scholarly journals A Blind Signal Samples Detection Algorithm for Accurate Primary User Traffic Estimation

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4136
Author(s):  
Jakub Nikonowicz ◽  
Aamir Mahmood ◽  
Mikael Gidlund
Keyword(s):  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Low Complexity ◽  
Primary User ◽  
Time Frame ◽  
Detection Algorithm ◽  
Experimental Simulation ◽  
Spectrum Access ◽  
Blind Signal ◽  
Occupancy Estimation

The energy detection process for enabling opportunistic spectrum access in dynamic primary user (PU) scenarios, where PU changes state from active to inactive at random time instances, requires the estimation of several parameters ranging from noise variance and signal-to-noise ratio (SNR) to instantaneous and average PU activity. A prerequisite to parameter estimation is an accurate extraction of the signal and noise samples in a received signal time frame. In this paper, we propose a low-complexity and accurate signal samples detection algorithm as compared to well-known methods, which is also blind to the PU activity distribution. The proposed algorithm is analyzed in a semi-experimental simulation setup for its accuracy and time complexity in recognizing signal and noise samples, and its use in channel occupancy estimation, under varying occupancy and SNR of the PU signal. The results confirm its suitability for acquiring the necessary information on the dynamic behavior of PU, which is otherwise assumed to be known in the literature.

scholarly journals A High-Precision Spectrum-Detection Algorithm Based on the Normalized Variance of Nonreconstruction Compression Sensing

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhiyuan Shen ◽  
Qianqian Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Primary User ◽  
Detection Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Sensing Matrix ◽  
Current State ◽  
Spectrum Detection ◽  
Compression Sensing

The traditional energy detection algorithm has been widely used in the field of signal detection, and a variety of improved algorithms have been derived. In the case of low signal-to-noise ratio, existing methods have shortcomings on achieving fast and accurate spectrum sensing that need to be resolved. This work proposes a normalized-variance-detection method based on compression sensing measurements of received signal. The discrete cosine transform sensing matrix is used to compress the signal, whose normalized variance is then calculated before being used as the testing variable for detecting the primary user signal. Taking the detection results as historical data into consideration, the classification model is obtained after training by applying a support vector machine for classifying and predicting test signals. Simulation results show that the proposed method outperforms the current state-of-the-art approaches by achieving faster and more accurate spectrum occupancy decisions.

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>

A Modified Energy Detection Algorithm Based on Primary User Activity for Cognitive Radio Networks

2014 ◽  
Vol 548-549 ◽  
pp. 1351-1354
Author(s):  
Gang Tao Han ◽  
Ying Qiang Ding ◽  
Xiao Min Mu ◽  
Jian Kang Zhang
Keyword(s):  
Spectrum Sensing ◽  
Dynamic Spectrum Access ◽  
Energy Detection ◽  
Primary User ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Spectrum Access ◽  
Theory Analysis ◽  
Channel State ◽  
User Activity

Spectrum sensing is used to identify the unused frequency bands and as such plays a key role in dynamic spectrum access. In most of the existing spectrum sensing models, the channel state of primary user is assumed unchanged within the spectrum sensing duration, which is not suitable for the channels with high activity, in which the primary user accesses or vacates from the channel frequently. In this paper, a Modified Energy Detection (MED) algorithm is proposed for this scenarios by considering the primary user activity within the spectrum sensing duration. Theory analysis and computer simulation results show that both the probability of detection and false alarm in this scenarios have been improved with our MED.

scholarly journals 5G cognitive radio system design with new algorithm asynchronous spectrum sensing

2021 ◽  
Vol 10 (4) ◽  
pp. 2046-2054
Author(s):  
Mohammed Mehdi Saleh ◽  
Ahmed A. Abbas ◽  
Ahmed Hammoodi
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Energy Detection ◽  
Primary User ◽  
Spectrum Access ◽  
Secondary Users ◽  
Radio System ◽  
Wireless Applications ◽  
Fifth Generation ◽  
Time Required

Due to the rapid increase in wireless applications and the number of users, spectrum scarcity, energy consumption and latency issues will emerge, notably in the fifth generation (5G) system. Cognitive radio (CR) has emerged as the primary technology to address these challenges, allowing opportunist spectrum access as well as the ability to analyze, observe, and learn how to respond to environmental 5G conditions. The CR has the ability to sense the spectrum and detect empty bands in order to use underutilized frequency bands without causing unwanted interference with legacy networks. In this paper, we presented a spectrum sensing algorithm based on energy detection that allows secondary user SU to transmit asynchronously with primary user PU without causing harmful interference. This algorithm reduced the sensing time required to scan the whole frequency band by dividing it into n sub-bands that are all scanned at the same time. Also, this algorithm allows cognitive radio networks (CRN) nodes to select their operating band without requiring cooperation with licensed users. According to the BER, secondary users have better performance compared with primary users.

scholarly journals A Two-Step Cooperative Energy Detection Algorithm Robust to Noise Uncertainty

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Tingting Yang ◽  
Yucheng Wu ◽  
Liang Li ◽  
Weiyang Xu ◽  
Weiqiang Tan
Keyword(s):  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Resonance Energy ◽  
Energy Detection ◽  
Detection Algorithm ◽  
Second Step ◽  
Final Decision ◽  
Noise Uncertainty ◽  
Robust To Noise ◽  
Significant Increment

In order to achieve accurate interference detection in complex electromagnetic environments, a two-step cooperative stochastic resonance energy detection (TCSRED) algorithm is proposed to address the problem, where the traditional energy detection (ED) performance is susceptible to noise uncertainty. By combining two thresholds and two-step cooperation, the generalized stochastic resonance is applied to the energy detection, which effectively reduces the complexity and detection time. In particular, when a certain decision result is obtained in the first step of detection, the decision is finished and the second step of detection is unnecessary. Otherwise, the second step of detection is performed to obtain the final decision result. Simulation results show that the proposed algorithm is robust to the noise uncertainty. Even in the case of a low signal-to-noise ratio (SNR), it also performs better than existing methods without significant increment of the complexity.

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.

A blind signal detection algorithm for passive location system based on troposcatter

2018 ◽  
Vol 10 (10) ◽  
pp. 1128-1133
Author(s):  
Zan Liu ◽  
Xihong Chen ◽  
Qiang Liu ◽  
Zedong Xie
Keyword(s):  
Signal Detection ◽  
Energy Detection ◽  
Detection Algorithm ◽  
Ensemble Empirical Mode Decomposition ◽  
Passive Location ◽  
Intrinsic Mode Functions ◽  
Location System ◽  
Blind Signal ◽  
Mode Decomposition ◽  
Signal Simulation

AbstractTo improve detection performance of passive location system based on troposcatter, we propose a blind signal detection algorithm. According to our algorithm, complementary ensemble empirical mode decomposition decomposes the received signal into several intrinsic mode functions (IMFs). To reconstruct the signal and background noises, difference between the entropy of adjacent IMFs is utilized as a standard. Different IMFs are utilized to estimate threshold of energy detection algorithm and energy level of received signal. Simulation examples indicate that the proposed algorithm can blindly and effectively detect the signal.

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.

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.

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