Implementation of an adaptive spectrum sensing technique in cognitive radio networks

Abstract

Cognitive radio techniques provide the capability to use or share the spectrum in an opportunistic manner. The major functions of cognitive radio include spectrum sensing, spectrum management, spectrum mobility and spectrum sharing. Spectrum sensing is the primary technique which is used by all the Cognitive radio users to harvest the advantage of Cognitive radio technique. In Spectrum sensing various popular methods have been already proposed and studied exhaustively by the CR researchers. In this paper spectrum sensing methods such as Eigen value based detection, energy detection and matched filter detection are implemented. All the above techniques have their own pros and cons. The main aim of this work is to classify the techniques based on the key parameters such as probability of detection, probability of false alarm and signal to noise ratio, to identify the adaptive threshold for each technique, implement and evaluate an adaptive sensing technique using MATLAB Keywords-cognitive radio, spectrum sensing, energy detection, matched filter detection, eigen value detection 978-1-4799-7623-2/15/$31.00 c 2015 IEEE

Retracted : Comparative analysis of distributive linear and nonlinear optimized spectrum sensing clustering techniques in cognitive radio network systems

“…Estimate the SNR for the detection of received signal and decide output from detection performance of spectrum sensing techniques. The threshold λ = 4 dB is based on the experimental results and observations [19]. If threshold value λ is greater than SNR (the primary user over channel is falsely detected ‘H 1 ’), then the hypothesis model is performed by the probability of false alarm technique.…”

“…The performance of the detector under various values of probability of false alarm for SNR = 4dB. However, the level of SNR = 4dB is a little high for a proper range in the spectrum sensing [19]. In the proposed method of DJFOC, the probability of detection is optimum when the P FA value is > 0.1 compared to DSOC and DFOC.…”

“…Estimate the SNR for the detection of received signal and decide output from detection performance of spectrum sensing techniques. The threshold λ = 4 dB is based on the experimental results and observations [19].…”

“…Whereas a large inertia weight enables a global search. DSOC provides a simple linear mechanism and the best of master particles use the objective function until an optimum global best position is reached [16][17][18][19][20]. The most important drawback of normal PSO is the fact that it has no proper communication between R e t r a c t e d particles for merging.…”

Adaptive Spectrum Sensing in Cognitive Radio Networks

“…In this paper we propose an Adaptive Spectrum Sensing Technique in which it adapts the sensing method according to the frequently changing wireless environment and the available information. The flowchart for the adaptive spectrum sensing technique is given in figure 5 [7].…”