Sub-Nyquist Wideband Spectrum sensing for Cognitive Radio Networks: Matrix Completion via seed values
DOI:
https://doi.org/10.17981/ingecuc.17.1.2021.10Keywords:
algorithm of spectrum sensing in wideband, Interest zone matrix approximation, Sub-Nyquist sampling, energy detection, cognitive radio, matrix completionAbstract
Introduction: Cognitive Radio (CR) makes efficient use of the radio resource, for this it performs Spectrum Sensing (SS) in order to identify the available spectrum. But due to the rapid evolution of transceivers, microelectronics and high propagation frequencies, it is necessary for SS algorithms to be applied in frequency bands in CR and for sampling below the Nyquist rate.
Objective: Adapt an algorithm for Wideband Sub-Nyquist Spectrum Detection (WBSS) for CR networks using Matrix Completion (MC) integrating seed values from known samples, in order to complete the unsampled inputs of the band to evaluate, reconstruct the signals and the identify the available spectrum.
Method: An adaptation to the Interest Zone Matrix Approximation (IZMA) algorithm was carried out, for this purpose the reconstruction stage is designed and a narrow band spectrum sensing method is chosen to form the detector bank; the algorithm called IZMA_SV is evaluated at the simulation level, therefore deterministic signals are reconstructed in different SNRs and the channel status is identified as busy or free.
Results: The simulations indicate that the adapted algorithm shows differences between the known values of the sampling matrix M and the recovered matrix X in SNRs lower than -8 dB, while the difference tends to zero in SNRs greater than 2 dB.
Conclusions: The IZMA-SV algorithm manages to reduce the number of operations to arrive at the approximate matrix X, reconstructing signals sampled at 75% of the Nyquist rate and even with a sampling of 20% the characteristics of the signal that make possible the detection of wideband spectrum.
Downloads
References
Cisco, “Cisco Visual Networking Index: Forecast and Trends, 2017–2022,” Informe técnico 2018-2023, [online , 2017. Available: https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-741490.html
Federal Communications Commission, “Spectrum Policy Task Force ,” Wikia.org, [online , Nov. 2002. Available: https://itlaw.wikia.org/wiki/Spectrum_Policy_Task_Force
J. Mitola & G. Q. Maguire, “Cognitive Radio: making software radios more personal,” IEEE Personal Communicant, vol. 6, no. 4 , pp. 13–18, Aug. 1999. https://doi.org/10.1109/98.788210
J. Mitola, “Cognitive Radio an Integrated Agent Architecture for Software Defined Radio,” Ph.D. dissertation, KTH, SK, SE, 2000 .
S. Haykin, “Cognitive Radio: Brain-Empowered Wireless Communications,” IEEE J Sel Areas Commun, vol. 23, no. 2, pp. 201–220, Feb. 2005. https://doi.org/10.1109/JSAC.2004.839380
J. C. Clement, K. V. Krishnan & A. Bagubali, “Cognitive Radio: Spectrum Sensing Problems in Signal Processing,” Int J Comput App, vol. 40, no. 16, pp. 37–40, Feb. 2012. https://doi.org/10.1109/JSAC.2004.839380
T. Yucek & H. Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications,” IEEE Commun Surv Tutor, vol. 11, no. 1, pp. 116–130, 2009. https://doi.org/10.1109/SURV.2009.090109
M. M. Mabrook & A. I. Hussein, “Major Spectrum Sensing Techniques for Cognitive Radio Networks: A Survey,” IJEIT, vol. 5, no. 3, pp. 24–37, Sep. 2015. Available from https://www.ijeit.com/Vol%205/Issue%203/IJEIT1412201509_05.pdf
S. Chinchu & T. R. Sangeeta, “Hybrid Detection Method for Improving Spectrum Sensing Performance in Cognitive Radio,” IJSR, vol. 5, no. 6, pp. 948–951, Jun. 2016. https://doi.org/10.21275/v5i6.NOV164316
H. Sun, A. Nallanathan, C.-X. Wang & Y. Chen, “Wideband spectrum sensing for cognitive radio networks: a survey,” IEEE Wirel Commun, vol. 20, no. 2, pp. 74–81, Apr. 2013. https://doi.org/10.1109/MWC.2013.6507397
H. Sun, W.-Y. Chiu, J. Jiang, A. Nallanathan & H. V. Poor , “Wideband spectrum sensing with sub-Nyquist sampling in cognitive radios,” IEEE Trans Signal Process, vol. 60, no 11, pp. 6068–6073, Nov. 2012. https://doi.org/10.1109/TSP.2012.2212892
