چكيده انگليسي :
Cooperative Users Selection in Spectrum Sensing of Cognitive Radio Networks Based on Random Matrix Theory Nariman Abdi n abdi@ec iut ac ir 1394 03 24 Department of Electrical and Computer Enginnering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language FarsiDr Ehsan Yazdian yazdian@cc iut ac irAbstractDevelopments in wireless communication systems and increasing demand for data transmission cause pushcommunication systems to acquire more and more spectral resources Thus fixed spectrum allocation is not anefficient technique for new communication networks Cognitive radio is a novel solution to overcome thislimitation Users in cognitive radio networks are secondary users SU who utilize spectrum opportunities forcommunication in the absence of a primary user PU who is the licensed owner of a frequency band In thisthesis we investigate the spectrum sensing challenges in cognitive radio networks Knowledge about PUspresence absence is a key problem because missing a PU results in interference and jeopardizes its performance Beginning with single user spectrum sensing methods we show that the cooperative spectrum sensingoutperforms the former approach considerably Then we study challenges of cooperative spectrum sensing incognitive radio networks Cooperative spectrum sensing methods utilize more available resource e g bandwidthand power We describe local sensing methods like energy cyclostationary and eigenvalue based detection thatare used in cooperative spectrum sensing in cognitive radio networks and evaluate their advantages and shortcomings Among these schemes energy detection and eigenvalue based methods need no information about PUsignal Energy detection is the simplest one but it needs the noise variance and shows poor performance if theestimated noise power is inaccurate Therefore it is not efficient for practical systems with a low PU SNRregime e g received SNR is about 20dB from a wireless microphone operating in TV bands several hundredmeters away Random matrix theory is a modern solution for complicated stochastic problems which studiesstochastical behavior of random matrix Random matrix theory provides asymptotical solutions to evaluatedetection threshold In this work we introduce popular random matrix like Gaussian matrix and Wishart matrixand study statistical distribution of random matrix eigenvalues and their extremes We analyze the eigenvaluebased spectrum sensing methods after the random matrix theory descriptions In a cognitive radio network detection perforamance could be enhanced by increasing number of cognitive users however more availableresources like bandwidth and power are needed On the other hand increasing the number of SUs leads to betterperformance of cooperative spectrum sensing however total probability of error has a lower bound andincreasing the number of SUs cannot decrease the bound further Due to this tradeoff between detectionperformance and available resource usage we investigate optimum number of cognitive users for cooperationand show that the maximal cooperation in spectrum sensing is not optimal However it is very important toselect the appropriate set of these users for cooperative spectrum sensing Due to this issue user selectionalgorithms are investigated and a new one is proposed We show that it outperforms random selection algorithmand decreases the computational complexity of eigenvalue based methods We find the optimum number ofusers for cooperative spectrum sensing via Monte Carlo simulations in MATLAB for several scenarios andshow the improvement Keywords Cooperative spectrum sensing Random matrix theory Eigenvalue basedspectrum sensing User selection Cognitive radio
