrum sensing. The network efficiency is evaluated in terms of throughput and energy consumption, under fading channel conditions. Simulation results show significant improvement of energy efficiency compared to the conventional non-clustering method by ensuring allowable interference with primary users.

scholarly journals Goodness-of-Fit Based Secure Cooperative Spectrum Sensing for Cognitive Radio Network

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Hiep Vu-Van ◽  
Insoo Koo
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Goodness Of Fit ◽  
Cognitive Radio Network ◽  
Cooperative Spectrum Sensing ◽  
Harmful Effect ◽  
Radio Network ◽  
Measured Distance ◽  
Simulation Results ◽  
Sample Set

Cognitive radio (CR) is a promising technology for improving usage of frequency band. Cognitive radio users (CUs) are allowed to use the bands without interference in operation of licensed users. Reliable sensing information about status of licensed band is a prerequirement for CR network. Cooperative spectrum sensing (CSS) is able to offer an improved sensing reliability compared to individual sensing. However, the sensing performance of CSS can be destroyed due to the appearance of some malicious users. In this paper, we propose a goodness-of-fit (GOF) based cooperative spectrum sensing scheme to detect the dissimilarity between sensing information of normal CUs and that of malicious users, and reject their harmful effect to CSS. The empirical CDF will be used in GOF test to determine the measured distance between distributions of observation sample set according to each hypothesis of licensed user signal. Further, the DS theory is used to combine results of multi-GOF tests. The simulation results demonstrate that the proposed scheme can protect the sensing process against the attack from malicious users.

Energy-Efficient Cooperative Spectrum Sensing for Cognitive Radio Networks

2015 ◽  
pp. 100-121
Author(s):  
Saud Althunibat ◽  
Sandeep Narayanan ◽  
Marco Di Renzo ◽  
Fabrizio Granelli
Keyword(s):  
Energy Consumption ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
High Energy ◽  
Radio Networks ◽  
Participation Decision ◽  
Consumption Energy ◽  
Fusion Centre

One of the main problems of Cooperative Spectrum Sensing (CSS) in cognitive radio networks is the high energy consumption. Energy is consumed while sensing the spectrum and reporting the results to the fusion centre. In this chapter, a novel partial CSS is proposed. The main concern is to reduce the energy consumption by limiting the number of participating users in CSS. Particularly, each user individually makes the participation decision. The energy consumption in a CSS round is expected by the user itself and compared to a predefined threshold. The corresponding user will participate only if the expected amount of energy consumed is less than the participation threshold. The chapter includes optimizing the participation threshold for energy efficiency maximization. The simulation results show a significant reduction in the energy consumed compared to the conventional CSS approach.

Machine Learning Approach in Cooperative Spectrum Sensing for Cognitive Radio Network: Survey

2021 ◽  
pp. 63-71
Author(s):  
Vaishali S. Kulkarni ◽  
Tanuja S. Dhope(Shendkar) ◽  
Swagat Karve ◽  
Pranav Chippalkatti ◽  
Akshay Jadhav
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Network ◽  
Cooperative Spectrum Sensing ◽  
Learning Approach ◽  
Radio Network ◽  
Machine Learning Approach
Sign in / Sign up

Export Citation Format

Share Document