Energy Detection In Cognitive Radio Research Articles

Spectrum Sensing based on Energy Detection for Cognitive Radio Using Poission Traffic

Spectrum Sensing based on Energy Detection for Cognitive Radio Using Poission Traffic

Performance of Threshold Detection in Cognitive Radio with Improved Otsu’s and Recursive One-Sided Hypothesis Testing Technique

Cognitive radio (CR) is a new technology proposed to enhance spectrum efficiency by enabling unlicensed secondary users to access the licensed frequency bands without getting involved with the primary users licensed. Although considered optimal, in order to calculate the signal threshold, this approach requires prior noise statistics information. Even though considered optimal, in order to calculate the signal threshold, this approach requires prior noise statistics information. A prominent example of an Adaptive Threshold Estimation Technique (ATT) for energy detection in Cognitive Radio (CR) is the Recursive One-sided Hypothesis Testing Technique (ROHT). Accurate threshold values are known to be calculated based on the correct choice of their parameter values, which include the standard deviation coefficient and the stop criteria. In this paper, for efficient threshold estimation, the improved Otsu and ROHT are combined for estimating threshold even in the presence of noise floor without need of prior knowledge. The proposed methodology for enactment in cognitive radio sensor networks (CRSN) system based on the adaptive threshold energy detection model with noise variance estimation. The simulation is carried out with the help of Matlab 2017a with the improved Otsu and ROHT techniques. The results obtained shows that improved Otsu and ROHT techniques outperforms that of fixed threshold energy detection in terms of different probability of false alarm rates and miss detections

A comparative analysis of local and global adaptive threshold estimation techniques for energy detection in cognitive radio

In this paper, we compare local and global adaptive threshold estimation techniques for energy detection in Cognitive Radio (CR). By this comparison, a sum-up synopsis is provided regarding the effective performance range and the operating conditions under which both classes best apply in CR. Representative methods from both classes were implemented and trained using synthesized signals to fine tune each algorithm’s parameter values. Further tests were conducted using real-life signals acquired via a spectrum survey exercise and results were analyzed using the probability of detection and the probability of false alarm computed for each algorithm. It is observed that while local based methods may be adept at maintaining a low constant probability of false alarm, they however suffer a grossly low probability of detection over a wide variety of CR spectra. Consequently, we concluded that global adaptive threshold estimation techniques are more suitable for signal detection in CR than their local adaptive thresholding counterparts.

On Optimal Cooperative Sensing with Energy Detection in Cognitive Radio.

In this paper, we propose an optimal cooperative sensing technique for cognitive radio to maximize sensing performance based on energy detection. In most spectrum sensing research, many cooperation methods have been proposed to overcome the sensitivity of energy detection so that both primary and secondary users are better off in terms of spectral efficiency. However, without assigning a proper sensing threshold to each sensing node, cooperation may not be effective unless the received average primary user signal-to-noise ratio (SNR) is identical. We show that equal threshold energy detection severely degrades sensing performance when cooperative sensing nodes experience diverse average SNRs, and it is not unusual for even single-node sensing to be better than cooperative sensing. To this end, based on the Neyman–Pearson criterion we formulate an optimization problem to maximize sensing performance by using optimized thresholds. Since this is a non-convex optimization problem, we provide a condition that convexifies the problem and thus serves as a sufficient optimality condition. We find that, perhaps surprisingly, in all practical cases one may consider this condition satisfied, and thus optimal sensing performance can be obtained. Through extensive simulations, we demonstrate that the proposed technique achieves a globally optimal solution, i.e., it maximizes the probability of detection under practical operating parameters such as the target probability of false alarm, different SNRs, and the number of cooperative sensing nodes.

Optimal Allocation Techniques for Reducing the Sensing Error Probability with Improved Energy Detection in Cognitive Radio

Optimal Allocation Techniques for Reducing the Sensing Error Probability with Improved Energy Detection in Cognitive Radio

Modeling and Analysis of Wireless Channels via the Mixture of Gaussian Distribution

In this paper, we consider a unified approach to model wireless channels by the mixture of Gaussian (MoG) distribution. The proposed approach provides an accurate approximation for the envelope and the signal-to-noise ratio (SNR) distributions of wireless fading channels. Simulation results have shown that the proposed model can accurately characterize multipath and composite fading channels. We utilize the well-known expectation-maximization (EM) algorithm to estimate the parameters of the MoG distribution and further utilize the Bayesian information criterion (BIC) to determine the number of mixture components automatically. We employ the Kullback-Leibler (KL) divergence and the mean-square-error (MSE) criteria to demonstrate that the proposed distribution provides both high accuracy and low computational complexity. Additionally, we provide closed-form expressions or approximations for several performance metrics used in wireless communication systems, including the moment generating function (MGF), the raw moments, the amount of fading (AF), the outage probability, the average channel capacity, and the probability of energy detection for cognitive radio (CR). Numerical analysis and Monte Carlo simulation results are presented to corroborate the analytical results.

