Spectrum Sensing Using Correlated Receiving Multiple Antennas in Cognitive Radios

Sedighi, Saeid; Taherpour, Abbas; Sala, J.

doi:10.1109/twc.2013.100213.130158

Cited by 23 publications

(7 citation statements)

References 39 publications

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“…We let n (m) ∈ C L denote the m th antenna zero-mean circular complex additive white Gaussian noise vector and N its corresponding time × antenna data matrix. The noise spatial covariance matrix [18]. Considering these assumptions, the data matrix Y is modeled with independent zero-mean rows, so that, for 1 ≤ i ≤ L, we may rewrite (1) as,…”

Section: Basic Assumption and System Modelmentioning

confidence: 99%

“…Those models, also contemplated in [18], reduce to Clark's model [21] for the parameter settings φ = 0 and ∆ = π. The expressions for C in [18] (Eqs.…”

Section: Basic Assumption and System Modelmentioning

confidence: 99%

“…Those models, also contemplated in [18], reduce to Clark's model [21] for the parameter settings φ = 0 and ∆ = π. The expressions for C in [18] (Eqs. 6,7,8) have been used in the simulations section as a validation scenario.…”

Section: Basic Assumption and System Modelmentioning

confidence: 99%

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Correlated Multiple Antennas Spectrum Sensing Under Calibration Uncertainty

Pourgharehkhan

Taherpour

Sala-Alvarez

et al. 2015

IEEE Trans. Wireless Commun.

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Abstract-We address the problem of spectrum sensing in Cognitive Radios (CRs) when the Secondary User (SU) is equipped with a multiantenna receiver. We consider scenarios with correlation between the received channels at different antennas and unequal per-antenna noise variances to accommodate calibration errors. First, we derive the genie-aided (benchmark) detector with perfect knowledge of the antenna correlation coefficients, Primary User (PU) signal power and noise covariance matrix, as well as its asymptotic performance. Then, we consider the sensing problem in which the SU is non-cognizant of the perantenna noise variances, the PU signal power and the correlation of channel gains, with a specific treatment of the two-antenna case. For a general multiantenna receiver, we propose to combine the derived test statistics among all antenna pairs. The related optimization problem to obtain the optimum combination weights is analyzed, which requires the analytical performance characterization of the constituent two-antenna detector. Thus, we compute the exact performance of the proposed detector in a special case (a particular case of the Hadamard Ratio test) in terms of its detection and false alarm probabilities. Performance analyses are verified with simulations, showing that the proposed detector outperforms several recently-proposed multiantenna detectors for CR in the scenarios considered.

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Section: Basic Assumption and System Modelmentioning

confidence: 99%

“…Those models, also contemplated in [18], reduce to Clark's model [21] for the parameter settings φ = 0 and ∆ = π. The expressions for C in [18] (Eqs.…”

Section: Basic Assumption and System Modelmentioning

confidence: 99%

See 1 more Smart Citation

Correlated Multiple Antennas Spectrum Sensing Under Calibration Uncertainty

Pourgharehkhan

Taherpour

Sala-Alvarez

et al. 2015

IEEE Trans. Wireless Commun.

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“…In [2], the authors derive the optimum Neyman-Pearson (NP) and sub-optimum GLRT-based multiantenna detectors of an Orthogonal Frequency Division Multiplexing (OFDM) signal with a cyclic prefix of known length. The Rao test is applied to derive sub-optimum multiantenna detectors under the correlated receiving antennas model in [3]. In [4]- [6], the authors derive the GLRT detectors of spatial rank-one Primary User (PU) signals robust to noise variance uncertainty.…”

Section: Introductionmentioning

confidence: 99%

SFET-Based Multiple Antenna Spectrum Sensing Using the Second Order Moments of Eigenvalues

Sedighi¹,

Taherpour²,

Gazor³

et al. 2014

2015 IEEE Global Communications Conference (GLOBECOM)

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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.

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“…It is shown that the performance of multiple antenna based spectrum sensing is much better than that from a single antenna [9] because the former fully utilizes the correlation among antennas.…”

Section: Introductionmentioning

confidence: 99%

Sensing and Recognition When Primary User Has Multiple Power Levels

Li,

Gao,

Jiang

et al. 2013

Preprint

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In this paper, we present a new cognitive radio (CR) scenario when the primary user (PU) operates under more than one transmit power levels. Different from the existing studies where PU is assumed to have only one constant transmit power, the new consideration well matches the practical standards, i.e., IEEE 802.11 Series, GSM, LTE, LTE-A, etc., as well as the adaptive power concept that has been studied over the past decades. The primary target in this new CR scenario is, of course, still to detect the presence of PU. However, there appears a secondary target as to identify the PU's transmit power level.Compared to the existing works where the secondary user (SU) only senses the "on-off" status of PU, recognizing the power level of PU achieves more "cognition", and could be utilized to protect different powered PU with different interference levels. We derived quite many closed-form results for either the threshold expressions or the performance analysis, from which many interesting points and discussions are raised. We then further study the cooperative sensing strategy in this new cognitive scenario and show its significant difference from traditional algorithms. Numerical examples are provided to corroborate the proposed studies.

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Spectrum Sensing Using Correlated Receiving Multiple Antennas in Cognitive Radios

Cited by 23 publications

References 39 publications

Correlated Multiple Antennas Spectrum Sensing Under Calibration Uncertainty

Correlated Multiple Antennas Spectrum Sensing Under Calibration Uncertainty

SFET-Based Multiple Antenna Spectrum Sensing Using the Second Order Moments of Eigenvalues

Sensing and Recognition When Primary User Has Multiple Power Levels

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