Spectrum Sensing technique for Cognitive Radio of multiple OFDM Signals based on Singular Value Decomposition

2016 ◽  
Author(s):  
F. Z. El Bahi ◽  
H. Ghennioui ◽  
M. Zouak
Keyword(s):  
Cognitive Radio ◽  
Singular Value Decomposition ◽  
Spectrum Sensing ◽  
Singular Value ◽  
Value Decomposition ◽  
Ofdm Signals

scholarly journals Adaptive and Blind Wideband Spectrum Sensing Scheme Using Singular Value Decomposition

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Zhuhua Hu ◽  
Yong Bai ◽  
Yaochi Zhao ◽  
Chong Shen ◽  
Mingshan Xie
Keyword(s):  
Singular Value Decomposition ◽  
Spectrum Sensing ◽  
Noise Intensity ◽  
Sampling Rate ◽  
Approximation Error ◽  
Singular Value ◽  
Decision Threshold ◽  
Wideband Spectrum Sensing ◽  
Reconstruction Performance ◽  
Value Decomposition

The Modulated Wideband Converter (MWC) can provide a sub-Nyquist sampling for continuous analog signal and reconstruct the spectral support. However, the existing reconstruction algorithms need a priori information of sparsity order, are not self-adaptive for SNR, and are not fault tolerant enough. These problems affect the reconstruction performance in practical sensing scenarios. In this paper, an Adaptive and Blind Reduced MMV (Multiple Measurement Vectors) Boost (ABRMB) scheme based on singular value decomposition (SVD) for wideband spectrum sensing is proposed. Firstly, the characteristics of singular values of signals are used to estimate the noise intensity and sparsity order, and an adaptive decision threshold can be determined. Secondly, optimal neighborhood selection strategy is employed to improve the fault tolerance in the solver of ABRMB. The experimental results demonstrate that, compared with ReMBo (Reduce MMV and Boost) and RPMB (Randomly Projecting MMV and Boost), ABRMB can significantly improve the success rate of reconstruction without the need to know noise intensity and sparsity order and can achieve high probability of reconstruction with fewer sampling channels, lower minimum sampling rate, and lower approximation error of the potential of spectral support.

Improving TF-IDF with Singular Value Decomposition (SVD) for Feature Extraction on Twitter

2017 ◽  
Author(s):  
Ammar Ismael Kadhim ◽  
Yu-N Cheah ◽  
Inaam Abbas Hieder ◽  
Rawaa Ahmed Ali
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition

Multi-scale singular value decomposition polarization image fusion defogging algorithm and experiment

2020 ◽  
Vol 13 (6) ◽  
pp. 1-10
Author(s):  
ZHOU Wen-zhou ◽  
◽  
FAN Chen ◽  
HU Xiao-ping ◽  
HE Xiao-feng ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Image Fusion ◽  
Singular Value ◽  
Multi Scale ◽  
Polarization Image ◽  
Value Decomposition
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