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See detailDirection of Arrival Estimation and Localization Exploiting Sparse and One-Bit Sampling
Sedighi, Saeid UL

Doctoral thesis (2021)

In the recent years, in order to reduce implementation costs and physical resources usage, there has been a growing tendency to exploit sparse and one-bit sampling in applications such as array processing ... [more ▼]

In the recent years, in order to reduce implementation costs and physical resources usage, there has been a growing tendency to exploit sparse and one-bit sampling in applications such as array processing, radar and wireless communication. This strategy for collecting data necessitates devising new algorithms for information retrieval in such applications. This fact has motivated further research in areas such as Directional of Arrival (DoA) estimation and source/target localization in the recent years. In this context, the first part of this thesis focuses on DoA estimation using Sparse Linear Arrays (SLAs). We consider this problem under three plausible scenarios from quantization perspective. Firstly, we assume that an SLA quantized the received signal to a large number of bits per samples such that the resulting quantization error can be neglected. Although the literature presents a variety of estimators under such circumstances, none of them are (asymptotically) statistically efficient. Motivated by this fact, we introduce a novel estimator for the DoA estimation from SLA data employing the Weighted Least Squares (WLS) method. We analytically show that the large sample performance of the proposed estimator coincides with the Cram\'{e}r-Rao Bound (CRB), thereby ensuring its asymptotic statistical efficiency. Next, we study the problem of DoA estimation from one-bit SLA measurements. The analytical performance of DoA estimation from one-bit SLA measurements has not yet been studied in the literature and performance analysis in the literature has be limited to simulations studies. Therefore, we study the performance limits of DoA estimation from one-bit SLA measurements through analyzing the identifiability conditions and the corresponding CRB. We also propose a new algorithm for estimating DoAs from one-bit quantized data. We investigate the analytical performance of the proposed method through deriving a closed-form expression for the covariance matrix of its asymptotic distribution and show that it outperforms the existing algorithms in the literature. Finally, the problem of DoA estimation from low-resolution multi-bit SLA measurements, e.g. $2$ or $4$ bit per sample, is studied. We develop a novel optimization-based framework for estimating DoAs from low-resolution multi-bit measurements. It is show that increasing the sampling resolution to $2$ or $4$ bits per samples could significantly increase the DoA estimation performance compared to the one-bit sampling case while the power consumption and implementation costs are still much lower compared to the high-resolution sampling scenario. In the second part of the thesis, the problem of target localization is addressed. Firstly, we consider the problem of passive target from one-bit data in the context of Narrowband Internet-of-Things (NB-IoT). In the recently proposed narrowband IoT (NB-IoT) standard, which trades off bandwidth to gain wide area coverage, the location estimation is compounded by the low sampling rate receivers and limited-capacity links. We address both of these NB-IoT drawbacks by consider a limiting case where each node receiver employs one-bit analog-to-digital-converters and propose a novel low-complexity nodal delay estimation method. Then, to support the low-capacity links to the fusion center (FC), the range estimates obtained at individual sensors are converted to one-bit data. At the FC, we propose a novel algorithm for target localization with the aggregated one-bit range vector. Our overall one-bit framework not only complements the low NB-IoT bandwidth but also supports the design goal of inexpensive NB-IoT location sensing. Secondly, in order to reduce bandwidth usage for performing high precision time of arrival-based localization, we developed a novel sparsity-aware target localization algorithm with application to automotive radars. The thesis concludes with summarizing the main research findings and some remarks on future directions and open problems. [less ▲]

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See detailLocalization Performance of 1-Bit Passive Radars in NB-IoT Applications using Multivariate Polynomial Optimization
Sedighi, Saeid UL; Mishra, Kumar Vijay; Mysore Rama Rao, Bhavani Shankar UL et al

in IEEE Transactions on Signal Processing (2021), 69

Several Internet-of-Things (IoT) applications provide location-based services, wherein it is critical to obtain accurate position estimates by aggregating information from individual sensors. In the ... [more ▼]

Several Internet-of-Things (IoT) applications provide location-based services, wherein it is critical to obtain accurate position estimates by aggregating information from individual sensors. In the recently proposed narrowband IoT (NB-IoT) standard, which trades off bandwidth to gain wide coverage, the location estimation is compounded by the low sampling rate receivers and limited-capacity links. We address both of these NB-IoT drawbacks in the framework of passive sensing devices that receive signals from the target-of-interest. We consider the limiting case where each node receiver employs one-bit analog-to-digital-converters and propose a novel low-complexity nodal delay estimation method using constrained-weighted least squares minimization. To support the low-capacity links to the fusion center (FC), the range estimates obtained at individual sensors are then converted to one-bit data. At the FC, we propose target localization with the aggregated one-bit range vector using both optimal and sub-optimal techniques. The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint r\textit{an}ge-\textit{tar}get location \textit{es}timation (ANTARES) algorithm. Our overall one-bit framework not only complements the low NB-IoT bandwidth but also supports the design goal of inexpensive NB-IoT location sensing. Numerical experiments demonstrate feasibility of the proposed one-bit approach with a 0.6\% increase in the normalized localization error for the small set of 20-60 nodes over the full-precision case. When the number of nodes is sufficiently large (>80), the one-bit methods yield the same performance as the full precision. [less ▲]