Function approximation based energy detection in cognitive radio using radial basis function network

AbstractIn this paper an attempt has been made to evolve a computationally intelligent energy detection method for spectrum sensing in Cognitive Radio (CR). The proposed method utilizes the function approximation capability of radial basis function (RBF) network to learn the threshold function for a pre-determined range of probability of false alarm and number of samples. The receiver operating characteristic (ROC) results obtained by the proposed method have been compared with the conventional energy detection scheme. It is validated from the results that, the proposed method provides enhanced probability of detection in some cases compared to the conventional one due to its inherent shortcoming in terms of computational intelligence.

An Algorithm Based on Double-Threshold Energy Detection in Cognitive Radio

Aiming at the situation where the double-threshold detection has been widely used without complete mathematical proof and condition of application, this paper proves its correctness under the circumstance of spectrum sensing, and circulates the condition where this method can work. The proof and simulation show that, comparing with traditional energy detection, this method can increase the probability of detection by 27% to 42% at most when the SNR is between-15dB and-2dB, while the probability of false alarm is increased by less than 2%.

Unlimited cooperative sensing with energy detection for cognitive radio

In this paper, we investigate the fundamental performance limits of the cooperative sensing using energy detection by considering the unlimited number of sensing nodes. Although a lot of cognitive radio research so far proposed various uses of energy detection because of its simplicity, the performance limits of energy detection have not been studied when a large number of sensing nodes exist. First, we show that when the sensing nodes see the independent and identically distributed channel conditions, then as the number of sensing nodes N goes to infinity, the OR rule of hard decision achieves zero of false alarm Pf for any given target probability of detection $\bar {P_d}$ irrespective of the non-zero received primary user signal to noise ratio ?. Second, we show that under the same condition, when the AND rule of hard decision is used, there exists a lower bound of Pf. Interestingly, however, for given $\bar {P_d}$, Pf goes to 1 as N goes to infinity. Third, we show that when the soft decision is used, there exists a way of achieving 100% utilization of secondary user, i.e., the sensing time overhead ratio goes to zero so does Pf. We verify our analyses by performing extensive simulations of the proposed unlimited cooperative sensing. Finally, we suggest a way of incorporating the unlimited cooperative sensing into a practical cellular system such as long term evolution-advanced by exploiting the existing frame structure of absolute blank subframe to implement the in-band sensing.

A Dynamic Threshold Optimization Algorithm for Spectrum Sensing with Energy Detection in Cognitive Radio

In order to improve an efficiency of energy detection for a spectrum sensing in cognitive radio (CR), this paper proposes a dynamic threshold optimization algorithm. The traditional energy detection algorithm uses a fixed threshold, and can't guarantee always the optimal sensing performance in any environment. The improvement for sensing performance need to minimize the undetected probability and the probability of false alarm, and it is dissimilar for different CR users to accept these two errors. We improve the traditional energy detection algorithm, and firstly introduce a preference factor to characterize CR users’ different requirements for these two errors, then, propose a dynamic threshold optimization algorithm by minimizing integrated detection error for different signal-to-noise ratio (SNR). The simulation results show that the proposed algorithm effectively reduces the integrated spectrum sensing error, and increases the probability of detection, especially in low SNR.

Robust energy detection in cognitive radio

The success of advanced dynamic utilisation of the scarce spectrum in cognitive radio depends upon reliable primary signal detection where accurate noise power estimation plays a critical role. However, in practical scenarios, the noise power cannot be accurately estimated, which significantly degrades the performance of primary signal detection. To avoid inaccurate noise power estimation and associated accumulated problems. A novel two-stage Bayesian estimation-based energy detection algorithm is introduced here. This algorithm, as supported by simulation results, shows two main features: (a) a superior performance of 1 dB compared with previous methods; (b) the consistency of the algorithm has been proved indicating that 100% correct primary user signal detection can be approached as the number of samples tends to infinity.