Multistage Spectrum Sensing for Cognitive Radio Using Energy and Maximum Eigenvalues Detection

2020 ◽  
Vol 11 (4) ◽  
pp. 41-59
Author(s):  
Faten Mashta ◽  
Mohieddin Wainakh ◽  
Wissam Altabban
Keyword(s):  
Cognitive Radio ◽  
Computational Complexity ◽  
Spectrum Sensing ◽  
Energy Detection ◽  
Probability Of Detection ◽  
Smoothing Factor ◽  
Third Stage ◽  
Simulation Results ◽  
Good Detection ◽  
High Computational Complexity

Spectrum sensing in cognitive radio has difficult and complex requirements such as requiring speed and sensing accuracy at very low SNRs. In this paper, the authors propose a novel fully blind sequential multistage spectrum sensing detector to overcome the limitations of single stage detector and make use of the advantages of each detector in each stage. In first stage, energy detection is used because of its simplicity. However, its performance decreases at low SNRs. In second and third stage, the maximum eigenvalues detector is adopted with different smoothing factor in each stage. Maximum eigenvalues detection technique provide good detection performance at low SNRs, but it requires a high computational complexity. In this technique, the probability of detection improves as the smoothing factor raises at the expense of increasing the computational complexity. The simulation results illustrate that the proposed detector has better sensing accuracy than the three individual detectors and a computational complexity lies in between the three individual complexities.

An Integrated Parallel Multistage Spectrum Sensing for Cognitive Radio

2021 ◽  
Vol 12 (2) ◽  
pp. 1-20
Author(s):  
Faten Mashta ◽  
Mohieddin Wainakh ◽  
Wissam Altabban
Keyword(s):  
Cognitive Radio ◽  
Computational Complexity ◽  
Spectrum Sensing ◽  
Energy Detection ◽  
Maximum Eigenvalue ◽  
Two Stage ◽  
Smoothing Factor ◽  
Low Snr ◽  
Simulation Results ◽  
Good Detection

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.

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

2014 ◽  
Vol 643 ◽  
pp. 105-110
Author(s):  
Yuan Li ◽  
Jia Yin Chen ◽  
Xiao Feng Liu ◽  
Ming Chuan Yang
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Mathematical Proof ◽  
Threshold Energy ◽  
Energy Detection ◽  
Probability Of Detection ◽  
Threshold Detection ◽  
Probability Of False Alarm ◽  
Double Threshold

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%.

Analysis of Spectrum Sensing in Cognitive Radio Network with OFDM

2018 ◽  
Vol 8 (6) ◽  
pp. 84
Author(s):  
Shadab Ahmed Khan ◽  
Pawandeep Kaur
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Network ◽  
Energy Detection ◽  
Radio Spectrum ◽  
Probability Of Detection ◽  
Spectrum Utilization ◽  
Wireless Applications ◽  
Novel Technology ◽  
Cognitive Environment

With the development of a new and ever expanding wireless applications and services, spectrum resources are facing in demands. In present scenario, the spectrum allotment has been done by providing each new service with its own fixed frequency Slot. Most of the user’s spectrum is already assigned, so it becomes very difficult to find spectrum for other users or existing users. This leads to the scarcity of available spectrum and inefficient channel utilization. Cognitive radio is a novel technology which improves the spectrum utilization by allowing secondary user to borrow unused radio spectrum from primary licensed users or to share the spectrum with the primary users. Present paper deals with the spectrum sensing in which multiple users detect the spectrum gap through energy detection and investigate the detection performance in an efficient and implementable way. Simulation results showed that the probability of detection is achieved at small value SNR in case of OFDM modulation as compare the other and simple cognitive environment.

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

2012 ◽  
Vol 236-237 ◽  
pp. 917-922
Author(s):  
Wei Ran Wang ◽  
Shu Bin Wang ◽  
Xin Yan Zhao
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Optimization Algorithm ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Dynamic Threshold ◽  
Threshold Optimization ◽  
Sensing Performance

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.

Improved Two-Stage Spectrum Sensing for Cognitive Radio Networks

2019 ◽  
Vol 23 (6) ◽  
pp. 1052-1062
Author(s):  
Fidel Wasonga ◽  
Thomas O. Olwal ◽  
Adnan Abu-Mahfouz ◽  
◽  
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Feature Detection ◽  
Energy Detection ◽  
Probability Of Detection ◽  
Detection Accuracy ◽  
Two Stage ◽  
Probability Of Error ◽  
Average Spectrum ◽  
Sensing Time

Cognitive radio employs an opportunistic spectrum access approach to ensure efficient utilization of the available spectrum by secondary users (SUs). To allow SUs to access the spectrum opportunistically, the spectrum sensing process must be fast and accurate to avoid possible interference with the primary users. Previously, two-stage spectrum sensing methods were proposed that consider the sensing time and sensing accuracy parameters independently at the cost of a non-optimal spectrum sensing performance. To resolve this non-optimality issue, we consider both parameters in the design of our spectrum sensing scheme. In our scheme, we first derive optimal thresholds using an optimization equation with an objective function of maximizing the probability of detection, subject to the minimal probability of error. We then minimize the average spectrum sensing time using signal-to-noise ratio estimation. Our simulation results show that the proposed improved two-stage spectrum sensing (ITSS) scheme provides a 4%, 7%, and 6% better probability of detection accuracy rate than two-stage combinations of energy detection (ED) and maximum eigenvalue detection, energy detection and cyclostationary feature detection (CFD), and ED and combination of maximum-minimum eigenvalue (CMME) detection, respectively. The ITSS is superior also to single-stage ED by 19% and shows an improved average spectrum sensing time.