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See detailDoA Estimation Using Low-Resolution Multi-BitSparse Array Measurements
Sedighi, Saeid UL; Mysore Rama Rao, Bhavani Shankar UL; Soltanalian, Mojtaba et al

in IEEE Signal Processing Letters (2021)

This letter studies the problem of Direction of Arrival (DoA) estimation from low-resolution few-bit quantized data collected by Sparse Linear Array (SLA). In such cases, contrary to the one-bit ... [more ▼]

This letter studies the problem of Direction of Arrival (DoA) estimation from low-resolution few-bit quantized data collected by Sparse Linear Array (SLA). In such cases, contrary to the one-bit quantization case, the well known arcsine law cannot be employed to estimate the covaraince matrix of unquantized array data. Instead, we develop a novel optimization-based framework for retrieving the covaraince matrix of unquantized array data from low-resolution few-bit measurements. The MUSIC algorithm is then applied to an augmented version of the recovered covariance matrix to find the source DoAs. The simulation results show that increasing the sampling resolution to $2$ or $4$ bits per samples could significantly increase the DoA estimation performance compared to the one-bit sampling regime while the power consumption and implementation costs is still much lower in comparison to the high-resolution sampling implementations. [less ▲]

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See detailOn the Asymptotic Performance of One-Bit Co-Array-Based Music
Sedighi, Saeid UL; Mysore Rama Rao, Bhavani Shankar UL; Soltanalian, Mojtaba et al

in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2021)

Co-array-based Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable attention in array processing thanks to its capability of providing enhanced degrees ... [more ▼]

Co-array-based Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable attention in array processing thanks to its capability of providing enhanced degrees of freedom for DoAs that can be resolved. Additionally, deployment of one-bit Analog-to-Digital Converters (ADCs) has become an important topic in array processing, as it offers both a low-cost and a low-complexity implementation. Although the problem of DoA estimation form one-bit SLA measurements has been studied in some prior works, its analytical performance has not yet been investigated and characterized. In this paper, to provide valuable insights into the performance of DoA estimation from one-bit SLA measurements, we derive an asymptotic closed-form expression for the performance of One-Bit Co-Array-Based MUSIC (OBCAB-MUSIC). Further, numerical simulations are provided to validate the asymptotic closed-form expression for the performance of OBCAB-MUSIC and to show an interesting use case of it in evaluating the resolution of OBCAB-MUSIC. [less ▲]

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See detailOne-bitDoA estimation via Sparse Linear Arrays
Sedighi, Saeid UL; Mysore Rama Rao, Bhavani Shankar UL; Soltanalian, Mojtaba et al

in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2020)

Parameter estimation from noisy and one-bit quantized data has become an important topic in signal processing, as it offers low cost and low complexity in the implementation. On the other hand, Direction ... [more ▼]

Parameter estimation from noisy and one-bit quantized data has become an important topic in signal processing, as it offers low cost and low complexity in the implementation. On the other hand, Direction-of-Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable interest in array processing due to their attractive capability of providing enhanced degrees of freedom. In this paper, the problem of DoA estimation from one-bit measurements received by an SLA is considered and a novel framework for solving this problem is proposed. The proposed approach first provides an estimate of the received signal covariance matrix through minimization of a constrained weighted least-squares criterion. Then, MUSIC is applied to the spatially smoothed version of the estimated covariance matrix to find the DoAs of interest. Several numerical results are provided to demonstrate the superiority of the proposed approach over its counterpart already propounded in the literature. [less ▲]

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See detailLocalization Performance of 1-Bit Passive Radars in NB-IoT Applications
Sedighi, Saeid UL; Mishra, Kumar Vijay; Shankar, Bhavani UL et al

in IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (2019, December 14)

Location-based services form an important use-case in emerging narrowband Internet-of-Things (NB-IoT) networks. Critical to this offering is an accurate estimation of the location without overlaying the ... [more ▼]

Location-based services form an important use-case in emerging narrowband Internet-of-Things (NB-IoT) networks. Critical to this offering is an accurate estimation of the location without overlaying the network with additional active sensors. The massive number of devices, low power requirement, and low bandwidths restrict the sampling rates of NB-IoT receivers. In this paper, we propose a novel low-complexity approach for NB-IoT target delay estimation in cases where one-bit analog-to-digital-converters (ADCs) are employed to sample the received radar signal instead of high-resolution ADCs. This problem has potential applications in the design of inexpensive NB-IoT radar and sensing devices. We formulate the target estimation as a multivariate fractional optimization problem and solve it via Lasserre's semi-definite program relaxation. Numerical experiments suggest feasibility of the proposed approach yielding high localization accuracy with a very low number of 1-bit samples. [less ▲]

