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

一种基于 K 均值聚类的认知无线电盲多带频谱感知算法

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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.

摘要

提出了基于 K 均值聚类(KMC)的盲多带频谱感知 (BMSS) 方法。 该方法不需要知道噪声方差、 主信号和无线信道的先验知识, 利用 KMC 算法区分被占用子带集合(OSS)和空闲子带集合 (ISS), 然后再根据 OSS 中的元素获得被占用信道的数量和位置信息。 与基于信息理论准则 (ITC) 的 BMSS 方法相比的结果为: 在低信噪比和小样本场景中, 新方法表现出更好的检测性能; 在噪声方差不确定或不一致的应用中, 其检测性能具有鲁棒性; 当被占用的子带数增加或信号经过多径衰落时, 其检测性能更优。 仿真结果验证了所提方法的有效性和优越性。

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References

  1. LIU X, CHEN K Q, YAN J 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. DOI: https://doi.org/10.1007/s11771-016-3225-7.

    Article  Google Scholar 

  2. XIAO N, LIANG J, ZHANG H Y, LIU Y, WANG Yi. 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. DOI: https://doi.org/10.11817/j.issn.1672-7207.2016.06.019. (in Chinese)

    Google Scholar 

  3. ABDELMOHSEN A, WALAA H. Advances on spectrum sensing for cognitive radio networks: Theory and applications [J]. IEEE Communications Surveys & Tutorials, 2016(99): 1–1. DOI: https://doi.org/10.1109/COMST.2016.2631080.

    Google Scholar 

  4. GOKCEOGLU A, DIKMESE S, VALKAMA M, RENFORS M. 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. DOI: https://doi.org/10.1109/JSAC.2014.1403001.

    Article  Google Scholar 

  5. JIN M, LI Y, HEUNG G R. On the performance of covariance based spectrum sensing for cognitive radio [J]. IEEE Transactions on Signal Processing, 2012, 60(7): 3670–3682. DOI: https://doi.org/10.1109/TSP.2012.2194708.

    Article  MathSciNet  MATH  Google Scholar 

  6. YANG X, PENG S L, LEI K J, CAO X Y. Spectrum sensing based on covariance matrix under noise uncertainty [C]// the 5th International Conference on Wireless Communications Networking and Mobile Computing. Beijing, China, 2009: 1649–1652. DOI: https://doi.org/10.1109/WICOM.2009.5304800.

    Google Scholar 

  7. YANG X, LEI K J, PENG S L, CAO X Y. Trace based semi-blind and blind spectrum sensing schemes for cognitive radio [C]// IEEE International Conference on Communications. Cape Town, South Africa, 2010: 1–5. DOI: https://doi.org/10.1109/ICC.2010.5501989.

    Google Scholar 

  8. LEI K J, YANG X, PENG S L, CAO X Y. A novel collaborative spectrum sensing scheme based on covariance matrix [C]// 5th International Conference on Wireless Communications, Networking and Mobile Computing. Beijing, China, 2009: 1–4. DOI: https://doi.org/10.1109/WICOM.2009.5303496.

    Google Scholar 

  9. EL-KHAMY S E, EL-MAHALLAWY M S, YOUSSEF E S. Improved wideband spectrum sensing techniques using waveletbased edge detection for cognitive radio [C]// International Conference on Computing, Networking and Communications. San Diego, USA, 2013: 418–423. DOI: https://doi.org/10.1109/ICCNC.2013.6504120.

    Google Scholar 

  10. QUAN Z, SHUGUANG C, ALI S, VINCENT P H. Optimal multiband joint detection for spectrum sensing in cognitive radio networks [J]. IEEE Trans Signal Process, 2009, 57(3): 1128–1140. DOI: https://doi.org/10.1109/TSP.2008.2008540.

    Article  MathSciNet  MATH  Google Scholar 

  11. PEDRAM P, NORMAN C B. Optimal wideband spectrum sensing framework for cognitive radio systems [J]. IEEE Trans Signal Process, 2011, 59(3): 1170–1182. DOI: https://doi.org/10.1109/TSP.2010.2096220.

    Article  MathSciNet  MATH  Google Scholar 

  12. SANNA M, MURRONI M. Optimization of non-convex multiband cooperative sensing with genetic algorithms [J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(1): 87–96. DOI: https://doi.org/10.1109/JSTSP.2010.2054064.

