A fast spectrum sensing for OFDM based on adaptive thresholding

Zhang, Mengbo; Wang, Lunwen; Ding, Li

doi:10.1109/iaeac.2017.8054511

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…The logistic regression, SVM, and deep learning OFDM (DP-OFDM) spectrum sensing model are used for training and testing, and the error threshold Ω th is adjusted to obtain the detection probability. As described in [28], a cyclic feature detection method based on compressed sensing, such as [11] proposed the energy detection method, and [15] proposed the autocorrelation detection method. Under the condition of false alarm probability P f = 0:05, the corresponding decision thresholds are set for each of the above methods, and the above methods are simulated in the simulation platform, respectively.…”

Section: Performance Comparison Analysis Of Algorithms [22]mentioning

confidence: 99%

“…[14] proposed a spectrum sensing method based on correlation detection, the correlation of cyclic prefix (CP) was used in OFDM, and the sampled data was subjected to correlation operation. In [15], the signal and noise were estimated simultaneously by the time domain correlation function, and the estimated threshold was continuously adjusted by the estimated value to complete the spectrum sensing of an OFDM signal. In [16], the received autocorrelation function was estimated at each OFDM symbol of its symbol period, and then, the multivariate statistical theory was used to calculate the judgment amount and the decision threshold.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Performance Comparison Analysis Of Algorithms [22]mentioning

confidence: 99%

Section: Introductionmentioning

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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“…to obtain measurement parameters [1,2] . Another commonly used method is to add recognition icons at different angles of the tested object for feature extraction and matching [7,8] . But these methods all have varying degrees of drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Research on rocket engine pose measurement technology based on monocular vision

Zhang,

Wu,

Liu

et al. 2023

Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023)

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In recent years, in the aerospace field, higher requirements have been put forward for the position and pose measurement tasks such as engines. Computer vision technology is the key technology to achieve the target measurement tasks. Rocket engine pose measurement provides technical support for engine pose control strategy optimization. Based on the principle of monocular vision, this article designs a measurement system with strong stability and high accuracy for launch vehicle engines, and verifies the effectiveness of the measurement system through two sets of simulation experiments. In terms of image preprocessing, the noise reduction effects of spatial filtering and wavelet transform were compared, and a preprocessing method combining wavelet transform and image enhancement was designed; In terms of feature extraction, improvements have been made to the traditional Accelerated Feature Segmentation Test (FAST) algorithm to reduce the impact of lighting on feature point extraction under different postures, while retaining the advantage of high-precision measurement algorithms under point features; In terms of pose measurement, the centroid and translation vector pre pose iterative optimization algorithm is adopted, which greatly reduces the computational complexity in the pose calculation iteration process and improves the speed of spatial target pose calculation.

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