scholarly journals SPECTRUM SENSING OF WIDE BAND SIGNALS BASED ON ENERGY DETECTION WITH COMPRESSIVE SENSING

2020 ◽  
Vol 24 (06) ◽  
pp. 83-90
Author(s):  
Ali Mohammad A. AL-Hussain ◽  
◽  
Maher K. Mahmood ◽  
Keyword(s):  
False Alarm ◽  
Compressive Sensing ◽  
Spectrum Sensing ◽  
High Speed ◽  
Sampling Rate ◽  
Wide Band ◽  
Probability Of Detection ◽  
Signal Spectrum ◽  
Radio System ◽  
Probability Of False Alarm

Compressive sensing (CS) technique is used to solve the problem of high sampling rate with wide band signal spectrum sensing where high speed analogue to digital converter is needed to do that. This leads to difficult hardware implementation, large time of sensing and detection with high consumptions power. The proposed approach combines energy-based detection, with CS compressive sensing and investigates the probability of detection, and the probability of false alarm as a function of the SNR, showing the effect of compression to spectrum sensing performance of cognitive radio system. The Discrete Cosine Transform (DCT) is used as a sparse representation basis of the received signal, and random matrix as a compressive matrix. The 𝓁1 norm algorithm is used to reconstruct the original signal. A closed form of probability of detection and probability of false alarm are derived. Computer simulation shows clearly that the compression ratio, recovery error and SNR level affect the probability of detection.

scholarly journals Spectrum sensing of wideband signals based on cyclostationary and compressive sensing

2020 ◽  
Vol 20 (3) ◽  
pp. 1361
Author(s):  
Ali Mohammad A. AL-Hussain ◽  
Maher Khudair Mahmood Al Azawi
Keyword(s):  
Compressive Sensing ◽  
Spectrum Sensing ◽  
Compression Ratio ◽  
High Speed ◽  
Processing Time ◽  
Sampling Rate ◽  
Probability Of Detection ◽  
Signal Spectrum ◽  
Sensing Matrix ◽  
Recovery Algorithm

Compressive sensing is a powerful technique used to overcome the problem of high sampling rate when dealing with wideband signal spectrum sensing which leads to high speed analogue to digital convertor (ADC) accompanied with large hardware complexity, high processing time, long duration of signal spectrum acquisition and high consumption power. Cyclostationary based detection with compressive technique will be studied and discussed in this paper. To perform the compressive sensing technique, Discrete Cosine Transform (DCT) is used as sparse representation basis of received signal and Gaussian random matrix as a sensing matrix, and then 𝓁1- norm recovery algorithm is used to recover the original signal. This signal is used with cyclostationary detector. The probability of detection as a function of SNR with several compression ratio and processing time versus compression ratio are used as performance parameters. The effect of the recovery error of reconstruction algorithm is presented as a function of probability of detection.

scholarly journals Optimization of spectrum sensing in cognitive radio using genetic algorithm

2012 ◽  
Vol 25 (3) ◽  
pp. 235-243 ◽  
Author(s):  
Rashmi Deka ◽  
Soma Chakraborty ◽  
Sekhar Roy
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Rapid Development ◽  
Probability Of Detection ◽  
Radio System ◽  
Probability Of False Alarm ◽  
Space Allocation ◽  
Wireless Environment ◽  
Better Than

Spectrum availability is becoming scarce due to the rise of number of users and rapid development in wireless environment. Cognitive radio (CR) is an intelligent radio system which uses its in-built technology to use the vacant spectrum holes for the use of another service provider. In this paper, genetic algorithm (GA) is used for the best possible space allocation to cognitive radio in the spectrum available. For spectrum reuse, two criteria have to be fulfilled - 1) probability of detection has to be maximized, and 2) probability of false alarm should be minimized. It is found that with the help of genetic algorithm the optimized result is better than without using genetic algorithm. It is necessary that the secondary user should vacate the spectrum in use when licensed users are demanding and detecting the primary users accurately by the cognitive radio. Here, bit error rate (BER) is minimized for better spectrum sensing purpose using GA.

