Amateur radio sensing technique using a combination of energy detection and waveform classification

A critical problem in spectrum sensing is to create a detection algorithm and test statistics. The existing approaches employ the energy level of each channel of interest. However, this feature cannot accurately characterize the actual application of public amateur radio. The transmitted signal is not continuous and may consist only of a carrier frequency without information. This paper proposes a novel energy detection and waveform feature classification (EDWC) algorithm to detect speech signals in public frequency bands based on energy detection and supervised machine learning. The energy level, descriptive statistics, and spectral measurements of radio channels are treated as feature vectors and classifiers to determine whether the signal is speech or noise. The algorithm is validated using actual frequency modulation (FM) broadcasting and public amateur signals. The proposed EDWC algorithm's performance is evaluated in terms of training duration, classification time, and receiver operating characteristic. The simulation and experimental outcomes show that the EDWC can distinguish and classify waveform characteristics for spectrum sensing purposes, particularly for the public amateur use case. The novel technical results can detect and classify public radio frequency signals as voice signals for speech communication or just noise, which is essential and can be applied in security aspects.

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A Diffusion-Based Distributed Collaborative Energy Detection Algorithm for Spectrum Sensing in Cognitive Radio

Communications and Network ◽

10.4236/cn.2013.53b2051 ◽

2013 ◽

Vol 05 (03) ◽

pp. 276-279

Author(s):

Junfang Li ◽

Wenxiao Chen ◽

Shaoli Kang ◽

Yongming Guo

Keyword(s):

Cognitive Radio ◽

Spectrum Sensing ◽

Energy Detection ◽

Detection Algorithm

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An New Optimal Decision Threshold Criterion for Broadband-Based Energy Detection with Performance Constraints

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.2242 ◽

2013 ◽

Vol 765-767 ◽

pp. 2242-2249

Author(s):

Wen Wen Liu ◽

Long Yuan ◽

Zi Yang Liu ◽

Tao Peng ◽

Wen Bo Wang

Keyword(s):

False Alarm ◽

Spectrum Sensing ◽

Spectrum Sharing ◽

Closed Form Solution ◽

Energy Detection ◽

Detection Algorithm ◽

Observation Time ◽

Optimal Decision ◽

Decision Threshold ◽

Threshold Criterion

We study on the energy detection algorithm of spectrum sensing. The performance of energy detection in spectrum sensing is measured by the false alarm probability and the missing detection probability. In a certain spectrum sharing environment, whether the energy detection algorithm can meet our requirement is depending on the length of observation time and the decision threshold selected. Several experiment results have shown that: when the decision threshold is too low, it will cause much more false alarm; when the decision threshold is too high, it will bring many missing detection. Therefore, it is crucial that choosing an optimal decision threshold according to the length of observation time. In this paper, the closed-form solution of the minimum-cost decision threshold is deduced by using the methodology of mathematical derivation, and a new criterion about selecting an optimal decision threshold is also proposed. At last, the conclusion is proved by simulation.

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

Wireless Communications and Mobile Computing ◽

10.1155/2021/5516540 ◽

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.236-237.917 ◽

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.

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Research on Dynamic Threshold Based Energy Detection in Cognitive Radio Systems

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.462.506 ◽

2012 ◽

Vol 462 ◽

pp. 506-511 ◽

Cited By ~ 1

Author(s):

Gui Cai Yu ◽

Cheng Zhi Long ◽

Man Tian Xiang

Keyword(s):

Cognitive Radio ◽

Spectrum Sensing ◽

Energy Detection ◽

Detection Algorithm ◽

Dynamic Threshold ◽

Noise Power ◽

Noise Uncertainty ◽

Spectrum Detection ◽

Radio Systems ◽

Short Time

In cognitive radio networks, nodes should have the capability to decide whether a signal from a primary transmitter is locally present or not in a certain spectrum within a short detection period. Traditional spectrum sensing schemes based on fixed threshold are sensitive to noise uncertainty, a fractional fluctuate of average noise power in a short time can lead the performance of spectrum detection drop seriously. This paper presents a new spectrum detection algorithm based on dynamic threshold. Theoretical results show that the proposed scheme debate the noise uncertainty, and good detection performance can be gained, if suitable dynamic threshold is chosen. In other words, the proposed scheme can enhance the robustness against noise and improve the capacity of spectrum sensing.

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Energy Detection Algorithm for Spectrum Sensing Using Three Consecutive Sensing Events

IEEE Wireless Communications Letters ◽

10.1109/lwc.2016.2543723 ◽

2016 ◽

Vol 5 (3) ◽

pp. 284-287 ◽

Cited By ~ 16

Author(s):

Calin Vladeanu ◽

Cosmina-Valentina Nastase ◽

Alexandru Martian

Keyword(s):

Spectrum Sensing ◽

Energy Detection ◽

Detection Algorithm

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

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v21.i1.pp472-478 ◽

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

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.

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Demand Response Management Research Based on Cognitive Radio for Smart Grid

Wireless Communications and Mobile Computing ◽

10.1155/2020/8827777 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Tingting Yang ◽

Tiancong Huang ◽

Haifeng Zhang ◽

Peiyi Li ◽

Canyun Xiong ◽

...

Keyword(s):

Cognitive Radio ◽

Smart Grid ◽

Outage Probability ◽

Spectrum Sensing ◽

Demand Response ◽

Loss Rate ◽

Energy Detection ◽

Detection Algorithm ◽

System Control ◽

Control Performance

Cognitive radio is introduced into the demand response management (DRM) of smart grid with the hope of alleviating the shortage of spectrum resources and improving communication quality. In this paper, we adopt an energy detection algorithm based on generalized stochastic resonance (GSRED) to improve the spectrum sensing accuracy under the circumstances of low signal-to-noise ratio without increasing system overhead. Specifically, a DRM scheme based on real-time pricing is investigated, and the social welfare is taken as the main index to measure system control performance. Furthermore, considering the adverse effects incurred by incorrect spectrum sensing, we incorporate the probability of the DRM system causing interference to primary user and spectrum loss rate into the evaluation index of the system control performance and give the final expression of the global optimization problem. The influence of sensing time on system communication outage probability and spectrum loss rate is elaborated in detail through theoretical derivation and simulation analysis. Simulation results show that the GSRED algorithm has higher detection probability under the same conditions compared with the traditional energy detection algorithm, thus guaranteeing lower communication outage probability and spectrum loss rate.

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