Spectrum Sensing Using a Uniform Uncalibrated Linear Antenna Array for Cognitive Radios

Pourgharehkhan, Zahra; Taherpour, Abbas; Gazor, Saeed

doi:10.1109/twc.2018.2840693

Cited by 15 publications

(5 citation statements)

References 37 publications

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“…However, this method results in a worse response with increasing collaboration and maintenance coefficients. In addition, in our previous work [23][24][25], the use of the multi-antenna system and cooperation between the sensor improved the results.…”

Section: Numerical Results and Discussionmentioning

confidence: 84%

Collaborative data aggregation using multiple antennas sensors and fusion centre with energy harvesting capability in WSN

Choubin¹,

Taherpour²,

Rahmani³

2019

IET Communications

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Section: Numerical Results and Discussionmentioning

confidence: 84%

Collaborative data aggregation using multiple antennas sensors and fusion centre with energy harvesting capability in WSN

Choubin¹,

Taherpour²,

Rahmani³

2019

IET Communications

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“…Our proposed model is better than the model of Nevat et al 13 in the ROC results for the optimal detection schemes with equal number of FC antennas, because of usage EH between the sensors in WSN. In addition, in our previous work, 29,[31][32][33][34][35][36] the use of the multi-antenna system and cooperation between the sensors improved the results.…”

Section: Probability Of Detection Pmentioning

confidence: 80%

Optimization of distributed detection in energy harvesting wireless sensor networks with multiple antenna fusion center

Choubin

Taherpour

2020

Trans Emerging Tel Tech

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This paper addresses the detection improvement using multiple antenna fusion center (FC) and optimal collaboration among sensors with energy harvesting (EH) capability in WSNs. Using multiple antennas at FC, we benefit from the available fundamental spatial diversity in distributed WSN. We formulate to maximize the probability of collaborative detection based on parameters of collaboration among sensors and the energy of each sensor for the different scenarios as the optimization problem. In particular, we consider several practical constraints for EH and battery energy storage to maintain network connectivity. The objective function is the nonconvex function so the optimization problem is obtained as the nonconvex problem. To resolve this issue, the objective function is converted to a convex function using a relaxation method, and an optimum solution is obtained for this problem using local optimum values. The numerical results show the impact of the different parameters on the performance of the collaborative decision. These results also demonstrate that using distributed spatial diversity at FC by multiple antennas leads to a better decision in terms of error probability, and distributed wireless sensor‐enabled with EH capability results to more robust network connectivity in practice.

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Section: Simulation Resultsmentioning

confidence: 99%

“…We now conduct experiments to compare the performance of the proposed NCC method with the following seven methods: CAV [5], LMPIT [6], HDM [7], VD [8], SFET [9], NC-HDM [12], and NC-LAV [13]. At each run, the elements in H are drawn independent and identically distributed from CN (0, 1), and the noise powers {σ 2 m } M m=1 are independently drawn from the uniform distribution within [−α, α] dB, while the noise parameter is set as α = 1 in the simulations.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In the literature, several robust multiantenna SS (MSS) methods have been proposed for overcoming this drawback, such as the covariance absolute value (CAV) [5], locally most powerful invariant test (LMPIT) [6], Hadamard (HDM) ratio test [7], volume-based detection (VD) [8] and separating function estimation test (SFET) [9] methods can deliver desirable performance under the scenario of unequal per-antenna noise variances. Nevertheless, all these methods above were devised for circular signals as they only use the standard covariance information.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Covariance-Based Spectrum Sensing for Noncircular Signal in Cognitive Radio Networks With Uncalibrated Multiple Antennas

Chen

Shi

2020

IEEE Wireless Commun. Lett.

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In this letter, the problem of spectrum sensing is addressed for noncircular (NC) signal in cognitive radio networks with uncalibrated multiple antennas. Specifically, by taking both the standard covariance and complementary covariance information of the NC signal into account, a new robust spectrum sensing method called NC covariance (NCC) is proposed, which can fully reap the statistical property of the NC signals. Meanwhile, we derive the asymptotic distribution of the NCC statistic under the signal-absence hypothesis and obtain the theoretical decision threshold of the NCC method. Simulation results demonstrate that the proposed method is capable of outperforming state-ofthe-art methods.

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Spectrum Sensing Using a Uniform Uncalibrated Linear Antenna Array for Cognitive Radios

Cited by 15 publications

References 37 publications

Collaborative data aggregation using multiple antennas sensors and fusion centre with energy harvesting capability in WSN

Collaborative data aggregation using multiple antennas sensors and fusion centre with energy harvesting capability in WSN

Optimization of distributed detection in energy harvesting wireless sensor networks with multiple antenna fusion center

Covariance-Based Spectrum Sensing for Noncircular Signal in Cognitive Radio Networks With Uncalibrated Multiple Antennas

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