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%.

scholarly journals Tree Copula Theory Based Fusion And Compressive Sensing For Activity Detection using Multi-Modal Data

2019 ◽  
Vol 8 (11) ◽  
pp. 4152-4158
Keyword(s):  
False Alarm ◽  
Compressive Sensing ◽  
Detection Method ◽  
Probability Of Detection ◽  
Stochastic Gradient Descent ◽  
Activity Detection ◽  
Copula Theory ◽  
Probability Of False Alarm ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Activity detection based on likelihood ratio in the presence of high dimensional multimodal data acts as a challenging problem as the estimation of joint probability density functions (pdfs) with intermodal dependence is tedious. The existing method with above expectations fails due to poor performance in the presence of strongly dependent data. This paper proposes a Compressive Sensing Based Detection method in the Multi-sensor signal using the deep learning method. The proposed Tree copula- Grasshopper optimization based Deep Convolutional Neural Network (TC-GO based DCNN) detection method comprises of three main steps, such as compressive sensing, fusion and detection. The signals are initially collected from the sensors in order to subject them under tensor based compressive sensing. The compressed signals are then fused together using tree copula theory, and the parameters are estimated with the Grasshopper optimization algorithm (GOA). The activity detection is finally performed using DCNN, which is trained with the Stochastic Gradient Descent (SGD) Optimizer. The performance of the proposed method is evaluated based on the evaluation metrics, such as probability of detection and probability of false alarm. The highest probability of detection and least probability of false alarm are obtained as 0.9083, and 0.0959, respectively using the proposed method that shows the effectiveness of the proposed method in activity detection.

scholarly journals Efficient spectrum sensing technique based on energy detector, compressive sensing, and de-noising techniques

2016 ◽  
Vol 6 (1) ◽  
pp. 1 ◽  
Author(s):  
Amr Hussein ◽  
Hossam Kasem ◽  
Mohamed Adel
Keyword(s):  
Compressive Sensing ◽  
Spectrum Sensing ◽  
Compression Ratio ◽  
System Performance ◽  
High Speed ◽  
Sampling Rate ◽  
Energy Detector ◽  
Recent Theory ◽  
Detector Performance ◽  
Compression Sensing

Highdata rate cognitive radio (CR) systems require high speed Analog-to-Digital Converters (ADC). This requirement imposes many restrictions on the realization of the CR systems. The necessity of high sampling rate can be significantly alleviated by utilizing analog to information converter (AIC). AIC is inspired by the recent theory of Compressive Sensing (CS), which states that a discrete signal has a sparse representation in some dictionary, which can be recovered from a small number of linear projections of that signal. This paper proposes an efficient spectrum sensing technique based on energy detection, compression sensing, and de-noising techniques. De-noising filters are utilized to enhance the traditional Energy Detector performance through Signal-to-Noise (SNR) boosting. On the other hand, the ordinary sampling provides an ideal performance at a given conditions. A near optimal performance can be achieved by applying compression sensing. Compression sensing allows signal to be sampled at sampling rates much lower than the Nyquist rate. The system performance and ADC speed can be easily controlled by adjusting the compression ratio. In addition, a proposed energy detector technique is introduced by using an optimum compression ratio. The optimum compression ratio is determined using a Genetic Algorithm (GA) optimization tool. Simulation results revealed that the proposed techniques enhanced system performance.

scholarly journals Compressive Spectrum Sensing with Temporal-Correlated Prior Knowledge Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jianfeng Liu ◽  
Xin-Lin Huang ◽  
Ping Wang
Keyword(s):  
Compressive Sensing ◽  
Prior Knowledge ◽  
Spectrum Sensing ◽  
High Speed ◽  
Sampling Rate ◽  
Temporal Correlation ◽  
False Alarm Probability ◽  
Spectrum Estimation ◽  
Sensing Data ◽  
Correlation Information

Cognitive radio (CR) has been proposed to mitigate the spectrum scarcity issue to support heavy wireless services on sub-3GHz. Recently, broadband spectrum sensing becomes a hot topic with the help of compressive sensing technology, which will reduce the high-speed sampling rate requirement of analog-to-digital converter. This paper considers sequential compressive spectrum sensing, where the temporal correlation information between neighboring compressive sensing data will be exploited. Different from conventional compressive sensing, the previous compressive sensing data will be fused into prior knowledge in current spectrum estimation. The simulation results show that the proposed scheme can achieve 98.7% detection probability under 3.5% false alarm probability and performs the best compared with the typical BPDN and OMP schemes.

scholarly journals Spectrum Sensing in Cognitive Radio

2012 ◽  
pp. 29-42
Author(s):  
Wei-Ho Chung
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Fading Channels ◽  
Spectrum Sensing ◽  
Cognitive Radios ◽  
Probability Of Detection ◽  
Sensor Nodes ◽  
Fusion Rules ◽  
Probability Of False Alarm ◽  
Individual Decisions

