Channel-Usage Model in Underlay Cognitive Radio Networks

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 131)

Abstract

In underlay cognitive radio networks (CRNs), a licensed channel can be shared with its primary users (PUs), provided the resultant interference power level at the primary receiver is below a predefined threshold, known as interference power constraint (IPC). Therefore, in such a scenario secondary users (SUs) have to perform the following—(a) perform spectrum sensing to detect the existence of PUs and (b) estimate the IPC to protect the PU from harmful interference. In this paper we develop a channel-usage model of PU’s in underlay mode of CRNs using a Hidden Markov Model (HMM) that allows SU to predict the unused/usable spectra. The model estimates the interference power level of a license channel due to the presence of SUs and decides on the availability of the channel for SU’s transmission imposing IPC. Further, a channel selection scheme is developed so that SUs can decide and select the best channel in the presence of multiple available licensed channels. Simulation results demonstrate the efficacy of the proposed model and its usability in underlay CRN.

References

  1. 1.
    Mitola J, Maguire G (1999) Cognitive radio: making software radios more personal. IEEE Pers Commun 6(4):13Google Scholar
  2. 2.
    Deka SK, Sarma N (eds) (2011) A survey on MAC protocols for cognitive radio networks. National Conference on Trends in Machine Intelligence (NCTMI), Assam, IndiaGoogle Scholar
  3. 3.
    Rabiner LR (ed) (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286Google Scholar
  4. 4.
    Tian Z, Yang C-G, Li JD (2010) Optimal power control for cognitive radio networks under coupled interference constraints: A cooperative game-theoretic perspective. IEEE Trans Veh Technol 59(4):1696Google Scholar
  5. 5.
    Zhang R (2009) Cooperative feedback for multiantenna cognitive radio networks. IEEE Trans Wirel Commun 8(4):2112Google Scholar
  6. 6.
    Zhang Z, Chen HH, Chen Y, Yu GG, Qui PL (2008) On cognitive radio networks with opportunistic power control strategies in fading channels. IEEE Trans Wirel Commun 7(7):2752Google Scholar
  7. 7.
  8. 8.
    Heidemann J, Ye W, Estrin D (2004) An energy-efficient mac protocol for wireless sensor networks. IEEE/ACM Trans Netw 12(3):493Google Scholar
  9. 9.
    Yang Z, Hamid M, Mohammed A (ed) (2010) On spectrum sharing and dynamic spectrum allocation : MAC layer spectrum sensing in cognitive radio networks In: International conference on communications and mobile, computing, 2010Google Scholar
  10. 10.
    Kim H, Shin KG (2008) Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks. IEEE Trans Mobile Comput 7(5):533Google Scholar
  11. 11.
    Fu H, Li H (ed) (2009) An adaptive sensing period algorithm in cognitive radio networks. In: Proceedings of ICCTA2009, IEEE, 2009Google Scholar
  12. 12.
    Kim H, Shin KG (2006) Adaptive mac-layer sensing of spectrum availability in cognitive radio networks, department of electrical engineering and computer science, university of michigan, ann arbor, mi. Tech. Rep. CSE-TR-518-06, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA (2006)Google Scholar
  13. 13.
    Tranter W, Akbar IA (2007) Dynamic spectrum allocation in cognitive radio using hidden markov models: Poisson distributed case. In: Proceedings of IEEE Southeast conference, pp 196–201Google Scholar
  14. 14.
    S.M.L.M.S.S. Chang-Hyun Park, Sang-Won Kim, Microwave Conference, APMC 2007. Asia-Pacific pp 1–4 (2007)Google Scholar
  15. 15.
    Hattangadi SM, Liu J, Wei Y (2011) In: ICCCTA. (http://www.ir.bbn.com/projects/xmac/rfc/rfcaf.pdf, 2011)
  16. 16.
    Wang B, Ji Z, Liu K, Clancy T (2009) Primary-prioritized Markov approach for efficient and fair dynamic spectrum allocation. IEEE Trans Wirel Commun 8(4):1854. doi: 10.1109/TWC.2008.080031 Google Scholar
  17. 17.
    Pla V, Vidal JR, Martinez-Bauset J, Guijarro L (2010) Modeling and characterization of spectrum white spaces for underlay cognitive radio networks. In: Proceedings of IEEE international conference on communications. ICC 2010, pp 1–5. doi: 10.1109/ICC.2010.5501788
  18. 18.
    Manuj Sharma AS, Nayak KD (eds) (2008) Channel modeling based on interference temperature in underlay cognitive wireless networks. In: Proceedings of IEEE international symposium on ISWCS (2008)Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringTezpur UniversityTezpurIndia

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