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    Multiple antenna spectrum sensing in cognitive radios

    , Article IEEE Transactions on Wireless Communications ; Volume 9, Issue 2 , 2010 , Pages 814-823 ; 15361276 (ISSN) Taherpour, A ; Nasiri-Kenari, M ; Gazor, S ; Sharif University of Technology
    2010
    Abstract
    In this paper, we consider the problem of spectrum sensing by using multiple antenna in cognitive radios when the noise and the primary user signal are assumed as independent complex zero-mean Gaussian random signals. The optimal multiple antenna spectrum sensing detector needs to know the channel gains, noise variance, and primary user signal variance. In practice some or all of these parameters may be unknown, so we derive the Generalized Likelihood Ratio (GLR) detectors under these circumstances. The proposed GLR detector, in which all the parameters are unknown, is a blind and invariant detector with a low computational complexity. We also analytically compute the missed detection and... 

    Spectrum Sensing in Cognitive Radio Networks

    , Ph.D. Dissertation Sharif University of Technology Taherpour, Abbas (Author) ; Nasiri Kenari, Masoumeh (Supervisor)
    Abstract
    In this thesis, we consider the problem of spectrum sensing in cognitive radio networks. First, the collaborative energy detectors based spectrum sensing are investigated in the case of known noise variance for two models of primary user (PU) signal, i.e. random and unknown deterministic signals. Since the derived optimum collaborative energy detector requires the signal-to-noise ratio (SNR) of secondary users (SU) and it has complex structure, the generalized likelihood ratio (GLR) detector is proposed for both models of PU signal which leads to the same decision rules for both models. Simulation results show that the performance of the proposed GLR detector is near to that of optimal... 

    Source enumeration in large arrays using moments of eigenvalues and relatively few samples

    , Article IET Signal Processing ; Volume 6, Issue 7 , 2012 , Pages 689-696 ; 17519675 (ISSN) Yazdian, E ; Gazor, S ; Bastani, H ; Sharif University of Technology
    IET  2012
    Abstract
    This study presents a method based on minimum description length criterion to enumerate the incident waves impinging on a large array using a relatively small number of samples. The proposed scheme exploits the statistical properties of eigenvalues of the sample covariance matrix (SCM) of Gaussian processes. The authors use a number of moments of noise eigenvalues of the SCM in order to separate noise and signal subspaces more accurately. In particular, the authors assume a Marcenko-Pastur probability density function (pdf) for the eigenvalues of SCM associated with the noise subspace. We also use an enhanced noise variance estimator to reduce the bias leakage between the subspaces.... 

    Wideband spectrum sensing in unknown white Gaussian noise

    , Article IET Communications ; Volume 2, Issue 6 , 2008 , Pages 763-771 ; 17518628 (ISSN) Taherpour, A ; Gazor, S ; Nasiri Kenari, M ; Sharif University of Technology
    2008
    Abstract
    The spectrum sensing of a wideband frequency range is studied by dividing it into multiple subbands. It is assumed that in each subband either a primary user (PU) is active or absent in a additive white Gaussian noise environment with an unknown variance. It is also assumed that at least a minimum given number of subbands are vacant of PUs. In this multiple interrelated hypothesis testing problem, the noise variance is estimated and a generalised likelihood ratio detector is proposed to identify possible spectrum holes at a secondary user (SU). Provided that it is known that a specific PU can occupy a subset of subbands simultaneously, a grouping algorithm which allows faster spectrum...