A Novel Distributed Compressive Wideband Spectrum Sensing Method in Cognitive Radio Networks

Chang Lin; Qi Zhu; Chang Shu

doi:10.4028/www.scientific.net/AMM.667.311

Paper Titles

A Tag-Based Improved LDA and Web Page Clustering Analysis
p.277

Applied Research of Weighted K-Means Algorithm in Social Networks
p.286

Cluster-Gossip Based Distributed Kalman Consensus Filter Algorithm with Energy Efficiency
p.291

Two-Subpopulation Particle Swarm Optimization Based on Pheromone Diffusion
p.300

A Novel Distributed Compressive Wideband Spectrum Sensing Method in Cognitive Radio Networks
p.311

Bias in Amplitude Estimation of a Sine Signal with Known Frequency due to the Rounding Quantization Noise
p.318

Big Data Sensing Information Processing Platform for Intelligent Traffic
p.324

Conductor Galloping Prediction Method Based on MEMs 3D Dynamic Displacement Sensors
p.328

Design and Calibration of a Six-Axis Force Sensor for Docking Mechanism
p.334

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 667A Novel Distributed Compressive Wideband Spectrum...

A Novel Distributed Compressive Wideband Spectrum Sensing Method in Cognitive Radio Networks

Abstract:

In this paper, we present an optimum weighted approach for wideband spectrum sensing. Distributed compressive sensing technology is exploited to obtain dramatic rate reductions while differential procedure is deduced to extremely enhance the detection sensitivity. The measurements are collected from each SU at a fusion center, where a C-out-of-J method is proposed to dramatically heighten the detection performance. SCSMP recovery algorithm is utilized to reconstruct the signals, which are then weighted by the estimated SNRs. Corroborating simulation results show that the raised algorithm can effectively reduce sampling rates at each SU, substantially raise the detection performance and saliently improve system robustness against noise.

You might also be interested in these eBooks

Advances in Computers, Electronics and Mechatronics

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 667)

Pages:

311-317

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.667.311

Citation:

Cite this paper

Online since:

October 2014

Authors:

Chang Lin*, Qi Zhu, Chang Shu

Keywords:

An Optimum Weighed Apprach, C-Out-Of-J Method, Differential Signal, Distributed Compressive Sensing, Wideband Spectrum Sensing

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Haykin S. Cognitive radio: brain-empowered wireless communications[J]. Selected Areas in Communications, IEEE Journal on, 2005, 23(2): 201-220.

DOI: 10.1109/jsac.2004.839380

Google Scholar

[2] Tian Z, Tafesse Y, Sadler B M. Cyclic feature detection with sub-Nyquist sampling for wideband spectrum sensing[J]. Selected topics in signal processing, IEEE Journal of, 2012, 6(1): 58-69.

DOI: 10.1109/jstsp.2011.2181940

Google Scholar

[3] Donoho D L. Compressed sensing [J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

DOI: 10.1109/tit.2006.871582

Google Scholar

[4] Shi G M, Liu D H, Gao D H, Liu Z, Lin J, Wang L J. Advances in Theory and Application of Compressed Sensing[J]. ACTA ELECTRONICA SINICA, 2009, 7(5): 1070-1081.

Google Scholar

[5] Tian Z, Giannakis G B. Compressed sensing for wideband cognitive radios[C]. International Conference on Acoustics, Speech, and Signal Prosessing, Honolulu, HI, USA, Apr. 15-20, 2007: 1357-1360.

DOI: 10.1109/icassp.2007.367330

Google Scholar

[6] Lin C, Zhu Q. Method of Achieving Broadband Spectrum Sensing by Distributed Compressed Sensing[J]. SIGNAL PROCESSING, Submitted. (In Chinese).

Google Scholar

[7] Gu B, Yang Z, Hu H, et al. Cooperative Compressed Sensing for Wide-Band Spectrum Detection[C]. Wireless Communications Networking and Mobile Computing (WiCOM), 2010 6th International Conference on. IEEE, 2010: 1-4.

DOI: 10.1109/icct.2010.5689191

Google Scholar

[8] Needell D, Tropp J A. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples[J]. Applied and Computational Harmonic Analysis, 2009, 26(3): 301-321.

DOI: 10.1016/j.acha.2008.07.002

Google Scholar