The cognitive radio has been widely investigated to support modern wireless applications. To exploit the spectrum vacancies in cognitive radios, the chapter considers the collaborative spectrum sensing by multiple sensor nodes in the likelihood ratio test (LRT) frameworks. In this chapter, the functions of sensors can be served through the cooperative regular nodes in the cognitive radio, or the specifically deployed sensor nodes for spectrum sensing. In the LRT, the sensors make individual decisions. These individual decisions are then transmitted to the fusion center to make the final decision, which provides better detection accuracy than the individual sensor decisions. The author provides the lowered-bounded probability of detection (LBPD) criterion as an alternative criterion to the conventional Neyman-Pearson (NP) criterion. In the LBPD criterion, the detector pursues the minimization of the probability of false alarm while maintaining the probability of detection above the pre-defined value. In cognitive radios, the LBPD criterion limits the probabilities of channel conflicts to the primary users. Under the NP and LBPD criteria, the chapter provides explicit algorithms to solve the LRT fusion rules, the probability of false alarm, and the probability of detection for the fusion center. The fusion rules generated by the algorithms are optimal under the specified criteria. In the spectrum sensing, the fading channels influence the detection accuracies. The chapter investigates the single-sensor detection and collaborative detections of multiple sensors under various fading channels and derives testing statistics of the LRT with known fading statistics.

scholarly journals Spectrum Sensing for Cognitive Radio Using Genetic Algorithm

2018 ◽  
Vol 14 (09) ◽  
pp. 190 ◽  
Author(s):  
Shewangi Kochhar ◽  
Roopali Garg
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Optimization Technique ◽  
Primary User ◽  
Time Frame ◽  
Probability Of False Alarm ◽  
Sensing Time ◽  
Better Than

<p>Cognitive Radio has been skillful technology to improve the spectrum sensing as it enables Cognitive Radio to find Primary User (PU) and let secondary User (SU) to utilize the spectrum holes. However detection of PU leads to longer sensing time and interference. Spectrum sensing is done in specific “time frame” and it is further divided into Sensing time and transmission time. Higher the sensing time better will be detection and lesser will be the probability of false alarm. So optimization technique is highly required to address the issue of trade-off between sensing time and throughput. This paper proposed an application of Genetic Algorithm technique for spectrum sensing in cognitive radio. Here results shows that ROC curve of GA is better than PSO in terms of normalized throughput and sensing time. The parameters that are evaluated are throughput, probability of false alarm, sensing time, cost and iteration.</p>

EVALUATION OF THE ACCURACY OF VOLUMETRIC LEAK DETECTION METHODS FOR UNDERGROUND STORAGE TANKS CONTAINING GASOLINE1

1989 ◽  
Vol 1989 (1) ◽  
pp. 27-35
Author(s):  
Joseph W. Maresca ◽  
James W. Starr ◽  
Robert D. Roach ◽  
John S. Farlow
Keyword(s):  
Data Analysis ◽  
False Alarm ◽  
Leak Detection ◽  
Test Methods ◽  
Probability Of Detection ◽  
Test Method ◽  
Underground Storage ◽  
Storage Tanks ◽  
Test Protocol ◽  
Probability Of False Alarm

ABSTRACT A United States Environmental Protection Agency (EPA) research program evaluated the current performance of commercially available volumetric test methods for the detection of small leaks in underground gasoline storage tanks. The evaluations were performed at the EPA Risk Reduction Engineering Laboratory's Underground Storage Tank Test Apparatus in Edison, New Jersey. The methodology used for evaluation made it possible to determine and resolve most of the technological and engineering issues associated with volumetric leak detection, as well as to define the current practice of commercially available test methods. The approach used (1) experimentally validated models of the important sources of ambient noise that affect volume changes in nonleaking and leaking tanks, (2) a large data base of product-temperature changes that result from the delivery of product to a tank at a different temperature than the product in the tank, and (3) a mathematical model of each test method to estimate the performance of that method. The test-method model includes the instrumentation noise, the configuration of the sensors, the test protocol, the data analysis algorithms, and the detection criterion. Twenty-five commercially available volumetric leak detection systems were evaluated. The leak rate measurable by these systems ranged from 0.26 to 6.78 L/h (0.07 to 1.79 gal/h), with a probability of detection of 0.95 and a probability of false alarm of 0.05. Five methods achieved a performance between 0.19 L/h (0.05 gal/h) and 0.57 L/h (0.15 gal/h). Only one method was able to detect leaks less than 0.57 L/h (0.15 gal/h) if the probability of detection was increased to 0.99 and the probability of false alarm was decreased to 0.01. The measurable leak rates ranged from 0.45 to 12.94 L/h (0.12 to 3.42 gal/h) with these more stringent detection and false alarm parameters. The performance of the methods evaluated was primarily limited by test protocol, operational sensor configuration, data analysis, and calibration, rather than by hardware. The experimental analysis and model calculations suggested that substantial performance improvements can be realized by making procedural changes. With modifications, it is estimated that more than 60 percent of the methods should be able to achieve a probability of detection of 0.99 and a probability of false alarm of 0.01 for leak rates between 0.19 L/h (0.05 gal/h) and 0.56 L/h (0.15 gal/h), and 100 percent should be able to achieve this performance for leak rates of approximately 0.76 L/h (0.20 gal/h).

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