References of "Taherpour, Abbas"
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See detailEigenvalue-Based Multiple Antenna Spectrum Sensing: Higher Order Moments
Sedighi, Saeid UL; Taherpour, Abbas; Gazor, Saeed et al

in IEEE Transactions on Wireless Communications (2017), 16(2), 11681184

The problem of multiple antenna spectrum sensing in cognitive radio (CR) networks is studied in this paper. We propose two new invariant constant false-alarm rate eigenvalue-based (EVB) detectors, using ... [more ▼]

The problem of multiple antenna spectrum sensing in cognitive radio (CR) networks is studied in this paper. We propose two new invariant constant false-alarm rate eigenvalue-based (EVB) detectors, using the higher order moments of the sample covariance matrix eigenvalues, by exploiting the separating function estimation test framework. We find closed-form expressions for the false-alarm and detection probabilities of the proposed detectors by providing moment-based approximations of their statistical distributions. The accuracy of the obtained closed-form expressions is validated by Monte Carlo simulations. In addition, we compare the performance of the proposed detectors with that of their two counterparts, i.e., John’s and the arithmetic to geometric mean (AGM) detectors, in terms of the asymptotic relative efficiency. This comparison enables us to demonstrate the superiority of our proposed detectors over those detectors within the typical range of signalto-noise ratio in CR application. The comparative simulation results also illustrate the superiority of the proposed detectors over John’s and the AGM detectors as well as some other state-of-the-art EVB algorithms given in the literature. [less ▲]

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See detailMultiantenna GLR Detection of Rank-One Signals With Known Power Spectral Shape Under Spatially Uncorrelated Noise
Sala, Josep; Vázquez-Vilar;, Gonzalo; López-Valcarce, Roberto et al

in IEEE Transactions on Signal Processing (2016), 64(23), 6269-6283

We establish the generalized likelihood ratio (GLR) test for a Gaussian signal of known power spectral shape and unknown rank-one spatial signature in additive white Gaussian noise with an unknown ... [more ▼]

We establish the generalized likelihood ratio (GLR) test for a Gaussian signal of known power spectral shape and unknown rank-one spatial signature in additive white Gaussian noise with an unknown diagonal spatial correlation matrix. This is motivated by spectrum sensing problems in dynamic spectrum access, in which the temporal correlation of the primary signal can be assumed known up to a scaling, and where the noise is due to an uncalibrated receive array. For spatially independent identically distributed (i.i.d.) noise, the corresponding GLR test reduces to a scalar optimization problem, whereas the GLR detector in the general non-i.i.d. case yields a more involved expression, which can be computed via alternating optimization methods. Low signal-to-noise ratio (SNR) approximations to the detectors are given, together with an asymptotic analysis showing the influence on detection performance of the signal power spectrum and SNR distribution across antennas. Under spatial rank-P conditions, we show that the rank-one GLR detectors are consistent with a statistical criterion that maximizes the output energy of a beamformer operating on filtered data. Simulation results support our theoretical findings in that exploiting prior knowledge on the signal power spectrum can result in significant performance improvement. [less ▲]

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See detailSFET-Based Multiple Antenna Spectrum Sensing Using the Second Order Moments of Eigenvalues
Sedighi, Saeid UL; Taherpour, Abbas; Gazor, Saeed et al

in IEEE Global Communications Conference (GLOBECOM) (2015)

In this paper, we propose a new detector for multiantenna spectrum sensing in cognitive radios (CR) by exploiting the Separating Function Estimation Test (SFET) framework. Specifically, we consider a ... [more ▼]

In this paper, we propose a new detector for multiantenna spectrum sensing in cognitive radios (CR) by exploiting the Separating Function Estimation Test (SFET) framework. Specifically, we consider a blind scenario for multiantenna spectrum sensing in which both the channel gains and noise variance are assumed to be unknown. For such a scenario, we find an appropriate Separating Function (SF) whose Maximum Likelihood Estimate (MLE) leads us to a SFET-based detector which uses the second order moments of the eigenvalues of the Sample Covariance Matrix (SCM). We also find closed-form expressions for the detection and false-alarm probabilities of the proposed detector. The performance of the proposed detector asymptotically tends to that of the Uniformly Most Powerful Unbiased (UMPU) detector as the number of independent and identically distributed observations increases. In addition, simulation results show that the proposed detector outperforms the state-of-art eigenvalue- based detectors because of using the second order moments of the SCM eigenvalues. [less ▲]

