Efficient spectrum sensing technique based on energy detector, compressive sensing, and de-noising techniques

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Highdata rate cognitive radio (CR) systems require high speed Analog-to-Digital Converters (ADC). This requirement imposes many restrictions on the realization of the CR systems. The necessity of high sampling rate can be significantly alleviated by utilizing analog to information converter (AIC). AIC is inspired by the recent theory of Compressive Sensing (CS), which states that a discrete signal has a sparse representation in some dictionary, which can be recovered from a small number of linear projections of that signal. This paper proposes an efficient spectrum sensing technique based on energy detection, compression sensing, and de-noising techniques. De-noising filters are utilized to enhance the traditional Energy Detector performance through Signal-to-Noise (SNR) boosting. On the other hand, the ordinary sampling provides an ideal performance at a given conditions. A near optimal performance can be achieved by applying compression sensing. Compression sensing allows signal to be sampled at sampling rates much lower than the Nyquist rate. The system performance and ADC speed can be easily controlled by adjusting the compression ratio. In addition, a proposed energy detector technique is introduced by using an optimum compression ratio. The optimum compression ratio is determined using a Genetic Algorithm (GA) optimization tool. Simulation results revealed that the proposed techniques enhanced system performance.


  • Keywords


    Cognitive Radio (CR); Compressive Sensing; De-Noising Filters; Genetic Algorithm (GA); Spectrum Sensing (SS).

  • References


      [1] P. SembaYawada, An Jian Wei and M. Mbyamm Kiki, "Performance evaluation of energy detection based on non-cooperative spectrum sensing in cognitive radio network," 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), Harbin, China, 2015.

      [2] Performance of Spectrum Sensing in Cognitive Radio”,I.J. Information Technology and Computer Science, 2012, 11, 11-17.

      [3] Hussein Al-Mood, H. S. Al-Raweshidy,”Energy Detection Performance Enhancement for Cognitive Radio Using Noise Processing, Approach”, IEEE, 2013.

      [4] Al-Hmood H. and Al-Raweshidy H. S., "Energy detection performance enhancement for cognitive radio using noise processing approach," Global Information Infrastructure Symposium - GIIS 2013, Trento, pp. 1-6, 2013.

      [5] Emmanuel Candes and Justin Romberg, “Practical signal recovery from random projections”, SPIE Symposium on Electronic Imaging, 2005.

      [6] Donoho, “Compressed sensing”, IEEE Transactions Info Theory, 52(4):12891306, 2006.

      [7] Z. Tian and G. B. Giannakis, “Compressed sensing for wideband cognitive radio”, in ICASSP, vol. 4, pp.1357-1360, April 2007.

      [8] Z. Tian and G. B. GiannakisPeh, E.C.Y.;Anh Tuan Hoang, “Sensing-Throughput Tradeoff for Cognitive Radio Networks ”, Wireless Communications, IEEE Transactions on, vol.7, no.4, pp.1326,1337, April 2008.

      [9] S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit”, SIAM journal on scientific computing, vol. 20, no. 1, pp. 3361, 1998. https://doi.org/10.1137/S1064827596304010.

      [10] E. J. Candes and T. Tao, “Decoding by linear programming, IEEE Transactions on Information Theory”, SIAM journal on scientific computing, vol. 51, no. 12, pp. 42034215, 2005.

      [11] F. Ghido, I. Tabus, “Sparse Modeling for Lossless Audio Compres- sion,”,IEEE Trans. on Audio, Speech, and Language Processing, vol.21, no.1, pp.14,28, 2013.

      [12] H.M. Kasem and M. Elsabrouty,“Perceptual compressed sensing and perceptual sparse fast fourier transform for audio signal compression,”, in; fifteenth International Workshop on Signal Processing Advances in Wireless Communications (SPAWC),2014, pp.444-448. https://doi.org/10.1109/spawc.2014.6941862.

      [13] J. Skilling and S. Gull, “Algorithms and applications, IEEE Transactions on Information Theory”, in Maximum-entropy and Bayesian methods in inverse problems, pp. 83132, Springer, 1985. https://doi.org/10.1007/978-94-017-2221-6_5.

      [14] Y. C. Pati, R. Rezaiifar, and P. Krishnaprasad, “Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition, IEEE Transactions on Information Theory”, in Signals, Systems and Computers, pp. 4044, IEEE, 1993.

      [15] G. Davis, S. Mallat, and M. Avellaneda, “Adaptive greedy approximations, Constructive approximation, ”, in IEEE Transactions on Information Theory Signals, Systems and Computers, vol. 13, no. 1, pp. 5798, 1997.

      [16] Miller, “Subset selection in regression”, iCRC Press, 2002.

      [17] R.Gribonval and M. Nielsen, “Highly sparse representations from dictionaries are unique and independent of the sparseness measure”,. iComputational Harmonic Analysis, vol. 22, no. 3, pp. 335355, 2007.

      [18] “CVX: Matlab software for disciplined convex programming, version 2.0”. I. CVX Research, http://cvxr.com/cvx, Aug. 2012.

      [19] M. Grant and S. Boyd, “Graph implementations for nonsmooth convex programs”,. in Recent Advances in Learning and Control (V. Blondel, S. Boyd, and H. Kimura, eds,Lecture Notes in Control and Information Sciences, pp. 95110, SpringerVerlag Limited, 2008.

      [20] Emmanuel C.Ifeachor,BarrieW.Jervis, “Digital Signal Processing, A Practical Approach ”,. Second edition 2002, chapter (10).

      [21] M. B. Sousa e Silva and A. N. Barreto, “Spectrum sensing in cognitive radio networks through change detection technique ”,. I Telecommunications Symposium (ITS), 2014 International, Sao Paulo, 2014, pp. 1-5.

      [22] GutoQuanXiang,YuXia Zhang, “Analysis of RLS Adaptive Filter in Signal De-noising ”,. TElectrical and Control Engineering (ICECE), 2011 International Conference on, Yichang.2011, pp. 5754-5758.

      [23] David L. Donoho and Iain M. Johnstone, “Minimax estimation via wavelet shrinkage”, Technical report, 1992.

      [24] T. E. Bogale, L. Vandendorpe and L. B. Le, "Sensing throughput tradeoff for cognitive radio networks with noise variance uncertainty,"2014 9th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), Oulu, 2014.

      [25] “IEEE Standard of Information Technology” Part 22: Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Policies and Procedures for Operation in the TV Bands, 1 July 2011.

      [26] R. L. Haupt and D. H. Werner Piscataway, “Genetic Algorithms in Electromagnetics”, NJ: IEEE PressWiley-Interscience, 2007. https://doi.org/10.1002/047010628X.


 

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Article ID: 6672
 
DOI: 10.14419/ijet.v6i1.6672




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