R. Al-Aomar, E. J. Williams & O. M. Ülgen, Process Simulation Using WITNESS. NJ, USA: John Wiley & Sons, 2015.
S. Qaisar, R. M. Bilal, W. Iqbal , M. Naureen & S. Lee, “Compressive Sensing: From Theory to Applications, A survey,” J Commun Net IEEE (KICS), vol. 15, no. 5, pp. 443–456, Oct. 2013. https://doi.org/10.1109/JCN.2013.000083
H. Huang, S. Misra, W. Tang, H. Barani & H. Al-Azzawi, “Applications of compressed sensing in communications networks,” ARXIV, vol. 1305.3002, pp. 1–18, Feb. 2014. Available: https://arxiv.org/pdf/1305.3002.pdf
F. Salahdine, N. Kaabouch & H. El Ghazi, “A survey on compressive sensing techniques for cognitive radio networks,” Phys Commun, vol. 20, no. 1, pp. 61–73, Sep. 2016. https://doi.org/10.1016/j.phycom.2016.05.002
E. J. Candes & B. Recht, “Exact Matrix completion via convex optimization,” FoCM, vol. 9, no. 6, pp. 717–766, Apr. 2009. https://doi.org/10.1007/s10208-009-9045-5
Z. Qin, Y. Gao, M. D. Plumbey & C. G. Parini, “Wideband Spectrum Sensing on Real-Time Signals at Sub-Nyquist Sampling Rates in Single and Cooperative Multiple Nodes,” IEEE Trans Signal Process , vol. 64, no. 12, pp. 3106–3117, Jun. 2016. https://doi.org/10.1109/TSP.2015.2512562
Z. Qin, Y. Liu, Y. Gao, M. Elkashlan & A. Nallanathan, “Wireless Powered Cognitive Radio Networks With Compressive Sensing and Matrix Completion,” IEEE Trans Commun, vol. 65, no. 4, pp. 1464–1476, Apr. 2017. https://doi.org/10.1109/TCOMM.2016.2623606
Z. Qin, Y. Gao & M. D. Plumbey, “Malicious User Detection Based on Low-Rank Matrix Completion in Wideband Spectrum Sensing,” IEEE Trans Signal Process, vol. 66, no. 1, pp. 5–17, Jan. 2018. https://doi.org/10.1109/TSP.2017.2759082
G. Shabat & A. Averbuch , “ Interest zone matrix approximation,” ELA, vol. 23, no. 1, pp. 678–702, Aug. 2012. https://doi.org/10.13001/1081-3810.1551
M. Subhedar & G. Birajdar, “Spectrum Sensing Techniques In Cognitive Radio Networks: A Survey,” IJNGN, vol. 3, no. 2, pp. 37–51, Jun. 2011. https://doi.org/10.5121/ijngn.2011.3203
M. A. Abdulsattar & Z. A. Hussein, “Energy Detection Technique For Spectrum Sensing In Cognitive, Radio: A Survey,” IJCNC, vol. 4, no. 5, pp. 223–242, Sep. 2012. https://doi.org/10.5121/ijcnc.2012.4514
P. S. Aparna & M. Jayasheela, “Cyclostationary Feature Detection in Cognitive Radio using Different Modulation Schemes,” Int J Comp App, vol. 47, no. 21, pp. 12–16, Jun. 2012. https://doi.org/10.5120/7472-0517
D. Bhargavi & C. R. Murthy, “Performance comparison of energy, matched-filter and cyclostationarity-based spectrum sensing,” IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC, MAR, MA, pp. 1–5, 20-23 Jun. 2010. https://doi.org/10.1109/SPAWC.2010.5670882
S. Pattanayak, P. Venkateswaran & R. Nandi, “Artificial Intelligence Based Model for Channel Status Prediction: A New Spectrum Sensing Technique for Cognitive Radio,” IJCNS, vol. 6, no. 3, pp. 139–148, Mar. 2013. https://doi.org/10.4236/ijcns.2013.63017
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 INGE CUC
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Published papers are the exclusive responsibility of their authors and do not necessary reflect the opinions of the editorial committee.
INGE CUC Journal respects the moral rights of its authors, whom must cede the editorial committee the patrimonial rights of the published material. In turn, the authors inform that the current work is unpublished and has not been previously published.
All articles are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
English
Español (España)
