Improvement of Noise Uncertainty and Signal-To-Noise Ratio Wall in Spectrum Sensing Based on Optimal Stochastic Resonance

He, Di; Chen, Xin; Pei, Ling; Jiang, Lingge; Wang, Yu

doi:10.3390/s19040841

“…is a nonlinear function representing the physical behavior. The transformation process can be defined by the Langevin equation [11]:…”

Section: Stochastic Resonancementioning

confidence: 99%

“…Although eigenvalue-based spectrum sensing algorithms can improve sensing quality under the SNR wall or noise uncertainty conditions [11], in the circumstance of low SNR, increasing the number of antennas is the primary method to compensate the deterioration of spectrum sensing performance. Therefore, the design cost and complexity of wireless mobile devices will be increased.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A covariance matrix-based spectrum sensing technology exploiting stochastic resonance and filters

Lu

¹

,

Huang

²

,

Yang

³

2021

EURASIP J. Adv. Signal Process.

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Cognitive radio (CR) is designed to implement dynamical spectrum sharing and reduce the negative effect of spectrum scarcity caused by the exponential increase in the number of wireless devices. CR requires that spectrum sensing should detect licenced signals quickly and accurately and enable coexistence between primary and secondary users without interference. However, spectrum sensing with a low signal-to-noise ratio (SNR) is still a challenge in CR systems. This paper proposes a novel covariance matrix-based spectrum sensing method by using stochastic resonance (SR) and filters. SR is implemented to enforce the detection signal of multiple antennas in low SNR conditions. The filters are equipped in the receiver to reduce the interference segment of noise frequency. Then, two test statistics computed by the likelihood ratio test (LRT) or the maximum eigenvalues detector (MED) are constructed by the sample covariance matrix of the processed signals. The simulation results exhibit the spectrum sensing performance of the proposed algorithms under various channel conditions, namely, additive white Gaussian noise (AWGN) and Rayleigh fading channels. The energy detector (ED) is also compared with LRT and MED. The simulation results demonstrate that SR and filter implementation can achieve a considerable improvement in spectrum sensing performance under a strong noise background.

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“…Although eigenvalue-based spectrum sensing algorithms can improve sensing quality under the SNR wall or noise uncertainty conditions [11], in the circumstance of low SNR, an eigenvalue-based detector can only increase the antenna number to compensate the deterioration of spectrum sensing performance. Therefore, the design cost and complexity of wireless mobile devices will be increased.…”

Section: Introductionmentioning

confidence: 99%

A Covariance Matrix-based Spectrum Sensing Technology Exploiting Stochastic Resonance and Filters

Lu

¹

,

Huang

²

,

Yang

³

2020

Preprint

0

View full text Add to dashboard Cite

Cognitive radio (CR) is designed to implement dynamical spectrum sharing and reduce the negative effect of spectrum scarcity caused by the exponential increase in the number of wireless devices. CR requires that spectrum sensing should detect licenced signals quickly and accurately and enable coexistence between primary and secondary users without interference. However, spectrum sensing with a low signal-to-noise ratio (SNR) is still a challenge in CR systems. This paper proposes a novel covariance matrix-based spectrum sensing method by using stochastic resonance (SR) and filters. SR is implemented to enforce the detection signal of multiple antennas in low SNR conditions. The filters are equipped in the receiver to reduce the interference segment of noise frequency. Then, two test statistics computed by the likelihood ratio test (LRT) or the maximum eigenvalues detector (MED) are constructed by the sample covariance matrix of the processed signals. The simulation results exhibit the spectrum sensing performance of the proposed algorithms under various channel conditions, namely, additive white Gaussian noise (AWGN) and Rayleigh fading channels. The energy detector (ED) is also compared with LRT and MED. The simulation results demonstrate that SR and filter implementation can achieve a considerable improvement in spectrum sensing performance under a strong noise background.

show abstract

“…The application of SR in partial polarized noise was investigated in [13]. Other SR-based detectors have also been studied, including detectors based on suprathreshold SR [14], particle swarm algorithm and tri-stable SR [15], and optimal dynamic overdamped SR [11].…”

Section: Introductionmentioning

confidence: 99%

A Covariance Matrix-based Spectrum Sensing Technology Exploiting Stochastic Resonance and Filters

Lu

¹

,

Huang

²

,

Yang

³

2020

Preprint

0

View full text Add to dashboard Cite

Cognitive radio (CR) is a dynamic spectrum sharing technology designed to reduce the negative effect of spectrum scarcity caused by the exponential increase in the number of wireless devices. CR requires that spectrum sensing should detect licenced signals quickly and accurately and enable coexistence between primary and secondary users without interference. However, spectrum sensing with a low signal-to-noise ratio (SNR) is still a challenge in CR systems. This paper proposes a novel covariance matrix-based spectrum sensing method by using stochastic resonance (SR) and filters. SR is implemented to enforce the detection signal of multiple antennas in low SNR conditions. The filters are equipped in the receiver to reduce the interference segment of noise frequency. Then, two test statistics computed by the likelihood ratio test (LRT) or the maximum eigenvalues detector (MED) are constructed by the sample covariance matrix of the processed signals. The simulation results exhibit the spectrum sensing performance of the proposed algorithms under various channel conditions, namely, additive white Gaussian noise (AWGN) and Rayleigh fading channels. The energy detector (ED) is also compared with LRT and MED. The simulation results demonstrate that SR and filter implementation can achieve a considerable improvement in spectrum sensing performance under a strong noise background.

show abstract

Improvement of Noise Uncertainty and Signal-To-Noise Ratio Wall in Spectrum Sensing Based on Optimal Stochastic Resonance

Cited by 12 publications

References 23 publications

A covariance matrix-based spectrum sensing technology exploiting stochastic resonance and filters

A covariance matrix-based spectrum sensing technology exploiting stochastic resonance and filters

A Covariance Matrix-based Spectrum Sensing Technology Exploiting Stochastic Resonance and Filters

A Covariance Matrix-based Spectrum Sensing Technology Exploiting Stochastic Resonance and Filters

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