A Novel Spectrum Sensing Method Based on Tri-Stable Stochastic Resonance and Quantum Particle Swarm Optimization

Lu, Jin; Huang, Ming; Yang, Jingjing

doi:10.1007/s11277-017-3945-5

Cited by 17 publications

(15 citation statements)

References 19 publications

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“…Then the potential wells will elevate alternatively and periodically with the frequency [14]. When only noise ( ) exists, the Brown particle will switch between two potential wells with the transition speed , which is named as Kramers rate [15]:…”

Section: Stochastic Resonancementioning

confidence: 99%

“…The joint effect of input ̅ ( ) and noise ( ) will resonate the SR system to achieve as high an amplitude as possible. But the classical SR theory, like adiabatic approximation theory and nonlinear response theory, indicates that SR can only work with small parameters [15]. Firstly, the output signal ( ) is mainly concentrated on low frequency components rather than higher harmonics, so the input carrier frequency should be a considerably small value (i.e., ≪ and ≪ ).…”

Section: Stochastic Resonancementioning

confidence: 99%

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

Huang

Yang

2020

Preprint

Self Cite

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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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Section: Stochastic Resonancementioning

confidence: 99%

Section: Stochastic Resonancementioning

confidence: 99%

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

Huang

Yang

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, the manual adjustment method can only be applied in offline detections. In order to solve these issues, by using some multiparameter optimization algorithms such as particle swarm optimization (PSO) [28] and genetic algorithm (GA) [22,29], some adaptive parameter-adjusting methods, which can realize SR in online conditions with high efficiency and high reliability, have been proposed and studied [30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance

Lai

Wang

Zhang

et al. 2020

Shock and Vibration

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The weak-signal detection technologies based on stochastic resonance (SR) play important roles in the vibration-based health monitoring and fault diagnosis of rolling bearings, especially at their early-fault stage. Aiming at the parameter-fixed vibration signals in practical engineering, it is feasible to diagnose the potential rolling bearing faults through adaptively adjusting the SR system parameters, as well as other generalized parameters such as the amplitude-transformation coefficient and scale-transformation coefficient. However, extant adaptive adjustment methods focus on the system parameters, while the adjustments of other adjustable parameters have not been fully studied, thus limiting the detection performance of the adaptive SR method. In order to further enhance the detection performance of adaptive SR methods and extend their application in rolling bearing fault diagnosis, an adaptive multiparameter-adjusting SR (AMPASR) method for bistable systems based on particle swarm optimization (PSO) algorithm is proposed in this paper. This method can produce optimal SR output through adaptively adjusting multiparameters, thus realizing fault feature extraction and further fault diagnosis. Furthermore, the influence of algorithm parameters on the optimization results is discussed, and the optimization results of the Langevin system and the Duffing system are compared. Finally, we propose a weak-signal detection method based on the AMPASR of the Duffing system and employ three diagnosis examples involving inner ring fault, outer ring fault, and rolling element fault diagnoses to demonstrate its feasibility in rolling bearing fault diagnosis.

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“…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

Huang

Yang

2020

Preprint

Self Cite

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

A Novel Spectrum Sensing Method Based on Tri-Stable Stochastic Resonance and Quantum Particle Swarm Optimization

Cited by 17 publications

References 19 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

Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance

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

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