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See detailDetection of Temporally Correlated Primary User Signal with Multiple Antennas
Hashemi, Hadi; Mohammadi Fard, Sina; Taherpour, Abbas et al

in International Conference on Cognitive Radio Oriented Wireless Networks (CROWNCOM) (2015)

In this paper, we address the problem of multiple antenna spectrum sensing in cognitive radios (CRs) when the samples of the primary user (PU) signal as well as samples of noise are assumed to be ... [more ▼]

In this paper, we address the problem of multiple antenna spectrum sensing in cognitive radios (CRs) when the samples of the primary user (PU) signal as well as samples of noise are assumed to be temporally correlated. We model and formulate this multiple antenna spectrum sensing problem as a hypothesis testing problem. First, we derive the optimum Neyman-Pearson (NP) detector for the scenario in which the channel gains, the PU signal and noise correlation matrices are assumed to be known. Then, we derive the sub-optimum generalized likelihood ratio test (GLRT)-based detector for the case when the channel gains and aforementioned matrices are assumed to be unknown. Approximate analytical expressions for the false-alarm probabilities of the proposed detectors are given. Simulation results show that the proposed detectors outperform some recently-purposed algorithms for multiple antenna spectrum sensing. [less ▲]

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See detailOn the Performance of Hadamard Ratio Detector-based Spectrum Sensing for Cognitive Radios
Sedighi, Saeid UL; Taherpour, Abbas; Sala, Josep et al

in IEEE Transactions on Signal Processing (2015), 63(14), 38093824

—We consider the problem of multiantenna spectrum sensing (SS) in cognitive radios (CRs) when the receivers are assumed to be uncalibrated across the antennas. The performance of the Hadamard Ratio ... [more ▼]

—We consider the problem of multiantenna spectrum sensing (SS) in cognitive radios (CRs) when the receivers are assumed to be uncalibrated across the antennas. The performance of the Hadamard Ratio Detector (HRD) is analyzed in such a scenario. Specifically, we first derive the exact distribution of the HRD statistic under the null hypothesis, which leads to an elaborate but closed-form expression for the false-alarm probability. Then, we derive a simpler and tight closed-form approximation for both the false-alarm and detection probabilities by using a moment-based approximation of the HRD statistical distribution under both hypotheses. Finally, the accuracy of the obtained results is verified by simulations. [less ▲]

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See detailMultiple Antenna Cyclostationary-Based Detection of Primary Users with Multiple Cyclic Frequency in Cognitive Radios
Sedighi, Saeid UL; Taherpour, Abbas; Khattab, Tamer et al

in IEEE Global Communications Conference (GLOBECOM) (2014)

In this paper, we study the problem of multiple antenna spectrum sensing by using cyclostationary features of Primary Users (PUs) signals in Cognitive Radios (CRs). We consider the general case of ... [more ▼]

In this paper, we study the problem of multiple antenna spectrum sensing by using cyclostationary features of Primary Users (PUs) signals in Cognitive Radios (CRs). We consider the general case of multiple antenna sensing in the presence of spatially and temporally correlated noise when the PU signal has more than one cyclic frequency. We model and formulate the multiple antenna sensing problem as a composite hypothesis testing problem and use the Generalized Likelihood Ratio Test (GLRT) to derive a detector for the general model mentioned above. Then, we also propose the GLRT-based detectors for the two special cases of: 1) spatially uncorrelated but colored noise; 2) spatially white noise. Moreover, in order to calculate the decision threshold, the asymptotic performance of the proposed detectors under the null hypothesis is given. The provided simulation results show the superiority of the performance of the proposed detectors compared to the recently-proposed cyclostationary-based detectors. [less ▲]

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See detailDistributed Spectrum Sensing of Correlated Observations in Cognitive Radio Networks,
Sedighi, Saeid UL; Pourgharehkhan, Zahra; Taherpour, Abbas et al

in IEEE GCC Conference and Exhibition (GCC), (2013)

In this paper, Collaborative Spectrum Sensing (CSS) as one of the most efficient sensing approaches in Cognitive Radio Networks (CRNs) is investigated when the Secondary Users (SUs) observations are ... [more ▼]

In this paper, Collaborative Spectrum Sensing (CSS) as one of the most efficient sensing approaches in Cognitive Radio Networks (CRNs) is investigated when the Secondary Users (SUs) observations are assumed to be correlated. A novel soft decision rule based on the covariance matrix of the SUs observations is proposed. By using the proposed scheme, we derive two Generalized Likelihood Ratio (GLR) detectors and then, we obtain the closed-form expressions for the detection and false-alarm probabilities. The proposed collaborative sensing method can control the available trade-off between efficient spectrum usage and more accurate spectrum sensing, which is not possible in the other counterpart collaborative sensing methods based on the soft decision rule. In order to have the best performance in the terms of spectral efficiency, power efficiency and spectrum sensing, we study the problem of designing the fusion parameter, the decision threshold and the number of SUs to maximize power efficiency and spectrum usage efficiency under the constraint that the Primary User (PU) is sufficiently protected. Finally, we provide the computer simulations to verify the validity of the obtained results. [less ▲]

