KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Adaptive Adjustment of Compressed Measurements for Wideband Spectrum Sensing

  • Gao, Yulong (Harbin Institute of Technology Communication Research Center) ;
  • Zhang, Wei (Harbin Institute of Technology Communication Research Center) ;
  • Ma, Yongkui (Harbin Institute of Technology Communication Research Center)
  • Received : 2015.07.21
  • Accepted : 2015.10.18
  • Published : 2016.01.31
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Abstract

Compressed sensing (CS) possesses the potential benefits for spectrum sensing of wideband signal in cognitive radio. The sparsity of signal in frequency domain denotes the number of occupied channels for spectrum sensing. This paper presents a scheme of adaptively adjusting the number of compressed measurements to reduce the unnecessary computational complexity when priori information about the sparsity of signal cannot be acquired. Firstly, a method of sparsity estimation is introduced because the sparsity of signal is not available in some cognitive radio environments, and the relationship between the amount of used data and estimation accuracy is discussed. Then the SNR of the compressed signal is derived in the closed form. Based on the SNR of the compressed signal and estimated sparsity, an adaptive algorithm of adjusting the number of compressed measurements is proposed. Finally, some simulations are performed, and the results illustrate that the simulations agree with theoretical analysis, which prove the effectiveness of the proposed adaptive adjusting of compressed measurements.

