A K-means clustering based blind multiband spectrum sensing algorithm for cognitive radio

Abstract

In this paper, a blind multiband spectrum sensing (BMSS) method requiring no knowledge of noise power, primary signal and wireless channel is proposed based on the K-means clustering (KMC). In this approach, the KMC algorithm is used to identify the occupied subband set (OSS) and the idle subband set (ISS), and then the location and number information of the occupied channels are obtained according to the elements in the OSS. Compared with the classical BMSS methods based on the information theoretic criteria (ITC), the new method shows more excellent performance especially in the low signal-to-noise ratio (SNR) and the small sampling number scenarios, and more robust detection performance in noise uncertainty or unequal noise variance applications. Meanwhile, the new method performs more stablely than the ITC-based methods when the occupied subband number increases or the primary signals suffer multi-path fading. Simulation result verifies the effectiveness of the proposed method.

Keywords

cognitive radio (CR) / blind multiband spectrum sensing(BMSS) / K-means clustering (KMC) / occupied subband set (OSS) / idle subband set (ISS) / information theoretic criteria (ITC) / noise uncertainty

References

[1] LiuX, ChenK Q, YanJ H. A novel weighed cooperative bandwidth spectrum sensing for spectrum occupancy of cognitive radio network [J]. Journal of Central South University, 2016, 23(7): 1709-1718

[2] XiaoN, LiangJ, ZhangH Y, LiuY, WangYi. A dynamic spectrum sensing strategy based on diversity reception for satellite cognitive communication [J]. Journal of Central South University: Science and Technology, 2016, 47(6): 1959-1966

[3] AbdelmohsenA, WalaaH. Advances on spectrum sensing for cognitive radio networks: Theory and applications [J]. IEEE Communications Surveys & Tutorials, 2016, 99: 1-1

[4] GokceogluA, DikmeseS, ValkamaM, RenforsM. Energy detection under IQ imbalance with single-and multi-channel direct-conversion receiver: analysis and mitigation [J]. IEEE Journal on Selected Areas in Communications, 2014, 32(3): 411-424

[5] JinM, LiY, HeungG R. On the performance of covariance based spectrum sensing for cognitive radio [J]. IEEE Transactions on Signal Processing, 2012, 60(7): 3670-3682

[6] YangX, PengS L, LeiK J, CaoX Y. Spectrum sensing based on covariance matrix under noise uncertainty [C]. the 5th International Conference on Wireless Communications Networking and Mobile Computing, 200916491652

[7] YangX, LeiK J, PengS L, CaoX Y. Trace based semi-blind and blind spectrum sensing schemes for cognitive radio [C]. IEEE International Conference on Communications, 201015

[8] LeiK J, YangX, PengS L, CaoX Y. A novel collaborative spectrum sensing scheme based on covariance matrix [C]. 5th International Conference on Wireless Communications, Networking and Mobile Computing, 200914

[9] El-KhamyS E, El-MahallawyM S, YoussefE S. Improved wideband spectrum sensing techniques using waveletbased edge detection for cognitive radio [C]. International Conference on Computing, Networking and Communications, 2013418423

[10] QuanZ, ShuguangC, AliS, VincentP H. Optimal multiband joint detection for spectrum sensing in cognitive radio networks [J]. IEEE Trans Signal Process, 2009, 57(3): 1128-1140

[11] PedramP, NormanC B. Optimal wideband spectrum sensing framework for cognitive radio systems [J]. IEEE Trans Signal Process, 2011, 59(3): 1170-1182

[12] SannaM, MurroniM. Optimization of non-convex multiband cooperative sensing with genetic algorithms [J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(1): 87-96

[13] LiuS, ShenJ, ZhangR, LiuY. Information theoretic criterion-based spectrum sensing for cognitive radio [J]. IET Commun, 2008, 2(6): 753-762

[14] QingH B, LiuY N, XieG. Smart antennas aided wideband detection for spectrum sensing in cognitive radio networks [J]. Electron Lett, 2014, 50(7): 490-492

[15] AndreaM, AndreaG, MarcoC. Wideband spectrum sensing for cognitive radio: A model order selection approach [C]. IEEE International Conference on Communications, 201413851389

[16] AndreaM, AndreaG, MarcoC. Wideband spectrum sensing by model order selection [J]. IEEE Trans Wireless Commun, 2015, 14(12): 6710-6721

[17] LeshemA V D, VeenA J. Multichannel detection of Gaussian signals with uncalibrated receivers [J]. IEEE Signal Process Lett, 2001, 8(4): 120-122

[18] SedighiS, TaherpourA, Sala-AlvarezJ, KhattabT. On the performance of hadamard ratio detector-based spectrum sensing for cognitive radios [J]. IEEE Transactions on Signal Processing, 2015, 63(14): 3809-3824

[19] MackayD J CInformation theory, inference, and learning algorithms [M], 2004, Cambridge, Cambridge University Press: 284290

[20] InabaM, KatohN, ImaiH. Applications of weighted voronoi diagrams and randomization to variance-based k-clustering [C]. ACM Symp on Computational Geometry, 1994, New York, USA, Stony Brook: 329332

[21] WuJAdvances in K-means clustering [M], 2012, Berlin Heidelberg, Springer: 213

[22] JohnsonB A, AbramovichY I, MestreX. Music, G-Music, and maximum-likelihood performance breakdown [J]. IEEE Trans Signal Process, 2008, 56(8): 3944-3958