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See detailOptimum Design for Sparse FDA-MIMO Automotive Radar
Sedighi, Saeid UL; Shankar, Bhavani UL; Mishra, Kumar Vijay et al

in Asilomar Conference on Signals, Systems, and Computers (2019, November 03)

Automotive radars usually employ multiple-input multiple-output (MIMO) antenna arrays to achieve high azimuthal resolution with fewer elements than a phased array. Despite this advantage, hardware costs ... [more ▼]

Automotive radars usually employ multiple-input multiple-output (MIMO) antenna arrays to achieve high azimuthal resolution with fewer elements than a phased array. Despite this advantage, hardware costs and desired radar size limits the usage of more antennas in the array. Similar trade-off is encountered while attempting to achieve high range resolution which is limited by the signal bandwidth. However, nowadays given the demand for spectrum from communications services, wide bandwidth is not readily available. To address these issues, we propose a sparse variant of Frequency Diverse Array MIMO (FDA-MIMO) radar which enjoys the benefits of both FDA and MIMO techniques, including fewer elements, decoupling, and efficient joint estimation of target parameters. We then employ the Cram\'{e}r-Rao bound for angle and range estimation as a performance metric to design the optimal antenna placement and carrier frequency offsets for the transmit waveforms. Numerical experiments suggest that the performance of sparse FDA-MIMO radar is very close to the conventional FDA-MIMO despite 50\% reduction in the bandwidth and antenna elements. [less ▲]

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See detailAn Asymptotically Efficient Weighted Least Squares Estimator for Co-Array-Based DoA Estimation
Sedighi, Saeid UL; Shankar, Bhavani UL; Ottersten, Björn UL

in IEEE Transactions on Signal Processing (2019)

Co-array-based Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable interest in array processing thanks to its capability of providing enhanced degrees ... [more ▼]

Co-array-based Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable interest in array processing thanks to its capability of providing enhanced degrees of freedom. Although the literature presents a variety of estimators in this context, none of them are proven to be statistically efficient. This work introduces a novel estimator for the co-array-based DoA estimation employing the Weighted Least Squares (WLS) method. An analytical expression for the large sample performance of the proposed estimator is derived. Then, an optimal weighting is obtained so that the asymptotic performance of the proposed WLS estimator coincides with the Cram\'{e}r-Rao Bound (CRB), thereby ensuring asymptotic statistical efficiency of resulting WLS estimator. This implies that the proposed WLS estimator has a significantly better performance compared to existing methods. Numerical simulations are provided to validate the analytical derivations and corroborate the improved performance. [less ▲]

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See detailDesigning (In) finite-alphabet Sequences via Shaping the Radar Ambiguity Function
Alaee-Kerahroodi, Mohammad UL; Sedighi, Saeid UL; MR, Bhavani Shankar et al

in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019)

In this paper, a new framework for designing the radar transmit waveform is established through shaping the radar Ambiguity Function (AF). Specifically, the AF of the phase coded waveforms are analyzed ... [more ▼]

In this paper, a new framework for designing the radar transmit waveform is established through shaping the radar Ambiguity Function (AF). Specifically, the AF of the phase coded waveforms are analyzed and it is shown that a continuous/discrete phase sequence with the desired AF can be obtained by solving an optimization problem promoting equality between the AF of the transmit sequence and the desired AF. An iterative algorithm based on Coordinate Descent (CD) method is introduced to deal with the resulting non-convex optimization problem. Numerical results illustrate the proposed algorithm make it possible to design sequences with remarkably high tolerance towards Doppler frequency shifts, which is of interest to the future generations of automotive radar sensors. [less ▲]

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See detailDesigning (In) finite-alphabet Sequences via Shaping the Radar Ambiguity Function
Alaee-Kerahroodi, Mohammad UL; Sedighi, Saeid UL; MR, Bhavani Shankar et al

in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019)

In this paper, a new framework for designing the radar transmit waveform is established through shaping the radar Ambiguity Function (AF). Specifically, the AF of the phase coded waveforms are analyzed ... [more ▼]

In this paper, a new framework for designing the radar transmit waveform is established through shaping the radar Ambiguity Function (AF). Specifically, the AF of the phase coded waveforms are analyzed and it is shown that a continuous/discrete phase sequence with the desired AF can be obtained by solving an optimization problem promoting equality between the AF of the transmit sequence and the desired AF. An iterative algorithm based on Coordinate Descent (CD) method is introduced to deal with the resulting non-convex optimization problem. Numerical results illustrate the proposed algorithm make it possible to design sequences with remarkably high tolerance towards Doppler frequency shifts, which is of interest to the future generations of automotive radar sensors. [less ▲]

