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Tytuł artykułu

Improving the Performance of Cognitive Radios through Classification, Learning, and Predictive Channel Selection

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Prediction of future idle times of different channels based on history information allows a cognitive radio (CR) to select the best channels for control and data transmission. In contrast to earlier work, the proposed method works not only with a specific type of traffic but learns and classifies the traffic type of each channel over time and can select the prediction method based on that. Different prediction rules apply to partially deterministic and stochastic ON-OFF patterns. New prediction methods for both traffic classes are developed in the paper. A CR predicts how long the channels are going to be idle. The channel with the longest predicted idle time is selected for secondary use. Simulations show that the proposed classification method works well and redictive channel selection method outperforms opportunistic random channel selection both with stochastic and deterministic ON-OFF patterns. Weibull, Pareto, and exponentially distributed traffic patterns are used in stochastic simulations to show general applicability of the proposed method. The classification-based method has even a higher gain when channels of interest include both stochastic and deterministic traffic. The collision rate with primary user over a given time interval can drop by more than 70% compared to the predictive system operating without classification.
Słowa kluczowe
Rocznik
Strony
28--38
Opis fizyczny
Bibliogr. 25 poz., rys.
Twórcy
autor
  • VTT Technical Research Center of Finland, Kaitoväaylä 1, 90571 Oulu, Finland
autor
  • University of California, Connectivity Lab, Berkeley, CA 94720, USA
  • Interuniversity Microelectronics Center (IMEC), Kapeldreef 75, B-3001 Leuven, Belgium
autor
  • VTT Technical Research Center of Finland, Kaitoväaylä 1, 90571 Oulu, Finland
Bibliografia
  • [1] V. Kanodia, A. Sabharwal, and E. Knightly, “MOAR: A Multi-channel opportunistic auto-rate media access protocol for ad hoc networks,” in Proc. of BROADNETS, Oct. 2004, pp. 600–610.
  • [2] N. Nie and C. Comaniciu, “Adaptive channel allocation spectrum etiquette for cognitive radio networks,” Mobile Networks and Applications, vol. 11, pp. 779–797, Dec. 2006.
  • [3] H. Zheng and L. Cao, “Device-centric spectrum management,” in Proc. of DySPAN, Dec. 2005, pp. 56–65.
  • [4] X. Jing, S.-C. Mau, D. Raychaudri, R., and Matyas, “Reactive cognitive radio algorithms for co-existence between ieee 802.11b and 802.16a networks,” in Proc. of GLOBECOM, November/ December 2005, pp. 2465–2469.
  • [5] T. C. Clancy, “Dynamic spectrum access in cognitive radio networks,” Ph.D. dissertation, University of Maryland, College Park, MD, 2006.
  • [6] S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, pp. 201–220, Feb. 2005.
  • [7] K. Takeuchi, S. Kaneko, and S. Nomoto, “Radio environment prediction for cognitive radio,” in Proc. of CrownCom, May 2008.
  • [8] S. Kaneko, S. Nomoto, T. Ueda, S. Nomura, and K. Takeuchi, “Predicting radio resource availability in cognitive radio – an experimental examination,” in Proc. of CrownCom, May 2008.
  • [9] T. C. Clancy and B. D. Walker, “Predictive dynamic spectrum access,” SDR Forum, Nov. 2006.
  • [10] P. A. K. Acharya, S. Singh, and H. Zheng, “Reliable open spectrum communications through proactive spectrum access,” in Proc. of TAPAS, Aug. 2006.
  • [11] X. Li and S. A. Zekavat, “Traffic pattern prediction and performance investigation for cognitive radio systems,” in Proc. of WCNC, March/April 2008, pp. 894–899.
  • [12] J. Hamkins and M. K. Simon, Eds., Autonomous Software-Defined Radio Receivers for Deep Space Applications. Wiley: New York, 2006.
  • [13] B. Ramkumar, “Automatic modulation classification for cognitive radios using cyclic feature detection,” IEEE Circuits Syst. Mag., vol. 9, pp. 27–45, Jun. 2009.
  • [14] K. Dostert, “Automatic classification of jammers in spread spectrum burst transmission systems,” in Proc. of NTC, Nov. 1983, pp. 420–424.
  • [15] L. Yang, L. Cao, and H. Zheng, “Proactive channel access in dynamic spectrum networks,” Physical Communication, pp. 103–111, Jun. 2008.
  • [16] M. Wellens, J. Riihij¨arvi, and P. M¨ah¨onen, “Empirical time and frequency domain models of spectrum use,” Physical Communcations, vol. 2, pp. 10–32, March/June 2009.
  • [17] M. Höyhtyä, S. Pollin, , and A. M¨ammel¨a, “Performance improvement with predictive channel selection for cognitive radios,” in Proc. Of CogART, Feb. 2008.
  • [18] M. Höyhtyä, T. Chen, and A. Mämmelä, “Interference management in frequency, time, and space domains for cognitive radios,” in Proc. of WTS, May 2009, pp. 226–232.
  • [19] M. Höyhtyä, S. Pollin, and A. Mämmelä, “Classification-based predictive channel selection for cognitive radios,” in Proc. of ICC, May 2010.
  • [20] A. Sang and S. Li, “A predictability analysis of network traffic,” Computer Networks, vol. 39, pp. 329–345, Jul. 2002.
  • [21] S. Haykin, Cognitive Radio Networks. Springer-Verlag, 2008, ch. Fundamental issues in cognitive radio, pp. 1–43.
  • [22] S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice-Hall: Englewood Cliffs, New Jersey, 1993.
  • [23] W. Willinger, M. S. Taqqu, R. Sherman, and D. V. Wilson, “selfsimilarity through high variability: statistical analysis of ethernet LAN traffic at the source level,” IEEE/ACM Trans. Netw., vol. 5, pp. 71–86, Feb. 1997.
  • [24] P. Pawelczak, “Opportunistic spectrum access: Designing link and transport layer,” Ph.D. dissertation, Delft University of Technology, 2009.
  • [25] M.Höyhtyä, J. Vartiainen, H. Sarvanko, and A. Mämmelä, “Combination of short term and long term database for cognitive radio resource management,” in Proc. of ISABEL, Nov. 2010.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-589f99ed-aec6-4f4c-9d50-b2b79d98fedd
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