scholarly journals Cooperative Spectrum Sensing Algorithm to Overcome Noise Fluctuations Based on Energy Detection in Sensing Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Guicai Yu ◽  
Han Wang ◽  
Wencai Du
Keyword(s):  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Average Power ◽  
Energy Detection ◽  
Detection Algorithm ◽  
Detection Sensitivity ◽  
Power Fluctuation ◽  
Sensing Systems ◽  
Noisy Signals

In sensing systems, nodes must be able to rapidly detect whether a signal from a primary transmitter is present in a certain spectrum. However, traditional energy-detection algorithms are poorly adapted to treating noisy signals. In this paper, we investigate how rapid energy detection and detection sensitivity are related to detection duration and average power fluctuation in noise. The results indicate that detection performance and detection sensitivity decrease quickly with increasing average power fluctuation in noise and are worse in situations with low signal-to-noise ratio. First, we present a dynamic threshold algorithm based on energy detection to suppress the influence of noise fluctuation and improve the sensing sensitivity. Then, we present a new energy-detection algorithm based on cooperation between nodes. Simulations show that the proposed scheme improves the resistance to average power fluctuation in noise for short detection timescales and provides sensitive detection that improves with increasing numbers of cooperative detectors. In other words, the proposed scheme enhances the ability to overcome noise and improves spectrum sensing performance.

Detection Sensitivity Based on Energy Detection in Cognitive Radio Systems

2012 ◽  
Vol 462 ◽  
pp. 500-505
Author(s):  
Gui Cai Yu ◽  
Cheng Zhi Long ◽  
Man Tian Xiang
Keyword(s):  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Detection Algorithm ◽  
Detection Sensitivity ◽  
Dynamic Threshold ◽  
Noise Power ◽  
Power Fluctuation ◽  
New Energy ◽  
And Performance ◽  
Short Time

Traditional energy detection algorithm is bad in anti-noise. In this paper, the relationship of energy detection performance and detection sensitivity with average noise power fluctuation in short time is investigated. Detection sensitivity drops quickly with the increment of average noise power fluctuation and becomes worse in low signal-to-noise ratio. To the characteristic, a new energy detection algorithm based on dynamic threshold is presented. Theoretic results and simulations show that the proposed scheme removes the falling proportion of performance and detection sensitivity caused by the average noise power fluctuation with a choice threshold, and also improves the antagonism of the average noise power fluctuation in short time and obtains a good performance. Detection sensitivity and performance improves as the dynamic threshold factor increasing.

scholarly journals Review on The Performance of Softened Two-Bit Hard Combination Scheme for Cooperative Spectrum Sensing

2018 ◽  
Vol 15 (1) ◽  
pp. 51-54
Author(s):  
Mohanad Abdulhamid
Keyword(s):  
False Alarm ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
False Alarm Probability ◽  
Energy Detection ◽  
Combination Scheme ◽  
Two Measures ◽  
Probability Number

Abstract This paper measures the performance of cooperative spectrum sensing, over Rayleigh fading channel and additive white Gaussian noise, based on softened two-bit hard combination scheme. Two measures based on energy detection are considered including effect of false alarm probability, and effect of number of users. Simulation results show that the detection probability increases with the increase of false alarm probability, number of users, and signal-to-noise-ratio.

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.

scholarly journals Adaptive Double-Threshold Cooperative Spectrum Sensing Algorithm Based on History Energy Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shanshan Yu ◽  
Ju Liu ◽  
Jing Wang ◽  
Inam Ullah
Keyword(s):  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Threshold Energy ◽  
Average Energy ◽  
Energy Detection ◽  
Detection Performance ◽  
Weighting Coefficient ◽  
Threshold Method ◽  
Double Threshold

Spectrum sensing is one of the key technologies in the field of cognitive radio, which has been widely studied. Among all the sensing methods, energy detection is the most popular because of its simplicity and no requirement of any prior knowledge of the signal. In the case of low signal-to-noise ratio (SNR), the traditional double-threshold energy detection method employs fixed thresholds and there is no detection result when the energy is between high and low thresholds, which leads to poor detection performance such as lower detection probability and longer spectrum sensing time. To address these problems, we proposed an adaptive double-threshold cooperative spectrum sensing algorithm based on history energy detection. In each sensing period, we calculate the weighting coefficient of thresholds according to the SNR of all cognitive nodes; thus, the upper and lower thresholds can be adjusted adaptively. Furthermore, in a single cognitive node, once the current energy is within the high and low thresholds, we utilize the average energy of history sensing times to rejudge. To ensure the real-time performance, if the average history energy is still between two thresholds, the single-threshold method will be used for the end decision. Finally, the fusion center aggregates the detection results of each node and obtains the final cooperative conclusion through “or” criteria. Theoretical analysis and simulation results show that the algorithm proposed in this paper improved detection performance significantly compared with the other four different double-threshold algorithms.

