Advertisement

Spectrum sensing in cognitive radio using multitaper method based on MIMO-OFDM techniques

  • Ahmed O. Abdul SalamEmail author
  • Ray E. Sheriff
  • Saleh R. Al-Araji
  • Kahtan Mezher
  • Qassim Nasir
Article
  • 11 Downloads

Abstract

The current inefficient utilization of frequency spectrum has alerted regulatory bodies to streamline improvements. Cognitive radio (CR) has recently received considerable attention and is widely perceived as a promising improvement tool in estimating, or equivalently sensing, the frequency spectrum for wireless communication systems. The cognitive cycle in CR systems is capable of recognizing and processing better spectrum estimation (SE) and hence promotes the efficiency of spectrum utilization. Among different SE methods, the multi-taper method (MTM) shows encouraging results. Further performance improvement in the SE for CR can be achieved by applying multiple antennas and combining techniques. This paper proposes a constructive development of SE using MTM, abbreviated as MTSE, and by employing multiple-input multiple-output (MIMO), parsed into separate parallel channels using singular value decomposition (SVD), and maximum ratio combining (MRC) configurations. Deviating from these improvements, however, multicarrier systems such as orthogonal frequency division multiplexing (OFDM) show inferior sensing performances due to the noise multiplicity generated and combined from all subcarrier channels. By means of the quadrature matrix form, the probabilities for such integrated settings of SE have been derived to reach at their approximate asymptotes. Numerical simulations revealed specific better performances stemmed from coupling the fashionable MTSE and MIMO technologies.

Keywords

Cognitive radio Spectrum estimation Multi-taper Multiple-input multiple-output Orthogonal frequency division multiplexing Singular value decomposition 

