SNR WALL AND COOPERATIVE SPECTRUM SENSING IN COGNITIVE RADIO UNDER NOISE UNCERTAINTY

Cooperative energy spectrum sensing has been proved effective to detect the spectrum holes in Cognitive Radio(CR).However,its performance may suffer from the noise uncertainty,which is portrayed by the SNR wall in some literatures.In this paper we analyze the spectrum sensing per-formance under noise uncertainty and find an alternative approach to obtain the SNR wall.Then the average SNR wall is proposed to illustrate the statistically average impact of noise uncertainty.In addition,the cooperative sensing performance under noise uncertainty with AND rule is discussed.Analyses and numerical results show that cooperative sensing can significantly improve the sensing performance under the condition of noise uncertainty.

Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.

Frequency Domain Goodness of Fit Test Based Spectrum Sensing Method with Dynamically Varying Noise

In cognitive radio networks,spectrum sensing under circumstances of dynamically varying noise and lacking prior information is a key challenge to the conventional spectrum sensing algorithms.Since the necessary information is rather difficult to obtain practically,most existing spectrum sensing methods are fettered in applications.Motivated by these,in this paper,a Frequency domain Goodness of Fit Test(FGoF)based spectrum sensing method is proposed.The FGoF makes full use of underlying information in Guard-Bands and the advantages of GoF test works for any distribution.Analytical and simulated results show that the FGoF is a robust spectrum sensing method in cognitive radio with the inherent advantages of invulnerability to dynamically varying noise.

Influence of the environmental noise on determining the period of a torsion pendulum

The environmental noise can restrict the accuracy of period estimation since the torsion pendulum is sensitive to weak forces. Two typical models for the environmental noise are proposed to make an evaluation. Generally, the stationary environmental noise is modeled as a white noise, and contributes to the period uncertainty as a function of the initial amplitude, the quality factor, the variance of noise and the time length. As to a sudden sharp disturbance acting on the pendulum, a narrow impulse model is constructed. It results in a sharp jump in the phase difference, which can be excluded with the 3σ criterion for a correction. An experimental data analysis for the measurement of the gravitational constant G with the time-of-swing method shows that the period uncertainty due to the environmental noise is about one and a half times the fundamental thermal noise limit. Though this result is dependent on the ambient environment, the analysis is instructive to improve the measurement accuracy of experiments.

A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems

Spectrum sensing is an essential component to realize the cognitive radio, and the requirement for real-time spectrum sensing in the case of lacking prior information, fading channel, and noise uncertainty, indeed poses a major challenge to the classical spectrum sensing algorithms. Based on the stochastic properties of scalar transformation of power spectral density（PSD）, a novel spectrum sensing algorithm, referred to as the power spectral density split cancellation method（PSC）, is proposed in this paper. The PSC makes use of a scalar value as a test statistic, which is the ratio of each subband power to the full band power. Besides, by exploiting the asymptotic normality and independence of Fourier transform,the distribution of the ratio and the mathematical expressions for the probabilities of false alarm and detection in different channel models are derived. Further, the exact closed-form expression of decision threshold is calculated in accordance with Neyman–Pearson criterion. Analytical and simulation results show that the PSC is invulnerable to noise uncertainty,and can achive excellent detection performance without prior knowledge in additive white Gaussian noise and flat slow fading channels. In addition, the PSC benefits from a low computational cost, which can be completed in microseconds.

A design of multi-cycle detector for cognitive radios

Cognitive radio(CR)is a promising technology.The most fundamental problem of CR is spectrum sensing.Energy detector is often considered for spectrum sensing in CR,and if the noise power is exactly known,energy detector has admirable performance.However,in practice,noise power is always inexactly known.To solve this problem,Dandawate[Dandawate et al.IEEE Transactions on Signal Processing,1994,42(9):2355-2369]has proposed a nonparametric single-cycle detector based on cyclostationarity,which is robust to noise uncertainty.In this paper,based on Dandawate’s single-cycle detector,a joint multi-cycle detector is further proposed,which is also nonparametric and immune from noise uncertainty.Simulation results have shown the validity and superiority over single-cycle detector of the proposed detector.