scholarly journals Fast and accurate primary user detection with machine learning techniques for cognitive radio networks

2021 ◽  
Vol 21 (1) ◽  
pp. 472
Author(s):  
G. A. Pethunachiyar ◽  
B. Sankaragomathi
Keyword(s):  
Machine Learning ◽  
Spectrum Sensing ◽  
Orthogonal Frequency Division Multiplexing ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Primary User ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques

<p class="IJASEITAbtract"><span>Spectrum decision is an important and crucial task for the secondary user to avail the unlicensed spectrum for transmission. Managing the spectrum is an efficient one for spectrum sensing. Determining the primary user presence in the spectrum is an essential work for using the licensed spectrum of primary user. The information which lacks in managing the spectrum are the information about the primary user presence, accuracy in determining the existence of user in the spectrum, the cost for computation and difficult in finding the user in low signal-to noise ratio (SNR) values. The proposed system overcomes the above limitations. In the proposed system, the various techniques of machine learning like decision tree, support vector machines, naive bayes, ensemble based trees, nearest neighbour’s and logistic regression are used for testing the algorithm. As a first step, the spectrum sensing is done in two stages with Orthogonal Frequency Division Multiplexing and Energy Detection algorithm at the various values of SNR. The results generated from the above algorithm is used for database generation. Next, the different machine learning techniques are trained and compared for the results produced by different algorithms with the characteristics like speed, time taken for training and accuracy in prediction. The accuracy and finding the presence of the user in the spectrum at low SNR values are achieved by all the algorithms. The computation cost of the algorithm differs from each other. Among the tested techniques, k-nearest neighbour (KNN) algorithm produces the better performance in a minimized time.</span></p>

scholarly journals Cooperative Spectrum Sensing Based on Convolutional Neural Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4440
Author(s):  
Youheng Tan ◽  
Xiaojun Jing
Keyword(s):  
Neural Networks ◽  
Spectrum Sensing ◽  
Convolutional Neural Networks ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Majority Voting ◽  
Signal To Noise ◽  
Hidden Terminal ◽  
Simulation Results ◽  
Sensing Performance

Cooperative spectrum sensing (CSS) is an important topic due to its capacity to solve the issue of the hidden terminal. However, the sensing performance of CSS is still poor, especially in low signal-to-noise ratio (SNR) situations. In this paper, convolutional neural networks (CNN) are considered to extract the features of the observed signal and, as a consequence, improve the sensing performance. More specifically, a novel two-dimensional dataset of the received signal is established and three classical CNN (LeNet, AlexNet and VGG-16)-based CSS schemes are trained and analyzed on the proposed dataset. In addition, sensing performance comparisons are made between the proposed CNN-based CSS schemes and the AND, OR, majority voting-based CSS schemes. The simulation results state that the sensing accuracy of the proposed schemes is greatly improved and the network depth helps with this.

scholarly journals Spectrum Sensing in Cognitive Radio using Frequency Domain

2019 ◽  
Vol 9 (2) ◽  
pp. 3475-3477
Keyword(s):  
Cognitive Radio ◽  
Frequency Domain ◽  
Spectrum Sensing ◽  
Bandwidth Allocation ◽  
Dynamic Spectrum Access ◽  
Signal To Noise Ratio ◽  
Wide Band ◽  
Energy Detection ◽  
Secondary Users ◽  
Sensing Applications

An efficient bandwidth allocation and dynamic bandwidth access away from its previous limits is referred as cognitive radio (CR).The limited spectrum with inefficient usage requires the advances of dynamic spectrum access approach, where the secondary users are authorized to utilize the unused temporary licensed spectrum. For this reason it is essential to analyze the absence/presence of primary users for spectrum usage. So spectrum sensing is the main requirement and developed to sense the absence/ presence of a licensed user. This paper shows the design model of energy detection based spectrum sensing in frequency domain utilizing Binary Symmetric Channel (BSC) ,Additive white real Gaussian channel (AWGN), Rayleigh fading channel users for 16-Quadrature Amplitude Modulation(QAM) which is utilized for the wide band sensing applications at low Signal to noise Ratio(SNR) level to reduce the false error identification. The spectrum sensing techniques has least computational complexity. Simulink model for the energy detection based spectrum sensing using frequency domain in MATLAB 2014a.

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