Similarity Analysis on Spectrum State Evolutions

  • Jiachen Sun
  • Ling Yu
  • Jingming Li
  • Guoru DingEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


The correlations between spectrum state evolutions, as a kind of similarity measure, have been revealed to optimize the spectrum usage model or improve the performance in spectrum prediction. However, most existing similarity analyses only end up with the superficial similarity phenomenon. It is of great need for us to conduct the deep investigation and analysis on the similarity of spectrum state evolutions. Firstly, we design a similarity index for spectrum state evolutions based on the Euclidean distance. Then, a network of spectrum state evolutions in the frequency domain can be formed for further analysis by comparing the proposed similarity indexes of frequency points with the decision threshold. Experiments with real-world spectrum data prove the feasibility and rationality of the above similarity analysis.


Spectrum state evolution Similarity analysis Complex network Scale-free 



This work is supported in part by the National Natural Science Foundation of China (Grants No. 61501510 and No. 61631020), Natural Science Foundation of Jiangsu Province (Grant No. BK20150717), China Postdoctoral Science Foundation Funded Project (Grant No. 2016M590398) and Jiangsu Planned Projects for Postdoctoral Research Funds (Grant No. 1501009A).


  1. 1.
    Wellens M. Empirical modelling of spectrum use and evaluation of adaptive spectrum sensing in dynamic spectrum access networks. Ph.D. Dissertation, RWTH Aachen University, May 2010.Google Scholar
  2. 2.
    López-Benítez M, Casadevall F. Spectrum usage models for the analysis, design and simulation of cognitive radio networks. In: Cognitive radio and its application for next generation cellular and wireless networks. The Netherlands: Springer; 2012. p. 27–73.Google Scholar
  3. 3.
    Axell E, Leus G, Larsson EG, Poor HV. Spectrum sensing for cognitive radio: State-of-the-art and recent advances. IEEE Signal Process Mag. 2012;29(3):101–16.CrossRefGoogle Scholar
  4. 4.
    Xing X, Jing T, Cheng W, Huo Y, Cheng X. Spectrum prediction in cognitive radio networks. IEEE Wireless Commun. 2013;20(2):90–6.CrossRefGoogle Scholar
  5. 5.
    Ding G, Jiao Y, Wang J, Zou Y, Wu Q, Yao Y, Hanzo L. Spectrum inference in cognitive radio networks: Algorithms and applications. IEEE Commun Surveys Tuts. 2018;20(1):150–82.CrossRefGoogle Scholar
  6. 6.
    Ding G, Wu F, Wu Q, Tang S, Song F, Vasilakos AV, Tsiftsis TA. Robust online spectrum prediction with incomplete and corrupted historical observations. IEEE Trans Veh Technol. 2017;66(9):8022–36.CrossRefGoogle Scholar
  7. 7.
    Palaios A, Riihijärvi J, Holland O, Mähönen P. Detailed measurement study of spatial similarity in spectrum use in dense urban environments. IEEE Trans Veh Technol. 2017;66(10):8951–63.CrossRefGoogle Scholar
  8. 8.
    Yuan C, Zhao Z, Li R, Li M, Zhang H. The emergence of scaling law, fractal patterns and small-world in wireless networks. IEEE Access. 2017;5:3121–30.CrossRefGoogle Scholar
  9. 9.
    Kim JS, Goh KI, Kahng B, Kim D. Fractality and self-similarity in scale-free networks. New J Phys. 2007;9(6):177.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Jiachen Sun
    • 1
  • Ling Yu
    • 1
  • Jingming Li
    • 2
  • Guoru Ding
    • 1
    • 3
    Email author
  1. 1.College of Communications EngineeringArmy Engineering UniversityNanjingChina
  2. 2.Nanjing University of Aeronautics and AstronauticsNanjingChina
  3. 3.National Mobile Communications Research LaboratorySoutheast UniversityNanjingChina

Personalised recommendations