Spectrum Sensing Algorithm Based on Double Threshold and Two-Stage Detection Under a Low SNR

Spectrum sensing for cognitive radio requires speed and good detection performance at very low SNR ratios. There is no single-stage spectrum sensing technique that is perfect enough to be implemented in practical cognitive radio. In this paper, the authors propose a new parallel fully blind multistage detector. They assume the appropriate stage based on the estimated SNR values that are achieved from the SNR estimator. Energy detection is used in first stage for its simplicity and sensing accuracy at high SNR. For low SNRs, they adopt the maximum eigenvalues detector with different smoothing factor in higher stages. The sensing accuracy for the maximum eigenvalue detector technique improves with higher value of the smoothing factor. However, the computational complexity will increase significantly. They analyze the performance of two cases of the proposed detector: two-stage and three-stage schemes. The simulation results show that the proposed detector improves spectrum sensing in terms of accuracy and speed.

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Novel Spectrum Sensing Technique and Its Evaluation for Cognitive Radio Wireless Sensor Network

Journal of Interconnection Networks ◽

10.1142/s0219265920500097 ◽

2020 ◽

Vol 20 (03) ◽

pp. 2050009

Author(s):

GARIMA MAHENDRU ◽

ANIL K. SHUKLA ◽

L. M. PATNAIK

Keyword(s):

Cognitive Radio ◽

Wireless Sensor Network ◽

Sensor Networks ◽

Sensor Network ◽

Spectrum Sensing ◽

Dynamic Spectrum Access ◽

Substantial Reduction ◽

Wireless Sensor ◽

Low Snr ◽

Double Threshold

Cognitive Radio based Wireless Sensor Network is a novel concept that integrates the dynamic spectrum access capability of cognitive radio into wireless sensor networks for the futuristic sensor networks and wireless communication technology. Spectrum sensing plays a quintessential role in a cognitive radio network but is a major constraint for a battery powered sensor with stringent energy limitations. The spectrum sensing algorithms are expected to yield acceptable detection probability at low SNR under noise uncertainty with minimum power consumption in a WSN. In this paper, a new spectrum sensing method has been proposed to overcome sensing failure under low SNR environment. The proposed technique is based on adaptive double threshold theory which improves the detection performance by 39.63 and 27.22% at SNR = −10dB as compared to the conventional energy detection and available double threshold-based method respectively. Furthermore, the proposed method of spectrum sensing is evaluated for its deployment into a CR-WSN using the evaluation metrics: Time and Sample Complexity. The comparative evaluation of the spectrum sensing method in a WSN through simulations shows that the proposed technique offers substantial reduction in sample and time complexity of the wireless sensor nodes.

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Two-Stage Spectrum Sensing for Cognitive Radio Using Eigenvalues Detection

International Journal of Interdisciplinary Telecommunications and Networking ◽

10.4018/ijitn.2020100102 ◽

2020 ◽

Vol 12 (4) ◽

pp. 18-36

Author(s):

Faten Mashta ◽

Wissam Altabban ◽

Mohieddin Wainakh

Keyword(s):

Cognitive Radio ◽

Spectrum Sensing ◽

Primary User ◽

Detection Performance ◽

Probability Of Detection ◽

Energy Detector ◽

Detection Methods ◽

Two Stage ◽

Low Snr ◽

Good Detection

Spectrum sensing in cognitive radio has difficult and complex requirements, requiring speed and good detection performance at low SNR ratios. As suggested in IEEE 802.22, the primary user signal needs to be detected at SNR = -21dB with a probability of detection exceeds 0.9. Conventional spectrum sensing methods such as the energy detector, which is characterized by simplicity with good detection performance at high SNR values, are ineffective at low SNR values, whereas eigenvalues detection methods have good detection performance at low SNR ratios, but they have high complexity. In this paper, the authors investigate the process of spectrum sensing in two stages: in the first stage (coarse sensing), the energy detector is adopted, while in the second stage (fine sensing), eigenvalues detection methods are used. This method improves performance in terms of probability of detection and computational complexity, as the authors compared the performance of two-stage sensing scheme with ones where only energy detection or eigenvalues detection is performed.

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