    Article  Google Scholar 

  13. LIU S, SHEN J, ZHANG R, LIU Y. Information theoretic criterion-based spectrum sensing for cognitive radio [J]. IET Commun, 2008, 2(6): 753–762. DOI: https://doi.org/10.1049/iet-com:20070405.

    Article  Google Scholar 

  14. QING H B, LIU Y N, XIE G. Smart antennas aided wideband detection for spectrum sensing in cognitive radio networks [J]. Electron Lett, 2014, 50(7): 490–492. DOI: https://doi.org/10.1049/el.2013.4295.

    Article  Google Scholar 

  15. ANDREA M, ANDREA G, MARCO C. Wideband spectrum sensing for cognitive radio: A model order selection approach [C]// IEEE International Conference on Communications. Sydney, Australia, 2014: 1385–1389. DOI: https://doi.org/10.1109/ICC.2014.6883515.

    Google Scholar 

  16. ANDREA M, ANDREA G, MARCO C. Wideband spectrum sensing by model order selection [J]. IEEE Trans Wireless Commun, 2015, 14(12): 6710–6721. DOI: https://doi.org/10.1109/ICC.2014.6883515.

    Article  Google Scholar 

  17. LESHEM A, VAN DER VEEN A J. Multichannel detection of Gaussian signals with uncalibrated receivers [J]. IEEE Signal Process Lett, 2001, 8(4): 120–122. DOI: https://doi.org/10.1109/97.911477.

    Article  Google Scholar 

  18. SEDIGHI S, TAHERPOUR A, SALA-ALVAREZ J, KHATTAB T. On the performance of hadamard ratio detector-based spectrum sensing for cognitive radios [J]. IEEE Transactions on Signal Processing, 2015, 63(14): 3809–3824. DOI: https://doi.org/10.1109/TSP.2015.2434330.

    Article  MathSciNet  MATH  Google Scholar 

  19. MACKAY D J C. Information theory, inference, and learning algorithms [M]. Cambridge: Cambridge University Press, 2004: 284–290.

    Google Scholar 

  20. INABA M, KATOH N, IMAI H. Applications of weighted voronoi diagrams and randomization to variance-based k-clustering [C]// ACM Symp on Computational Geometry. New York, USA: Stony Brook 1994: 329–332. DOI: https://doi.org/10.1145/177424.178042.

    Google Scholar 

  21. WU J. Advances in K-means clustering [M]. Berlin Heidelberg: Springer, 2012: 2–13.

    Book  Google Scholar 

  22. JOHNSON B A, ABRAMOVICH Y I, MESTRE X. Music, G-Music, and maximum-likelihood performance breakdown [J]. IEEE Trans Signal Process, 2008, 56(8): 3944–3958. DOI: https://doi.org/10.1109/TSP.2008.921729.

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

    Ke-jun Lei  (雷可君) & Yang-hong Tan  (谭阳红)

  2. College of Information Science and Engineering, Jishou University, Jishou, 416000, China

    Xi Yang  (杨喜) & Han-rui Wang  (王韩瑞)

  3. National Mobile Communications Research Laboratory, Southeast University, Nanjing, 210096, China

    Xi Yang  (杨喜)

Authors
  1. Ke-jun Lei  (雷可君)
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  2. Yang-hong Tan  (谭阳红)
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  3. Xi Yang  (杨喜)
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  4. Han-rui Wang  (王韩瑞)
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Corresponding author

Correspondence to Yang-hong Tan  (谭阳红).

Additional information

Foundation item: Projects(61362018, 61861019) supported by the National Natural Science Foundation of China; Project(1402041B) supported by the Jiangsu Province Postdoctoral Scientific Research Project, China; Project(16A174) supported by the Scientific Research Fund of Hunan Provincial Education Department, China; Project([2016]283) supported by the Research Study and Innovative Experiment Project of College Students, China

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Lei, Kj., Tan, Yh., Yang, X. et al. A K-means clustering based blind multiband spectrum sensing algorithm for cognitive radio. J. Cent. South Univ. 25, 2451–2461 (2018). https://doi.org/10.1007/s11771-018-3928-z

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  • DOI: https://doi.org/10.1007/s11771-018-3928-z

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