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See detailSpectrum sensing using correlated receiving multiple antennas in cognitive radios
Sedighi, Saeid UL; Taherpour, Abbas; Sala, Josep

in IEEE Transactions on Wireless Communications (2013), 12(11), 57545766

In this paper, we address the problem of multiantenna spectrum sensing in Cognitive Radios (CRs) by considering the correlation between the received channels at different antennas. First, we derive the ... [more ▼]

In this paper, we address the problem of multiantenna spectrum sensing in Cognitive Radios (CRs) by considering the correlation between the received channels at different antennas. First, we derive the optimum genie-aided detector which assumes perfect knowledge of the antenna correlation coefficients, Primary User (PU) signal power and noise variance. This is used as a benchmark for comparing with more practical detectors when some or all of these parameters are unknown to the Secondary User (SU). Two scenarios are considered: 1) the antenna correlation coefficients and PU signal power are unknown to the SU; 2) the antenna correlation coefficients, PU signal power and noise variance are unknown to the SU. To derive sub-optimum detectors for these two scenarios, we apply the Rao test, an asymptotically equivalent test to the Generalized Likelihood Ratio Test (GLRT) that does not require the Maximum Likelihood (ML) estimates of unknown parameters. Additionally, we calculate analytical approximations to the detection and false-alarm probabilities of the proposed detectors and verify them with Monte-Carlo simulations. The simulation results show that these new detectors outperform several recently proposed detectors for CR using multiple antennas. [less ▲]

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See detailFinite-Sample Size Multiple Antennas Spectrum Sensing
Sedighi, Saeid UL; Taherpour, Abbas; Khattab, Tamer

in International Conference on Wireless Communications and Signal Processing (WCSP) (2012)

In this paper, we consider the problem of multiple antenna spectrum sensing in Cognitive Radios (CR) by exploiting the prior information about unknown parameters. Specifically, we consider a blind ... [more ▼]

In this paper, we consider the problem of multiple antenna spectrum sensing in Cognitive Radios (CR) by exploiting the prior information about unknown parameters. Specifically, we consider a blind spectrum sensing problem when the channel gains and the noise variance are unknown for the Secondary User (SU). Under assumption that additional statistical side-information is available about unknown parameters, we use a novel Generalized Likelihood Ratio (GLR) test, which is optimal under finite number of samples, in order to derive our proposed detector. As it has been shown, this novel GLR test need to obtain the Maximum A-posteriori Probability (MAP) estimation of unknown parameters which it is impossible to obtain them in closed form for our case. Thus, we calculate them based on the Expectation-Maximization (EM) algorithm. The simulation results show that our proposed detector has good performance even for finite number of samples and also outperforms the classical GLR detector. [less ▲]

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See detailSpectrum Sensing of Correlated Subbands With Colored Noise in Cognitive Radios
Pourgharehkhan, Zahra; Sedighi, Saeid UL; Taherpour, Abbas et al

in IEEE Wireless Communications and Networking Conference (WCNC) (2012)

In this paper, we consider the problem of wideband spectrum sensing by using the correlation among the observation samples in different subbands. The Primary User (PU) signal samples in occupied subbands ... [more ▼]

In this paper, we consider the problem of wideband spectrum sensing by using the correlation among the observation samples in different subbands. The Primary User (PU) signal samples in occupied subbands are assumed to be zero-mean correlated Gaussian random variables and additive noise is modeled as colored zero-mean Gaussian random variables independent of the PU signal. It is also assumed that there is at least a minimum given number of subbands that are vacant of PU signals. First we derive the optimal detector and the Generalized Likelihood Ratio (GLR) detector for the case that the covariance matrix of PUs signal samples is unknown and the noise variance in the different subbands is known. Then, we propose an iterative algorithm for GLR test when both the covariance matrix of the PUs signal samples and the noise variances in the different subbands, are unknown. For analytical performance evaluation, we derive some closed-form expressions for detection and false alarm probabilities of the proposed detectors in low Signal to Noise Ratio (SNR) regime. The simulation results are further presented to compare the performance of the proposed detectors. [less ▲]

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