Wideband spectrum sensing using compressive sampling based energy reconstruction

2012 ◽  
Author(s):  
Davood Mardani Najafabadi ◽  
Ali A. Tadaion ◽  
Masoud Reza Aghabozorgi Sahaf
Keyword(s):  
Spectrum Sensing ◽  
Compressive Sampling ◽  
Wideband Spectrum Sensing ◽  
Energy Reconstruction

scholarly journals Compressed Wideband Spectrum Sensing Based on Discrete Cosine Transform

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Yulin Wang ◽  
Gengxin Zhang
Keyword(s):  
Fourier Transform ◽  
Discrete Cosine Transform ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
Compressive Sampling ◽  
Cosine Transform ◽  
Wideband Spectrum Sensing ◽  
Communication Signal ◽  
Dct Domain ◽  
Sparsity Structure

Discrete cosine transform (DCT) is a special type of transform which is widely used for compression of speech and image. However, its use for spectrum sensing has not yet received widespread attention. This paper aims to alleviate the sampling requirements of wideband spectrum sensing by utilizing the compressive sampling (CS) principle and exploiting the unique sparsity structure in the DCT domain. Compared with discrete Fourier transform (DFT), wideband communication signal has much sparser representation and easier implementation in DCT domain. Simulation result shows that the proposed DCT-CSS scheme outperforms the conventional DFT-CSS scheme in terms of MSE of reconstruction signal, detection probability, and computational complexity.

Photonics-assisted compressive sampling system for wideband spectrum sensing (Invited Paper)

2017 ◽  
Vol 15 (1) ◽  
pp. 010012-10017 ◽  
Author(s):  
Qiang Guo Qiang Guo ◽  
Minghua Chen Minghua Chen ◽  
Yunhua Liang Yunhua Liang ◽  
Hongwei Chen Hongwei Chen ◽  
Sigang Yang Sigang Yang ◽  
...  
Keyword(s):  
Spectrum Sensing ◽  
Compressive Sampling ◽  
Sampling System ◽  
Wideband Spectrum Sensing

scholarly journals Bayesian Compressive Sampling Based Wideband Spectrum Sensing in Cognitive Radio Network using Wavelet Transform

2019 ◽  
Vol 8 (4) ◽  
pp. 1412-1419
Keyword(s):  
Wavelet Transform ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Network ◽  
Primary User ◽  
Compressive Sampling ◽  
Signal Recovery ◽  
Radio Network ◽  
Recovery Algorithm ◽  
Wideband Spectrum Sensing

This paper deals with the implementation of sub Nyquist sampling for the efficient wideband spectrum sensing in cognitive radio network. Cognitive radio is a very promising technology in the field of wireless communication which has drastically changed the spectral dynamics through the opportunistic utilization of frequency band by the secondary users when it is not utilized by the primary users. The complexity of spectral detection strategy is reduced using the compressive sensing method. Bayesian technique is utilized in the compressive sampling to deal with uncertainty of the process and increase the speed of detection. This technique recovers the wideband signals even with few measurements via Laplace prior and Toeplitz matrix. Sparse signal recovery algorithm is used for the extraction of primary user frequency location. The condition of the detection of primary user even in the low regulated transmission from unlicensed user is been resolved in this paper through Wavelet transform. This approach enables the evaluation of all possible hypotheses simultaneously in the global optimization framework. Simulation analysis is performed to verify the effectiveness of the proposed technique over the cognitive radio network.

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