Notes

References

  1. 1.
    Ali A, Hamouda W (2017) Advances on spectrum sensing for cognitive radio networks: theory and applications. IEEE Communications Surveys and Tutorials 19(2):1277–1303CrossRefGoogle Scholar
  2. 2.
    Wang B, Liu K (2011) Advances in cognitive radio networks: a survey. IEEE J Select Topics Signal Process 5(1):5–23CrossRefGoogle Scholar
  3. 3.
    Yucek T, Arslan H (2009) A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun Surv Tutorials 11(1):116–130CrossRefGoogle Scholar
  4. 4.
    Axell E, Leus G, Larsson EG, Poor HV (2012) Spectrum sensing for cognitive radio: state-of-the-art and recent advances. IEEE Signal Process Mag 29:101–116CrossRefGoogle Scholar
  5. 5.
    Haykin S, Thomson DJ, Reed JH (2009) Spectrum sensing for cognitive radio. Proc IEEE 97(5):849–877CrossRefGoogle Scholar
  6. 6.
    Haykin S (2005) Cognitive radio: brain-empowered wireless communications. IEEE Trans Select Areas Commun 23(2):201–220CrossRefGoogle Scholar
  7. 7.
    Ma J, Li GY, Juang BH (2009) Signal processing in cognitive radio. Proc IEEE 97(5):805–823CrossRefGoogle Scholar
  8. 8.
    Wang J, Zhang QT (2009) A multitaper spectrum based detector for cognitive radio. Proc Conf Wireless Communications and Networks, Budapest, Hungary, pp 1–5Google Scholar
  9. 9.
    Zhang Q (2011) Multitaper based spectrum sensing for cognitive radio: design and performance. Proc Conf Vehicular Technology, Yokohama, Japan, pp 1–5Google Scholar
  10. 10.
    Jataprolu MK, Koilpillai RD, Bhashyam S (2012) Optimal MTM spectral estimation based detection for cognitive radio in HDTV. Proc Nat Conf Communications, Kharagpur, India, pp 1–5Google Scholar
  11. 11.
    Yu T, Parera S, Markovic D, Cabric D (2010) Cognitive radio wideband spectrum sensing using multitap windowing and power detection with threshold adaptation. Proc Int Conf Communications, Cape Town, South Africa, pp 1–6Google Scholar
  12. 12.
    Gismalla EH, Alsusa E (2012) New and accurate results on the performance of the multitaper-based detector. Proc Int Conf Communications, Ottawa, Canada, pp 1609–1613Google Scholar
  13. 13.
    Yousif EHG, Ratnarajah T, Sellathurai M (2015) Modelling and performance analysis of multitaper detection using phase-type distributions over MIMO fading channels. IEEE Trans on Signal Processing 63(22):5882–5896MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Alghamdi OA, Abu-Rgheff MA (2010) Local MTM-SVD based spectrum sensing in SIMO OFDM cognitive radio under bandwidth constraint. Proc Int Conf Cognitive Radio Oriented Wireless Networks and Communications, Cannes, France, pp 1–6Google Scholar
  15. 15.
    Stuber GL, Barry JR, McLaughlin SW, LI Y, Ingram MA, Pratt TG (2004) Broadband MIMO-OFDM wireless communications. Proc of the IEEE 92(2):271–294CrossRefGoogle Scholar
  16. 16.
    Hwang T, Yang C, Wu G, Li S, Li GY (2009) OFDM and its wireless applications: a survey. IEEE Trans Veh Technol 58(4):1673–1694CrossRefGoogle Scholar
  17. 17.
    Mahmoud HA, Yucek T, Arslan H (2009) OFDM for cognitive radio: merits and challenges. IEEE Wirel Commun 16:6–14CrossRefGoogle Scholar
  18. 18.
    Gupta A, Jha RK (2015) A survey of 5G network: architecture and emerging technologies. IEEE Access 3:1206–1232CrossRefGoogle Scholar
  19. 19.
    Brennan D (2003) Linear diversity combining techniques. Proc of the IEEE 91(2):331–356CrossRefGoogle Scholar
  20. 20.
    Clerckx B, Oestges C (2013). MIMO wireless networks: channels, techniques and standards for multi-antenna, multi-user and multi-cell systems. Academic Press, 2013Google Scholar
  21. 21.
    Goldsmith A (2005). Wireless communications. Cambridge University PressGoogle Scholar
  22. 22.
    Al-Juboori S, Fernando X (2015) Unified approach for performance analysis of cognitive radio spectrum sensing over correlated multipath fading channels. Proc IEEE Int Sym World of Wireless, Mobile and Multimedia Networks, Boston, USA, pp 1–6Google Scholar
  23. 23.
    Kuppusamy V, Mahapatra R (2008) Primary user detection in OFDM based MIMO cognitive radio. Proc Int Conf Cognitive Radio Oriented Wireless Networks and Communications, Singapore, pp 1–5Google Scholar
  24. 24.
    Nafkha A, Aziz B (2014) Closed-form approximation for the performance of finite sample-based energy detection using correlated receiving antennas. IEEE Wireless Communications Letters 3(6):577–580CrossRefGoogle Scholar
  25. 25.
    Wang N, Gao Y (2013) Optimal threshold of Welch’s periodogram for sensing OFDM signals at low SNR levels. Proc Conf European Wireless, Guildford, UK, pp 1–5Google Scholar
  26. 26.
    Joshi DR, Popescu DC, Dobre OA (2010) Dynamic threshold adaptation for spectrum sensing in cognitive radio systems. Proc IEEE Radio and Wireless Symposium, New Orleans, USA, pp 468–471Google Scholar
  27. 27.
    Kay SM (1998). Fundamentals of statistical signal processing: detection theory Prentice-Hall PTRGoogle Scholar
  28. 28.
    Quan Z, Cui S, Sayed AH, Poor HV (2009) Optimal multiband joint detection for spectrum sensing in cognitive radio networks. IEEE Trans Signal Process 57(3):1128–1140MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Bogale TE, Vandendorpe L, Le LB (2015) Wideband sensing and optimization for cognitive radio networks with noise variance uncertainty. IEEE Trans Commun 63(4):1091–1105CrossRefGoogle Scholar
  30. 30.
    Taherpour A, Gazor S, Kenari MN (2008) Wideband spectrum sensing in unknown white Gaussian noise. IET Commun 2(6):763–771CrossRefGoogle Scholar
  31. 31.
    Qing H, Liu Y, Xie G, Gao J (2015) Wideband spectrum sensing for cognitive radios: a multistage Wiener filter perspective. IEEE Signal Processing Letters 22(3):332–335CrossRefGoogle Scholar
  32. 32.
    Betlehem T, Coulson AJ, Reid AB (2010) Wide-band spectrum sensing for cognitive radio by combining antenna signals. Proc Australian Communications Theory Workshop, Canberra, Australia, pp 111–116Google Scholar

Copyright information

© Institut Mines-Télécom and Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Engineering and InformaticsUniversity of BradfordBradfordUK
  2. 2.School of EngineeringUniversity of BoltonBoltonUK
  3. 3.(formerly) College of EngineeringKhalifa University for Science and TechnologyAbu DhabiUnited Arab Emirates
  4. 4.College of EngineeringUniversity of SharjahSharjahUnited Arab Emirates

Personalised recommendations