Keywords

References

  1. B.Wang and K.J.R.Liu, “Advances in Cognitive Radio Networks: A Survey,” IEEE Journal of Selected Topics in Signal Processing, vol.5, no.1, pp.5-23, Feb 2011. Article (CrossRef Link) https://doi.org/10.1109/JSTSP.2010.2093210
  2. C.Zou and C.Chigan, "Dynamic Spectrum Allocation Based Confidentiality for Cognitive Radio Networks," IEEE GlobeCom 2010, pp.1-6, Dec 2010. Article(CrossRef Link)
  3. E.Axell, G.Leus, and E.G.Larsson et al, “Spectrum Sensing for Cognitive Radio:State-of-the-Art and Recent Advances,” IEEE Signal Processing Magazine, vol.29, no.3, pp.101-11, May 2012. Article(CrossRef Link) https://doi.org/10.1109/MSP.2012.2183771
  4. B.Farhang-Boroujeny, “Filter Bank Spectrum Sensing for Cognitive Radios,” IEEE Transactions on Signal Processing, vol.56, no.5, pp.1801-1811, May 2008. Article(CrossRef Link) https://doi.org/10.1109/TSP.2007.911490
  5. Y.Hur, J.Park, W.Woo, K.Lim, C.H.Lee, H.S.Kim, and J.Laskar, "A Wideband Analog Multi-resolution Spectrum Sensing Technique for Cognitive Radio Systems," IEEE Int.Symp.Circuits and Systems(ISCAS), pp.4090-4093, May 2006. Article (CrossRef Link)
  6. S.E.El-Khamy, M.S.El-Mahallawy, and E.S.Youssef, "Improved Wideband Spectrum Sensing Techniques Using Wavelet-based Edge Detection for Cognitive Radio," 2013 International Conference on Computing, Networking and Communications (ICNC), pp.418-423, Jan 2013. Article (CrossRef Link)
  7. M.Sanna and M.Murroni, “Optimization of Non-convex Multiband Cooperative Sensing with Genetic Algorithms,” IEEE Journal of Selected Topics in Signal Processing, vol.5, no.1, pp.87-96, Feb 2011. Article (CrossRef Link) https://doi.org/10.1109/JSTSP.2010.2054064
  8. K.Koufos, K.Ruttik, and R.D.Jantti, “Distributed Sensing in Multiband Cognitive Networks,” IEEE Transactions on Wireless Communications, vol.10, no.5, pp.1667-1677, May 2011. Article (CrossRef Link) https://doi.org/10.1109/TWC.2011.020111.101528
  9. R.Daniel and L.Geert, “Wideband Spectrum Sensing from Compressed Measurements Using Spectral Prior Information,” IEEE Transactions on Signal Processing, vol.61, no.24, pp. 6232-6246, Dec 2013. Article (CrossRef Link) https://doi.org/10.1109/TSP.2013.2283473
  10. Y.Wang, C.Guo, X.Sun, and C.Feng, "Time-efficient Wideband Spectrum Sensing Based on Compressive Sampling," 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), pp.1-5, May 2015. Article (CrossRef Link)
  11. D.L.Donoho, "Compressed Sensing," IEEE Transactions on Information Theory, vol.52, no.4, pp.1289-1306, Apr 2006. Article (CrossRef Link) https://doi.org/10.1109/TIT.2006.871582
  12. M.F.Duarte and Y.C.Eldar, “Structured Compressed Sensing: from Theory to Applications,” IEEE Transactions on Signal Processing, vol.59, no.9, pp.4053-4085, Sep 2011. Article (CrossRef Link) https://doi.org/10.1109/TSP.2011.2161982
  13. E.J.Candes and T.Tao, “Near-optimal Signal Recovery from Random Projections: Universal Encoding Strategies,” IEEE Transactions on Information Theory, vol.52, no.12, pp. 5406-5425, Dec 2006. Article (CrossRef Link) https://doi.org/10.1109/TIT.2006.885507
  14. Z.Tian and G.B.Giannakis, "Compressed Sensing for Wideband Cognitive Radios," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.1357-1360, Apr 2007. Article (CrossRef Link)
  15. T.Zhi, Y.Tafesse and B.M.Sadle, “Cyclic Feature Detection With Sub-Nyquist Sampling for Wideband Spectrum Sensing,” IEEE Journal of Selected Topics in Signal Processing, vol.6, no.1, pp.58-69, Feb 2012. Article (CrossRef Link) https://doi.org/10.1109/JSTSP.2011.2181940
  16. Z.Zhang, Z.Han, H.Li, D.Yang, and C.Pei, “ Belief Propagation Based Cooperative Compressive Spectrum Sensing in Wideband Cognitive Radio Networks,” IEEE Transactions on Wireless communications, vol.10, no.9, pp.3020-3031, Sep 2011. Article (CrossRef Link) https://doi.org/10.1109/TWC.2011.071411.101929
  17. M.A.T.Fiqueiredo, R.D.Nowak, and S.J.Wright, “Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems,” IEEE Journal of Selected Topics in Signal Processing, vol.1, no.4, pp.586-597, Dec 2007. Article (CrossRef Link) https://doi.org/10.1109/JSTSP.2007.910281
  18. J.A.Tropp and A.C.Gilbert, “Signal Recovery from Random Measurements via Orthogonal Matching Pursuit,” IEEE Transactions on Information Theory, vol.53, no.12, pp.4655-4666, Dec 2007. Article (CrossRef Link) https://doi.org/10.1109/TIT.2007.909108
  19. D.Needell and J.A.Tropp, “CoSaMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples," Applied and Computational Harmonic Analysis, vol.26, no.3, pp.301-321, May 2009. Article (CrossRef Link) https://doi.org/10.1016/j.acha.2008.07.002
  20. D.L.Donoho, Y.Tsaig, Droril, and J.L.Starck, “Sparse Solution of Underdetermined Linear Equations by Stagewise Orthogonal Matching Pursuit,” IEEE Transactions on Information Theory, vol.58, no.2, pp.1094-1121, Feb 2012. Article (CrossRef Link) https://doi.org/10.1109/TIT.2011.2173241
  21. C.Fu, X.Ji, and Q.Dai, “Adaptive Compressed Sensing Recovery Utilizing the Property of Signal's Autocorrelations,” IEEE Transactions on Image Processing, vol.21, no.5, pp.2369-2378, May 2012. Article (CrossRef Link) https://doi.org/10.1109/TIP.2011.2177989
  22. J.Haupt, R.Nowak, and R.Castro, "Adaptive Sensing for Sparse Signal Recovery," in Proc. of IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp.702-707, Jan 2009.Article (CrossRef Link)
  23. M.A.Iwen and A.H.Tewfik, "Adaptive Compressed Sensing for Sparse Signals in Noise," in Proc. of IEEE 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers, pp.1240-1244, Nov 2011. Article (CrossRef Link)
  24. M.L.Malloy and R.D.Nowak, “Near-optimal Adaptive Compressed Sensing,” IEEE Transactions on Information Theory, vol.60, no.7, pp.4001-4012, July 2014. Article (CrossRef Link) https://doi.org/10.1109/TIT.2014.2321552
  25. J.Zhang, D.Zhu, and G.Zhang, “Adaptive Compressed Sensing Radar Oriented toward Cognitive Detection in Dynamic Sparse Target Scene,” IEEE Transactions on Signal Processing, vol.60, no.4, pp.1718-1729, Apr 2012. Article (CrossRef Link) https://doi.org/10.1109/TSP.2012.2183127
  26. Z.Liu, J.Liu, and Z.Qiu, "A New Adaptive Compressed Sensing Algorithm for Wireless Sensor Networks," 2010 IEEE 10th International Conference on Signal Processing (ICSP), pp.2452-2455, Oct 2010. Article (CrossRef Link)
  27. J.A.Tropp, J.N.Laska, and M.F.Duarte et al, “Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals,” IEEE Transactions on Information Theory, vol.56, no.1, pp.520-544, Jan 2010. Article (CrossRef Link) https://doi.org/10.1109/TIT.2009.2034811
  28. W.Tlan, G.Rui, and J.Kanf, “Divide and Conquer Method for Sparsity Estimation within Compressed Sensing Framework,” Electronics Letters, vol.50, no.9, pp.677-678, Apr 2014. Article (CrossRef Link) https://doi.org/10.1049/el.2013.4271
  29. S.K.Sharma, S.Chatzinotas, and B.Ottersten, “Compressive Sparsity Order Estimation for Wideband Cognitive Radio Receiver,” IEEE Transactions on Signal Processing, vol.62, no.19, pp.4984-499, Oct 2014. Article (CrossRef Link) https://doi.org/10.1109/TSP.2014.2343949
  30. Y.Wang, Z.Tian, and C.Feng, “Sparsity Order Estimation and Its Application in Compressive Spectrum Sensing for Cognitive Radios,” IEEE Transactions on Wireless Communications, vol.11, no.6, pp.2116-2125, Jun 2012. Article (CrossRef Link) https://doi.org/10.1109/TWC.2012.050112.110505
  31. Y.Wang, Z.Tian, and C.Feng, "A Two-step Compressed Spectrum Sensing Scheme for Wideband Cognitive Radios," in Proc. of 2010 IEEE Global Telecommunications Conference, pp.1-5, Dec 2010. Article (CrossRef Link)
  32. X.Zhu, J.Wang, and L.Dai et al, “Sparsity-aware Adaptive Channel Estimation Based on SNR Detection,” IEEE Transactions on Broadcasting, vol.61, no.1, pp.119-126, Mar 2015. Article (CrossRef Link) https://doi.org/10.1109/TBC.2014.2372271
  33. T.Cai, J.Fan, and T.Jiang, “Distributions of Angles in Random Packing on Spheres,” The Journal of Machine Learning Research, vol.14, no.1, pp.1837-1864, Jun 2013. Article (CrossRef Link)