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Neural Computing and Applications

, Volume 29, Issue 12, pp 1363–1376 | Cite as

Spectrum occupancy prediction based on functional link artificial neural network (FLANN) in ISM band

  • Deepa DasEmail author
  • David W. Matolak
  • Susmita Das
Original Article

Abstract

Recent advancements in artificial neural networks (ANNs) motivated us to design a simple and faster spectrum prediction model termed the functional link artificial neural network (FLANN). The main objective of this paper is to gather realistic data to obtain utilization statistics for the industrial, scientific and medical band of 2.4–2.5 GHz. To present the occupancy statistics, we conducted measurement in indoors at the Swearingen Engineering Center, University of South Carolina. Further, we introduce different threshold-based spectrum prediction schemes to show the impact of threshold on occupancy, and propose a spectrum prediction algorithm based on FLANN to forecast a future spectrum usage profile from historical occupancy statistics. Spectrum occupancy is estimated and predicted by employing different ANN models including the Feed-forward multilayer perceptron (MLP), Recurrent MLP, Chebyshev FLANN and Trigonometric FLANN. It is observed that the absence of a hidden layer in FLANN makes it more efficient than the MLP model in predicting the occupancy faster and with less complexity. A set of illustrative results are presented to validate the performance of our proposed learning scheme.

Keywords

ANN model FLANN model ISM band Cognitive radio Spectrum occupancy prediction 

References

  1. 1.
    Hossain E, Niyato D, Kim DI (2015) Evolution and future trends of research in cognitive radio: a contemporary survey. Wirel Commun Mobile Comput 15(11):1530–1564CrossRefGoogle Scholar
  2. 2.
    Liang Q, Leung VCM, Meng W, Adachi F (2014) Cooperative communications and sensing. Wirel Commun Mobile Comput 14(13):1201–1203CrossRefGoogle Scholar
  3. 3.
    Nekovee M (2009) Quantifying the availability of TV white spaces for cognitive radio operation in the UK. Communications workshops, 2009. In: ICC Workshops 2009. IEEE International Conference 2009; pp 1–5Google Scholar
  4. 4.
    Jayavalan S, Mohamad H, Aripin NM, Ismail A, Ramli N, Yaacob A, Ng MA (2014) Measurements and analysis of spectrum occupancy in the cellular and TV bands. Lect Notes Softw Eng 2(2):133–138CrossRefGoogle Scholar
  5. 5.
    Martin FL, Correal NS, Ekl RL, Gorday P, O’Dea R (2008) Early opportunities for commercialization of TV whitespace in the US. In: Cognitive Radio Oriented Wireless Networks And Communications, 2008. CrownCom, pp 1–5Google Scholar
  6. 6.
    Petty VR, Rajbanshi R, Datla D, Weidling F, DePardo D, Kolodzy PJ, Marcus MJ, Wyglinski AM, Evans JB, Minden GJ, Roberts JA (2007) Feasibility of dynamic spectrum access in underutilized television bands. In: New frontiers in dynamic spectrum access networks, pp 331–339Google Scholar
  7. 7.
    López-Benítez M, Umbert A, Casadevall F (2009) Evaluation of spectrum occupancy in Spain for cognitive radio applications. In: Vehicular Technology Conference, 2009. VTC Spring, pp 1–5Google Scholar
  8. 8.
    Xue J, Feng Z, Zhang P (2013) Spectrum occupancy measurements and analysis in Beijing. IERI Procedia 4:295–302CrossRefGoogle Scholar
  9. 9.
    Valenta V, Maršálek R, Baudoin G, Villegas M, Suarez M, Robert F (2010) Survey on spectrum utilization in Europe: measurements, analyses and observations. Cognitive Radio Oriented Wireless Networks & Communications (CROWNCOM), pp 1–5Google Scholar
  10. 10.
    Biggs M, Henley A, Clarkson T (2004) Occupancy analysis of the 2.4 GHz ISM band. IEE Proc Commun 151(5):481–488CrossRefGoogle Scholar
  11. 11.
    Kokkoniemi J, Lehtomäki J (2012) Spectrum occupancy measurements and analysis methods on the 2.45 GHz ISM band. In: Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), pp 285–290Google Scholar
  12. 12.
    Matinmikko M, Mustonen M, Höyhtyä M, Rauma T, Sarvanko H, Mämmelä A (2010) Distributed and directional spectrum occupancy measurements in the 2.4 GHz ISM band. In: ISWCS, pp 676–980Google Scholar
  13. 13.
    Jianli Z, Mingwei W, Jinsha Y (2011) Based on neural network spectrum prediction of cognitive radio. In: Electronics, Communications and Control (ICECC), pp 762–765Google Scholar
  14. 14.
    Najashi BG, Feng W (2014) Cooperative spectrum occupancy based spectrum prediction modeling. J Comput Inf Syst 10(10):4093–4100Google Scholar
  15. 15.
    Tumuluru VK, Wang P, Niyato D (2010) A neural network based spectrum prediction scheme for cognitive radio. Communications (ICC), pp 1–5Google Scholar
  16. 16.
    Shamsi N, Mousavinia A, Amirpour H (2013) A channel state prediction for multi-secondary users in a cognitive radio based on neural network. In: Electronics, Computer and Computation (ICECCO), pp 200–203Google Scholar
  17. 17.
    Akbar IA, Tranter WH (2007) Dynamic spectrum allocation in cognitive radio using hidden Markov models: Poisson distributed case. SoutheastCon, pp 196–201Google Scholar
  18. 18.
    Liu X, Zhang C, Tan X (2014) Double-threshold cooperative detection for cognitive radio based on weighing. Wirel Commun Mobile Comput 14(13):1530–8677CrossRefGoogle Scholar
  19. 19.
    Bai Z, Wang L, Kwak KS (2014) Different sensing durations-based cooperative spectrum sensing in cognitive radio systems. Wirel Commun Mobile Comput 14(16):1522–1529CrossRefGoogle Scholar
  20. 20.
    Pao Y-H, Phillips SM, Sobajic DJ (1992) Neural-net computing and the intelligent control of systems. Int J Control 56(2):263–289MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Majhi R, Panda G, Sahoo G (2009) Development and performance evaluation of FLANN based model for forecasting of stock markets. Expert Syst Appl 36(3):6800–6808CrossRefGoogle Scholar
  22. 22.
    Patra JC, Kot AC (2002) Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks. IEEE Trans Syst Man Cybern Part B: Cybern 32(4):505–511CrossRefGoogle Scholar
  23. 23.
    Liang Y-C, Zeng Y, Peh EC, Hoang AT (2008) Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans Wirel Commun 7(4):1326–1337CrossRefGoogle Scholar
  24. 24.
    Bureau R (2002) HANDBOOK spectrum monitoring. In: International Telecommunication Union (ITU)Google Scholar
  25. 25.
    Luenberger DG, Ye Y (2008) Linear and nonlinear programming. Springer, Berlin, p 116zbMATHGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  1. 1.Department of Electrical EngineeringNITRourkelaIndia
  2. 2.Department of Electrical EngineeringUniversity of South CarolinaColumbiaUSA

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