Adaptive Two-Stage Sensing Based on Energy Detection and Cyclostationary Feature Detection for Cognitive Radio Systems

2013 ◽  
Vol 411-414 ◽  
pp. 1521-1528 ◽  
Author(s):  
Yu Yang ◽  
Yan Li Ji ◽  
Han Hui Li ◽  
Du Lei ◽  
Meng Rui
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Feature Detection ◽  
Energy Detection ◽  
Probability Of Detection ◽  
Two Stage ◽  
Probability Of False Alarm ◽  
Detection Model ◽  
Radio Systems ◽  
Cyclostationary Feature Detection

In this paper, we investigate the features of energy detection and cyclostationary feature detection for spectrum sensing. In order to combine their advantages, we propose an adaptive two-stage sensing scheme which performs spectrum sensing using an energy detector first in cognitive radio networks. Then in the second stage, this scheme decides whether or not to implement cyclostationary feature detection based on the sensing results of the first stage. On the premise of meeting a given constraint on the probability of false alarm, the goal of our proposed scheme is to optimize the probability of detection and sensing speed at the same time. In order to obtain the optimal detection thresholds, we can formulate the detection model as a nonlinear optimization problem. Furthermore, the simulation results show that the proposed scheme improves the performance of spectrum sensing compared with the ones where only energy detection or cyclostationary feature detection is performed.

scholarly journals DWT Based Energy Detection Spectrum Sensing Method for Cognitive Radio System

2020 ◽  
Vol 3 (3) ◽  
pp. 1-11
Author(s):  
Muntaser S. Falih ◽  
Hikmat N. Abdullah
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Primary User ◽  
Radio System ◽  
Processed Sample ◽  
Simulation Results ◽  
Sensing Time

In this paper a new blind energy detection spectrum-sensing method based on Discreet Wavelet Transform (DWT) is proposed. The method utilizes the DWT sub-band to collects the received energy. The proposed method recognizes the Primary User (PU) signal from noise only signal using the differences in the collected energy in first and last sub-bands of one level DWT. The simulation results show that the proposed method achieves improved detection probability especially at low Signal to Noise Ratio (SNR) compared to Conventional Energy Detector (CED). The results also show that the proposed method has shorter sensing time and less Energy Consumption (EC) compared to CED due to using small number of processed sample. Therefore, this method is suitable for Cognitive Radio (CR) applications where only limited energy like device battery is available.

An Overview of the Different Principal Spectrum Sensing Techniques in Cognitive Radio Systems

2020 ◽  
Vol 12 (3) ◽  
pp. 342-347
Author(s):  
Asmaa Maali ◽  
Hayat Semlali ◽  
Sara Laafar ◽  
Najib Boumaaz ◽  
Abdallah Soulmani
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Primary User ◽  
Operational Characteristic ◽  
Main Function ◽  
Maximum Eigenvalue ◽  
Smoothing Factor

Cognitive radio is a technology proposed to increase the effective use of the spectrum. This can be done through the main function of cognitive radio technology, which is the spectrum sensing. In our work, we propose an analysis of the following spectrum sensing techniques: the matched filter detector, the cyclostationary feature detector, the energy detector and the maximum eigenvalue detector. More attention is given to blind sensing techniques that they do not need any knowledge of the primary user signal characteristics, namely the energy detection and maximum eigenvalue detection. These methods are evaluated in terms of Receiver Operational Characteristic curves and detection probability for various values of Signal to Noise Ratio based on Monte Carlo simulations, using MATLAB. As a result of this study, we found that the energy detection offers a good performance only for high SNR. Furthermore, with the maximum eigenvalue detector, the noise uncertainty problem encountered by the energy detection is solved when the value of the smoothing factor L ≥ 8 and. Finally, a summary of the comparative analysis is presented.

Two-Stage Spectrum Sensing for Cognitive Radio Using Eigenvalues Detection

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.

scholarly journals Efficient Approximations for Optimization of N-Out-of-K Rule for Heterogeneous Cognitive Radio Networks

2021 ◽  
Vol 11 (7) ◽  
pp. 3083
Author(s):  
Youheng Tan ◽  
Xiaojun Jing
Keyword(s):  
Cognitive Radio ◽  
Computational Complexity ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Closed Form Expression ◽  
Sampling Point ◽  
Mutual Cooperation ◽  
Communication Time ◽  
Sensing Time

Spectrum sensing (SS) has attracted much attention due to its important role in the improvement of spectrum efficiency. However, the limited sensing time leads to an insufficient sampling point due to the tradeoff between sensing time and communication time. Although the sensing performance of cooperative spectrum sensing (CSS) is greatly improved by mutual cooperation between cognitive nodes, it is at the expense of computational complexity. In this paper, efficient approximations of the N-out-of-K rule-based CSS scheme under heterogeneous cognitive radio networks are provided to obtain the closed-form expression of the sensing threshold at the fusion center (FC), where the false alarm probability and its corresponding detection probability are approximated by the Poisson distribution. The computational complexity required to obtain the optimal sensing threshold at the FC has greatly decreased and theoretical derivations state that the approximation error is negligible. The simulations validate the effectiveness of the proposed scheme.

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