Wideband spectrum sensing in cognitive radio using discrete wavelet packet transform and principal component analysis

2020 ◽  
Vol 38 ◽  
pp. 100918 ◽  
Author(s):  
P.Y. Dibal ◽  
E.N. Onwuka ◽  
J. Agajo ◽  
C.O. Alenoghena
Keyword(s):  
Principal Component Analysis ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Wavelet Packet ◽  
Principal Component ◽  
Component Analysis ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Discrete Wavelet Packet Transform ◽  
Wideband Spectrum Sensing

Principal component analysis‐based blind wideband spectrum sensing for cognitive radio

2016 ◽  
Vol 52 (16) ◽  
pp. 1416-1418 ◽  
Author(s):  
Kejun Lei ◽  
Xi Yang ◽  
Yanghong Tan ◽  
Shengliang Peng ◽  
Xiuying Cao
Keyword(s):  
Principal Component Analysis ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Principal Component ◽  
Component Analysis ◽  
Wideband Spectrum Sensing

scholarly journals Advanced Multiresolution Wavelet Based Wideband Spectrum Sensing Technique for Cognitive Radio

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jai Sukh Paul Singh ◽  
Mritunjay Kumar Rai ◽  
Gulshan Kumar ◽  
Rajwinder Singh ◽  
Hye-Jin Kim ◽  
...  
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Wavelet Packet ◽  
Cognitive Radios ◽  
Computing System ◽  
Discrete Wavelet ◽  
Radio System ◽  
Discrete Wavelet Packet Transform ◽  
Wideband Spectrum Sensing ◽  
Multiresolution Wavelet

An advance multiresolution wavelet based approach for wideband spectrum sensing for cognitive radio system is proposed in this paper. Prime focus is made on the coarse detection part for interweaved system, in which unoccupied spectrum can be used efficiently by the cognitive users. Quick and immediate shifting over the sensed vacant channel is extremely vital and is a challenging task. To overcome this issue, fast and efficient spectrum sensing technique is proposed for cognitive radios by improvising the Discrete Wavelet Packet Transform (DWPT) for multiresolution interweaved systems. This proposed scheme not only increases the system speed but also reduces complexity. Simulation results are used to analyse the system performance and numerical analysis for computing system complexity.

Generation of critical aftershocks using stochastic neural networks and wavelet packet transform

2019 ◽  
Vol 26 (5-6) ◽  
pp. 331-351
Author(s):  
Elham Rajabi ◽  
Gholamreza Ghodrati Amiri
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Response Spectrum ◽  
Wavelet Packet ◽  
Principal Component ◽  
Response Spectra ◽  
Component Analysis ◽  
Wavelet Packet Transform ◽  
Stochastic Neural Networks ◽  
Transform Coefficients

This paper proposes a methodology using wavelet packet transform, principal component analysis, and neural networks in order to generate artificial critical aftershock accelerograms which are compatible with the response spectra. This procedure uses the learning abilities of neural networks, principal component analysis as a dimension reduction technique, and decomposing capabilities of wavelet packet transform on consecutive earthquakes. In fact, the proposed methodology consists of two steps and expands the knowledge of the inverse mapping from mainshock response spectrum to aftershock response spectrum and aftershock response spectrum to wavelet packet transform coefficients of the aftershocks. This procedure results in a stochastic ensemble of response spectra of aftershock (first step) and corresponding wavelet packet transform coefficients (second step) which are then used to generate the aftershocks through applying the inverse wavelet packet transform. Finally, in order to demonstrate the effectiveness of the proposed method, three examples are presented in which recorded critical successive ground motions are used to train and test the neural networks.

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