Blind Spectrum Sensing Using Antenna Arrays and Path Correlation

Orooji, Mahdi; Soosahabi, Reza; Naraghi-Pour, Mort

doi:10.1109/tvt.2011.2166283

Cited by 25 publications

(19 citation statements)

References 36 publications

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“…The cross correlation among the received signals is a result of the correlation among the channel path coefficients from the primary user (PU) transmitter to different antenna elements of the secondary receiver [12]. …”

Section: Literature Surveymentioning

confidence: 99%

Performance Analysis of Cyclostationary Spectrum Sensing Over Different Fading Channels

Bhandari¹,

Singh²

2015

IJCA

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In research area of wireless communication, cognitive radio gets more endearment in recent times. The main motive behind the use of cognitive radio is to sense the available spectrum, which is very limited, for the users who wish to use it for the transmission purpose. The users can be primary or secondary, based on, whether they are licensed or unlicensed. Different goals of cognitive radio include spectrum sensing, spectrum sharing, spectrum management, and spectrum mobility. Spectrum sensing plays vital role in the cognitive radio system since it is used to detect signal presence on the air. This paper signifies the role of Cyclostationary Spectrum Sensing technique to define a device capable of detecting OFDM signals in a noisy environment. The work has been done for the applications employed in high frequency, such as, Wi-Fi and WiMAX.

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Section: Literature Surveymentioning

confidence: 99%

Performance Analysis of Cyclostationary Spectrum Sensing Over Different Fading Channels

Bhandari¹,

Singh²

2015

IJCA

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“…We also assume, following related works [25], [27], [29], that the signal is low-rate (low bandwidth) so that a flat-fading model constitutes a close approximation to reality. For an M -antenna SU receiver, let z m (t) = x P,m (t) + n m (t) denote the signal at the m-th antenna, with x P,m (t) the received noiseless pass-band PU signal and n m (t) the corresponding additive white Gaussian noise process of power spectral density …”

Section: A Basic Assumptions and Problem Statementmentioning

confidence: 99%

“…However, most research works on multiantenna spectrum sensing have assumed unknown deterministic channel gains at each antenna and have not considered statistical correlations between the antenna channel 1536-1276/13$31.00 c 2013 IEEE gains. Multiantenna spectrum sensing considering statistical correlations between the antenna channel gains has recently been studied in some works [25]- [29]. The performance of the multiantenna ED over correlated Rayleigh and Nakagamim faded channels is derived in [25] and [26], respectively.…”

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confidence: 99%

“…In [27], the authors propose a weighted ED for multiantenna spectrum sensing with correlated receiving antennas when the antenna correlation coefficients, PU signal power and noise variance are known to the SU; Moreover, the authors derive a GLRT-based detector by using an approximation in the low-SNR regime when only the noise variance is unknown to the SU. In [29], the authors propose a detector that uses the weighted linear combination of the estimates of the cross correlation among the signals received at different antennas when the noise variance is unknown but the PU signal power and antenna correlation coefficients are known to the SU. Nevertheless, as shown in [25] and [26], the performance of the ED degrades significantly under the correlated receiving antennas model.…”

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confidence: 99%

“…Nevertheless, as shown in [25] and [26], the performance of the ED degrades significantly under the correlated receiving antennas model. Moreover, the proposed detectors in [27] and [29] have been derived assuming that the PU signal power and the antenna correlation coefficients are known to the SU, which, although useful for benchmarking, is not realistic in practice.…”

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confidence: 99%

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Spectrum Sensing Using Correlated Receiving Multiple Antennas in Cognitive Radios

Sedighi

Taherpour

Sala

2013

IEEE Trans. Wireless Commun.

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Abstract-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.Index Terms-Cognitive radio, spectrum sensing, multiple antennas, Rao test, antenna correlations, Fisher information matrix, noise variance mismatch, antenna array.

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Fast adjustable spectrum sensing circuit for cognitive radio applications

Vahidpoor

Forooraghi

Fotowat-Ahmady

et al. 2013

Circuit Theory & Apps

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A fast and adjustable spectrum sensing method is proposed using a phase frequency detector for cognitive radio applications. Using this method, spectral sensing can be done in a very short time by a low-cost, low-power circuit, compared with available implemented methods.Different aspects of sensing procedure in different situations are investigated including frequency sweep time and system errors. Accurate operation of this method is verified through simulations, and comparison with other methods shows faster sweep rates.

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Blind Spectrum Sensing Using Antenna Arrays and Path Correlation

Cited by 25 publications

References 36 publications

Performance Analysis of Cyclostationary Spectrum Sensing Over Different Fading Channels

Performance Analysis of Cyclostationary Spectrum Sensing Over Different Fading Channels

Spectrum Sensing Using Correlated Receiving Multiple Antennas in Cognitive Radios

Fast adjustable spectrum sensing circuit for cognitive radio applications

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