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See detailDesigning (In) finite-alphabet Sequences via Shaping the Radar Ambiguity Function
Alaee-Kerahroodi, Mohammad UL; Sedighi, Saeid UL; MR, Bhavani Shankar et al

in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019)

In this paper, a new framework for designing the radar transmit waveform is established through shaping the radar Ambiguity Function (AF). Specifically, the AF of the phase coded waveforms are analyzed ... [more ▼]

In this paper, a new framework for designing the radar transmit waveform is established through shaping the radar Ambiguity Function (AF). Specifically, the AF of the phase coded waveforms are analyzed and it is shown that a continuous/discrete phase sequence with the desired AF can be obtained by solving an optimization problem promoting equality between the AF of the transmit sequence and the desired AF. An iterative algorithm based on Coordinate Descent (CD) method is introduced to deal with the resulting non-convex optimization problem. Numerical results illustrate the proposed algorithm make it possible to design sequences with remarkably high tolerance towards Doppler frequency shifts, which is of interest to the future generations of automotive radar sensors. [less ▲]

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See detailA Statistically Efficient Estimator for Co-array Based DoA Estimation
Sedighi, Saeid UL; Shankar, Bhavani UL; Ottersten, Björn UL

in Asilomar Conference on Signals, Systems, and Computers (2018, October)

Co-array-based Direction of Arrival (DoA) estimation using Sparse linear arrays (SLAs) has recently gained considerable interest in array processing due to the attractive capability of providing enhanced ... [more ▼]

Co-array-based Direction of Arrival (DoA) estimation using Sparse linear arrays (SLAs) has recently gained considerable interest in array processing due to the attractive capability of providing enhanced degrees of freedom. Although a variety of estimators have been suggested in the literature for co-array-based DoA estimation, none of them are statistically efficient. This work introduces a novel Weighted Least Squares (WLS) estimator for the co-array-based DoA estimation employing the covariance fitting method. Then, an optimal weighting is given so that the asymptotic performance of the proposed WLS estimator coincides with the Cram\'{e}r-Rao Bound (CRB), thereby ensuring statistical efficiency of resulting WLS estimator. This implies that the proposed WLS estimator has significantly better performance compared to existing methods in the literature. Numerical simulations are provided to corroborate the asymptotic statistical efficiency and the improved performance of the proposed estimator. [less ▲]

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See detailCONSISTENT LEAST SQUARES ESTIMATOR FOR CO-ARRAY-BASED DOA ESTIMATION
Sedighi, Saeid UL; Shankar, Bhavani UL; Maleki, Sina et al

in IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) (2018, July)

Sparse linear arrays (SLAs), such as nested and co-prime arrays, have the attractive capability of providing enhanced degrees of freedom by exploiting the co-array model. Accordingly, co-array-based ... [more ▼]

Sparse linear arrays (SLAs), such as nested and co-prime arrays, have the attractive capability of providing enhanced degrees of freedom by exploiting the co-array model. Accordingly, co-array-based Direction of Arrivals (DoAs) estimation has recently gained considerable interest in array processing. The literature has suggested applying MUSIC on an augmented sample covariance matrix for co-array-based DoAs estimation. In this paper, we propose a Least Squares (LS) estimator for co-array-based DoAs estimation employing the covariance fitting method as an alternative to MUSIC. We show that the proposed LS estimator provides consistent estimates of DoAs of identifiable sources for SLAs. Additionally, an analytical expression for the large sample performance of the proposed estimator is derived. Numerical results illustrate the finite sample behavior in relation to the derived analytical expression. Moreover, the performance of the proposed LS estimator is compared to the co-array-based MUSIC. [less ▲]

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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 detailMulti-Target Localization in Asynchronous MIMO Radars Using Sparse Sensing
Sedighi, Saeid UL; Shankar, Bhavani UL; Maleki, Sina UL et al

in IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (2017)

Multi-target localization, warranted in emerging applications like autonomous driving, requires targets to be perfectly detected in the distributed nodes with accurate range measurements. This implies ... [more ▼]

Multi-target localization, warranted in emerging applications like autonomous driving, requires targets to be perfectly detected in the distributed nodes with accurate range measurements. This implies that high range resolution is crucial in distributed localization in the considered scenario. This work proposes a new framework for multi-target localization, addressing the demand for the high range resolution in automotive applications without increasing the required bandwidth. In particular, it employs sparse stepped frequency waveform and infers the target ranges by exploiting sparsity in target scene. The range measurements are then sent to a fusion center where direction of arrival estimation is undertaken. Numerical results illustrate the impact of range resolution on multi-target localization and the performance improvement arising from the proposed algorithm in such scenarios. [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 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 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 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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