OFDM system with cyclostationary feature detection spectrum sensing

Кумар, Арун; Pandi, Nandhakumar

doi:10.1016/j.icte.2018.01.007

Cited by 55 publications

(17 citation statements)

References 9 publications

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“…The detection probability of the proposed method (PDPRO) is a composite detection probability includes detection probability of CED and the DCT based assisting module as in (11).…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…PD PRO =P INCED *PD CED +(1-P INCED )*PD DCT (11) where P INCED is the probability of falling in CED sensing event, PD CED is the detection probability of the CED method, and PD DCT is the detection probability of DCT based assisting module.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Spectrum sensing is the process that achieved by Secondary User (SU) side to know the PU status which either idle or busy with a purpose to detect the spectrum holes [5][6][7][8][9]. There are many narraw-band spectrum sensing methods such as Matched Filter [10], Cyclostationary [11], Energy Detector [12], features based [13] and eigen value based [14] . Onother hand, there aremany spectrum sensing methods specialized for wide-band channles such as compressive sensing [15,16] and edge based detectiom methods [17].…”

Section: Introductionmentioning

confidence: 99%

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A combined spectrum sensing method based DCT for cognitive radio system

Falih

Abdullah

2020

IJECE

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In this paper a new hybrid blind spectrum sensing method is proposed. The method is designed to enhance the detection performance of Conventional Energy Detector (CED) through combining it with a proposed sensing module based on Discrete Cosine Transform (DCT) coefficient’s relationship as operation mode at low Signal to Noise Ratio (SNR) values. In the proposed sensing module a certain factor called Average Ratio (AR) represent the ratio of energy in DCT coefficients is utilized to identify the presence of the Primary User (PU) signal. The simulation results show that the proposed method improves PU detection especially at low SNR values.

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“…The detection probability of the proposed method (PDPRO) is a composite detection probability includes detection probability of CED and the DCT based assisting module as in (11).…”

Section: The Proposed Methodsmentioning

confidence: 99%

Section: The Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A combined spectrum sensing method based DCT for cognitive radio system

Falih

Abdullah

2020

IJECE

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“…The existing spectrum sensing methods mainly use the neural network as a classifier, and the data set is a complex feature statistic, and the sensing effect is not ideal [18][19][20][21]. [22] proposed multigene genetic programming-(MGGP-) based feature engineering was conducted to transform the cumulants of the received signals into highly discriminative features, and then, authors use a logistic regression classifier to achieve classification of overlapping signal modulation.…”

Section: Related Workmentioning

confidence: 99%

A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum

Pan

Lin

2020

International Journal of Digital Multimedia Broadcasting

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In a cognitive radio network (CRN), spectrum sensing is an important prerequisite for improving the utilization of spectrum resources. In this paper, we propose a novel spectrum sensing method based on deep learning and cycle spectrum, which applies the advantage of the convolutional neural network (CNN) in an image to the spectrum sensing of an orthogonal frequency division multiplex (OFDM) signal. Firstly, we analyze the cyclic autocorrelation of an OFDM signal and the cyclic spectrum obtained by the time domain smoothing fast Fourier transformation (FFT) accumulation algorithm (FAM), and the cyclic spectrum is normalized to gray scale processing to form a cyclic autocorrelation gray scale image. Then, we learn the deep features of layer-by-layer extraction by the improved CNN classic LeNet-5 model. Finally, we input the test set to verify the trained CNN model. Simulation experiments show that this method can complete the spectrum sensing task by taking advantage of the cycle spectrum, which has better spectrum sensing performance for OFDM signals under a low signal-noise ratio (SNR) than traditional methods.

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“…In recent years, some new end-point prediction methods have emerged, such as smelting noise analysis, pattern recognition, and texture analysis [3]. Because the light intensity spectrum of the converter will show certain rules along with the process of blowing, as the accurate theory of light intensity spectrum detection matures [4], there have been many attempts to use optical methods to judge the end point of converter steelmaking. Professor Yanru Chen of Nanjing University of Science and Technology found that the light intensity and image characteristic information have remarkable changes in the steelmaking blowing process and established the end-point time mathematical model using regression analysis method [5].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Converter Combustion Flame Spectrum Big Data Sets Based on HHT

Chang

Wang

et al. 2018

Complexity

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The characteristics of the converter combustion flame are one of the key factors in the process control and end-point control of steelmaking. In a big data era, it is significant to carry out high-speed and effective processing on frame spectrum data. By installing data acquisition devices at the converter mouth and separating the spectrum according to the wave length, high-dimensional converter flame spectrum big data sets are achieved. The data of each converter is preprocessed after information fusion. By applying the SM software, the correspondence with the carbon content is obtained. Selecting the relative data of the two peak ratios and the single-peak absolute data as a one-dimensional signal, due to the obvious nonlinear and nonstationary characteristics, using HHT to do empirical mode decomposition and Hilbert spectrum analysis, the variation characteristics after 70% of the converter steelmaking process are obtained. From data acquisition, data preprocessing to data analysis and results, it provides a new perspective and method for the study of similar problems.

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OFDM system with cyclostationary feature detection spectrum sensing

Cited by 55 publications

References 9 publications

A combined spectrum sensing method based DCT for cognitive radio system

A combined spectrum sensing method based DCT for cognitive radio system

A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum

Analysis of Converter Combustion Flame Spectrum Big Data Sets Based on HHT

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